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Related Experiment Video

Updated: May 12, 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

Unveiling cell-type-specific mode of evolution in comparative single-cell expression data.

Tian Qin¹, Hongjiu Zhang², Zhengting Zou¹

¹State Key Laboratory of Animal Biodiversity Conservation and Integrated Pest Management, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 101408, China.

Journal of Genetics and Genomics = Yi Chuan Xue Bao

|May 9, 2025

Summary

This summary is machine-generated.

We developed EVaDe, a new framework to analyze adaptive evolution in single-cell expression data. It identifies genes showing significant expression changes linked to adaptation, particularly in human neurodevelopment and naked mole-rat immunity.

Keywords:

Adaptive evolution Cell type Gene expression Naked mole-rat Phenotypic evolution Prefrontal cortex Single-cell transcriptomics

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Area of Science:

Evolutionary biology
Genomics
Computational biology

Background:

Established methods for coding sequence evolution analysis exist.
Evaluating adaptation in phenotype data, like gene expression, requires new approaches.

Purpose of the Study:

To propose a novel analysis framework, EVaDe (expression variance decomposition), for comparative single-cell expression data.
To identify genes and cell types that have undergone adaptive evolution using phenotypic evolution theory.

Main Methods:

Decompose gene expression variance into components.
Identify genes with high between-taxon divergence and low within-cell-type noise.
Apply EVaDe to primate prefrontal cortex and naked mole-rat vs. mouse datasets.

Main Results:

Human-specific genes in prefrontal cortex are linked to neurodevelopment and show adaptive patterns.
Specific neuron types exhibit more adaptive genes, suggesting targeted evolution.
Naked mole-rats show adaptive expression changes in innate immunity genes and cell types.
EVaDe framework effectively identifies adaptive evolution in single-cell expression data.

Conclusions:

The EVaDe framework provides a robust method for studying adaptive evolution in single-cell expression data.
Identified adaptive changes in human neurodevelopment and naked mole-rat immunity highlight specific evolutionary trajectories.