SpatialKNifeY (SKNY): Extending from spatial domain to surrounding area to identify microenvironment features with...

Area of Science:

• Computational biology
• Bioinformatics
• Cancer research

Background:

• Single-cell spatial omics analysis is crucial for understanding biological functions within a microenvironment.
• Current bioinformatic methods for microenvironment analysis are limited in detecting histological morphology and its spatial extension.

Purpose of the Study:

• To develop an image-processing toolkit, SpatialKNifeY (SKNY), for enhanced spatial omics analysis.
• To identify spatial domains, analyze tumor microenvironment (TME) characteristics, and understand cancer progression mechanisms.

Main Methods:

• Developed SpatialKNifeY (SKNY), an image-processing toolkit.
• Applied SKNY to spatial transcriptomic data from breast cancer and metastatic colorectal cancer.
• Utilized domain detection, clustering, trajectory estimation, and spatial extension to the TME.

Main Results:

• SKNY identified tumor spatial domains and extended them to the TME.
• Trajectory estimation results aligned with known cancer progression mechanisms.
• Observed tumor vascularization and immunodeficiency in mid- to late-stage TME.
• Clustered spatial domains from metastatic colorectal cancer patients based on TME characteristics.

Conclusions:

• SKNY facilitates the determination of microenvironment functions and mechanisms.
• The toolkit aids in cataloguing TME features for better cancer research.
• SKNY enhances the analysis of spatial omics data for biological insights.