Spatialproteomics: an interoperable toolbox for analyzing highly multiplexed fluorescence image data

Area of Science:

• Biotechnology
• Computational Biology
• Pathology

Background:

• Highly multiplexed immunofluorescence (mIF) imaging offers high-resolution protein quantification in tissues.
• Analyzing mIF data is complex, requiring adaptable methods for segmentation, image processing, and cell classification.
• A unified, flexible computational toolbox is needed to streamline the mIF analysis workflow.

Purpose of the Study:

• To introduce 'spatialproteomics', a Python package designed for end-to-end analysis of mIF imaging data.
• To provide a scalable and adaptable solution for researchers working with large-scale spatial proteomics datasets.
• To facilitate the statistical characterization of cell types and their spatial distributions in complex tissue microenvironments.

Main Methods:

• Development of the 'spatialproteomics' Python package.
• Implementation of image processing steps including segmentation and cell-type classification.
• Synchronization of spatial coordinates across multiple data modalities for integrated analysis.

Main Results:

• Demonstrated the utility of 'spatialproteomics' on mIF images from reactive lymph nodes and B cell non-Hodgkin lymphomas (n=132).
• Showcased an end-to-end analysis pipeline from raw images to statistical insights on cell composition and spatial distribution.
• Validated the package's capability to process large Gigapixel whole-slide images.

Conclusions:

• 'spatialproteomics' provides a comprehensive and flexible solution for analyzing complex mIF imaging data.
• The package enables detailed characterization of cellular heterogeneity and spatial organization in disease contexts.
• Spatialproteomics significantly advances the accessibility and efficiency of spatial proteomics research.