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Cell Specific Gene Expression

Cell Specific Gene Expression

Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Updated: Nov 9, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Bayesian estimation of cell type-specific gene expression with prior derived from single-cell data.

Jiebiao Wang¹, Kathryn Roeder^2,3, Bernie Devlin⁴

¹Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA.

Genome Research

|April 10, 2021

Summary

We developed bMIND, a Bayesian method to integrate bulk and single-cell RNA sequencing (scRNA-seq) data. This approach enhances cell type-specific expression analysis, improving the discovery of disease-related genes and genetic variants.

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Last Updated: Nov 9, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Bulk RNA sequencing offers reliable tissue-level gene expression data, while single-cell RNA sequencing (scRNA-seq) provides cellular-level insights.
scRNA-seq data can be noisy and limited by small sample sizes, hindering analyses like gene expression quantitative trait loci (eQTL) identification.
Integrating these data types can leverage their respective strengths for more robust biological discoveries.

Purpose of the Study:

To develop a Bayesian method (bMIND) for integrating bulk and scRNA-seq data.
To enable accurate estimation of sample-level cell type-specific (CTS) expression from bulk data using scRNA-seq priors.
To facilitate large-scale downstream analyses, including CTS differentially expressed genes (DEGs) and eQTL detection.

Main Methods:

Developed a Bayesian method (bMIND) to integrate bulk and scRNA-seq data.
Utilized scRNA-seq data to derive priors for estimating sample-level CTS expression from bulk RNA sequencing data.
Applied bMIND to autism spectrum disorder and Alzheimer's disease brain tissue data, and Genotype-Tissue Expression Project data.

Main Results:

bMIND improves the accuracy of sample-level CTS expression estimates compared to existing methods.
The method increases the power to discover CTS DEGs.
Identified CTS DEGs in brain tissues relevant to autism spectrum disorder and Alzheimer's disease, and generated a new resource of CTS eQTLs for 11 brain regions.

Conclusions:

bMIND effectively integrates bulk and scRNA-seq data to enhance cell type-specific expression analysis.
The approach facilitates the discovery of novel CTS DEGs and eQTLs, advancing our understanding of complex diseases.
The generated CTS eQTLs provide a valuable resource for future biological research.