Automated Protoplast Screening System for High-Throughput Plant Cell Analysis

Overview

Protoplasts—plant cells with their cell walls removed—are critical tools for genetic transformation, breeding, and cellular studies, but traditional screening relies on manual microscopy and cell counting, which are labor-intensive, subjective, and prone to contamination. The Automated Protoplast Screening System (APSS) addresses these limitations by combining established laboratory automation technologies into a single coordinated platform. It integrates robotic sample handling with 96- or 384-well culture plates, automated bright-field and fluorescence imaging, machine-learning-based image analysis, and centralized data management to enable objective, high-throughput assessment of protoplast health and regeneration potential. The system reduces manual handling, operator bias, contamination risk, and processing time, and can screen hundreds to thousands of samples in parallel, making it suitable for research and commercial applications in plant biotechnology.

Technical specifications

Core components:

• Robotic sample handling for consistent, automated preparation and transfer of protoplast samples
• 96- or 384-well culture plate formats for parallel high-throughput screening
• Automated bright-field and fluorescence imaging to capture viability, morphology, cell-wall regeneration, cell division, and fluorescent marker expression
• Machine-learning models trained on annotated images for objective counting and classification of protoplast features
• Centralized data management with automated scheduling, data storage, and a decision-support dashboard

Key capabilities:

• Monitors viability, morphology, cell-wall regeneration, cell division, fluorescence markers, and regeneration potential
• Designed for accuracy, throughput, repeatability, and reduced manual intervention in routine laboratory operation
• Modular architecture supports stepwise development and validation against manual methods
• Applicable across multiple plant species and experimental conditions

Technology readiness level

The APSS is currently in the development phase. The project plan includes four stages: (1) design of the modular platform, (2) development of image-analysis and machine-learning tools, (3) assembly and integration of a working prototype with automated imaging, scheduling, data storage, and dashboard, and (4) validation across multiple plant species and conditions. Validation will assess accuracy, throughput, repeatability, processing time, and reduction in manual intervention, with a target of at least 90% viability-detection accuracy.