Autonomous screening of C. elegans identifies genes implicated in synaptogenesis

Journal name:
Nature Methods
Volume:
9,
Pages:
977–980
Year published:
DOI:
doi:10.1038/nmeth.2141
Received
Accepted
Published online

Morphometric studies in multicellular organisms are generally performed manually because of the complexity of multidimensional features and lack of appropriate tools for handling these organisms. Here we present an integrated system that identifies and sorts Caenorhabditis elegans mutants with altered subcellular traits in real time without human intervention. We performed self-directed screens 100 times faster than manual screens and identified both genes and phenotypic classes involved in synapse formation.

At a glance

Figures

  1. Integrated system for autonomous screening of C. elegans expressing fluorescent reporters.
    Figure 1: Integrated system for autonomous screening of C. elegans expressing fluorescent reporters.

    (a) Schematic of the microfluidic device used to automate sample handling of a mutagenized C. elegans population that allows imaging and sorting. Scale bar, 150 μm. (bd) Computer-vision framework used to identify the fluorescent reporter in a low signal-to-noise environment. Scale bars, 20 μm. (b) Maximum intensity–projection image of a representative wild-type animal acquired in the device; boxes show three distinct regions of the animal: (i) gut, (ii) fat granule and (iii) synapse. (c) Computer-vision framework applied to identify the objects of interest (synapses, S). For each pixel in b, local features are used to predict the probability that a pixel is a synapse; for points likely to be synapses, a second layer of information incorporating the spatial relationship between potential synapses is used to distinguish between gut or background autofluorescence and the fluorescent reporter of interest. (d) Left, a map of the image in b showing the probability that each point is a synapse; right, a thresholded image showing the identified synapses. (e,f) Statistical framework for quantitative phenotyping and autonomous decision-making during screening. (e) Representative images of wild-type animals and lin-44−/− mutants acquired in the device, and the resulting identified synapse locations. Scale bar, 20 μm. (f) Quantitative phenotypic descriptors extracted from the representative images. These descriptors are used to train the classifier for performing autonomous screens and predicting whether an animal is a mutant. The full list of the 30 descriptors can be found in Supplementary Note 2.

  2. Autonomous screens for C. elegans mutants with altered synaptic patterns.
    Figure 2: Autonomous screens for C. elegans mutants with altered synaptic patterns.

    (a) Linear discriminant analysis projection of the phenotypic descriptors from wild-type and lin-44−/− animals used to train the discriminative classifier. Classification during screening was performed in the original high-dimensional space using a radial basis function–kernel support vector machine. A representative decision boundary is shown. (b) Principal-component analysis projection of the phenotypic descriptors from wild-type animals used for the outlier detection screen, showing a representative decision boundary. Classification during screening was performed in the original high-dimensional space. (c) Schematic location of the DA9 neuron (blue) within C. elegans, and its synapses (green). (d) Schematics of the phenotypic classes identified during autonomous screening, including both previously identified and novel phenotypic classes, are shown at left, with one or more representative images of alleles with phenotypes falling into the appropriate categories to the right. Asterisks, DA9 soma; red arrowheads, synapses in asynaptic regions; yellow arrowheads, gaps in synaptic regions. (e) Candidate mutants of a single phenocluster, trafficking, were selected for further investigation. Complementarity tests were performed between these new alleles and genes known to cause a similar dendritic puncta phenotype (cdk-5 encodes a homolog of mammalian cyclin-dependent kinase 5; cyy-1 encodes a cyclin Y homolog; pct-1 encodes PCTAIRE class cell cycle kinase; cdka-1 encodes a cyclin-dependent kinase 5–activating protein ortholog; unc-6 encodes a netrin ortholog). Two alleles complemented genes known to act within this pathway (+), and two alleles failed to complement any of the known genes (−). (f) Images corresponding to the mutant a085 phenotype, showing a novel pattern of enlarged spine-like protrusions. Scale bar, 20 μm; asterisk indicates DA9 soma.

References

  1. Jorgensen, E.M. & Mango, S.E. Nat. Rev. Genet. 3, 356369 (2002).
  2. Huang, K. & Murphy, R.F. BMC Bioinformatics 5, 78 (2004).
  3. Whitehurst, A.W. et al. Nature 446, 815819 (2007).
  4. Jones, T.R. et al. Proc. Natl. Acad. Sci. USA 106, 18261831 (2009).
  5. Collinet, C. et al. Nature 464, 243249 (2010).
  6. Bakal, C., Aach, J., Church, G. & Perrimon, N. Science 316, 17531756 (2007).
  7. Perlman, Z.E. et al. Science 306, 11941198 (2004).
  8. Doitsidou, M., Flames, N., Lee, A.C., Boyanov, A. & Hobert, O. Nat. Methods 5, 869872 (2008).
  9. Chung, K., Crane, M.M. & Lu, H. Nat. Methods 5, 637643 (2008).
  10. Crane, M.M., Chung, K. & Lu, H. Lab Chip 9, 3840 (2009).
  11. Rohde, C.B., Zeng, F., Gonzalez-Rubio, R., Angel, M. & Yanik, M.F. Proc. Natl. Acad. Sci. USA 104, 1389113895 (2007).
  12. Murray, J.I. et al. Nat. Methods 5, 703709 (2008).
  13. Klassen, M.P. & Shen, K. Cell 130, 704716 (2007).
  14. Poon, V.Y., Klassen, M.P. & Shen, K. Nature 455, 669673 (2008).
  15. Ou, C.-Y. et al. Cell 141, 846858 (2010).
  16. Brenner, S. Genetics 77, 7194 (1974).
  17. Wood, W.B. The Nematode Caenorhabditis elegans (Cold Spring Harbor Laboratory Press, 1988).
  18. Unger, M.A., Chou, H.P., Thorsen, T., Scherer, A. & Quake, S.R. Science 288, 113116 (2000).
  19. Duffy, D.C., McDonald, J.C., Schueller, O.J.A. & Whitesides, G.M. Anal. Chem. 70, 49744984 (1998).
  20. Chung, K. & Lu, H. Lab Chip 9, 27642766 (2009).
  21. Ellis, R.E., Jacobson, D.M. & Horvitz, H.R. Genetics 129, 7994 (1991).

Download references

Author information

Affiliations

  1. Interdisciplinary Program in Bioengineering, Georgia Institute of Technology, Atlanta, Georgia, USA.

    • Matthew M Crane,
    • Jeffrey N Stirman &
    • Hang Lu
  2. School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.

    • Jeffrey N Stirman &
    • Hang Lu
  3. Department of Biology, Howard Hughes Medical Institute, Stanford University, California, USA.

    • Chan-Yen Ou,
    • Peri T Kurshan &
    • Kang Shen
  4. School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, USA.

    • James M Rehg
  5. Present address: SynthSys, University of Edinburgh, Edinburgh, UK.

    • Matthew M Crane

Contributions

M.M.C. wrote the software, designed the microfluidic device and performed the screens. M.M.C. and J.N.S. designed and built the external system components. C.-Y.O. performed the complementation tests. P.T.K. performed the mapping and provided valuable reagents. M.M.C., J.N.S., J.M.R., K.S. and H.L. designed the experiments and prepared the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Supplementary information

PDF files

  1. Supplementary Text and Figures (799K)

    Supplementary Figures 1–9, Supplementary Tables 1 and 2 and Supplementary Notes 1–4

Zip files

  1. Supplementary Software (283K)

    Supplementary Software

Additional data