<figure><img src="image_1.png" /></figure>

# Plant cell wall enzymatic hydrolysis: Predicting yield dynamics from autofluorescence and morphological temporal changes

Solmaz Hossein Khani<sup>a,†</sup>, Khadidja Ould Amer<sup>a,b †</sup>, Fatemeh Shiasi Ghalemaleki<sup>a,c †</sup>, Maxime Corré<sup>a †</sup>, Noah Remy<sup>a †</sup>, Anouck Habrant<sup>a †</sup>, Berangère Lebas<sup>a †</sup>, Jafar Massah<sup>c</sup>, Ali Faraj<sup>a †</sup>, Gabriel Paës<sup>a †,*</sup>, Yassin Refahi<sup>a †,*</sup>

## GRAPHICAL ABSTRACT

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

Dataset link: https://gitlab.com/farelab/teamy r/publications/AIMTrack

**Keywords:**

Lignocellulosic biomass
Enzymatic hydrolysis

## ABSTRACT

Enzymatic hydrolysis of plant cell walls into fermentable sugars is a critical step in biotechnological conversion, yet efficiency is limited by cell wall recalcitrance. Predicting conversion yields of cell wall-derived sugars during hydrolysis is challenging due to the complex underlying mechanisms and the labor-intensive nature of conventional methods. This study introduces an innovative pipeline that accurately quantifies cell wall

E-mail addresses: solmaz.hossein-khani@univ-reims.fr (S. Hossein Khani), khadidja.ouldamer@isen-ouest.yncrea.fr (K. Ould Amer), fatemeh.shiasi@ut.ac.ir (F. Shiasi Ghalemaleki), maxime.corre@inrae.fr (M. Corré), noah.remy@inrae.fr (N. Remy), anouck.habrant@inrae.fr (A. Habrant), berangere.lebas@inrae.f (B. Lebas), jmassah@ut.ac.ir (J. Massah), ali.faraj@inrae.fr (A. Faraj), gabriel.paes@inrae.fr (G. Paës), yassin.refahi@inrae.fr (Y. Refahi).

<footnote><sup>a</sup> Université de Reims-Champagne-Ardenne, INRAE, FARE, UMR A 614, Reims, France</footnote>

<footnote><sup>b</sup> LabISEN-KlaIM, ISEN Yncrea Ouest, Carquefou, France</footnote>

<footnote><sup>c</sup> Department of Agrotechnology, College of Aburaihan, University of Tehran, Tehran, Iran</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote n="1">¹ S.HK. and K.OA. contributed equally to this work.</footnote>
Fluorescence microscopy

4D imaging

Drift compensation

Morphodynamics

autofluorescence intensity and morphological descriptors during enzymatic hydrolysis. The pipeline incorporates a novel adaptive drift compensation strategy that dynamically adjusts to the progression and extent of deconstruction, ensuring robust analysis. Applied to time-lapse images of spruce wood enzymatic deconstruction, the pipeline revealed strong negative correlations between conversion yields during hydrolysis and both the dynamics of cell wall autofluorescence intensity and morphological descriptors. Phase-specific analysis uncovered distinct correlation patterns dependent on hydrolysis stage and sugar type. This nondestructive pipeline eliminates the need for extensive sampling and time-consuming chemical assays, establishing plant cell wall autofluorescence and morphological descriptors as accurate predictive real-time biomarkers of dynamics of sugar conversion yields. The findings provide a framework for accelerating the development of optimized biotechnological conversion processes.

# 1. Introduction

Addressing global sustainability challenges such as climate change and depletion of fossil-based resources has intensified the research on using renewable resources for energy, chemicals, and materials. Lignocellulosic biomass from plants, as the most abundant renewable bioresource, can serve as a fundamental component of a suite of solutions, such as wind power and photovoltaic systems, to ensure energy security and a sustainable future (Cavelius et al., 2023).

Lignocellulosic biomass is mainly composed of three biopolymers: cellulose (45%–55%), hemicellulose (25%–35%), and lignin (20%–30%) (Peng and She, 2014). Cellulose, as the most abundant biopolymer, is a linear polysaccharide composed of β-D-glucose monomers linked by β(1 → 4) glycosidic bonds. Bundles of aligned β(1 → 4)-linked D-glucose chains form cellulose microfibrils (Pérez et al., 2002). Hemicellulose is an amorphous heterogeneous group of polysaccharides (e.g. xylans, mannans, galactans) composed of various sugar monomers, including pentoses (e.g. arabinose and xylose) and hexoses (e.g. mannose, and galactose) (Qaseem et al., 2021). Lignin is a structurally complex aromatic biopolymer which confers hydrophobicity and mechanical strength to the plant cell walls (Yoo et al., 2020).

The recalcitrance of lignocellulosic biomass poses a major challenge to its efficient bioconversion (McCann and Carpita, 2015). Key factors contributing to recalcitrance include the chemical composition and structural characteristics of lignocellulosic biomass. The interactions between hemicelluloses, cellulose, and lignin limit enzyme accessibility and enzymatic hydrolysis efficiency. Hemicelluloses create an enveloping matrix around cellulose microfibrils forming a physical shield that restricts enzyme access. Structural and physical properties of cellulose, such as crystallinity and degree of polymerization, are key contributors to recalcitrance (Jang et al., 2023). High cellulose crystallinity and degree of polymerization negatively impact the enzymatic hydrolysis efficiency (Hallac and Ragauskas, 2011). Lignin-related characteristics are widely recognized as major contributors of recalcitrance (Yoo et al., 2020). These characteristics include lignin content, composition, molecular weights, and phenolic hydroxyl groups (Yoo et al., 2020). In addition to making a natural barrier to cellulose, lignin also contributes to creating non-specific interactions with enzymes, reducing their efficiency (Dos Santos et al., 2019; Yoo et al., 2020). Structural features of lignocellulosic biomass such as pore size and cellulose accessibility, also referred to as accessible surface area of cellulose, significantly impact conversion efficiency (Meng et al., 2015; Herbaut et al., 2018).

Biotechnological conversion of lignocellulosic biomass offers advantages such as mild operating conditions, enhanced selectivity for target product, and environmental sustainability (Chatterjee et al., 2015). This process involves three main steps: pretreatment, enzymatic hydrolysis, and fermentation. Pretreatment disrupts the complex structure of the biomass and reduces the biomass recalcitrance. Pretreatment is followed by enzymatic hydrolysis, a key bottleneck limiting the efficiency of lignocellulosic biomass biorefineries, to generate simple fermentable sugars (Guo et al., 2023). The sugars are then converted into bioproducts, like bioethanol, via fermentation through separate or integrated process strategies (Malik et al., 2022). Enzymatic saccharification can be considered the most critical step (Guo et al., 2023) and

has a decisive role in determining the economic viability of producing bioethanol from lignocellulose (Binod et al., 2011). However, dynamics of conversion yields of cell wall-derived sugars remain elusive to predict, primarily due to cell walls' complex and heterogeneous nature. In addition, yield conversion measurements remain time-consuming and laborious (Singh et al., 2020). The unpredictability combined with procedural demands impedes rapid screening of biomass feedstocks and process conditions in research contexts and constrains industrial efforts to optimize enzyme formulations and reaction parameters in a cost-effective manner.

Study of plant cell wall digestibility increasingly relies on advanced imaging and spectroscopic techniques such as atomic force microscopy (AFM), AFM-based approaches such as nano-Fourier transform infrared spectroscopy (nano-FTIR) (Bhagia et al., 2022; Li and Kasal, 2025) and confocal Raman microscopy (Gierlinger et al., 2012; Zoglami et al., 2019), which have advanced our understanding of the chemical composition, structural heterogeneity, and digestibility of cell walls. Solid-state nuclear magnetic resonance (ssNMR) techniques have contributed to understanding of cell wall molecular architecture elucidating specific interactions between cell wall components (Zhao et al., 2020; Ghassemi et al., 2021). While most investigations are at nanoscale, microscale studies have also advanced our understanding of cell wall enzymatic hydrolysis. Fluorescent probes, immunolabeling, and cellulose staining have been employed for imaging enzymatic hydrolysis, offering visual insights into cell wall polymer deconstruction and its dynamics (Li et al., 2018). Furthermore, recent studies have also demonstrated the relevance and importance of microscale quantitative investigation of cell wall enzymatic hydrolysis (Refahi et al., 2024; Hossein Khani et al., 2025). Despite progress, cell and tissue scale study of cell wall morphological parameters during enzymatic deconstruction remains challenging and quantitatively underexplored.

