Single-cell colocalization analysis using a deep generative model

• DOI: 10.1016/j.cels.2024.01.007
• Publication Date: 2024-02-21T15:32:31Z
• Authors: Yasuhiro Kojima; Shinji Mii; Shuto Hayashi; Haruka Hirose; Masato Ishikawa; Masashi Akiyama; Atsushi Enomoto; Teppei Shimamura
• Keywords: Colocalization; Single-Cell Analysis

1AbstractAnalyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, cellular responses to external stimuli, and their biological functions in diseases and tissues. However, high-throughput methods for identifying spatial proximity at single-cell resolution are practically unavailable. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers inter-cellular colocalization networks with single cell resolution by the integration of single cell and spatial transcriptomes. It segregates cell populations defined by the colocalization relationships and predicts cell-cell interactions between colocalized single cells. DeepCOLOR could identify plausible cell-cell interaction candidates in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2 by reconstructing spatial colocalization maps at single-cell resolution. DeepCOLOR is typically applicable to studying cell-cell interactions in any spatial niche. Our newly developed computational framework could help uncover molecular pathways across single cells connected with colocalization networks.