Patent Publication Number: US-2023138672-A1

Title: Analysis and screening of cell secretion profiles

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a division of U.S. patent application Ser. No. 17/344,505, filed Jun. 10, 2021, which is a continuation application of U.S. patent application Ser. No. 16/773,535, filed Jan. 27, 2020, now issued as U.S. Pat. No. 11,066,689, which is a continuation application of U.S. patent application Ser. No. 15/532,428, filed Jun. 1, 2017, now issued as U.S. Pat. No. 10,584,366, which is a National Stage Application, filed under 35 U.S.C §371, of International Application No. PCT/US2015/063754 filed Dec. 3, 2015, which claims the benefit of priority to U.S. Provisional Patent Application Ser. No. 62/087,147 filed Dec. 3, 2014, the contents of each of which are incorporated by reference herein in their entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     The present invention generally relates to cellular analysis, and more particularly to apparatus, methods, and software for biochemical assessment and functional characterization of cellular states. 
     BACKGROUND 
     In fields including cell biology and immunology, development of analytical techniques for evaluation and quantitation of cellular protein expression and secretion profiles is of significant importance to elucidate underlying biochemical processes and cell signaling mechanisms. Due in part to the heterogeneous behaviors often exhibited by cells, a need exists for tools and procedures capable of assaying large numbers of discrete cell populations that are also suitable for detection of biomolecules at the single cell level. Sensitive and accurate assessment of cellular phenotypes and functionalities as well as identification of drivers and interactions between individual cells have been shown to be important indicia of the capabilities and operation of biological systems. 
     As one example, immune cell response is directed by a large number of secreted proteins including cytokines, chemokines, and growth factors which represent important functional regulators mediating a range of cellular behaviors and cell-cell signaling processes. Monitoring these complex immuno-signaling pathways and cellular interactions present significant challenges to identifying clinically relevant measurements that can be used to understand that state of the immune system, predict clinical outcomes, and direct treatment or therapies. Increasingly, there is a demand for sensitive and highly-multiplexed technologies for cellular analysis that can be used to identify and rapidly evaluate correlatives of disease, cellular responses to various chemicals and therapeutic agents, and other cell-based processes involved in immunological interventions. Such technologies can also be used to better understand the underlying mechanisms of immunity. 
     SUMMARY OF THE DISCLOSURE 
     The present disclosure provides apparatus, methods and software useful for determining and investigating cellular processes and functional profiles. In various embodiments, the present teachings may be advantageously applied in the context of cellular analysis of immune cells. For example, T-cell functionality and correlative response to various therapies, chemical interactions and/or disease states may be assessed at the single cell level. 
     In various embodiments, apparatus, methods and software are disclosed for performing biological screening and analysis implemented using an instrument platform capable of detecting a wide variety of cell-based secretions, expressed proteins, and other cellular components. The platform may be configured for simultaneous multiplexed detection of a plurality of biological components such that a large number of discrete samples may be individually sequestered and evaluated to detect or identify constituents from the samples in a highly parallelized and scalable manner. In various embodiments, the platform is configured for automated or semi-automated processing significantly improving time-to-result, sample throughput, detection accuracy, and sensitivity. 
     The platform may be advantageously adapted for use in applications to analyze small numbers of cells and single cells. As disclosed herein, analysis of single cells and cellular interactions between single cells (e.g. cell-cell interactions) is particularly useful in immunological applications to aid in the determination of functional profiles for immuno cells such as B-cells, T cells (e.g. CD4+, CD8+ cells), and/or macrophage cells. While various examples and workflows are provided and discussed involving immuno cells, it will be appreciated that the apparatus, methods and software of the present teachings may be adapted for use with a wide variety of different cell types. Furthermore, the sample analysis techniques may be extended outside of cellular or biological analysis applications to be used in other chemical surveys where multiple discrete samples are desirably evaluated substantially simultaneously for a plurality of different analytes. Additionally, sample constituents other than cells may be evaluated in parallel, for example, beads or other particles containing chemicals, compounds or other analytes of interest. In various embodiments, the technologies disclosed herein are sufficiently sensitive to detect, distinguish, and quantify multiple analytes present in very small liquid or aqueous volumes (for example, from nanoliter and picoliter volumes or less). 
     In various embodiments, a system is described for discretely resolving analytes associated with a cellular population comprising: (a) a plurality of sample retention regions that receive at least one cell distributed from a population of cells and retain an associated plurality of analytes released by the at least one cell; (b) a plurality of analyte detection regions patterned with a plurality of discretely positioned analyte detection moieties, the analyte detection regions disposed in an ordered pattern alignable with the sample retention regions whereby, upon coupling, released analytes selectively associate with the plurality of analyte detection moieties forming an analyte pattern for each analyte detection region; (c) a plurality of first alignment markers disposed about the plurality of sample retention regions and a plurality of second alignment markers disposed about the plurality of analyte detection regions; and (d) an imaging apparatus that generates a plurality of images for selected sample retention regions and retained at least one cell and additionally at least one first alignment marker, the imaging apparatus further generating a plurality of images for analyte detection regions corresponding to the selected sample regions and associated analyte patterns and additionally at least one second alignment marker; and an image processor that aligns associated images for selected sample retention regions using the at least one first alignment marker and further aligns images for analyte detection regions with corresponding images for selected sample retention regions using the at least one second alignment marker, the image processor further identifying retained at least one cell in respective sample retention regions and corresponding analyte patterns to discretely resolve released analytes associated with the retained at least one cell based on the analyte detection moieties detected in the analyte pattern. 
     In other embodiments, the a method is described for discretely resolving analytes associated with a cellular population comprising: (a) acquiring a plurality of images for a plurality of sample retention regions that receive at least one cell distributed from a population of cells and retain an associated plurality of analytes released by the at least one cell; (b) acquiring a plurality of images for a plurality of analyte detection regions patterned with a plurality of discretely positioned analyte detection moieties, the analyte detection regions disposed in an ordered pattern alignable with the sample retention regions whereby, upon coupling, released analytes selectively associate with the plurality of analyte detection moieties forming an analyte pattern for each analyte detection region; (c) resolving a plurality of first alignment markers disposed about the plurality of sample retention regions; (d) resolving a plurality of second alignment markers disposed about the plurality of analyte detection regions; (e) aligning associated images for selected sample retention regions using at least one of the plurality of first alignment markers and further aligning images for analyte detection regions with corresponding images for selected sample retention regions using the at least one of the plurality of second alignment markers; and (f) identifying retained at least one cell in respective sample retention regions and corresponding analyte patterns to discretely resolve released analytes associated with the retained at least one cell based on the analyte detection moieties detected in the analyte pattern. 
     In further embodiments, described is non-transitory computer-readable media having computer executable code stored thereon for discretely resolving analytes associated with a cellular population, the code comprising: (a) an executable routine for acquiring a plurality of images for a plurality of sample retention regions that receive at least one cell distributed from a population of cells and retain an associated plurality of analytes released by the at least one cell; (b) an executable routine for acquiring a plurality of images for a plurality of analyte detection regions patterned with a plurality of discretely positioned analyte detection moieties, the analyte detection regions disposed in an ordered pattern alignable with the sample retention regions whereby, upon coupling, released analytes selectively associate with the plurality of analyte detection moieties forming an analyte pattern for each analyte detection region; (c) an executable routine for resolving a plurality of first alignment markers disposed about the plurality of sample retention regions; (d) an executable routine for resolving a plurality of second alignment markers disposed about the plurality of analyte detection regions; (e) an executable routine for aligning associated images for selected sample retention regions using at least one of the plurality of first alignment markers and further aligning images for analyte detection regions with corresponding images for selected sample retention regions using the at least one of the plurality of second alignment markers; and (f) an executable routine for identifying retained at least one cell in respective sample retention regions and corresponding analyte patterns to discretely resolve released analytes associated with the retained at least one cell based on the analyte detection moieties detected in the analyte pattern. 
     Additional objects and advantages of the disclosed embodiments will be set forth in part in the description that follows, and in part will be apparent from the description, or may be learned by practice of the disclosed embodiments. The objects and advantages of the disclosed embodiments will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the scope of disclosed embodiments, as set forth by the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other embodiments of the disclosure will be discussed with reference to the following exemplary and non-limiting illustrations, in which like elements are numbered similarly, and where: 
         FIG.  1 A  depicts an exemplary high-level workflow for performing sample analysis according to the present disclosure. 
         FIG.  1 B  illustrates an exemplary high-level workflow for performing sample analysis according to the present disclosure. 
         FIG.  1 C  depicts an exemplary sample array and an analyte detection substrate with magnified sub-portions according to the present disclosure. 
         FIG.  1 D  depicts an exemplary detailed analysis workflow for imaging and resolution of sample array data according to the present disclosure. 
         FIG.  1 E  depicts an exemplary outline of processes associated with multiple imaging of sub-regions of a sample array according to the present disclosure. 
         FIG.  1 F  depicts exemplary imaging of a selected sub-regions of sample array and cells/particulates according to the present disclosure. 
         FIG.  1 G  depicts exemplary signal scans for analyte patterns and alignment markers according to the present disclosure. 
         FIG.  1 H  depicts exemplary light field and fluorescent image panels for a sample array with associated image panel for a corresponding analyte detection substrate and the merging of the images panels according to the present disclosure. 
         FIG.  2    depicts an exemplary process for sample retention region location determination and resolution according to the present disclosure. 
         FIG.  3    depicts an exemplary method for location of alignment features according to the present disclosure. 
         FIG.  4    illustrates exemplary operation of a sample retention region identification process according to the present disclosure. 
         FIG.  5    depicts exemplary processes for alignment feature location and resolution according to the present disclosure. 
         FIG.  6 A- 1    and  FIG.  6 A- 2    depicts an exemplary cell/particulate location identification process according to the present disclosure. 
         FIG.  6 B  illustrates an exemplary cell detection and visualization method for sample region boundaries according to the present disclosure. 
         FIG.  6 C  depicts an exemplary cell template matching processes according to the present disclosure. 
         FIG.  6 D  depicts an exemplary cell template matching processes according to the present disclosure. 
         FIG.  6 E  depicts an exemplary process for generating a cell/particulate template library according to the present disclosure. 
         FIG.  7 A  depicts an exemplary workflow for alignment of images and scans according to the present disclosure. 
         FIG.  7 B  illustrates exemplary operations applied to exemplary images and scans using alignment markers according to the present disclosure. 
         FIG.  7 C  depicts an exemplary method for first and second image alignment according to the present disclosure. 
         FIG.  7 D  illustrates an exemplary process for first and second image alignment according to the present disclosure. 
         FIG.  8 A  depicts an exemplary process  800  for association and alignment of features according to the present disclosure. 
         FIG.  8 B  illustrates an exemplary process for association and alignment of features according to the present disclosure. 
         FIG.  9 A- 1    and  FIG.  9 A- 2    depicts an exemplary method for cell/particulate position identification and discrimination according to the present disclosure. 
         FIG.  9 B  illustrates an exemplary positioning and discrimination process for cells labelled with surface markers according to the present disclosure. 
         FIG.  10 A  depicts an exemplary process for merging sample retention region images/analyte scans according to the present disclosure. 
         FIG.  10 B  illustrates exemplary imaging and pixel comparison processes according to the present disclosure. 
         FIG.  10 C- 1    and  FIG.  10 C- 2    illustrates an example of an imaging and pixel comparison process showing pairwise readouts associated with marker patterns for exemplary sample retention regions according to the present disclosure. 
         FIG.  11    depicts an exemplary process for gating outlier data and signals according to the present disclosure. 
         FIG.  12 A  illustrates exemplary images of a portion of a sample retention region with intersecting analyte detection regions according to the present disclosure. 
         FIG.  12 B  depicts an exemplary scatterplot for evaluating a plurality of analytes according to the present disclosure. 
         FIG.  12 C  illustrates exemplary imaging results for marker patterns/readouts from detected analytes associated with two cells cell types according to the present disclosure. 
         FIG.  13 A  depicts exemplary methods and tools for presenting and interpreting analyte data and results according to the present disclosure.  FIG.  13 B  depicts exemplary methods and tools for presenting and interpreting analyte data and results according to the present disclosure. 
         FIG.  14 A  depicts an exemplary database interface for evaluating experimental results according to the present disclosure. 
         FIG.  14 B  depicts an exemplary results interface for displaying information from an experiment according to the present disclosure. 
         FIG.  14 C  depicts a workflow for analysis functionalities and database querying according to the present disclosure. 
         FIG.  14 D  depicts an exemplary platform comprising components for image acquisition and computational components for image analysis and subsequent analyte evaluation according to the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Efforts to characterize the state or capabilities of a biological system or sample may be confounded or obscured by bulk analysis methods. This is evident in cellular analysis such as immunological surveys wherein a sample comprising many cells analyzed in a common volume or medium may act to mask or dilute analytes. It is therefore desirable in many instances to provide very small volume and single cell sample analysis capabilities. For small sample volumes including those involving a single or a few cells it is further desirable to sequester multiple samples in discrete reaction regions or areas so they may be subjected to parallel analysis. Such methods may require avoiding sample-to-sample cross-talk and contamination while providing the ability to subject the samples to uniform and discrete treatment. Collection of accurate and sensitive data in a scalable and multiplexed format with minimal user intervention is also important. 
     A particular example of a biochemical analysis particularly amenable to discrete cellular analysis relates to the evaluation of functional states based on secreted proteins from individual cells or arising from cell-to-cell interactions. In a model system comprising immune cell analysis, cell protein or chemical secretion profiles can be correlated with the quality of immune response at the single-cell level. For example, multiple cytokines may define or distinguish the differentiation stages in both CD4 and CD8 cell populations. Further, central memory and memory/effector cells may be associated with increased probabilities of secreting particular cytokines such as tumor necrosis factor alpha (TNF-α), interleukin 2 (IL2) and/or interferon gamma (IFN-γ). In contrast, cells that principally produce and secrete IFN-γ may be more likely to exhibit terminally differentiated effectors associated with programmed cell death (e.g. apoptosis). 
     To facilitate screening for a multiplicity of potential functional phenotypes such as those demonstrated in CD4 cells as well as to allow measurement of poly-functionality such as that associated with cellular immune response it is desirable to analyze multiple analytes simultaneously. For such analysis it is further desirable to enable detection of multiple analytes substantially simultaneously from individual or few numbers of cells where many hundreds, thousands, millions, or more discrete cells, samples, and/or discrete cellular interactions are evaluated for multiple analytes. Furthermore, the number of analytes that are screened for each cell or sample population may be between relatively few (for example between approximately 1-5 analytes) or more (for example between approximately 5 and 100 analytes) and perhaps even greater (for example more than 100 analytes) for each sample. 
