Unsupervised automated high throughput phenotyping of RNAi time-lapse movies

Background: Gene perturbation experiments in combination with
fluorescence time-lapse cell imaging are a powerful tool in reverse
genetics. High content applications require tools for the automated
processing of the large amounts of data. These tools include in
general several image processing steps, the extraction of
morphological descriptors, and the grouping of cells into phenotype
classes according to their descriptors. This phenotyping can be
applied in a supervised or an unsupervised manner. Unsupervised
methods are suitable for the discovery of formerly unknown
phenotypes, which are expected to occur in high-throughput RNAi
time-lapse screens. Results: We developed an unsupervised
phenotyping approach based on Hidden Markov Models (HMMs) with
multivariate Gaussian emissions for the detection of
knockdown-specific phenotypes in RNAi time-lapse movies. The
automated detection of abnormal cell morphologies allows us to
assign a phenotypic fingerprint to each gene knockdown. By applying
our method to the Mitocheck database, we show that a phenotypic
fingerprint is indicative of a gene's function. Conclusion: Our
fully unsupervised HMM-based phenotyping is able to automatically
identify cell morphologies that are specific for a certain
knockdown. Beyond the identification of genes whose knockdown
affects cell morphology, phenotypic fingerprints can be used to
find modules of functionally related genes.