Cargando…

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

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 m...

Descripción completa

Detalles Bibliográficos
Autores principales: Patino, Cesar A., Pathak, Nibir, Mukherjee, Prithvijit, Park, So Hyun, Bao, Gang, Espinosa, Horacio D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307252/
https://www.ncbi.nlm.nih.gov/pubmed/35867793
http://dx.doi.org/10.1126/sciadv.abn7637
_version_ 1784752716980944896
author Patino, Cesar A.
Pathak, Nibir
Mukherjee, Prithvijit
Park, So Hyun
Bao, Gang
Espinosa, Horacio D.
author_facet Patino, Cesar A.
Pathak, Nibir
Mukherjee, Prithvijit
Park, So Hyun
Bao, Gang
Espinosa, Horacio D.
author_sort Patino, Cesar A.
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9307252
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher American Association for the Advancement of Science
record_format MEDLINE/PubMed
spelling pubmed-93072522022-08-09 Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering Patino, Cesar A. Pathak, Nibir Mukherjee, Prithvijit Park, So Hyun Bao, Gang Espinosa, Horacio D. Sci Adv Biomedicine and Life Sciences 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. American Association for the Advancement of Science 2022-07-22 /pmc/articles/PMC9307252/ /pubmed/35867793 http://dx.doi.org/10.1126/sciadv.abn7637 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Biomedicine and Life Sciences
Patino, Cesar A.
Pathak, Nibir
Mukherjee, Prithvijit
Park, So Hyun
Bao, Gang
Espinosa, Horacio D.
Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering
title Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering
title_full Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering
title_fullStr Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering
title_full_unstemmed Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering
title_short Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering
title_sort multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering
topic Biomedicine and Life Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307252/
https://www.ncbi.nlm.nih.gov/pubmed/35867793
http://dx.doi.org/10.1126/sciadv.abn7637
work_keys_str_mv AT patinocesara multiplexedhighthroughputlocalizedelectroporationworkflowwithdeeplearningbasedanalysisforcellengineering
AT pathaknibir multiplexedhighthroughputlocalizedelectroporationworkflowwithdeeplearningbasedanalysisforcellengineering
AT mukherjeeprithvijit multiplexedhighthroughputlocalizedelectroporationworkflowwithdeeplearningbasedanalysisforcellengineering
AT parksohyun multiplexedhighthroughputlocalizedelectroporationworkflowwithdeeplearningbasedanalysisforcellengineering
AT baogang multiplexedhighthroughputlocalizedelectroporationworkflowwithdeeplearningbasedanalysisforcellengineering
AT espinosahoraciod multiplexedhighthroughputlocalizedelectroporationworkflowwithdeeplearningbasedanalysisforcellengineering