Revealing the spatio-phenotypic patterning of cells in healthy and tumor tissues with mLSR-3D and STAPL-3D thumbnail

Revealing the spatio-phenotypic patterning of cells in healthy and tumor tissues with mLSR-3D and STAPL-3D

Abstract

Despite advances in three-dimensional (3D) imaging, it remains challenging to profile all the cells within a large 3D tissue, including the morphology and organization of the many cell types present. Here, we introduce eight-color, multispectral, large-scale single-cell resolution 3D (mLSR-3D) imaging and image analysis software for the parallelized, deep learning–based segmentation of large numbers of single cells in tissues, called segmentation analysis by parallelization of 3D datasets (STAPL-3D). Applying the method to pediatric Wilms tumor, we extract molecular, spatial and morphological features of millions of cells and reconstruct the tumor’s spatio-phenotypic patterning. In situ population profiling and pseudotime ordering reveals a highly disorganized spatial pattern in Wilms tumor compared to healthy fetal kidney, yet cellular profiles closely resembling human fetal kidney cells could be observed. In addition, we identify previously unreported tumor-specific populations, uniquely characterized by their spatial embedding or morphological attributes. Our results demonstrate the use of combining mLSR-3D and STAPL-3D to generate a comprehensive cellular map of human tumors.

Access options

Subscribe to Journal

Get full journal access for 1 year

$59.00

only $4.92 per issue

All prices are NET prices.

VAT will be added later in the checkout.

Tax calculation will be finalised during checkout.

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

Data are publicly available. Processed results (that is, cells × features matrices and clustering and pseudotime results) and imaging data are made available through public repositories for which the links are posted on the STAPL-3D GitHub page.

Code availability

We provide the STAPL-3D framework as a Python package on github (https://github.com/RiosGroup/STAPL3D).

References

  1. 1.

    Coutu, D. L., Kokkaliaris, K. D., Kunz, L. & Schroeder, T. Multicolor quantitative confocal imaging cytometry. Nat. Methods 15, 39–46 (2018).

    CAS 
    Article 

    Google Scholar
     

  2. 2.

    Li, W., Germain, R. N. & Gerner, M. Y. High-dimensional cell-level analysis of tissues with Ce3D multiplex volume imaging. Nat. Protoc. 14, 1708–1733 (2019).

    CAS 
    Article 

    Google Scholar
     

  3. 3.

    Rios, A. C. et al. Intraclonal plasticity in mammary tumors revealed through large-scale single-cell resolution 3D imaging. Cancer Cell 35, 618–632.e6 (2019).

    CAS 
    Article 

    Google Scholar
     

  4. 4.

    Segovia-Miranda, F. et al. Three-dimensional spatially resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression. Nat. Med. 25, 1885–1893 (2019).

    CAS 
    Article 

    Google Scholar
     

  5. 5.

    Steinert, E. M. et al. Quantifying memory CD8 T cells reveals regionalization of immunosurveillance. Cell 161, 737–749 (2015).

    CAS 
    Article 

    Google Scholar
     

  6. 6.

    Mosaliganti, K. R., Noche, R. R., Xiong, F., Swinburne, I. A. & Megason, S. G. ACME: automated cell morphology extractor for comprehensive reconstruction of cell membranes. PLoS Comput. Biol. 8, e1002780 (2012).

    CAS 
    Article 

    Google Scholar
     

  7. 7.

    Stegmaier, J. et al. Real-time three-dimensional cell segmentation in large-scale microscopy data of developing embryos. Dev. Cell 36, 225–240 (2016).

    CAS 
    Article 

    Google Scholar
     

  8. 8.

    McQuin, C. et al. CellProfiler 3.0: Next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).

    Article 

    Google Scholar
     

  9. 9.

    Dunn, K. W. et al. DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data. Sci. Rep. 9, 18295 (2019).

    CAS 
    Article 

    Google Scholar
     

  10. 10.

    Wolny, A. et al. Accurate and versatile 3D segmentation of plant tissues at cellular resolution. Elife 9, 1–34 (2020).

    Article 

    Google Scholar
     

  11. 11.

