Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Childhood cerebellar tumours mirror conserved fetal transcriptional programs

Subjects

Abstract

Study of the origin and development of cerebellar tumours has been hampered by the complexity and heterogeneity of cerebellar cells that change over the course of development. Here we use single-cell transcriptomics to study more than 60,000 cells from the developing mouse cerebellum and show that different molecular subgroups of childhood cerebellar tumours mirror the transcription of cells from distinct, temporally restricted cerebellar lineages. The Sonic Hedgehog medulloblastoma subgroup transcriptionally mirrors the granule cell hierarchy as expected, while group 3 medulloblastoma resembles Nestin+ stem cells, group 4 medulloblastoma resembles unipolar brush cells, and PFA/PFB ependymoma and cerebellar pilocytic astrocytoma resemble the prenatal gliogenic progenitor cells. Furthermore, single-cell transcriptomics of human childhood cerebellar tumours demonstrates that many bulk tumours contain a mixed population of cells with divergent differentiation. Our data highlight cerebellar tumours as a disorder of early brain development and provide a proximate explanation for the peak incidence of cerebellar tumours in early childhood.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Identification of cell types in the developing mouse cerebellum.
Fig. 2: Reconstruction of cerebellar developmental lineages through pseudo-temporal ordering of cells.
Fig. 3: Deconvolution analyses of bulk human medulloblastoma tumour transcriptomes.
Fig. 4: Temporal transcriptional matching of normal cerebellar cell clusters with bulk human tumours.
Fig. 5: Cell-type deconvolution analyses of tumour-cell-specific clusters from human medulloblastoma scRNA-seq.
Fig. 6: Cell-type deconvolution analyses of tumour-cell-specific clusters from human PFA and C-PA scRNA-seq.

Similar content being viewed by others

Data availability

The datasets generated and analysed during the current study are available in the Gene Expression Omnibus (GEO) and European Genome-phenome Archive (EGA; https://www.ebi.ac.uk/ega/studies/) repositories: BAMs and filtered gene matrices of mouse developmental time points scRNA-seq (GSE118068), FASTQs of PFB bulk RNA-seq and microarray expression (EGAS00001002696, GSE64415), BAMs of human tumour scRNA-seq and either BAMs or FASTQs of bulk PFA/C-PA RNA-seq (EGAS00001003170) and FASTQs of MB bulk RNA-seq (EGAD00001004435).

Code availability

The following packages were used for the data analysis: Cell Ranger v1.2.1, R v3.4.4, Seurat v1.4.0, v2.3.0 and v2.3.4, Monocle v2.6.3 and CIBERSORT (absolute mode beta).

References

  1. Northcott, P. A. et al. Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature 488, 49–56 (2012).

    Article  ADS  CAS  Google Scholar 

  2. Mack, S. C. et al. Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling. Nature 553, 101–105 (2018).

    Article  ADS  CAS  Google Scholar 

  3. Jones, D. T. et al. Dissecting the genomic complexity underlying medulloblastoma. Nature 488, 100–105 (2012).

    Article  ADS  CAS  Google Scholar 

  4. Pugh, T. J. et al. Medulloblastoma exome sequencing uncovers subtype-specific somatic mutations. Nature 488, 106–110 (2012).

    Article  ADS  CAS  Google Scholar 

  5. Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).

    Article  ADS  CAS  Google Scholar 

  6. Jones, D. T. et al. Recurrent somatic alterations of FGFR1 and NTRK2 in pilocytic astrocytoma. Nat. Genet. 45, 927–932 (2013).

    Article  CAS  Google Scholar 

  7. Mack, S. C. et al. Epigenomic alterations define lethal CIMP-positive ependymomas of infancy. Nature 506, 445–450 (2014).

