Browsing by Author "Bondesson, Maria"

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    Acoustic assembly of cell spheroids in disposable capillaries
    (IOP, 2018-12) Wu, Yue; Ao, Zheng; Chen, Bin; Muhsen, Maram; Bondesson, Maria; Lu, Xiongbin; Guo, Feng; Medical and Molecular Genetics, School of Medicine
    Multicellular spheroids represent a promising approach to mimic 3D tissues in vivo for emerging applications in regenerative medicine, therapeutic screening, and drug discovery. Conventional spheroid fabrication methods, such as the hanging drop method, suffer from low-throughput, long time, complicated procedure, and high heterogeneity in spheroid size. In this work, we report a simple yet reliable acoustic method to rapidly assemble cell spheroids in capillaries in a replicable and scalable manner. Briefly, by introducing a coupled standing surface acoustic wave, we are able to generate a linear pressure node array with 300 trapping nodes simultaneously. This enables us to continuously fabricate spheroids in a high-throughput manner with minimal variability in spheroid size. In a proof of concept application, we fabricated cell spheroids of mouse embryonic carcinoma (P19) cells, which grew well and retained differentiation potential in vitro. Based on the advantages of the non-invasive, contactless and label-free acoustic cell manipulation, our method employs the coupling strategy to assemble cells in capillaries, and further advances 3D spheroid assembly technology in an easy, cost-efficient, consistent, and high-throughput manner. This method could further be adapted into a novel 3D biofabrication approach to replicate compilated tissues and organs for a wide set of biomedical applications.
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    Microfluidics guided by deep learning for cancer immunotherapy screening
    (National Academy of Science, 2022) Ao, Zheng; Cai, Hongwei; Wu, Zhuhao; Hu, Liya; Nunez, Asael; Zhou, Zhuolong; Liu, Hongcheng; Bondesson, Maria; Lu, Xiongbin; Lu, Xin; Dao, Ming; Guo, Feng; Medical and Molecular Genetics, School of Medicine
    Immune-cell infiltration and cytotoxicity to pathogens and diseased cells are ubiquitous in health and disease. To better understand immune-cell behavior in a 3D environment, we developed an automated high-throughput microfluidic platform that enables real-time imaging of immune-cell infiltration dynamics and killing of the target cancer cells. We trained a deep learning algorithm using clinical data and integrated the algorithm with our microfluidic platform to effectively identify epigenetic drugs that promote T cell tumor infiltration and enhance cancer immunotherapy efficacy both in vitro and in vivo. Our platform provides a unique method to investigate immune-tissue interactions, which can be widely applied to oncology, immunology, neurology, microbiology, tissue engineering, regenerative medicine, translational medicine, and so on.