Acquiring time-lapse image datasets is notoriously demanding, and their accurate quantitative analysis is challenging, with sample drift being a common obstacle. A widely used strategy for drift compensation relies on tracking fiducial markers, such as gold nanoparticles or fluorescent beads to quantify drift (Bates et al., 2007; Ma et al., 2024). Alternatively, marker-free drift correction methods are developed, offering the advantage of eliminating the need for specialized sample preparation or modifications to the imaging system (Diao et al., 2023). Existing drift compensation approaches include (Refahi et al., 2024; Hossein Khani et al., 2025): i) global approach which registers images to a single reference image but fails under extensive hydrolysis; ii) local approach which registers consecutive images which is suitable for handling substantial changes between images under high deconstruction conditions, but accumulates error through repeated compositions of transformations; and iii) fixed-size clustering approach which divides time-lapse images into clusters of predefined sizes. This approach offers a balance between prior approaches and uses a global approach, used for intra-cluster images, and local approach across the clusters. However, this clustering strategy requires manual cluster-size tuning and is suboptimal under natural variable deconstruction rates, often resulting in excessive registrations and reduced accuracy.

This study introduces AIMTrack (Adaptive autofluorescence Intensity and Morphology Tracking), a novel computational pipeline devised
to quantify dynamics of autofluorescence intensity and morphological
changes of cell walls during enzymatic deconstruction. AIMTrack em-
loys an adaptive clustering strategy to compensate for sample drift
and deformation where the cluster sizes are adapted to deconstruction.
AIMTrack is applied to time-lapse images of spruce wood pretreated
with sodium chlorite for partial delignification (Gupta et al., 2011).
Spruce was chosen due to the impact of climate change on Euro-
pean forests experiencing widespread spruce dieback (Netherer et al.,
2024). Using AIMTrack dynamics of enzyme-driven changes in cell
wall autofluorescence and morphological descriptors are quantified.
Finally, the relationship between quantified dynamics and cell wall
monosaccharide conversion yield dynamics is investigated.

**2. Materials and methods**

**2.1. Pretreatment and composition analysis**

Spruce (Picea abies) samples, supplied by INRAE, Grand-Est, France, were first cut into fragments measuring 0.5 × 0.5 × 1 cm. Sample fragments were then pretreated using sodium chlorite to achieve partial delignification. Specifically, the samples were immersed in eight consecutive treatment baths, each lasting 1 h at 70 °C, in a solution composed of 1.25 g of sodium chlorite, 150 µL acetic acid, and 40 mL of water (Gupta et al., 2011).

To determine lignin content and polysaccharide composition, the
3 mm end segments of raw and pretreated sample fragments, selected
for consistency with the subsequent microscopy acquisitions, were first
ground. Lignin content was measured by acetyl bromide assay in tripli-
cate (Moreira-Vilar et al., 2014). Sugar composition was determined
in triplicate following acid hydrolysis. Hydrolysates were analyzed
by high-pressure anion exchange chromatography (HPAEC - Dionex
ICS5000, Thermo Fisher, USA), equipped with Carbopac PA1 column
(4 × 25 mm) and precolumn, using gradient elution with sodium
hydroxide and sodium hydrogenocarbonate and detection by pulsed
amperometry (Bleuze et al., 2018).

**2.2. 4D (3D + time) confocal fluorescence microscopy imaging**

To reduce sample spatial heterogeneity that could be introduced
by pretreatment differential impact, the terminal 3 mm regions along
the 1 cm longitudinal axis of the pretreated samples were selected
for sectioning. These extremities were selected as they were consis-
tently and thoroughly exposed during the pretreatment. Sections of
40 µm thickness were then prepared from these specific regions using
a microtome (HM340E, Thermo Scientific, Waltham, USA) equipped
with a disposable blade. A sample section was then placed onto a
glass slide with a Gene Frame positioned around it. 60 µL of acetate
buffer (50 mM, pH 5) was then added within the Gene Frame and
incubated for 30 min. Another sample section was weighed with a pre-
cision balance. A cellulolytic enzyme cocktail (IFPEN, Rueil-Malmaison,
France) was diluted with 50 mM acetate buffer, to reach 15 and 30
FPU/g biomass. At the end of the 30-minute incubation, filter paper
was used to remove the buffer within the Gene Frame. A volume
of 60 µL of enzyme solution (or acetate buffer in case of control
dataset) was then added into the frame, which was subsequently sealed
with a coverslip. The prepared slide was placed onto the stage of a
confocal microscope (Olympus Fluoview FV3000, Tokyo, Japan) within
a temperature-controlled incubation chamber (OKOlab, Napoli, Italy)
maintained at 50 °C (Zoghlami et al., 2020). Time-lapse imaging was
then performed by acquiring z-stacks at 1 h intervals for a period of
24 h. Imaging was performed using a 405 nm excitation laser set to 10%
power, with fluorescence emission detection in 415–515 nm range.
Scanning was conducted at 4 µs/pixel with a resolution of 512 × 512
pixels, and the gain was set to 600 V. The confocal pinhole aperture was
adjusted to 50 µm. For time-lapse imaging, a 20× objective lens was
used with a 2× digital zoom. Z-stacks, each containing 400 sequential
optical sections, were acquired with a step size of 0.3 µm along the
Z-axis at 12-bit depth.

**2.3. Conversion yields quantification during enzymatic hydrolysis**

The prepared sample sections were placed into individual 200 µL mini-reactors, to which 60 µL of enzyme solution (or acetate buffer to collect control dataset) was added. Each mini-reactor tube was closed with a lid and wrapped with some Parafilm to ensure effective prevention of evaporation. Experiments were performed in triplicate mini-reactor tubes for each enzyme loading. Each tube was then placed in the incubation chamber stabilized at 50 °C. Sampling of the reaction mix into an Eppendorf tube was performed at 0 h, 15 min, 1 h, 2 h, 4 h, 8 h, 12 h, 16 h, 20 h, 24 h after incubation launch. Hydrolysates were then boiled for 5 min, then briefly centrifuged and stored at -20 °C prior to analysis. For quantification of released sugars, 30 µL aliquots of enzymatic hydrolysates were mixed with an internal standard and diluted prior to analysis by High-Performance Anion Exchange Chromatography (HPAEC) using a Dionex ICS-5000 system (Thermo Fisher Scientific, USA).

**2.4. 4D image processing**

Reliable quantification of cell wall enzymatic deconstruction from time-lapse images requires an effective sample drift and deformation compensation. The existing drift compensation strategies – global, local, and fixed-size clustering – have limitations: the global approach fails as deconstruction progresses, the local approach accumulates errors from repeated transformation composition, and fixed-size clustering requires manual tuning and performs poorly under natural temporal heterogeneity of enzymatic deconstruction. To address these limitations, an adaptive clustering method named AIMTrack (Adaptive autofluorescence Intensity and Morphology Tracking) was devised to track cell wall autofluorescence intensity and morphodynamics during enzymatic deconstruction.

Let {$I_{t_0}, \dots, I_{t_n$} denote the set of time-lapse 3D images of plant cell wall deconstruction, where $I_{t_i}$ represents the i-th 3D confocal image (z-stack) acquired at time $t_i$, with $0 \le i \le n$, and $I_{t_0}$ and $I_{t_n}$ denoting the pre-hydrolysis and final acquisitions respectively. The time-lapse 3D images represent cell wall autofluorescence intensity images. The AIMTrack workflow involves the following steps: i) adaptive temporal clustering of time-lapse images; ii) adaptive compensation of drift and deformation; iii) tracking cell walls in time-lapse autofluorescence intensity images using a propagation strategy iv) identification of individual cell walls. In the following, a detailed explanation of each step is provided.

