American Journal of Transplantation 2015; XX: 1–7 Wiley Periodicals Inc.
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Copyright 2015 The American Society of Transplantation and the American Society of Transplant Surgeons doi: 10.1111/ajt.13193
A Mine Is a Terrible Thing to Waste: High Content, Single Cell Technologies for Comprehensive Immune Analysis P. K. Chattopadhyay* and M. Roederer Vaccine Research Center, National Institutes of Health, Bethesda, MD Corresponding author: Pratip K. Chattopadhyay, [email protected]
In recent years, an incredible variety of single cell technologies have become available to analyze immune responses. These technologies include polychromatic flow cytometry, mass cytometry, highly multiplexed single cell qPCR, RNA sequencing, microtools, and high-resolution imaging. In this article, we review these platforms, describing their power and limitations for comprehensive analysis of the immune system. We relate the properties of these technologies to the various cellular states relevant to an immune response, in order to address which technologies are most appropriate for which settings. Abbreviations: AIDS, acquired immunodeficiency syndrome; CyTOF, cytometry by time of flight; FlowCAP, flow cytometry critical assessment of population identification methods; HIV, human immunodeficiency virus; qPCR, quantitative real-time polymerase chain reaction; RNAseq, RNA sequencing Received 05 November 2014, revised 22 December 2014 and accepted for publication 26 December 2014
Introduction For decades, flow cytometry has been the dominant technology for interrogating individual cells (1,2). The ability to make multiparameter measurements on single cells, at rates approaching 50 000 cells/s, has revealed remarkable diversity in leukocyte phenotype and function. Results have been far-reaching, feeding a broad set of disciplines ranging from fundamental biology to medicine and drug development (3). In recent years, however, there has been a revolution in the technology used to measure cells. ‘‘Classical’’ flow cytometry has become increasingly multiparametric (4–6), and in parallel, new imaging, molecular, and mass
spectrometry-based tools have emerged. A common feature of these tools is the enormous data content they provide, but the problem has become: How can the high content data available in cells be mined most effectively? Here, we review the particular challenges researchers face with these new approaches, along with a discussion of emerging tools.
The Miner’s Toolbox: Available Single Cell Technologies Polychromatic flow cytometry Since the 1970s, flow cytometry has been the leading single cell technology because it is high-throughput, allows live cell sorting, and is quantitative. With each decade, the introduction of new classes of fluorescent dyes has geometrically increased the number of parameters that could be simultaneously measured, from 2 to (most recently) 30 (7). One and two-color cytometry began with the use of small organic fluorochromes (fluorescein, rhodamine) in the 1970s, first applied to distinguish, e.g. B and T cell lineages (8,9). The 1980s saw the advent of phycobiliprotein dyes: incredibly bright, protein-based fluorochromes that enabled 4-color analysis, used to identify major subsets of lymphocytes (such as differentiation stages) (10). The introduction of a variety of resonance energy tandem dyes in the 1990s allowed 8-color measurements that distinguished fine memory T cell populations, revealing, e.g. TH1/TH2 cytokine polarization of cells (11). In the 2000s, the development of quantum dots brought cytometry to 20-parameter capability (6), allowing highlymultiplexed detection/enumeration of antigen-specific T cells and the identification of cells capable of making multiple cytokines simultaneously (12). In the most recent decade, two technologies have pushed the ceiling beyond 30 parameters. Cytometry by time-offlight (CyTOF) uses mass-spectrometry based detection combined with lanthanide-labeled reagents (13), and has been used for as much as 40-parameter measurements (without the possibility of cell sorting); this technology is discussed more, below. On the fluorescence side, the ‘‘Brilliant’’ family of dyes was introduced (14), enabling 30 (this year [7]) and perhaps 40 (in the next 2 years) parameter fluorescence analysis (compatible with live-cell sorting). 1
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The extraordinary brightness (14) of these dyes allows for easy, and highly sensitive antibody multiplexing, a key challenge in polychromatic flow cytometry. Although spectral overlap of fluorochromes is commonly thought to be the primary problem in panel development, the main reason why panels fail is because low-level protein expression cannot be resolved, a problem exacerbated by Poisson errors in photon counting leading to ‘‘spillover spreading error’’ (15). This impact is minimized when antibodies against low-expression proteins are paired with very bright dyes, such as Brilliant Violet 421 (and other similar dyes in development). For this reason, the Brilliant family of dyes holds great promise for simplifying panel design. In the near future, tools that calculate the spreading error between dyes and detectors will further simplify panel design by helping researchers to pair reagents (monoclonal antibodies) with dyes depending on the properties and requirements of each. Such tools may be used to predict the likelihood that a panel will work well before extensive experiments are performed; they will be based simply on antibody titration data and measures of instrument performance (16). The mathematics underlying such tools have been described (17), and incorporated into commercial software to report the spillover/spreading (SSM) matrix. Ultimately, these measures will automate much of the panel design process. In the meantime, previously developed panels are now reported in a new print and online resource (18), ‘‘Optimized Muliticolor Immunophenotyping Panels’’ (OMIPs). There are currently 22 unique panels described in this publication format, to query, e.g. antigenspecific cells (19–25), chemokine receptors (26), B cells (27), innate cells (28), regulatory T cells (29,30), and gamma-delta T cells (31). Some reagents and panels developed for polychromatic flow cytometry may be used on other platforms as well. For example, on the Amnis Imagestream, fluorescence flow cytometry is combined with microscopic imaging to visualize protein distribution within a cell. These instruments are particularly useful for monitoring cellular morphology, receptor capping, or phagocytosis (32). Current versions can be configured with up to seven lasers, for detection of 12 fluorescence parameters, including the same quantum dot and brilliant violet dyes used in flow cytometry. These dyes offer particular advantages over other imaging dyes because they can be multiplexed easily, are brighter (14), and exhibit reduced photobleaching (33). Finally, in recent years spectral flow cytometry systems have been commercialized (34). These instruments collect all the fluorescent light from stained cells, and then use algorithms to un-mix (or deconvolute (34)) this signal into the component signal from each fluorochrome (a process identical to compensation on existing cytometers). The primary advantage of spectral flow cytometers is that there is no need to optimize nor change detector filters depending 2
