Spatial Proteomics

Spatial Proteomics enables end-to-end spatial proteomic image analysis through a modular, containerized workflow that maps the location and abundance of proteins within cells and tissues.

Key Features:

• Cell Segmentation: The workflow performs cell segmentation as the initial analytical step for identifying cellular boundaries in imaging data.
• Batch Correction: The workflow includes batch correction to address technical variation across samples.
• Unsupervised Clustering: The workflow applies unsupervised clustering to group cells based on protein expression patterns.
• Cluster Validation on Image: The workflow enables validation of clusters directly on the image to confirm spatial patterns.
• Cell Type Clustering Visualization: The workflow provides visualization of cell type clustering results for biological interpretation.
• QuPath Integration: The workflow integrates with QuPath for spatial proteomics analysis.
• Containerized Steps: Individual workflow steps are containerized to ensure reproducible execution.
• Modular Architecture: The workflow has a modular design that allows customization of complex analyses.

Scientific Applications:

• Cancer Tissue Microarray Analysis: The workflow was demonstrated on a tissue microarray stained by high-plex immunohistochemistry containing various cancer and tissue cell types to assess broad applicability to different biopsy and tissue types.
• Spatial Protein Expression Mapping: The workflow provides spatially resolved views of protein expression and localization within cells and tissues.
• Cell Type Identification in Tissues: The workflow supports identification and characterization of cell types based on spatial proteomic profiles in complex tissue samples.

Methodology:

The workflow consists of cell segmentation, batch correction, unsupervised clustering, validation of clusters on the image, and cell type clustering results visualization, with individual steps containerized for reproducibility.