Dissecting subcellular and cellular organization by spatial molecular neighborhood networks

细胞内分子的亚细胞组织调节细胞的基本功能，如生长、分裂、增殖和分化。通过调节RNA和蛋白质的亚细胞邻域，细胞得以生存，但也在多细胞系统中特化。虽然亚细胞定位机制被广泛研究，但亚细胞邻域在确定细胞对刺激和多种药物的反应中的作用仅限于少数低通量和低灵敏度的分子研究。关于这些高度协调的亚细胞邻域如何产生组织特异性、组织状态以及它们如何在不同的亚细胞体积的组织状态和表型中调节细胞功能，人们知之甚少。基于图像的可视化的分子邻域只探索了少数目标分子，产生的证据的转录本和蛋白质因子的亚细胞定位的重要性。亚细胞调控的一个瓶颈是缺乏解释分子的邻近相互作用的模型，这些分子在不同的异常组织中驱动细胞反应。以前的工作建立了多路复用的基因表达测量模型的亚细胞RNA的邻居和多参数蛋白质分析，以量化空间信号蛋白的邻居，改变响应扰动。然而，在广泛使用的间充质和上皮细胞中，尚未以物理上有意义的分辨率建立显性信号蛋白和RNA邻域，因为它们响应于扰动，经历分化事件，或适应用基于细胞或小分子的干预治疗的个体的组织环境，以校正由于遗传或分子功能障碍引起的异常。因此，该项目的长期目标是利用先进的高通量筛选和单细胞中的空间分辨基因组和蛋白质测量来建模群体和组织环境中的分子邻域。为了实现这些任务，利用顺序荧光原位杂交和多重蛋白质成像的最新进展，该提议计划1）使用邻近连接测定来机械地解剖分子RNA和蛋白质邻域，2）使用3D超分辨率显微镜增强分子邻域的空间信息能力，并通过伪时间分化模型来增强分子邻域的时间演化，3）评估从健康恢复干预中分离的人样本的血管附近的间充质细胞和衬里附近的上皮细胞的亚细胞邻域组织和组织背景的结果。分子基准和正常组织注释将与信号生物学，生物信息学，生物制造和病理学专家合作进行。从亚细胞邻域到组织的跨尺度分子控制将揭示如何通过分子邻域的网络方差来重新分层对治疗的系统反应。MIRA的建议揭示了健康和疾病中的空间分辨亚细胞组织，为破译许多疾病中的组织状态控制和改变提供了预测指标。

Subcellular organization of intracellular molecules regulates basic cell functions such as growth, division, proliferation, and differentiation. By regulating subcellular neighborhoods of RNAs and proteins, cells survive but also specialize in a multicellular system. While subcellular localization mechanisms are widely studied, the role of subcellular neighborhoods in determining the cellular response to stimuli and multiple drugs has been limited to a few low-throughput and low-sensitivity molecular studies. Little is known about how these highly coordinated subcellular neighborhoods give rise to tissue-specificity, tissue-sate, and how they regulate cell function in distinct subcellular volumes of tissue states and phenotypes. Image-based visualization of molecular neighborhoods has explored only a few target molecules, yielding evidence of the importance of subcellular localization of transcripts and protein factors. One bottleneck in subcellular regulation is the lack of models explaining neighborhood interactions of molecules that are driving cellular response in tissues from distinct abnormalities. Previous work established multiplexed gene expression measurements to model subcellular RNA neighborhoods and multiparameter protein analysis to quantify spatial signaling protein neighborhoods that are altered in response to perturbations. However, dominant signaling protein and RNA neighborhoods have not been established at a physically meaningful resolution in widely employed mesenchymal and epithelial cells as they respond to perturbations, experience differentiation events, or adapt to tissue environments of individuals treated with cell-based or small molecule interventions for correcting abnormalities due to genetic or molecular dysfunction. Thus, the long-term goal of this project is to leverage advanced high-throughput screening and spatially resolved genomic and protein measurements in single cells for modeling molecular neighborhoods in populations and tissue contexts. To achieve these tasks, using recent advances in sequential fluorescence in situ hybridization and multiplexed protein imaging, this proposal plans to 1) mechanistically dissect the molecular RNA and protein neighborhoods using proximity ligation assays, 2) enhance the spatial information capacity of molecular neighborhoods using 3D super-resolution microscopy and temporal evolution of molecular neighborhoods by pseudo-temporal differentiation models, 3) evaluate the outcome of subcellular neighborhood organization and tissue context of mesenchymal cells near vessels and epithelial cells near linings from human specimens isolated from health restoring interventions. Molecular benchmarking and normal tissue annotations will be performed in collaboration with signaling biology, bioinformatics, biomanufacturing, and pathology experts. Cross-scale molecular control from subcellular neighborhoods to tissue organization will reveal how a systemic response to treatments can be re-stratified by network variance of molecular neighborhoods. This MIRA proposal sheds light on the spatially resolved subcellular organization in health and disease, providing a predictive metric for deciphering tissue-state control and alterations in many disorders.