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Updated: Dec 12, 2025

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

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

Published on: November 11, 2014

Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis.

Sagar¹, Dominic Grün^1,2

¹Max Planck Institute of Immunobiology and Epigenetics, D-79108 Freiburg, Germany.

Annual Review of Biomedical Data Science

|August 12, 2020

Summary

This summary is machine-generated.

Single-cell genomics reveals how cells differentiate and specialize. Computational tools analyze single-cell RNA sequencing data to understand cell fate decisions in development and disease.

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Published on: October 25, 2018

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Last Updated: Dec 12, 2025

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

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

Published on: November 11, 2014

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

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

Area of Science:

Molecular Biology
Developmental Biology
Genomics

Background:

Cellular differentiation is fundamental to multicellular life, enabling specialized cell functions.
Understanding cell fate decisions is crucial for development, regeneration, homeostasis, and disease.
Advances in single-cell biology offer high-resolution insights into cell fate specification.

Purpose of the Study:

To review single-cell genomic methods for studying cell differentiation.
To highlight computational tools for analyzing cell fate trajectories and biases.
To provide mechanistic insights into cell fate decisions.

Main Methods:

Single-cell RNA sequencing (scRNA-seq)
Sequencing of other molecular modalities
Computational analysis of single-cell data (lineage reconstruction, dimensionality reduction)

Main Results:

Single-cell genomic approaches elucidate molecular mechanisms of lineage specification.
Computational tools enable reconstruction of cell differentiation trajectories.
Analysis quantifies cell fate bias and provides visualization for mechanistic insights.

Conclusions:

Single-cell genomics is revolutionizing the study of cellular differentiation.
Understanding cell fate decisions is key to comprehending health and disease.
This approach promises detailed insights into cellular behavior.