Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection

Seal, Srijit; Carreras-Puigvert, Jordi; Trapotsi, Maria-Anna; Yang, Hong-Chang; Spjuth, Ola; Bender, Andreas

doi:10.1038/s42003-022-03763-5

Cited by 54 publications

(56 citation statements)

References 99 publications

(103 reference statements)

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“…Additionally, MID performs better than CellProfiler and DeepProfiler in task 2, with results consistent with those reported in the literature [21] (Figure 2).…”

Section: Discussionsupporting

confidence: 90%

“…Multiple mechanisms contribute to mitochondrial toxicity [19] , resulting in various changes in cell phenotype. The alterations in cell morphology, texture, and intensity caused by compounds are strongly correlated with mitochondrial toxicity, suggesting that cell phenotype analysis is a reliable method for predicting mitochondrial toxicity [21] .…”

Section: Mid Performance On Mitochondrial Toxicity Classificationmentioning

confidence: 99%

See 1 more Smart Citation

More Is Different: Drug Property Analysis on Cellular High-Content Images Using Deep Learning

Gao¹,

Guo²,

Zhang³

et al. 2023

Preprint

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High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noise and high similarity in cell images impede the accuracy of deep learning-based image analysis. To address these issues, we introduce More Is Different (MID), a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. MID effectively combines the convolutional neural network and Transformer to encode high-content images, effectively filtering out noisy signals and characterizing cell phenotypes with high precision. In comparative tests on drug-induced cardiotoxicity and mitochondrial toxicity classification, as well as compound classification, MID outperformed both DeepProfiler and CellProfiler, which are two highly recognized methods in HCA. We believe that our results demonstrate the utility and versatility of MID and anticipate its widespread adoption in HCA for advancing drug development and disease research.

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“…Additionally, MID performs better than CellProfiler and DeepProfiler in task 2, with results consistent with those reported in the literature [21] (Figure 2).…”

Section: Discussionsupporting

confidence: 90%

Section: Mid Performance On Mitochondrial Toxicity Classificationmentioning

confidence: 99%

More Is Different: Drug Property Analysis on Cellular High-Content Images Using Deep Learning

Gao¹,

Guo²,

Zhang³

et al. 2023

Preprint

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“…Note that not all bioactivity classes might be as well-populated or as easily annotated with a “mode of action”, and hence also here some kind of self-selection has been performed in this study. The general finding, of improved classification performance, is nonetheless consistent with a study cited above, which in particular for novel scaffolds underlined the value of including Cell Painting readouts in predicting biological endpoints.…”

Section: Cell Painting Datasupporting

confidence: 86%

“…46 The predictive value of a readout, given a particular assay setup and data processing pipeline, needs to be validated for every endpoint individually. This has been done for mitochondrial toxicity, 65 using Cell Painting, gene expression, and compound structural information, for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. Mitochondrial toxicants were found to differ from nontoxic compounds in morphological space, and when included in predictive models this combination of features improved model performance on an external test set of 244 significantly, thereby improving extrapolation to new chemical space.…”

Section: Acs Medicinalmentioning

confidence: 99%

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

Pruteanu

Bender

2023

ACS Med. Chem. Lett.

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Gene expression and cell morphology data are highdimensional biological readouts of much recent interest for drug discovery. They are able to describe biological systems in different states (e.g., healthy and diseased), as well as biological systems before and after compound treatment, and they are hence useful for matching both spaces (e.g., for drug repurposing) as well as for characterizing compounds with respect to efficacy and safety endpoints. This Microperspective describes recent advances in this direction with a focus on applied drug discovery and drug repurposing, as well as outlining what else is needed to advance further, with a particular focus on better understanding the applicability domain of readouts and their relevance for decision making, which is currently often still unclear.

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“…This showed that fusing models built on two different feature spaces that provide complementary information were able to improve the prediction of bioactivity endpoints. Previous work has also shown that combinations of descriptors can significantly improve prediction for MOA classification 25,26,15 (using gene expression and cell morphology data), cytotoxicity 16 , mitochondria toxicity 18 and anonymised assay activity 27 (using chemical, gene expression, cell morphology and predicted bioactivity data), prediction of sigma 1 (σ1) receptor antagonist 28 (using cell morphology data and thermal proteome profiling), and even organism-level toxicity 29 (using chemical, protein target and cytotoxicity qHTS data). Thus, the combination of models built from complementary feature spaces can expand a model’s applicability domain by allowing predictions in new structural space.…”

Section: Introductionmentioning

confidence: 99%

Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data

Seal

Yang

Trapotsi

et al. 2022

Preprint

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The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints). Using a combination of a decision tree and logistic regression models on the structural versus morphological feature space of the training data, which leveraged the similarity of test compounds to training compounds, the similarity-based merger models used logistic equations to weigh individual model outputs. We applied these models to predict assay hit calls of 92 assays from ChEMBL and PubChem and 89 anonymised assays released by the Broad Institute, where the required Cell Painting annotations were available. We found that for the 181 assays used in this study the similarity-based merger model improved AUC in relative terms by 16.3% compared to the models using chemical structure alone (mean AUC of 0.75 vs. 0.64), and by 21.3% compared to the models using Cell Painting data alone (mean AUC of 0.62). Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.

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Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection

Cited by 54 publications

References 99 publications

More Is Different: Drug Property Analysis on Cellular High-Content Images Using Deep Learning

More Is Different: Drug Property Analysis on Cellular High-Content Images Using Deep Learning

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data

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