Single‐Cell Transcriptomic Analysis

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Zhihong Zheng, Enguo Chen, Weiguo Lu, Gary Mouradian, Matthew Hodges, Mingyu Liang, Pengyuan Liu, Yan Lu

10.1002/cphy.c190037

Source: Volume 10, Issue 2, April 2020

Published online: March 2020

Full Article on Wiley Online Library

Abstract
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Abstract

Single‐cell sequencing measures the sequence information from individual cells using optimized single‐cell isolation protocols and next‐generation sequencing technologies. Recent advancement in single‐cell sequencing has transformed biomedical research, providing insights into diverse biological processes such as mammalian development, immune system function, cellular diversity and heterogeneity, and disease pathogenesis. In this article, we introduce and describe popular commercial platforms for single‐cell RNA sequencing, general workflow for data analysis, repositories and databases, and applications for these approaches in biomedical research. © 2020 American Physiological Society. Compr Physiol 10:767‐783, 2020.

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	Figure 1. A workflow for analyzing the single‐cell transcriptomic data.
	Figure 2. Major biological questions addressed by single‐cell sequencing technologies. (A) Classify cell types. (B) Construct cell developmental trajectories. (C) Quantify cell cycles. (D) Identify marker genes. (E) Identify related pathways.

Figure 1. A workflow for analyzing the single‐cell transcriptomic data.

Figure 2. Major biological questions addressed by single‐cell sequencing technologies. (A) Classify cell types. (B) Construct cell developmental trajectories. (C) Quantify cell cycles. (D) Identify marker genes. (E) Identify related pathways.

