Dataset Information

Unsupervised automated high throughput phenotyping of RNAi time-lapse movies.

ABSTRACT:

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.

SUBMITTER: Failmezger H

PROVIDER: S-EPMC3851277 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Publications

Unsupervised automated high throughput phenotyping of RNAi time-lapse movies.

Failmezger Henrik H Fröhlich Holger H Tresch Achim A

BMC bioinformatics 20131004

<h4>Background</h4>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 unsupervise ...[more]

PMID: 24090185

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Project description:The mitotic spindle is a microtubule-based structure that elongates to accurately segregate chromosomes during anaphase. Its position within the cell also dictates the future cell cleavage plan, thereby determining daughter cell orientation within a tissue or cell fate adoption for polarized cells. Therefore, the mitotic spindle ensures at the same time proper cell division and developmental precision. Consequently, spindle dynamics is the matter of intensive research. Among the different cellular models that have been explored, the one-cell stage C. elegans embryo has been an essential and powerful system to dissect the molecular and biophysical basis of spindle elongation and positioning. Indeed, in this large and transparent cell, spindle poles (or centrosomes) can be easily detected from simple DIC microscopy by human eyes. To perform quantitative and high-throughput analysis of spindle motion, we developed a computer program ACT for Automated-Centrosome-Tracking from DIC movies of C. elegans embryos. We therefore offer an alternative to the image acquisition and processing of transgenic lines expressing fluorescent spindle markers. Consequently, experiments on large sets of cells can be performed with a simple setup using inexpensive microscopes. Moreover, analysis of any mutant or wild-type backgrounds is accessible because laborious rounds of crosses with transgenic lines become unnecessary. Last, our program allows spindle detection in other nematode species, offering the same quality of DIC images but for which techniques of transgenesis are not accessible. Thus, our program also opens the way towards a quantitative evolutionary approach of spindle dynamics. Overall, our computer program is a unique macro for the image- and movie-processing platform ImageJ. It is user-friendly and freely available under an open-source licence. ACT allows batch-wise analysis of large sets of mitosis events. Within 2 minutes, a single movie is processed and the accuracy of the automated tracking matches the precision of the human eye.

Project description:BackgroundThe software available to date for analyzing image sequences from time-lapse microscopy works only for certain bacteria and under limited conditions. These programs, mostly MATLAB-based, fail for microbes with irregular shape, indistinct cell division sites, or that grow in closely packed microcolonies. Unfortunately, many organisms of interest have these characteristics, and analyzing their image sequences has been limited to time consuming manual processing.ResultsHere we describe BactImAS - a modular, multi-platform, open-source, Java-based software delivered both as a standalone program and as a plugin for Icy. The software is designed for extracting and visualizing quantitative data from bacterial time-lapse movies. BactImAS uses a semi-automated approach where the user defines initial cells, identifies cell division events, and, if necessary, manually corrects cell segmentation with the help of user-friendly GUI and incorporated ImageJ application. The program segments and tracks cells using a newly-developed algorithm designed for movies with difficult-to-segment cells that exhibit small frame-to-frame differences. Measurements are extracted from images in a configurable, automated fashion and an SQLite database is used to store, retrieve, and exchange all acquired data. Finally, the BactImAS can generate configurable lineage tree visualizations and export data as CSV files. We tested BactImAS on time-lapse movies of Mycobacterium smegmatis and achieved at least 10-fold reduction of processing time compared to manual analysis. We illustrate the power of the visualization tool by showing heterogeneity of both icl expression and cell growth atop of a lineage tree.ConclusionsThe presented software simplifies quantitative analysis of time-lapse movies overall and is currently the only available software for the analysis of mycobacteria-like cells. It will be of interest to the community of both end-users and developers of time-lapse microscopy software.

Dataset Information

Unsupervised automated high throughput phenotyping of RNAi time-lapse movies.

Background

Results

Conclusion

Publications

Unsupervised automated high throughput phenotyping of RNAi time-lapse movies.

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