Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen, William M Mauck, Shiwei Zheng, Andrew Butler, Maddie J Lee, Aaron J Wilk, Charlotte Darby, Michael Zager, Paul Hoffman, Marlon Stoeckius, Efthymia Papalexi, Eleni P Mimitou, Jaison Jain, Avi Srivastava, Tim Stuart, Lamar M Fleming, Bertrand Yeung, Angela J Rogers, Juliana M McElrath, Catherine A Blish, Raphael Gottardo, Peter Smibert, Rahul Satija
Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA; New York Genome Center, New York, NY 10013, USA.
Technology Innovation Lab, New York Genome Center, New York, NY 10013, USA.
Cell. 2021 Jun 24;184(13):3573-3587.e29. doi: 10.1016/j.cell.2021.04.048. Epub 2021 May 31.
The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
多种模态的同步测量是单细胞基因组学一个令人兴奋的前沿领域，需要能够基于多模态数据定义细胞状态的计算方法。在此，我们引入“加权最近邻”分析，这是一个无监督框架，用于了解每种数据类型在每个细胞中的相对效用，从而实现对多种模态的综合分析。我们将我们的方法应用于一个包含211,000个人类外周血单个核细胞（PBMC）的CITE-seq数据集，该数据集的抗体面板扩展到228种，以构建循环免疫系统的多模态参考图谱。多模态分析显著提高了我们解析细胞状态的能力，使我们能够识别和验证以前未报告的淋巴细胞亚群。此外，我们展示了如何利用这个参考图谱快速映射新数据集，并解释对疫苗接种和2019年冠状病毒病（COVID-19）的免疫反应。我们的方法代表了一种广泛适用的策略，用于分析单细胞多模态数据集，并超越转录组，朝着细胞身份的统一多模态定义迈进。
