Evidence map›Paper›PMID 38716457›Full record

ReviewAnalytical science advances2021

The rise of single-cell proteomics.

Claudia Ctortecka, Karl Mechtler

Abstract readReview
In one paragraph

Review in Analytical science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing 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

51 citing papers in PubMed.

  1. Article
  2. Article
  3. MR-SPJournal of proteome research · 2026
    Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Article
  20. Review
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.

Claudia CtorteckaResearch Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC) Campus-Vienna-Biocenter 1 Vienna 1030 Austria.ORCID https://orcid.org/0000-0002-6886-8936
Karl MechtlerResearch Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC) Campus-Vienna-Biocenter 1 Vienna 1030 Austria.ORCID https://orcid.org/0000-0002-3392-9946

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mass spectrometry-based proteomics comprehensively defines proteome expression patterns in thousands of cells majorly contributing to our current understanding of many biological processes. More recently, single-cell transcriptome and genome studies, however, have demonstrated overwhelming heterogeneity of tissues and cellular subpopulations. These studies have indicated different cellular functionality and identity, which are mainly driven by proteins and their posttranscriptional modifications. The rapidly emerging field of single-cell proteomics aims at complementing transcriptome and genome data by generating comparative protein expression profiles from individual cells. Recent developments demonstrated tremendous improvements in sample preparation workflows and MS instrumentation, quantifying over 1000 proteins from a single cell. Efficient and reproducible sample processing in conjunction with sensitive MS acquisition strategies will allow to further increase the proteome coverage of tissues with single-cell resolution. The required throughput and data reliability of such studies are still subject to further developments. Therefore, we herein discuss recent progress on specialized workflows and instrumentation next to advancements outside the field, which we expect to contribute to the development of comprehensive single-cell proteomics.

Indexed as

mass spectrometrysingle‐cell proteomicsultra‐low input proteomics

Identifiers

PMID38716457
PMCPMC10989620

What OpenQuestion 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 OpenQuestion graph.