Genetically engineered cerebral organoids model brain tumor formation

An Author Correction to this article was published on 22 August 2018

This article has been updated

Abstract

Brain tumors are among the most lethal and devastating cancers. Their study is limited by genetic heterogeneity and the incompleteness of available laboratory models. Three-dimensional organoid culture models offer innovative possibilities for the modeling of human disease. Here we establish a 3D in vitro model called a neoplastic cerebral organoid (neoCOR), in which we recapitulate brain tumorigenesis by introducing oncogenic mutations in cerebral organoids via transposon- and CRISPR–Cas9-mediated mutagenesis. By screening clinically relevant mutations identified in cancer genome projects, we defined mutation combinations that result in glioblastoma-like and central nervous system primitive neuroectodermal tumor (CNS-PNET)-like neoplasms. We demonstrate that neoCORs are suitable for use in investigations of aspects of tumor biology such as invasiveness, and for evaluation of drug effects in the context of specific DNA aberrations. NeoCORs will provide a valuable complement to the current basic and preclinical models used to study brain tumor biology.

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Fig. 1: Introducing genome-editing constructs into neural stem/precursor cells of cerebral organoids.
Fig. 2: Clonal mutagenesis in organoids induces tumor overgrowth.
Fig. 3: MYCOE and GBM-like neoCORs have distinct transcriptional profiles and cellular identities.
Fig. 4: NeoCORs expand in renal subcapsular xenografts.
Fig. 5: GBM neoCORs exhibit features of GBM invasion.
Fig. 6: NeoCORs are suitable for preclinical investigations.

Change history

  • 22 August 2018

    In the originally published paper, the “before” image for the afatinib condition in Fig. 6c was incorrect. Instead of an image displaying a GBM-3 neoplastic organoid before afatinib treatment, this panel showed an image from the GBM-2 control (DMSO) group before treatment. This error has now been corrected in the HTML and PDF versions of the article; the “before, afatinib” panel in Fig. 6c now shows a representative image from the indicated experiment. The color of all error bars in Fig. 6 has also been changed to black, for consistency. All statistical analysis and all conclusions presented in the article are unaffected by this error. Nevertheless, we apologize for the mistake.

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Acknowledgements

We are grateful to all members of the Knoblich laboratory for discussions, and H. Gustafson and S. Wolfinger for technical support. We thank O. Wüseke and F. Bonnay for comments on the manuscript. We thank T. Müller, P. Pasierbek, G. Petri, M. Weninger, G. Schmauss, and T. Lendl for FACS and help with imaging. We thank A. Piszczek, T. Engelmaier, J. Klughofer, and M. Zeba for tissue processing and immunohistochemical staining. We thank the Next Generation Sequencing Facility for next-generation sequencing. We thank Boehringer Ingelheim RCV GmbH & Co KG for providing EGFR inhibitors. We thank W. Hu for statistical advice. J.A. Bagley received funding from an EMBO postdoctoral fellowship, and from the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 707109. Work in the Knoblich laboratory is supported by the Austrian Academy of Sciences, the Austrian Science Fund (Z_153_B09), and an advanced grant from the European Research Council (ERC).

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Contributions

S.B. and J.A.K. conceived the project and experimental design and wrote the manuscript. S.B. performed experiments and analyzed data. M.R., Z.G., and C.K. performed experiments. A.K. contributed the histopathology data. T.B. performed bioinformatics analysis of RNA-seq data. J.A.B. contributed to RNA-seq analysis and quantification of immunostained tissues. J.A.K. directed and supervised the project.

Corresponding author

Correspondence to Jürgen A. Knoblich.

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Competing interests

S.B. and J.A.K. have filed a patent application (EP 17190447.7) for use of this method in future disease modeling and preclinical investigation.

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Integrated supplementary information

Supplementary Figure 1 The strategy to introduce gene aberrations into neural stem/precursor cells in cerebral organoids.

