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

Simple item page
dc.citation.articleNumbereabn7637en_US
dc.citation.issueNumber29en_US
dc.citation.journalTitleScience Advancesen_US
dc.citation.volumeNumber8en_US
dc.contributor.authorPatino, Cesar A.en_US
dc.contributor.authorPathak, Nibiren_US
dc.contributor.authorMukherjee, Prithvijiten_US
dc.contributor.authorPark, So Hyunen_US
dc.contributor.authorBao, Gangen_US
dc.contributor.authorEspinosa, Horacio D.en_US
dc.contributor.orgBioengineeringen_US
dc.date.accessioned2022-08-09T17:09:32Zen_US
dc.date.available2022-08-09T17:09:32Zen_US
dc.date.issued2022en_US
dc.description.abstractManipulation 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.en_US
dc.identifier.citationPatino, Cesar A., Pathak, Nibir, Mukherjee, Prithvijit, et al.. "Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering." <i>Science Advances,</i> 8, no. 29 (2022) AAAS: https://doi.org/10.1126/sciadv.abn7637.en_US
dc.identifier.digitalsciadv-abn7637en_US
dc.identifier.doihttps://doi.org/10.1126/sciadv.abn7637en_US
dc.identifier.urihttps://hdl.handle.net/1911/113089en_US
dc.language.isoengen_US
dc.publisherAAASen_US
dc.rightsDistributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_US
dc.titleMultiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineeringen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
sciadv-abn7637.pdf
Size:
6.14 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications