RapID Cell Counter: Semi-automated and mid-throughput estimation of cell density within diverse cortical layers

Abstract

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, in situ hybridization, and expression of transgenes via stable lines or transient in utero electroporations—depend upon 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 Cell Counter, 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, that facilitates quick and consistent quantifications of cell density within user-defined boundaries that can be divided into equally-partitioned segments. Compared to 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 in utero electroporation experiments. Using RapID, we recapitulated previously published work focusing on two genes, Srgap2 and Cul5, important for projection neuron migration in the neocortex and used it to quantify projection neuron displacement in a mouse knockout model of Rbx2. 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 high-throughput manner.

SIGNIFICANCE STATEMENT

Most studies in neuroscience rely on imaging to elucidate key neurodevelopmental processes including cell migration and proliferation. Many imaging techniques, including in utero electroporation and immunohistochemistry, produce multitudes of images that require accurate quantification, often via labor-intensive manual counting by multiple individuals that may delay follow-up experiments. To address this problem, we developed RapID, an efficient and semi-automated cell counting software platform, that reduces the time spent to 1/10^th compared to one of the most popular quantification methods used for imaging studies today. RapID is flexible across imaging platforms and easily implemented through a graphical user interface.