(a) Schematic of the strategy of genome-editing techniques to introduce oncogene amplification and/or tumour suppressor mutation/deletion. Sleeping Beauty transposon system was used to integrate oncogene-expression and GFP-expression elements into genome. CRISPR-Cas9 system was applied to introduce mutation/deletion of tumour suppressors. (b) Quantification of cellular identities of nucleofected cells in EBs one day after nucleofection by immunofluorescence staining on serial cryo-sections. The percentage of marker+ GFP+ cells in total GFP+ cells from different sections were presented. Cell number for different cellular markers analysed in this study was labelled under the graph. This experiment was performed twice independently with same results. (c, d) Immunofluorescence images (c) and quantification (d) of adherent cell culture of dissociated EBs one day after nucleofection stained with different cell markers. The percentage of marker+ GFP+ cells in total GFP+ cells from different EBs were presented. Cell number for different cellular markers analysed in this study was labelled under the graph. This experiment was performed once. All the detailed sample size and mean±SD were provided in Source Data.Scale bar: c, 50 μm.

Supplementary Figure 2 Verification of gene aberrations introduced by genome-editing techniques.

(a) RNA-seq and RT-PCR analysis showed that tumour cells from MYCOE neoCORs exhibit high MYC expression levels. The TPM value from RNA-seq analysis was analyzed using four organoids from three independent cultures (P<0.0001). The TPM values of three organoids from CTRL groups from three independent cultures were presented as control. (b) Three example sequences of CRISPR-Cas9 targeting CDKN2A and CDKN2B locus in tumour cells from GBM-1 neoCORs. RNA-seq and RT-PCR analysis showed that tumour cells from GBM-1 neoCORs exhibit high expression levels of both EGFR and EGFRvIII. The TPM value from RNA-seq analysis was analyzed using four organoids from three independent cultures (p = 0.0068). The TPM values of three organoids from CTRL groups from three independent cultures were presented as control. (c) Three example sequences of CRISPR-Cas9 targeting NF1, PTEN, and p53 locus in tumour cells from GBM-2 neoCORs. (d) Three example sequences of CRISPR-Cas9 targeting CDKN2A and PTEN locus in tumour cells from GBM-3 neoCORs. RNA-seq and RT-PCR analysis showed that tumour cells from GBM-3 neoCORs exhibit high expression level of EGFRvIII, but not EGFR. The TPM value from RNA-seq analysis was analyzed using three organoids from three independent cultures (P = 0.0124). The TPM values of three organoids from CTRL groups from three independent cultures were presented as control. Statistical analysis of quantification was performed using unpaired two-tailed Student’s t-test. Data were presented as mean±s.d. All the detailed sample size, mean±s.d., as well as P value were provided in the Source Data. The percentage of non-homologous end joining (NHEJ) for individual genes by sequencing TA-vector colonies carrying amplified targeting locus were also provided in Source Data. *, P<0.05; **, P<0.01; ***, P<0.001.

Supplementary Figure 3 Venn diagram hypergeometric and KEGG pathway analysis of tumor cells from the cluster 2 and cluster 3 neoCORs.

(a) Venn diagram hypergeometric test showed overlap of differentially expressed genes (DESeq, adjusted absolute log2fc value >0.5 and adjusted P value <0.05) in Cluster 2 (n = 3 organoids from one experiment) or Cluster 3 (n = 7 organoids from one experiment), in each case relative to CTRL organoids (n = 3 organoids from one experiment). P values for overlaps were calculated by hypergeometric test. (b) KEGG pathway enrichment analysis for differentially expressed genes (DESeq, adjusted absolute log2fc value >0.5 and adjusted P value <0.05) in tumour cells from Cluster 2 and Cluster 3 neoCORs.

Supplementary Figure 4 Low-magnification images revealed that 4-month-old neoCORs showed brain-tumor-subtype-specific cellular identities.

(a-f) Immunofluorescence images and quantification of neuronal marker HuC/D (a, magenta), precursor marker SOX2 (b, magenta), cell cycle marker Ki67 (c, magenta), glial marker S100β (d, magenta) and GFAP (e, magenta), as well as CNS-PNET marker CD99 (f, magenta). The staining was performed from six independent experiments with the similar results. Scale bar: a-f, 1000 μm.

Supplementary Figure 5 High-magnification images revealed that 4-month-old neoCORs showed brain-tumor-subtype-specific cellular identities.

