Neuronal tracing

Neuron tracing, or Neuron reconstruction is a technique used in neuroscience to determine the path of neural axons and dendrites (many times also called neuronal processes). From a sample preparation point of view, it may refer to some of the following as well as other genetic neuron labeling techniques,

In broad sense, neuron tracing is more often related to digital reconstruction of a neuron's morphology from imaging data of above samples.

Digital Neuron Reconstruction and Neuron Tracing

Digital reconstruction or tracing of neuron morphology is a fundamental task in computational neuroscience.[1][2][3] It is also critical for mapping neuronal circuits based on advanced microscope images, usually based on light microscopy (e.g. laser scanning microscopy, bright field imaging) or electron microscopy or other methods. Due to the high complexity of neuron morphology and often seen heavy noise in such images, as well as the typically encountered massive amount of image data, it has been widely viewed as one of the most challenging computational tasks for computational neuroscience. Many image analysis based methods have been proposed to trace neuron morphology, usually in 3D, manually, semi-automatically or completely automatically. There are normally two processing steps: generation and proof editing of a reconstruction.[4][5]

History

The need to describe or reconstruct a neuron's morphology probably began already in early days when neurons were labeled or visualized using Golgi's methods. Many of the known neuron types, such as pyramidal neurons and Chandelier cells, were described based on their morphological characterization.

The first computerized microscope for analyzing neuronal morphology created by Dr. Edmund Glaser and Dr. Hendrik Van der Loos in the 1960’s

The first computer-assisted neuron reconstruction system, now known as Neurolucida© , was developed by Dr. Edmund Glaser and Dr. Hendrik Van der Loos in the 1960’s. [6]

Modern approaches to trace a neuron started when digitized pictures of neurons were acquired using microscopes. Initially this was done in 2D. Quickly after the advanced 3D imaging, especially the fluorescence imaging and electron microscopic imaging, there were a huge demand of tracing neuron morphology from these imaging data.

Methods

Schematic illustration of digital tracing of a neuron's morphology

Neurons can be often traced manually either in 2D or 3D. To do so, one may either directly paint the trajectory of neuronal processes in individual 2D sections of a 3D image volume and manage to connect them, or use the 3D Virtual Finger painting which directly converts any 2D painted trajectory in a projection image to real 3D neuron processes. The major limitation of manual tracing of neurons is the huge amount of labor in the work.

Automated reconstructions of neurons can be done using model (e.g. spheres or tubes) fitting and marching,[7] pruning of over-reconstruction,[8] minimal cost connection of key points, ray-bursting and many others.[9] Skeletonization is a critical step in automated neuron reconstruction, but in the case of all-path-pruning and its variants[10] it is combined with estimation of model parameters (e.g. tube diameters). The major limitation of automated tracing is the lack of precision especially when the neuron morphology is complicated or the image has substantial amount of noise.

Semi-automated neuron tracing often depends on two strategies. One is to run the completely automated neuron tracing followed by manual curation of such reconstructions. The alternative way is to produce some prior knowledge, such as the termini locations of a neuron, with which a neuron can be more easily traced automatically. Semi-automated tracing is often thought to be a balanced solution that has acceptable time cost and reasonably good reconstruction accuracy. The Open Source tool Vaa3D-Neuron provides both categories of methods.

Tracing of electron microscopy image is thought to be more challenging than tracing light microscopy images, while the latter is still quite difficult, according to the DIADEM competition.[11] For tracing electron microscopy data, manual tracing is used more often than the alternative automated or semi-automated methods.[12] For tracing light microscopy data, more times the automated or semi-automated methods are used.

Since tracing electron microscopy images takes substantial amount time, collaborative manual tracing software is useful. Crowdsourcing is an alternative way to effectively collect collaborative manual reconstruction results for such image data sets.[13]

Tools and Software

A number of neuron tracing tools especially software packages are available. One comprehensive Open Source software package that contains implementation of a number of neuron tracing methods developed in different research groups as well as many neuron utilities functions such as quantitative measurement, parsing, comparison, is Vaa3D and its Vaa3D-Neuron modules (http://vaa3d.org). Some other free tools such as NeuronStudio also provide tracing function based on specific methods. People also use commercial tools such as Neurolucida, Neurolucida 360, Amira, etc. to trace neurons.

Neuron Formats and Databases

Reconstructions of single neurons can be stored in various formats. This largely depends on the software that have been used to trace such neurons. The SWC format, which consists of a number of topologically connected structural compartments (e.g. a single tube or sphere), is often used to store digital traced neurons, especially when the morphology lacks or does not need detailed 3D shape models for individual compartments. Other more sophisticated neuron formats have separate geometrical modeling of the neuron cell body and neuron processes using Neurolucida [14][15][16] among others.

