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.

  • Correspondence
  • Published:

SimuCell: a flexible framework for creating synthetic microscopy images

Nature Methods volume 9, pages 634–635 (2012)Cite this article

Subjects

To the Editor:

SimuCell differs from existing cell-population generators5 in three ways. First, SimuCell can generate heterogeneous cellular populations composed of diverse cell types. Each cell type can be defined independently by specifying models for cell and organelle shape and distributions of markers over these shapes. Models are typically algorithmic, but there is support for rendering produced by other tools, such as the highly realistic models learned from image data by CellOrganizer3 (via the new SLML markup language). Second, SimuCell allows users to specify interdependencies among population, biomarker and cell phenotypes. For example, a marker's cellular distribution can be affected by the cell's microenvironment (Fig. 1b, marker 1) as well as the localization pattern of another marker (Fig. 1b, markers 2 and 3). These definable image properties are accessible to users either via a novel scripting syntax built on top of MATLAB or through a graphical user interface; intermediate results can be used to define further 'ground truths' (for example, cell boundaries can be used to validate segmentation algorithms). Finally, SimuCell is easily extensible, providing a standard framework for defining new plugins that can also be shared through the SimuCell website. Users interested in adding novel phenotypes to SimuCell's palette can typically do so by writing just a few lines of code, in part because of MATLAB's extensive library of functions. We also intend to implement a user forum to share ideas, scripts, plug-ins and images. Thus SimuCell allows the definition of a broad range of phenotypes, encompassing nontrivial population-level effects such as cell-type heterogeneity or local cell-density effects (Fig. 1c). Although realistic synthetic data cannot replace true experimental data6, SimuCell can be a useful part of the algorithm developer's toolbox by generating rich, flexible test image data sets containing specified, parameterized 'biological' effects.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Synthetic image generation using SimuCell.

References

  1. Snijder, B. et al. Nature 461, 520–523 (2009).

    Article  CAS  Google Scholar 

  2. Bray, M.A., Fraser, A.N., Hasaka, T.P. & Carpenter, A.E. J. Biomol. Screen. 17, 266–274 (2012).

    Article  CAS  Google Scholar 

  3. Zhao, T. & Murphy, R.F. Cytometry A 71A, 978–990 (2007).

    Article  Google Scholar 

  4. Svoboda, D., Kozubek, M. & Stejskal, S. Cytometry A 75A, 494–509 (2009).

    Article  Google Scholar 

  5. Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H. & Yli-Harja, O. IEEE Trans. Med. Imaging 26, 1010–1016 (2007).

    Article  Google Scholar 

  6. Anonymous. Nat. Methods 8, 885 (2011).

Download references

Acknowledgements

We thank R. Murphy and members of the Altschuler and Wu laboratories for helpful feedback and discussions. This research was supported by US National Institutes of Health R01 grants (GM085442 to S.J.A. and GM081549 to L.F.W.), the Welch Foundation (I-1619 to S.J.A. and I-1644 to L.F.W.), CPRIT RP10900 (L.F.W.) and the University of Texas Southwestern Medical Center (QP-SURF to N.E.F.H.).

Author information

Author notes

  1. Satwik Rajaram and Benjamin Pavie: These authors contributed equally to the work

Authors and Affiliations

  1. Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Satwik Rajaram, Benjamin Pavie, Steven J Altschuler & Lani F Wu

  2. Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Satwik Rajaram, Benjamin Pavie, Steven J Altschuler & Lani F Wu

  3. University of Virginia School of Medicine, Charlottesville, Virginia, USA

    Nicholas E F Hac

Authors
  1. Satwik Rajaram
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Benjamin Pavie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicholas E F Hac
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Steven J Altschuler
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lani F Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Steven J Altschuler or Lani F Wu.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Software

SimuCell code (v0.9) (ZIP 9533 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rajaram, S., Pavie, B., Hac, N. et al. SimuCell: a flexible framework for creating synthetic microscopy images. Nat Methods 9, 634–635 (2012). https://doi.org/10.1038/nmeth.2096

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.2096

This article is cited by

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