利用SpatialMETA整合空间转录组学和代谢组学的跨样本和跨模式数据。

IF 15.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Ruonan Tian,Ziwei Xue,Yiru Chen,Yicheng Qi,Jian Zhang,Jie Yuan,Dengfeng Ruan,Junxin Lin,Jia Liu,Di Wang,Youqiong Ye,Wanlu Liu
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引用次数: 0

摘要

在相同或相邻的组织切片上同时分析空间转录组学(ST)和空间代谢组学(SM)为解码组织微环境和确定癌症免疫治疗的潜在治疗靶点提供了一种革命性的方法。与其他空间组学不同,由于转录本数量和代谢物强度的特征分布差异以及空间形态和分辨率的固有差异,ST和SM数据的跨模式整合具有挑战性。此外,跨样本整合对于捕获空间共识和异质模式至关重要,但往往因批量效应而变得复杂。在这里,我们介绍了一个基于条件变分自编码器(CVAE)的框架SpatialMETA,用于ST和SM数据的跨模态和跨样本集成。SpatialMETA采用定制的解码器和损失函数来增强模态融合、批量效应校正和生物保护,实现空间相关ST-SM模式的可解释整合和下游分析。SpatialMETA识别癌症中具有不同代谢特征的免疫空间簇,揭示了超出原始研究的见解。与现有工具相比,SpatialMETA展示了卓越的重建能力和融合模态表示,准确捕获ST和SM特征分布。综上所述,SpatialMETA为推进空间多组学研究和完善对组织微环境中代谢异质性的理解提供了一个强大的平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating cross-sample and cross-modal data for spatial transcriptomics and metabolomics with SpatialMETA.
Simultaneous profiling of spatial transcriptomics (ST) and spatial metabolomics (SM) on the same or adjacent tissue sections offers a revolutionary approach to decode tissue microenvironment and identify potential therapeutic targets for cancer immunotherapy. Unlike other spatial omics, cross-modal integration of ST and SM data is challenging due to differences in feature distributions of transcript counts and metabolite intensities, and inherent disparities in spatial morphology and resolution. Furthermore, cross-sample integration is essential for capturing spatial consensus and heterogeneous patterns but is often complicated by batch effects. Here, we introduce SpatialMETA, a conditional variational autoencoder (CVAE)-based framework for cross-modal and cross-sample integration of ST and SM data. SpatialMETA employs tailored decoders and loss functions to enhance modality fusion, batch effect correction and biological conservation, enabling interpretable integration of spatially correlated ST-SM patterns and downstream analysis. SpatialMETA identifies immune spatial clusters with distinct metabolic features in cancer, revealing insights that extend beyond the original study. Compared to existing tools, SpatialMETA demonstrates superior reconstruction capability and fused modality representation, accurately capturing ST and SM feature distributions. In summary, SpatialMETA offers a powerful platform for advancing spatial multi-omics research and refining the understanding of metabolic heterogeneity within the tissue microenvironment.
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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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