1932

Abstract

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-020422-050255
2023-08-10
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/biodatasci/6/1/annurev-biodatasci-020422-050255.html?itemId=/content/journals/10.1146/annurev-biodatasci-020422-050255&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    O'Neill K, Aghaeepour N, Spidlen J, Brinkman R. 2013. Flow cytometry bioinformatics. PLOS Comput. Biol. 9:12e1003365
    [Google Scholar]
  2. 2.
    Ornatsky OI, Lou X, Nitz M, Schäfer S, Sheldrick WS et al. 2008. Study of cell antigens and intracellular DNA by identification of element-containing labels and metallointercalators using inductively coupled plasma mass spectrometry. Anal. Chem. 80:72539–47
    [Google Scholar]
  3. 3.
    Bendall SC, Simonds EF, Qiu P, Amir E-AD, Krutzik PO et al. 2011. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332:6030687–96
    [Google Scholar]
  4. 4.
    Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK et al. 2017. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14:9865–68
    [Google Scholar]
  5. 5.
    den Braanker H, Bongenaar M, Lubberts E. 2021. How to prepare spectral flow cytometry datasets for high dimensional data analysis: a practical workflow. Front. Immunol. 12:768113
    [Google Scholar]
  6. 6.
    Giesen C, Wang HAO, Schapiro D, Zivanovic N, Jacobs A et al. 2014. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11:4417–22
    [Google Scholar]
  7. 7.
    Angelo M, Bendall SC, Finck R, Hale MB, Hitzman C et al. 2014. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20:4436–42
    [Google Scholar]
  8. 8.
    Goltsev Y, Samusik N, Kennedy-Darling J, Bhate S, Hale M et al. 2018. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174:4968–81.e15
    [Google Scholar]
  9. 9.
    Lin J-R, Fallahi-Sichani M, Chen J-Y, Sorger PK. 2016. Cyclic immunofluorescence (CycIF), a highly multiplexed method for single-cell imaging. Curr. Protoc. Chem. Biol. 8:4251–64
    [Google Scholar]
  10. 10.
    Flynn E, Almonte-Loya A, Fragiadakis GK. 2023. Single-cell multiomics. Annu. Rev. Biomed. Data Sci. 6:313–37
    [Google Scholar]
  11. 11.
    Finck R, Simonds EF, Jager A, Krishnaswamy S, Sachs K et al. 2013. Normalization of mass cytometry data with bead standards. Cytometry A 83:5483–94
    [Google Scholar]
  12. 12.
    Zunder ER, Finck R, Behbehani GK, Amir E-AD, Krishnaswamy S et al. 2015. Palladium-based mass-tag cell barcoding with a doublet-filtering scheme and single cell deconvolution algorithm. Nat. Protoc. 10:2316–33
    [Google Scholar]
  13. 13.
    Fread KI, Strickland WD, Nolan GP, Zunder ER. 2017. An updated debarcoding tool for mass cytometry with cell type-specific and cell sample-specific stringency adjustment. Pac. Symp. Biocomput. 22:588–98
    [Google Scholar]
  14. 14.
    Chen TJ, Kotecha N. 2014. Cytobank: providing an analytics platform for community cytometry data analysis and collaboration. Curr. Top. Microbiol. Immunol. 377:127–57
    [Google Scholar]
  15. 15.
    Kotecha N, Krutzik PO, Irish JM. 2010. Web-based analysis and publication of flow cytometry experiments. Curr. Protoc. Cytom. 53:10.17.1–10.17.24
    [Google Scholar]
  16. 16.
    Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P et al. 2009. flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinform. 10:106
    [Google Scholar]
  17. 17.
    Finak G, Frelinger J, Jiang W, Newell EW, Ramey J et al. 2014. OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. PLOS Comput. Biol. 10:8e1003806
    [Google Scholar]
  18. 18.
    Rybakowska P, Alarcón-Riquelme ME, Marañón C. 2020. Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry. Comput. Struct. Biotechnol. J. 18:874–86
    [Google Scholar]
  19. 19.
    Saeys Y, Van Gassen S, Lambrecht BN. 2016. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat. Rev. Immunol. 16:7449–62
    [Google Scholar]
  20. 20.
    Kruskal JB, Wish M. 1978. Multidimensional Scaling Thousand Oaks, CA: SAGE
  21. 21.
    Jackson JE. 1991. A User's Guide to Principal Components Hoboken, NJ: Wiley-Intersci.
  22. 22.