References
1.	Abugessaisa I, Noguchi S, Böttcher M, Hasegawa A, Kouno T, Kato S, Tada Y, Ura H, Abe K, Shin JW, Plessy C, Carninci P, Kasukawa T. SCPortalen: Human and mouse single‐cell centric database. Nucleic Acids Res 46: D781‐D787, 2018.
2.	Adam M, Potter AS, Potter SS. Psychrophilic proteases dramatically reduce single‐cell RNA‐seq artifacts: A molecular atlas of kidney development. Development 144: 3625‐3632, 2017.
3.	Aizarani N, Saviano A, Sagar, Mailly L, Durand S, Herman JS, Pessaux P, Baumert TF, Grun D. A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature 572: 199‐204, 2019.
4.	Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 11: R106, 2010.
5.	Anders S, Pyl PT, Huber W. HTSeq—A Python framework to work with high‐throughput sequencing data. Bioinformatics 31: 166‐169, 2015.
6.	Andrews S. FastQC: A quality control tool for high throughput sequence data. 2010. http://www.bioinformatics.babraham.ac.uk/projects/fastqc.
7.	Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR, Butte AJ, Bhattacharya M. Reference‐based analysis of lung single‐cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 20: 163‐172, 2019.
8.	Arneson D, Zhang G, Ying Z, Zhuang Y, Byun HR, Ahn IS, Gomez‐Pinilla F, Yang X. Single cell molecular alterations reveal target cells and pathways of concussive brain injury. Nat Commun 9: 3894, 2018.
9.	Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, Nainys J, Wu K, Kiseliovas V, Setty M, Choi K, Fromme RM, Dao P, McKenney PT, Wasti RC, Kadaveru K, Mazutis L, Rudensky AY, Pe'er D. Single‐cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174: 1293‐1308, 2018.
10.	Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30: 2114‐2120, 2014.
11.	Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA‐Seq experiments. BMC Bioinformatics 11: 94, 2010.
12.	Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single‐cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36: 411‐420, 2018.
13.	Cadwell CR, Scala F, Li S, Livrizzi G, Shen S, Sandberg R, Jiang X, Tolias AS. Multimodal profiling of single‐cell morphology, electrophysiology, and gene expression using Patch‐seq. Nat Protoc 12: 2531‐2553, 2017.
14.	Cao Y, Zhu J, Jia P, Zhao Z. scRNASeqDB: A database for RNA‐seq based gene expression profiles in human single cells. Genes (Basel) 8. pii: E368, 2017.
15.	Chang JC. Cancer stem cells: Role in tumor growth, recurrence, metastasis, and treatment resistance. Medicine 95: S20‐S25, 2016.
16.	Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348: aaa6090, 2015.
17.	Chen L, Lee JW, Chou CL, Nair AV, Battistone MA, Paunescu TG, Merkulova M, Breton S, Verlander JW, Wall SM, Brown D, Burg MB, Knepper MA. Transcriptomes of major renal collecting duct cell types in mouse identified by single‐cell RNA‐seq. Proc Natl Acad Sci U S A 114: E9989‐E9998, 2017.
18.	Collins FS, Morgan M, Patrinos A. The Human Genome Project: Lessons from large‐scale biology. Science 300: 286‐290, 2003.
19.	Colonna M, Butovsky O. Microglia function in the central nervous system during health and neurodegeneration. Annu Rev Immunol 35: 441‐468, 2017.
20.	Corces MR, Buenrostro JD, Wu B, Greenside PG, Chan SM, Koenig JL, Snyder MP, Pritchard JK, Kundaje A, Greenleaf WJ, Majeti R, Chang HY. Lineage‐specific and single‐cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat Genet 48: 1193‐1203, 2016.
21.	Crinier A, Milpied P, Escaliere B, Piperoglou C, Galluso J, Balsamo A, Spinelli L, Cervera‐Marzal I, Ebbo M, Girard‐Madoux M, Jaeger S, Bollon E, Hamed S, Hardwigsen J, Ugolini S, Vely F, Narni‐Mancinelli E, Vivier E. High‐dimensional single‐cell analysis identifies organ‐specific signatures and conserved NK cell subsets in humans and mice. Immunity 49: 971‐986.e975, 2018.
22.	Cui Y, Zheng Y, Liu X, Yan L, Fan X, Yong J, Hu Y, Dong J, Li Q, Wu X, Gao S, Li J, Wen L, Qiao J, Tang F. Single‐cell transcriptome analysis maps the developmental track of the human heart. Cell Rep 26: 1934‐1950.e1935, 2019.
23.	Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L, Gunderson KL, Steemers FJ, Trapnell C, Shendure J. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348: 910‐914, 2015.
