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

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.

SUBMITTER: Patino CA

PROVIDER: S-EPMC9307252 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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

Patino Cesar A CA Pathak Nibir N Mukherjee Prithvijit P Park So Hyun SH Bao Gang G Espinosa Horacio D HD

Science advances 20220722 29

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 versati ...[more]

PMID: 35867793

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Project description:BackgroundDiagnosis of mitral regurgitation (MR) requires careful evaluation by echocardiography with Doppler imaging. This study presents the development and validation of a fully automated deep learning pipeline for identifying apical 4-chamber view videos with color Doppler echocardiography and detecting clinically significant (moderate or severe) MR from transthoracic echocardiograms.MethodsA total of 58 614 transthoracic echocardiograms (2 587 538 videos) from Cedars-Sinai Medical Center were used to develop and test an automated pipeline to identify apical 4-chamber view videos with color Doppler across the mitral valve and then assess MR severity. The model was tested internally on a test set of 1800 studies (80 833 videos) from Cedars-Sinai Medical Center and externally evaluated in a geographically distinct cohort of 915 studies (46 890 videos) from Stanford Healthcare.ResultsIn the held-out Cedars-Sinai Medical Center test set, the view classifier demonstrated an area under the curve (AUC) of 0.998 (0.998-0.999) and correctly identified 3452 of 3539 echocardiography videos as having color Doppler information across the mitral valve (sensitivity of 0.975 [0.968-0.982] and specificity of 0.999 [0.999-0.999] compared with manually curated videos). In the external test cohort from Stanford Healthcare, the view classifier correctly identified 1051 of 1055 manually curated videos with color Doppler information across the mitral valve (sensitivity of 0.996 [0.990-1.000] and specificity of 0.999 [0.999-0.999]). In the Cedars-Sinai Medical Center test cohort, MR moderate or greater in severity was detected with an AUC of 0.916 (0.899-0.932) and severe MR was detected with an AUC of 0.934 (0.913-0.953). In the Stanford Healthcare test cohort, the model detected MR moderate or greater in severity with an AUC of 0.951 (0.924-0.973) and severe MR with an AUC of 0.969 (0.946-0.987).ConclusionsIn this study, a novel automated pipeline for identifying clinically significant MR from full transthoracic echocardiography studies demonstrated excellent performance across large numbers of studies and across multiple institutions. Such an approach has the potential for automated screening and surveillance of MR.

Project description:BackgroundRice is a major staple food crop for more than half the world's population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed.ResultsWe use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area.ConclusionsWe have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.

Dataset Information

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

Publications

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

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