Poster Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

SWATH Proteomics for Human Personalized Omics Profiling (hPOP) (#514)

Kevin P Erazo Castillo 1 , Sara Ahadi 2 , Daniel Hornsburg 2 , Tejaswini Mishra 2 , Emily Higgs 2 , Orit Dagan-Rosenfeld 2 , Monika Avina 2 , Michael P Snyder 2
  1. Chemistry Department, Stanford University, Stanford, CA, USA
  2. Genetics Department, Stanford University, Stanford, CA, US

The aim of the hPOP project is to study the variance of molecular markers across a large and heterogeneous cohort. To that end, we have collected plasma, stool and urine samples from 400 participants from North America, Europe and Asia during the 2015-2019 HUPO conferences. The hPOP cohort is quite diverse, incorporating participants of different ages (average age is 43.5), sexes (30% female), ethnicities, BMIs, and various other medical markers. In collaboration with 17 labs across the globe, we aim to map the multi-molecular landscape of the participants with more than 20 omics assays covering various aspects of cellular biology. Among other molecular classes, we profiled plasma proteins via data-independent acquisition SWATH mass spectrometry using a TripleTOF 6600 System equipped with a DuoSpray Source and 25mm I.D. electrode (SCIEX). The SWATH MS data was analyzed using OpenSWATH/PyProphet and all the sample runs were aligned using TRIC software. This work complements lipidomics and metabolomics done on the same cohort and a similar longitudinal analysis in the iPOP (Integrated Personalized Omics Profiling) project where a cohort of 100 individuals was profiled over time. In that instance, various medical risks were identified along with multiple changes in diverse biological pathways across healthy and diseased conditions. We expect to analyze hPOP data and uncover comparable trends across various segments of the world population. This study represents a large cross lab effort to explore and connect multi-omics data and a significant application of DIA SWATH mass spectrometry to broadly profile heterogeneous populations as opposed to deeply profiling individuals.