Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Geometric morphometrics of microscopic animals as exemplified by model nematodes

Nature Protocols volume 15, pages 2611–2644 (2020)Cite this article

Subjects

Abstract

While a host of molecular techniques are utilized by evolutionary developmental (evo-devo) biologists, tools for quantitative evaluation of morphology are still largely underappreciated, especially in studies on microscopic animals. Here, we provide a standardized protocol for geometric morphometric analyses of 2D landmark data sets using a combination of the geomorph and Morpho R packages. Furthermore, we integrate clustering approaches to identify group structures within such datasets. We demonstrate our protocol by performing exemplary analyses on stomatal shapes in the model nematodes Caenorhabditis and Pristionchus. Image acquisition for 80 worms takes 3–4 d, while the entire data analysis requires 10–30 min. In theory, this approach is adaptable to all microscopic model organisms to facilitate a thorough quantification of shape differences within and across species, adding to the methodological toolkit of evo-devo studies on morphological evolution and novelty.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The stomatal polyphenism in Pristionchus pacificus.
Fig. 2: Analysis of shape differences between the stomata of C. elegans (N2) and C. briggsae (AF16).
Fig. 3: Analysis of shape differences between the two stomatal morphs of P. pacificus (RS5205).
Fig. 4: Analysis of shape differences between the stomatal morphs of P. pacificus (RS5205), P. bucculentus (RS5596) and P. elegans (RS5698).
Fig. 5: k-medoid clustering performed on the two Pristionchus datasets.

Similar content being viewed by others

Data availability

All data generated and analyzed in this study are included in the paper or its Supplementary Data 1–3. Upon request, the raw data that were used to generate the example results are available from the corresponding authors.

Code availability

The entire code for an analysis based on geomorph can be copied from the different steps of the procedure. In addition, two ready-to-use R markdown files that contain the code for both geometric morphometrics packages (geomorph and Morpho) and a R routine that allows the generation of landmark files can be downloaded from the supplementary material of this paper (Supplementary Data 4–6).

References

  1. Pigliucci, M. Phenotypic Plasticity: Beyond Nature and Nurture (Johns Hopkins University Press, 2001).

  2. West-Eberhard, M. J. Developmental Plasticity and Evolution (Oxford University Press, 2003).

  3. Shubin, N., Tabin, C. & Carroll, S. Deep homology and the origins of evolutionary novelty. Nature 457, 818 (2009).

    CAS  PubMed  Google Scholar 

  4. Wagner, G. P. Homology, Genes, and Evolutionary Innovation (Princeton University Press, 2014).

  5. Moczek, A. P. et al. The significance and scope of evolutionary developmental biology: a vision for the 21st century. Evol. Dev. 17, 198–219 (2015).

    PubMed  Google Scholar 

  6. Minelli, A. Grand challenges in evolutionary developmental biology. Front. Eco. Evol. 2, 1–11 (2015).

    Google Scholar 

  7. Mallarino, R. & Abzhanov, A. Paths less traveled: evo-devo approaches to investigating animal morphological evolution. Annu. Rev. Cell Dev. Biol. 28, 743–763 (2012).

    CAS  PubMed  Google Scholar 

  8. Klingenberg, C. P. Evolution and development of shape: integrating quantitative approaches. Nat. Rev. Genet. 11, 623–635 (2010).

    CAS  PubMed  Google Scholar 

  9. Parsons, K. J. & Albertson, R. C. Unifying and generalizing the two strands of evo-devo. Trends Ecol. Evol. 28, 584–591 (2013).

    PubMed  Google Scholar 

  10. Andersen, E. C. et al. A powerful new quantitative genetics platform, combining Caenorhabditis elegans high-throughput fitness assays with a large collection of recombinant strains. G3 (Bethesda) 5, 911–920 (2015).

    Google Scholar 

  11. Seydoux, G. & Fire, A. Whole-mount in situ hybridization for the detection of RNA in Caenorhabditis elegans embryos. Methods Cell Biol. 48, 323–337 (2015).

