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Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx

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Stem Cell Transcriptional Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2117))

Abstract

CIBERSORTx is a suite of machine learning tools for the assessment of cellular abundance and cell type-specific gene expression patterns from bulk tissue transcriptome profiles. With this framework, single-cell or bulk-sorted RNA sequencing data can be used to learn molecular signatures of distinct cell types from a small collection of biospecimens. These signatures can then be repeatedly applied to characterize cellular heterogeneity from bulk tissue transcriptomes without physical cell isolation. In this chapter, we provide a detailed primer on CIBERSORTx and demonstrate its capabilities for high-throughput profiling of cell types and cellular states in normal and neoplastic tissues.

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References

  1. Maman S, Witz IP (2018) A history of exploring cancer in context. Nat Rev Cancer 18(6):359–376. https://doi.org/10.1038/s41568-018-0006-7

    Article  CAS  PubMed  Google Scholar 

  2. Valkenburg KC, de Groot AE, Pienta KJ (2018) Targeting the tumour stroma to improve cancer therapy. Nat Rev Clin Oncol 15(6):366–381. https://doi.org/10.1038/s41571-018-0007-1

    Article  PubMed  PubMed Central  Google Scholar 

  3. Ribas A, Wolchok JD (2018) Cancer immunotherapy using checkpoint blockade. Science 359(6382):1350–1355. https://doi.org/10.1126/science.aar4060

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Fan HC, Fu GK, Fodor SPA (2015) Combinatorial labeling of single cells for gene expression cytometry. Science 347(6222). https://doi.org/10.1126/science.1258367

    Article  PubMed  Google Scholar 

  5. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, Peshkin L, Weitz DA, Kirschner MW (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161(5):1187–1201. https://doi.org/10.1016/j.cell.2015.04.044

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM, Trombetta JJ, Weitz DA, Sanes JR, Shalek AK, Regev A, McCarroll SA (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161(5):1202–1214. https://doi.org/10.1016/j.cell.2015.05.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, Desai TJ, Krasnow MA, Quake SR (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509(7500):371–375. https://doi.org/10.1038/nature13173

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Abbas AR, Wolslegel K, Seshasayee D, Modrusan Z, Clark HF (2009) Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS One 4(7):e6098. https://doi.org/10.1371/journal.pone.0006098

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ahn J, Yuan Y, Parmigiani G, Suraokar MB, Diao L, Wistuba II, Wang W (2013) DeMix: deconvolution for mixed cancer transcriptomes using raw measured data. Bioinformatics 29(15):1865–1871

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Angelova M, Charoentong P, Hackl H, Fischer ML, Snajder R, Krogsdam AM, Waldner MJ, Bindea G, Mlecnik B, Galon J, Trajanoski Z (2015) Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy. Genome Biol 16:64. https://doi.org/10.1186/s13059-015-0620-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Aran D, Hu Z, Butte AJ (2017) xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 18(1):220. https://doi.org/10.1186/s13059-017-1349-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gong T, Hartmann N, Kohane IS, Brinkmann V, Staedtler F, Letzkus M, Bongiovanni S, Szustakowski JD (2011) Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples. PLoS One 6(11):e27156. https://doi.org/10.1371/journal.pone.0027156

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kuhn A, Thu D, Waldvogel HJ, Faull RL, Luthi-Carter R (2011) Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain. Nat Methods 8(11):945–947. https://doi.org/10.1038/nmeth.1710

    Article  CAS  PubMed  Google Scholar 

  14. Li B, Severson E, Pignon JC, Zhao H, Li T, Novak J, Jiang P, Shen H, Aster JC, Rodig S, Signoretti S, Liu JS, Liu XS (2016) Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol 17(1):174. https://doi.org/10.1186/s13059-016-1028-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Liebner DA, Huang K, Parvin JD (2014) MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples. Bioinformatics 30(5):682–689. https://doi.org/10.1093/bioinformatics/btt566

    Article  CAS  PubMed  Google Scholar 

  16. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA (2015) Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12(5):453–457. https://doi.org/10.1038/nmeth.3337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Qiao W, Quon G, Csaszar E, Yu M, Morris Q, Zandstra PW (2012) PERT: a method for expression deconvolution of human blood samples from varied microenvironmental and developmental conditions. PLoS Comput Biol 8(12):e1002838. https://doi.org/10.1371/journal.pcbi.1002838

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Quon G, Haider S, Deshwar AG, Cui A, Boutros PC, Morris Q (2013) Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction. Genome Med 5(3):29

    Article  PubMed  PubMed Central  Google Scholar 

  19. Shen-Orr SS, Tibshirani R, Khatri P, Bodian DL, Staedtler F, Perry NM, Hastie T, Sarwal MM, Davis MM, Butte AJ (2010) Cell type-specific gene expression differences in complex tissues. Nat Methods 7(4):287

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhong Y, Wan Y-W, Pang K, Chow LM, Liu Z (2013) Digital sorting of complex tissues for cell type-specific gene expression profiles. BMC bioinformatics 14(1):89

    Article  PubMed  PubMed Central  Google Scholar 

  21. Newman AM, Alizadeh AA (2016) High-throughput genomic profiling of tumor-infiltrating leukocytes. Curr Opin Immunol 41:77–84. https://doi.org/10.1016/j.coi.2016.06.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM, Melton DA, Yanai I (2016) A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst 3(4):e344–e360. https://doi.org/10.1016/j.cels.2016.08.011

    Article  CAS  Google Scholar 

  23. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D, Diehn M, Alizadeh AA (2019) Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. https://doi.org/10.1038/s41587-019-0114-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, Leeson R, Kanodia A, Mei S, Lin JR, Wang S, Rabasha B, Liu D, Zhang G, Margolais C, Ashenberg O, Ott PA, Buchbinder EI, Haq R, Hodi FS, Boland GM, Sullivan RJ, Frederick DT, Miao B, Moll T, Flaherty KT, Herlyn M, Jenkins RW, Thummalapalli R, Kowalczyk MS, Canadas I, Schilling B, Cartwright ANR, Luoma AM, Malu S, Hwu P, Bernatchez C, Forget MA, Barbie DA, Shalek AK, Tirosh I, Sorger PK, Wucherpfennig K, Van Allen EM, Schadendorf D, Johnson BE, Rotem A, Rozenblatt-Rosen O, Garraway LA, Yoon CH, Izar B, Regev A (2018) A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175 (4):984–997. e924. doi:https://doi.org/10.1016/j.cell.2018.09.006

    Article  PubMed  PubMed Central  Google Scholar 

  25. Li H, van der Leun AM, Yofe I, Lubling Y, Gelbard-Solodkin D, van Akkooi ACJ, van den Braber M, Rozeman EA, Haanen J, Blank CU, Horlings HM, David E, Baran Y, Bercovich A, Lifshitz A, Schumacher TN, Tanay A, Amit I (2019) Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176 (4):775–789. e718. doi:https://doi.org/10.1016/j.cell.2018.11.043

    Article  PubMed  Google Scholar 

  26. Tirosh I, Izar B, Prakadan SM, Wadsworth MH 2nd, Treacy D, Trombetta JJ, Rotem A, Rodman C, Lian C, Murphy G, Fallahi-Sichani M, Dutton-Regester K, Lin JR, Cohen O, Shah P, Lu D, Genshaft AS, Hughes TK, Ziegler CG, Kazer SW, Gaillard A, Kolb KE, Villani AC, Johannessen CM, Andreev AY, Van Allen EM, Bertagnolli M, Sorger PK, Sullivan RJ, Flaherty KT, Frederick DT, Jane-Valbuena J, Yoon CH, Rozenblatt-Rosen O, Shalek AK, Regev A, Garraway LA (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352(6282):189–196. https://doi.org/10.1126/science.aad0501

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Puram SV, Tirosh I, Parikh AS, Patel AP, Yizhak K, Gillespie S, Rodman C, Luo CL, Mroz EA, Emerick KS, Deschler DG, Varvares MA, Mylvaganam R, Rozenblatt-Rosen O, Rocco JW, Faquin WC, Lin DT, Regev A, Bernstein BE (2017) Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171(7):1611–1624. e1624. https://doi.org/10.1016/j.cell.2017.10.044

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Andor N, Simonds EF, Czerwinski DK, Chen J, Grimes SM, Wood-Bouwens C, Zheng GXY, Kubit MA, Greer S, Weiss WA, Levy R, Ji HP (2019) Single-cell RNA-seq of follicular lymphoma reveals malignant B-cell types and coexpression of T-cell immune checkpoints. Blood 133(10):1119–1129. https://doi.org/10.1182/blood-2018-08-862292

