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、表观基因组和空间特征来定量定义细胞身份 摘要 确定识别人类体内无数专门化细胞亚群的分子特征 身体是基因组医学方法的基础。高通量单细胞测序最近 为全面表征人类细胞的分子特性打开了大门。多种类型的 特征有助于细胞同一性，包括基因表达、表观基因组修饰和空间定位 在组织内，但目前不可能同时测量同一组织内的所有这些模式 单细胞。每种实验背景和测量方式都提供了对细胞的不同一瞥 细胞身份，以及如何将这些观点结合成细胞身份的统一图景仍不清楚。 在不同的单个细胞上进行的多个单细胞实验的计算积分。 尽管面临这些挑战，但仍有一条前进的道路。然而，现有的方法还不够健壮，不能插入- 在所有生物环境中收集单细胞数据，也不够灵活，无法利用独特的 不同单细胞模式的特性，并且需要在每次新的数据点到达时重新计算结果。 我们最近开发了Liger，这是一种高度健壮和灵活的算法，可以集成单元格数据 在广泛的生物学背景和模式中共享一组共同的以基因为中心的特征。一个 我们方法的关键特性是能够识别定义像元的共享要素和特定于数据集的要素 不同生物背景下的类型。此外，Liger构建在一个强大的矩阵分解框架之上 这是很容易扩展的。在初步分析中，我们表明我们的方法可以识别特定类型的细胞 性别二态基因表达和人类受试者变异，绘制跨物种的细胞类型，并联合 定义共享相应特征的多个单细胞形态的细胞类型。 在这里，我们以Liger为基础，以多种方式开发一个全面的框架，该框架可以最大限度地- 有效地利用转录、表观基因组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，它可以轻易地扩展到数百万个细胞，并且可以 不断合并新数据，允许反复改进小区身份(目标1)。第二，我们发展 集成分析不同类型特征的单细胞模式的新方法(如基因和 基因间峰)和包含缺失数据(如在空间转录数据集中)，从而能够推断EIGE- 经济调控和跨模式数据推算(目标2)。通过与生物医学科学家的合作，我们应用 我们对从小鼠骨骼中新产生的单细胞转录和单细胞表观基因组数据的方法- Tal干细胞，并从实验上验证这些数据模式之间的预测联系(目标3)。我们的 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差距，并为建立一种 包含多种类型的细胞特征的细胞身份的定性定义。

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.