    Article  ADS  CAS  Google Scholar 

  8. Cavalli, F. M. G. et al. Intertumoral heterogeneity within medulloblastoma subgroups. Cancer Cell 31, 737–754 (2017).

    Article  CAS  Google Scholar 

  9. Dubuc, A. M., Mack, S., Unterberger, A., Northcott, P. A. & Taylor, M. D. The epigenetics of brain tumors. Methods Mol. Biol. 863, 139–153 (2012).

    Article  CAS  Google Scholar 

  10. Dubuc, A. M. et al. Subgroup-specific alternative splicing in medulloblastoma. Acta Neuropathol. 123, 485–499 (2012).

    Article  CAS  Google Scholar 

  11. Northcott, P. A. et al. Medulloblastoma comprises four distinct molecular variants. J. Clin. Oncol. 29, 1408–1414 (2011).

    Article  Google Scholar 

  12. Wechsler-Reya, R. J. & Scott, M. P. Control of neuronal precursor proliferation in the cerebellum by Sonic Hedgehog. Neuron 22, 103–114 (1999).

    Article  CAS  Google Scholar 

  13. Wallace, V. A. Purkinje-cell-derived Sonic Hedgehog regulates granule neuron precursor cell proliferation in the developing mouse cerebellum. Curr. Biol. 9, 445–448 (1999).

    Article  CAS  Google Scholar 

  14. Li, P. et al. A population of Nestin-expressing progenitors in the cerebellum exhibits increased tumorigenicity. Nat. Neurosci. 16, 1737–1744 (2013).

    Article  CAS  Google Scholar 

  15. Kawauchi, D. et al. A mouse model of the most aggressive subgroup of human medulloblastoma. Cancer Cell 21, 168–180 (2012).

    Article  CAS  Google Scholar 

  16. Pei, Y. et al. An animal model of MYC-driven medulloblastoma. Cancer Cell 21, 155–167 (2012).

    Article  CAS  Google Scholar 

  17. Swartling, F. J. et al. Pleiotropic role for MYCN in medulloblastoma. Genes Dev. 24, 1059–1072 (2010).

    Article  CAS  Google Scholar 

  18. Gibson, P. et al. Subtypes of medulloblastoma have distinct developmental origins. Nature 468, 1095–1099 (2010).

    Article  ADS  CAS  Google Scholar 

  19. Vanner, R. J. et al. Quiescent Sox2+ cells drive hierarchical growth and relapse in Sonic Hedgehog subgroup medulloblastoma. Cancer Cell 26, 33–47 (2014).

    Article  CAS  Google Scholar 

  20. Pajtler, K. W. et al. The current consensus on the clinical management of intracranial ependymoma and its distinct molecular variants. Acta Neuropathol. 133, 5–12 (2017).

    Article  CAS  Google Scholar 

  21. Pajtler, K. W. et al. Molecular classification of ependymal tumors across all CNS compartments, histopathological grades, and age groups. Cancer Cell 27, 728–743 (2015).

    Article  CAS  Google Scholar 

  22. Witt, H. et al. Delineation of two clinically and molecularly distinct subgroups of posterior fossa ependymoma. Cancer Cell 20, 143–157 (2011).

    Article  CAS  Google Scholar 

  23. Johnson, R. A. et al. Cross-species genomics matches driver mutations and cell compartments to model ependymoma. Nature 466, 632–636 (2010).

    Article  ADS  CAS  Google Scholar 

  24. Taylor, M. D. et al. Radial glia cells are candidate stem cells of ependymoma. Cancer Cell 8, 323–335 (2005).

    Article  CAS  Google Scholar 

  25. Pomeroy, S. L. et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415, 436–442 (2002).

    Article  ADS  CAS  Google Scholar 

  26. Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).