     In a highly multiplexed system where many discrete samples may be desirably evaluated for many different analytes, a particular challenge arises in terms of quickly and accurately acquiring the associated data and performing high quality analysis. The small scale and large number of discrete samples to be simultaneously analyzed by such systems may make it impractical to perform these analyses adequately without a high degree of automation and/or user-independent computational analysis. This is particularly evident for developing single-cell technologies and cellular analysis systems intended to evaluate many hundreds, thousands or more discrete samples. 
     Exemplary single-cell analysis technologies have recently been described for example in “High-Throughput Secretomic Analysis of Single Cells to Assess Functional Cellular Heterogeneity” Analytical Chemistry, 2013, Volume 85, Pages 2548-2556, “Microfluidics-Based Single-Cell Functional Proteomics for Fundamental and Applied Biomedical Applications,” Annual Review of Analytical Chemistry, 2014, Volume 7, Pages 275-95, and “Single-cell technologies for monitoring immune systems,” Nature Immunology, 2014, Volume 15, Number 2, Pages 128-135 secreted proteins may be measured on a per-cell basis. Additionally, exemplary apparatus and platforms for single cell and few cell analyses are disclosed in PCT Application Serial Number PCT/US2013/056454 (PCT Patent Publication WO2014031997), U.S. patent application Ser. No. 12/174,601 (US Patent Publication 2009/0036324), U.S. patent application Ser. No. 12/174,598 (US Patent Publication 2009/0053732), U.S. patent application Ser. No. 12/857510 (US Patent Number 8,865,479), and U.S. patent application Ser. No. 13/132858 (US Patent Publication 2012/0015824). 
     As will be described in greater detail hereinbelow, systems such as the aforementioned single cell analysis and few-cell analyses platforms and technologies may be adapted to benefit from the automated and semi-automated analysis methods of the present teachings to improve throughput, accuracy, inference discovery and results confidence. Furthermore, the apparatus, methods, and software of the present teachings may facilitate analysis of very large numbers of discrete samples when performing multiplexed chemical, biochemical, or cellular secretion analysis including those where multiple analytes are detected, identified, and quantified for each sample. 
     According to various embodiments, the disclosed detection methods may be used to analyze a wide variety of compounds including for example biochemicals, cellular secretions and/or expressed proteins associated with a biological system (for example, immunological cells) in a multiplexed manner. Additionally, a multiplicity of different proteins and/or compounds associated with a single or few cells (including cell-cell interactions) may be detected and analyzed to generate a cellular secretion or expression profile reflecting the response or status of a representative cellular population, cellular sub-population, or single cell for a subject or sample. 
     In the context of immunological analysis, due to the phenotypic and functional heterogeneity of immuno cells and the plasticity of immune cell differentiation, conventional methods that analyze cells in bulk create difficulties in defining or identifying correlates of immune protection against diseases such as cancers and infectious agents such as pathogens. Secretion and expression profiles associated with immune protection are potentially valuable and measurable predictors of an individual&#39;s immunity. Such profiles may be used to evaluate and quantitate response to a pathogen, disease, or treatment and are helpful tools in clinical analysis. 
     Correlates of protective immunity (for example associated with T cells) have been particularly challenging for immunologists to identify at least in part as the degree of protection may not clearly match or have similarities with known cellular phenotypes and/or surface markers for the cells. Further, functional profiles for TH1 cells (Type I helper T cells), one of the major functional subsets differentiated from naïve CD4 T cells, have demonstrated marked heterogeneity as reflected by their various cytokine profiles. Using conventional methods based on flow cytometry, functional analysis of effector T cells have attempted to delineate functional subsets of cells. It has been shown that each of these cellular groupings or classes can produce and release different combinations of cytokines within an immune response such as that elicited by bacterial infection. 
     In various embodiments, the system and methods of the present disclosure provide novel methods to measure cellular secretions and/or cell signals (such as those associated with immune cells or immune response). Analysis may be conducted at the single or few cell level (including cell-cell interactions) where the effector level for a plurality of cytokines may be evaluated per cell across a relatively large number of cells in parallel. 
     As will be described in greater detail hereinbelow, the disclosed methods enhance and improve the performance of analysis platforms addressing particular issues related to distinguishing cellular retention regions or areas, identifying and classifying cells/particulates, addressing systematic errors, and automating analysis workflows to enable high throughput sensitive and accurate assessments required to analyze many hundreds, thousands, millions, or more discrete cells or samples in parallel. Such methods may advantageously be used to improve existing analysis platforms and technologies to create valuable discovery and screening tools that may be used to understand and survey many different types of cellular poly-functionality, such as those implicated and correlated with immune response. 
       FIGS.  1 A / 1 B depict an exemplary high-level workflow  100  for performing sample analysis. The analysis may include evaluation of one or more samples  102 ,  104  comprising a population or distribution of cells, beads, or other particles  106  harvested or collected from one or more selected sample sources (for example, cells obtained from an individual or culture representative of a selected biological state, chemically responding to a disease, pathogen, or therapeutic treatment). According to the present disclosure, where reference is made to one or more populations of cells, beads, or particles as comprising the selected or desired sample to be analyzed other types of particulates associated with analytes to be detected and evaluated may also comprise the sample(s). Furthermore, the sample(s) may comprise various distinct or different populations of cells, beads, or other particles forming one or more mixed populations of materials to be analyzed collectively. It will be appreciated that references to “cells” or “beads” are intended as exemplary of classes of particles that may be analyzed for associated analytes. Thus, the present teachings can be applied to a wide variety of different types and compositions of particulates  106  without departing from the scope of the present teachings. 
     As referred to above, the cells/particulates  106  of the sample(s)  102 ,  104  are evaluated for constituents associated with or comprising a plurality of analytes  108 . Analytes  108  according to the present disclosure may take many forms. For example, analytes  108  may comprise chemicals, compounds or materials that are labile, dispersed, diffused or dissolved within an aqueous medium (for example a cell culture medium) or a fluidic/semi-fluidic carrier that are associated, secreted or released by the cells/particulates  106 . Analytes  108  may further comprise biomolecules or organic molecules such as nucleic acids, peptides, peptide fragments, cells surface receptors, nucleic acids, hormones, antigens, growth factors, proteins, antibodies, cytokines, chemokines, or other molecules. Analytes  108  may further comprise other types of materials or compounds such as ions or inorganic chemicals associated, secreted or released by the cells/particulates  106 . 
     According to various embodiments, the apparatus and methods of the present disclosure are particularly well suited for the analysis of small concentrations of analytes  108  associated with discrete single or few-cells contained within or comprising the samples  102 ,  104 . Such analytes  108  may include for example cell-membrane associated proteins, cytokines, chemokines, or other biochemicals secreted or released by the cellular samples  102 ,  104  that are desirably analyzed in parallel in a highly-multiplexed manner. Sample populations or cellular constituents thereof may further be compared for similarities and/or differences in analyte presence, expression, or abundance such as may arise from a first control, normal, or untreated cellular population compared with a second test, abnormal/diseased, or treated cellular population. 
     According to  FIGS.  1 A / 1 B, one or more samples  102 ,  104  containing cells / particulates  106  to be analyzed are harvested or collected in Step  110 . The cells/particulates  106  are then distributed and retained in discrete portions in Step  120 . Sample distribution is accomplished using a substrate comprising a sample array  122  having a plurality of discrete chambers, wells, troughs, channels or other features/areas that comprise retention regions  124 . The retention regions  124  are further suitably configured to contain, hold, or sequester at least a portion of the cells/particulates  106 . In various embodiments, the array  122  used for distributing the cells/particulates  106  comprises a plurality of retention regions  124  such as wells, troughs, cavities, or depressions formed in the sample array  122  that are suitably sized based on the dimensionality of the cells/particulates  106 . Distribution of the cells/particulates in Step  120  may involve dispersing the sample in a selected fluidic volume such that a desired number of cells/particulates are expected to be disposed in one or more of the retention regions based on the volume of the regions and the amount of fluid-containing sample to be located, disposed, or placed therein. 
     In various embodiments, the sample array  122  may comprise a plurality of discrete retention regions each configurable to hold or position a desired number of cells/particulates. The sample array  122  according to the present teachings may include a large number of retention regions  124 . For example, many hundreds, thousands, or millions of discrete retention regions  124  may be formed in the sample array  122 . In one exemplary embodiment, the sample array  122  may comprise a structure of between approximately 1-10 cm in length and/or width having between approximately 1,000-100,000 discrete retention regions formed therein. 
     In various embodiments, the sample retention regions  124  comprise microchambers/microwells having dimensions of approximately 0.01-5 millimeters in length and about 0.5-100 micrometers in depth. In other embodiments, the microchambers/microwells have a generally rectangular profile with a length of approximately 0.1-2000 micrometers, a width of about 0.1-100 micrometers and a depth of about 1-100 micrometers. The size and shape of the microchambers/microwells can be configured for a variety of applications and cell/particulate types. For example, to accommodate larger cell/particulates commensurately larger microwells may be desirably used. Additionally, for experiments involving more than a single cell/particulate to be evaluated in each microchamber/microwell the size and/or dimensionality of the the microchamber/microwell can be configured accordingly. Additionally, the density of the sample array  122  can be flexibly configured, for example, with between approximately 100-50,000 microchambers/microwells per cm 2 . 
     Using dilution methods such as those based on Poisson statistics, an expected distribution of cells/particulates  106  may be achieved resulting in at least a portion of the retention regions  124  receiving a small number of cells/particulates or in various embodiments a single cell/particulate  106 . According to this approach for sample dilution/distribution some retention regions  124  may receive no cells/particulates  106  while others may receive more than the desired amount of cells/particulates  106  (e.g. more than one or a few cells). In various embodiments, analysis of analytes  108  associated with sample populations of a few cells/particulates  106  and more particularly single cells/particulates  106  includes identification of the presence and numerosity of cells/particulates  106  within respective retention regions  124 . As will be described hereinbelow, the system and methods of the present disclosure are able to effectively image and distinguish desired cell/particulate distributions for the various retention regions to identify those which contain the desired amount or number of cells/particulates. 
     Following sample distribution, the cells/particulates  106  are incubated in respective retention regions for a selected period or duration in State  130 . Incubating according to selected criteria or protocols, the retained cells/particulates  106  release analytes  108  into a surrounding volume  132  within the discrete retention regions  124  (e.g. the volume in which medium containing discrete single or few cells /particulates are retained). The configuration of the sample array  122  allows the cells/particulates  106  of the sample(s)  102 ,  104  to secrete/release analytes  108  into the volume  132 . During the incubation period analyte concentrations may build up and/or diffuse within the volume  132  of the retention regions  124 . 
     Analytes  108  released into the volume  132  of the retention regions  124 , for example by dispersal or diffusion throughout at least a portion of the retention region, may further associate or react with one or more detection moieties  135  disposed or located about selected positions of an analyte detection substrate  134  in Step  140 . The detection moieties  135  may comprise antibodies, nucleic acids, proteins or other chemical/biochemical constituents that selectively react, couple, recognize, or otherwise distinguish and/or detect the presence of one or more selected analytes  108 . In general, the size and configuration of the retention regions  124  provides sufficient volume/area to allow analytes  108  to be dispersed in a manner so that they may be discretely detected by the detection moieties  135  through association with or positioning about selected portions or regions (e.g. analyte detection regions  136  where the detection moieties  135  are positioned or disposed) of the analyte detection substrate  134 . 
     In various embodiments, secretions, biochemicals, cytokines, chemokines, proteins, peptides, peptide fragments, cells surface receptors, nucleic acids, hormones, antigens, growth factors or expressed proteins associated with the distributed cells/particulates  106  retained in discrete retention regions  124  diffuse and distribute in the volume  132  and become selectively associated with, captured, or retained by one or more detection moieties  135  disposed or positioned in analyte detection regions  136  associated with the analyte detection substrate  134  forming a detectible or discernable pattern, fingerprint, or barcode  142  which when detected in State  150  forms a representation of the presence and composition of analytes  108  associated with respective cells/particulates  106  contained within occupied retention regions  124 . 
     In various embodiments, the analyte detection substrate  134  is configured with a plurality of analyte capture or detection moieties  135  capable of detecting and distinguishing numerous analytes  108  (for example between approximately 10-1000 different types of analytes). The positioning or association of the analyte detection moieties  135  in analyte detection regions  136  on the analyte detection substrate  134  is conducted in such a manner so that simultaneous determinations of analyte  108  presences and/or abundance may be determined in a highly multiplexed manner based on the pattern, fingerprint, or barcode  142 . In various embodiments, the disposition or positioning of the analyte detection moieties  135  disposed or positioned in analyte detection regions  136  on the analyte detection substrate  134  permit secreted and/or released cytokines, chemokines, proteins, or other cellular constituents to be readily and individually classified based on the types and presence of the analytes  108 . 
     The analyte detection substrate  134  may be comprised of a plurality of capture agents or detection moieties  135  each discretely or specifically recognizing a compound, chemical or biochemical of interest. The analyte detection moieties  135  may further be arranged in distinct or positionally discrete features (e.g. analyte detection regions  136 ) about the analyte detection substrate  134  providing spatial separation between the various analyte detection moieties  135 . Such spatially separate arrangements of analyte detection moieties  135  provide for spatial encoding or patterning the detected analytes  108  in or about analyte detection regions  136  that may be resolved based on a known distribution of the analyte detection moieties  135  in or about analyte detection regions  136  associated with the analyte detection substrate  134 . In various embodiments, the analyte detection substrate  134  may comprise analyte detection moieties  135  arranged in the analyte detection regions  136  as a plurality of lines, spots or other discrete shapes or combinations of shapes. 
     Each analyte detection region  136  may further be comprised of one or more types of analyte detection moieties  135  capable of being distinguished from one another through the use of different markers, labels, dyes, or other means generating distinct signals or other spectrally separable characteristics that may be discerned upon imaging, for example, using different optical characteristics or wavelengths during imaging. In various embodiments, the analyte detection substrate  134  comprises one or more duplicative, redundant, or repeating analyte detection moieties  135  or analyte detection regions  136  that may be used, for example, to provide multiple opportunities for analyte presence to be discerned and compared using signals, readouts, or patterns obtained for the corresponding analyte detection regions  136 . 
