A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images

Thi Kim Ngan Ngo, Sze Jue Yang, Bin Hsu Mao, Thi Kim Mai Nguyen, Qi Ding Ng, Yao Lung Kuo, Jui Hung Tsai, Shier Nee Saw, Ting Yuan Tu

研究成果: Article同行評審

摘要

Metastasis is the leading cause of cancer-related deaths. During this process, cancer cells are likely to navigate discrete tissue-tissue interfaces, enabling them to infiltrate and spread throughout the body. Three-dimensional (3D) spheroid modeling is receiving more attention due to its strengths in studying the invasive behavior of metastatic cancer cells. While microscopy is a conventional approach for investigating 3D invasion, post-invasion image analysis, which is a time-consuming process, remains a significant challenge for researchers. In this study, we presented an image processing pipeline that utilized a deep learning (DL) solution, with an encoder-decoder architecture, to assess and characterize the invasion dynamics of tumor spheroids. The developed models, equipped with feature extraction and measurement capabilities, could be successfully utilized for the automated segmentation of the invasive protrusions as well as the core region of spheroids situated within interfacial microenvironments with distinct mechanochemical factors. Our findings suggest that a combination of the spheroid culture and DL-based image analysis enable identification of time-lapse migratory patterns for tumor spheroids above matrix-substrate interfaces, thus paving the foundation for delineating the mechanism of local invasion during cancer metastasis.

原文English
文章編號100820
期刊Materials Today Bio
23
DOIs
出版狀態Published - 2023 12月

All Science Journal Classification (ASJC) codes

  • 生物技術
  • 生物工程
  • 生物材料
  • 生物醫學工程
  • 分子生物學
  • 細胞生物學

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