on the fluorochromes used. However, for a given configuration with optimized filters, there is no theoretical advantage. Mass cytometry Recently, inductively-coupled mass spectrometry was adapted for cytometry (13). This platform is the basis for CyTOF instrumentation that measures cells tagged with antibodies conjugated to rare isotopes of heavy metal elements (rather than fluorescent dyes). In principle, because these rare isotopes are not present in cells, biological background should be very low, allowing exquisite resolution of low-level protein expression (practically, the experimental background from nonspecific binding and so on becomes the limiting factor for sensitivity). Moreover, there should be minimal overlap between isotopic signals, allowing complete freedom in designing multiplexed panels. Finally, with the availability of over 40 unique isotopes, the high content of this platform is as yet unparalleled. However, in practice, there are some limitations to the advantages of mass cytometry. First, cell staining can be weak for some antibodies, and nonspecific antibody binding can contribute substantial signal (32), diminishing the advantages afforded by low system background. Second, although there should be essentially no overlap between isotopic elements, in practice there are impurities in the raw materials used as tags for antibodies (35). Moreover, the formation of oxides can shift signals 16 mass units, causing overlap between probes, leading to the need for signal deconvolution (i.e. compensation) (35). Finally, CyTOF is generally low-throughput (with collection of one sample every 5–7 min) and not compatible with live cell sorting (because cells are destroyed as they are introduced into the instrument). Despite these caveats, the future of mass cytometry for certain applications (see below) is particularly bright. Transcriptomics Analysis of gene expression has been possible for many years on bulk cell populations, using microarrays (for relative, multiplexed analysis) or qPCR (for quantitative analysis of a single gene). Recently, methods for single cell gene expression have been optimized, allowing multiplexed qPCR (36) or sequencing of RNA transcripts (RNAseq) (37). In terms of content provided, RNAseq is unmatched. In principle, every transcript within a single cell can be quantified and mapped to a gene. Thus, there is no selection bias in an RNA sequencing experiment: results are not dependent on the investigator’s choice of probes, as is the case with other technologies. However, there are some major challenges associated with applying this technology to studies of lymphocytes. First, the small size of lymphocytes provides very little starting RNA, so minimum quality standards for a sequencing run are not often met (based on picogreen quantification or Bioanalyzer analysis). Second, obtaining enough single cells for American Journal of Transplantation 2015; XX: 1–7
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statistically significant results may be challenging— primarily because of the cell-to-cell variability. Little work has been done to establish guidelines as to how many single cells are needed for comparison of gene signatures. Third, data analysis tools are relatively underdeveloped for single cell RNA sequencing: for example, how does one ensure enough ‘‘reads’’ were available from a single cell experiment to have confidence in the results? How does one generate and compare single cell transcriptional profiles, on a per gene basis and—more importantly— based on differential expression of combinations of tens of thousands of genes? For simpler transcriptomic technologies, some of these concerns have been addressed. Quality control and data analysis approaches are available for the Fluidigm BioMark platform, an instrument that can quantify up to 96 transcripts from each of 96 samples, which could be single cells. The approaches include methods for qualifying primers in the system’s unique microfluidic platform (36), and algorithms for distinguishing PCR failures from cells or genes with low or absent expression (38). In addition, new tools process data both in terms of the percentage of cells expressing a gene and in terms of the expression level per cell (38), and offer methods to visualize and cluster the data (39). Finally, methods have been developed to allow more sensitive discrimination of treatment groups (40).
Important Features of Single Cell Technologies High content The primary feature of new single cell technologies is that they can measure multiple parameters simultaneously, from the same sample. This is particularly useful given the remarkable diversity and complexity of leukocytes. Even among a specific subset of cells—memory T cells, for example—expression of hundreds of cytokines, signaling molecules, and cell surface molecules have been described in the literature. Measurement of a single marker rarely captures the entirety of a population; for example, IFNg expression only captures a fraction of T cells responding to antigen (many of these cells make other cytokines like IL2 and TNF (12). Importantly, this diversity shapes the immune response and results in qualitative differences that impact disease and survival. High content technologies also account for the limitations of leukocyte classification schemes. For example, in a large study HIV patients, we hypothesized that, early in HIV disease, central memory T cell levels (based on a phenotype that included CCR7) would predict how quickly an individual developed AIDS (41). Although this cell population had no predictive power, we found that elevated frequencies of a related cell type (CD127þ memory cells) did confer a survival advantage (41). Because both sets of markers were measured (along with others defining T cell American Journal of Transplantation 2015; XX: 1–7
maturity) in our high content PFC experiments, an immunological correlate could be found in the dataset, even though the primary hypothesis of the study failed. Thus, high content technologies provide a sample-sparing means to screen for new immunologic correlates with greater efficiency, and do not rely too heavily on cell classification dogma (42). Automated data analysis Robust data analysis methods are a critical need for most single cell, high content systems. A variety of approaches and tools are available, but systematic methods for evaluating these within a technology platform are mostly lacking. Recent work with polychromatic flow cytometry data provides a potential roadmap for objectively comparing algorithms. The FlowCAP project first compared manual data analysis methods for discriminating cell populations (‘‘gating’’) to automated algortihms (43). This was an important need for the field, since the inherent subjectivity of gating contributes variability in multi-site studies. FlowCAP-I (43) found that when various automated approaches are used together they identify the same cell populations as a panel of experts performing manual analysis. The next FlowCAP challenges searched for cellular correlates of external variables, such as clinical outcome or response to an antigen (43). A number of automated algorithms performed well in the challenges, presumably because the cell populations of interest were frequent and had strong associations with the external variable. The most recent challenge, FlowCAP-IV (44), tested the ability of algorithms to find weak correlates among infrequent cell populations. Of particular note, we found early correlates of HIV disease progression using the R-chyoptimyx algorithm that involved rare cells (45), defined by complex phenotypes, which we would not have thought to measure by manual analysis. This approach could be applied to other technology platforms. For example, algorithms might first be compared for their automated quality control or data preprocessing abilities. Thus, for transcriptomic tools, algorithms could be compared for their ability to distinguish a failed PCR reaction from a cell lacking expression of a particular gene. Algorithms could also be compared for their accuracy in setting thresholds that discriminate expressing from nonexpressing cells. Next, the ability to find cellular or mRNA correlates of immunity could be compared across tools. This comparison would be performed on a variety of ‘‘standard’’ datasets, covering a wide range of applications. These publicly available datasets would contain a large numbers of samples that had been collected and processed with rigorous quality control. For single cell transcriptomic technologies, there are not enough unique tools for algorithm comparisons as yet. 3
