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，细胞存活，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，但亚细胞邻域在决定细胞对刺激和多种药物的反应中的作用仅限于一些低通量和低灵敏度的分子研究。对于这些高度协调的亚细胞邻域如何产生组织特异性和组织安全性，以及它们如何在不同的组织状态和表型的亚细胞体积中调节细胞功能，我们知之甚少。基于图像的分子邻域可视化仅探索了少数目标分子，从而证明了转录物和蛋白质因子亚细胞定位的重要性。亚细胞调控的一个瓶颈是缺乏模型来解释驱动组织中不同异常的细胞反应的分子的邻近相互作用。先前的工作建立了多重基因表达测量来模拟亚细胞RNA邻域和多参数蛋白质分析，以量化空间信号蛋白邻域在响应扰动时的改变。然而，在广泛应用的间充质细胞和上皮细胞中，显性信号蛋白和RNA邻域尚未在物理上有意义的分辨率上建立起来，因为它们对扰动做出反应，经历分化事件，或适应个体的组织环境，这些个体接受基于细胞或小分子干预来纠正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空间分辨率的基因组和蛋白质测量来模拟群体和组织背景下的分子邻域。为了完成这些任务，利用序列荧光原位杂交和多路复用蛋白质成像的最新进展，本提案计划1)利用近距离连接法对分子RNA和蛋白质邻域进行机械解剖，2)利用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增强分子邻域的时间进化。3)评估从健康恢复干预中分离的人类标本中血管附近的间充质细胞和内膜附近的上皮细胞的亚细胞邻近组织和组织环境的结果。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组织的跨尺度分子控制将揭示如何通过分子邻域的网络变异对治疗的系统反应进行重新分层。这一MIRA提案揭示了健康和疾病中空间分解的亚细胞组织，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.