![]() ![]() It is an inexpensive methodology that captures the rich information in cell morphology and has shown promising utility in bioactivity prediction, identification of cytotoxicity and prediction of mechanisms of action 3, 4, 5. We demonstrate that label-free Cell Painting has the potential to be used for downstream analyses and could allow for repurposing imaging channels for other non-generic fluorescent stains of more targeted biological interest.Ĭell Painting 1 is a high-content assay used for image-based profiling in drug discovery 2. ![]() We provide a breakdown of the feature profiles by channel and feature type to understand the potential and limitations of label-free morphological profiling. Toxicity analysis on the label-free Cell Painting resulted a sensitivity of 62.5% and specificity of 99.3% on images from unseen batches. Additionally, we identified 30 features which correlated greater than 0.8 to the ground truth. Without incorporating features into the model training, we achieved a mean correlation of 0.45 with ground truth features extracted using a segmentation-based feature extraction pipeline. The mean Pearson correlation coefficient of the predicted images across all channels is 0.84. We train and validate two deep learning models with a dataset representing 17 batches, and we evaluate on batches treated with compounds from a phenotypic set. We investigate label-free Cell Painting by predicting the five fluorescent Cell Painting channels from brightfield input. ![]() Cell Painting is a high-content image-based assay applied in drug discovery to predict bioactivity, assess toxicity and understand mechanisms of action of chemical and genetic perturbations. ![]()
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