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However, tools for analyzing bulk cell populations by microarray and RNA sequencing may be readily adaptable for single cell work, and could be objectively evaluated as they are being adapted using a FlowCAP-style approach. For proteomics work using mass cytometry or imaging, a variety of automated algorithms have been reported in the literature (46,47), but none have been compared or evaluated on the same data set. Automated tools for high dimensional data analysis have three basic functions: (1) preprocessing data for quality control, (2) identifying, classifying, or grouping cell types (and in some cases graphically representing relationships), or (3) comparing data across groups or against a clinical outcome. A number of considerations should go into the development and use of these tools. First, methods for preprocessing data, such as normalization for interinstrument or run-to-run variation, should not replace careful instrument and experimental quality control and should report the degree of preprocessing required for each parameter in the dataset (i.e. the original variation in data collection). For interpretation of high dimension datasets, it is important to know which of the measured parameters are the noisiest. Second, methods for identifying, classifying, or grouping cells should report the minimum set of markers that allows manual identification of each cell population, so that the results of the automated analysis can be interpreted and confirmed easily. Finally, when comparing data across groups, it is critical to consider the problem of multiple comparisons. High dimensional datasets allow testing of hundreds of thousands, if not millions, of cell populations so the number of populations coincidentally related to an outcome can be very high at the p ¼ 0.05 threshold for significance. Bonferroni correction for multiple comparisons is generally too conservative, because this method assumes all tests are independent. In fact, in high dimension datasets, many parameters mark the same types of cells (thus, some of the comparisons performed overlap). For this reason, other methods of controlling false discovery rates (FDR) have emerged, including bootstrapping. This is an active area of research, with continually improving methods, so users of high dimension technology must collaborate with bioinformaticians and statisticians experienced in this area. Because of the complexity of multiple comparisons adjustments, users of high dimension technology should also consider including testing/training datasets and confirmation cohorts when designing studies. Ultrasensitivity The promise of ultrasensitive detection is another important feature of today’s single cell technologies. Sensitivity is a critical issue in immunoassays because the populations of interest are often rare. For example, antigen-specific T cells can be present at frequencies less than 0.1% of all T cells. 4
Recent advances in antibody tags have supported higher sensitivity measurements. For example, phycoerythrin (PE) and allophycocyanin (APC) are currently commonly used for flow cytometric analysis of low-density antigens and detection of infrequent populations. However, new dyes from the Brilliant family will be increasingly employed as well, providing more (and better) high sensitivity dyes. The higher sensitivity of these dyes arises from their conductive polymer properties, the discovery of which resulted in a Nobel Prize and spawned a wide variety of research and commercial applications (48). Dyes containing conductive polymers share electrons across their monomeric subunits during excitation. As a result, more electrons are harvested from the excitation, building up until amplified fluorescence is emitted. Remarkably, this signal is as bright, or brighter than, PE (14). The development of a number of these dyes, across the fluorescence spectrum, will have dramatic consequences for multiplexing with increased sensitivity. Finally, PCR-based technology may have the most sensitive detection capabilities. In a typical BioMark experiment, RNA transcripts undergo 18 rounds of cDNA amplification after reverse transcription; this represents a theoretical 218 (262 000) amplification of signal. Experiments defining the limit of detection show that an additional 28 rounds of PCR on the BioMark chip can quantify even single transcripts within a cell (36). In theory, these remarkably low limits of detection should provide exquisite sensitivity when comparing cell types or patient groups in BioMark studies. However, this advantage may often be abrogated by the low-throughput of the assay; the measurement of only 96 single cells at a time limits sample size and experimental power. Moreover, there is substantial stochastic variation in transcription, as evidenced by the wide distribution of mRNA expression observed even in cell line cultures (49).
Choosing the ‘‘Right’’ Technology: The Four Cellular States of Immunity With the emergence of new platforms, it can be difficult to determine the best technology for an experimental question. By defining the cellular states important in immunity, we can relate these to the properties of each technology; this can help identify which technologies are best suited for which applications. The resting state The resting state of immune cells is the most easily accessible to researchers, and is measured using a variety of technologies. Typically, the resting state is assessed by phenotypic analysis, and provides information on the lineage, maturity, and homing properties of the cell. In phenotyping studies, the correct discrimination of positive and negative staining (commonly known as ‘‘gating’’) is crucial; therefore, ultrasensitive detection should be a key American Journal of Transplantation 2015; XX: 1–7
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property of technologies used to analyze resting cells. In theory, highly sensitive PCR-based transcriptomic tools should provide a powerful phenotyping platform, but in practice, the presence of mRNA for a gene does not assure that the protein is expressed. This complicates biological interpretation, especially since transcription occurs in bursts (49), giving rise to seemingly stochastic variation in gene expression from cell to cell. Mass cytometry offers high content, but the low signal strength of many reagents is an important limitation. Thus, although the elegant studies introducing these technologies have applied them to phenotyping (50,51), polychromatic flow cytometry is likely to remain the most common and best-suited platform for this work, especially given recent reports of higher content and ultrasensitive dyes. The availability of automated data analysis algorithms is particularly important for phenotyping applications, because of the need for reliable gating and accurate population identification. Both mass cytometry and highlymultiplexed fluorescence cytometry are heavily reliant on automated tools, because the high content precludes examination of all cell populations in the dataset manually. A number of innovative tools have been developed (46,47), but users should take care employing them, since there is no gold standard to test the veracity of population identification. Cells types may be defined in a multidimensional space that is impossible to recreate manually, and classification of cells may be the consequence of undetected gating error or experimental artefact. Perhaps most troubling, visualization modules for automated data analysis tools provide no indication of the statistical confidence in the clustering or grouping of cells (nor reproducibility). For example, an algorithm may not cluster a rare cell population (like an antigen-specific cell population) reliably because there are too few events for statistical power; this is not typically reported in the visualization. With automated tools, one must also consider whether to employ a method that identifies all possible cell populations in the sample (considering multiple comparisons issues), or whether to use an approach that reduces the data to unique clusters (that ignore some markers). The latter approach provides simpler answers to experimental questions; this has interpretive advantages, provided the clusters reported are correct and biological meaningful. However, data reduction discards information that may be at the cusp of statistical significance, but might have provided more meaningful biological information than the markers ultimately reported. The activated state Static cells do not drive the immune response, so the activation and functional properties of leukocytes is often of interest. A number of functional assays are available to assess cytokine production (12), signaling (52), and cytotoxic potential (53). The utility of cytometry for American Journal of Transplantation 2015; XX: 1–7
