A Systematic, Open-Science Framework for Quantification of Cell-Types in Mouse Brain Sections Using Fluorescence Microscopy

Abstract

The ever-expanding availability and evolution of microscopy tools has enabled ground-breaking discoveries in neurobiology, particularly with respect to the analysis of cell-type density and distribution. Widespread implementation of many of the elegant image processing tools available continues to be impeded by the lack of complete workflows that span from experimental design, labeling techniques, and analysis workflows, to statistical methods and data presentation. Additionally, it is important to consider open science principles (e.g., open-source software and tools, user-friendliness, simplicity, and accessibility). In the present methodological article, we provide a compendium of resources and a FIJI-ImageJ-based workflow aimed at improving the quantification of cell density in mouse brain samples using semi-automated open-science-based methods. Our proposed framework spans from principles and best practices of experimental design, histological and immunofluorescence staining, and microscopy imaging to recommendations for statistical analysis and data presentation. To validate our approach, we quantified neuronal density in the mouse barrel cortex using antibodies against pan-neuronal and interneuron markers. This framework is intended to be simple and yet flexible, such that it can be adapted to suit distinct project needs. The guidelines, tips, and proposed methodology outlined here, will support researchers of wide-ranging experience levels and areas of focus in neuroscience research.

Editorial: What the New White House Rules on Equitable Access Mean for the Neurosciences

“…The UK Biobank -a rich, remarkable, and growing archive of neuroimaging, genomic, biomarker, lifestyle, and health information -has set the bar high for large sample neuroimaging size studies. These and other compendia (Van Horn, 2021) have expanded the utility of their data's original collection, contributed greatly to current knowledge about brain function, structure, and connectivity, and provided grist for the mill in novel new computational methods (Sanchez-Arias et al, 2021).…”