Cross-Feature Correlations Define Cell Types, Asymmetric Cell Division, and Variant Networks

跨特征相关性定义细胞类型、不对称细胞分裂和变体网络

• 批准号: 10040076
• 负责人: Scott R Tyler
• 金额: $ 14.32万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-07 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10040076
• 关键词: Address; Algorithms; Benchmarking; Biological Assay; Biological Models; Biology; Catalogs; Cell Culture Techniques; Cell Line; Cell Lineage; Cell division; Cells; Collaborations; Communities; Complex; Consensus; Data; Data Science; Data Set; Development; Disease; Environment; Environmental Risk Factor; Event; Failure; Future; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Transcription; Genome; Genomics; Geography; Goals; Grant; Graph; High Performance Computing; Housekeeping Gene; Human; Institutes; Laboratories; Messenger RNA; Methods; Microscopy; Molecular Biology; Mutation; National Human Genome Research Institute; Pathology; Pathway Analysis; Phase; Population; Process; Property; Protocols documentation; Research; Resolution; Resources; Systems Biology; T-Lymphocyte; Technology; Testing; Tissues; Training; Variant; Work; algorithm development; base; career; cell immortalization; cell type; daughter cell; differential expression; feature selection; fluorescence microscope; genome wide association study; guided inquiry; human disease; human tissue; innovation; instrument; interest; live cell microscopy; medical schools; nanofluidic; novel; open source; prevent; programs; segregation; simulation; single cell technology; single-cell RNA sequencing; socioeconomics; stem; transcriptome; transcriptomics; vector

Project Summary/Abstract Research: Here we aim to use cross-feature correlations in three different contexts in single cell omics to (Aim1) solve critical issues in single cell RNAseq (scRNAseq) cell type identification, (Aim2) discover subtypes of asymmetric cell division (ACD) by the creation of a new genomics technology [single cell ACD transcriptomics (scACDt)], and (Aim3) create an anthology of scRNAseq co-expression networks across human tissues. (Aim1) We have found that status quo cell type identification algorithms (1) cannot identify immortalized cell lines as a single cell type, and (2) have no unbiased mechanism to prevent a user from repeatedly ‘sub-clustering’ populations of interest, which can result in false discoveries. These problems have immediate implications for the analysis of all scRNAseq, thus requiring an urgent resolution. We have created an anti-correlation-based algorithm that appears to pass these tests, but must expand our benchmarking with more simulation studies, more competing algorithms, and real-world datasets. (Aim2) Similar to Aim1, we anticipate that anti-correlated vectors will define subtypes of ACD. Using an opto-electric nano-fluidic chip, we will track daughter cells by microscopy and pair them with their transcriptomes by scRNAseq following cell division to calculate the asymmetry in mRNA segregation between daughter cells. We have previously performed all needed functions to achieve these goals; here we propose to merge these protocols to create a new genomics assay (scACDt). (Aim3) Lastly, we will use cross-feature correlations to build consensus tissue and pan-tissue co-expression networks from publicly available human scRNAseq datasets. This will enable functional annotation of the entire NHGRI GWAS catalogue using graph theoretic approaches from gene-gene correlations. Career Goals: My future laboratory will use transdisciplinary approaches to develop new genomic technologies and algorithms to uncover the mechanisms by which the genome, integrated with environmental input, results in a diverse array of cell types and expression programs. Through integrated data science, algorithm development, and basic molecular biology, my lab will generate data-driven hypotheses and validate them at the bench. These approaches will broadly impact all of biology rather than on a single disease. Lastly, an important goal is to create a socio- economic and geographically diverse lab-environment. The training and aims I propose here will guide me to these goals. Environment: The Icahn School of Medicine at Mount Sinai (ISMMS) has an established systems biology track record with access to and expertise in massively scalable computation, which will be important for Aims1&3. Additionally, ISMMS is the only academic institute to own the Beacon platform let alone have the expertise to operate this instrument for Aim2. Through our collaborations within the institute, our team at Mount Sinai is uniquely situated to (Aim1) create innovative algorithms to identify cell types from scRNAseq, (Aim2) begin the scACDt field, (Aim3) create an anthology of scRNAseq co-expression networks across human tissues.

项目概要/摘要 研究：在这里，我们的目标是在单细胞组学的三种不同背景下使用跨特征相关性（目标1） 解决单细胞 RNAseq (scRNAseq) 细胞类型鉴定中的关键问题，(目标 2) 发现细胞亚型 通过创建新的基因组学技术实现不对称细胞分裂（ACD）[单细胞ACD转录组学] (scACDt)] 和 (Aim3) 创建了跨人体组织的 scRNAseq 共表达网络的选集 (Aim1)。 我们发现现状细胞类型识别算法（1）无法将永生化细胞系识别为 单细胞类型，(2) 没有公正的机制来防止用户重复“子聚类” 感兴趣的人群，这可能导致错误的发现，这些问题对我们有直接的影响。 对所有 scRNAseq 的分析，因此需要紧急解决方案，我们创建了基于反相关的解决方案。 算法似乎通过了这些测试，但必须通过更多的模拟研究来扩展我们的基准测试， 更多竞争算法和现实世界数据集（目标 2）与目标 1 类似，我们预计反相关。 向量将定义 ACD 的亚型 使用光电纳米流体芯片，我们将通过以下方式追踪子细胞。 显微镜观察，并在细胞分裂后通过 scRNAseq 将它们与转录组配对，以计算 子细胞之间 mRNA 分离的不对称性我们之前已经执行了所有需要的功能。 实现这些目标；在这里，我们建议合并这些协议以创建新的基因组学测定（scACDt）（Aim3）。 最后，我们将使用跨特征相关性来构建共识组织和泛组织共表达网络 来自公开可用的人类 scRNAseq 数据集，这将使整个 NHGRI 的功能注释成为可能。 GWAS 目录使用基因-基因相关性的图论方法：我的未来。 实验室将使用跨学科方法开发新的基因组技术和算法来揭示 基因组与环境输入整合产生多种细胞的机制 通过综合数据科学、算法开发和基础分子。 生物学，我的实验室将生成数据驱动的假设并在实验室验证它们。 最后，一个重要的目标是创建一个社会- 我在这里提出的培训和目标将指导我实现经济和地理上多样化的实验室环境。 这些目标。 环境：西奈山伊坎医学院 (ISMMS) 拥有一套既定的系统。 具有大规模可扩展计算能力和专业知识的生物学记录，这对于 目标 1 和 3 此外，ISMMS 是唯一拥有 Beacon 平台的学术机构，更不用说拥有 Beacon 平台了。 通过我们在研究所内的合作，我们在 Mount 的团队拥有为 Aim2 操作该仪器的专业知识。 西奈半岛拥有独特的地理位置，可以 (Aim1) 创建创新算法来从 scRNAseq 中识别细胞类型，(Aim2) 开始 scACDt 领域，（目标 3）创建跨人体组织的 scRNAseq 共表达网络的选集。