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An Open-Source Framework for Automated High-Throughput Cell Biology Experiments.

ABSTRACT: Modern data analysis methods, such as optimization algorithms or deep learning have been successfully applied to a number of biotechnological and medical questions. For these methods to be efficient, a large number of high-quality and reproducible experiments needs to be conducted, requiring a high degree of automation. Here, we present an open-source hardware and low-cost framework that allows for automatic high-throughput generation of large amounts of cell biology data. Our design consists of an epifluorescent microscope with automated XY stage for moving a multiwell plate containing cells and a perfusion manifold allowing programmed application of up to eight different solutions. Our system is very flexible and can be adapted easily for individual experimental needs. To demonstrate the utility of the system, we have used it to perform high-throughput Ca²⁺ imaging and large-scale fluorescent labeling experiments.

SUBMITTER: Katunin P

PROVIDER: S-EPMC8498207 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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An Open-Source Framework for Automated High-Throughput Cell Biology Experiments.

Katunin Pavel P Zhou Jianbo J Shehata Ola M OM Peden Andrew A AA Cadby Ashley A Nikolaev Anton A

Frontiers in cell and developmental biology 20210924

Modern data analysis methods, such as optimization algorithms or deep learning have been successfully applied to a number of biotechnological and medical questions. For these methods to be efficient, a large number of high-quality and reproducible experiments needs to be conducted, requiring a high degree of automation. Here, we present an open-source hardware and low-cost framework that allows for automatic high-throughput generation of large amounts of cell biology data. Our design consists of ...[more]

PMID: 34631697

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Project description:Research in photobiology is currently limited by a lack of devices capable of delivering precise and tunable irradiation to cells in a high-throughput format. This limits researchers to using expensive commercially available or custom-built light sources which make it difficult to replicate, standardize, optimize, and scale experiments. Here we present an open-source Microplate Photoirradiation System (MPS) developed to enable high-throughput light experiments in standard 96 and 24-well microplates for a variety of applications in photobiology research. This open-source system features 96 independently controlled LEDs (4 LEDs per well in 24-well), Wi-Fi connected control and programmable graphical user interface (GUI) for control and programming, automated calibration GUI, and modular control and LED boards for maximum flexibility. A web-based GUI generates light program files containing irradiation parameters for groups of LEDs. These parameters are then uploaded wirelessly, stored and used on the MPS to run photoirradiation experiments inside any incubator. A rapid and semi-quantitative porphyrin metabolism assay was also developed to validate the system in wild-type fibroblasts. Protoporphyrin IX (PpIX) fluorescence accumulation was induced by incubation with 5-aminolevulinic acid (ALA), a photosensitization method leveraged clinically to destroy malignant cell types in a process termed photodynamic therapy (PDT), and cells were irradiated with 405nm light with varying irradiance, duration and pulsation parameters. Immediately after light treatment with the MPS, subsequent photobleaching was measured in live, adherent cells in both 96-well and a 24-well microplates using a microplate reader. Results demonstrate the utility and reliability of the Microplate Photoirradiation System to irradiate cells with precise irradiance and timing parameters in order to measure PpIx photobleaching kinetics in live adherent cells and perform comparable experiments with both 24 and 96 well microplate formats. The high-throughput capability of the MPS enabled measurement of enough irradiance conditions in a single microplate to fit PpIX fluorescence to a bioexponential decay model of photobleaching, as well as reveal a dependency of photobleaching on duty-cycle-but not frequency-in a pulsed irradiance regimen.

Project description:PurposeThe primary aim of this study was to develop an open-source Python-based software for the automated analysis of dynamic cell behaviors in microphysiological models using non-confocal microscopy. This research seeks to address the existing gap in accessible tools for high-throughput analysis of endothelial tube formation and cell invasion in vitro, facilitating the rapid assessment of drug sensitivity.MethodsOur approach involved annotating over 1000 2 mm Z-stacks of cancer and endothelial cell co-culture model and training machine learning models to automatically calculate cell coverage, cancer invasion depth, and microvessel dynamics. Specifically, cell coverage area was computed using focus stacking and Gaussian mixture models to generate thresholded Z-projections. Cancer invasion depth was determined using a ResNet-50 binary classification model, identifying which Z-planes contained invaded cells and measuring the total invasion depth. Lastly, microvessel dynamics were assessed through a U-Net Xception-style segmentation model for vessel prediction, the DisPerSE algorithm to extract an embedded graph, then graph analysis to quantify microvessel length and connectivity. To further validate our software, we reanalyzed an image set from a high-throughput drug screen involving a chemotherapy agent on a 3D cervical and endothelial co-culture model. Lastly, we applied this software to two naive image datasets from coculture lumen and microvascular fragment models.ResultsThe software accurately measured cell coverage, cancer invasion, and microvessel length, yielding drug sensitivity IC50 values with a 95% confidence level compared to manual calculations. This approach significantly reduced the image processing time from weeks down to h. Furthermore, the software was able to calculate cell coverage, microvessel length, and invasion depth from two additional microphysiological models that were imaged with confocal microscopy, highlighting the versatility of the software.ConclusionsOur free and open source software offers an automated solution for quantifying 3D cell behavior in microphysiological models assessed using non-confocal microscopy, providing the broader Cellular and Molecular Bioengineering community with an alternative to standard confocal microscopy paired with proprietary software.This software can be found in our GitHub repository: https://github.com/fogg-lab/tissue-model-analysis-tools.Supplementary informationThe online version contains supplementary material available at 10.1007/s12195-024-00821-2.

Dataset Information

An Open-Source Framework for Automated High-Throughput Cell Biology Experiments.

Publications

An Open-Source Framework for Automated High-Throughput Cell Biology Experiments.

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