Quantitative Definition of Cell Identity by Integrating Transcriptomic, Epigenomic, and Spatial Features of Individual Cells

通过整合单个细胞的转录组、表观基因组和空间特征来定量定义细胞身份

• 批准号: 10428484
• 负责人: Joshua Welch
• 金额: $ 23.51万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-03 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10428484
• 关键词: Address; Algorithms; Beds; Benchmarking; Biological; Biological Assay; Cataloging; Catalogs; Cell Differentiation process; Cell Nucleus; Cells; Chromatin; Collaborations; DNA; Data; Data Set; Disease; E-learning; Female; Foundations; Gene Expression; Genes; Genomic approach; Human; Human body; In Situ; Individual; Location; Maps; Measurement; Measures; Medicine; Memory; Mesenchymal Stem Cells; Methods; Modality; Modeling; Modification; Molecular; Morphology; Mus; Pathologic; Performance; Property; Protocols documentation; Publishing; Regulation; Regulatory Element; Resolution; Scientist; Speed; Substantia nigra structure; Techniques; Technology; Time; Tissues; Variant; Visual Cortex; Work; base; biological systems; biomedical scientist; cell type; computer framework; data integration; data sharing; epigenome; epigenomics; experimental study; flexibility; genomic data; human subject; in situ imaging; learning algorithm; male; man; new technology; novel strategies; progenitor; prototype; sex; sexual dimorphism; single cell sequencing; single-cell RNA sequencing; skeletal; skeletal stem cell; transcriptomics; trend

Quantitative Definition of Cell Identity by Integrating Transcriptomic, Epigenomic, and Spatial Features of Individual Cells Abstract Defining the molecular features that identify the myriad specialized subsets of cells within the human body is foundational to a genomic approach to medicine. High-throughput single-cell sequencing has recently opened the door to comprehensively characterizing the molecular identities of human cells. Multiple types of features contribute to cell identity, including gene expression, epigenomic modifications, and spatial location within a tissue, but it is not currently possible to simultaneously measure all of these modalities within the same single cells. Each experimental context and measurement modality provides a different glimpse into cellular identity, and how to combine these views into a unified picture of cell identity remains unclear. Computational integration of multiple single cell experiments performed on different individual cells pro- vides a way forward despite these challenges. However, existing approaches are not sufficiently robust to inte- grate single cell data across the full range of biological contexts, nor flexible enough to leverage the unique properties of different single cell modalities, and require recalculating results each time new data points arrive. We recently developed LIGER, a highly robust and flexible algorithm that can integrate single cell data sharing a common set of gene-centric features across a wide range of biological contexts and modalities. A key property of our approach is the ability to identify both shared and dataset-specific features that define cell types across biological contexts. Additionally, LIGER is built upon a powerful matrix factorization framework that is readily extensible. In preliminary analysis, we showed that our approach can identify cell-type-specific sexually dimorphic gene expression and human subject variation, map cell types across species, and jointly define cell types from multiple single cell modalities that share corresponding features. Here, we build upon LIGER in several ways to develop a comprehensive framework that can most ef- fectively leverage the unique aspects of transcriptomic, epigenomic, and spatial data for quantitative definition of cell identity. First, we develop an “online learning” algorithm that readily scales to millions of cells and can continually incorporate new data, allowing iterative refinement of cell identity (Aim 1). Second, we develop novel approaches to integrate single-cell modalities that assay different types of features (such as genes and intergenic peaks) and contain missing data (as in spatial transcriptomic datasets), enabling inference of epige- nomic regulation and cross-modal data imputation (Aim 2). In collaboration with biomedical scientists, we apply our approach to newly generated single cell transcriptomic and single cell epigenomic data from mouse skele- tal stem cells and experimentally validate the predicted linkage between these data modalities (Aim 3). Our work addresses a crucial gap in analysis methods for single cell genomic data and paves the way for a quanti- tative definition of cell identity that incorporates multiple types of cellular features.

通过整合单个细胞的转录组学、表观基因组学和空间特征来定量定义细胞身份 摘要 定义识别人体内无数专门细胞亚群的分子特征 身体是基因医学的基础。高通量单细胞测序最近 开启了全面表征人类细胞分子身份的大门。多种类型的 特征有助于细胞身份，包括基因表达、表观基因组修饰和空间位置 但是目前不可能在同一组织内同时测量所有这些模态。 单细胞每个实验背景和测量方式提供了不同的一瞥细胞 身份，以及如何将这些观点联合收割机组合成一个统一的细胞身份的图片仍然不清楚。 对不同单个细胞进行的多个单细胞实验的计算积分 尽管面临这些挑战，但仍指明了前进的道路。然而，现有的方法并不足以鲁棒地整合， 在整个生物学背景下的单细胞数据，也没有足够的灵活性来利用独特的 不同的单细胞模式的属性，并且每次新的数据点到达时都需要重新计算结果。 我们最近开发了LIGER，这是一种高度稳健和灵活的算法，可以整合单细胞数据 在广泛的生物学背景和模式中共享一组共同的基因中心特征。一 我们的方法的一个关键特性是能够识别定义单元的共享特征和特定于子网的特征 生物学背景下的类型。此外，LIGER是建立在一个强大的矩阵分解框架 其易于扩展。在初步分析中，我们表明我们的方法可以识别细胞类型特异性 性二态性基因表达和人类个体变异，跨物种绘制细胞类型， 从共享相应特征的多个单细胞模态定义细胞类型。 在这里，我们以几种方式建立在LIGER之上，以开发一个全面的框架， 有效地利用转录组学、表观基因组学和空间数据的独特方面进行定量定义 细胞的身份。首先，我们开发了一种“在线学习”算法，可以轻松扩展到数百万个单元格，并且可以 不断纳入新数据，允许迭代细化细胞身份（目标1）。第二，我们发展 新的方法来整合单细胞模式，测定不同类型的特征（如基因和 基因间峰）并包含缺失数据（如在空间转录组数据集中），从而能够推断表观- 经济法规和跨模态数据插补（目标2）。与生物医学科学家合作，我们应用 我们的方法，新产生的单细胞转录组和单细胞表观基因组数据从小鼠胚胎， tal干细胞，并通过实验验证这些数据模式之间的预测联系（目标3）。我们 这项工作解决了单细胞基因组数据分析方法中的一个关键空白，并为定量分析铺平了道路。 结合多种类型的细胞特征的细胞身份的典型定义。