     A plurality of analytes  108  are desirably identifiable and/or quantifiable for the cell(s)/particulate(s)  106  associated with respective sample retention regions  124 . In various embodiments, the analyte detection substrate  134  provides the ability to detect and resolve approximately 5-1000 or more different analytes  108  using corresponding analyte detection moieties  135 /analyte detection regions  136 . The analyte detection moieties  135 /analyte detection regions  136  disposed or located on the analyte detection substrate  134  are typically ordered or patterned in a manner that corresponds with or is complimentary to the sample retention regions  124  of the sample array  122 . Thus the length and width of respective analyte detection regions  136  and the overall size and/or groupings of analyte detection regions  136  may be variably configured based on the corresponding or available area determined by the sample retention regions  124 . 
     As will be described in greater detail hereinbelow, signals resulting from the coupling or association of analytes  108  with corresponding analyte detection moieties  135  associated with the analyte detection regions  136  may be imaged forming signal fingerprints, barcodes or other discernable patterns that may be analyzed and resolved to determine analytes  108  present in respective sample retention regions  124  of the sample array  122 . These analytes  108  may further be correlated with cell(s)/particulate(s) determined to be present in respective sample retention region  124 . 
     Detected analyte patterns may be resolved to the presence or quantity of discrete analytes based on the spatial separation, spectral separation, or a combination of both spatial and spectral separation for the analyte detection moieties  135 /analyte detection regions  136 . The present apparatus and methods may be used to resolve the presence of a plurality of analytes  108  by multiplexed detection of 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 20 or more, 40 or more, 50 or more, 100 or more, 1000 or more analytes of interest. The image analysis apparatus and methods of the present disclosure thus provide the ability to detect large numbers of different analytes for large numbers of discrete single cells/particulates or discrete few cells/particulates (co-located or co-positioned in corresponding sample retention regions  124 ) in parallel. 
     Analyte detection in Step  150  may comprise imaging the analyte detection substrate  134  (either directly or following separation from the sample array  122 ) to identify signals or markers (e.g. for example fluorescent and or radioactive) representative of the presence of analytes  108  that have coupled or associated with analyte detection moieties  135 . In various embodiments, the analyte detection moieties  135  may comprise light-generating markers, energy-emitting markers, dyes, or other labels  136  and utilize techniques such as antibody capture and ELISA analysis for analyte detection. 
     Cells/particulates  106  and their corresponding analytes  108  are characterized in Step  160  by association of detected or discerned patterns, fingerprints, signal readouts or barcodes  142  with respective retention regions  124 . This processing includes identifying and distinguishing discrete retention regions  124  associated with corresponding areas for the analyte detection substrate  134 . As will be described in greater detail hereinbelow, this process may include a number of operations that determine the position and/or orientation of corresponding cells/particulates  106 , retention regions  124 , and detected or discerned patterns, fingerprints, or barcodes  142  in a highly multiplexed manner. This analysis further provides a basis to discern the secretions, biochemicals, and/ or expressed proteins for individual or a few cells in a highly parallel manner allowing other analysis such as phenotypic characterization to be performed from the resulting patterns of detected analytes to characterize individual samples including quantitation of analytes, determination of expression patterns, categorization or grouping of samples into classes, and other analytical functions. 
     A particular challenge in high throughput single cell analysis results from the large number of very small features that must be rapidly and accurately assessed in a substantially parallel manner. Such apparatus and methods should be capable of not only separating and retaining single cells in discrete regions but also should accurately discern large numbers of discrete low abundance analytes with a high degree of confidence. Sample and substrate features are typically diminutive in size necessitating high-resolution and careful imaging (for example using high resolution instruments and/or microscopes). In various embodiments, potentially complex analyte detection patterns, fingerprints, signal readouts or barcodes must also be discerned or associated with individual wells or analytes  113  or analytes  115  to provide the ability to simultaneously detect and evaluate multiple analytes from each sample. Again the size of the analyte regions or areas and the closely spaced disposition of the analytes to be detected presents a particular challenge in terms of performing accurate analysis. 
       FIG.  1 C  depicts an image of a portion of an exemplary sample array  122  with magnified sub-portion  180  according to the present disclosure. The sample array  122  may comprise many hundreds, thousands or more of discrete sample retention areas  124  as shown in the magnified sub-portion  182 . Each sample retention area  124  may further retain a single cell or few cells (or similarly particles)  106 . Individual cells/particulates  106  should not only be confidently discernable and associated with a respective sample retention area  124  in which they reside but also must be distinguished from various potential artifacts, noise and other sources of error. Various factors that may directly influence imaging quality include for example, the size of the cells versus potential contaminants, distinguishing particular cell-types in mixed populations, low quality/contrast in the image acquisition, deformities in the sample array, bubbles and other artifacts. As will be appreciated by those of skill in the art, imaging and resolving cells/particulates in the presence of such confounding factors presents particular challenges. 
       FIG.  1 C  further depicts an image of a portion of an exemplary analyte detection substrate  134  with magnified sub-portions  280 ,  282  according to the present disclosure. The analyte detection substrate  134  may comprise many hundreds, thousands or more of discrete analyte detection regions  136  as shown in the magnified sub-portion  282 . Each analyte detection region  136  may form an analyte pattern  142  corresponding to markers  135  detecting analytes  108  associated with cells/particulates  106 . Various factors that may directly influence imaging quality include for example, the noise and background for regions where analyte markers  135 /patterns  142  are detected. As will be appreciated by those of skill in the art, imaging and resolving markers  135 /analyte patterns  142  in the presence of such confounding factors presents particular challenges. 
       FIG.  1 C  further depicts an exemplary overlay, merging, or association  291  of corresponding images of portions of the sample array  122  and the analyte detection substrate  134 . Alignment and positioning of corresponding portions of the images of the sample array  122  and the analyte detection substrate  134  provides a means to associate the signals from various markers  135  detecting analytes  108  forming the analyte patterns  142  with the cell/particulate  106  for which the analytes  108  are further associated. Mechanisms for aligning and positioning the corresponding portions of the images and analyzing the associated data is described in greater detail hereinbelow. 
     As previously described and in various embodiments, features that are to be desirably detected and associated during analysis  100  include the various cells or particles  106 , the retention regions or microchambers  124 , and the patterns, fingerprints, signal readouts or barcodes. The small size (for example, typically on the order of 20 micrometers or less in at least one dimension) and distinctive properties associated with imaging and resolving the various features presents numerous challenges that are to be desirably overcome, especially when applying automated or semi-automated data acquisition and processing mechanisms required to achieve acceptable throughput and accuracy. The present methods and apparatus are capable of achieving requisite high-quality data for single- or two-cell/particulate chemical or biochemical profile readouts, in particular, per assay chamber or retention region  124  while imaging large numbers of these features including for example thousands of cells/particulates  106 , thousands of microchambers or retention regions  124 , and for hundreds of thousands if not millions of readouts for ease of detection and analysis. 
     For cellular analysis involving sample arrays with very small feature sizes imaging at very high resolutions may be impractical and/or infeasible necessitating alternative methods for data acquisition and analysis. One reason for this limitation is that imaging devices, such as scanning microscopes and fluorimeters have resolution limitations. Upgrades to such hardware components can be very costly and integration of newer components difficult to perform for standardized or calibrated workflows that may be necessary in clinical and research settings. Additionally, addressing limitations in the field of view of imaging devices such as scanning microscopes that may be used for feature resolution scans, by leveraging conventional capture and tiling approaches is impractical or infeasible due to time, cost, and computational constraints. For example, imaging and analyzing a full sample array  122  with the many thousands or more of retention regions  124 , cells/particulates  106 , and analyte signal readouts could easily involve capturing and tiling thousands of images which may be overly time consuming or computationally prohibitive. The size of such a tiled image may readily exceed hundreds of millions of pixels or more making it very difficult to process and analyze. Alternatively, imaging at lower resolutions may be performed, however, such an approach may contribute to diminished analysis quality and result in high quantization errors where each feature has a limited number of pixels that may prevent accurate assessments of cell type, noise correction, and/or signal resolution. 
     A further limitation of some workflows is that the optical/detection characteristics and requirements of the cell/particulate sample array may be distinct and different from those leveraged to resolve/image the secretion profiles or detected compounds. Simultaneous imaging on a single device may therefore be impossible/impractical to conduct. Thus it may be desirable if not a prerequisite to perform multiple imagings using different signal/data acquisition devices (e.g. multiple microscopes/signal capture devices). These devices may not output data in identical formats (for example, at the same resolution or orientation) and therefore additional challenges are present to align and associate detected secretion patterns with specific retention regions and cells/particulates. 
     At the resolutions used to image both the sample array and chemical signal fingerprints the minute measurements required are potentially confounded by many sources of imperfections. For example, even micron levels of warping or distortion in the substrate surfaces as well as very small contaminants should be accounted for to achieve sufficiently high quality and accurate results. Additionally, imaging biological data including cells often results in inconsistencies in cell morphology and observed structure as well as the intensity and quality of the signal (secretion) readouts. The system, methods, and software of the present disclosure effectively address potentially observable variations such as those exemplified above while distinguishing noise/background with a high degree of confidence. 
     According to various embodiments, desirable features of the present teachings include the ability to automate data acquisition/extraction processes avoiding time-consuming and error-prone manual methods for obtaining data. Acquisition of assay data, even when attempting to leverage existing off-the-shelf/conventional image processing software, is both time-consuming (on the order of days per assay) and inaccurate. A further benefit of the present methods is that development of the integrated and optimized imaging solution disclosed herein provides the ability to confidently detect large numbers of very small features with a high degree of accuracy remaining economical and efficient time-wise while accommodating inconsistency of features, presence of noise, distortions or deformities in the substrate materials among other challenges. In various embodiments, the methods may be applied to develop automated procedures that are simultaneously very accurate, but likewise sufficiently practical to analyze, both from a time standpoint (for example less than 30 minutes of hands-on time) and a hardware standpoint (can be performed on a conventional computer). 
     As discussed above, the methods and apparatus for cellular analysis address many issues and shortcomings of conventional single cell image analysis solutions.  FIG.  1 D  depicts a detailed analysis workflow  1000  that may be used for imaging and resolution of sample array data (for example single cell analysis arrays) according to the present disclosure. The workflow  1000  commences in state  1100  with collection of a plurality of images associated with sample retention regions  124  of the sample array  122  and associated patterns, fingerprints, signal readouts or barcodes  142  representing signal acquisitions from detected analytes  108  for various cells/particulates  106  disposed in the sample retention regions  124 . 
     In various embodiments, sample imaging and analysis operates by evaluation of groupings or collections of associated images and signal acquisition information for selected regions of the sample array  122 . A selected imaged region of the sample array  122  may represent a sub-region  180 / 182  (see  FIG.  1 C ) where a portion of sample retention regions  124  from the sample array  122  are captured in an image. According to the present disclosure, rather than attempting to stitch a representation of the entire sample array together (a laborious, computationally expensive and error-prone process), each sub-region  180 ,  182  may be independently imaged and evaluated to achieve high quality data and results rapidly using inexpensive commodity computing systems. Fiducials, labels, identifiers or other positional features (collectively, alignment markers) may be included, etched, or printed on the sample array  122 . The alignment markers may further be used to facilitate orienting or identifying sample retention regions  124  through or across multiple images and scans such that discrete sample retention regions  124  of the sample array  122  may be associated with cells/particulates  106  residing or disposed therein. The cells/particulates  106  may further be associated with the identified patterns, fingerprints, signal readouts or barcodes (collectively analyte patterns) for the cells/particulates  106  facilitating high accuracy profiling of the detected released secretions and analytes  108 . 
     As will be described in greater detail hereinbelow, a first image or scan (for example, a high resolution light field image) is obtained for a selected sub-region  180 ,  182  of the sample array  122 . The high resolution image (such as may be obtained using a light microscope with a 10×-50× objective) may be used for initial cell/particulate detection. Using this first image the location, number, and distribution of cells/particulates  106  in the various sample retention regions  124  of the sub-region  180 ,  182  of the sample array  122  may be discerned. Additionally, sample retention regions  122  containing a desired number of cells/particulates  106  (for example single cells or two or more interacting cells) may be identified and distinguished from sample retention regions  122  containing no cells/particulates  106  or more than a desired number of cells/particulates  106 . The high resolution image is also helpful for identifying and discriminating between relatively small effector cells/particulates  106  (for example in immunological assays identifying and distinguishing T-cells and natural killer cells from other cells which may be present in the sample.) 
     To further aid in accurate alignment of images and/or alignment markers associated with sample retention regions  124  and associated analyte detection regions  136 , a second image for substantially the same or similar sub-region  180 ,  182  may also be obtained. The second image may comprise a lower resolution image or other image type applying different optical parameters compared to the first image. Together, the first and second images for imaged sub-regions  180 , 182  may aid in the alignment of various images/scans and provide improved cell/particulate detection and identification accuracy compared to the use of single imagings. In various embodiments, the second image may be obtained in various manners including using different optical settings such as adjusting contrast or focal plane orientation, changing digital resolutions, applying a designated fraction or percentage reduction in magnification compared to the first image (for example, 1×-5× for a lower resolution image as compared to 10×-50× for the first image or scan), and may be taken with different illumination/microscopy methods, (e.g., darkfield, phase contrast, fluorescence). In various embodiments, the analysis system may be preconfigured with desired optical settings or may perform a determination on-the-fly based on the data quality and experimental design parameters as to what types of optical settings are applied to both first and second images at each resolution setting. It will be appreciated that higher and/or lower resolutions and magnifications can be readily adopted for use in the imaging processes. In various embodiments, the magnification and other optical parameters for imaging will be determined, at least in part, by the dimensions of the sample array  122 /sample retention features  124  and analyte detection substrate  134 /analyte detection regions  136  as well as the type of cell/particulates  106  and analyte detection moieties used. 
     In addition to the first and second cell/particulate images obtained for a selected sub-region  180 ,  182 , an additional scan or signal acquisition is performed to obtain or identify the analyte pattern  142  representative of the detected, released, or secreted compounds or analytes  108  associated with the cells/particulates  106  present in the sample retention regions  122  of the sub-region  180 ,  182 . In various embodiments, the scan or signal acquisition information obtained for each sub-region  180 ,  182  may be obtained on separate instruments and/or at different times providing for more flexible data acquisition. Additionally, first and second imagings may be based on different optical properties or characteristics from one another and as compared to the analyte pattern scan. For example, the first and second imagings may be visible light-based images while the analyte pattern scan acquired from fluorescent signals. 
