How to get the most out of your cancer spatial transcriptomics data

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Spatial transcriptomics (ST) is revolutionizing cancer research by enabling detailed study of tumor microenvironments.
• A wide array of ST platforms and analysis tools are available, creating complexity for researchers.
• Extracting biological insights from ST data requires understanding diverse analytical approaches.

Purpose of the Study:

• To provide a guide for cancer researchers on analyzing spatial transcriptomics data.
• To cover common and emerging analysis techniques for ST data in cancer research.
• To assist researchers in navigating the complexities of ST data analysis workflows.

Main Methods:

• Review of current spatial transcriptomics analysis approaches.
• Highlighting commonly used tools for ST data visualization and interpretation.
• Discussion of emerging machine learning and spatial statistics techniques for ST data.

Main Results:

• Spatial transcriptomics enables visualization of gene expression within tissue context.
• Analysis techniques allow identification of cell neighborhoods and quantification of tumor heterogeneity.
• ST data facilitates mapping of cell-cell signaling networks within the tumor microenvironment.

Conclusions:

• This review offers a roadmap for cancer researchers beginning their spatial transcriptomics data analysis journey.
• It covers essential analysis strategies and highlights emerging techniques for deeper biological insights.
• Considerations for experimental design and team assembly are discussed for successful ST projects.