Understanding Transcriptional Noise in Cells
Author Information
Author(s): Khetan Neha, Zuckerman Binyamin, Calia Giuliana P., Chen Xinyue, Garcia Arceo Ximena, Weinberger Leor S.
Primary Institution: Gladstone|UCSF Center for Cell Circuitry, University of California, San Francisco
Hypothesis
How can we best quantify genome-wide transcriptional noise?
Conclusion
The study finds that most single-cell RNA sequencing algorithms underestimate transcriptional noise changes compared to single-molecule RNA imaging.
Supporting Evidence
- scRNA-seq validates that IdU orthogonally amplifies transcriptome-wide noise.
- smFISH validates IdU-induced noise amplification for most genes.
- scRNA-seq algorithms underestimate the fold change in noise compared to smFISH.
- The underestimation of changes in noise holds even after extrinsic factor corrections.
Takeaway
This study looks at how noise in gene expression can change without affecting the average amount of gene product, and it shows that some methods for measuring this noise don't work as well as others.
Methodology
The study used single-cell RNA sequencing and single-molecule RNA fluorescence in situ hybridization to analyze transcriptional noise in human and mouse datasets.
Potential Biases
Potential biases from technical noise in scRNA-seq methods could affect the quantification of transcriptional noise.
Limitations
The study is limited by a single replicate in mouse embryonic stem cells and smaller sequencing depth in Jurkat cells.
Participant Demographics
The study involved mouse embryonic stem cells and human Jurkat T lymphocytes.
Statistical Information
P-Value
p<10−17
Statistical Significance
p<10−17
Digital Object Identifier (DOI)
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