SCORPy: Lowering the computational barrier to reproducible multiplexed imaging spatial single cell proteomics analysis
Spatially resolved single-cell proteomic imaging technologies, including cyclic immunofluorescence (CycIF), generate high-dimensional data, critical for tissue-scale biological analysis. However, single-cell analysis remains computationally demanding, lacks standardization across platforms and is often inaccessible to experimental biologists without programming expertise. Here we present SCORPy…
Single-cell proteomic imaging technologies, such as cyclic immunofluorescence (CycIF), produce high-dimensional data crucial for analyzing tissues at the scale of individual cells. Nevertheless, analyzing single-cell data remains computationally intensive, lacks consistency across different platforms, and can be challenging for experimental biologists who do not have programming skills.
To address these issues, researchers have developed SCORPy (Single-Cell proteOmics Research Platform), a desktop application that functions independently of other systems. This software provides a complete, user-friendly workflow for analyzing single-cell proteomic data acquired through imaging experiments.
SCORPy introduces several methodological enhancements specifically tailored for handling multiplexed imaging data. These include a background correction strategy that takes into account the varying exposure times of different cycles, and a normalization framework that ensures consistency in the signal distribution across various markers. This enables researchers to compare data from different experiments more effectively.
These features are integrated with a quality control system, interactive thresholding, and cell phenotyping capabilities using a hierarchical cell reference library. This library can be customized to suit specific research questions. The software also offers downstream compositional and spatial analyses within a single interface.
Researchers can incorporate sample-level metadata at any stage of the workflow. This allows for integrative analyses, which can help in generating publication-ready visualizations. By incorporating such robust preprocessing techniques into an easily accessible interface, SCORPy significantly lowers the technical barriers associated with spatial single-cell proteomics analysis, making it more widely available to experimental biologists.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
