Compartmentalization of constituents into distinct membrane bound organelles is an essential element of mammalian cells. Lipid metabolism and homeostasis are critical for organelle maintenance in health, and abnormalities in cellular lipids underlie numerous chronic diseases. While methods of spatial proteomics are relatively well established, high throughput organellar lipidomics methods are largely unexplored. This study aims to develop a high-throughput method that enables proteo-lipidomic profiling of organelles, termed integrative Protein and Lipid Organelle Profiling (iPLOP). Starting on the same premise as PCP and hyperLOPIT methods previously reported for spatial proteomics, organelle identities are inferred from known marker proteins with a single known organelle localization. As most lipid species localize to several organelles at varying abundances, lipidomics aimed to quantify the lipid species at each organelle. iPLOP uses a continuous sucrose gradient to roughly separate various membrane bound organelles into different profiles. Lipids and proteins are extracted from the same sucrose gradient fractions and subjected to shotgun proteomics and targeted lipidomics using mass spectrometry. Computational analysis use correlation profiles and network classifiers to infer organelle proteome and lipidome. This presentation will report the development and proof of concept data for iPLOP. We anticipate that applications of iPLOP method will contribute to an improved understanding of the spatial relationship between cellular lipids and proteins in health and disease.