    Schuyler RP, Jackson C, Garcia-Perez JE, Baxter RM, Ogolla S et al. 2019. Minimizing batch effects in mass cytometry data. Front. Immunol. 10:2367
    [Google Scholar]
  23. 23.
    Trussart M, Teh CE, Tan T, Leong L, Gray DHD, Speed TP. 2020. Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets. eLife 9:e59630
    [Google Scholar]
  24. 24.
    Van Gassen S, Gaudilliere B, Angst MS, Saeys Y, Aghaeepour N. 2020. CytoNorm: a normalization algorithm for cytometry data. Cytometry A 97:3268–78
    [Google Scholar]
  25. 25.
    Ogishi M, Yang R, Gruber C, Zhang P, Pelham SJ et al. 2021. Multibatch cytometry data integration for optimal immunophenotyping. J. Immunol. 206:206–13
    [Google Scholar]
  26. 26.
    Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F et al. 2019. Fast, sensitive, and accurate integration of single cell data with Harmony. Nat. Methods 16:121289–96
    [Google Scholar]
  27. 27.
    Pedersen CB, Dam SH, Barnkob MB, Leipold MD, Purroy N et al. 2022. cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies. Nat. Commun. 13:1698
    [Google Scholar]
  28. 28.
    Johnson WE, Li C, Rabinovic A. 2007. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–27
    [Google Scholar]
  29. 29.
    Lo Y-C, Keyes TJ, Jager A, Sarno J, Domizi P et al. 2022. CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors. Nat. Commun. 13:934
    [Google Scholar]
  30. 30.
    Newell EW, Sigal N, Bendall SC, Nolan GP, Davis MM. 2012. Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 36:142–52
    [Google Scholar]
  31. 31.
    Amir E-AD, Davis KL, Tadmor MD, Simonds EF, Levine JH et al. 2013. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31:6545–52
    [Google Scholar]
  32. 32.
    van der Maaten L, Hinton G. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:2579–605
    [Google Scholar]
  33. 33.
    Becht E, McInnes L, Healy J, Dutertre C-A, Kwok IWH et al. 2019. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37:38–44
    [Google Scholar]
  34. 34.
    McInnes L, Healy J, Melville J. 2018. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 [stat.ML]
  35. 35.
    Amodio M, van Dijk D, Srinivasan K, Chen WS, Mohsen H et al. 2019. Exploring single-cell data with deep multitasking neural networks. Nat. Methods 16:111139–45
    [Google Scholar]
  36. 36.
    Ding J, Condon A, Shah SP. 2018. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat. Commun. 9:2002
    [Google Scholar]
  37. 37.
    Wang K, Yang Y, Wu F, Song B, Wang X, Wang T. 2023. Comparative analysis of dimension reduction methods for cytometry by time-of-flight data. bioRxiv 2022.04.26.489549. https://doi.org/10.1101/2022.04.26.489549
    [Crossref]
  38. 38.
    Moon KR, van Dijk D, Wang Z, Gigante S, Burkhardt DB et al. 2019. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37:121482–92
    [Google Scholar]
  39. 39.
    Mair F, Hartmann FJ, Mrdjen D, Tosevski V, Krieg C, Becher B. 2016. The end of gating? An introduction to automated analysis of high dimensional cytometry data. Eur. J. Immunol. 46:134–43
    [Google Scholar]
  40. 40.
    Van Gassen S, Callebaut B, Van Helden MJ, Lambrecht BN, Demeester P et al. 2015. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87:7636–45
    [Google Scholar]
  41. 41.
    Quintelier K, Couckuyt A, Emmaneel A, Aerts J, Saeys Y, Van Gassen S. 2021. Analyzing high-dimensional cytometry data using FlowSOM. Nat. Protoc. 16:83775–801
    [Google Scholar]
  42. 42.
    Zaki MJ. 2001. SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42:31–60
    [Google Scholar]
  43. 43.
    Shekhar K, Brodin P, Davis MM, Chakraborty AK. 2014. Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE). PNAS 111:1202–7
    [Google Scholar]
  44. 44.
    Aghaeepour N, Nikolic R, Hoos HH, Brinkman RR. 2011. Rapid cell population identification in flow cytometry data. Cytometry A 79:16–13
    [Google Scholar]
  45. 45.
    Lo K, Hahne F, Brinkman RR, Gottardo R. 2009. flowClust: a Bioconductor package for automated gating of flow cytometry data. BMC Bioinform. 10:145
    [Google Scholar]
  46. 46.
    Weber LM, Robinson MD. 2016. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytometry A 89:121084–96
    [Google Scholar]
  47. 47.
    Gupta T, Panda SP. 2019. A comparison of K-means clustering algorithm and CLARA clustering algorithm on Iris Dataset. Int. J. Eng. Technol. 7:44766–68
    [Google Scholar]
  48. 48.
    Kaufman L, Rousseeuw PJ. 2008. Finding Groups in Data Hoboken, NJ: Wiley
  49. 49.
    Schubert E, Rousseeuw PJ 2019. Faster k-medoids clustering: improving the PAM, CLARA, and CLARANS algorithms. Similarity Search and Applications G Amato, C Gennaro, V Oria, M Radovanović 171–87. Cham, Switz.: Springer
    [Google Scholar]
  50. 50.
    Spitzer MH, Gherardini PF, Fragiadakis GK, Bhattacharya N, Yuan RT et al. 2015. An interactive reference framework for modeling a dynamic immune system. Science 349:62441259425
    [Google Scholar]
  51. 51.
    Kodinariya TM, Makwana PR. 2013. Review on determining number of cluster in k-Means clustering. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 1:690–95
    [Google Scholar]
  52. 52.
    Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. 2008. Fast unfolding of communities in large networks. J. Stat. Mech. 2008:10P10008
    [Google Scholar]
  53. 53.
    Traag VA, Waltman L, van Eck NJ. 2019. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9:5233
    [Google Scholar]
  54. 54.
    Levine JH, Simonds EF, Bendall SC, Davis KL, Amir E-AD et al. 2015. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162:1184–97
    [Google Scholar]
  55. 55.
    Liu X, Song W, Wong BY, Zhang T, Yu S et al. 2019. A comparison framework and guideline of clustering methods for mass cytometry data. Genome Biol. 20:297
    [Google Scholar]
  56. 56.
    Palit S, Heuser C, de Almeida GP, Theis FJ, Zielinski CE. 2019. Meeting the challenges of high-dimensional single-cell data analysis in immunology. Front. Immunol. 10:1515
    [Google Scholar]
  57. 57.
    Bruggner RV, Bodenmiller B, Dill DL, Tibshirani RJ, Nolan GP. 2014. Automated identification of stratifying signatures in cellular subpopulations. PNAS 111:26E2770–77
    [Google Scholar]
  58. 58.
    Spitzer MH, Carmi Y, Reticker-Flynn NE, Kwek SS, Madhireddy D et al. 2017. Systemic immunity is required for effective cancer immunotherapy. Cell 168:3487–502.e15
    [Google Scholar]
  59. 59.
    Arvaniti E, Claassen M. 2017. Sensitive detection of rare disease-associated cell subsets via representation learning. Nat. Commun. 8:14825
    [Google Scholar]
  60. 60.
    Lun ATL, Richard AC, Marioni JC. 2017. Testing for differential abundance in mass cytometry data. Nat. Methods 14:7707–9
    [Google Scholar]
  61. 61.
    Robinson MD, McCarthy DJ, Smyth GK. 2009. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:1139–40
    [Google Scholar]
  62. 62.
    McCarthy DJ, Chen Y, Smyth GK. 2012. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40:104288–97
    [Google Scholar]
  63. 63.
    Weber LM, Nowicka M, Soneson C, Robinson MD. 2019. diffcyt: differential discovery in high-dimensional cytometry via high-resolution clustering. Commun. Biol. 2:183
    [Google Scholar]
  64. 64.
    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW et al. 2015. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:7e47
    [Google Scholar]
  65. 65.
    Law CW, Chen Y, Shi W, Smyth GK. 2014. Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15:2R29
    [Google Scholar]
  66. 66.
    Seiler C, Ferreira A-M, Kronstad LM, Simpson LJ, Le Gars M et al. 2021. CytoGLMM: conditional differential analysis for flow and mass cytometry experiments. BMC Bioinform. 22:137
    [Google Scholar]
  67. 67.
    Orlova DY, Zimmerman N, Meehan S, Meehan C, Waters J et al. 2016. Earth mover's distance (EMD): a true metric for comparing biomarker expression levels in cell populations. PLOS ONE 11:3e0151859
    [Google Scholar]
  68. 68.
    Haghverdi L, Buettner F, Theis FJ. 2015. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31:182989–98
    [Google Scholar]
  69. 69.
    Coifman RR, Lafon S, Lee AB, Maggioni M, Nadler B et al. 2005. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. PNAS 102:217426–31
    [Google Scholar]
  70. 70.
    Wolf FA, Hamey FK, Plass M, Solana J, Dahlin JS et al. 2019. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20:59
    [Google Scholar]
  71. 71.
    Qiu P, Simonds EF, Bendall SC, Gibbs KD Jr., Bruggner RV et al. 2011. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29:10886–91
    [Google Scholar]
  72. 72.
    Bendall SC, Davis KL, Amir E-AD, Tadmor MD, Simonds EF et al. 2014. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157:3714–25
    [Google Scholar]
  73. 73.
    Cannoodt R, Saelens W, Sichien D, Tavernier S, Janssens S et al. 2016. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. bioRxiv 079509. https://doi.org/10.1101/079509
    [Crossref]
  74. 74.
    Dai Y, Xu A, Li J, Wu L, Yu S et al. 2021. CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data. BMC Bioinform. 22:138
    [Google Scholar]
  75. 75.
    Qiu X, Mao Q, Tang Y, Wang L, Chawla R et al. 2017. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14:10979–82
    [Google Scholar]
  76. 76.
    Street K, Risso D, Fletcher RB, Das D, Ngai J et al. 2018. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genom. 19:477
    [Google Scholar]
  77. 77.
    Haghverdi L, Büttner M, Wolf FA, Buettner F, Theis FJ. 2016. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13:10845–48
    [Google Scholar]
  78. 78.
    Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM et al. 2016. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34:6637–45
    [Google Scholar]
  79. 79.
    Saelens W, Cannoodt R, Todorov H, Saeys Y. 2019. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37:5547–54
    [Google Scholar]
  80. 80.
    Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP. 2005. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308:5721523–29
    [Google Scholar]
  81. 81.
    Krishnaswamy S, Spitzer MH, Mingueneau M, Bendall SC, Litvin O et al. 2014. Conditional density-based analysis of T cell signaling in single-cell data. Science 346:62131250689
    [Google Scholar]
  82. 82.
    Mukherjee S, Jensen H, Stewart W, Stewart D, Ray WC et al. 2017. In silico modeling identifies CD45 as a regulator of IL-2 synergy in the NKG2D-mediated activation of immature human NK cells. Sci. Signal. 10:485eaai9062
    [Google Scholar]
  83. 83.
    Keren L, Bosse M, Thompson S, Risom T, Vijayaragavan K et al. 2019. MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci. Adv. 5:10eaax5851
    [Google Scholar]
  84. 84.
    Risom T, Glass DR, Averbukh I, Liu CC, Baranski A et al. 2022. Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. Cell 185:2299–310.e18
    [Google Scholar]
  85. 85.
    McCaffrey EF, Donato M, Keren L, Chen Z, Delmastro A et al. 2022. The immunoregulatory landscape of human tuberculosis granulomas. Nat. Immunol. 23:2318–29
    [Google Scholar]
  86. 86.
    Keren L, Bosse M, Marquez D, Angoshtari R, Jain S et al. 2018. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174:61373–87.e19
    [Google Scholar]
  87. 87.
    Baharlou H, Canete NP, Cunningham AL, Harman AN, Patrick E. 2019. Mass cytometry imaging for the study of human diseases-applications and data analysis strategies. Front. Immunol. 10:2657
    [Google Scholar]
  88. 88.
    Chang Q, Ornatsky OI, Siddiqui I, Loboda A, Baranov VI, Hedley DW. 2017. Imaging mass cytometry. Cytometry A 91:2160–69
    [Google Scholar]
  89. 89.
    Hickey JW, Neumann EK, Radtke AJ, Camarillo JM, Beuschel RT et al. 2022. Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging. Nat. Methods 19:3284–95
    [Google Scholar]
  90. 90.
    Black S, Phillips D, Hickey JW, Kennedy-Darling J, Venkataraaman VG et al. 2021. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat. Protoc. 16:83802–35
    [Google Scholar]
  91. 91.
    Kennedy-Darling J, Bhate SS, Hickey JW, Black S, Barlow GL et al. 2021. Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction. Eur. J. Immunol. 51:51262–77
    [Google Scholar]
  92. 92.
    Phillips D, Schürch CM, Khodadoust MS, Kim YH, Nolan GP, Jiang S. 2021. Highly multiplexed phenotyping of immunoregulatory proteins in the tumor microenvironment by CODEX tissue imaging. Front. Immunol. 12:687673
    [Google Scholar]
  93. 93.
