Collaborative Research: SCH: Integrated Analysis of Single-Cell and Spatially Resolved Omics Data

细胞中表达的基因决定了细胞的功能和行为。在特定时间在细胞中表达的一组基因受到高度调控，并可能受到许多因素的影响，包括细胞的空间环境。因此，理解细胞在健康和疾病中的功能和行为的一个基本问题是，在健康和疾病细胞中，这些基因表达水平可能会有什么不同。最近的技术进步，包括单细胞rna-seq和空间分辨组学，现在允许我们在保持原始组织内细胞空间排列的同时，测量哪些基因在哪些细胞中同时表达数百到数千个基因和数千到数百万个细胞。这些技术进步和由此产生的数据为更深入地了解不同的基因表达模式如何定义不同的细胞类型或细胞状态，这些不同的细胞类型和细胞状态在组织中是如何组织的，以及健康组织和患病组织中的基因表达和空间组织模式可能有何不同提供了机会。然而，由于这些数据集庞大而复杂，需要新的分析工具以可解释的方式提取相关的生物学见解。该项目旨在开发和测试这些工具，特别强调建立简单的解释，以促进潜在的临床翻译到成本效益的程序。PIs计划将研究与教育相结合，通过让学生参与研究来培训学生，特别是年轻女性，并与公众分享开发的课程材料和计算机代码。细胞特性和异质性由多种内在的分子和空间背景因素塑造。在其固有的空间环境中分析细胞及其分子状态提供了一种连接细胞组织和功能的方式。最近的技术进步使转录组和蛋白质组的全基因组分子图谱能够以单细胞和接近单细胞的分辨率以空间分辨的方式进行。然而，仍然需要新的统计方法和计算工具来对这些高维空间分辨组学测量进行建模和分析，以便以可解释的方式提取相关的生物学见解。这项研究旨在从这些数据模式提供的丰富信息中推断出一个压缩的、但可解释的摘要，该摘要基于捕捉潜在细胞转录异质性的最小基因集。科学的方法是基于统计空间建模和信息论，设计算法来近似随机变量组的熵，并使用整数规划方法来搜索最能代表最初观察到的整个集合的变量的小子集。研究开发和验证将基于多个数据集，其中一些是公开可用的，另一些是由威尔-康奈尔医学的合作者共享的。这些数据集将涉及最近推出的三种主要的空间分辨转录模式(维西姆、食典、水鱼)，并将包含使用免疫组织化学的传统成像模式。了解如何使用这些模式中的一些来增强在其他模式中的部分观察也是研究计划的一部分。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

What genes are expressed in a cell shapes a cell's function and behavior. The set of genes that are expressed in a cell at a given time is highly regulated and can be impacted by many factors, including the cell's spatial context. Consequently, a fundamental question for understanding the function and behavior of cells in both health and disease is how such gene expression levels may be different in healthy versus diseased cells. Recent technological advances, including single-cell RNA-seq and spatially resolved omics, now allow us to measure what genes are expressed in what cells simultaneously for hundreds to thousands of genes and thousands to millions of cells while preserving the spatial arrangement of cells within the original tissue. Such technological advances and the resulting data generated present the opportunity to develop a deeper understanding of how different patterns of gene expression define different cell-types or cell-states, how these different cell-types and cell-states are organized within tissues, and how gene expression and spatial organizational patterns may be different in health versus diseased tissues. However, as these datasets are huge and complex, new analytical tools are required to extract relevant biological insights in an interpretable manner. This project aims to develop and test these tools, with a particular emphasis on building simple interpretations to facilitate potential clinical translations to cost-effective procedures. The PIs plan to integrate the research with education, train students, especially young women, by involving them in the research, and share the developed curricula materials and computer code with the public.Cellular identity and heterogeneity are shaped by a multitude of intrinsic molecular and spatial-contextual factors. Profiling cells and their molecular states within their native spatial context provides a modality to connect cellular organization and function. Recent technological advances are enabling such genome-wide molecular profiling of the transcriptome and proteome in a spatially resolved manner at single-cell and near-single-cell resolution. However, new statistical methods and computational tools are still needed to model and analyze these high-dimensional spatially resolved omics measurements in order to extract relevant biological insights in an interpretable manner. This research aims to deduce from the abundant information that these data modalities provide, a compressed, but interpretable, summary, based on minimal sets of genes that capture the underlying cellular transcriptional heterogeneity. The scientific approach is based on statistical spatial modeling and information theory, designing algorithms for approximating the entropy of groups of random variables, and using integer programming methods to search for small subsets of variables that represent best the whole collection initially observed. The research development and validation will be based on multiple datasets, some of them being publicly available and some shared by collaborators at Weill-Cornell Medicine. These datasets will involve three of the main spatially resolved transcriptomic modalities recently introduced (VISIUM, CODEX, MERFISH), and will contain traditional imaging modalities using immunohistochemistry as well. Understanding how some of these modalities can be used to enhance partial observation in other modalities is also part of the research program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.