Summary of ssbd-repos-000264

Name
URL
DOI

Title
Raman Images of Rat Liver Tissues underlying Non-Alcoholic Fatty Liver Disease
Description

An essential challenge in diagnosing states of non-alcoholic fatty liver disease (NAFLD) is the early prediction of progression from non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH) before the disease progresses. However, histological diagnoses of NAFLD rely on the appearance of anomalous tissue morphologies, and it is difficult to objectively segment the biomolecular environment of the tissue through a conventional histopathological approach. Here, we show that hyper-spectral Raman imaging provides objective diagnostic information on NAFLD in rats, as spectral changes among disease states can be detected before histological characteristics emerge. Our results demonstrate that Raman imaging of NAFLD can be a useful tool for histopathologists, offering biomolecular distinctions among tissue states that cannot be observed through standard histopathological means.

Submited Date
2023-02-04
Release Date
2023-02-07
Updated Date
-
License
Funding information
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File formats
MATLAB .mat files
Data size
268.7 MB

Organism
NA
Strain
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Cell Line
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Genes
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Proteins
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GO Molecular Function (MF)
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GO Biological Process (BP)
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GO Cellular Component (CC)
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Study Type
-
Imaging Methods
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Method Summary

Raman images of tissue slices were acquired using a confocal Raman microscope system, Raman-11 (Nanophoton, Japan), described elsewhere [25]. 532 nm excitation light was delivered through a 20x/0.75 dry-type objective lens (Olympus, Japan). Epi-detected Raman signal was passed through a 50 μm pinhole and read by a thermoelectrically cooled charge-coupled device (CCD) camera, Pixis 400BR, at -70°C (Princeton Instrument, USA). In the Raman spectral mapping of the liver tissues, excitation power measured on the sample plane was 40 mW. The size of the laser spot on the sample was estimated to be 0.865 µm in diameter according to the theory of a point spread function [26]. Point scanning with 5 μm steps and 1 second exposure times yielded Raman images of 95 μm × 345 μm in the area (20 × 70 pixels). For Raman spectral measurements of pure substances (cytochrome c, retinol, linoleic acid, and oleic acid), Raman spectra were collected from 400 different points on each sample, with excitation laser power of 3.6 mW at the sample plane and exposure time of 5 seconds.

Related paper(s)

Khalifa Mohammad Helal, Harsono Cahyadi, J Nicholas Taylor, Akira Okajima, Koji Tabata, Yasuaki Kumamoto, Kentaro Mochizuki, Yoshito Itoh, Tetsuro Takamatsu, Hideo Tanaka, Katsumasa Fujita, Tamiki Komatsuzaki, Yoshinori Harada (2023) Raman imaging of rat non-alcoholic fatty liver tissues reveals distinct biomolecular states., FEBS letters

Published in 2023 Feb 17 (Electronic publication in Feb. 17, 2023, midnight )

(Abstract) An essential challenge in diagnosing states of non-alcoholic fatty liver disease (NAFLD) is the early prediction of progression from non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH) before the disease progresses. Histological diagnoses of NAFLD rely on the appearance of anomalous tissue morphologies, and it is difficult to segment the biomolecular environment of the tissue through a conventional histopathological approach. Here, we show that hyper-spectral Raman imaging provides diagnostic information on NAFLD in rats, as spectral changes among disease states can be detected before histological characteristics emerge. Our results demonstrate that Raman imaging of NAFLD can be a useful tool for histopathologists, offering biomolecular distinctions among tissue states that cannot be observed through standard histopathological means.

Contact(s)
Khalifa Mohamamd Helal
Organization(s)
Hokkaido University, Japan; Comilla University, Bangladesh , Graduate School of Life Science, Transdisciplinary Life Science Course, Hokkaido University, Kita 12, Nishi 6, Kita-ku, Sapporo 060-0812, Japan; Department of Mathematics, Comilla University, Cumilla-3506, Bangladesh.
Image Data Contributors
Harsono Cahyadi, Harsono Cahyadi , Harsono Cahyadi
Quantitative Data Contributors
Harsono Cahyadi, Harsono Cahyadi, Harsono Cahyadi

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