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

CAJAL：细胞形态学、转录组学和生理学综合分析的计算框架

• 批准号: 10509196
• 负责人: Pablo Gonzalez Camara
• 金额: $ 125.47万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10509196
• 关键词: 3-Dimensional; Accounting; Address; Algorithms; Archives; Atlases; BRAIN initiative; Biological Process; Biology; Brain; Brain Diseases; Cell Shape; Cell physiology; Cells; Cellular Morphology; Cellular biology; Censuses; Classification; Closure by clamp; Cloud Computing; Communities; Complex; Computer software; Computing Methodologies; Consensus; Data; Data Analyses; Databases; Dendrites; Descriptor; Disease; Educational workshop; Electron Microscopy; Electrophysiology (science); FAIR principles; Fostering; Foundations; Gene Expression; Genetic; Geometry; Goals; Graph; Individual; Informatics; Mathematics; Methods; Modality; Molecular; Morphogenesis; Morphology; Neuroglia; Neurons; Neurosciences; Outcome; Pathway interactions; Phenotype; Physiological; Physiology; Population; Prevention; Process; Property; Research; Research Personnel; Shapes; Stains; Techniques; Technology; Three-dimensional analysis; Tissue-Specific Gene Expression; Work; algorithm development; base; brain cell; cell type; computer framework; computerized tools; data archive; experimental study; falls; interest; migration; morphometry; multimodality; novel strategies; open source; outreach; patch clamp; patch sequencing; programs; single cell analysis; transcriptomics

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.

抽象的 形态是细胞及其状态表征的重要表型。它反映了进展 功能性细胞过程，例如形态发生，迁移或树突植物，可以是 指示疾病。描述形态表型基础的分子途径至关重要 了解大脑中细胞的遗传途径，形态和功能之间的关系。最近的 诸如Patch-seq之类的技术可用于同时介绍细胞形态，基因表达和 单个细胞的电生理特性。但是，缺乏计算方法可以 总结大脑中发现的复杂细胞形态的大量多样性，并从统计上推断与与 基因表达和生理特性。为了克服这一障碍，我们开创了一本小说 基于度量几何概念的细胞形态计量学和多模式分析的方法。我们的研究 证明该方法可以与单细胞形态，转录组和生理学有效使用 来自大脑启动细胞普查网络的数据，以描绘出基于的分子途径 脑细胞的形态和生理表型。我们的目标是建立这种方法以建立 可扩展算法框架和用于3D细胞形态和单细胞组合分析的软件 转录组和生理数据。本提案的目标1将建立一种通用且可解释的算法 用于复杂和异质3D细胞形态的表征，分类和整合。我们 将使用度量几何形状构建精确的细胞形态摘要空间，其中表示细胞 按点，细胞之间的距离表示改变3D形态所需的变形量 一个细胞进入另一个单元。我们还将解决这些空间的集成和批化校正。目标2 将基于独立于聚类的光谱方法来开发建立统计框架 细胞形态与单细胞转录组和电生理数据之间的关联。我们将使用 贴片序列，f几种，Morf，Muselight和串行电子显微镜数据来自大脑启动细胞 人口普查网络，Allen Brain Atlas细胞类型数据库和Microns Consortium以及模拟 数据，评估和优化这些算法。我们将通过将这些方法实施到 一种开源，支持云的软件，用于组合形态计量学，分子和生理分析 通过组织几个研讨会和教程来培养活跃的用户社区。拍摄 总之，我们的研究计划的结果将填补单细胞综合分析的关键空白 形态学，分子和生理数据，将有助于建立新的基础 单细胞形态学学的领域。

项目成果

CAJAL enables analysis and integration of single-cell morphological data using metric geometry.

• DOI: 10.1038/s41467-023-39424-2
• 发表时间: 2023-06-21
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Govek, Kiya W.;Nicodemus, Patrick;Lin, Yuxuan;Crawford, Jake;Saturnino, Artur B.;Cui, Hannah;Zoga, Kristi;Hart, Michael P.;Camara, Pablo G.
• 通讯作者: Camara, Pablo G.