Cited by 187Single-cell and spatial transcriptomicsDevelopmental Biology and Gene RegulationPluripotent Stem Cells Research

Single-cell RNA-seq analysis unveils a prevalent epithelial/mesenchymal hybrid state during mouse organogenesis

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Dong, Ji, Hu, Yuqiong, Fan, Xiaoying, Wu, Xinglong, Mao, Yunuo, Hu, Boqiang, Guo, Hongshan, Wen, Lu +1 more·2018·Genome Biology

Abstract

BACKGROUND: Organogenesis is crucial for proper organ formation during mammalian embryonic development. However, the similarities and shared features between different organs and the cellular heterogeneity during this process at single-cell resolution remain elusive. RESULTS: We perform single-cell RNA sequencing analysis of 1916 individual cells from eight organs and tissues of E9.5 to E11.5 mouse embryos, namely, the forebrain, hindbrain, skin, heart, somite, lung, liver, and intestine. Based on the regulatory activities rather than the expression patterns, all cells analyzed can be well classified into four major groups with epithelial, mesodermal, hematopoietic, and neuronal identities. For different organs within the same group, the similarities and differences of their features and developmental paths are revealed and reconstructed. CONCLUSIONS: We identify mutual interactions between epithelial and mesenchymal cells and detect epithelial cells with prevalent mesenchymal features during organogenesis, which are similar to the features of intermediate epithelial/mesenchymal cells during tumorigenesis. The comprehensive transcriptome at single-cell resolution profiled in our study paves the way for future mechanistic studies of the gene-regulatory networks governing mammalian organogenesis.

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  • Epithelial-mesenchymal plasticity has been previously identified within hepatic tissues and cell lines.

    “epithelial-mesenchymal plasticity has been previously identified within hepatic tissues and cell lines”

    From Single-Cell Transcriptomics Reveals Early Emergence of Liver Parenchymal and Non-parenchymal Cell Lineages (2020) · cites this paper as Dong et al., 2018

    The Results section on the epithelial/mesenchymal hybrid state reports co-expression of epithelial and mesenchymal markers in developing mouse liver epithelial cells, supporting the hepatic-tissue portion. The paper does not identify this state in hepatic cell lines, and its Discussion says adult liver epithelial cells did not show the same hybrid state. (LLM, conf=0.87)

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References 49 · Cited by 187

