Repository logo
 

Label-free prediction of cell painting from brightfield images.

Published version
Peer-reviewed

Change log

Authors

Cross-Zamirski, Jan Oscar 
Mouchet, Elizabeth 
Williams, Guy 
Schönlieb, Carola-Bibiane 
Turkki, Riku 

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.

Description

Funder: BBSRC DTP


Funder: AstraZeneca; doi: http://dx.doi.org/10.13039/100004325


Funder: AstraZeneca, Sweden

Keywords

Biological Assay, Drug Discovery, Image Processing, Computer-Assisted

Journal Title

Sci Rep

Conference Name

Journal ISSN

2045-2322
2045-2322

Volume Title

12

Publisher

Springer Science and Business Media LLC
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)
Relationships
Is derived from: