SPACEc: A Streamlined, Interactive Python Workflow for Multiplexed Image Processing and Analysis

Yuqi Tan; Tim Noah Kempchen; Martin Becker; Maximilian Haist; Dorien Feyaerts; Jiaqi Liu; Marieta Ioana Toma; Yang Xiao; Graham Su; Andrew J. Rech; Michael Hölzel; Rong Fan; John W. Hickey; Garry P. Nolan

doi:10.1038/s41467-025-65658-3

BioinformaticsNature CommunicationsNov 27, 2025

Peer-reviewed journal article

SPACEc: A Streamlined, Interactive Python Workflow for Multiplexed Image Processing and Analysis

Yuqi Tan iD, Tim Noah Kempchen iD, Martin Becker iD, Maximilian Haist iD, Dorien Feyaerts iD, Jiaqi Liu iD, Marieta Ioana Toma iD, Yang Xiao iD, Graham Su iD, Andrew J. Rech iD, Michael Hölzel iD, Rong Fan iD, John W. Hickey iD, Garry P. Nolan iD

Abstract

Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface.

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