Automated counting of Drosophila imaginal disc cell nuclei.

Automated image quantification workflows have dramatically improved over the past decade, enriching image analysis and enhancing the ability to achieve statistical power. These analyses have proved especially useful for studies in organisms such as Drosophila melanogaster, where it is relatively simple to obtain high sample numbers for downstream analyses. However, the developing wing, an intensively utilized structure in developmental biology, has eluded efficient cell counting workflows due to its highly dense cellular population. Here, we present efficient automated cell counting workflows capable of quantifying cells in the developing wing. Our workflows can count the total number of cells or count cells in clones labeled with a fluorescent nuclear marker in imaginal discs. Moreover, by training a machine-learning algorithm we have developed a workflow capable of segmenting and counting twin-spot labeled nuclei, a challenging problem requiring distinguishing heterozygous and homozygous cells in a background of regionally varying intensity. Our workflows could potentially be applied to any tissue with high cellular density, as they are structure-agnostic, and only require a nuclear label to segment and count cells.