Neuronal tracing

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Neuronal tracing, or neuron reconstruction is a technique used in neuroscience to determine the pathway of the neurites or neuronal processes, the axons and dendrites, of a neuron. From a sample preparation point of view, it may refer to some of the following as well as other genetic neuron labeling techniques,

Contents

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

Digital neuronal reconstruction and neuronal 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 in early days of neuroscience 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 computer-assisted neuron reconstruction system, now known as Neurolucida, was developed by Dr. Edmund Glaser and Dr. Hendrik Van der Loos in the 1960s. [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 Neuron reconstruction and tracing illustration.png
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 software Vaa3D-Neuron, Neurolucida 360, Imaris Filament Tracer and Aivia all provide 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. Some other free tools such as NeuronStudio [14] also provide tracing function based on specific methods. Neuroscientists also use commercial tools such as Neurolucida, Neurolucida 360, Aivia, Amira, etc. to trace and analyse neurons. A 2012 study show that Neurolucida is cited over 7 times more than all other available neuron tracing programs combined, [15] and is also the most widely used and versatile system to produce neuronal reconstruction. [16] The BigNeuron project (https://alleninstitute.org/bigneuron/about/) [17] is a recent substantial international collaboration effort to integrate the majority of known neuron tracing tools onto a common platform to facilitate Open Source, easy accessing of various tools at one single place. Powerful new tools such as UltraTracer, [18] that can trace arbitrarily large image volume, have been produced through this effort. The online tool WEBKNOSSOS has a Flight Mode for high-speed tracing of axons or dendrites, in which trained annotator crowds achieve tracing speeds of 1.5 ± 0.6 mm/h for axons and 2.1 ± 0.9 mm/h for dendrites in 3D electron microscopy data. [19]

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 [20] [21] [22] among others.

There are a few common single neuron reconstruction databases. A widely used database is http://NeuroMorpho.Org [23] which contains over 86,000 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.

Related Research Articles

Neuroinformatics is the emergent field that combines informatics and neuroscience. Neuroinformatics is related with neuroscience data and information processing by artificial neural networks. There are three main directions where neuroinformatics has to be applied:

<span class="mw-page-title-main">NeuronStudio</span>

NeuronStudio was a non-commercial program created at Icahn School of Medicine at Mount Sinai by the Computational Neurobiology and Imaging Center. This program performed automatic tracing and reconstruction of neuron structures from confocal image stacks. The resulting models were then exported to file using standard formats for further processing, modeling, or for statistical analyses. NeuronStudio handled morphologic details on scales spanning local Dendritic spine geometry through complex tree topology to the gross spatial arrangement of multi-neuron networks. Its capability for automated digitization avoided the subjective errors inherent in manual tracing. The program ceased to be supported in 2012 and the project pages were eventually removed from the ISMMS Website. Its documentation and the Windows source code however are still available via the Internet Archive.

Neuromorphology is the study of nervous system form, shape, and structure. The study involves looking at a particular part of the nervous system from a molecular and cellular level and connecting it to a physiological and anatomical point of view. The field also explores the communications and interactions within and between each specialized section of the nervous system. Morphology is distinct from morphogenesis. Morphology is the study of the shape and structure of biological organisms, while morphogenesis is the study of the biological development of the shape and structure of organisms. Therefore, neuromorphology focuses on the specifics of the structure of the nervous system and not the process by which the structure was developed. Neuromorphology and morphogenesis, while two different entities, are nonetheless closely linked.

Winfried Denk is a German physicist. He built the first two-photon microscope while he was a graduate student in Watt W. Webb's lab at Cornell University, in 1989.

<span class="mw-page-title-main">Connectome</span> Comprehensive map of neural connections in the brain

A connectome is a comprehensive map of neural connections in the brain, and may be thought of as its "wiring diagram". An organism's nervous system is made up of neurons which communicate through synapses. A connectome is constructed by tracing the neuron in a nervous system and mapping where neurons are connected through synapses.

Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The nervous system is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a high-throughput application of functional and structural neural imaging, most commonly magnetic resonance imaging (MRI), electron microscopy, and histological techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected spanning the nervous system including the various areas of cortex, cerebellum, the retina, the peripheral nervous system and neuromuscular junctions.

In neuroscience, anterograde tracing is a research method that is used to trace axonal projections from their source to their point of termination. A hallmark of anterograde tracing is the labeling of the presynaptic and the postsynaptic neuron(s). The crossing of the synaptic cleft is a vital difference between the anterograde tracers and the dye fillers used for morphological reconstruction. The complementary technique is retrograde tracing, which is used to trace neural connections from their termination to their source. Both the anterograde and retrograde tracing techniques are based on the visualization of the biological process of axonal transport.

<span class="mw-page-title-main">Fiji (software)</span> Open-source image-processing software

Fiji is an open source image processing package based on ImageJ2.

Bioimage informatics is a subfield of bioinformatics and computational biology. It focuses on the use of computational techniques to analyze bioimages, especially cellular and molecular images, at large scale and high throughput. The goal is to obtain useful knowledge out of complicated and heterogeneous image and related metadata.

Sholl analysis is a method of quantitative analysis commonly used in neuronal studies to characterize the morphological characteristics of an imaged neuron, first used to describe the differences in the visual and motor cortices of cats in the early 1950s. Sholl was interested in comparing the morphology of different types of neurons, such as the star-shaped stellate cells and the cone-shaped pyramidal cells, and of different locations in the dendritic field of the same type of neurons, such as basal and apical processes of the pyramidal neuron. He looked at dendritic length and diameter and also the number of cells per volume.

<span class="mw-page-title-main">Retrograde tracing</span> Technique for mapping neural circuits in the "upstream" direction, from target to source

Retrograde tracing is a research method used in neuroscience to trace neural connections from their point of termination to their source. Retrograde tracing techniques allow for detailed assessment of neuronal connections between a target population of neurons and their inputs throughout the nervous system. These techniques allow the "mapping" of connections between neurons in a particular structure and the target neurons in the brain. The opposite technique is anterograde tracing, which is used to trace neural connections from their source to their point of termination. Both the anterograde and retrograde tracing techniques are based on the visualization of axonal transport.

<span class="mw-page-title-main">MBF Bioscience</span>

MBF Bioscience is a biotech company that develops microscopy software and hardware for bioscience research and education. MBF Bioscience’s primary location is Williston, Vermont, United States, but has offices that market, sell, and support its line of hardware and software products throughout North America, Europe, and Asia.

<span class="mw-page-title-main">IMOD (software)</span>

IMOD is an open-source, cross-platform suite of modeling, display and image processing programs used for 3D reconstruction and modeling of microscopy images with a special emphasis on electron microscopy data. IMOD has been used across a range of scales from macromolecule structures to organelles to whole cells and can also be used for optical sections. IMOD includes tools for image reconstruction, image segmentation, 3D mesh modeling and analysis of 2D and 3D data.

<i>Eyewire</i> Human-based computation game

Eyewire is a citizen science game from Sebastian Seung's Lab at Princeton University. It is a human-based computation game that uses players to map retinal neurons. Eyewire launched on December 10, 2012. The game utilizes data generated by the Max Planck Institute for Medical Research.

<span class="mw-page-title-main">Amira (software)</span> Software platform for 3D and 4D data visualization

Amira is a software platform for visualization, processing, and analysis of 3D and 4D data. It is being actively developed by Thermo Fisher Scientific in collaboration with the Zuse Institute Berlin (ZIB), and commercially distributed by Thermo Fisher Scientific — together with its sister software Avizo.

A Drosophila connectome is a list of neurons in the Drosophila melanogaster nervous system, and the chemical synapses between them. The fly's nervous system consists of the brain plus the ventral nerve cord, and both are known to differ considerably between male and female. Dense connectomes have been completed for the female adult brain, the male nerve cord, and the female larval stage. The available connectomes show only chemical synapses - other forms of inter-neuron communication such as gap junctions or neuromodulators are not represented. Drosophila is the most complex creature with a connectome, which had only been previously obtained for three other simpler organisms, first C. elegans. The connectomes have been obtained by the methods of neural circuit reconstruction, which over the course of many years worked up through various subsets of the fly brain to the almost full connectomes that exist today.

Vaa3D is an Open Source visualization and analysis software suite created mainly by Hanchuan Peng and his team at Janelia Research Campus, HHMI and Allen Institute for Brain Science. The software performs 3D, 4D and 5D rendering and analysis of very large image data sets, especially those generated using various modern microscopy methods, and associated 3D surface objects. This software has been used in several large neuroscience initiatives and a number of applications in other domains. In a recent Nature Methods review article, it has been viewed as one of the leading open-source software suites in the related research fields. In addition, research using this software was awarded the 2012 Cozzarelli Prize from the National Academy of Sciences.

Neural circuit reconstruction is the reconstruction of the detailed circuitry of the nervous system of an animal. It is sometimes called EM reconstruction since the main method used is the electron microscope (EM). This field is a close relative of reverse engineering of human-made devices, and is part of the field of connectomics, which in turn is a sub-field of neuroanatomy.

Jeff W. Lichtman is an American neuroscientist. He is the Jeremy R. Knowles Professor of Molecular and Cellular Biology and Santiago Ramón y Cajal Professor of Arts and Sciences at Harvard University. He is best known for his pioneering work developing the neuroimaging connectomic technique known as Brainbow.

Patch-sequencing (patch-seq) is a modification of patch-clamp technique that combines electrophysiological, transcriptomic and morphological characterization of individual neurons. In this approach, the neuron's cytoplasm is collected and processed for RNAseq after electrophysiological recordings are performed on it. The cell is simultaneously filled with a dye that allows for subsequent morphological reconstruction.

