The environmental risks of climate change promotes the use of lignocellulosic biomass (LB) as a natural renewable resource to make bioproducts. However, LB is recalcitrant to enzymatic deconstruction due to chemical and structural complexity, making the cost of LB transformation not competitive. Therefore, it is necessary to identify and overcome the markers governing LB recalcitrance. Despite extensive research on recalcitrance, the identified markers are at the molecular scale while markers at the cellular and tissular scale remain poorly understood. This is mainly due to the notorious challenge of acquiring and processing time-lapse images to quantify the markers. We devised an original quantitative imaging pipeline including time-lapse 4D (space + time) confocal imaging, and a novel computational tool to segment cell walls and track their evolution during enzymatic hydrolysis to quantitatively characterize the LB deconstruction at the cellular and tissular scale. We first set up a protocol to collect 4D data : the LB section was placed with the enzymatic cocktail in a sealed chamber at a constant optimal temperature to keep the sample stable and to avoid evaporation. We then developed an original high-throughput 4D image segmentation and tracking pipeline, called LIBSTRAC (LIgnocellulosic Biomass Segmentation and Tracking), to segment and track individual cell walls. A particular challenge to overcome was the cell wall degradation during hydrolysis which made classical segmentation algorithms unsuitable. Therefore, an innovative method was developed which uses segmentation before hydrolysis to compute segmentation of images during hydrolysis. This strategy of propagating spatial information over time allows the identification of individual degraded cell walls using their earlier state (i.e. before deconstruction when the structure is intact). Applying this method on poplar wood, we computed dynamics of several cellular parameters (e.g. cell wall volumes) while measuring cellulose conversion rate. The quantifications suggested correlation between cell wall volume degradation rate and cellulose conversion rate linking deconstruction markers across scales. In addition, a negative correlation between cell wall compactness and cell volume degradation rate was also observed. Overall, our pipeline allows quantitative investigation of structural markers underlying LB recalcitrance paving the way to computational modelling of LB transformation.

Imaging plant cell wall deconstruction in 4D: Quantitative pipeline to investigate enzymatic hydrolysis across scales

ACS Spring 2022

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