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Related Experiment Video

Updated: Jan 20, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing.

Allegra A Petti^1,2, Stephen R Williams³, Christopher A Miller^1,2

¹Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA.

Nature Communications

|August 16, 2019

Summary

This study links genetic mutations to gene expression in Acute Myeloid Leukemia (AML) cells. Researchers identified distinct expression patterns for different AML cell groups, aiding in subclone identification.

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Last Updated: Jan 20, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

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Published on: February 17, 2017

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

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Published on: September 20, 2016

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

Area of Science:

Genomics
Molecular Biology
Cancer Research

Background:

Tumors exhibit genetic heterogeneity with distinct subclonal populations and phenotypes.
Single-cell RNA sequencing reveals transcriptional heterogeneity in tumors, but its link to subclonal architecture is unclear.

Purpose of the Study:

To investigate the relationship between gene expression heterogeneity and subclonal architecture in Acute Myeloid Leukemia (AML).
To integrate whole genome sequencing with single-cell RNA sequencing to connect genotype with phenotype in AML.

Main Methods:

Applied whole genome sequencing and 10x Genomics Chromium Single Cell 5' Gene Expression workflow to five cryopreserved AML samples.
Analyzed data to identify tumor-specific mutations within individual cells and distinguish AML cells from normal cells.

Main Results:

Identified hundreds to thousands of cells with tumor-specific mutations per sample.
Discovered expression signatures associated with subclonal mutations in AML.
Found potential cell surface markers for purifying AML subclones.

Conclusions:

The integrated approach successfully connects genotype to phenotype in heterogeneous AML samples.
This method is broadly applicable for studying phenotypically and genetically heterogeneous samples.