PNNL

Abstract

Beneficial,facultative endophytes help plants thrive in challenging environments by altering their host’s metabolism, but how these cellular scale metabolic changes propagate to the systems biology scale is unknown. • In this work, we employed a high-resolution chemical imaging approach to map metabolic changes at the Populus trichocarpa root zone and cell type levels combined with machine learning (ML) models to identify root metabolites and exudates that have predictive power over treatment class. • We found that a 9-strain consortium of beneficial endophytes differentially altered the metabolome of droughted root tissues in a manner specific to cell type and root zone, with endophyte abundance showing clear correlation to individual metabolites. • Our study demonstrates that integrating spatial metabolomics with ML can reveal localized metabolic patterns linked to root–microbe interactions and generate novel hypotheses about underlying biological mechanisms.