The cellular complexity and functional diversity of the human immune system necessitate the use of high-dimensional single-cell tools to uncover its role in multifaceted diseases such as rheumatic diseases, as well as other autoimmune and inflammatory disorders. Proteomic technologies that use elemental (heavy metal) reporter ions, such as mass cytometry (also known as CyTOF) and analogous high-dimensional imaging approaches (including multiplexed ion beam imaging (MIBI) and imaging mass cytometry (IMC)), have been developed from their low-dimensional counterparts, flow cytometry and immunohistochemistry, to meet this need. A growing number of studies have been published that use these technologies to identify functional biomarkers and therapeutic targets in rheumatic diseases, but the full potential of their application to rheumatic disease research has yet to be fulfilled. This Review introduces the underlying technologies for high-dimensional immune monitoring and discusses aspects necessary for their successful implementation, including study design principles, analytical tools and future developments for the field of rheumatology.
Immune monitoring of human cells using systems immunology approaches has the potential to produce new insights into pathological processes and therapeutic opportunities for rheumatic disease research.
Proteomic approaches that use elemental (heavy metal) reporter ions, such as mass cytometry and high-dimensional imaging techniques, might be of value for the study of a wide variety of clinical samples.
Mass cytometry enables in-depth analysis of the phenotype and functional state of immune cells at the single-cell level.
High-dimensional imaging techniques use concepts analogous to mass cytometry to image cells in their histological context, providing spatial and cell–cell interaction information.
A combination of these technologies with data-driven analytical approaches can give predictive insights into disease mechanisms for rheumatic diseases.
Subscribe to Journal
Get full journal access for 1 year
only $17.75 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Robinson, W. H. & Mao, R. Technological advances transforming rheumatology. Nat. Rev. Rheumatol. 11, 626–628 (2015).
Hartmann, F. J. et al. Comprehensive immune monitoring of clinical trials to advance human immunotherapy. Cell Rep. 28, 819–831.e4 (2019).
von Herrath, M. G. & Nepom, G. T. Lost in translation: barriers to implementing clinical immunotherapeutics for autoimmunity. J. Exp. Med. 202, 1159–1162 (2005).
Davis, M. M. A prescription for human immunology. Immunity 29, 835–838 (2008).
Davis, M. M. & Brodin, P. Rebooting human immunology. Annu. Rev. Immunol. 36, 843–864 (2018).
Robinson, W. H. & Mao, R. Biomarkers to guide clinical therapeutics in rheumatology? Curr. Opin. Rheumatol. 28, 168–175 (2016).
Ermann, J., Rao, D. A., Teslovich, N. C., Brenner, M. B. & Raychaudhuri, S. Immune cell profiling to guide therapeutic decisions in rheumatic diseases. Nat. Rev. Rheumatol. 11, 541–551 (2015).
Gaudillière, B. et al. Clinical recovery from surgery correlates with single-cell immune signatures. Sci. Transl Med. 6, 255ra131 (2014).
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
Good, Z. et al. Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nat. Med. 24, 474–483 (2018).
Nair, N. et al. Mass cytometry as a platform for the discovery of cellular biomarkers to guide effective rheumatic disease therapy. Arthritis Res. Ther. 17, 127 (2015).
Maecker, H. T., McCoy, J. P. & Nussenblatt, R. Standardizing immunophenotyping for the Human Immunology Project. Nat. Rev. Immunol. 12, 191–200 (2012).
Hao, Y., O’Neill, P., Naradikian, M. S., Scholz, J. L. & Cancro, M. P. A B-cell subset uniquely responsive to innate stimuli accumulates in aged mice. Blood 118, 1294–1304 (2011).
Chattopadhyay, P. K. & Roederer, M. Good cell, bad cell: flow cytometry reveals T-cell subsets important in HIV disease. Cytometry A 77, 614–622 (2010).
Bandura, D. R. et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81, 6813–6822 (2009).
Ornatsky, O. et al. Highly multiparametric analysis by mass cytometry. J. Immunol. Methods 361, 1–20 (2010).
Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).
Lou, X. et al. Polymer-based elemental tags for sensitive bioassays. Angew. Chem. Int. Ed. 46, 6111–6114 (2007).
Ornatsky, O. I. et al. Study of cell antigens and intracellular DNA by identification of element-containing labels and metallointercalators using inductively coupled plasma mass spectrometry. Anal. Chem. 80, 2539–2547 (2008).
Ornatsky, O. I. et al. Development of analytical methods for multiplex bio-assay with inductively coupled plasma mass spectrometry. J. Anal. At. Spectrom. 23, 463 (2008).
Majonis, D. et al. Synthesis of a functional metal-chelating polymer and steps toward quantitative mass cytometry bioassays. Anal. Chem. 82, 8961–8969 (2010).
Mei, H. E., Leipold, M. D. & Maecker, H. T. Platinum-conjugated antibodies for application in mass cytometry. Cytometry A 89, 292–300 (2016).
Han, G. et al. Atomic mass tag of bismuth-209 for increasing the immunoassay multiplexing capacity of mass cytometry. Cytometry A 91, 1150–1163 (2017).
Han, G., Spitzer, M. H., Bendall, S. C., Fantl, W. J. & Nolan, G. P. Metal-isotope-tagged monoclonal antibodies for high-dimensional mass cytometry. Nat. Protoc. 13, 2121–2148 (2018).
Wagner, J. et al. A single-cell atlas of the tumor and immune ecosystem of human breast cancer. Cell 177, 1330–1345.e18 (2019).
Chang, Q. et al. Single-cell measurement of the uptake, intratumoral distribution and cell cycle effects of cisplatin using mass cytometry. Int. J. Cancer 136, 1202–1209 (2015).
Chang, Q. et al. Biodistribution of cisplatin revealed by imaging mass cytometry identifies extensive collagen binding in tumor and normal tissues. Sci. Rep. 6, 36641 (2016).
Newell, E. W., Sigal, N., Bendall, S. C., Nolan, G. P. & Davis, M. M. Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 36, 142–152 (2012).
Newell, E. W. et al. Combinatorial tetramer staining and mass cytometry analysis facilitate T-cell epitope mapping and characterization. Nat. Biotechnol. 31, 623–629 (2013).
Newell, E. W. & Davis, M. M. Beyond model antigens: high-dimensional methods for the analysis of antigen-specific T cells. Nat. Biotechnol. 32, 149–157 (2014).
Leong, M. L. & Newell, E. W. in Single Cell Protein Analysis (eds. Singh, A. K. & Chandrasekaran, A.) 115–131 (Humana, 2015).
Simoni, Y. et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).
Spitzer, M. H. et al. An interactive reference framework for modeling a dynamic immune system. Science 349, 1259425 (2015).
Hartmann, F. J. et al. High-dimensional single-cell analysis reveals the immune signature of narcolepsy. J. Exp. Med. 213, 2621–2633 (2016).
Galli, E. et al. GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis. Nat. Med. 25, 1290–1300 (2019).
Bodenmiller, B. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867 (2012).
Fienberg, H. G., Simonds, E. F., Fantl, W. J., Nolan, G. P. & Bodenmiller, B. A platinum-based covalent viability reagent for single-cell mass cytometry. Cytometry A 81A, 467–475 (2012).
Hartmann, F. J., Simonds, E. F. & Bendall, S. C. A universal live cell barcoding-platform for multiplexed human single cell analysis. Sci. Rep. 8, 10770 (2018).
Frei, A. P. et al. Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nat. Methods 13, 269–275 (2016).
Duckworth, A. D. et al. Multiplexed profiling of RNA and protein expression signatures in individual cells using flow or mass cytometry. Nat. Protoc. 14, 901–920 (2019).
Behbehani, G. K., Bendall, S. C., Clutter, M. R., Fantl, W. J. & Nolan, G. P. Single-cell mass cytometry adapted to measurements of the cell cycle. Cytometry A 81A, 552–566 (2012).