    Weigert, M., Schmidt, U., Haase, R., Sugawara, K. & Myers, G. Star-convex polyhedra for 3D object detection and segmentation in microscopy. in Proceedings – 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 3655–3662 (2020). https://doi.org/10.1109/WACV45572.2020.9093435

  12. 12.

    Zhao, S. et al. Cellular and molecular probing of intact human organs. Cell 180, 796–812.e19 (2020).

    CAS 
    Article 

    Google Scholar
     

  13. 13.

    Kraus, B., Ziegler, M. & Wolff, H. Linear fluorescence unmixing in cell biological research. Mod. Res. Educ. Top. Microsc. 2, 863–872 (2007).


    Google Scholar
     

  14. 14.

    Valm, A. M. et al. Applying systems-level spectral imaging and analysis to reveal the organelle interactome. Nature 546, 162–167 (2017).

    CAS 
    Article 

    Google Scholar
     

  15. 15.

    Hochane, M. et al. Single-cell transcriptomics reveals gene expression dynamics of human fetal kidney development. PLoS Biol. 17, e3000152 (2019).

    Article 

    Google Scholar
     

  16. 16.

    Tustison, N. J. et al. N4ITK: Improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010).

    Article 

    Google Scholar
     

  17. 17.

    Berg, S. et al. Ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).

    CAS 
    Article 

    Google Scholar
     

  18. 18.

    Gut, G., Herrmann, M. D. & Pelkmans, L. Multiplexed protein maps link subcellular organization to cellular states. Science 80, 361 (2018).


    Google Scholar
     

  19. 19.

    Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).

    CAS 
    Article 

    Google Scholar
     

  20. 20.

    Young, M. D. et al. Single cell derived mRNA signals across human kidney tumors. Preprint at bioRxiv https://doi.org/10.1101/2020.03.19.998815 (2020).

  21. 21.

    McInnes, L. PCA, t-SNE, and UMAP: modern approaches to dimension reduction. PyData Conference 2018 (2018).

  22. 22.

    Reinhard, H. et al. Outcome of relapses of nephroblastoma in patients registered in the SIOP/GPOH trials and studies. Oncol. Rep. 20, 463–467 (2008).

    PubMed 

    Google Scholar
     

  23. 23.

    Wegert, J. et al. Mutations in the SIX1/2 pathway and the DROSHA/DGCR8 miRNA microprocessor complex underlie high-risk blastemal type Wilms tumors. Cancer Cell 27, 298–311 (2015).

    CAS 
    Article 

    Google Scholar
     

  24. 24.

    Glaser, A. K. et al. Multi-immersion open-top light-sheet microscope for high-throughput imaging of cleared tissues. Nat. Commun. 10, 2781 (2019).

    Article 

    Google Scholar
     

  25. 25.

    Stoltzfus, C. R. et al. CytoMAP: a spatial analysis toolbox reveals features of myeloid cell organization in lymphoid tissues. Cell Rep. 31, 107523 (2020).

    CAS 
    Article 

    Google Scholar
     

  26. 26.

    Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599 (2020).

    CAS 
    Article 

    Google Scholar
     

  27. 27.

    Dekkers, J. F. et al. High-resolution 3D imaging of fixed and cleared organoids. Nat. Protoc. 14, 1756–1771 (2019).

    CAS 
    Article 

    Google Scholar
     

  28. 28.

    van Ineveld, R. L., Ariese, H. C. R., Wehrens, E. J., Dekkers, J. F. & Rios, A. C. Single-cell resolution three-dimensional imaging of intact organoids. J. Vis. Exp. 2020, 1–8 (2020).


    Google Scholar
     

  29. 29.

    Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31, 1116–1128 (2006).

    Article 

    Google Scholar
     

  30. 30.

    Sauvola, J. & Pietikäinen, M. Adaptive document image binarization. Pattern Recognit. 33, 225–236 (2000).

    Article 

    Google Scholar
     

  31. 31.

    Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. P. W. Elastix: A toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196–205 (2010).

    Article 

    Google Scholar
     

  32. 32.

    Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945).

    Article 

    Google Scholar
     

  33. 33.

