Fetal Gene Atlas Fetal Cells Track Settings
 
Fetal Gene Atlas binned by cell type from Cao et al 2020

Track collection: Fetal Gene Atlas from Cao et al 2020

+  Description
+  All tracks in this collection (8)

Display mode:      Duplicate track

Label: Name or ID of item    Alternative name for item   
Log10(x+1) transform:    View limits maximum: UMI/cell (range 0-10000)

Clear All
cell_class
 epithelial (6)

color organ and cell type count organ cell_class cell_type
   
lung bronchiolar/alveolar epithelial ... 101249 lung epithelial bronchiolar/alveolar epithelial cell
   
eye corneal/conjunctival epithelial cell 104 eye epithelial corneal/conjunctival epithelial cell
   
intestine intestinal epithelial cell 24545 intestine epithelial intestinal epithelial cell
   
mixed mesothelial cell 2048 mixed epithelial mesothelial cell
   
mixed squamous epithelial cell 2238 mixed epithelial squamous epithelial cell
   
thymus thymic epithelial cell 259 thymus epithelial thymic epithelial cell
View table schema
Data last updated at UCSC: 2021-01-11 04:16:11

Description

This group of tracks shows data from A human cell atlas of fetal gene expression. This is a collection of single cell and single nucleus combinatorial indexing-based RNA-seq data covering 4 million cells from 15 organs obtained during mid-gestation. The cells were sequenced in a highly multiplexed fashion and then clustered with annotations as described in Cao et al., 2020.

The Fetal Cells subtrack contains the data organized by cell type, with RNA signals from all cells of a given type pooled and averaged into one bar for each cell type. The Fetal Lineage subtrack shows similar data, but with the cell types subdivided more finely and by organ. Additional bar chart subtracks pool the cell by other characteristics such as by sex (Fetal Sex), assay (FetalAssay), donor (Fetal Donor ID), experiment (Fetal Exp), organ (Fetal Organ), and reverse transcription group (Fetal RT Group).

Please see descartes.brotmanbaty.org for further interactive displays and additional data.

Display Conventions and Configuration

The cell types are colored by which class they belong to according to the following table. The coloring algorithm allows cells that show some blended characteristics to show blended colors so there will be some color variation within a class. The colors will be purest in the Fetal Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well.

Color Cell classification
neural
adipose
fibroblast
immune
muscle
hepatocyte
trophoblast
secretory
ciliated
epithelial
endothelial
glia

Methods

Three-level single-cell combinatorial indexing (sci-RNAseq3) as described in Cao et al., 2020 was used on 121 samples from 28 fetuses estimated 72 to 129 days post-conception. This included samples from 15 organs. and resulted in RNA profiles for 4 million cells. The samples were flash-frozen for majority of the experiments and then nuclei extracted for sequencing. Samples from tissues from the kidney and digestive system were fixed after disassociation to deactivate endogenous RNases and proteases.

The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server.

Data Access

The raw bar chart 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.

Credits

Thanks to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative.

References

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