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Updated: Jan 16, 2026

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Single-cell splicing QTL analysis in pancreatic islets.

Jae-Won Cho¹, Jingyi Cao¹, Martin Hemberg¹

¹The Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital, and Harvard Medical School, Boston, MA, United States.

Frontiers in Bioinformatics

|September 26, 2025

Summary

This summary is machine-generated.

This study identified 228 cell type-specific splicing quantitative trait loci (sQTLs) in pancreatic islet cells, revealing novel genetic variants impacting gene regulation and cell function. These findings advance our understanding of splicing regulation at a single-cell level.

Keywords:

CDC42 alternative splicing pancreatic islet single-cell RNA-sequencing splicing quantitative trait loci

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Last Updated: Jan 16, 2026

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Single-cell Transcriptomic Analyses of Mouse Pancreatic Endocrine Cells

Single-cell Transcriptomic Analyses of Mouse Pancreatic Endocrine Cells

Published on: September 30, 2018

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Published on: October 9, 2016

Area of Science:

Genomics
Molecular Biology
Transcriptomics

Background:

Alternative splicing (AS) significantly expands transcriptome diversity and is implicated in various diseases.
Genetic variants, known as splicing quantitative trait loci (sQTLs), influence AS.
Single-cell technologies enable the study of genetic effects at a cell-type-specific resolution.

Purpose of the Study:

To identify cell type-specific splicing quantitative trait loci (sQTLs) in pancreatic islet cells.
To investigate the genetic regulation of alternative splicing in distinct pancreatic cell types.
To explore the functional implications of cell type-specific sQTLs.

Main Methods:

Utilized eight full-length single-cell RNA sequencing (scRNA-seq) pancreatic islet datasets.
Performed individual genotyping using CTAT and Streka2 pipelines.
Quantified isoform expression with RSEM and identified sQTLs using sQTLseeker2.

Main Results:

Identified 228 cell type-level sQTLs across 152 genes in alpha and beta cells.
Discovered four novel variants affecting CDC42, a gene crucial for cell morphology, not detected in bulk analyses.
Demonstrated the feasibility of identifying cell type-specific genetic influences on splicing.

Conclusions:

This study provides a proof of concept for identifying cell type-specific sQTLs.
Future, larger-scale studies can further elucidate the genetic control of splicing.
Cell type-specific sQTL analysis offers deeper insights into disease mechanisms and gene regulation.