**2.4.1. Adaptive temporal clustering of 3D time-lapse images**

Direct registration of temporally close images with conventional
registration algorithms can be performed with reasonable accuracy.
However, direct registration often fails when applied to temporally
distant images, in particular under extensive deconstruction condi-
tions, leading to excessive deformation of the floating image (Hossein
Khani et al., 2025). Indirect registration approaches (Hossein Khani
et al., 2025), address this issue by dividing time-lapse images into
clusters of fixed size, constraining the registration time-frame to the
images within clusters and composing the resulting transformations
to register temporally distant images. This fixed size clustering has
the following limitations: i) the cluster size needs to be determined
manually which is laborious and time-consuming, as it requires re-
peated trials and visual inspection by an expert to determine the
cluster size; ii) fixed-size clusters implicitly assume a uniform rate of
deconstruction throughout the hydrolysis process. Consequently, the
use of a fixed cluster size requires setting the cluster size according
to the fastest deconstruction stage, leading to unnecessary subdivision
of image sequences during stages with limited deconstruction. This
over-clustering compromises the quality of results and downstream
quantifications (Hossein Khani et al., 2025). AIMTrack addresses these
limitations by defining clusters as maximal contiguous sequences of
time points within which the cell wall deformation remains below a
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Tracking of cell wall during enzymatic hydrolysis. (A) Time-lapse images of cell wall hydrolysis. (B) AIMTrack's adaptive clustering framework: (B.1) $I_t$ is the $i$-th 3D image collected at time $t$, $0 \le i \le n$. AIMTrack's variable cluster size strategy employs larger clusters during periods of slower cell wall modification, and adaptively reduces cluster size during periods of faster structural changes. (B.2) AIMTrack ensures that deformation magnitude, $D(\cdot, \cdot)$, within each cluster does not exceed a predefined threshold, $\epsilon$. The last image of a cluster is the final image in the contiguous sequence for which the deformation does not exceed $\epsilon$; the image immediately following exceeds this threshold. The transformations are computed in forward and backward directions within each cluster which are used to evaluate structural changes induced by enzymatic hydrolysis. Composition of intra-cluster transformations registers hydrolysis-phase images to the pre-hydrolysis image, effectively compensating for drift and deformation. (C) Pre-hydrolysis cell walls consistently captured throughout the time-lapse are rendered with increased brightness. (D) Corresponding tracking of these cell walls during hydrolysis, with enhanced brightness indicating their position at each time point. (E) Identification of individual cells in the time-lapse images.</figcaption></figure>

specified threshold. In doing so, AIMTrack automatizes the clustering process and reduces the number of clusters required to achieve accurate registration compared to fixed-size clustering, which can excessively fragment time-lapse sequences, and introduce accumulation of registration errors generated by an increased frequency of transformation compositions.

To achieve this, AIMTrack divides the time-lapse images into $v$ sequential clusters of variable sizes, denoted by $C_q$, $q \in \{0, 1, ..., v-1\}$. Each cluster shares a single image with its preceding cluster such that its first image is the last image of the previous cluster (Fig. 1). Each cluster $C_q$ consists of $m_q + 1$ successive images. The clusters are defined as follows:
* The first cluster, $C_0$, consists of the first $m_0 + 1$ images:

$$C_0 = \{I_{t_0}, I_{t_1}, \dots, I_{t_{m_0}}\}.$$

* For $q \ge 1$, the cluster $q$ is defined as:

$$C_q = \{I_{t_p}, I_{t_{p+1}}, \dots, I_{t_{p+m_q}}\}, \quad \text{where} \quad p = \sum_{j=0}^{q-1} m_j.$$

Cluster sizes, $m_q$, are determined adaptively according to the extent of deconstruction over specific intervals, defining cluster boundaries based on deformation that reflect the extent of structural change over time. To accomplish this, AIMTrack initiates clustering from the pre-hydrolysis image and assesses deformation between the first image of the cluster (here pre-hydrolysis image) and subsequent hydrolysis-phase images using registration both in forward and backward directions. This deformation is quantified as a vector field resulting from nonlinear registration, performed after sequentially applying rigid and affine registrations. The nonlinear registration computes the residual deformation as a vector displacement field. The block matching framework is used for the registrations (Ourselin et al., 2000). Deformation magnitude, defined as the largest of the means of Euclidean norms of vectors comprising these vector fields derived from forward and backward registrations, is then computed. Cluster expansion proceeds progressively, integrating subsequent images while the deformation magnitude does not exceed a predefined threshold value (denoted by $\epsilon$ and set at 4.1 for hydrolysis and control datasets, chosen by an expert as the maximum tolerable deformation). Upon exceeding this threshold, AIMTrack terminates the current cluster and initiates a new cluster starting from the last image of the preceding cluster. This iterative clustering process continues systematically until reaching the last image of the time-lapse series. The adaptive clustering ensures that intra-cluster deformations remain below the predefined threshold. Consider a given cluster, $C_q = \{I_{t_p}, I_{t_{p+1}}, \dots, I_{t_{p+m_q}}\}$ with $0 \le q < \nu$ and $p = \sum_{j=0}^{q-1} m_j$. Let $D(I_{t_p}, I_{t_q})$ denote the deformation field magnitude. By construction, the deformation within the cluster satisfies $D(I_{t_p}, I_{t_q}) \le \epsilon$, $p < u \le p + m_q$, while including of the next image would exceed the threshold: $D(I_{t_p}, I_{t_{p+m_q}}) > \epsilon$ (Fig. 1).

## Benchmarking aimtrack adaptive clustering

To assess the accuracy and performance of the AIMTrack clustering, the clusters generated with AIMTrack were compared with expert determined clusters obtained through expert visual inspection. Expert determination of clusters involved visual inspection of the registration quality by generating an RGB image in which the tracked region of the reference image was assigned to the green channel and the registered floating image to the red channel and subsequently inspecting the overlay of the green channel and red channel with ImageJ. In the RGB image, the untracked region of the reference image due to sample drift was saved in blue channel. Cluster boundaries were manually defined at time points where misalignments appeared as color fringes which indicated registration failure. In fact, successful registrations are characterized by overlapping red and green signals which combine to produce yellow in the RGB overlay while imperfect registration creates color fringes as visible edges of pure red or pure green. These expert-defined clusters served as a benchmark for evaluating the clustering results produced by AIMTrack. The cluster time points identified by AIMTrack were compared to those annotated manually, revealing strong agreement: 213 out of 225 time points (approximately 95%) matched the expert annotations (Table 1).

To further evaluate the performance of adaptive clustering of AIM-Track, the average cluster sizes obtained using AIMTrack were compared to fixed-size clustering strategy (Fig. 2) given that in the fixed cluster size clustering the smallest cluster size detected by AIMTrack should be used uniformly across all time points of a time-lapse sequence. Larger clusters imply fewer required successive application (or compositions) of transformations across clusters and smaller clusters

<figure><img src="image_4.png" /><figcaption>Fig. 2. Comparison of average cluster sizes produced by fixed-size clustering and AIMTrack under different enzymatic conditions. In the presence of enzymes at enzyme loadings of 30 FPU/g biomass and 15 FPU/g biomass, AIMTrack provides larger clusters than fixed-size clustering strategy. Both strategies generate comparable cluster sizes for control time-lapse dataset. By producing larger clusters where appropriate, AIMTrack reduces the number of transformation compositions, therefore improves accuracy.</figcaption></figure>

result in higher number of compositions, increasing cumulative registration error (Hossein Khani et al., 2025). This relationship between cluster size and accuracy was considered in the comparative evaluation of AIMTrack and fixed-size clustering strategies.

## 2.4.2. Adaptive compensation of drift and deformation

AIMTrack uses the pre-hydrolysis image ($I_{t_0}$) as the reference image for drift and deformation compensation. The hydrolysis-phase images are registered with pre-hydrolysis image. To achieve this, following adaptive clustering of the time-lapse images, intra-cluster bidirectional transformations are computed: in the forward direction, the first image of each cluster is registered to subsequent images within the cluster; in the backward direction, each cluster image is registered to the first image of the cluster (Fig. 1.B.2, and (see supplementary material)). Each registration involves first computing a rigid transformation followed by computing an affine transformation. This is then followed by computing a nonlinear transformation. The rigid, affine, and nonlinear transformations are computed using the block matching framework.

To register the hydrolysis-phase image, $I_{t_k}$, $0 < k \le n$ with the pre-hydrolysis image, the cluster $C_s$ which includes $I_{t_k}$ is first identified. If $I_{t_k}$ is a boundary image, $I_{t_k}$ belongs to two consecutive clusters and the left one is chosen. $C_s$ can be written as:

$$C_s = \begin{cases} C_0, & k \le m_0 \\ \{I_{t_g}, I_{t_{g+1}}, \dots, I_{t_d}\}, & k > m_0, \end{cases} \quad \text{where } g = \sum_{j=0}^{s-1} m_j, \text{ and } d = g + m_s.$$

Let $T_{t_i \leftarrow t_j}$ denote the transformation that registers $I_{t_i}$ (as the floating image), with the $I_{t_j}$ (as the reference image), for $0 \le i, j \le n$. The resampled image is denoted by $I_{t_i \leftarrow t_j}$ which is obtained by applying the resulting transformation to the floating image, $I_{t_i} \circ T_{t_i \leftarrow t_j}$, where $\circ$ denotes the application of the transformation throughout this manuscript.

