Mass spectrometry imaging (MSI) has become a powerful tool to probe molecule events in biological tissue. However, it is a widely held viewpoint that one of the biggest challenges is an easy-to-use data processing software for discovering the underlying biological information from complicated and huge MSI dataset. Here, a user-friendly and full-featured MSI software including three subsystems, Solution, Visualization and Intelligence, named MassImager, is developed focusing on interactive visualization, in-situ biomarker discovery and artificial intelligent pathological diagnosis. Simplified data preprocessing and high-throughput MSI data exchange, serialization jointly guarantee the quick reconstruction of ion image and rapid analysis of dozens of gigabytes datasets. It also offers diverse self-defined operations for visual processing, including multiple ion visualization, multiple channel superposition, image normalization, visual resolution enhancement and image filter. Regions-of-interest analysis can be performed precisely through the interactive visualization between the ion images and mass spectra, also the overlaid optical image guide, to directly find out the region-specific biomarkers. Moreover, automatic pattern recognition can be achieved immediately upon the supervised or unsupervised multivariate statistical modeling. Clear discrimination between cancer tissue and adjacent tissue within a MSI dataset can be seen in the generated pattern image, which shows great potential in visually in-situ biomarker discovery and artificial intelligent pathological diagnosis of cancer. All the features are integrated together in MassImager to provide a deep MSI processing solution at the in-situ metabolomics level for biomarker discovery and future clinical pathological diagnosis.

中文翻译：

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MassImager：用于交互式和深入分析质谱成像数据的软件

质谱成像 (MSI) 已成为探测生物组织中分子事件的强大工具。然而，普遍认为最大的挑战之一是易于使用的数据处理软件，用于从复杂而庞大的 MSI 数据集中发现潜在的生物信息。在这里，开发了一个用户友好且功能齐全的MSI软件，包括解决方案、可视化和智能三个子系统，名为MassImager，专注于交互式可视化、原位生物标志物发现和人工智能病理诊断。简化的数据预处理和高通量的MSI数据交换、序列化共同保证了离子图像的快速重建和数十GB数据集的快速分析。它还为视觉处理提供了多种自定义操作，包括多离子可视化、多通道叠加、图像归一化、视觉分辨率增强和图像过滤。通过离子图像和质谱之间的交互式可视化，以及重叠的光学图像指南，可以精确地执行感兴趣区域分析，以直接找出区域特定的生物标志物。此外，在有监督或无监督的多元统计建模后，可以立即实现自动模式识别。在生成的模式图像中可以看到 MSI 数据集中的癌组织和邻近组织之间的明显区分，这在视觉原位生物标志物发现和癌症的人工智能病理诊断方面显示了巨大的潜力。