24.	DeLaughter DM, Bick AG, Wakimoto H, McKean D, Gorham JM, Kathiriya IS, Hinson JT, Homsy J, Gray J, Pu W, Bruneau BG, Seidman JG, Seidman CE. Single‐cell resolution of temporal gene expression during heart development. Dev Cell 39: 480‐490, 2016.
25.	Der E, Ranabothu S, Suryawanshi H, Akat KM, Clancy R, Morozov P, Kustagi M, Czuppa M, Izmirly P, Belmont HM, Wang T, Jordan N, Bornkamp N, Nwaukoni J, Martinez J, Goilav B, Buyon JP, Tuschl T, Putterman C. Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis. JCI Insight 2: e93009, 2017.
26.	Ding B, Zheng L, Wang W. Assessment of single cell RNA‐seq normalization methods. G3 (Bethesda) 7: 2039‐2045, 2017.
27.	Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: Ultrafast universal RNA‐seq aligner. Bioinformatics 29: 15‐21, 2013.
28.	Du Y, Guo M, Whitsett JA, Xu Y. ‘LungGENS’: A web‐based tool for mapping single‐cell gene expression in the developing lung. Thorax 70: 1092‐1094, 2015.
29.	Du Y, Kitzmiller JA, Sridharan A, Perl AK, Bridges JP, Misra RS, Pryhuber GS, Mariani TJ, Bhattacharya S, Guo M, Potter SS, Dexheimer P, Aronow B, Jobe AH, Whitsett JA, Xu Y. Lung Gene Expression Analysis (LGEA): An integrative web portal for comprehensive gene expression data analysis in lung development. Thorax 72: 481‐484, 2017.
30.	Fincher CT, Wurtzel O, de Hoog T, Kravarik KM, Reddien PW. Cell type transcriptome atlas for the planarian. Science 360: eaaq1736, 2018.
31.	Frankish A, Diekhans M, Ferreira AM, Johnson R, Jungreis I, Loveland J, Mudge JM, Sisu C, Wright J, Armstrong J, Barnes I, Berry A, Bignell A, Carbonell Sala S, Chrast J, Cunningham F, Di Domenico T, Donaldson S, Fiddes IT, Garcia Giron C, Gonzalez JM, Grego T, Hardy M, Hourlier T, Hunt T, Izuogu OG, Lagarde J, Martin FJ, Martinez L, Mohanan S, Muir P, Navarro FCP, Parker A, Pei B, Pozo F, Ruffier M, Schmitt BM, Stapleton E, Suner MM, Sycheva I, Uszczynska‐Ratajczak B, Xu J, Yates A, Zerbino D, Zhang Y, Aken B, Choudhary JS, Gerstein M, Guigo R, Hubbard TJP, Kellis M, Paten B, Reymond A, Tress ML, Flicek P. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res 47: D766‐D773, 2019.
32.	Franzen O, Gan LM, Bjorkegren JLM. PanglaoDB: A web server for exploration of mouse and human single‐cell RNA sequencing data. Database (Oxford) 2019: 46, 2019.
33.	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. Cell composition analysis of bulk genomics using single‐cell data. Nat Methods 16: 327‐332, 2019.
34.	Fuzik J, Zeisel A, Máté Z, Calvigioni D, Yanagawa Y, Szabó G, Linnarsson S, Harkany T. Integration of electrophysiological recordings with single‐cell RNA‐seq data identifies neuronal subtypes. Nat Biotechnol 34: 175‐183, 2016.
35.	Gardeux V, David FPA, Shajkofci A, Schwalie PC, Deplancke B. ASAP: A web‐based platform for the analysis and interactive visualization of single‐cell RNA‐seq data. Bioinformatics 33: 3123‐3125, 2017.
36.	Gole J, Gore A, Richards A, Chiu YJ, Fung HL, Bushman D, Chiang HI, Chun J, Lo YH, Zhang K. Massively parallel polymerase cloning and genome sequencing of single cells using nanoliter microwells. Nat Biotechnol 31: 1126‐1132, 2013.
37.	Goodwin S, McPherson JD, McCombie WR. Coming of age: Ten years of next‐generation sequencing technologies. Nat Rev Genet 17: 333‐351, 2016.
38.	Green ED, Watson JD, Collins FS. Human Genome Project: Twenty‐five years of big biology. Nature 526: 29‐31, 2015.
39.	Grün D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N, Clevers H, van Oudenaarden A. Single‐cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525: 251‐255, 2015.
40.	Guo M, Du Y, Gokey JJ, Ray S, Bell SM, Adam M, Sudha P, Perl AK, Deshmukh H, Potter SS, Whitsett JA, Xu Y. Single cell RNA analysis identifies cellular heterogeneity and adaptive responses of the lung at birth. Nat Commun 10: 37, 2019.
41.	Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, Saadatpour A, Zhou Z, Chen H, Ye F, Huang D, Xu Y, Huang W, Jiang M, Jiang X, Mao J, Chen Y, Lu C, Xie J, Fang Q, Wang Y, Yue R, Li T, Huang H, Orkin SH, Yuan GC, Chen M, Guo G. Mapping the mouse cell atlas by microwell‐seq. Cell 172: 1091‐1107.e1017, 2018.
42.	Hashimshony T, Wagner F, Sher N, Yanai I. CEL‐seq: Single‐cell RNA‐seq by multiplexed linear amplification. Cell Rep 2: 666‐673, 2012.
