Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images
2016 (English)In: Frontiers in Plant Science, ISSN 1664-462X, E-ISSN 1664-462X, Vol. 7, 119Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2016. Vol. 7, 119
automated image analysis; confocal microscopy; Arabidopsis; hypocotyl; automated phenotyping; code:matlab
Research subject Computerized Image Processing
IdentifiersURN: urn:nbn:se:uu:diva-252412DOI: 10.3389/fpls.2016.00119ISI: 000369802700001OAI: oai:DiVA.org:uu-252412DiVA: diva2:810172