Developing novel algorithms for spatial molecular profiling technologies

项目摘要 组织内mRNA和蛋白质的位置、时间和丰度是基本分子生物学的基础。 细胞功能和生理病理发展的机制。例如，研究 数千个基因在不同位置同时表达可以揭示胚胎发育的重要见解。 发展，高级心理功能的分子和细胞过程的合作， 肿瘤内异质性的分子基础和临床影响。空间领域的最新技术突破 分子谱分析（SMP），包括基于成像的技术和基于测序的技术， 能够对单细胞进行全面的分子表征，同时保留其空间和 形态学语境由于巨大的潜力，以加深我们的理解的分子机制， 细胞和生理表型，SMP技术正在迅速获得关注， 这些数据将被生成。然而，只有很少的计算方法被开发来分析这种情况。 丰富但复杂的数据，以及计算方法的局限性导致这些有价值的数据在很大程度上被 未充分利用本研究的首要目标是开发计算方法来分析SMP数据， 表征详细的分子空间分布并将这些信息与细胞表型相关联， 生理表型具体目标如下：1.开发新的空间统计方法， 表征基因表达的空间分布; 2.开发计算方法来表征细胞 空间结构及其与分子空间分布和疾病状态的关系; 3. 开发用户友好的软件，以方便研究人员在SMP数据分析和可视化。为了实现 为了实现这一目标，我们组建了一支强大的团队，在单细胞基因组学、组织成像 分析、空间建模、机器学习和软件开发。如果成功实施， 该平台将极大地方便用户理解生物学中的分子和细胞空间组织， 组织，并提供对潜在生物过程的全面见解。

Project Summary The location, timing and abundance of mRNA and proteins within a tissue underlie the basic molecular mechanisms of cell functions and physiological and pathological developments. For example, the study of expression of thousands of genes simultaneously at different locations could reveal great insights into embryo development, the cooperation of molecular and cellular processes for high-order mental functions, and the molecular basis and clinical impact of intra-tumor heterogeneity. Recent technology breakthroughs in spatial molecular profiling (SMP), including both imaging-based technologies and sequencing-based technologies, have enabled the comprehensive molecular characterization of single cells while preserving their spatial and morphological contexts. Due to the huge potential to deepen our understanding of the molecular mechanisms of cellular and physiological phenotypes, SMP technologies are rapidly gaining attention and a large amount of such data will be generated. However, there are only few computational methods developed to analyze such rich but complex data, and the limitations of computational methods lead to such valuable data being largely under-used. The overarching goal of this study is to develop computational methods to analyze SMP data to characterize detailed molecular spatial distributions and associate such information with cellular phenotypes and physiological phenotypes. The specific aims are as follows: 1. develop novel spatio-statistical methods to characterize spatial distributions of gene expression; 2. develop computational methods to characterize cellular spatial organizations and investigate their relationship with molecular spatial distributions and disease status; 3. develop user-friendly software to facilitate researchers in SMP data analysis and visualization. In order to achieve this goal, we have assembled a strong team with complementary expertise in single-cell genomics, tissue image analysis, spatial modelling, machine learning and software development. If implemented successfully, this platform will greatly facilitate users in understanding molecular and cellular spatial organization in biological tissues and provide comprehensive insights into the underlying biological processes.