43.	He L, Vanlandewijck M, Mae MA, Andrae J, Ando K, Del Gaudio F, Nahar K, Lebouvier T, Lavina B, Gouveia L, Sun Y, Raschperger E, Segerstolpe A, Liu J, Gustafsson S, Rasanen M, Zarb Y, Mochizuki N, Keller A, Lendahl U, Betsholtz C. Single‐cell RNA sequencing of mouse brain and lung vascular and vessel‐associated cell types. Sci Data 5: 180160, 2018.
44.	Hochane M, van den Berg PR, Fan X, Berenger‐Currias N, Adegeest E, Bialecka M, Nieveen M, Menschaart M, Chuva de Sousa Lopes SM, Semrau S. Single‐cell transcriptomics reveals gene expression dynamics of human fetal kidney development. PLoS Biol 17: e3000152, 2019.
45.	Huang S. Non‐genetic heterogeneity of cells in development: More than just noise. Development 136: 3853‐3862, 2009.
46.	Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ, Marioni JC, Teichmann SA. Classification of low quality cells from single‐cell RNA‐seq data. Genome Biol 17: 29, 2016.
47.	Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, Lonnerberg P, Linnarsson S. Quantitative single‐cell RNA‐seq with unique molecular identifiers. Nat Methods 11: 163‐166, 2014.
48.	Ji Z, Ji H. TSCAN: Pseudo‐time reconstruction and evaluation in single‐cell RNA‐seq analysis. Nucleic Acids Res 44: e117, 2016.
49.	Jia G, Preussner J, Chen X, Guenther S, Yuan X, Yekelchyk M, Kuenne C, Looso M, Zhou Y, Teichmann S, Braun T. Single cell RNA‐seq and ATAC‐seq analysis of cardiac progenitor cell transition states and lineage settlement. Nat Commun 9: 4877, 2018.
50.	Jiang P, Thomson JA, Stewart R. Quality control of single‐cell RNA‐seq by SinQC. Bioinformatics 32: 2514‐2516, 2016.
51.	Jolliffe IT. Principal component analysis and factor analysis. In: Jolliffe IT, editor. Principal Component Analysis. New York, NY: Springer New York, 1986, p. 115‐128.
52.	Julia M, Telenti A, Rausell A. Sincell: An R/Bioconductor package for statistical assessment of cell‐state hierarchies from single‐cell RNA‐seq. Bioinformatics 31: 3380‐3382, 2015.
53.	Kim D, Langmead B, Salzberg SL. HISAT: A fast spliced aligner with low memory requirements. Nat Methods 12: 357‐360, 2015.
54.	Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN, Reik W, Barahona M, Green AR, Hemberg M. SC3: Consensus clustering of single‐cell RNA‐seq data. Nat Methods 14: 483‐486, 2017.
55.	Kitzman JO. Haplotypes drop by drop. Nat Biotechnol 34: 296‐298, 2016.
56.	Kivioja T, Vaharautio A, Karlsson K, Bonke M, Enge M, Linnarsson S, Taipale J. Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods 9: 72‐74, 2011.
57.	Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, Peshkin L, Weitz DA, Kirschner MW. Droplet barcoding for single‐cell transcriptomics applied to embryonic stem cells. Cell 161: 1187‐1201, 2015.
58.	Lareau CA, Duarte FM, Chew JG, Kartha VK, Burkett ZD, Kohlway AS, Pokholok D, Aryee MJ, Steemers FJ, Lebofsky R, Buenrostro JD. Droplet‐based combinatorial indexing for massive‐scale single‐cell chromatin accessibility. Nat Biotechnol 37: 916‐924, 2019.
59.	Lee C‐Y, Chiu Y‐C, Wang L‐B, Kuo Y‐L, Chuang EY, Lai L‐C, Tsai M‐H. Common applications of next‐generation sequencing technologies in genomic research. Transl Cancer Res 2: 33‐45, 2013.
60.	Leinonen R, Sugawara H, Shumway M, International Nucleotide Sequence Database C. The sequence read archive. Nucleic Acids Res 39: D19‐D21, 2011.
61.	Li L, Clevers H. Coexistence of quiescent and active adult stem cells in mammals. Science 327: 542‐545, 2010.
62.	Li L, Dong J, Yan L, Yong J, Liu X, Hu Y, Fan X, Wu X, Guo H, Wang X, Zhu X, Li R, Yan J, Wei Y, Zhao Y, Wang W, Ren Y, Yuan P, Yan Z, Hu B, Guo F, Wen L, Tang F, Qiao J. Single‐cell RNA‐seq analysis maps development of human germline cells and gonadal niche interactions. Cell Stem Cell 20: 891‐892, 2017.
63.	Li Q, Cheng Z, Zhou L, Darmanis S, Neff NF, Okamoto J, Gulati G, Bennett ML, Sun LO, Clarke LE, Marschallinger J, Yu G, Quake SR, Wyss‐Coray T, Barres BA. Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single‐cell RNA sequencing. Neuron 101: 207‐223, 2019.
64.	Liao Y, Smyth GK, Shi W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30: 923‐930, 2014.
65.	Lim JS, Choi BS, Lee JS, Shin C, Yang TJ, Rhee JS, Lee JS, Choi IY. Survey of the applications of NGS to whole‐genome sequencing and expression profiling. Genomics Inf 10: 1‐8, 2012.
