Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris

Nature

Volumn 562, Issue 7727, 2018, Pages 367-372

  Schaum, Nicholas ^a   Karkanias, Jim ^b   Neff, Norma F ^b   May, Andrew P ^b   Quake, Stephen R ^b,c   Wyss Coray, Tony ^a,d   Darmanis, Spyros ^b   Batson, Joshua ^b   Botvinnik, Olga ^b   Chen, Michelle B ^c   Chen, Steven ^b   Green, Foad ^b   Jones, Robert C ^c   Maynard, Ashley ^b   Penland, Lolita ^b   Pisco, Angela Oliveira ^b   Sit, Rene V ^b   Stanley, Geoffrey M ^c   Webber, James T ^b   Zanini, Fabio ^c   Baghel, Ankit S ^a   Bakerman, Isaac ^a   Bansal, Ishita ^b   Berdnik, Daniela ^a   Bilen, Biter ^a   Brownfield, Douglas ^a   Cain, Corey ^d   Cho, Min ^b   Cirolia, Giana ^b   Conley, Stephanie D ^a   Demers, Aaron ^b   Demir, Kubilay ^a,e   de Morree, Antoine ^a   Divita, Tessa ^b   du Bois, Haley ^a   Dulgeroff, Laughing Bear Torrez ^a   Ebadi, Hamid ^b   Espinoza, F Hernán ^a   Fish, Matt ^a,e   Gan, Qiang ^a   George, Benson M ^a   Gillich, Astrid ^a   Genetiano, Geraldine ^b   Gu, Xueying ^a   Gulati, Gunsagar S ^a   Hang, Yan ^a   Hosseinzadeh, Shayan ^b   Huang, Albin ^a   Iram, Tal ^a   Isobe, Taichi ^a   Ives, Feather ^b   Kao, Kevin S ^a   Karnam, Guruswamy ^f   Kershner, Aaron M ^a   Kiss, Bernhard M ^a   Kong, William ^a   Kumar, Maya E ^a   Lam, Jonathan Y ^a   Lee, Davis P ^d   Lee, Song E ^a   Li, Guang ^a   Li, Qingyun ^a   Liu, Ling ^a   Lo, Annie ^b   Lu, Wan Jin ^a   Manjunath, Anoop ^a   May, Kaia L ^b   May, Oliver L ^b   McKay, Marina ^b   Metzger, Ross J ^a   Mignardi, Marco ^c   Min, Dullei ^a   Nabhan, Ahmad N ^a   Ng, Katharine M ^c   Noh, Joseph ^a   Patkar, Rasika ^f   Peng, Weng Chuan ^a   Puccinelli, Robert ^b   Rulifson, Eric J ^a   Sikandar, Shaheen S ^a   Sinha, Rahul ^a,g   Szade, Krzysztof ^a,h   Tan, Weilun ^b   Tato, Cristina ^b   Tellez, Krissie ^a   Travaglini, Kyle J ^a   Tropini, Carolina ^a   Waldburger, Lucas ^b   van Weele, Linda J ^a   Wosczyna, Michael N ^a   Xiang, Jinyi ^a   Xue, Soso ^c   Youngyunpipatkul, Justin ^b   Zardeneta, Macy E ^d   Zhang, Fan ^a   Zhou, Lu ^a   Castro, Paola ^b   Croote, Derek ^c   DeRisi, Joseph L ^b,i   Kuo, Christin S ^a,e   Lehallier, Benoit ^a   Nguyen, Patricia K ^a   Tan, Serena Y ^a   Wang, Bruce M ^f   Yousef, Hanadie ^a   Beachy, Philip A ^a,e   Chan, Charles K F ^e   Huang, Kerwyn Casey ^a,b   Weinberg, Kenneth ^a   Wu, Sean M ^a   Barres, Ben A ^a   Clarke, Michael F ^a   Kim, Seung K ^a   Krasnow, Mark A ^a,e   Nusse, Roel ^a,e   Rando, Thomas A ^a,d   Sonnenburg, Justin ^a,b   Weissman, Irving L ^a,g   more..

