Local concentration gradients of the plant growth regulator auxin (indole-3-acetic acid [IAA]) are thought to instruct the positioning of organ primordia and stem cell niches and to direct cell division, expansion, and differentiation. Highresolution measurements of endogenous IAA concentrations in support of the gradient hypothesis are required to substantiate this hypothesis. Here, we introduce fluorescence-activated cell sorting of green fluorescent protein-marked cell types combined with highly sensitive mass spectrometry methods as a novel means for analyses of IAA distribution and metabolism at cellular resolution. Our results reveal the presence of IAA concentration gradients within the Arabidopsis thaliana root tip with a distinct maximum in the organizing quiescent center of the root apex. We also demonstrate that the root apex provides an important source of IAA and that cells of all types display a high synthesis capacity, suggesting a substantial contribution of local biosynthesis to auxin homeostasis in the root tip. Our results indicate that local biosynthesis and polar transport combine to produce auxin gradients and maxima in the root tip.

In metabolomics, the objective is to identify differences in metabolite profiles between samples. A widely used tool in metabolomics investigations is gas chromatography-mass spectrometry (GC/MS). More than 400 compounds can be detected in a single analysis, if overlapping GC/MS peaks are deconvoluted. However, the deconvolution process is time-consuming and difficult to automate, and additional processing is needed in order to compare samples. Therefore, there is a need to improve and automate the data processing strategy for data generated in GC/MS-based metabolomics; if not, the processing step will be a major bottleneck for high-throughput analyses. Here we describe a new semiautomated strategy using a hierarchical multivariate curve resolution approach that processes all samples simultaneously. The presented strategy generates (after appropriate treatment, e.g., multivariate analysis) tables of all the detected metabolites that differ in relative concentrations between samples. The processing of 70 samples took similar time to that of the GC/TOFMS analyses of the samples. The strategy has been validated using two different sets of samples: a complex mixture of standard compounds and Arabidopsis samples.