Evidence map›Paper›PMID 36806919›Full record

ReviewProteomics2023

Label-free single cell proteomics utilizing ultrafast LC and MS instrumentation: A valuable complementary technique to multiplexing.

Manuel Matzinger, Rupert L Mayer, Karl Mechtler

Abstract readReview
In one paragraph

Review in Proteomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Article
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  9. bioRxiv : the preprint server for biology · 2025
    Article
  10. Article
  11. Article
  12. Mass Spectrometry-based Solutions for Single-cell Proteomics.Genomics, proteomics & bioinformatics · 2025
    Review
  13. Article
  14. Review
  15. Review
  16. Review
  17. Spatial Proteomics towards cellular Resolution.Expert review of proteomics · 2024
    Article
  18. Article
  19. Article
  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

3 authors.

Manuel MatzingerResearch Institute of Molecular Pathology (IMP), Vienna BioCenter, Vienna, Austria.ORCID 0000-0002-9765-7951
Rupert L MayerResearch Institute of Molecular Pathology (IMP), Vienna BioCenter, Vienna, Austria.
Karl MechtlerResearch Institute of Molecular Pathology (IMP), Vienna BioCenter, Vienna, Austria.ORCID 0000-0002-3392-9946

Funding

Austrian Science Fund ERA-CAPS I 3686Austrian Science Fund P35045-BHorizon 2020 Framework Programme 823839Vienna Science and Technology Fund LS20-079
6 · The paper itself

Abstract

The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.

Indexed as

AlgorithmsProteomicsMass SpectrometryProteomeProteomebioinformaticscell biologylabel-freequantificationtechnology

Identifiers

PMID36806919
PMCPMC10909491

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.