Grant Number:	5UH3CA256962-04 Interpret this number
Primary Investigator:	Stockwell, Brent
Organization:	Columbia Univ New York Morningside
Project Title:	Multimodal Mass Spectrometry Imaging of Mouse and Human Liver
Fiscal Year:	2023

We propose to develop a multimodal mass spectrometry imaging pipeline with novel desorption sources and data integration that will enable simultaneously mapping of biomolecule abundance in 3-dimensions in biological tissues at high spatial resolution (micron to submicron) and high speed (>10 ms/pixel) in a near-native environment. This would provide previously inaccessible information on cellular and tissue organization, and how homeostasis and disease intersect at the level of tissue physiology. A major challenge for performing multi- omics using mass spectrometry imaging has been the (i) lack of universal ionization methods, (ii) limited sample preparation protocols for preserving chemical gradients, (iii) low sensitivity, and (iv) limited tools for integration of large quantities of data. Our laboratories are developing systematic MS imaging for high sensitivity and high resolution analysis of diverse tissues. We discovered that water-based gas cluster ion beams (H2O-GCIB) operating at high energy yield ionization enhancements of multiple biomolecules (e.g., metabolites, lipids, and peptides/protein fragments) with high sensitivity at 1 µm lateral resolution and without labeling or complicated sample preparation. Coupled with unique Secondary Ion Mass Spectrometry (SIMS) instrumentation and cryogenic sample handling, we have imaged biomolecules directly in cells and tissues in a near-native state (i.e., frozen-hydration) with feature resolution of 1-10 µm. Low concentration biomolecules (e.g. cardiolipin and metabolites) that were impossible to localize in single cells previously are now visible with 3-dimensional localization. Moreover, the sufficient signal per pixel, we can use automated data analysis to characterize biologically active functional sites within 1 µm2 and areas of interest in single cells. We further developed data integration methods to combine imaging data from adjacent sections to create a multi-model imaging data sets. We propose to develop a pipeline for MS imaging analysis of biomolecules, and to elucidate molecular heterogeneity in tissues using multimodal imaging. To support the multi-modal analysis pipeline, we will develop an integrated data analysis platform. Integration of multiomics remains challenging, particularly spatially localize multiple biomolecules at single cell level. The direct visualization of cellular contents provides information on biomolecular composition, interactions and functions. This network of biomolecules is the driving force of specific behavior of cells in physiological states. Despite this, a comprehensive grasp of these interactions at cellular level has not moved beyond segregated methods. Our efforts will result in an integrated multimodal imaging platform to summon the best characteristics of each image form, acquiring a complete picture the biomolecular network at spatial resolution of 1 µm. With this direct visualization, we will address how metabolism links with functional biomarkers that stem from metabolism-associated protein complexes and phase-separated membrane-less organelles at the subcellular level, and how this drive different cell death modalities, including different modes of cell death.





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