LA-ICP-MS and MALDI-MS image registration for correlating nanomaterial biodistributions and their biochemical effects

Laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) imaging and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) are complementary methods that measure distributions of elements and biomolecules in tissue sections. Quantitative correlations of the information provided by these two imaging modalities requires that the datasets be registered in the same coordinate system, allowing for pixel-by-pixel comparisons. We describe here a computational workflow written in Python that accomplishes this registration, even for adjacent tissue sections, with accuracies within +/- 50 mu m. The value of this registration process is demonstrated by correlating images of tissue sections from mice injected with gold nanomaterial drug delivery systems. Quantitative correlations of the nanomaterial delivery vehicle, as detected by LA-ICP-MS imaging, with biochemical changes, as detected by MALDI-MSI, provide deeper insight into how nanomaterial delivery systems influence lipid biochemistry in tissues. Moreover, the registration process allows the more precise images associated with LA-ICP-MS imaging to be leveraged to achieve improved segmentation in MALDI-MS images, resulting in the identification of lipids that are most associated with different sub-organ regions in tissues.

Automated summary

Laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) imaging and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) are complementary methods used to measure distributions of elements and biomolecules in tissue sections. A computational workflow written in Python is described to achieve accurate registration between datasets from these two imaging modalities, allowing for pixel-by-pixel comparisons. The registration process provides deeper insights into how nanomaterial delivery systems influence lipid biochemistry in tissues and allows for improved segmentation in MALDI-MS images for identification of lipids associated with different sub-organ regions in tissues.