Abstract
BackgroundHigh content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will generate multiple large datasets that are often stored in an ad-hoc manner. This hinders identification of previously gathered data that may be relevant to current analyses. Whilst solutions exist for managing image data, they are primarily concerned with storage and retrieval of the images themselves and not the data derived from the images. There is therefore a requirement for an information management solution that facilitates the indexing of experimental metadata and results of high content live cell imaging experiments.ResultsWe have designed and implemented a data model and information management solution for the data gathered through high content live cell imaging experiments. Many of the experiments to be stored measure the translocation of fluorescently labelled proteins from cytoplasm to nucleus in individual cells. The functionality of this database has been enhanced by the addition of an algorithm that automatically annotates results of these experiments with the timings of translocations and periods of any oscillatory translocations as they are uploaded to the repository. Testing has shown the algorithm to perform well with a variety of previously unseen data.ConclusionOur repository is a fully functional example of how high throughput imaging data may be effectively indexed and managed to address the requirements of end users. By implementing the automated analysis of experimental results, we have provided a clear impetus for individuals to ensure that their data forms part of that which is stored in the repository. Although focused on imaging, the solution provided is sufficiently generic to be applied to other functional proteomics and genomics experiments. The software is available from:
Highlights
The combination of fluorescently labelled proteins, combined with automated focus and image capture, has led to a large increase in "high content screening" of cells, for phenotype and protein localisation in response to a variety of environmental perturbations (e.g. [1,2,3,4]). The majority of these assays deal with cells at single time points, but more challenging, and potentially more useful for a systems biology approach involving mathematical modelling, is being able to track cellular functions in a number of individual live cells over a period of time
The pathway is complex, and high throughput screening of samples is proving to be essential for investigation of the mechanisms that regulate these dynamic processes [5]
This paper presents an information management solution for high throughput cell imaging experiments, illustrated in the context of the NF-κB pathway, that addresses the above requirements by: R1
Summary
High content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will generate multiple large datasets that are often stored in an ad-hoc manner. This hinders identification of previously gathered data that may be relevant to current analyses. There is a requirement for an information management solution that facilitates the indexing of experimental metadata and results of high content live cell imaging experiments. The relationship between NF-κB and IκB leads to delayed negative feedback, which generates oscillatory behaviour in NF-κB localisation [5] These oscillations can be studied effectively using fluorescently tagged NF-κB and IκB proteins (Figure 1). An important part of our work concerns the construction of an accurate dynamic mathematical model of the NF-κB pathway which must be fitted and verified using experimental data
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