Label-free prediction of cell painting from brightfield images.
Authors
Cross-Zamirski, Jan Oscar
Mouchet, Elizabeth
Williams, Guy
Schönlieb, Carola-Bibiane
Turkki, Riku
Wang, Yinhai
Publication Date
2022-06-15Journal Title
Sci Rep
ISSN
2045-2322
Publisher
Springer Science and Business Media LLC
Volume
12
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Cross-Zamirski, J. O., Mouchet, E., Williams, G., Schönlieb, C., Turkki, R., & Wang, Y. (2022). Label-free prediction of cell painting from brightfield images.. Sci Rep, 12 (1) https://doi.org/10.1038/s41598-022-12914-x
Description
Funder: BBSRC DTP
Funder: AstraZeneca; doi: http://dx.doi.org/10.13039/100004325
Funder: AstraZeneca, Sweden
Abstract
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.
Keywords
Biological Assay, Drug Discovery, Image Processing, Computer-Assisted
Sponsorship
BBSRC (2103688)
Engineering and Physical Sciences Research Council (EP/N014588/1)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (777826)
EPSRC (EP/S026045/1)
EPSRC (EP/T017961/1)
EPSRC (EP/T003553/1)
Wellcome Trust (215733/Z/19/Z)
Wellcome Trust (221633/Z/20/Z)
Wellcome Trust (223131/Z/21/Z)
Identifiers
s41598-022-12914-x, 12914
External DOI: https://doi.org/10.1038/s41598-022-12914-x
This record's URL: https://www.repository.cam.ac.uk/handle/1810/338124
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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