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Karaayvaz M, Cristea S, Gillespie SM, Patel AP, Mylvaganam R, Luo CC, Specht MC, Bernstein BE, Michor F, Ellisen LW (2018) Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq. Nat Commun 9(1):3588. https://doi.org/10.1038/s41467-018-06052-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Giraddi RR, Chung CY, Heinz RE, Balcioglu O, Novotny M, Trejo CL, Dravis C, Hagos BM, Mehrabad EM, Rodewald LW, Hwang JY, Fan C, Lasken R, Varley KE, Perou CM, Wahl GM, Spike BT (2018) Single-cell transcriptomes distinguish stem cell state changes and lineage specification programs in early mammary gland development. Cell Rep 24(6):1653–1666. e1657. https://doi.org/10.1016/j.celrep.2018.07.025

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36(5):411–420. https://doi.org/10.1038/nbt.4096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ji Z, Ji H (2016) TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res 44(13):e117. https://doi.org/10.1093/nar/gkw430

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN, Reik W, Barahona M, Green AR, Hemberg M (2017) SC3: consensus clustering of single-cell RNA-seq data. Nat Methods 14(5):483–486. https://doi.org/10.1038/nmeth.4236

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lin P, Troup M, Ho JW (2017) CIDR: ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol 18(1):59. https://doi.org/10.1186/s13059-017-1188-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, Trapnell C (2017) Reversed graph embedding resolves complex single-cell trajectories. Nat Methods 14(10):979–982. https://doi.org/10.1038/nmeth.4402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Senabouth A, Lukowski SW, Alquicira Hernandez J, Andersen S, Mei X, Nguyen QH, Powell JE (2017, 2017) ascend: R package for analysis of single cell RNA-seq data. bioRxiv:207704. https://doi.org/10.1101/207704

  37. Zurauskiene J, Yau C (2016) pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinformatics 17:140. https://doi.org/10.1186/s12859-016-0984-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, Gregory MT, Shuga J, Montesclaros L, Underwood JG, Masquelier DA, Nishimura SY, Schnall-Levin M, Wyatt PW, Hindson CM, Bharadwaj R, Wong A, Ness KD, Beppu LW, Deeg HJ, McFarland C, Loeb KR, Valente WJ, Ericson NG, Stevens EA, Radich JP, Mikkelsen TS, Hindson BJ, Bielas JH (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049. https://doi.org/10.1038/ncomms14049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lenz G, Wright G, Dave SS, Xiao W, Powell J, Zhao H, Xu W, Tan B, Goldschmidt N, Iqbal J, Vose J, Bast M, Fu K, Weisenburger DD, Greiner TC, Armitage JO, Kyle A, May L, Gascoyne RD, Connors JM, Troen G, Holte H, Kvaloy S, Dierickx D, Verhoef G, Delabie J, Smeland EB, Jares P, Martinez A, Lopez-Guillermo A, Montserrat E, Campo E, Braziel RM, Miller TP, Rimsza LM, Cook JR, Pohlman B, Sweetenham J, Tubbs RR, Fisher RI, Hartmann E, Rosenwald A, Ott G, Muller-Hermelink HK, Wrench D, Lister TA, Jaffe ES, Wilson WH, Chan WC, Staudt LM, Lymphoma/Leukemia Molecular Profiling P (2008) Stromal gene signatures in large-B-cell lymphomas. N Engl J Med 359(22):2313–2323. https://doi.org/10.1056/NEJMoa0802885

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J Jr, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769):503–511. https://doi.org/10.1038/35000501

    Article  CAS  PubMed  Google Scholar 

  41. Diaz-Mejia J, Meng E, Pico A, MacParland S, Ketela T, Pugh T, Bader G, Morris J (2019) Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data [version 1; peer review: 3 approved with reservations]. F1000Research 8(296). https://doi.org/10.12688/f1000research.18490.1

    Article  Google Scholar 

  42. Zhong Y, Liu Z (2011) Gene expression deconvolution in linear space. Nat Methods 9(1):8–9.; author reply 9. https://doi.org/10.1038/nmeth.1830

