Abstract
Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy or gene expression. Recently, developments in tissue preparation and microscopy allow cellular populations to be imaged throughout the entire rodent brain. However, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an open-source algorithm for fully automated 3D detection of neuronal somata in mouse whole-brain microscopy images using standard desktop computer hardware. We demonstrate the applicability and power of our approach by mapping the brain-wide locations of large populations of cells labeled with cytoplasmic fluorescent proteins expressed via retrograde trans-synaptic viral infection.
Highlights
To understand the circuits underlying computations in the brain, it is necessary to map cell types, connections and activity across the entire structure
Mapping cells in the brain is a key method in neuroscience, and was traditionally carried out on manually prepared thin sections
Modern microscopy approaches allow the entire mouse brain to be imaged in 3D at high resolution
Summary
To understand the circuits underlying computations in the brain, it is necessary to map cell types, connections and activity across the entire structure. Advances in labelling [1,2,3], tissue clearing [4,5,6] and imaging [7,8,9,10,11,12] allow for the meso- and microscopic study of brain structure and function across the rodent brain Analysis of these whole-brain images has lagged behind the developments in imaging [13]. An increasingly common whole-brain image analysis task is the identification of individual, labelled cells across the entire brain This was carried out manually [18,19,20,21], but this approach does not scale to all biological questions, when many cells are labelled. Considering that a mouse brain has around 100 million neurons [22], even if only 0.01% of cells in the brain are labelled, a manual approach becomes impractical for any kind of routine analysis
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