Evidence map›Paper›PMID 35954294›Full record

ArticleCells2022

Spectral Library-Based Single-Cell Proteomics Resolves Cellular Heterogeneity.

Lakmini Senavirathna, Cheng Ma, Ru Chen, Sheng Pan

Open access · goldAbstract read
In one paragraph

Article in Cells, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

7 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Bottom-Up Proteomics: Advancements in Sample Preparation.International journal of molecular sciences · 2023
    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

4 authors at 2 institutions in 1 country.

Lakmini SenavirathnaThe Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.ORCID 0000-0003-0911-7320
Cheng MaThe Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Ru ChenDepartment of Medicine, Baylor College of Medicine, Houston, TX 77030, USA.
Sheng PanThe Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Brown Foundation · USBaylor College of Medicine · US

Funding

Tumor BiologyP30CA125123 · NCI · BAYLOR COLLEGE OF MEDICINE · PI DAVID R ROWLEY · 2007 to 2026
$73.9M
Early detection of pancreatic cancer in diabeticsR01CA180949 · NCI · UNIVERSITY OF WASHINGTON · PI CHEN, RU, PAN, SHENG · 2014 to 2018
$2.9M
Biomarkers for Early Detection of Colorectal Cancer in Ulcerative ColitisR01CA211892 · NCI · UNIVERSITY OF WASHINGTON · PI CHEN, RU · 2017 to 2022
$2.3M
NCI NIH HHS P30 CA125123NCI NIH HHS R01 CA180949NCI NIH HHS R01 CA211892
6 · The paper itself

Abstract

Dissecting the proteome of cell types and states at single-cell resolution, while being highly challenging, has significant implications in basic science and biomedicine. Mass spectrometry (MS)-based single-cell proteomics represents an emerging technology for system-wide, unbiased profiling of proteins in single cells. However, significant challenges remain in analyzing an extremely small amount of proteins collected from a single cell, as a proteome-wide amplification of proteins is not currently feasible. Here, we report an integrated spectral library-based single-cell proteomics (SLB-SCP) platform that is ultrasensitive and well suited for a large-scale analysis. To overcome the low MS/MS signal intensity intrinsically associated with a single-cell analysis, this approach takes an alternative approach by extracting a breadth of information that specifically defines the physicochemical characteristics of a peptide from MS1 spectra, including monoisotopic mass, isotopic distribution, and retention time (hydrophobicity), and uses a spectral library for proteomic identification. This conceptually unique MS platform, coupled with the DIRECT sample preparation method, enabled identification of more than 2000 proteins in a single cell to distinguish different proteome landscapes associated with cellular types and heterogeneity. We characterized individual normal and cancerous pancreatic ductal cells (HPDE and PANC-1, respectively) and demonstrated the substantial difference in the proteomes between HPDE and PANC-1 at the single-cell level. A significant upregulation of multiple protein networks in cancer hallmarks was identified in the PANC-1 cells, functionally discriminating the PANC-1 cells from the HPDE cells. This integrated platform can be built on high-resolution MS and widely accepted proteomic software, making it possible for community-wide applications.

Indexed as

ProteomeProteomicsPeptidesSoftwareTandem Mass SpectrometryPeptidesProteomecellular heterogeneitymass spectrometryproteomicssingle-cell proteomicsspectral librarysystems biology

Identifiers

PMID35954294
PMCPMC9368228
OpenAlexW4290466098

What OpenQuestion holds

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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.