Wess Maximilian, Andersen Maria K, Midtbust Elise, Guillem Juan Carlos Cabellos, Viset Trond, Størkersen Øystein, Krossa Sebastian, Rye Morten Beck, Tessem May-Britt
Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, 7491, Norway.
ELIXIR, Norway.
Gigascience. 2025 Jan 6;14. doi: 10.1093/gigascience/giaf035.
Truly understanding the cancer biology of heterogeneous tumors in precision medicine requires capturing the complexities of multiple omics levels and the spatial heterogeneity of cancer tissue. Techniques like mass spectrometry imaging (MSI) and spatial transcriptomics (ST) achieve this by spatially detecting metabolites and RNA but are often applied to serial sections. To fully leverage the advantage of such multi-omics data, the individual measurements need to be integrated into 1 dataset.
We present the Multi-Omics Imaging Integration Toolset (MIIT), a Python framework for integrating spatially resolved multi-omics data. A key component of MIIT's integration is the registration of serial sections for which we developed a nonrigid registration algorithm, GreedyFHist. We validated GreedyFHist on 244 images from fresh-frozen serial sections, achieving state-of-the-art performance. As a proof of concept, we used MIIT to integrate ST and MSI data from prostate tissue samples and assessed the correlation of a gene signature for citrate-spermine secretion derived from ST with metabolic measurements from MSI.
MIIT is a highly accurate, customizable, open-source framework for integrating spatial omics technologies performed on different serial sections.
在精准医学中真正理解异质性肿瘤的癌症生物学，需要捕捉多个组学水平的复杂性以及癌组织的空间异质性。质谱成像（MSI）和空间转录组学（ST）等技术通过对代谢物和RNA进行空间检测来实现这一点，但通常应用于连续切片。为了充分利用此类多组学数据的优势，需要将各个测量值整合到一个数据集中。
我们展示了多组学成像整合工具集（MIIT），这是一个用于整合空间分辨多组学数据的Python框架。MIIT整合的一个关键组件是连续切片的配准，为此我们开发了一种非刚性配准算法GreedyFHist。我们在来自新鲜冷冻连续切片的244张图像上验证了GreedyFHist，实现了最先进的性能。作为概念验证，我们使用MIIT整合了前列腺组织样本的ST和MSI数据，并评估了源自ST的柠檬酸盐 - 精胺分泌基因特征与MSI代谢测量值之间的相关性。
MIIT是一个高度准确、可定制的开源框架，用于整合在不同连续切片上进行的空间组学技术。
