Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering

Cesar A. Patino, Nibir Pathak, Prithvijit Mukherjee, So Hyun Park, Gang Bao, Horacio D. Espinosa*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Manipulation of cells for applications such as biomanufacturing and cell-based therapeutics involves introducing biomolecular cargoes into cells. However, successful delivery is a function of multiple experimental factors requiring several rounds of optimization. Here, we present a high-throughput multiwell-format localized electroporation device (LEPD) assisted by deep learning image analysis that enables quick optimization of experimental factors for efficient delivery. We showcase the versatility of the LEPD platform by successfully delivering biomolecules into different types of adherent and suspension cells. We also demonstrate multicargo delivery with tight dosage distribution and precise ratiometric control. Furthermore, we used the platform to achieve functional gene knockdown in human induced pluripotent stem cells and used the deep learning framework to analyze protein expression along with changes in cell morphology. Overall, we present a workflow that enables combinatorial experiments and rapid analysis for the optimization of intracellular delivery protocols required for genetic manipulation.

Original languageEnglish (US)
Article numbereabn7637
JournalScience Advances
Volume8
Issue number29
DOIs
StatePublished - Jul 2022

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering'. Together they form a unique fingerprint.

Cite this