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".

翻译：单组织空间多组学数据的整合对推动生物学研究至关重要。然而，严重的数据失衡阻碍了研究进展：尽管空间转录组数据相对丰富，但由于技术限制和高成本，空间蛋白质组数据仍然稀缺。为应对这一挑战，我们提出了STProtein——一种利用图神经网络与多任务学习策略的新型框架。STProtein旨在利用更易获取的空间多组学数据（如空间转录组数据）准确预测未知的空间蛋白质表达。我们相信STProtein能有效缓解空间蛋白质组数据的稀缺问题，加速空间多组学整合，并可能催化生命科学领域的突破性进展。该工具通过识别组织中复杂且先前隐藏的蛋白质空间模式、揭示不同标记基因间的新关联、探索生物学"暗物质"，助力科学家加速科学发现。