Mass spectrometry imaging is a valuable tool for visualizing the localization of drugs in tissues, a critical issue especially in cancer pharmacology where treatment failure may depend on poor drug distribution within the tumours. Proper preprocessing procedures are mandatory to obtain quantitative data of drug distribution in tumours, even at low intensity, through reliable ion peak identification and integration. We propose a simple preprocessing and quantification pipeline. This pipeline was designed starting from classical peak integration methods, developed when "microcomputers" became available for chromatography, now applied to MSI. This pre-processing approach is based on a novel method using the fixed mass difference between the analyte and its 5 d derivatives to set up a mass range gate. We demonstrate the use of this pipeline for the evaluating the distribution of the anticancer drug paclitaxel in tumour sections. The procedure takes advantage of a simple peak analysis and allows to quantify the drug concentration in each pixel with a limit of detection below 0.1 pmol mm-2 or 10 μg g-1. Quantitative images of paclitaxel distribution in different tumour models were obtained and average paclitaxel concentrations were compared with HPLC measures in the same specimens, showing <20% difference. The scripts are developed in Python and available through GitHub, at github.com/FrancescaFalcetta/Imaging_of_drugs_distribution_and_quantifications.git.

中文翻译：

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过去的未来。峰检测改进了靶向质谱成像

质谱成像是可视化组织中药物定位的重要工具，这是一个关键问题，尤其是在癌症药理学中，治疗失败可能取决于肿瘤内药物分布不佳。通过可靠的离子峰识别和积分，适当的预处理程序是强制性的，以获得肿瘤中药物分布的定量数据，即使在低强度下也是如此。我们提出了一个简单的预处理和量化管道。该管道是从经典的峰积分方法开始设计的，当“微型计算机”可用于色谱时开发，现在应用于 MSI。这种预处理方法基于一种新方法，该方法使用分析物与其 5 d 衍生物之间的固定质量差异来设置质量范围门。我们展示了该管道用于评估抗癌药物紫杉醇在肿瘤切片中的分布。该程序利用简单的峰分析，并允许量化每个像素中的药物浓度，检测限低于 0.1 pmol mm-2 或 10 μg g-1。获得了不同肿瘤模型中紫杉醇分布的定量图像，并将平均紫杉醇浓度与相同样本中的 HPLC 测量值进行比较，显示 <20% 的差异。这些脚本是用 Python 开发的，可通过 GitHub 获得，网址为 github.com/FrancescaFalcetta/Imaging_of_drugs_distribution_and_quantifications.git。该程序利用简单的峰分析，并允许量化每个像素中的药物浓度，检测限低于 0.1 pmol mm-2 或 10 μg g-1。获得了不同肿瘤模型中紫杉醇分布的定量图像，并将平均紫杉醇浓度与相同样本中的 HPLC 测量值进行比较，显示 <20% 的差异。这些脚本是用 Python 开发的，可通过 GitHub 获得，网址为 github.com/FrancescaFalcetta/Imaging_of_drugs_distribution_and_quantifications.git。该程序利用简单的峰分析，并允许量化每个像素中的药物浓度，检测限低于 0.1 pmol mm-2 或 10 μg g-1。获得了不同肿瘤模型中紫杉醇分布的定量图像，并将平均紫杉醇浓度与相同样本中的 HPLC 测量值进行比较，显示 <20% 的差异。这些脚本是用 Python 开发的，可通过 GitHub 获得，网址为 github.com/FrancescaFalcetta/Imaging_of_drugs_distribution_and_quantifications.git。