Leveraging Spatial Location for Single-Cell Molecular and Morphological Characterization

利用空间定位进行单细胞分子和形态学表征

• 批准号: 10534272
• 负责人: April Rose Kriebel
• 金额: $ 3.95万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10534272
• 关键词: Algorithms; Anatomy; Axon; Bar Codes; Biochemistry; Bioinformatics; Biological Assay; Calcium; Cell physiology; Cells; Cellular Assay; Characteristics; Chromatin; Communication; Complex; Computing Methodologies; Critical Thinking; Data; Data Set; Development; Disease; Electrophysiology (science); Fluorescence; Genes; Genomics; Graph; Human body; Image; Intuition; Knowledge; Light; Link; Location; Measurement; Measures; Methods; Microscopy; Modality; Molecular; Molecular Profiling; Morphology; Mosaicism; Neurons; Neurosciences; Optics; Pattern; Physiological; Physiology; Process; Property; RNA; Research; Resolution; Scientist; Series; Signal Transduction; Specificity; Stimulus; Surveys; Technical Expertise; Techniques; Technology; Tissues; Training; Transcript; Translations; Transposase; Ultrasonography; Update; algorithm development; base; cell type; cohesion; computerized tools; epigenome; epigenomics; experience; extracellular; improved; in vivo; innovation; innovative technologies; insight; novel; reconstruction; response; single cell sequencing; spatial integration; spatial relationship; success; technology/technique; tomography; tool; transcriptome; transcriptomics; two-photon

Abstract Innovative developments in single-cell sequencing technologies and techniques are providing increased resolution and novel ways to define and characterize cellular profiles. Despite this progress, linking different aspects of a cell’s identity, such as transcriptome, spatial location, morphology, and physiological response remains challenging. Spatial transcriptomic technologies, while providing transcriptomic data within a spatial framework, frequently must compromise achieving single-cell resolution in order to survey a wider panel of genes. Similarly, while techniques such as fluorescent micro-optical section tomography (fMOST) and functional ultrasound imaging (fUSI) provide detailed reconstructions of neuron morphology and physiological response, these data modalities lack the ability to simultaneously capture molecular information. As a result, while technological advances for each distinct modality continue to resolve finer and more complex cell type distinctions, cohesive cellular profiles that combine all aspects of a cell’s identity, from transcriptome to physiological response, have yet to be captured. Thus, understanding how the transcriptomic and morphological composition of a cell influences its physiological response is a key barrier for the field. This proposal aims to develop computational tools that will connect multiple facets of cellular identity. In Aim 1, we propose the addition of graph-regularization into the integrative non-negative matrix factorization algorithm (GRINMF). The use of GRINMF to include spatial information will result in more refined cell-type characterizations for cells assayed with spatial transcriptomics technologies. In Aim 2, we will validate a spatial deconvolution algorithm that leverages non-negative matrix factorization to calculate cell-type proportions within spatially registered transcriptomic data. We will anchor our derived cell-type proportion voxels in the same coordinate framework as a series of morphological and physiological datasets. By completing the proposed research, I will gain extensive experience in the development of algorithms to synthesize physiological, transcriptomic, and spatial data. This training will facilitate advancement of my communication, critical thinking, and translational technical skills, providing me with the tools necessary to pursue my ambition of becoming a research scientist at the interface of neuroscience and bioinformatics.

抽象的 单细胞测序技术和技术的创新发展正在提供 增加了分辨率和新颖的方式来定义和表征细胞谱。尽管取得了这种进展，链接 细胞身份的不同方面，例如转录组，空间位置，形态和生理学 反应仍然是挑战。空间转录学技术，同时提供转录组数据 空间框架，经常必须损害实现单细胞分辨率以调查更广泛的面板 基因。同样，虽然诸如荧光微光学截面层析成像（f最）和 功能超声成像（FUSI）提供了神经元形态和生理的详细重建 响应，这些数据模式缺乏简单地捕获分子信息的能力。因此， 虽然每种不同模式的技术进步继续解决更精细，更复杂的单元格类型 区别，结合细胞身份的各个方面的凝聚力细胞谱，从转录组到 生理反应，尚未捕获。这是了解转录组和 细胞的形态组成影响其物理反应是该领域的关键障碍。 该建议旨在开发将连接蜂窝身份多个方面的计算工具。在 AIM 1，我们建议将图形定制添加到集成的非负矩阵分解中 算法（grinmf）。使用GrinMF包含空间信息将导致更精致的细胞类型 用空间转录组学测定的细胞表征。在AIM 2中，我们将验证一个空间 利用非阴性基质分解来计算细胞类型属性的反卷积算法 在空间注册的转录组数据中。我们将锚定我们派生的细胞类型比例体体内体内 与一系列形态和物理数据集相同的坐标框架。通过完成 拟议的研究，我将在算法的开发中获得丰富的经验以综合 物理，转录组和空间数据。这项培训将促进我的沟通进步， 批判性思维和翻译技术技能，为我提供了追求野心所需的工具 成为神经科学和生物信息学界面的研究科学家。