When $k \le m_0$, the resampled image is directly computed as

$$I_{t_k \leftarrow t_0} = I_{t_k} \circ T_{t_k \leftarrow t_0}.$$

For $k > m_0$, $I_{t_k \leftarrow t_0}$, which is the resampled image, is obtained through the recursive application of transformations:

$$I_{t_k \leftarrow t_0} = (\dots (I_{t_k} \circ T_{t_k \leftarrow t_g}) \circ T_{t_g \leftarrow t_{g-m_s-1}}) \circ \dots \circ T_{t_{m_0} \leftarrow t_0}.$$
<caption>**Table 1**</caption>

Quantitative benchmarking of AIMTrack adaptive clustering. Cluster size values correspond to those shown in Fig. 2 but are provided here for precision and to facilitate direct comparison with additional performance indicators.

<table><thead><tr><th>Metric</th><th>AIMTrack</th><th>Fixed-size clustering</th><th>Improvement</th></tr></thead><tbody><tr><td>Agreement with expert annotations (%)</td><td>95</td><td>–</td><td>High accuracy</td></tr><tr><td>Average cluster size (30 FPU g<sup>-1</sup> biomass)</td><td>5.23</td><td>2.66</td><td>×1.97</td></tr><tr><td>Average cluster size (15 FPU g<sup>-1</sup> biomass)</td><td>13.00</td><td>8.00</td><td>×1.63</td></tr><tr><td>Average cluster size (control dataset)</td><td>21.00</td><td>20.00</td><td>×1.05</td></tr><tr><td>Manual intervention required</td><td>No</td><td>Yes</td><td>Eliminated</td></tr><tr><td>Impact on cumulative error</td><td>Reduced</td><td>Higher</td><td>Improved precision</td></tr></tbody></table>

### 2.4.3. Tracking cell walls in time-lapse autofluorescence intensity images using a propagation strategy

Following the approach described in (Hossein Khani et al., 2025), the resampled images $I_{t_k \leftarrow t_0}$ are used to detect the pairwise temporally consistent imaged regions between $I_{t_k}$ and $I_{t_0}$, $0 < k \le n$. For each $I_{t_k \leftarrow t_0}$, a binary image, denoted by $\Psi_{t_k \leftarrow t_0}^{\text{cons}}$, is created where voxels which map back to voxels in $I_{t_k}$ are set to 1. Therefore, $\Psi_{t_k \leftarrow t_0}^{\text{cons}}$ marks the subset of $I_{t_0}$ which is overlapped by $I_{t_k \leftarrow t_0}$. The consistently imaged region, denoted by $\Psi_{t_0}^{\text{cons}}$, which corresponds to the subset of the sample that remains within the microscope's field of view during time-lapse acquisitions (Fig. 1.C), is determined by computing the intersection of these masks obtained using a logical AND:

$$ \Psi_{t_0}^{\text{cons}} = \bigcap_{k=1}^{n} \Psi_{t_k \leftarrow t_0} $$

The cell walls located within this region, referred to as the pre-hydrolysis consistently imaged cell walls and denoted by $\Omega_{t_0}$, are then identified using thresholding. To track this region, temporal propagation of spatial information strategy is employed using the forward transformations to propagate $\Omega_{t_0}$. This region at time point $t_k$ is denoted by $\Omega_{t_k}$, $0 < k \le n$ (Fig. 1.D, and (see supplementary material)) and is obtained as follows:

* If $k \le m_0$

$$ \Omega_{t_k} = \Omega_{t_0} \circ T_{t_0 \leftarrow t_k} $$

* For $k > m_0$, $\Omega_{t_k}$ is computed using:

$$ \Omega_{t_k} = (\dots ((\Omega_{t_0} \circ T_{t_0 \leftarrow t_{m_0}}) \circ T_{t_{m_0} \leftarrow t_{m_0+m_1}}) \circ \dots) \circ T_{t_g \leftarrow t_k} $$

where $T_{t_g \leftarrow t_k}$ is the transformation which enables to register the left boundary image, $I_{t_g}$, of the cluster including $I_{t_k}$, with $I_{t_k}$. In case $I_{t_k}$ itself is a boundary image belonging to two consecutive clusters, and $I_{t_g}$ corresponds to the left boundary image of the preceding (left) cluster.

### 2.4.4. Identification of individual cell walls

To identify and track individual cells in the time-lapse images, the pre-hydrolysis image is first segmented using the previously developed method (Hossein Khani et al., 2025) which involves computing the convex hull of the imaged sample in pre-hydrolysis image. The individual cells are then identified by spatially constraining the watershed algorithm to the region inside the convex hull, generating a segmented image denoted by $S_{t_0}$. Similar to computation of $\Omega_{t_k}$, $S_{t_0}$ is then propagated throughout the hydrolysis (Fig. 1.E, and (see supplementary material)) phase as follows:

* When $k \le m_0$:

$$ S_{t_k} = S_{t_0} \circ T_{t_0 \leftarrow t_k} $$

* For $k > m_0$:

$$ S_{t_k} = (\dots ((S_{t_0} \circ T_{t_0 \leftarrow t_{m_0}}) \circ T_{t_{m_0} \leftarrow t_{m_0+m_1}}) \circ \dots) \circ T_{t_g \leftarrow t_k} $$

The reduced number of clusters achieved through AIMTrack's adaptive clustering strategy, and consequently the decreased frequency of required registrations and transformation composition and application, positively influences the identification of individual cell walls in hydrolysis-phase images and quality of segmentations as previously demonstrated (Hossein Khani et al., 2025).

### 2.5. Estimation of cell-scale accessible surface area

To estimate the morphology-based accessible surface area at the cell scale (ASA), defined as the surface area of the interface between the cell walls of individual cells and the background, the Marching Cubes (MC) algorithm from the Visualization Toolkit (VTK) (Schroeder et al., 2006) library was employed to generate a mesh of the cell walls. This mesh was then decimated, smoothed using a Laplacian filter, and subsequently used to estimate the surface area. The decimation and smoothing steps were used to optimize the surface area estimation. Two parameters in the VTK decimation and smoothing methods were optimized to improve estimation accuracy: TargetReduction, which defines the level of mesh reduction ($0 \le TargetReduction \le 1$), and NumberOfIterations, which sets the maximum number of smoothing iterations. For this optimization, 100,000 three-dimensional objects in the form of cell-shaped polyhedra (right prisms with randomly generated holed polygonal bases) were generated with known surface areas. The parameters were tuned on these randomly generated objects to obtain surface area estimations with minimal mean squared error (MSE).

Due to the possibility of local minima, three criteria were used to select the optimal parameters, which are listed in the following in descending order of importance: (1) the MSE, (2) the estimation bias, and (3) the mean CPU time required to perform a single surface area estimation, including decimation and smoothing. These criteria were evaluated using a Monte Carlo approximation, which generated optimal parameter values of 0.1 for TargetReduction and 50 for NumberOfIterations.

The resolution chosen for the optimization was 80, meaning that for each generated cell-shaped polyhedron, the cell diameter corresponded to 80 times the voxel side in the horizontal $(x, y)$ plane, and the cell thickness was set to half of the cell diameter. These dimensions were selected because they most closely represent the true cell dimensions observed in the pre-hydrolysis images.

To evaluate the performance of the optimal VTK parameters, 1,656 virtual cell-shaped polyhedra approximating the original true cells were generated. This generation process involved: i) projecting the original cells onto the horizontal $(x, y)$ plane, ii) approximating the resulting two-dimensional digital objects using the approximate_polygon function from the scikit-image package, and iii) constructing three-dimensional cell-shaped polyhedra by assigning each polygon a uniform thickness equal to the original cell wall thickness, obtained from projections onto the vertical $z$ axis. Using the optimal parameters values, a mean AD of 0.9% and a CV of 1.97% were obtained for the virtual cells. The mean AD denotes the mean absolute deviation defined as:

$$ \text{mean AD} = \mathbb{E} \left[ \left| \frac{\hat{A} - A}{A} \right| \right], $$
with $\hat{A}$ denoting the estimated surface area, A the ground truth surface area, $E[·|·]$ the expectation operator, and $|·|$ the absolute value. CV denotes the coefficient of variation, defined as $\sigma/\mu$, where $\sigma$ is the standard deviation of the estimation errors and $\mu$ is their mean. These results demonstrate that using optimized parameters enable highly accurate surface area estimations for cell-shaped polyhedra closely approximating the true cells in pre-hydrolysis images. Subsequently, these parameter values were used to estimate the cell-scale ASA. The accuracy achieved in estimating surface area combined with the precision of AIMTrack's image processing framework, underscores the overall robustness and reliability of the quantifications obtained using AIMTrack pipeline.

