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histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data



Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell–cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.

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Figure 1: Multiscale analysis of the tissue ecosystem.
Figure 2: Round-trip analysis of unique cell types in high-dimension images of breast cancer.
Figure 3: Neighborhood analysis of breast cancer cell phenotypes.


  1. Tirosh, I. et al. Science 352, 189–196 (2016).

    Article  CAS  Google Scholar 

  2. Bendall, S.C. et al. Science 332, 687–696 (2011).

    Article  CAS  Google Scholar 

  3. Bodenmiller, B. et al. Nat. Biotechnol. 30, 858–867 (2012).

    Article  CAS  Google Scholar 

  4. Tabassum, D.P. & Polyak, K. Nat. Rev. Cancer 15, 473–483 (2015).

    Article  CAS  Google Scholar 

  5. Lee, J.H. et al. Science 343, 1360–1363 (2014).

    Article  CAS  Google Scholar 

  6. Chen, K.H., Boettiger, A.N., Moffitt, J.R., Wang, S. & Zhuang, X. Science 348, aaa6090 (2015).

    Article  Google Scholar 

  7. Lin, J.-R., Fallahi-Sichani, M. & Sorger, P.K. Nat. Commun. 6, 8390 (2015).

    Article  CAS  Google Scholar 

  8. Schubert, W. et al. Nat. Biotechnol. 24, 1270–1278 (2006).

    Article  CAS  Google Scholar 

  9. Gerdes, M.J. et al. Proc. Natl. Acad. Sci. USA 110, 11982–11987 (2013).

    Article  CAS  Google Scholar 

  10. Angelo, M. et al. Nat. Med. 20, 436–442 (2014).

    Article  CAS  Google Scholar 

  11. Giesen, C. et al. Nat. Methods 11, 417–422 (2014).

    Article  CAS  Google Scholar 

  12. Bodenmiller, B. Cell Syst. 2, 225–238 (2016).

    Article  CAS  Google Scholar 

  13. Ding, H., Wang, C., Huang, K. & Machiraju, R. BMC Bioinformatics 16, S10 (2015).

    Article  Google Scholar 

  14. Beck, A.H. et al. Sci. Transl. Med. 3, 108ra113 (2011).

    Article  Google Scholar 

  15. Jones, T.R. et al. BMC Bioinformatics 9, 482 (2008).

    Article  Google Scholar 

  16. Amir, A.D. et al. Nat. Biotechnol. 31, 545–552 (2013).

    Article  CAS  Google Scholar 

  17. Shekhar, K., Brodin, P., Davis, M.M. & Chakraborty, A.K. Proc. Natl. Acad. Sci. USA 111, 202–207 (2014).

    Article  CAS  Google Scholar 

  18. Rieckmann, J.C. et al. Nat. Immunol. 18, 583–593 (2017).

    Article  CAS  Google Scholar 

  19. Levine, J.H. et al. Cell 162, 184–197 (2015).

    Article  CAS  Google Scholar 

  20. Ostuni, R., Kratochvill, F., Murray, P.J. & Natoli, G. Trends Immunol. 36, 229–239 (2015).

    Article  CAS  Google Scholar 

  21. Kononen, J. et al. Nat. Med. 4, 844–847 (1998).

    Article  CAS  Google Scholar 

  22. Catena, R., Özcan, A., Jacobs, A., Chevrier, S. & Bodenmiller, B. Genome Biol. 17, 142 (2016).

    Article  Google Scholar 

  23. Wang, H.A.O. et al. Anal. Chem. 85, 10107–10116 (2013).

    Article  CAS  Google Scholar 

  24. Sommer, C., Straehle, C., Köthe, U. & Hamprecht, F.A. in Proc. 2011 8th IEEE International Symposium on. Biomedical Imaging: From Nano to Macro 230–233 (IEEE, 2011).

  25. Schüffler, P.J. et al. Cytometry A 87, 936–942 (2015).

    Article  Google Scholar 

  26. Weber, L.M. & Robinson, M.D. Cytometry A 89, 1084–1096 (2016).

    Article  CAS  Google Scholar 

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We would like to thank the Bodenmiller lab for support and fruitful discussions. Thank you to open-source softwares like “cyt”, “CellProfiler,” and many others. This work was supported by the Swiss National Science Foundation (SNSF) R'Equip grant 316030-139220, an SNSF Assistant Professorship grant PP00P3-144874, a Swiss Cancer League grant, the PhosphonetPPM and MetastasiX SystemsX grants, and funding from the European Research Council (ERC) under the European Union′s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. 336921. D. Schapiro was supported by the Forschungskredit of the University of Zurich, grant FK-74419-01-01, and the BioEntrepreneur-Fellowship of the University of Zurich, reference no. BIOEF-17-001. H.W.J. and D. Schulz are supported by European Molecular Biology Organization (EMBO) Long Term Fellowships cofunded by the European Commission (LTFCOFUND2013 and 2014), grants ALTF-711 2015 and ALTF-970 2014, respectively. H.W.J. was also supported by a Transition Postdoc Fellowship from the Swiss initiative ref. 2015/344, evaluated by the Swiss National Science Foundation.

Author information

Authors and Affiliations



D. Schapiro, H.W.J., and B.B. conceived of the project and software. H.W.J., C.G., and R.C. collected samples and validated antibodies. Z.V. assembled, classified, and provided tumor samples. H.W.J. completed the staining and image acquisition. D. Schapiro, S.R., and J.R.F. wrote the code. D. Schapiro, H.W.J., and D. Schulz tested software on multiple data sources. D. Schapiro, H.W.J., and V.R.T.Z. analyzed the images and single-cell data. D. Schapiro, H.W.J., and B.B. prepared the figures and wrote the manuscript. B.B. directed the project.

Corresponding author

Correspondence to Bernd Bodenmiller.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9, Supplementary Table 1 and Supplementary Notes 1–4. (PDF 69995 kb)

Life Sciences Reporting Summary

Life Sciences Reporting Summary (PDF 129 kb)

Supplementary Table 2

Patient Metadata (XLSX 47 kb)

Supplementary Dataset 1

Source Data for Figure 2 (TXT 12656 kb)

Supplementary Dataset 2

Source Data for Supplementary Figure 3 (XLS 80 kb)

Supplementary Software 1

histoCAT_MacOS12 (ZIP 17932 kb)

Supplementary Software 2

histoCAT_Windows7 (EXE 20372 kb)

Supplementary Software 3

histoCAT_Windows10 (EXE 19986 kb)

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Schapiro, D., Jackson, H., Raghuraman, S. et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat Methods 14, 873–876 (2017).

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