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: 10275922
• 负责人: Jean Fan
• 金额: $ 38.79万
• 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/10275922
• 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 健康和疾病状态下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。健康和疾病之间的基因表达差异 细胞状态可表现为重要调节因子的表达量改变，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同种型。同样， mRNA在细胞内空间定位在调节局部蛋白质中起重要的调节作用 在疾病中可能被破坏的翻译。最后，细胞存在于不同的微环境中 在那里它们发出信号并与不同的细胞相互作用以维持组织内的稳态。定量 评估健康和患病细胞之间转录异质性的这些不同方面 状态对于我们理解疾病病因和疾病发病机制至关重要。 下一代测序和成像技术的最新进展使 研究人员在转录组水平上定量测量单个细胞中的基因表达， 不同的生物和疾病设置以高通量的方式。因此，执行任务的能力 计算分析正变得越来越重要，以便从这些数据中提取生物学见解。 数据我的研究计划开发统计方法和计算工具，以确定和 表征这些方面的转录和空间异质性，并定量评估 这一变化的后果。 在这里，我们将集中于开发计算工具来描述1）转录异质性 2）细胞内的亚细胞空间转录异质性，和3）空间- 组织中细胞之间的背景异质性。具体来说，我将根据我以前的经验 开发统一聚类分析的统计方法，以确定适当的正常 用于与来自转录异质性疾病状态的细胞进行比较的细胞。我将进一步加强 我以前的经验检测选择性剪接，以表征异常的选择性剪接内 单个细胞，并评估这种选择性剪接如何通过亚细胞 本地化我将进一步评估mRNA定位模式如何通过动态过程发生变化 如细胞周期和组织内神经胶质细胞的成熟，从而影响细胞的命运。最后，我将评估如何 组织内细胞的空间背景组织可能影响细胞-细胞通信网络。 虽然我们专注于在模型系统中建立概念证明，但对这些研究目标的追求将 导致新的计算方法的发展，可作为开源软件， 定制并应用于解决各种疾病环境中的基本生物学问题。