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.

摘要 单细胞测序技术和技术的创新发展提供了 更高的分辨率和定义和表征细胞轮廓的新颖方法。尽管取得了这一进展，但链接 细胞特性的不同方面，如转录组、空间位置、形态和生理 应对措施仍然具有挑战性。空间转录技术，同时在 空间框架，为了调查更广泛的面板，往往必须牺牲实现单个像元的分辨率 基因。同样，虽然诸如荧光显微光学切片层析成像(FMOST)和 功能超声成像(FUSI)提供了神经元形态和生理的详细重建 作为回应，这些数据模式缺乏同时捕获分子信息的能力。结果, 虽然每种不同形态的技术进步继续解决更精细和更复杂的细胞类型 区别，结合了细胞身份的方方面面的紧密结合的细胞轮廓，从转录组到 生理反应，尚未被捕捉到。因此，理解转录和转录是如何 细胞的形态组成影响其生理反应，是该领域的关键障碍。 这项提议旨在开发将连接蜂窝身份的多个方面的计算工具。在……里面 目的1.将图的正则化引入到积分非负矩阵因式分解中 算法(GRINMF)。使用GRINMF来包含空间信息将导致更精细的像元类型 用空间转录组学技术分析细胞的特征。在目标2中，我们将验证一个空间 一种利用非负矩阵因式分解来计算单元类型比例的去卷积算法 在空间注册的转录数据中。我们将派生的单元格类型比例体素锚定在 与一系列形态和生理数据集的坐标框架相同。通过填写 提出的研究，我将获得广泛的开发经验的算法来综合 生理、转录和空间数据。这次培训将促进我的沟通， 批判性思维和翻译技术技能，为我实现抱负提供了必要的工具 成为神经科学和生物信息学交界处的研究科学家。