The need for a clinically accessible method with the ability to match protein activity to defined regions in heterogeneous tissues is currently unmet by existing technologies. Our proteomics sample preparation platform, named microPOTS (Microdroplet Processing in One pot for Trace Samples), can be used to measure relative protein abundance in micron-scale samples alongside the spatial location of each measurement, thereby tying biologically interesting proteins and pathways to distinct regions. However, given the smaller sample number and amount of tissue measured, standard mass spectrometric analysis pipelines have proven inadequate. Here we describe how existing computational approaches can be adapted to focus on the specific biological questions asked in spatial proteomics experiments. Herein we present a novel, unbiased characterization of the human islet microenvironment comprising the entire complex array of tissues involved while maintaining spatial information and the degree of the islet’s sphere of influence. We identify specific functional activity unique to the pancreatic islet cells and demonstrate how far their signature can be measured. Our results show that we can distinguish pancreatic islet cells from the neighboring exocrine tissue environment, recapitulate known biological functions of islet cells, and we identify a novel spatial gradient in the expression of RNA processing proteins within the islet microenvironment.
The data for this project is on Synapse at http://synapse.org/microPotsPanc. To access the data you will need to register for synapse.. We have created a single file that downloads the processed data used for this analysis, you can find it at ./loadPancData.R.
The analysis for this manuscript is found in this repository.