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X-Ray Microscopy of the Larval Crustacean Brain

  • Jakob KriegerEmail author
  • Franziska Spitzner
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2047)

Abstract

Micro-computed X-ray tomography (μCT) coupled with visualization techniques such as three-dimensional reconstruction of internal morphological structures has opened up new pathways for analyzing the anatomy of nervous systems in intact specimens. The possibility for combining μCT with other techniques is one of the major advantages of μCT scanning, and the technical development of higher resolutions in lab-based μCT-scanners allows for investigating the anatomy of specimens in the sub-milimeter range. The European shore crab Carcinus maenas features a larval development over four zoeal and one megalopal stage with body lengths ranging from 500 μm to 2000 μm. The developing nervous system in the larvae of C. maenas is organized into a central brain which is connected via esophageal connectives with a ventral nerve chord and segmental ganglia. Since soft tissues such as the nervous tissues feature low contrasts compared to other tissues such as muscles or cuticularized body parts, the interpretation in μCT scans is challenging and needs some practice. The protocol described here is also applicable for larger specimens of a variety of species and spans over 2–3 days resulting in an image stack ready for postprocessing and visualization.

Keywords

μCT Brain Contrast-enhancement 3D reconstruction Carcinus maenas Crustacean Larva 

Notes

Acknowledgments

We cordially thank Marie K. Hörnig for macrophotographs of specimen preparation as well as Steffen Harzsch for reviewing and constructive criticism of the first version of the manuscript. This work was supported by the German Science Foundation (Research Training Group 2010 RESPONSE, DFG INST 292/119-1 FUGG, and DFG INST 292/120-1 FUGG).

