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

• Published in: Scientific reports Vol. 12; no. 1; pp. 11180 - 10
• Main Authors: Subramanian, Ramkumar, Tang, Rui, Zhang, Zunming, Joshi, Vaidehi, Miner, Jeffrey N., Lo, Yu-Hwa
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 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
• DOI: 10.1038/s41598-022-15364-7

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.