PANADE automated nanoliter screening platform for protoplast and algae discovery

Overview

PANADE (Protoplast & Algae NAnoculture Discovery Engine) is an automated platform for high-throughput, plant-relevant biological screening. The technology addresses the limitations of conventional protoplast and algae screening, which relies on multiwell plates, repeated pipetting, and relatively large sample and reagent volumes. By miniaturizing and automating the workflow, PANADE reduces manipulation of fragile protoplasts and enables far more conditions to be tested per experiment, accelerating discovery while reducing hands-on time.

Technical specifications

PANADE uses microfluidics to encapsulate protoplasts or algae within semi-permeable nanoliter compartments that retain cells while allowing controlled exchange of nutrients, signaling molecules, and screening compounds. Key features include:

• Semi-permeable, translucent nanocultures acting as miniature biological reactors
• Parallel testing of hundreds to thousands of conditions with minimal sample handling and reagent consumption
• Automated high-content imaging to monitor viability, growth, morphology, fluorescence, and treatment responses over time
• Machine-learning analysis to identify nanocultures, quantify phenotypes, and rank promising conditions
• Scalable workflow from cell encapsulation to actionable screening data

The platform is designed for longitudinal monitoring of algae growth, fluorescence, and responses to chemical or environmental perturbations, as well as for screening applications involving fragile protoplasts.

Technology readiness level

PANADE is currently at an early development stage. A three-phase validation plan is proposed: first, optimizing the existing microfluidic encapsulation workflow for representative algae and protoplasts by tuning nanoculture size, cell loading, shell permeability, media composition, and osmotic conditions to maximize viability and stability. Second, establishing parallel screening workflows using nutrients, environmental stressors, and selected compounds across concentration gradients, with automated high-content imaging quantifying viability, growth, morphology, chlorophyll fluorescence, reporter expression, and treatment responses. Third, integrating machine-learning analysis to automatically identify nanocultures, quantify phenotypes, and rank conditions, followed by benchmarking against conventional multiwell assays for throughput, reproducibility, reagent consumption, hands-on time, and biological response. Successful completion will deliver a validated and scalable prototype.