Reactive oxygen species (ROS) production and breakdown have been studied in detail in plant-pathogenic fungi, including the rice blast fungus, Magnaporthe oryzae; however, the examination of the dynamic process of ROS production in real time has proven to be challenging. We resynthesized an existing ROS sensor, called HyPer, to exhibit optimized codon bias for fungi, specifically Neurospora crassa, and used a combination of microscopy and plate reader assays to determine whether this construct could detect changes in fungal ROS during the plant infection process. Using confocal microscopy, we were able to visualize fluctuating ROS levels during the formation of an appressorium on an artificial hydrophobic surface, as well as during infection on host leaves. Using the plate reader, we were able to ascertain measurements of hydrogen peroxide (H2 O2 ) levels in conidia as detected by the MoHyPer sensor. Overall, by the optimization of codon usage for N. crassa and related fungal genomes, the MoHyPer sensor can be used as a robust, dynamic and powerful tool to both monitor and quantify H2 O2 dynamics in real time during important stages of the plant infection process.

Journal Article Optimizing Workflows in Correlative Light and Electron Microscopy Get access Kirk Czymmek, Kirk Czymmek Carl Zeiss Microcopy, LLC, One Zeiss Drive, Thornwood, NY 10594, USA Search for other works by this author on: Oxford Academic Google Scholar Jeff Caplan, Jeff Caplan 15 Innovation Way, Delaware Biotechnology Institute, University of Delaware, Newark DE 19711, USA Search for other works by this author on: Oxford Academic Google Scholar Cherish Warner, Cherish Warner 15 Innovation Way, Delaware Biotechnology Institute, Department of Plant and Soil Sciences, University of Delaware, Newark DE 19711, USA Search for other works by this author on: Oxford Academic Google Scholar Janine Sherrier, Janine Sherrier 15 Innovation Way, Delaware Biotechnology Institute, Department of Plant and Soil Sciences, University of Delaware, Newark DE 19711, USA Search for other works by this author on: Oxford Academic Google Scholar Alexandra Elli Alexandra Elli Carl Zeiss Microscopy, Carl-Zeiss-Strasse 22, 73447 Oberkochen, Germany Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 21, Issue S3, 1 August 2015, Pages 1377–1378, https://doi.org/10.1017/S1431927615007679 Published: 23 September 2015

Correlative microscopy combines information from light and electron microscopy (LM and EM) into one comprehensive dataset. The “Shuttle & Find” interface from Zeiss was the first easy to use solution for imaging one and the same sample region in different microscopes, i.e. from widefield or confocal LSMs to environmental or field emission SEMs (FE-SEM). However, Shuttle & Find is limited to 2D applications. Correlative Array Tomography (CAT) is a correlative volumetric microscopy method on a high-throughput basis. Ordered arrays of ultrathin, resin-embedded serial sections can be imaged with different microscopical modalities. Serial sections are prepared using an ultramicrotome and for correlative array tomography, sections were stained for imaging with a widefield microscope and a scanning electron microscope. CAT is a non-destructive method that allows multiple investigations of the same sample and even multiple successive fluorescent staining procedures [1]. Multiple staining cycles of a large number of antigens or the use of different fluorescent proteins followed by the ultrastructural investigation in a SEM enables the analysis of functional and structural information in the same context. The software module “ ZEN Correlative Array Tomography” enables an easy and efficient workflow from LM (e.g. fluorescence) to FE-SEM or vice versa. The first step after inserting the sample into one microscope (LM or SEM) is to perform a quick calibration to establish a coordinate system. Subsequently the software guides the user through an automated process to recognize the ribbons of serial section. Regions of interest (ROI) can then be selected in one section and these ROIs are then automatically duplicated to all other recognized section. Once this process is completed image acquisition of all ROIs is performed automatically by the software thus generating a 3D volume stack from the serial sections. In the second step the sample along with the acquired data is transferred to next microscope (LM to SEM or vice versa). After the initial calibration of the coordinates the subsequent information on section and ROI position can be loaded and re-used for a fast set-up of the imaging parameters. Again, the microscope will then automatically acquire all images at the same regions that were already investigated. Finally a correlative 3D dataset can be created by first aligning both LM and SEM image stack separately. Afterwards the user correlates both volumes by identifying the same four reference points in both dataset thus obtaining a full 3D correlative volume.