Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform

Patino, Cesar A.; Mukherjee, Prithvijit; Lemaı̂tre, V.; Pathak, Nibir; Espinosa, Horacio D.

doi:10.1177/2472630320982320

Cited by 13 publications

(18 citation statements)

References 38 publications

(46 reference statements)

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“…A fully convolutional network (FCN) architecture ( 42 ) was implemented and trained to segment individual cells or nuclei in fluorescent or phase-contrast micrographs. The FCN contains 20 hidden layers arranged in an encoder-decoder scheme ( 43 , 44 ) ( Fig. 2B ) that outputs a multiclass probability used to classify each pixel in the image into three classes (interior, boundary, or exterior).…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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“…[ 44 ] We previously trained our algorithm for segmentation of a wide variety of cell lines. [ 45 ] Here, we trained and optimized the algorithm to precisely identify the nuclear locations of human primary dermal fibroblasts (HDFs) and hiPSCs from phase contrast images, by using corresponding fluorescence images with nuclear stains as the ground truth. The trained network enabled localization of live cell nuclei from the phase images only, without any additional nuclear labeling.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning‐Assisted Automated Single Cell Electroporation Platform for Effective Genetic Manipulation of Hard‐to‐Transfect Cells

Mukherjee

Patino

Pathak

et al. 2022

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Genome engineering of cells using CRISPR/Cas systems has opened new avenues for pharmacological screening and investigating the molecular mechanisms of disease. A critical step in many such studies is the intracellular delivery of the gene editing machinery and the subsequent manipulation of cells. However, these workflows often involve processes such as bulk electroporation for intracellular delivery and fluorescence activated cell sorting for cell isolation that can be harsh to sensitive cell types such as human‐induced pluripotent stem cells (hiPSCs). This often leads to poor viability and low overall efficacy, requiring the use of large starting samples. In this work, a fully automated version of the nanofountain probe electroporation (NFP‐E) system, a nanopipette‐based single‐cell electroporation method is presented that provides superior cell viability and efficiency compared to traditional methods. The automated system utilizes a deep convolutional network to identify cell locations and a cell‐nanopipette contact algorithm to position the nanopipette over each cell for the application of electroporation pulses. The automated NFP‐E is combined with microconfinement arrays for cell isolation to demonstrate a workflow that can be used for CRISPR/Cas9 gene editing and cell tracking with potential applications in screening studies and isogenic cell line generation.

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“…Nanofountain probes (NFP) have also been fabricated for more spatially controlled single-cell EP (Figure E). − NFP chips are designed with a 750 nm tip opening, a ∼3 pL microreservoir, and a connection to a conductive wire. Low-voltage, bilevel pulses are sufficient to permeabilize individual cells with good cell–probe contact.…”

Section: Technological Improvementsmentioning

confidence: 99%

Recent Advances in Microscale Electroporation

2022

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Electroporation (EP) is a commonly used strategy to increase cell permeability for intracellular cargo delivery or irreversible cell membrane disruption using electric fields. In recent years, EP performance has been improved by shrinking electrodes and device structures to the microscale. Integration with microfluidics has led to the design of devices performing static EP, where cells are fixed in a defined region, or continuous EP, where cells constantly pass through the device. Each device type performs superior to conventional, macroscale EP devices while providing additional advantages in precision manipulation (static EP) and increased throughput (continuous EP). Microscale EP is gentle on cells and has enabled more sensitive assaying of cells with novel applications. In this Review, we present the physical principles of microscale EP devices and examine design trends in recent years. In addition, we discuss the use of reversible and irreversible EP in the development of therapeutics and analysis of intracellular contents, among other noteworthy applications. This Review aims to inform and encourage scientists and engineers to expand the use of efficient and versatile microscale EP technologies.

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Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform

Cited by 13 publications

References 38 publications

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

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

Deep Learning‐Assisted Automated Single Cell Electroporation Platform for Effective Genetic Manipulation of Hard‐to‐Transfect Cells

Recent Advances in Microscale Electroporation

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