Abstract
INTRODUCTION: Fibrosis can damage the normal function of many organs, such as cardiac function, for which no effective clinical therapies exist. However, traditional approaches to anti-fibrosis drug discovery have primarily focused on the final biological indicators, often overlooking the dynamic morphological changes during fibrosis progression. Here, we present a novel approach, deep-DPC, which integrates label-free, time-series digital phase contrast (DPC) imaging with cell morphology analysis and unsupervised machine learning to dynamically control and monitor cell morphology. OBJECTIVES: This method enables discrimination between resting and activated fibrocytes and facilitates the discovery of non-invasive labeled anti-fibrotic lead compounds. METHODS: The deep-DPC comprises two major steps: (1) preliminary analysis by Harmony 4.9 software and (2) image classification via a neural network. For the experiment dataset, label-free time-series imaging was acquired from each well at 10 × magnification using the high-content imaging system, equipped with a high-speed charge-coupled device (CCD) camera. Dual-channel output images were generated through the imaging system, with one channel for bright-field and the other for DPC imaging, captured at 30-minute intervals. Firstly, applying the anti-fibrotic cell model as a case, a label-free time-series DPC imaging was developed by combining cell morphological analysis and deep learning, and its stability was verified by training with 12,000 images. Furthermore, the application of deep-DPC in the discovery of anti-fibrotic lead compounds. RESULTS: Using the deep-DPC platform, over 100,000 images generated from 1,400 compounds were processed, identifying Neo-Przewaquinone A as a potent anti-fibrosis agent. Neo-Przewaquinone A exerts its effects by inhibiting TGF-β receptor I, thereby maintaining cells in a resting state and arresting the cell cycle. CONCLUSION: The deep-DPC offers a promising strategy for fibrosis assessment by combining deep learning with dynamic cell morphology analysis based on time-series DPC images. Additionally, the platform holds potential as a novel therapeutic approach for anti-myocardial fibrosis by regulating cell morphology.