    Wei, T. & Simko, V. corrplot. R Package, v. 0.84 (2017).

  34. 34.

    Basser, P. J. & Pierpaoli, C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. Ser. B 111, 209–219 (1996).

    CAS 
    Article 

    Google Scholar
     

  35. 35.

    Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: Large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article 

    Google Scholar
     

  36. 36.

    Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, (2019).

  37. 37.

    Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).


    Google Scholar
     

  38. 38.

    Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    CAS 
    Article 

    Google Scholar
     

Download references

Acknowledgements

We are grateful for the technical support from the Princess Máxima Center for Pediatric Oncology and Zeiss for imaging support. We acknowledge the Gynaikon Clinic in Rotterdam for their efforts to provide the human fetal material and the laboratory of Hans Clevers and the Hubrecht Organoid Technology (HUB, www.hub4organoids.nl) for access to the breast cancer organoid biobank. We also acknowledge the Utrecht Bioinformatics Center High Performance Computing Facility for data processing infrastructure. We thank R.R. de Krijger for useful discussions. All the imaging was performed at the Princess Máxima Imaging Center. This work was financially supported by the Princess Máxima Center for Pediatric Oncology and St. Baldrick’s Robert J. Arceci International Innovation award. J.F.D. is supported by a VENI grant from the Netherlands Organisation for Scientific Research (NWO). A.C.R is supported by an ERC-starting grant 2019 project no. 804412.

Author information

Author notes

  1. These authors contributed equally: Ravian L. van Ineveld, Michiel Kleinnijenhuis.

Affiliations

  1. Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands

    Ravian L. van Ineveld, Michiel Kleinnijenhuis, Maria Alieva, Sam de Blank, Mario Barrera Roman, Esmée J. van Vliet, Clara Martínez Mir, Hannah R. Johnson, Frank L. Bos, Jarno Drost, Johanna F. Dekkers, Ellen J. Wehrens & Anne C. Rios

  2. Cancer Genomics Netherlands, Oncode Institute, Utrecht, the Netherlands

    Ravian L. van Ineveld, Michiel Kleinnijenhuis, Maria Alieva, Sam de Blank, Mario Barrera Roman, Esmée J. van Vliet, Clara Martínez Mir, Hannah R. Johnson, Frank L. Bos, Jarno Drost, Johanna F. Dekkers, Ellen J. Wehrens & Anne C. Rios

  3. QVQ Holding BV, Utrecht, the Netherlands

    Raimond Heukers

  4. Department of Anatomy and Embryology, Leiden University Medical Center, Leiden, the Netherlands

    Susana M. Chuva de Sousa Lopes

Contributions

R.L.v.I. and M.K. contributed equally. M.A. and S.d.B. contributed equally. R.L.v.I. developed mLSR-3D and performed microscopy. M.K. developed STAPL-3D and performed the computational methods. R.L.v.I. and M.K. analyzed the data. S.d.B. assisted with computational analysis. C.M.M. assisted with microscopy. M.B.R. performed mLSR-3D imaging of breast and neuronal tumor tissue, rendered data and made videos. E.J.v.V. performed mLSR-3D imaging of breast and neuronal tumor tissue. M.A. performed quality control and computational analysis. J.F.D. provided organoid and xenograft material. H.R.J. assisted with sample preparation. F.L.B. provided microscopy support. R.H. provided the NCAM nanobody used in this study. S.M.C.d.S.L provided human fetal material. J.F.D., M.A., E.J.v.V., S.d.B., F.L.B. and J.D. provided critical feedback on the work. R.L.v.I., M.K. and A.C.R. designed the study and wrote the manuscript with support from E.J.W., and A.C.R. supervised this work.

Corresponding author

Correspondence to
Anne C. Rios.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Biotechnology thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

About this article

Verify currency and authenticity via CrossMark

Cite this article

van Ineveld, R.L., Kleinnijenhuis, M., Alieva, M. et al. Revealing the spatio-phenotypic patterning of cells in healthy and tumor tissues with mLSR-3D and STAPL-3D.
Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-00926-3

Download citation

Read More

Leave a Reply

Your email address will not be published. Required fields are marked *