New avenues for systematically inferring cellcell communication: through single-cell transcriptomics data

doi:10.1007/s13238-020-00727-5

Protein Cell

2020, Vol. 11

Issue (12) : 866-880 https://doi.org/10.1007/s13238-020-00727-5

REVIEW

New avenues for systematically inferring cellcell communication: through single-cell transcriptomics data

Xin Shao¹, Xiaoyan Lu¹, Jie Liao¹, Huajun Chen^2,³, Xiaohui Fan^1,⁴(

)

¹. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
². College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
³. The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
⁴. The Save Sight Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2000, Australia

Download: PDF(1082 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

For multicellular organisms, cell-cell communication is essential to numerous biological processes. Drawing upon the latest development of single-cell RNA-sequencing (scRNA-seq), high-resolution transcriptomic data have deepened our understanding of cellular phenotype heterogeneity and composition of complex tissues, which enables systematic cell-cell communication studies at a single-cell level. We first summarize a common workflow of cell-cell communication study using scRNA-seq data, which often includes data preparation, construction of communication networks, and result validation. Two common strategies taken to uncover cell-cell communications are reviewed, e.g., physically vicinal structure-based and ligand-receptor interaction-based one. To conclude, challenges and current applications of cell-cell communication studies at a single-cell resolution are discussed in details and future perspectives are proposed.

Keywords cell-cell communication single-cell RNA sequencing physical contact-dependent communication chemical signal-dependent communication ligand-receptor interaction network biology

Corresponding Author(s): Xiaohui Fan

Online First Date: 14 September 2020 Issue Date: 22 December 2020

Cite this article:

Xin Shao,Xiaoyan Lu,Jie Liao, et al. New avenues for systematically inferring cellcell communication: through single-cell transcriptomics data[J]. Protein Cell, 2020, 11(12): 866-880.

URL:

https://academic.hep.com.cn/pac/EN/10.1007/s13238-020-00727-5
https://academic.hep.com.cn/pac/EN/Y2020/V11/I12/866

