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

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.