assessing the activated state has been well-documented, with a number of studies reporting clinical correlations between levels of cytokine expressing cells (particularly polyfunctional cells (54)) or signaling networks (55) and clinical disease. Given the enormous variety of molecules that are modulated during an immune response, the high content of transcriptomic platforms may be particularly useful. However, data analysis tools are not available for the classification or grouping of cells based on expression of dozens/hundreds of transcripts. Even with such tools, the sample sizes in a typical single cell transcriptomics experiment may be too small to derive multidimensional signatures (or combinatorial phenotypes) with any statistical confidence. Still, the analysis of small sets of sortpurified cells (‘‘nanoarray’’ (36)) can provide lists of genes up- or down-regulated with an immune response. These may provide new targets for flow and mass cytometry assays. Dynamic states Together, assessment of cell phenotype and function comprise the vast majority of cytometry experiments. However, these technologies take ‘‘snapshot’’ measurements, providing information about the state of the cell only at the time of the assay. In contrast, immune responses are dynamic processes, characterized by the up- and downregulation of receptors, patterns of cytokine production, and cellular proliferation. Because each cell is interrogated only once by flow cytometry or mass cytometry, these technologies cannot measure dynamic processes at the cell level. In contrast, newer imaging-based technologies, such as microtools, offer the ability to examine the kinetics of cell secretion (56). Microtools consist of fabricated chips with nanoliter-scale channels and wells. When cell suspensions are introduced into a microtool, single cells distribute across the wells and are exposed to antigencontaining medium. The stimulated cells then release cytokines, which can be assayed by overlaying a slide coated with capture antibodies. The slide is then treated with detection antibodies and imaged, for quantitative measurement of cytokine production. Since the secreting cell is not disturbed, fresh capture slides can be overlaid at various time points, resulting in longitudinal measurements of cytokine production from a single cell. These tools have demonstrated that single cell cytokine profiles integrated over a time course are heterogeneous (32,56,57), and can be used to predict cellular differentiation states. This demonstrates the value of high content technologies that measure cytokines and phenotypic markers simultaneously, and dynamic measurements. A similar technology captures two cells within a well, in order to examine the kinetics of cell-to-cell interactions, like cytotoxic killing (58). 5
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The interactive state The techniques described thus far consider the characteristics of cells within suspension; however, immune responses in vivo are organized and launched by cell-tocell interactions. Thus, technologies that query cells within tissues may be particularly powerful. Recently, mass cytometry was adapted to allow imaging at a subcellular resolution. Using breast cancer tissue, Giesen et al (59) performed immunocytochemical and immunohistochemical staining with antibodies tagged to rare earth metal isotopes. The stained tissue was then placed in a laser ablation chamber that provided spot-by-spot and line-by-line ‘‘addresses’’ within the tissue. Next, an ultraviolet laser ablated the tissue, releasing isotopic ions for collection and quantitation by the adapted mass cytometer. The data from the 32 metal isotopes from each address were overlaid in image processing software, and the expression of each marker on single cells was extracted. The method was validated against classical single-plex immunohistochemical staining and dualplex immunofluorescence microscopy. The work revealed the heterogeneity of signaling molecules and pathways within tumor tissue (59), confirming findings from past studies of single cell suspensions. Although this work was mostly limited to signaling molecules, the potential of imaging mass cytometry was clear: the technology can reveal differential expression of resting and activated cell markers in the context of tissue architecture, without the destructive processing necessary to make single cell suspensions. Moreover, with the high content of mass cytometry, a variety of cell populations could theoretically be queried at once (in great depth) allowing analysis of cell-to-cell interactions in situ. In fact, an analogous multiparameter fluorescence-based technology—known as ‘‘histo-cytometry’’ (60)—recently demonstrated that different dendritic cell subsets preferentially reside in different areas of draining lymph nodes, creating discrete anatomical microdomains that contain different types of T cells. Notably, histocytometry studies can be performed with more commonly available equipment than imaging mass cytometry.
Conclusions Leukocytes express a wide array of proteins, associated with an enormous variety of maturational, homing, and/ or functional properties. Our understanding of the cell subsets that express these proteins has grown tremendously over the past 15 years, largely because of the increased adoption of polychromatic flow cytometry technology. Recently, a number of new technologies have been developed to examine the immune response. These technologies have specific virtues, but also differ in content, data analysis methodology, and sensitivity. By considering the types of cells important in immunological responses against the properties of various tools, the best tool for a given experimental setting can be chosen. 6
Disclosure The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
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41. Ganesan A, Chattopadhyay PK, Brodie TM, et al. Immunologic and virologic events in early HIV infection predict subsequent rate of progression. J Infect Dis 2010; 201: 272–284. 42. Chattopadhyay PK, Roederer M. Good cell, bad cell: Flow cytometry reveals T-cell subsets important in HIV disease. Cytometry A 2010; 77: 614–622. 43. Aghaeepour N, Finak G, Flow CAPC, et al. Critical assessment of automated flow cytometry data analysis techniques. Nat Methods 2013; 10: 228–238. 44. Aghaeepour N, Chattopadhyay PK, Chikina M, et al. A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry 2014 (Submitted). 45. Aghaeepour N, Brodie T, Ganesan A, et al. Early Correlates of HIV-1 disease progression within complex, high dimensional combinations of maturation and functional markers. J Immunol 2014 (Submitted). 46. Qiu P, Simonds EF, Bendall SC, et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 2011; 29: 886–891. 47. Amir el AD, Davis KL, Tadmor MD, et al. ViSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 2013; 31: 545–552. 48. Balint R, Cassidy NJ, Cartmell SH. Conductive polymers: Towards a smart biomaterial for tissue engineering. Acta Biomater 2014; 10: 2341–2353. 49. Livak KJ, Wills QF, Tipping AJ, et al. Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells. Methods 2013; 59: 71–79. 50. Bendall SC, Simonds EF, Qiu P, et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 2011; 332: 687–696. 51. Newell EW, Sigal N, Bendall SC, Nolan GP, Davis MM. Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8þ T cell phenotypes. Immunity 2012; 36: 142–152. 52. Krutzik PO, Irish JM, Nolan GP, Perez OD. Analysis of protein phosphorylation and cellular signaling events by flow cytometry: Techniques and clinical applications. Clin Immunol 2004; 110: 206–221. 53. Zaritskaya L, Shurin MR, Sayers TJ, Malyguine AM. New flow cytometric assays for monitoring cell-mediated cytotoxicity. Expert Rev Vaccines 2010; 9: 601–616. 54. Makedonas G, Betts MR. Polyfunctional analysis of human t cell responses: Importance in vaccine immunogenicity and natural infection. Springer Semin Immunopathol 2006; 28: 209–219. 55. Irish JM, Myklebust JH, Alizadeh AA, et al. B-cell signaling networks reveal a negative prognostic human lymphoma cell subset that emerges during tumor progression. Proc Natl Acad Sci USA 2010; 107: 12747–12754. 56. Han Q, Bagheri N, Bradshaw EM, Hafler DA, Lauffenburger DA, Love JC. Polyfunctional responses by human T cells result from sequential release of cytokines. Proc Natl Acad Sci USA 2012; 109: 1607–1612. 57. Han Q, Bradshaw EM, Nilsson B, Hafler DA, Love JC. Multidimensional analysis of the frequencies and rates of cytokine secretion from single cells by quantitative microengraving. Lab Chip 2010; 10: 1391–1400. 58. Varadarajan N, Kwon DS, Law KM, et al. Rapid, efficient functional characterization and recovery of HIV-specific human CD8þ T cells using microengraving. Proc Natl Acad Sci USA 2012; 109: 3885–3890. 59. Giesen C, Wang HA, Schapiro D, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods 2014; 11: 417–422. 60. Gerner MY, Kastenmuller W, Ifrim I, Kabat J, Germain RN. Histocytometry: A method for highly multiplex quantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes. Immunity 2012; 37: 364–376.
