Plant cell wall enzymatic hydrolysis: Predicting hydrolysis yield dynamics

In this study, an innovative imaging-based computational pipeline, named AIMTrack (Adaptive autofluorescence Intensity and Morphology Tracking), was developped to track plant cell wall enzymatic deconstruction in real time. AIMTrack employs an adaptive clustering strategy to compensate for sample drift and deformation occurring during enzymatic hydrolysis, enabling robust tracking and quantifying of cell wall autofluorescence intensity and morphological changes during hydrolysis. Application of the AIMTrack pipeline to the time-lapse 3D images of chlorite-treated spruce wood samples undergoing enzymatic hydrolysis revealed strong negative correlations between dynamics of sugar conversion yields measured during hydrolysis and both the dynamics of cell wall autofluorescence intensity and morphological descriptors. Phase-specific analysis revealed hydrolysis stage– and sugar type–dependent correlation patterns.

These results demonstrate that cell wall autofluorescence and morphodynamics can serve as accurate real-time biomarkers of sugar conversion yield dynamics, without the need for extensive sampling or time-consuming chemical assays, which can accelerate optimization of biotechnological conversion processes.

This work is part of the PhD thesis of Solmaz Hossein Khani conducted under the supervision of Gabriel Paës and Yassin Refahi, with fundings from ANR (FillingGaps and BIOMOD projects) and from Grand Est Region (BIOMODEL project).

Read: Hossein Khani S, Ould Amer K, Shiasi Ghalemaleki F, Corré M, Remy M, Habrant A, Lebas B, Massah J, Faraj A, Paës G*, Refah Y*.  Plant cell wall enzymatic hydrolysis: Predicting yield dynamics from autofluorescence and morphological temporal changes. Bioresource Technology, February 2026, 133642. https://doi.org/10.1016/j.biortech.2025.133642

Contacts: Dr Yassin Refahi (yassin.refahi@inrae.fr) et Dr Gabriel Paës (gabriel.paes@inrae.fr)