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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
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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 |
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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 |
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