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Transcriptomics at the Single Cell Level and Human Diseases: Opportunities and Challenges in Data Processing and Analysis

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Transcriptomics in Health and Disease

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Abstract

Despite breakthroughs in precision medicine during the past decade due to bulk RNA profiling approaches, those conceal gene expression heterogeneity expected in samples and tissues. Recent developments in single-cell RNA sequencing (scRNA-seq) have enabled researchers to dissect this heterogeneity through genome-wide expression profiling at cellular resolution. This new level of information on cell types and cell states may shed light on molecular mechanisms underlying the pathogenesis of complex diseases, leading to the re-evaluation of the current hypothesis discriminating diseases and treatment subgroups. In this chapter, features of the most relevant technologies for single-cell isolation and library preparation for scRNA-seq are presented, and pipelines for data analysis are discussed, including challenging technical aspects specific to single-cell analysis.

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References

  • Agresti J, Antipov E, Abate AR, Ahn K, Rowat AC, Baret JC, Marquez M, Klibanov AM, Griffiths AD, Weitz DA (2010) Ultrahigh-throughput screening in drop-based microfluidics for directed evolution. Proc Natl Acad Sci U S A 107:4004–4009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Armingol E, Officer A, Harismendy O, Lewis NE (2021) Deciphering cell-cell interactions and communication from gene expression. Nat Rev Genet 22(2):71–88

    Article  CAS  PubMed  Google Scholar 

  • Bevilacqua C, Makhzami S, Helbling JC, Defrenaix P, Martin P (2010) Maintaining RNA integrity in a homogeneous population of mammary epithelial cells isolated by laser capture microdissection. BMC Cell Biol 11:95

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cao J, Packer JS, Ramani V et al (2017) Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357:661–667

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cao J, Spielmann M, Qiu X et al (2019) The single-cell transcriptional landscape of mammalian organogenesis. Nature 566:496–502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cardozo LE, Russo PST, Gomes-Correia B, Araujo-Pereira M, Sepúlveda-Hermosilla G, Maracaja-Coutinho V, Nakaya HI (2019) webCEMiTool: co-expression modular analysis made easy. Front Genet 10(March):146

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chan TE, Stumpf MPH, Babtie AC (2017) Gene regulatory network inference from single-cell data using multivariate information measures. Cell Syst 5(3):251–267.e3

    PubMed  PubMed Central  Google Scholar 

  • Choi H, Sheng J, Gao D, Li F, Durrans A, Ryu S, Lee SB et al (2015) Transcriptome analysis of individual stromal cell populations identifies stroma-tumor crosstalk in mouse lung cancer model. Cell Rep 10(7):1187–1201

    Article  CAS  PubMed  Google Scholar 

  • Citri A, Pang ZP, Südhof TC, Wernig M, Malenka RC (2011) Comprehensive qPCR profiling of gene expression in single neuronal cells. Nat Protoc 7:118–127

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • da Rocha LE, Rowe RG, Lundin V, Malleshaiah M, Jha DK, Rambo CR, Li H, North TE, Collins JJ, Daley GQ (2018) Reconstruction of complex single-cell trajectories using CellRouter. Nat Commun 9(1):892

    Article  CAS  Google Scholar 

  • Duncombe TA, Tentori AM, Herr AE (2015) Microfluidics: reframing biological enquiry. Nat Rev Mol Cell Biol 16:554–567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Espina V, Heiby M, Pierobon M, Liotta (2007) LA Laser capture microdissection technology. Expert Rev Mol Diagn 7:647–657

    Article  CAS  PubMed  Google Scholar 

  • Esumi S, Wu X, Yanagawa Y, Obata K, Sugimoto Y, Tamamaki N (2008) Method for single-cell microarray analysis and application to gene-expression profiling of GABAergic neuron progenitors. Neurosci Res 60:439–451

    Article  CAS  PubMed  Google Scholar 

  • Fend F (2000) Laser capture microdissection in pathology. J Clin Pathol 53:666–672

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Franzén O, Gan L-M, Björkegren JLM (2019) PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019(January). https://doi.org/10.1093/database/baz046

  • Fuller SA, Takahashi M, Hurrell JG (2001) Cloning of hybridoma cell lines by limiting dilution. Curr Protoc Mol Biol:Chapter 11:Unit11.8

    Google Scholar 

  • Gierahn TM, Wadsworth M, Hughes T et al (2017) Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods 14:395–398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Han X, Wang R, Zhou Y et al (2018) Map** the mouse cell atlas by microwell-seq. Cell 172:1091–1107.e17

    Article  PubMed  CAS  Google Scholar 

  • Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ et al (2021) Integrated analysis of multimodal single-cell data. Cell. https://doi.org/10.1016/j.cell.2021.04.048

