Year: 2026
Type: preprint
Abstract: The integration of spatial multi-omics data from single tissues is crucial for advancing biological research. However, a significant data imbalance impedes progress: while spatial transcriptomics data is relatively abundant, spatial proteomics data remains scarce due to technical limitations and high costs. To overcome this challenge we propose STProtein, a novel framework leveraging graph neural networks with multi-task learning strategy. STProtein is designed to accurately predict unknown spatial protein expression using more accessible spatial multi-omics data, such as spatial transcriptomics. We believe that STProtein can effectively addresses the scarcity of spatial proteomics, accelerating the integration of spatial multi-omics and potentially catalyzing transformative breakthroughs in life sciences. This tool enables scientists to accelerate discovery by identifying complex and previously hidden spatial patterns of proteins within tissues, uncovering novel relationships between different marker genes, and exploring the biological "Dark Matter".
Source: arXiv (Cornell University)
Authors: Jiang, Zhaorui, Yuan, Yingfang, Hu, Lei, Pang, Wei
Institutions: Heriot-Watt University, Westlake University
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7127967330