Guo Lei, Hu Zhenxing, Zhao Chao, Xu Xiangnan, Wang Shujuan, Xu Jingjing, Dong Jiyang, Cai Zongwei
National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China.
State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China.
Anal Chem. 2021 Mar 23;93(11):4788-4793. doi: 10.1021/acs.analchem.0c05242. Epub 2021 Mar 8.
Mass spectrometry imaging (MSI) could provide vast amounts of data at the temporal-spatial scale in heterogeneous biological specimens, which challenges us to segment accurately suborgans/microregions from complex MSI data. Several pipelines had been proposed for MSI spatial segmentation in the past decade. More importantly, data filtering was found to be an efficient procedure to improve the outcomes of MSI segmentation pipelines. It is not clear, however, how the filtering procedure affects the MSI segmentation. An improved pipeline was established by elaborating the filtering prioritization and filtering algorithm. Lipidomic-characteristic-based MSI data of a whole-body mouse fetus was used to evaluate the established pipeline on localization of the physiological position of suborgans by comparing with three commonly used pipelines and commercial SCiLS Lab software. Two structural measurements were used to quantify the performances of the pipelines including the percentage of abnormal edge pixel (PAEP) and CHAOS. Our results demonstrated that the established pipeline outperformed the other pipelines in visual inspection, spatial consistence, time-cost, and robustness analysis. For example, the dorsal pallium (isocortex) and hippocampal formation (Hpf) regions, midbrain, cerebellum, and brainstem on the mouse brain were annotated and located by the established pipeline. As a generic pipeline, the established pipeline could help with the accurate assessment and screening of drug/chemical-induced targeted organs and exploration of the progression and molecular mechanisms of diseases. The filter-based strategy is expected to become a critical component in the standard operating procedure of MSI data sets.
质谱成像（MSI）能够在异质生物样本的时空尺度上提供大量数据，这对我们从复杂的MSI数据中准确分割亚器官/微区提出了挑战。在过去十年中，已经提出了几种用于MSI空间分割的流程。更重要的是，数据过滤被发现是一种提高MSI分割流程结果的有效方法。然而，尚不清楚过滤过程如何影响MSI分割。通过详细阐述过滤优先级和过滤算法，建立了一种改进的流程。利用基于脂质组学特征的全身小鼠胎儿MSI数据，通过与三种常用流程和商业SCiLS Lab软件进行比较，来评估所建立的流程在亚器官生理位置定位方面的性能。使用两种结构测量方法来量化这些流程的性能，包括异常边缘像素百分比（PAEP）和CHAOS。我们的结果表明，所建立的流程在视觉检查、空间一致性、时间成本和稳健性分析方面优于其他流程。例如，所建立的流程对小鼠大脑中的背侧皮质（同型皮质）和海马结构（Hpf）区域、中脑、小脑和脑干进行了标注和定位。作为一种通用流程，所建立的流程有助于准确评估和筛选药物/化学诱导的靶器官，以及探索疾病的进展和分子机制。基于过滤的策略有望成为MSI数据集标准操作程序中的关键组成部分。
