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Related Experiment Video

Updated: Sep 1, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

CellRegMap: a statistical framework for mapping context-specific regulatory variants using scRNA-seq.

Anna S E Cuomo^1,2, Tobias Heinen^3,4,5, Danai Vagiaki^3,4,6

¹European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.

Molecular Systems Biology

|August 16, 2022

Summary

This summary is machine-generated.

Cell Regulatory Map (CellRegMap) analyzes genetic effects on gene expression in single cells. This new framework reveals how genetic variants influence subtle cell types and states, advancing precision medicine.

Keywords:

cell-type specificity eQTL genetic interaction single-cell sequencing

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Area of Science:

Genomics
Computational Biology
Systems Biology

Background:

Single-cell RNA sequencing (scRNA-seq) allows detailed analysis of cellular heterogeneity.
Current genetic analysis methods for scRNA-seq data are limited by discrete cell type definitions.
Assessing genetic effects across subtle cell states and continuous transitions remains a challenge.

Purpose of the Study:

To develop a statistical framework, Cell Regulatory Map (CellRegMap), for analyzing genetic effects on gene expression at the single-cell level.
To identify and quantify genotype-context interactions for known expression quantitative trait loci (eQTL) variants using scRNA-seq data.
To resolve allelic effects across diverse cellular contexts, including subtypes and continuous cell transitions.

Main Methods:

Developed Cell Regulatory Map (CellRegMap), a model-based statistical framework.
Applied CellRegMap to analyze scRNA-seq data from differentiating induced pluripotent stem cells (iPSCs).
Validated the framework using simulated data.

Main Results:

Uncovered hundreds of eQTLs exhibiting heterogeneous genetic effects across cellular contexts in differentiating iPSCs.
Demonstrated CellRegMap's ability to resolve allelic effects in cell subtypes and continuous cell transitions.
Identified fine-grained genetic regulation in neuronal subtypes for eQTLs linked to human diseases.

Conclusions:

CellRegMap provides a principled approach to analyze genetic regulation in individual cells using scRNA-seq.
The framework enables the discovery of genotype-context interactions and genetic effects in subtle cellular states.
This work advances our understanding of genetic influences on cellular heterogeneity and disease.