Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
Viscient Biosciences Inc, San Diego, CA, 92121, USA.
Sci Rep. 2022 Jul 1;12(1):11180. 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.
为了深入理解非酒精性脂肪性肝炎（NASH）发展背后的复杂生物学过程，本研究采用具备透射和侧向散射成像功能的三维成像流式细胞术（3D-IFC），以单细胞分辨率对肝星状细胞（HSC）和肝内皮细胞（LEC）的形态进行了特征描述。本研究中，HSC 和 LEC 取自活检证实的早期 NASH（F2-F3）和健康对照者。我们通过单细胞成像和对健康和患病细胞的 3D 数字重建，分析了一组具有空间分辨率的形态计量学细胞和纹理参数，这些参数随疾病进展而呈现出退行性变化。我们基于从 3D 成像流式细胞仪获得的无标记细胞透射和侧向散射图像，开发了定制的自动编码器卷积神经网络（CNN），从而证明了参与 NASH 发展的关键调节细胞类型，并实现了优于传统机器学习方法的细胞分类性能。
