Learning to see colours: Biologically relevant virtual staining for adipocyte cell images

Wieslander, Håkan; Gupta, Ankit; Bergman, Ebba; Hallström, Erik; Harrison, Philip J.

doi:10.1371/journal.pone.0258546

Cited by 10 publications

(6 citation statements)

References 37 publications

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“…Training with an adversarial scheme using a conditional GAN approach has been shown to enhance performance in virtual staining tasks 35 . In preliminary experiments, we tested different GAN models and adversarial weightings, but we chose cWGAN-GP due to its stability of training on our dataset.…”

Section: Discussionmentioning

confidence: 99%

Label-free prediction of cell painting from brightfield images

Cross-Zamirski

Mouchet

Williams

et al. 2022

Sci Rep

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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. We investigate label-free Cell Painting by predicting the five fluorescent Cell Painting channels from brightfield input. 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. The mean Pearson correlation coefficient of the predicted images across all channels is 0.84. 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. Additionally, we identified 30 features which correlated greater than 0.8 to the ground truth. Toxicity analysis on the label-free Cell Painting resulted a sensitivity of 62.5% and specificity of 99.3% on images from unseen batches. We provide a breakdown of the feature profiles by channel and feature type to understand the potential and limitations of label-free morphological profiling. 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.

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Section: Discussionmentioning

confidence: 99%

Label-free prediction of cell painting from brightfield images

Cross-Zamirski

Mouchet

Williams

et al. 2022

Sci Rep

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“…Had the performance been significantly poorer for the BF models, than those based on FL, one avenue for exploration would have been to perform virtual staining [15, 16, 17] to generate virtually stained images from which to subsequently base the MoA prediction. However, going via this route would lose any morphological information that is only present in the BF images.…”

Section: Discussionmentioning

confidence: 99%

Is brightfield all you need for mechanism of action prediction?

Gupta

Harrison

Wieslander

et al. 2022

Preprint

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Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, and labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments.

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“…Consequently, so‐called label‐free or virtual staining approaches have been considered which may allow bypassing fluorescent labeling altogether. Recent studies in fluorescent imaging have used deep learning models to virtually stain brightfield images of adipose tissue [11], detect acute lymphoblastic leukemia cells [12] and to discriminate between different cell lines [13]. Machine learning on IFC brightfield and darkfield images have been used to distinguish cell types [8] and transitions between cell states [7].…”

Section: Introductionmentioning

confidence: 99%

DeepIFC: Virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning

et al. 2023

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Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high‐throughput single‐cell fluorescent imaging. However, fluorescent labeling is costly and time‐consuming. We present a computational method called DeepIFC based on the Inception U‐Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single‐cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.

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Learning to see colours: Biologically relevant virtual staining for adipocyte cell images

Cited by 10 publications

References 37 publications

Label-free prediction of cell painting from brightfield images

Label-free prediction of cell painting from brightfield images

Is brightfield all you need for mechanism of action prediction?

DeepIFC: Virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning

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