Abstract
Knowledge about neuron morphology is key to understanding brain structure and function. There are a variety of software tools that are used to segment and trace the neuron morphology. However, these tools usually utilize proprietary formats. This causes interoperability problems since the information extracted with one tool cannot be used in other tools. This article aims to improve neuronal reconstruction workflows by facilitating the interoperability between two of the most commonly used software tools—Neurolucida (NL) and Imaris (Filament Tracer). The new functionality has been included in an existing tool—Neuronize—giving rise to its second version. Neuronize v2 makes it possible to automatically use the data extracted with Imaris Filament Tracer to generate a tracing with dendritic spine information that can be read directly by NL. It also includes some other new features, such as the ability to unify and/or correct inaccurately-formed meshes (i.e., dendritic spines) and to calculate new metrics. This tool greatly facilitates the process of neuronal reconstruction, bridging the gap between existing proprietary tools to optimize neuroscientific workflows.
Highlights
In recent years, the emergence of novel methods that provide new insights into the organization of the brain has produced a wealth of data that needs to be analyzed, shifting the bottleneck from the acquisition to the analysis of data
Analyzing neuron morphology is essential to better understand cell functioning (Segev and Rall, 1998; Spruston, 2008), including dendritic spines, which are of great relevance for the study of brain processing (Yuste, 2010; Heck and Benavides-Piccione, 2015)
There are several software tools that aim to facilitate the reconstructions of the neurons, employing different approaches—either extracting tracings from stacks of images, as is the case for Snake (Wang et al, 2011), APP2 (Xiao and Peng, 2013), flNeuronTool (Ming et al, 2013), SmartTracing (Chen et al, 2015), NeuTube (Feng et al, 2015), Rivulet (Liu et al, 2016), TreeMap (Zhou et al, 2016), NeuroGPS-Tree (Quan et al, 2016), Neuron tracer (Wang et al, 2017) and ShuTu (Jin et al, 2019); or processing neuronal tracings as occurs with Lasserre (Lasserre et al, 2012), Filament editor (Dercksen et al, 2014), NeuTube (Feng et al, 2015) and NeuroMorphoVis (Abdellah et al, 2018)
Summary
The emergence of novel methods that provide new insights into the organization of the brain has produced a wealth of data that needs to be analyzed, shifting the bottleneck from the acquisition to the analysis of data. Several laboratories have made significant contributions in recent times to gathering data on and analyzing neuron morphology These studies have contributed significantly to better understand the diversity and regional specialization of the cortical organization (e.g., Huttenlocher and Dabholkar, 1997; Cline, 1999; Preuss, 2001; Elston and DeFelipe, 2002; Jacobs and Scheibel, 2002; Elston, 2003; Luebke, 2017). Some tools use the neuronal tracings to create 3D meshes; for example, NeuroTessMesh (GarciaCantero et al, 2017), NeuroMorphoVis (Abdellah et al, 2018), Neuronize (Brito et al, 2013), and Lasserre (Lasserre et al, 2012). Some of the most frequently used available reconstruction software suites are Neurolucida (NL; MicroBrightfield, VT, USA) and Imaris (BitplaneAG, Zurich, Switzerland) These tools are based on proprietary formats. Proprietary formats and lack of interoperability make data sharing difficult since the data files are linked to a specific tool
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