    Google Scholar 

  12. Pertea, M., Kim, D., Pertea, G. M., Leek, J. T. & Salzberg, S. L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 11, 1650–1667 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Friedland, A. E. et al. Heritable genome editing in C. elegans via a CRISPR-Cas9 system. Nat. Methods 10, 741–743 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Witte, H. et al. Gene inactivation using the CRISPR/Cas9 system in the nematode Pristionchus pacificus. Dev. Genes Evol. 225, 55–62 (2015).

    CAS  PubMed  Google Scholar 

  15. Au, V. et al. CRISPR/Cas9 methodology for the generation of knockout deletions in Caenorhabditis elegans. G3 (Bethesda) 9, 135–144 (2019).

    CAS  Google Scholar 

  16. Yoshida, K. et al. Two new species of Pristionchus (Nematoda: Diplogastridae) from Taiwan and the definition of the pacificus species-complex sensu stricto. J. Nematol. 50, 355–368 (2018).

    PubMed  PubMed Central  Google Scholar 

  17. Susoy, V., Ragsdale, E. J., Kanzaki, N. & Sommer, R. J. Rapid diversification associated with a macroevolutionary pulse of developmental plasticity. Elife 4, e05463 (2015).

    PubMed Central  Google Scholar 

  18. Susoy, V. et al. Large-scale diversification without genetic isolation in nematode symbionts of figs. Sci. Adv. 2, e1501031 (2016).

    PubMed  PubMed Central  Google Scholar 

  19. Sieriebriennikov, B., Markov, G. V., Witte, H. & Sommer, R. J. The role of DAF-21/Hsp90 in mouth-form plasticity in Pristionchus pacificus. Mol. Biol. Evol. 34, 1644–1653 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Sieriebriennikov, B. et al. Conserved nuclear hormone receptors controlling a novel trait target fast-evolving genes expressed in a single cell. PLoS Genet 16, e1008687 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Hong, R. L. & Sommer, R. J. Pristionchus pacificus: a well‐rounded nematode. Bioessays 28, 651–659 (2006).

    CAS  PubMed  Google Scholar 

  22. Sommer, R. J. & McGaughran, A. The nematode Pristionchus pacificus as a model system for integrative studies in evolutionary biology. Mol. Ecol. 22, 2380–2393 (2013).

    PubMed  Google Scholar 

  23. Sommer, R. J. Pristionchus pacificus: A Nematode Model for Comparative and Evolutionary Biology (Brill, 2015).

  24. Sommer, R. J. et al. The genetics of phenotypic plasticity in nematode feeding structures. Open Biol. 7, 160332 (2017).

    PubMed  PubMed Central  Google Scholar 

  25. De Ley, P., Van De Velde, M. C., Mounport, D., Baujard, P. & Coomans, A. Ultrastructure of the stoma in Cephalobidae, Panagrolaimidae and Rhabditidae, with a proposal for a revised stoma terminology in Rhabditida (Nematoda). Nematologica 41, 153–182 (1995).

    Google Scholar 

  26. Von Lieven, A. F. & Sudhaus, W. Comparative and functional morphology of the buccal cavity of Diplogastrina (Nematoda) and a first outline of the phylogeny of this taxon. J. Zool. Syst. Evol. Res. 38, 37–63 (2000).

    Google Scholar 

  27. Jay Burr, A. & Baldwin, J. G. The nematode stoma: homology of cell architecture with improved understanding by confocal microscopy of labeled cell boundaries. J. Morphol. 277, 1168–1186 (2016).

    CAS  PubMed  Google Scholar 

  28. Wilecki, M., Lightfoot, J. W., Susoy, V. & Sommer, R. J. Predatory feeding behaviour in Pristionchus nematodes is dependent on phenotypic plasticity and induced by serotonin. J. Exp. Biol. 218, 1306–1313 (2015).

    PubMed  Google Scholar 

  29. Ragsdale, E. J., Müller, M. R., Rödelsperger, C. & Sommer, R. J. A developmental switch coupled to the evolution of plasticity acts through a sulfatase. Cell 155, 922–933 (2013).

    CAS  PubMed  Google Scholar 

  30. Kieninger, M. R. et al. The nuclear hormone receptor NHR-40 acts downstream of the sulfatase EUD-1 as part of a developmental plasticity switch in Pristionchus. Curr. Biol. 26, 2174–2179 (2016).

