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

Nature Biotechnology volume 39, pages 1239–1245 (2021)Cite this article

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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.

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Fig. 1: mLSR-3D imaging and STAPL-3D.
Fig. 2: Spatio-phenotypic patterning of HFK and Wilms tumor reveals expanded, cycling, early epithelial compartment in Wilms tumor.
Fig. 3: Comparative analysis reveals spindle-shaped SIX2+ cells in surrounding tumor stoma.

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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).

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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.

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Author notes

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

Authors and 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

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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.

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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 39, 1239–1245 (2021). https://doi.org/10.1038/s41587-021-00926-3

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