66.	Lindström NO, Guo J, Kim AD, Tran T, Guo Q, De Sena Brandine G, Ransick A, Parvez RK, Thornton ME, Baskin L, Grubbs B, McMahon JA, Smith AD, McMahon AP. Conserved and divergent features of mesenchymal progenitor cell types within the cortical nephrogenic niche of the human and mouse kidney. J Am Soc Nephrol 29: 806‐824, 2018.
67.	Lindström NO, McMahon JA, Guo J, Tran T, Guo Q, Rutledge E, Parvez RK, Saribekyan G, Schuler RE, Liao C, Kim AD, Abdelhalim A, Ruffins SW, Thornton ME, Baskin L, Grubbs B, Kesselman C, McMahon AP. Conserved and divergent features of human and mouse kidney organogenesis. J Am Soc Nephrol 29: 785‐805, 2018.
68.	Lindström NO, Tran T, Guo J, Rutledge E, Parvez RK, Thornton ME, Grubbs B, McMahon JA, McMahon AP. Conserved and divergent molecular and anatomic features of human and mouse nephron patterning. J Am Soc Nephrol 29: 825‐840, 2018.
69.	Loo L, Simon JM, Xing L, McCoy ES, Niehaus JK, Guo J, Anton ES, Zylka MJ. Single‐cell transcriptomic analysis of mouse neocortical development. Nat Commun 10: 134, 2019.
70.	Lovatt D, Ruble BK, Lee J, Dueck H, Kim TK, Fisher S, Francis C, Spaethling JM, Wolf JA, Grady MS, Ulyanova AV, Yeldell SB, Griepenburg JC, Buckley PT, Kim J, Sul JY, Dmochowski IJ, Eberwine J. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat Methods 11: 190‐196, 2014.
71.	Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA‐seq data with DESeq2. Genome Biol 15: 550, 2014.
72.	Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T. Transcriptomics technologies. PLoS Comput Biol 13: e1005457, 2017.
73.	Lu F, Liu Y, Inoue A, Suzuki T, Zhao K, Zhang Y. Establishing chromatin regulatory landscape during mouse preimplantation development. Cell 165: 1375‐1388, 2016.
74.	Lun AT, McCarthy DJ, Marioni JC. A step‐by‐step workflow for low‐level analysis of single‐cell RNA‐seq data with Bioconductor. F1000Research 5: 2122, 2016.
75.	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. Highly parallel genome‐wide expression profiling of individual cells using nanoliter droplets. Cell 161: 1202‐1214, 2015.
76.	MacParland SA, Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK, Manuel J, Khuu N, Echeverri J, Linares I, Gupta R, Cheng ML, Liu LY, Camat D, Chung SW, Seliga RK, Shao Z, Lee E, Ogawa S, Ogawa M, Wilson MD, Fish JE, Selzner M, Ghanekar A, Grant D, Greig P, Sapisochin G, Selzner N, Winegarden N, Adeyi O, Keller G, Bader GD, McGilvray ID. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun 9: 4383, 2018.
77.	McInnes L, Healy J, Melville J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv e‐prints, 2018.
78.	Moffitt JR, Bambah‐Mukku D, Eichhorn SW, Vaughn E, Shekhar K, Perez JD, Rubinstein ND, Hao J, Regev A, Dulac C, Zhuang X. Molecular, spatial, and functional single‐cell profiling of the hypothalamic preoptic region. Science 362: eaau5324, 2018.
79.	Ozsolak F, Milos PM. RNA sequencing: Advances, challenges and opportunities. Nat Rev Genet 12: 87‐98, 2011.
80.	Parekh S, Ziegenhain C, Vieth B, Enard W, Hellmann I. zUMIs ‐ A fast and flexible pipeline to process RNA sequencing data with UMIs. Gigascience 7: giy059, 2018.
81.	Park J, Shrestha R, Qiu C, Kondo A, Huang S, Werth M, Li M, Barasch J, Suszták K. Single‐cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360: 758‐763, 2018.
82.	Park JH, Yu Q, Erman B, Appelbaum JS, Montoya‐Durango D, Grimes HL, Singer A. Suppression of IL7Ralpha transcription by IL‐7 and other prosurvival cytokines: A novel mechanism for maximizing IL‐7‐dependent T cell survival. Immunity 21: 289‐302, 2004.
83.	Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, Cahill DP, Nahed BV, Curry WT, Martuza RL, Louis DN, Rozenblatt‐Rosen O, Suva ML, Regev A, Bernstein BE. Single‐cell RNA‐seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344: 1396‐1401, 2014.
84.	Picelli S, Faridani OR, Bjorklund AK, Winberg G, Sagasser S, Sandberg R. Full‐length RNA‐seq from single cells using Smart‐seq2. Nat Protoc 9: 171‐181, 2014.
85.	Pierson E, Yau C. ZIFA: Dimensionality reduction for zero‐inflated single‐cell gene expression analysis. Genome Biol 16: 241, 2015.