There are a few common single neuron reconstruction databases. A widely used database is http://NeuroMorpho.org [17] which contains over 10,000 neuron morphology of >40 species contributed worldwide by a number of research labs. Allen Institute for Brain Science, HHMI's Janelia Research Campus, and other institutes are also generating large-scale single neuron databases. Many of related neuron data databases at different scales also exist.

References

  1. ↑ Peng, Hanchuan; Roysam, Badri; Ascoli, Giorgio (2013). "Automated image computing reshapes computational neuroscience". BMC Bioinformatics 14: 293. doi:10.1186/1471-2105-14-293.
  2. ↑ Meijering, Erik (2010). "Neuron Tracing in Perspective". Cytometry Part A 77 (7): 693–704. doi:10.1002/cyto.a.20895.
  3. ↑ Schwartz E (1990). Computational neuroscience. Cambridge, Mass: MIT Press. ISBN 0-262-19291-8.
  4. ↑ Peng, H., Long, F., Zhao, T., and Myers, E.W. "Proof-editing is the bottleneck of 3D neuron reconstruction: the problem and solutions". NeuroInformatics 9 (2-3): 103–105. doi:10.1007/s12021-010-9090-x.
  5. ↑ Peng, H., Tang, J., Xiao, H., Bria, A., et al., (2014). "Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis". Nature Communications 5. doi:10.1038/ncomms5342.
  6. ↑ Glaser, E. M.; Vanderloos, H. (1965-01-01). "A SEMI-AUTOMATIC COMPUTER-MICROSCOPE FOR THE ANALYSIS OF NEURONAL MORPHOLOGY". IEEE transactions on bio-medical engineering 12: 22–31. ISSN 0018-9294. PMID 14291539.
  7. ↑ Al-Kofahi,K.A.; et al. (2002). "Rapid automated three-dimensional tracing of neurons from confocal image stacks". IEEE Trans. Inf. Technol. Biomed. 6: 171–187. doi:10.1109/titb.2002.1006304.
  8. ↑ Peng, H.; et al. "Automatic 3D neuron tracing using all-path pruning". Bioinformatics 27 (13): i239-i247. doi:10.1093/bioinformatics/btr237.
  9. ↑ Rodriguez,A.; et al. (2009). "Three-dimensional neuron tracing by voxel scooping". J. Neurosci. Methods 184: 169–175. doi:10.1016/j.jneumeth.2009.07.021.
  10. ↑ Xiao, H; et al. (2013). "APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of gray-weighted image distance-trees". Bioinformatics 29 (11): 1448–1454. doi:10.1093/bioinformatics/btt170. PMID 23603332.
  11. ↑ Liu, Y (2011). "The DIADEM and beyond". Neuroinformatics 9: 99–102. doi:10.1007/s12021-011-9102-5.
  12. ↑ Helmstaedter M, Briggman KL, Denk W (2011). "High-accuracy neurite reconstruction for high-throughput neuroanatomy". Nat Neurosci 14 (8): 1081–1088. doi:10.1038/nn.2868.
  13. ↑ Kim; et al. (2014). "Space–time wiring specificity supports direction selectivity in the retina". Nature 509: 331–336. doi:10.1038/nature13240.
  14. ↑ Bianchi, Serena; Stimpson, Cheryl D.; Bauernfeind, Amy L.; Schapiro, Steven J.; Baze, Wallace B.; McArthur, Mark J.; Bronson, Ellen; Hopkins, William D.; Semendeferi, Katerina (2013-10-01). "Dendritic Morphology of Pyramidal Neurons in the Chimpanzee Neocortex: Regional Specializations and Comparison to Humans". Cerebral Cortex (New York, NY) 23 (10): 2429–2436. doi:10.1093/cercor/bhs239. ISSN 1047-3211. PMC 3767963. PMID 22875862.
  15. ↑ Silberberg, Gilad; Markram, Henry (2007-03-01). "Disynaptic Inhibition between Neocortical Pyramidal Cells Mediated by Martinotti Cells". Neuron 53 (5): 735–746. doi:10.1016/j.neuron.2007.02.012. ISSN 0896-6273. PMID 17329212.
  16. ↑ Bianchi, Serena; Bauernfeind, Amy L.; Gupta, Kanika; Stimpson, Cheryl D.; Spocter, Muhammad A.; Bonar, Christopher J.; Manger, Paul R.; Hof, Patrick R.; Jacobs, Bob (2011-04-01). "Neocortical neuron morphology in Afrotheria: comparing the rock hyrax with the African elephant". Annals of the New York Academy of Sciences 1225: 37–46. doi:10.1111/j.1749-6632.2011.05991.x. ISSN 1749-6632. PMID 21534991.
  17. ↑ Ascoli GA, Donohue DE, Halavi M (2007). "NeuroMorpho.Org - A central resource for neuronal morphologies". J Neurosci 27: 9247–9251. doi:10.1523/jneurosci.2055-07.2007.
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