A categorization of spatial transcriptomics methods for cell-cell communication analysis

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Cell-cell communication (CCC) is crucial for tissue regulation.
• Spatial transcriptomics provides local gene expression context, enhancing CCC inference over single-cell RNA-seq.
• Numerous CCC inference methods have been rapidly developed.

Purpose of the Study:

• To review and categorize recent CCC inference methods for spatial transcriptomics.
• To provide a structured guide for selecting appropriate CCC methods based on research goals.
• To highlight the strengths, limitations, and features of various CCC inference approaches.

Main Methods:

• Systematic review of 33 recent CCC inference methods.
• Categorization of methods into three main frameworks: network inference, microenvironment modeling, and spatial signaling analysis.
• Analysis of analytical frameworks, strengths, and limitations of each method.

Main Results:

• The reviewed methods were organized into three categories: cell-type/single-cell resolution networks, microenvironment-driven variability/modules, and spatially informed signaling associations/structures.
• Key technical features, strengths, and limitations of each method were identified.
• A structured guide for method selection was developed.

Conclusions:

• The review provides a comprehensive overview of current CCC inference methods in spatial transcriptomics.
• The structured guide facilitates informed selection of CCC methods tailored to specific research questions.
• This work supports the effective application of CCC inference in advancing spatial transcriptomics research.