    Hickey JW, Tan Y, Nolan GP, Goltsev Y. 2021. Strategies for accurate cell type identification in CODEX multiplexed imaging data. Front. Immunol. 12:727626
    [Google Scholar]
  94. 94.
    Greenwald NF, Miller G, Moen E, Kong A, Kagel A et al. 2022. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40:4555–65
    [Google Scholar]
  95. 95.
    Liu CC, Bosse M, Kong A, Kagel A, Kinders R et al. 2022. Reproducible, high-dimensional imaging in archival human tissue by multiplexed ion beam imaging by time-of-flight (MIBI-TOF). Lab. Investig. 102:7762–70
    [Google Scholar]
  96. 96.
    Liu CC, McCaffrey EF, Greenwald NF, Soon E, Risom T et al. 2022. Multiplexed ion beam imaging: insights into pathobiology. Annu. Rev. Pathol. 17:403–23
    [Google Scholar]
  97. 97.
    Phillips D, Matusiak M, Gutierrez BR, Bhate SS, Barlow GL et al. 2021. Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma. Nat. Commun. 12:6726
    [Google Scholar]
  98. 98.
    Schürch CM, Bhate SS, Barlow GL, Phillips DJ, Noti L et al. 2020. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182:51341–59.e19
    [Google Scholar]
  99. 99.
    Damond N, Engler S, Zanotelli VRT, Schapiro D, Wasserfall CH et al. 2019. A map of human type 1 diabetes progression by imaging mass cytometry. Cell Metab. 29:3755–68.e5
    [Google Scholar]
  100. 100.
    Kueckelhaus J, von Ehr J, Ravi VM, Will P, Joseph K et al. 2020. Inferring spatially transient gene expression pattern from spatial transcriptomic studies. bioRxiv 2020.10.20.346544. https://doi.org/10.1101/2020.10.20.346544
    [Crossref]
  101. 101.
    Pham D, Tan X, Xu J, Grice LF, Lam PY et al. 2020. stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues. bioRxiv 2020.05.31.125658. https://doi.org/10.1101/2020.05.31.125658
  102. 102.
    Burnett CE, Okholm TLH, Tenvooren I, Marquez DM, Tamaki S et al. 2022. Mass cytometry reveals a conserved immune trajectory of recovery in hospitalized COVID-19 patients. Immunity 55:71284–98.e3
    [Google Scholar]
  103. 103.
    Keeler AB, Van Deusen AL, Cheng I, Williams CM, Goggin SM et al. 2022. A developmental atlas of somatosensory diversification and maturation in the dorsal root ganglia by single-cell mass cytometry. Nat. Neurosci. 25:1543–58
    [Google Scholar]
  104. 104.
    Molania R, Gagnon-Bartsch JA, Dobrovic A, Speed TP. 2019. A new normalization for Nanostring nCounter gene expression data. Nucleic Acids Res. 47:126073–83
    [Google Scholar]
  105. 105.
    Teh CE, Tan T, Trussart M, Luo M, Thijssen R et al. 2021. Deep profiling of chronic lymphocytic leukaemia (CLL) and healthy immune cells by mass cytometry resolves impacts of venetoclax pressure. Blood 138:Suppl. 13710
    [Google Scholar]
  106. 106.
    Wilk AJ, Lee MJ, Wei B, Parks B, Pi R et al. 2021. Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19. J. Exp. Med. 218:8e20210582
    [Google Scholar]
  107. 107.
    Spaan AN, Neehus A-L, Laplantine E, Staels F, Ogishi M et al. 2022. Human OTULIN haploinsufficiency impairs cell-intrinsic immunity to staphylococcal α-toxin. Science 376:6599eabm6380
    [Google Scholar]
  108. 108.
    Kohonen T. 1982. Self-organized formation of topologically correct feature maps. Biol. Cybernet. 43:159–69
    [Google Scholar]
  109. 109.
    Huang Y, Shin JE, Xu AM, Yao C, Joung S et al. 2022. Evidence of premature lymphocyte aging in people with low anti-spike antibody levels after BNT162b2 vaccination. iScience 25:10105209
    [Google Scholar]
  110. 110.
    Fletez-Brant K, Špidlen J, Brinkman RR, Roederer M, Chattopadhyay PK. 2016. flowClean: automated identification and removal of fluorescence anomalies in flow cytometry data. Cytometry A 89:5461–71
    [Google Scholar]
  111. 111.