  • Hashimoto S (2019) Nx1-Seq well based single-cell analysis system. Adv Exp Med Biol 1129:51–61

    Article  CAS  PubMed  Google Scholar 

  • Hashimshony T, Wagner F, Sher N, Yanai I (2012) CEL-Seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep 2:666–673

    Article  CAS  PubMed  Google Scholar 

  • Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y, Anavy L, Gennert D et al (2016) CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol 17(April):77

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Hayashi T et al (2018) Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs. Nat Commun 9:619

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Herzenberg LA, Parks D, Sahaf B, Perez O, Roederer M, Herzenberg LA (2002) The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford. Clin Chem 48:1819–1827

    Article  CAS  PubMed  Google Scholar 

  • Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, Lönnerberg P, Linnarsson S (2014) Quantitative single-cell RNA-Seq with unique molecular identifiers. Nat Methods 11(2):163–166

    Article  CAS  PubMed  Google Scholar 

  • ** S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan C-H, Myung P, Plikus MV, Nie Q (2021) Inference and analysis of cell-cell communication using CellChat. Nat Commun 12(1):1088

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kaminow B, Yunusov D, Dobin A (n.d.) STARsolo: accurate, fast and versatile map**/quantification of single-cell and single-nucleus RNA-Seq data. https://doi.org/10.1101/2021.05.05.442755

  • Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN et al (2017) SC3: consensus clustering of single-cell RNA-Seq data. Nat Methods 14(5):483–486

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 161:1187–1201

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kolodziejczyk AA, Lönnberg T (2018) Global and targeted approaches to single-cell transcriptome characterization. Brief Funct Genomics 17:209–219

    Article  CAS  PubMed  Google Scholar 

  • Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA (2015) The technology and biology of single-cell RNA sequencing. Mol Cell 58:610–620

    Article  CAS  PubMed  Google Scholar 

  • Kouno T, Moody J, Kwon ATJ et al (2019) C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution. Nat Commun 10:360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lindeboom RGH, Regev A, Teichmann SA (2021) Towards a human cell atlas: taking notes from the past. Trends Genet 37:625–630

    Article  CAS  PubMed  Google Scholar 

  • Liu A (2010) Laser capture microdissection in the tissue biorepository. J Biomol Tech 21:120–125

    CAS  PubMed  PubMed Central  Google Scholar 

  • Lu Z, Moraes C, Ye G, Simmons CA, Sun Y (2010) Single cell deposition and patterning with a robotic system. PLoS One 5(10):e13542

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • 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 161:1202–1214

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • McCarthy DJ, Campbell KR, Lun ATL, Wills QF (2017) Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-Seq data in R. Bioinformatics. https://doi.org/10.1093/bioinformatics/btw777

  • Moignard V, Woodhouse S, Haghverdi L, Lilly AJ, Tanaka Y, Wilkinson AC, Buettner F, Macaulay IC, Jawaid W, Diamanti E, Nishikawa SI, Piterman N, Kouskoff V, Theis FJ, Fisher J, Göttgens B (2015) Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat Biotechnol 33:269–276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nakamura N, Ruebel K, ** L, Qian X, Zhang H, Lloyd RV (2007) Laser capture microdissection for analysis of single cells. Methods Mol Med 132:11–18

    Article  CAS  PubMed  Google Scholar 

  • Noël F, Massenet-Regad L, Carmi-Levy I, Cappuccio A, Grandclaudon M, Trichot C, Kieffer Y, Mechta-Grigoriou F, Soumelis V (2021) Dissection of intercellular communication using the transcriptome-based framework ICELLNET. Nat Commun 12(1):1089

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Picelli S et al (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10:1096–1100

    Article  CAS  PubMed  Google Scholar 

  • Rajan S, Djambazian H, Dang HCP et al (2011) The living microarray: a high-throughput platform for measuring transcription dynamics in single cells. BMC Genomics 12:115

    Article  PubMed  PubMed Central  Google Scholar 

  • Ramos TAR, Maracaja-Coutinho V, Miguel Ortega J, do Rêgo TG (2020) CORAZON: a web server for data normalization and unsupervised clustering based on expression profiles. BMC Res Notes 13(1):338

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, Daniels GA, Khrebtukova I, Loring JF, Laurent LC, Schroth GP, Sandberg R (2012) Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30:777–782

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Regev A, Teichmann SA, Lander ES et al (2017) The human cell atlas. elife 6:e27041

    Article  PubMed  PubMed Central  Google Scholar 

  • Rozenblatt-Rosen O, Shin JW, Rood JE, Hupalowska A, Regev A, Heyn H (2021) Building a high-quality human cell atlas. Nat Biotechnol 39(2):149

