Motivation Spatial transcriptomics enables the dissection of tissue heterogeneity within native contexts, yet current platforms are inherently constrained by high sparsity and low signal-to-noise ratios that obscure fine-grained biological signals. Current efforts to recover these signals are limited by image registration dependencies or the inherent context-blindness of implicit neural representations. Results We introduce the Cell Positioning System (CPS), a context-aware implicit neural representation framework designed to map physical coordinates to high-fidelity spatial transcriptomics via a privileged multi-scale context distillation strategy. CPS treats multi-scale tissue niches as privileged information, employing a teacher network equipped with a multi-scale niche attention mechanism to capture adaptive biological interactions during training. This structural knowledge is explicitly distilled into a student coordinate network, enabling the generation of context-aware expression landscapes solely from spatial coordinates during inference. Benchmarking on the DLPFC dataset demonstrates that CPS achieves state-of-the-art performance in spatial and gene expression imputation and denoising. Furthermore, CPS enables super-resolution to recover high-resolution mouse brain anatomical details and offers interpretability by identifying the scale effective size of biological interactions within human breast cancer tissues. Finally, the framework exhibits superior scalability for large-scale datasets with linear computational complexity. Availability Software is available online at https://github.com/tju-zl/CPS.

Publication:

Bioinformatics

http://dx.doi.org/10.1093/bioinformatics/btag218

Author:

Lei Zhang

Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China

Kai Cao

Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States

Shuqiao Zheng

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

Shu Liang

Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China

Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, 201210, China

Corresponding authors

mail: sliang@tongji.edu.cn

Lin Wan

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

Corresponding authors

Email: lwan@amss.ac.cn