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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

Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging.

Léonard Hérault^1,2, Mathilde Poplineau^2,3, Elisabeth Remy¹

¹I2M, CNRS, Aix Marseille University, 13009 Marseille, France.

|October 14, 2022

Summary

This summary is machine-generated.

Single-cell transcriptomics reveals hematopoietic stem cell (HSC) heterogeneity and aging. New bioinformatics and mathematical modeling tools help understand HSC deregulation during aging.

Keywords:

Boolean modeling HSC aging bioinformatics single cell transcriptomic

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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

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Published on: November 11, 2014

Area of Science:

Hematology
Genomics
Computational Biology

Background:

Cellular heterogeneity is crucial for understanding biological systems.
The hematopoietic system, with its distinct cellular states, is an ideal model for studying heterogeneity.
Hematopoietic stem cells (HSCs) undergo aging, leading to decreased efficiency.

Purpose of the Study:

To review advances in single-cell transcriptomics for understanding HSC heterogeneity.
To explore HSC deregulations associated with aging.
To discuss bioinformatics and mathematical modeling approaches for analyzing HSC aging data.

Main Methods:

Single-cell transcriptomic technologies for high-resolution cellular analysis.
Bioinformatics tools for analyzing large-scale transcriptomic datasets.
Mathematical modeling for understanding regulatory networks and predicting HSC behavior.

Main Results:

Single-cell transcriptomics provides insights into HSC heterogeneity and its role in aging.
Analysis of transcriptomic data reveals deregulations in HSCs during aging.
Mathematical models can integrate multilayered information to predict HSC aging dynamics.

Conclusions:

Single-cell transcriptomics is a powerful tool for dissecting HSC heterogeneity and aging.
Advanced bioinformatics and mathematical modeling are essential for interpreting complex HSC aging data.
Understanding HSC aging is critical for addressing age-related hematological disorders.