Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning

Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection. Now, automated image analysis can effectively score many phenotypes. In practical application, customizing an image-analy...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2009-02, Vol.106 (6), p.1826-1831
Main Authors: Jones, Thouis R, Carpenter, Anne E, Lamprecht, Michael R, Moffat, Jason, Silver, Serena J, Grenier, Jennifer K, Castoreno, Adam B, Eggert, Ulrike S, Root, David E, Golland, Polina, Sabatini, David M
Format: Article
Language:English
Subjects:
Actins
Algorithms
Animals
Artificial Intelligence
Biological Sciences
Cell cycle
Cells
Cells - chemistry
Cells - cytology
Cells - ultrastructure
Computer software
Diagnostic Imaging - methods
Drosophila
Feedback
Genetic screening
Genotype & phenotype
HeLa cells
Humans
Image analysis
Image Cytometry - methods
Image Interpretation, Computer-Assisted - methods
Machine learning
Mutation
Pattern Recognition, Automated - methods
Penetrance
Phenotype
Phenotypes
Ribonucleic acid
RNA
RNA Interference
Tissue Array Analysis
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Summary:Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection. Now, automated image analysis can effectively score many phenotypes. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. First, automated cytological profiling extracts hundreds of numerical descriptors for every cell in every image. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0808843106