SPACEc: a streamlined, interactive Python workflow for multiplexed image processing and analysis

Yuqi Tan (), Tim N. Kempchen, Martin Becker, Maximilian Haist, Dorien Feyaerts, Jiaqi Liu, Marieta Toma, Yang Xiao, Graham Su, Andrew J. Rech, Michael Hölzel, Rong Fan, John W. Hickey and Garry P. Nolan ()
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Yuqi Tan: Stanford University, Department of Microbiology and Immunology
Tim N. Kempchen: Stanford University, Department of Microbiology and Immunology
Martin Becker: University of Rostock, Institute for Visual and Analytic Computing
Maximilian Haist: Stanford University, Department of Microbiology and Immunology
Dorien Feyaerts: Perioperative and Pain Medicine, Department of Anesthesia
Jiaqi Liu: Duke University, Department of Biomedical Engineering
Marieta Toma: University Hospital Bonn, Institute of Pathology
Yang Xiao: Columbia University, Department of Biomedical Engineering
Graham Su: Yale University, Department of Biomedical Engineering
Andrew J. Rech: University of Pennsylvania, Department of Pathology and Laboratory Medicine
Michael Hölzel: University of Bonn, Institute of Experimental Oncology, Medical Faculty, University Hospital Bonn
Rong Fan: Yale University, Department of Biomedical Engineering
John W. Hickey: Stanford University, Department of Microbiology and Immunology
Garry P. Nolan: Stanford University, Department of Microbiology and Immunology

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract Multiplexed imaging has transformed our ability to study tissue organization by capturing thousands of cells and molecules in their native context. However, these datasets are enormous, often comprising tens of gigabytes per image, and require complex workflows that limit their broader use. Current tools are often fragmented, inefficient, and difficult to adopt across disciplines. Here we show that SPACEc, a scalable Python platform, streamlines spatial imaging analysis from start to finish. The platform integrates image processing, cell segmentation, and data preprocessing into a single workflow, while improving computational performance through parallelization and GPU acceleration. We introduce innovative methods, including patch proximity analysis, to more accurately map local cellular neighborhoods and interactions. SPACEc also simplifies advanced approaches such as deep-learning annotation, making them accessible through an intuitive interface. By combining efficiency, accuracy, and usability, this platform enables researchers from diverse backgrounds to gain deeper insights into tissue architecture and cellular microenvironments.

Date: 2025
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DOI: 10.1038/s41467-025-65658-3

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