    Chevrier S, Crowell HL, Zanotelli VRT, Engler S, Robinson MD, Bodenmiller B. 2018. Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst. 6:5612–20.e5
    [Google Scholar]
  112. 112.
    Nowicka M, Krieg C, Crowell HL, Weber LM, Hartmann FJ et al. 2017. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Research 6:748
    [Google Scholar]
  113. 113.
    Chen H, Lau MC, Wong MT, Newell EW, Poidinger M, Chen J. 2016. Cytofkit: a bioconductor package for an integrated mass cytometry data analysis pipeline. PLOS Comput. Biol. 12:9e1005112
    [Google Scholar]
  114. 114.
    Casado J, Lehtonen O, Rantanen V, Kaipio K, Pasquini L et al. 2021. Agile workflow for interactive analysis of mass cytometry data. Bioinformatics 37:91263–68
    [Google Scholar]
  115. 115.
    Olsen LR, Leipold MD, Pedersen CB, Maecker HT. 2019. The anatomy of single cell mass cytometry data. Cytometry A 95:2156–72
    [Google Scholar]
  116. 116.
    Patel AJ, Willsmore ZN, Khan N, Richter A, Naidu B et al. 2022. Regulatory B cell repertoire defects predispose lung cancer patients to immune-related toxicity following checkpoint blockade. Nat. Commun. 13:3148
    [Google Scholar]
  117. 117.
    Turner TC, Sok MCP, Hymel LA, Pittman FS, York WY et al. 2020. Harnessing lipid signaling pathways to target specialized pro-angiogenic neutrophil subsets for regenerative immunotherapy. Sci. Adv. 6:44eaba7702
    [Google Scholar]
  118. 118.
    Hymel LA, Ogle ME, Anderson SE, San Emeterio CL, Turner TC et al. 2021. Modulating local S1P receptor signaling as a regenerative immunotherapy after volumetric muscle loss injury. J. Biomed. Mater. Res. A 109:5695–712
    [Google Scholar]
  119. 119.
    Jeger-Madiot R, Vaineau R, Heredia M, Tchitchek N, Bertrand L et al. 2022. Naive and memory CD4+ T cell subsets can contribute to the generation of human Tfh cells. iScience 25:1103566
    [Google Scholar]
  120. 120.
    Taverna JA, Hung C-N, DeArmond DT, Chen M, Lin C-L et al. 2020. Single-cell proteomic profiling identifies combined AXL and JAK1 inhibition as a novel therapeutic strategy for lung cancer. Cancer Res 80:71551–63
    [Google Scholar]
  121. 121.
    Hartmann FJ, Mrdjen D, McCaffrey E, Glass DR, Greenwald NF et al. 2021. Single-cell metabolic profiling of human cytotoxic T cells. Nat. Biotechnol. 39:2186–97
    [Google Scholar]
  122. 122.
    Cheng Y, Zhu YO, Becht E, Aw P, Chen J et al. 2019. Multifactorial heterogeneity of virus-specific T cells and association with the progression of human chronic hepatitis B infection. Sci. Immunol. 4:32eaau6905
    [Google Scholar]
  123. 123.
    Melsen JE, van Ostaijen-Ten Dam MM, Lankester AC, Schilham MW, van den Akker EB. 2020. A comprehensive workflow for applying single-cell clustering and pseudotime analysis to flow cytometry data. J. Immunol. 205:3864–71
    [Google Scholar]
  124. 124.
    Pardieck IN, van der Sluis TC, van der Gracht ETI, Veerkamp DMB, Behr FM et al. 2022. A third vaccination with a single T cell epitope confers protection in a murine model of SARS-CoV-2 infection. Nat. Commun. 13:3966
    [Google Scholar]
  125. 125.
    Klocperk A, Friedmann D, Schlaak AE, Unger S, Parackova Z et al. 2022. Distinct CD8 T cell populations with differential exhaustion profiles associate with secondary complications in common variable immunodeficiency. J. Clin. Immunol. 42:61254–69
    [Google Scholar]
  126. 126.
    Chretien A-S, Devillier R, Granjeaud S, Cordier C, Demerle C et al. 2021. High-dimensional mass cytometry analysis of NK cell alterations in AML identifies a subgroup with adverse clinical outcome. PNAS 118:22e2020459118
    [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-020422-050255
Loading
/content/journals/10.1146/annurev-biodatasci-020422-050255
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error