ArticleScientific reports2016
Concurrent profiling of polar metabolites and lipids in human plasma using HILIC-FTMS.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 32 citations in OpenAlex.
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- Approaches to Nanoparticle Labeling: A Review of Fluorescent, Radiological, and Metallic Techniques.Environment & health (Washington, D.C.) · 2023Review
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- Observational
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- Single-Step Extraction Coupled with Targeted HILIC-MS/MS Approach for Comprehensive Analysis of Human Plasma Lipidome and Polar Metabolome.Metabolites · 2020Article
- Phosphate Transporter PstSCAB of Campylobacter jejuni Is a Critical Determinant of Lactate-Dependent Growth and Colonization in Chickens.Journal of bacteriology · 2020Article
- Lipidomics unveils the complexity of the lipidome in metabolic diseases.Clinical and translational medicine · 2018Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Blood plasma is the most popularly used sample matrix for metabolite profiling studies, which aim to achieve global metabolite profiling and biomarker discovery. However, most of the current studies on plasma metabolite profiling focused on either the polar metabolites or lipids. In this study, a comprehensive analysis approach based on HILIC-FTMS was developed to concurrently examine polar metabolites and lipids. The HILIC-FTMS method was developed using mixed standards of polar metabolites and lipids, the separation efficiency of which is better in HILIC mode than in C5 and C18 reversed phase (RP) chromatography. This method exhibits good reproducibility in retention times (CVs < 3.43%) and high mass accuracy (<3.5 ppm). In addition, we found MeOH/ACN/Acetone (1:1:1, v/v/v) as extraction cocktail could achieve desirable gathering of demanded extracts from plasma samples. We further integrated the MeOH/ACN/Acetone extraction with the HILIC-FTMS method for metabolite profiling and smoking-related biomarker discovery in human plasma samples. Heavy smokers could be successfully distinguished from non smokers by univariate and multivariate statistical analysis of the profiling data, and 62 biomarkers for cigarette smoke were found. These results indicate that our concurrent analysis approach could be potentially used for clinical biomarker discovery, metabolite-based diagnosis, etc.
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