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Related Experiment Video

Updated: Apr 18, 2026

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Integrated genome and transcriptome sequencing of the same cell.

Siddharth S Dey^1,2, Lennart Kester^1,2, Bastiaan Spanjaard^1,2

¹Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, The Netherlands.

Nature Biotechnology

|January 20, 2015

Summary

This summary is machine-generated.

This study introduces a novel method for single-cell sequencing, enabling simultaneous analysis of genomic DNA and messenger RNA. This integrated approach reveals copy number variations influencing gene expression variability in individual cells.

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Transcriptome Analysis of Single Cells

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Published on: April 25, 2011

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Area of Science:

Molecular Biology
Genomics
Transcriptomics

Background:

Single-cell genomics and transcriptomics are vital for understanding cellular biology at a genome-wide scale.
A key challenge is simultaneously analyzing genomic DNA and mRNA from the same cell for direct comparison.

Purpose of the Study:

To develop an integrated sequencing strategy for quantifying both genomic DNA and mRNA from individual cells.
To enable direct correlation of genomic variation with transcriptome heterogeneity within single cells.

Main Methods:

A quasilinear amplification strategy was developed to quantify genomic DNA and mRNA from the same cell without prior physical separation.
The efficiency of this integrated approach was compared to existing single-cell sequencing methods for DNA or mRNA alone.

Main Results:

The integrated approach demonstrated comparable efficiency to existing single-cell sequencing methods.
Genes with high cell-to-cell transcript variability typically showed lower genomic copy numbers, and vice versa.
This suggests a link between copy number variations and gene expression variability.

Conclusions:

The developed integrated sequencing method allows for simultaneous analysis of DNA and RNA in single cells.
Copy number variations appear to be a significant driver of gene expression heterogeneity among individual cells.
This approach has potential applications in studying cancer evolution and the effects of copy number variations in various tissues.