# 3. Results and discussion

## 3.1. Dynamics of enzymatic hydrolysis in raw and chlorite-treated spruce wood samples

Chemical compositions of the raw and sodium chlorite-pretreated spruce wood samples were determined to assess the extent of chemical changes of the pretreatment process (see supplementary material). The raw sample was mainly composed of glucose (46.35%), lignin (30.4%), mannose (11.8%), xylose (5.5%), galactose (2.6%) with only minor amounts of other sugars including arabinose, galacturonic acid, glucuronic acid, rhamnose, and fucose. In contrast, the compositional analysis of the chlorite-pretreated sample revealed a remarkable increase in the proportion of glucose (58.9%). Mannose, xylose, and galactose content also shifted to 15%, 6.5%, 1.4% respectively. Lignin content dropped to 11.3% following pretreatment, in line with expected compositional shift, while contents of other sugars present in small amounts changed slightly.

To characterize the kinetics of enzymatic hydrolysis, conversion yields of the sugars identified in the compositional analysis were measured during 24 h for both pretreated and raw samples under three experimental conditions: a control without enzymes, and two enzyme loadings of 15 and 30 FPU/g biomass. Significant conversion yields were observed in pretreated samples under both enzyme loadings (see supplementary material). In contrast, only minor saccharification was observed in the raw samples at both enzyme loadings (see supplementary material). In both pretreated and raw samples under control conditions, sugar conversion yields were negligible.

More specifically, pretreated samples subjected to enzymatic hydrolysis at 30 FPU/g biomass consistently exhibited the highest conversion yields at every time point. Conversion yield of glucose showed fast increase during the first 4 h and started to plateau after 8 h. Similarly, xylose exhibited an initial high rate of enzymatic hydrolysis, with a slower, but steady conversion rate over the subsequent 20 h. In contrast, mannose and galactose conversion yields maintained consistently steady dynamics throughout the hydrolysis process over 24 h. Analysis of grouped hemicellulosic sugars conversion yields revealed a consistent increase over time with a moderately accelerated conversion rate during the first 4 h. However, this early-stage trend was less pronounced than that seen for glucose over the same period. Pretreated samples hydrolyzed at 15 FPU/g biomass exhibited similar trends of conversion yields dynamics, but with lower conversion yields compared to pretreated samples hydrolyzed at 30 FPU/g biomass. Altogether, these results highlight an enzyme activity-dependent effect: higher enzyme loading (30 FPU/g biomass) leads to faster and more complete sugar conversion, while lower enzyme loading (15 FPU/g biomass) achieves a significant, but comparatively lower yields. Furthermore, while glucose conversion reaches a plateau after approximately 8 h, hemicellulosic sugars conversion continues to progress after that time frame.

## 3.2. Adaptive pipeline to track cell wall autofluorescence intensity and morphology during hydrolysis

To assess the impact of enzymatic hydrolysis on plant cell wall, confocal fluorescence time-lapse 3D images of pretreated spruce wood samples were collected over the course of hydrolysis, with two enzyme loadings of 15 and 30 FPU/g biomass utilizing the inherent autofluorescence of cell wall (Donaldson and Radotic, 2013). Additionally, control time-lapse images were acquired under the same conditions but without enzymes. Time-lapse imaging and the subsequent analysis were limited to pretreated samples, as raw samples exhibited only minor saccharification. The images were acquired with a one hour interval over 24 h. Hereafter, the control time-lapse images are referred to as *control dataset*, and those collected with two enzyme loadings of 15 and 30 FPU/g biomass are collectively referred to as *hydrolysis dataset*. The first image in a time-lapse series is designated as pre-hydrolysis image and the following images are referred to as hydrolysis-phase images. For consistency, the term hydrolysis-phase images is also used for the post-initial time-lapse images in the control dataset, even though no enzymatic hydrolysis occurs.

The hydrolysis dataset revealed highly deconstructed cell walls exhibiting material loss, structural deformations, a progressive decline in autofluorescence intensity and gradual sample drift during time-lapse imaging (Fig. 1). Consequently, tracking cell walls undergoing deconstruction from time-lapse images required efficient drift and deformation compensation to ensure accurate, and reliable quantification.

The existing drift compensation methods demonstrated limitations and reduced accuracy. The global approach which involves registering the hydrolysis-phase images directly with pre-hydrolysis, although initially effective, failed as deconstruction advanced and hydrolysis-induced changes accumulated. Local approach which involves indirect registration of hydrolysis-phase images and pre-hydrolysis image by registering pair of consecutive images and composing the resulting transformations, became inefficient by accumulating errors from repeated registration and compositions. The clustering approach is a compromise between the global and local approaches by limiting the registration to clusters, thus reducing registration time-frame compared to global approach, and reducing the required number of transformation composition compared to local strategy (Hossein Khani et al., 2025). However, clustering strategy with fixed-size clusters has some limitations. The optimal cluster size selection relies on iterative visual assessment of registration quality which can be a laborious and subjective process. Moreover, the optimal cluster size has to minimize the registration and transformation composition frequency, therefore needs to be as large as possible without comprising the intra-cluster registration precision. However, a considerable temporal variability is observed in hydrolysis rates and associated changes in cell walls (Hossein Khani et al., 2025). Significant cell wall modification induced by enzymatic hydrolysis within short time intervals, necessitates a smaller cluster size to achieve precise registration. Using a fixed cluster size implies the consistent use of this smaller cluster size throughout the entire sequence of time-lapse images, even during intervals lacking significant cell wall modifications. This results in an increased number of registration and transformation compositions, and reduces the quality of indirect registration.

To address these limitations, an adaptive clustering method named AIMTrack (Adaptive autofluorescence Intensity and Morphology Tracking) was developed to track cell wall autofluorescence intensity and morphodynamics during enzymatic hydrolysis. AIMTrack includes a hybrid drift and deformation compensation strategy by using a global strategy within clusters and a local strategy between clusters (Fig. 1.B and (see supplementary material)) and introduces adaptivity by dynamically adjusting cluster sizes according to the extent of deconstruction with larger clusters for slow deconstruction stages and smaller clusters for stages involving more significant deconstruction. This adaptive
clustering approach builds upon the divide-and-conquer principle (Cor- men et al., 2009) by adopting a dynamically modulated partitioning strategy. More precisely, AIMTrack initiates clustering from the pre- hydrolysis image and assesses deformation between the pre-hydrolysis image and subsequent hydrolysis-phase images via registration (see supplementary material). The cluster progressively expands with im- ages exhibiting only gradual changes in average deformation. A pro- nounced increase in average deformation indicates the limit of effective registration and consequently marks the end of cluster expansion. AIMTrack initiates a new cluster by including the final image of the previous cluster and image associated with the rapid average deforma- tion increase, and the same process used for determining the preceding cluster repeats to determine the current cluster's right-most image. This process repeats until reaching the last image of the time-lapse dataset. The resulting transformations are then used to compensate for sample drift and deformation enabling identification of the consistently imaged region, defined as the intersection of resampled hydrolysis-phase im- ages in frame of the pre-hydrolysis image. The cell walls within this region, called consistently imaged cell walls (Fig. 1.C), are then prop- agated across the hydrolysis-phase images using the transformations enabling tracking of cell walls and their autofluorescence intensity over time (Fig. 1.D and (see supplementary material)). Furthermore, this propagation strategy is also used to identify the individual cell walls during hydrolysis using their initial states in pre-hydrolysis image (Fig. 1.E and (see supplementary material)).

To assess the performance of the AIMTrack adaptive clustering, its output was compared with expert determined clusters based on visual inspection. This comparison revealed strong agreement with approximately 95% of the images clustered by AIMTrack matching the expert annotations (Table 1). When compared with fixed-size clustering, AIMTrack generated larger average cluster sizes, particularly for the hydrolysis dataset (Fig. 2). At 30 and 15 FPU/g biomass, AIMTrack provided larger clusters (5.23 and 13, respectively), compared to the fixed-size cluster method (2.66 and 8.00, respectively). For the control dataset, AIMTrack produced an average cluster size of 21.0, closely matching the fixed-size strategy (20.0). Larger cluster sizes reduce the frequency of registrations and transformation compositions, therefore minimize cumulative error and improve overall accuracy (Hossein Khani et al., 2025). The benchmarking metrics, including agreement with expert annotations, manual intervention requirements, and error impact, are summarized in Table 1. Collectively, these results demonstrated the ability of AIMTrack in autonomously determining the clusters, eliminating the need for manual intervention, and enhancing precision. Furthermore, the larger clusters generated by AIMTrack, and the correspondingly reduced number of clusters, results in enhanced accuracy of segmentations of hydrolysis-phase images as previously demonstrated (Hossein Khani et al., 2025).

