Evidence map›Paper›PMID 35810282›Full record

ArticleClinical proteomics2022

Optimized data-independent acquisition approach for proteomic analysis at single-cell level.

Yuefan Wang, Tung-Shing Mamie Lih, Lijun Chen, Yuanwei Xu, Morgan D Kuczler, Liwei Cao, Kenneth J Pienta, Sarah R Amend, Hui Zhang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical proteomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
4.2field-weighted citation impact, top 4% 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

18 citing papers in PubMed, 29 citations in OpenAlex.

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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

9 authors at 1 institution in 1 country.

Yuefan WangDepartment of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA.
Tung-Shing Mamie LihDepartment of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA.
Lijun ChenDepartment of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA.
Yuanwei XuDepartment of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA.
Morgan D KuczlerCancer Ecology Center, The Brady Urological Institute, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.
Liwei CaoDepartment of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA.
Kenneth J PientaCancer Ecology Center, The Brady Urological Institute, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.
Sarah R AmendCancer Ecology Center, The Brady Urological Institute, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.
Hui ZhangDepartment of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA. huizhang@jhu.edu.
Johns Hopkins University · US

Funding

TRANS_NETWORK PROJECTSU54CA143803 · NCI · PRINCETON UNIVERSITY · PI AUSTIN, ROBERT H. · 2009 to 2013
$14.2M
The Comprehensive Proteome Characterization Center at Johns Hopkins: High Precision Discovery and Confirmation of Genoproteomic TargetsU24CA210985 · NCI · JOHNS HOPKINS UNIVERSITY · PI CHAN, DANIEL WANYUI, ZHANG, HUI · 2016 to 2020
$5.3M
NCI NIH HHS U24 CA210985NCI NIH HHS U24CA210985NCI NIH HHS U54 CA143803NCI NIH HHS U54CA143803U.S. Department of Defense W81XWH-20-10353
6 · The paper itself

Abstract

backgroundSingle-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis.

methodsWe report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow.

resultsWe found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number.

conclusionsOur results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

Identifiers

PMID35810282
PMCPMC9270744
OpenAlexW4284991879

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.