Hossein Khani Solmaz, Remy Noah, Ould Amer Khadidja, Lebas Berangère, Habrant Anouck, Malandain Grégoire, Paës Gabriel, Refahi Yassin
Université de Reims-Champagne-Ardenne, INRAE, FARE, UMR A 614, Reims, France.
Université Côte d'Azur, Inria, CNRS, I3S, Nice, France.
Data Brief. 2025 May 7;60:111618. doi: 10.1016/j.dib.2025.111618. eCollection 2025 Jun.
The transition to use plant cell walls as an alternative to fossil carbon resources is important in the context of climate change. To achieve an economically viable plant cell wall transformation into biofuels and biomaterials, it is essential to better understand cell wall enzymatic deconstruction and overcome its recalcitrance to deconstruction. While identification of nanoscale markers of recalcitrance has been the focus of the majority of studies, quantitative investigation of cell wall hydrolysis at microscale, particularly the cell wall morphological parameters, remains relatively insufficiently addressed. This is mainly due to the lack of quantitative data on cell wall enzymatic deconstruction at microscale. Acquisition and processing of reliable microscale datasets are notoriously challenging; the sample needs to be kept at a constant temperature for efficient enzymatic hydrolysis and imaged over a considerable number of hours. Processing the acquired datasets to extract cell wall morphological parameters is also challenging due to cell wall deconstruction and deformations occurring during enzymatic hydrolysis. This becomes particularly challenging under high deconstruction conditions. The datasets presented here include time-lapse 3D images of highly deconstructed pretreated spruce wood acquired using fluorescence confocal microscopy, together with cell resolution segmentations of the acquired time-lapses. Along with this hydrolysis dataset, control time-lapse images of pretreated spruce wood samples acquired without adding enzymatic cocktail are also presented. The control dataset includes 6505 segmented and tracked cells. The hydrolysis dataset includes 6699 tracked cells at various stages of extensive deconstruction. Overall, these datasets provide a reliable and comprehensive set of time-lapse 3D images to study cell wall enzymatic deconstruction at cell and tissue scales, which can be used to better understand the microscale limiting factors of efficient transformation of plant biomass into sustainable products.
在气候变化的背景下，转向使用植物细胞壁作为化石碳资源的替代品具有重要意义。为了实现将植物细胞壁经济可行地转化为生物燃料和生物材料，必须更好地理解细胞壁的酶解过程并克服其对酶解的抗性。虽然大多数研究都聚焦于抗性纳米级标志物的识别，但在微观尺度上对细胞壁水解的定量研究，特别是细胞壁形态参数的研究，仍相对不足。这主要是由于缺乏微观尺度上细胞壁酶解的定量数据。获取和处理可靠的微观尺度数据集极具挑战性；样品需要保持在恒定温度下以实现高效酶解，并在相当长的时间内进行成像。由于酶解过程中细胞壁的解构和变形，处理获取的数据集以提取细胞壁形态参数也具有挑战性。在高解构条件下，这一挑战尤为突出。这里展示的数据集包括使用荧光共聚焦显微镜获取的高度解构预处理云杉木的延时三维图像，以及所获取延时图像的细胞分辨率分割图。除了这个水解数据集，还展示了未添加酶混合物情况下获取的预处理云杉木样品的对照延时图像。对照数据集包括6505个分割和跟踪的细胞。水解数据集包括6699个处于广泛解构不同阶段的跟踪细胞。总体而言，这些数据集提供了一组可靠且全面的延时三维图像，用于在细胞和组织尺度上研究细胞壁的酶解过程，可用于更好地理解植物生物质高效转化为可持续产品的微观尺度限制因素。
