Postdoctoral Research Associate Pacific Northwest National Laboratory Richland, Washington
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is often used to investigate the spatial distribution of biomolecules in mammalian tissue samples, but it has seen limited use in plants. Herein, we applied MALDI-MSI to Sorghum bicolor, a critical food and biofuel crop that can be resilient to both drought and heat stress. Sorghum features a notably deep and biomass dense root system that contributes crucial metabolic functions for its development. There have been numerous studies exploring metabolic changes within bulk Sorghum root tissue, but these findings do not put metabolic changes into a spatial context. Here, we prepared young root tips (7 days), mature roots (13 weeks), and mature stems (77 days after emergence). All samples, once harvested, were flash frozen, embedded, cryosectioned, and thaw-mounted on conductive glass slides before matrix application. MALDI-MSI was performed with a 12 Tesla Fourier transform ion cyclotron resonance mass spectrometer, with a 30 µm resolution, and all datasets were uploaded to METASPACE for molecular annotation and visualization. While many key metabolites with roles in plant metabolism and stress responses, including citrate, malate, and palmitic acid, were visualized using commonly used MALDI matrices, we found that our on-tissue chemical derivatization method enhances the detection of many carbonyl-containing phytocompounds, particularly key plant phytohormones and components of the citric acid cycle. Our optimized matrix spraying protocols allowed for the visualization of distinct chemical regions that match physiological regions for these intact sorghum tissues with minimal molecular delocalization. This permitted us to molecularly image distinct regions that included the root cap, apical meristem, elongation zone, as well as internal vasculature and endodermis. By better understanding the distribution of metabolites in sorghum roots and stems, and correlating it with other spatial omics data, we aim to advance our comprehension of key metabolic pathways of growth and stress responses.