    CAS  PubMed  Google Scholar 

  31. Namdeo, S. et al. Two independent sulfation processes regulate mouth-form plasticity in the nematode Pristionchus pacificus. Development 145, dev166272 (2018).

    PubMed  Google Scholar 

  32. Moreno, E., Lightfoot, J. W., Lenuzzi, M. & Sommer, R. J. Cilia drive developmental plasticity and are essential for efficient prey detection in predatory nematodes. Proc. Biol. Sci. 286, 20191089 (2019).

    CAS  PubMed  Google Scholar 

  33. Bardua, C., Wilkinson, M., Gower, D. J., Sherratt, E. & Goswami, A. Morphological evolution and modularity of the caecilian skull. BMC Evol. Biol. 19, 30 (2019).

    PubMed  PubMed Central  Google Scholar 

  34. Tatsuta, H., Takahashi, K. H. & Sakamaki, Y. Geometric morphometrics in entomology: basics and applications. Entomol. Sci. 21, 164–184 (2018).

    Google Scholar 

  35. Siriwut, W., Edgecombe, G. D., Sutcharit, C. & Panha, S. The centipede genus Scolopendra in mainland Southeast Asia: molecular phylogenetics, geometric morphometrics and external morphology as tools for species delimitation. PLoS One 10, e0135355 (2015).

    PubMed  PubMed Central  Google Scholar 

  36. Zelditch, M. L., Swiderski, D. L. & Sheets, H. D. Geometric Morphometrics for Biologists: A Primer (Academic Press, 2004).

  37. Adams, D. C., Rohlf, F. J. & Slice, D. E. Geometric morphometrics: ten years of progress following the ‘revolution’. Ital. J. Zool. 71, 5–16 (2004).

    Google Scholar 

  38. Webster, M. & Sheets, H. D. A practical introduction to landmark-based geometric morphometrics. Paleontological Soc. Pap. 16, 163–188 (2010).

    Google Scholar 

  39. Adams, D. C., Rohlf, F. J. & Slice, D. E. A field comes of age: geometric morphometrics in the 21st century. Hystrix It. J. Mamm. 24, 7–14 (2013).

    Google Scholar 

  40. Collyer, M. L. & Adams, D. C. Phenotypic trajectory analysis: comparison of shape change patterns in evolution and ecology. Hystrix It. J. Mamm. 24, 75–83 (2013).

    Google Scholar 

  41. Feilich, K. L. & López-Fernández, H. When does form reflect function? Acknowledging and supporting ecomorphological assumptions. Integr. Comp. Biol. 59, 358–370 (2019).

    PubMed  Google Scholar 

  42. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).

  43. Claude, J. Morphometrics with R (Springer, 2008).

  44. Claude, J. Log-shape ratios, Procrustes superimposition, elliptic Fourier analysis: three worked examples in R. Hystrix It. J. Mamm. 24, 94–102 (2013).

    Google Scholar 

  45. Adams, D. C. & Otárola‐Castillo, E. geomorph: an R package for the collection and analysis of geometric morphometric shape data. Methods Ecol. Evol. 4, 393–399 (2013).

    Google Scholar 

  46. Adams, D. C., Collyer, M. & Kaliontzopoulou, A. Geomorph: Software for geometric morphometric analyses. R package version 3.2.1. https://cran.r-project.org/package=geomorph (2020).

  47. Schlager, S. Morpho and Rvcg–Shape Analysis in R: R-Packages for geometric morphometrics, shape analysis and surface manipulations. in Statistical Shape and Deformation Analysis (eds. Zheng, G., Li, S. & Szekely, G.) 217–256 (Academic Press, 2017).

  48. Schlager, S. Morpho: calculations and visualisations related to geometric morphometrics. R package version 2.8. https://rdrr.io/cran/Morpho/ (2020).

  49. Gunz, P. & Mitteroecker, P. Semilandmarks: a method for quantifying curves and surfaces. Hystrix It. J. Mamm. 24, 103–109 (2013).

    Google Scholar 

  50. Klingenberg, C. P. Visualizations in geometric morphometrics: how to read and how to make graphs showing shape changes. Hystrix It. J. Mamm. 24, 15–24 (2013).

