{"title":"云杉木材酶解的延时三维图像数据集","authors":"Solmaz Hossein Khani , Noah Remy , Khadidja Ould Amer , Berangère Lebas , Anouck Habrant , Grégoire Malandain , Gabriel Paës , Yassin Refahi","doi":"10.1016/j.dib.2025.111618","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111618"},"PeriodicalIF":1.0000,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Time-lapse 3D image datasets of spruce tree wood enzymatic deconstruction\",\"authors\":\"Solmaz Hossein Khani , Noah Remy , Khadidja Ould Amer , Berangère Lebas , Anouck Habrant , Grégoire Malandain , Gabriel Paës , Yassin Refahi\",\"doi\":\"10.1016/j.dib.2025.111618\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":10973,\"journal\":{\"name\":\"Data in Brief\",\"volume\":\"60 \",\"pages\":\"Article 111618\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2025-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data in Brief\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352340925003506\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352340925003506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Time-lapse 3D image datasets of spruce tree wood enzymatic deconstruction
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.
期刊介绍:
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