Well Plate-Based Localized Electroporation Workflow for Rapid Optimization of Intracellular Delivery.

Area of Science:

• Cell biology
• Biotechnology
• Bioengineering

Background:

• Efficient and safe intracellular delivery of molecules is crucial for biological research and cell engineering.
• Optimizing electroporation parameters (voltage, duration, buffer, cargo concentration) is essential but complex for each application.
• Current methods lack high-throughput capabilities for rapid parameter optimization.

Purpose of the Study:

• To present a protocol for a high-throughput multi-well localized electroporation device (LEPD).
• To integrate deep learning-based image analysis for rapid optimization of molecular delivery parameters.
• To enable efficient and nontoxic delivery of diverse molecular cargoes into various cell types.

Main Methods:

• Fabrication and utilization of a novel multi-well localized electroporation device (LEPD).
• Implementation of a deep learning algorithm for automated image analysis and parameter optimization.
• Conducting multiplexed combinatorial experiments with varying electroporation conditions and molecular cargoes (DNA, RNA, proteins).

Main Results:

• The LEPD facilitates high-throughput, multiplexed optimization of electroporation parameters.
• The integrated deep learning analysis enables rapid identification of optimal conditions for efficient and nontoxic delivery.
• The workflow is adaptable to both adherent and suspended cell types and various molecular cargoes.

Conclusions:

• The LEPD combined with deep learning offers a powerful tool for accelerating cell engineering workflows.
• This approach significantly improves the efficiency and safety of intracellular molecular delivery.
• The presented protocol is versatile and applicable to a wide range of cell-based applications, including biomanufacturing and therapeutics.