Merged Cells Track Settings
 
Single cell RNA expression levels cell types from many organs   (All Single Cell RNA-seq tracks)

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Log10(x+1) transform:    View limits maximum: ppm/cell (range 0-10000)

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cell_class
 epithelial (0)
cell_type
 fibroblast (0)

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Data last updated at UCSC: 2022-04-11 21:59:21


new Note: Released May 5, 2022

Description

This track displays single-cell data from 12 papers covering 14 organs. Cells are grouped together by organ and cell type. The cell types are based on annotations published alongside the papers. These were curated at UCSC as much as possible to use the same cell type terminologies across papers and organs. In some cases, we merged together small populations of cells annotated as distinct and related types into a single type so as to have enough cells to call gene expression levels accurate. The gene expression levels are normalized so that the total level of expression for all genes in a single cell or cell type adds up to one million.

Display Conventions and Configuration

The cell types are colored by which class they belong to according to the following table.

Please note, the coloring algorithm allows cells that show some mixed characteristics to = show blended colors so there will be some color variation within a class. In addition, cells with less than 100 transcripts will be a lighter shade and less concentrated in color to represent a low number of transcripts.

Color Cell classification
neural
adipose
fibroblast
immune
muscle
hepatocyte
trophoblast
secretory
ciliated
epithelial
endothelial
glia
stem cell or progenitor cell

Methods

Each organ or tissue was integrated and curated into the Genome Browser indiviually.

All components were normalized to be in parts per million using the matrixNormalize command available from UCSC. Metadata was cleaned up using the tabToTabDir tool. The major clean-ups were unpacking abbreviations, replacing jargon with standard English, choosing shorted terms to shorten long labels, labeling outliers, etc. Before integration we invited the original data producers as well as local biologists and informaticions to view the data.

Credits

Many thanks to the data contributing labs for sharing their high quality research. Thanks to the Cell Browser team including Matt Speir and Max Haeussler, for their work in integratinging these datasets into the Cell Browser. In most cases, their efforts were ahead of our own and we could leverage their work making the job much easier. Within the Genome Browser group, Jim Kent did the initial wrangling, and Brittney Wick did substantial data cleanup and coordination with the labs.

References

Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM et al. A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Syst. 2016 Oct 26;3(4):346-360.e4. PMID: 27667365; PMC: PMC5228327

Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123

Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952

De Micheli AJ, Spector JA, Elemento O, Cosgrove BD. A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations. Skelet Muscle. 2020 Jul 6;10(1):19. PMID: 32624006; PMC: PMC7336639

Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. PMID: 34062119; PMC: PMC8238499

Litviňuková M, Talavera-López C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775

MacParland SA, Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK, Manuel J, Khuu N, Echeverri J, Linares I et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun. 2018 Oct 22;9(1):4383. PMID: 30348985; PMC: PMC6197289

Solé-Boldo L, Raddatz G, Schütz S, Mallm JP, Rippe K, Lonsdorf AS, Rodríguez-Paredes M, Lyko F. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun Biol. 2020 Apr 23;3(1):188. PMID: 32327715; PMC: PMC7181753

Stewart BJ, Ferdinand JR, Young MD, Mitchell TJ, Loudon KW, Riding AM, Richoz N, Frazer GL, Staniforth JUL, Vieira Braga FA et al. Spatiotemporal immune zonation of the human kidney. Science. 2019 Sep 27;365(6460):1461-1466. PMID: 31604275; PMC: PMC7343525

Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697

Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, Bhaduri A, Goyal N, Rowitch DH, Kriegstein AR. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019 May 17;364(6441):685-689. PMID: 31097668; PMC: PMC7678724

Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Polański K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548

Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z, Fu W, Yang X, Chen YG. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2020 Feb 3;217(2). PMID: 31753849; PMC: PMC7041720