Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.
Multimodal integration between mass spectrometry imaging (MSI) and radiology-established modalities such as magnetic resonance imaging (MRI) would allow the investigations of key questions in complex biological systems such as the central nervous system. Such integration would provide complementary multiscale data to bridge the gap between molecular and anatomical phenotypes, potentially revealing new insights into molecular mechanisms underlying anatomical pathologies presented on MRI. Automatic co-registration between 3D MSI/MRI is a computationally challenging process due to dimensional complexity, MSI data sparsity, lack of direct spatial-correspondences, and non-linear tissue deformation. Here, we present a new computational approach based on stochastic neighbor embedding to non-linearly align 3D MSI to MRI data, identify and reconstruct biologically-relevant molecular patterns in 3D, and fuse the MSI datacube to the MRI space. We demonstrate our method using multimodal high-spectral resolution MALDI 9.4 Tesla MSI and 7 Tesla in vivo MRI data, acquired from a patient-derived, xenograft mouse brain model of glioblastoma following administration of the EGFR inhibitor drug of Erlotinib. Results show the distribution of some identified molecular ions of the EGFR inhibitor
Head and Neck Squamous Cell Carcinoma (HNSCC) remains among the most aggressive human cancers. Tumor progression and aggressiveness in SCC are largely driven by Tumor Propagating Cells (TPCs). Aerobic glycolysis, also known as the Warburg Effect, represents a characteristic of many cancers, yet whether this adaptation is functionally important in SCC, and at which stage, remains poorly understood. Here, we show that the NAD+-dependent histone deacetylase Sirtuin 6 (SIRT6) is a robust tumor suppressor in SCC, acting as a modulator of glycolysis in these tumors. Remarkably, rather than a late adaptation, we find enhanced glycolysis specifically in TPCs. More importantly, using single cell RNA sequencing of TPCs, we identify a subset of TPCs with higher glycolysis and enhanced pentose phosphate pathway and glutathione metabolism, characteristics that are strongly associated with a better antioxidant response. Altogether, our studies uncover enhanced glycolysis as a main driver in SCC, and, more importantly, identify a subset of TPCs as the cell-of-origin for the Warburg effect, defining metabolism as a key feature of intra-tumor heterogeneity.
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