Evolution and impact of high content imaging

Way, Gregory P.; Sailem, Heba; Shave, Steven; Kasprowicz, Richard; Carragher, Neil O.

doi:10.1016/j.slasd.2023.08.009

Cited by 9 publications

(8 citation statements)

References 141 publications

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“…We select features using Pycytominer 48 . For any pair of features, if their correlation is higher than 0.9, then it will exclude the feature with the highest sum of correlation with the other features in the dataset.…”

Section: Methodsmentioning

confidence: 99%

Evaluating batch correction methods for image-based cell profiling

Arevalo,

Su,

van Dijk

et al. 2023

Preprint

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High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects pose severe limitations to community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we evaluated seven top-ranked batch correction strategies for mRNA profiles in the context of a newly released Cell Painting dataset, the largest publicly accessible image-based dataset. We focused on five different use scenarios with varying complexity, and found that Harmony, a nonlinear method, consistently outperformed the other tested methods. Furthermore, we provide a framework, benchmark, and metrics for the future assessment of new batch correction methods. Overall, this work paves the way for improvements that allow the community to make best use of public Cell Painting data for scientific discovery.

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Section: Methodsmentioning

confidence: 99%

Evaluating batch correction methods for image-based cell profiling

Arevalo,

Su,

van Dijk

et al. 2023

Preprint

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“…One such method is high-content screening (HCS), also known as high-content imaging (HCI), simply defined as the usage of automated imaging and quantitative image analysis to investigate biological questions. 10,11 These methods are gaining popularity in the realm of toxicology, particularly in pharmaceuticals research, and have been validated across diverse human-derived cell types. 12 However, high-content imaging has also been applied in nanotoxicology—for example, in evaluating the impacts of transition metal oxide nanoparticles to zebrafish.…”

Section: Introductionmentioning

confidence: 99%

The effect of nanoparticle surface charge on freshwater algae growth, reproduction, and lipid production

McKeel,

Kim,

Jeon

et al. 2024

Environ. Sci.: Nano

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“…Increasingly, a combination of high-throughput drug screening and machine learning is used to advance drug discovery [27, 28]. In vitro cell lines are treated with libraries of small-molecule compounds, and high-content imaging is used to automatically acquire images of cells after treatment [29]. Pipelines capable of analysing a large number of images search for compounds which lead to cell death or phenotypic change, by first performing in-depth image processing [30] and then applying machine learning algorithms [31].…”

Section: Introductionmentioning

confidence: 99%

Machine learning recognises senescence in glioblastoma and discovers senescence-inducing compounds

Martin,

Irving,

Suwanlikit

et al. 2024

Preprint

Self Cite

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Senescence is a cell-intrinsic tumour suppressive response. A one-two-punch cancer treatment strategy aims to induce senescence in cancerous cells before removing them with a senolytic. It is important to accurately recognise senescent cells to investigate the feasibility of such a treatment strategy and identify compounds that induce senescence in cancer. We focus specifically on the terminal brain cancer glioblastoma, firstly identifying senescent glioblastoma cells with conventional stains, before training a machine learning model to distinguish senescent cells using only a DAPI nuclear stain. To demonstrate how our method can aid drug discovery, we apply our pipeline to existing glioblastoma high-throughput phenotypic drug screening imaging data to identify compounds that induce senescence in glioblastoma and verify these predictions experimentally.

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Evolution and impact of high content imaging

Cited by 9 publications

References 141 publications

Evaluating batch correction methods for image-based cell profiling

Evaluating batch correction methods for image-based cell profiling

The effect of nanoparticle surface charge on freshwater algae growth, reproduction, and lipid production

Machine learning recognises senescence in glioblastoma and discovers senescence-inducing compounds

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