Quantitative mass spectrometry imaging (MSI) is a robust approach that provides both quantitative and spatial information for drug candidates’ research. However, because of complicated signal suppression and interference, acquiring accurate quantitative information from MSI data remains a challenge, especially for whole-body tissue sample. Ambient MSI techniques using spray-based ionization appear to be ideal for pharmaceutical quantitative MSI analysis. However, it is more challenging, as it involves almost no sample preparation and is more susceptible to ion suppression/enhancement. Herein, based on our developed air flow-assisted desorption electrospray ionization (AFADESI)-MSI technology, an ambient quantitative MSI method was introduced by integrating inkjet-printing technology with normalization of the signal extinction coefficient (SEC) using the target compound itself. The method utilized a single calibration curve to quantify multiple tissue types. Basic blue 7 and an antitumor drug candidate (S-(+)-deoxytylophorinidine, CAT) were chosen to initially validate the feasibility and reliability of the quantitative MSI method. Rat tissue sections (heart, kidney, and brain) administered with CAT was then analyzed. The quantitative MSI analysis results were cross-validated by LC-MS/MS analysis data of the same tissues. The consistency suggests that the approach is able to fast obtain the quantitative MSI data without introducing interference into the in-situ environment of the tissue sample, and is potential to provide a high-throughput, economical and reliable approach for drug discovery and development.

定量质谱成像（MSI）是一种强大的方法，可为候选药物的研究提供定量和空间信息。然而，由于复杂的信号抑制和干扰，从MSI数据获取准确的定量信息仍然是一个挑战，尤其是对于全身组织样本。使用基于喷雾的电离的环境MSI技术似乎是药物定量MSI分析的理想选择。但是，它更具挑战性，因为它几乎不需要样品制备，并且更容易受到离子抑制/增强的影响。在此，根据我们开发的气流辅助解吸电喷雾电离（AFADESI）-MSI技术，通过将喷墨打印技术与目标化合物本身的信号消光系数（SEC）归一化相结合，引入了一种环境定量MSI方法。该方法利用单个校准曲线来量化多种组织类型。选择碱性蓝7和候选抗肿瘤药物（S-（+）-脱氧酪氨酸碱，CAT）来初步验证定量MSI方法的可行性和可靠性。然后分析了施用CAT的大鼠组织切片（心脏，肾脏和大脑）。MSI定量分析结果通过相同组织的LC-MS / MS分析数据进行交叉验证。一致性表明，该方法能够快速获得定量MSI数据，而不会在组织样本的原位环境中引入干扰，