CAREER: Statistical approaches and computational tools for analyzing spatially-resolved single-cell transcriptomics data

健康和患病的组织都由代表许多不同细胞类型和细胞状态的大量相互作用的细胞组成。测序和成像技术的快速进步使以空间分辨的方式描绘成百上千个单个细胞和一小群细胞中成百上千个基因的这些差异成为可能。然而，仍然需要统计方法和计算工具来对这些高维空间分辨率测量进行建模和分析，以便提取相关的生物学见解。该项目将开发统计方法来分析这些空间分辨的转录组数据集，以便表征空间基因表达模式，并将它们与潜在的细胞表型和功能联系起来，特别是在组织中。这样的特征将有助于深入了解空间背景和组织如何影响细胞表型和功能。所开发的方法还将作为开放源码软件提供给科学界，并适用于其他生物学领域。最后，通过这个项目开发的包括案例研究教程在内的教学材料将被用于一个动手学习课程，以吸引年轻女孩参与计算机科学。在技术层面上，为了分析组织的空间分辨转录数据，该项目将使用一种密度不可知的空间位置信息编码，使用Voronoi镶嵌来适应组织常见的细胞密度的变化。空间自相关和互相关分析将被用来识别具有显著空间模式的基因，这些模式可能进一步指示细胞间的通信。基于卷积神经网络的方法将在组织内细胞分割中整合mRNAs的亚细胞空间组织，并与先前的RNA速度模型相结合，以推断时间动力学和描绘组织内潜在的细胞迁移。为了探究基因表达变化与细胞表型的关系，神经网络也将被应用于直接从组织的组织染色图像中预测分子特征。最后，将开发一个客户端网络应用程序，以便能够在浏览器内对空间分辨的转录组数据进行这种探索和分析。有关该项目正在进行的结果的更多信息，请参阅：https://jef.works/NSF_CAREERThis奖项反映了美国国家科学基金会的法定使命，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。

Both healthy and diseased tissues are comprised of a multitude of interacting cells representing many different cell-types and cell-states. Rapid advances in sequencing and imaging technologies are making it possible to profile these differences for hundreds to thousands of genes in hundreds to thousands of individual cells and small groups of cells in a spatially resolved manner. However, statistical methods and computational tools are still needed to model and analyze these high-dimensional spatially resolved measurements in order to extract relevant biological insights. This project will develop statistical approaches for analyzing these spatially resolved transcriptomics datasets in order to characterize spatial gene expression patterns and associate them with underlying cellular phenotypes and functions, particularly within tissues. Such characterization will help provide insights into how spatial context and organization may impact cellular phenotype and function. The developed approaches will also be made available to the scientific community as open-source software and be applicable to other biological contexts. Finally, instructional material including case study tutorials developed through this project will be used in a hands-on-learning course to engage young girls in computer science. On a technical level, to analyze spatially resolved transcriptomics data of tissues, this project will use a density agnostic encoding of spatial positional information using Voronoi tessellation to accommodate variations in cell density common to tissues. Spatial autocorrelation and cross-correlation analyses will be used to identify genes with significant spatial patterns that may further be indicative of cell-cell communication. Convolutional neural network-based approaches will be applied to integrate the subcellular spatial organization of mRNAs in cell segmentation within tissues and be combined with previous RNA velocity models in order to infer temporal dynamics and delineate potential cellular migration within tissues. To interrogate how gene expression variation may be associated with cellular phenotype, neural networks will also be applied to predict molecular features directly from histological staining images of tissues. Finally, a client-side web application will be developed to enable such exploration and analysis of spatially resolved transcriptomics data within the browser. For more information regarding the ongoing results of this project, please see: https://jef.works/NSF_CAREERThis 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.