     In various embodiments, the various first and second images for selected sub-regions  180 ,  182  are associated and accurately aligned using fiducials or alignment markers that are detected and resolved in selected corresponding image sets. Furthermore, a separate set of alignment markers (or additional imaging of the first set of alignment markers at a different wavelengths using different markers, dyes, or labels) may be imaged in both the sample array/particulate imaging and the analyte detection scans. Alignment between the sample array images and the analyte detection scans may thus be performed to match or associate the secretions or released analytes  108  detected from the analyte pattern  142  associated with a selected sample retention region  124  with the corresponding cell(s)/particulate(s)  106  located or disposed therein. 
     It will be appreciated that many factors contribute to the overall quality of each image and the visibility of features/details in the respective images. Factors such as differences in lighting conditions, brightness, contrast, and focus can significantly affect imaging. By obtaining multiple images in the manner described above, it will be appreciated that the combined results of the images may contribute to improved identification reliability of both sample retention areas  124  and cell(s)/particulate(s)  106  contained therein. Furthermore, due to the generally small size of features (such as sample retention area outlines/borders and cell/particulate profiles in the images) having more than a single image and at different resolutions can prove to be very helpful in resolving/imaging the sample array  122  and constituents therein. As will be described in greater detail hereinbelow, various potential sources of noise and error that can otherwise act to obscure portions or entire features within a sub-region  180 ,  182  or result in misregistration of cell(s)/particulate(s)  106  and/or analyte patterns  142  can be reduced or eliminated using the multiple imaging methods of the present disclosure. These methods can be helpful in resolving or distinguishing useable data with increased accuracy and improved quantification capabilities from the sample array  122 . 
       FIG.  1 E  depicts an outline of the processes associated with the multiple imaging of sub-regions  180 ,  182  of the sample array  122  described in Step  1100  in  FIG.  1 D . High resolution images  1150  may comprise one or more light field/bright field images  1155  of the microchambers/sample retention regions  124  and associated or nearby first alignment markers (designated in the diagram by an exemplary “A”). Second images  1160  may likewise comprise one or more light field images  1165  having a similar field of view for the sample retention areas  124  and associated cell(s)/particulate(s)  106  as the high resolution images  1155 . First and second images may be aligned using alignment markers present on the sample array  122  and identified within the corresponding images to provide the ability to orient first and second images  1155 / 1165  with respect to one another. 
     Corresponding analyte pattern(s)  142  obtained, for example, through a fluorescent imaging or scan  1170  comprise one or more scans  1175  of analyte patterns  142 . The analyte patterns may be further resolved to individual detected analytes  108  released or secreted by cell(s)/particulate(s)  106  present in the sample retention regions  124 . The scans  1175  may further comprise discrete discernable patterns, fingerprints, detection patterns or barcodes  142  obtained by imaging at various selected wavelengths or using two or more modes of detection to form a collection of analyte patterns that are combined and evaluated to identify detected analytes  108  associated with, released, or secreted by the cell(s)/particulate(s)  108 . 
     In various embodiments, a second imaging  1166  of the areas corresponding to the first and/or second images is obtained to identify one or more second alignment markers. In various embodiments, the second imaging is acquired at a different wavelength or emission spectrum for the second alignment markers such that they may be readily distinguished from the one or more first alignment markers. For example, a fluorescent imaging or scan of the corresponding regions of the first and/or second images may be obtained to distinguish a discrete marker set designated “B” in the Figure. Using the image and associated alignment marker sets, the microarray scan/analyte pattern (corresponding to the discernable pattern, fingerprint, or barcode  142  of analytes  108 ) including corresponding alignment markers “B” may be used to align or orient the first and/or second images with the corresponding microarray scan/analyte pattern. Taken together using the processes described above, the various images and scans for respective portions on the sample array  122  may be desirably oriented and aligned to associate cell(s)/particulate(s)  106  with corresponding analytes  108  to enable high quality profiling and compound analysis. 
     In certain embodiments, one or more additional imagings  1167  of the cell(s)/particulate(s)  106  associated with the selected sub-region  180 ,  182  imaged in the corresponding first and second images may be obtained. The cell imaging process  1167  may utilize similar or different wavelengths or scanning configurations as the alignment marker imagings or analyte detection scans. In various embodiments, the various cells or particulates  106  may be labelled with fluorescent or light-emitting dyes, stains, or other identification means to resolve the location or disposition of the cell(s)/particulate(s)  106  in various sample retention regions  109  of the sample array  122 . This information may further be used to definitively identify the presence and/or number of cell(s)/particulate(s)  106  in a selected sample retention region  108  and may further be used to aid in associating the identified cell(s)/particulate(s)    106  with their corresponding discernable analyte pattern  142  and corresponding released or secreted analytes  108 . 
     In various embodiments, the dimensionality and other characteristics of the cell/particulate  106  being analyzed may determine the number and or type of images to be acquired for subsequent detection and analysis. For example, relatively large cells (for example THP-1 cells) may be imaged without staining and subsequent fluorescent imaging. For relatively small cells (for example CD4 cells) fluorescent stains may not be required with multiple light field images acquired at various magnifications. For other small cell types, staining and fluorescent imaging as well as one or more light field images may be used. For mixed populations of cells and very small cells, surface markers may be used and detected with appropriate imaging parameters to distinguish cell types and/or to verify the presence of cells. 
       FIG.  1 F  panel (a) depicts an exemplary high resolution imaging of a selected sub-region  180  of the sample array  122  comprising a plurality of sample retention regions  124 . Cell(s)/particulate(s)  106  are further positioned in various sample retention regions  124  (further shown in expanded view). First and second positional/alignment markers  1168 ,  1171  (e.g. markers sets comprising “A” and “B”) are further located about the sample retention regions  122  for alignment of images as discussed above.  FIG.  1 F  panel (b) depicts an exemplary corresponding second image of a selected sub-region  180  of the sample array  122  comprising the same plurality of sample retention region  124 . Cell(s)/particulate(s)  106  are positioned in the sample retention regions  124  at similar or identical locations as visualized in the first image. Positional alignment markers  1168  are visible about the sample retention regions  122  for alignment as discussed above.  FIG.  1 F  panel (c) depicts the additional imaging of cells/particulates  106  resultant from fluorescent labeling and imaging (step  1166  in  FIG.  1 E ). Cell/particulate positioning and orientation with respect to other cells/particulates may be preserved and comparable to that obtained for the light field images in panels (a) and (b) above. Cell/particulate positions are not necessarily required to be fixed or maintained in a single position however as the sample retention region  122  in which it resides may limit overall movement of the cell/particulate  106 . 
       FIG.  1 G  panel (a) depicts exemplary signal and alignment scans showing microarray/analyte patterns associated with detected analytes  108  released or secreted by cells/particulates  106 . As described above the analyte pattern  142  may be resolved to individual or discretely detected analytes  108  associated with cells/particulates  106  disposed or retained in various retention regions  122 . By correlating the analyte scans and underlying detected analyte patterns  142  with the first and/or second images for the sample retention regions  122  with cells/particulates  106 , individual cells/particulates or collections of cells/particulates can be attributed to releasing or secreting multiple analytes  108  in a parallel and multiplexed manner. 
     According to the device imaging process ( FIG.  1 E , state  1175 ) analyte patterns  142  may be detected using multiple wavelengths or imaging/scan modes. For example, multiple discretely and differentially tagged or labelled analyte detection moieties  135  may be disposed in or co-occupy generally the same analyte detection region(s) or area(s)  136  of the analyte detection substrate  134 . Different analytes  108  may selectively bind or associate with the differentially labelled analyte detection moieties  135  and be separately resolved or detected on the basis of the tags or labels  135  associated with the analyte detection moieties  135 . Discrete resolution of co-located analytes  108  in the same analyte detection region  136  may be determined on the basis of different emitted signal/wavelengths associated with the tags or labels. Additionally, multiple images may be obtained at different wavelengths or use different filters or optical settings to separately resolve the analytes  108  in these regions. In certain embodiments, separate imagings may be acquired for the same analyte detection regions  136  to generate different analyte patterns  142  based on the analytes  108  present and the tag, marker, dye or label associated with the analyte detection moiety  135 . 
       FIG.  1 G  panel (b) depicts detected second alignment markers  1171  associated with the analyte detection substrate  134 . The second alignment markers  1171  may be resolvable in the device imaging process as previously described ( FIG.  1 E , state  1166 ).  FIG.  1 G  panel (c) depicts detected second alignment markers  1171  associated with the sample array  122 . The second alignment markers  1171  may be resolvable in the device imaging process as previously described ( FIG.  1 E , state  1166 ). According to various embodiments, the detected alignment markers for the analyte detection substrate  134  and the sample array  122  may be used to associate and orient the first and/or second images of the sample array  122  ( FIG.  1 E , state  1168 ). Alignment between these imagings and scans thus provides the ability to associate respective analyte patterns  142  with specific cell/particulates  106  releasing or secreting analytes  106  within analyte detection regions  136  as will be described in greater detail hereinbelow. 
       FIG.  1 H  depicts a light field image (panel a) for a portion of an exemplary sample array  122  with sample retention regions  124  retaining single cells/particulates  106 .  FIG.  1 H  further depicts a corresponding fluorescent image (panel b) showing the cells  106  visualized by for example by a stain indicating the presence of a selected surface marker on the cells/particulates  106 .  FIG.  1 H  further depicts a corresponding image (panel c) for a portion of an exemplary analyte detection substrate  134  with analyte patterns  142  corresponding to markers  135  detecting analytes  108  associated with the cells/particulates  106 .  FIG.  1 H  further depicts the merging of the various images (panel d) to provide a mechanism to associate respective analyte patterns  142  with the associated cells/particulates  106  whose analytes  108  were detected. 
     Referring again to  FIG.  1 D , collected images of the various sub-regions  180 ,  182  of the sample array  122  and analyte detection substrate  134  are used in further processing steps according to the detailed analysis workflow  1000 . Initially, in state  1200  microchambers / sample retention regions  124  of the sample array  124  are located and resolved for respective images. Due, in part, to small feature size of the sample retention regions  124  as well as potential artifacts and distortions associated with the structures themselves and resulting images, it is desirable to conduct detailed evaluation of the various images and scans. These processes contribute to high quality analysis and aid in accurate determination of the various analytes associated with respective cells/particulates  106 . 
     According to various embodiments, the sample array  122  may be configured with an ordered pattern or positioning of sample retention regions  124 . An exemplary pattern may take the form of a two dimensional grid having rows and columns as depicted in  FIG.  1 C . To aid in orientation and determination of sample retention regions  124  and cell/particulates  106  residing therein, a regular patterning may be used where the dimensions of the sample retention regions  124  are generally similar and typically approximately equal to one another across various portions or sections of the sample array  122 . Offsets between sample retention regions  124  may likewise be configured as generally similar and typically approximately equivalent to one another across various portions or sections of the sample array  122 . 
     A principal operation during sample retention region identification processes is to determine the relative position or location of the imaged sample retention regions. The graphical or pixel area of selected sample retention regions  124  may then be used for determining respective positioning of cells/particulates  106  localized therein and alignment with analyte patterns  142 . Improving the automated analysis capabilities of the imaging system is an important consideration to increasing overall analysis throughput. Thus, it is desirable to increase or maximize the number or proportion of sample retention regions  122  that may be detected. 
     In various embodiments, the regular ordering or patterning of the sample retention regions  122  provide generally consistent or expected dimensions and spacing that is helpful to leverage in automated analysis. Further, the associated detection procedures may be extendable or configurable to a number of different orientations, dimensions, spacings, and quantities of sample retention regions  124  on the sample array  122 . The system may also be configured for analysis of different array configurations depending on the particular assay or preference of the user. 
     The imaging and identification processes may further accommodate various imperfections and deformities in the sample array  122  and associated sample retention regions  124 . In certain embodiments, the sample array  122  is fabricated in such a manner that various deformities and/or imperfections may be present as a result of manufacture and/or processing. In some instances, the sample retention regions  124  may be slightly out of alignment or off-axis with respect to each other or have variations from lot-to-lot or array-to-array. There may also exist minor variations in sample retention region dimensions and spacing across the sample array  122 . Further, various artifacts and debris may be present on the sample array or associated with various sample retention regions  124  that obscure sample retention region outlines or profiles. Additionally, the resultant images of the sample array and sample retention regions may include imperfections such as blurry areas or broken/distorted sample retention region outlines. 
     A more detailed process  400  for microchamber/sample retention region  124  location and resolution associated with step  1200  is shown in  FIG.  2   . Additionally,  FIG.  4    illustrates the principal operations  500  of sample retention region identification in a pictorial manner. As shown in  FIG.  4   , exemplary sample retention regions  124  may be subject to various imperfections  512  and artifacts  514  that obscure or confound analysis and are desirably addressed during automated image analysis. 
     The process  400  may be performed for selected, desired, or substantially all imagings of the sample array  122 , for example using the high resolution light field images represented in  FIG.  1 F (a) and/or the second light field images represented in  FIG.  1 F (b). For each input image ( 410 ), representative of a selected sub-region  180 ,  182  of the sample array  122 , a noise removal process ( 415 / 515 ) may be applied. The noise removal process may use various methods to identify noise and artifacts in the images including applying Gaussian difference methods. The results of the noise removal processing may exclude portions of sample retention regions of low quality or that cannot be confidently resolved or distinguished from surrounding background, noise or artifacts and may also be used to counteract or address small imperfections and image blurriness. Additionally, artifacts including imaged dust and debris may be removed from the image and/or excluded from further processing and image analysis. Artifact removal may be conducted in such a manner so as to avoid high likelihoods of excluding cells/particulates from the image. 
     Following noise removal ( 415 / 515 ), an outline detection routine ( 420 / 520 ) may be performed to detect the peripheries, boundaries, or edges of the various sample retention regions  124  in the image  410 . Edges, contours, and other shape-based characteristics for the sample retention regions  124  may be determined by a number of methods including application of Canny edge detection methods. The morphology or profile of the sample retention regions  124  may be evaluated as part of this process to identify various distortions affecting selected sample retention regions  124  such as dilations  532  and/or erosions  534 . Left unaddressed, these distortions may create difficulties in subsequent operations and potentially result in inaccurate or erroneous analyte analysis. 