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Copyright 2015 The American Society of Transplantation and the American Society of Transplant Surgeons doi: 10.1111/ajt.13193
A Mine Is a Terrible Thing to Waste: High Content, Single Cell Technologies for Comprehensive Immune Analysis P. K. Chattopadhyay* and M. Roederer Vaccine Research Center, National Institutes of Health, Bethesda, MD Corresponding author: Pratip K. Chattopadhyay, [email protected]
In recent years, an incredible variety of single cell technologies have become available to analyze immune responses. These technologies include polychromatic flow cytometry, mass cytometry, highly multiplexed single cell qPCR, RNA sequencing, microtools, and high-resolution imaging. In this article, we review these platforms, describing their power and limitations for comprehensive analysis of the immune system. We relate the properties of these technologies to the various cellular states relevant to an immune response, in order to address which technologies are most appropriate for which settings. Abbreviations: AIDS, acquired immunodeficiency syndrome; CyTOF, cytometry by time of flight; FlowCAP, flow cytometry critical assessment of population identification methods; HIV, human immunodeficiency virus; qPCR, quantitative real-time polymerase chain reaction; RNAseq, RNA sequencing Received 05 November 2014, revised 22 December 2014 and accepted for publication 26 December 2014
Introduction For decades, flow cytometry has been the dominant technology for interrogating individual cells (1,2). The ability to make multiparameter measurements on single cells, at rates approaching 50 000 cells/s, has revealed remarkable diversity in leukocyte phenotype and function. Results have been far-reaching, feeding a broad set of disciplines ranging from fundamental biology to medicine and drug development (3). In recent years, however, there has been a revolution in the technology used to measure cells. ‘‘Classical’’ flow cytometry has become increasingly multiparametric (4–6), and in parallel, new imaging, molecular, and mass
spectrometry-based tools have emerged. A common feature of these tools is the enormous data content they provide, but the problem has become: How can the high content data available in cells be mined most effectively? Here, we review the particular challenges researchers face with these new approaches, along with a discussion of emerging tools.
The Miner’s Toolbox: Available Single Cell Technologies Polychromatic flow cytometry Since the 1970s, flow cytometry has been the leading single cell technology because it is high-throughput, allows live cell sorting, and is quantitative. With each decade, the introduction of new classes of fluorescent dyes has geometrically increased the number of parameters that could be simultaneously measured, from 2 to (most recently) 30 (7). One and two-color cytometry began with the use of small organic fluorochromes (fluorescein, rhodamine) in the 1970s, first applied to distinguish, e.g. B and T cell lineages (8,9). The 1980s saw the advent of phycobiliprotein dyes: incredibly bright, protein-based fluorochromes that enabled 4-color analysis, used to identify major subsets of lymphocytes (such as differentiation stages) (10). The introduction of a variety of resonance energy tandem dyes in the 1990s allowed 8-color measurements that distinguished fine memory T cell populations, revealing, e.g. TH1/TH2 cytokine polarization of cells (11). In the 2000s, the development of quantum dots brought cytometry to 20-parameter capability (6), allowing highlymultiplexed detection/enumeration of antigen-specific T cells and the identification of cells capable of making multiple cytokines simultaneously (12). In the most recent decade, two technologies have pushed the ceiling beyond 30 parameters. Cytometry by time-offlight (CyTOF) uses mass-spectrometry based detection combined with lanthanide-labeled reagents (13), and has been used for as much as 40-parameter measurements (without the possibility of cell sorting); this technology is discussed more, below. On the fluorescence side, the ‘‘Brilliant’’ family of dyes was introduced (14), enabling 30 (this year [7]) and perhaps 40 (in the next 2 years) parameter fluorescence analysis (compatible with live-cell sorting). 1
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The extraordinary brightness (14) of these dyes allows for easy, and highly sensitive antibody multiplexing, a key challenge in polychromatic flow cytometry. Although spectral overlap of fluorochromes is commonly thought to be the primary problem in panel development, the main reason why panels fail is because low-level protein expression cannot be resolved, a problem exacerbated by Poisson errors in photon counting leading to ‘‘spillover spreading error’’ (15). This impact is minimized when antibodies against low-expression proteins are paired with very bright dyes, such as Brilliant Violet 421 (and other similar dyes in development). For this reason, the Brilliant family of dyes holds great promise for simplifying panel design. In the near future, tools that calculate the spreading error between dyes and detectors will further simplify panel design by helping researchers to pair reagents (monoclonal antibodies) with dyes depending on the properties and requirements of each. Such tools may be used to predict the likelihood that a panel will work well before extensive experiments are performed; they will be based simply on antibody titration data and measures of instrument performance (16). The mathematics underlying such tools have been described (17), and incorporated into commercial software to report the spillover/spreading (SSM) matrix. Ultimately, these measures will automate much of the panel design process. In the meantime, previously developed panels are now reported in a new print and online resource (18), ‘‘Optimized