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  1. [1]Cunha GR Baskin L Mesenchymal-epithelial interaction techniques Differentiation 2016 91 20 27 10.1016/j.diff.2015.10.006 26610327 PMC4874915 · DOI
  2. [2]Baskin L Hayward S Sutherland R DiSandro M Thomson A Goodman J Cunha G Mesenchymal-epithelial interactions in the bladder World J Urol 1996 14 301 309 10.1007/BF00184602 8912470 · DOI
  3. [3]Cunha GR Hom YK Role of mesenchymal-epithelial interactions in mammary gland development J Mammary Gland Biol Neoplasia 1996 1 21 35 10.1007/BF02096300 10887478 · DOI
  4. [4]Thiery JP Epithelial-mesenchymal transitions in development and pathologies Curr Opin Cell Biol 2003 15 740 746 10.1016/j.ceb.2003.10.006 14644200 · DOI
  5. [5]Lim J Thiery JP Epithelial-mesenchymal transitions: insights from development Development 2012 139 3471 3486 10.1242/dev.071209 22949611 · DOI · Paper page
  6. [7]Nieto MA Huang RY-J Jackson RA Thiery JP EMT: 2016 Cell 2016 166 21 45 10.1016/j.cell.2016.06.028 27368099 · DOI · Paper page
  7. [8]Mitiku N Baker JC Genomic analysis of gastrulation and organogenesis in the mouse Dev Cell 2007 13 897 907 10.1016/j.devcel.2007.10.004 18061570 · DOI
  8. [9]Gerrard DT Berry AA Jennings RE Hanley KP Bobola N Hanley NA An integrative transcriptomic atlas of organogenesis in human embryos elife 2016 5 e15657 10.7554/eLife.15657 27557446 PMC4996651 · DOI
  9. [10]Diez-Roux G Banfi S Sultan M Geffers L Anand S Rozado D Magen A Canidio E Pagani M Peluso I A high-resolution anatomical atlas of the transcriptome in the mouse embryo PLoS Biol 2011 9 e1000582 10.1371/journal.pbio.1000582 21267068 PMC3022534 · DOI · Paper page
  10. [12]Zeisel A Muñoz-Manchado AB Codeluppi S Lönnerberg P La Manno G Juréus A Marques S Munguba H He L Betsholtz C Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq Science 2015 347 1138 1142 10.1126/science.aaa1934 25700174 · DOI · Paper page
  11. [13]DeLaughter DM Bick AG Wakimoto H McKean D Gorham JM Kathiriya IS Hinson JT Homsy J Gray J Pu W Single-cell resolution of temporal gene expression during heart development Dev Cell 2016 39 480 490 10.1016/j.devcel.2016.10.001 27840107 PMC5198784 · DOI
  12. [14]Li G Xu A Sim S Priest JR Tian X Khan T Quertermous T Zhou B Tsao PS Quake SR Transcriptomic profiling maps anatomically patterned subpopulations among single embryonic cardiac cells Dev Cell 2016 39 491 507 10.1016/j.devcel.2016.10.014 27840109 PMC5130110 · DOI
  13. [16]Li L Dong J Yan L Yong J Liu X Hu Y Fan X Wu X Guo H Wang X Single-cell RNA-seq analysis maps development of human germline cells and gonadal niche interactions Cell Stem Cell 2017 20 858 73.e4 10.1016/j.stem.2017.03.007 28457750 · DOI
  14. [17]Cao J Packer JS Ramani V Cusanovich DA Huynh C Daza R Qiu X Lee C Furlan SN Steemers FJ Comprehensive single-cell transcriptional profiling of a multicellular organism Science 2017 357 661 667 10.1126/science.aam8940 28818938 PMC5894354 · DOI · Paper page
  15. [18]Haber AL Biton M Rogel N Herbst RH Shekhar K Smillie C Burgin G Delorey TM Howitt MR Katz Y A single-cell survey of the small intestinal epithelium Nature 2017 551 333 339 10.1038/nature24489 29144463 PMC6022292 · DOI · Paper page
  16. [20]Graf T Enver T Forcing cells to change lineages Nature 2009 462 587 594 10.1038/nature08533 19956253 · DOI · Paper page
  17. [21]Wagner GP The developmental genetics of homology Nat Rev Genet 2007 8 473 479 10.1038/nrg2099 17486120 · DOI
  18. [22]Kin K Nnamani MC Lynch VJ Michaelides E Wagner GP Cell-type phylogenetics and the origin of endometrial stromal cells Cell Rep 2015 10 1398 1409 10.1016/j.celrep.2015.01.062 25732829 · DOI
  19. [24]Tripathi S Pohl MO Zhou Y Rodriguez-Frandsen A Wang G Stein DA Moulton HM DeJesus P Che J Mulder LC Meta-and orthogonal integration of influenza “OMICs” data defines a role for UBR4 in virus budding Cell Host Microbe 2015 18 723 735 10.1016/j.chom.2015.11.002 26651948 PMC4829074 · DOI
  20. [25]Varga J Greten FR Cell plasticity in epithelial homeostasis and tumorigenesis Nat Cell Biol 2017 19 1133 1141 10.1038/ncb3611 28945230 · DOI
  21. [26]Chung VY Tan TZ Tan M Wong MK Kuay KT Yang Z Ye J Muller J Koh CM Guccione E GRHL2-miR-200-ZEB1 maintains the epithelial status of ovarian cancer through transcriptional regulation and histone modification Sci Rep 2016 6 19943 10.1038/srep19943 26887977 PMC4757891 · DOI
  22. [27]Hong T Watanabe K Ta CH Villarreal-Ponce A Nie Q Dai X An Ovol2-Zeb1 mutual inhibitory circuit governs bidirectional and multi-step transition between epithelial and mesenchymal states PLoS Comput Biol 2015 11 e1004569 10.1371/journal.pcbi.1004569 26554584 PMC4640575 · DOI
  23. [28]Jolly MK Tripathi SC Jia D Mooney SM Celiktas M Hanash SM Mani SA Pienta KJ Ben-Jacob E Levine H Stability of the hybrid epithelial/mesenchymal phenotype Oncotarget 2016 7 27067 10.18632/oncotarget.8166 27008704 PMC5053633 · DOI
  24. [31]Halpern KB Shenhav R Matcovitch-Natan O Tóth B Lemze D Golan M Massasa EE Baydatch S Landen S Moor AE Single-cell spatial reconstruction reveals global division of labour in the mammalian liver Nature 2017 542 352 356 10.1038/nature21065 28166538 PMC5321580 · DOI · Paper page
  25. [32]Kim K-T Lee HW Lee H-O Kim SC Seo YJ Chung W Eum HH Nam D-H Kim J Joo KM Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells Genome Biol 2015 16 127 10.1186/s13059-015-0692-3 26084335 PMC4506401 · DOI
  26. [34]Shibue T Weinberg RA EMT, CSCs, and drug resistance: the mechanistic link and clinical implications Nat Rev Clin Oncol 2017 14 611 629 10.1038/nrclinonc.2017.44 28397828 PMC5720366 · DOI · Paper page
  27. [35]Palis J Hematopoietic stem cell-independent hematopoiesis: emergence of erythroid, megakaryocyte, and myeloid potential in the mammalian embryo FEBS Lett 2016 590 3965 3974 10.1002/1873-3468.12459 27790707 · DOI
  28. [36]Aceto N Toner M Maheswaran S Haber DA En route to metastasis: circulating tumor cell clusters and epithelial-to-mesenchymal transition Trends Cancer 2015 1 44 52 10.1016/j.trecan.2015.07.006 28741562 · DOI
  29. [37]Cicchini C de Nonno V Battistelli C Cozzolino AM Puzzonia MDS Ciafrè SA Brocker C Gonzalez FJ Amicone L Tripodi M Epigenetic control of EMT/MET dynamics: HNF4α impacts DNMT3s through miRs-29 Biochim Biophys Acta 2015 1849 919 929 10.1016/j.bbagrm.2015.05.005 26003733 PMC6319628 · DOI
  30. [38]Matsui A Fujimoto J Ishikawa K Ito E Goshima N Watanabe S Semba K Hepatocyte nuclear factor 1 beta induces transformation and epithelial-to-mesenchymal transition FEBS Lett 2016 590 1211 1221 10.1002/1873-3468.12147 27001343 · DOI
  31. [39]Trapnell C Pachter L Salzberg SL TopHat: discovering splice junctions with RNA-Seq Bioinformatics 2009 25 1105 1111 10.1093/bioinformatics/btp120 19289445 PMC2672628 · DOI
  32. [40]Anders S Pyl PT Huber W HTSeq–a Python framework to work with high-throughput sequencing data Bioinformatics 2015 31 166 169 10.1093/bioinformatics/btu638 25260700 PMC4287950 · DOI · Paper page
  33. [42]Lake BB Ai R Kaeser GE Salathia NS Yung YC Liu R Wildberg A Gao D Fung H-L Chen S Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain Science 2016 352 1586 1590 10.1126/science.aaf1204 27339989 PMC5038589 · DOI · Paper page
  34. [44]Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner H, Trapnell C. Reversed graph embedding resolves complex single-cell developmental trajectories. bioRxiv. 2017:110668. 10.1101/110668 PMC5764547 28825705 · DOI
  35. [45]Macosko EZ Basu A Satija R Nemesh J Shekhar K Goldman M Tirosh I Bialas AR Kamitaki N Martersteck EM Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets Cell 2015 161 1202 1214 10.1016/j.cell.2015.05.002 26000488 PMC4481139 · DOI · Paper page
  36. [46]Tirosh I Izar B Prakadan SM Wadsworth MH Treacy D Trombetta JJ Rotem A Rodman C Lian C Murphy G Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq Science 2016 352 189 196 10.1126/science.aad0501 27124452 PMC4944528 · DOI · Paper page
  37. [47]Zhang HM Liu T Liu CJ Song SY Zhang XT Liu W Jia HB Xue Y Guo AY AnimalTFDB 2.0: a resource for expression, prediction and functional study of animal transcription factors Nucleic Acids Res 2015 43 D76 D81 10.1093/nar/gku887 25262351 PMC4384004 · DOI
  38. [48]Zhou F Li X Wang W Zhu P Zhou J He W Ding M Xiong F Zheng X Li Z Tracing haematopoietic stem cell formation at single-cell resolution Nature 2016 533 487 492 10.1038/nature17997 27225119 · DOI
  39. [49]Dong J, Hu Y, Fan X, Wu X, Tang F. Single-cell RNA-seq analysis unveils a prevalent epithelial/mesenchymal hybrid state during mouse organogenesis. Gene Expression Omnibus. 2018; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE87038 PMC5853091 29540203