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 . PMC   3853071 . PMID   24090217.
  2. ↑ Meijering, Erik (2010). "Neuron Tracing in Perspective". Cytometry Part A. 77 (7): 693–704. CiteSeerX   10.1.1.623.3000 . doi:10.1002/cyto.a.20895. PMID   20583273. S2CID   14047936.
  3. ↑ Schwartz E (1990). Computational neuroscience. Cambridge, Mass: MIT Press. ISBN   978-0-262-19291-0.
  4. ↑ Peng, H., Long, F., Zhao, T., and Myers, E.W. (2011). "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. PMID   21170608. S2CID   4995280.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  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: 4342. Bibcode:2014NatCo...5.4342P. doi:10.1038/ncomms5342. PMC   4104457 . PMID   25014658.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  6. ↑ Glaser, E. M.; Vanderloos, H. (1965-01-01). "A Semi-Automatic Computer-Microscope for the Analysis of Neuronal Morphology". IEEE Transactions on Biomedical Engineering. 12: 22–31. doi:10.1109/TBME.1965.4502337. 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 (2): 171–187. CiteSeerX   10.1.1.57.9339 . doi:10.1109/titb.2002.1006304. PMID   12075671. S2CID   12413677.
  8. ↑ Peng, H.; et al. (2011). "Automatic 3D neuron tracing using all-path pruning". Bioinformatics. 27 (13): i239–i247. doi:10.1093/bioinformatics/btr237. PMC   3117353 . PMID   21685076.
  9. ↑ Rodriguez, A.; et al. (2009). "Three-dimensional neuron tracing by voxel scooping". J. Neurosci. Methods. 184 (1): 169–175. doi:10.1016/j.jneumeth.2009.07.021. PMC   2753723 . PMID   19632273.
  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. PMC   3661058 . PMID   23603332.
  11. ↑ Liu, Y (2011). "The DIADEM and beyond". Neuroinformatics. 9 (2–3): 99–102. doi: 10.1007/s12021-011-9102-5 . PMID   21431331.
  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. PMID   21743472. S2CID   17795934.
  13. ↑ Kim; et al. (2014). "Space–time wiring specificity supports direction selectivity in the retina". Nature. 509 (7500): 331–336. Bibcode:2014Natur.509..331.. doi:10.1038/nature13240. PMC   4074887 . PMID   24805243.
  14. ↑ Rodriguez, Alfredo (2010-02-18). "Tools: NeuronStudio (Beta) Version 0.9.92, last updated on November 19, 2009". CNIC, Mount Sinai School of Medicine. Archived from the original on 2018-09-16.
  15. ↑ Halavi, Maryam; Hamilton, Kelly A.; Parekh, Ruchi; Ascoli, Giorgio A. (2012-01-01). "Digital reconstructions of neuronal morphology: three decades of research trends". Frontiers in Neuroscience. 6: 49. doi: 10.3389/fnins.2012.00049 . ISSN   1662-453X. PMC   3332236 . PMID   22536169.
  16. ↑ Aguiar, Paulo; Sousa, Mafalda; Szucs, Peter (2013-06-14). "Versatile Morphometric Analysis and Visualization of the Three-Dimensional Structure of Neurons". Neuroinformatics. 11 (4): 393–403. doi:10.1007/s12021-013-9188-z. ISSN   1539-2791. PMID   23765606. S2CID   16591493.
  17. ↑ Peng, Hanchuan; Hawrylycz, Michael; Roskams, Jane (2015-07-15). "BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images". Neuron. 87 (2): 252–256. doi:10.1016/j.neuron.2015.06.036. PMC   4725298 . PMID   26182412.
  18. ↑ Peng, Hanchuan; Zhou, Zhi; Meijering, Erik (2016). "Automatic Tracing of Ultra-Volume of Neuronal Images". bioRxiv   10.1101/087726 .
  19. ↑ Boergens, Kevin M.; Berning, Manuel; Bocklisch, Tom; Bräunlein, Dominic; Drawitsch, Florian; Frohnhofen, Johannes; Herold, Tom; Otto, Philipp; Rzepka, Norman; Werkmeister, Thomas; Werner, Daniel; Wiese, Georg; Wissler, Heiko; Helmstaedter, Moritz (July 2017). "webKnossos: efficient online 3D data annotation for connectomics". Nature Methods. 14 (7): 691–694. doi:10.1038/nmeth.4331. ISSN   1548-7105. PMID   28604722. S2CID   30609228.
  20. ↑ 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. 23 (10): 2429–2436. doi:10.1093/cercor/bhs239. ISSN   1047-3211. PMC   3767963 . PMID   22875862.
  21. ↑ 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. S2CID   15624023.
  22. ↑ 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 (1): 37–46. Bibcode:2011NYASA1225...37B. doi:10.1111/j.1749-6632.2011.05991.x. ISSN   1749-6632. PMID   21534991. S2CID   18281955.
  23. ↑ Ascoli GA, Donohue DE, Halavi M (2007). "NeuroMorpho.Org - A central resource for neuronal morphologies". J Neurosci. 27 (35): 9247–9251. doi:10.1523/jneurosci.2055-07.2007. PMC   6673130 . PMID   17728438.
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