Image-based phenotyping of disaggregated cells using deep learning
Open Access
- 13 November 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in Communications Biology
- Vol. 3 (1), 1-9
- https://doi.org/10.1038/s42003-020-01399-x
Abstract
The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used for image cytometry but has been limited by cell agglomeration and it is currently unclear if this approach can reliably phenotype cells that are difficult to distinguish by the human eye. Here, we show disaggregated single cells can be phenotyped with a high degree of accuracy using low-resolution bright-field and non-specific fluorescence images of the nucleus, cytoplasm, and cytoskeleton. Specifically, we trained a convolutional neural network using automatically segmented images of cells from eight standard cancer cell-lines. These cells could be identified with an average F1-score of 95.3%, tested using separately acquired images. Our results demonstrate the potential to develop an "electronic eye" to phenotype cells directly from microscopy images. Berryman et al demonstrate that disaggregated cells can be phenotyped with a high degree of accuracy from bright-field and non-specifically stained microscopy images using a trained convolutional neural network (CNN). This approach allows for the identification of cell types without the need for specific markers.Funding Information
- Mitacs (IT09621, IT09621, IT13817)
- Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (2020-05412, 2015-06541, 2020-00530, 508392-17)
- Gouvernement du Canada | Canadian Institutes of Health Research (381129, 322375, 426032)
- Michael Smith Foundation for Health Research (18714)
This publication has 29 references indexed in Scilit:
- Automated analysis of high‐content microscopy data with deep learningMolecular Systems Biology, 2017
- A multi-scale convolutional neural network for phenotyping high-content cellular imagesBioinformatics, 2017
- Single-Cell Phenotype Classification Using Deep Convolutional Neural NetworksSLAS Discovery, 2016
- Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping ConsortiumScientific Reports, 2016
- HEp-2 Cell Image Classification With Deep Convolutional Neural NetworksIEEE Journal of Biomedical and Health Informatics, 2016
- Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology ImagesIEEE Transactions on Medical Imaging, 2016
- U-Net: Convolutional Networks for Biomedical Image SegmentationPublished by Springer Science and Business Media LLC ,2015
- Assessing the efficacy of low-level image content descriptors for computer-based fluorescence microscopy image analysisJournal of Microscopy, 2011
- Image-based multivariate profiling of drug responses from single cellsNature Methods, 2007
- Integrated classification of lung tumors and cell lines by expression profilingProceedings of the National Academy of Sciences, 2002