     Application of the above-described processes helps identify and remove potentially problematic features and components of the image and may be used in connection with a filtering process ( 425 / 525 ). Excluding sample retention regions that are affected by artifacts and noise as well as those subject to significant deformities or other problems leaves a subset of filtered sample retention regions  542  having generally desirable and/or tolerable characteristics that may be readily analyzed in downstream operations. Certain filtering process may include gating out or excluding chambers by applying various criteria. For example, selected sample retention regions may be gated on the basis of discrepancies or deviations in contour length, width, height, or as a result of observed overlaps in the retention chambers. 
     It will be appreciated that exclusion of sample retention regions affected by noise, artifacts, and other issues results in a potential loss of data and information associated with cell(s)/particulate(s)  106  and detected analytes  108 . In certain instances, a sample used in the analysis may be particularly rare or valuable, the analytes  108  or the detection moieties  135  used may be expensive or timely results are required which makes it important to capture as much information as practical from a given analysis or run. 
     A notable enhancement to the image analysis routines of the present disclosure is the application of a recapture or fill-in procedure ( 430 / 530 ). According to this approach, the expected or predicted location of a sample retention region  124  may be determined using the pattern of regular orderings among sample retention regions  124 . For example, for a sample retention region  124  that has not been located or which may have been excluded in previous operations the relative location of neighboring sample retention regions  124  may be determined. Using an iterative process, existing/identified sample retention regions  544  immediately above and/or below a missing region  546  may be used to help identify the expected location of a sample retention region in the missing region  546 . Attempts may further be made to recreate or add a missing sample retention region  546  on the basis of distance or space separating adjacent identified sample retention regions  124 . Outlining and resizing operations may also be used to determine the approximate or expected position of sample retention regions  124 . Taken together, where these operations are successfully applied, missing sample retention regions may be added back or constructed into the image. Application of these processes therefore provides the ability to rehabilitate or recover data from the image that might otherwise be lost or discarded. 
     Following the above-described operations, identified/remaining sample retention regions in the various images may be sorted and organized ( 435 / 535 ). In various embodiments, the sample retention regions  124  in a selected image may be oriented and/or grouped with respect to neighboring sample retention regions. For example, vertically aligned sample retention regions may be labelled in a columnar manner and sorted/cataloged for ease of further analysis. Detected, located, and/or identified sample retention regions  124  within the image may then be outputted ( 440 ) completing the process ( 1200 / 400 / 500 ). 
     In addition to the microchamber/sample retention region identification process  1200  described above, additional operations  1220 ,  1240  may be performed to identify and locate associated alignment features or markers associated with analyte scans and corresponding cell/particulate imagings. Such processes may be performed for selected, desired, or substantially all imagings/scans of the sample array  122  and analyte detection substrate  136 . For example, a first alignment feature identification process  1220  may be performed using scans for the detected second alignment markers  1171  associated with the sample array  122  represented in  FIG.  1 G (c). A corresponding second alignment feature identification process  1240  may be performed using scans for detected second alignment markers  1171  associated with the analyte detection substrate  134  represented in  FIG.  1 G (b). Location of the alignment features for scans representing portions of the sample array  122  and the corresponding portions of the analyte detection substrate  134  may be performed according to the steps  450  outlined in  FIG.  3    and illustrated renditions  550  in  FIG.  5   . 
     Using selected input images/scans ( 455 ,  555 ), alignment feature outlines, edges, or peripheries may first be detected ( 460 ,  560 ) using Canny edge detection methods and/or applying line detection approaches such as Hough transforms. In various embodiments, the alignment features comprise fine details, dimensions, and/or geometries  558  including for example angles, turns, and profiles that may aid in confident determination and resolution of the position of the alignment features. The alignment features, may further be resolved as a grouping of one or more segments to aid in determination of portions of the alignment features that are clearly identifiable and portions which may be less-clearly resolved. 
     In various embodiments, alignment portions or segments that are clearly resolved are filtered and grouped ( 465 ,  565 ). Filtering operations may include identifying selected expected or imaged angles, determined orientation and/or locations for portions of the segments and merging similar or expected portions/segments while removing or excluding less-confidently evaluated alignment portions. The remaining segments/alignment portions may be further identified, labelled, and/or ordered ( 470 / 570 ) for example according to angle and positioning. A portion of the operations may classify and/or label segment portions based on orientation or angle  572  as shown in  FIG.  5   . Using known or expected orientations for the alignment markers, portions that may be missing or poorly resolved from the images/scans may be filled in or reconstructed ( 475 / 575 ). Comparisons between expected and actual intervals between alignment markers may be used to reconstruct missing regions or segments. 
     Using the actual and reconstructed alignment markers, additional interpolation operations ( 480 / 580 ) may then be performed, for example, between adjacent segments to obtain expected orientations and positioning of analyte detection regions, flow lanes, and cell/particulate retention regions. Applying these operations to respective images for analyte detection regions and analyte patterns thus facilitate determination of expected and actual regions where cell/particulates  106  and analyte detection moieties  134  reside. 
     Referring again to  FIG.  1 D , using located sample retention regions  122  determined in step  1200 , a subsequent stage  1300  of the analysis  1000  determines potential cells/particulates  106  that may reside in the sample retention regions  122 . The operations of cell/particulate localization  1300  address issues associated with variances that may occur between cells/particulates and facilitate identifying/distinguishing desired cells/particulates  106  from both other cells/particulates  106  that may reside in the same sample retention region  122  as well as discriminating cells/particulates from other materials such as dust or particles that may not be associated with analyte release or secretions. 
     In various embodiments, a cell may have an expected morphology, shape, or size when analyzed. For example, cells may be expected to be approximately round and convex. Similarly, particulates may have known dimensions and uniformity. During imaging, the image of a cell may have an expected profile, for example, with a generally darker outline or peripheral portion as compared to a generally lighter interior portion. Similarly, particulates may have other expected characteristics such as being uniformly illuminated or displaying various other optical properties. Cells in particular can be challenging to identify with a high degree of confidence due to variance between the physical characteristics of individual cells. For example, cell size can vary, but may be assumed to reside within a selected range (for example approximately in the range of 5-20 microns) where cell morphology remains fairly consistent and independent of size. Further, in assays involving few or single cell(s)/particulate(s), a majority of the space within the sample retention regions  124  may remain open and cells/particulates  106  may be located in a variety of different positions within the sample retention regions  124 . 
     According to the location identification step  1300 , the occupancy and location (e.g. pixel area in a corresponding image) of one or more cells/particulates  106  within selected previously identified microchamber/sample retention regions  124  may be determined. These processes desirably accommodate cell-cell variances and deformities, variations in image quality, and help provide precise and accurate detection. The detection processes  1300  are also desirably configured as extendable to different cell/particulate types having different physical properties and dimensions while able to handle large numbers of discrete identifications associated with the large number of sample retention regions  124  to be analyzed. 
       FIG.  6 A  illustrates an exemplary detailed process  600  for cell/particulate location identification. Commencing in state  610  acquired light field images for the various sample retention regions  124  may be used. Such images may include the first or second light field images depicted in  FIG.  1 F (a) and  1 F(b). Cell localization  600  may include establishing an upper and lower boundary or gradient threshold (step  615 ) used to determine criteria for including or excluding cells based on candidate identifications. In various embodiments, thresholds may be determined by computing an average or mean image intensity gradient for selected chambers/sample retention regions  124 . Thereafter, upper and lower boundary thresholds may be determined using statistical parameters or calculations such as identifying boundaries based on a selected number or percentage of standard deviations from the average or mean image intensity gradient. The image intensity gradient may then be used in subsequent analysis to include or exclude candidate cells/particulates in a readily automated manner. 
     To establish potential locations  660  where candidate cells/particulates may reside, one or more sample region masks or boundaries are identified (state  620 ). Depicted in the exemplary visualization  650  in  FIG.  6 B , the sample regions boundary mask  655  provides a convenient tool to delineate areas  658  that are to be searched or analyzed for cells / particulates  106 . The masks  655  may further serve to enumerate chamber or particle retention regions or areas to be considered and a logical flow may be applied where each area  658  is analyzed separately (serially or in parallel) proceeding until all areas  658  have been processed. In state  625 , for each area  658  delineated by a boundary mask  655 , putative cell/particulate identifications  662  may be made. The identifications  662  may be based on selected criteria such as size/dimensionality of features located in the area  658  under consideration. These criteria may further match or conform to expected proportionality or criteria that cells /particulates  106  located in the areas  658  may be expected to meet. Cell/particulate identification may include one or more features within the respective areas  658  delineating one or more potential cell/particulate identifications. Each putative cell/particulate identification  662  may further comprise performing a gradient intensity analysis where, based on established gradient thresholds determined above, candidate cells/particulates  106  may be identified as described in greater detail hereinbelow. 
     To improve the accuracy of cell/particulate determination and/or to select or discriminate between desired cells/particulates  106 , a feature template comparison is performed (state  630 ).  FIGS.  6 C and  6 D  depict exemplary cell template matching processes  670 ,  672  corresponding to the feature template comparison process. In various embodiments, a collection or catalog of multiple cell/particulate template images  674  may be obtained. The catalog may further be used to compare against images or regions having a putative cell(s)/particulate(s). 
     In various embodiments, cell templates  674  retrieved from a database are compared to designated regions/areas corresponding to putative cells/particulates of interest for each sample retention area  124 . Multiple imagings/renditions  678  of both the cell/particulate template  674  and/or the selected region  676  under consideration may be made. The images  674 ,  676 ,  678  may further be normalized, scaled, and /or balanced by various operations (for example normalizing intensities to a range of approximately 0-1). In various embodiments, the multiple renditions  678  may comprise various intensity gradients and/or magnitude for the various images (for example, representing changes in intensity or magnitudes for the original and/or template image). 
     Applying a directional gradient imaging approach, selected or calculated changes in intensity for the images may be determined. As illustrated, three gradient images are depicted for the putative  676  and template  674  images. Each of the three corresponding pairs of images may then be compared to each other (for example pixel by pixel) to determine similarities and differences between the putative  676  and template  674  images. In various embodiments, a total intensity difference across the various images is computed and a similarity measure between the putative region  676  and the template  674  is determined. This information may be used in connection with designated thresholds to both include or exclude putative cell/particulate identifications as well as to further classify the identifications as potential/lower accuracy/false positive identifications  682  or definitive/higher accuracy/cell match identifications  684 . State  630  is completed with the retention of definitive/higher accuracy/cell match identifications  661 . 
     To increase the percentage of productive cell/particulate analyte analysis, potential/lower accuracy/false positive identifications  682  may be re-examined in state  635 . For example, putative identifications that were not designated as definitive but nonetheless were highly ranked or likely cell/particulate candidates may be considered against the previously described criteria where additional cells/particulates may be included or retained as definitive/higher accuracy/cell match identifications  661 . 
     As shown in  FIG.  6 B , sample region masks or boundaries  655  and corresponding areas  658  may further be classified on the basis of cells/particulates identified in the processes above. For example, from the number of cells/particulates associated with a selected area  658 , the region may be designated or flagged to be excluded from further analysis  667  (for example as containing multiple cells/particulates in a single cell experiment or analysis). Other areas  658  containing a desired number or cells/particulates may be designated or flagged to be included in further analysis  668  (for example as containing a single cell/particulate in a single cell experiment or analysis). Further, regions or chambers for which ambiguous results were obtained (for example where a clear identification of the presence or absence of a cell/particulate could not be made) may be flagged  669 . In various embodiments, ambiguous chambers or regions  669  may be subjected to further processing or examination  645 . In some instances, such regions  669  may be evaluated in connection with additional images/scans obtained for the sample array  122  such as the fluorescent cell stain/marker image depicted in  FIG.  1 F (c). In some cases, additional processing in the aforementioned manner may improve the confidence of cell/particulate identification and “rescue” associated regions which may be retained for further analysis. 
     The cell/particulate location identification process  600  depicted in  FIG.  6 A  is completed with the output of image classifications in state  649 . Output from the process  600  may include information relating to the identified cell/particulates  102  (e.g. location or positioning within the respective sample retention region, confidence determinations, cell/particulate classification or identification, etc.). Further information concerning the identified sample retention regions  122  may additionally be output including information relating to the number of cells/particulates  106  contained in respective regions and the overall quality of region imaging. 
       FIG.  6 E  depicts an exemplary process for generating a cell/particulate template library, database or population  674  for comparison in connection with the aforementioned methods identified in association with  FIGS.  6 C and  6 D . Using previous imagings or scans  691  of known cells/particulates, one or more representative cell/particulate images  692  may be identified. In various embodiments, identification of a subset of cells/particulates  692  having varied characteristics (e.g. different morphologies, sizes, intensities, or other properties) may be associated with a selected type or class of cell/particulate  106 . The subset  692  may be further refined and improved to form a representative cell/particulate subset  693  using data and information obtained from experimental results and analysis. In various embodiments, automated methods may select representative cell/particulate templates by applying criteria that consider similarities in morphology, experimental quality, confidence in match rates, and other criteria. Various representative cell/particulate subsets  693  may be collected from different experiments, literature descriptors and information, and other sources, improving the dataset over time. Additionally, in some instances, a representative cell/particulate subset may be determined in real time using images and data from a current experiment in which selected cell/particulate images are used as a template or basis for comparison against other cell/particulate images. In various embodiments, representative cell/particulate images are preferentially selected that are dissimilar in appearance to other false positive or undesired cells or particles that may reside in the sample retention regions  124 . 
     Referring again to  FIG.  1 D , analyte patterns  142  associated with one or more scans obtained from imaging of the analyte detection substrate  134  are aligned and oriented (steps  1310 ,  1320 ) with selected images (e.g. first and second images for example corresponding to the high and lower resolution images) of the sample array  122 . As previously described, association of the sample retention regions  124  with the analyte patterns  142  provides the ability to determine analytes  108  secreted, released, or associated with cells/particulates  108  residing in selected sample retention regions  124 . In various embodiments, a plurality of imagings of the analyte patterns  142  may be obtained, for example, using differing spectral or optical characteristics during the imaging process. Multiple imagings in this manner may be used to resolve co-located or multiplexed analyte detection moieties  135  within the same analyte detection region  136  having discretely identifiable and/or spectrally separable labels or dyes providing the ability to distinguish multiple analytes  108  from a particular region of the analyte detection substrate  134 . 
       FIG.  7 A  depicts a detailed workflow for alignment of images and scans discussed above.  FIG.  7 B  further illustrates the operations applied to exemplary images and scans using fiducials/alignment markers  1168 . According to various embodiments, the first and second images of the sample retention regions  124  corresponding, for example, to higher and lower resolution images, may have different regions of interest. The imaging apparatus (e.g. a scanning optical microscope) may acquire multiple sub-images (e.g. on the order of tens or hundreds) based on different selected regions of interest. While these images may be tiled or aligned with respect to one another, such processes may be imperfect and results may vary significantly between high and lower resolution images. 
