An Open-Source Workflow for Semi-Automated Spatial Profiling of Multiplex Immunofluorescent Images

Area of Science:

• Biomedical Imaging
• Computational Biology
• Pathology

Background:

• Multiplex immunofluorescence microscopy enables detailed cellular and spatial analysis in tissues.
• Advances in computational methods enhance data extraction from multiplex images.
• Existing spatial analysis pipelines often lack accessibility and reproducibility due to technical complexity or proprietary software.

Purpose of the Study:

• To present accessible, standardized protocols for semi-automated spatial analysis of multiplex immunofluorescence images using free, open-source software.
• To detail methods for quantifying cellular organization and tissue structure in multiplex images.
• To facilitate reproducible spatial profiling for translational research and biomarker discovery.

Main Methods:

• Utilized QuPath software for semi-automated spatial analysis of 2D multiplex immunofluorescence images.
• Employed object and pixel classification, cell distance, and cluster measurements for spatial profiling.
• Incorporated training images, basic scripting for batch processing, and a browser-based tool for data consolidation and reporting.

Main Results:

• Developed and validated protocols for spatial analysis of multiplex immunofluorescence images using QuPath.
• Demonstrated objective and standardized analysis through training images and batch processing.
• Provided a pipeline for automated reporting of spatial data, including cell plots, measurements, and clustering.

Conclusions:

• QuPath offers an accessible, reproducible, and scalable solution for spatial analysis of multiplex immunofluorescence microscopy images.
• The presented protocols enhance the quantification of cellular organization and tissue structure.
• This approach strengthens the integration of spatial data into translational research, biomarker discovery, and mechanistic studies.