Muliticolor Immunophenotyping Panels’’ (OMIPs). There are currently 22 unique panels described in this publication format, to query, e.g. antigenspecific cells (19–25), chemokine receptors (26), B cells (27), innate cells (28), regulatory T cells (29,30), and gamma-delta T cells (31). Some reagents and panels developed for polychromatic flow cytometry may be used on other platforms as well. For example, on the Amnis Imagestream, fluorescence flow cytometry is combined with microscopic imaging to visualize protein distribution within a cell. These instruments are particularly useful for monitoring cellular morphology, receptor capping, or phagocytosis (32). Current versions can be configured with up to seven lasers, for detection of 12 fluorescence parameters, including the same quantum dot and brilliant violet dyes used in flow cytometry. These dyes offer particular advantages over other imaging dyes because they can be multiplexed easily, are brighter (14), and exhibit reduced photobleaching (33). Finally, in recent years spectral flow cytometry systems have been commercialized (34). These instruments collect all the fluorescent light from stained cells, and then use algorithms to un-mix (or deconvolute (34)) this signal into the component signal from each fluorochrome (a process identical to compensation on existing cytometers). The primary advantage of spectral flow cytometers is that there is no need to optimize nor change detector filters depending 2
on the fluorochromes used. However, for a given configuration with optimized filters, there is no theoretical advantage. Mass cytometry Recently, inductively-coupled mass spectrometry was adapted for cytometry (13). This platform is the basis for CyTOF instrumentation that measures cells tagged with antibodies conjugated to rare isotopes of heavy metal elements (rather than fluorescent dyes). In principle, because these rare isotopes are not present in cells, biological background should be very low, allowing exquisite resolution of low-level protein expression (practically, the experimental background from nonspecific binding and so on becomes the limiting factor for sensitivity). Moreover, there should be minimal overlap between isotopic signals, allowing complete freedom in designing multiplexed panels. Finally, with the availability of over 40 unique isotopes, the high content of this platform is as yet unparalleled. However, in practice, there are some limitations to the advantages of mass cytometry. First, cell staining can be weak for some antibodies, and nonspecific antibody binding can contribute substantial signal (32), diminishing the advantages afforded by low system background. Second, although there should be essentially no overlap between isotopic elements, in practice there are impurities in the raw materials used as tags for antibodies (35). Moreover, the formation of oxides can shift signals 16 mass units, causing overlap between probes, leading to the need for signal deconvolution (i.e. compensation) (35). Finally, CyTOF is generally low-throughput (with collection of one sample every 5–7 min) and not compatible with live cell sorting (because cells are destroyed as they are introduced into the instrument). Despite these caveats, the future of mass cytometry for certain applications (see below) is particularly bright. Transcriptomics Analysis of gene expression has been possible for many years on bulk cell populations, using microarrays (for relative, multiplexed analysis) or qPCR (for quantitative analysis of a single gene). Recently, methods for single cell gene expression have been optimized, allowing multiplexed qPCR (36) or sequencing of RNA transcripts (RNAseq) (37). In terms of content provided, RNAseq is unmatched. In principle, every transcript within a single cell can be quantified and mapped to a gene. Thus, there is no selection bias in an RNA sequencing experiment: results are not dependent on the investigator’s choice of probes, as is the case with other technologies. However, there are some major challenges associated with applying this technology to studies of lymphocytes. First, the small size of lymphocytes provides very little starting RNA, so minimum quality standards for a sequencing run are not often met (based on picogreen quantification or Bioanalyzer analysis). Second, obtaining enough single cells for American Journal of Transplantation 2015; XX: 1–7
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statistically significant results may be challenging— primarily because of the cell-to-cell variability. Little work has been done to establish guidelines as to how many single cells are needed for comparison of gene signatures. Third, data analysis tools are relatively underdeveloped for single cell RNA sequencing: for example, how does one ensure enough ‘‘reads’’ were available from a single cell experiment to have confidence in the results? How does one generate and compare single cell transcriptional profiles, on a per gene basis and—more importantly— based on differential expression of combinations of tens of thousands of genes? For simpler transcriptomic technologies, some of these concerns have been addressed. Quality control and data analysis approaches are available for the Fluidigm BioMark platform, an instrument that can quantify up to 96 transcripts from each of 96 samples, which could be single cells. The approaches include methods for qualifying primers in the system’s unique microfluidic platform (36), and algorithms for distinguishing PCR failures from cells or genes with low or absent expression (38). In addition, new tools process data both in terms of the percentage of cells expressing a gene and in terms of the expression level per cell (38), and offer methods to visualize and cluster the data (39). Finally, methods have been developed to allow more sensitive discrimination of treatment groups (40).
