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An open development kit for ambient ionisation enables the fast scanning and visualisation of TLC plates with high lateral resolution.

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Ambient ionisation mass spectrometry (AIMS) enables studying biological systems in their native state and direct high-throughput analyses. The ionisation occurs in the physical conditions of the surrounding environment. Simple spray or plasma-based AIMS devices allow the desorption and ionisation of molecules from solid, liquid and gaseous samples. 3D printing helps to implement new ideas and concepts in AIMS quickly. Here, we present examples of 3D printed AIMS sources and devices for ion transfer and manipulation. Further, we show the use of 3D printer parts for building custom AIMS sampling robots and imaging systems. Using 3D printing technology allows upgrading existing mass spectrometers with relatively low cost and effort.

RATIONALE Ambient ionization mass spectrometry (AIMS) delivers realistic data from samples in their native state. In addition, AIMS methods reduce time and costs for sample preparation and have less environmental impact. However, AIMS data are often complex and require substantial processing before interpretation. METHODS We developed an interactive R script for the guided processing of mass spectrometry (MS) data. The ‘MQ_Assistant’ is based on MALDIquant, a popular R package for MS data processing. In each step, the user can try and pre-view the effect of chosen parameters before deciding on the values with the best result and proceeding to the next stage. The outcome of the MQ_Assistant is a feature matrix that can be further analyzed in R and statistics tools such as MetaboAnalyst. RESULTS Using 360 AIMS example spectra, we demonstrate the step-by-step processing for creating a feature matrix. In addition, we show how to visualize the results of tree biological replicates of a plant-microbe interaction between Arabidopsis and Trichoderma as a heatmap using R and upload them to MetaboAnalyst. The final parameter set can be saved for reuse in MALDIquant workflows of similar data. CONCLUSIONS The MQ_Assistant helps novices and experienced users to develop workflows for (AI)MS data processing. The interactive procedure supports the quick finding of appropriate settings. These parameters can be exported and reused in future projects. The step-wise operation with visual feedback also suggests the use of the MQ_Assistant in education.