||1UG3CA256962-01 Interpret this number
||Columbia Univ New York Morningside
||Multimodal Mass Spectrometry Imaging of Mouse and Human Liver
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.
Organ Mapping Antibody Panels: a community resource for standardized multiplexed tissue imaging.
, Saunders D.C.
, McDonough E.
, Hickey J.W.
, Werlein C.
, Surrette C.
, Rajbhandari P.
, Casals A.M.
, Tian H.
, Lowery L.
, et al.
Nature methods, 2023 Aug; 20(8), p. 1174-1178.
Ferroptosis turns 10: Emerging mechanisms, physiological functions, and therapeutic applications.
Cell, 2022-07-07; 185(14), p. 2401-2421.
Machine Learning Classifies Ferroptosis and Apoptosis Cell Death Modalities with TfR1 Immunostaining.
, Schorpp K.
, Samaga D.
, Unger K.
, Hadian K.
, Stockwell B.R.
ACS chemical biology, 2022-03-18; 17(3), p. 654-660.
Successive High-Resolution (H2O)n-GCIB and C60-SIMS Imaging Integrates Multi-Omics in Different Cell Types in Breast Cancer Tissue.
, Sparvero L.J.
, Anthonymuthu T.S.
, Sun W.Y.
, Amoscato A.A.
, He R.R.
, Bayır H.
, Kagan V.E.
, Winograd N.
Analytical chemistry, 2021-06-15; 93(23), p. 8143-8151.
Multiomics Imaging Using High-Energy Water Gas Cluster Ion Beam Secondary Ion Mass Spectrometry [(H2O)n-GCIB-SIMS] of Frozen-Hydrated Cells and Tissue.
, Sheraz Née Rabbani S.
, Vickerman J.C.
, Winograd N.
Analytical chemistry, 2021-06-08; 93(22), p. 7808-7814.