PNNL

Abstract

Human tissues are known to exhibit inter-individual proteomic variability, but a deeper understanding of the different factors affecting peptide expression is necessary to further apply this knowledge. Our goal was to explore proteomic discrepancies between individuals as well as between healthy and diseased samples, and to test whether heterogeneous data from many different individuals could be used to build a tissue classifier. In order to investigate whether disparate proteomics datasets may be combined to provide meaningful insights, we performed a retrospective analysis of proteomics data from 9 different human tissues. These datasets represent several different sample prep methods, mass spectrometry instruments, and tissue health. Using this data we examined inter-individual and inter-tissue variability in peptide expression, and analyzed the methods required to build accurate tissue classifiers. We also evaluated the limits of tissue classification, and how results may be improved. These tests provided insights about how classification techniques may be applied to situations where less data is available, such as clinical biopsies, laser capture microdissected samples or potentially to classify cells types based on single cell proteomics. Our findings reveal the strong potential for utilizing proteomics data to build robust tissue classifiers, which has many prospective clinical applications.