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Decode Mitochondrial Morphology Dynamics to Predict Cell Fate Decisions

Decode Mitochondrial Morphology Dynamics to Predict Cell Fate Decisions
解码线粒体形态动力学以预测细胞命运决策
批准号:
10473200
负责人:
Johannes Schoeneberg
金额:
$142.2万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-09-08 至 2025-08-31

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Project Summary/Abstract ABSTRACT Mitochondria not only provide 90% of the energy required for reading these very lines but they are also responsible for the correct differentiation of the cells lining your gut. The recent recognition that mitochondria play an active role in stem cell fate decisions has moved them from passive power plants to active centers of cell signaling. Our inability to automatically track mitochondria and link them to the fate of a cell is in stark contrast to the increasing relevance of this organelle in stem cell fate decisions and intestinal diseases such as Crohn’s disease and colorectal cancer. Mitochondria have always been too small and too fast for volumetric imaging and tracking. Our preliminary data show that a combination of recently developed lattice light-sheet microscopy and our in-house developed computational image processing pipeline can succeed in the four-dimensional tracking of the entire cellular mitochondrial network. Here we propose to 1) expand our prototype into a general tool that can track mitochondria in multiple cell types and network morphologies, to 2) elucidate the coupling between mitochondrial network morphology and cellular fate, and to 3) create a predictive model that links the mitochondrial network morphology to its underlying signaling drivers and to the fate decisions that are caused by different morphologies. We propose to use intestinal epithelial organoids and the differentiation of intestinal stem cells to paneth cells as a model system for a mitochondria-directed fate determination. This proposal will open a new window into mitochondrial biology that will translate to a large number of mitochondria-related diseases such as cancer and neurological disorders such as epilepsy, Parkinson’s disease, and Alzheimer’s disease.
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Modeling and Analysis of the Spatio-Temporal Dynamics of the Mitochondrial Network