Transformative Technology Development
Columbia University / Penn State University
Multimodal mass spectrometry imaging of mouse and human liver
This group is working to develop a multimodal mass spectrometry imaging pipeline with novel desorption sources and data integration that will enable cell-type specific multi-omic profiling in 3-dimensions in biological tissues at high spatial resolution (micron to submicron) and high speed (>10 ms/pixel) in biological tissues in a near-native environment. This would provide previously inaccessible information on cellular and tissue organization, and on 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 pristine chemical gradients, (iii) low sensitivity, (iv) integration of different omics in the same sample and at the single cell level, and (v) limited tools for integration of large quantities of data. These laboratories are developing systematic MS imaging for high sensitivity and high resolution analysis of diverse tissues. They 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, they 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, with sufficient signal per pixel, they can use automated data analysis to characterize biologically active functional sites within 1 µm2 and areas of interest in single cells. They further developed data integration methods to combine imaging data from the same and adjacent sections to create multi-model imaging data sets. The labs will 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, they will develop an integrated data analysis platform. Integration of multi-omics remains challenging, particularly spatially localizing 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. These efforts will result in an integrated multimodal imaging platform to summon the best characteristics of each image form, acquiring a complete picture of the biomolecular network at spatial resolution of 1 µm. With this direct visualization, they will address how lipidomics and 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 drives different cell death modalities, including different modes of cell death.
|Project title:||Multimodal mass spectrometry imaging of mouse and human liver|
|PIs:||Brent Stockwell, Columbia, and Hua Tian, Penn State|
|Co-Investigator:||Nicholas Winograd, Penn State|
|Project Manager:||Hua Tian, Penn State|
|Assay Types:||DESI, TOF-SIMS, spatial transcriptomics|
Return to Home.