Rapid cell counter: Semi-automated and mid-throughput estimation of cell density within diverse cortical layers

Aarthi Sekar, Thiago M. Sanches, Keiko Hino, Matangi Kumar, Juliann Wang, Elisa Ha, Blythe Durbin-Johnson, Sergi Simó, Megan Y. Dennis

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Tracking and quantifying the abundance and location of cells in the developing brain is essential in neuroscience re-search, 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), in situ hybridization, and expression of transgenes via stable lines or transient in utero 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 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 (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 stand-ard 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, SRGAP2 and CUL5, important for projection neuron (PN) migration in the neocortex and used it to quantify PN displacement in a mouse knock-out 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 mid-throughput manner.

Original languageEnglish (US)
Article numberENEURO.0185-21.2021
JournaleNeuro
Volume8
Issue number6
DOIs
StatePublished - Nov 1 2021

Keywords

  • Automated
  • Brain imaging
  • Cell quantification
  • Fluorescence
  • Microscopy

ASJC Scopus subject areas

  • Neuroscience(all)

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