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Related Experiment Video

Updated: Jul 2, 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

Population Diversity at the Single-Cell Level.

M Grace Gordon¹, Pooja Kathail², Bryson Choy^3,4

¹Genentech, San Francisco, California, USA.

Annual Review of Genomics and Human Genetics

|February 21, 2024

Summary

This summary is machine-generated.

Population-scale single-cell genomics reveals how genetic and environmental factors shape cellular diversity. Advanced multiomics and computational methods deepen our understanding of molecular variation and human disease genetics.

Keywords:

computational methodologies disease-associated genetic variants genetic multiplexing population genetics single-cell genomics

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Last Updated: Jul 2, 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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Published on: May 23, 2018

Area of Science:

Genomics
Cellular Biology
Computational Biology

Background:

Population-scale single-cell genomics is revolutionizing the study of genetic and cellular variation.
High-throughput single-cell multiomics and multiplexed perturbations are key experimental advancements.
Understanding cellular heterogeneity requires examining genetic variants in diverse cellular contexts.

Purpose of the Study:

To explore the interplay between genetics and environment in shaping cellular heterogeneity.
To quantitatively analyze molecular variation and its sources (stochastic, intercellular, interindividual).
To highlight future directions for advancing single-cell genomics research.

Main Methods:

High-throughput single-cell multiomics.
Multiplexed environmental and genetic perturbations.
Advanced computational methodologies for quantitative analysis.

Main Results:

Insights into the mutual influence of genetics and environment on cellular heterogeneity.
Detailed quantitative analysis of molecular variation.
Examination of stochastic, intercellular, and interindividual variation.

Conclusions:

Leveraging long-read sequencing, refined cellular models, and machine learning will advance the field.
Deeper understanding of the genetic architecture of human molecular traits.
Significant implications for elucidating the genetic causes of human disease.