A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning  

作　　者：Haojun Hua Shangjie Zou Zhiqiang Ma Wang Guo Ching Yin Fong Bee Luan Khoo 

机构地区：[1]City University of Hong Kong,83 Tat Chee Avenue,Kowloon,Hong Kong 999077,China [2]Hong Kong Center for Cerebro-Cardiovascular Health Engineering(COCHE),Hong Kong 999077,China [3]City University of Hong Kong Futian-Shenzhen Research Institute,Shenzhen 518057,China

出　　处：《Microsystems & Nanoengineering》2023年第5期161-176,共16页微系统与纳米工程（英文）

基　　金：supported in part by InnoHK Project on[Project 1-5 Multimodal spectroscopy(MMS)&biosensor platforms for monitoring CVDs]at Hong Kong Centre for Cerebro-cardiovascular Health Engineering(COCHE),in part by City University of Hong Kong(7020002,7005464,7005208,9667220);which is funded by the Research Grants Council(RGC),in part by Pneumoconiosis Compensation Fund Board(9211276);in part by Research Grants Council of the Hong Kong Special Administrative Region,China(CityU 21200921).

摘　　要：Cellular deformability is a promising biomarker for evaluating the physiological state of cells in medical applications.Microfluidics has emerged as a powerful technique for measuring cellular deformability.However,existing microfluidic-based assays for measuring cellular deformability rely heavily on image analysis,which can limit their scalability for high-throughput applications.Here,we develop a parallel constriction-based microfluidic flow cytometry device and an integrated computational framework(ATMQcD).The ATMQcD framework includes automatic training set generation,multiple object tracking,segmentation,and cellular deformability quantification.The system was validated using cancer cell lines of varying metastatic potential,achieving a classification accuracy of 92.4%for invasiveness assessment and stratifying cancer cells before and after hypoxia treatment.The ATMQcD system also demonstrated excellent performance in distinguishing cancer cells from leukocytes(accuracy=89.5%).We developed a mechanical model based on power-law rheology to quantify stiffness,which was fitted with measured data directly.The model evaluated metastatic potentials for multiple cancer types and mixed cell populations,even under realworld clinical conditions.Our study presents a highly robust and transferable computational framework for multiobject tracking and deformation measurement tasks in microfluidics.We believe that this platform has the potential to pave the way for high-throughput analysis in clinical applications,providing a powerful tool for evaluating cellular deformability and assessing the physiological state of cells.

关 键 词：DEFORMABILITY METASTATIC PRECISE 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]