Important Features of Single Cell Technologies High content The primary feature of new single cell technologies is that they can measure multiple parameters simultaneously, from the same sample. This is particularly useful given the remarkable diversity and complexity of leukocytes. Even among a specific subset of cells—memory T cells, for example—expression of hundreds of cytokines, signaling molecules, and cell surface molecules have been described in the literature. Measurement of a single marker rarely captures the entirety of a population; for example, IFNg expression only captures a fraction of T cells responding to antigen (many of these cells make other cytokines like IL2 and TNF (12). Importantly, this diversity shapes the immune response and results in qualitative differences that impact disease and survival. High content technologies also account for the limitations of leukocyte classification schemes. For example, in a large study HIV patients, we hypothesized that, early in HIV disease, central memory T cell levels (based on a phenotype that included CCR7) would predict how quickly an individual developed AIDS (41). Although this cell population had no predictive power, we found that elevated frequencies of a related cell type (CD127þ memory cells) did confer a survival advantage (41). Because both sets of markers were measured (along with others defining T cell American Journal of Transplantation 2015; XX: 1–7
maturity) in our high content PFC experiments, an immunological correlate could be found in the dataset, even though the primary hypothesis of the study failed. Thus, high content technologies provide a sample-sparing means to screen for new immunologic correlates with greater efficiency, and do not rely too heavily on cell classification dogma (42). Automated data analysis Robust data analysis methods are a critical need for most single cell, high content systems. A variety of approaches and tools are available, but systematic methods for evaluating these within a technology platform are mostly lacking. Recent work with polychromatic flow cytometry data provides a potential roadmap for objectively comparing algorithms. The FlowCAP project first compared manual data analysis methods for discriminating cell populations (‘‘gating’’) to automated algortihms (43). This was an important need for the field, since the inherent subjectivity of gating contributes variability in multi-site studies. FlowCAP-I (43) found that when various automated approaches are used together they identify the same cell populations as a panel of experts performing manual analysis. The next FlowCAP challenges searched for cellular correlates of external variables, such as clinical outcome or response to an antigen (43). A number of automated algorithms performed well in the challenges, presumably because the cell populations of interest were frequent and had strong associations with the external variable. The most recent challenge, FlowCAP-IV (44), tested the ability of algorithms to find weak correlates among infrequent cell populations. Of particular note, we found early correlates of HIV disease progression using the R-chyoptimyx algorithm that involved rare cells (45), defined by complex phenotypes, which we would not have thought to measure by manual analysis. This approach could be applied to other technology platforms. For example, algorithms might first be compared for their automated quality control or data preprocessing abilities. Thus, for transcriptomic tools, algorithms could be compared for their ability to distinguish a failed PCR reaction from a cell lacking expression of a particular gene. Algorithms could also be compared for their accuracy in setting thresholds that discriminate expressing from nonexpressing cells. Next, the ability to find cellular or mRNA correlates of immunity could be compared across tools. This comparison would be performed on a variety of ‘‘standard’’ datasets, covering a wide range of applications. These publicly available datasets would contain a large numbers of samples that had been collected and processed with rigorous quality control. For single cell transcriptomic technologies, there are not enough unique tools for algorithm comparisons as yet. 3
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However, tools for analyzing bulk cell populations by microarray and RNA sequencing may be readily adaptable for single cell work, and could be objectively evaluated as they are being adapted using a FlowCAP-style approach. For proteomics work using mass cytometry or imaging, a variety of automated algorithms have been reported in the literature (46,47), but none have been compared or evaluated on the same data set. Automated tools for high dimensional data analysis have three basic functions: (1) preprocessing data for quality control, (2) identifying, classifying, or grouping cell types (and in some cases graphically representing relationships), or (3) comparing data across groups or against a clinical outcome. A number of considerations should go into the development and use of these tools. First, methods for preprocessing data, such as normalization for interinstrument or run-to-run variation, should not replace careful instrument and experimental quality control and should report the degree of preprocessing required for each parameter in the dataset (i.e. the original variation in data collection). For interpretation of high dimension datasets, it is important to know which of the measured parameters are the noisiest. Second, methods for identifying, classifying, or grouping cells should report the minimum set of markers that allows manual identification of each cell population, so that the results of the automated analysis can be interpreted and confirmed easily. Finally, when comparing data across groups, it is critical to consider the problem of multiple comparisons. High dimensional datasets allow testing of hundreds of thousands, if not millions, of cell populations so the number of populations coincidentally related to an outcome can be very high at the p ¼ 0.05 threshold for significance. Bonferroni correction for multiple comparisons is generally too conservative, because this method assumes all tests are independent. In fact, in high dimension datasets, many parameters mark the same types of cells (thus, some of the comparisons performed overlap). For this reason, other methods of controlling false discovery rates (FDR) have emerged, including bootstrapping. This is an active area of research, with continually improving methods, so users of high dimension technology must collaborate with bioinformaticians and statisticians experienced in this area. Because of the complexity of multiple comparisons adjustments, users of high dimension technology should also consider including testing/training datasets and confirmation cohorts when designing studies. Ultrasensitivity The promise of ultrasensitive detection is another important feature of today’s single cell technologies. Sensitivity is a critical issue in immunoassays because the populations of interest are often rare. For example, antigen-specific T cells can be present at frequencies less than 0.1% of all T cells. 4
Recent advances in antibody tags have supported higher sensitivity measurements. For example, phycoerythrin (PE) and allophycocyanin (APC) are currently commonly used for flow cytometric analysis of low-density antigens and detection of infrequent populations. However, new dyes from the Brilliant family will be increasingly employed as well, providing more (and better) high sensitivity dyes. The higher sensitivity of these dyes arises from their conductive polymer properties, the discovery of which resulted in a Nobel Prize and spawned a wide variety of research and commercial applications (48). Dyes containing conductive polymers share electrons across their monomeric subunits during excitation. As a result, more electrons are harvested from the excitation, building up until amplified fluorescence is emitted. Remarkably, this signal is as bright, or brighter than, PE (14). The development of a number of these dyes, across the fluorescence spectrum, will have dramatic consequences for multiplexing with increased sensitivity. Finally, PCR-based technology may have the most sensitive detection capabilities. In a typical BioMark experiment, RNA transcripts undergo 18 rounds of cDNA amplification after reverse transcription; this represents a theoretical 218 (262 000) amplification of signal. Experiments defining the limit of detection show that an additional 28 rounds of PCR on the BioMark chip can quantify even single transcripts within a cell (36). In theory, these remarkably low limits of detection should provide exquisite sensitivity when comparing cell types or patient groups in BioMark studies. However, this advantage may often be abrogated by the low-throughput of the assay; the measurement of only 96 single cells at a time limits sample size and experimental power. Moreover, there is substantial stochastic variation in transcription, as evidenced by the wide distribution of mRNA expression observed even in cell line cultures (49).
