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.

项目概要 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空间组织至关重要。 发育过程和疾病形成。空间转录组技术的快速发展 能够对组织样本中数千个位置的完整转录组进行分析。此外 除了转录测量之外，该技术还获得了组织的配对组织学成像。 基因表达的空间解析图谱有可能带来突破性的发现， 然而，分析这些数据存在关键障碍，尤其是涉及复杂组织样本的数据。 在捕获位置（点）混合多种不同的细胞类型。分辨率低，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 互动。此外，整合多个组织样本的空间转录组数据集并不是 由于这种技术限制，很简单。用于分析高维的现有计算工具 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 指导空间转录组数据的分析。此外，当前的方法不考虑空间 组织学图像中嵌入的依赖性和信息。 该提案的总体目标是开发新颖的机器学习方法来分析新的 空间转录组数据波，无需配对单细胞数据作为参考。这些 创新框架将能够通过 数据的半监督反卷积（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来整合空间转录组和 用于改进细胞间相互作用推断的组织学成像（目标 2）。通过结合图像处理 用于对齐重复样本中的图像的算法，我们将扩展此框架以集成 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，并具有 在健康发展和指导过程中理解空间动态方面具有变革性的潜力 基于疾病特有细胞类型的空间组织的诊断和治疗策略 微环境。