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

Proc Natl Acad Sci U S A
Authors
Keywords
Abstract

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.

Year of Publication
2009
Journal
Proc Natl Acad Sci U S A
Volume
106
Issue
6
Pages
1826-31
Date Published
2009 Feb 10
ISSN
1091-6490
URL
DOI
10.1073/pnas.0808843106
PubMed ID
19188593
PubMed Central ID
PMC2634799
Links
Grant list
R01 AI047389 / AI / NIAID NIH HHS / United States
RL1 CA133834-02 / CA / NCI NIH HHS / United States
DK070069-01 / DK / NIDDK NIH HHS / United States
R01 GM0725555 / GM / NIGMS NIH HHS / United States
T90 DK070069 / DK / NIDDK NIH HHS / United States
RL1 CA133834 / CA / NCI NIH HHS / United States