Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells

To improve the understanding of the complex biological process underlying the development of non-alcoholic steatohepatitis (NASH), 3D imaging flow cytometry (3D-IFC) with transmission and side-scattered images were used to characterize hepatic stellate cell (HSC) and liver endothelial cell (LEC) mor...

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Vydáno v:Scientific reports Ročník 12; číslo 1; s. 11180 - 10
Hlavní autoři: Subramanian, Ramkumar, Tang, Rui, Zhang, Zunming, Joshi, Vaidehi, Miner, Jeffrey N., Lo, Yu-Hwa
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 01.07.2022
Nature Publishing Group
Nature Portfolio
Témata:
631/114/2397
631/80/2373
Artificial intelligence
Biomarkers
Biopsy
Cytology
Disease
Electronic health records
Endothelial cells
Fatty liver
Flow cytometry
Hepatocytes
Humanities and Social Sciences
Liver
Machine learning
Magnetic resonance imaging
Mortality
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
Three dimensional imaging
Ultrasonic imaging
ISSN:2045-2322, 2045-2322
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Shrnutí:To improve the understanding of the complex biological process underlying the development of non-alcoholic steatohepatitis (NASH), 3D imaging flow cytometry (3D-IFC) with transmission and side-scattered images were used to characterize hepatic stellate cell (HSC) and liver endothelial cell (LEC) morphology at single-cell resolution. In this study, HSC and LEC were obtained from biopsy-proven NASH subjects with early-stage NASH (F2-F3) and healthy controls. Here, we applied single-cell imaging and 3D digital reconstructions of healthy and diseased cells to analyze a spatially resolved set of morphometric cellular and texture parameters that showed regression with disease progression. By developing a customized autoencoder convolutional neural network (CNN) based on label-free cell transmission and side scattering images obtained from a 3D imaging flow cytometer, we demonstrated key regulated cell types involved in the development of NASH and cell classification performance superior to conventional machine learning methods.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-15364-7