Image processing algorithm.

<p>Digrammatic representation of the different image processing steps.</p

Examples of applications of DeadEasy MitoGlia to address biological questions.

<p>(A) Automatic quantification of mitotic pH3 positive cells in vivo in wild-type and <i>pros^J013</i> null mutants, showing that proliferation increases in the mutants throughout embryogenesis. (B) Automatic quantification of Repo positive glia in wild-type and <i>cycE^AR95</i> mutant embryos, showing a decrease in glial number when cell division is compromised. Only a subset of dorsal glia are counted here. Numbers within bars indicate sample sizes. Error bars are s.e.m.</p

Image processing steps.

<p>(A) Images showing the image processing steps starting from the raw image and finishing in the result, which corresponds to the identified objects (cells). (B) Histogram of a typical pH3 stained image. (C) Higher magnification examples to show, as in (A), the different processing steps. This example shows the power of the programme to separate cells that in some slices may appear to be joined. (D) Example of a faintly stained sample that DeadEasy MitoGlia cannot process and must be discarded.</p

Cell division and glia in the embryonic VNC.

<p>(A) Diagram showing an embryo (left) and a cross-section view of the ventral nerve cord (VNC, right). The red box indicates an example of a region of interest (ROI) comprising the VNC and excluding the epidermis; any ROI of choice can be used. (B) Characteristic embryonic VNCs labelled with the mitotic marker pH3 and the glial marker Repo. (C) Higher magnification views of details from specimens in (B) to show the properties of the images. (D) Interactive window to enable users to change the parameters to apply the programme to other markers or sample types.</p

Parameters that can be altered by the user and the consequences.

<p>This table sows the parameters that the user can modify after accessing through ImageJ theprogrammes written in Java. Any parameter modification will require cycles of validation and further modification by the user, until the desired accuracy for the sample and staining in use is reached.</p

Validation of DeadEasy MitoGlia in identifying mitotic and glia cells.

<p>Validation of DeadEasy MitoGlia in identifying mitotic and glia cells.</p

Examples of application of DeadEasy Caspase to address biological questions.

<p>DeadEasy Caspase is used to count the number of apoptotic cells stained with Caspase in wild-type embryos at different stages and in H99 mutants lacking apoptosis. Numbers over box-plots indicate number of embryos analysed per genotype.</p

The extent of apoptosis in the Drosophila embryonic VNC.

<p>(A) A Drosophila embryonic VNC. The Region Of Interest (ROI) box in red indicates the areas analysed in (B,C). (B) 3D rendering of confocal stacks of sections through the VNC to show the abundant number of embryonic apoptotic cells stained with anti-cleaved-Caspase-3. (C) Cross section view of the image in (B).</p

Validation of DeadEasy Caspase.

<p>(A) During processing, DeadEasy creates a second stack of confocal images reproducing the identified objects in locations that correspond to those of cells in the original raw stack. By placing the mouse over each of the objects, an identifying number is revealed (as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005441#pone-0005441-g002" target="_blank">Figure 2</a>), showing whether a cell is counted appropriately. We compared one cell at a time in this way through multiple stacks. (B) Using a second validation method, a merged stack can be created from the raw images (green) and the processed images with the identified objects (red). Colocalising cells in the merged stack (yellow) indicate the identified cells, green cells are false negatives and red cells are false positives.</p

Parameters than can be modified and effects on performance.

<p>Parameters than can be modified and effects on performance.</p