RapID Cell Counter: Semi-Automated and Mid-Throughput Estimation of Cell Density within Diverse Cortical Layers.

Tracking and quantifying the abundance and location of cells in the developing brain is essential in neuroscience research, enabling a greater understanding of mechanisms underlying nervous system morphogenesis. Widely used experimental methods to quantify cells labeled with fluorescent markers, such as immunohistochemistry (IHC), hybridization, and expression of transgenes via stable lines or transient electroporations (IUEs), depend on accurate and consistent quantification of images. Current methods to quantify fluorescently-labeled cells rely on labor-intensive manual counting approaches, such as the Fiji plugin , which requires custom macros to enable higher-throughput analyses. Here, we present RapID Cell Counter, a semi-automated cell-counting tool with an easy-to-implement graphical user interface (GUI), which facilitates quick and consistent quantifications of cell density within user-defined boundaries that can be divided into equally-partitioned segments. Compared with the standard manual counting approach, we show that RapID matched accuracy and consistency and only required ∼10% of user time relative to manual counting methods, when quantifying the distribution of fluorescently-labeled neurons in mouse IUE experiments. Using RapID, we recapitulated previously published work focusing on two genes, and , important for projection neuron (PN) migration in the neocortex and used it to quantify PN displacement in a mouse knock-out model of Moreover, RapID is capable of quantifying other cell types in the brain with complex cell morphologies, including astrocytes and dopaminergic neurons. We propose RapID as an efficient method for neuroscience researchers to process fluorescently-labeled brain images in a consistent, accurate, and mid-throughput manner.