Evidence map›Paper›PMID 28265968›Full record

ArticleJournal of the American Society for Mass Spectrometry2017

Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation.

Jeremy P Koelmel, Nicholas M Kroeger, Emily L Gill, Candice Z Ulmer, John A Bowden, Rainey E Patterson, Richard A Yost, Timothy J Garrett

Abstract read
In one paragraph

Article in Journal of the American Society for Mass Spectrometry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.

0numbers the graph read from it
0cells of the map it votes in
96citing papers in PubMed
6.4field-weighted citation impact, top 3% 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

96 citing papers in PubMed, 219 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

36 more citing papers are in PubMed but not listed here.

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 at 2 institutions in 1 country.

Jeremy P KoelmelDepartment of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA.
Nicholas M KroegerCollege of Engineering, University of Florida, 412 Newell Drive, Gainesville, FL, 32611, USA.
Emily L GillDepartment of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA.
Candice Z UlmerDepartment of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA.
John A BowdenHollings Marine Laboratory, National Institute of Standards and Technology, 331 Ft. Johnson Rd., Charleston, SC, 29412, USA.
Rainey E PattersonDepartment of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA.
Richard A YostDepartment of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA.
Timothy J GarrettDepartment of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA. tgarrett@ufl.edu.
University of Florida · USNational Institute of Standards and Technology · US

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
Southeast Resource Center for Integrated Metabolomics (SECIM)U24DK097209 · NIDDK · UNIVERSITY OF FLORIDA · PI EDISON, ARTHUR S, YOST, RICHARD A. · 2013 to 2017
$9.2M
Intramural NIST DOC 9999-NISTNCATS NIH HHS UL1 TR001427NIDDK NIH HHS U24 DK097209
6 · The paper itself

Abstract

Untargeted omics analyses aim to comprehensively characterize biomolecules within a biological system. Changes in the presence or quantity of these biomolecules can indicate important biological perturbations, such as those caused by disease. With current technological advancements, the entire genome can now be sequenced; however, in the burgeoning fields of lipidomics, only a subset of lipids can be identified. The recent emergence of high resolution tandem mass spectrometry (HR-MS/MS), in combination with ultra-high performance liquid chromatography, has resulted in an increased coverage of the lipidome. Nevertheless, identifications from MS/MS are generally limited by the number of precursors that can be selected for fragmentation during chromatographic elution. Therefore, we developed the software IE-Omics to automate iterative exclusion (IE), where selected precursors using data-dependent topN analyses are excluded in sequential injections. In each sequential injection, unique precursors are fragmented until HR-MS/MS spectra of all ions above a user-defined intensity threshold are acquired. IE-Omics was applied to lipidomic analyses in Red Cross plasma and substantia nigra tissue. Coverage of the lipidome was drastically improved using IE. When applying IE-Omics to Red Cross plasma and substantia nigra lipid extracts in positive ion mode, 69% and 40% more molecular identifications were obtained, respectively. In addition, applying IE-Omics to a lipidomics workflow increased the coverage of trace species, including odd-chained and short-chained diacylglycerides and oxidized lipid species. By increasing the coverage of the lipidome, applying IE to a lipidomics workflow increases the probability of finding biomarkers and provides additional information for determining etiology of disease. Graphical Abstract ᅟ.

Indexed as

Data-dependent AnalysisDiglycerideLipidomicsLipidsMass spectrometryMetabolomicsTandem mass spectrometry

Identifiers

PMID28265968
PMCPMC5408749
OpenAlexW2593533643

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.