    Article  ADS  CAS  Google Scholar 

  27. Goldowitz, D. & Hamre, K. The cells and molecules that make a cerebellum. Trends Neurosci. 21, 375–382 (1998).

    Article  CAS  Google Scholar 

  28. Leto, K. et al. Consensus paper: cerebellar development. Cerebellum 15, 789–828 (2016).

    Article  Google Scholar 

  29. Sillitoe, R. V. & Joyner, A. L. Morphology, molecular codes, and circuitry produce the three-dimensional complexity of the cerebellum. Annu. Rev. Cell Dev. Biol. 23, 549–577 (2007).

    Article  CAS  Google Scholar 

  30. Fleming, J. T. et al. The Purkinje neuron acts as a central regulator of spatially and functionally distinct cerebellar precursors. Dev. Cell 27, 278–292 (2013).

    Article  CAS  Google Scholar 

  31. Parmigiani, E. et al. Heterogeneity and bipotency of astroglial-like cerebellar progenitors along the interneuron and glial lineages. J. Neurosci. 35, 7388–7402 (2015).

    Article  CAS  Google Scholar 

  32. Wojcinski, A. et al. Cerebellar granule cell replenishment postinjury by adaptive reprogramming of Nestin+ progenitors. Nat. Neurosci. 20, 1361–1370 (2017).

    Article  CAS  Google Scholar 

  33. Saunders, A. et al. Molecular diversity and specialization among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).

    Article  CAS  Google Scholar 

  34. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).

    Article  CAS  Google Scholar 

  35. Yeung, J. & Goldowitz, D. Wls expression in the rhombic lip orchestrates the embryonic development of the mouse cerebellum. Neuroscience 354, 30–42 (2017).

    Article  CAS  Google Scholar 

  36. Yeung, J. et al. Wls provides a new compartmental view of the rhombic lip in mouse cerebellar development. J. Neurosci. 34, 12527–12537 (2014).

    Article  Google Scholar 

  37. Garzia, L. et al. A hematogenous route for medulloblastoma leptomeningeal metastases. Cell 173, 1549 (2018).

    Article  CAS  Google Scholar 

  38. Morrissy, A. S. et al. Spatial heterogeneity in medulloblastoma. Nat. Genet. 49, 780–788 (2017).

    Article  CAS  Google Scholar 

  39. Morrissy, A. S. et al. Divergent clonal selection dominates medulloblastoma at recurrence. Nature 529, 351–357 (2016).

    Article  ADS  CAS  Google Scholar 

  40. Wu, X. et al. Clonal selection drives genetic divergence of metastatic medulloblastoma. Nature 482, 529–533 (2012).

    Article  ADS  CAS  Google Scholar 

  41. Tan, I. L. et al. Lateral cerebellum is preferentially sensitive to high Sonic Hedgehog signaling and medulloblastoma formation. Proc. Natl Acad. Sci. USA 115, 3392–3397 (2018).

    Article  CAS  Google Scholar 

  42. Kim, J. A., Sekerková, G., Mugnaini, E. & Martina, M. Electrophysiological, morphological, and topological properties of two histochemically distinct subpopulations of cerebellar unipolar brush cells. Cerebellum 11, 1012–1025 (2012).

    Article  Google Scholar 

  43. Nguyen, Q. H. et al. Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity. Nat. Commun. 9, 2028 (2018).

    Article  ADS  Google Scholar 

  44. De Simoni, A. & Yu, L. M. Preparation of organotypic hippocampal slice cultures: interface method. Nat. Protocols 1, 1439–1445 (2006).

    Article  Google Scholar 

  45. Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  ADS  CAS  Google Scholar 

  46. Lun, A. T. L., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

    Article  Google Scholar 

  47. Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761 (2018).

    Article  CAS  Google Scholar 

  48. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  Google Scholar 

  49. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  Google Scholar 

  50. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  Google Scholar 

  51. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    Article  CAS  Google Scholar 

  52. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    Article  CAS  Google Scholar 

  53. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  Google Scholar 

  54. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  55. Whitfield, M. L. et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell. 6, 1977–2000 (2002).

