Evidence map›Paper›PMID 33226031›Full record

ArticleJournal of visualized experiments : JoVE2020

Carrier-assisted One-pot Sample Preparation for Targeted Proteomics Analysis of Small Numbers of Human Cells.

Kendall Martin, Tong Zhang, Pengfei Zhang, William B Chrisler, Fillmore L Thomas, Fen Liu, Tao Liu, Wei-Jun Qian, Richard D Smith, Tujin Shi

Abstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Kendall MartinBiological Sciences Division, Pacific Northwest National Laboratory.
Tong ZhangBiological Sciences Division, Pacific Northwest National Laboratory.
Pengfei ZhangNHC Key Laboratory of Cancer Proteomics, Department of Oncology, Xiangya Hospital, Central South University.
William B ChrislerBiological Sciences Division, Pacific Northwest National Laboratory.
Fillmore L ThomasEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory.
Fen LiuDepartment of Critical Care Medicine, The First Affiliated Hospital of Nanchang University.
Tao LiuBiological Sciences Division, Pacific Northwest National Laboratory.
Wei-Jun QianBiological Sciences Division, Pacific Northwest National Laboratory.
Richard D SmithBiological Sciences Division, Pacific Northwest National Laboratory.
Tujin ShiBiological Sciences Division, Pacific Northwest National Laboratory; tujin.shi@pnnl.gov.

Funding

PNNL Proteome Characterization CenterU24CA210955 · NCI · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI LIU, TAO, SMITH, RICHARD D · 2016 to 2020
$5.3M
A streamlined platform for phosphoproteome mapping of human tissuesUH3CA256967 · NCI · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI LIU, HUIPING, SHI, TUJIN · 2022 to 2023
$1.2M
A streamlined platform for phosphoproteome mapping of human tissuesUG3CA256967 · NCI · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI LIU, HUIPING, SHI, TUJIN · 2020 to 2021
$800k
An ultrasensitive targeted mass spectrometry system for proteomics analysis of single cellsR21CA223715 · NCI · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI SHI, TUJIN · 2018 to 2020
$691k
NCI NIH HHS R21 CA223715NCI NIH HHS U24 CA210955NCI NIH HHS UG3 CA256967NCI NIH HHS UH3 CA256967
6 · The paper itself

Abstract

Protein analysis of small numbers of human cells is primarily achieved by targeted proteomics with antibody-based immunoassays, which have inherent limitations (e.g., low multiplex and unavailability of antibodies for new proteins). Mass spectrometry (MS)-based targeted proteomics has emerged as an alternative because it is antibody-free, high multiplex, and has high specificity and quantitation accuracy. Recent advances in MS instrumentation make MS-based targeted proteomics possible for multiplexed quantification of highly abundant proteins in single cells. However, there is a technical challenge for effective processing of single cells with minimal sample loss for MS analysis. To address this issue, we have recently developed a convenient protein carrier-assisted one-pot sample preparation coupled with liquid chromatography (LC) - selected reaction monitoring (SRM) termed cLC-SRM for targeted proteomics analysis of small numbers of human cells. This method capitalizes on using the combined excessive exogenous protein as a carrier and low-volume one-pot processing to greatly reduce surface adsorption losses as well as high-specificity LC-SRM to effectively address the increased dynamic concentration range due to the addition of exogeneous carrier protein. Its utility has been demonstrated by accurate quantification of most moderately abundant proteins in small numbers of cells (e.g., 10-100 cells) and highly abundant proteins in single cells. The easy-to-implement features and no need for specific devices make this method readily accessible to most proteomics laboratories. Herein we have provided a detailed protocol for cLC-SRM analysis of small numbers of human cells including cell sorting, cell lysis and digestion, LC-SRM analysis, and data analysis. Further improvements in detection sensitivity and sample throughput are needed towards targeted single-cell proteomics analysis. We anticipate that cLC-SRM will be broadly applied to biomedical research and systems biology with the potential of facilitating precision medicine.

Indexed as

AlkylationCell CountCell FractionationCell LineChromatography, LiquidData AnalysisErbB ReceptorsFlow CytometryHumansMAP Kinase Signaling SystemMass SpectrometryProtein DenaturationProteomicsTrypsinErbB ReceptorsTrypsin

Identifiers

PMID33226031
PMCPMC8349108

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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.