1	R Albert, H Jeong, AL Barabasi (2000) Error and attack tolerance of complex networks. Nature 406:378–382 https://doi.org/10.1038/35019019
2	D Aran, AP Looney, L Liu, E Wu, V Fong, A Hsu, S Chak, RP Naikawadi, PJ Wolters, AR Abateet al. (2019) Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 20:163–172 https://doi.org/10.1038/s41590-018-0276-y
3	DR Bandura, VI Baranov, OI Ornatsky, A Antonov, R Kinach, X Lou, S Pavlov, S Vorobiev, JE Dick, SD Tanner (2009) Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem 81:6813–6822 https://doi.org/10.1021/ac901049w
4	AL Barabasi, ZN Oltvai (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113 https://doi.org/10.1038/nrg1272
5	M Bessis(1958) Erythroblastic island, functional unity of bone marrow. Rev Hematol 13:8–11
6	JC Boisset, J Vivie, D Grun, MJ Muraro, A Lyubimova, A van Oudenaarden(2018) Mapping the physical network of cellular interactions. Nat Methods 15:547–553 https://doi.org/10.1038/s41592-018-0009-z
7	VM Braga (2002) Cell-cell adhesion and signalling. Curr Opin Cell Biol 14:546–556 https://doi.org/10.1016/S0955-0674(02)00373-3
8	B Budnik, E Levy, G Harmange, N Slavov (2018) SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol 19:161 https://doi.org/10.1186/s13059-018-1547-5
9	JC Burns, MC Kelly, M Hoa, RJ Morell, MW Kelley (2015) Single-cell RNA-Seq resolves cellular complexity in sensory organs from the neonatal inner ear. Nat Commun 6:8557 https://doi.org/10.1038/ncomms9557
10	JG Camp, K Sekine, T Gerber, H Loeffler-Wirth, H Binder, M Gac, S Kanton, J Kageyama, G Damm, D Seehoferet al. (2017) Multilineage communication regulates human liver bud development from pluripotency. Nature 546:533–538 https://doi.org/10.1038/nature22796
11	J Cao, JS Packer, V Ramani, DA Cusanovich, C Huynh, R Daza, X Qiu, C Lee, SN Furlan, FJ Steemerset al. (2017) Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357:661–667 https://doi.org/10.1126/science.aam8940
12	J Cao, DA Cusanovich, V Ramani, D Aghamirzaie, HA Pliner, AJ Hill, RM Daza, JL McFaline-Figueroa, JS Packer, L Christiansenet al. (2018) Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361:1380–1385 https://doi.org/10.1126/science.aau0730
13	LF Cheow, ET Courtois, Y Tan, R Viswanathan, Q Xing, RZ Tan, DS Tan, P, Robson YH Loh, SR Quakeet al.(2016) Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat Methods 13:833–836 https://doi.org/10.1038/nmeth.3961
14	M Cohen, A Giladi, AD Gorki, DG Solodkin, M Zada, A Hladik, A Miklosi, TM Salame, KB Halpern, E Davidet al. (2018) Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175:1031–1044 e1018 https://doi.org/10.1016/j.cell.2018.09.009
15	BC Collins, R Aebersold (2018) Proteomics goes parallel. Nat Biotechnol 36:1051–1053 https://doi.org/10.1038/nbt.4288
16	L Duan, XD Zhang, WY Miao, YJ Sun, G Xiong, Q, Wu G, Li P Yang, H Yu, H Liet al. (2018) PDGFRbeta cells rapidly relay inflammatory signal from the circulatory system to neurons via chemokine CCL2. Neuron 100:183–200 e188 https://doi.org/10.1016/j.neuron.2018.08.030
17	M Efremova, M Vento-Tormo , SA Teichmann, R Vento-Tormo (2020) CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc 15(4):1484–1506 https://doi.org/10.1038/s41596-020-0292-x
18	CL Eng, M Lawson, Q Zhu, R Dries, N Koulena, Y Takei, J Yun, C Cronin, C, Karp GC Yuanet al. (2019) Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568:235–239 https://doi.org/10.1038/s41586-019-1049-y
19	WH Evans (2015) Cell communication across gap junctions: a historical perspective and current developments. Biochem Soc Trans 43:450–459 https://doi.org/10.1042/BST20150056
20	DM Fernandez, AH Rahman, NF Fernandez, A Chudnovskiy, ED Amir, L Amadori, NS Khan, CK Wong, R Shamailova, CA Hillet al. (2019) Single-cell immune landscape of human atherosclerotic plaques. Nat Med 25:1576–1588 https://doi.org/10.1038/s41591-019-0590-4
