SPATIAL METABOLOME–TRANSCRIPTOME INTEGRATION TO DECODE NUTRIENT COMPETITION BETWEEN PLANTS AND NECROTROPHIC FUNGI

Abstract

Plant–necrotrophic fungal interactions represent a spatially dynamic molecular battlefield where host plants and pathogens compete for metabolic control over infected tissues. Traditional bulk omics approaches homogenize these intricate spatial architectures, obscuring localized metabolic fluxes and intercellular signaling events critical for understanding nutrient competition. This review synthesizes recent advances in spatial multi-omics technologies that enable high-resolution mapping of host–pathogen metabolic interactions. Spatial transcriptomics platforms, including array-based sequencing (10x Visium, Stereo-seq) and imaging-based methods (MERFISH, CosMx SMI), provide in situ gene expression profiles at resolutions ranging from subcellular to 55 µm, while Spatial MetaTranscriptomics (SmT) enables simultaneous host and fungal transcript detection. Concurrently, Mass Spectrometry Imaging techniques such as MALDI-MSI, DESI-MSI, and AFADESI-MSI facilitate untargeted spatial mapping of primary metabolites, phytoalexins, and phytotoxins across infection fronts. The integration of these complementary datasets through computational frameworks like SpatialMETA (deep generative modeling) and Spatial Flux Balance Analysis (spFBA) bridges resolution mismatches and translates transcriptomic data into predictive metabolic flux maps. Integrated spatial analyses reveal that infected tissues organize into concentric micro-niches necrotic cores exhibiting fungal nutrient depletion, lesion interfaces characterized by host cell-wall invertase and phytoalexin accumulation forming defensive perimeters, and distal zones undergoing systemic metabolic priming. This spatial framework illuminates how host plants rewire carbon and nitrogen metabolism to starve invading pathogens while necrotrophic fungi employ detoxification mechanisms to dismantle chemical barriers. Despite technical challenges including resolution disparities, tissue permeabilization optimization, and metabolite identification limitations, spatial metabolome–transcriptome integration promises transformative insights for engineering climate-resilient crops with enhanced metabolic defense barriers.