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Cell Specific Gene Expression

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Updated: Jun 30, 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

Predicting cell population-specific gene expression from genomic sequence.

Lieke Michielsen^1,2,3, Marcel J T Reinders^1,2,3, Ahmed Mahfouz^1,2,3

¹Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands.

Frontiers in Bioinformatics

|March 19, 2024

Summary

This summary is machine-generated.

This study introduces a new computational model using single-cell RNA sequencing data to predict gene expression. Cell population-specific models show improved accuracy over tissue-specific ones, aiding in identifying regulatory elements.

Keywords:

cell populations gene expression prediction sequence to prediction models single-cell RNA-sequencing transcriptional regulation

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Regulatory elements, particularly enhancers, are crucial for defining cell populations.
Identifying cell-specific regulatory elements and their impact on gene expression is challenging.
Current computational models predict gene expression from genomic sequence but are limited to bulk and tissue-specific predictions.

Purpose of the Study:

To develop a computational model that predicts gene expression using single-cell RNA sequencing data.
To demonstrate the superiority of cell population-specific models over tissue-specific models.
To explore the model's utility in prioritizing genetic variants and identifying transcription factor binding sites.

Main Methods:

Leveraging single-cell RNA sequencing (scRNA-seq) data.
Developing and applying cell population-specific predictive models for gene expression.
Evaluating model performance against tissue-specific models.

Main Results:

Cell population-specific models significantly outperform tissue-specific models, especially when expression profiles differ.
The model successfully prioritizes genome-wide association study (GWAS) variants.
The model can effectively learn motifs for transcription factor binding sites.

Conclusions:

Single-cell RNA sequencing data enables the development of accurate, cell population-specific gene expression prediction models.
These models offer a powerful approach for discovering cell-specific regulatory elements.
The developed model has implications for understanding gene regulation and prioritizing disease-associated genetic variants.