Computational toolbox for spatial transcriptomic analysis of complex tissues

用于复杂组织空间转录组分析的计算工具箱

• 批准号: 10666294
• 负责人: Elham Azizi
• 金额: $ 43.55万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10666294
• 关键词: Address; Algorithms; Anatomy; Biomedical Research; Brain Diseases; Cell Communication; Cells; Clinical; Communication; Complex; Computer software; Computing Methodologies; Data; Data Set; Dependence; Development; Diagnosis; Disease; Disease Progression; Evaluation; Funding Mechanisms; Gene Expression; Genetic Transcription; Genomics; Goals; Histology; Histopathology; Image; Immune; Location; Machine Learning; Mammary Neoplasms; Maps; Measurement; Methods; Modality; Modeling; Modernization; Molecular; Molecular Profiling; Morphology; Neighborhoods; Normal Range; Organoids; Patients; Pattern; Play; Procedures; Process; Publishing; Resolution; Role; Sampling; Spottings; System; Techniques; Technology; Therapeutic; Tissue Sample; Tissue imaging; Tissues; Tumor Tissue; Visualization; Work; brain tissue; cell type; cohort; computerized tools; data integration; deep learning; design; genomic data; high dimensionality; histological image; image processing; improved; innovation; insight; machine learning framework; machine learning method; machine learning model; multidimensional data; multimodality; novel; open source; single cell analysis; single-cell RNA sequencing; spatial integration; supervised learning; tool; transcriptome; transcriptomics; treatment response

PROJECT SUMMARY Mapping the spatial organization of cells and their communication in tissues is essential to understanding the process of development and disease formation. The rapid development of spatial transcriptomic technologies has enabled the profiling of the full transcriptome across thousands of locations in a tissue sample. In addition to transcriptional measurements, this technology also obtains paired histological imaging of the tissue. The spatially resolved profiling of gene expression has the potential to unlock groundbreaking discoveries, however, there are critical barriers in analyzing this data especially in complex tissue samples that involve the mixing of many diverse cell types in capture locations (spots). The low resolution makes it difficult to discern diverse cell types which is essential for downstream analysis of their spatial organization, dynamics, and interactions. Additionally, integrating spatial transcriptomic datasets across multiple tissue samples is not straightforward due to this technical limitation. Existing computational tools for analyzing high-dimensional genomic data were either built for single-cell resolution data or require paired single-cell transcriptomic data to guide the analysis of spatial transcriptomic data. Additionally, current methods do not consider spatial dependencies and information embedded in the histology image. The overarching goal of this proposal is to develop novel machine learning methods for analyzing the new wave of spatial transcriptomic data without the need for paired single-cell data as a reference. These innovative frameworks will enable characterizing diverse cell states and their spatial dynamics through a semi-supervised deconvolution of data (Aim 1) which will also allow integration of data from multiple tissue samples. We will also develop a multi-view framework for the integration of spatial transcriptomic and histological imaging for improved inference of intercellular interactions (Aim 2). By combining image processing algorithms for the alignment of images from replicate samples, we will extend this framework for integrating tissue samples. Our toolbox will be applicable to a broad range of tissue systems and larger clinical cohorts and has the potential to be transformative in understanding spatial dynamics during healthy development and guiding diagnosis and therapeutic strategies based on the spatial organization of cell types specific to the disease microenvironment.

项目摘要 绘制细胞的空间组织及其在组织中的通讯对于理解细胞的功能是至关重要的。 发展和疾病形成的过程。空间转录组学技术的快速发展 已经能够在组织样本中的数千个位置上对全转录组进行分析。此外 对于转录测量，该技术还获得组织的成对组织学成像。 基因表达的空间分辨分析有可能带来突破性的发现， 然而，在分析这些数据时存在关键的障碍，特别是在复杂的组织样本中， 在捕获位置（斑点）混合许多不同的细胞类型。低分辨率使其难以辨别 不同的细胞类型，这对于下游分析其空间组织，动力学和生物学特性至关重要。 交互.此外，整合多个组织样本的空间转录组数据集并不容易。 由于这种技术限制，现有的计算工具，用于分析高维 基因组数据要么是为单细胞分辨率数据构建的，要么需要配对的单细胞转录组数据， 引导空间转录组学数据的分析。此外，当前的方法不考虑空间 依赖关系和信息嵌入在组织学图像中。 该提案的总体目标是开发新的机器学习方法，用于分析新的 空间转录组学数据的波，而不需要配对的单细胞数据作为参考。这些 创新的框架将能够通过一个 数据的半监督去卷积（目标1），这也将允许整合来自多个组织的数据 样品我们还将开发一个多视图框架，用于整合空间转录组学和 组织学成像用于改善细胞间相互作用的推断（目的2）。通过结合图像处理 算法的图像从重复样本的对齐，我们将扩展这个框架，整合 组织样本 我们的工具箱将适用于广泛的组织系统和更大的临床队列， 在理解健康发展过程中的空间动态方面具有变革性潜力， 基于疾病特异性细胞类型的空间组织的诊断和治疗策略 微环境