Combining phenotypic profiling and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: Retinoic acid as an example

Nyffeler, Johanna; Willis, Clinton; Harris, Felix R.; Taylor, Laura W.; Judson, Richard S.; Everett, Logan J.; Harrill, Joshua

doi:10.1016/j.taap.2022.116032

Cited by 12 publications

(9 citation statements)

References 68 publications

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“…116 Another study combined RNA-Seq and Cell Painting data to estimate the phenotype altering concentration of a set of 11 mechanistically diverse compounds, and found that for most of the compounds, the phenotype altering concentration from Cell Painting and biological phenotype altering concentration from RNA-Seq were within half an order of magnitude. 117 Furthermore, they found that combining both modalities provided the best potency estimates, particularly for compounds with strong morphological signatures that did not affect expression of target genes (ATRA in this study).…”

Section: Integrating Cell Painting Transcriptomics and Proteomics Datamentioning

confidence: 65%

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

Seal,

Trapotsi,

Spjuth

et al. 2024

Preprint

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High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting’s ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

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Section: Integrating Cell Painting Transcriptomics and Proteomics Datamentioning

confidence: 65%

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

Seal,

Trapotsi,

Spjuth

et al. 2024

Preprint

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“…ToxCast data is supplied post-pipelining with information that users can apply to filter or “clean” the data for specific use cases with the stringency required in terms of including potential false positives. For instance, curves can be filtered by effect size using fit category, filters such as a ‘top over cutoff’ of ≥1 ( Nyffeler et al, 2022 ), by evaluating cautionary flags for borderline activity, or examining the sheer number of cautionary flags on fitting. Cautionary flags and fitc have been used in the past to rapidly indicate curve quality, along with other potential indicators such as the reproducibility of the hitc ( Watt and Judson, 2018 ; Paul Friedman et al, 2020 ; Carstens et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…A salient need moving forward is for curve-fitting of bioactivity data of any tier in the CompTox Blueprint to be as analogous as practicable. The primary objective of the work described herein was to implement changes in tcpl and subsequently in invitrodb to include curve-fitting models available in BMDExpress version 2 ( Phillips et al, 2019 ) and used to curve-fit Tier 1 bioactivity data ( Harrill et al, 2021 ; Nyffeler et al, 2021 ; Nyffeler et al, 2022 ), and then to evaluate the potential impacts on ToxCast data interpretation. To accomplish this, tcpl v3.0, released in August 2022, incorporates a new dependency for curve-fitting: R package tcplfit2 ( Sheffield et al, 2021 ; USEPA, 2022c ).…”

Section: Introductionmentioning

confidence: 99%

“…To accomplish this, tcpl v3.0, released in August 2022, incorporates a new dependency for curve-fitting: R package tcplfit2 ( Sheffield et al, 2021 ; USEPA, 2022c ). The R package tcplfit2 is already employed for analysis in Tier 1 high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP) screening results ( Nyffeler et al, 2020 ; Harrill et al, 2021 ; Nyffeler et al, 2022 ; Speen et al, 2022 ). Existing annotation of ToxCast assays to gene and intended target family (a label intended to capture biological processes or types of macromolecular interaction), along with upgrading curve-fitting using tcplfit2, will enable a more seamless integration with HTPP and HTTr results.…”

Section: Introductionmentioning

confidence: 99%

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The ToxCast pipeline: updates to curve-fitting approaches and database structure

Feshuk,

Kolaczkowski,

Dunham

et al. 2023

Front. Toxicol.

