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

项目总结/摘要 研究：在这里，我们的目标是在单细胞组学的三个不同背景下使用交叉特征相关性（Aim 1） 解决单细胞RNAseq（scRNAseq）细胞类型鉴定中的关键问题，（Aim 2）发现 不对称细胞分裂（ACD）通过创建一个新的基因组学技术[单细胞ACD转录组学 （scACDt）]和（Aim 3）创建了跨人类组织的scRNAseq共表达网络的选集。（目标1） 我们已经发现，现状的细胞类型鉴定算法（1）不能将永生化细胞系鉴定为细胞系。 单个小区类型，以及（2）没有无偏机制来防止用户重复地“子聚类” 感兴趣的人群，这可能导致错误的发现。这些问题直接影响到 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性的 算法，似乎通过这些测试，但必须扩大我们的基准与更多的模拟研究， 更多的竞争算法和真实世界的数据集。（Aim 2）与Aim 1类似，我们预计反相关 载体将定义ACD的亚型。使用光电纳米流体芯片，我们将通过以下方式跟踪子细胞： 在显微镜下观察，并在细胞分裂后通过scRNAseq将它们与它们的转录组配对，以计算它们的转录水平。 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，以创建一个新的基因组学检测（scACDt）。（目标3） 最后，我们将使用交叉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。这将启用整个NHGRI的功能注释 GWAS目录使用图论方法从基因-基因相关性。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，以揭示 基因组与环境输入相结合，导致细胞多样化的机制 类型和表达式程序。通过整合数据科学、算法开发和基本分子 生物学，我的实验室将产生数据驱动的假设，并在实验台上验证它们。这些办法将 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- 经济和地理上多样化的实验室环境。我在这里提出的训练和目标将指导我 这些目标。环境：西奈山伊坎医学院（ISMMS）有一个既定的系统， 生物跟踪记录与访问和专业知识，在大规模可扩展的计算，这将是重要的， 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 专业知识来操作Aim 2的仪器。通过我们在研究所内的合作，我们在Mount的团队 西奈半岛是独特的位置（Aim 1）创建创新的算法，以确定从scRNAseq细胞类型，（Aim 2） 开始scACDt领域，（目标3）创建跨人体组织的scRNAseq共表达网络选集。

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.