Kimmey, S. C., Borges, L., Baskar, R. & Bendall, S. C. Parallel analysis of tri-molecular biosynthesis with cell identity and function in single cells. Nat. Commun. 10, 1185 (2019).
Poreba, M. et al. The Activome: multiplexed probing of activity of proteolytic enzymes using mass cytometry-compatible activity-based probes (TOF-probes). Preprint at bioRxiv https://doi.org/10.1101/775627 (2019).
Edgar, L. J. et al. Identification of hypoxic cells using an organotellurium tag compatible with mass cytometry. Angew. Chem. Int. Ed. 53, 11473–11477 (2014).
Schulz, D., Severin, Y., Zanotelli, V. R. T. & Bodenmiller, B. In-depth characterization of monocyte-derived macrophages using a mass cytometry-based phagocytosis assay. Sci. Rep. 9, 1925 (2019).
Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83, 483–494 (2013).
Kleinsteuber, K. et al. Standardization and quality control for high-dimensional mass cytometry studies of human samples. Cytometry A 89, 903–913 (2016).
Leipold, M. D. et al. Comparison of CyTOF assays across sites: results of a six-center pilot study. J. Immunol. Methods 453, 37–43 (2018).
Zunder, E. R. et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10, 316–333 (2015).
Mei, H. E., Leipold, M. D., Schulz, A. R., Chester, C. & Maecker, H. T. Barcoding of live human peripheral blood mononuclear cells for multiplexed mass cytometry. J. Immunol. 194, 2022–2031 (2015).
Lai, L., Ong, R., Li, J. & Albani, S. A CD45-based barcoding approach to multiplex mass-cytometry (CyTOF). Cytometry A 87, 369–374 (2015).
Bodenmiller, B. Multiplexed epitope-based tissue imaging for discovery and healthcare applications. Cell Syst. 2, 225–238 (2016).
Gerner, M. Y., Kastenmuller, W., Ifrim, I., Kabat, J. & Germain, R. N. Histo-cytometry: a method for highly multiplex quantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes. Immunity 37, 364–376 (2012).
Gerdes, M. J. et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc. Natl Acad. Sci. USA 110, 11982–11987 (2013).
Lin, J.-R. et al. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife 7, e31657 (2018).
Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).
Davis, A. S. et al. Characterizing and diminishing autofluorescence in formalin-fixed paraffin-embedded human respiratory tissue. J. Histochem. Cytochem. 62, 405–423 (2014).
Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).
Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).
Keren, L. et al. MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci. Adv. 5, eaax5851 (2019).
Damond, N. et al. A map of human type 1 diabetes progression by imaging mass cytometry. Cell Metab. 29, 755–768.e5 (2019).
Wang, Y. J. et al. Multiplexed in situ imaging mass cytometry analysis of the human endocrine pancreas and immune system in type 1 diabetes. Cell Metab. 29, 769–783.e4 (2019).
Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387.e19 (2018).
Schulz, D. et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 6, 25–36.e5 (2018).
Catena, R., Montuenga, L. M. & Bodenmiller, B. Ruthenium counterstaining for imaging mass cytometry. J. Pathol. 244, 479–484 (2018).
Skinner, P. J., Daniels, M. A., Schmidt, C. S., Jameson, S. C. & Haase, A. T. Cutting edge: in situ tetramer staining of antigen-specific T cells in tissues. J. Immunol. 165, 613–617 (2000).
Steinert, E. M. et al. Quantifying memory CD8 T cells reveals regionalization of immunosurveillance. Cell 161, 737–749 (2015).
Li, S. et al. Simian immunodeficiency virus-producing cells in follicles are partially suppressed by CD8+ cells in vivo. J. Virol. 90, 11168–11180 (2016).
Mair, F. et al. The end of gating? An introduction to automated analysis of high dimensional cytometry data. Eur. J. Immunol. 46, 34–43 (2016).
Saeys, Y., Gassen, S. Van & Lambrecht, B. N. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat. Rev. Immunol. 16, 449–462 (2016).