    Article  CAS  PubMed  Google Scholar 

  • Russo PST, Ferreira GR, Cardozo LE, Bürger MC, Arias-Carrasco R, Maruyama SR, Thiago D. C. Hirata, et al. (2018) CEMiTool: a bioconductor package for performing comprehensive modular co-expression analyses. BMC Bioinf 19(1):56

    Article  CAS  Google Scholar 

  • Sasagawa Y, Nikaido I, Hayashi T, Danno H, Uno KD, Imai T, Ueda HR (2013) Quartz-Seq: a highly reproducible and sensitive single-cell RNA-Seq reveals non-genetic gene expression heterogeneity. Genome Biol 14:R31

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Srivastava A, Malik L, Smith T, Sudbery I, Patro R (2019) Alevin efficiently estimates accurate gene abundances from dscRNA-Seq data. Genome Biol 20(1):65

    Article  PubMed  PubMed Central  Google Scholar 

  • Stegle O, Teichmann SA, Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16:133–145

    Article  CAS  PubMed  Google Scholar 

  • Svensson V, Vento-Tormo R, Teichmann SA (2018) Exponential scaling of single-cell RNA-seq in the past decade. Nat Protoc 13:599–604

    Article  CAS  PubMed  Google Scholar 

  • Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, Lao K, Surani MA (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6:377–382

    Article  CAS  PubMed  Google Scholar 

  • Tian L, Su S, Dong X, Amann-Zalcenstein D, Biben C, Seidi A, Hilton DJ, Naik SH, Ritchie ME (2018) scPipe: a flexible R/bioconductor preprocessing pipeline for single-cell RNA-sequencing data. PLoS Comput Biol 14(8):e1006361

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL (2014) The Dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32(4):381–386

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Van de Sande B, Flerin C, Davie K, De Waegeneer M, Hulselmans G, Aibar S, Seurinck R et al (2020) A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc 15(7):2247–2276

    Article  PubMed  CAS  Google Scholar 

  • Wang Y, Wang R, Zhang S, Song S, Jiang C, Han G, Wang M, Ajani J, Futreal A, Wang L (n.d.) iTALK: an R package to characterize and illustrate intercellular communication. https://doi.org/10.1101/507871

  • White AK, VanInsberghe M, Petriv OI, Hamidi M, Sikorski MMA, Piret J, Aparicio S, Hansen CL (2011) High-throughput microfluidic single-cell RT-qPCR. Proc Natl Acad Sci U S A 108:13999–14004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Whitesides GM (2006) The origins and the future of microfluidics. Nature 442:368–373

    Article  CAS  PubMed  Google Scholar 

  • Wolf FA, Angerer P, Theis FJ (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19(1):15

    Article  PubMed  PubMed Central  Google Scholar 

  • Wright G, Tucker MJ, Morton PC, Sweitzer-Yoder CL, Smith SE (1998) Micromanipulation in assisted reproduction: a review of current technology. Curr Opin Obstet Gynecol 10:221–226

    Article  CAS  PubMed  Google Scholar 

  • Wu M, Singh AK (2012) Single-cell protein analysis. Curr Opin Biotechnol 23:83–88

    Article  CAS  PubMed  Google Scholar 

  • **n Y, Kim J, Ni M, Wei Y, Okamoto H, Lee J, Adler C, Cavino K, Murphy AJ, Yancopoulos GD, Lin HC, Gromada J (2016) Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. Proc Natl Acad Sci U S A 113:3293–3298

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, Xu L et al (2019) CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res 47(D1):D900–D908

    Article  CAS  PubMed  Google Scholar 

  • Zhang X, Lan Y, Xu J, Quan F, Zhao E, Deng C, Luo T et al (2019) CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res 47(D1):D721–D728

    Article  CAS  PubMed  Google Scholar 

  • Zheng GXY, 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhong Q, Bhattacharya S, Kotsopoulos S, Olson J, Taly V, Griffiths AD, Link DR, Larson JW (2011) Multiplex digital PCR: breaking the one target per color barrier of quantitative PCR. Lab Chip 11:2167–2174

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Advanced Center for Chronic Diseases – ACCDiS, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile

    Vinicius Maracaja-Coutinho

  2. Instituto Israelita de Ensino e Pesquisa Albert Einstein – Hospital Israelita Albert Einstein, São Paulo, SP, Brazil

    Patricia Severino

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  1. Vinicius Maracaja-Coutinho
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  2. Patricia Severino
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Correspondence to Patricia Severino .

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  1. Molecular Immunogenetics Group, Department of Genetics, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil

    Geraldo A. Passos

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Maracaja-Coutinho, V., Severino, P. (2022). Transcriptomics at the Single Cell Level and Human Diseases: Opportunities and Challenges in Data Processing and Analysis. In: Passos, G.A. (eds) Transcriptomics in Health and Disease. Springer, Cham. https://doi.org/10.1007/978-3-030-87821-4_5

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