Evidence map›Paper›PMID 39662473›Full record

ArticleCell reports methods2024

Single-cell RNA sequencing algorithms underestimate changes in transcriptional noise compared to single-molecule RNA imaging.

Neha Khetan, Binyamin Zuckerman, Giuliana P Calia, Xinyue Chen, Ximena Garcia Arceo, Leor S Weinberger

Abstract readComparative Study
In one paragraph

Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Neha KhetanGladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, San Francisco, CA 94158, USA.
Binyamin ZuckermanGladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, San Francisco, CA 94158, USA.
Giuliana P CaliaGladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, San Francisco, CA 94158, USA.
Xinyue ChenGladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, San Francisco, CA 94158, USA.
Ximena Garcia ArceoGladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, San Francisco, CA 94158, USA.
Leor S WeinbergerGladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Evolvable Medicines, Oakland, CA, USA; Autonomous Therapeutics, Inc., Rockville, MD, USA. Electronic address: leor.weinberger@gmail.com.

Funding

Modulating Stochastic Gene Expression for Cell-fate Control and TherapeuticsR37AI109593 · NIAID · J. DAVID GLADSTONE INSTITUTES · PI WEINBERGER, LEOR S · 2021 to 2025
$6.2M
NIAID NIH HHS R37 AI109593
6 · The paper itself

Abstract

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome-wide noise remains unclear. Here, we utilize a small-molecule perturbation (5'-iodo-2'-deoxyuridine [IdU]) to amplify noise and assess noise quantification from numerous single-cell RNA sequencing (scRNA-seq) algorithms on human and mouse datasets and then compare it to noise quantification from single-molecule RNA fluorescence in situ hybridization (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise-without altered mean expression levels-for ∼90% of genes and that smFISH analysis verifies noise amplification for the vast majority of tested genes. Collectively, the analyses suggest that most scRNA-seq algorithms (including a simple normalization approach) are appropriate for quantifying noise, although all algorithms appear to systematically underestimate noise changes compared to smFISH. For practical purposes, this analysis further argues that IdU noise enhancement is globally penetrant-i.e., homeostatically increasing noise without altering mean expression levels-and could enable investigations of the physiological impacts of transcriptional noise.

Indexed as

AlgorithmsSequence Analysis, RNASingle-Cell AnalysisSingle Molecule ImagingAnimalsHumansIn Situ Hybridization, FluorescenceMiceRNATranscription, GeneticRNACP: imagingCP: systems biologynoise-enhancer moleculescRNA-seqsingle-cell RNA sequencingsingle-molecule RNA FISHsmFISHtranscriptional noise

Identifiers

PMID39662473
PMCPMC11704610

What Socratic holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.