- Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences
- Umeå University
Escamez, Sacha; Terryn, Christine; Gandla, Madhavi Latha; Yassin, Zakiya; Scheepers, Gerhard; Nasholm, Torgny; Sundman, Ola; Jonsson, Leif J.; Lundberg-Felten, Judith; Tuominen, Hannele; Niittyla, Totte; Paes, Gabriel
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.
chemotyping in situ; FLIM; lignin; machine learning; statistical modeling; wood
ACS Sustainable Chemistry and Engineering
2021, Volume: 9, number: 51, pages: 17381-17392
Publisher: AMER CHEMICAL SOC