Chester, C. & Maecker, H. T. Algorithmic tools for mining high-dimensional cytometry data. J. Immunol. 195, 773–779 (2015).
Newell, E. W. & Cheng, Y. Mass cytometry: blessed with the curse of dimensionality. Nat. Immunol. 17, 890–895 (2016).
Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019).
Van Valen, D. A. et al. Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLOS Comput. Biol. 12, e1005177 (2016).
Sommer, C., Straehle, C., Kothe, U. & Hamprecht, F. A. in 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 230–233 (IEEE, 2011).
Dao, D. et al. CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets. Bioinformatics 32, 3210–3212 (2016).
Schapiro, D. et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873–876 (2017).
Haberl, M. G. et al. CDeep3M–Plug-and-play cloud-based deep learning for image segmentation. Nat. Methods 15, 677–680 (2018).
Gabriel, K. R. & Sokal, R. R. A new statistical approach to geographic variation analysis. Syst. Zool. 18, 259–278 (1969).
Finak, G., Perez, J.-M., Weng, A. & Gottardo, R. Optimizing transformations for automated, high throughput analysis of flow cytometry data. BMC Bioinformatics 11, 546 (2010).
Bagwell, C. B. Hyperlog?A flexible log-like transform for negative, zero, and positive valued data. Cytometry A 64A, 34–42 (2005).
Parks, D. R., Roederer, M. & Moore, W. A. A new ‘Logicle’ display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytometry A 69, 541–551 (2006).
Maaten, L. Van Der & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Amir, E. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).
Kotecha, N., Krutzik, P. O. & Irish, J. M. Web-based analysis and publication of flow cytometry experiments. Curr. Protoc. Cytom. 53, 10.17.1–10.17.24 (2010).
van der Maaten, L. Barnes-Hut-SNE. Preprint at arXiv https://arxiv.org/abs/1301.3342v2 (2013).
Pezzotti, N. et al. Approximated and user steerable tSNE for progressive visual analytics. IEEE Trans. Vis. Computer Graph. 23, 1739–1752 (2016).
van Unen, V. et al. Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types. Nat. Commun. 8, 1740 (2017).
Cho, H., Berger, B. & Peng, J. Generalizable and scalable visualization of single-cell data using neural networks. Cell Syst. 7, 185–191.e4 (2018).
McInnes, L. & Healy, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://arxiv.org/abs/1802.03426v2 (2018).
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).
Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886–891 (2011).
Samusik, N., Good, Z., Spitzer, M. H., Davis, K. L. & Nolan, G. P. Automated mapping of phenotype space with single-cell data. Nat. Methods 13, 493–496 (2016).
Zunder, E. R., Lujan, E., Goltsev, Y., Wernig, M. & Nolan, G. P. A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. Cell Stem Cell 16, 323–337 (2015).
Mrdjen, D. et al. High-dimensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity 48, 380–395.e6 (2018).
Weber, L. M. & Robinson, M. D. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytometry A 89, 1084–1096 (2016).
Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).
Bruggner, R. V., Bodenmiller, B., Dill, D. L., Tibshirani, R. J. & Nolan, G. P. Automated identification of stratifying signatures in cellular subpopulations. Proc. Natl Acad. Sci. USA 111, E2770–E2777 (2014).
Spitzer, M. H. et al. Systemic immunity is required for effective cancer immunotherapy. Cell 168, 487–502.e15 (2017).
Lun, A. T. L., Richard, A. C. & Marioni, J. C. Testing for differential abundance in mass cytometry data. Nat. Methods 14, 707–709 (2017).
Arvaniti, E. & Claassen, M. Sensitive detection of rare disease-associated cell subsets via representation learning. Nat. Commun. 8, 14825 (2017).
Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).
Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).
Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).
Krishnaswamy, S. et al. Systems biology. Conditional density-based analysis of T cell signaling in single-cell data. Science 346, 1250689 (2014).
