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PhenoRipper: software for rapidly profiling microscopy images

Nature Methods volume 9, pages 635–637 (2012)Cite this article

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To minimize user input, PhenoRipper automatically identifies features from the images; users may only be required to modify default values of a few, visually interpretable, parameters. To increase speed, we chose a segmentation-free approach4,5: the software breaks images down into a square grid of blocks6,7,8 and performs analysis on these blocks rather than on individual cells. To capture heterogeneity, PhenoRipper identifies characteristic patterns of neighboring blocks and describes each image in terms of the occurrence frequencies of these patterns6,8. Finally, a simple graphical user interface, PhenoBrowser, is used to tie together images, features and profiles. Profiles can be annotated or combined (for example, by experimental or replicate conditions) to help interpret and explore their visual grouping. These design choices let users analyze their images an order of magnitude faster than existing unsupervised platforms (Supplementary Fig. 2). PhenoRipper does not replace traditional single cell–based analysis approaches2,9,10 as it does not quantify properties such as area or average nuclear biomarker intensity. Nevertheless, the statistical properties of subcellular-scale phenotypes captured by PhenoRipper can be sufficient to accurately group cellular perturbations and identify outliers (Supplementary Fig. 3a).

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Figure 1: Overview of the PhenoRipper platform.

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Acknowledgements

We thank R. Murphy, M. Slack, R.J. Steininger III, C.A. Thorne, P. Xie and members of the Altschuler and Wu laboratories for helpful feedback and discussions. This research was supported by National Institutes of Health grants R01 GM085442 (S.J.A.) and R01 GM081549 (L.F.W.), Welch Foundation grants I-1619 (S.J.A.) and I-1644 (L.F.W.) and CPRIT RP10900 (L.F.W).

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Authors and Affiliations

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

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

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

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

Authors
  1. Satwik Rajaram
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  2. Benjamin Pavie
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  3. Lani F Wu
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  4. Steven J Altschuler
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Correspondence to Lani F Wu or Steven J Altschuler.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 and Supplementary Methods (PDF 1465 kb)

Supplementary Software

PhenoRipper executables (v0.11) (ZIP 379 kb)

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Rajaram, S., Pavie, B., Wu, L. et al. PhenoRipper: software for rapidly profiling microscopy images. Nat Methods 9, 635–637 (2012). https://doi.org/10.1038/nmeth.2097

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