PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.

CP: biotechnology CP: metabolism SPME integrated omics metabolomics nLC-MS proteomics

Journal

Cell reports methods
ISSN: 2667-2375
Titre abrégé: Cell Rep Methods
Pays: United States
ID NLM: 9918227360606676

Informations de publication

Date de publication:
26 Jun 2024
Historique:
received: 19 05 2023
revised: 16 03 2024
accepted: 05 06 2024
medline: 4 7 2024
pubmed: 4 7 2024
entrez: 3 7 2024
Statut: aheadofprint

Résumé

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

Identifiants

pubmed: 38959888
pii: S2667-2375(24)00159-0
doi: 10.1016/j.crmeth.2024.100803
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

100803

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests The authors declare no competing interests.

Auteurs

Weiwei Lin (W)

Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA; Department of Pharmacology, Physiology & Biophysics, Boston University School of Medicine, Boston, MA, USA. Electronic address: linww@bu.edu.

Fatemeh Mousavi (F)

Center for Network Systems Biology, Boston University, Boston, MA, USA.

Benjamin C Blum (BC)

Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.

Christian F Heckendorf (CF)

Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.

Matthew Lawton (M)

Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.

Noah Lampl (N)

Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.

Ryan Hekman (R)

Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.

Hongbo Guo (H)

Center for Network Systems Biology, Boston University, Boston, MA, USA.

Mark McComb (M)

Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.

Andrew Emili (A)

Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA; Department of Biology, Boston University, Boston, MA, USA. Electronic address: emili@ohsu.edu.

Classifications MeSH