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.

项目摘要/摘要 研究：在这里，我们旨在在单个单元格中使用三种不同情况下使用交叉功能相关性到（AIM1） 解决单细胞RNASEQ（SCRNASEQ）细胞类型识别中的关键问题，（AIM2）发现的亚型 通过创建新的基因组技术的非对称细胞分裂（ACD）[单细胞ACD转录组学 （SCACDT）]和（AIM3）在人体组织中创建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）无法识别永生的细胞系 单个单元格类型，（2）没有公正的机制来防止用户反复“子群集” 感兴趣的人群，这可能导致错误的发现。这些问题对 对所有scrnaseq的分析，因此需要紧急解决。我们创建了一个基于反相关的 似乎通过这些测试的算法，但必须通过更多的仿真研究扩展我们的基准测试， 更多竞争算法和现实世界数据集。 （AIM2）类似于AIM1，我们预计这是反相关的 向量将定义ACD的亚型。使用选择的纳米流体芯片，我们将通过 显微镜检查并通过scrnaseq将它们与细胞分裂后的转录组配对以计算 子细胞之间mRNA分离的不对称性。我们以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法（SCACDT）。 （AIM3） 最后，我们将使用交叉功能相关性建立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。这将实现整个NHGRI的功能注释 GWAS目录使用基因基因相关性的图理论方法。职业目标：我的未来 实验室将使用跨学科的方法来开发新的基因组技术和算法来发现 基因组与环境输入集成的机制导致细胞的潜水员阵列 类型和表达程序。通过综合数据科学，算法开发和基本分子 生物学，我的实验室将生成数据驱动的假设并在板凳上验证它们。这些方法会 广泛影响所有生物学，而不是单一疾病。最后，一个重要的目标是建立一个社会 - 经济和地理上不同的实验室环境。我在这里提出的培训和目标将指导我 这些目标。环境：西奈山（ISMMS）的伊坎医学院有一个已建立的系统 生物学往绩记录具有访问和巨大可扩展计算方面的专业知识，这对于 AIMS1和3。此外，ISMMS是唯一拥有Beacon平台的学术机构，更不用说拥有 为AIM2操作此工具的专业知识。通过我们在研究所内的合作，我们在芒特的团队 Sinai独特地位于（AIM1）创建创新算法以识别Scrnaseq的细胞类型（AIM2） 开始SCACDT领域，（AIM3）在人体组织中创建了Scrnaseq共表达网络的选集。