21	S, Gao L Yan, R Wang, J Li, J Yong, X Zhou, Y Wei, X Wu, X Wang, X Fanet al. (2018) Tracing the temporal-spatial transcriptome landscapes of the human fetal digestive tract using single-cell RNA-sequencing. Nat Cell Biol 20:721–734 https://doi.org/10.1038/s41556-018-0105-4
22	ZJ Gartner, JA Prescher, LD Lavis (2017) Unraveling cell-to-cell signaling networks with chemical biology. Nat Chem Biol 13:564–568 https://doi.org/10.1038/nchembio.2391
23	D Grun, A Lyubimova, L, Kester K Wiebrands, O Basak, N Sasaki, H Clevers, A van Oudenaarden (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525:251–255 https://doi.org/10.1038/nature14966
24	KB Halpern, R Shenhav, O, Matcovitch-Natan B Toth, D Lemze, M Golan, EE Massasa, S, Baydatch S Landen, AE Mooret al. (2017) Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542:352–356 https://doi.org/10.1038/nature21065
25	T, Hashimshony F, Wagner N Sher, I Yanai (2012) CEL-Seq: singlecell RNA-Seq by multiplexed linear amplification. Cell Rep 2:666–673 https://doi.org/10.1016/j.celrep.2012.08.003
26	Y, Hu X Wang, B Hu, Y Mao, Y Chen, L, Yan J Yong, J Dong, Y Wei, W Wanget al. (2019) Dissecting the transcriptome landscape of the human fetal neural retina and retinal pigment epithelium by single-cell RNA-seq analysis. PLoS Biol 17:e3000365 https://doi.org/10.1371/journal.pbio.3000365
27	DA Jaitin, E Kenigsberg, H Keren-Shaul, N Elefant, F Paul, I, Zaretsky A Mildner, N, Cohen S Jung, A Tanayet al. (2014) Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343:776–779 https://doi.org/10.1126/science.1247651
28	DC Kirouac, GJ Madlambayan, M Yu, EA Sykes, C Ito, PW Zandstra (2009) Cell-cell interaction networks regulate blood stem and progenitor cell fate. Mol Syst Biol 5:293 https://doi.org/10.1038/msb.2009.49
29	VY Kiselev, A Yiu, M Hemberg (2018) scmap: projection of singlecell RNA-seq data across data sets. Nat Methods 15:359–362 https://doi.org/10.1038/nmeth.4644
30	AM Klein, L Mazutis, I Akartuna, N Tallapragada, A Veres, V, Li L Peshkin, DA Weitz, MW Kirschner (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–1201 https://doi.org/10.1016/j.cell.2015.04.044
31	V Kumar, L Donthireddy, D Marvel, T Condamine, F, Wang S Lavilla-Alonso, A Hashimoto, P Vonteddu, R Behera, MA Goinset al. (2017) Cancer-associated fibroblasts neutralize the anti-tumor effect of CSF1 receptor blockade by inducing PMN-MDSC infiltration of tumors. Cancer Cell 32:654–668 e655 https://doi.org/10.1016/j.ccell.2017.10.005
32	MP Kumar, J Du, G, Lagoudas Y Jiao, A Sawyer, DC Drummond, DA Lauffenburger, A Raue (2018) Analysis of single-cell RNASeq identifies cell-cell communication associated with tumor characteristics. Cell Rep 25:1458–1468e1454 https://doi.org/10.1016/j.celrep.2018.10.047
33	L Li, J, Dong L Yan, J Yong, X Liu, Y, Hu X Fan, X Wu, H Guo, X Wanget al. (2017) Single-cell RNA-Seq analysis maps development of human germline cells and gonadal niche interactions. Cell Stem Cell 20:858–873 e854 https://doi.org/10.1016/j.stem.2017.03.007
34	J Liao, C, Hao W, Huang X Shao, Y, Song L Liu, N Ai, X Fan (2018) Network pharmacology study reveals energy metabolism and apoptosis pathways-mediated cardioprotective effects of Shenqi Fuzheng. J Ethnopharmacol 227:155–165 https://doi.org/10.1016/j.jep.2018.08.029
35	X Lin, TJ Spindler, MA de Souza Fonseca, RI Corona, JH Seo, FS Dezem, L Li, JM, Lee HW Long, TA Sellerset al. (2019) Superenhancer-associated LncRNA UCA1 interacts directly with AMOT to activate YAP target genes in epithelial ovarian cancer. iScience 17:242–255 https://doi.org/10.1016/j.isci.2019.06.025
36	EZ Macosko, A Basu, R Satija, J Nemesh, K Shekhar, M Goldman, I Tirosh, AR Bialas, N Kamitaki, EM Marterstecket al. (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–1214 https://doi.org/10.1016/j.cell.2015.05.002
37	D Manwani, JJ Bieker (2008) The erythroblastic island. Curr Top Dev Biol 82:23–53 https://doi.org/10.1016/S0070-2153(07)00002-6