     To alleviate potential problems in alignment and to improve the overall accuracy alignment markers  1168  are located about the sample array  122 . In various embodiments, the alignment markers  1168  may appear adjacent or in proximity to selected sample retention regions  124  as previously described. Alignment markers may be present for each column of sample retention regions  124  or disposed in various other positions about the sample array  122 . In various embodiments, the number and/or size of the alignment markers  1168  may be varied in a repeating fashion or with a selected ordering further facilitating adjustment and alignment of the images and scans. 
     In various embodiments, due, in part, to inconsistencies or variations in the regions of interest, images may be desirably aligned to allow precise matching or alignment of scans corresponding to the detected analytes  108  with the corresponding images identifying the cells/particulates  106 . In some instances, due for example to tiling inconsistences as well as the small feature sizes (e.g. sample retention regions  124 /analyte detection regions  136 ) of the sample array  122  and the analyte detection substrate  134  and further including potential variations, inconsistencies, and issues associated with the features themselves, a single global alignment operation may be insufficient for accurately aligning all regions in all images/scans. Consequently, it is often desirable to provide mechanisms to locally align regions of each image/scan using subsets of alignment markers. 
     According to methods described in association with  FIG.  7 A  first (e.g. for example high resolution) normal or light-field images may be readily aligned with second (e.g. for example lower resolution) normal or light-field images. In various embodiments, the process of image and scan alignment is facilitated by the lower resolution images having similar fields of view and tiling as the analyte scans reducing alignment complexities. In step  710 , one or more first (high) and second (lower) resolution images (corresponding for example to those depicted in  FIG.  1 F (a) and  1 F(b)) may be selected as inputs into the marker detection and alignment process  700 . A noise removal operation (step  715 ) improves the image quality and removes artifacts. Such as process may be accomplished using for example a difference of Gaussians approach as well as other noise removal methods. Input images are subdivided (step  720 ) such that each image comprises a range of selected or desired number of sample retention regions  124 . For example, image subdivisions may be performed such that each sub-image comprises approximately a single column of sample retention regions  124  (for example between 10 and 100 or other selected amount). 
     For each sub-image, alignment markers  1168  may be identified by applying a marker outline detection method (step  725 ). In various embodiments, a Canny edge detection approach may be utilized. Edge detection may further comprise performing other quality assessments of the features contained in the images, such as, dilation and erosion morphology assessments of the alignment marker regions as well as feature contour detection.  FIG.  7 B  illustrates an exemplary processing of sub-image region  755  comprising a plurality of alignment markers  1168  and associated sample retention regions  124 . The original image  755  may be transformed according to the contour detection and size gating operations described above to discretely identify or locate the various alignment markers  1168 . Following marker outline detection method (step  725 ), false or erroneous marker identification may be performed (step  730 ). To help insure actual or well resolved alignment markers are used in subsequent alignment operations, the detected markers may be evaluated based on various criteria. For example, the dimensionality or positioning of the alignment markers and sample analyte regions may be evaluated as well as the pixel areas associated with the various alignment markers. Overlapping contours representative of poorly resolved alignment markers may further be evaluated. In the various alignment marker assessment operations described above, low quality and/or poorly resolved alignment markers may be excluded from further analysis therefore desirably avoiding potential downstream alignment issues. In various embodiments, the number of alignment markers present in a respective sub-image is desirably redundant or in excess of what may be used for image/scan alignment. Such redundancy provides greater tolerance and flexibility to the alignment processes. 
     In addition to identifying false positive, low quality, and/or poorly resolved markers, the method  700  may include processes for identifying the expected locations for missing alignment markers or providing fill-in operations to correct for low quality and/or poorly resolved markers (step  735 ). Following these processes, remaining alignment markers  1168  may be more clearly identified by shading, coloring, or other methods to help insure the markers are clearly distinguishable within respective sub-images (step  740  and further illustrated in  FIG.  7 B (c)). The resulting identified and processed alignment markers  1168  and associated sub-images  765  may then be output for further processing (step  745 ). 
     Referring to  FIG.  1 D , as discussed above, similar operations  1310 ,  1320  may be used for each sub-image  755  associated with the various images obtained in the high and lower resolution imagings (for example,  FIG.  1 F (a) and  1 F(b)) resulting in processed images having high quality alignment markers  1168  identified in each instance. Thereafter, corresponding first (e.g. high resolution) and second (e.g. lower resolution) images may be aligned with respect to each other. 
     An exemplary method  770  for first and second image alignment is shown in  FIG.  7 C  with corresponding exemplary illustration  792  for the alignment process shown in  FIG.  7 D . Initially, sub-images with detected and validated alignment markers for corresponding first (e.g. high resolution) and second (e.g. lower resolution) images are received for processing (step  775 ). As previously described, multiple sub-images may be desirably aligned corresponding to similar or identical regions of interest. Sub-image alignment proceeds selecting a first sub-image to be overlaid/oriented by a corresponding second sub-image applying one or more offsets to aid in the alignment process (step  780 ). 
     Each offset may be representative of a shift in positioning the respective image frames with an offset distance determined, for example, by a number of pixels reflecting the offset distance or position. For each offset, detected alignment markers present in each sub-image are evaluated to determine the number of alignment markers that overlap between the respective sub-images. As will be described in greater detail, the number and degree of overlap between the alignment markers may be used as a factor in determining the extent or quality of sub-image overlap. For the various selected or determined sub-image offsets, the image offset with the largest pixel/alignment marker overlap may be identified. 
       FIG.  7 D  illustrates the multiple offset process of step  780  where a selected sub-image  793  is overlaid/oriented with another associated sub-image  794  for discrete offset intervals (a), (b), (c), and (d) corresponding to approximate alignment marker overlaps of  450  pixels,  800  pixels,  1200  pixels, and  1800  pixels respectively. Using the image offset corresponding to the largest pixel overlap (e.g. (d) in  FIG.  7 D ), sample retention regions  124  within the sub-image are assigned or associated with the selected offset (step  785 ). This process may then be repeated for additional corresponding sub-images until substantially all corresponding sub-images across the one or more first and second images is completed. The output of the process  770  provides details and values for the alignment of the associated sample retention regions  124  in each sub-image for each corresponding first and second images. The aligned images may then be used in downstream processing and further associated with the analyte patterns as will be described in greater detail hereinbelow. 
     Referring again to  FIG.  1 D , detection and resolution of the discrete analytes  108  secreted or released by cells/particulates  106  located in respective sample retention regions  124  of the sample array  122  requires association and alignment of the corresponding fingerprint, barcode, or analyte patterns  142  formed on or associated with the analyte detection substrate  134 . As noted elsewhere, the small size and large number of features visualized across multiple images and scans make it important to provide highly accurate means for orienting and aligning the images and scans with respect to each other. Misalignments can lead to poor quality data analysis including missed and erroneous analyte identifications. The methodologies described herein desirably avoid such problems and provide highly accurate means to associate and evaluate the images and scans. 
     In various embodiments, addressable fiducials or alignment markers  1168  are advantageously leveraged identifying and positioning associated images and scans with respect to one another. For example, alignment markers  1168  associated with, positioned about, or aligned with sample retention regions  124  of the sample array  122  (e.g. detected in images  1 F (b)) may be used in connection with corresponding or other alignment markers  1171  associated with or positioned about analyte detection regions  136  of the analyte detection substrate  134  (e.g. detected in images  1 G(b)). 
     It will be appreciated that a precise and/or unique alignment is desired between the sample array  122  and the analyte detection substrate  134 . Such alignment helps insure microchambers/sample retention regions  124  are appropriately aligned with corresponding flow lanes/analyte detection regions  136 . In this regard, various types, numbers and/or positionings of alignment markers  1168 ,  1171  may help facilitate identification of optimal alignment between the various images and scans of the sample array  122  and analyte detection substrate  134 . 
     Alignment markers  1168 ,  1171  may further aid in resolving variations in scale or differences in imaging characteristics between scans and/or images. For example, rotational and/or translational differences may exist between the images and scans that are desirably accounted for and corrected to improve alignment. To address these issues, accurate alignment marker detection is desirable and may be used to precisely align the various imagings. Several variables may be considered during alignment marker identification and resolution including, for example, x and y translations or offsets, image/scan rotational angle differences, and image/scan scaling factors. Taking these factors into account, detected alignment markers may be used to orient the sample retention regions  124  with analyte detection regions  136  with a high level of accuracy.  FIG.  8 A  illustrates a detailed process  800  for association and alignment of the above-indicated features.  FIG.  8 B  provides an illustration  850  of the processes described in  FIG.  8 A  for an exemplary sub-image portion of sample retention regions  124  with corresponding analyte detection regions  136 . 
     In step  805 , alignment markers detected from corresponding scans of the analyte detection substrate  134  and sample array  122  may be collected and associated. (For example  FIGS.  1 F ( 2   b ) and  1 F ( 3   b )). Alignment markers may be positioned about the surfaces of the analyte detection substrate  134  and sample array  122  in various manners to aid in orientation in various directions such as horizontally, vertically, and/or diagonally. By way of example, alignment marker details and alignment information may be obtained from preceding steps such as information generated during analysis of analyte detection region location and identification (ex:  FIG.  5   ). In step  810 , an approximate position and orientation of selected sample retention regions  124  may be determined using selected alignment markers (ex: diagonal markers). In step  815 , additional operations to refine the first determined positioning and orientation of the sample retention regions  124  may be performed. Using one or more different sets of alignment markers (ex: horizontal markers and/or vertical markers) more precise positioning and scaling of the sample retention regions  124  may be performed according to one or more operations. The updated position and alignment details of the sample retention regions  124  may then be used to align against the positioning for associated analyte detection regions  136 . The results of the alignment for the respective images and scans are then output in state  820 . 
       FIG.  8 B  provides an illustration of alignment  850  between sample retention regions  124  and analyte detection regions  136 . In panel  855 , a sub-image is depicted for detected flow lanes corresponding to the analyte detection regions  136  that may be determined from positioning of associated alignment markers  856 . In panel  860 , a sub-image with corresponding sample retention regions  124  is depicted with alignment markers  857 . It can be noted from the image that the relative positioning of the two images appears slightly offset with respect to one another. Using the processes described above, the alignment markers  856 ,  857  from the two sub-images  855 ,  860  can be leveraged to help provide more precise alignment between the two sub-images. Thus, in sub-image  860  positioning and aligning using the two sets of alignment markers  856 ,  857  provides an accurate approach to visualizing analyte patterns  142  formed at intersections of the sample retention regions  124  and analyte detection regions  136 . These regions of overlap further correspond to positions where signals for analytes  108  are expected to reside based on the type of analyte detection moiety  135  disposed therein as well as the presence of a detected analyte  108  secreted or released from one or more cells/particulates associated with the sample retention chamber  124 . 
     Referring again to  FIG.  1 D , in step  1340  the location and positioning of analyte detection regions  136  is determined. In various embodiments, location information for the analyte detection regions  136  may be aided in part from previous analysis, calculations, and evaluations. For example, image analysis operations and information for alignment feature identification in steps  1220 ,  1240  may be leveraged to further identify associated flow lanes/analyte detection regions  136  (see also  FIGS.  3  and  5   ). As shown in  FIG.  5 ( f ) , located positions and orientations of alignment features/markers may be used to further identify flow lanes/analyte detection regions  136  associated with detected alignment features/markers based on expected positioning of the flow lanes/analyte detection regions with respect to the alignment feature/markers. 
     As shown in the analysis workflow  1000 , steps  1400 ,  1410 , and  1420  may be used to perform additional operations for cell/particulate identification. For example, in cell-based assays (e.g. single cell assays and/or cell-cell interaction assays) and associated analyte  108  analysis it may be desirable to conduct additional operations to discriminate between various different cell types. Additionally, it may be desirable to evaluate or determine the physiological or biochemical status of selected cells  106  for which analytes  108  are detected. According to various embodiments, one or more cell surface markers (e.g. for example surface markers corresponding to CD4+, CD8, and/or CD3) may be detected and/or other stains or dyes evaluated (e.g. for example vitality stains or dyes). 
     According to various embodiments, selected cell populations and cell characteristics may be identified, sorted, and/or distinguished by detection of one or more representative cell surface markers or indicators of expressed membrane associated/secreted proteins. Singular cells as well as multiple cells  106  retained in selected sample retention regions  124  may thus be exposed to various markers, dyes, stains, and/or reagents that are evaluated in addition to or in connection with the analyte patterns  142  resulting from secreted or released chemicals, biochemicals or other cell-associated constituents. 
     During single cell or few-cell analysis, the above-described identification and characterization operations may further be used to refine and/or confirm cell location and positioning within a selected microchamber or sample retention region  124  (step  1400 ). Such processes may proceed using the various acquired images and scans evaluating characteristic optical properties or cellular features associated with cells visible in the images and scans. For example, it may be observed that when a cell is labeled, marked or stained with appropriate dyes and/or antibodies, the cell position may be determined by a generally high fluorescence or signal intensity. Such signals may further indicate, for example, the presence of one or more corresponding and discernable surface markers (e.g. CD4+, CD8, and/or CD3) associated with the cell or in the case of vital stains or other dyes some other discernable characteristic (e.g. for example live vs dead cells). 
     Detected fluorescence or high signal intensities for cell surface markers provide a good indication of the positioning of the cell which is expected to be located close to or exactly in the same area as the observed fluorescence or observed signal. In some instances, however, surface marker antibodies, dyes, stains, or other markers may generate relatively noisy or diffuse signals that may result in areas of relatively high background within the sample retention region  124  containing the cell  106 . High background or noise may obscure visualization or detection of cells/particulates  106  associated with the sample retention region  124 . These high-background sample retention regions  124  may be desirably identified, flagged, and/or excluded from further analysis (step  1410 ). 
     Additionally, signal intensity or fluorescence may exhibit variance between cells/particulates  106  in different sample retention regions  124 , vary from experiment to experiment, and/or vary with the quality or characteristics of the stain, marker, or dye used. In some instances, there may be a population of cells/particulates  106  that do not exhibit a detectible signal or possess only a weak signal despite having a corresponding surface marker or having been labelled with a selected stain, marker, or dye. Furthermore, in instances where two or more cells/particulates  106  of the same type or general characteristics reside in close proximity or next to each other, difficulties in distinguishing the origin of the fluorescence or signal may be observed. For cellular secretion, protein expression, and other types of bioanalysis it may be important to accurately determine the cell type and surface markers or other characteristic associated with each detected cell. Accordingly, it may be desirable to label potential or candidate cell identifications as definite cells or false positive cells based on cell surface markers or other signal properties associated with the cell (step  1420 ). 
