Neuronal morphology goes digital: a research hub for cellular and system neuroscience

Abstract

The importance of neuronal morphology in brain function has been recognized for over a century. The broad applicability of "digital reconstructions" of neuron morphology across neuroscience subdisciplines has stimulated the rapid development of numerous synergistic tools for data acquisition, anatomical analysis, three-dimensional rendering, electrophysiological simulation, growth models, and data sharing. Here we discuss the processes of histological labeling, microscopic imaging, and semiautomated tracing. Moreover, we provide an annotated compilation of currently available resources in this rich research "ecosystem" as a central reference for experimental and computational neuroscience.

Figure 1. Morphological diversity: A representative sample of reconstructed neurons from NeuroMorpho.Org

a. Rat neocortex Martinotti cell (NMO_00351). b. Rat neocortex bipolar cell (NMO_06144). c. Rat neocortex pyramidal cell (NMO_05729). d. Mouse neocortex pyramidal cell (NMO_05538). e. Mouse hippocampus Schaffer collateral associated neuron (NMO_07893). f. Mouse cerebellum Golgi cell (NMO_06902). g. Cat brainstem vertical cell (NMO_06171). h. Rat olfactory bulb deep short-axon cell (NMO_06222). i. Mouse neocortex Cajal-Retzius cell (NMO_07521) j. Mouse retina ganglion cell (NMO_06379). k. Spiny lobster stomatogastric ganglion motoneuron (NMO_06635). l. Rat hippocampus granule cell (NMO_06778). m. Mouse cerebellum Purkinje cell (NMO_00865). n. Rat neocortex layer 2/3 interneuron (NMO_04548). Scale bar: 100µm; somas and dendrites: black; axons: red.

Figure 2. From nervous systems to digital reconstructions of neuronal morphology

a. Staining techniques that label individual neurons include bulk extracellular loads, intracellular tracer injection, immunolabeling of cellular proteins, and genetic labeling that marks neurons intrinsically, b. Various optical microscopy visualization techniques (also depending on the labeling method) can be used to acquire images, which are then used to trace individual neurons, c. Tracing techniques have evolved over the years starting with pencil drawings using camera lucida, to a digitizing tablet that logs the tracing coordinates, followed by semi-automated methods with a computer interface and automatic algorithm-generated reconstructions that minimize manual intervention, d. Digital reconstructions files are produced by all systems interfaced with a computer. Analog camera lucida tracings can be scanned but require substantial post-processing for conversion to ‘vector’-style representation. Immonulabeling (adapted from oncoprof.net/Generale2000/g04_Diagnostic/Histologie/Technique-texte/dg_gb_ap_tech06.html with permission from Prof. J.F.Heron) Bright field microscopy (from Lanciego and Wouterlood, 2011) Confocal, 2-photon microscopy (from Lemmens et al., 2010) Camera lucida (adapted from e-book.lib.sjtu.edu.cn/iupsys/Proc/mont2/mpv2ch05.html with permission from Dr. Nick Hammond) Digitizing tablet (This image is included under the fair use exemption and is restricted from further use) Semi-automated (with permission from MBF Bioscience, Inc.) Automated (with permission from Dr. A. McKinney, McGill University)

Figure 3. Scientific applications of digital reconstructions

Three-dimensional tracing of axonal and dendritic morphology are typically acquired for one of several purposes, such as establishing neuronal identity, implementing anatomically and biophysically realistic simulations of neuronal electrophysiology, performing morphometric and stereological analyses, and determining potential connectivity. Data deposition in central repositories makes the reconstructions easily accessible for reuse in any of these applications, as well as data mining, education, and outreach. Morphometric analysis (from Costa et al., 2010) Potential connectivity (from Ropireddy and Ascoli, 2011)

Figure 4. The ecosystem of digital reconstructions: representative resources and tools

a. Neurolucida allows live or offline tracing and its companion module NeuroExplorer (inset) performs quantitative analyses, b. Digital reconstructions can be viewed and edited using Cvapp, and extensive morphometric analysis can be performed using L-Measure (inset. c. The NEURON simulation environment can distribute biophysical properties on imported neuronal reconstructions (in this example, a hippocampal interneuron; top left) for electrophysiological simulations. Here, the membrane depolarization is recorded (top right) at the soma (blue), proximal dendrite (green) and distal dendrite (red). The peak of back-propagating action potential along the dendrite decreases in amplitude with distance from the soma (bottom left), due to distinct current contributions (bottom right). d. NeuroMorpho.Org hosts digital reconstruction of neuronal morphologies that are published in peer-reviewed journals. Searching across different metadata categories (left) returns a summary result list (middle), which can be individually browsed to download the reconstruction as a standard SWC file (top right) or for additional metadata details (bottom right).

Figure 5. Literature database of references reporting digital reconstructions of neuronal morphology

a. Articles describing neuromorphological tracings are categorized in NeuroMorpho.Org based on availability of the reconstructions, year of publication, and species from which the morphologies are traced. b. Each publication is listed with its PubMed identifier (PMID) linking to the online abstract. c. If the reconstructions from the publication are available in the repository, they can be directly retrieved from PubMed via linkout mechanism.