Strauss-Albee, D. M. et al. Human NK cell repertoire diversity reflects immune experience and correlates with viral susceptibility. Sci. Transl Med. 7, 297ra115 (2015).
Good, Z. et al. Proliferation tracing with single-cell mass cytometry optimizes generation of stem cell memory-like T cells. Nat. Biotechnol. 37, 259–266 (2019).
Aghaeepour, N. et al. GateFinder: projection-based gating strategy optimization for flow and mass cytometry. Bioinformatics 34, 4131–4133 (2018).
Spitzer, M. H. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).
Hartmann, F. J. et al. Scalable conjugation and characterization of immunoglobulins with stable mass isotope reporters for single-cell mass cytometry analysis. Methods Mol. Biol. 1989, 55–81 (2019).
Leipold, M. D., Newell, E. W. & Maecker, H. T. Multiparameter phenotyping of human PBMCs using mass cytometry. Methods Mol. Biol. 1343, 81–95 (2015).
Takahashi, C. et al. Mass cytometry panel optimization through the designed distribution of signal interference. Cytometry A 91, 39–47 (2017).
Chevrier, S. et al. Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst. 6, 612–620.e5 (2018).
Bhattacharya, S. et al. ImmPort: disseminating data to the public for the future of immunology. Immunol. Res. 58, 234–239 (2014).
Spidlen, J., Breuer, K., Rosenberg, C., Kotecha, N. & Brinkman, R. R. FlowRepository: a resource of annotated flow cytometry datasets associated with peer-reviewed publications. Cytometry A 81A, 727–731 (2012).
Lee, J. A. et al. MIFlowCyt: the minimum information about a flow cytometry experiment. Cytometry A 73A, 926–930 (2008).
Hu, Z. et al. MetaCyto: a tool for automated meta-analysis of mass and flow cytometry data. Cell Rep. 24, 1377–1388 (2018).
Williams, E. et al. Image Data Resource: a bioimage data integration and publication platform. Nat. Methods 14, 775–781 (2017).
Brito-Zerón, P. et al. Sjögren syndrome. Nat. Rev. Dis. Primers 2, 16047 (2016).
Angiolilli, C. et al. New insights into the genetics and epigenetics of systemic sclerosis. Nat. Rev. Rheumatol. 14, 657–673 (2018).
Smolen, J. S. et al. Rheumatoid arthritis. Nat. Rev. Dis. Primers 4, 18001 (2018).
Kaul, A. et al. Systemic lupus erythematosus. Nat. Rev. Dis. Primers 2, 16039 (2016).
Tsokos, G. C., Lo, M. S., Reis, P. C. & Sullivan, K. E. New insights into the immunopathogenesis of systemic lupus erythematosus. Nat. Rev. Rheumatol. 12, 716–730 (2016).
Mavragani, C. P. & Moutsopoulos, H. M. Sjögren’s syndrome. Annu. Rev. Pathol. Mech. Dis. 9, 273–285 (2014).
Sedger, L. M. & McDermott, M. F. TNF and TNF-receptors: from mediators of cell death and inflammation to therapeutic giants – past, present and future. Cytokine Growth Factor. Rev. 25, 453–472 (2014).
Hofmann, K., Clauder, A.-K. & Manz, R. A. Targeting B cells and plasma cells in autoimmune diseases. Front. Immunol. 9, 835 (2018).
Rao, D. A. et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542, 110–114 (2017).
Fonseka, C. Y. et al. Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis. Sci. Transl Med. 10, eaaq0305 (2018).
Al-Mossawi, M. H. et al. Unique transcriptome signatures and GM-CSF expression in lymphocytes from patients with spondyloarthritis. Nat. Commun. 8, 1510 (2017).
Noster, R. et al. IL-17 and GM-CSF expression are antagonistically regulated by human T helper cells. Sci. Transl Med. 6, 241ra80 (2014).
Hartmann, F. J. et al. Multiple sclerosis-associated IL2RA polymorphism controls GM-CSF production in human TH cells. Nat. Commun. 5, 5056 (2014).
