Computational toolbox for spatial transcriptomic analysis of complex tissues

项目总结 绘制细胞的空间组织及其在组织中的通信图对于理解 发展和疾病形成的过程。空间转录技术的快速发展 已经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 对于转录测量，这项技术还可以获得组织的配对组织成像。 基因表达的空间分辨图谱有可能揭开突破性发现的面纱， 然而，在分析这些数据方面存在关键障碍，特别是在涉及到 在捕获位置(斑点)混合多种不同类型的细胞。分辨率低，很难辨别 不同的单元类型，这对于后续分析其空间组织、动力学和 互动。此外，跨多个组织样本整合空间转录数据集并不是 由于这一技术限制，这一点很简单。用于分析高维数据的现有计算工具 基因组数据要么是为单细胞分辨率数据构建的，要么是需要配对的单细胞转录数据来 指导空间转录数据分析。此外，目前的方法不考虑空间 组织学图像中嵌入的依赖项和信息。 这项提议的总体目标是开发新的机器学习方法来分析新的 波的空间转录数据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 半监督数据去卷积(目标1)，还将允许整合来自多个组织的数据 样本。我们还将开发一个多视图框架，用于整合空间转录和 用于改进细胞间相互作用推断的组织成像(目标2)。通过结合图像处理 用于从复制样本中对齐图像的算法，我们将扩展该框架以用于集成 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 在健康发展和引导中理解空间动力学具有变革的潜力 基于疾病特定细胞类型的空间组织的诊断和治疗策略 微环境。

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.