Pancreas Baron Pancreas Cells Track Settings
 
Pancreas cells binned by cell type from Baron et al 2016

Track collection: Pancreas single cell sequencing from Baron et al 2016

+  Description
+  All tracks in this collection (4)

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)

Categories:  
 acinar cell
 stellate (activated) cell
 islet alpha cell
 islet beta cell
 islet delta cell
 ductal cell
 endothelial cell
 islet epsilon cell
 islet gamma cell
 other
 stellate (quiescent) cell
Data schema/format description and download
Assembly: Human Dec. 2013 (GRCh38/hg38)
Data last updated at UCSC: 2022-05-11 19:20:56

Description

This track shows data from A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Pancreas tissue was analyzed using droplet-based single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 14 pancreas-resident cell types based on their identified marker genes found in Baron et al., 2016.

There are four bar chart tracks in this track collection with pancreas cells grouped by either batch (Pancreas Batch), cell type (Pancreas Cells), detailed cell type (Pancreas Details) and donor (Pancreas Donor). The default track displayed is pancreas cells grouped by cell type.

Display Conventions

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

Color Cell classification
secretory
endothelial
epithelial
fibroblast

Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Pancreas 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.

Method

Human islets were obtained from two female cadaveric donors ages 51 (human2) and 59 (human4) and two male cadaveric donors ages 17 (human1) and 38 (human3). The samples collected from human 1-3 were non-diabetic and human 4 had type 2 diabetes mellitus. Using single-cell RNA-sequencing ~10,000 human pancreatic cells were isolated and sequenced. For each donor, several separate batches of ~800 cells were prepared and sequenced to obtain an average of about 100,000 reads per cell. Cells were barcoded using the inDrop platform which follows the CEL-Seq protocol for library construction. Paired end sequencing was done on the Illumina Hiseq 2500. After filtering out cells with limited numbers of detected genes, the dataset contained 8,629 cells from the four donors.

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.

Credit

Thanks to Mayaan Baron, Adrian Veres, Samuel L. Wolock, Aubrey L. Faust, and 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

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