CAJAL: A computational framework for the combined morphometric, transcriptomic, and physiological analysis of cells

摘要 形态学是表征细胞及其状态的基本表型。它反映了 功能性细胞过程，如形态发生、迁移或树突分支， 表明疾病。描绘出构成形态表型基础的分子途径对于 了解遗传途径，形态和脑细胞功能之间的关系。最近 像Patch-seq这样的技术可以用来同时分析细胞形态、基因表达和 单个细胞的电生理特性。然而，缺乏计算方法， 总结大脑中发现的复杂细胞形态的巨大多样性，并从统计学上推断与 基因表达和生理特性。为了克服这一障碍，我们开创了一部小说 基于度量几何学概念的细胞形态测量和多模态分析方法。我们的研究 表明这种方法可以有效地用于单细胞形态学、转录组学和生理学研究， 来自BRAIN Initiative Cell Census Network的数据，以描绘出 脑细胞的形态和生理表型。我们的目标是在此基础上建立一个 可扩展的算法框架和软件，用于3D细胞形态和单细胞的组合分析 转录组学和生理学数据。本建议的目标1将建立一个通用的和可解释的算法 用于复杂和异质3D细胞形态的表征、分类和整合。我们 将使用度量几何构造精确的细胞形态摘要空间，其中细胞被表示为 细胞之间的距离表示改变3D形态所需的变形量 从一个细胞到另一个细胞我们还将解决这些空间的集成和批量校正。目的2 将建立在聚类独立的光谱方法，以建立一个统计框架， 细胞形态学与单细胞转录组学和电生理学数据之间的关联。我们将使用 来自BRAIN Initiative细胞的Patch-seq、fMOST、MORF、MouseLight和连续电子显微镜数据 人口普查网络、艾伦脑图谱细胞类型数据库和MICrONS联盟，以及模拟的 数据，以评估和优化这些算法。我们将通过实施这些方法， 一个开源的、支持云的软件，用于结合形态测量、分子和生理分析。 此外，还举办了若干讲习班和辅导班，以培养一个活跃的用户群体。采取 总之，我们的研究计划的成果将填补单细胞综合分析的关键空白， 形态，分子和生理数据，并将有助于建立新的基础， 单细胞形态基因组学

ABSTRACT Morphology is an essential phenotype in the characterization of cells and their states. It reflects the progression of functional cellular processes, such as morphogenesis, migration, or dendrite arborization, and can be indicative of disease. Delineating the molecular pathways that underlie morphological phenotypes is critical to understanding the relation between genetic pathways, morphology, and function of cells in the brain. Recent techniques like Patch-seq can be used to simultaneously profile cell morphology, gene expression, and electrophysiological properties of individual cells. However, there is a lack of computational methods that can summarize the great diversity of complex cell morphologies found in the brain and statistically infer relations with gene expression and physiological properties. To overcome this impediment, we have pioneered a novel approach for cell morphometry and multi-modal analysis based on concepts from metric geometry. Our studies show that this approach can be efficiently used with single-cell morphological, transcriptomic, and physiological data from the BRAIN Initiative Cell Census Network to delineate the molecular pathways that underlie the morphological and physiological phenotypes of brain cells. Our goal is to build upon this approach to establish a scalable algorithmic framework and a software for the combined analysis of 3D cell morphologies and single-cell transcriptomic and physiological data. Aim 1 of this proposal will establish a general and interpretable algorithm for the characterization, classification, and integration of complex and heterogeneous 3D cell morphologies. We will use metric geometry to construct accurate cell morphology summary spaces, where cells are represented by points, and distances between cells indicate the amount of deformation needed to change the 3D morphology of one cell into that of another. We will also address the integration and batch correction of these spaces. Aim 2 will build upon clustering-independent spectral methods to develop a statistical framework for establishing associations between cell morphology and single cell transcriptomic and electrophysiological data. We will use Patch-seq, fMOST, MORF, MouseLight, and serial electron microscopy data from the BRAIN Initiative cell Census Network, the Allen Brain Atlas Cell Types Database, and the MICrONS consortium, as well as simulated data, to evaluate and optimize these algorithms. We will disseminate these methods by implementing them into an open-source, cloud-enabled, software for the combined morphometric, molecular, and physiological analysis of cells, and by organizing several workshops and tutorials to foster an active community of users. Taken together, the outcomes of our research program will fill a critical gap in the integrative analysis of single-cell morphological, molecular, and physiological data and will contribute to establishing the foundations of the new field of single-cell morphogenomics.