    Google Scholar 

  51. Bookstein, F. L. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11, 567–585 (1989).

    Google Scholar 

  52. Rohlf, F. J. Shape statistics: Procrustes superimpositions and tangent spaces. J. Classif. 16, 197–223 (1999).

    Google Scholar 

  53. Dryden, I. L. & Mardia, K. V. Multivariate shape analysis. Sankhya 55, 460–480 (1993).

    Google Scholar 

  54. Anderson, M. J. A new method for non‐parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).

    Google Scholar 

  55. Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). in Wiley StatsRef: Statistics Reference Online (eds. Balakrishnan, N. et al.) 1–15. https://onlinelibrary.wiley.com/doi/full/10.1002/9781118445112.stat07841 (2014).

  56. Goodall, C. Procrustes methods in the statistical analysis of shape. J. R. Stat. Soc. Ser. B Stat. Methodol. 53, 285–321 (1991).

    Google Scholar 

  57. Abdi, H. & Williams, L. J. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2, 433–459 (2010).

    Google Scholar 

  58. Kassambara, A. & Mundt, F. Factoextra: extract and visualize the results of multivariate data analyses. R package version 1.0.7. https://cran.r-project.org/web/packages/factoextra/index.html (2020).

  59. Jin, X. & Han, J. K-medoids clustering. in Encyclopedia of Machine Learning and Data Mining (eds. Sammut, C. & Webb, G. I.) 697–700 (Springer, 2017).

  60. Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. Cluster: cluster analysis basics and extensions. R package version 2.1.0. https://cran.r-project.org/web/packages/cluster/index.html (2019).

  61. Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R. J. 8, 289–317 (2016).

    PubMed  PubMed Central  Google Scholar 

  62. Fraley, C., Raftery, A. & Scrucca, L. mclust: normal mixture modeling for model-based clustering, classification, and density estimation. R package version 5.4.6. https://cran.r-project.org/web/packages/mclust/index.html (2020).

  63. Stiernagle, T. Maintenance of C. elegans. In WormBook (eds. The C. elegans research community, WormBook). http://www.wormbook.org (2006).

  64. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676 (2012).

    CAS  PubMed  Google Scholar 

  65. Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed‐effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).

    Google Scholar 

  66. Schielzeth, H. & Nakagawa, S. rptR: Repeatability for Gaussian and non-Gaussian data. R package version 0.9.22. https://cran.r-project.org/web/packages/rptR/index.html (2019).

  67. Yezerinac, S. M., Lougheed, S. C. & Handford, P. Measurement error and morphometric studies: statistical power and observer experience. Syst. Biol. 41, 471–482 (1992).

    Google Scholar 

  68. Adams, D. C. & Nistri, A. Ontogenetic convergence and evolution of foot morphology in European cave salamanders (Family: Plethodontidae). BMC Evol. Biol. 10, 216 (2010).

    PubMed  PubMed Central  Google Scholar 

  69. Esquerré, D., Sherratt, E. & Keogh, J. S. Evolution of extreme ontogenetic allometric diversity and heterochrony in pythons, a clade of giant and dwarf snakes. Evolution 71, 2829–2844 (2017).

    PubMed  Google Scholar 

  70. Mitteroecker, P. et al. A brief review of shape, form, and allometry in geometric morphometrics, with applications to human facial morphology. Hystrix It. J. Mamm. 24, 59–66 (2013).

    Google Scholar 

  71. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  72. Bookstein, F. L. Measuring and Reasoning: Numerical Inference in the Sciences (Cambridge University Press, 2014).

  73. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  74. Collyer, M. L. & Adams, D. C. RRPP: An r package for fitting linear models to high‐dimensional data using residual randomization. Methods Ecol. Evol. 9, 1772–1779 (2018).

    Google Scholar 

  75. Collyer, M. & Adams, D. RRPP: linear model evaluation with randomized residuals in a permutation procedure. R package version 0.5.0. https://cran.r-project.org/web/packages/RRPP/index.html (2019).

  76. Kanzaki, N. et al. Pristionchus bucculentus n. sp. (Rhabditida: Diplogastridae) isolated from a shining mushroom beetle (Coleoptera: Scaphidiidae) in Hokkaido, Japan. J. Nematol. 45, 78–86 (2013).

