PMID- 26904081 OWN - NLM STAT- PubMed-not-MEDLINE VI - 7 TI - Precision Automation of Cell Type Classification and Sub-Cellular Fluorescence Quantification from Laser Scanning Confocal Images. PG - 119 LA - eng PT - Journal Article PL - Switzerland TA - Front Plant Sci JT - Frontiers in plant science JID - 101568200 IS - 1664-462X (Print) LID - 10.3389/fpls.2016.00119 [doi] FAU - Hall, Hardy C AU - Hall HC AD - Department of Forest Genetics and Plant Physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences Umeå, Sweden. FAU - Fakhrzadeh, Azadeh AU - Fakhrzadeh A AD - Centre for Image Analysis, Uppsala University Uppsala, Sweden. FAU - Luengo Hendriks, Cris L AU - Luengo Hendriks CL AD - Centre for Image Analysis, Uppsala University Uppsala, Sweden. FAU - Fischer, Urs AU - Fischer U AD - Department of Forest Genetics and Plant Physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences Umeå, Sweden. IS - 1664-462X (Linking) OTO - NOTNLM OT - Arabidopsis OT - automated image analysis OT - automated phenotyping OT - code:matlab OT - confocal microscopy OT - hypocotyl PMC - PMC4746258 DCOM- 20160223 LR - 20200930 DP - 2016 DEP - 20160209 AB - 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.