Dual-channel laser confocal microscopy lignocellulose enzymatic digestion dataset
Description
This dataset contains original 3D volumetric images (exported as 2×512×512×64 .npz files) capturing the microstructural evolution of Caragana korshinskii biomass under various enzymatic treatments. Key features include: Substrate: Ball-milled C. korshinskii (1–100 µm particles). Treatments: Cellulase (CEL), Lignin Peroxidase (LIP), Laccase (LAC), and their synergistic combinations (LL, LLC). Imaging Technology: Dual-channel Confocal Laser Scanning Microscopy (CLSM) using 405 nm and 552 nm lasers to distinguish lignin autofluorescence (435–500 nm) and cellulose-Congo red fluorescence (570–620 nm). Standardization: All samples were imaged within a custom-fabricated microfluidic chip to ensure geometric confinement and eliminate deformation artifacts. Data Format: Each volume is a 4-dimensional NumPy array representing (Channels, Height, Width, Depth). This dataset provides a spatially resolved, non-destructive foundation for training 3D deep learning models to identify degradation signatures in biorefinery processes.
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Steps to reproduce
Biomass Preparation: C. korshinskii was hammer-milled, sieved through a 1 mm screen, and ball-milled in water for 4 hours. Cellulose was stained with 1% Congo Red. Enzymatic Reaction: Substrate (5%) was incubated with enzymes (Cellulase, LIP, LAC) at 50°C and 90 rpm for 4 hours before activity was quenched at 100°C. Microfluidic Injection: Treated samples were injected into a PDMS-quartz microfluidic chip (channels: 0.1 mm × 1.2 mm) using a syringe to maintain consistent thickness and hydration. CLSM Scanning: Imaging was performed using a Leica SP8 with a 20× objective. XY resolution: 512×512 pixels (1.48 µm/pixel); Z-step: 1.33 µm across 64 slices. Data Processing: Raw .lif files were converted to .tiff and then compiled into 2×512×512×64 .npz files using Python. (Note: This version excludes augmented data) .
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Funders
- Key Research and Development Program of ChinaGrant ID: 2022YFD1300902