Tissue Morphogenesis pp 79-97

Part of the Methods in Molecular Biology book series (MIMB, volume 1189) | Cite as

Light Sheet-Based Imaging and Analysis of Early Embryogenesis in the Fruit Fly

  • Khaled Khairy
  • William C. Lemon
  • Fernando Amat
  • Philipp J. Keller

Abstract

The fruit fly is an excellent model system for investigating the sequence of epithelial tissue invaginations constituting the process of gastrulation. By combining recent advancements in light sheet fluorescence microscopy (LSFM) and image processing, the three-dimensional fly embryo morphology and relevant gene expression patterns can be accurately recorded throughout the entire process of embryogenesis. LSFM provides exceptionally high imaging speed, high signal-to-noise ratio, low level of photoinduced damage, and good optical penetration depth. This powerful combination of capabilities makes LSFM particularly suitable for live imaging of the fly embryo.

The resulting high-information-content image data are subsequently processed to obtain the outlines of cells and cell nuclei, as well as the geometry of the whole embryo tissue by image segmentation. Furthermore, morphodynamics information is extracted by computationally tracking objects in the image. Towards that goal we describe the successful implementation of a fast fitting strategy of Gaussian mixture models.

The data obtained by image processing is well-suited for hypothesis testing of the detailed biomechanics of the gastrulating embryo. Typically this involves constructing computational mechanics models that consist of an objective function providing an estimate of strain energy for a given morphological configuration of the tissue, and a numerical minimization mechanism of this energy, achieved by varying morphological parameters.

In this chapter, we provide an overview of in vivo imaging of fruit fly embryos using LSFM, computational tools suitable for processing the resulting images, and examples of computational biomechanical simulations of fly embryo gastrulation.

Key words

Light sheet microscopy Computational modeling Tissue biomechanics Live imaging Quantitative developmental biology Drosophila melanogaster Embryonic development Image processing 