    PubMed  PubMed Central  Google Scholar 

  77. Kanzaki, N. et al. Two new species of Pristionchus (Rhabditida: Diplogastridae): P. fissidentatus n. sp. from Nepal and La Réunion Island and P. elegans n. sp. from Japan. J. Nematol. 44, 80–91 (2012).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

J. Claude (Institut des Sciences de l’Évolution-Montpellier, Montpellier, France) is gratefully acknowledged for his advice on geometric morphometrics using geomorph and Morpho in RStudio during a workshop given in Calgary (attended by B.S. in 2017). Earlier drafts of this manuscript were improved by advice on many steps of data analysis by F. Chan (Friedrich Miescher Laboratory, Tübingen, Germany) and critical comments on the general methodology of geometric morphometrics by P. Arnold (University of Potsdam, Potsdam, Germany). Furthermore, we would like to thank A. Rohrlach (Max Planck Institute for the Science of Human History, Jena, Germany) for his comments on statistical procedures and multivariate hypothesis testing using R. N. Kanzaki (Forestry and Forest Products Research Institute, Tsukuba, Japan) provided expertise on nematode stomatal morphology in an early phase of this project. Further thanks go to M. Riebesell for providing the TEM image of the P. pacificus stoma (Supplementary Fig. 1a). D. Sharma and J. de la Cuesta are to be thanked for general discussion. This work was funded by the Max Planck Society. T.T. was supported by the IMPRS ‘From Molecules to Organisms’.

Author information

Author notes

  1. Bogdan Sieriebriennikov

    Present address: Department of Biology, New York University, New York, NY, USA

Authors and Affiliations

  1. Department for Integrative Evolutionary Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany

    Tobias Theska, Bogdan Sieriebriennikov, Sara S. Wighard, Michael S. Werner & Ralf J. Sommer

Authors
  1. Tobias Theska
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bogdan Sieriebriennikov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sara S. Wighard
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael S. Werner
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ralf J. Sommer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: T.T., B.S. and M.S.W. Coding: B.S. and T.T. Data acquisition: T.T., S.S.W. and M.S.W. Data analysis: T.T. Writing the original draft: T.T. and S.S.W. Reviewing and editing the initial draft: B.S., M.S.W. and R.J.S. Revision of the original manuscript: T.T., B.S., S.S.W., M.S.W. and R.J.S. Supervision: M.S.W. and R.J.S.

Corresponding authors

Correspondence to Michael S. Werner or Ralf J. Sommer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Ming Bai and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using earlier versions of this protocol

Sieriebriennikov, B. et al. Mol. Biol. Evol. 34, 1644–1653 (2017): https://academic.oup.com/mbe/article/34/7/1644/3067497

Sieriebriennikov, B. et al. PLoS Genetics. 16, e1008687 (2020): https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008687

Early paper from our lab using a similar approach but a different protocol

Susoy, V. et al. Elife 4, e05463 (2015): https://elifesciences.org/articles/05463

Supplementary information

Supplementary Information

Supplementary Figs. 1–12 and Supplementary Tables 1 and 2.

Reporting Summary

Supplementary Data 1

Landmark file for the P. pacificus dataset (including the two different morphs).

Supplementary Data 2

Landmark file for the Pristionchus dataset (including P. pacificus, P. bucculentus and P. elegans).

Supplementary Data 3

Landmark file for the Caenorhabditis dataset (including C. elegans and C. briggsae).

Supplementary Data 4

R markdown file containing the code for geometric morphometrics based on the geomorph package (main text code).

Supplementary Data 5

Alternative R markdown file containing the code for geometric morphometrics based on the Morpho package.

Supplementary Data 6

R routine to automatically reformat original landmark files to fit the requirements of the readlands.tps() function. Note that functionality might be affected by the text editor that was used to generate the ‘lands.txt’ file.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Theska, T., Sieriebriennikov, B., Wighard, S.S. et al. Geometric morphometrics of microscopic animals as exemplified by model nematodes. Nat Protoc 15, 2611–2644 (2020). https://doi.org/10.1038/s41596-020-0347-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-020-0347-z

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics