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Creators/Authors contains: "Laskin, Julia"

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  1. Free, publicly-accessible full text available June 3, 2024
  2. The skeletal muscle is a highly heterogeneous tissue comprised of different fiber types with varying contractile and metabolic properties. The complexity in the analysis of skeletal muscle fibers associated with their small size (30–50 μm) and mosaic-like distribution across the tissue tnecessitates the use of high-resolution imaging to differentiate between fiber types. Herein, we use a multimodal approach to characterize the chemical composition of skeletal fibers in a limb muscle, the gastrocnemius. Specifically, we combine high-resolution nanospray desorption electrospray ionization (nano-DESI) mass spectrometry imaging (MSI) with immunofluorescence (IF)-based fiber type identification. Computational image registration and segmentation approaches are used to integrate the information obtained with both techniques. Our results indicate that the transition between oxidative and glycolytic fibers is associated with shallow chemical gradients (<2.5 fold change in signals). Interestingly, we did not find any fiber type-specific molecule. We hypothesize that these findings might be linked to muscle plasticity thereby facilitating a switch in the metabolic properties of fibers in response to different conditions such as exercise and diet, among others. Despite the shallow chemical gradients, cardiolipins (CLs), acylcarnitines (CAR), monoglycerides (MGs), fatty acids, highly polyunsaturated phospholipids, and oxidized phospholipids, were identified as molecular signatures of oxidative metabolism. In contrast, histidine-related compounds were found as molecular signatures of glycolytic fibers. Additionally, the presence of highly polyunsaturated acyl chains in phospholipids was found in oxidative fibers whereas more saturated acyl chains in phospholipids were found in glycolytic fibers which suggests an effect of the membrane fluidity on the metabolic properties of skeletal myofibers. 
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  3. Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. One of key challenges in molecular colocalization is that complex MSI data are too large for manual annotation but too small for training deep neural networks. Herein, we introduce a self-supervised clustering approach based on contrastive learning, which shows an excellent performance in clustering of MSI data. We train a deep convolutional neural network (CNN) using MSI data from a single experiment without manual annotations to effectively learn high-level spatial features from ion images and classify them based on molecular colocalizations. We demonstrate that contrastive learning generates ion image representations that form well-resolved clusters. Subsequent self-labeling is used to fine-tune both the CNN encoder and linear classifier based on confidently classified ion images. This new approach enables autonomous and high-throughput identification of co-localized species in MSI data, which will dramatically expand the application of spatial lipidomics, metabolomics, and proteomics in biological research. 
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  4. Abstract

    Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high‐spatial resolution and high‐throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation‐driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

     
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