### 3.3. Enzyme-driven cell wall autofluorescence intensity dynamics are monotonically and depend on enzyme loading

To investigate the cell wall autofluorescence intensity evolution during enzymatic hydrolysis, normalized average autofluorescence intensity dynamics, hereafter referred to as AFI dynamics, were first computed directly from the unprocessed control and hydrolysis datasets (Fig. 3.A). The computed AFI dynamics exhibited gradual decreases, with control dataset showing the lowest drop compared to the hydrolysis dataset. The collected time-lapse images were then processed using AIMTrack to correct the sample drift and compute the AFI dynamics of the consistently imaged region (Fig. 3.B). The AFI dynamics showed comparable overall trends between the unprocessed and drift-corrected image datasets. In both cases, AFI dynamics of the hydrolysis dataset showed larger reductions than the control. However, the drift-corrected datasets undergoing enzymatic hydrolysis displayed a more pronounced AFI dynamics decline compared to the unprocessed hydrolysis dataset without drift correction (Fig. 3.A, B). The drift correction

ensured a more precise extracted AFI dynamics by focusing on the same spatial region of the sample which is common to all images, the consistently imaged region, and removing the inaccuracy induced by positional inconsistency. Following the identification of the consistently imaged region in the pre-hydrolysis images, the consistently imaged cell walls within this region were isolated by removing background noise. AFI dynamics quantification was restricted to these consistently imaged cell walls, meaning that only time-dependent changes in autofluorescence intensity within this region were tracked (Fig. 3.C). Consistent with the previously observed trends, the computed AFI dynamics displayed a gradual decline over time, with control dataset exhibiting the smallest reductions and the hydrolysis dataset demonstrating more significant decreases. However, the decrease in autofluorescence intensity became markedly more pronounced under different experimental conditions. Specifically, distinctions in AFI dynamics between control and hydrolysis datasets became clearer, with higher enzyme loadings exhibiting a distinctly greater decline. This exclusive focus on cell wall voxels (3D pixels) enabled effective isolation of the cell wall-specific signal by discarding the background and avoiding underestimation or misinterpretation of AFI dynamics.

To further characterize the autofluorescence intensity evolution during hydrolysis, temporal evolution of skewness and kurtosis of the autofluorescence intensity distributions were quantified as measures of asymmetry, tailedness, and peakedness (see supplementary material). In the control dataset, both skewness and kurtosis remained consistently low and stable after an initial decrease. The hydrolysis dataset exhibited a small initial reduction in skewness, followed by a general upward trend, with kurtosis showing a similar trend. These results indicate that during enzymatic hydrolysis, the distributions shift away from nearly symmetrical distributions and progressively develop pronounced asymmetry and heavier tails. These results are in line with previous results reporting distinct autofluorescence distribution signatures during enzymatic hydrolysis (Hossein Khani et al., 2025). No clear differences in skewness or kurtosis dynamics between datasets collected at 15 and 30 FPU/g biomass enzyme loadings. However, both skewness and kurtosis dynamics in the hydrolysis dataset were clearly and markedly different from the control dataset.

The autofluorescence intensity decrease observed in control dataset is due to photobleaching (Fig. 3), a phenomenon that reduces the fluorescent signal independent of enzyme-driven biochemical changes. To accurately quantify AFI dynamics resulting exclusively from enzymatic deconstruction, the AFI dynamics observed in control dataset were used to correct those from hydrolysis dataset, by assuming that the losses attributable to enzymatic deconstruction and photobleaching combine multiplicatively (Vicente et al., 2007). The control is a pretreated, enzyme-free dataset acquired under identical imaging conditions as the hydrolysis dataset. The correction involved scaling the AFI decline in hydrolysis dataset by the relative AFI reduction observed in the control dataset. Therefore, at each time point, the autofluorescence intensity computed in the hydrolysis dataset was divided by the corresponding reduction factor measured in the control dataset. This correction enabled isolation of the AFI loss exclusively due to enzymatic action, hereafter referred to as the enzyme-driven AFI dynamics, which exhibited a consistent, monotonic decrease over time and a dependence on enzyme loading, with higher loadings producing more pronounced reductions.

### 3.4. Enzyme-driven autofluorescence intensity dynamics are strongly correlated with conversion yields during enzymatic hydrolysis

Following photobleaching correction, AFI dynamics attributable solely to enzymatic hydrolysis exhibited a sustained, enzyme-loading-dependent monotonic decline over the 24 h period (Fig. 3.D). This clear monotonicity and dose dependence suggested that AFI dynamics could be biochemically linked to hydrolysis efficiency, motivating a direct comparison with cell wall conversion yields. Therefore, to assess the biochemical relevance of the enzyme-induced AFI dynamics, and
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Autofluorescence intensity dynamics. (A) Normalized AFI dynamics extracted from original (unprocessed) datasets. (B) Normalized AFI dynamics after drift correction using AIMTrack pipeline. (C) Normalized AFI dynamics of consistently imaged pre-hydrolysis cell walls over time obtained using AIMTrack's propagation strategy, including drift correction. (D) Normalized enzyme-driven AFI dynamics obtained using the AIMTrack pipeline, corrected for photobleaching. Error bars indicate the standard deviation of three replicates at each time point.</figcaption></figure>

to investigate the relationship between AFI changes and the cell wall conversion dynamics, Pearson correlation analysis was performed over 24 h period between enzyme-driven AFI dynamics and conversion yields of cell wall-derived sugars. The correlation analysis was performed separately for individual and grouped cell wall-derived sugars to determine the strength and direction of the relationship (Fig. 4).

At 15 FPU/g biomass enzyme loading, glucose conversion yields showed strong negative correlation with enzyme-driven AFI dynamics ($r = -0.81$, *p*-value < 0.005). The predominant hemicellulose-derived sugars – xylose, mannose, and galactose – showed even stronger negative correlations, with $r = -0.9$, $r = -0.98$, and $r = -0.96$ respectively (all *p*-values < 0.005). When all hemicellulosic sugars were considered collectively, a very strong negative correlation was observed with $r = -0.95$ (*p*-value < 0.005). Grouping these hemicellulose-derived sugars with glucose (represented as “total sugars” in Fig. 4) resulted again in a strong negative correlation coefficient of –0.84 (*p*-value < 0.005) (Fig. 4.A.1). Similarly, in the dataset collected with enzyme loading of 30 FPU/g biomass, the correlation between enzyme-driven AFI dynamics and glucose conversion yields remained strong with $r = -0.79$ (*p*-value < 0.05). Xylose, mannose, and galactose again showed very strong negative correlations with $r = -0.92$, $r = -0.99$, and $r = -0.97$ respectively (all *p*-value < 0.005). Grouped hemicellulose-derived sugars, exhibited a nearly perfect correlation with $r = -0.98$ (*p*-value < 0.005). Total sugars showed a lower absolute correlation coefficient of 0.85, which remained nonetheless statistically significant and strong (*p*-value < 0.005) (Fig. 4.B.1). A Spearman correlation analysis was also performed which revealed near-perfect to perfect monotonic associations between enzyme-driven AFI dynamics and conversion yields across all enzyme loadings and sugar types. Taken together, these strong inverse relationships between the enzyme-driven AFI dynamics and

dynamics of conversion yields indicate that autofluorescence intensity reduction closely tracks enzymatic hydrolysis of cell wall polysaccharides into fermentable simple sugars and AFI dynamics can serve as a robust real-time proxy for assessing enzymatic hydrolysis efficiency, and most notably, for capturing the dynamics of cell wall-derived sugar conversion yields.

### 3.5. Cell wall enzymatic hydrolysis exhibits time- and sugar-type-specific correlation patterns with enzyme-driven autofluorescence intensity dynamics

The correlation analysis over the whole 24 h period provided a broad overview of the relationship between conversion yields and enzyme-driven AFI. However, this can mask phase-specific variations in the dynamics. Notably, some sugar conversion yields, particularly glucose, exhibited distinct stages, with an initial phase of rapid production during the early hours, followed by a plateau (see supplementary material). To investigate phase-specific relationships between enzyme-driven AFI changes and the conversion yields dynamics, the 24 h reaction time was divided into two intervals: the initial 8 h and the last 16 h. Separate correlation analyses were conducted for these intervals to investigate the temporal variations in the relationships between enzyme-driven AFI dynamics and sugar yield dynamics (Fig. 4.A.2, A.3, B.2, B.3).

