The absorption of X-rays as a function of an object's physical and chemical properties makes possible spatial segmentation by material type in biological organisms. Recently, X-ray microcomputed tomography (microCT) has emerged as a nondestructive technique that can be used to create digital cross-sections of a physical object, such as plant tissue, with micrometer- and submicrometer-scale resolution. To obtain a more comprehensive understanding of the role of this transient compound, it is necessary to develop tools that can capture the spatiotemporal dynamics of processes related to carbohydrate accumulation and depletion in the intact plant (Bansal & Germino, 2009 Adams et al., 2013 Rosas et al., 2013 Richardson et al., 2015). Problems related to quantification of starch also arise from the spatial and temporal dynamics of the storage and subsequent utilization of starch by the plant. Furthermore, a recent multi-laboratory experiment testing identical starch samples (Germino, 2015 Quentin et al., 2015) showed that methodological variability leads to highly inconsistent results between research groups, which precludes direct, quantitative comparisons between datasets. These technical limitations eliminate the opportunity for repeated measurements, introduce substantial across-sample variability, and limit conclusions to bulk tissue. Despite the importance of starch for plant function, only destructive methods with limited spatial resolution exist for quantifying and monitoring starch storage and depletion. Further, during periods of low to no photosynthesis, depletion of stored starch is thought to underlie stress-induced mortality associated with drought and pathogenic infection (McDowell, 2011 Sevanto et al., 2014 Dickman et al., 2015). Consequently, starch is a central molecule involved in the metabolic regulation of a plant's growth trajectory. Plants adjust starch synthesis and degradation rates in response to various environmental and phenological cues with the associated kinetics varying along hourly to seasonal timescales (Smith & Stitt, 2007 Gibon et al., 2009 Sulpice et al., 2009). Immobilization, aggregation and storage of photosynthate as starch provide a buffer mechanism that permits plants to maintain cellular processes, growth and defense functions in periods when metabolic demand exceeds energetic supply. Using X-ray microCT technology for in vivo starch monitoring should enable novel research directed at resolving the spatial and temporal patterns of starch accumulation and depletion in woody plant species.After validating our machine learning algorithm, we then characterized the spatial distribution of starch concentration in living stems at micrometer resolution, and identified starch depletion in live plants under experimental conditions designed to halt photosynthesis and starch production, initiating the drawdown of stored starch pools. Starch content estimated for xylem axial and ray parenchyma cells from microCT images was correlated strongly with enzymatically measured bulk-tissue starch concentration on the same stems.Here, we demonstrate how X-ray microcomputed tomography (microCT) and a machine learning algorithm can be coupled to quantify plant starch content in vivo, repeatedly and nondestructively over time in grapevine stems ( Vitis spp.).Destructive techniques at coarse spatial scales exist to quantify starch, but these techniques face methodological challenges that can lead to uncertainty about the lability of tissue-specific starch pools and their role in plant survival. Starch is the primary energy storage molecule used by most terrestrial plants to fuel respiration and growth during periods of limited to no photosynthesis, and its depletion can drive plant mortality.
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