Patino Cesar A, Sarikaya Sevketcan, Mukherjee Prithvijit, Pathak Nibir, Espinosa Horacio D
Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA.
Theoretical and Applied Mechanics Program, Northwestern University, Evanston, IL, USA.
Bio Protoc. 2024 Jul 20;14(14):e5037. doi: 10.21769/BioProtoc.5037.
Efficient and nontoxic delivery of foreign cargo into cells is a critical step in many biological studies and cell engineering workflows with applications in areas such as biomanufacturing and cell-based therapeutics. However, effective molecular delivery into cells involves optimizing several experimental parameters. In the case of electroporation-based intracellular delivery, there is a need to optimize parameters like pulse voltage, duration, buffer type, and cargo concentration for each unique application. Here, we present the protocol for fabricating and utilizing a high-throughput multi-well localized electroporation device (LEPD) assisted by deep learning-based image analysis to enable rapid optimization of experimental parameters for efficient and nontoxic molecular delivery into cells. The LEPD and the optimization workflow presented herein are relevant to both adherent and suspended cell types and different molecular cargo (DNA, RNA, and proteins). The workflow enables multiplexed combinatorial experiments and can be adapted to cell engineering applications requiring in vitro delivery. Key features • A high-throughput multi-well localized electroporation device (LEPD) that can be optimized for both adherent and suspended cell types. • Allows for multiplexed experiments combined with tailored pulse voltage, duration, buffer type, and cargo concentration. • Compatible with various molecular cargoes, including DNA, RNA, and proteins, enhancing its versatility for cell engineering applications. • Integration with deep learning-based image analysis enables rapid optimization of experimental parameters.
将外源物质高效且无毒地递送至细胞内是许多生物学研究和细胞工程工作流程中的关键步骤,这些工作流程应用于生物制造和基于细胞的治疗等领域。然而,将分子有效递送至细胞内需要优化多个实验参数。对于基于电穿孔的细胞内递送而言,针对每个独特的应用,都需要优化诸如脉冲电压、持续时间、缓冲液类型和物质浓度等参数。在此,我们展示了一种基于深度学习图像分析辅助的高通量多孔局部电穿孔装置(LEPD)的制造及使用方案,以实现对实验参数的快速优化,从而将分子高效且无毒地递送至细胞内。本文介绍的LEPD及优化工作流程适用于贴壁细胞和悬浮细胞类型以及不同的分子物质(DNA、RNA和蛋白质)。该工作流程能够进行多重组合实验,并且可适用于需要体外递送的细胞工程应用。关键特性 • 一种高通量多孔局部电穿孔装置(LEPD),可针对贴壁细胞和悬浮细胞类型进行优化。 • 允许结合定制的脉冲电压、持续时间、缓冲液类型和物质浓度进行多重实验。 • 与包括DNA、RNA和蛋白质在内的各种分子物质兼容,增强了其在细胞工程应用中的通用性。 • 与基于深度学习的图像分析相结合,能够快速优化实验参数。