    Article  CAS  PubMed  Google Scholar 

  43. Sinha R, Stanley G, Gulati GS, Ezran C, Travaglini KJ, Wei E, Chan CKF, Nabhan AN, Su T, Morganti RM, Conley SD, Chaib H, Red-Horse K, Longaker MT, Snyder MP, Krasnow MA, Weissman IL (2017) Index switching causes “spreading-of-signal” among multiplexed samples in Illumina HiSeq 4000 DNA sequencing. bioRxiv 2017:125724. https://doi.org/10.1101/125724

    Article  CAS  Google Scholar 

  44. Smyth GK, Speed T (2003) Normalization of cDNA microarray data. Methods 31(4):265–273. https://doi.org/10.1016/S1046-2023(03)00155-5

    Article  CAS  PubMed  Google Scholar 

  45. Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8(1):118–127. https://doi.org/10.1093/biostatistics/kxj037

    Article  PubMed  Google Scholar 

  46. Giordan M (2014) A two-stage procedure for the removal of batch effects in microarray studies. Stat Biosci 6(1):73–84. https://doi.org/10.1007/s12561-013-9081-1

    Article  Google Scholar 

  47. Bacher R, Chu LF, Leng N, Gasch AP, Thomson JA, Stewart RM, Newton M, Kendziorski C (2017) SCnorm: robust normalization of single-cell RNA-seq data. Nat Methods 14(6):584–586. https://doi.org/10.1038/nmeth.4263

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Haghverdi L, Lun ATL, Morgan MD, Marioni JC (2018) Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol 36(5):421–427. https://doi.org/10.1038/nbt.4091

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hie B, Bryson B, Berger B (2019) Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat Biotechnol. https://doi.org/10.1038/s41587-019-0113-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Johnson M, Purdom E (2017) Clustering of mRNA-Seq data based on alternative splicing patterns. Biostatistics 18(2):295–307. https://doi.org/10.1093/biostatistics/kxw044

    Article  PubMed  Google Scholar 

  51. Schelker M, Feau S, Du J, Ranu N, Klipp E, MacBeath G, Schoeberl B, Raue A (2017) Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Nat Commun 8(1):2032. https://doi.org/10.1038/s41467-017-02289-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wang Z, Cao S, Morris JS, Ahn J, Liu R, Tyekucheva S, Gao F, Li B, Lu W, Tang X, Wistuba II, Bowden M, Mucci L, Loda M, Parmigiani G, Holmes CC, Wang W (2018) Transcriptome deconvolution of heterogeneous tumor samples with immune infiltration. iScience 9:451–460. https://doi.org/10.1016/j.isci.2018.10.028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wang X, Park J, Susztak K, Zhang NR, Li M (2019) Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat Commun 10(1):380. https://doi.org/10.1038/s41467-018-08023-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Frishberg A, Peshes-Yaloz N, Cohn O, Rosentul D, Steuerman Y, Valadarsky L, Yankovitz G, Mandelboim M, Iraqi FA, Amit I, Mayo L, Bacharach E, Gat-Viks I (2019) Cell composition analysis of bulk genomics using single-cell data. Nat Methods 16(4):327–332. https://doi.org/10.1038/s41592-019-0355-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

We would like to thank B. Chen, B. Nabet, and M. Matusiak for assistance in beta-testing CIBERSORTx. This work was supported by grants from the 2019 AACR-AstraZeneca Lymphoma Research Fellowship (C.B.S., 19-40-12-STEE), the National Cancer Institute (A.M.N., R00CA187192; A.A.A., U01CA194389; A.A.A., R01CA188298), the Stinehart-Reed foundation (A.M.N., A.A.A.), the Stanford Bio-X Interdisciplinary Initiatives Seed Grants Program (IIP) (A.M.N.), the Virginia and D.K. Ludwig Fund for Cancer Research (A.M.N., A.A.A.), the US Department of Defense (A.M.N., W81XWH-12-1-0498), anonymous donors (A.A.A., A.M.N.), the Shanahan and Bronzini Family Funds (A.A.A.), the V Foundation for Cancer Research (A.A.A.), the Leukemia and Lymphoma Society (A.A.A.), and the Damon Runyon Cancer Research Foundation (A.A.A.), the American Society of Hematology (A.A.A.).

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Steen, C.B., Liu, C.L., Alizadeh, A.A., Newman, A.M. (2020). Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx. In: Kidder, B. (eds) Stem Cell Transcriptional Networks. Methods in Molecular Biology, vol 2117. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0301-7_7

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