       FIG.  9 A  depicts a detailed method  900  for cell/particulate position identification and discrimination.  FIG.  9 B  further illustrates the positioning and discrimination process  970  depicting an exemplary sub-set of sample retention regions  124  with cells labelled with surface markers  985  (CD8 &amp; CD3) for cell characterization and discrimination. The process  900  commences with receiving input images corresponding to cell/particulates  106  identifiable with dyes, markers, or labels that can be visualized for example by fluorescence detection ( FIG.  1 F (c)). As discussed above, the markers may comprise one or more cell/particulate surface markers imaged/scanned simultaneously or at different times and whose data is aggregated through the alignment and positioning mechanisms described herein. Additional information may also be utilized in the alignment process  900  corresponding to identified microchambers/sample retention regions, definite and potential cells candidates, and aligned microchamber/sample retention region information. (for example, obtained from the processes  400 ,  600 ,  700  described in association with  FIGS.  4 ,  6 , and  7    respectively) 
     Initially, in step  910  an average background signal or noise ratio may be determined (for example, to identify background fluorescence due to dyes, markers, or labels used in the analysis). The average background signal may further be determined by evaluation of one or more sample retention regions  124  lacking a cell/particulate  106  residing therein. In various embodiments, sample retention regions  124  lacking a cell/particulate  106  may be expected to exhibit lower background or reflect average nominal background present throughout each of the sample retention regions  124 . 
     In step  915 , sample retention regions or areas  124  are correlated with the input images (ex:  FIG.  1 F (c)). This process  915  may further utilize offset information determined previously for first and second aligned images (ex:  FIG.  1 F (a),  1 F(b)). In step  920 , a microchamber or sample retention region mask is determined to isolate or localize respective areas to be evaluated. A portion of this process  920  may include identifying an average pixel intensity or density in regions outside of the locations where cell/particulates  106  are detected or predicted to determine background within a respective sample retention region  124 . For example, an average pixel intensity may be determined for portions of a selected sample retention region  124  by excluding detected or potential cell areas, signals, or intensities. Thereafter, the background signal (e.g. fluorescence) for a selected microchamber/sample retention region  124  may be determined. 
     In step  925 , the sample retention region  124  may be evaluated further identifying high signal intensities in areas proximal to identified or predicted cell/particulate locations. For example, the highest fluorescent signal residing around or in proximity to a cell/particulate  106  may be determined for one or more surface markers or images. Taking into account the background signal/intensity information identified above and the maximal cell/particulate associated signal present, the relative position of cells/particulates  106  within respective images of sample retention regions  124  may be determined with a high degree of accuracy. This information may be further correlated with the one or more scans of analyte patterns  142  associated with the cell(s)/particulate(s)  106  in the corresponding sample retention region  124 . 
     In step  930 , cell/particulate surface marker intensities may be evaluated to determine and quantitate detected markers. In various embodiments, for each cell undergoing analysis the presence of one or more surface markers detected or associated with the cell may be used to determine the characteristics and/or status of the cell. Additionally, for instances where high background signals exist based on the determinations and calculations described above, associated sample retention regions  124  may be flagged and/or excluded from further analysis in step  935 . Flagging or excluding sample retention regions in this manner may aid in generation of high quality results and/or prevent anomalous or erroneous interpretation of the sample data. Finally, in step  940  sample retention regions having acceptable background are output along with associated cell/particulate position and identification characteristics including surface marker patterns or descriptors (if present) for further processing. 
       FIG.  9 B  provides a pictorial representation  970  of the processes described in  FIG.  9 A  where in an exemplary single cell assay, surface markers for CD3 and CD8 are imaged discretely and aligned. Imaging  972  reflects detected signals for CD8 surface marker detection. A portion of cells  106  in the imaging  972  exhibit sufficient signals to identify the presence of the CD8 marker. In imaging  974 , an exemplary light field image of the same sample retention regions  124  indicate the presence the cells present in imaging  972  and other cells  106 . Comparing, overlaying, or merging of the signals associated with the two imagings  972 ,  974  as depicted by composite imaging  978  can be accomplished using alignment markers and processes similar to those previously described. 
     Similarly, imaging  976  reflects detected signals for CD3 surface marker detection. A portion of cells  106  in the imaging  976  exhibit sufficient signals to identify the presence of the CD8 marker. Comparing, overlaying, or merging of the signals associated with the two imagings  972 ,  976  as depicted by composite imaging  980  can be accomplished using alignment markers and processes similar to those previously described. The composite imagings  978  and  980  can be further compared or overlaid to provide a final imaging that combines the data and signal information from each of the previous panels. Taken together, cell position as well as detected cell surface markers  985  may be determined. The presence of differentially detected markers (CD8, CD3) indicate differing characteristics and/or expression patterns for the cells. This information can further be used to classify or group the cells and discriminate between different cell types and/or states. Such classifications and groupings may be further considered in relation to respective detected analyte patterns  142  discussed below. 
     Referring again to  FIG.  1 D , following positioning and alignment operations for the various images and scans associated with sample retention regions  124  of the sample array  122  and the analyte detection regions  136  of the analyte detection substrate  134 , the analysis workflow  1000  may proceed with locating and identifying analyte patterns  142  associated with or attributable to selected cells/particulates  106  (step  1430 ).  FIG.  10 A  provides details of a workflow  1510  for secretion readout determination. Additional secretion readout illustrations are provided in  FIG.  10 B . 
     In various embodiments, a selected analyte pattern  142  corresponding to one or more detected analytes  108  expressed, secreted, or released by the cell/particulate  106  comprise one or more discrete signals each associated with a respective analyte that are positioned about various known or expected locations with respect to the analyte detection substrate  134 . The manner of fabricating, positioning and/or depositing the various analyte detection moieties  135 /analyte detection regions  136  about the analyte detection substrate  134  determines the orientation and position of resultant detected analytes  108 . 
     In various embodiments, two or more analytes may be detected in approximately the same position or region but observed in different scans. Positionally multiplexed analyte detection moieties  135  may thus be resolved from one another by the type of signal emitted and/or the scan or imaging in which they appear. (For example, co-located or positionally multiplexed analyte detection moieties may comprise discrete labels or fluorophores that may be separately detected and/or distinguished). 
     An analyte pattern  142  for a selected cell/particulate  106  may be identified or “read” by associating a selected sample retention region  124  with a corresponding series of one or more positions or regions  136  on the analyte detection substrate  134  where analyte detection moieties  135  are located. The disposition of the respective cell/particulate  106  that results in detected analytes  108  may therefore be determined by evaluating one or more selected pixel area(s) or regions for the cell images and the analyte detection scans where overlap occurs in a manner described in greater detail below. Signal intensity values identified in the representative signal/scan images may be mapped to discrete pixels and/or assigned numerical values representative of pixels in the imaging area for ease of identification and processing. In various embodiments, identified signal intensities of a selected threshold may be averaged to obtain a value that may be associated with the detected analyte  108 . In various embodiments, higher accuracy quantification of analytes  108  and/or confirmation of the presence of selected analytes  108  may be determined using two or more regions of analyte detection/pixel analysis. Scans/imagings for these regions provide discrete overlap in two or more areas or locations between the sample retention regions  124  and the analyte detection regions  136 . In various embodiments, the observed intensity values for each corresponding region of overlap may be compared and /or averaged to yield a higher confidence identification of respective detected analytes  108 . 
     As shown in  FIG.  10 A , the analysis process  1510  may commence with acquiring detected flow lanes or analyte detection regions  136  that have been aligned with corresponding microchamber or sample retention regions  124 . In various embodiments, sub-images corresponding to detected analytes  108  may be utilized to identify regions of intensity where one or more analytes are detected (see for example  FIG.  1 G  (a) and (b)). Corresponding flow lanes or sample retention regions  124  may then be used to overlay or merge the analyte scans to locate regions of overlap. As discussed above, overlap may be determined according to pixels in the various images and scans. Regions or areas of overlap may then be assigned to corresponding sample retention regions  124 /analyte detection regions  136  in a pairwise manner. 
     For assigned areas or regions of identified overlap the associated intensity data or signal information may be extracted from each position (step  1525 ). Associated or corresponding signals from selected areas may further be averaged to provide an output signal or readout that corresponds to the detected analyte  108 . In certain instances, low confidence or outlier data or signals may be removed or flagged as described in greater detail with respect to  FIG.  11    below. Duplicate or redundant signal intensities associated with the same cell/particulate  106  and analyte  108  pairings may further be compared and/or averaged for validity and/or confidence before generating a final analyte readout of the detected analyte  108 . Thereafter, valid detected analytes  108 , associated intensities, and other characteristics may be output (step  1530 ). 
       FIG.  10 B  illustrates an example  1550  of the imaging and pixel comparison processes described in  FIG.  10 A . As shown in sub-image  1555 , a plurality of analyte detection regions  136  (identified as “protein lanes”) may be overlapping with a plurality of sample retention regions  124  (identified as “chambers”). According to the exemplary sub-image  1555 , analyte detection regions  136  are generally vertically disposed overlaid or oriented with generally horizontally disposed sample retention regions  124 . Overlapping regions  1557  with observable signal intensities are further identified. Sub-image  1560  provides a magnified view of a selected overlap region between analyte “lane”  15  and sample “chamber”  23 . As depicted by the overlap region  1558 , an analyte readout area may be designated indicative of the presence of a detected analyte  108 . Sub-image  1570  further illustrates pairwise readouts for two distinct overlap regions  1572 ,  1574  corresponding to identical cell/particulate  106  and detected analyte  108  regions. These regions may be evaluated independently and the results compared and/or averaged as described above. 
       FIG.  10 C  illustrates a further example  1551  of the imaging and pixel comparison processes described in  FIG.  10 B  above showing pairwise readouts  1552  associated with marker patterns  142  for exemplary sample retention regions  124 . A plurality of analytes (e.g. cytokines)  108  may be discretely detected simultaneously and resolved from the marker patterns  142 . As depicted in image panels (a) and (b), a plurality of images may be acquired using, for example, different wavelengths or with differing image acquisition characteristics to discretely identify markers  135  that may be co-located or positioned about the same analyte detection region  135 . Images acquired with differing image acquisition characteristics may further be overlaid or merged as shown in panel (c). 
     Referring again to  FIG.  1 D , following analyte/secretion location identification and analysis, a background signal evaluation and outlier detection analysis may be conducted (step  1440 ).  FIG.  11    depicts a process  1610  for gating, excluding, and/or flagging potentially low confidence, error-prone or outlier data and signals. As discussed above in connection with  FIG.  10   , various scans or images (or portions thereof) may be subject to regions of high background and noise as well as other potential artifacts that may diminish the quality of signal data and results output by the analysis method  1000 . To help avoid outputting erroneous and low quality results from the analysis, various quality checks or gating procedures  1620  may be applied to the output analyte identifications, readouts, and associated image/scan data obtained from step  1530 . This data may be first received for quality review (step  1625 ) where one or more quality checks  1620  are applied. 
     One exemplary quality test  1630  may comprise an assessment of image/scan quality based on pixel information. A Grubb&#39;s test may be performed to exclude or flag pixels in the images/scans that are identified as potential outliers. In various embodiments, pixel data corresponding to images/scans for portions of the sample retention regions  124  overlapping with the analyte detection regions  136  (exemplified in  FIG.  10 B ) may be considered as well as other regions of pixel data. Pixel data failing the Grubb&#39;s test may serve as an indicator that associated image/scan data in these regions are suspect and results/readouts may be excluded or flagged for further review. 
     Another exemplary quality test  1635  may comprise evaluating pixel areas or sizes of regions where intensity information was extracted and evaluated. Size thresholds may be applied to the image/scan data and analyte results/readouts associated with pixel areas that fall below or do not meet the selected threshold excluded or flagged for further review. As one example, insufficient overlap between an analyte detection region  136  and an associated sample retention region  124  may result in insufficient pixel information to accurately determine or assess the data. In regions with insufficient overlap as determined by preselected criteria the results/readouts may be excluded or flagged for further review. 
     A further exemplary quality test  1640  may comprise evaluating image/scan data to identify regions of high interstitial background. In various embodiments, evaluation of images/scans in areas substantially adjacent or proximal to identified analyte detection regions  136  and/or sample retention regions  124  (for example, above, below, to the right, or to the left) may indicate one or more of the interstitial regions exceed a threshold associated with the analyte signals. Such a result may suggest the associated data/readout is indicative of a false positive result. Consequently, such results/readouts may be excluded or flagged for further review. 
     Another exemplary quality test  1645  may consider observed analyte results associated with selected sample detection chambers  124 . In various embodiments, sample retention chambers that are not associated with at least one reliable analyte identification/readout for selected analytes may be excluded or flagged for further review. In some instances, at least one reliable readout should be obtained from two or more associated analyte imaging areas. 
     Following application of one or more of the various gating and quality assessment checks described above, the resulting analyte data/readouts may be considered valid and output (step  1650 ). It will be appreciated that applying various quality assessments desirably improve the overall confidence and quality of the data analysis. Additional quality assessments may thus be applied to further enhance the output analyte data/readout quality. 
     Referring again to  FIG.  1 D , following exclusion of high background signals and associated analyte data, data analysis  1000  may proceed with quantification of signals for analytes  108  detected in one or more analyte detection regions  136  (step  1450 ).  FIG.  12 A  illustrates exemplary imagings  1610 ,  1620  depicting a portion of selected sample retention regions  124  with intersecting analyte detection regions  136 . Shown in image panel  1610 , sample retention regions  124  (identified as “zero-cell-chambers” chambers  3 ,  5 ,  22 ) that have previously been determined to lack a cell/particulate  106  contained or residing therein are identified. Signal intensities in the regions associated with the various intersecting analyte detection regions  136  (e.g. lane  15 ) may be leveraged to determine or characterize background signal intensities. 
     For example, the signal intensities associated with one or more of the sample analyte regions lacking a cell/particulate  106  may be evaluated in the selected intersection regions  1630  to determine an associated background signal. In various embodiments, background signals may be determined for one or more analyte detection regions  136  and/or for one or more sample retention region  124 . Background results may be further processed by combining, averaging, or other methods to determine an overall background signal value to be applied to other signal intensity data or applied individually to selected or corresponding signal intensity data for sample retention regions  124  located in proximity to a region  1630  where a background signal was determined. 
