Evidence map›Paper›PMID 38740383›Full record

ArticleJournal of the American Society for Mass Spectrometry2024

Multiplexing the Identification of Microorganisms via Tandem Mass Tag Labeling Augmented by Interference Removal through a Novel Modification of the Expectation Maximization Algorithm.

Gelio Alves, Aleksey Y Ogurtsov, Harry Porterfield, Tapan Maity, Lisa M Jenkins, David B Sacks, Yi-Kuo Yu

Abstract read
In one paragraph

Article in Journal of the American Society for Mass Spectrometry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

7 authors.

Gelio AlvesNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States.ORCID 0000-0002-1595-1445
Aleksey Y OgurtsovNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States.
Harry PorterfieldDepartment of Laboratory Medicine, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892, United States.
Tapan MaityLaboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, United States.
Lisa M JenkinsLaboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, United States.
David B SacksDepartment of Laboratory Medicine, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892, United States.
Yi-Kuo YuNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States.ORCID 0000-0002-6213-7665

Funding

Robust Accurate Identification of peptides from tandem mass spectrometry dataZIALM092404 · NLM · NATIONAL LIBRARY OF MEDICINE · PI YU, YI-KUO · 2009 to 2025
$13.2M
CPTR - Mass Spectrometry UnitZICBC011430 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI JENKINS, LISA · 2012 to 2025
$9.4M
Robust Accurate Identification of peptides from tandem mass spectrometry dataZ01LM092404 · NLM · NATIONAL LIBRARY OF MEDICINE · PI YU, YI-KUO · 2004 to 2008
$593k
Intramural NIH HHS Z01 LM092404
6 · The paper itself

Abstract

Having fast, accurate, and broad spectrum methods for the identification of microorganisms is of paramount importance to public health, research, and safety. Bottom-up mass spectrometer-based proteomics has emerged as an effective tool for the accurate identification of microorganisms from microbial isolates. However, one major hurdle that limits the deployment of this tool for routine clinical diagnosis, and other areas of research such as culturomics, is the instrument time required for the mass spectrometer to analyze a single sample, which can take ∼1 h per sample, when using mass spectrometers that are presently used in most institutes. To address this issue, in this study, we employed, for the first time, tandem mass tags (TMTs) in multiplex identifications of microorganisms from multiple TMT-labeled samples in one MS/MS experiment. A difficulty encountered when using TMT labeling is the presence of interference in the measured intensities of TMT reporter ions. To correct for interference, we employed in the proposed method a modified version of the expectation maximization (EM) algorithm that redistributes the signal from ion interference back to the correct TMT-labeled samples. We have evaluated the sensitivity and specificity of the proposed method using 94 MS/MS experiments (covering a broad range of protein concentration ratios across TMT-labeled channels and experimental parameters), containing a total of 1931 true positive TMT-labeled channels and 317 true negative TMT-labeled channels. The results of the evaluation show that the proposed method has an identification sensitivity of 93-97% and a specificity of 100% at the species level. Furthermore, as a proof of concept, using an in-house-generated data set composed of some of the most common urinary tract pathogens, we demonstrated that by using the proposed method the mass spectrometer time required per sample, using a 1 h LC-MS/MS run, can be reduced to 10 and 6 min when samples are labeled with TMT-6 and TMT-10, respectively. The proposed method can also be used along with Orbitrap mass spectrometers that have faster MS/MS acquisition rates, like the recently released Orbitrap Astral mass spectrometer, to further reduce the mass spectrometer time required per sample.

Indexed as

AlgorithmsProteomicsTandem Mass SpectrometryBacteriaBacterial ProteinsHumansBacterial Proteins

Identifiers

PMID38740383
PMCPMC11157548

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.