38	JC Martin, C Chang, G, Boschetti R Ungaro, M Giri, JA Grout, K Gettler, LS Chuang, S Nayar, AJ Greensteinet al. (2019) Single-cell analysis of Crohn’s disease lesions identifies a pathogenic cellular module associated with resistance to anti- TNF therapy. Cell 178:1493–1508e1420 https://doi.org/10.1016/j.cell.2019.08.008
39	V Marx (2019) A dream of single-cell proteomics. Nat Methods 16:809–812 https://doi.org/10.1038/s41592-019-0540-6
40	K Mittal, E Eremenko, O Berner, Y Elyahu, I Strominger, D Apelblat, A Nemirovsky, I Spiegel, A Monsonego (2019) CD4 T cells induce a subset of MHCII-expressing microglia that attenuates Alzheimer pathology. iScience 16:298–311 https://doi.org/10.1016/j.isci.2019.05.039
41	S Nestorowa, FK Hamey, B Pijuan Sala, E Diamanti, M Shepherd, E Laurenti, NK Wilson, DG Kent, B Gottgens (2016) A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128:e20–31 https://doi.org/10.1182/blood-2016-05-716480
42	M Nitzan, N Karaiskos, N Friedman, N Rajewsky (2019) Gene expression cartography. Nature 576:132–137 https://doi.org/10.1038/s41586-019-1773-3
43	G Pan, M Cavalli, B Carlsson, S Skrtic, C Kumar, C Wadelius (2020) rs953413 Regulates polyunsaturated fatty acid metabolism by modulating ELOVL2 expression. iScience 23:100808 https://doi.org/10.1016/j.isci.2019.100808
44	F Petersen, L Bock, HD Flad, E Brandt (1999) Platelet factor 4-induced neutrophil-endothelial cell interaction: involvement of mechanisms and functional consequences different from those elicited by interleukin-8. Blood 94:4020–4028 https://doi.org/10.1182/blood.V94.12.4020
45	VM Peterson, KX Zhang, N Kumar, J Wong, L Li, DC Wilson, R Moore, TK, McClanahan S Sadekova, JA Klappenbach (2017) Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol 35:936–939 https://doi.org/10.1038/nbt.3973
46	DW Pfaff, MJ Baum (2018) Hormone-dependent medial preoptic/ lumbar spinal cord/autonomic coordination supporting male sexual behaviors. Mol Cell Endocrinol 467:21–30 https://doi.org/10.1016/j.mce.2017.10.018
47	S Picelli, AK Bjorklund, OR Faridani, S Sagasser, G Winberg, R Sandberg (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10:1096–1098 https://doi.org/10.1038/nmeth.2639
48	P Rajbhandari, D Arneson, SK Hart, IS Ahn, G Diamante, LC Santos, N Zaghari, AC Feng, BJ Thomas, L Vergneset al. (2019) Single cell analysis reveals immune cell-adipocyte crosstalk regulating the transcription of thermogenic adipocytes. Elife 8:e49501 https://doi.org/10.7554/eLife.49501.021
49	JA Ramilowski, T Goldberg, J Harshbarger, E Kloppmann, M Lizio, VP Satagopam, M Itoh, H Kawaji, P Carninci, B Rostet al. (2015) A draft network of ligand-receptor-mediated multicellular signalling in human. Nat Commun 6:7866 https://doi.org/10.1038/ncomms8866
50	P, Ramos C Casu, S Gardenghi, L, Breda BJ Crielaard, E, Guy MF Marongiu, R Gupta, RL Levine, O Abdel-Wahabet al. (2013) Macrophages support pathological erythropoiesis in polycythemia vera and beta-thalassemia. Nat Med 19:437–445 https://doi.org/10.1038/nm.3126
51	SG Rodriques, RR Stickels, A Goeva, CA Martin, E Murray, CR Vanderburg, J, Welch LM Chen, F Chen, EZ Macosko (2019) Slide-seq: a scalable technology for measuring genomewide expression at high spatial resolution. Science 363:1463–1467 https://doi.org/10.1126/science.aaw1219
52	M Rothbauer, H Zirath, P Ertl (2018) Recent advances in microfluidic technologies for cell-to-cell interaction studies. Lab Chip 18:249–270 https://doi.org/10.1039/C7LC00815E
53	R Satija, JA Farrell, D Gennert, AF Schier, A Regev (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33:495–502 https://doi.org/10.1038/nbt.3192
54	CL Scott, M Guilliams (2018) Tissue unit-ed: lung cells team up to drive alveolar macrophage development. Cell 175:898–900 https://doi.org/10.1016/j.cell.2018.10.031
