Abstract
Fluorescence confocal microscopy has become increasingly more important in neuroscience due to its applications in image-based screening and profiling of neurons. Multispectral confocal imaging is useful to simultaneously probe for distribution of multiple analytes over networks of neurons. However, current automated image analysis algorithms are not designed to extract single-neuron arbors in images where neurons are not separated, hampering the ability map fluorescence signals at the single cell level. To overcome this limitation, we introduce NeuroTreeTracer – a novel image processing framework aimed at automatically extracting and sorting single-neuron traces in fluorescent images of multicellular neuronal networks. This method applies directional multiscale filters for automated segmentation of neurons and soma detection, and includes a novel tracing routine that sorts neuronal trees in the image by resolving network connectivity even when neurites appear to intersect. By extracting each neuronal tree, NeuroTreetracer enables to automatically quantify the spatial distribution of analytes of interest in the subcellular compartments of individual neurons. This software is released open-source and freely available with the goal to facilitate applications in neuron screening and profiling.
Highlights
Neuronal reconstruction is critical in a variety of neurobiological studies
To successfully separate distinct neuronal arbors, NeuroTreeTracer combines an automated method for soma detection and extraction that relies on multiscale directional filters, and a novel centerline tracing routine that identifies the neurites associated with each individual neuron using a front-propagation approach initiated from each soma location
The tracing routine may stop before a neurite is completely traced. We found that this situation is rare in the images we considered but it is a potential cause of errors in images containing a denser population of neurons
Summary
Neuronal reconstruction is critical in a variety of neurobiological studies. During the last two decades, a large number of algorithms and software toolkits were developed aiming at providing digital reconstruction of neurons from images acquired using bright field or fluorescent microscopy[1]. Extracting individual neuronal traces from an image of a multicellular network is a challenging task, in general, even when the entire image has been traced, due to the need to separate vessels that appear to cross or run very close to each other, and the need to resolve the connectivity of a networks that can be topologically complex (what path leads from a neurite location to its soma?) To address this limitation and facilitate the automated collection of local fluorescent expression measures from individual neurons in a network, in this paper we introduce a novel neurite tracing and sorting algorithm, called NeuroTreeTracer, designed to identify and trace individual neuronal trees in 2-dimensional fluorescent images of networks containing multiple (non-separated) neurons. In addition to identifying the neuritic branches belonging to each neuron in a multicellular image, for each neuron NeuroTreeTracer labels its sub-compartments, i.e., soma, dendrites and axon, and determines the paths connecting soma to neurites, enabling the computation of geometrical characteristics and the quantification of local expression levels of analytes of interest with respect to their location along the neurites
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