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Imaging mass spectrometry reveals heterogeneity of proliferation and metabolism in atherosclerosis.

ABSTRACT: Atherosclerotic plaques feature local proliferation of leukocytes and vascular smooth muscle cells (VSMCs) and changes in cellular metabolism. Yet the relationship between glucose utilization and proliferation has been technically impossible to study directly in cells of atherosclerotic plaques in vivo. We used multi-isotope imaging mass spectrometry (MIMS), a quantitative imaging platform, to measure coincident cell division and glucose utilization at suborganelle resolution in atherosclerotic plaques. In established plaques, 65% of intimal foam cells and only 4% of medial VSMCs were labeled with 15N-thymidine after 1 week of isotope treatment. Dividing cells demonstrated heightened glucose labeling. MIMS detected 2H-glucose label in multiple subcellular compartments within foam cells, including lipid droplets, the cytosol, and chromatin. Unexpectedly, we identified an intensely focal region of 2H-label in VSMCs underlying plaques. This signal diminished in regions of aorta without atherosclerosis. In advanced plaques, 15N-thymidine and 2H-glucose labeling in foam cells and VSMCs significantly decreased. These data demonstrate marked heterogeneity in VSMC glucose metabolism that was dependent on both proliferative status and proximity of VSMCs to plaques. Furthermore, these results reveal how quantitative mass spectrometry coupled with isotope imaging can complement other methods used to study cell biology directly in the growing atherosclerotic plaque in vivo.

SUBMITTER: Guillermier C

PROVIDER: S-EPMC6629145 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Imaging mass spectrometry reveals heterogeneity of proliferation and metabolism in atherosclerosis.

Guillermier Christelle C Doherty Sean P SP Whitney Adam G AG Babaev Vladimir R VR Linton MacRae F MF Steinhauser Matthew L ML Steinhauser Matthew L ML Brown Jonathan D JD

JCI insight 20190606 11

Atherosclerotic plaques feature local proliferation of leukocytes and vascular smooth muscle cells (VSMCs) and changes in cellular metabolism. Yet the relationship between glucose utilization and proliferation has been technically impossible to study directly in cells of atherosclerotic plaques in vivo. We used multi-isotope imaging mass spectrometry (MIMS), a quantitative imaging platform, to measure coincident cell division and glucose utilization at suborganelle resolution in atherosclerotic ...[more]

PMID: 31167964

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Project description:BackgroundImaging mass spectrometry enables in situ label-free detection of thousands of metabolites from intact tissue samples. However, automated steps for multi-omics analyses and interpretation of histological images have not yet been implemented in mass spectrometry data analysis workflows. The characterization of molecular properties within cellular and histological features is done via time-consuming, non-objective, and irreproducible definitions of regions of interest, which are often accompanied by a loss of spatial resolution due to mass spectra averaging.MethodsWe developed a new imaging pipeline called Spatial Correlation Image Analysis (SPACiAL), which is a computational multimodal workflow designed to combine molecular imaging data with multiplex immunohistochemistry (IHC). SPACiAL allows comprehensive and spatially resolved in situ correlation analyses on a cellular resolution. To demonstrate the method, matrix-assisted laser desorption-ionization (MALDI) Fourier-transform ion cyclotron resonance (FTICR) imaging mass spectrometry of metabolites and multiplex IHC staining were performed on the very same tissue section of mouse pancreatic islets and on human gastric cancer tissue specimens. The SPACiAL pipeline was used to perform an automatic, semantic-based, functional tissue annotation of histological and cellular features to identify metabolic profiles. Spatial correlation networks were generated to analyze metabolic heterogeneity associated with cellular features.ResultsTo demonstrate the new method, the SPACiAL pipeline was used to identify metabolic signatures of alpha and beta cells within islets of Langerhans, which are cell types that are not distinguishable via morphology alone. The semantic-based, functional tissue annotation allows an unprecedented analysis of metabolic heterogeneity via the generation of spatial correlation networks. Additionally, we demonstrated intra- and intertumoral metabolic heterogeneity within HER2/neu-positive and -negative gastric tumor cells.ConclusionsWe developed the SPACiAL workflow to provide IHC-guided in situ metabolomics on intact tissue sections. Diminishing the workload by automated recognition of histological and functional features, the pipeline allows comprehensive analyses of metabolic heterogeneity. The multimodality of immunohistochemical staining and extensive molecular information from imaging mass spectrometry has the advantage of increasing both the efficiency and precision for spatially resolved analyses of specific cell types. The SPACiAL method is a stepping stone for the objective analysis of high-throughput, multi-omics data from clinical research and practice that is required for diagnostics, biomarker discovery, or therapy response prediction.

Project description:MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information.

Dataset Information

Imaging mass spectrometry reveals heterogeneity of proliferation and metabolism in atherosclerosis.

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Imaging mass spectrometry reveals heterogeneity of proliferation and metabolism in atherosclerosis.

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