Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

The importance of metadata to assess information content in digital reconstructions of neuronal morphology

Abstract

Digital reconstructions of axonal and dendritic arbors provide a powerful representation of neuronal morphology in formats amenable to quantitative analysis, computational modeling, and data mining. Reconstructed files, however, require adequate metadata to identify the appropriate animal species, developmental stage, brain region, and neuron type. Moreover, experimental details about tissue processing, neurite visualization and microscopic imaging are essential to assess the information content of digital morphologies. Typical morphological reconstructions only partially capture the underlying biological reality. Tracings are often limited to certain domains (e.g., dendrites and not axons), may be incomplete due to tissue sectioning, imperfect staining, and limited imaging resolution, or can disregard aspects irrelevant to their specific scientific focus (such as branch thickness or depth). Gauging these factors is critical in subsequent data reuse and comparison. NeuroMorpho.Org is a central repository of reconstructions from many laboratories and experimental conditions. Here, we introduce substantial additions to the existing metadata annotation aimed to describe the completeness of the reconstructed neurons in NeuroMorpho.Org. These expanded metadata form a suitable basis for effective description of neuromorphological data.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

References

  1. Anderson SA, Classey JD, Condé F, Lund JS, Lewis DA (1995) Synchronous development of pyramidal neuron dendritic spines and parvalbumin-immunoreactive chandelier neuron axon terminals in layer III of monkey prefrontal cortex. Neuroscience 67:7–22

  2. Ascoli GA, Donohue DE, Halavi M (2007) NeuroMorpho.Org: a central resource for neuronal morphologies. J Neurosci 27:9247–9251

  3. Ascoli GA, Brown KM, Calixto E et al (2009) Quantitative morphometry of electrophysiologically identified CA3b interneurons reveals robust local geometry and distinct cell classes. J Comp Neurol 515:677–695

  4. Banke TG, McBain CJ (2006) GABAergic input onto CA3 hippocampal interneurons remains shunting throughout development. J Neurosci 26:11720–11725

  5. Bannister NJ, Larkman AU (1995) Dendritic morphology of CA1 pyramidal neurones from the rat hippocampus: I. Branching patterns. J Comp Neurol 360:150–160

  6. Brown KM, Sugihara I, Shinoda Y, Ascoli GA (2012) Digital morphometry of rat cerebellar climbing fibers reveals distinct branch and bouton types. J Neurosci 32:14670–14684

  7. Brunjes PC, Kay RB, Arrivillaga JP (2011) The mouse olfactory peduncle. J Comp Neurol 519:2870–2886

  8. Eyre MD, Antal M, Nusser Z (2008) Distinct deep short-axon cell subtypes of the main olfactory bulb provide novel intrabulbar and extrabulbar GABAergic connections. J Neurosci 28:8217–8229

  9. Gibson F, Overton P, Smulders T, Schultz S, Eglen S, Ingram C, Panzeri S, Bream P, Whittington M, Sernagor E, Cunningham M, Adams C, Echtermeyer C, Simonotto J, Kaiser M, Swan D, Fletcher M, Lord P (2009) Minimum Information about a Neuroscience Investigation (MINI): electrophysiology. Available from Nature Precedings <http://hdl.handle.net/10101/npre.2009.1720.2>

  10. Glickfeld LL, Scanziani M (2006) Distinct timing in the activity of cannabinoid-sensitive and cannabinoid-insensitive basket cells. Nat Neurosci 9:807–815

  11. Golding NL, Mickus TJ, Katz Y et al (2005) Factors mediating powerful voltage attenuation along CA1 pyramidal neuron dendrites. J Physiol 568:69–82

  12. Halavi M, Polavaram S, Donohue DE et al (2008) NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF. Neuroinformatics 6:241–252

  13. Halavi M, Hamilton KA, Parekh R, Ascoli GA (2012) Digital reconstructions of neuronal morphology: three decades of research trends. Front Neurosci 6:49

  14. Hayes TL, Lewis DA (1996) Magnopyramidal neurons in the anterior motor speech region. Dendritic features and interhemispheric comparisons. Arch Neurol 53:1277–1283

  15. Horcholle-Bossavit G, Gogan P, Ivanov Y et al (2000) The problem of the morphological noise in reconstructed dendritic arborizations. J Neurosci Methods 95:83–93

