SpaRx: Elucidate single-cell spatial heterogeneity of drug responses for personalized treatment
Ziyang TangiD, Xiang LiuiD, Zuotian LiiD, Tonglin ZhangiD, Baijian YangiD, Jing SuiD, Qianqian SongiD
Abstract
Spatial cellular heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx SMI, MERSCOPE, and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels, and transcriptomics coverage. Further application of SpaRx to the state-of-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance, and identifies personalized drug targets and effective drug combinations. Key PointsO_LIWe have developed a novel graph-based domain adaption model named SpaRx, to reveal the heterogeneity of spatial cellular response to different types of drugs, which bridges the gap between pharmacogenomics knowledgebase and single-cell spatial transcriptomics data. C_LIO_LISpaRx is developed tailored for single-cell spatial transcriptomics data and is provided available as a ready-to-use open-source software, which demonstrates high accuracy and robust performance. C_LIO_LISpaRx uncovers that tumor cells located in different areas within tumor lesion exhibit varying levels of sensitivity or resistance to drugs. Moreover, SpaRx reveals that tumor cells interact with themselves and the surrounding microenvironment to form an ecosystem capable of drug resistance. C_LI
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 2
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- STDrug enables spatially informed personalized drug repurposing from spatial transcriptomics
Yiwen Yang, Thatchayut Unjitwattana, … Lana X. Garmire·bioRxiv·Apr 7, 2026
- CSGDA: A Cell State-Guided Graph Domain Adaptation Network for Single-Cell Drug Response Prediction
Yan Fen, 谢东贵, … Yu‐An Huang·BioinformaticsPeer-reviewed·Sep 28, 2026
- Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis
C. Shiau, J. Cao, … W. L. Hwang·bioRxiv·Jun 28, 2023
- Identifying drug-resistant individual cells within tumors by semi-supervised transfer learning from bulk to single-cell transcriptome
Kaishun Huang, Hui Liu·bioRxiv·Aug 18, 2024
- Connecting spatial regions to clinical phenotypes by transferring knowledge from bulk patient data
Bayarbaatar Amgalan, Ermin Hodzic, … Teresa M. Przytycka·bioRxiv·Dec 12, 2025
- Domain-adaptation deep learning models do not outperform simple baseline models in single-cell anti-cancer drug sensitivity prediction
Michael Bohl, Marina Esteban‐Medina, … Kerstin Lenhof·bioRxiv·Feb 25, 2026