A Single-Cell Transcriptome Atlas of the Human Pancreas

By Mauro J. Muraro, Gitanjali Dharmadhikari, Dominic Grün, N. Groen, Tim Dielen, Erik Jansen, Léon van Gurp, M. Engelse, F. Carlotti, E. D. de Koning and 1 more. Cell Systems, 2016

1,203 citations recorded by the source corpus.

Summary To understand organ function, it is important to have an inventory of its cell types and of their corresponding marker genes. This is a particularly challenging task for human tissues like the pancreas, because reliable markers are limited. Hence, transcriptome-wide studies are typically done on pooled islets of Langerhans, obscuring contributions from rare cell types and of potential subpopulations. To overcome this challenge, we developed an automated platform that uses FACS, robotics, and the CEL-Seq2 protocol to obtain the transcriptomes of thousands of single pancreatic cells from deceased organ donors, allowing in silico purification of all main pancreatic cell types. We identify cell type-specific transcription factors and a subpopulation of REG3A-positive acinar cells. We also show that CD24 and TM4SF4 expression can be used to sort live alpha and beta cells with high purity. This resource will be useful for developing a deeper understanding of pancreatic biology and pathophysiology of diabetes mellitus.

Subfields: Molecular Biology, Oncology, Surgery

View Paper: https://www.semanticscholar.org/paper/a616c39a2b8a7b66017ebe62b787f9bf92eb0e75

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Citations in the corpus

Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (2019, methodology): “The CEL-Seq2 Muraro dataset [34] was annotated with 9 types of pancreatic cells.”
Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data (2022, methodology): “The RNA-seq datasets used in the benchmarking originated from various tissues, including human liver 10 , pancreas 24 , peripheral blood mononuclear cells (PBMCs) 25 , brain 26 , as well as mouse lung 27 and retina samples 20 .”
SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species (2018, background): “, 2016), and human pancreas cells profiled by CELSeq2 as the query (Muraro et al., 2016).”
A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “Dataset 4 was constructed using human pancreatic data from five different sources, available under accession numbers GSE85241 [33], E-MTAB-5061 [34], GSE84133 [35] GSE83139 [36], and GSE81608 [37].”
A comparison of automatic cell identification methods for single-cell RNA sequencing data (2019, background): “The brain datasets, including Allen Mouse Brain (AMB), VISp, ALM (GSE115746), and MTG (phs001790), were downloaded from the Allen Institute Brain Atlas http://celltypes.brain-map.org/rnaseq.”
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama (2019, methodology): “…k-means clustered the cells in the corrected gene expression space, obtaining 40 clusters, and assigned cell types to each cluster based on previously provided cell type labels and the relative expression levels of cell type-specific marker genes from previous analyses 10,21,23 (Supplementary Fig.”
Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (2018, methodology): “For comparison, we repeated the DE analysis using only cells from each batch in which both cell types were present [21] [22] [20].”