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Updated: Jul 15, 2025

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

scQTLbase: an integrated human single-cell eQTL database.

Ruofan Ding¹, Qixuan Wang¹, Lihai Gong¹

¹Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China.

Nucleic Acids Research

|October 4, 2023

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The first human single-cell eQTLs (sc-eQTLs) database, scQTLbase, aids in discovering disease risk genes. It integrates millions of genetic variants and provides tools for analysis, advancing genetic research.

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Genome-wide association studies (GWAS) identify disease-associated genetic variants, but functional interpretation is challenging.
Expression quantitative trait loci (eQTLs) link genetic variants to gene expression, yet explain only a portion of disease-related variants.
Single-cell eQTLs (sc-eQTLs) offer higher resolution for identifying disease risk genes and understanding transcriptional regulation.

Purpose of the Study:

To develop the first comprehensive, integrated human single-cell eQTLs (sc-eQTLs) portal.
To provide a centralized resource for searching, analyzing, and visualizing sc-eQTL data.
To facilitate the discovery of novel disease susceptibility genes through fine-resolution genetic and gene expression analysis.

Main Methods:

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Last Updated: Jul 15, 2025

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

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

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Compiled 304 human datasets covering 57 cell types and 95 cell states.
Integrated approximately 16 million single nucleotide polymorphisms (SNPs) associated with cell-type/state gene expression.
Incorporated around 0.69 million disease-associated sc-eQTLs from 3,333 traits/diseases.

Main Results:

Developed scQTLbase, the first human sc-eQTLs portal (http://bioinfo.szbl.ac.cn/scQTLbase).
The database contains extensive sc-eQTL data, including millions of SNPs and hundreds of thousands of disease-associated sc-eQTLs.
Implemented functionalities for sc-eQTL search, gene expression visualization (UMAP plots), genome browsing, and colocalization analysis with GWAS data.

Conclusions:

scQTLbase serves as a valuable one-stop portal for single-cell eQTL research.
The resource significantly advances the identification and understanding of disease susceptibility genes.
Facilitates deeper insights into genetic regulation of gene expression at the single-cell level.