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

职业：推进单细胞 RNA 测序的生物信息学基础设施和方法

• 批准号: 1846216
• 负责人: Jingyi Jessica Li
• 金额: $ 59.96万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846216&HistoricalAwards=false
• 关键词: CAREER; Advancing; Bioinformatic; Infrastructure; Methodology

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.

最近引入的单细胞RNA测序（scRNA-seq）通过揭示个体全基因组基因表达反应水平，即，一个细胞接一个细胞的转录组。研究人员第一次能够评估和比较单个细胞的转录组，例如来自相同组织但不同微环境的两个细胞，或不同发育状态的两个神经元，或比较正常细胞和正在经历退化过程的细胞。然而，该技术仍然存在限制其量化能力的局限性。研究人员面临着一个实验权衡，要么探索更少的细胞以获得更高的准确性，要么探索更多的细胞以进行更广泛的基因表达调查。此外，scRNA-seq技术仍然是如此新，以至于存在各种实验方案，这些方案会受到不同的偏见和错误的影响，这对数据验证，实验室之间的交叉引用以及来自公共存储库的数据的标准化和整合构成了障碍。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，包括癌症和神经系统疾病等疾病状态，从而加强使用这种类型的检测方法进行的许多学科的生物学研究。由于scRNA-seq技术仍然是新的，该项目计划处于教育和方法开发的前沿，向统计学和生物学的各级受训者传播信息。通过scRNA-seq数据，利用细胞个体分析带来的兴奋感，提高本科生对统计分析的理解，吸引少数民族学生学习定量科学。本项目将为scRNA-seq分析的实验设计和数据分析建立必要的计算基础设施。将开发一个统计和计算模拟器，使研究人员能够以更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，该数据库以细胞类型的分层分类法组织单个细胞转录组，为计算方法开发提供基准资源。在模拟器和数据库的帮助下，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。这些方法将作为研究人员的有效生物信息学工具，使他们可以量化单个细胞中的全基因组转录本，以细胞亚型分辨率识别差异表达的基因，并比较单个人类和小鼠细胞的转录组。该项目中开发的基础设施和方法将使scRNA-seq数据能够加速科学发现，并将适用于scRNA-seq领域的实验学家和计算学家。该项目的成果，包括研究论文、软件包和视频教程，将在www.example.com上提供http://jsb.ucla.edu.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Simulating Single-Cell Gene Expression Count Data with Preserved Gene Correlations by scDesign2

• DOI: 10.1089/cmb.2021.0440
• 发表时间: 2022-01
• 期刊: Journal of computational biology : a journal of computational molecular cell biology
• 作者: Tianyi Sun;Dongyuan Song;W. Li;J. Li

DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features.

• DOI: 10.1126/sciadv.aba6784
• 发表时间: 2020-11
• 期刊: Science advances
• 影响因子: 13.6
• 作者: Lyu J;Li JJ;Su J;Peng F;Chen YE;Ge X;Li W
• 通讯作者: Li W

Cellular Heterogeneity-Adjusted cLonal Methylation (CHALM) improves prediction of gene expression.

• DOI: 10.1038/s41467-020-20492-7
• 发表时间: 2021-01-15
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Xu J;Shi J;Cui X;Cui Y;Li JJ;Goel A;Chen X;Issa JP;Su J;Li W
• 通讯作者: Li W

Clipper: p-value-free FDR control on high-throughput data from two conditions.

• DOI: 10.1186/s13059-021-02506-9
• 发表时间: 2021-10-11
• 期刊: Genome biology
• 影响因子: 12.3
• 作者: Ge X;Chen YE;Song D;McDermott M;Woyshner K;Manousopoulou A;Wang N;Li W;Wang LD;Li JJ
• 通讯作者: Li JJ

The concurrence of DNA methylation and demethylation is associated with transcription regulation.

• DOI: 10.1038/s41467-021-25521-7
• 发表时间: 2021-09-06
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Shi J;Xu J;Chen YE;Li JS;Cui Y;Shen L;Li JJ;Li W
• 通讯作者: Li W