Evidence map›Paper›PMID 39018178›Full record

ArticleBioinformatics (Oxford, England)2024

Controlled noise: evidence of epigenetic regulation of single-cell expression variability.

Yan Zhong, Siwei Cui, Yongjian Yang, James J Cai

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

4 authors.

Yan ZhongSchool of Statistics, KLATASDS-MOE, East China Normal University, Shanghai, 200062, China.ORCID 0000-0003-2412-043X
Siwei CuiSchool of Statistics, KLATASDS-MOE, East China Normal University, Shanghai, 200062, China.
Yongjian YangDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States.ORCID 0000-0002-4135-5014
James J CaiDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States.ORCID 0000-0002-8081-6725

Funding

Cancer Prevention & Research Institute of Texas RP230204National Key R&D Program of China 2021YFA1000100National Key R&D Program of China 2021YFA1000101National Natural Science Foundation of China 12301336National Natural Science Foundation of China 72331005
6 · The paper itself

Abstract

motivationUnderstanding single-cell expression variability (scEV) or gene expression noise among cells of the same type and state is crucial for delineating population-level cellular function. While epigenetic mechanisms are widely implicated in gene expression regulation, a definitive link between chromatin accessibility and scEV remains elusive. Recent advances in single-cell techniques enable the study of single-cell multiomics data that include the simultaneous measurement of scATAC-seq and scRNA-seq within individual cells, presenting an unprecedented opportunity to address this gap.

resultsThis article introduces an innovative testing pipeline to investigate the association between chromatin accessibility and scEV. With single-cell multiomics data of scATAC-seq and scRNA-seq, the pipeline hinges on comparing the prediction performance of scATAC-seq data on gene expression levels between highly variable genes (HVGs) and non-highly variable genes (non-HVGs). Applying this pipeline to paired scATAC-seq and scRNA-seq data from human hematopoietic stem and progenitor cells, we observed a significantly superior prediction performance of scATAC-seq data for HVGs compared to non-HVGs. Notably, there was a substantial overlap between well-predicted genes and HVGs. The gene pathways enriched from well-predicted genes are highly pertinent to cell type-specific functions. Our findings support the notion that scEV largely stems from cell-to-cell variability in chromatin accessibility, providing compelling evidence for the epigenetic regulation of scEV and offering promising avenues for investigating gene regulation mechanisms at the single-cell level. AVAILABILITY AND IMPLEMENTATION: The source code and data used in this article can be found at https://github.com/SiweiCui/EpigeneticControlOfSingle-CellExpressionVariability.

Identifiers

PMID39018178
PMCPMC11283284

What Socratic holds

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LicenceCC BY
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Registered trials

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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.