References

  1. 1.
    Holst S, Michalik P, Noske M, Krieger J, Sötje I (2016) Potential of X-ray micro-computed tomography for soft-bodied and gelatinous cnidarians with special emphasis on scyphozoan and cubozoan statoliths. J Plankton Res 38:1225–1242.  https://doi.org/10.1093/plankt/fbw054CrossRefGoogle Scholar
  2. 2.
    Henne S, Friedrich F, Hammel JU, Sombke A, Schmidt-Rhaesa A (2016) Reconstructing the anterior part of the nervous system of Gordius aquaticus (Nematomorpha, cycloneuralia) by a multimethodological approach. J Morphol 278:106–118.  https://doi.org/10.1002/jmor.20623CrossRefPubMedGoogle Scholar
  3. 3.
    O’Sullivan JDB, Behnsen J, Starborg T, MacDonald AS, Phythian-Adams AT, Else KJ, Cruickshank SM, Withers PJ (2018) X-ray micro-computed tomography (μCT): an emerging opportunity in parasite imaging. Parasitology 145:1–7.  https://doi.org/10.1017/s0031182017002074CrossRefGoogle Scholar
  4. 4.
    Dinley J, Hawkins L, Paterson G, Ball AD, Sinclair I, Sinnett-Jones P, Lanham S (2010) Micro-computed X-ray tomography: a new non-destructive method of assessing sectional, fly-through and 3D imaging of a soft-bodied marine worm. J Microsc 238:123–133.  https://doi.org/10.1111/j.1365-2818.2009.03335.xCrossRefPubMedGoogle Scholar
  5. 5.
    Faulwetter S, Vasileiadou A, Kouratoras M, Dailianis T, Arvanitidis C (2013) Micro-computed tomography: introducing new dimensions to taxonomy. ZooKeys 263:1–45.  https://doi.org/10.3897/zookeys.263.4261CrossRefGoogle Scholar
  6. 6.
    Handschuh S, Baeumler N, Schwaha T, Ruthensteiner B (2013) A correlative approach for combining microCT, light and transmission electron microscopy in a single 3D scenario. Front Zool 10:44.  https://doi.org/10.1186/1742-9994-10-44CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Akkari N, Enghoff H, Metscher BD (2015) A new dimension in documenting new species: high-detail imaging for myriapod taxonomy and first 3D cybertype of a new millipede species (Diplopoda, Julida, Julidae). PLoS One 10:e0135243.  https://doi.org/10.1371/journal.pone.0135243CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Michalik P, Piacentini L, Lipke E, Ramirez M (2013) The enigmatic Otway odd-clawed spider (Progradungula otwayensis Milledge, 1997, Gradungulidae, Araneae): natural history, first description of the female and micro-computed tomography of the male palpal organ. ZooKeys 335:101–112.  https://doi.org/10.3897/zookeys.335.6030CrossRefGoogle Scholar
  9. 9.
    Nischik ES, Krieger J (2018) Evaluation of standard imaging techniques and volumetric preservation of nervous tissue in genetically identical offspring of the crayfish Procambarus fallax cf. virginalis (Marmorkrebs). PeerJ 6:e5181.  https://doi.org/10.7717/peerj.5181CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Sombke A, Lipke E, Michalik P, Uhl G, Harzsch S (2015) Potential and limitations of X-Ray micro-computed tomography in arthropod neuroanatomy: a methodological and comparative survey. J Comp Neurol 523:1281–1295.  https://doi.org/10.1002/cne.23741CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Steinhoff POM, Sombke A, Liedtke J, Schneider JM, Harzsch S, Uhl G (2017) The synganglion of the jumping spider Marpissa muscosa (Arachnida: Salticidae): insights from histology, immunohistochemistry and microCT analysis. Arthropod Struct Dev 46:156–170.  https://doi.org/10.1016/j.asd.2016.11.003CrossRefPubMedGoogle Scholar
  12. 12.
    Metscher BD (2009) MicroCT for comparative morphology: simple staining methods allow high-contrast 3D imaging of diverse non-mineralized animal tissues. BMC Physiol 9:11.  https://doi.org/10.1186/1472-6793-9-11CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Köhnk S, Baudewig J, Brandis D, Boretius S (2017) What’s in this crab? MRI providing high-resolution three-dimensional insights into recent finds and historical collections of Brachyura. Zoology 121:1–9.  https://doi.org/10.1016/j.zool.2016.11.004CrossRefPubMedGoogle Scholar
  14. 14.
    Hounsfield GN (1973) Computerized transverse axial scanning (tomography): part 1. Description of system. Br J Radiol 46:1016–1022.  https://doi.org/10.1259/0007-1285-46-552-1016CrossRefPubMedGoogle Scholar
  15. 15.
    Bucher D, Scholz M, Stetter M, Obermayer K, Pflüger H-J (2000) Correction methods for three-dimensional reconstructions from confocal images: I. Tissue shrinking and axial scaling. J Neurosci Methods 100:135–143.  https://doi.org/10.1016/S0165-0270(00)00245-4CrossRefPubMedGoogle Scholar
  16. 16.
    Huys R, Olesen JT, Petrunina A, Martin JW (2014) Tantulocarida. In: Martin JW, Olesen JT, Høeg JT (eds) Atlas of crustacean larvae. Johns Hopkins University Press, Baltimore, pp 122–127Google Scholar
  17. 17.
    Ahyong ST, Haug JT, Haug C (2014) Stomatopoda. In: Martin JW, Olesen JT, Høeg JT (eds) Atlas of crustacean larvae. Johns Hopkins University Press, Baltimore, pp 185–187Google Scholar
  18. 18.
    Anger K (2001) The biology of decapod crustacean larvae. AA Balkema Publishers, RotterdamGoogle Scholar
  19. 19.
    Anger K (2006) Contributions of larval biology to crustacean research: a review. Invertebr Reprod Dev 49:175–205.  https://doi.org/10.1080/07924259.2006.9652207CrossRefGoogle Scholar
  20. 20.