    Article  Google Scholar 

Download references

Acknowledgements

M.D.T. is supported by the NIH (R01CA148699 and R01CA159859), The Pediatric Brain Tumor Foundation, The Terry Fox Research Institute, The Canadian Institutes of Health Research, The Cure Search Foundation, b.r.a.i.n.child, Meagan’s Walk, The SWIFTY Foundation, Genome Canada, Genome BC, Genome Quebec, the Ontario Research Fund, Worldwide Cancer Research, V-Foundation for Cancer Research and the Ontario Institute for Cancer Research through funding provided by the Government of Ontario. M.D.T. is also supported by a Canadian Cancer Society Research Institute Impact grant and by a Stand Up To Cancer (SU2C) St. Baldrick’s Pediatric Dream Team Translational Research Grant (SU2C-AACR-DT1113) and SU2C Canada Cancer Stem Cell Dream Team Research Funding (SU2C-AACR-DT-19-15) provided by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, with supplementary support from the Ontario Institute for Cancer Research through funding provided by the Government of Ontario. Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research. M.D.T. is also supported by the Garron Family Chair in Childhood Cancer Research at the Hospital for Sick Children and the University of Toronto. L.S. and I.E.-H. were supported by funding provided by the Government of Ontario. M.C.V is supported by The Canadian Institutes of Health Research Doctoral scholarship. A.L.J. was supported by NIMH-R37MH085726, NCI-CA192176 and NINDS-R01NS092096 and a National Cancer Institute Cancer Center Support Grant (P30 CA008748-48). This study was conducted with the support of the Ontario Institute for Cancer Research’s Genomics & Bioinformatics platform (https://genomics.oicr.on.ca/) through funding provided by the Government of Ontario.

Reviewer information

Nature thanks Kathleen Millen and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Authors and Affiliations

Authors

Contributions

M.C.V., I.E.-H. and L.K.D. designed, performed and analysed the majority of the experiments in this study. H.F. contributed to the pre-processing of scRNA-seq data. B.L.H. contributed to timed mating and tissue isolation in developing mouse embryos. Y.S. and L.H. contributed to the SNV and CNV analysis of the scRNA-seq data. V.R. contributed to the clinical analysis of the primary tumour samples. L.D.H contributed to the differential gene expression analysis. S.K. contributed to primary tissue isolation, preparation and statistical analysis of scRNA-seq data. V.F. contributed to literature review, marker gene discovery and cell cluster annotations. P.S., H.S., A.S.M. and F.M.G.C. contributed to the analysis of the bulk RNA-seq data and the data availability submission. J.J.Y.L., K.J., D.P., A.M., B.L. and M.A.Q. contributed to the collection and processing of primary tumour samples. K.N. optimized the 10X Genomics scRNA-seq protocols. C.D., X.W. and F.N. provided reagents, equipment and expert advice. S.C.M., L.G., S.K.S., J.A.C., M.A.M., D.M., P.D., T.P., F.N., E.M.T. and C.L.K. provided clinical samples and helped design the study. A.L.J provided expert advice on identification of developing cerebellar cell populations. N.J., L.S. and M.D.T. supervised the project and wrote the manuscript.

Corresponding authors

Correspondence to Nada Jabado, Lincoln Stein or Michael D. Taylor.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Characterization of cell types in the mouse developing cerebellum.

a, t-SNE visualization demonstrating 34 unique clusters of 62,040 single cells. b, Bar chart displaying the number of cells collected during each developmental time point (n = 9). c, Bar plot displaying the number of cells within each identified cluster belonging to specific developmental time points. d, Circles showing the normalized average expression as indicated by the scale at the bottom right of established developmental lineage marker genes (n = 24) specific to each cell cluster.

Extended Data Fig. 2 Clustering analysis of scRNA-seq data of mouse developing cerebellum from seven time points used for generating CIBERSORT expression signatures.

a, b, t-SNE visualization (using the Seurat package) of transcriptionally distinct cell populations from 44,461 single cells from seven developmental time points annotated by cluster identity (n = 31) and by time point (n = 7).

