Matrix-assisted laser desorption/ionization matrix incorporation evaluation algorithm for improved peak coverage and signal-to-noise ratio in mass spectrometry imaging.

Rationale: Despite decades of implementation, the selection of optimal sample preparation conditions for matrix-assisted laser desorption/ionization (MALDI) imaging is still ambiguous due to the lack of a universal and comprehensive evaluation methodology. Thus, numerous experiments with different matrix application conditions accompany a translation of the method to novel sample types and matrices.

Methods: Mouse brain tissues were covered with 9-aminoacridine through sublimation, followed by recrystallization in vapors of 5% (v/v) methanol solution in water. The samples were analyzed by MALDI time-of-flight mass spectrometry, and the efficiency of lipid and small-molecule ionization was evaluated with different metrics.

Results: We first investigate the dependency of matrix density and recrystallization conditions on the thickness of an analyte-empty matrix layer to roughly evaluate the laser shot number required to obtain an intense signal with minimal noise. Then, we introduce metrics for the analysis of small imaging datasets (small sample regions) of model samples based on median quantity of peaks in spectra (medQP) and weighted median signal-to-noise ratio (wmSNR). The evaluation of small regions and taking median values for metrics help overcome the sample heterogeneity and allow for the simultaneous comparison of different acquisition parameters.

Conclusions: Here, we propose a methodology based on gradual laser ablation of small regions of sample and further implementation of weighted signal-to-noise ratio to assess various matrix application conditions. The proposed approach helps reduce the number of test samples required to determine optimal sample preparation conditions and improve the overall quality of images.