Evidence map›Paper›PMID 33897275›Full record

ArticleInternational journal of mass spectrometry2021

Application of frequency multiple FT-ICR-MS signal acquisition for improved proteome research.

Sung-Gun Park, Jared P Mohr, Gordon A Anderson, James E Bruce

Abstract read
In one paragraph

Article in International journal of mass spectrometry, 2021. 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

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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

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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. Review
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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

4 authors.

Sung-Gun ParkDepartment of Genome Sciences, University of Washington, Seattle, WA 98109.
Jared P MohrDepartment of Genome Sciences, University of Washington, Seattle, WA 98109.
Gordon A AndersonGAA Custom Engineering, LLC, Benton City, WA 99320.
James E BruceDepartment of Genome Sciences, University of Washington, Seattle, WA 98109.

Funding

Dynamics of the cellular interactomeR35GM136255 · NIGMS · UNIVERSITY OF WASHINGTON · PI James Edward Bruce · 2020 to 2026
$4.0M
Instrumentation Development: MS Array for Quantitative ProteomicsR01GM097112 · NIGMS · UNIVERSITY OF WASHINGTON · PI BRUCE, JAMES EDWARD · 2011 to 2020
$2.9M
NIGMS NIH HHS R01 GM097112NIGMS NIH HHS R35 GM136255
6 · The paper itself

Abstract

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) coupled with liquid chromatography (LC) is a powerful combination useful in many research areas due to the utility of high mass resolving power and mass measurement accuracy for studying highly complex samples. Ideally, every analyte in a complex sample can be subjected to accurate mass MS/MS analysis to aid in identification. FT-ICR MS can provide high mass resolving power and mass accuracy at the cost of long data acquisition periods, reducing the number of spectra that can be acquired per unit time. Frequency multiple signal acquisition has long been realized as an attractive method to obtain high mass resolving power and mass accuracy with shorter data acquisition periods. However, one of the limitations associated with frequency multiple signal acquisition is reduced signal intensity as compared to a traditional dipole detector. In this study, we demonstrated the use of a novel ICR cell to improve frequency multiple signal intensity and investigated the potential use of frequency multiple acquisition for proteome measurements. This novel ICR cell containing both dipole and frequency multiple detection electrodes was installed on a 7T FT-ICR MS coupled to an LC system. Tryptic digests of HeLa cell lysates were analyzed using dipole and frequency multiple detectors by holding either the mass resolving power or signal acquisition time constant. Compared to dipole detection, second frequency multiple detection yielded 36% or 45% more unique identified peptides from HeLa cell lysates at twice the scan rate or twice the mass resolving power, respectively. These results indicate that frequency multiple signal acquisition with either the same resolving power or the same signal acquisition duration as used with dipole signals can produce a significant increase in the number of peptides identified in complex proteome samples.

Identifiers

PMID33897275
PMCPMC8059610

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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.