Extended Data Fig. 3 Reconstruction of cerebellar developmental lineages through pseudo-temporal ordering of cells.

ae, t-SNE visualization and two-dimensional embedding showing constructed pseudo-time trajectories of different lineages in the developing cerebellum: early germinal zones (n = 6,096 cells), GABAergic interneurons lineage (n = 13,432 cells), Purkinje cells (n = 6,048 cells), granule cells (n = 15,011 cells) and oligodendrocytes (n = 1, 433 cells). Cells within specific lineage clusters were selected, visualized using t-SNE visualization (using the Seurat package) and then ordered based on a reversed graph embedding method (Monocle 2). Heat maps demonstrate gene-normalized expression levels of cluster-specific markers, red being highest and blue being lowest. MOs, myelinating oligodendrocytes; PCs, Purkinje cells.

Extended Data Fig. 4 Diagram of developing cerebellar lineages showing relative abundance of cell type clusters across time.

a, Line plot showing the number of cells of each glutamatergic lineage cluster at each collected time point. b, Line plot showing the number of glial population clusters at each collected time point. c, Line plot showing the number of GABAergic cells at each collected time point. d, Cartoon of individual cell clusters identified through unsupervised hierarchical clustering of single-cell transcriptomes from the developing mouse cerebellum. Cell clusters were arranged in their respective developmental hierarchies based on the expression of known marker genes as well as the results of pseudo-time analyses. Cluster annotations are found on the bottom right.

Extended Data Fig. 5 Deconvolution analyses of bulk human PFA/PFB ependymoma and C-PA tumour transcriptomes.

Hierarchical clustering of patient samples of known molecular subgroups based on calculated relative abundance values of the mouse cell-type clusters in each sample, obtained from CIBERSORT. Expression signatures from 26 mouse cell clusters were selected to deconvolute bulk RNA-seq of human PFA (n = 22) and PFB (n = 21) ependymomas and C-PAs (n = 10).

Extended Data Fig. 6 Clustering analysis and t-SNE visualization of human scRNA-seq data.

ae, t-SNE visualization of scRNA-seq data used as input for the CIBERSORT deconvolution analysis of SHH MB (n = 2), group 3 MB (n = 2), group 4 MB (n = 4), PFA (n = 4) and C-PA (n = 3) patient samples. Cluster annotations were established by expression of known marker genes unique to tumour and cell type and are defined as follows: SHH-1, tumour clusters: 1, 2, 3, 4, 5; monocyte/microglia: 6. SHH-2, tumour clusters: 1, 2, 3, 4, 7; monocyte/microglia: 5, 6; T cells: 8. G3-1, tumour clusters: 1, 2, 3, 5, 6; monocyte/microglia: 4. G3-2, tumour clusters: 1 ,2, 3, 5, 6 ,7; monocyte/microglia: 4. G4-1, tumour clusters: 1, 2, 3, 4, 5, 6; microglia/monocytes: 8; T cells: 7. G4-2, tumour clusters:1, 2, 3, 4, 5, 7; microglia/monocytes: 6. G4-3, tumour clusters: 1, 2, 3, 4, 5, 6, 7, 8, 9; monocytes/microglia: 10. G4-4, tumour clusters: 1, 2. PFA-1, tumour clusters: 4, 6; monocytes/microglia: 1, 3, 5; T cells: 2; B cells: 7. PFA-2, tumour clusters: 1, 2; monocytes/microglia: 3. PFA-3, tumour clusters: 1, 4, 6, 7; microglia/monocytes: 2, 3, 5. PFA-4, tumour clusters: 1, 3, 6, 7; monocytes/microglia: 2, 4, 5; T cells: 9; pericytes: 8; endothelial cells: 10. C-PA-1, tumour cluster: 3; monocytes/microglia: 1, 2, 4, 5, 6, 7, 9, 10, 11; T cells: 8. C-PA-2, tumour clusters: 4, 5, 7; monocytes/microglia: 1, 2, 3, 6, 8, 10, 11, 12; T cells: 9. C-PA-3, tumour clusters: 2, 4, 5, 7; monocytes/microglia: 1, 3, 8; T cells: 6.