Choosing the ‘‘Right’’ Technology: The Four Cellular States of Immunity With the emergence of new platforms, it can be difficult to determine the best technology for an experimental question. By defining the cellular states important in immunity, we can relate these to the properties of each technology; this can help identify which technologies are best suited for which applications. The resting state The resting state of immune cells is the most easily accessible to researchers, and is measured using a variety of technologies. Typically, the resting state is assessed by phenotypic analysis, and provides information on the lineage, maturity, and homing properties of the cell. In phenotyping studies, the correct discrimination of positive and negative staining (commonly known as ‘‘gating’’) is crucial; therefore, ultrasensitive detection should be a key American Journal of Transplantation 2015; XX: 1–7
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property of technologies used to analyze resting cells. In theory, highly sensitive PCR-based transcriptomic tools should provide a powerful phenotyping platform, but in practice, the presence of mRNA for a gene does not assure that the protein is expressed. This complicates biological interpretation, especially since transcription occurs in bursts (49), giving rise to seemingly stochastic variation in gene expression from cell to cell. Mass cytometry offers high content, but the low signal strength of many reagents is an important limitation. Thus, although the elegant studies introducing these technologies have applied them to phenotyping (50,51), polychromatic flow cytometry is likely to remain the most common and best-suited platform for this work, especially given recent reports of higher content and ultrasensitive dyes. The availability of automated data analysis algorithms is particularly important for phenotyping applications, because of the need for reliable gating and accurate population identification. Both mass cytometry and highlymultiplexed fluorescence cytometry are heavily reliant on automated tools, because the high content precludes examination of all cell populations in the dataset manually. A number of innovative tools have been developed (46,47), but users should take care employing them, since there is no gold standard to test the veracity of population identification. Cells types may be defined in a multidimensional space that is impossible to recreate manually, and classification of cells may be the consequence of undetected gating error or experimental artefact. Perhaps most troubling, visualization modules for automated data analysis tools provide no indication of the statistical confidence in the clustering or grouping of cells (nor reproducibility). For example, an algorithm may not cluster a rare cell population (like an antigen-specific cell population) reliably because there are too few events for statistical power; this is not typically reported in the visualization. With automated tools, one must also consider whether to employ a method that identifies all possible cell populations in the sample (considering multiple comparisons issues), or whether to use an approach that reduces the data to unique clusters (that ignore some markers). The latter approach provides simpler answers to experimental questions; this has interpretive advantages, provided the clusters reported are correct and biological meaningful. However, data reduction discards information that may be at the cusp of statistical significance, but might have provided more meaningful biological information than the markers ultimately reported. The activated state Static cells do not drive the immune response, so the activation and functional properties of leukocytes is often of interest. A number of functional assays are available to assess cytokine production (12), signaling (52), and cytotoxic potential (53). The utility of cytometry for American Journal of Transplantation 2015; XX: 1–7
assessing the activated state has been well-documented, with a number of studies reporting clinical correlations between levels of cytokine expressing cells (particularly polyfunctional cells (54)) or signaling networks (55) and clinical disease. Given the enormous variety of molecules that are modulated during an immune response, the high content of transcriptomic platforms may be particularly useful. However, data analysis tools are not available for the classification or grouping of cells based on expression of dozens/hundreds of transcripts. Even with such tools, the sample sizes in a typical single cell transcriptomics experiment may be too small to derive multidimensional signatures (or combinatorial phenotypes) with any statistical confidence. Still, the analysis of small sets of sortpurified cells (‘‘nanoarray’’ (36)) can provide lists of genes up- or down-regulated with an immune response. These may provide new targets for flow and mass cytometry assays. Dynamic states Together, assessment of cell phenotype and function comprise the vast majority of cytometry experiments. However, these technologies take ‘‘snapshot’’ measurements, providing information about the state of the cell only at the time of the assay. In contrast, immune responses are dynamic processes, characterized by the up- and downregulation of receptors, patterns of cytokine production, and cellular proliferation. Because each cell is interrogated only once by flow cytometry or mass cytometry, these technologies cannot measure dynamic processes at the cell level. In contrast, newer imaging-based technologies, such as microtools, offer the ability to examine the kinetics of cell secretion (56). Microtools consist of fabricated chips with nanoliter-scale channels and wells. When cell suspensions are introduced into a microtool, single cells distribute across the wells and are exposed to antigencontaining medium. The stimulated cells then release cytokines, which can be assayed by overlaying a slide coated with capture antibodies. The slide is then treated with detection antibodies and imaged, for quantitative measurement of cytokine production. Since the secreting cell is not disturbed, fresh capture slides can be overlaid at various time points, resulting in longitudinal measurements of cytokine production from a single cell. These tools have demonstrated that single cell cytokine profiles integrated over a time course are heterogeneous (32,56,57), and can be used to predict cellular differentiation states. This demonstrates the value of high content technologies that measure cytokines and phenotypic markers simultaneously, and dynamic measurements. A similar technology captures two cells within a well, in order to examine the kinetics of cell-to-cell interactions, like cytotoxic killing (58). 5
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The interactive state The techniques described thus far consider the characteristics of cells within suspension; however, immune responses in vivo are organized and launched by cell-tocell interactions. Thus, technologies that query cells within tissues may be particularly powerful. Recently, mass cytometry was adapted to allow imaging at a subcellular resolution. Using breast cancer tissue, Giesen et al (59) performed immunocytochemical and immunohistochemical staining with antibodies tagged to rare earth metal isotopes. The stained tissue was then placed in a laser ablation chamber that provided spot-by-spot and line-by-line ‘‘addresses’’ within the tissue. Next, an ultraviolet laser ablated the tissue, releasing isotopic ions for collection and quantitation by the adapted mass cytometer. The data from the 32 metal isotopes from each address were overlaid in image processing software, and the expression of each marker on single cells was extracted. The method was validated against classical single-plex immunohistochemical staining and dualplex immunofluorescence microscopy. The work revealed the heterogeneity of signaling molecules and pathways within tumor tissue (59), confirming findings from past studies of single cell suspensions. Although this work was mostly limited to signaling molecules, the potential of imaging mass cytometry was clear: the technology can reveal differential expression of resting and activated cell markers in the context of tissue architecture, without the destructive processing necessary to make single cell suspensions. Moreover, with the high content of mass cytometry, a variety of cell populations could theoretically be queried at once (in great depth) allowing analysis of cell-to-cell interactions in situ. In fact, an analogous multiparameter fluorescence-based technology—known as ‘‘histo-cytometry’’ (60)—recently demonstrated that different dendritic cell subsets preferentially reside in different areas of draining lymph nodes, creating discrete anatomical microdomains that contain different types of T cells. Notably, histocytometry studies can be performed with more commonly available equipment than imaging mass cytometry.
Conclusions Leukocytes express a wide array of proteins, associated with an enormous variety of maturational, homing, and/ or functional properties. Our understanding of the cell subsets that express these proteins has grown tremendously over the past 15 years, largely because of the increased adoption of polychromatic flow cytometry technology. Recently, a number of new technologies have been developed to examine the immune response. These technologies have specific virtues, but also differ in content, data analysis methodology, and sensitivity. By considering the types of cells important in immunological responses against the properties of various tools, the best tool for a given experimental setting can be chosen. 6
Disclosure The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
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