During the initial 8 h at 15 FPU/g biomass, glucose conversion yields showed very strong negative correlation with enzyme-driven AFI dynamics with $r = -0.97$ (*p*-value < 0.005) (Fig. 4.A.2). Xylose, mannose, galactose, and grouped hemicellulosic sugars exhibited a strong negative correlation with $r = -0.93, -0.87, -0.86$, and $-0.93$ respectively with *p*-value < 0.05. Grouping hemicellulose-derived sugars with glucose resulted in a very strong negative correlation with *r*
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Correlation analysis between enzyme-driven autofluorescence intensity dynamics and sugar conversion yield dynamics. (A.1–3) Pearson correlation coefficients between enzyme-driven AFI dynamics and conversion yield dynamics of glucose, xylose, mannose, galactose, combined hemicellulosic sugars, and total sugars conversion yield dynamics at an enzyme loading of 15 FPU/g biomass, computed over the entire 24 h period (A.1), the initial 8 h (A.2), and the subsequent 16 h (A.3). (B.1–3) Corresponding Pearson correlation coefficients at an enzyme loading of 30 FPU/g biomass, evaluated over 24 h (B.1), the first 8 h (B.2), and the final 16 h (B.3). Statistically significant correlations are denoted by solid black bullets and asterisks: (* *p*-value < 0.05, ** *p*-value < 0.005). Non-significant correlations are indicated by unfilled bullets and absence of asterisks. Left-margin labels indicate the parameter correlated with conversion yields together with enzyme loading.</figcaption></figure>

= -0.96 and *p*-value < 0.005. At enzyme loading of 30 FPU/g biomass, during the first 8 h, the correlations were even stronger and, in most cases, approached near-perfect levels (Fig. 4.B.2). Glucose conversion yields exhibited nearly perfect negative correlation with enzyme-driven AFI dynamics with *r* = -0.99 (*p*-value < 0.005). Xylose, mannose, galactose and grouped hemicellulosic sugars, also demonstrated very strong to near-perfect negative correlations with *r* = -0.99, -0.94, -0.95, and -0.99 respectively (all *p*-value < 0.005, except mannose with *p*-value < 0.05). When glucose was grouped with hemicellulosic sugars, the correlation remained exceptionally strong at -0.99 (*p*-value < 0.005).

Over the last 16 h at 15 and 30 FPU/g biomass, glucose did not exhibit statistically significant correlations (*p*-value > 0.05) (Fig. 4.A.3, B.3). In contrast, xylose, mannose, and galactose showed very strong correlations with *r* = -0.98, -0.99, and -0.96 at 15 FPU/g biomass and *r* = -0.97, -0.98, and -0.98 at 30 FPU/g biomass, respectively (all *p*-values < 0.05). Grouped hemicellulosic sugars, showed nearly perfect correlations with *r* = 0.98 and *p*-value < 0.05 for both enzyme loadings. The total sugars showed very strong correlations with *r* = 0.96 (*p*-value < 0.05) at 15 FPU/g biomass, without a significant correlation (*p*-value > 0.05) at 30 FPU/g biomass.

Overall, phase-specific analysis revealed that during the initial 8 h, glucose showed nearly perfect correlations, whereas in the final 16 h, glucose correlations became statistically insignificant, while hemicellulosic sugars maintained very strong correlations.

### 3.6. Temporal and sugar-specific correlations link cell wall morphological descriptors to enzymatic hydrolysis yields dynamics

Following the analysis of the relationship between enzyme-driven AFI dynamics and sugar conversion yields, the association between cell

wall morphodynamics and conversion efficiency dynamics was examined. Two morphological descriptors were selected for this analysis: cell wall volume and cell-scale accessible surface area (ASA) (Fig. 5). Unlike cellulose-specific accessibility measurements reported in previous studies, the accessible surface area in this work represents the total cell-scale surface area of individual cell walls, computed from segmentations of time-lapse images generated by AIMTrack. Hereafter, ASA refers specifically to cell-scale accessible surface area.

This analysis revealed strong and significant negative correlations between dynamics of conversion yields and temporal evolution of both normalized average cell wall volume and ASA, over a period of 24 h (Fig. 5.A.1, A.4 (ASA), B.1, B.4 (volume)), with all *p*-values < 0.005 except between ASA and glucose at 30 FPU/g biomass, where *p*-value < 0.05. At 15 FPU/g biomass, ASA was correlated with individual and grouped sugars (coefficients ranging from -0.85 to -0.98), and cell wall volume showed even stronger correlations (coefficients ranging from -0.94 to -0.99). At 30 FPU/g biomass, ASA correlations ranged from -0.78 to -0.99 and volume correlations ranged from -0.92 to -0.99.

To assess how the relationship may evolve over time between both cell wall volume and cell-scale ASA and dynamics of conversion yields, the 24 h hydrolysis period was divided into two phases: an early phase (0–8 h) and a late phase (8–24 h), following the phase-specific analysis previously applied to AFI dynamics and conversion yield dynamics. Pearson correlation analyses were performed separately for early and late phases. The results showed that during the initial 8 h, strong negative correlations were observed between dynamics of conversion yields and the dynamics of normalized average cell wall volume and ASA at both enzyme loadings. At 15 FPU/g biomass, correlation coefficients ranged from -0.91 to -0.99 for volume (Fig. 5.B.2) and
<figure><img src="image_7.png" /><figcaption>Fig. 6. Cell wall deformation dynamics during enzymatic hydrolysis for con-
trol and hydrolysis datasets.</figcaption></figure>

results revealed minor deformations in the control dataset. In contrast,
the hydrolysis dataset showed a time-dependent, monotonic increase
in deformation that scaled with enzyme loading: the hydrolysis dataset
at 30 FPU/g biomass exhibited the greatest deformation with a steep
increase after 8 h, remarkably exceeding that observed at hydrolysis
dataset at 15 FPU/g biomass. These observations indicate that, whereas
bulk glucose production is early-phase dominated, deformation reflects
cumulative loss of mechanical integrity that accelerates once a critical
fraction of the wall matrix has been removed, yielding a visible post-
8 h rise in morphodynamics despite the glucose plateau. A further
correlation analysis revealed that cell wall deformation is tightly linked
to normalized average volume and ASA dynamics during enzymatic
hydrolysis. At 15 FPU/g biomass, the correlation coefficients were
similarly high for ASA (r = -0.96, p-value < 0.005) and volume (r
= -0.97, p-value < 0.005), indicating nearly equivalent relationships.
In contrast, at 30 FPU/g biomass, ASA showed a stronger negative
correlation with deformation (r = -0.95, p-value < 0.005) compared to
volume (r = -0.82, p-value < 0.005), suggesting that at higher enzyme
loadings, changes in ASA are more tightly associated with structural
deformation than changes in volume.

3.7. Image-based tracking of enzymatic deconstruction: significance and implications

A key contribution of this study is the development of the AIM-
Track pipeline, which enables automated analysis of time-lapse 3D
acquisitions of plant cell wall enzymatic deconstruction. AIMTrack
provides accurate quantification of autofluorescence intensity dynamics
together with dynamics of morphological descriptors of the cell wall,
including volume, cell-scale ASA, and deformation. AIMTrack, includes
a novel, efficient adaptive compensation strategy for sample drift and
deformation that adapts to the extent of deconstruction. Sample drift is
a common challenge in imaging that should be appropriately addressed
to ensure reliability and accuracy of downstream quantitative analyses.
Most drift correction methods have been developed for and applied to
nanoscale imaging such as single-molecule localization microscopy (Ma
et al., 2024; Diao et al., 2023). In microscale imaging, drift com-
pensation remains challenging, particularly in studies of plant cell
wall enzymatic deconstruction where different approaches have been
proposed (Refahi et al., 2024; Hossein Khani et al., 2025). AIMTrack
addresses the limitations of the existing drift compensation strategies
such as global, local, or fixed-size clustering, by introducing an innova-
tive adaptive clustering approach that dynamically adjusts cluster sizes
based on the extent of deconstruction, employing larger clusters during
slow hydrolysis periods and smaller ones during periods of significant
structural changes. This adaptive clustering strategy is an enhanced
divide-and-conquer strategy with a context-aware partitioning mech-
anism, where the cluster boundaries are modulated by progression of
enzymatic hydrolysis. Given the intrinsic temporal heterogeneity of en-
zymatic hydrolysis dynamics, this data-driven adaptivity is essential to

achieve accurate characterization. These advantages position AIMTrack
as a broadly applicable pipeline for microscale structural analyses of
lignocellulosic biomass during enzymatic hydrolysis.

