SCORPy: Lowering the computational barrier to reproducible multiplexed imaging spatial single cell proteomics analysis

Gerber, Z.; Simard, S.; Kolipaka, H.; Drouin, Z.; Sevigny, J.; Pourcel, V.; del Carmen Crespo Oliva, C.; Tate, B.; Mouzakitis, K.; Placet, M.; Jean, D.; Deuel, K.; Pavlatos, E.; Sturgill, E.; Pucilowska, J.; Mills, G. B.; Labrie, M.

doi:10.64898/2026.08.24.746722

bioRxiv · 10.64898/2026.08.24.746722

SCORPy: Lowering the computational barrier to reproducible multiplexed imaging spatial single cell proteomics analysis

Gerber, Z.·Simard, S.·Kolipaka, H.·Drouin, Z.·Sevigny, J.·Pourcel, V.·del Carmen Crespo Oliva, C.·Tate, B.·Mouzakitis, K.·Placet, M.·Jean, D.·Deuel, K.·Pavlatos, E.·Sturgill, E.·Pucilowska, J.·Mills, G. B.·Labrie, M.

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Abstract

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 proteOmics Research Platform), a standalone, cross-platform desktop application that provides an end-to-end, code-free workflow for the analysis of single-cell proteomic data extracted from imaging experiments. SCORPy introduces methodological advances for preprocessing multiplexed imaging data: an exposure-aware, cycle-matched background correction strategy, and a normalization framework that harmonizes signal distributions across markers while enabling batch correction across experiments. These approaches are integrated with quality control, interactive thresholding and cell phenotyping using a hierarchical cell reference library, and downstream compositional and spatial analyses within a unified interface. Sample-level metadata can be incorporated throughout the workflow to support integrative analyses and facilitate generation of publication-ready visualizations. By combining robust preprocessing methods with an accessible implementation, SCORPy reduces computational barriers and promotes broader adoption of spatial single-cell proteomics analysis.

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Gerber, Z., Simard, S., Kolipaka, H., Drouin, Z., Sevigny, J., Pourcel, V., del Carmen Crespo Oliva, C., Tate, B., Mouzakitis, K., Placet, M., Jean, D., Deuel, K., Pavlatos, E., Sturgill, E., Pucilowska, J., Mills, G. B., Labrie, M.. 2026-08-25. SCORPy: Lowering the computational barrier to reproducible multiplexed imaging spatial single cell proteomics analysis. https://doi.org/10.64898/2026.08.24.746722

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