ImageJ, MiToBo, stromule, cell biology
plastid, nucleus, peroxisome, image analysis,
Organelle morphology as well as subcellular organisation can drastically change in response to a changing cellular environment. However our knowledge about the functionality as well as the regulation of organelle form changes, such as plastid tubule formation (stromules) and organelle rearrangements, is limited. Monitoring changes to organelle morphology and subcellular organisation in response to experimental treatments and in different mutant backgrounds is a promising strategy to address open questions. However, for a detailed comparison of different treatments and mutants, the quantification of subcellular phenotypes is crucial. Unfortunately, for many specific subcellular parameters, such as stromule frequency, software tools supporting data extraction from images are not readily available or are highly specialized in their purpose. In order to quantify stromule frequency in a semi-automated manner we developed the 'MTB Cell Counter', which combines automated plastid detection with manual counting tools. We show that with the use of our plugin stromule frequency can be quantified up to 90% faster. We further demonstrate, with the detection of peroxisomes and nuclei, that due to its adaptable detection algorithm, which is based on scale-adaptive analysis of wavelet coefficients, the plugin can be used to reliably detect and count organelles of different size and brightness. In addition to the analysis of CLSM images, the 'MTB Cell Counter' is easily adapted to particle detection in challenging epifluorescence images, making it a versatile, semi-automated tool capable of quantifying a wide variety of subcellular phenotypes.