Automated analysis of intracellular calcium fluorescence in rat organotypic hippocampal cultures: Comparison to a manual, observer based method

• Manual object detection and subsequent analysis in fluorescent microscopy shows great variability.
• Here we utilise automated object detection script for high throughput analysis of complex data.
• Comparison of manual versus automated object detection reveals the automated method to be more efficient and reliable.
• Our method is fully open source and customisable allowing it to be applied to other analyses.

The technical advances made in microscopy have been matched by an increase in the application of fluorescent microscopy to answer scientific questions. While analysis of fluorescent microscopy images represents a powerful tool, one must be aware of the potential pitfalls. Frequently, the analysis methods applied involve at least some manual steps which are dependent on an observers input. Typically these steps are laborious and time consuming, but more importantly they are also influenced by an individual observer's bias, drift or imprecision. This raises concerns about the repeatability and definitiveness of the reported observations. Using calcium fluorescence in organotypic hippocampal slices as an experimental platform, we demonstrate the influence that manual interventions can exert on an analysis. We show that there is a high degree of variability between observers, and that this can be sufficient to affect the outcome of an experiment. To counter this, and to eliminate the disagreement between observers, we describe an alternative fully automated method which was created using EBImage package for R. This method has the added advantage of being fully open source and customisable, allowing for this approach to be applied to other analyses.