  16. Ishizuka N, Cowan WM, Amaral DG (1995) A quantitative analysis of the dendritic organization of pyramidal cells in the rat hippocampus. J Comp Neurol 362:17–45

  17. Jacobs B, Driscoll L, Schall M (1997) Life-span dendritic and spine changes in areas 10 and 18 of human cortex: a quantitative Golgi study. J Comp Neurol 386:661–680

  18. Jacobs B, Schall M, Prather M et al (2001) Regional dendritic and spine variation in human cerebral cortex: a quantitative golgi study. Cereb Cortex 11:558–571

  19. Kaspirzhny AV, Gogan P, Horcholle-Bossavit G, Tyc-Dumont S (2002) Neuronal morphology data bases: morphological noise and assesment of data quality. Network 13:357–380

  20. Kole MHP, Costoli T, Koolhaas JM, Fuchs E (2004) Bidirectional shift in the cornu ammonis 3 pyramidal dendritic organization following brief stress. Neuroscience 125:337–347

  21. Li Y, Brewer D, Burke RE, Ascoli GA (2005) Developmental changes in spinal motoneuron dendrites in neonatal mice. J Comp Neurol 483:304–317

  22. Lu J, Tapia JC, White OL, Lichtman JW (2009) The interscutularis muscle connectome. PLoS Biol 7:e32

  23. Marx M, Feldmeyer D (2012) Morphology and physiology of excitatory neurons in layer 6b of the somatosensory rat barrel cortex. Cereb Cortex 23:2803–2817

  24. Mizuseki K, Diba K, Pastalkova E et al (2014) Neurosharing: large-scale data sets (spike, LFP) recorded from the hippocampal-entorhinal system in behaving rats. F1000Res 3:98

  25. Parekh R, Ascoli GA (2013) Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron 77:1017–1038

  26. Parekh R, Ascoli GA (2014) Quantitative investigations of axonal and dendritic arbors: development, structure, function, and pathology. Neuroscientist 1–15

  27. Rihn LL, Claiborne BJ (1990) Dendritic growth and regression in rat dentate granule cells during late postnatal development. Dev Brain Res 54:115–124

  28. Santiago LF, Rocha EG, Santos CLA et al (2010) S1 to S2 hind- and forelimb projections in the agouti somatosensory cortex: axon fragments morphological analysis. J Chem Neuroanat 40:339–345

  29. Takemura S-Y, Bharioke A, Lu Z et al (2013) A visual motion detection circuit suggested by Drosophila connectomics. Nature 500:175–181

  30. Tamamaki N, Nojyo Y (1993) Projection of the entorhinal layer II neurons in the rat as revealed by intracellular pressure-injection of neurobiotin. Hippocampus 3:471–480

  31. Wang Y, Gupta A, Toledo-Rodriguez M et al (2002) Anatomical, physiological, molecular and circuit properties of nest basket cells in the developing somatosensory cortex. Cereb Cortex 12:395–410

  32. Wittner L, Henze DA, Zaborszky L, Buzsáki G (2007) Three-dimensional reconstruction of the axon arbor of a CA3 pyramidal cell recorded and filled in vivo. Brain Struct Funct 212:75–83

  33. Yu J, Proddutur A, Elgammal FS et al (2013) Status epilepticus enhances tonic GABA currents and depolarizes GABA reversal potential in dentate fast-spiking basket cells. J Neurophysiol 109:1746–1763

Download references

Acknowledgments

The authors are grateful to Namra Ansari, Julian Burke, Meghan Eyerman, and Lauretta Wilkerson for their literature mining efforts. This work is supported by NIH R01 NS39600 from NINDS, ONR MURI 14101-0198, and Keck NAKFI to G.A.A.

Author information

Correspondence to Giorgio A. Ascoli.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Parekh, R., Armañanzas, R. & Ascoli, G.A. The importance of metadata to assess information content in digital reconstructions of neuronal morphology. Cell Tissue Res 360, 121–127 (2015). https://doi.org/10.1007/s00441-014-2103-6

Download citation

Keywords

  • Neuron morphology
  • Metadata
  • Digital reconstruction
  • Data standards
  • Completeness