55	AK Shalek, R Satija, J Shuga, JJ Trombetta, D Gennert, D Lu, P, Chen RS Gertner, JT Gaublomme, N Yosefet al. (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510:363–369 https://doi.org/10.1038/nature13437
56	X Shao, N Ai, D Xu, X Fan (2016) Exploring the interaction between Salvia miltiorrhiza and human serum albumin: Insights from herbdrug interaction reports, computational analysis and experimental studies. Spectrochim Acta A Mol Biomol Spectrosc 161:1–7 https://doi.org/10.1016/j.saa.2016.02.015
57	X Shao, N Lv, J, Liao J, Long R Xue, N Ai, D Xu, X Fan (2019) Copy number variation is highly correlated with differential gene expression: a pan-cancer study. BMC Med Genet 20:175 https://doi.org/10.1186/s12881-019-0909-5
58	X Shao, J Liao, X Lu, R Xue, N Ai, X Fan (2020) scCATCH: automatic annotation on cell types of clusters from single-cell RNA sequencing data. iScience 23:100882 https://doi.org/10.1016/j.isci.2020.100882
59	BR Shrestha, C Chia, L Wu, SG Kujawa, MC Liberman, LV Goodrich (2018) Sensory neuron diversity in the inner ear is shaped by activity. Cell 174:1229–1246 e1217 https://doi.org/10.1016/j.cell.2018.07.007
60	RE Sicard (1986) Hormones, neurosecretions, and growth factors as signal molecules for intercellular communication. Dev Comp Immunol 10:269–272 https://doi.org/10.1016/0145-305X(86)90011-X
61	SJ Singer (1992) Intercellular communication and cell-cell adhesion. Science 255:1671–1677 https://doi.org/10.1126/science.1313187
62	DA Skelly, GT Squiers, MA McLellan, MT Bolisetty, P Robson, NA Rosenthal, AR Pinto (2018) Single-cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart. Cell Rep 22:600–610 https://doi.org/10.1016/j.celrep.2017.12.072
63	Y, Song X, Xu W Wang, T Tian, Z Zhu, C Yang (2019) Single cell transcriptomics: moving towards multi-omics. Analyst 144:3172–3189 https://doi.org/10.1039/C8AN01852A
64	RB Stagg, WH Fletcher (1990) The hormone-induced regulation of contact-dependent cell-cell communication by phosphorylation. Endocr Rev 11:302–325 https://doi.org/10.1210/edrv-11-2-302
65	M Stoeckius, C Hafemeister, W, Stephenson B, Houck-Loomis PK Chattopadhyay, H Swerdlow, R Satija, P Smibert (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 14:865–868 https://doi.org/10.1038/nmeth.4380
66	T Stuart, R Satija (2019) Integrative single-cell analysis. Nat Rev Genet 20:257–272 https://doi.org/10.1038/s41576-019-0093-7
67	E Sugiyama, MM Guerrini, K Honda, Y Hattori, M Abe, P Kallback, PE Andren, KF Tanaka, M Setou, S Fagarasanet al. (2019) Detection of a high-turnover serotonin circuit in the mouse brain using mass spectrometry imaging. iScience 20:359–372 https://doi.org/10.1016/j.isci.2019.09.036
68	J Swaminathan, AA Boulgakov, ET Hernandez, AM Bardo, JL Bachman, J Marotta, AM Johnson, EV Anslyn, EM Marcotte (2018) Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures. Nat Biotechnol 36:1076–1082 https://doi.org/10.1038/nbt.4278
69	BM Szczerba, F Castro-Giner , M Vetter, I, Krol S Gkountela, J Landin, MC Scheidmann, C Donato, R, Scherrer J Singeret al. (2019) Neutrophils escort circulating tumour cells to enable cell cycle progression. Nature 566:553–557 https://doi.org/10.1038/s41586-019-0915-y
70	I Tirosh, B Izar, SM Prakadan, MH 2nd Wadsworth, D Treacy, JJ Trombetta, A Rotem, C Rodman, C Lian, G Murphyet al. (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352:189–196 https://doi.org/10.1126/science.aad0501
71	R Vento-Tormo, M Efremova, RA Botting, MY Turco, M Vento-Tormo, KB Meyer, JE Park, E Stephenson, K Polanski, A Goncalveset al. (2018) Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 563:347–353 https://doi.org/10.1038/s41586-018-0698-6
72	SA Vitak, KA Torkenczy, JL Rosenkrantz, AJ Fields, L Christiansen, MH Wong, L Carbone, FJ Steemers, A Adey (2017) Sequencing thousands of single-cell genomes with combinatorial indexing. Nat Methods 14:302–308 https://doi.org/10.1038/nmeth.4154
