Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan.
The Metabolomics Core Laboratory, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei 10617, Taiwan.
Anal Chem. 2023 Feb 14;95(6):3317-3324. doi: 10.1021/acs.analchem.2c04360. Epub 2023 Feb 1.
Mass spectrometry imaging (MSI) is a powerful tool that can be used to simultaneously investigate the spatial distribution of different molecules in samples. However, it is difficult to comprehensively analyze complex biological systems with only a single analytical technique due to different analytical properties and application limitations. Therefore, many analytical methods have been combined to extend data interpretation, evaluate data credibility, and facilitate data mining to explore important temporal and spatial relationships in biological systems. Image registration is an initial and critical step for multimodal imaging data fusion. However, the image registration of multimodal images is not a simple task. The property difference between each data modality may include spatial resolution, image characteristics, or both. The image registrations between MSI and different imaging techniques are often achieved indirectly through histology. Many methods exist for image registration between MSI data and histological images. However, most of them are manual or semiautomatic and have their prerequisites. Here, we built MSI Registrar (MSIr), a web service for automatic registration between MSI and histology. It can help to reduce subjectivity and processing time efficiently. MSIr provides an interface for manually selecting region of interests from histological images; the user selects regions of interest to extract the corresponding spectrum indices in MSI data. In the performance evaluation, MSIr can quickly map MSI data to histological images and help pinpoint molecular components at specific locations in tissues. Most registrations were adequate and were without excessive shifts. MSIr is freely available at https://msir.cmdm.tw and https://github.com/CMDM-Lab/MSIr.
质谱成像（MSI）是一种强大的工具，可用于同时研究样品中不同分子的空间分布。然而，由于不同的分析特性和应用限制，仅使用单一分析技术很难全面分析复杂的生物系统。因此，许多分析方法已经结合在一起，以扩展数据解释、评估数据可信度，并促进数据挖掘，以探索生物系统中的重要时空关系。图像配准是多模态成像数据融合的初始和关键步骤。然而，多模态图像的图像配准并不是一项简单的任务。每个数据模态之间的属性差异可能包括空间分辨率、图像特征或两者兼而有之。MSI 与不同成像技术之间的图像配准通常通过组织学间接实现。有许多方法可用于 MSI 数据和组织学图像之间的图像配准。然而，大多数方法是手动或半自动的，并且有其前提条件。在这里，我们构建了 MSI Registrar（MSIr），这是一种用于 MSI 和组织学自动配准的网络服务。它可以帮助有效减少主观性和处理时间。MSIr 为从组织学图像中手动选择感兴趣区域提供了一个接口；用户选择感兴趣的区域，以提取 MSI 数据中的相应光谱指数。在性能评估中，MSIr 可以快速将 MSI 数据映射到组织学图像上，并帮助确定组织中特定位置的分子成分。大多数配准都足够，并且没有过度的移位。MSIr 可在 https://msir.cmdm.tw 和 https://github.com/CMDM-Lab/MSIr 免费获取。
