ArticleCommunications biology2024
LiLA: lipid lung-based ATLAS built through a comprehensive workflow designed for an accurate lipid annotation.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- MALDI Mass Spectrometry Imaging in Alzheimer's Disease Lipidomics: Matrix Selection, Spatial Lipid Pathology and Emerging Analytical Strategies.International journal of molecular sciences · 2026Review
- Plasma miRNA-Metabolite Dysregulation in People with HIV with Cirrhosis Despite Successful HCV Cure.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Subcutaneous and visceral adipose tissue lipidome in children reveals novel lipid species involved in obesity.Journal of physiology and biochemistry · 2026Article
- Metabolic alterations associated with cirrhosis after HCV cure in people with HIV: metabolomic and lipidomic findings at one and five years.Frontiers in immunology · 2026Article
- Review
- Lipidomics Unveils Critical Lipid Pathway Shifts in Alport Syndrome.Kidney international reports · 2025Article
- Lipid and immune dysregulation and risk of metabolic disorders after HCV clearance in HIV/HCV-coinfected participants with cACLD: a retrospective study.Frontiers in immunology · 2025Article
- Plasma Lipidomics Reveals Lipid Signatures of Early Pregnancy in Mares.International journal of molecular sciences · 2024Article
- FAIMS Shotgun Lipidomics for Enhanced Class- and Charge-State Separation Complemented by Automated Ganglioside Annotation.Analytical chemistry · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
Abstract
Accurate lipid annotation is crucial for understanding the role of lipids in health and disease and identifying therapeutic targets. However, annotating the wide variety of lipid species in biological samples remains challenging in untargeted lipidomic studies. In this work, we present a lipid annotation workflow based on LC-MS and MS/MS strategies, the combination of four bioinformatic tools, and a decision tree to support the accurate annotation and semi-quantification of the lipid species present in lung tissue from control mice. The proposed workflow allowed us to generate a lipid lung-based ATLAS (LiLA), which was then employed to unveil the lipidomic signatures of the Mycobacterium tuberculosis infection at two different time points for a deeper understanding of the disease progression. This workflow, combined with manual inspection strategies of MS/MS data, can enhance the annotation process for lipidomic studies and guide the generation of sample-specific lipidome maps. LiLA serves as a freely available data resource that can be employed in future studies to address lipidomic alterations in mice lung tissue.
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Registered trials
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.