References

  1. 1.
    Khairy K, Keller PJ (2011) Reconstructing embryonic development. Genesis 49(7):488–513PubMedCrossRefGoogle Scholar
  2. 2.
    Pawley JB (2006) Handbook of biological confocal microscopy. Springer, New York, NYCrossRefGoogle Scholar
  3. 3.
    Diaspro A, Chirico G, Collini M (2005) Two-photon fluorescence excitation and related techniques in biological microscopy. Q Rev Biophys 38(2):97–166PubMedCrossRefGoogle Scholar
  4. 4.
    Helmchen F, Denk W (2005) Deep tissue two-photon microscopy. Nat Methods 2(12):932–940PubMedCrossRefGoogle Scholar
  5. 5.
    Graf R, Rietdorf J, Zimmermann T (2005) Live cell spinning disk microscopy. Adv Biochem Eng Biotechnol 95:57–75PubMedGoogle Scholar
  6. 6.
    Keller PJ, Dodt HU (2011) Light sheet microscopy of living or cleared specimens. Curr Opin Neurobiol 22(1):138–143PubMedCrossRefGoogle Scholar
  7. 7.
    Tomer R, Khairy K, Keller PJ (2011) Shedding light on the system: studying embryonic development with light sheet microscopy. Curr Opin Genet Dev 21(5):558–565PubMedCrossRefGoogle Scholar
  8. 8.
    Weber M, Huisken J (2011) Light sheet microscopy for real-time developmental biology. Curr Opin Genet Dev 21(5):566–572PubMedCrossRefGoogle Scholar
  9. 9.
    Mertz J (2011) Optical sectioning microscopy with planar or structured illumination. Nat Methods 8(10):811–819PubMedCrossRefGoogle Scholar
  10. 10.
    Siedentopf H, Zsigmondy R (1903) Über Sichtbarmachung und Größenbestimmung ultramikroskopischer Teilchen, mit besonderer Anwendung auf Goldrubingläser. Ann Phys 315(1):1–39CrossRefGoogle Scholar
  11. 11.
    Voie AH, Burns DH, Spelman FA (1993) Orthogonal-plane fluorescence optical sectioning: three-dimensional imaging of macroscopic biological specimens. J Microsc 170(3):229–236PubMedCrossRefGoogle Scholar
  12. 12.
    Huisken J et al (2004) Optical sectioning deep inside live embryos by selective plane illumination microscopy. Science 305(5686):1007–1009PubMedCrossRefGoogle Scholar
  13. 13.
    Keller PJ et al (2008) Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322(5904):1065–1069PubMedCrossRefGoogle Scholar
  14. 14.
    Tomer R et al (2012) Quantitative high-speed imaging of entire developing embryos with simultaneous multiview light-sheet microscopy. Nat Methods 9(7):755–763PubMedCrossRefGoogle Scholar
  15. 15.
    Arnaout R et al (2007) Zebrafish model for human long QT syndrome. Proc Natl Acad Sci U S A 104(27):11316–11321PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Scherz PJ et al (2008) High-speed imaging of developing heart valves reveals interplay of morphogenesis and function. Development 135(6):1179–1187PubMedCrossRefGoogle Scholar
  17. 17.
    Swoger J et al (2007) Multi-view image fusion improves resolution in three-dimensional microscopy. Opt Express 15(13):8029–8042PubMedCrossRefGoogle Scholar
  18. 18.
    Truong TV et al (2011) Deep and fast live imaging with two-photon scanned light-sheet microscopy. Nat Methods 8(9):757–760PubMedCrossRefGoogle Scholar
  19. 19.
    Huisken J, Stainier DY (2007) Even fluorescence excitation by multidirectional selective plane illumination microscopy (mSPIM). Opt Lett 32(17):2608–2610PubMedCrossRefGoogle Scholar
  20. 20.
    Dodt HU et al (2007) Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain. Nat Methods 4(4):331–336PubMedCrossRefGoogle Scholar
  21. 21.
    Preibisch S et al (2010) Software for bead-based registration of selective plane illumination microscopy data. Nat Methods 7(6):418–419PubMedCrossRefGoogle Scholar
  22. 22.
    Rubio-Guivernau JL et al (2012) Wavelet-based image fusion in multi-view three-dimensional microscopy. Bioinformatics 28(2):238–245PubMedCrossRefGoogle Scholar
  23. 23.
    Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639CrossRefGoogle Scholar
  24. 24.
    Lucy LB (1974) An iterative technique for the rectification of observed distributions. Astron J 79(6):745–754CrossRefGoogle Scholar
  25. 25.
    Santella A et al (2010) A hybrid blob-slice model for accurate and efficient detection of fluorescence labeled nuclei in 3D. BMC Bioinformatics 11:580PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Li G et al (2007) 3D cell nuclei segmentation based on gradient flow tracking. BMC Cell Biol 8:40PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Xu C, Prince J (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Proc 7(3):359–369CrossRefGoogle Scholar
  28. 28.
    Lin G et al (2003) A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry A 56(1):23–36PubMedCrossRefGoogle Scholar
  29. 29.
    Dufour A et al (2005) Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. IEEE Trans Image Process 14(9):1396–1410PubMedCrossRefGoogle Scholar
  30. 30.
    Pop S et al (2013) Extracting 3D cell parameters from dense tissue environments: application to the development of the mouse heart. Bioinformatics 29(6):772–779PubMedCrossRefGoogle Scholar
  31. 31.
    Smal I et al (2008) Multiple object tracking in molecular bioimaging by Rao-Blackwellized marginal particle filtering. Med Image Anal 12(6):764–777PubMedCrossRefGoogle Scholar
  32. 32.
    Kausler BX et al (2012) A discrete chain graph model for 3D + t cell tracking with high misdirection robustness. In: Fitzgibbon A et al (eds) Computer vision - ECCV 2012. Springer, Berlin, pp 144–157. doi:10.1007/978-3-642-33712-3_11 CrossRefGoogle Scholar
  33. 33.
    McMahon A et al (2008) Dynamic analyses of Drosophila gastrulation provide insights into collective cell migration. Science 322(5907):1546–1550PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Megason SG (2009) In toto imaging of embryogenesis with confocal time-lapse microscopy. Methods Mol Biol 546:317–332PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Giurumescu CA et al (2012) Quantitative semi-automated analysis of morphogenesis with single-cell resolution in complex embryos. Development 139(22):4271–4279PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Farge E (2003) Mechanical induction of Twist in the Drosophila foregut/stomodeal primordium. Curr Biol 13(16):1365–1377PubMedCrossRefGoogle Scholar
  37. 37.
    Wyczalkowski MA et al (2012) Computational models for mechanics of morphogenesis. Birth Def Res C Emb Today 96(2):132–152CrossRefGoogle Scholar
  38. 38.
    Leptin M (1999) Gastrulation in Drosophila: the logic and the cellular mechanisms. EMBO J 18(12):3187–3192PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Gelbart MA et al (2012) Volume conservation principle involved in cell lengthening and nucleus movement during tissue morphogenesis. Proc Natl Acad Sci U S A 109(47):19298–19303PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Martin AC, Kaschube M, Wieschaus EF (2009) Pulsed contractions of an actin-myosin network drive apical constriction. Nature 457(7228):495–499PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Munoz JJ, Barrett K, Miodownik M (2007) A deformation gradient decomposition method for the analysis of the mechanics of morphogenesis. J Biomech 40(6):1372–1380PubMedCrossRefGoogle Scholar
  42. 42.
    Conte V et al (2009) Robust mechanisms of ventral furrow invagination require the combination of cellular shape changes. Phys Biol 6(1):016010PubMedCrossRefGoogle Scholar
  43. 43.
    Conte V, Munoz JJ, Miodownik M (2008) A 3D finite element model of ventral furrow invagination in the Drosophila melanogaster embryo. J Mech Behav Biomed Mater 1(2):188–198PubMedCrossRefGoogle Scholar
  44. 44.
    Munoz JJ (2008) Modelling unilateral frictionless contact using the null-space method and cubic B-Spline interpolation. Comput Methods Appl Mech Eng 197:979–993CrossRefGoogle Scholar
  45. 45.
    Kellog OD (1953) Foundations of potential theory. Springer, BerlinGoogle Scholar
  46. 46.
    Allena R, Aubry D (2012) An extensive numerical simulation of the cephalic furrow formation in Drosophila embryo. Comput Methods Biomech Biomed Engin 15(5):445–455PubMedCrossRefGoogle Scholar
  47. 47.
    Brodland GW et al (2010) Video force microscopy reveals the mechanics of ventral furrow invagination in Drosophila. Proc Natl Acad Sci U S A 107(51):22111–22116PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Viens D, Brodland GW (2007) A three-dimensional finite element model for the mechanics of cell-cell interactions. J Biomech Eng 129(5):651–657PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Khaled Khairy
    • 1
  • William C. Lemon
    • 1
  • Fernando Amat
    • 1
  • Philipp J. Keller
    • 1
  1. 1.Howard Hughes Medical InstituteJanelia Farm Research CampusAshburnUSA

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