Extended Data Fig. 7 Re-clustering of the gliogenic progenitors and roof-plate-like stem cells with comparison to PFA/PFB ependymomas and C-PAs.

a, t-SNE visualization of the eight sub-clusters obtained from combined re-clustering of roof-plate-like stem cells and gliogenic progenitor clusters (n = 2,525 cells). b, Gene expression of gliogenic progenitor and roof-plate-like stem cell marker genes onto t-SNE of sub-clusters (n = 2,525 cells). c, Pseudo-time trajectory analysis of the eight sub-clusters annotated by sub-cluster (top) and developmental time point (bottom) (n = 2,525 cells). d, Deconvolution analysis heat map of tumour single-cell PFA clusters (n = 9) (top) and tumour single-cell C-PA clusters (n = 6) (bottom) against expression signatures of the 8 mouse developmental sub-clusters. eg, t-SNE visualizations of clustered populations of PFA (n = 4) and C-PA (n = 3) scRNA-seq patient samples used for CIBERSORT deconvolution analysis. t-SNE visualization of the six sub-clusters obtained from re-clustering of only the gliogenic progenitor cluster (n = 1,709 cells). h, Pseudo-time trajectory analysis of the gliogenic progenitor sub-clusters (n = 1,709 cells) annotated by sub-cluster (top) and developmental time point (bottom). i, Deconvolution analysis heat map of samples from patients with bulk PFA (n = 22), PFB (n = 21) and C-PA (n = 10) against expression signatures of the six gliogenic progenitor sub-clusters.

Extended Data Fig. 8 Re-clustering of the granule cell lineage with comparison to SHH MBs.

a, t-SNE visualization showing seven distinct sub-clusters from re-clustering of the granule cell lineage (n = 15,011 cells). b, Pseudo-time trajectory analysis of the seven granule cell sub-clusters annotated by sub-cluster (top) and developmental time point (bottom) (n = 15,011 cells). c, Deconvolution analysis heat map of bulk SHH MB (n = 60) patient sample transcriptomes against expression signatures of the seven granule cell sub-clusters. d, Deconvolution analysis heat map of SHH MB scRNA-seq tumour-specific clusters (n = 10) against signatures of the seven granule cell sub-clusters. e, t-SNE plot of clustered populations of SHH MB scRNA-seq samples (n = 2). f, Comparison of clinical characteristics based on clustering by similarity to different points in GCP lineage of SHH-1 (n = 15) and SHH-2 (n = 45), comparing age at diagnosis. Box-plot centre lines show data medians; box limits indicate 25th and 75th percentiles; lower and upper whiskers extend to 1.5 times the interquartile range (IQR) from the 25th and 75th percentiles, respectively; outliers are represented by individual points; P value (P = 0.07) was determined by Wilcoxon test. g, Survival curve, corrected for metastatic dissemination and molecular subtype, of SHH-1 (n = 15) and SHH-2 (n = 45) identified through matching to a re-clustered granule cell lineage. P value (P = 0.00442) was determined by log-rank test and ‘+’ indicates censored cases. hk, Comparison of additional clinical characteristics including histology, sex, molecular subtype affiliation and metastatic status of SHH-1 (n = 15) and SHH-2 (n = 45) patient samples. P values were determined using Fisher’s exact test.

Extended Data Fig. 9 Re-clustering of the UBC lineage with comparison to group 4 MBs.

a, t-SNE visualization of 6 distinct sub-clusters obtained from re-clustering of the UBC lineage (n = 9,605 cells). b, Gene expression of UBC lineage marker genes onto t-SNE of sub-clusters (n = 9,605 cells). c, Pseudo-time trajectory analysis of the six sub-clusters, showing clear branching of the GCP and UBC lineage annotated by sub-clusters (top) and developmental time point (bottom) (n = 9,605 cells). d, t-SNE visualization of the scRNA-seq clustered populations of group 4 MB human tumour samples (n = 4). e, t-SNE visualization of scRNA-seq clustering analysis of four group 4 MB patient sample tumours coloured by transcriptional match to both UBC and GCP gene expression signatures (9,895 cells positive out of n = 12,129 cells). f, Pie charts showing the percentage of cells at various states of differentiation in three G4 tumour samples based on their matches to UBC precursors, UBCs or postnatal GCPs. g, Deconvolution analysis heat map of group 4 MB (n = 45) bulk patient sample transcriptomes against expression signatures of the 6 UBC sub-clusters. h, Deconvolution analysis heat map of group 4 MB scRNA-seq tumour cell clusters (n = 15) against signatures of the 6 UBC sub-clusters. ik, t-SNE visualization of re-clustered UBC and GCP progenitor cluster coloured by the number of cells expressing UBC transcriptional signature genes (573 cells positive out of n = 2,866 cells), the number of cells expressing GCP transcriptional signature genes (159 cells positive out of n = 4,607 cells) and the number of cells expressing both UBC and GCP gene signatures (75 cells positive out of n = 4,607 cells). l, Venn diagram showing that group 4 GCP-like clusters express 308 of 600 GCP signatures and 149 of 500 UBC signatures (n = 3,050 genes) (top) compared to group 4 UBC-like clusters which express 136 of 600 GCP signatures and 182 of 500 UBC signatures (n = 3,177 genes) (bottom). m, Comparison of clinical characteristics based on clustering by similarity to E16 and E18 time points in UBC lineage of group 4 MB labelled as group 4 (E16) (n = 17) and group 4 (E18) (n = 28), comparing age at diagnosis. Box-plot centre lines show data medians; box limits indicate 25th and 75th percentiles; lower and upper whiskers extend to 1.5 times the interquartile range (IQR) from the 25th and 75th percentiles, respectively; outliers are represented by individual points; P value (P = 0.45) was determined by Wilcoxon test. n, Survival curve, corrected for metastatic dissemination and molecular subtype, of group 4 (E16) (n = 17) and group 4 (E18) (n = 28) identified through matching to a re-clustered granule cell lineage. P value (P = 0.168) was determined by log-rank test and ‘+’ indicates censored cases. or, Comparison of additional clinical characteristics including sex, histology, metastatic status and molecular subtype affiliation of samples from patients with group 4 (E16) (n = 17) and group 4 (E18) (n = 28). P values were determined using Fisher’s exact test.

Extended Data Fig. 10 Cell cycle analysis of human scRNA-seq data.

ao, Dot plots showing the normalized ratio values of G1/S against G2/M ratios within each cell annotated by cluster identity (left) for SHH (n = 2), group 3 MB (n = 2), group 4 MB (n = 4), PFA (n = 4) and C-PA (n = 3). Re-clustering t-SNE visualization of the single-cell human tumours displaying cluster annotations (middle). Re-clustering t-SNE visualization with cell cycle phase ratios (G1/S, G2/M) projections (right).

Supplementary information

Life Sciences Reporting Summary

Supplementary Table 1

scRNA-seq sequencing metrics and CIBERSORT deconvolution validations. CellRanger count pipeline outputs were imported for each of the single-cell samples including both human tumours and mouse developing time points. Sequencing metrics contain the number of barcodes associated with cell-containing partitions, sequencing depth and median number of genes detected per cell (tab 1). Deconvolution analysis of same and cross-species of previously annotated brain cell types (above), low grade glioma (LGG) and diffuse large B-cell lymphoma (DLBCL) (below) against our 31 mouse clusters (tab 2). Table displays relative abundance values inferred from CIBERSORT of the 31 mouse cell clusters against gene signatures of previously annotated clusters. Mouse data was obtained from Saunders et al., 2018 (number of transcripts/cluster); Human data was obtained from Zhang et al., 2016 (FPKM values), 5 LGG patient samples and DLBCL collected from NCI Genomic Commons (GDC).

Supplementary Table 2

Patient information of SHH, group 3 and group 4 MBs, PFA and PFB ependymomas and C-PAs of bulk and single-cell RNA-seq data. Clinical characteristics of patient tumour samples including age at diagnosis, sex and final diagnosis.

Supplementary Table 3

Top 50 differentially expressed marker genes within each ‘cell of origin’ mouse cluster. Differential gene expression from Seurat’s likelihood ratio test (LRT) (two-sided) method was used to generate the marker genes for all of mouse developing time points (n=34 clusters) (Figure-1 and Extended Figure-1) (tab 1) and curated and filtered mouse developing time points used for the generation of CIBERSORT deconvolution signatures (n=31 clusters) (Extended Figure-2) (tab 2). The top 50 genes were selected based on the log2 fold change.

Supplementary Table 4

Top 50 differentially expressed marker genes of each cluster of the human single-cell tumours. Differential gene expression from Seurat’s likelihood ratio test (LRT) (two-sided) method was used to generate the marker genes for SHH (n=2) (tab 1-2), group 3 (n=2) (tab 3-4), group 4 (n=4) (tab 5-8) MBs, PFA (n=4) (tab 9-12) ependymomas and C-PA (n=3) (tab 13-15) patient samples. The top 50 genes were selected based on the log2 fold change.

Supplementary Table 5

scRNA-seq CIBERSORT’s input and signature matrices. Human single-cell gene input expression matrices (tab 1-15) and mouse cluster signatures (tab 16) used for CIBERSORT’s deconvolution analysis.

Supplementary Table 6

Bulk RNA-seq CIBERSORT’s input matrices. Log normalized expression matrices of genes shared between mouse ‘Cell of origin’ clusters and PFA (tab 1), C-PA (tab 2), group 3 (tab 3), group 4 (tab 4) and SHH (tab 5) bulk tumours. Top expressed genes of human bulk MBs input expression matrices used for CIBERSORT’s deconvolution analysis (tab 6).

Supplementary Table 7

Summary of Copy Number Variation (CNV) and Single Nucleotide Variation (SNV) analysis of human tumour scRNA-seq. CNV analysis was generated by HoneyBadger and inferCNVs; analysis displaying the position, the overlap, number of cells and the clusters associated with each cell (tab 1). SNV analysis was generated by MuTect and Strelka; filtered summary of human single-cell SNV analysis displaying the position, number of counts, type, gene, number of single cells and cluster association of each cell (tab 2); unfiltered SNV analysis of human single-cell analysis (tab 3-17) displaying all of the detected SNV calls.

Supplementary table 8

Additional validation of single-cell Grp4 MB matching to both UBC and GCP progenitor model. Deconvolution analysis results, through CIBERSORT, of group 4 MB tumour-specific clusters (n=15) against signatures of unipolar brush cells (UBCs) and proliferating GCP (tab 1). Differentially expressed genes in the UBC (tab 2) and GCP (tab 3) cell clusters compared to each other, obtained through a differential gene expression analysis using edgeR. List of UBC signature genes present in the group 4 UBC-like clusters (tab 4). List of GCP signature genes present in group 4 GCP-like clusters (tab 5).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vladoiu, M.C., El-Hamamy, I., Donovan, L.K. et al. Childhood cerebellar tumours mirror conserved fetal transcriptional programs. Nature 572, 67–73 (2019). https://doi.org/10.1038/s41586-019-1158-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-019-1158-7

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer