METHOD FOR DESIGNING A PROBE PANEL FOR A FLOW CYTOMETER

专利摘要:
systems and methods for flow cytometry panel design. embodiments of the present invention encompass systems and methods for determining detection limits for various antibody-dye conjugates for flow cytometry. the technical examples involve an overflow linear over-positioning approach induced by typically distributed measurement error spreads.
公开号:BR112015022201B1
申请号:R112015022201-3
申请日:2014-03-14
公开日:2021-06-22
发明作者:Michael KAPINSKY
申请人:Beckman Coulter, Inc；
IPC主号:

专利说明:

REFERENCES TO RELATED DEPOSIT REQUESTS
[0001] The present disclosure claims priority from US Provisional Patent Application No. 61/791,492 filed on March 15, 2013, which is incorporated herein by way of reference. BACKGROUND OF THE INVENTION
[0002] The embodiments of the present invention relate generally to systems and methods for evaluating cells from a biological sample and, in particular, to techniques for selecting different combinations of antibody-dye conjugate for use in flow cytometry . Other modalities generally refer to automated systems and methods for the analysis of positivity in multicolor flow cytometry.
[0003] Cell surface immunophenotyping using fluorescent flow cytometry has become a relatively routine process for differentiating and counting cells of interest in a cell sample containing many different cell types. Typically, cell surface probes, eg, fluorochrome-labeled monoclonal antibodies (MAbs) or other suitably labeled ligands, specific for antigens on the outer surface of the cells of interest, are used to selectively label or "stain" such cells for detection subsequent. Flow cytometry operates to detect stained cells by irradiating individual cells in the sample, one by one, with specially adapted radiation to excite the fluorochrome labels. When irradiated, the tags fluoresce and their associated cells scatter the incident radiation in a pattern determined by the physical and optical characteristics of the irradiated cell. Appropriate photodetectors within the flow cytometer detect fluorescence and scattered radiation, and their respective output signals are used to differentiate different cell types based on their respective fluorescence and light scattering signatures.
[0004] Flow cytometric immunophenotyping typically involves the selection of a set of physiologically appropriate probes or reagents for the desired assessment or monitoring procedure. Similarly, because certain disease conditions can be characterized by the expression of various antigens on the surface of cells or within a patient's cells, panels of antibody probe reagents that match these antigen profiles can be selected. . For example, the Solastra™ 5-Color reagent panel is a panel of antibody-conjugated cocktails for use in flow cytometry characterization of hematolymphoid neoplasia. The panel can be used for identification and enumeration of relevant leukocyte surface molecules, and as an aid in the differential diagnosis of patients with certain abnormal hematologic findings and/or presence of blasts in blood, bone marrow, and/or lymphoid tissues. Solastra™ 5-Color reagents are composed of antibodies directed to B, T and myelomonocytic lineage antigens. Such panels can be used in flow cytometry analysis for hematopathology applications.
[0005] The measurement of samples through a flow cytometry device produces a photonic signature characteristic of scattered light, fluorescence light or a combination thereof. By analyzing the signature, it is possible to infer the physical and chemical characteristics of the particle. Often, protein expression, a biological feature of an exemplary particle, is subject to interrogation. Particle signatures from a blood sample can be presented on a dot plot, and keying can be used to interpret these signatures. Keying is generally used to classify a subscription as positive or negative. For example, keying can be used to determine whether a particle is a blood cell or a piece of debris, or whether the blood cell contains a marker for disease. Thus, switching is important for diagnostic and clinical hematology applications. However, it can be difficult to determine whether a particle belongs to a positive or negative population, such as when the positive and negative signatures look similar. A variety of specificity control or keying techniques such as isotypic controls, models that apply cluster analysis algorithms such as principal component analysis, and fluorescence minus one (FMO) have been proposed to help determine whether a particle should be classified as positive or negative.
[0006] Although currently known antibody panel selection techniques provide many benefits to those performing cell monitoring and assessment procedures, further improvements are still desired. In addition, switch control techniques for evaluating samples can be improved. Embodiments of the present invention provide solutions to at least some of these pending needs. BRIEF SUMMARY OF THE INVENTION
[0007] Embodiments of the present invention encompass systems and methods for selecting and simulating antibody-dye conjugate panels for use in flow cytometry, and other related cell evaluation and monitoring techniques. Often, such panels can be used or designed to evaluate cells from a biological sample. Such cells can be obtained from a single person, from a cell culture, from a pool of human or non-human donors, or the like. According to some embodiments, the techniques disclosed herein can be used to evaluate any material that includes particles (eg, biological cells) that are present in a suspension, and that have structures on their surface or in their interior that can be recognized by fluorochrome-labeled specific biological probes that non-covalently bind to such structures, such as antibodies, toxins, receptor ligands, or their derivatives or similar compounds. As discussed elsewhere in this document, exemplary probes, which may include fluorochrome-labeled monoclonal antibodies (MAbs) or other suitably labeled ligands, may be specific for antigens on the outer surface of, or within, cells of interest. Although different sample preparation procedures can be used depending on whether the analysis involves external or internal antigens, the data acquisition techniques discussed here are equally applicable to any type of analysis. Appropriate photodetectors within the flow cytometer detect fluorescence and scattered radiation, and their respective output signals are used to differentiate between different cell types (or subtypes of a certain cell type or different functional states between a certain cell type ) based on their respective fluorescence and light scattering signatures. These fluorescence signatures can be resolved by computer through a procedure called fluorescence compensation, thus transmitting quantitative information about the presence of each unique antigen queried on the surface or inside the cell/particle.
[0008] Multicolor immunophenotypic analysis by flow cytometry typically involves the use of panels or cocktails of antibody-dye conjugates. Panels can be configured so that individual probes, having their individual dyes, match the individual color detection channels of a flow cytometry device. As discussed here, the selection of probe panels can be automated, thus streamlining the multicolor flow cytometry analysis process. The use of such probe panels in flow cytometry can provide the efficient acquisition of excellent quality data using multiple detection channels. Thus, downtime can be reduced and lab productivity can be maximized. Similarly, embodiments of the present invention provide improved sensitivity for detecting prioritized antigens, and markedly facilitate data analysis.
[0009] Exemplary flow cytometry devices can include various laser configurations (eg, multiple solid-state lasers), providing excitation spectra corresponding to red, blue, violet, yellow, and the like. Interchangeable optical filters can be used to facilitate detection of a variety of dyes and wavelengths. Exemplary systems can be used to analyze multiple fluorescent labels simultaneously. For example, systems that have six fluorescence detectors can provide simultaneous acquisition of up to six fluorescence signals. Additional fluorescence detectors and/or lasers can be added to a system, allowing for simultaneous reading of up to ten or more colors. Embodiments of the present invention provide graphical display techniques to provide a user with plots, graphs, and other visual features that can facilitate the analysis of complex flow cytometry data.
[0010] In one aspect, embodiments of the present invention encompass systems and methods for determining a probe panel for analyzing a biological sample in a flow cytometry procedure. Exemplary methods include entering a flow cytometer hardware configuration, entering a list comprising a plurality of probes, where the individual probes in the list are associated with respective individual channel-specific detection limits, entering a antigenic coexpression pattern, and probe panel determination based on the flow cytometer hardware configuration, specific detection limits for individual channels, and the antigenic coexpression pattern. The probe panel can include a subset of probes from the list.
[0011] Additional embodiments of the present invention encompass systems and methods for assessing positivity in multicolor flow cytometry. Exemplary specificity or switch control techniques can be used to assess a signature of individual particles, for example, to determine whether a blood cell is positive or negative for a particular protein expression, as a disease marker. In some cases, these control techniques can be used to position a graphic region or gate in relation to acquired data in order to sort the cells from which the data is obtained. Exemplary control techniques can be used in multicolor procedures after compensation. In some cases, the methods described here provide a level of standardization that is not present in currently used techniques. Furthermore, the control techniques disclosed here are fast, economical and effective for heterogeneous expression patterns, and allow for the quantification of positives.
[0012] Other features described above and many others and attendant advantages of embodiments of the present invention will be apparent and better understood by reference to the following detailed description when considered in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Aspects of systems and methods according to embodiments of the present disclosure are described by the illustrations and figures presented below.
[0014] Figure 1 represents aspects of flow cytometry systems and methods according to some modalities.
[0015] Figure 1A represents an exemplary hardware illustration of a flow cytometry device, having a ten-color filter block configuration, with three lasers, according to some embodiments.
[0016] Figure 1B represents aspects of a probe panel selection technique according to some embodiments.
[0017] Figure 1C represents aspects of the expression of antibody-dye conjugates that may refer to a set of antibody-dye conjugates within a database containing information about flow cytometry probes, according to some modalities.
[0018] Figure 1D represents aspects of database queries for specific antibodies to the antigen selected by a user to return information associated with the respective probes, according to some modalities.
[0019] Figures 1E to 1M represent aspects of a panel evaluation technique according to the embodiments of the present invention, according to some embodiments.
[0020] Figures 1N to 1O represent examples for associations and calculations for three conjugates, to associate a given CD with a particular dye, according to some modalities.
[0021] Figures 1P to 1U represent additional aspects of a panel evaluation technique, according to embodiments of the present invention, according to some embodiments.
[0022] Figures 1V to 1W represent aspects of monitors for results for probe panels with ten exemplifying colors, according to some modalities.
[0023] Figure 2 represents aspects of an operator selection process, according to some modalities.
[0024] Figures 2A to 2C represent operation selection features to target the phenotype, phenotype exclusion, and parent/descendant schemes, respectively, according to some modalities.
[0025] Figure 2D represents operation selection characteristics for antigen density parameters, according to some modalities.
[0026] Figure 3 represents aspects of a probe panel selection technique, according to some modalities.
[0027] Figure 4 represents certain aspects of isotype signaling and resolution sensitivity, according to some embodiments.
[0028] Figure 5 represents a comparison between coefficiency of variance and standard deviation for expression events, according to some modalities.
[0029] Figures 6A to 6B show aspects of a bimodal distribution for determining negative sign events, according to some modalities.
[0030] Figure 7 represents aspects of an extended distribution of signal events, according to some modalities.
[0031] Figure 8 represents aspects of actual data results from a staining protocol using a single antibody-dye conjugate, according to some modalities.
[0032] Figure 9 represents aspects of detection limits that can be independent of, or be adjusted for, compensation factors, according to some modalities.
[0033] Figure 10 represents aspects of an exemplary overflow pattern distortion matrix for certain dyes, according to some modalities.
[0034] Figure 11 represents aspects of a coexpression matrix, according to some modalities.
[0035] Figures 12A to 12I represent schemes of estimated color patterns, according to some modalities.
[0036] Figure 13 represents aspects of probe panel evaluation, including categorization of expression patterns, according to some modalities.
[0037] Figure 14 represents aspects of probe panel evaluation, including relative fluorophore contribution, according to some modalities.
[0038] Figure 15 represents aspects of probe panel evaluation, including relative dye expression brightness, according to some modalities.
[0039] Figure 16A describes an exemplary scheme for a probe panel system, according to some embodiments.
[0040] Figures 16B to 16C represent aspects of a user input module for a probe panel, according to some embodiments.
[0041] Figures 16D to 16E represent aspects of simulator graphics modules for expression relations in table form, according to some modalities.
[0042] Figures 16F to 16G represent aspects of simulator graphic modules for predicted result profiles, according to some modalities.
[0043] Figures 16H to 16J represent aspects of a simulator numerical values module for distortion calculations, according to some modalities.
[0044] Figures 16K to 16L represent aspects of overflow pattern modules, according to some modalities.
[0045] Figures 16M to 16O represent aspects of an antibody database module, according to some embodiments.
[0046] Figure 17 represents aspects of a numerical approach to modeling overflow patterns including detection radar graphics for multivariate analysis, according to some modalities.
[0047] Figures 18A to 18B represent additional aspects of a numerical approach to modeling overflow patterns including detection radar graphics for multivariate analysis, according to some embodiments.
[0048] Figures 19A to 19B represent additional aspects of a numerical approach to modeling overflow patterns including detection radar graphics for multivariate analysis, according to some embodiments.
[0049] Figures 20A to 20G represent aspects of a computerized interface for the design and simulation of a probe panel, according to some embodiments.
[0050] Figures 21A to 21B represent additional aspects of a computerized interface for designing and simulating a probe panel, according to some embodiments.
[0051] Figures 22A to 22B represent aspects, according to some embodiments.
[0052] Figures 23 to 26 represent aspects of a computerized interface for the design and simulation of a probe panel, according to some embodiments.
[0053] Figure 27 represents a three-dimensional graph modeling distortion in a PMT channel, caused by the signal strength of two dyes which the PMT is not directed to detect, according to modalities.
[0054] Figure 28 represents an exemplary distortion table, according to some modalities.
[0055] Figures 29 to 29E represent real data aspects, plotting event data acquired from a flow cytometry instrument applying keying to the data where applicable, according to some modalities.
[0056] Figures 30 to 30A depict real data aspects, plot event data acquired from a flow cytometry instrument without applying keying to the data, according to some modalities. DETAILED DESCRIPTION OF THE INVENTION
[0057] Flow cytometry often involves labeling a sample of particles with fluorochrome dyes, and then evaluating the properties of the individual particles in the sample using various fluorescence detectors specific for various wavelengths. In this way, it is possible to obtain qualitative and quantitative data on the particle sample. For example, different cell surface receptors on a blood cell can be labeled with different fluorochrome dyes, and a flow cytometer can use separate fluorescence channels to detect the resulting emitted light. In exemplary embodiments, multiple wavelengths of excitation light can be used in conjunction with multiple fluorochrome dyes and multiple fluorescence detectors to simultaneously obtain multiple parameters from a sample. In particular embodiments, distortion factors resulting from the joint use of multiple fluorochrome dyes and multiple fluorescence detectors can be quantified.
[0058] The term "event", as used herein, may refer to a particle passing through a beam of light, or to data or signature representing the particle. An event can be evaluated using multiple detectors, and each detector can provide its own intensity or signal parameter. Similarly, each detector can be associated with a respective flow cytometer channel. For example, a measurement from an individual detector can be called a parameter (eg, forward scatter, side scatter, or measured fluorescence), and the data obtained on each parameter for a particle can be called an event.
[0059] In some cases, a measured parameter may not reach a specific threshold for the detector channel, and therefore may not register as an event. In this sense, the interaction between a light beam and a particle flowing through the cytometer may or may not produce a particle event. Optionally, such threshold can be used to reduce or eliminate signals caused by noise, debris and the like.
[0060] Embodiments of the present invention encompass systems and methods involving detection limit determination of fluorescence signals in photomultipliers for use in flow cytometry applications. In some cases, the determination of a detection limit may be based on an expected expression pattern of a target cell (which may be labeled with antibody-fluorochrome conjugates), on the expected fluorescence signal intensities for individual fluorescent markers that arise of the fluorescent labeling of the antigens comprised by the expression pattern, and in an expected overflow matrix for the fluorochromes in the different photomultipliers.
[0061] According to some embodiments, there may be an additional entry that covers data dissemination that is specific to a given wavelength detection range (as determined by the bandpass filter in front of a photomultiplier), as the latter can determine the respective sensitivity of the photomultiplier and therefore the measurement error (data dissemination). The relationship between spillover and resulting data dissemination can be evaluated experimentally.
[0062] Exemplary modalities allow a cytometry device user to select a desired fluorochrome antibody combination (eg, probe panel), which can be used to construct flow cytometry experiments including a prediction of detection limits for fluorochrome conjugates; this can also be called a simulation panel. Furthermore, a user of the cytometry device can select a combination of antibodies without assigning fluorescent labels to each of them, respectively, in order to obtain a proposal for a probe panel with minimized detection limits for individual probes desired within that panel. of probe. In some cases, panel design or evaluation techniques may involve the use of a linear superposition model of overflow-induced broadening of normally distributed measurement errors.
[0063] Probe panel design and evaluation techniques, as disclosed in this document, can use single dye reference fluorochrome measurement data for calculations of an overflow (which may alternatively be called overflow/spread, spillage or interference). In some cases, a distortion factor can be characterized by the following formula:

[0064] In other words, the determination of a distortion factor can quantify the spillover effect of a first label (which is part of a probe-marker conjugate), wherein the first label is intended or configured to be measured in a first channel (e.g. a PMT detector) in at least a second channel, wherein the second channel (or other additional channels) is intended and configured to measure a different mark. In some aspects, the distortion factor may be an estimate of an increase in the second channel detection limit as a function of an emission intensity of a first probe-marker combination. In other aspects, the distortion factor may be a linear function of the emission intensity of a first probe-marker combination. In other respects, the distortion factor can be calculated using an interference index. In some respects, the distortion factor is mathematically modified by a coefficient that represents the pattern of antigen coexpression corresponding to a first probe-marker combination and a second probe-marker combination, with the second probe-marker combination being intended or configured to be measured on a second channel. In additional aspects, determining a distortion factor for each marker in a first potential probe panel can be done to calculate an overall increase in detection limit in a second channel.
[0065] In some cases, a probe panel design or evaluation technique may involve the use of an expected antigen expression pattern in different target cells, such as deletion, subpopulation (e.g., parent-descendant, as depicted in Figure 1D), non-exclusive co-expression characteristics, or expected antigen expression characteristics, such as distinct or modulated. In some cases, a probe panel design or evaluation technique may involve predicting, estimating, or otherwise determining detection limits for individual detection channels, optionally for a variety of different cell types. or from antigen expression patterns.
[0066] As used herein, in describing antigens, the genealogical terms "father", "son", "brother", "aunt", and "cousin" are used to identify and describe the relationships between differentiating groupings (" CD") for specific antigens, or the corresponding antibody. Note, however, that in the context of the present disclosure, these terms do not refer to generations of cell reproduction, but rather refer to developmental stages of a particular cell as antigens on a given cell surface change due to differentiation or specialization. For example, an antigen such as CD45 may be present on a native T cell; if the native T cell specializes to become a killer T cell, the CD45 antigen can act as a precursor to, and become a CD2 antigen (daughter), whereas if the native T cell specializes to become a cell T helper, the CD45 antigen can act as a precursor to, and become a CD4 (child) antigen. In such a situation, the CD45 antigen can be called the parent antigen for both the CD4 and CD2 sibling antigens.
[0067] The possible developmental relationships between antigens are generally presented as follows. In some cells, two different antigens can occur on the same cell surface, each with its typical average density and expression range, resulting in a case called "co-expression". The presence of one antigen on the cell surface can exclude the presence of another specific antigen on the same cell surface, resulting in a case called "deletion". Antigens with an identical "father" are called "brothers". In some cells, a first antigen may occur on a cell surface, where a second antigen is also expressed, however, the second antigen may also occur on a cell surface without the first antigen being present. In such cases, the second antigen is called the "parent" antigen, while the first antigen is called the "descendant" or "son" antigen, resulting in a case called the "parent-descendant" or "parent-child". The "brother" of a "father" of a particular antigen is called the "uncle" of that antigen. When considering a specific antigen, your non-excluded "siblings", your children, your additional children (or "grandchildren"), other descendant antigens, your "father", your additional "parents" (or "grandparents"), and all others ascending antigens and their non-excluded "uncles" may be called the developmental genealogy (or developmental tree) of the specific antigen. It may happen that a single protein is expressed in different types of cells that belong to different developmental genealogies, possibly also having different characteristics of density, range and mean distribution of expression of this antigen. The multiple cellular entities of this antigen expressing multiplicity can be called "multiples". Furthermore, two co-expressed antigens can have inversely correlated expression densities, resulting in a process called "inverse correlation".
[0068] A set of guidelines for creating developmental genealogies for a given antigen may include the following rules: (1) each antigen must be assigned to at least one "parent", if the "parent" is not known, "unknown " is assigned as "parent"; (2) if existing, for each antigen, "exclusions", "correlations" or "inverse correlations" with "siblings" should be identified, in aspects, the order of consideration of siblings can be based on which antigen has the name in previous alphabetical order; (3), if any, for each antigen, "exclusions", "correlations" or "inverse correlations" with "uncles" must be identified, unless the uncles are excluded by the parent of the respective antigen; (4) "multiples" should be handled as if they were multiple different antigens, in order to maintain consistency of individual antigen genealogies, this is done by assigning different excluded "parents", "siblings" and "uncles" or inversely correlated, and (5) correlated antigens share the same exclusions and inverse correlations but do not necessarily have the same multiples. By this set of rules, the complete set of developmental antigen genealogies for a given species and body compartment can be created, establishing all possible relationships between all antigens.
[0069] Under some embodiments, detection limits can be used to classify, compare, or otherwise evaluate fluorochrome-antibody panels, based on antigen expression patterns (which may involve antigen expression densities and fluorochrome gloss), optionally in combination with experimental data. In some aspects, calculations related to determining detection limits may include inputting, or receiving, information about a maximum expected signal from a first probe-marker combination, based on the characteristics of a first probe and first marker. In an instrument or system where the signal from a first probe-marker combination can create spillover into at least a second detection channel (i.e., a channel not configured to specifically detect the first probe-marker conjugate), the calculation of the increase in detection limits of a second channel may be based on a distortion factor and the maximum expected signal from the first probe-marker combination. Consequently, a probe-marker combination can be selected or chosen to include the probe panel based on the calculated increase(s) in detection threshold(s). In further aspects, the increase in a detection limit in at least a second channel may be caused by an increase in measurement error, as a function of emission intensity, of a first probe-marker combination.
Exemplary probe panel systems and methods, as described herein, are suitable for use in evaluating various conjugate combinations (e.g., panel-marker combinations, fluorochrome labeled antibodies, etc.) having patterns overflow complexes. Such systems and methods may involve the use of channel detection and specific expression pattern determination of a typical signal strength and an increase in detection limit related to a typical signal strength. Consequently, systems and methods can function based on specific parameters such as expression densities, fluorochrome intensities, coexpression patterns, and distortion factors. In some cases, evaluating a probe panel may include determining a maximum difference for a typical signal strength and for an increase in detection limit. In some cases, evaluation of a probe panel may include determining the ratio of a typical signal intensity to a detection limit for one or more specific antibody-dye conjugates. Exemplary probe panel evaluation systems and methods can be based on consideration of coexpression patterns and simulation of experimental results. The selection of probe-marker combinations can additionally be based on a comparison of the calculated total increase in detection limit with an expected minimum increase in at least one second channel. In addition, a total increase in detection limits, for all channels, can be calculated for each probe in one or more potential probe panels. In some aspects, systems and methods for evaluating various conjugate combinations may further include calculating a total increase in the detection limit for each probe in a second potential probe panel and selecting the probe panel, based on a comparison of the calculated total increase in detection limit for each probe in the first potential probe panel with the calculated total increase in detection limit for each probe in the second potential probe panel. In some aspects, such systems and methods may include calculating a total increase in detection limit for each probe in a second potential probe panel and selecting the probe panel, based on the calculated total increase in detection limit for one probe. priority values on the first potential probe panel and on the second potential probe panel. In other aspects, a first channel and a second channel can be adjacent channels.
[0071] Furthermore, in some embodiments, methods for designing a probe panel for a flow cytometer may include: identifying a first probe and a second probe; identify a minimum expected signal from the first probe; determining a first detection limit of the first probe based on a potential marker associated with the second probe; determining a second detection limit for the first probe based on a different potential marker associated with the second probe; and selecting which marker to associate with the second probe for the probe panel, based on the first detection limit, the second detection limit, and the minimum expected signal from the first probe. In aspects, determining a first detection limit may include multiplying a distortion factor of a maximum expected signal in a detection channel designed to measure the potential marker associated with the second probe. In other respects, determining the first detection limit may include multiplying the distortion factor by a coefficient that represents an antigenic co-expression pattern, where, in some cases, the coefficient is either one or zero. In further aspects, a maximum expected signal may be based, in part, on any or all of an expected antigen density in a target cell, the potential marker associated with the second probe, and an antigenic co-expression pattern. Some embodiments of such methods or systems include a first probe, which is intended to be detected on a first channel, and wherein determining a first detection limit of the first probe includes multiplying a distortion factor by a maximum expected signal for each flow cytometer channel other than the first channel. In these aspects, the determination of the first detection limit of the first probe is based on a linear superposition model of CV broadenings. In some respects, the distortion factor can be a measure of the CV widening caused by color compensation.
[0072] In some embodiments, a method for designing a probe panel for a flow cytometer may include: identifying a first probe, a second probe, and a third probe; identifying a plurality of possible probe panels, each possible probe panel including a combination of the first probe, the second probe or the third probe, each probe having a possible label associated therewith; evaluating a first possible probe panel by determining the detection limit of the first probe based on spectrum spillover effects of the combination of the second probe and its associated possible label; evaluating a second possible probe panel by determining the detection limit of the second probe based on spectrum spillover effects of the combination of the third probe and its associated possible label; and selecting the probe panel from the plurality of possible probe panels based on the determined detection limits. In these respects, at least one of the first probe, the second probe and the third probe can specifically bind to an antigen. In similar respects, at least one of the first probe, the second probe and the third probe can specifically bind to an analyte. In further aspects, evaluation of a possible first probe panel includes determining the detection limit of a first probe based, in part, on a co-expression pattern of antigens associated with the first probe and a second probe. In some aspects, the antigen co-expression pattern may include information about the co-expression relationships between antigens for a specific cell type. In aspects, the overflow spectrum effects of combinations of a second probe and its associated label can be determined to be zero, if the co-expression pattern of antigens associated with a first probe and the second probe indicates that the probes are mutually exclusive . In further aspects, the combination spectrum overflow effects of a second probe and its associated label can be determined to be zero if the antigen associated with the second probe is a descendant of the antigen associated with a first probe. In aspects, the combination spectrum overflow effects of the second probe and its associated label can be quantified as a function of a distortion factor and an antigenic co-expression pattern. Together, or alternatively, the spillover spectrum effects of the combination of the second probe and its associated label can be quantified, as a function of expected antigen density in a target cell. In some aspects, such methods or systems may include displaying a graphical representation of a population distribution of expected signals for a pair of probes on a selected probe panel, where displaying the population distribution graphical representation may include displaying the determined detection limit of the first probe and the second probe.
[0073] The probe panel techniques, as disclosed herein, are suitable for use with various automated devices, including the Navios™ and Gallios™ Flow Cytometry systems (Beckman Coulter, Brea, California, USA). In some cases distortion calculations may be based on specific properties or performance characteristics of filter sets and/or photodetectors (eg PMTs). In addition, probe panel techniques can be based on spectral properties of the dyes, optionally, which are available within a specific library or repository of dyes or antibody-dye conjugates.
[0074] In some cases, evaluation or simulation of probe panels can be determined based on certain patterns or profiles of antigen expression. Such profiles or patterns may or may not be associated with a specific cell type. In operation, a user can select antibody-dye conjugates according to a specific expression pattern, which can be a target expression pattern selected through a designed experiment. In some cases, an instrument such as a flow cytometer may be configured to accept multiple colors (eg ten colors), and the user may wish to choose a smaller number of probes. Thus, the user can expressly assign a first number of probes, while leaving a second number of probes as a dummy variable, so that the sum total of the probes is equivalent to the number of color channels in the flow cytometer. In some cases, different dyes (eg PC5 and PC5.5) may be detected in the same channel and therefore may not involve simultaneous application.
[0075] As disclosed herein in another section, once a user selects or enters certain parameters from a probe panel, the system can operate to retrieve or upload part numbers or other identifying indicia of a base module of antibody data. If, for example, a desired conjugate (ie probe) is not contained in the library, the part number (PN) can be indicated as a customer design service probe (CDS). In some cases, systems and methods involve evaluating a panel of a probe based on a respective fluorochrome property and a signal intensity (eg, assumed) that could be expected based on a target antigen expression density. , and such parameters can be retrieved or read from an antibody database module. In some cases, an antibody database module may include data relating to various parameters for use in evaluating probe panels, including probe signal intensity, which may be based on an antigen density for probe specificity in together with the conjugated fluorochrome. Conjugate intensity data can be based on estimates, or can be based on experimental data, for example, which can be obtained as part of a manufacturing quality control process.
[0076] Other embodiments of the present disclosure encompass systems and methods for assessing positivity in multicolor flow cytometry. Exemplary specificity or switch control techniques can be used to assess a signature of individual particles, for example, to determine whether a blood cell is positive or negative for a particular protein expression as a marker of disease. In some cases, these control techniques can be used to position a graphic region or gate in relation to acquired data in order to sort the cells from which the data is obtained. Exemplary control techniques can be used in multicolor procedures after compensation. In some cases, the methods described here provide a level of standardization that is not present in currently used techniques. Furthermore, the control techniques disclosed here are fast, economical and effective for heterogeneous expression patterns, and provide quantification of the positive aspects.
[0077] In some cases, the emission spectra measured from different fluorescent dyes can overlap, and can be useful to compensate for the signals obtained by the detectors. For example, a fluorescence compensation technique can be applied during data analysis to determine how much interference Fluorochrome A is having on channel B (which is assigned to specifically measure Fluorochrome B). As a result, it is possible to obtain the total fluorescence measured in Channel B, and subtract the contribution of Fluorochrome A, in order to determine the fluorescence of Fluorochrome B in Channel B. According to some modalities, it is possible to obtain the total fluorescence measured in the Channel B and eliminate the Fluorochrome A contribution in order to determine the Fluorochrome B fluorescence in Channel B, for example, which can be performed using a matrix-based compensation approach that involves digital compensation.
[0078] Event data can be visually represented in a variety of ways. For example, a histogram can be used to display a single measurement parameter (eg fluorescence) on the horizontal X-axis and the number of events (eg, cell count) on the vertical Y-axis. In this way, it is possible to determine the number of cells in a sample having certain characteristics. For example, a short peak on the left side of the graph could represent a small group of cells having an obscure fluorescence (events within a negative population), and a high peak on the right side of the graph could represent a large group of cells having a bright fluorescence (events within a positive population).
[0079] As used here, a "gate" can be used as a threshold to differentiate between a positive population and a negative population. Likewise, a port can be used as a boundary to define a subpopulation of events. A gate can be defined, for example, by delineating a boundary around a subset of events in a data plot, such as a point plot or histogram. A gate can be inclusive, so as to select events that fall within a boundary, or exclusive, so as to select events that fall outside the boundaries. Consequently, the number of positive events (on a specific side of the boundary) can refer to the number of cells that exhibit a physical feature or marker of interest. In accordance with some embodiments of the present invention, keying can be used to distinguish signals corresponding to fluorescent objects from signals corresponding to non-fluorescent objects. According to some embodiments, any event detected with a photomultiplier tube (PMT) can emit a fluorescence signal. Thus, an emitted fluorescence can be associated with a specific marker.
[0080] Specific switching protocols are available for diagnostic and clinical purposes in the field of hematology. For example, ports can be used in flow cytometry data to selectively visualize certain cells of interest, such as white blood cells (white blood cells), eliminating the results of unwanted particles such as dead cells and debris. In some situations it can be difficult to determine where to place a door in order to effectively classify an event as positive or negative. With the use of an appropriate control, it is possible to help identify the difference between a positive population and a negative population. The embodiments of the present invention can be used in conjunction with multicolor cytometry techniques in general, including, without limitation, the hematological field. In some cases, embodiments of the present invention can be applied to any measurement where a Fluorescence Minus One (FMO) control is useful or necessary.
[0081] Embodiments of the present invention provide systems and methods for performing automated positive analysis in multicolor flow cytometry, which is characterized by overflow-induced broadening of measurement errors. Modalities additionally cover techniques to calculate measurement errors according to each individual event expression pattern based on a fluorochrome spillover linear overlay model, as well as applied to panel design and simulation applications, with use of the same or similar techniques to determine the prediction limits for multicolor flow cytometry, taking into account the co-expression pattern of antigens of a detected particle, so as to allow the determination of an overflow rate for one fluorochrome on all others detection channels by an overlap calculation.
[0082] Similarly, embodiments of the present invention encompass post-acquisition correction techniques for flow cytometry so that compensation errors associated with acquired sample results can be minimized. For example, the use of standard compensation approaches in multicolor channel designs with fixed correction values may tend to either little or little compensate for the design results at a switching boundary. Embodiments of the present invention encompass the use of correctly compensated data, or avoid such overcompensation or undercompensation.
[0083] In some cases, the automatic correction techniques disclosed here can operate to improve the compensated results of a flow cytometry device so as to enhance the identification of specific positive results after data acquisition. In some embodiments, the systems and methods disclosed herein can operate to calculate a corrected detection limit or corrected borders between two sections of results or corrected limits between specific positivity and specific negativity for each fluorescence signal channel (e.g., photomultiplier), based on an overlap model of the overflow of all other dyes detected in all other channels. With the use of these corrected boundaries between specific positivity and negativity, it is possible to proceed automatically without manual switching of the result sets, and critical results in the boundary area between the two result sections are observed to be unambiguously assigned to the appropriate section. Overview
[0084] Returning now to the drawings, Figure 1 represents aspects of flow cytometry systems and methods according to some modalities. Configuration 100 of a flow cytometry device typically includes certain fluorescence signal detector assembly 110 parameters as well as laser excitation wavelength parameters 120.
[0085] Flow cytometry devices can be configured with any of a variety of laser excitation parameters. For example, laser arrays can be configured to produce excitation spectra at 355 nm (ultraviolet), 405 nm (violet), 488 nm (blue), 532 nm (green), 561 (yellow), 633 nm (red) , 638 nm (red), and the like. Similarly, laser assemblies can include any number of laser excitation devices. For example, a dual laser assembly may include a first laser for delivering excitation energy at 488 nm and a second laser for delivering energy at 638 nm.
[0086] As shown in Figure 1, laser excitation energy can be impinged upon a dye from an antibody-dye conjugate probe. Typically, a specific dye will be excited with a characteristic wavelength, and subsequently fluoresces characteristic emission spectra. There may be one or several fluorescence emission maxima. Emission spectra can cover a range of wavelengths. For example, fluorescein isothiocyanate (FITC) is a fluorochrome that is excited by 488 nm light and that produces a maximum fluorescence emission around 520 nm. At at least one wavelength, PC5 and PC5.5, for example, are excited by two wavelengths of 488 and 638 nm, which add complexity to the overflow patterns.
[0087] Signal detector assemblies 110 may include various combinations of filters and detectors. As shown here, a detector assembly may include a 525/40 bandpass filter for use with a PMT1 photomultiplier tube device, which is designed for detecting FITC dye emission. Such settings can be designed to provide the desired detection parameters for a specific fluorochrome. Additional configurations, as shown, may include: a 575/30 bandpass filter for use with a PMT2 photomultiplier tube device, which is designed for PE dye emission detection; a 620/30 bandpass filter for use with a PMT3 photomultiplier tube device, which is designed for detecting ECD dye emission; a 675/20 bandpass filter for use with a PMT4 photomultiplier tube device, which is designed for detecting PC5 dye emission; a 695/30 bandpass filter for use with a PMT5 photomultiplier tube device, which is designed for detecting PE-Cy7 dye emission; and a 660/20 bandpass filter for use with a PMT6 photomultiplier tube device, which is designed for the detection of APC dye emission. Individual detectors (eg, PMT1, PMT2, PMT4) can be designed to detect wavelengths of light from their respective primary dyes (eg, FITC, PE, PC5), as indicated by the emission spectrum graph 130. Bandpass filters can be used to allow a certain amount of emitted light to pass through, and into a photomultiplier tube (PMT). The specific PMT with its associated bandpass filters can be dedicated to detect the emission of conjugates excited by specific wavelengths of light.
[0088] An exemplary hardware illustration of a flow cytometry device having a ten-color filter block configuration with three lasers is depicted in Figure 1A. As shown here, a flow cytometry device can include a variety of band-pass (BP) filters, as well as dichroic short-pass (SP) and long-pass (LP) (DC) filters.
[0089] Referring again to Figure 1, it can be seen that the exemplifying techniques can involve a user or operator performing certain actions. For example, user actions 140 may include determining or selecting a configuration of a flow cytometry device, as indicated in step 142, and determining or selecting an antigenic expression pattern of interest, as indicated in step 144. With regard to the step of selecting a configuration device, such selection can be made using a dropdown button (database) to choose from a variety of predefined hardware configurations (eg like the hardware configuration depicted in Figure 1A).
[0090] According to some modalities, the selection of various hardware configurations and the selection of the antigen expression patterns of interest can be implemented in a web-based version. Under some modalities, systems and methods may involve database means that can provide an indication as to whether there are potentially more antigens outside the probe panel that are included in the expression pattern than the user has assigned to a certain pattern. . If so, this could result in pointing out to the user that there are more antigens including these probes influences the detection limits.
[0091] According to some embodiments, the hardware configuration can represent any of a variety of laser set, filter set and detection channel combinations. For example, a specific hardware configuration might include one or more lasers that emit at different wavelengths. In some cases, a hardware configuration might include six detection channels and two laser colors (for example, 488 nm and 638 nm). In some cases, a hardware configuration might include eight detection channels and two laser colors (for example, 488 nm and 638 nm). In some cases, a hardware configuration might include ten detection channels and three laser colors (for example, 405 nm, 488 nm, and 638 nm). In some cases, a hardware configuration might include ten detection channels and four laser colors (for example, 405 nm, 488 nm, 561 nm, and 638 nm).
[0092] Exemplary cell monitoring procedures involve evaluating individual cells for the relative quantitative presence or absence of certain cell surface or internal antigens. As shown in Figure 1, a specific cell at a certain stage of development or in a certain disease state may exhibit a distinct antigenic expression pattern 150 characterized by the presence of certain antigens (eg, CD28, CD26, CD3, CD15, CD59 , CD71) on the cell surface or inside the cell. Thus, an antibody-dye conjugate panel containing probes specific for these antigens (eg CD28-specific conjugate, CD26-specific conjugate, respectively) that produce specific emission spectra upon excitation can be used to analyze a biological sample for determine the extent to which the sample contains cells that express such antigens, and also the relative amount of antigen expression in cell expression. In this way, the probe panel can be used to assess or monitor the physiological state of a patient. Figures 1B through 1H provide additional details on processing user input, returning database entries and subsequent calculations, and interpreting the monitors.
[0093] Figure 1B represents aspects of a probe panel selection technique according to some modalities. As shown here, a user can introduce a set of antibodies corresponding to a specific antigenic expression pattern of interest (eg, CD57, CD45, and other cell markers). In some cases, selected antibodies can be assigned to, or associated with, the respective predefined dyes. Under some arrangements, a user can assign each specificity to a predefined dye. Under some embodiments, a user may have the opportunity to input specificity only and allow the software to propose the optimal antibody dye assignments. The selection of probe panels can further include "dummy" channels where a specific channel and related dye are not intended to be used, the dummy designation and panel design can be used as a "silent" channel and negative control. As used here, a "silent" channel indicates a detector channel that does not cause any signal overflow on any other channel and can be called a "clean column" when viewed as part of a distortion table. As used here, an "untouched" channel indicates a detector channel that does not receive any signal overflow from dyes that the channel is not configured to detect, and may be called a "clean line" when viewed as part of a table. of distortion.
[0094] As shown in Figure 1B, the use of PC5.5 as a dye for the detection of the CD33 antigen in the FL4 detection channel would be complicated by the use of PC5 or the tentative detection of PC5 in the FL4 detection channel. Consequently, PC5 is assigned as dummy in that probe panel. Figure 1B further shows that a user can assign a specific antigen to a desired dye in a specific detection channel, particularly, in the example shown, the Pacific Blue dye conjugated to CD57, which will be excited by light at 405 nm and detected in PMT FL9 detection channel. In this context, FL9 is the primary channel for the detection of the excited Pacific Blue dye (and therefore the detection of CD57), and, consequently, FL1-8 and FL-10 are secondary channels that can detect the fluorescence signal from unwanted overflow from Pacific Blue dye.
[0095] As illustrated in Figure 1C, a probe panel, which may refer to a set of antibody-dye conjugates, can be linked with a database containing information about the probes. For example, the database may contain fluorescence intensity information for each probe in the panel. In some embodiments, the term "panel" may also refer to a sequence or a group of various conjugate combinations. As shown (and discussed in more detail with respect to Figure 8 below), the database can provide information on a set of conjugates (also called a panel), which allows the calculation of the typical average fluorescence intensity of a bright positive population. , as indicated by population events in decades above 10A0, as shown in the graphs, identified as section (A) in Figure 1C. The database may also allow the calculation of the minimum fluorescence intensity of a weak positive population. In a characteristic assessment of distinct antigen expression, as seen with the CD4-PE plot, the positive and negative populations are clearly separated, and the minimum fluorescence intensity of the weak positive population is identical to 1. In a characteristic assessment of modulated antigen expression, as seen in the graph of CD184-PE versus CD57-PacB, however, the positive and negative populations are not clearly separated, as antigen densities can vary between positive cells, and the minimum fluorescence intensity is assumed to be within decade zero (ie, less than 10A0). In addition, the database can allow the calculation of the increase of a detection limit for a conjugate (and the corresponding loss of sensitivity) in decades for each conjugate resulting from the combined effects of all spillover contributions that occur, identified as ( B) in Figure 1C. In some respects, the distortion factor from one conjugate to another can be characterized as the ratio of (B) over (A).
[0096] Similarly, as shown in Figure 1D, based on the set of antibodies specific to antigen selected by the user, which can be called input 160, it is possible to query a database 162 for information associated with the respective probes. As discussed in another section here, the calculations 164 discussed in relation to Figure 1D can be used for reference, for example, in relation to the techniques described in Figure 8. Querying the database 162 on user input 160 of a conjugate can include, for example: (C) the intensity (in decades above 10A0) of a PE conjugate (or other reference dye) for each conjugate; (D) the fluorochrome brightness relative to PE for each conjugate, where PE (or other reference dye) is equal to 1 or 100%; (E) the typical increase in detection limit caused by secondary channels by decades of signal strength on a primary channel for each fluorochrome usage, ie, a distortion factor; (F) typical expression characteristics of each antigen, assessed as distinct or modulated; (G) the typical antigen coexpression patterns for each individual antigen with all other antigens, that is, the coexpressions of interest, where codes "1" are for coexpression and codes "0" are for an absence or exclusion of coexpression; and (H) the typical parent-descendant antigen coexpression patterns for each individual antigen with all other antigens, where "0" codes are for a descendant property and "1" codes are for an absence of a descendant property.
[0097] The output 166 of a database, as described in Figure 1D, depends on the area of your application. Particularly, the cell type considered of interest is different for blood cell disorders and immunomonitoring and therefore the typical co-expression patterns and the typical expression characteristics related thereto vary depending on the cell type of interest. Calculations 164 based on data retrieved from the database due to search queries can provide the following output. A value for (I), the typical mean fluorescence intensity above 10A0 for each conjugate for the bright positive populations (thus directed at distinct plots), can be calculated as:
where addition and subtraction of 10^0 provide consistency on a plot scale. A value for (J), the typical fluorescence intensity of each conjugate for weak positive populations (thus directed towards modulated plots) based on expression characteristics, is similar to the equation for (I) above, but with the value for (C) set to 0 decades above 10A0.
[0098] The value for (K), the increase in the detection limit (DL) in a secondary detection channel by overflowing an individual conjugate of a main channel, can be determined as: (K) = (I) * DF * (G) * (H) where the value of (K) is calculated for each individual conjugate detected in a primary channel, which, for example, can be nine (9) values for (K), calculated for channels on a panel of ten (10) colors. Furthermore, a value (L) for the overall increase of DL in a secondary detection channel through the combined overflow of all detected conjugates in their respective primary channels can be given by:
where, again, the addition and subtraction of 10A0 provides consistency on a plot scale. A value of (L) can be calculated for each secondary channel. The results of these calculations 164 can subsequently be output166 to either or both of a monitor and further processing.
[0099] Figure 1E represents aspects of a panel evaluation technique according to some modalities. The graphical display charts shown here can be useful in analyzing or ranking certain dashboard designs. As shown here, a particular probe, or dye, is provided having an antibody specific to the CD45 antigen, conjugated to a PE fluorophore dye. The PE dye can be excited by a 488 nm laser, and can emit spectra that are detected in an FL2 channel (eg, at approximately 575 nm). The CD45 antigen is typically strongly expressed on leukocyte cells, and PE is a bright fluorophore. Thus, it is possible to observe a strong signal intensity when applying a CD45-PE probe to a laboratory sample of human origin. As shown here, the result is a full-scale intensity. The dashed (minimum separation, square data symbols) and dotted (maximum separation, triangular data point symbols) lines coincide, indicating a distinct expression. That is, the positive and negative populations are clearly separated.
[0100] Figure 1F represents the addition of a CD25-ECD probe. The ECD dye can also be excited by a 488 nm laser, and can emit spectra that are detected in an FL3 channel (eg, at approximately 625 nm). As shown here, CD25 is a modulated antigen. That is, there is a difference between dashed (minimum separation) and dotted (maximum separation). Depending on the activation state of the cells, the cells can have an elevated expression of CD25, even a negative expression of CD25. Here, the dashed line (minimum separation) coincides with the boundary between the first and second decades. This corresponds to a desired detection threshold, whereby cells with very low expression (weak signal) can be detected, as well as cells with higher expression (bright signal). As depicted here, cells with a higher expression (maximum separation; dotted line) are about 0.75 decades higher than cells with a lower expression (minimum separation; dashed line). The difference (A) between the minimum (dashed) and the maximum (dotted) represents the range of expression of CD25 according to this modality. Note that these data may also take into account parameters associated with the ECD dye itself. The smaller panel bubble plot in Figure 1F indicates that the emission spectrum of the PE dye is overflowing into the ECD detection channel and causes an estimated increase in the detection limit according to the position of the center of the circle when projected to the y axis (in decades) (FL3).
[0101] As discussed here in another section, these plots can include different lines corresponding to different laser settings (eg 405 nm, 488 nm and 638 nm) The threshold between the first and second decades can be used to evaluate certain data of the sign. In some cases, a specific background can be assumed for a specific cell type or co-expression pattern. For example, a T cell might have a certain pattern compared to a B cell. Thus, a user can select a phenotype (and corresponding co-expression pattern) that they might want to see in a simulation output. In some cases, for a given expression pattern, the detection limits for a specific cell type may be different from the detection limits for another specific cell type, for example, depending on the presence or absence and quantitative characteristics of the given pattern. of expression.
[0102] As shown in Figure 1F, a detection limit can be represented by a dashed line (square data point symbols). In cases where the expression characteristics are distinct, there may be either a negative population or a positive population, but no intermediaries. That is, there will not be cells with different degrees of antigen densities in the specific population. Thus, the dashed and dotted lines will coincide (as with CD45-PE at about 575 nm). At this point, the highest expression density is equal to the lowest expression density. In contrast, with modulated expression, an antigen can be found in a specific cell type at a very low expression density, up to a very high expression density. As shown here, a modulated expression can be represented by a dotted line. For example, in a specific CD3+ cell type, CD4 can be present (CD4+, CD8) or absent (CD4-, CD8+). This represents a strong positivity, and corresponds to a distinct expression feature. As discussed in another section here, the probe scoreboard can also be based on specific phenotypes (eg, assuming a standard phenotype such as T cells or monocytes). Thus, users can assess probe panel characteristics according to different cell types and expression patterns, and determine how expression patterns can affect detection limits for specific antigens.
[0103] In contrast to Figure 1F, where the CD25 probe has an ECD tag, Figure 1G represents the results of a CD25 probe having a stronger fluorochrome, PC5.5. The desired detection limit of Figure 1G is the same as shown in Figure 1F. However, the range (A) between dotted (maximum) and dashed (minimum), which the modality assumes, for the signal strength dynamics is much greater in Figure 1G. As shown here, the range is about 1 decade. The bottom bubble plot of the Figure 1G panel indicates that PE dye emission spectra are spilling over into detection channel PC5.5 (FL4).
[0104] In Figure 1H, the CD25-ECD probe is reintroduced. Thus, it is possible to compare the highest expression (signal intensity) that could be expected for a CD25-ECD marker with the highest expression density that could be expected for the CD25-PC5.5 marker. Assuming, again, on a logarithmic scale, the latter is considerably larger.
[0105] It is also useful to consider the background (solid line). For example, as illustrated in Figure 1H, some background BG content may occur due to high lymphocyte expression levels of CD45, which is tagged with the CD45-PE marker. As shown here, that BG background signal from PE in the ECD channel is larger than the ECD signal in the ECD channel. Thus, there is a significant loss of information, and such a panel design may not be favorable.
[0106] In Figure 1I, the CD25-PC5.5 probe is removed and a CD25-PC7 probe is added. There are overflows from PE and PC7 to channel FL3. In Figure 1J, the CD45-PE probe is removed, and PC7 is transferred to channel FL3. A favorable result is provided when the solid line (bottom, BG) coincides with or approaches the threshold between the first and second logarithmic decades, as shown in Figure 1J. As shown here, there is not a huge amount of background that is caused by overflow in the ECD channel. Instead, there is a small amount of overflow from the CD45-PC7 probe emission to the FL3 ECD detection channel. Thus, there is a gap between the background (BG) and the highest expression density (maximum; dotted line). Consequently, the detection scheme misses only those cells that have very low levels of CD25 expression.
[0107] Figure 1K represents the replacement of the CD25-ECD probe of Figure 1J with a CD25-PE probe. The PE fluorochrome is stronger than the ECD fluorochrome. As shown in Figure 1K, there is a similar BG background provided by the CD45-PC7 probe in the PE FL2 detection channel. However, the distance between background and maximum is greater in Figure 1K compared to Figure 1J, due to the stronger fluorochrome PE over the CD25 probe.
[0108] In Figure 1L, the CD25-ECD probe is again included in the panel. Here, it can be seen that the distance between the background (solid) and the maximum signal (dotted) is greater for CD25-PE than for CD25-ECD. Thus, the overall sensitivity is greater. This result takes into account the intensity of the fluorochrome used in the CD25 probe (eg PE intensity > ECD intensity), and also the overflow pattern that originates from other antigens (eg CD45), which are also expressed in the cells. Both the fluorochrome intensity and the co-expression overflow pattern can contribute to sensitivity.
[0109] In Figure 1M, a CD45 antibody is conjugated to an APC-AF750 fluorophore and it can be seen that there is no overflow. Here, the solid line (BG) coincides with the threshold between the first and second decades and therefore there is no added background. Here, there is no complete sensitivity, so signals can be detected even when there are cells with low expression densities for CD25. As discussed here in another section, as more probes are added to the panel, more complex results will be observed. Such results can take into account the various contributions (eg, fluorophore intensity), in which the co-expression pattern is relevant for the overflow.
[0110] Figures 1N and 1O provide an example for three conjugates, in which a given CD can be associated with a given dye. As shown in Figure 1N, each CD dye conjugate has an MFI for PE conjugation above 10A0 decades (PE being the reference dye); as shown, CD-X has an MFI of 0.5, CD-Y has an MFI of 1, and CD-Z has an MFI of 2.5. In some embodiments, the parent's descendant array can be symmetric. In some embodiments, the parent's descendant array may be asymmetric. As shown here, the coexpression matrix can be symmetric, although in other respects the coexpression matrix can be asymmetric. Additionally, as shown in Figure 10, each dye of the CD dye conjugates has a relative intensity compared to PE; dye-A has an intensity of 0.2, dye-B has an intensity of 0.45, and dye-C has an intensity of 0.85. These CD-dye conjugate data values allow the calculation of a skew factor table, as seen in Figure 1O, where, in part, the B-dye has a skew value of 0.25 resulting in PMT FL1 which is directed towards the detection of the A-dye, and has a distortion value of 0.75 resulting in the PMT FL3 which is directed towards the detection of the C-dye. In contrast, dye-C has no distortion effect on PMT FL1, and similarly, dye-A has no distortion effect on PMT FL3 which is directed towards detection of dye-C. In further contrast, dye-A has a skew value of 0.65 and dye-C has a skew value of 0.1 in PMT FL2 which is directed towards detection of dye-B. The calculations shown in Figure 1O are applications of the equations as determined by Figure 1D, using the relevant values given in Figures 1N and 1O.
[0111] When the coexpression pattern has a certain parent/descendant scheme, or an exclusion scheme, then the calculated overflow may not add to the overall distortion. For example, Figure 1P represents the calculated result for a single dye (CD25-PE probe). When a second dye (CD45-FITC) is added to the panel, as shown in Figure 1Q, it can be seen that the FITC dye emission exerts an overflow into the PE FL2 detection channel. The circle in the bubble plot corresponding to the FITC overflow has a certain Y-intercept, and a certain diameter. As shown here, the CD45-FITC probe can take about half a decade of sensitivity in the FL2 PE channel. For example, the distance between the bottom and the maximum in Figure 1Q is about half the distance as depicted in Figure 1P.
[0112] In Figure 1R, the CD45-FITC probe of Figure 1Q is replaced with a CD15-FITC probe. The CD15 antigen is less highly expressed and therefore the signal in the FITC detection channel is lower in Figure 1R compared to Figure 1Q. For example, it can be seen that the maximum (dotted line) is no longer between the third and fourth decades. However, CD45 is co-expressed with CD25, while CD15 is not co-expressed with CD25 (for example, according to the default settings in the database). Thus, there is no effective distortion caused by CD15-FITC probe overflow in the FL2 PE channel. Similarly, there is no background addition in the PE channel. In other words, the overflow is still present, but it does not affect CD15 negative cells with respect to analysis of their CD25 expression potential. CD15+ cells in this example do not express CD25. Thus, based on cell biology, CD25 expression would not be analyzed in CD15+ cells. These two different antigens could thus be evaluated using different ports.
[0113] In another illustration of a coexpression scheme, Figure 1S represents results in which both a CD3-FITC probe and a CD45-ECD probe are generating overflow spectral emission to the PE FL2 detection channel. The global distortion is a little bigger than half a decade. Such a result can be consistent with an observable co-expression pattern in a T cell. It can also be seen that there is a smaller amount of spillover to the FL1 FITC detection channel. Bubble plotting is useful in solving contributions. For example, when considering the Y-intercept values corresponding to the overflow in the FL2 channel, it can be seen that the CD45-ECD overflow is greater than the CD3-FITC overflow. The respective bubble diameters in the FL2 channel indicate that ECD provides a greater contribution to the overall distortion.
[0114] Thus, to improve the signal in the FL2 channel (eg increase sensitivity), it may be more desirable to remove the ECD overflow in FL2 (eg keeping the FITC overflow). Figure 1T depicts such a result, in which the CD45-ECD probe of Figure 1S is replaced with a CD45-APC probe. Thus, there is a considerable improvement in the FL2 PE channel for the detection limit. That is, there is a greater distance between the background (solid) and maximum (dotted) lines.
[0115] In Figure 1U, the CD3-FITC and CD45-APC probes of Figure 1T are replaced with the CD3-APC and CD45-ECD probes, respectively. As shown in Figure 1U, there is still about a half decades of signal loss in the FL2 PE channel.
[0116] Bubble plots provide a useful indication of how a probe configuration (eg, antigen specificity, fluorophore) can contribute to the detection limit for a detection channel.
[0117] The display of results for an exemplary ten-color panel probe is represented in Figure 1V. As shown in Section A, to prioritize conjugates that detect antigens having a modulated expression characteristic, a large distance between an estimated mean fluorescence intensity (triangle) and an overall increase in DL (circle) may be desirable, and may serve as a criterion for classifying different combinations of conjugates for identical sets of antigens. As shown in Section B, the bright (triangle) and faint (box) estimates mean that fluorescence intensities can be based on typical antigen expression densities and relative fluorochrome intensity (eg, = (I) & (J ) as depicted in Figure 1D), and, for example, may coincide due to the distinct expression characteristics for the conjugate-CDxy-APCA750 detected in the respective primary channel of FL8, assigned to a red laser. As shown in Section C, there may be an overall increase from DL = (L) to FL8, attributed to a red laser. As shown in Section D, due to the modulated expression characteristics of CDqw-ECD the typical weak expression density (triangle) can be adjusted for zero decades above 10A0.
[0118] Another display of results for an exemplary ten-color panel probe is shown in Figure 1W. As shown in Section A, a PE-conjugate (see caption) in this panel causes a 0.6 decade increase in the detection limit (DL) above 10A0 in the third detection channel of FL3 (eg when referring to intercept on the Y axis), and the diameter of the bubble circle corresponds to the contribution of the PE-conjugate to the global increase in the detection limit at FL3. Thus, it can be seen that the PE conjugate provides the greatest contribution to increasing the detection limit in FL3. As shown in Section B, the PC5/PC5.5 conjugate (see caption) in this panel causes a low-to-moderate increase of 0.2 decades above 10A0. In many cases, such an increase will be acceptable. Section B also indicates that, based on the magnitude of the bubble diameter, the PC5/PC5.5 conjugate can be considered as the main contributor to the overall increase in DL in detection channel 6.
[0119] Thus, in a typical procedure, the user may select a specific hardware configuration, which may involve certain filters (eg, bandpass filters) associated with various detectors. In some embodiments, a specific hardware configuration can be assigned or predetermined without allowing such selection. Various hardware configurations can have associated distortion factor values. In some cases, hardware configurations or corruption data can be retrieved from a database. For example, a database may include data that indicates that a distortion factor of a specific bandpass detector configuration (eg, infrared) is greater than some other detector configuration. Consequently, sensitivity and/or measurement error may vary based on hardware configuration. In some modalities, a database can include a predefined number of hardware configurations, and the user can select among them. For example, a specific hardware configuration might include data related to the Navios™ Flow Cytometry system or the Gallios™ Flow Cytometry system (both available from Beckman Coulter, Brea, California, USA). In some cases, a specific hardware configuration will have an associated distortion factor profile. The number of lasers and/or the number of detection channels contained in the hardware configuration may have an influence on the number of fluorochromes used in a specific probe panel. In some cases, the characteristics of bandpass filters influence distortion factors. Similarly, the quality of a PMT or other detectors such as an avalanche photodiode can influence the rate of distortion. ANTIBODY SELECTION FOR STIMULUS AND PANEL DESIGN
[0120] Figure 2 depicts aspects of an operator selection procedure 200, whereby the operator enters certain antibody and dye selection information for use in selecting an antibody panel. For example, as shown in step 210a, the operator can select an antibody specific for antigen 1 (eg, CD28), optionally together with a corresponding dye (eg, FITC), as indicated in step 210b. Furthermore, the operator can select additional antibodies specific to respective antigens of an expression profile (eg steps 220a, 230a, and 240a), optionally, together with the respective corresponding dyes (eg steps 220b, 220c, 220d). As shown here, antigen-specific antibody parameters can be selected from a database drop-down menu, and dyes can also be selected from a database drop-down menu. In some cases, the colorant selection process can be configured so that no colorant is expressly selected by the operator. Instead, a dye associated with the selected antibody can be provided by the database. Under some arrangements, if the user does not assign dyes to the antibodies, then the system can identify the most suitable dyes for all unassigned antibodies based on consideration of the entire probe panel (sensitivity, etc.) outside the base of data that will provide all available conjugations for the respective antibody. The fluorochromes chosen by the system cannot be displayed until the system has performed the necessary iterations and calculations, that is, when simulated data is output.
[0121] Systems and methods as disclosed herein may incorporate user interface and dropdown database features shown here. Thus, the user can designate the antigens of interest, and the designation of the antigen, in turn, will influence the selected antibody. According to some modalities, the user will directly choose the antibodies in the interface. In some cases, the antigens of interest can correspond to a specific cell type, such as a T cell. Thus, a user can select a collection of antibodies that corresponds to a panel of T cells.
[0122] The menu shown in Figure 2 includes two columns that can be presented to the user. The left column corresponds to antibody specificity, and the right column corresponds to dye selection or assignment. At this stage of the process, the user may or may not have a specific expression pattern in mind. For example, the user might only have a list of antigens that might be of interest. In some cases, a user may know when to select certain antigens whether it is desirable to be more sensitive when selecting a dye or, alternatively, less sensitive. Similarly, a database often includes data relating to the expression density of a specific antigen, and this data can be taken into account when the dye is selected or assigned by the system. In some cases, expression density may depend on a specific cell type. For example, the database may include information that a specific marker is minimally expressed for a given cell type, whereas the same marker is highly expressed in another cell type. If a user does not select a dye to go along with a selected antibody, the system may take other characteristics, such as cell type, into account when recommending or assigning a dye to that antibody. According to some modalities, at this stage of the process, the system will still not assign a colorant in case the user does not select one.
[0123] Typically, different dyes will have different brightness quantified by the quantum yield and dye absorption coefficient, indicating how much of the light passage is absorbed and how much light absorbed by the dye will translate the fluorescence emission, respectively. In many cases, the absorption coefficient and the quantum yield match each other. For example, a PE dye is considered to have a high quantum yield and absorption, and a high percentage of the absorbed light is converted to fluorescence emission. In contrast, FITC has a lower absorption coefficient and quantum yield, and a lower percentage of absorbed light is translated into fluorescence emission. As a result, the selection of a specific dye can determine the sensitivity that can be obtained. single antibody molecule can be covalently linked to multiple FITC molecules due to the small size of the FITC molecule (<1 kD), which cannot be realized for the large PE molecule (> 200 kD)).
[0124] In some cases, the user may choose to accept one or more of the default colorant selections provided by the system database. Optionally, the user can choose to modify the default selections. Under some embodiments, at this stage of the process the system may not recommend that dyes be used with antibodies as the full range of information provided by all user interface inputs may be required to select the appropriate dyes for the antibodies. Similarly, a system database may include a certain assumed expression density for a typical/specific T cell antigen (eg, CD3 antigen). By selecting a specific antigen specificity, the system can obtain a specific expression density that is typical for an antigen of a certain cell type. For example, when selecting an antibody specific for a CD3 antigen, the database may include information relating the association of the CD3 antigen with a T cell. As another example, a user may select the specificity of the antibody for CD16, CD38 , and the like. Some antigens have multiple expression densities, present in multiple cell types. Thus, expression densities can vary between T cells, B cells and monocytes, for example. For example, when selecting an antigen specificity, the database can have three typical expression densities for three different cell types. In some modalities, the user can choose to modify a default or default density, if desired.
[0125] Figures 2A, 2B and 2C illustrate similar operation selection characteristics for target phenotype, phenotype exclusion, and parent/descendant schemes, respectively. Figure 2D represents the operation selection characteristics for the antigen density parameters.
[0126] As shown in Figure 2A, the user has the option to define various phenotypes, if desired, based on the selection of antibodies that may be contained in a database. In some embodiments, information about typical antigen co-expression patterns that correlate with defined phenotypes can be included in a database as pre-selected or pre-defined typical antigen co-expression patterns. The database may include standard information about the co-expression patterns of normally occurring antigens. This is less specific than predefined cell phenotypes and allows for combinations of antigens that do not occur at the same port. As an example, the T cell is a prominent type of cell phenotype that is used in cytometry. Optionally, the user can define their own phenotype. In some cases, a user can assign a certain expression pattern to a phenotype. As shown here, the user also has the option to skip this step and not specify a phenotype. By selecting a specific phenotype, the database can provide an associated set of antigen-specific antibodies. According to some modalities, the user can define the phenotype by directly choosing the antibodies. For example, if a dye has been assigned to an antibody in Figure 2, then the formula may display that preselection in the right column in response to the choice of the respective antibody in the left column of Figure 2A. The column on the right side can allow the user to pre-select colorants in an auto-fill form. Under some modalities, at this stage of the process, the user may no longer be able to assign dyes to antibodies (for example, as was done in Figure 2). However, if the user has assigned dyes to antibodies in Figure 2, then those dyes will be displayed in the right column by choosing the respective antibodies in the left column. In some cases, it is possible to select between different phenotypes, or give priority to phenotypes. For example, phenotypes can be prioritized based on a target population, or based on a specific antigen that must be detected in a specific phenotype. In some cases, the system may prioritize an antigen based on expression density. For example, a high priority can be given to an antigen with a low expression density.
[0127] As shown in Figure 2B, the user has the option to select various phenotype exclusions, if desired, which may be contained in a database. For example, a database may contain information indicating that a certain antigen never occurs on the same cell surface with another certain antigen. Optionally, the user can define certain exclusions. As described here in the left column, the user can define a first exclusion, which corresponds to an antibody that was previously selected as discussed in relation to Figure 2 and is displayed in the header section of the left column. The user can select multiple exclusions in this way for multiple panel antibodies. For example, the user can indicate that a CD-X antigen must be unique to the CD-Y and CD-Z antigens. These selections can have an impact on a graphical display for a probe panel, as discussed elsewhere here. According to some modalities, the exclusion of a specific antigen may not influence a respective detection limit, because only markers that are on the same cell surface can influence each other's channel detection limits. In other words, since the antigens are not on the same cell surface, the labeling dyes will also not be on the same cell surface and, therefore, there can be no interference between the respective emission spectra. As shown in Figure 2B, the user also has the option of not specifying exclusions.
[0128] In some cases, a user may select two antigens that are never expressed together. Optionally, the database can make certain assumptions about the exclusions. For example, the system can assume that a specific T cell marker and a specific B cell marker are unique to each other. In some cases, a system may allow a user to indicate that there are no exclusions. Consequently, the user may be presented with several options, including (a) accepting exclusions provided by the database, (b) actively assigning exclusions, and/or (c) not assigning any exclusions, so that any antigen can occur with a another antigen, on any cell.
[0129] As shown in Figure 2C, the user has the option to select multiple parent-descendant relationships, if desired, which can be contained in a database. For example, a database may contain information indicating that a certain antigen has a parent or descendant relationship with another antigen. Optionally, the user can choose not to specify this relationship. As described here in the left column, the user can define a first parent and any related descendants, which can correspond to antibodies that were previously selected as discussed in relation to Figure 2. As an example of a parent-descendant scheme, a relationship parent-descendant can mean that a parent marker has expressed itself in parent cells, and that cell A expresses a parent protein in addition to the subpopulation/descendant protein A, and that the B cell expresses the parent protein in addition to a protein B of subpopulation/descendant. For example, a parent T cell may include a CD3 antigen, and some T cell dependents may express the CD3 antigen along with a CD4 daughter T cell antigen, and some T cell dependents may express the CD3 antigen along with a CD8 daughter T cell antigen. The expression of CD4 and CD8 on daughter cells may be mutually exclusive. For example, daughter cells can be CD4+/CD8- or CD4/CD8+. In some cases, each CD4+ daughter cell is CD3+, each CD8+ daughter cell is CD3+. In this way, parent-descendant relationships can be considered to have an effect on patterns of expression. Similarly, parent-descendant relationships can be considered to have an impact on the relevance of the associated misstatement. As an example, if a descendant antigen is tagged with a dye that can cause a distortion in the channel when the tagged parent antigen is detected, then it may not be necessary to take that distortion into account. For example, when all CD4+ cells are also CD3 positive, there may not be any CD4+ that can be in the background (for example, there is no CD4+ that is also CD3-). In some cases, a parent CD3 marker can cause distortion in a child CD4 channel.
[0130] Thus, parent-descendant relationships can have an effect on background distortion. When the distortion is applied to a positive population, it can be described as a population that is in a larger range of the logarithmic scale. In some related cases, such measurement error may not be substantially relevant with respect to the detection limit of the channel in which the positive population is detected. Under some embodiments, there is no effect caused by a parent-descendant relationship other than producing an overflow and related distortion irrelevant if the overflow occurs from the descendant channel to the parent channel. If the overflow occurs from the parent to the descendant channel "normal" distortion applies.
[0131] As discussed in another section here, the primary detection channel for a specific fluorochrome is the channel in which the user intends to detect the signal associated with the fluorochrome. With respect to a single primary channel, there may be one or more secondary channels (eg where a fluorochrome for the primary channel has emission spectra that spread out to the other channels). For example, a system may be configured to primarily detect PE dye in one FL2 channel, however, the PE emission spectrum may still spill over to other channels such as FL3, FL4, FL5, and the like. In this sense, secondary channels can indicate where undesirable signal strengths are detected. Such overflow characteristics can be used in conjunction with parent-descendant relationships to evaluate or configure probe panel designs. In some cases, a probe can be assigned to a primary channel while having strong spillover to a secondary channel, where the secondary channel represents a primary channel. As also described elsewhere in this document, parent/descendant systems for typical populations can be represented in a database. In some cases, the user may choose to proceed with a predefined parent-descendant scheme. In some cases, a user may choose to define their own parent-descendant scheme. In some cases, a user may choose to specify that there is no parent-descendant scheme. For example, with respect to the analysis of blood cell disorders, certain patterns of expression can be surprisingly aberrant. In such cases, it may be desirable for the user to indicate that there are no parent-descendent relationships.
[0132] As shown in Figure 2D, the user has the option to select certain antigen density parameters, if desired, which may be contained in a database. For example, standard antigen densities may be contained in the database. Such patterns may refer to typical populations that are present in human peripheral blood. Other types of patterns can be implemented. Thus, depending on whether a user is interested in evaluating material associated with cell culture cells, or material associated with bone marrow, for example, different expression densities and/or patterns may be available.
[0133] As depicted in Figure 2D, for individual antibodies, it is possible to select a phenotype as indexed according to Figure 2A. Each antibody selected for a specific phenotype can be represented on a histogram. In addition, a user may have the option to accept or modify what is considered a typical expression density. With reference to the bar below the histogram, a button can be moved to shift the positive population versus the negative population. In this way, it is possible to influence the signal-to-noise ratio that can be assumed for a given population. Changes in the signal-to-noise ratio can influence interference or overflow that can be expected to be present on other channels. Under some modalities, the terms interference and overflow may be used interchangeably. Thus, when the brightest signal is assumed, or when a brighter signal is selected or adjusted, a greater amount of interference or overflow can be assumed to be present on other channels. In some cases, a user may choose to proceed with a pattern (eg antigen density and/or signal strength). In some cases, a user can modify the signal intensities according to the expected characteristics of a specific desired phenotype, such as a T cell population or a monocyte population or a non-human cell population or a non-blood-related population human peripheral or bone marrow or others.
[0134] Figure 3 represents aspects of a probe panel selection technique 300 according to the modalities. As shown here, data such as user-selected antibody parameters 310 (for example, as described in Figure 2), a flow cytometry device configuration 320 (for example, as described in Figure 1), and a database 330 of antibody, can be introduced or accessed by a simulator module 340, which operates for the proposed optimal combinations according to prioritized phenotypes and/or antigen expressions, and for the production of graphics (eg, A, B, and C ) showing detection limits and population separations based on simulated probe panels. As illustrated by step 350, you can select a probe panel based on the simulations. In the embodiment shown here, the selected probe panel is for configuring the flow cytometry device having three lasers and ten color detection channels (PC5 and PC5.5 can be detected by the same channel). The panel includes antibody-dye conjugates specific for ten CD antigens.
[0135] Table 1 below represents aspects of an exemplary probe list that may be associated with, or part of, the probes in the database. As depicted herein, individual probes can include an antibody or other specific binding agent for a specific antigen, conjugated to a dye. Table 1

[0136] It is understood that a surface or cell structure such as a cluster of differentiation (CD) and antibody directed against the structure or cluster may be referred to interchangeably. For example, an antibody that recognizes cluster of differentiation 3 (CD3) may also be called a CD3 or CD3 antibody. In some cases, the term anti-CD3 antibody may be used. In some cases, there are cellular structures that have not been assigned a CD number. In such cases, the structure name itself can also be used to name the antibody. In some cases, the nomenclature of an anti-"framework" antibody may be used.
[0137] As discussed here in another section, according to an expression pattern that is retrieved from a database (either assumed or assigned by a user), a specific expression pattern may correspond to a coexpression schema, a schema exclusion, or a parent-descendant scheme. In some cases, selected antigens may be attributed to a certain phenotype. Based on these categories of information, you can graphically display data in two-dimensional plot plot representations, such as those shown in panels A, B, and C. In some cases, the representations may also incorporate estimates of background distortions.
[0138] As shown here, population X may reflect cases where the intention is to detect antigen X in a specific population. In panel A, there is an acute critical point of the straight line that delimits the upper left quadrant of the upper right quadrant, indicating a strong increase in a detection limit for the Y antigen for the population expressing the Y antigen, depending on the antigen density Y. These detection limit characteristics are very different from those for antigen X for the population expressing antigen Y. The line delimiting the lower right quadrant of the upper right quadrant has no critical point but is parallel with the outline of the diagram indicating Thus, there is no dependence of the detection limit for antigen Y on the density of expression of antigen X in the respective cells. Thus, this graphic production can indicate to the user that this specific choice may not be optimal.
[0139] Therefore, when the user selects or enters a certain antigen expression pattern (eg, as associated with 310 antibody parameters), the system can retrieve certain antibody-dye conjugates from a database based on the selection or user input. The user can then look at the various graphical outputs associated with the antigen expression pattern. According to some modalities, the system can generate an optimized probe panel proposal according to the antigen selections made by the user. Under some embodiments, a user can select a specific antibody specificity profile or antigen expression pattern, and the system can return a permutation of all possible conjugate combinations for the profile or pattern. In some cases, the system may display conjugate combinations selected according to priority antigens. Thus, there may be a priority ranking for certain antigens, and the ranking can be used to determine which conjugate combinations are expected to provide the optimal results with respect to sensitivity and which conjugate combinations to display in a graphic output. According to some embodiments, the system can return optimized conjugate combinations according to phenotype/prioritized conjugates related to fluorochrome brightness expression patterns and the like. In some cases, antigens can be prioritized based on sensitivity. For example, it may be desirable to have a higher sensitivity for one antigen, while a lower specificity for a second antigen is considered sufficient.
[0141] As discussed here in another section, graphic production can be influenced by several factors. For example, the display may depend on expression pattern, flow cytometry device configuration, and/or probe characteristics (eg, whether dyes are considered optimal for a given specificity within a given antibody combination, or the dyes are specifically selected).
[0142] According to some arrangements, some or all of the entries retrieved from the database may be predefined. Similarly, some or all of the proposals for probe panels as recommended by the system can be calculated based on an entire set of antigens, including expression patterns. For example, ten pattern sets for ten antigens may result in a different probe panel recommendation when, for example, different expected expression patterns are chosen by the user. Modifying certain parameters, such as an expression pattern or signal strength, can result in a change in the monitor. In some cases, a simulation monitor can indicate a specific system recommendation. In some cases, a simulation monitor can show the output according to pre-set colorants. In some cases, a simulation monitor may show output according to a mixed case, where some colorants are selected by the user and some colorants are selected by the system. Under some embodiments, systems can return optimized conjugate combinations according to phenotype/prioritized conjugates related to gloss expression patterns of fluorochromes and the like. Relevant antigens for a certain phenotype can be shown in bivariate point plots or other representations. In Figure 3, there are three plots shown. Embodiments of the present invention encompass the display of any number of plots. For example, when five antigens are selected, there can be ten plots, as represented in Table 2.

[0143] Under some modalities, the user can review the display output plot, and take into account any antigens that have been prioritized, any knowledge of target populations, and/or any knowledge of target coexpression antigens, and then , evaluate the detection limits and determine if a large distortion is expected or not. In some cases, a system may recommend a specific probe panel, and the user may select the recommended panel, regardless of what is shown in a graphical display.
[0144] For example, in the case where CD3 is co-expressed with a specific antigen, and there is no significant population bias because there are too many antigens spreading into the detection channel, the user may decide not to proceed with such a panel of probe. The user can then go back to any step as seen in Figures 2-2D and modify their entries and/or default entries as retrieved from the database. Detection limits
[0145] One goal in flow cytometry, for example, where cells are stained with immunofluorescence dyes, is to analyze the proteins or markers that are expressed on the surface or inside of the cell. In this way, it is possible to classify cells (or subsets of cells) as being positive or negative for a specific antigen or marker. Thus, for example, some cells may be considered "negative" for a specific marker or antigen, and other cells may be considered "positive" for a specific marker or antigen. To determine whether a cell is negative or positive, it may be useful to define a signal strength threshold, whereby cells that express minimal or no levels of antigen to produce a signal strength below the threshold are considered "negative " and cells that express sufficient levels of antigen to produce a signal intensity above the threshold are considered "positive". Such a signal strength threshold may also be called a detection threshold.
[0146] Thus, in some cases, a detection limit may refer to a threshold between a negative event and a positive event on a specific scale. That is, there may be a positive/negative cutoff, whereby an undetectable or low level of signal or fluorescence corresponds to a negative event. In some cases, non-specific fluorescence may correspond to a negative event. In contrast, a sufficiently high level of signal or fluorescence can be considered a positive event.
[0147] As discussed here in another section, a specific detection limit can be based on a formulation that involves isotype data, fluorescence intensities, distortion factors, and the like. Isotype control staining
[0148] A typical immunophenotyping protocol involves labeling cells from a biological sample with antibody-dye conjugates that specifically bind to proteins on the surface of cells. In this way, the proteins expressed by cells can be analyzed. Due to the nature of conjugate probes, however, unwanted non-specific binding can occur between probes and cells. That is, a probe can bind to a certain cell, even though the probe is not designed to bind to the cell.
[0149] Isotype control antibodies can be used to provide a negative control for such fluorescence or non-specific binding. Typically, isotype control is generated from the same host from which the antibody probe is generated. For example, if an antibody to a conjugate probe is generated from a mouse, then the antibody isotype used to control the probe is also generated from a mouse. Furthermore, the isotype can be generated so that it has a specificity for an antigen that is not known to occur in the sample (or otherwise, which does not occur in a target cell).
[0150] When staining a sample with an isotype control, the resulting signal may be an indication of non-specific binding of an antibody-dye conjugate to a cell surface. In this way, an isotype control can be used to assess the level of background intensity that might occur in a conjugate staining procedure. For example, isotype control can mean the level at which the fluorescence intensity obtained with a probe can be considered specific. That is, when coloring with the projected probe, a detected signal exceeds the background intensity level provided by the isotype control, so it is possible to deduce that the detected signal corresponds to a positive event.
[0151] In practice, an isotype control can be used to distinguish specific binding from a non-specific binding, to define specificity or switching controls, to designate the location of gates or graphic regions or boundaries used to classify cells , to determine positivity or negativity for specific antigens, to assign positive/negative boundaries in the data, and the like. Antibody isotype controls can be used in flow cytometry procedures. It may be specifically useful to use antibody isotype controls when employing multiple stains in a flow cytometry procedure.
[0152] In some cases, for example, when an antigen has a distinct bimodal expression, with no overlap between positive and negative populations (eg, CD4 and CD8 on T cells), it may be possible to proceed without the use of a control.
[0153] Isotype controls can help resolve signal to noise issues in a cell analysis protocol. By determining whether an expression pattern or cell of interest can be positive for a particular marker, an isotype control can help optimize resolution sensitivity without unduly sacrificing specificity. When using an isotype control, it is possible to assess whether a detected signal can only correspond to non-specific binding, or to some combination of non-specific and specific binding. As discussed in another section here, isotype controls can be useful in determining a detection limit. Isotypes can contribute to background, and spectral overlap or overflow can contribute to background scattering, both of which can play a role in sensitivity.
[0154] Figure 4 describes certain aspects of isotype signaling and resolution sensitivity. For example, as shown in panel A, the isotype sign on the left corresponds to a negative event, and the detected sign on the right corresponds to a positive event. Here, the negative population has a low background relative to the positive population, and populations can be easily resolved (eg with a well-defined detection limit). In contrast, panel B shows a situation where the negative population has a high background relative to the positive population and, consequently, it may be difficult to resolve populations. Similarly, panel C shows a situation where the negative population has a low background (relative to the positive population) and a high coefficient of variation (CV) and therefore populations can be difficult to resolve. Such a situation may be present when an isotype control has significant overflow. Thus, to be able to resolve the positive and negative populations, it is useful to have a negative sign with low background relative to the positive sign, and to have a negative sign with low scattering or variance.
[0155] As discussed here in another section, a dye in one probe may have an emission that spills over into a specific channel designed to detect the emission of a dye from another probe. In such cases, the spillover can be analogous to the scattering of negative population data as described above.
[0156] In some cases, the population amplitude in a secondary channel can increase due to scattered data as caused by the overflow of another fluorescent marker in the same population detected in a primary channel, which can contribute to an increase in the detection limit in the secondary channel. For example, as the measurement error for a negative population increases, the range of distribution may also increase. In some cases, there may be a large measurement error, and a signal detected on a secondary channel in the presence of an overflow from a primary channel may be beyond an isotype control signal on a secondary channel in the absence of an overflow of a primary channel, but can still correspond to a negative event. In some cases, overflow can contribute to an increase in the detection limit. For example, an increase in the amount of overflow for a specific detection channel can operate to increase the detection threshold for that channel. This can be associated with greater measurement error which is caused by a more intense form of fluorescence detection in the specific channel.
[0157] In some cases, an isotype antibody can be used to obtain a baseline. In some cases, an unstained cell can be used to obtain a baseline. The greater the number of wash steps applied after staining during the process, the more likely the isotype functionality approaches the functionality of the unstained cell population, with respect to background fluorescence. That is, extensive washing can operate to equalize the background, such that even if isotype dye has been added, the washing acts to remove all non-specifically bound isotype.
[0158] Following a staining protocol with a multicolor probe panel, there may be multiple fluorochromes associated with the cell surface. To assess antigen-specific positivity as detected by a single certain conjugate on these cells, it may be useful to take into account the emission of all other fluorochromes in these cells, which may contribute to increasing the detection limit for the respective single fluorochrome . Fluorescence intensity
[0159] Typically, flow cytometry involves the use of dyes or fluorochromes that have certain properties, such as brightness values or fluorescence intensity. For example, PE dye can have a gloss intensity of 3420,000, whereas FITC can have a gloss intensity of 39,000. These intensities can be measured as relative to each other, with one dye being chosen as a reference for the maximum intensity. As discussed in another section here, the intensity of the individual fluorochromes can be considered when evaluating the probe panels. Distortion
[0160] A distortion of a background population and, consequently, an increase in the detection limit may correspond to the number of fluorochromes that spread on the cell surface or inside the cell or the intensity of the overflow signal related to the respective channel. detection. So, for example, a greater amount of overflow can correspond to a wider background distortion. In some cases, it is possible to consider the number of antibodies bound to the cell surface, it is considered that this larger number of antibodies labeled with the specific spreading fluorochrome contributes more to the background distortion. In some cases, a database may contain estimates of background distortion, for example, based on assumed intensities that are typical for a certain cell type. In other words, the distortion may be based on the number of antibodies tagged with spreading fluorochromes that are bound to that cell type.
[0161] As an example, it is possible to consider a T cell that expresses the CD3 antigen along with some other Z antigens co-expression. Typically, T cells have a very strong expression of CD3 on the surface. Thus, when staining with an anti-CD3 probe, which is intended to provide the emission signal for a specific channel, it is possible that the dye-CD3 antibody conjugate probe actually interferes with speech with the intended channel. to detect the Z probe, then the detection limit for the Z probe can be increased. When performing a multicolor experiment, complex overflow patterns can occur. For example, for a ten-color experiment, it may be necessary to take into account the spillage of another nine colors when considering a specific detection channel. That is, each of the other nine colors can contribute to an increase in the detection limit for that specific channel. In some cases, the distortion may be dependent on the cell type and related expression patterns being analyzed. In some cases, the distortion may be dependent on the number of antibodies bound to the cell type that carries a spreading fluorochrome.
[0162] Returning to the example of the anti-CD3 probe, it is possible to consider a T cell (high density of CD3 antigen) and a B cell (absence of CD3 antigen), in which both the T cell and the B cell express a Z cell antigen When staining a T cell with the CD3 probe, the dye can provide an overflow to the Z channel detector, thus increasing the detection limit of the Z channel. In contrast, when staining a B cell with that same CD3 probe, there is no binding (specific) because CD3 is absent on the B cell, and therefore there is no CD3 probe dye overflow, and the CD3 probe is not considered to cause an increase in the detection limit on the Z channel. Thus, it can be seen that distortion factors may vary depending on cell type and related antigen expression patterns. For example, a specific probe channel can result in one set of detection limits for one cell type, and another set of detection limits for another cell type. Similarly, the set of detection limits for a given channel probe may depend on what types of antigens are expressed on the cells being analyzed.
[0163] For example, when a cell type is free of antigen A (and therefore no binding to an antigen-specific probe), an increase in detection limit may not be observed in a detection channel that is specific to the B antigen. In contrast, when a cell type abundantly expresses the A antigen (which is bound by a probe to the A antigen, which also provides an overflow into the detection channel for the B antigen), then there may be a increase in detection limit in the detection channel that is specific for antigen B. Thus, the increase in detection limit (or lack of increase) can be based on the expression pattern of stained cells.
[0164] The strain factor can thus have an impact on the detection limit. For example, when there is an increase in the detection limit (eg, a decade-long increase due to overflow), it may be necessary to observe or detect a larger signal in the channel to conclude that there is a positive event.
[0165] As discussed with reference to Figure 8, it can be seen that there is a strong overflow of the PE dye (from the CD4-PE probe) to the FL3 channel that detects ECD. Due to the overflow, the decompensated ECD signal from the specifically PE-labeled population is a few hundred times brighter than the decompensated ECD signal from the non-PE-specifically labeled background population compared to a non-specific isotype dye.
[0166] In some cases, with an increase in signal, there is an increase in the spread of the CD4+ population. Thus, there may be a sign of higher PE in this population. Consequently, for marker FL3 (y axis) there may be an increase in the detection limit. Compensation
[0167] Typically, a higher fluorescence signal may be accompanied by a higher absolute measurement error. For example, referring to Figure 5, the positive population of PE causes a signal in the detection channel from FL3 to ECD, due to the spillover of the dye emission PE to the detection channel from FL3 to ECD and therefore absolute errors can be much higher for this population. This can be represented as a standard deviation. In some cases, measurement errors can be affected according to Poisson distribution characteristics and cannot be reduced by compensation processes, since they can only correct the average intensity of a population specifically marked with spread conjugates versus a population not specifically tagged with spread conjugates. If the comparison of relative measurement errors (as represented by the respective CV coefficients of variation) indicates a sufficient degree of similarity, this may be a sign of detection linearity.
[0168] In some cases, there may be a factor of two between the coefficients of variation, so that the CVs are relatively similar and of the same order of magnitude, and yet the standard deviation may be more than 100 times different, or more .
[0169] As depicted in Figures 6A and 6B, the negative fluorescence values, for the expression signal events measured by a PE detection PMT and by an ECD detection PMT, plotted against each other, can be artificially generated in a bimodal distribution through computational compensation. The bimodal calculation as shown can operate to distinguish events that are negative for the first of the two measured antibody-dye conjugates, the second of the two measured antibody-dye conjugates, or both of the two measured antibody-dye conjugates. Figure 7 shows an increase in distribution amplitude for the expression signal events measured by an ECD detection PMT and a PC5 detection PMT, plotted against each other. Figure 7 in part reflects the fact that coexpression can affect the proliferation of a negative population to an overflow channel. Although not represented in these figures, the scattering for positive events is the same as the scattering for artificially negative events.
[0170] As shown in Figure 8, there may be an increase in the detection limit due to heavy overflow. In some cases, calculations implemented through software and/or hardware may work to correct the overflow, in an offsetting procedure. At a certain offset point, negative PE and positive PE can have the same mean on the DPI axis, which can refer to a correct offset (see, for example, Figure 5). As depicted in Figure 5, the compensated PE positive population may have a greater measurement error such that there is greater data spread compared to the PE negative population. Therefore, there may be a certain range of ECD signal that is less intense than the measurement error increase. Such signals may no longer be detectable as positive events. In some cases, there may also be a bifurcation of the compensated population.
[0171] When transforming a certain portion of the Y scale to a linear scale (see, for example, Figure 7), it is possible to compress the lower end of the logarithmic scale. Such a transformation may indicate that the measurement error (eg, amplitude, or scattering) of the positive population of ECD is greater than that of the negative population of ECD. In some cases, an overflow from a positive population of FL3 (ECD) to a detection channel of FL4 (PC5) can be translated to a larger error measurement after computational correction of the mean intensities referred to as offset. In some cases where there are cell-bound fluorophores that spill over into an FL4 detection channel (PC5), such dyes as ECD and PE can be attached to antibodies that detect cell structures in the same cells resulting in double positive cells of FL3 (ECD) and FL2 (PE) can be overlapped by overflowing into the FL4 channel. This can translate into an overlapping measurement error. It may be possible to account for such an error. For example, each of the contributions to global data scattering can be estimated as a linear value, and the linear values can be summed. The result can then be turned into a log value again. Furthermore, a database can contain information about which antigens can be expressed on the same cell surface. Such information can be used as a basis for selecting which of the overflow signals should be matched for this population that is positive for FL3. Thus, there may be dyes that also have an overflow into the FL4 channel like PE, however, antigens associated with such dyes cannot be expressed in the same cell type that is FL3+, and thus do not contribute to the overall measurement error on that channel.
[0172] Under some modalities, cell type or expression pattern or profile may have an impact on results. For example, results may depend on the pattern of antigens that is expressed in the cell, and that is recognized by antibody dye conjugate probes. Because of the potential spillover of some dyes into secondary channels, the detection limits for each of the fluorochromes can be influenced. In some cases where there is overflow, the population in the negative range of the scale may be scattered or skewed, and detection limits may increase. Typically, a specific expression pattern will be unique for a cell type. For example, it is unlikely that two different cell types can have identical expression patterns. To provide an estimate of the increase in the detection limit, it may be useful to identify which fluorescence signals will be present in the cells. Consequently, the techniques as discussed herein encompass identifying an expression pattern for use in evaluating probe panels.
[0173] The graph in Figure 8 represents the results of a staining protocol using an antibody specific for the CD4 antigen, conjugated to a PE dye. As shown here, the FL3 detection channel (sensitive to ECD dye emission) is registering overflow and therefore background distortion of the PE fluorochrome.
[0174] As an example, it is useful to consider a helper T cell, which is positive for CD3 and CD4. In some cases, it may be desirable to evaluate the cell to see if a cytokine receptor such as CD25 (IL-2 receptor) or CD184 (CXCR4) is present. Often, a cytokine receptor will have a low copy number and thus will be faintly colored on the surface of helper T cells. Thus, when it is desirable to assess cytokine receptor staining in the FL3 channel (DPI), it may not be desirable to assign a helper T cell marker (e.g., CD4 cells) to the PE channel. This is because CD4+ cells that are stained with the PE marker will show an increase in the limit of detection in FL3 (ECD) where cytokine detection is desired.
[0175] As depicted in Figure 8, there is an increase in the detection limit associated with the difference between the detection limit for the CD4- population and the CD4+ population. +. The line at the bottom of the y-axis delimits the CD population by limiting the negative population. This particular graph depicts a significant amount of overflow and therefore background distortion from the PE emission spectrum to the ECD detection channel. Thus, the increase in the detection limit is considerable. Comparing this result with the negative population on the left side of the graph, it can be seen that a substantial amount of sensitivity (eg, about half a decade) is lost. Consequently, the number of copies that would be detected with the ECD marker would need to be several times (eg 6x) higher (eg brighter signal) in a CD4+ population compared to a CD4- population to be detected or registered as a positive event.
[0176] In a related example, it is useful to consider a different detection channel, such as the FL5 channel which is intended to detect the PC7 dye emission spectrum. In this example, the FL5 channel detects a certain wavelength that is farthest away from the PE fluorochrome emission. Thus, there will be less overflow of the PE emission spectrum in the FL5 channel and thus the slope of the critical point line (eg as shown in Figure 8) would be less pronounced. In other words, the lower the amount of overflow for a particular channel, the smaller the critical point line slope (for example, as shown in Figure 8) associated with that channel.
[0177] The specific wavelength value is another parameter that can impact the result. Often, the wavelength range associated with a specific detector or PMT is at least in part determined by a bandpass filter that is in front of the filter. Typically, the longer the wavelength to be detected by a specific PMT, the less sensitive the PMT will be, and consequently the PMT will have a greater intrinsic error. For example, a PC7 detection channel can be configured to detect light with a wavelength greater than 755 nm, while a PE detection channel can be configured to detect light at a wavelength of 575 nm+/- 15. Consequently, there may be more distortion associated with the PC7 channel. These effects can be taken into account in the database or table. Another factor that can be considered involves the amount of light spilling over into a secondary channel. For example, it may be helpful to consider how much light a particular dye would typically spill over into another secondary channel. The properties of the secondary channel, with respect to an intrinsic measurement error, which may depend on the wavelength, can be considered.
[0178] As represented in Figure 8, the distortion factor is provided by the slope of the critical point line (detection limit). The strain factor can be based on an intensity parameter (eg PE staining signal intensity). Also, a longer wavelength in the secondary channel on the y-axis may correspond to a higher slope. Thus, the distortion factor can increase with wavelength. In some cases this can be applied to the PE signal strength. For example, the PE overflow distortion factor for an ECD channel may be different from the PE overflow distortion factor for an infrared channel, where the distortion factor for the infrared channel will be larger. Similarly, because the infrared channel may be at a greater spectral distance from the PE channel, the amount of overflow light may be less. Typically, PMT detectors can provide a good range of linearity and therefore a linear approach is helpful. The global distortion factor can be approximated or correlated with increasing detection limit. As shown in Figure 8, the distortion factor can be the slope of the critical point line in the quadrant, and thus the detection limit can be determined in a linear fashion.
[0179] In one modality of detection limit calculation, it is possible to obtain (i) the value of X on the X axis and (ii) the distortion factor or slope and thus generate the value of Y or limit of detection.
[0180] For example, it is possible to provide the relative FL(x) expression density of a signal that may spill over (eg, based on CD4 antigen density), and this value can be derived from empirical data. It is also possible to give the fluorochrome intensity of relative FL(x) expression, e.g. PE tag, corresponding to the brightness of the fluorochrome.
[0181] An exemplary empirical approach might involve staining a population of T cells with a CD4-PE probe, and evaluating the population positive (eg, separated by 2.5 decades from a negative population). A results table can be generated based on the data.
[0182] Thus, by inputting an X value (for example, an estimate according to a typical expression density and dye brightness) along with an angular coefficient or a linear relationship, it is possible to determine the Y value or threshold of detection. By considering a probe panel that includes several antibody-dye conjugates, it is possible to obtain a distortion factor for each of the markers that provide an overflow into the FL3 channel. Results may vary depending on colorant selected. For example, a CD4-PE probe may provide a greater degree of separation, and a CD4-FITC probe may provide a lesser degree of separation.
[0183] In some cases, the values can be linearized, added, and the sum of the linearized values subsequently transformed to a log value, in order to obtain a log distance. For each of the colorants, different distortion factors can be applied. Furthermore, there may be different signal-to-noise ratios between positive and negative populations.
[0184] According to some modalities, for a given conjugate combination, for a population of CD-Y, where CD-Y is a specific antigen of interest being measured by a single channel, an estimate of the signal ratio distances -to-noise "untouched" (Y/N) can be given by: FL(y) = relative density of expression FL(y) * relative intensity of the fluorochrome FL(y) where in the case of a modulated marker, the distance of Y/N FL(y) can be set to zero (0). For the given conjugate combination, an estimate of the overflow of one or more CD-X populations causing distortions to the channel measurement for CD-Y for each individual in the CD-X population can be given by: Distortion FL(x) -> FL(y) = Distortion factor FL(x) -> FL(y) * relative density of expression FL(x) * relative intensity of the fluorochrome FL(x) where in the case where DC-X is a subpopulation of CD-Y, or CD-Y is a set of exclude markers for CD-X, S/N distortion from FL (x) to FL (y) can be set to zero (0). The distortions of each channel in a system that has a plurality of channels (eg 10 channels in total and therefore nine CDX channels) can be accumulated as given by:

[0185] The set of estimates and aggregation can be performed for each channel in a system, for each antigen of interest individually, allowing the calculation of an effective S/N distance for each antigen as given by:
where the effective S/N distance for each antigen in each channel can be used to determine detection limits for the panel and the global system.
[0186] According to some embodiments, the detection limit (DL) for the specific positivity of a fluorescent member of CD-Y, represented by FL(y), is an almost linear function of the positive fluorescence spillage from a member of CD-X, represented by FL(x) according to the equations:
where DLIC(FL y)) is defined as the detection limit of CD-Y fluorescence for an isotype control stain indicated as coordinates between 0 and 1023; where RI(FL(x)) is defined as the fluorescence intensity of a CD-X above its indicated DLIC(FL(x)) in the decades of FL(x); and where DF(FL(x) -> FL(y) is defined as the distortion factor, measured as the increase of DL(FL(y)) by RI(FL(x)), indicated in decades of FL(y) divided by decades of FL(x).
[0187] In some respects, DF(FL(x)->FL(y)) can be calculated from the positive/negative compensation procedures and correlated according to the equation:
where FL(y, x) is defined as the intensity FL(y) of the positive events of FL(x), and where FLIC(x) and FLIC(y) are defined as the intensity of the isotype control stains for your respective antigens. The accumulated effects of the spread of intensities compensated from members of CD-X, FL(x1, x2, ... x(n-1)) to CD-Y can be given by:
These calculations are also described in the second paragraph of Figure 1D.
[0188] Under some modalities, isotype control data can be provided per closed patient in a "silent" stained (leukocyte) population or scatter. In other modalities, the compensation data can be generated per application (or per panel) by a positive/negative algorithm, where a mathematical or an experimental procedure can be established for the calculation of a distortion factor. In other embodiments, the distortion matrix can be generated based on one or both of the isotype and offset data. After compensation, a distortion matrix can be applied to each event. In some embodiments, knowledge of compensation factors may not be sufficient to complete calculations as described here. In other embodiments, knowledge of a distortion matrix, which can be experimentally determined on a given instrument or an instrument configuration and dye set, may be sufficient to complete the calculations as described herein. In still other embodiments, specifically positive closed values can be presented in a prism shape, a tree, a three-dimensional overlay, a comparison plot, a profile plot, or the like.
[0189] Referring to Figure 8, it can be seen that the techniques may involve the input or selection of a value for the X axis, which corresponds to an expression density multiplied by the fluorescence intensity. Such data can be implemented in an antibody table module (and read or retrieved from it), as discussed in other sections of this document. In some cases, values can be estimated. In some cases, values can be based on experimental data. For example, it is possible to analyze the CD4-PE staining experiments to obtain such data. Furthermore, the methods may involve multiplying the distortion factor by the intensity (actual or estimated) of the marker. In some cases, expression densities or marker patterns (eg for target cells) may have predefined parameters or values. Such information can be implemented in an antibody database module. As described here, the second paragraph of Figure 1D provides additional details for sampler detection limit calculations.
[0190] As depicted in Figure 9, an increase in detection limits can be independent of compensation factors and/or PMT voltages. Instead, the detection limit and/or increase thereof may depend on emission spectra and filter configuration. As illustrated, the settings for how the results are represented can be adapted to remove distortion (or, in other words, the application of a compensation factor) that can be caused by sensor readings from a desired PMT being measured. incorrectly in combination with sensor readings from a separate PMT channel. Specifically, Figure 9 displays detection results, fetched from an ECD channel, with a percentage of sensor readings from a PE channel subtracted from the detection results. Three exemplary PE channel subtraction variations are shown: one with 34% of the PE channel signal subtracted, one with 46% of the PE channel signal subtracted, and one with 62% of the PE channel signal subtracted. In each detection limit result plot, the number of events identified as positive or negative changes changes, but the detection critical point line between the two compared populations remains the same. Removing distortion provides a more accurate and sensitive set of results.
[0191] Figure 10 shows aspects of an exemplary overflow pattern distortion matrix for certain dyes, according to some modalities. Overflow can be independent of PMT settings and compensation factors, and can be dependent on fluorochromes, filters, and alignment accuracy. In aspects as illustrated, the overflow matrix can be qualitative, to quickly show an operator the effect on the interface of specific dyes or PMT detectors.
[0192] Figure 11 shows aspects of a coexpression matrix, according to some modalities. In aspects as illustrated, the coexpression matrix can be qualitative, to quickly show an operator the effect on the interface of specific antigens, dyes, or PMT detectors. As illustrated in Figure 11, a coexpression matrix can indicate interactions where: a column is not exclusively expressed with a row, a column is an exclusive subpopulation of a row, or where a column is mutually exclusive of a row (which provides a symmetric plot of expression events).
[0193] Figure 12A represents estimates of schematic staining patterns, according to some modalities. When there is positivity for a particular marker spreading to another channel, a critical point can be observed, as shown in the graph on the right. The user can change certain inputs for a simulation in order to vary the output shown. For example, a user can select or change multiple dyes or antibodies contained in a probe panel in order to obtain improved sensitivity for a certain co-expressed antigen. The situation shown in the middle graph of Figure 12A is specifically desirable, as the detection limit of the positive FL(x) population is not influenced by the FL(x) positivity compared to the negative one.
[0194] Figure 12B represents a variable PE detection threshold signal according to some embodiments. The PE signal of Figure 12B may correspond to the horizontal direction represented in the graph of Figure 12C. That is, the overflow that is manifested in Figure 12B can also be manifested in Figure 12C, resulting in a variable PE detection limit that depends on the fluorescence intensity of the spreading dye whose intensity is scaled along the y-axis. In comparison, in Figure 12D the spread antigen has been moved to a position where it does not overflow into the PE channel (eg CD45 moved from ECD excited the blue laser to APC excited by the red laser with a very strong signal ), so the CD45-APC signal does not impact the background in the PE channel. In addition, PE and APC fluorophores are excited with different laser wavelengths. Such non-overflow situations can be rare, specifically in multicolor panel configurations (eg 10), as there will typically be fluorescent markers in cells that influence detection limits in multiple channels.
[0195] In Figure 12E, there is a high sensitivity for the expression of the Y antigen in cells that are positive for the X antigen. This may be a desirable situation where the expression of the Y is prioritized. When comparing positive and negative populations, the detection limit is unchanged. There is a low detection limit for FLy over the FLx population. According to some modalities, Figure 12E may refer to a special situation involving high cell expression of a non-unique co-expressed antigen that is at risk of being specifically detected. In some cases, it may be helpful to refer to an uncommented duplicate of Figure 12C.
[0196] In comparison, in Figure 12F, it can be seen that detection of the X antigen marker in the cell involves a fluorochrome emission that influences the detection limit in FLy. An overlay of populations is also shown. In contrast, there is no such overlap in Figure 12E (or an uncommented duplicate of Figure 12C). Thus, a user who obtains results like those depicted in Figure 12E may decide to proceed with the specific probe panel. In contrast, if this coexpression is prioritized, a user who gets results such as those depicted in Figure 12F may decide not to proceed with the particular probe panel.
[0197] Figure 12G (or an uncommented duplicate of Figure 12C) represents a plot of bivariate points showing a double positive event. Figure 12H represents a plot of bivariate points where there is no double positive event. The plot in Figure 12H is representative of a result of an exclusion.
[0198] Figure 12I represents another exclusion situation, in which there are no double positive events, which can present an acceptable result for the user. Considering the negative for Antigen 1, which excludes Antigen 2, it can be seen that these two antigens do not occur on the same cell type. Thus, a user who is interested in the assessment for Antigen 2 in a cell characterized by the fact that the lower left quadrant can observe an unbiased detection limit. The user would not look for a cell that expresses Antigen 2, because due to the biological characteristic of the cell, a double positive population does not occur. Thus, a double positive may not be a target population in the analysis. Discrimination between Antigen 2 positive (AG2-POS) and Antigen 2 negative (AG2-NEG) can, however, be a target of analysis. Likewise, the discrimination between Antigen 1 positive (AG1-POS) and Antigen 1 negative (AG1-NEG) can also be a target of the analysis. In each of these situations, the detection limits are unchanged.
[0199] Because there is no expression of Y antigen on X positive cells, nor any expression of X antigen on Y positive cells, the upper right quadrant is not filled.
[0200] Figure 13 represents aspects of the systems and methods of evaluation of the probe panel according to some modalities. In some cases, expression patterns can be classified based on multiple criteria. For example, in some embodiments, expression patterns can be categorized as normal, lymphoproliferative, or immature blood cell disorders. As shown here, several factors can be taken into account for expression patterns, such as fluorescent intensity and the like. In some cases, cells in the upper table of Figure 13 that identify expression characteristics can be annotated with a "1" to represent the coexpression of the marker, and with a "0" to represent the absence of the coexpression marker. In some cases, cells in the lower table of Figure 13 that also identify expression characteristics can be annotated with a "0" to represent co-expression of the descendant to parent marker, and with a "1" to represent the absence of co-expression of descendant to parent marker. Consequently, these data can be used to indicate whether a fluorochrome marker may or may not result in an increased detection limit for a secondary fluorophore marker. For example, when there is no coexpression, then it may be possible to disregard the fluorescent label in the cell. That is, there will be no production of an overflow signal, due to the absence in the same cell, and thus, there will be no increase in the detection limit. As discussed in another section here, the modalities of the present disclosure encompass aspects of other patterns of expression, such as parent-descendant patterns. The expression pattern information can be used to determine whether a measurement error of a certain channel for a certain population with a certain expression pattern will actually be increased or not. Similarly, expression pattern information can be used to estimate or determine distortion, and/or for overlapping. In some cases, the sample tables are responsible for overflow that occurs in order to provide a combined distortion result.
[0201] Figure 14 represents aspects of probe panel evaluation techniques, specifically numerical simulation techniques, according to some modalities. The tables provided as Figure 14 show, in part, the relative contributions of the various fluorochromes excited by one or more excitation lasers and detected in the FL-channels (ie, PMT channels). Relative contributions provide a basis for correcting, removing parameters or events undesirably measured by a given PMT relative to fluorochromes to be measured by another detector and PMT channel. An effective distortion matrix can be calculated to determine such relative contributions for any given combination of excitation lasers, fluorochromes and PMT detectors.
[0202] Figure 15 represents aspects of probe panel evaluation techniques, specifically overflow pattern techniques, according to some modalities. The tables provided as Figure 15 show, in part, the relative brightness of the fluorochrome data relative to a single fluorochrome in the pool, chosen as a reference for 100% brightness or intensity in that dye panel. The overflow pattern and relative brightness can be classified as creating a specific level of distortion, indicating the relevance of a particular fluorochrome member in affecting the detection of other fluorochromes by related PMT detectors. These values can be used to calculate an increase in detection limits, per decades of signal strength distortion, for each given channel or dye combination.
[0203] Figure 16A illustrates an exemplary scheme for a probe panel system according to some embodiments. As depicted herein, the system includes a simulator graphics module, a numerical simulator module, a standard overflow module, and an antibody database module. The simulator graphics module can be used for user input of aspects of a desired antibody panel, for graphical output of simulation results, and for modifying default simulation parameters. The simulator numerical module can be used for the numerical production of simulation results. The overflow patterns module can be used to standard simulation parameters for fluorochrome properties and distortion factors according to conventional optical filter sets. The antibody database module can be used to standard simulation parameters for antigen expression densities, coexpression patterns, and parent-descendant schemes.
[0204] In some cases, for example, as shown in Figure 13, the simulator graphics module can be configured to implement user input related to an antibody panel, the display of standard data on antigen co-expression (for example , "may be co-expressed with / the co-expression may be of interest"), the display of default data in the sub-population co-expression (eg "cells (which express brightness) are not a (exclusive) sub-population of"), the display of data estimated in conjugate decades of the mean signal strength above the positive-negative threshold, and the graphical output of simulation results.
[0205] Figures 16B and 16C represent aspects of a user input module for a probe panel, according to some embodiments. After user input of antigen specificity, the respective part numbers (PN) can be retrieved from the Antibody Database module and displayed in the fields below the user input fields. For unoccupied channels, the user can enter a dummy designation. When conjugated antibodies are not available in the Antibody Database repertoire, and when antigen specificity is available although conjugated to dye markers other than the desired markers, it is possible to designate as a Customer Assignment Service (CDS).
[0206] Figure 16D represents aspects of a graphic simulator module according to some modalities. As shown here, the system can provide a standard data display on antigen co-expression (eg, "may be co-expressed with / the co-expression may be of interest"). In some cases, the source range (eg, normal, lymphoproliferative, or leukemic disease, corresponding to the expression pattern, can be changed or selected by entering 0, 1, or 2). Specifics as provided by a user can be displayed, for example, as column and row headers. In some embodiments, the system can be configured to take user input in order to override default data. In some modalities, table entries can be replaced by a user, if necessary or desired. In some cases, co-expression data (eg standard inputs) can be retrieved from the Antibody Database module As indicated here, a value of "1" can represent a True case, so co-expression occurs and it is of interest. Similarly, a value of "0" can represent a False case, so the antigens are not co-expressed, or their co-expression is not of interest. Such co-expression data can be stored in and retrieved from the Antibody Database module. There is symmetry along the diagonal axis of the blank fields.
[0207] Figure 16E represents aspects of a graphic simulator module according to some modalities. As shown here, the system can provide a standard data display on antigen co-expression (eg, "may be co-expressed with / the co-expression may be of interest"). Again, a source range can be changed by entering the value 0, 1 or 2. As further shown here, antigen specifics according to user input can be displayed as column headers and table rows. In some cases, entries can be overwritten by a user if necessary or desired. In some cases, parent-descendant data (eg patterns) can be retrieved from an Antibody Database module. A value of "1" can represent a true case where the positive cells (spine antigen) are not a descendant population and therefore cause distortion in the row antigen channel. A value of "0" may represent a false case where the (spine antigen) positive cells are a descendant population and therefore do not cause distortion in the row antigen channel. For example, according to the table shown here, lambda is not a descendant of kappa, while FMC7 is a descendant of CD19. According to some modalities, FMC7 can also be kappa negative.
[0208] Figure 16F represents aspects of a graphic simulator module according to some modalities. As shown here, different lines or curves can correspond to different excitation wavelengths. Here the wavelengths are 488 nm, 638 nm and 405 nm. Circles, connected by solid lines, correspond to the background for most complex expression patterns. The squares, connected by dashed lines, correspond to the lowest expected fluorescence intensity for a conjugate. The triangles, connected by dotted lines, correspond to the brightest fluorescence intensity expected from a conjugate. Each individual indicator (ie, circle, square, or triangle) is positioned above an X-axis value that corresponds to a central wavelength for a specific sensing channel or bandpass filter band. The Y axis represents the upper four log decades of fluorescence intensity, with a negative population centered on the lower decade. As discussed in another section here, for an antigen having distinct expression characteristics, the dashed and dotted lines will coincide for a respective X-axis location (bandpass wavelength). Also, as discussed in other sections of this document, it is possible to classify different probe panel designs based on distances between various indicators. For example, probe panel designs can be classified based on a maximum distance between a triangle (dashed line) and a circle (solid line) and can also be based on a minimum distance between a square (dashed line) and a circle (solid line).
[0209] In some cases, classification may involve prioritizing probe panels that match complex overflow patterns and that are associated with weakly expressed antigens.
[0210] Figure 16G represents aspects of a graphic simulator module according to some modalities. Here, a display of all distortion contributions for a specific sensing channel is positioned above each individual channel. Circles or bubbles can be coded (eg color coded) according to the respective spreading fluorochrome. The Y-axis value or intercept indicates an absolute increase in the detection limit in decades caused by a torque in a respective channel. For example, the Y-axis might represent an increase in fund, over decades, where 0.00 is a threshold between the first and second decades (for example, negative population centered on the first decade). The diameter of the circle can represent the relative contribution of a conjugate to the overall distortion of the background of a respective channel. As discussed here in another section, to reduce background distortion for a given channel, it may be useful to address, for example, the largest circle positioned above the specific channel. In some cases, it may be useful to minimize or reduce the number of circles for channels used to detect modulated antigens.
[0211] Figure 16H represents aspects of a numerical simulator module according to some modalities. As shown here, the table includes values for all absolute distortions resolved by conjugate sprawl (column headings) and skewed channel, and also illustrates an increase in funds in decades per decade of fluorochrome overflow signal strength in its primary channel . Also included are columns for the size of the maximum contributions to total background distortion and total background distortions. The superimposed column indicates a position of the region or quadrant on a 1024-unit graphic scale of a positive-negative threshold according to a position parameter. Source data for the graphical representation of background distortion is also shown.
[0212] According to some embodiments, it is possible to determine an absolute distortion caused by a single overflow conjugate based on the following formula: (Conjugate strength)*(distortion factor)*(coexpression index)*(parent index) -downward)
[0213] According to some modalities, conjugate intensity can be represented in decades, and can be determined as an estimate based on antigen density and fluorochrome brightness or by using empirical data on conjugate intensity.
[0214] According to some modalities, it is possible to determine a total distortion in a given channel based on the following formula: LOG10 (sum of linearized absolute distortions caused by each conjugate)
[0215] According to some modalities, a position of the positive-negative threshold quadrant/region can be determined based on the following formula: (decades of total distortion)*(256)+256 where 256 represents the graphic threshold between the first and second decade assuming that the negative population is delineated by this graphic threshold.
[0216] Figure 16I represents aspects of a numerical simulator module according to some modalities. The table shown here includes contributions of linear relative distortions resolved by interference from the conjugate (column headings) and the distorted channel in decades. This table can provide source data for the graphical representation of background distortion (for example, graphs, "distortion contributions"). Table values can be based on the following formula:

[0217] Figure 16J represents aspects of an overflow pattern module, according to some modalities. The table shown here includes distortion factors, which can be applied by decade of interference fluorescence intensity (ie, scattering) and can be determined experimentally or based on the following approximate equation: distortion factor = [interference index]* [bandpass temperature factor]
[0218] Figure 16K represents aspects of an overflow pattern module, according to some modalities. A definition of the interference index can be seen here as: Interference index = LOG(SNR(secondary signal) / LOG(SNR(primary signal); where: SNR = signal-to-noise ratio = MFI (positive population) / MFI ( negative population); and with MFI = mean fluorescence intensity.
[0219] Figure 16L represents aspects of an overflow pattern module, according to some modalities. As shown here, for 525 nm (FITC) the absolute distortion can be calculated according to the following formula where the ratio of DF (distortion factor) to CI (interference index) is 0.15: absolute distortion = 0 .15 x LOG(SNR(secondary)
[0220] That is, there is 0.15 decades of distortion per decade of secondary signal strength. As also shown here, for 660 nm (APC) the absolute distortion can be calculated according to the following formula where the ratio of DF to Cl is 0.42: absolute distortion = 0.42 x LOG(SNR(secondary) )
[0221] That is, there is 0.42 decades of distortion per decade of secondary signal strength in the APC channel (660/20 bandpass).
[0222] Figures 16M and 16N represent aspects of an antibody database module, according to some embodiments. The table in Figure 16M includes data for antigen expression densities (eg, scaled according to CD8-PE = 2.5 decades of intensity above the pos-neg threshold), part numbers, and expression characteristics (per example, 0 = modulated, 1 = distinct). The table in Figure 16N includes data for coexpression patterns (eg, "may be coexpressed with / the coexpression may be of interest"). As shown here, there is symmetry in the table across the diagonal table cells.
[0223] Figure 16O represents aspects of an antibody database module, according to some embodiments. In some embodiments, the antibody database modules may include information about parent-descendant patterns (e.g., "cells (which express brightness) are not a descendant population of"), and may be asymmetric. Under some embodiments, such database information may include estimated values. Under some embodiments, such database information may include empirical values.
[0224] Figure 17 represents aspects of a numerical approach to model overflow patterns, according to some modalities. As shown here, the evaluation of overflow patterns can involve a radar detection approach to processing a multivariate dataset. As shown in Figure 17, the multivariate radar representation can display detection limits for antigens according to their detection channels as they are arranged in panel configuration 1702. As shown in a first image layer 1704, each The detection radar's radial axis represents a fluorescence channel from the third to sixth decade of measured signal, each decade being a 20-bit segment. The first interior shaded portion 1706 represents untouched detection limits, within and below that decade, assuming that isotype control is centered on the third decade. As shown in a second image layer 1708, a second interior shaded portion 1710 underlies the first interior shaded portion 1706, wherein the second image layer 1708 represents the estimated background distortion for each fluorescence channel. The estimated values for the distortion that generate the second inner shaded portion 1710 are based on the data conjugated according to their combination: overflow pattern, relative fluorochrome intensities, antigen densities, coexpression matrix, and distortion matrix.
[0225] A third layer of the 1712 image further includes a lower limit 1714 that represents the lower limit of expected fluorescence intensity for each conjugate. All modulated or fuzzy expressions are set to zero as part of the lower limit of 1714 expected fluorescence intensity. A fourth image layer 1716 further includes an upper limit 1718 representing the upper limit of fluorescence expected for each conjugate. Distinct expressions of a specific antigen and dye have equal lower limit 1714 and upper limit 1718 values for the expected fluorescence intensity.
[0226] Figures 18A and 18B represent aspects of a numerical approach to model overflow patterns, according to some modalities. As shown here, the assessment of overflow patterns may involve a detection radar approach and/or a distortion indicator approach. In such skew indicator representations, each DL may have a color that represents a skew dye. The Y axis can represent the amount of distortion, in decades, while the X axis can represent each distorted FL channel. The diameter of a data point (ie, the size of a DL) can represent the relative contribution of a single conjugate to global distortion.
[0227] Figures 19A and 19B represent aspects of a numerical approach to model overflow patterns, according to some modalities. As shown here, the evaluation of overflow patterns can involve a clonality screen approach, which can further be represented with a multivariate radar approach.
[0228] After simulating a probe panel design as set forth here, a flow cytometry device can be configured and operated using said probe panel design. In particular, a processor or system, which may be a non-temporary computer readable medium, may receive information about a flow cytometer hardware configuration from, information about a list comprising a plurality of probes, the individual probes of the list being associated with respective individual channel-specific detection limits and information about an antigenic co-expression pattern. The processor, or one or more informationally linked processors, can evaluate individual probe combinations like the probe panel, based on the flow cytometer hardware configuration, the specific detection limits of individual channels, and the pattern. of antigenic coexpression, the combinations being subsets of probes from the list, and can additionally determine the probe panel for use with the flow cytometer hardware configuration, the specific detection limits of individual channels, and antigenic coexpression pattern. Finally, a probe panel for use in a flow cytometry procedure can be issued, and used by an operator to design the probe panel for a flow cytometry instrument and experiment. Interface and parameter selection for panel design and simulation
[0229] The determination of a probe panel can be presented in a web-based interface, which allows a user to design a probe panel for a flow cytometry experiment using online tools and databases. Specific modalities of systems and methods (as described above in Figures 2-2D) can be provided to an operator as shown in Figures 20A-20F. Aspects of the system or method can be provided to an operator as a single shape, a transient shape, or as multiple shapes that represent the method steps.
[0230] Figure 20A is an example image of an interface screen that allows the selection of a 2000 hardware configuration (also called a 2000 hardware configuration screen). A 2002 tab selection allows movement between various fields into which data can be entered, which as shown are tabs for the steps of a method of simulating a dashboard. The 2004 tab title field can indicate which step of a method a user is viewing or editing, which in Figure 20A is "Hardware Configuration", indicated as "Step 1" in the 2002 tab selection. The 2006 dropdown field allows an operator to select a hardware configuration that provides information from a database to establish parameters for performing a panel simulation. The 2008 hardware configuration graphical display can show an operator details of a hardware configuration, including, but not limited to, one or more excitation lasers, excitation wavelengths of one or more excitation lasers, voltage or other energy values for the one or more excitation lasers, one or more PMT detectors, voltages or other energy values for one or more PMT detectors, or bandpass filters for each of the one or more PMT detectors . In some respects, the selection of a hardware configuration can be automatically determined by the database and system. The selected hardware configuration can set parameters and values for a panel design and simulation described here. A 2010 selectable "Next>" field is provided to advance to a later step in the method, A generally selectable "Back>" field 2012 is provided to advance to an earlier step in the method, however, in the Hardware Setup screen shown 2000, no there is no previous step, and thus the “<Back” 2012 field is not selectable in the Hardware Setup screen shown 2000.
[0231] Figure 20B is an exemplary image of an interface that allows 2014 antibody selection (also called a 2014 Antibody Selection screen). The 2004 tab title field in Figure 20B is titled "Antibody Selection", and is indicated as "Step 2" within the 2002 tab selection. A plurality of 2016 specificity drop-down fields allows an operator to select a specificity, and a plurality of drop-down dye fields 2018 allows an operator to select a dye. In some modalities, the specificity to be selected in the 2016 specificity drop-down fields may be an antigen or antibody selection. For each specificity selected in the 2016 specificity drop-down fields, a corresponding dye can be selected from the plurality of 2018 dye drop-down fields. In some aspects, the selection of 2016 specificity drop-down fields and 2018 dye drop-down fields can be determined automatically by system database. Selected antibodies can set parameters and values for a panel design and simulation described here. A 2010 selectable "Next>" field is provided to advance to a later step in the method. A 2012 selectable "Back>" field is provided to advance to an earlier step in the method.
[0232] Figure 20C is an example image of an interface that allows the 2020 target phenotype selection (also called a 2020 Target Phenotypes screen). The 2004 tab title field in Figure 20C is titled "Target Phenotypes", and is indicated as "Step 3" within the 2002 tab selection. A 2022 target population dropdown field allows an operator to select a target population in relation to specific antigens that express or should express a target phenotype. A plurality of "Unrelated Antigens" 2024 is provided, listing a selection of antibodies and dye conjugates that are not, or cannot be, relative to a selected target population. Each antibody and dye pair in the plurality of 2024 Unrelated Antigens includes a selectable check box to indicate that the antibody and dye pair is an antigen of interest. A plurality of "Antigens of Interest" 2026 are provided, listing a selection of antibody and dye conjugates that are, or believed to be, related to a selected target population. Members of the plurality of 2026 Antigens of Interest may be populated by antibody and dye pairs originally provided in the plurality of 2024 Unrelated Antigens, or may be self-filled according to data relationships stored in the database and system. The indication that a specific antibody and dye pair is a member of the plurality of 2026 Antigens of Interest can be removed by excluding a selectable checkbox for the antibody and dye pair. In some aspects, the selection of a plurality of 2024 Unrelated Antigens and a plurality of 2026 Interest Antigens may be automatically determined by the database and system. In some aspects, a global Antigen of Interest 2028 selection field can be provided, having an enableable field to select all antibody and dye pairs as members of the plurality of Antigens of Interest and an enableable field 2026 for all antibody pairs and dyes indicated as members of the plurality of Unrelated Antigens 2024. Target phenotypes, unrelated antigens, and selected antigens of interest can define parameters and values for a design and simulation of the panel described here. Although not expressly shown in Figure 20C, a selectable "Next>" field is provided to advance to a later step in the method and a selectable "<Back" field is provided to advance to an earlier step in the process.
[0233] Figure 20D is an exemplary image of an interface that allows the identification of 2030 mutually deleting antigens (also called the 2030 Mutually Deleting Antigen screen). The 2004 tab title field in Figure 20D is titled "Mutually Deleting Antigen", and is indicated as "Step 4" within the 2002 tab selection. A selection of expandable 2032 antigen-antibody pairing fields is provided in that, when expanded, the antigen-antibody pairing field provides a listing of antigens to exclude 2034; this can be a list of potential antigens that can be mutually exclusive with the antigen-antibody pairing for an individual field. The 2034 Exclude Antigen List may have a checkbox per each antigen listed to select a specific antigen to indicate as excluded, which is an indication that the selected antigen or antigens is not co-expressed with the antigen identified as the part of the antigen-antibody pairing for that field. The listing of antigens to exclude 2034 may further include one or more selection links that can cause all antigens in listing 2034 to be selected, or exclude all antigens in listing 2034. Antigens that are indicated as excluded may be listed in a 2036 identified excluded antigens list. The 2036 identified excluded antigens list may have a checkbox selected for each listed antigen, which can be excluded to indicate that the specific antigen is not mutually excluded for a given antigen-antibody pairing field . In aspects, the antigen listing to exclude 2034 can be selected by an operator that selects one or more antigens listed based on operator-generated logic. In other respects, the 2036 identified excluded antigen listing may be specified by the database and system; a 2038 selectable "Auto-Specify Exclusions" field is provided to allow an operator to indicate mutually excluded antigens according to data stored in the database and system. On the other hand, a 2040 selectable "Discard All Exclusions" field is provided to allow an operator to exclude all previously indicated mutexes for one or more antigen-antibody pairing fields. Selected exclusion antigens can set parameters and values for a panel design and simulation described here. Although not expressly shown in Figure 20D, a selectable "Next>" field is provided to advance to a later step in the method and a selectable "<Back" field is provided to advance to an earlier step in the process.
[0234] Figure 20E is an exemplary image of an interface that allows the identification of 2042 parent and descendant antigens (also called the 2042 Parent & Descendant Antigens screen). The 2004 tab title field in Figure 20E is titled "Parents & Descendants", and is indicated as "Step 5" in the 2002 tab selection. A selection of 2044 expandable antigen-antibody pairing fields is provided, where, when expanded, the antigen-antibody pairing field provides a listing of 2046 descendant antigens; this can be a list of descendant antigens, descendants being a parent antigen indicated as the antigen of the antigen-antibody pairing for an individual field. The 2046 descendant antigens listing may have a checkbox by each antigen listed to select a specific antigen to indicate as a descendant, which is an indication that the selected antigen or antigens is a more later differentiating grouping in one step or developmental progression than the cluster of differentiation indicated as the selected antigen of the antigen-antibody pairing for that field. The 2046 descendant antigen listing may further include one or more selection links that can cause all antigens in listing 2046 to be selected, or exclude all antigens in listing 2046. Antigens that are indicated as descendants may be listed in a listing of 2048 identified descendant antigens. The 2048 identified descendant antigens listing may have a checkbox selected for each antigen listed, which can be deleted to indicate that the specific antigen is not a descendant for a given antigen-antibody pairing field. In aspects, the 2046 descendant antigen listing can be selected by an operator that selects one or more listed antigens based on operator-generated logic. In other respects, the 2046 descendant antigen listing may be specified by the database and system; a selectable 2050 "Auto-Specify Family Patterns" field is provided to allow an operator to indicate parent and descendant antigen relationships according to data stored in the database and system. On the other hand, a 2052 selectable "Discard All Family Patterns" field is provided to allow an operator to delete all previously indicated parent and descendant antigen relationships for one or more antigen-antibody pairing fields. Selected parent and descendant antigen relationships can define parameters and values for a panel design and simulation described here. Although not expressly shown in Figure 20E, a selectable "Next>" field is provided to advance to a later step in the method and a selectable "<Back" field is provided to advance to an earlier step in the process.
[0235] Figure 20F is an exemplary image of an interface that allows the identification of 2052 antigen densities (also called the 2052 Antigen Density screen). The 2004 tab title field in Figure 20F is titled "Antigen Densities", and is denoted as "Step 6" within the 2002 tab selection. A 2056 target population dropdown field allows an operator to select a target population in relation to specific antigens, and particularly the density of the specific antigen for a specific target population or cell type. The density of antigens in a region of a cell, or for a cell type or target population, corresponds to the strength of expression of that antigen, and how it may interact or be measured with populations of other antigens. A 2058 signal-to-noise display can indicate over decades of expression the expected signal ratio from a target population and corresponding antigen over background noise. A modulated/distinct selection field 2060 may be provided, which in aspects may be a radio button selection field, to configure the signal-to-noise display 2058 to display the expression of the target population when evaluated according to the discrete or modulated parameters as described above. Likewise, discrimination selection field 2062 may be provided, which in aspects may be a radio button selection field, to configure the signal-to-noise display 2058 to discriminate target population results, such as any between positive and negative expression or as between bright positive and weak positive expression, as described above. A slider setting can be set for the 2058 signal-to-noise display to adjust the scale and scope of the region of the 2058 signal-to-noise display displayed. In some respects, the slider setting can be chosen with a selectable 2064 "Slider Autoset" field, using target population display data and antigen to automatically configure the 2058 signal-to-noise display range. other aspects, an operator can direct the 2058 signal-to-noise display to have a specific scale by manually adjusting a 2066 slider interface, which may be a radio slider interface. Target populations, distinct versus modulated display selection, discrimination selection, slider configuration, and selected antigen densities can set parameters and values for a panel design and simulation described here. Although not expressly shown in Figure 20F, a selectable "Next>" field is provided to advance to a later step in the method and a selectable "<Back" field is provided to advance to an earlier step in the process.
[0236] Figure 20G is an exemplary image of an interface that displays a panel expression simulation estimate for a given 2068 antigen panel design (also called the 2068 Panel Simulation screen). The information obtained from the input to the Hardware Setup 2000 screen, Antibody Selection screen 2014, Target Phenotypes 2020 screen, Mutually Exclusion Antigen 2030 screen, Parent & Descendant Antigens screen 2042, and Density of Density screen Antigen 2052 can be collected and used to calculate an expected expression or staining pattern. The 2004 tab title field in Figure 20G is titled "Estimated Color Patterns", and is indicated as "Step 7" in the 2002 tab selection. A 2070 display field can provide either or both of the numeric and graphical representations of the estimated antigen expression for a given panel design, including but not limited to the graphical representations discussed herein. The 2010 generally selectable "Next>" field has shown that it is not selectable in the 2068 Panel Simulation screen because there is no further step in the corresponding method or evaluation to proceed. The 2012 selectable "<Back" field is provided to advance to an earlier step in the method.
[0237] In some embodiments, a web-based interface can present information to an operator to further detail the relationships between multiple antigens to a cell phenotype. Figure 21A is an exemplary developmental tree (similar in shape to a geological tree) that illustrates the relationships between CD members (ie, antigens) for a cell phenotype. This developmental tree can illustrate parent-child relationships between antigens and thus further illustrate sibling relationships, cousin relationships, and uncle relationships between antigens. In some respects, antigens can be identified as developing along more than one developmental trajectory, and can be identified as a "twin antigen" along with occurrences of the same antigen in the developmental tree. In additional modalities, temporary selection or highlighting (eg, hovering over a software-defined field to represent a CD member or antigen) of a specific antigen field can be used as a stimulus for other display expression relationships with other antigens on the developmental tree. For example, selection or temporary highlighting of an antigen field may indicate, for the indicated antigen, that other antigens in the developmental tree are mutually excluded from expression with the selected antigen, that other antigens in the developmental tree have correlation expression with the selected antigen, that other antigens in the developmental tree have expression inversely correlated with the selected antigen, that other antigens in the developmental tree are twins to the selected antigen, or that other antigens in the developmental tree otherwise co-express with the selected antigen. In other respects, the developmental relationship between antigens may be provided in a listing or tabular form, as illustrated in Figure 21B, as an exemplary modality. MULTICOLOR COMPENSATION WITH MEASURED PANEL
[0238] As noted above, the methodology to predict and simulate panel expression can also be applied to derive or extrapolate the magnitudes and signal sources of the measured panel and antigens in a sample. Distortion and coexpression matrices can be calculated and used to compensate for mutual coexpression or parent-descendant overflow, which in aspects can be performed via suitable switching parameters. In some aspects, lasers used to excite antibody-coupled dyes can excite more than one intended target dye, and compensation tables or matrices can be used to accommodate and correct for such overflow. Figure 22A depicts intralaser compensation aspects according to some embodiments. Likewise, Figure 22B represents intralaser compensation aspects according to the modalities. Both Figures 22A and 22B represent the same compensation table 2200, which shows a configuration of fluorochromes in columns 2202 and a set of PMTs in rows 2204. Compensation table 2200 is determined by the individual identities of the fluorochromes in columns 2212 and the PMT in lines 2204, which is specific to each hardware configuration and design of the chosen panel. Figure 22A focuses on intralaser compensation values, values for which the overflow of a fluorochrome triggered by a given excitation laser wavelength can affect a PMT for a separate fluorochrome also triggered by the same excitation laser wavelength . In contrast, Figure 22B focuses on interlaser compensation values, values for which the overflow of a fluorochrome triggered by a given laser excitation wavelength can affect a PMT for a separate fluorochrome triggered by an excitation wavelength of different laser.
[0239] As illustrated in Figure 22A, the fluorochromes triggered by excitation light having a wavelength (À) of 405 nm are located in columns FL9 and FL10, with the corresponding detection channels in lines FL9 and FL10. As shown, FL9 and FL10 detection is for the Pacific Blue and Pacific Orange fluorochromes, respectively. The comparison plot at 405 nm 2206 reflects the region of overlap of each fluorochrome, and is represented in compensation table 2200 by the region of intersection of the columns and rows of FL9 and FL10. The region where the columns and rows of FL9 and FL10 intersect are filled with values or factors used to correct distortion, as discussed above. Likewise, fluorochromes elicited by excitation light having a wavelength (À) of 638 nm are located in columns FL6, FL7, and FL8, with corresponding detection channels in lines FL6, FL7, and FL8. As shown, detection of FL6, FL7, and FL8 is for the fluorochromes APC, APC-Cy5 or APC-A700, and APC-Cy7 or APC-A750, respectively. The comparison plot at 638 nm 2208 reflects the overlap region of each fluorochrome, and is represented in compensation table 2200 by the region where the columns and rows of FL6, FL7, and FL8 intersect. The region where the FL6, FL7, and FL8 columns and rows intersect is filled with values or factors used to correct distortion, as discussed above. Furthermore, the fluorochromes elicited by the excitation light having a wavelength (À) of 488 nm, are located in the columns FL1, FL2, FL3, FL4, and FL5, with the corresponding detection channels in the lines FL1, FL2, FL3 , FL4, and FL5. As shown, detection of FL1, FL2, FL3, FL4, and FL5 is for the fluorochromes FITC, PE, ECD, PC5 or PC5.5, and PC7, respectively. The comparison plot at 488 nm 2210 reflects the region of overlap of each fluorochrome, and is represented in compensation table 2200 by the region of intersection of the columns and rows of FL1, FL2, FL3, FL4, and FL5. The region where the columns and rows FL1, FL2, FL3, FL4, and FL5 intersect are filled with values or factors used to correct distortion, as discussed above.
[0240] As would be expected, the intersection between a given fluorochrome and the channel that is configured to detect that fluorochrome does not require any compensation value or factor, and is indicated in compensation table 2200 as the diagonal without any numerical values that fill in the 2200 compensation table cells.
[0241] As illustrated in Figure 22B, fluorochromes can also be triggered by an excitation light at a wavelength not configured or intended to excite the fluorochrome. Interlaser compensation region 2212 may include the two sections of compensation table 2200 outside of the intralaser compensation regions. The two sections of the 2212 interlaser compensation region indicate compensation values or factors for: FL1-FL5 fluorochromes, affecting the detection channels for FL6-FL8 and FL9-FL10; FL6-FL8 fluorochromes, affecting the detection channels for FL1-FL5 and FL9-FL10; and FL9-FL10 fluorochromes, affecting the detection channels for FL1-FL5 and FL6-FL8. The two sections of the 2212 interlaser compensation region are populated with values or factors used to correct distortion, as discussed above.
[0242] Figure 23 represents an approach for determining a distortion calculation according to some modalities. In particular, Figure 23 displays six plots of results of fluorochrome expression measured using ten (10) dyes for a cell phenotype [LEUKO]. Table 2301 provides a listing of antibody-dye conjugates that can be detected and used to develop switching parameters and related plots, where Figure 23 provides an exemplary selection of plots and switching techniques based on the listing of antibody-dye conjugates. dye in table 2301. Plot 2302 displays the signal of a CD45-KRO antibody-dye conjugate against general lateral scattered light (SSC) measured from a flow cytometry system. In plot 2302, the expression sign in the first decade is identified as [LEUKO], in relation to this phenotype. The remaining five plots are detailed extrapolations, applying switching techniques, of the signal in the first decade of the signal [LEUKO]. Plot 2304 displays the SSC signal against CD13-PC5.5 (the CD13 signal measured in the PMT for PC5.5) and identifies the region of expression and density for CD13 in it. Plot 2306 displays the SSC signal against HLADR-PC7 (the HLADR signal measured in the PMT for PC7) and identifies the region of expression and density for HLADR therein. Plot 2308 displays the SSC signal against CD117-APC (the CD117 signal measured in the PMT for APC) and identifies the region of expression and density for CD117 in it. Plot 2310 displays the SSC signal against CD34-APCA700 (the CD34 signal measured in the PMT for APCA700) and identifies the region of expression and density for CD34 in it. Plot 2312 displays the SSC signal against CD33-APCA750 (the CD33 signal measured in the PMT for APCA750) and identifies the region of expression and density for CD33 in it. As evident from the six lots of Figure 23, the result profile may look different for any given antibody-dye conjugate measured for, and the density of any given antibody-dye conjugate can be isolated from the signal for, antibody-dye conjugate combinations present in the cell or phenotype.
[0243] Figure 24 represents an approach for determining a distortion calculation according to some modalities. In particular, Figure 24 displays six plots of results of fluorochrome expression measured using ten (10) dyes for a cell phenotype [LEUKO], indicating quadrants for predicted detection levels for different switching parameters. Table 2401 provides a listing of antibody-dye conjugates that can be detected and used to develop switching parameters and related plots, while Figure 24 provides an exemplary selection of plots and switching techniques based on the listing of antibody-dye conjugates. dye in table 2401. The first plot 2402 displays the overall SSC signal against the signal measured from a CD34-APCA700 antibody-dye conjugate for a cell phenotype [LEUKO]. The signal indicating the population of CD34 positive events is identified as region 2414. The remaining five plots reflect the application of switching techniques to identify the positivity or negativity of the signal compared to the signal measured by the system from other conjugates of fluorochrome. Plot 2404 shows the signal measured from CD34-APCA700 with switching parameters applied to further indicate the positive signal measured from CD13 antigens while remaining negative for CD33 and CD117 antigens. Plot 2406 displays the signal measured from CD34-APCA700 with switching parameters applied to further indicate the positive signal measured from CD17 antigens while remaining negative for CD13 and CD33 antigens. Plot 2408 displays the signal measured from CD34-APCA700 with switching parameters applied to further indicate the positive signal measured from CD33 antigens while remaining negative for CD13 and CD117 antigens. Plot 2410 displays the signal measured from CD34-APCA700 with switching parameters applied to further indicate the positive signal measured from CD13 and CD33 antigens while remaining negative for CD117 antigens. Plot 2412 displays the signal measured from CD34-APCA700 with switching parameters applied to further indicate the positive signal measured from the CD13, CD33, and CD117 antigens.
[0244] Figure 25 represents an approach for determining a distortion calculation according to some modalities. In particular, Figure 25 displays six plots of results of fluorochrome expression measured using ten (10) dyes for a cell phenotype [LEUKO], indicating quadrants for predicted detection levels for different switching parameters. Table 2501 provides a listing of antibody-dye conjugates that can be detected and used to develop switching parameters and related plots, where Figure 25 provides an exemplary selection of plots and switching techniques based on the listing of antibody-dye conjugates. dye in table 2501. Plot 2502 displays the overall SSC signal against the signal measured from a CD117-APC antibody-dye conjugate for a cell phenotype [LEUKO]. The signal that indicates the population of CD117 positive events is identified as region 2514. The remaining five plots reflect the application of switching techniques to identify the positivity or negativity of the signal compared to the signal measured by the system from other conjugates of fluorochrome. Plot 2504 displays the signal measured from CD117-APC with switching parameters applied to further indicate the positive signal measured from CD13 antigens while remaining negative for CD33 and CD34 antigens. Plot 2506 displays the signal measured from CD117-APC with switching parameters applied to further indicate the positive signal measured from CD33 antigens while remaining negative for CD13 and CD34 antigens. Plot 2508 displays the signal measured from CD117-APC with switching parameters applied to further indicate the positive signal measured from CD34 antigens while remaining negative for CD13 and CD33 antigens. Plot 2510 displays the signal measured from CD117-APC with switching parameters applied to further indicate the positive signal measured from CD13 and CD33 antigens while remaining negative for CD34 antigens. Plot 2512 displays the signal measured from CD117-APC with switching parameters applied to further indicate the positive signal measured from the CD13, CD33, and CD43 antigens.
[0245] Figure 26 represents an approach for determining a distortion calculation according to some modalities. In particular, Figure 26 displays seven plots of results of fluorochrome expression measured using ten (10) dyes for a cell phenotype [LEUKO], indicating quadrants for predicted detection levels for different switching parameters. Table 2601 provides a listing of antibody-dye conjugates that can be detected and used to develop switching parameters and related plots, where Figure 26 provides an exemplary selection of plots and switching techniques based on the listing of antibody-dye conjugates. dye in table 2601. Plot 2602 displays the overall SSC signal against the signal measured from a CD33-APCA750 antibody-dye conjugate for a cell phenotype [LEUKO]. The signal indicating the population of CD33 positive events is identified as region 2616. The remaining six plots reflect the application of switching techniques to identify the positivity or negativity of the signal compared to the signal measured by the system from other conjugates of fluorochrome. The 2604 plot displays the signal measured from CD33-APCA750 with switching parameters applied to further indicate the positive signal measured from CD13 antigens, while remaining negative for CD34, CD117, and HLADR antigens. Plot 2606 displays the signal measured from CD33-APCA750 with switching parameters applied to further indicate the positive signal measured from CD34 antigens while remaining negative for CD13, CD117, and HLADR antigens. The 2608 plot displays the signal measured from CD33-APCA750 with switching parameters applied to further indicate the positive signal measured from CD117 antigens while remaining negative for CD13, CD34, and HLADR antigens. The 2610 plot displays the signal measured from CD33-APCA750 with switching parameters applied to further indicate the positive signal measured from HLADR antigens while remaining negative for CD13, CD34, and CD117 antigens. The 2612 plot displays the signal measured from CD33-APCA750 with switching parameters applied to further indicate the positive signal measured from CD13 antigens, while remaining negative for CD34, and CD117 antigens. Plot 2614 displays the signal measured from CD33-APCA750 with switching parameters applied to further indicate the positive signal measured from CD34, CD117, and HLADR antigens remaining negative for CD13 antigens.
[0246] Figure 27 represents aspects of an APCA700/A750 distortion model in an APC channel, according to the modalities. The three-dimensional graph plots the increase in APCA750 signal strength detected by a PMT APC channel, against the increase in APCA700 signal strength detected by the PMT APC channel, both plotted against the overall detection level measured by the PMT channel. APC to PMT. At the selected set point, for example above 1.00, as detected by the APC channel, event signals are predicted or calculated to be positive events. On the other hand, below the selected reference point, event signals are predicted or calculated as being negative events. At a point of maximum intensity of APCA750 and zero intensity of APCA700, the event signal as measured by the APC channel is subject to APCA750 distortion only or "pure". At a point of maximum intensity of APCA700 and zero intensity of APCA750, the event signal as measured by the APC channel is subject to APCA700 distortion only or "pure". Such models as shown in Figure 27 can be expanded in other dimensions for an arbitrary number of fluorochrome distortion. Such models as shown in Figure 27 can be used in calculations to remove the effect of the fluorochrome distortion event signal on measurements recorded by a channel and PMT for a desired fluorophore.
[0247] Figure 28 represents an exemplary distortion table 2800, similar to the distortion table 2200 discussed in relation to Figure 22A and Figure 22B. As can be seen from the 2800 distortion table, the specific colorants used by a panel (or, alternatively, used in a panel design simulation) can be specifically identified. Similarly, the bandpass filter properties of hardware PMT channel detectors used by an emission measurement instrument (or, alternatively, used in a panel design simulation) can be specifically identified. In aspects, the PMT channel dye grouping can be based on the excitation laser wavelength for a given dye, however, displaying a distortion table can present the distortion value groups in any order that is useful or appropriate for an operator. As is evident from the comparison of the two distortion tables, the 2200 distortion table and the 2800 distortion table, the identity of PMT detectors and dyes used for any specific hardware configuration or panel design may change the distortion values used for do the correction calculations.
[0248] Figure 29 is a table 2900 identifying an exemplary array of 2902 target antigens, 2904 dyes, and 2906 excitation lasers to activate the identified dyes. As indicated, the 2906 excitation lasers are operative to excite the respective 2904 dyes, which, in turn, are conjugated to their 2902 target antigens. In aspects, the compensated data that is acquired using these measurements can be exported to a processor and operable system or program, such as a 20-bit table for Excel, via software, such as Kaluza 1.2 beta. Positive and negative event scores can be calculated for each event individually, particularly in the context of specifically evaluated antibody-dye conjugates. Figures 29A-29E represent actual data aspects according to the modalities, using the 2902 target antigen array, 2904 dyes, and 2906 excitation lasers as shown in Figure 29.
[0249] Figure 29A depicts real data aspects according to an embodiment of the present invention, particularly the plotting of event data acquired from a flow cytometry instrument, applying switching techniques to CD62L-FITC event signal against CD4-PacBlue 2902. The plot of the CD62L-FITC event signal against CD4-PacBlue (PacBlue) 2902 shows the differentiation we evaluated. Thus classified as negative for both CD62L-FITC and CD4-PacBlue 2904 population, one positive for CD62L-FITC and negative for CD4-PacBlue 2906 population, one negative for both CD62L-FITC and one positive for CD4- population PacBlue 2908, and one positive for both CD62L-FITC and CD4-PacBlue 2910 population.
[0250] Figure 29B represents real data aspects according to the modalities of the present disclosure, particularly the plotting of event data acquired from a flow cytometry instrument, applying switching techniques to CD117-PE event signal against CD45RA-ECD 2912. Thus, the population of all events can be classified as event populations that are: one negative for both CD117-PE and CD45RA-ECD 2914 population, one positive for CD117-PE and negative for CD45RA population - ECD 2916, one negative for both CD117-PE and one positive for the CD45RA-ECD 2918 population, and one positive for both CD117-PE against the CD45RA-ECD 2920 population. The plot of CD117-PE against the sign of event CD45RA-ECD 2912 indicates at least one "critical point" that affects the classification of individual events as positive or negative, reflective of the evaluated channels. A first critical point can be identified as in the region between the population of 2914 events and the population of 2920 events, while a second critical point can be identified in the region between the population of 2916 events and the population of 2920 events. further multiple overlapping coefficients of variation (CV) between different populations.
[0251] Figure 29C represents real data aspects according to the modalities of the present disclosure, particularly the plotting of event data acquired from a flow cytometry instrument, applying switching techniques for event signal CD69-PC5 against CD45RA-ECD 2922. Thus, the population of all events can be classified as event populations that are: one negative for both CD69-PC5 and CD45RA-ECD 2924 population, one positive for CD69-PC5 and negative for CD45RA population - ECD 2926, one negative for both CD69-PC5 and one positive for the CD45RA-ECD 2928 population, and one positive for both CD69-PC5 against the CD45RA-ECD 2930 population. The plot of CD69-PC5 against the sign of event CD45RA-ECD 2922 indicates at least one "critical point" that affects the classification of individual events as positive or negative, reflective of the evaluated channels. A first critical point can be identified as in the region between the population of 2924 events and the population of 2930 events, while a second critical point can be identified in the region between the population of 2926 events and the population of 2930 events. further multiple overlapping coefficients of variation (CV) between different populations.
[0252] Figure 29D represents aspects of real data according to the modalities of the present disclosure, particularly the plotting of event data acquired from a flow cytometry instrument, when the application of switching techniques may not be useful or possible. In particular, the plot of event data acquired from a flow cytometry instrument for the event signal CD69-PC5 versus CD16-APCA750 2932 is shown. Thus, the population of all events can be classified as event populations that are: one negative for both CD69-PC5 and CD16-APCA750 population 2934, one positive for CD69-PC5 and negative for CD16-APCA750 population 2936, one negative for both CD69-PC5 and one positive for the CD16-APCA750 2938 population, and one positive for both CD69-PC5 against the CD16-APCA750 2940 population. The plotting of the event signal CD69-PC5 against CD16-APCA750 2932, however, it does not indicate a clear region that can define a "hot spot" to reliably classify individual events as positive or negative in relation to the evaluated channels. These results may reflect multiple overlapping coefficients of variation (CV) between different populations. Thus, in some respects, the analysis of CD69-PC5 versus CD16-APCA750 in combination can be considered as not subject to switching techniques, and can be avoided in further analysis or panel design.
[0253] Figure 29E represents real data aspects according to the modalities of the present disclosure, particularly the plotting of event data acquired from a flow cytometry instrument, when the application of switching techniques may not be useful or possible. In particular, the plot of event data acquired from a flow cytometry instrument for the event signal CD69-PC5 against CD56-APC 2942 is shown. Thus, the population of all events can be classified as event populations that are: one negative for both CD69-PC5 and CD56-APC 2944 population, one positive for CD69-PC5 and negative for CD56-APC 2946 population, one negative for both CD69-PC5 and one positive for the CD56-APC 2948 population, and one positive for both CD69-PC5 against the CD56-APC 2950 population. The plotting of the event signal CD69-PC5 against CD56-APC 2942, however, it does not indicate a clear region that can define a "hot spot" to reliably classify individual events as positive or negative in relation to the evaluated channels. These results may reflect multiple overlapping coefficients of variation (CV) between different populations. Thus, in some respects, the analysis of CD69-PC5 versus CD56-APC in combination can be considered as not subject to switching techniques, and can be avoided in further analysis or panel design.
[0254] Figure 30 is a table 3000 identifying an exemplary array of target antigens 3002, dyes 3004, and excitation lasers 3006 to activate the identified dyes. As indicated, excitation lasers 3006 are operative to excite respective dyes 3004, which, in turn, are conjugated to their target antigens 3002.
[0255] Figure 30A represents actual data aspects according to the modalities of the present disclosure, using the 3002 target antigen array, 3004 dyes, and 3006 excitation lasers as shown in Figure 30, particularly the plotting of data from events acquired from a flow cytometry instrument. In Figure 30A, switching techniques or algorithms are not applied, allowing a holistic assessment of the positive and negative classification based on the PMT detection channels used to evaluate the event signals. In particular, the plot of event data acquired from a flow cytometry instrument for the event signal CD27-PC7 against CD3-PacO (Pacific Orange) 3008 is shown. Thus, the population of all events can be classified as event populations that are: one negative for both CD27-PC7 and CD3-PacO 3010 population, one positive for CD27-PC7 and negative for CD3-PacO 3012 population, one negative for both CD27-PC7 and one positive for the CD3-PacO 3014 population, and one positive for both CD27-PC7 against the CD3-PacO 3016 population. disclosure, an event signal population from a different antigen-antibody conjugate may be incorrectly grouped as positive for the other event signal population. For example, in Figure 30A, a population of positive event signal for CD19-PC5.5 3018 may be mistakenly grouped with positive event signal for CD27-PC7 and negative for CD3-PacO 3012. These results may again reflect multiple overlaps of coefficients of variation (CV) between the various populations, and further reflect the errors that can occur without the application of switching techniques.
[0256] Several principles can be used and applied when predicting or determining detection limits. For example, under some modalities, bright dyes may work well with weakly expressed antigens, whereas both weak and bright dyes may work well with strongly expressed antigens. Furthermore, untouched channels may work well with weakly expressed antigens, whereas silent channels may work well with strongly expressed antigens. In some cases, it may be desirable to allow for spillover between deletion antigens. In some cases, it may be desirable to avoid spillover between non-exclusively co-expressed markers. In some cases, it may be desirable to allow the spillover from subpopulation markers to parent markers. In some cases, it may be desirable to avoid spillover from parent markers to subpopulation markers or coexpressed markers to subpopulation or other coexpressed markers if the goal is to discriminate within the positive range, ie, discriminate prominent positive events against weak positive events . In some cases, it may be desirable to minimize the number of fluorochrome distortions per detection channel.
[0257] Various embodiments of the present disclosure are considered as follows. In aspects, the present disclosure is directed to a method of determining a probe panel for analyzing a biological sample in a flow cytometry procedure, the method including: receiving information about a list comprising a plurality of probes, the individual probes from the list being associated with respective individual channel-specific detection limits; receive information about an antigenic co-expression pattern; evaluate individual probe combinations such as the probe panel, based on the flow cytometer hardware configuration, individual channel-specific detection limits, and antigenic co-expression pattern, the combinations being subsets of the listed probes; determine the probe panel for use with the flow cytometer hardware configuration, individual channel-specific detection limits, and antigenic co-expression pattern; and produce a probe panel for use in a flow cytometry process. In some aspects, the method may further include receiving information about a flow cytometer hardware configuration. In other aspects, information received about the flow cytometer hardware configuration may include any or all information about at least one excitation laser intensity, at least one excitation laser wavelength, and at least one band. of photomultiplier tube detection channel. In other aspects, the information received about the list comprising a plurality of probes may further involve any or all of accessing a non-temporary computer readable medium having a library of channel-specific detection limits for the plurality of probes, an operator selecting an antibody and a corresponding dye, as at least one probe panel member, automatically selecting an antibody and a corresponding dye from a library, as at least one probe panel member, and automatically selecting an antibody and a corresponding dye from a library for each member of the probe panel. In some embodiments, the list may include one or more fictitious members. In some aspects, receiving information about the antigenic co-expression pattern may include accessing a non-temporary computer-readable medium having a library of co-expression relationships. In still other aspects, evaluating combinations of individual probes such as the probe panel may include calculating any overlap or distortion between the channel-specific detection limits of two or more individual probes. In some aspects, the pattern of antigen co-expression may include co-expression relationships between antigens for a specific cell type.
[0258] Additional embodiments of the present disclosure may be directed to a system for determining a probe panel for analyzing a biological sample in a flow cytometry procedure, wherein the system may include: an information input device; a flow cytometer having a hardware configuration having at least one excitation laser and at least one photomultiplier tube detector; a probe library stored in a database, wherein individual probes from the library are associated with respective individual channel-specific detection limits; an antigenic co-expression pattern stored in the database; a processor configured to evaluate a list of individual probes selected from the probe library based on the flow cytometer's hardware configuration, the channel-specific detection limits of individual probes, and the antigenic co-expression pattern; and an output device that provides a determination of detection limits for the probe panel, the probe panel comprising a subset of individual probes from the list. In aspects, the system's flow cytometer hardware configuration may include up to ten photomultiplier tube detectors, while in other embodiments, the flow cytometer hardware configuration may have more than ten photomultiplier tube detectors. In other aspects, the system's flow cytometer hardware configuration may also include up to four excitation lasers, although in other embodiments, the flow cytometer hardware configuration may have more than four excitation lasers. In some respects, the information input device can be configured to allow any or all of the following: an operator to select individual probes from the probe library for evaluation from the list, an operator to enter channel-specific detection limits for individual probes in the probe library, the processor to automatically select an antibody and corresponding dye from the probe library for each member of the probe panel. In further aspects, the combination evaluation processor can calculate any overlap or distortion between the channel-specific detection limits of two or more individual probes.
[0259] Other embodiments of the present invention are directed to a method of analyzing a biological sample in a flow cytometry procedure, wherein the method includes: measuring the light output of a multiplicity of probes in a biological sample with a cytometer flow; receive information about a flow cytometer hardware configuration; receiving information about a list comprising a plurality of probes used in the biological sample, the individual probes from the list being associated with respective individual channel-specific detection limits; receive information about an antigenic co-expression pattern; determine a positivity criterion and a negativity criterion for each individual probe in the list and determine the switching parameters for the list based on the flow cytometer's hardware configuration, individual channel-specific detection limits, and the coexpression pattern antigenic; and evaluating the light output of the plurality of probes in the biological sample according to the positivity criteria, the negativity criteria, and the switching parameters. In aspects, receiving information about the flow cytometer hardware configuration further includes receiving information about at least one excitation laser intensity, at least one excitation laser wavelength, and at least one channel band. of photomultiplier tube detection. In some aspects, receiving information about the list comprising a plurality of probes may further include either or both of accessing a non-temporary computer readable medium having a library of channel-specific detection limits for the plurality of probes and operator selection of an antibody and a corresponding dye for the plurality of probes. In additional aspects, the list may include one or more fictitious members. In some aspects, receiving information about the antigenic co-expression pattern may include accessing a non-temporary computer-readable medium having a library of co-expression relationships. In other aspects, evaluating individual probe combinations such as the probe panel may include calculating any overlap or distortion between the channel-specific detection limits of two or more individual probes. In still other aspects, the antigenic co-expression pattern may include co-expression relationships between antigens for a specific cell type.
[0260] Other embodiments of the present disclosure may be directed to a system for analyzing a biological sample in a flow cytometry procedure, wherein the system may include: an operator input device, through which a list comprising a plurality of probes used in a biological sample can be introduced; a flow cytometer hardware configuration having at least one excitation laser and at least one photomultiplier tube detector; a probe library stored in a database, wherein individual probes from the library are associated with respective individual channel-specific detection limits; an antigenic co-expression pattern stored in the database; a processor configured to evaluate light emitted by a plurality of probes and detected by at least one photomultiplier tube detector, calculating any overlap and distortion between the channel-specific detection limits of the plurality of probes; and an output device that provides a determination of the presence or absence of a probe in the biological sample. In some respects, the system may have a flow cytometer hardware configuration that includes up to ten photomultiplier tube detectors. In other respects, the system can have a flow cytometer hardware configuration that includes up to four excitation lasers. In other aspects, the system can include an operator input device that can be configured to allow an operator to select individual probes from the probe library for evaluation from the list, or that can be configured to allow an operator to enter detection limits channel specifics for individual probes in the probe library.
[0261] Additional embodiments of the present disclosure may be directed to a method for determining a probe panel for analyzing a biological sample in a flow cytometry procedure, including: providing a selection of flow cytometry hardware configurations; provide a plurality of antibody pairing selections; provide a selection of at least one target population; providing a selection for a plurality of antibodies, and providing at least one selection to indicate whether an antibody from the plurality of antibodies is an antigen of interest or an unrelated antigen; provide a plurality of a selection of antigen-antibody pairings, and for an individual antigen-antibody pairing, provide a selection of antigens for which the individual antigen-antibody pairing is mutually exclusive; provide a plurality of an antigen-antibody pairing selection, and for an individual antigen-antibody pairing, provide a selection of antigens that are developmental descendants of the individual antigen-antibody pairing; provide a selection of adjustable antigen density parameters; and responsive to the selection of a variety of flow cytometry hardware configurations, a plurality of antibody pairing selections, a selection of at least one target population, a selection for a plurality of antibodies, a selection of antigens, to which at least one antigen-antibody pairing is mutually excluding, a selection of antigens that are developmental descendants of at least one antigen-antibody pairing, and a selection of adjustable antigen density parameters, providing a display of detection limit estimates for the probe panel. In embodiments, providing a plurality of antibody pairing selections can further include providing a selectivity selection for each antibody pairing and providing a dye section for each antibody pairing. In aspects, providing a selection of at least one target population may include providing a selection corresponding to a phenotype of the target population. In some aspects, providing a selection of at least one target population is configurable to allow for the addition or removal of target populations. In some aspects, the selection of antigens for which an individual antigen-antibody pairing is mutually excluding can be determined automatically by a mutual exclusion database, and the selection of antigens for which an individual antigen-antibody pairing is mutually excluding mutually is automatically selected. In other respects, a selection of antigens that are developmental descendants of individual antigen-antibody pairings can be determined automatically by a database of family developmental patterns, and the selection of antigens that are developmental descendants of individual antigen-antibody pairings is automatically selected. In some respects, the provided tunable antigen density parameters may include a selection to discriminate, on the monitor, between positive and negative detection limit estimates, while in other respects, the provided tunable antigen density parameters may include a selection for on-screen discrimination between bright positive and weak positive detection limit estimates. In other respects, the provided tunable antigen density parameters include a selection to display both distinct and modulated detection limit estimates, while in still other respects the provided tunable antigen density parameters include a selection to size the monitor accordingly with an estimated probe panel detection limit.
[0262] Each of the calculations or operations described herein can be done using a computer, communication network, or other processor that has hardware, software and/or firmware. An exemplary system with an attendant computer, communication network, or other processor having hardware, software, and/or firmware can be found in US patent application no. 13/935,154, which is incorporated herein by reference. The various steps of the method may be performed in modules, and the modules may comprise any of a wide variety of digital and/or analog data processing hardware and/or software arranged to perform the method steps described herein. The modules optionally comprise data processing hardware adapted to perform one or more of these steps having suitable machine programming code associated therewith, the modules of two or more steps (or portions of two or more steps) being integrated into a single board. processor or separated on different processor boards in any one of a wide variety of integrated and/or distributed processing architectures. These methods and systems will often use a tangible medium including machine-readable code with instructions to perform the steps of the method described above. Suitable tangible media may comprise memory (including volatile memory and/or non-volatile memory), storage media (such as a magnetic recorder on a floppy disk, hard disk, tape, or the like; in an optical memory such as a CD, a CD-R/W, a CD-ROM, a DVD, or similar; or any other digital or analog storage media)
[0263] It is appreciated that a flow cytometry system as described herein can be configured to carry out various aspects of the methods of the present invention. For example, a processor component or module of a system may be a microprocessor control module configured to receive cellular parameter signals from a sensor input device or module from a sensor input device or module. user interface, and/or from an analyzer system, optionally through an analyzer system interface and/or a network interface and a communication network. In some cases, the sensor input device(s) may include or be part of a cell analysis system such as a flow cytometer. In some cases, the user interface(s) and/or network interface input device(s) may be configured to receive cellular parameter signals generated by a cellular analysis system such as a flow cytometer. In some cases, the analyzer system may include or be part of a cell analysis system such as a flow cytometer.
[0264] Processor component or module may also be configured to transmit cellular parameter signals, optionally processed according to any of the techniques described herein, to a sensor output device or module, to a sensor output device or module. user interface, to a network interface device or module, to an analyzer system interface, or any combination thereof. Each of the devices or modules, in accordance with embodiments of the present invention, may include one or more software modules on a computer-readable medium that is processed by a processor, or hardware modules, or any combination thereof. Any of a variety of commonly used platforms, such as Windows, Macintosh, and Unix, along with any of a variety of commonly used programming languages, can be used to implement the embodiments of the present invention.
[0265] User interface input devices can include, for example, a touchpad, a keyboard, pointing devices such as a mouse, a trackball, a graphics tablet, a digitizer, a joystick, a touch screen built in to a monitor, audio input devices such as speech recognition systems, microphones, and other types of input devices. User input devices may also download computer executable code from a tangible storage medium or communication network, the code incorporating any of the methods or aspects thereof disclosed herein. It will be appreciated that the terminal software may be updated from time to time and transferred to the terminal as appropriate. In general, the use of the term "input device" is intended to include a variety of conventional and proprietary devices and ways to input information into the module system.
[0266] User interface output devices may include, for example, a display subsystem, a printer, a fax machine, or non-visual monitors such as audio output devices. The display subsystem can be a cathode ray tube (CRT), a flat panel device such as a liquid crystal display (LCD), a projection device, or the like. The display subsystem can also provide a non-visual screen such as through audio output devices. In general, the use of the term "output device" is intended to include a variety of conventional and proprietary devices and ways to produce information from the module system for a user.
[0267] The bus subsystem can provide a mechanism to allow the various components and subsystems of the module system to communicate with each other as envisioned or desired. The various components and subsystems of the module system do not need to be in the same physical location, but can be distributed across multiple locations within a distributed network. A bus subsystem can be a single bus or it can use multiple buses.
[0268] The network interface can provide an interface to an external network or other devices. The external communication network can be configured to carry out communications as needed or desired with other parties. In many embodiments, the communication network can be a cloud-based or web-based processing system, allowing remote access and processing. It can thus receive an electronic packet from the 600 module system and transmit any information as needed or desired back to the module system. In addition to providing such infrastructure communications links internal to the system, the communications network system may also provide a connection to other networks such as the Internet and may comprise a wired, wireless, modem and/or other interface connection of interface connection.
[0269] All patents, patent publications, patent applications, journal articles, books, technical references and the like discussed in the present disclosure are hereby incorporated by reference in their entirety for all purposes.
[0270] It should be understood that the figures and descriptions of the invention have been simplified to illustrate elements that are relevant to a clear understanding of the invention. It should be understood that the figures are presented for illustrative purposes and not as construction drawings. Omitted details and modifications or alternative modalities are within the scope of those with basic knowledge of the technique.
[0271] It can be understood that, in certain aspects of the invention, a single component may be replaced by multiple components, and multiple components may be replaced by a single component, to provide an element or structure or to perform a particular function or functions. Except where such substitution is not operational to practice certain embodiments of the invention, such substitution is considered within the scope of the invention.
[0272] The examples presented in the present invention are intended to illustrate possible and specific implementations of the invention. It will be understood that the examples are intended primarily for purposes illustrating the invention to those skilled in the art. There may be variations on these diagrams or on the operations described herein without departing from the spirit of the invention. For example, in certain cases, method steps or operations can be performed or executed in different orders, or operations can be added, deleted or modified.
[0273] Different arrangements of components represented in the drawings and described above, as well as components and steps not shown or described are possible. Similarly, some features and subcombinations are useful and can be used without reference to other features and subcombinations. Embodiments of the invention have been described for illustrative and non-restrictive purposes, and alternative embodiments will be apparent to readers of this patent. Accordingly, the present invention is not limited to the embodiments described above or shown in the drawings, and various embodiments and modifications can be made without departing from the scope of the claims below.

权利要求:
Claims (15)
[0001]
1. A method for designing a probe panel for a flow cytometer comprising: determining a distortion factor that quantifies the overflow effect caused by the emission of a first marker, intended to be measured in a first channel, to a second channel; and inputting a maximum expected signal from a first probe-marker combination including the first marker and a first probe, characterized in that it further comprises: calculating an increase in the detection limit in the second channel based on the distortion factor and the signal maximum expected from the first probe-marker combination; and selecting a probe-marker combination to include the probe panel based on the calculated increase in detection limit.
[0002]
2. Method according to claim 1, characterized in that the distortion factor is an increase in the detection limit of the second channel as a function of an emission intensity of a first probe-marker combination.
[0003]
3. Method according to claim 2, characterized in that the distortion factor is a linear function of the emission intensity of the first probe-marker combination.
[0004]
4. Method according to claim 2, characterized in that the increase in the detection limit in the second channel is caused by an increase in a measurement error as a function of the emission intensity of the first probe-marker combination.
[0005]
5. Method according to claim 2, characterized in that the distortion factor is calculated using an interference index.
[0006]
6. Method according to claim 1, characterized in that the distortion factor is mathematically modified by a coefficient that represents the co-expression pattern of antigens corresponding to the first probe-marker combination and a second probe-marker combination , the second probe-marker combination being intended to be measured in the second channel.
[0007]
7. The method of claim 1, further comprising: determining a distortion factor for each marker in a first potential probe panel to calculate a total increase in detection limit in the second channel.
[0008]
8. Method according to claim 7, characterized in that the selection of the probe-marker combination is based on a comparison of the calculated total increase in detection limit with an expected minimum signal in the second channel.
[0009]
9. Method according to claim 7, characterized in that it further comprises calculating a total increase in the detection limit for each probe in the first potential probe panel.
[0010]
10. Method according to claim 9, characterized in that it further comprises: calculating a total increase in the detection limit for each probe in a second potential probe panel; and selecting the probe panel based on a comparison of the calculated total increase in detection limit for each probe in the first potential probe panel with the calculated total increase in detection limit for each probe in the second potential probe panel.
[0011]
11. Method according to claim 9, characterized in that it further comprises: calculating a total increase in the detection limit for each probe in a second potential probe panel; and selecting the probe panel based on the calculated total increase in detection threshold for a priority probe in the first potential probe panel and the second potential probe panel.
[0012]
12. Method according to claim 6, characterized in that the coefficient is one or zero.
[0013]
13. Method according to claim 1, characterized in that said maximum expected signal is based, in part, on an expected antigen density in a target cell.
[0014]
14. Method according to claim 6, characterized in that the coefficient is determined to be zero if the co-expression pattern of antigens corresponding to the first probe-marker combination and the second probe-marker combination is mutually exclusive.
[0015]
15. Method according to claim 6, characterized in that the coefficient is determined to be zero if the antigen corresponding to the second probe-marker combination is a descendant of the antigen corresponding to the first probe-marker combination.

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法律状态:
2018-11-13| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|

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2020-01-21| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|

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2020-11-10| B06A| Patent application procedure suspended [chapter 6.1 patent gazette]|

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2021-04-13| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|

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2021-06-22| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 14/03/2014, OBSERVADAS AS CONDICOES LEGAIS. |

优先权:

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申请号 | 申请日 | 专利标题

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US201361791492P| true| 2013-03-15|2013-03-15|

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US61/791,492|2013-03-15|

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PCT/US2014/029400|WO2014144826A1|2013-03-15|2014-03-14|Systems and methods for panel design in flow cytometry|

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