86.	Plass M, Solana J, Wolf FA, Ayoub S, Misios A, Glažar P, Obermayer B, Theis FJ, Kocks C, Rajewsky N. Cell type atlas and lineage tree of a whole complex animal by single‐cell transcriptomics. Science 360. pii: eaaq1723, 2018.
87.	Proserpio V, Lonnberg T. Single‐cell technologies are revolutionizing the approach to rare cells. Immunol Cell Biol 94: 225‐229, 2016.
88.	Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C. Single‐cell mRNA quantification and differential analysis with Census. Nat Methods 14: 309‐315, 2017.
89.	Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, Trapnell C. Reversed graph embedding resolves complex single‐cell trajectories. Nat Methods 14: 979‐982, 2017.
90.	Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, Bodenmiller B, Campbell P, Carninci P, Clatworthy M, Clevers H, Deplancke B, Dunham I, Eberwine J, Eils R, Enard W, Farmer A, Fugger L, Gottgens B, Hacohen N, Haniffa M, Hemberg M, Kim S, Klenerman P, Kriegstein A, Lein E, Linnarsson S, Lundberg E, Lundeberg J, Majumder P, Marioni JC, Merad M, Mhlanga M, Nawijn M, Netea M, Nolan G, Pe'er D, Phillipakis A, Ponting CP, Quake S, Reik W, Rozenblatt‐Rosen O, Sanes J, Satija R, Schumacher TN, Shalek A, Shapiro E, Sharma P, Shin JW, Stegle O, Stratton M, Stubbington MJT, Theis FJ, Uhlen M, van Oudenaarden A, Wagner A, Watt F, Weissman J, Wold B, Xavier R, Yosef N, Human cell atlas meeting participants. The human cell atlas. elife 6. pii: e27041, 2017.
91.	Reyfman PA, Walter JM, Joshi N, Anekalla KR, McQuattie‐Pimentel AC, Chiu S, Fernandez R, Akbarpour M, Chen CI, Ren Z, Verma R, Abdala‐Valencia H, Nam K, Chi M, Han S, Gonzalez‐Gonzalez FJ, Soberanes S, Watanabe S, Williams KJN, Flozak AS, Nicholson TT, Morgan VK, Winter DR, Hinchcliff M, Hrusch CL, Guzy RD, Bonham CA, Sperling AI, Bag R, Hamanaka RB, Mutlu GM, Yeldandi AV, Marshall SA, Shilatifard A, Amaral LAN, Perlman H, Sznajder JI, Argento AC, Gillespie CT, Dematte J, Jain M, Singer BD, Ridge KM, Lam AP, Bharat A, Bhorade SM, Gottardi CJ, Budinger GRS, Misharin AV. Single‐cell transcriptomic analysis of human lung provides insights into the pathobiology of pulmonary fibrosis. Am J Respir Crit Care Med 199: 1517‐1536, 2019.
92.	Risso D, Ngai J, Speed TP, Dudoit S. Normalization of RNA‐seq data using factor analysis of control genes or samples. Nat Biotechnol 32: 896‐902, 2014.
93.	Robinson MD, McCarthy DJ, Smyth GK. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26: 139‐140, 2009.
94.	Ryerson AB, Eheman CR, Altekruse SF, Ward JW, Jemal A, Sherman RL, Henley SJ, Holtzman D, Lake A, Noone AM, Anderson RN, Ma J, Ly KN, Cronin KA, Penberthy L, Kohler BA. Annual Report to the Nation on the Status of Cancer, 1975‐2012, featuring the increasing incidence of liver cancer. Cancer 122: 1312‐1337, 2016.
95.	Sarda S, Hannenhalli S. Next‐generation sequencing and epigenomics research: A hammer in search of nails. Genomics Inform 12: 2‐11, 2014.
96.	Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single‐cell gene expression data. Nat Biotechnol 33: 495‐502, 2015.
97.	Schmelzer E, Zhang L, Bruce A, Wauthier E, Ludlow J, Yao HL, Moss N, Melhem A, McClelland R, Turner W, Kulik M, Sherwood S, Tallheden T, Cheng N, Furth ME, Reid LM. Human hepatic stem cells from fetal and postnatal donors. J Exp Med 204: 1973‐1987, 2007.
98.	Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D, Lu D, Chen P, Gertner RS, Gaublomme JT, Yosef N, Schwartz S, Fowler B, Weaver S, Wang J, Wang X, Ding R, Raychowdhury R, Friedman N, Hacohen N, Park H, May AP, Regev A. Single‐cell RNA‐seq reveals dynamic paracrine control of cellular variation. Nature 510: 363‐369, 2014.
99.	Shendure J, Ji H. Next‐generation DNA sequencing. Nat Biotechnol 26: 1135‐1145, 2008.
100.	Smith T, Heger A, Sudbery I. UMI‐tools: Modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res 27: 491‐499, 2017.
101.	Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, Giacomello S, Asp M, Westholm JO, Huss M, Mollbrink A, Linnarsson S, Codeluppi S, Borg Å, Pontén F, Costea PI, Sahlén P, Mulder J, Bergmann O, Lundeberg J, Frisén J. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353: 78‐82, 2016.
102.	Stegle O, Teichmann SA, Marioni JC. Computational and analytical challenges in single‐cell transcriptomics. Nat Rev Genet 16: 133‐145, 2015.
