Using imaging ToF‐SIMS data to determine the cell wall thickness of fibers in wood

Mapping Dicorynia guianensis Amsh. wood constituents by submicron resolution cluster-TOF-SIMS imaging

The preparation of tropical wood surface sections for time-of-flight secondary ion mass spectrometry imaging is described, and the use of delayed extraction of secondary ions and its interest for the analysis of vegetal surface are shown. The method has been applied to the study by time-of-flight secondary ion mass spectrometry imaging with a resolution of less than one micron of a tropical wood species, Dicorynia guianensis, which is one of the most exploited wood in French Guiana for its durable heartwood. The heartwood of this species exhibits an economical importance, but its production is not controlled in forestry. Results show an increase of tryptamine from the transition zone and a concomitant decrease of inorganic ions and starch fragment ions. These experiments lead to a better understanding of the heartwood formation and the origin of the natural durability of D. guianensis. Copyright © 2016 John Wiley & Sons, Ltd.

Precision Automation of Cell Type Classification and Sub-Cellular Fluorescence Quantification from Laser Scanning Confocal Images

While novel whole-plant phenotyping technologies have been successfully implemented into functional genomics and breeding programs, the potential of automated phenotyping with cellular resolution is largely unexploited. Laser scanning confocal microscopy has the potential to close this gap by providing spatially highly resolved images containing anatomic as well as chemical information on a subcellular basis. However, in the absence of automated methods, the assessment of the spatial patterns and abundance of fluorescent markers with subcellular resolution is still largely qualitative and time-consuming. Recent advances in image acquisition and analysis, coupled with improvements in microprocessor performance, have brought such automated methods within reach, so that information from thousands of cells per image for hundreds of images may be derived in an experimentally convenient time-frame. Here, we present a MATLAB-based analytical pipeline to (1) segment radial plant organs into individual cells, (2) classify cells into cell type categories based upon Random Forest classification, (3) divide each cell into sub-regions, and (4) quantify fluorescence intensity to a subcellular degree of precision for a separate fluorescence channel. In this research advance, we demonstrate the precision of this analytical process for the relatively complex tissues of Arabidopsis hypocotyls at various stages of development. High speed and robustness make our approach suitable for phenotyping of large collections of stem-like material and other tissue types.