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Fluorescence Lifetime Imaging as an in Situ and Label-Free Readout for the Chemical Composition of Lignin
SLU Swedish University of Agricultural Sciences, Sweden; Umeå University, Sweden.
Université de Reims Champagne Ardenne, France.
Umeå University, Sweden.
RISE Research Institutes of Sweden, Built Environment, Building and Real Estate.ORCID iD: 0000-0001-6878-3361
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2021 (English)In: ACS Sustainable Chemistry and Engineering, E-ISSN 2168-0485, Vol. 9, no 51Article in journal (Refereed) Published
Abstract [en]

Naturally fluorescent polymeric molecules such as collagen, resilin, cutin, suberin, or lignin can serve as renewable sources of bioproducts. Theoretical physics predicts that the fluorescence lifetime of these polymers is related to their chemical composition. We verified this prediction for lignin, a major structural element in plant cell walls that form woody biomass. Lignin is composed of different phenylpropanoid units, and its composition affects its properties, biological functions, and the utilization of wood biomass. We carried out fluorescence lifetime imaging microscopy (FLIM) measurements of wood cell wall lignin in a population of 90 hybrid aspen trees genetically engineered to display differences in cell wall chemistry and structure. We also measured the wood cell wall composition by classical analytical methods in these trees. Using statistical modeling and machine learning algorithms, we identified parameters of fluorescence lifetime that predict the content of S-type and G-type lignin units, the two main types of units in the lignin of angiosperm (flowering) plants. In a first step toward tailoring lignin biosynthesis toward improvement of woody biomass feedstocks, we show how FLIM can reveal the dynamics of lignin biosynthesis in two different biological contexts, including in vivo while lignin is being synthesized in the walls of living cells. © 2021 The Authors.

Place, publisher, year, edition, pages
American Chemical Society , 2021. Vol. 9, no 51
Keywords [en]
chemotyping in situ, FLIM, lignin, machine learning, statistical modeling, wood, Biochemistry, Biomass, Biosynthesis, Cells, Cytology, Fluorescence imaging, Forestry, Learning algorithms, Statistical methods, Chemical compositions, Fluorescence lifetime imaging, Fluorescence lifetime imaging microscopy, Fluorescence lifetimes, Label free, Lignin biosynthesis, Polymeric molecules, Statistic modeling, Wood cell walls
National Category
Wood Science
Identifiers
URN: urn:nbn:se:ri:diva-57904DOI: 10.1021/acssuschemeng.1c06780Scopus ID: 2-s2.0-85121639271OAI: oai:DiVA.org:ri-57904DiVA, id: diva2:1626092
Note

Funding details: Energimyndigheten; Funding text 1: The authors wish to acknowledge the valuable technical help from Junko Takahashi-Schmidt (pyrolysis-GC/MS, UPSC biopolymer analytical platform), Anouck Habrant (synthesis of DHP, FARE lab), and David Crônier (2D NMR analysis, FARE lab). This work was funded by the Swedish Strategic Research Environment Bio4Energy ( https://www.bio4energy.se ) through two grants: the Bio4Energy Special Call Grant 2018 and the Bio4Energy Strategic Funds Grant 2019. Bio4Energy is one of three strategic research environments funded by the Swedish Energy Agency.

Available from: 2022-01-10 Created: 2022-01-10 Last updated: 2023-12-06Bibliographically approved

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Yassin, ZakiyaScheepers, Gerhard

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