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.

Methods

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

• Blood (PBMC) Hao - This track displays peripheral blood mononuclear cell expression data from Hao et al., 2020 for 3 levels of cell type annotations, donor, phase, and time.
• Colon Wang - This track shows colon expression data from Wang et al., 2020 grouped by cell type and donor.
• Cortex Velmeshev - This track shows cortex expression data from Velmeshev et al., 2019 grouped by cell type, sex, sample, donor, and diagnosis.
• Fetal Gene Atlas - This track shows expression data from Cao et al., 2020 binned by cell type and other categories including sex, organ, experiment, donor, etc.
• Heart Cell Atlas - This track shows heart expression data from Litviňuková et al., 2020 binned by cell type and various categories including cell state, sample, region, donor, age, etc.
• Ileum Wang - This track shows ileum expression data from Wang et al., 2020 grouped by cell type and donor.
• Kidney Stewart - This track shows kidney expression data from Stewart et al., 2019 grouped by cell type, detailed cell type, project, experiment, etc.
• Liver MacParland - This track shows liver expression data from MacParland et al., 2018 grouped by cell type, broad cell type, and donor.
• Lung Travaglini - This track shows lung expression data from Travaglini et al., 2020 binned by categories such as cell type, sample, donor, compartment, etc. using both 10x and Smart-seq2 library preparation methods.
• Muscle De Micheli - This track shows muscle expression data from De Micheli et al., 2020 grouped by cell type and sample.
• Pancreas Baron - This track shows pancreas expression data from Baron et al., 2016 grouped by cell type, detailed cell type, donor, and batch.
• Placenta Vento-Tormo - This track shows placenta and matched decidua and maternal PBMCs expression data from Vento-Tormo et al., 2018 grouped by cell type, detailed cell type, stage, etc. using both 10x and Smart-seq2 library preparation methods.
• Rectum Wang - This track shows rectum expression data from Wang et al., 2020 grouped by cell type and donor.
• Skin Sole-Boldo - This track shows skin expression data from Solé-Boldo et al., 2020 grouped by cell type, cell type with donor's age, donor, and age.

Data Access

The raw barChart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information.

The expScores field for this track contains a comma-separated list of values for each cell type, and the expCount field is the total cell count. The value in the expScores field corresponds to the read count for that cell type, and the order of the cell types is defined by the barChartBars line in the trackDb file for this track.

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