Evidence map›Paper›PMID 33153500›Full record

ArticleBiology of sex differences2020

Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types.

Tianyuan Lu, Jessica C Mar

Open access · goldAbstract readComparative StudyValidation Study
In one paragraph

Article in Biology of sex differences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
3.6field-weighted citation impact, top 6% of its field
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

20 citing papers in PubMed, 74 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. New role for cardiomyocytebioRxiv : the preprint server for biology · 2024
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Big Data in Gastroenterology Research.International journal of molecular sciences · 2023
    Review
  15. Article
  16. Review
  17. Article
  18. Matters of the heart: Cellular sex differences.Journal of molecular and cellular cardiology · 2021
    Review
  19. Review
  20. 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

2 authors at 1 institution in 2 countries.

Tianyuan LuAustralian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia.
Jessica C MarAustralian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia. j.mar@uq.edu.au.ORCID 0000-0002-5147-9299
The University of Queensland · AU

Funding

Australian Research Council FT170100047National Stem Cell Foundation of Australia Metcalf Prize
6 · The paper itself

Abstract

backgroundIt is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level.

methodsOur study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism.

resultsFor each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF.

conclusionsWe explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.

Indexed as

Gene ExpressionSex CharacteristicsTranscriptomeAnimalsBrainDatasets as TopicFemaleGene Regulatory NetworksMaleMiceMice, Inbred C57BLMyocardiumSequence Analysis, RNASingle-Cell AnalysisSoftwareCell clusterDifferential distributionDifferential expressionPathway analysisSex dimorphismSingle cell RNA sequencingTranscription regulatory network

Identifiers

PMID33153500
PMCPMC7643324
OpenAlexW3097522371

What Socratic holds

Textmetadata
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.