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 更高的分辨率和新颖的方法来定义和表征细胞概况。尽管取得了这些进展，但链接 细胞身份的不同方面，例如转录组、空间位置、形态和生理学 应对措施仍然具有挑战性。空间转录组技术，同时提供转录组数据 空间框架，经常必须妥协实现单细胞分辨率才能调查更广泛的面板 基因。同样，虽然荧光微光学断层扫描 (fMOST) 和 功能性超声成像 (fUSI) 提供神经元形态和生理学的详细重建 响应，这些数据模式缺乏同时捕获分子信息的能力。因此， 同时每种不同模式的技术进步继续解决更精细和更复杂的细胞类型 区别，有凝聚力的细胞特征，结合了细胞身份的各个方面，从转录组到 生理反应，尚未被捕获。因此，了解转录组学和 细胞的形态组成影响其生理反应是该领域的关键障碍。 该提案旨在开发连接细胞身份多个方面的计算工具。在 目标 1，我们建议将图正则化添加到积分非负矩阵分解中 算法（GRINMF）。使用 GRINMF 包含空间信息将产生更精细的细胞类型 使用空间转录组学技术测定细胞的表征。在目标 2 中，我们将验证空间 利用非负矩阵分解来计算细胞类型比例的反卷积算法 在空间登记的转录组数据内。我们将把派生的细胞类型比例体素锚定在 与一系列形态学和生理数据集相同的坐标框架。通过完成 提出的研究，我将在开发合成算法方面获得丰富的经验 生理、转录组和空间数据。这次培训将促进我的沟通进步， 批判性思维和转化技术技能为我提供了实现我的抱负所需的工具 成为神经科学和生物信息学交叉领域的研究科学家。