Computational methods for delineating subcellular and cellular spatial transcriptional heterogeneity along developmental trajectories

沿着发育轨迹描绘亚细胞和细胞空间转录异质性的计算方法

• 批准号: 10474625
• 负责人: Jean Fan
• 金额: $ 40.89万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10474625
• 关键词: Address; Alternative Splicing; Biological; Biological Models; Cell Communication; Cell Cycle; Cell physiology; Cells; Cluster Analysis; Computer Analysis; Computer software; Computing Methodologies; Data; Development; Disease; Etiology; Gene Expression; Genetic Transcription; Goals; Heterogeneity; Homeostasis; Image; Imaging technology; Individual; Measures; Messenger RNA; Molecular; Neuroglia; Normal Cell; Pathogenesis; Pattern; Play; Population; Process; Protein Isoforms; Proteins; Quantitative Evaluations; Research; Research Personnel; Role; Signal Transduction; Spliced Genes; Tissues; Translations; Variant; Work; computerized tools; differential expression; experience; insight; next generation sequencing; open source; programs; single-cell RNA sequencing; transcriptome

Project Summary Biological differences between cells in healthy and diseased states are molecularly encoded in part by coordinated differences in gene expression. Gene expression differences between healthy and disease cell states may manifest as altered expression magnitudes of important regulatory factors, as well as aberrant alternative splicing of genes to produce protein isoforms with divergent functions. Likewise, the spatial localization of mRNAs within cells play important regulatory roles in modulating local protein translation that may be disrupted in disease. And finally, cells exist within diverse microenvironments where they signal and interact with different cells to maintain homeostasis within tissues. Quantitatively evaluating these different aspects of transcriptional heterogeneity between cells in healthy and diseased states is paramount to our understanding of disease etiology and the mechanisms for disease pathogenesis. Recent advancements in next-generation sequencing and imaging technologies are enabling investigators to quantitatively measure gene expression in individual cells at transcriptome-scale across different biological and disease settings in a high-throughput manner. As such, the ability to perform computational analysis is becoming increasingly paramount in order to extract biological insights from such data. My research program develops statistical approaches and computational tools to identify and characterize these aspects of transcriptional and spatial heterogeneity and quantitatively evaluate the functional consequences of this variation. Here, we will focus on developing computational tools to delineate 1) transcriptional heterogeneity across populations of cells, 2) subcellular spatial transcriptional heterogeneity within cells, and 3) spatial- contextual heterogeneity among cells in tissues. Specifically, I will build on my previous experience developing statistical approaches for unified clustering analysis in order to identify the appropriate normal cells for comparison with cells from transcriptionally heterogeneous diseased states. I will further build on my previous experience detecting alternative splicing to characterize aberrant alternative splicing within individual cells and assess how such alternative splicing may impact cellular function through subcellular localization. I will further assess how mRNA localization patterns may change through dynamic processes such as the cell-cycle and neuroglia maturation within tissues to impact cell-fate. Finally, I will assess how the spatial-contextual organization of cells within tissues may impact cell-cell communication networks. Although we focus on establishing proof of concept in model systems, pursuit of these research goals will result in the development of new computational methods available as open-source software that can be tailored and applied to address fundamental biological questions in a variety of disease settings.

项目摘要 健康和患病状态的细胞之间的生物学差异部分是由分子编码的 通过基因表达的协调差异。健康人群与疾病人群的基因表达差异 细胞状态可能表现为重要调节因子的表达水平改变，以及 基因的异常选择性剪接，以产生具有不同功能的蛋白质亚型。同样， MRNAs在细胞内的空间定位在调节局部蛋白质方面起着重要的调节作用 翻译可能会因疾病而中断。最后，细胞存在于不同的微环境中 它们向不同的细胞发出信号并与之相互作用，以维持组织内的动态平衡。数量上 评估健康和疾病患者细胞间转录异质性的这些不同方面 状态对于我们理解疾病的病因和疾病的发病机制是至关重要的。 新一代测序和成像技术的最新进步使 研究人员将在转录组范围内定量测量单个细胞的基因表达 以高通量方式设置不同的生物学和疾病环境。因此，表演的能力 为了从中提取生物学见解，计算分析正变得越来越重要 数据。我的研究项目开发了统计方法和计算工具，以识别和 描述转录和空间异质性的这些方面，并定量评估 这一变化的功能后果。 在这里，我们将专注于开发计算工具来描绘转录异质性 跨细胞群体，2)细胞内亚细胞空间转录异质性，以及3)空间- 组织中细胞间的背景异质性。具体地说，我将以我以前的经验为基础 开发统计方法进行统一的聚类分析，以确定适当的正态分布 用于与转录异质性疾病状态的细胞进行比较。我将在此基础上 我以前的经验是检测选择性剪接来表征异常的选择性剪接 并评估这种选择性剪接如何通过亚细胞影响细胞功能 本地化。我将进一步评估如何通过动态过程改变mrna的本地化模式。 例如，细胞周期和神经胶质细胞在组织内的成熟影响细胞的命运。最后，我将评估如何 组织内细胞的空间-背景组织可能会影响细胞间的通讯网络。 尽管我们专注于在模型系统中建立概念证明，但追求这些研究目标将 结果是开发了新的计算方法，可以作为开源软件使用，可以 量身定做并应用于解决各种疾病环境中的基本生物学问题。