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您的检索式：作者名="Shihai Lan"
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| 1 | Quantifying 3D cell-matrix interactions during mitosis and the effect of anticancer drugs on the interactions显示文摘The mechanical force between cells and the extracellular microenvironment is crucial to many physiological processes such as cancer metastasis and stem cell differentiation. Mitosis plays an essential role in all these processes and thus an in-depth understanding of forces during mitosis gains insight into disease diagnosis and disease treatment. Here, we develop a traction force microscope method based on monolayer fluorescent beads for measuring the weak traction force (tens of Pa) of mitotic cells in three dimensions. We quantify traction forces of human ovarian granulosa (KGN) cells exerted on the extracellular matrix throughout the entire cell cycle in three dimensions. Our measurements reveal how forces vary during the cell cycle, especially during cell division. Furthermore, we study the effect of paclitaxel (PTX) and nocodazole (NDZ) on mitotic KGN cells through the measurement of traction forces. Our results show that mitotic cells with high concentrations of PTX exert a larger force than those with high concentrations of NDZ, which proved to be caused by changes in the structure and number of microtubules. These findings reveal the key functions of microtubule in generating traction forces during cell mitosis and explain how dividing cells regulate themselves in response to anti-mitosis drugs. This work provides a powerful tool for investigating cell-matrix interactions during mitosis and may offer a potential way to new therapies for cancer. | Yongman Liu Jianye Wang Yong Su Xiaohai Xu Hong Liu Kainan Mei Shihai Lan Shubo Zhang Xiaoping Wu Yunxia Cao Qingchuan Zhang Shangquan Wu | 2021 | Nano Research2021，14，11： | 3 |
| 2 | Deep learning for complex displacement field measurement显示文摘Traction force microscopy(TFM)is one of the most successful and broadly-used force probing technologies to quantify the mechanical forces in living cells.The displacement recovery of the fluorescent beads within the gel substrate,which serve as the fiducial markers,is one of the key processes.The traditional methods of extracting beads displacements,such as PTV,PIV,and DIC,persistently suffer from mismatching and loss of high-frequency information while dealing with the complex deformation around the focal adhesions.However,this information is crucial for the further analysis since the cells mainly transmit the force to the extracellular surroundings through focal adhesions.In this paper,we introduced convolutional neural network(CNN)to solve the problem.We have generated the fluorescent images of the non-deformable fluorescent beads and the displacement fields with different spatial complexity to form the training dataset.Considering the special image feature of the fluorescent images and the deformation with high complexity,we have designed a customized network architecture called U-DICNet for the feature extraction and displacement estimation.The numerical simulation and real experiment show that U-DICNet outperforms the traditional methods(PTV,PIV,and DIC).Particularly,the proposed U-DICNet obtains a more reliable result for the analysis of the local complex deformation around the focal adhesions. | LAN ShiHai SU Yong GAO ZeRen CHEN Ye TU Han ZHANG QingChuan | 2022 | Science China(Technological Sciences)2022，65，12： | 0 |
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