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b CHAN ZUCKERBERG BIOHUB   (United States)

^c Stanford University   (United States)

^d VA PALO ALTO HEALTH CARE SYSTEM   (United States)

^e STANFORD UNIVERSITY   (United States)

^f UNIVERSITY OF CALIFORNIA   (United States)

^g STANFORD CANCER INSTITUTE   (United States)

^h JAGIELLONIAN UNIVERSITY   (Poland)

ⁱ UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ATLAS; BIOLOGY; CELL COMPONENT; GAMETOGENESIS; GENE; GENE EXPRESSION; GENETIC ANALYSIS; RODENT;

ANIMAL CELL; ANIMAL EXPERIMENT; ARTICLE; CANCER MODEL; CELL POPULATION; CYTOLOGY; ENDOTHELIUM CELL; FLUORESCENCE ACTIVATED CELL SORTING; MOUSE; MUS MUSCULUS; NONHUMAN; T LYMPHOCYTE; TRANSCRIPTOMICS; ANIMAL; ANTIBODY SPECIFICITY; C57BL MOUSE; FEMALE; FLOW CYTOMETRY; GENE EXPRESSION PROFILING; GENETICS; MALE; METABOLISM; MICROFLUIDICS; SINGLE CELL ANALYSIS;

MUS MUSCULUS;

TRANSCRIPTION FACTOR; TRANSCRIPTOME;

ANIMALS; CELL BIOLOGY; ENDOTHELIAL CELLS; FEMALE; FLOW CYTOMETRY; GENE EXPRESSION PROFILING; MALE; MICE; MICE, INBRED C57BL; MICROFLUIDICS; ORGAN SPECIFICITY; SINGLE-CELL ANALYSIS; T-LYMPHOCYTES; TRANSCRIPTION FACTORS; TRANSCRIPTOME;

EID: 85055080981     PISSN: 00280836     EISSN: 14764687     Source Type: Journal    
DOI: 10.1038/s41586-018-0590-4     Document Type: Article

References (30)

1. Alberts, B. et al. Essential Cell Biology (W.W. Norton & Company, New York, 2016).
2. Guo, G. et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev. Cell 18, 675–685 (2010).
3. Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011).
4. Thorsen, T., Roberts, R. W., Arnold, F. H. & Quake, S. R. Dynamic pattern formation in a vesicle-generating microfluidic device. Phys. Rev. Lett. 86, 4163–4166 (2001).
5. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
6. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).
7. Ramsköld, D. et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).
8. Wu, A. R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).
9. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).
10. Enge, M. et al. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171, 321–330.e14 (2017).
11. Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).
12. Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017).
13. Villani, A.-C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).
14. Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).
15. Gokce, O. et al. Cellular taxonomy of the mouse striatum as revealed by single-cell RNA-seq. Cell Rep. 16, 1126–1137 (2016).
16. Usoskin, D. et al. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat. Neurosci. 18, 145–153 (2015).
17. Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
18. Li, H. et al. Classifying Drosophila olfactory projection neuron subtypes by single-cell RNA sequencing. Cell 171, 1206–1220.e22 (2017).
19. Bakken, T. et al. Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinformatics 18 (Suppl 17), 559 (2017).
20. Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018).
21. Freytag, S., Tian, L., Lonnstedt, I., Ng, M. & Bahlo, M. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. F1000Res 7, 1297 (2018).
22. Holling, T. M., Schooten, E. & van Den Elsen, P. J. Function and regulation of MHC class II molecules in T-lymphocytes: of mice and men. Hum. Immunol. 65, 282–290 (2004).
23. Reichardt, J. & Bornholdt, S. Statistical mechanics of community detection. Phys. Rev. E 74, 016110 (2006).
24. van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).
25. Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018).
26. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).
27. Liu, Z. et al. Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte. Nature 551, 100–104 (2017).
28. Darmanis, S. et al. Single-cell RNA-seq analysis of infiltrating neoplastic cells at the migrating front of human glioblastoma. Cell Rep. 21, 1399–1410 (2017).
29. Kassambara, A. Practical guide to cluster analysis in R: unsupervised machine learning 1st edn (CreateSpace, North Charleston, 2017).
30. Díaz-Uriarte, R. & Alvarez de Andrés, S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7, 3 (2006).