Neural Proteomics Fields for Super-resolved Spatial Proteomics Prediction

• URL: http://arxiv.org/abs/2508.17389v1
• Date: Sun, 24 Aug 2025 14:53:12 GMT
• Title: Neural Proteomics Fields for Super-resolved Spatial Proteomics Prediction
• Authors: Bokai Zhao, Weiyang Shi, Hanqing Chao, Zijiang Yang, Yiyang Zhang, Ming Song, Tianzi Jiang,
• Abstract summary: We introduce the novel task of spatial super-resolution for sequencing-based spatial (seq-SP)<n>Neural Proteomics Fields (NPF) formulates seq-SP as a protein reconstruction problem in continuous space by training a dedicated network for each tissue.<n>NPF achieves state-of-the-art performance with fewer learnable parameters, underscoring its potential for advancing spatial research.
• Score: 8.424059461071614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial proteomics maps protein distributions in tissues, providing transformative insights for life sciences. However, current sequencing-based technologies suffer from low spatial resolution, and substantial inter-tissue variability in protein expression further compromises the performance of existing molecular data prediction methods. In this work, we introduce the novel task of spatial super-resolution for sequencing-based spatial proteomics (seq-SP) and, to the best of our knowledge, propose the first deep learning model for this task--Neural Proteomics Fields (NPF). NPF formulates seq-SP as a protein reconstruction problem in continuous space by training a dedicated network for each tissue. The model comprises a Spatial Modeling Module, which learns tissue-specific protein spatial distributions, and a Morphology Modeling Module, which extracts tissue-specific morphological features. Furthermore, to facilitate rigorous evaluation, we establish an open-source benchmark dataset, Pseudo-Visium SP, for this task. Experimental results demonstrate that NPF achieves state-of-the-art performance with fewer learnable parameters, underscoring its potential for advancing spatial proteomics research. Our code and dataset are publicly available at https://github.com/Bokai-Zhao/NPF.

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