Patch-Seq Links Single-Cell Transcriptomes to Human Islet Dysfunction in Diabetes

Abstract

Pancreas patch-seq provides a single-cell survey of function-transcriptome pairing in 1,369 islet cells from donors with and without diabetes • Expression of a specific subset of genes predicts b-cell electrophysiology in transcriptomefunction networks.• Compromised b-cell function in T2D correlates with altered ETV1 expression and inflammatory pathways• Functional heterogeneity in a-cells maps to ER stress and islet lineage markers• Application of patch-seq to cells from rare cryopreserved islets from donors with T1D SummaryPancreatic islet cells regulate glucose homeostasis through insulin and glucagon secretion; dysfunction of these cells leads to severe diseases like diabetes. Prior single-cell transcriptome studies have shown heterogeneous gene expression in major islet cell-types; however it remains challenging to reconcile this transcriptomic heterogeneity with observed islet cell functional variation. Here we achieved electrophysiological profiling and single-cell RNA sequencing in the same islet cell (pancreas patch-seq) thereby linking transcriptomic phenotypes to physiologic properties. We collected 1,369 cells from the pancreas of donors with or without diabetes and assessed function-gene expression networks. We identified a set of genes and pathways that drive functional heterogeneity in b-cells and used these to predict b-cell electrophysiology. We also report specific transcriptional programs that correlate with dysfunction in type 2 diabetes (T2D) and extend this approach to cryopreserved cells from donors with type 1 diabetes (T1D), generating a valuable resource for understanding islet cell heterogeneity in health and disease..

SARS-CoV-2 Cell Entry Factors ACE2 and TMPRSS2 are Expressed in the Pancreas but are Not Enriched in Islet Endocrine Cells

“…HPAP (Kaestner et al, 2019), Baron et al (Baron et al, 2016), and Camunas-Soler et al (Camunas-Soler et al, 2020) datasets showed that less than 1% of acinar or ductal cells expressed ACE2, whereas ~35% of both cell types expressed TMPRSS2. In the dataset by Segerstolpe et al (Segerstolpe et al, 2016), ACE2 was expressed in ~20% of acinar and ductal cells, whereas TMPRSS2 was expressed in greater than 75% of both populations ( Figure 1B).…”

“…Median expression level of ACE2 and TMPRSS2 mRNA was much less than transcripts of such key islet-enriched genes in human a and b cells (~84% and 92% lower than a and b cellenriched transcripts, respectively, for DNA-binding transcription factors) ( Figure 1A; n=7-8 adult donors per study). In addition, analysis of four single-cell (sc) RNA-seq datasets of human pancreatic islets (Baron et al, 2016;Camunas-Soler et al, 2020;Kaestner et al, 2019;Segerstolpe et al, 2016) revealed that ACE2 and TMPRSS2 transcripts were either undetectable or minimally expressed in all endocrine subsets ( Figure 1B and S3J). We note that this includes analysis of the robust Human Pancreas Analysis Program (HPAP) dataset that includes more than 25,000 cells (Kaestner et al, 2019) from 11 normal donors and is confirmed by the analysis of three previously reported, but smaller, datasets ( Figure 1B and S3J).…”

“…Given that ACE2 and TMPRSS2 co-expression is required for SARS-CoV-2 host cell entry (Hoffmann et al, 2020), we evaluated this occurrence and found that on average, less than 1% of acinar, ductal, endothelial and stellate cells co-expressed both transcripts in the HPAP (Kaestner et al, 2019), Baron et al (Baron et al, 2016) and Camunas-Soler et al (Camunas-Soler et al, 2020) datasets. In the Segerstolpe et al dataset (Segerstolpe et al, 2016), ~5% of acinar cells and 15% of ductal cells co-expressed ACE2 and TMPRSS2.…”

“…All data generated or analyzed during this study are included in this published article or in the data repositories listed in the Key Resources Table. Original/source data for Figure 1A (bulk RNA-seq of FACS sorted human islet a and b cells) is available under NCBI GEO accession numbers GSE67543 (Blodgett et al, 2015) and GSE57973 (Arda et al, 2016). Original/source data for Figure 1B and Figure S3J (single cell RNA-seq of human islets) is available under NCBI GEO accession numbers GSE84133 (Baron et al, 2016), GSE124742 (Camunas-Soler et al, 2020), and ArrayExpress (EBI) accession number E-MTAB-5061 (Segerstolpe et al, 2016). This manuscript used data acquired from the Coate-17 (IIDP), and local organ procurement organizations.…”

“…The sources of the datasets are summarized in the Key Resources Table. GraphPad Prism v8 was used to generate plots in Figures 1A. Single Cell RNA-seq Data Acquisition. Raw gene count matrices were extracted from existing single cell RNA-seq datasets (Baron et al, 2016;Segerstolpe et al, 2016, Camunas-Soler et al, 2020 and from the Human Pancreas Analysis Program (HPAP) Database (https://hpap.pmacs.upenn.edu), a Human Islet Research Network consortium. The sources of the datasets are summarized in the Key Resources Table. Gene count matrices were further analyzed using the R package Seurat version 3.1 .…”