Cesaro Giulia, Nagai James Shiniti, Gnoato Nicolò, Chiodi Alice, Tussardi Gaia, Klöker Vanessa, Musumarra Carmelo Vittorio, Mosca Ettore, Costa Ivan G, Di Camillo Barbara, Calura Enrica, Baruzzo Giacomo
Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131, Padova, Italy.
RWTH Aachen Medical Faculty, Institute for Computational Genomics, Pauwelsstrasse 19, 52074, Aachen, Germany.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf280.
Recent advancements in high-resolution and high-throughput sequencing technologies have significantly enhanced the study of cell-cell communication inference using single-cell and spatial transcriptomics data. Over the past 6 years, this growing interest has led to the development of more than 100 bioinformatics tools and nearly 50 resources, primarily in the form of ligand-receptor databases. These tools vary widely in their requirements, scoring approaches, ability to infer inter- and/or intra-cellular communication, assumptions, and limitations. Similarly, cell-cell communication resources differ in many aspects, mainly in the number of annotated interactions, species coverage, and their focus on inter-cellular signaling or both inter- and intra-cellular communication. This abundance and diversity create challenges in identifying compatible and suitable tools and resources to meet specific user needs. In this collaborative effort, we aim to provide a comprehensive report on the current state of cell-cell communication analysis derived from single-cell or spatial transcriptomics data. The report reviews existing methods and resources, addressing all relevant aspects from the user's perspective. It also explores current limitations, pitfalls, and unresolved issues in cell-cell communication inference, offering an aggregated analysis of the existing literature on the topic. Furthermore, we highlight potential future directions in the field and consolidate the collected knowledge into CCC-Catalog (https://sysbiobig.gitlab.io/ccc-catalog), a centralized web platform designed to serve as a hub for bioinformaticians and researchers interested in cell-cell communication inference.
高分辨率和高通量测序技术的最新进展显著促进了利用单细胞和空间转录组学数据进行细胞间通讯推断的研究。在过去6年中,这种日益增长的兴趣推动了100多种生物信息学工具和近50种资源的开发,主要形式为配体-受体数据库。这些工具在要求、评分方法、推断细胞间和/或细胞内通讯的能力、假设和局限性等方面差异很大。同样,细胞间通讯资源在许多方面也存在差异,主要体现在注释相互作用的数量、物种覆盖范围以及对细胞间信号传导或细胞间和细胞内通讯两者的关注重点上。这种丰富性和多样性给识别兼容且合适的工具和资源以满足特定用户需求带来了挑战。在这项合作工作中,我们旨在提供一份关于从单细胞或空间转录组学数据得出的细胞间通讯分析现状的综合报告。该报告回顾了现有方法和资源,从用户角度探讨了所有相关方面。它还探讨了细胞间通讯推断中当前的局限性、陷阱和未解决的问题,对该主题的现有文献进行了综合分析。此外,我们强调了该领域未来可能的发展方向,并将收集到的知识整合到CCC-Catalog(https://sysbiobig.gitlab.io/ccc-catalog)中,这是一个集中式网络平台,旨在作为对细胞间通讯推断感兴趣的生物信息学家和研究人员的中心枢纽。