73	C Vogel, EM Marcotte (2012) Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet 13:227–232 https://doi.org/10.1038/nrg3185
74	X Wang, W Song, N Kawazoe, G Chen (2013) The osteogenic differentiation of mesenchymal stem cells by controlled cell-cell interaction on micropatterned surfaces. J Biomed Mater Res A 101:3388–3395 https://doi.org/10.1002/jbm.a.34645
75	X Wang, WE Allen, MA Wright, EL Sylwestrak, N Samusik, S Vesuna, K Evans, C Liu, C Ramakrishnan, J Liuet al. (2018) Threedimensional intact-tissue sequencing of single-cell transcriptional states. Science 361:eaat5691 https://doi.org/10.1126/science.aat5691
76	S Wang, M Karikomi, AL MacLean, Q Nie(2019) Cell lineage and communication network inference via optimization for single-cell transcriptomics. Nucleic Acids Res 47(11):e66 https://doi.org/10.1093/nar/gky882
77	X Xiong, H Kuang, S Ansari, T Liu, J Gong, S Wang, XY Zhao, Y Ji, C Li, L Guoet al.(2019) Landscape of intercellular crosstalk in healthy and NASH liver revealed by single-cell secretome gene analysis. Mol Cell 75:644–660e645 https://doi.org/10.1016/j.molcel.2019.07.028
78	Y Xu, K Ji, M Wu, B Hao, KT Yao, Y Xu (2019) A miRNA-HERC4 pathway promotes breast tumorigenesis by inactivating tumor suppressor LATS1. Protein Cell 10:595–605 https://doi.org/10.1007/s13238-019-0607-2
79	R Xue, J, Liao X Shao, K Han, J Long, L Shao, N Ai, X Fan (2020) Prediction of adverse drug reactions by combining biomedical tripartite network and graph representation model. Chem Res Toxicol 33:202–210 https://doi.org/10.1021/acs.chemrestox.9b00238
80	JA Zepp, WJ Zacharias, DB Frank, CA Cavanaugh, S Zhou, MP Morley, EE Morrisey (2017) Distinct mesenchymal lineages and niches promote epithelial self-renewal and myofibrogenesis in the lung. Cell 170:1134–1148 e1110 https://doi.org/10.1016/j.cell.2017.07.034
81	L Zhang, A Vertes (2018) Single-cell mass spectrometry approaches to explore cellular heterogeneity. Angew Chem Int Ed Engl 57:4466–4477 https://doi.org/10.1002/anie.201709719
82	Y Zhang, Z Yan, Q Qin, V Nisenblat, HM Chang, Y Yu, T Wang, C, Lu M Yang, S Yanget al.(2018) Transcriptome landscape of human folliculogenesis reveals oocyte and granulosa cell interactions. Mol Cell 72:1021–1034 e1024 https://doi.org/10.1016/j.molcel.2018.10.029
83	X Zhang, Y, Lan J Xu, F Quan, E Zhao, C Deng, T Luo, L Xu, G Liao, M Yanet al. (2019) CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res 47:D721–D728 https://doi.org/10.1093/nar/gky900
84	L Zhang, A Vertes (2018) Single-cell mass spectrometry approaches to explore cellular heterogeneity. Angew Chem Int Ed Engl 57:4466–4477 https://doi.org/10.1002/anie.201709719
85	GX Zheng, JM Terry, P, Belgrader P, Ryvkin ZW Bent, R Wilson, SB Ziraldo, TD Wheeler, GP McDermott, J Zhuet al. (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049 https://doi.org/10.1038/ncomms14049
86	G Zheng, C Jiang, Y, Li D Yang, Y, Ma B Zhang, X Li, P Zhang, X Hu, X Zhaoet al.(2019) TMEM43-S358L mutation enhances NFkappaB- TGFbeta signal cascade in arrhythmogenic right ventricular dysplasia/cardiomyopathy. Protein Cell 10:104–119 https://doi.org/10.1007/s13238-018-0563-2
87	B Zhou, C Liu, L Xu, Y Yuan, J Zhao, W Zhao, Y Chen, J Qiu, M Meng, Y Zhenget al.(2020) N(6)-methyladenosine reader protein Ythdc2 suppresses liver steatosis via regulation of mRNA stability of lipogenic genes. Hepatology. https://doi.org/10.1002/hep.31220
88	C, Zhu S Preissl, B Ren (2020) Single-cell multimodal omics: the power of many. Nat Methods 17:11–14 https://doi.org/10.1038/s41592-019-0691-5

[1]	Yugong Ho, Peng Hu, Michael T. Peel, Sixing Chen, Pablo G. Camara, Douglas J. Epstein, Hao Wu, Stephen A. Liebhaber. Single-cell transcriptomic analysis of adult mouse pituitary reveals sexual dimorphism and physiologic demand-induced cellular plasticity[J]. Protein Cell, 2020, 11(8): 565-583.

Viewed

Full text

Abstract

Cited

Shared

Discussed