Evidence map›Paper›PMID 33725447›Full record

ArticleJournal of the American Society for Mass Spectrometry2021

Quantitative Top-Down Proteomics in Complex Samples Using Protein-Level Tandem Mass Tag Labeling.

Dahang Yu, Zhe Wang, Kellye A Cupp-Sutton, Yanting Guo, Qiang Kou, Kenneth Smith, Xiaowen Liu, Si Wu

Abstract read
In one paragraph

Article in Journal of the American Society for Mass Spectrometry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Article
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  11. Article
  12. Review
  13. Analytical and bioanalytical chemistry · 2024
    Article
  14. Article
  15. Top-down proteomics.Nature reviews. Methods primers · 2024
    Article
  16. Article
  17. Multidimensional Separations in Top-Down Proteomics.Analytical science advances · 2023
    Article
  18. Article
  19. Article
  20. 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

8 authors.

Dahang YuDepartment of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019, United States.
Zhe WangDepartment of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019, United States.
Kellye A Cupp-SuttonDepartment of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019, United States.
Yanting GuoDepartment of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019, United States.
Qiang KouSchool of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States.ORCID http://orcid.org/0000-0001-6786-5453
Kenneth SmithDepartment of Arthritis and Clinical Immunology, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma 73104, United States.
Xiaowen LiuSchool of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States.
Si WuDepartment of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019, United States.ORCID http://orcid.org/0000-0002-6346-7359

Funding

Translation of immunologic technologies from basic research into pre-clinical nonU19AI062629 · NIAID · OKLAHOMA MEDICAL RESEARCH FOUNDATION · PI COGGESHALL, KENNETH MARK · 2004 to 2023
$56.2M
Defining the role of altered cytokine signaling pathways on autoimmunityU01AI101990 · NIAID · BENAROYA RESEARCH INST AT VIRGINIA MASON · PI BUCKNER, JANE HOYT · 2012 to 2016
$12.1M
Computational tools for top down mass spectrometry based proteoform identification and proteogenomicsR01GM118470 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Xiaowen Liu · 2016 to 2026
$2.7M
Quantitative Analysis of Serum Autoantibody Repertories in Systemic Lupus ErythematosusR01AI141625 · NIAID · UNIVERSITY OF OKLAHOMA · PI SINGH, SHANTERI, SMITH, KENNETH MICHAEL · 2019 to 2023
$2.1M
NIAID NIH HHS R01 AI141625NIAID NIH HHS U01 AI101990NIAID NIH HHS U19 AI062629NIGMS NIH HHS R01 GM118470
6 · The paper itself

Abstract

Labeling approaches using isobaric chemical tags (e.g., isobaric tagging for relative and absolute quantification, iTRAQ and tandem mass tag, TMT) have been widely applied for the quantification of peptides and proteins in bottom-up MS. However, until recently, successful applications of these approaches to top-down proteomics have been limited because proteins tend to precipitate and "crash" out of solution during TMT labeling of complex samples making the quantification of such samples difficult. In this study, we report a top-down TMT MS platform for confidently identifying and quantifying low molecular weight intact proteoforms in complex biological samples. To reduce the sample complexity and remove large proteins from complex samples, we developed a filter-SEC technique that combines a molecular weight cutoff filtration step with high-performance size exclusion chromatography (SEC) separation. No protein precipitation was observed in filtered samples under the intact protein-level TMT labeling conditions. The proposed top-down TMT MS platform enables high-throughput analysis of intact proteoforms, allowing for the identification and quantification of hundreds of intact proteoforms from

Indexed as

Chromatography, GelChromatography, LiquidEscherichia coli ProteinsMolecular WeightPeriplasmic ProteinsPeroxidasesProteomicsRibosomal ProteinsTandem Mass SpectrometryEscherichia coli ProteinsPeriplasmic ProteinsPeroxidasesribosomal protein L6Ribosomal ProteinsTpx protein, E coliisobaric labelingliquid chromatographymass spectrometryquantitative proteomicstandem mass tags (TMT)top-down proteomics

Identifiers

PMID33725447
PMCPMC8323476

What OpenQuestion holds

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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.