Label-free Evaluation of Hepatic Microvesicular Steatosis with Multimodal Coherent Anti-Stokes Raman Scattering MicroscopyReportar como inadecuado




Label-free Evaluation of Hepatic Microvesicular Steatosis with Multimodal Coherent Anti-Stokes Raman Scattering Microscopy - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Hepatic microvesicular steatosis is a hallmark of drug-induced hepatotoxicity and early-stage fatty liver disease. Current histopathology techniques are inadequate for the clinical evaluation of hepatic microvesicular steatosis. In this paper, we explore the use of multimodal coherent anti-Stokes Raman scattering CARS microscopy for the detection and characterization of hepatic microvesicular steatosis. We show that CARS microscopy is more sensitive than Oil Red O histology for the detection of microvesicular steatosis. Computer-assisted analysis of liver lipid level based on CARS signal intensity is consistent with triglyceride measurement using a standard biochemical assay. Most importantly, in a single measurement procedure on unprocessed and unstained liver tissues, multimodal CARS imaging provides a wealth of critical information including the detection of microvesicular steatosis and quantitation of liver lipid content, number and size of lipid droplets, and lipid unsaturation and packing order of lipid droplets. Such information can only be assessed by multiple different methods on processed and stained liver tissues or tissue extracts using current standard analytical techniques. Multimodal CARS microscopy also permits label-free identification of lipid-rich non-parenchymal cells. In addition, label-free and non-perturbative CARS imaging allow rapid screening of mitochondrial toxins-induced microvesicular steatosis in primary hepatocyte cultures. With its sensitivity and versatility, multimodal CARS microscopy should be a powerful tool for the clinical evaluation of hepatic microvesicular steatosis.



Autor: Thuc T. Le , Amy Ziemba, Yasuyo Urasaki, Steven Brotman, Giuseppe Pizzorno

Fuente: http://plos.srce.hr/



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