Evidence map›Paper›PMID 32894271›Full record

ArticleJournal of visualized experiments : JoVE2020

High-throughput and Deep-proteome Profiling by 16-plex Tandem Mass Tag Labeling Coupled with Two-dimensional Chromatography and Mass Spectrometry.

Zhen Wang, Kanisha Kavdia, Kaushik Kumar Dey, Vishwajeeth Reddy Pagala, Kiran Kodali, Danting Liu, Dong Geun Lee, Huan Sun, Surendhar Reddy Chepyala, Ji-Hoon Cho and 3 more

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 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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

13 authors.

Zhen Wang *Departments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital.
Kanisha Kavdia *Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital.
Kaushik Kumar DeyDepartments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital.
Vishwajeeth Reddy PagalaCenter for Proteomics and Metabolomics, St. Jude Children's Research Hospital.
Kiran KodaliCenter for Proteomics and Metabolomics, St. Jude Children's Research Hospital.
Danting LiuDepartments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital.
Dong Geun LeeDepartments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital.
Huan SunDepartments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital.
Surendhar Reddy ChepyalaDepartments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital.
Ji-Hoon ChoCenter for Proteomics and Metabolomics, St. Jude Children's Research Hospital.
Mingming NiuDepartments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital.
Anthony A HighCenter for Proteomics and Metabolomics, St. Jude Children's Research Hospital; anthony.high@stjude.org.
Junmin PengDepartments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital; Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital; junmin.peng@stjude.org.

Funding

Viral Vector Technology (VVTSR)P30CA021765 · NCI · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI CHARLES ROBERTS · 1985 to 2026
$166.9M
Utilization of proteomics and lipidomics to identify modifiers of LBDU54NS110435 · NINDS · MAYO CLINIC JACKSONVILLE · PI DICKSON, DENNIS WILLIAM · 2019 to 2023
$14.5M
Systems Approaches to Novel Molecular Mechanism in Alzheimer's DiseaseRF1AG064909 · NIA · UT SOUTHWESTERN MEDICAL CENTER · PI PENG, JUNMIN, YU, GANG · 2019 to 2024
$6.4M
Multi-level Integrative Proteomics to Alzheimer's Disease PathwaysR01AG053987 · NIA · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI PENG, JUNMIN · 2016 to 2020
$2.2M
RNA splicing dysfunction in Alzheimer's diseaseR01AG047928 · NIA · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI PENG, JUNMIN · 2015 to 2019
$1.8M
Proteomics Approaches to Protein TurnoverR01GM114260 · NIGMS · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI PENG, JUNMIN · 2016 to 2019
$1.4M
NCI NIH HHS P30 CA021765NIA NIH HHS R01 AG047928NIA NIH HHS R01 AG053987NIA NIH HHS RF1 AG064909NIGMS NIH HHS R01 GM114260NINDS NIH HHS U54 NS110435
6 · The paper itself

Abstract

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an expanded 16-plex TMT method has been introduced, which further increases the throughput of proteomic studies. In this manuscript, we present an optimized protocol for 16-plex TMT-based deep-proteome profiling, including protein sample preparation, enzymatic digestion, TMT labeling reaction, two-dimensional reverse-phase liquid chromatography (LC/LC) fractionation, tandem mass spectrometry (MS/MS), and computational data processing. The crucial quality control steps and improvements in the process specific for the 16-plex TMT analysis are highlighted. This multiplexed process offers a powerful tool for profiling a variety of complex samples such as cells, tissues, and clinical specimens. More than 10,000 proteins and posttranslational modifications such as phosphorylation, methylation, acetylation, and ubiquitination in highly complex biological samples from up to 16 different samples can be quantified in a single experiment, providing a potent tool for basic and clinical research.

Indexed as

Chromatography, Reverse-PhaseComputational BiologyProteomeProteomicsTandem Mass SpectrometryProteome

Identifiers

PMID32894271
PMCPMC7752892

What OpenQuestion holds

Textmetadata
LicenceTDM
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.