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Introduction: The US Environmental Protection Agency Toxicity Forecaster (ToxCast) program makes in vitro medium- and high-throughput screening assay data publicly available for prioritization and hazard characterization of thousands of chemicals. The assays employ a variety of technologies to evaluate the effects of chemical exposure on diverse biological targets, from distinct proteins to more complex cellular processes like mitochondrial toxicity, nuclear receptor signaling, immune responses, and developmental toxicity. The ToxCast data pipeline (tcpl) is an open-source R package that stores, manages, curve-fits, and visualizes ToxCast data and populates the linked MySQL Database, invitrodb.Methods: Herein we describe major updates to tcpl and invitrodb to accommodate a new curve-fitting approach. The original tcpl curve-fitting models (constant, Hill, and gain-loss models) have been expanded to include Polynomial 1 (Linear), Polynomial 2 (Quadratic), Power, Exponential 2, Exponential 3, Exponential 4, and Exponential 5 based on BMDExpress and encoded by the R package dependency, tcplfit2. Inclusion of these models impacted invitrodb (beta version v4.0) and tcpl v3 in several ways: (1) long-format storage of generic modeling parameters to permit additional curve-fitting models; (2) updated logic for winning model selection; (3) continuous hit calling logic; and (4) removal of redundant endpoints as a result of bidirectional fitting.Results and discussion: Overall, the hit call and potency estimates were largely consistent between invitrodb v3.5 and 4.0. Tcpl and invitrodb provide a standard for consistent and reproducible curve-fitting and data management for diverse, targeted in vitro assay data with readily available documentation, thus enabling sharing and use of these data in myriad toxicology applications. The software and database updates described herein promote comparability across multiple tiers of data within the US Environmental Protection Agency CompTox Blueprint.

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“…The diversity and number of these toxic phenotypes led us to quantify the changes happening in the cell morphology, starting from the automatization of cell count calculations from these CPA images. The best phenotypes were obtained using the Robust MAD (Median Absolute Deviation) normalization method, as previously recommended by the Carpenter-Singh lab directives and tends to be more popular than a traditional Z-score scaling in recent studies (26)(27)(28)(29)(30). These resulting phenotypes were used for further analysis.…”

Section: Cell Painting Imaging and Phenotypingmentioning

confidence: 99%

Drug-induced cytotoxicity prediction in muscle cells, an application of the Cell Painting assay

Lambert,

Aparicio,

Candelas

et al. 2024

Preprint

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In silicotoxicity prediction offers the chance of reducing or replacing most animal testing through the integration of large experimental assay datasets with the appropriate computational approaches. The use of Cell Painting to detect various phenotypic changes induced by chemicals is emerging as a powerful technique in toxicity prediction. However, most Cell Painting approaches use cancer cells that are less relevant for many toxicological endpoints, which may limit the usefulness of this data. In this study, a myoblast cell line is used to characterize cellular responses to a panel of 30 known myotoxicants. In place of traditional structural descriptors, here each perturbation is described by a fingerprint of calculated properties, deducted from the intensity, shape, or texture of individual cells. We show that these kinds of descriptors convey information to allow the prediction of the cellular viability and fate of cells in myoblasts and differentiated myotubes of the C2C12 cell line, and the clustering of drugs by their cytotoxicity responses.Author SummaryStudying the toxicity of chemical compounds and drugs is crucial to avoid potentially lethal adverse effects of commercialized products, but also to detect the unsuspected toxicity of existing drugs. While these assays traditionally rely on animal models raising important ethical concerns, a need forin vitroandin silicomodels is present and increasing in recent years. We here propose a predictive model capable of predicting the values of a cell viability assay using cell morphology profiles captured with a microscopy experiment. This model predicts the healthiness of muscle cells treated with 30 compounds suspected to induce muscular damage or even myopathies in humans. We also use these profiles to find an interesting morphological similarity between two different classes of drugs: statins (used for cholesterol treatments) and tyrosine kinase inhibitors (anti-cancer drugs). This analysis opens a new perspective for understanding the mechanisms responsible for drug-induced muscular toxicity, an area of toxicology that is currently under-researched.

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Combining phenotypic profiling and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: Retinoic acid as an example

Cited by 12 publications

References 68 publications

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

The ToxCast pipeline: updates to curve-fitting approaches and database structure

Drug-induced cytotoxicity prediction in muscle cells, an application of the Cell Painting assay

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