Browsing by Author "Hu, Liya"

Now showing 1 - 4 of 4
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    Acoustofluidic Assembly of 3D Neurospheroids to Model Alzheimer’s Disease
    (Royal Society of Chemistry, 2020-09-28) Cai, Hongwei; Ao, Zheng; Hu, Liya; Moon, Younghye; Wu, Zhuhao; Lu, Hui-Chen; Kim, Jungsu; Guo, Feng; Medical and Molecular Genetics, School of Medicine
    Neuroinflammation plays a central role in the progression of many neurodegenerative diseases such as Alzheimer's disease, and challenges remain in modeling the complex pathological or physiological processes. Here, we report an acoustofluidic method that can rapidly construct 3D neurospheroids and inflammatory microenvironments for modeling microglia-mediated neuroinflammation in Alzheimer's disease. By incorporating a unique contactless and label-free acoustic assembly, this cell culture platform can assemble dissociated embryonic mouse brain cells into hundreds of uniform 3D neurospheroids with controlled cell numbers, composition (e.g. neurons, astrocytes, and microglia), and environmental components (e.g. amyloid-β aggregates) in hydrogel within minutes. Moreover, this platform can maintain and monitor the interaction among neurons, astrocytes, microglia, and amyloid-β aggregates in real-time for several days to weeks, after the integration of a high-throughput, time-lapse cell imaging approach. We demonstrated that our engineered 3D neurospheroids can represent the amyloid-β neurotoxicity, which is one of the main pathological features of Alzheimer's disease. Using this method, we also investigated the microglia migratory behaviors and activation in the engineered 3D inflammatory microenvironment at a high throughput manner, which is not easy to achieve in 2D neuronal cultures or animal models. Along with the simple fabrication and setup, the acoustofluidic technology is compatible with conventional Petri dishes and well-plates, supports the fine-tuning of the cellular and environmental components of 3D neurospheroids, and enables the high-throughput cellular interaction investigation. We believe our technology may be widely used to facilitate 3D in vitro brain models for modeling neurodegenerative diseases, discovering new drugs, and testing neurotoxicity.
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    Evaluation of cancer immunotherapy using mini-tumor chips
    (Ivyspring International, 2022-05-01) Ao, Zheng; Cai, Hongwei; Wu, Zhuhao; Hu, Liya; Li, Xiang; Kaurich, Connor; Gu, Mingxia; Cheng, Liang; Lu, Xin; Guo, Feng; Pathology and Laboratory Medicine, School of Medicine
    Rationale: Predicting tumor responses to adjuvant therapies can potentially help guide treatment decisions and improve patient survival. Currently, tumor pathology, histology, and molecular profiles are being integrated into personalized profiles to guide therapeutic decisions. However, it remains a grand challenge to evaluate tumor responses to immunotherapy for personalized medicine. Methods: We present a microfluidics-based mini-tumor chip approach to predict tumor responses to cancer immunotherapy in a preclinical model. By uniformly infusing dissociated tumor cells into isolated microfluidic well-arrays, 960 mini-tumors could be uniformly generated on-chip, with each well representing the ex vivo tumor niche that preserves the original tumor cell composition and dynamic cell-cell interactions and autocrine/paracrine cytokines. Results: By incorporating time-lapse live-cell imaging, our mini-tumor chip allows the investigation of dynamic immune-tumor interactions as well as their responses to cancer immunotherapy (e.g., anti-PD1 treatment) in parallel within 36 hours. Additionally, by establishing orthotopic breast tumor models with constitutive differential PD-L1 expression levels, we showed that the on-chip interrogation of the primary tumor's responses to anti-PD1 as early as 10 days post tumor inoculation could predict the in vivo tumors' responses to anti-PD1 at the endpoint of day 24. We also demonstrated the application of this mini-tumor chip to interrogate on-chip responses of primary tumor cells isolated from primary human breast and renal tumor tissues. Conclusions: Our approach provides a simple, quick-turnaround solution to measure tumor responses to cancer immunotherapy.
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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.
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    Rapid Profiling of Tumor-Immune Interaction Using Acoustically Assembled Patient-Derived Cell Clusters
    (Wiley, 2022) Ao, Zheng; Wu, Zhuhao; Cai, Hongwei; Hu, Liya; Li, Xiang; Kaurich, Connor; Chang, Jackson; Gu, Mingxia; Cheng, Liang; Lu, Xin; Guo, Feng; Pathology and Laboratory Medicine, School of Medicine
    Tumor microenvironment crosstalk, in particular interactions between cancer cells, T cells, and myeloid‐derived suppressor cells (MDSCs), mediates tumor initiation, progression, and response to treatment. However, current patient‐derived models such as tumor organoids and 2D cultures lack some essential niche cell types (e.g., MDSCs) and fail to model complex tumor‐immune interactions. Here, the authors present the novel acoustically assembled patient‐derived cell clusters (APCCs) that can preserve original tumor/immune cell compositions, model their interactions in 3D microenvironments, and test the treatment responses of primary tumors in a rapid, scalable, and user‐friendly manner. By incorporating a large array of 3D acoustic trappings within the extracellular matrix, hundreds of APCCs can be assembled within a petri dish within 2 min. Moreover, the APCCs can preserve sensitive and short‐lived (≈1 to 2‐day lifespan in vivo) tumor‐induced MDSCs and model their dynamic suppression of T cell tumor toxicity for up to 24 h. Finally, using the APCCs, the authors succesully model the combinational therapeutic effect of a multi‐kinase inhibitor targeting MDSCs (cabozantinib) and an anti‐PD‐1 immune checkpoint inhibitor (pembrolizumab). The novel APCCs may hold promising potential in predicting treatment response for personalized cancer adjuvant therapy as well as screening novel cancer immunotherapy and combinational therapy.