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第十二届全国免疫学学术大会

作者：张保军 Chenfeng He Siqi Liu Mingjuan Qu Ning Jiang Yuan Zhuang Qijing Li 西安交通大学医学部 Department of Immunology Duke University Medical Center Department of Biomedical Engineering University of Texas at Austin Institute for Cellular and Molecular Biology University of Texas at Austin

Regulatory T cells(Tregs) play a critical role in the maintenance of gut homeostasis by suppressing conventional CD4helper T cells(Tconvs) that are activated by microbial antigens.Tregs can directly generate from the thymus or be converted from peripheral Tconvs under various antigen stimulations.But it is still unclear about the frequency of this lineage conversion from Tconvs to Tregs in na(i|¨)ve animals.Here we used a newly established Tlox reporter system to track the proliferative expansion of activated Tconvs and Tregs.We found that marked Tregs(eTregs) exhibit an enhanced expression of effector molecules and a stronger suppressive ability than non-lineage marked Tregs.TCR repertoire analysis of Peyer’s patch T cells revealed that eTregs are clonally related to Tconvs,but not to non-lineage tracked Tregs.Thus,this study suggests that only a fraction of clonally activated T cells from the natural T cell repertoire exhibits lineage conversion to Tregs in response to commensal microbes under homeostatic conditions.

关键词： Immune tolerance Regulatory T cells Gut homeostasis lineage tracking

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Cellbow:a robust customizable cell segmentation program

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Quantitative Biology 2020年第3期8卷 245-255页

作者： Huixia Ren Mengdi Zhao Bo Liu Ruixiao Yao Qi liu Zhipeng Ren Zirui Wu Zongmao Gao Xiaojing Yang Chao Tang Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences Academy for Advaneed Interdisciplinary StudiesPeking UniversityBeijing 100871China

Backgrounds Time-lapse live cell imaging of a growing cell population is routine in many biological investigations.A major challenge in imaging analysis is accurate segmentation,a process to define the boundaries of cells based on raw image data.Current segmentation methods relying on single boundary features have problems in robustness when dealing with inhomogeneous foci which invariably happens in cell population imaging.Methods:Combined with a multi-layer training set strategy,we developed a neural-network-based algorithm—Cellbow.Results'Cellbow can achieve accurate and robust segmentation of cells in broad and general settings.It can also facilitate long-term tracking of cell growth and division.To facilitate the application of Cellbow,we provide a website on which one can online test the software,as well as an I mage J plugin for the user to visualize the performance before software installation.Conclusions Cellbow is customizable and generalizable.It is broadly applicable to segmenting fluorescent images of diverse cell types with no further training needed.For bright-field images,only a small set of sample images of the specific cell type from the user may be needed for training.

关键词： deep neural network cell segmentation fluorescent cell imaging bright-field cell imaging lineage tracking

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