Proteome mapping of the human pancreatic islet microenvironment reveals endocrine-exocrine signaling sphere of influence

Abstract

The need for a clinically accessible method with the ability to match protein activity within 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 measu red, 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. We apply this approach to present an 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 identify a spatial gradient in the expression of RNA processing proteins within the islet microenvironment.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses

“…Using LCM, it is possible to cut out a single cell, 26,73 or to cut out larger pixels of an arbitrary shape and size. 91,97,98 Larger pixels will contain more peptides and increase MS signal. This is important as the number of peptide identifications in SCP is heavily determined by the amount of peptide material.…”

Spatial top-down proteomics for the functional characterization of human kidney

“…Nanodroplet processing in onepot for trace samples (nanoPOTS), and its companion microPOTS, have enabled routine single cell proteomics and BUP processing of dissected tissues. [28][29][30] In a previous study, we demonstrated the coupling of nanoPOTS sample preparation with TDP enabling the identification of over 150 proteoforms along with various PTMs from roughly 70 cultured HeLa cells. 31 This technology was further extended to spatial TDP by integrating nanoPOTS with LCM to identify an average of 509 proteoforms per 100,000 µm 2 dissection in brain tissue.…”

Clinical Proteomics: A Promise Becoming Reality

“…This special issue of Molecular and Cellular Proteomics presents a wide range of primary research, reviews, and perspectives pertaining to clinical proteomics. The expanding scope of proteomics applications is reflected in the spectrum of disease foci represented, including neurodegenerative diseases ( 1 , 2 , 3 , 4 ), infectious disease ( 5 , 6 ), endocrinology ( 7 , 8 ), cardiovascular disease ( 9 ), inflammatory disease ( 10 ), kidney disease ( 11 ), and cancer ( 12 , 13 , 14 , 15 ), as well as in the burgeoning areas of health monitoring and exercise ( 16 , 17 ).…”

“…Complex cross-sectional, longitudinal and multi-omic analyses are facilitated by accelerating computer speeds, storage capabilities, and the promulgation of computational methods focused on proteomics ( 17 , 18 ). Improved sample-handling methods and instrument sensitivity have contributed to the expansion of spatially-resolved proteomics applications ( 8 ). Prior technological limitations that have prevented large numbers of patient samples from being analyzed have been greatly reduced owing to highly multiplexed strategies ( 3 , 4 , 6 , 8 ), far more sensitive MS systems, and new scanning methods such as data independent acquisition.…”

“…Improved sample-handling methods and instrument sensitivity have contributed to the expansion of spatially-resolved proteomics applications ( 8 ). Prior technological limitations that have prevented large numbers of patient samples from being analyzed have been greatly reduced owing to highly multiplexed strategies ( 3 , 4 , 6 , 8 ), far more sensitive MS systems, and new scanning methods such as data independent acquisition.…”