Evidence map›Paper›PMID 38816402›Full record

ArticleScientific data2024

Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells.

Emily Y Su, Kristen Fread, Sarah Goggin, Eli R Zunder, Patrick Cahan

Abstract readDatasetComparative Study
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. CD4Frontiers in immunology · 2026
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. 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

5 authors.

Emily Y Su *Institute for Cell Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA.ORCID 0000-0002-9098-6541
Kristen Fread *Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
Sarah GogginDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
Eli R ZunderDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA. ezunder@virginia.edu.
Patrick CahanInstitute for Cell Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA. patrick.cahan@jhmi.edu.ORCID 0000-0003-3652-2540

Funding

From intra to intercellular regulatory networks that define cell type identityR35GM124725 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI Patrick Cahan · 2017 to 2026
$4.5M
Biotechnology Training ProgramT32GM136615 · NIGMS · UNIVERSITY OF VIRGINIA · PI Silvia Salinas Blemker, Kimberly A. Kelly · 2020 to 2026
$3.4M
Deciphering Trophic Signaling Programs Governing Peripheral Sensory Nervous System DevelopmentR01NS111220 · NINDS · UNIVERSITY OF VIRGINIA · PI ZUNDER, ELI · 2020 to 2024
$2.6M
Transdisciplinary Big Data Science Training at UVaT32LM012416 · NLM · UNIVERSITY OF VIRGINIA · PI BROWN, DONALD E, LOUGHRAN, THOMAS P. · 2016 to 2020
$1.3M
National Science Foundation (NSF) DGE-1746891NIGMS NIH HHS R35 GM124725NIGMS NIH HHS T32 GM136615NINDS NIH HHS R01 NS111220NLM NIH HHS T32 LM012416U.S. Department of Health & Human Services | National Institutes of Health (NIH) 5T32LM012416U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) 5T32GM136615U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM124725U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) 5R01NS111220
6 · The paper itself

Abstract

Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.

Indexed as

Flow CytometryLeukocytes, MononuclearSequence Analysis, RNASingle-Cell AnalysisHumansProteomicsTranscriptome

Identifiers

PMID38816402
PMCPMC11139855

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.