     As shown in image panel  1620 , background signal intensities may then be applied to the signal intensity data associated detected analytes  108  for sample retention regions  124  that have been previously identified as containing at least one cell/particulate  106  (ex: chambers  19 ,  23 ). In various embodiments, background signal intensities may be used to evaluate the analyte signals/readouts associated with selected analyte detection regions  136  to further characterize the resultant detected analyte signals. For example, as shown in image panel  1620 , signal intensities for a selected analyte detection region  136  (e.g. lane  15 ) may be evaluated for respective sample retention regions  124  (e.g. chambers  19 ,  23 ) and characterized with respect to background. For example, subtraction of background signal from associated signal intensities may be performed and corresponding regions characterized as above-background (ex:  1640 ) or below-background (ex:  1645 ). Background analysis in this manner may further be used to qualify the presence or absence of analyte  108  corresponding to the background-subtracted regions. 
       FIG.  12 B  depicts an exemplary scatterplot  1700  for an experiment in which a plurality of samples (e.g. cells  106 ) are evaluated for a plurality of analytes (e.g. cytokines  108 ). Analysis results are shown as a number or percentage  1710  of cells exhibiting analyte signals/readouts with respect to a background signal or background threshold  1720 . The vertical axis  1730  provides relative intensity values or units for selected cytokines depicted on the horizontal axis  1740 . Background thresholds  1720  are determined for each cytokine independently and cells characterized by the detected presence of an analyte above-threshold  1750  or below-threshold  1760 . 
     Analyte secretion or detection results depicted in the above-indicated manner provide a convenient method to evaluate overall expression, secretion, or presence of selected analytes for a potentially large number of cells simultaneously. Using this information, cells may be evaluated for analyte presence and/or response on a single cell basis while allowing potentially large populations of cells to be collectively profiled to reveal insights regarding the behaviors, responses, or characteristics of the cells. Applying the disclosed analysis methods therefore provides a practical means by which to assess for multiple analytes including biochemicals, cytokines, and chemokines secreted from the same cell across significant numbers of cells simultaneously while maintaining sensitivity and accuracy to detect analyte secretions for single cells or cell-cell interactions. 
       FIG.  12 C  illustrates exemplary imaging results  1701  for merged images of a selected subset of sample retention regions  124  with associated marker patterns/readouts resulting from detected analytes  108  associated with one or more cells  106  contained within the sample retention regions  124 . Pairwise analyte analysis results are shown as described previously and further associated with a cell number  1702  and cell type  1703 . Evaluating detected analyte results from respective sample retention regions  124  containing singular cell types  1703  (e.g. K-562 and NK cells) versus analyte results for sample retention regions  124  containing two or more cells of different types can provide, for example, information and insights into cell-cell interactions and associated changes in cellular analyte response or cellular secretions. Additionally, evaluating analyte results for sample retention regions  124  containing two or more cells of the same type can provide, for example, information and insights into cell-cell interactions and further be used for quantitative analyte analysis correlated with the number of cells present in the chamber. 
     Referring again to  FIG.  1 D , the data analysis  1000  may include additional operations and tools for evaluation of populations of discrete cells to determine correlative analyte (e.g. cytokine/chemokine) response and functional profiling of samples (step  1460 ). In various embodiments, experimental data and results obtained according to the disclosed methods may be further analyzed, compared, and associated providing powerful tools for discovery, screening, surveying many different types of cellular characteristics, responses, and functionalities, such as those implicated and correlated with immune response. As shown in  FIG.  13 A  and  FIG.  13 B , various methods and tools  1800  for presenting and interpreting analyte data and results may be utilized. For example, in single cell experiments, evaluation of poly-functionality across a population of cells may be provided as output from the data analysis. Single cell expression data and information obtained from one or more experiments may be related to identify patterns and trends in polyfunctional expression (e.g. the ability to secrete multiple effector proteins/cytokines from the same cell). Secretion profiles corresponding to an exemplary polyfunctional assay for selected cytokines is depicted in exemplary pie charts  1805 , bar graphs  1810 , and heat maps  1815 . Additional correlations may be made, for example, comparing cell types from multiple samples and comparing detected analytes to known or established parameters to determine analyte expression or secretion profiles, physiological correlations, disease states, and/or treatment response. 
     Referring again to  FIG.  1 D , the data analysis  1000  may include additional operations and tools to facilitate comparison and/or correlation of experimental analyte response results with other information, for example, by comparison with a database of existing literature/publications/datasets (step  1470 ).  FIGS.  14 A  depicts a database interface  1850  containing information that may be used to evaluate single cell experimental results providing a tool for inference determination and discovery. 
     The database  1850  for example may comprise single cell secretion/analyte expression information from previous experiments as well as literature/published data. Exemplary experiment filters  1855 , publication filters  1860 , and data filters  1865  provide users with the ability to focus on desired or relevant information which may be used to query stored experimental data and publication information. The experimental filters  1855 , for example, may allow selection or retrieval of data/information based on particular or selected informational sources, diseases/conditions, subject types, cell types and/or technologies (used for sample analysis, such as flow cytometry, Enzyme-Linked ImmunoSpot (ELISPOT), the presently described technology, etc.). The publications filters  1860 , for example, may allow selection or retrieval of relevant literature/information based on particular or selected areas of interest, scientific categories, authors, publication year, and/or keywords. The data filter  1865 , for example, may allow selection of data/information/literature based on particular or selected cytokines or other analytes of interest. The data filter  1865  may further provide functionality for data retrieval based comparisons between cytokine/analyte secretion profiles or values as well as selections based on polyfunctional cytokine/analyte response. Various metrics can be chosen to automatically compare the similarity of the overall cytokine/analyte secretion and/or polyfunctional profiles. To further facilitate user interaction with the database tool, various filters may be auto-populated with search and data selection criteria. In conjunction with any user-selected filters, an overall determination can be made as far as the relevance or similarity of a catalogued dataset with the user&#39;s current dataset. In various embodiments, where a conclusive correlation cannot be determined, providing a small set of relevant past experiments/datasets can help guide or aid a user in determining what subsequent tests may be informative to run, how a treatment could be performed relative to others, etc. 
       FIGS.  14 B  depicts an exemplary results interface  1875  for displaying information from a selected single cell experiment. Results and relevant associated datasets  1880  may be selected and examined in more detail with convenient links to publications and associated data. A score or rank  1885  may further be attributed to the results  1880  based, for example, on their similarity to an experimental or desired cell profile providing details and correlations between the literature /publications with the obtained/experimental cellular response data. 
       FIG.  14 C  depicts an overall workflow  1900  for the above-described data analysis functionalities and database to provide single-cell profiling capabilities in the context of immunological profiling analysis. Leveraging the throughput, sensitivity, and accuracy of the systems and methods disclosed herein enables functional characterization of single cells, populations of cells, and classes or types of cells providing additional data gathering, evidence evaluation, and inference determination functionalities to evaluate experimental results. 
     In various embodiments, and as shown in  FIG.  14 D  the methods and processes described above and depicted in the associated figures may be implemented using a platform  1950  comprising components for image acquisition further comprising computational components for image analysis and subsequent analyte evaluation. The platform  1950  may further comprise an integrated system, for example, a singular device or instrument capable of performing each of the functions to output desired analyte results and associated data. Also, the platform  1950  may include discrete or separate components comprising various instruments and computing components for performing various desired functionalities. 
     In various embodiments, sample arrays  122  and associated analyte detection substrates  134  are processed by an imaging apparatus  1952  that acquires a plurality of images/scans. The imaging apparatus  1952  may include one more more imaging devices including by way of example microscopes, florescence detectors, microarray scanners, and/or other optical and signal detection apparatus. An image processor  1953  comprising, for example, a computing device with suitable image processing and data analysis software may be configured to receive signal information, images, scans, and other data associated with the imaging process. The image processor  1953  may further evaluate and analyze the information determine or associate analytes responsible for exhibited signals along with determining corresponding cell(s)/particulate(s) associated with the detected analytes. 
     The image processor  1953 , for example, may determine correspondence between cytokine signals and detected single-cell chambers/microwells detected by the imaging apparatus  1952  and further associate the cytokine signals with one or more specific cells to generate a cell-by-cell profile of secreted proteins for individual cells. The image processor  1953  may further quantify detected analytes and associate this information with detected cells/particulates. The image processor  1953  may further perform additional analytics that associate detected analytes for the various detected cell(s)/particulate(s) across one or more analytes per cell/particulate. The image processor may further determine cell/particulate functional responses and characteristics and leverage information contained in one or more data repositories  1954  to perform further associations, determine outcome data and compare against existing information/literature as described above. 
     The imaging and analysis methods may be implemented using software comprising computer programs using standard programming techniques. Such programs may be executed on programmable computers each including an electronic processor, a data storage system (including memory and/or storage elements), at least one input device, and least one output device, such as a display or printer. 
     In some embodiments, the code is applied to obtaining and analyzing image and scan data (e.g., images and scans for sample retention regions  124 , analyte detection regions  136 , and associated cell(s)/particulate(s)  106  and analyte pattern(s)  142 ), to perform the functions described herein, and to generate output information (e.g., assessment/quantitation of analytes, polyfunctionality determinations, and associated correlations/inferences), which may be applied to one or more output devices. Each such computer program can be implemented in a high-level procedural or object-oriented programming language, or an assembly or machine language. Furthermore, the language can be a compiled or interpreted language. Each such computer program can be stored on a computer readable storage medium (e.g., CD ROM or magnetic diskette) that when read by a computer can cause the processor in the computer to perform the analysis described herein. 
     The speed, accuracy, and throughput capabilities of the system and methods of the present disclosure are useful for cell population analysis at the level of single cell assays and cell signaling assays between singular cells. In particular, the present teachings may be advantageously applied to cell population analysis to discriminate between highly polyfunctional classes of cells such as immunological cells (e.g. B-cells, T-cells, macrophages, and other immuno-cell types). Immuno-cells are associated with the secretion of many cytokines per cell and often exhibit significant differences between cytokine secretion profiles for individual cells. Applying the teachings disclosed herein, clinicians and researchers are able to analyze cell and protein secretion profiles in a sensitive and reproducible manner. Single cell cytokine secretion profiles (e.g. analyte readouts) may be analyzed for one or more patients or samples simultaneously and evaluated against databases of immuno-cell cytokine responses to evaluate immune functionality, regulation and toxicity. As previously described, the system provides a high degree of automation for processing of various cellular images, analyte detection scans, and querying databases of information to automate the single-cell functional analysis. 
     The composition of T-cell subpopulations (based for example on CD4/CD8 ratios) as well as their associated effector/cytokine secretion profiles may be correlative of clinical outcome and/or therapeutic response. Examples of where such analysis may useful include but are not limited to immune cell response for patients with human acquired immune deficiency syndrome (AIDS), as well as adaptive immune protection responses against pathogen infections. 
     In various instances, the same or similar cell type (e.g. for example CD4 or CD8 cells) may comprise highly heterogeneous populations with dynamic and evolving functional phenotypes. Such cells may be desirably classified at the single cell level by their immune effector functions (e.g. cytokine secretion profiles). Potency and durability of immune response can further be correlated with polyfunctionality and thus it is desirable to analyze multiple immune effector proteins from the same cell. In considering the nature of cytokine secretions, the degree of immune protection may be correlated with both the frequency of poly-functional immune cells (for example T-cells) secreting distinct cytokines simultaneously, as well as the quality and/or amount of cytokine secretion. 
     Immune cells not only display distinctive and disparate immunological secretion profiles but also influence activity in the micro-environment where they reside. Cytokines such as the tumor-necrosis factor (TNF) are produced by immune cells, and can improve the efficacy of T-cell priming and induce adaptive anti-tumor immunity once introduced into the cancer environment. Conversely, other cytokines have been associated with poor patient outcomes, and have been reported to promote tumor growth and inhibit anti-tumor immune response. For example, imbalanced production of interleukin 6 (IL-6), vascular endothelial growth factor (VEGF), or macrophage colony-stimulating factor (M-CSF) inhibit adaptive anti-tumor immunity by suppressing dendritic cell maturation and activating regulatory T cells (Treg) to aid tumor cells in evading immune-surveillance. Transforming growth factor beta (TGF-β), which is abundantly expressed in many pathological conditions, heavily influences tumor growth and maintenance, as the cytokine has an important role in forming a tumor microenvironment and facilitating angiogenesis. Applying the teachings of the present disclosure, accurate measures of the preceding immunological cytokine actors at the single-cell level, across multiple cells and cell populations/samples may be conducted. Cytokine secretion profiles for individual cells or collections of cells may be further evaluated and compared to existing information and literature vales to create actionable patient immunological profiles and/or provide indicators or correlates of immune response and functional activation against cancers. 
     In other applications, polyfunctionality for diseased cells such as malignant hematological cells or solid tumor cells may be evaluated on the basis of cytokine secretions with various secretion profiles correlated with pathogenesis. Cytokines that may be analyzed may include by way of example, CCI-11, GM-CSF, Granzyme B, IFN-γ, IL-10, IL-12, IL-13, IL-17A, IL-17F, IL-18, IL-1β3, IL-2, IL-21, IL-22, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IP-10, MCP-1, MCP-4, MIP-1α, MIP-1β, Perforin, RANTES, sCD137, sCD40L, TGF-β1, TNF-α, TNF-β. Additionally, the system and methods of the present disclosure may be desirably applied in various clinical applications where characterization of effector function across wide spectra or populations of single cells, for example, in areas of cellular immunity and oncology tools is beneficial for accurate diagnosis or therapeutic evaluation. 
     While the principles of the disclosure have been illustrated in relation to the exemplary embodiments shown herein, the principles of the disclosure are not limited thereto and include any modification, variation or permutation thereof. Further, the examples set forth above are provided to give those of ordinary skill in the art a complete disclosure and description of how to make and use the embodiments of the devices, systems and methods of the disclosure, and are not intended to limit the scope of what the inventors regard as their disclosure. Modifications of the above-described modes for carrying out the disclosure that are obvious to persons of skill in the art are intended to be within the scope of the following claims. All patents and publications mentioned in the specification are indicative of the levels of skill of those skilled in the art to which the disclosure pertains. 
     The terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the content clearly dictates otherwise. The term “plurality” includes two or more referents unless the content clearly dictates otherwise. A number of embodiments of the disclosure have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the present disclosure. Accordingly, other embodiments are within the scope of the following claims.