Evidence map›Paper›PMID 36864196›Full record

ReviewNature methods2023

Single-cell proteomics enabled by next-generation sequencing or mass spectrometry.

Hayley M Bennett, William Stephenson, Christopher M Rose, Spyros Darmanis

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 201 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
201citing papers in PubMed, 1 pooled it
47.4field-weighted citation impact, top 1% 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

201 citing papers in PubMed, 1 synthesis or guideline pooled it, 309 citations in OpenAlex.

  1. Pooled it
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  15. Thousandfold Expansion Microscopy.bioRxiv : the preprint server for biology · 2026
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141 more citing papers are in PubMed but not listed here.

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 at 1 institution in 1 country.

Hayley M BennettDepartment of Next Generation Sequencing and Microchemistry, Proteomics and Lipidomics, Genentech, South San Francisco, CA, USA.ORCID http://orcid.org/0000-0001-8879-2855
William StephensonDepartment of Next Generation Sequencing and Microchemistry, Proteomics and Lipidomics, Genentech, South San Francisco, CA, USA.
Christopher M RoseDepartment of Next Generation Sequencing and Microchemistry, Proteomics and Lipidomics, Genentech, South San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-7502-3368
Spyros DarmanisDepartment of Next Generation Sequencing and Microchemistry, Proteomics and Lipidomics, Genentech, South San Francisco, CA, USA. darmanis@gene.com.ORCID http://orcid.org/0000-0003-4002-8158
Gene Therapy Laboratory · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the last decade, single-cell RNA sequencing routinely performed on large numbers of single cells has greatly advanced our understanding of the underlying heterogeneity of complex biological systems. Technological advances have also enabled protein measurements, further contributing to the elucidation of cell types and states present in complex tissues. Recently, there have been independent advances in mass spectrometric techniques bringing us one step closer to characterizing single-cell proteomes. Here we discuss the challenges of detecting proteins in single cells by both mass spectrometry and sequencing-based methods. We review the state of the art for these techniques and propose that there is a space for technological advancements and complementary approaches that maximize the advantages of both classes of technologies.

Indexed as

ProteomeProteomicsHigh-Throughput Nucleotide SequencingMass SpectrometryProteome

Identifiers

PMID36864196
OpenAlexW4322766901

What OpenQuestion holds

Textmetadata
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.