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.

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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.

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).

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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.

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

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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 or Lincoln Stein or Michael D. Taylor.

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The authors declare no competing interests.

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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).

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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

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