Enabling Lipidomic Biomarker Studies for Protected Populations by Combining Noninvasive Fingerprint Sampling with MS Analysis and Machine Learning

Abstract

Triacylglycerols and wax esters are two lipid classes that have been linked to diseases, including autism, Alzheimer's disease, dementia, cardiovascular disease, dry eye disease, and diabetes, and thus are molecules worthy of biomarker exploration studies. Since triacylglycerols and wax esters make up the majority of skin-surface lipid secretions, a viable sampling method for these potential biomarkers would be that of groomed latent fingerprints. Currently, however, blood-based sampling protocols predominate in the field. The invasiveness of a blood draw limits its utility to protected populations, including children and the elderly. Herein we describe a noninvasive means for sample collection (from fingerprints) paired with fast MS data-acquisition (MassIVE data set MSV000092742) and efficient data analysis via machine learning. Using both supervised and unsupervised classification, we demonstrate the usefulness of this method in determining whether a variable of interest imparts measurable change within the lipidomic data set. As a proof-of-concept, we show that the method is capable of distinguishing between the fingerprints of different individuals as well as between anatomical sebum collection regions. This noninvasive, high-throughput approach enables future lipidomic biomarker researchers to more easily include underrepresented, protected populations, such as children and the elderly, thus moving the field closer to definitive disease diagnoses that apply to all.

Celebrating Women in Proteomics and Metabolomics

“…A focus on histone PTM regulation also comes from Ming-Ming Zhang from the Xin-Qing Zhao group in a study examining the role of the histone H4 methyltransferase Set5p in acetic acid stress . In a creative study from the group of Heather Desaire, a noninvasive method is described as a possible means for future biomarker studies by combining sample collection from fingerprints with lipidomic MS analyses . Seeking to optimize workflows for metabolomics studies, a report from Andrea Bileck emphasizes the preferred use of plasma rather than serum samples for clinical metabolomics studies, and a paper from Jennifer Kirwan compares extraction protocols for the analysis of polar and nonpolar compounds in mouse plasma …”