O’Gorman, W. E. et al. Single-cell systems-level analysis of human Toll-like receptor activation defines a chemokine signature in patients with systemic lupus erythematosus. J. Allergy Clin. Immunol. 136, 1326–1336 (2015).
O’Gorman, W. E. et al. Mass cytometry identifies a distinct monocyte cytokine signature shared by clinically heterogeneous pediatric SLE patients. J. Autoimmun. 81, 74–89 (2017).
US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT02535689 (2018).
US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT02446899 (2019).
Ramsköld, D. et al. B cell alterations during BAFF inhibition with belimumab in SLE. EBioMedicine 40, 517–527 (2019).
Rubtsova, K., Rubtsov, A. V., Cancro, M. P. & Marrack, P. Age-associated B cells: a T-bet-dependent effector with roles in protective and pathogenic immunity. J. Immunol. 195, 1933–1937 (2015).
Wang, S. et al. IL-21 drives expansion and plasma cell differentiation of autoreactive CD11chiT-bet+ B cells in SLE. Nat. Commun. 9, 1758 (2018).
Mingueneau, M. et al. Cytometry by time-of-flight immunophenotyping identifies a blood Sjögren’s signature correlating with disease activity and glandular inflammation. J. Allergy Clin. Immunol. 137, 1809–1821.e12 (2016).
Christophersen, A. et al. Distinct phenotype of CD4+ T cells driving celiac disease identified in multiple autoimmune conditions. Nat. Med. 25, 734–737 (2019).
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).
Falk, T. et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16, 67–70 (2019).
Gupta, A. et al. Deep learning in image cytometry: a review. Cytometry A 95, 366–380 (2018).
Rovira-Clave, X. et al. Subcellular localization of drug distribution by super-resolution ion beam imaging. Preprint at bioRxiv https://doi.org/10.1101/557603 (2019).
Coskun, A. F. et al. Ion beam subcellular tomography. Preprint at bioRxiv https://doi.org/10.1101/557728 (2019).
Olin, A. et al. Stereotypic immune system development in newborn children. Cell 174, 1277–1292.e14 (2018).
Pedersen, H. K. et al. A computational framework to integrate high-throughput ‘-omics’ datasets for the identification of potential mechanistic links. Nat. Protoc. 13, 2781–2800 (2018).
Ghaemi, M. S. et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 35, 95–103 (2018).
Huang, S., Chaudhary, K. & Garmire, L. X. More is better: recent progress in multi-omics data integration methods. Front. Genet. 8, 84 (2017).
Cheung, P. et al. Single-cell chromatin modification profiling reveals increased epigenetic variations with aging. Cell 173, 1385–1397.e14 (2018).
Zhang, Z. & Zhang, R. Epigenetics in autoimmune diseases: pathogenesis and prospects for therapy. Autoimmun. Rev. 14, 854–863 (2015).
Jeffries, M. A. & Sawalha, A. H. Autoimmune disease in the epigenetic era: how has epigenetics changed our understanding of disease and how can we expect the field to evolve? Expert. Rev. Clin. Immunol. 11, 45–58 (2015).
Munroe, M. E. et al. Discerning risk of disease transition in relatives of systemic lupus erythematosus patients utilizing soluble mediators and clinical features. Arthritis Rheumatol. 69, 630–642 (2017).
van der Woude, D. et al. Epitope spreading of the anti-citrullinated protein antibody response occurs before disease onset and is associated with the disease course of early arthritis. Ann. Rheum. Dis. 69, 1554–1561 (2010).
Leong, J. Y. et al. Immunome perturbation is present in patients with juvenile idiopathic arthritis who are in remission and will relapse upon anti-TNFα withdrawal. Ann. Rheum. Dis. 78, 1712–1721 (2019).
Blicharz, T. M. et al. Microneedle-based device for the one-step painless collection of capillary blood samples. Nat. Biomed. Eng. 2, 151–157 (2018).