    Anger K, Queiroga H, Calado R (2015) Larval development and behaviour strategies in Brachyura. In: Castro P, Davie P, Guinot D, Schram F, von Vaupel Klein JC (eds) Treatise on zoology—anatomy, taxonomy, biology. The crustacea, volume 9, part C-I, Decapoda: Brachyura. Brill, Leiden, Boston, pp 317–374Google Scholar
  21. 21.
    Haug JT, Haug C (2015) “Crustacea”: comparative aspects of larval development. In: Wanninger A (ed) Evolutionary developmental biology of invertebrates 4: ecdysozoa II: crustacea. Springer Vienna, Vienna, pp 1–37Google Scholar
  22. 22.
    Martin JW (2014) Brachyura. In: Martin JW, Olesen JT, Høeg JT (eds) Atlas of crustacean larvae. Johns Hopkins University Press, Baltimore, pp 295–310Google Scholar
  23. 23.
    Carlton JT, Cohen AN (2003) Episodic global dispersal in shallow water marine organisms: the case history of the European shore crabs Carcinus maenas and C. aestuarii. J Biogeogr 30:1809–1820.  https://doi.org/10.1111/j.1365-2699.2003.00962.xCrossRefGoogle Scholar
  24. 24.
    Cohen AN, Carlton JT, Fountain MC (1995) Introduction, dispersal and potential impacts of the green crab Carcinus maenas in San Francisco Bay, California. Mar Biol 122:225–237.  https://doi.org/10.1007/BF00348935CrossRefGoogle Scholar
  25. 25.
    Grosholz ED, Ruiz GM (1995) Spread and potential impact of the recently introduced European green crab, Carcinus maenas, in central California. Mar Biol 122:239–247.  https://doi.org/10.1007/BF00348936CrossRefGoogle Scholar
  26. 26.
    Hidalgo FJ, Barón PJ, Orensanz JM (2005) A prediction come true: the green crab invades the Patagonian coast. Biol Invasions 7:547–552.  https://doi.org/10.1007/s10530-004-5452-3CrossRefGoogle Scholar
  27. 27.
    Yamada SB, Dumbauld BR, Kalin A, Hunt CE, Figlar-Barnes R, Randall A (2005) Growth and persistence of a recent invader Carcinus maenas in estuaries of the northeastern pacific. Biol Invasions 7:309–321.  https://doi.org/10.1007/s10530-004-0877-2CrossRefGoogle Scholar
  28. 28.
    Young A, Elliott J (2018) Population dynamics of green crabs (Carcinus maenas)—a review. Preprints.  https://doi.org/10.20944/preprints201807.0436.v2
  29. 29.
    Epifanio CE, Cohen JH (2016) Behavioral adaptations in larvae of brachyuran crabs: a review. J Exp Mar Biol Ecol 482:85–105.  https://doi.org/10.1016/j.jembe.2016.05.006CrossRefGoogle Scholar
  30. 30.
    Forward RB (2009) Larval biology of the crab Rhithropanopeus harrisii (Gould): a synthesis. Biol Bull 216:243–256.  https://doi.org/10.1086/BBLv216n3p243CrossRefPubMedGoogle Scholar
  31. 31.
    Spitzner F, Meth R, Krüger C, Nischik E, Eiler S, Sombke A, Torres G, Harzsch S (2018) An atlas of larval organogenesis in the European shore crab Carcinus maenas L. (Decapoda, Brachyura, Portunidae). Front Zool 15:27.  https://doi.org/10.1186/s12983-018-0271-zCrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Krieger J, Sombke A, Seefluth F, Kenning M, Hansson BS, Harzsch S (2012) Comparative brain architecture of the European shore crab Carcinus maenas (Brachyura) and the common hermit crab Pagurus bernhardus (Anomura) with notes on other marine hermit crabs. Cell Tissue Res 348:47–69.  https://doi.org/10.1007/s00441-012-1353-4CrossRefPubMedGoogle Scholar
  33. 33.
    Harzsch S, Dawirs RR (1993) On the morphology of the central nervous system in larval stages of Carcinus maenas L. (Decapoda, Brachyura). Helgolander Meeresunters 47:61–79.  https://doi.org/10.1007/BF02366185CrossRefGoogle Scholar
  34. 34.
    Panel on Animal Health and Welfare (2005) Opinion of the scientific panel on Animal Health and Welfare (AHAW) on a request from the Commission related to the aspects of the biology and welfare of animals used for experimental and other scientific purposes. EFSA J 3:292.  https://doi.org/10.2903/j.efsa.2005.292CrossRefGoogle Scholar
  35. 35.
    Jahn H, Oliveira IDS, Gross V, Martin C, Hipp A, Mayer G, Hammel JU (2018) Evaluation of contrasting techniques for X-ray imaging of velvet worms (Onychophora). J Microsc 270:343–358.  https://doi.org/10.1111/jmi.12688CrossRefPubMedGoogle Scholar
  36. 36.
    Gutiérrez Y, Ott D, Töpperwien M, Salditt T, Scherber C (2018) X-ray computed tomography and its potential in ecological research: a review of studies and optimization of specimen preparation. Ecol Evol 8:1–18.  https://doi.org/10.1002/ece3.4149CrossRefGoogle Scholar
  37. 37.
    Betz O, Wegst U, Weide D, Heethoff M, Helfen L, Lee W-K, Cloetens P (2007) Imaging applications of synchrotron X-ray phase-contrast microtomography in biological morphology and biomaterials science. I. General aspects of the technique and its advantages in the analysis of millimetre-sized arthropod structure. J Microsc 227:51–71.  https://doi.org/10.1111/j.1365-2818.2007.01785.xCrossRefPubMedGoogle Scholar
  38. 38.
    Wipfler B, Pohl H, Yavorskaya MI, Beutel RG (2016) A review of methods for analysing insect structures—the role of morphology in the age of phylogenomics. Curr Opin Insect Sci 18:60–68.  https://doi.org/10.1016/j.cois.2016.09.004CrossRefPubMedGoogle Scholar
  39. 39.
    Lösel P, Heuveline V (2016) Enhancing a diffusion algorithm for 4D image segmentation using local information. In: Medical Imaging 2016: image processing. International Society for Optics and Photonics, Bellingham WA, p 97842LGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Cytology and Evolutionary Biology, Zoological Institute and MuseumUniversity of GreifswaldGreifswaldGermany

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