Representative immunofluorescence images of four-month-old neoCORs from GBM-2 and GBM-3 groups. Neuronal marker HuC/D (magenta), precursor marker SOX2 (magenta), cell cycle marker Ki67 (magenta), glial marker S100β (magenta) and GFAP (magenta), as well as CNS-PNET marker CD99 (magenta) were presented. The staining was performed from six independent experiments with the similar results. Scale bar: 100 μm.

Supplementary Figure 6 High-magnification images revealed that 1-month-old neoCORs showed brain-tumor-subtype-specific cellular identities.

(a) Immunofluorescence images of control and neoplastic groups one day and one month after nucleofection confirmed the tumour-initiation capability of genetic disruptions. (b) Immunofluorescence images of DAPI (blue) and GFP (green) of control and tumour groups one month after nucleofection. (c-e) Immunofluorescence images and quantification of neuronal marker HuC/D (c, magenta), precursor marker SOX2 (c, blue), cell cycle marker Ki67 (d, magenta), as well as glial marker S100β (e, magenta). The staining was performed from three independent experiments with the similar results. Scale bar: a, upper panel: 200 μm, lower panel: 1000 μm; b, 1000 μm; c-e, 100 μm.

Supplementary Figure 7 Low-magnification images revealed that 1-month-old neoCORs showed brain-tumor-subtype-specific cellular identities.

(a) Immunofluorescence images of control and neoplastic groups one day and one month after nucleofection confirmed the tumour-initiation capability of genetic disruptions. (b) Immunofluorescence images of DAPI (blue) and GFP (green) staining of control and neoplastic groups one month after nucleofection. (c-e) Immunofluorescence images and quantification of neuronal marker HuC/D (c, magenta), precursor marker SOX2 (c, blue), cell cycle marker Ki67 (d, magenta), as well as glial marker S100β (e, magenta). The staining was performed from three independent experiments with the similar results. Scale bar: a, upper panel: 200 μm, lower panel: 1000 μm; b-e, 1000 μm.

Supplementary Figure 8 In vivo expansion of neoCORs after renal subcapsular implantation.

(a) Schematic of renal subcapsular xenograft procedure. (b) NeoCORs from MYCOE group and GBM-1 group were implanted into kidney capsule. Engrafted kidneys were analysed at one week and one and half months after xenograft to evaluate the in vivo expansion of neoCORs from MYCOE and GBM-1 groups. (c) Immunohistochemical staining of neuronal marker MAP2 in the MYCOE implant. Arrowhead shows a neuron. (d-f) H&E staining of implanted MYCOE organoids showing cell sheet (e) and rosette (f) structures. The implantation experiments were performed three times independently with similar results. Scale bar: b, 500 mm; d, 1000 μm; e,f, 50 μm.

Supplementary Figure 9 Drug testing assay showed the drug-screening potential of neoCORs.

(a) Schematic of luciferase assay-based drug testing strategy on neoCORs from GBM-1 group. (b) Quantification of relative luciferase activity revealed that EGFR inhibitors Afatinib (725.57±253.71; P = 0.0076) and Erlotinib (716.10±424.94; P = 0.0074) significantly reduced luciferase activity in GBM-1 (CDKN2A-/CDKN2B-/EGFROE/EGFRvIIIOE) neoCORs (DMSO: n = 9; Canertinib: n = 9; Pelitinib: n = 8; Afatinib: n = 9; Gefitinib: n = 9; Erlotinib: n = 9). Normalized luciferase activity was presented. This experiment was performed once. Statistical analysis of quantifications was performed using one-way ANOVA with Dunnett’s test. Data were presented as mean±SD. All the detailed sample size, mean±SD, as well as p value were provided in Source Data. **, P<0.01.

Supplementary Figure 10 An example of the gating strategy for FACS analysis.

(a) Gating for live cells. (b) Gating to exclude doublets and cell aggregates. (c) Gating for GFP+ cells.

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Bian, S., Repic, M., Guo, Z. et al. Genetically engineered cerebral organoids model brain tumor formation. Nat Methods 15, 631–639 (2018). https://doi.org/10.1038/s41592-018-0070-7

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