Machine learning methods for the analysis and modeling of spatial proteomics data

项目摘要 一张全面的人体3D分子图将提供有价值的信息，即 对于研究与人类相关的过程和生物系统，如发育、衰老和 疾病。为了实现构建这样一张地图的目标，多学科联盟，如人类细胞 阿特拉斯(HCA)和人类生物分子图集计划(HuBMAP)已经开发出分析技术 单细胞的转录组和蛋白质组。在这些技术中，用于单细胞空间的方法 蛋白质组学只是最近才发展起来的；例如，最近在多路成像方面的进展已经 使每个细胞能够分析数十到数百种蛋白质。而单细胞空间的生成 蛋白质组学数据有望彻底改变我们研究细胞间相互作用的能力，它还提出了几个 计算和建模挑战。细胞分割仍然是一个长期存在的问题，通常 每个生物成像实验都需要量身定做的解决方案。即使在分割单元之后，也可以使用表达式 推断细胞类型和组织的价值是具有挑战性的。目前还没有标准化的方法 开发了联合整合空间和分子信息来分析复杂生物的 来自丰富的空间蛋白质组学数据集的相互作用。 该项目建议开发计算方法，以提供全面的解决方案 利用空间蛋白质组学数据构建人体3D分子图谱。我们共同假设 从空间蛋白质组学数据集中分析空间和分子关系捕获了 否则就会被错过。在目标1中，将开发一种用于对细胞分割的标记进行排序的方法 (RAMCES)，以便选择用于细胞分割的最佳蛋白质标记。在目标2中，统一的 结合蛋白质表达和细胞邻域信息的学习框架将是 构建的目的是将细胞分配给表型并揭示空间模式。在目标3中，方法将是 开发用于在空间蛋白质组学数据中推断细胞-细胞和蛋白质-蛋白质相互作用。开发的方法 在这个项目中，将整合到HuBMAP处理管道中，以分析空间蛋白质组数据集。 我们还将应用和验证这些方法，使用来自胰腺淋巴结的数据来描述个体 在有和没有1型糖尿病的情况下，以前所未有的规模分析与疾病相关的变化。 总之，完成提出的目标将使HuBMAP项目能够发现新的生物相互作用 并扩大了我们在单细胞水平上对分子相互作用的理解。 该提案概述了一项培训计划，该计划包括有指导的研究培训、课程作业和 职业发展。在培训期间开发的知识和技能将是必要的 申请人的长期目标是成为一名成功的独立科学家，在 机器学习、计算机科学和生物学。

Project Summary A comprehensive 3D molecular map of the human body would provide valuable information that is critical for studying human-related processes and biological systems such as development, aging, and disease. Towards this goal of constructing such a map, multidisciplinary consortia such as the Human Cell Atlas (HCA) and the Human BioMolecular Atlas Program (HuBMAP) have developed technologies for profiling the transcriptome and proteome in single cells. Out of these technologies, methods for single-cell spatial proteomics have only very recently been developed; for example, recent advances in multiplexed imaging have enabled the profiling of tens to hundreds of proteins per cell. While the generation of single-cell spatial proteomics data promise to revolutionize our ability to study cell-cell interactions, it also raises several computational and modeling challenges. Cell segmentation remains a long-standing problem that usually requires tailored solutions for each bioimaging experiment. Even after cells are segmented, using expression values to infer cell type and organization is challenging. There are currently no standardized methods developed that jointly incorporate spatial and molecular information to analyze the complex biological interactions from rich spatial proteomics datasets. This project proposes to develop computational methods to provide a comprehensive solution for the use of spatial proteomics data for building 3D molecular maps of the human body. We hypothesize that jointly profiling spatial and molecular relationships from spatial proteomics datasets captures biological patterns that would otherwise be missed. In Aim 1, a method will be developed for RAnking Markers for CEll Segmentation (RAMCES) in order to choose the optimal protein markers to use for cell segmentation. In Aim 2, a unified learning framework that incorporates both protein expression and cell neighborhood information will be constructed in order to assign cells to phenotypes and reveal spatial patterns. In Aim 3, methods will be developed to infer cell-cell and protein-protein interactions in spatial proteomics data. The methods developed in this project will be integrated into the HuBMAP processing pipeline to analyze spatial proteomics datasets. We will also apply and validate these methods using data from pancreatic lymph nodes that profile individuals with and without Type 1 diabetes to analyze changes associated with the disease at an unprecedented scale. Together, completing the proposed aims will enable the HuBMAP project to uncover new biological interactions in cells and tissues and expand our understanding of molecular interactions at a single-cell level. This proposal outlines a training plan that comprises of mentored research training, coursework, and professional development. The knowledge and skillset developed during the training period will be necessary for the applicant's long-term goal of becoming a successful independent scientist working at the interface of machine learning, computer science, and biology.