• SPAC: a scalable, integrated e...

*Result*: SPAC: a scalable, integrated enterprise platform for end-to-end single cell spatial analysis of multiplexed tissue imaging.

Title:
SPAC: a scalable, integrated enterprise platform for end-to-end single cell spatial analysis of multiplexed tissue imaging.
Authors:
Liu F; Biomedical and Computational Science Directorate, Frederick National Laboratory for Cancer Research, Rockville, MD, USA., He R; Essential Software Inc., Gaithersburg, MD, USA.; Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA., Sheeley T; Department of Biochemistry, Purdue University, West Lafayette, IN, USA., Scheiblin D; Optical Microscopy and Analysis Laboratory, Frederick National Laboratory for Cancer Research; Leidos Biomedical Research Inc. for the National Cancer Institute, Frederick, MD, USA., Lockett SJ; Optical Microscopy and Analysis Laboratory, Frederick National Laboratory for Cancer Research; Leidos Biomedical Research Inc. for the National Cancer Institute, Frederick, MD, USA., Ridnour LA; Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA., Wink DA; Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA., Jensen M; Biomedical and Computational Science Directorate, Frederick National Laboratory for Cancer Research, Rockville, MD, USA., Cortner J; Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA., Zaki G; Biomedical and Computational Science Directorate, Frederick National Laboratory for Cancer Research, Rockville, MD, USA.
Source:
BioRxiv : the preprint server for biology [bioRxiv] 2025 Apr 08. Date of Electronic Publication: 2025 Apr 08.
Publication Type:
Journal Article; Preprint
Language:
English
Journal Info:
Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Comments:
Update in: BMC Bioinformatics. 2026 Jan 29;27(1):25. doi: 10.1186/s12859-025-06339-2.. (PMID: 41612174)
Grant Information:
75N91019D00024 United States CA NCI NIH HHS; P30 CA023168 United States CA NCI NIH HHS
Contributed Indexing:
Keywords: Spatial proteomics; high-performance computing; interactive visualization; multiplex imaging; scalable analysis; single-cell analysis; tumor microenvironment
Entry Date(s):
Date Created: 20250428 Date Completed: 20260203 Latest Revision: 20260203
Update Code:
20260203
PubMed Central ID:
PMC12026498
DOI:
10.1101/2025.04.02.646782
PMID:
40291751
Database:
MEDLINE

*Further Information*

*Background: Multiplexed tissue imaging enables the simultaneous detection of dozens of proteins at single-cell resolution, providing unprecedented insights into tissue organization and disease microenvironments. However, the resulting high-dimensional, gigabyte-scale datasets pose significant computational and methodological challenges. Existing analytical workflows, often fragmented between bespoke scripts and static visualizations, lack the scalability and user-friendly interfaces required for efficient, reproducible analysis. To overcome these limitations, we developed SPAC (analysis of SPAtial single-Cell datasets), a scalable, web-based ecosystem that integrates modular pipelines, high-performance computing (HPC) connectivity, and interactive visualization to democratize end-to-end single-cell spatial analysis applied to cellular positional data and protein expression levels.
Results: SPAC is built on a modular, layered architecture that leverages community-based and newly developed tools for single-cell and spatial proteomics analysis. A specialized Python package extends these functionalities with custom analysis routines and established software engineering practices. An Interactive Analysis Layer provides web-hosted pipelines for configuring and executing complex workflows, and scalability enhancements that support distributed or parallel execution on GPU-enabled clusters. A Real-Time Visualization Layer delivers dynamic dashboards for immediate data exploration and sharing. As a showcase of its capabilities, SPAC was applied to a 4T1 breast cancer model, analyzing a multiplex imaging dataset comprising 2.6 million cells. GPU acceleration reduced unsupervised clustering runtimes from several hours to under ten minutes, and real-time visualization enabled detailed spatial characterization of tumor subregions.
Conclusions: SPAC effectively overcomes key challenges in spatial single-cell analysis by streamlining high-throughput data processing and spatial profiling within an accessible and scalable framework. Its robust architecture, interactive interface and ease of access have the potential to accelerate biomedical research and clinical applications by converting complex imaging data into actionable biological and clinical insights.*

*Declarations The authors have declared that no conflict of interest exists.*