Application of AIMTrack to time-lapse images of pretreated spruce wood enzymatic hydrolysis revealed strong and consistent negative correlations between enzyme-driven AFI dynamics and dynamics of conversion yields of sugars over 24 h, considered either individually – glucose and hemicellulose-derived sugars – or collectively. This strong inverse relationship indicated that enzyme-driven AFI dynamics closely tracks enzymatic hydrolysis yield dynamics of cell wall polysaccharides into fermentable simple sugars. Wood cell wall autofluorescence primarily originates from phenolic compounds that constitute lignin (Maceda and Terrazas, 2022), and its intensity depends on a range of chemical and physical factors, including fluorophore content, inter-linkage types, and interactions with other cell wall polymers (Donaldson, 2020). These dependencies suggest that enzymatic deconstruction not only alters polysaccharide content but also leads to substantial modifications of the lignin environment, indirectly reflected by the progressive decrease in autofluorescence intensity and directly supported by the release of sugars from hydrolyzed polysaccharides. To further validate this interpretation, future work should include enzyme-deactivated controls (e.g., heat-inactivated cocktails) and cell wall component-specific staining imaging to further elucidate cell wall changes during enzymatic hydrolysis. While previous work showed that initial cell wall autofluorescence intensity correlates with final released glucose concentration after prolonged hydrolysis (Auxenfans et al., 2017), this study takes a dynamic approach to investigate morphodynamics of cell wall hydrolysis.

More importantly, phase-specific analysis revealed distinct tem-
poral and sugar-type-dependent patterns in the correlation between
enzyme-induced AFI dynamics and sugar conversion yields. During
the initial 8 h, dynamics of glucose conversion yields, which mostly
originated from cellulose (representing nearly 60% of the sugars (see
supplementary material)) exhibited near-perfect negative correlations
with fluorescence loss, indicating rapid early-stage hydrolysis. In con-
trast, over the final 16 h, glucose correlations became statistically
insignificant while hemicellulose-derived sugars – particularly xylose,
mannose, and galactose – maintained strong correlations, indepen-
dently of enzyme loading. These results reveal temporally resolved
deconstruction phases and substrate-specific enzymatic activity dy-
namics. Given that chlorite-based pretreatment leads to a significant
removal of lignin (nearly 63%), cellulose fibers become much more
accessible, as previously illustrated for instance for poplar wood (Ding
et al., 2016). The discrepancy between the first 8-h phase and the
following phase highlights the phenomenon known as cellulosic hy-
drolysis slowdown (Ahamed et al., 2019), mostly attributed to the
irreversible deposition of non-cellulosic species (either reaction side
products or denatured enzymes, or both) on the cellulosic substrate sur-
face (Kafle et al., 2015). Conversely, hemicellulosic sugars conversion
appears consistent over hydrolysis time, probably because enzymatic
activities are in limited amounts given the relative hemicellulose con-
tent in comparison to cellulose, in particular for enzymatic activities
degrading mannan which is abundant in spruce (Nargotra et al., 2022).
These results suggest that autofluorescence intensity dynamics allows
tracking distinct phases of cell wall enzymatic deconstruction and
offers a kinetic window into cell wall deconstruction, which should be
taken into account when used for predictive computational modeling of
lignocellulosic biomass enzymatic deconstruction and conversion opti-
mization strategies such as enzyme cocktail composition and tailored
enzyme application strategies.

Finally, our findings also revealed enzyme-mediated cell wall struc-
tural changes including cell wall volume, ASA, and deformation reflect-
ing enzymatic deconstruction efficiency dynamics. Strong, statistically
significant negative correlations were observed across enzyme load-
ings (15 and 30 FPU/g biomass) between dynamics of individual and
grouped sugar conversion yield and dynamics of cell wall volume
and ASA, over 24 h. Temporal analysis revealed that this inverse relationship was most pronounced during the early hydrolysis phase (0–8 h), where correlations remained consistently strong and significant across all sugars and enzyme loadings. In contrast, during the late phase (8–24 h), correlations involving glucose and total sugars weakened or lost significance, while those for hemicellulosic sugars remained robust. Quantification of cell wall deformation further supported these findings, showing monotonic increases with enzyme loading and time. Deformation dynamics were strongly correlated with both ASA and volume, with ASA showing a stronger association with deformation than volume. Overall, these results indicate that enzymatic hydrolysis progressively compromises cell wall structural integrity, leading to reductions in both volume and cell-scale ASA, and resulted in increased mechanical deformability. In return, the observed correlations with sugar conversion dynamics underscore the predictive potential of quantitative morphology-based analyses for understanding and predicting hydrolytic efficiency and conversion dynamics.

Previous studies and models of enzymatic cellulose digestion based on quantitative AFM data, revealed formation of elongated cracks on the substrate surface upon cellulase treatment, highlighting localized structural changes during hydrolysis (Eibinger et al., 2014). However, these findings are limited in scope due to their reliance on purified substrates lacking native cell wall complexity. Similarly, particle-scale imaging of hardwood and switchgrass hydrolysis revealed progressive shape alterations over time (Luterbacher et al., 2015), aligning conceptually with our observations. Yet, these analyses were conducted at the 10–100 µm scale using pure cellulosic cocktails, precluding identification of specific structural markers at the cell wall level. Progress has been made for providing 3D analysis of cell wall after pretreatment of spruce (Muzamal et al., 2015) or during enzymatic hydrolysis but without a detailed investigation of the relationship between structural changes, biochemical composition, and deconstruction dynamics (Blosse et al., 2023; Chandrasekar et al., 2024). In contrast, and to the best of our knowledge, this study is the first to establish a direct and quantitative relationship between dynamics of conversion yields, cell wall autofluorescence intensity, and morphological descriptors at the cell wall scale. Furthermore, it characterizes the temporal dynamics of these interrelated parameters under conditions of extensive substrate conversion, an essential context for optimizing hydrolytic efficiency. The results align with prior work demonstrating the relevance of microscale structural parameters to investigate the enzymatic deconstruction efficiency under limited conversion conditions in poplar wood (Refahi et al., 2024).

## 4. Conclusions

Enzymatic hydrolysis of lignocellulosic biomass is a key step in biotechnological conversion, but efficiency is hindered by cell wall re-calcitrance. Predicting hydrolysis yield dynamics has remained elusive, and traditional sugar-release assays are time-consuming and labor-intensive. This study introduces a robust, quantitative, and nondestractive framework for assessing cell wall conversion efficiency. By establishing autofluorescence intensity and morphological descriptors as reliable real-time biomarkers, the framework provides a powerful alternative to conventional assays and enables dynamic prediction of sugar release. The approach offers substantial potential to guide enzymatic hydrolysis studies and accelerate the optimization of biotechnological strategies for efficient plant biomass conversion.

### CRediT authorship contribution statement

**Solmaz Hossein Khani:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Khadidja Ould Amer:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Fatemeh Shiasi Ghalemaleki:**

Writing – review & editing, Formal analysis.
**Maxime Corré:** Software, Formal analysis, Data curation.
**Noah Remy:** Writing – review & editing, Writing – original draft, Investigation.
**Anouck Habrant:** Writing – review & editing, Writing – original draft, Investigation.
**Berangère Lebas:** Investigation.
**Jafar Massah:** Writing – review & editing, Formal analysis.
**Ali Faraj:** Writing – original draft, Software, Methodology.
**Gabriel Paës:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.
**Yassin Refahi:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

### Funding

This work was funded by French Research Minister aid managed by the Agence Nationale de la Recherche as part of the France 2030 investment plan through the grant reference ANR-23-PEBB-0006 (FillingGaps project). This work was also funded by Agence Nationale de la Recherche (ANR) under grant ANR-19-CE43-0010 (BIOMOD project), and by Grand Est Region through "BIOMODEL" doctoral funding.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gabriel Paës reports financial support was provided by French Research Minister. Yassin Refahi reports financial support was provided by French National Research Agency. Solmaz Hossein Khani reports was provided by La Région Grand Est. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

We gratefully acknowledge Simon Arragain and Antoine Margeot (IFPEN, Rueil-Malmaison, France) for provision of enzymatic cocktail. We also wish to thank Juliette Floret (FARE laboratory, Reims, France) for measuring enzymatic activity and François Gaudard (FARE laboratory, Reims, France) for his help for preparation of the samples and measuring conversion yields. We also thank Grégoire Malandain (MORPHEME team, I3S laboratory, Sophia Antipolis, France) for providing the block matching algorithm.

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at
https://doi.org/10.1016/j.biortech.2025.133642. Supplementary material for this work can be found in e-version of this paper online.

### Data availability

AIMTrack code is available at:
https://gitlab.com/farelab/teamyr/publications/AIMTrack. Further information and data are available upon request.

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