Scalable analysis of whole slide spatial proteomics with Harpy

Area of Science:

• Spatial Omics
• Proteomics Data Analysis
• Bioinformatics Workflows

Background:

• Current spatial proteomics workflows struggle with gigapixel datasets, lacking efficiency, scalability, and quality control.
• Limited interoperability with existing spatial omics analysis ecosystems hinders comprehensive data utilization.

Purpose of the Study:

• Introduce Harpy, a novel Python workflow for accelerated spatial proteomics data processing.
• Enhance efficiency, scalability, and quality control in analyzing large-scale spatial proteomics datasets.

Main Methods:

• Developed a Python workflow (Harpy) for accelerated processing of large spatial proteomics datasets.
• Implemented parallel processing for state-of-the-art segmentation and feature extraction.
• Integrated quality control steps and scalable clustering for cell and pixel analysis.

Main Results:

• Harpy demonstrated rapid application of segmentation and feature extraction on four datasets.
• Achieved up to 27x faster processing for scalable clustering and cell type identification.
• Enabled local or high-performance computing processing and visualization.

Conclusions:

• Harpy significantly improves the efficiency and scalability of spatial proteomics data analysis.
• The workflow integrates seamlessly with existing Python and R spatial single-cell analysis tools.
• Harpy provides robust quality control and facilitates rapid cell type identification from large datasets.