103.	Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive integration of single‐cell data. Cell 177: 1888‐1902.e1821, 2019.
104.	Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, Lao K, Surani MA. mRNA‐Seq whole‐transcriptome analysis of a single cell. Nat Methods 6: 377‐382, 2009.
105.	Tang F, Lao K, Surani MA. Development and applications of single‐cell transcriptome analysis. Nat Methods 8: S6‐S11, 2011.
106.	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. Dissecting the multicellular ecosystem of metastatic melanoma by single‐cell RNA‐seq. Science 352: 189‐196, 2016.
107.	Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32: 381‐386, 2014.
108.	Trapnell C, Pachter L, Salzberg SL. TopHat: Discovering splice junctions with RNA‐Seq. Bioinformatics 25: 1105‐1111, 2009.
109.	Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA‐Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28: 511‐515, 2010.
110.	Turner R, Lozoya O, Wang Y, Cardinale V, Gaudio E, Alpini G, Mendel G, Wauthier E, Barbier C, Alvaro D, Reid LM. Human hepatic stem cell and maturational liver lineage biology. Hepatology 53: 1035‐1045, 2011.
111.	van den Hurk M, Erwin JA, Yeo GW, Gage FH, Bardy C. Patch‐seq protocol to analyze the electrophysiology, morphology and transcriptome of whole single neurons derived from human pluripotent stem cells. Front Mol Neurosci 11: 261, 2018.
112.	van der Maaten LJP, Hinton GE. Visualizing data using t‐SNE. J Mach Learn Res 9: 27, 2008.
113.	Wagner GP, Kin K, Lynch VJ. Measurement of mRNA abundance using RNA‐seq data: RPKM measure is inconsistent among samples. Theory Biosci 131: 281‐285, 2012.
114.	Wang Y, Tang Z, Huang H, Li J, Wang Z, Yu Y, Zhang C, Li J, Dai H, Wang F, Cai T, Tang N. Pulmonary alveolar type I cell population consists of two distinct subtypes that differ in cell fate. Proc Natl Acad Sci U S A 115: 2407‐2412, 2018.
115.	Wang Z, Gerstein M, Snyder M. RNA‐Seq: A revolutionary tool for transcriptomics. Nat Rev Genet 10: 57‐63, 2009.
116.	Welch JD, Hartemink AJ, Prins JF. SLICER: Inferring branched, nonlinear cellular trajectories from single cell RNA‐seq data. Genome Biol 17: 106, 2016.
117.	Wu H, Kirita Y, Donnelly EL, Humphreys BD. Advantages of single‐nucleus over single‐cell RNA sequencing of adult kidney: Rare cell types and novel cell states revealed in fibrosis. J Am Soc Nephrol 30: 23‐32, 2019.
118.	Xie T, Wang Y, Deng N, Huang G, Taghavifar F, Geng Y, Liu N, Kulur V, Yao C, Chen P, Liu Z, Stripp B, Tang J, Liang J, Noble PW, Jiang D. Single‐cell deconvolution of fibroblast heterogeneity in mouse pulmonary fibrosis. Cell Rep 22: 3625‐3640, 2018.
119.	Yang S, Corbett SE, Koga Y, Wang Z, Johnson WE, Yajima M, Campbell JD. Decontamination of ambient RNA in single‐cell RNA‐seq with DecontX. bioRxiv 704015, 2019.
120.	Yang X, Liu D, Liu F, Wu J, Zou J, Xiao X, Zhao F, Zhu B. HTQC: A fast quality control toolkit for Illumina sequencing data. BMC Bioinformatics 14: 33, 2013.
121.	Yu D, Huber W, Vitek O. Shrinkage estimation of dispersion in Negative Binomial models for RNA‐seq experiments with small sample size. Bioinformatics 29: 1275‐1282, 2013.
122.	Yuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, Xu L, Luo T, Yan H, Long Z, Shi A, Zhao T, Xiao Y, Li X. CancerSEA: A cancer single‐cell state atlas. Nucleic Acids Res 47: D900‐D908, 2019.
123.	Zappia L, Phipson B, Oshlack A. Exploring the single‐cell RNA‐seq analysis landscape with the scRNA‐tools database. PLoS Comput Biol 14: e1006245, 2018.
124.	Zheng C, Zheng L, Yoo JK, Guo H, Zhang Y, Guo X, Kang B, Hu R, Huang JY, Zhang Q, Liu Z, Dong M, Hu X, Ouyang W, Peng J, Zhang Z. Landscape of infiltrating T cells in liver cancer revealed by single‐cell sequencing. Cell 169: 1342‐1356, 2017.
125.	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. Massively parallel digital transcriptional profiling of single cells. Nat Commun 8: 14049, 2017.
126.	Zhong S, Zhang S, Fan X, Wu Q, Yan L, Dong J, Zhang H, Li L, Sun L, Pan N, Xu X, Tang F, Zhang J, Qiao J, Wang X. A single‐cell RNA‐seq survey of the developmental landscape of the human prefrontal cortex. Nature 555: 524‐528, 2018.

Teaching Material

Zhihong Zheng, Enguo Chen, Weiguo Lu, Gary Mouradian, Matthew Hodges, Mingyu Liang, Pengyuan Liu, and Yan Lu. Single-cell Transcriptomic Analysis. Compr Physiol 10 : 2020, 767-783.

Didactic Synopsis

Major Teaching Points:

* Single cell sequencing measures the sequence information from individual cells using optimized single-cell isolation protocols and next-generation sequencing technologies.

* Single-cell RNA sequencing platforms commonly used include 10x Chromium system, C1 System (Fluidigm), BD Rhapsody™ Single-Cell Analysis System, and llumina® Bio-Rad® Single-Cell Sequencing Solution.

* Many software tools used in bulk sequencing are also suitable for analyzing single-cell sequencing data. Major steps in single-cell transcriptome analysis include quality control, alignment, read quantification, expression matrix filter, normalization, and visualization.

* A large amount of single cell sequencing data has been generated and deposited into public domains, which is a huge treasure for the research community.

* Single-cell sequencing provides insights into diverse biological processes such as mammalian development, immune system, cellular diversity and heterogeneity, and disease pathogenesis.

* Single-cell sequencing technology is evolving rapidly. With the significant reduction in sequencing costs, single-cell sequencing technology will be increasingly popular in biomedical and genomic research. Meanwhile, it presents a big challenge for efficient data storage, processing, and analysis.

Didactic Legends

The following legends to the figures that appear throughout the article are written to be useful for teaching.

Figure 1. Teaching P oints: A workflow for analyzing the single-cell transcriptomic data

Figure 2. Teaching Points: Major biological questions addressed by single cell sequencing technologies. (A) Classify cell types. (B) Construct cell developmental trajectories. (C) Quantify cell cycles. (D) Identify marker genes. (E) Identify related pathways.

Teaching Material

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How to Cite

Zhihong Zheng, Enguo Chen, Weiguo Lu, Gary Mouradian, Matthew Hodges, Mingyu Liang, Pengyuan Liu, Yan Lu. Single‐Cell Transcriptomic Analysis. Compr Physiol 2020, 10: 767-783. doi: 10.1002/cphy.c190037

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