CAREER: Advancing the Bioinformatic Infrastructure and Methodology for Single-cell RNA Sequencing

最近引入的单细胞RNA测序(scRNA-seq)通过揭示单个基因组范围的基因表达水平，即转录水平，逐个细胞地揭示了生物科学的研究。研究人员首次能够评估和比较单个细胞的转录本，例如来自同一组织中不同微环境的两个细胞，或处于不同发育状态的两个神经元，以比较正常细胞和正在经历退化过程的细胞。然而，这项技术仍然存在限制其量化能力的限制。研究人员面临着一个实验性的权衡，要么为了更高的准确性探索更少的细胞，要么为了更广泛的基因表达调查探索更多的细胞。此外，scRNA-seq技术仍然很新，以至于存在各种实验方案，它们受到不同偏见和错误的影响，这给数据验证、实验室之间的交叉参考以及来自公共存储库的数据的标准化和整合带来了障碍。该项目将通过推进scRNA-seq数据分析，并提供新的关键工具来研究特定状态下的分子机制，包括癌症和神经疾病等疾病状态，从而加强使用这种类型的分析方法在许多学科中进行的生物学研究。由于scRNA-seq技术仍然是新技术，该项目计划走在教育和方法开发的前沿，向各级统计和生物学受训人员传播信息。教学活动将利用个体细胞分析的兴奋，通过scRNA-seq数据，提高本科生对统计分析的理解，并吸引未被充分代表的少数族裔学生学习量化科学。该项目将为设计实验和分析scRNA-seq分析产生的数据建立必要的计算基础设施。将开发一个统计和计算模拟器，使研究人员能够以显著较低的成本设计更有效的scRNA-seq实验。另一个目标是开发一个scRNA-seq数据库，将单个细胞转录按细胞类型的层次分类组织起来，为计算方法的开发提供基准资源。在模拟器和数据库的协助下，将开发一套新的统计和计算方法，以提高scRNA-seq数据分析的分辨率和准确性。这些方法将成为研究人员的有效生物信息学工具，以便他们可以量化单个细胞中的全基因组转录，以细胞亚型分辨率识别差异表达的基因，并比较单个人和小鼠细胞的转录。本项目开发的基础设施和方法将能够并加速从scRNA-seq数据中进行科学发现，并将适用于scRNA-seq领域的实验学家和计算学家。该项目的结果，包括研究论文、软件包和视频教程，将在http://jsb.ucla.edu.This上提供，该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。

The recent introduction of single-cell RNA-sequencing (scRNA-seq) has revolutionized research in the biological sciences by revealing the individual genome-wide gene expression response levels, i.e., transcriptomes, cell by cell. For the first time, researchers are able to evaluate and compare the transcriptomes of individual cells, for instance from two cells in the same tissue but different microenvironments, or two neurons in different developmental states, of to compare a normal cell and one that is undergoing a degenerative process. However, the technology still has limitations that restrict its quantitative power. Researchers face an experimental trade-off between exploring either fewer cells for higher accuracy or a greater number of cells for a broader survey of gene expression. Further, scRNA-seq technology is still so new that a variety of experimental protocols exist that are subject to different bias and errors, presenting a hurdle for data validation, cross-referencing between labs, and normalization and integration of data from public repositories. This project will enhance biological research across the many disciplines using this type of assay, by advancing scRNA-seq data analysis and providing new critical tools for investigating molecular mechanisms underlying particular states, including disease states like cancers and neurological disorders. As scRNA-seq technology is still new, the project plans to be on the frontier of education and method development, disseminating information to all levels of trainees in statistics and biology. Teaching activities will capitalize on the excitement of individual cell analysis, through scRNA-seq data, to heighten undergraduate students' understanding of statistical analysis and to attract underrepresented minority students to study quantitative sciences.This project will establish a necessary computational infrastructure for the design of experiments and analysis of data that arise from scRNA-seq assays. A statistical and computational simulator will be developed to enable researchers to design more effective scRNA-seq experiments at a significantly lower cost. Another goal is to develop an scRNA-seq database that organizes individual cell transcriptomes in a hierarchical taxonomy of cell types, providing a benchmark resource for computational method development. Assisted by the simulator and the database, a new suite of statistical and computational methods will be developed to increase the resolution and accuracy of scRNA-seq data analysis. Those methods will serve as effective bioinformatic tools for researchers such that they may quantify genome-wide transcripts in individual cells, identify differentially expressed genes at a cell-subtype resolution, and compare the transcriptomes of individual human and mouse cells. The infrastructure and methods developed in this project will enable and expedite scientific discoveries from scRNA-seq data and will be applicable to both experimentalists and computationalists in the scRNA-seq field. Results of this project, including research papers, software packages, and video tutorials, will be made available at http://jsb.ucla.edu.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.