Quantitative profiling and mapping of small molecules by laser desorption/ionization mass spectrometry: combinations of carbon-based nano-matrices and sample preparation protocols

Abstract

The choices of matrices and protocols for sample deposition are critical factors, which impact each other in the matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). Previous reports on MALDI MS matrices have only compared their performances in terms of their MS signal intensities and provided optical microphotos or MALDI MS images of sample spots but typically lacked quantitative evaluation. Therefore, there is an urgent need to develop a multivariate model to evaluate the performance of different combinations of matrices and sample protocols. Herein, we report a weighted multivariate model for the systematic quantitative profiling of combinations of matrices/protocols. Our work covers three types of carbon-based nano-matrices, namely, two-dimensional graphite oxide (GO) nanosheets, one-dimensional carbon nanotubes (CNTs), and zero-dimensional graphite dots (GDs). Four representative sample protocols for MALDI MSI, namely, rapid evaporation, seed layer, sandwich, and dry droplet, were examined in our model. We selected five important parameters, namely, (1) planar dispersity grading by SEM, (2) planar fluorescence homogeneity, (3) coefficient variations in MS signals, (4) MS signal-to-noise ratio, and (5) planar MS signal heatmap grading, and individually assigned weights according to their significance in MSI performance. Calculation of the radar chart areas based on our model provided quantitative profiles for individual combinations. Among these, the dry droplet protocol using a matrix of GDs offered the largest radar chart area, indicating that it was a favorable combination choice for MSI. Thus, this work proposes a multivariate model to systematically compare different combinations of matrices and sample protocols, thereby promising a useful tool for mapping small molecules through MALDI MS.