Authors
Dong, J., Hu, Y., Fan, X., Wu, X., Mao, Y., Hu, B., Guo, H., Wen, L., & Tang, F.
Affiliations
Peking University (CN)
Corresponding author
Tang, Fuchou
Venue
Genome Biology
Published
2018-03-14
Volume
19
Issue
1
Pages
31-31
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
OpenAlex
W2800064031
Cited by
187
References
49
Citations by year
2026: 9 · 2025: 15 · 2024: 25 · 2023: 17 · 2022: 29 · 2021: 31 · 2020: 31 · 2019: 16 · 2018: 13
FWCI
8.89
Type
journal article
Language
en
Open access
Locations
  • Genome biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Natural Science Foundation of China (81561138005, 31322037, 81521002)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Pluripotent Stem Cells Research
Keywords
organogenesis, epithelial-mesenchymal transition, embryonic development, mouse embryo, cellular heterogeneity, epithelial-mesenchymal interactions, transcriptome, forebrain, hindbrain, somites, heart, lung, liver, intestine, skin, hematopoietic cells, neurons, gene regulatory networks
MeSH terms
Animals, Epithelial Cells, Epithelium, Hematopoietic Stem Cells, Mesoderm, Neurons, Sequence Analysis, RNA, Gene Expression Profiling, Organogenesis, Mice, Single-Cell Analysis, Transcriptome

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