Evidence map›Paper›PMID 36611134›Full record

ArticleClinical proteomics2023

Combination of automated sample preparation and micro-flow LC-MS for high-throughput plasma proteomics.

Xueting Ye, Xiaozhen Cui, Luobin Zhang, Qiong Wu, Xintong Sui, An He, Xinyou Zhang, Ruilian Xu, Ruijun Tian

Open access · goldAbstract read
In one paragraph

Article in Clinical proteomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.3field-weighted citation impact, top 24% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  5. Automated Sample Preparation Workflow for Tandem Mass Tag-Based Proteomics.Journal of the American Society for Mass Spectrometry · 2023
    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

9 authors at 3 institutions in 1 country.

Xueting Ye *The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China.
Xiaozhen Cui *Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055, China.
Luobin ZhangThe Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China.
Qiong WuDepartment of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055, China.
Xintong SuiDepartment of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055, China.
An HeDepartment of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055, China.
Xinyou ZhangThe Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China. zhangxinyou0518@sina.com.
Ruilian XuThe Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China. xuruilian@126.com.
Ruijun TianDepartment of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055, China. tianrj@sustech.edu.cn.
Southern University of Science and Technology · CNJinan University · CNFirst Affiliated Hospital of Jinan University · CN

Funding

China State Key Basic Research Program Grants 2021YFA1302603, 2020YFE0202200, 2021YFA1301601, and 2021YFA1301602Guangdong Provincial Fund for Distinguished Young Scholars 2019B151502050National Natural Science Foundation of China 22125403, 91953118, 32171433, 22104047, and 22004056Shenzhen Innovation of Science and Technology Commission JCYJ20200109141212325, JCYJ20210324120210029, and JCYJ20200109140814408
6 · The paper itself

Abstract

backgroundNon-invasive detection of blood-based markers is a critical clinical need. Plasma has become the main sample type for clinical proteomics research because it is easy to obtain and contains measurable protein biomarkers that can reveal disease-related physiological and pathological changes. Many efforts have been made to improve the depth of its identification, while there is an increasing need to improve the throughput and reproducibility of plasma proteomics analysis in order to adapt to the clinical large-scale sample analysis.

methodsWe have developed and optimized a robust plasma analysis workflow that combines an automated sample preparation platform with a micro-flow LC-MS-based detection method. The stability and reproducibility of the workflow were systematically evaluated and the workflow was applied to a proof-of-concept plasma proteome study of 30 colon cancer patients from three age groups.

resultsThis workflow can analyze dozens of samples simultaneously with high reproducibility. Without protein depletion and prefractionation, more than 300 protein groups can be identified in a single analysis with micro-flow LC-MS system on a Orbitrap Exploris 240 mass spectrometer, including quantification of 35 FDA approved disease markers. The quantitative precision of the entire workflow was acceptable with median CV of 9%. The preliminary proteomic analysis of colon cancer plasma from different age groups could be well separated with identification of potential colon cancer-related biomarkers.

conclusionsThis workflow is suitable for the analysis of large-scale clinical plasma samples with its simple and time-saving operation, and the results demonstrate the feasibility of discovering significantly changed plasma proteins and distinguishing different patient groups.

Indexed as

Automated samples preparationHigh throughputMicro-flow LC–MSPlasma proteome profiling

Identifiers

PMID36611134
PMCPMC9824974
OpenAlexW4313656554

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.