Correlative Quantitative Raman Chemical Imaging and MCR–ALS in Mouse NASH Model Reveals Direct Relationships between Diet and Resultant Liver Pathology
Alison J. Hobro*, Takatoshi Sakaguchi, Shizuo Akira and Nicholas I. Smith*,
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Abstract
Raman imaging has the capability to provide unlabeled, spatially aware analysis of chemical components, with no a priori assumptions. Several lifestyle diseases such as nonalcoholic steatohepatitis (NASH) can appear in the liver as changes in the nature, abundance, and distribution of lipids, proteins, and other biomolecules and are detectable by Raman imaging. In order to identify which of these liver-associated changes occur as a direct result of the diet and which are secondary effects, we developed correlative imaging and analysis of diet and liver samples. Oleic acid was found to be a direct contributor to NASH liver composition, whereas protein and collagen distributions were found to be affected in a manner consistent with early fibrotic transformation, as a secondary consequence of the high-fat diet.
期刊介绍：
Chemical & Biomedical Imaging is a peer-reviewed open access journal devoted to the publication of cutting-edge research papers on all aspects of chemical and biomedical imaging. This interdisciplinary field sits at the intersection of chemistry physics biology materials engineering and medicine. The journal aims to bring together researchers from across these disciplines to address cutting-edge challenges of fundamental research and applications.Topics of particular interest include but are not limited to:Imaging of processes and reactionsImaging of nanoscale microscale and mesoscale materialsImaging of biological interactions and interfacesSingle-molecule and cellular imagingWhole-organ and whole-body imagingMolecular imaging probes and contrast agentsBioluminescence chemiluminescence and electrochemiluminescence imagingNanophotonics and imagingChemical tools for new imaging modalitiesChemical and imaging techniques in diagnosis and therapyImaging-guided drug deliveryAI and machine learning assisted imaging
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