Evidence map›Paper›PMID 35831612›Full record

ReviewNature protocols2022

Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions.

Kaylie I Kirkwood, Brian S Pratt, Nicholas Shulman, Kaipo Tamura, Michael J MacCoss, Brendan X MacLean, Erin S Baker

Abstract readReview
In one paragraph

Review in Nature protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

40 citing papers in PubMed.

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  17. Analytical chemistry · 2024
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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.

Kaylie I KirkwoodDepartment of Chemistry, North Carolina State University, Raleigh, NC, USA.ORCID http://orcid.org/0000-0003-3248-820X
Brian S PrattDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0001-5880-6734
Nicholas ShulmanDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Kaipo TamuraDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Michael J MacCossDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0003-1853-0256
Brendan X MacLeanDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Erin S BakerDepartment of Chemistry, North Carolina State University, Raleigh, NC, USA. ebaker@ncsu.edu.ORCID http://orcid.org/0000-0001-5246-2213

Funding

Single cell, multi-parametric high throughput platform to classify endocrine disruptor potential of mixturesP42ES027704 · NIEHS · TEXAS A&M UNIVERSITY · PI Weihsueh A Chiu · 2017 to 2026
$21.2M
TrainingP41GM103533 · NIGMS · UNIVERSITY OF WASHINGTON · PI YATES III, JOHN R · 2012 to 2021
$20.7M
Translational Research Support CoreP30ES025128 · NIEHS · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI Sue Fenton · 2015 to 2026
$18.3M
Uncovering the Mechanisms of PFAS-induced ImmunotoxicityP42ES031009 · NIEHS · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI HOPPIN, JANE · 2020 to 2024
$9.3M
Seattle Quant: A Resource for the Skyline Software EcosystemR24GM141156 · NIGMS · UNIVERSITY OF WASHINGTON · PI Michael MacCoss · 2021 to 2026
$6.7M
Skyline Targeted Proteomics EnvironmentR01GM103551 · NIGMS · UNIVERSITY OF WASHINGTON · PI MACCOSS, MICHAEL · 2012 to 2020
$3.4M
NC STATE MOLECULAR BIOTECHNOLOGY TRAINING PROGRAM (MBTP)T32GM133366 · NIGMS · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI Jason M. Haugh, Robert M Kelly · 2020 to 2026
$3.3M
NIEHS NIH HHS P30 ES025128NIEHS NIH HHS P42 ES027704NIEHS NIH HHS P42 ES031009NIGMS NIH HHS P41 GM103533NIGMS NIH HHS R01 GM103551NIGMS NIH HHS R24 GM141156NIGMS NIH HHS T32 GM133366
6 · The paper itself

Abstract

Lipidomics studies suffer from analytical and annotation challenges because of the great structural similarity of many of the lipid species. To improve lipid characterization and annotation capabilities beyond those afforded by traditional mass spectrometry (MS)-based methods, multidimensional separation methods such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation and MS (LC-IMS-CID-MS) may be used. Although LC-IMS-CID-MS and other multidimensional methods offer valuable hydrophobicity, structural and mass information, the files are also complex and difficult to assess. Thus, the development of software tools to rapidly process and facilitate confident lipid annotations is essential. In this Protocol Extension, we use the freely available, vendor-neutral and open-source software Skyline to process and annotate multidimensional lipidomic data. Although Skyline ( https://skyline.ms/skyline.url ) was established for targeted processing of LC-MS-based proteomics data, it has since been extended such that it can be used to analyze small-molecule data as well as data containing the IMS dimension. This protocol uses Skyline's recently expanded capabilities, including small-molecule spectral libraries, indexed retention time and ion mobility filtering, and provides a step-by-step description for importing data, predicting retention times, validating lipid annotations, exporting results and editing our manually validated 500+ lipid library. Although the time required to complete the steps outlined here varies on the basis of multiple factors such as dataset size and familiarity with Skyline, this protocol takes ~5.5 h to complete when annotations are rigorously verified for maximum confidence.

Indexed as

Ion Mobility SpectrometryLipidomicsChromatography, LiquidLipidsMass SpectrometryLipids

Identifiers

PMID35831612
PMCPMC9633456

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.