Josyula, V. S., Lakshmikanth, T., Mikes, J., Chen, Y. & Brodin, P. Systems-level immunomonitoring using self-sampled capillary blood. Preprint at bioRxiv https://doi.org/10.1101/694521 (2019).
Tatovic, D. et al. Fine-needle aspiration biopsy of the lymph node: a novel tool for the monitoring of immune responses after skin antigen delivery. J. Immunol. 195, 386–392 (2015).
Mandal, A. et al. Cell and fluid sampling microneedle patches for monitoring skin-resident immunity. Sci. Transl Med. 10, eaar2227 (2018).
Perfetto, S. P., Chattopadhyay, P. K. & Roederer, M. Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol. 4, 648–655 (2004).
Cossarizza, A. et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition). Eur. J. Immunol. 49, 1457–1973 (2019).
Bendall, S. C., Nolan, G. P., Roederer, M. & Chattopadhyay, P. K. A deep profiler’s guide to cytometry. Trends Immunol. 33, 323–332 (2012).
The work of F.J.H. is supported by the EMBO organization (EMBO Long-Term Fellowship ALTF 1141-2017), the Novartis Foundation for Medical-Biological Research (16C148) and the Swiss National Science Foundation (SNF Early Postdoc Mobility P2ZHP3-171741). The work of S.C.B. is supported by the Damon Runyon Cancer Research Foundation Fellowship (DRG-2017-09), the NIH (1DP2OD022550-01, 1R01AG056287-01, 1R01AG057915-01, 1-R00-GM104148-01, 1U24CA224309-01, 5U19AI116484-02 and U19 AI104209) and a Translational Research Award from the Stanford Cancer Institute.
S.C.B. declares that he is an inventor of multiplexed ion beam imaging technology and a scientific founder of IONpath Inc., the company that commercialized this technology. F.J.H. declares no competing interests.
Peer review information
Nature Reviews Rheumatology thanks J. Lederer, P. Brodin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- Inductively coupled plasma
A type of plasma in which the energy is supplied through electromagnetic induction (changes in magnetic fields).
The time taken by a particle to travel through a medium; measuring the time-of-flight of ions in an electric field can be used to infer the ions’ mass-to-charge ratio and therefore its identity.
- Cell barcoding
A method of labelling cells with a sample-specific signature that can be used to subsequently pool cells from several samples for downstream staining and processing.
A pattern of scanning in which an area is scanned (for example, with an ion beam) in lines from side to side, starting at the top.
- Image segmentation
The process of identifying and partitioning an image into meaningful objects (such as cells) in order to facilitate their downstream analysis.
- Deep learning
A type of machine learning in which artificial neural networks with multiple layers of adjustable nodes are used to learn how to perform specific tasks from large amounts of data.
An algorithm that has been trained to predict the class of data points.
- Artificial neural networks
A type of machine learning framework inspired by the biological structure of the brain, in which (potentially many) layers of interconnected nodes transmit information to each other and apply transformations to perform classification or prediction tasks.
- Minimum spanning tree
In a graph consisting of points (nodes) connected through edges, the minimum spanning tree represents the subset of the graph that connects all nodes with the minimum total edge weight, usually representing the length of the edge.
- Force-directed layouts
Graphical renderings that assign spring-like (attractive and repulsive) forces between the edges and nodes of a graph to position them in 2D space.
Grouping a set of points that are similar to each other.
- Self-organizing maps
A type of unsupervised clustering and dimensionality reduction approach that preserves the topological information of the input data.
- Representation learning
The automated process of transforming raw data into useful features that are subsequently used in other machine learning applications.
- Simpson’s diversity index
A measurement of diversity that takes into account the number of different groups present in a dataset, as well as their relative abundance.
A type of artificial neural network that aims to learn a lower-dimensional data representation from which the original input can be reconstructed as closely as possible.
About this article
Cite this article
Hartmann, F.J., Bendall, S.C. Immune monitoring using mass cytometry and related high-dimensional imaging approaches. Nat Rev Rheumatol 16, 87–99 (2020). https://doi.org/10.1038/s41584-019-0338-z