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SimuCell: a flexible framework for creating synthetic microscopy images

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

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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.

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Figure 1: Synthetic image generation using SimuCell.

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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.).

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  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
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  2. Benjamin Pavie
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  3. Nicholas E F Hac
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  4. Steven J Altschuler
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  5. Lani F Wu
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Corresponding authors

Correspondence to Steven J Altschuler or Lani F Wu.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Software

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

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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

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