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Spatial and cell type transcriptional landscape of human cerebellar development

Abstract

The human neonatal cerebellum is one-fourth of its adult size yet contains the blueprint required to integrate environmental cues with developing motor, cognitive and emotional skills into adulthood. Although mature cerebellar neuroanatomy is well studied, understanding of its developmental origins is limited. In this study, we systematically mapped the molecular, cellular and spatial composition of human fetal cerebellum by combining laser capture microscopy and SPLiT-seq single-nucleus transcriptomics. We profiled functionally distinct regions and gene expression dynamics within cell types and across development. The resulting cell atlas demonstrates that the molecular organization of the cerebellar anlage recapitulates cytoarchitecturally distinct regions and developmentally transient cell types that are distinct from the mouse cerebellum. By mapping genes dominant for pediatric and adult neurological disorders onto our dataset, we identify relevant cell types underlying disease mechanisms. These data provide a resource for probing the cellular basis of human cerebellar development and disease.

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Fig. 1: Overview of prenatal cerebellar development and the data generated in this study.
Fig. 2: Spatial transcriptional analysis of the developing human cerebellum.
Fig. 3: Identifying the major cell types of the developing human cerebellum.
Fig. 4: Analysis of RL compartments at single-cell resolution.
Fig. 5: Characterization of the RL trajectory.
Fig. 6: Purkinje cells.
Fig. 7: Human–mouse cross-species analysis.
Fig. 8: Cerebellar cell type enrichment in pediatric and adult diseases.

Data availability

Processed data are available through the Human Cell Atlas (https://www.covid19cellatlas.org/aldinger20), the UCSC Cell Browser (https://cbl-dev.cells.ucsc.edu) and upon request. Sequence data were deposited into the Database of Genotypes and Phenotypes, under accession number phs001908.v2.p1, and are available upon request.

Code availability

No custom code was used in this study. Open-source algorithms were used as detailed in analysis methods. Details on how these algorithms were used are available from the corresponding author upon request.

References

  1. Sathyanesan, A. et al. Emerging connections between cerebellar development, behaviour and complex brain disorders. Nat. Rev. Neurosci. 20, 298–313 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Schmahmann, J. D. The cerebellum and cognition. Neurosci. Lett. 688, 62–75 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Leto, K. et al. Consensus Paper: Cerebellar Development. Cerebellum 15, 789–828 (2016).

    Article  PubMed  Google Scholar 

  4. Rakic, P. & Sidman, R. L. Histogenesis of cortical layers in human cerebellum, particularly the lamina dissecans. J. Comp. Neurol. 139, 473–500 (1970).

    Article  CAS  PubMed  Google Scholar 

  5. Aldinger, K. A. & Doherty, D. The genetics of cerebellar malformations. Semin. Fetal Neonatal Med. 21, 321–332 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hoxha, E. et al. The emerging role of altered cerebellar synaptic processing in Alzheimer’s disease. Front. Aging Neurosci. 10, 396 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Klockgether, T., Mariotti, C. & Paulson, H. L. Spinocerebellar ataxia. Nat. Rev. Dis. Prim. 5, 24 (2019).

    Article  PubMed  Google Scholar 

  8. Corrales, J. D., Rocco, G. L., Blaess, S., Guo, Q. & Joyner, A. L. Spatial pattern of sonic hedgehog signaling through Gli genes during cerebellum development. Development 131, 5581–5590 (2004).

    Article  CAS  PubMed  Google Scholar 

  9. Dahmane, N. & Ruiz i Altaba, A. Sonic hedgehog regulates the growth and patterning of the cerebellum. Development 126, 3089–3100 (1999).

    Article  PubMed  Google Scholar 

  10. Haldipur, P. et al. Spatiotemporal expansion of primary progenitor zones in the developing human cerebellum. Science 366, 454–460 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Holgado, B. L., Guerreiro Stucklin, A., Garzia, L., Daniels, C. & Taylor, M. D. Tailoring medulloblastoma treatment through genomics: making a change, one subgroup at a time. Annu. Rev. Genomics Hum. Genet. 18, 143–166 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Volpe, J. J. Cerebellum of the premature infant: rapidly developing, vulnerable, clinically important. J. Child Neurol. 24, 1085–1104 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Johnson, M. B. et al. Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron 62, 494–509 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mu, Q., Chen, Y. & Wang, J. Deciphering brain complexity using single-cell sequencing. Genomics Proteomics Bioinformatics 17, 344–366 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005).

    Article  PubMed  Google Scholar 

  20. Lange, W. Cell number and cell density in the cerebellar cortex of man and some other mammals. Cell Tissue Res. 157, 115–124 (1975).

    Article  CAS  PubMed  Google Scholar 

  21. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Aldinger, K. A. et al. Redefining the etiologic landscape of cerebellar malformations. Am. J. Hum. Genet. 105, 606–615 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 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  PubMed  PubMed Central  Google Scholar 

  24. Machold, R. & Fishell, G. Math1 is expressed in temporally discrete pools of cerebellar rhombic-lip neural progenitors. Neuron 48, 17–24 (2005).

    Article  CAS  PubMed  Google Scholar 

  25. Wang, V. Y., Rose, M. F. & Zoghbi, H. Y. Math1 expression redefines the rhombic lip derivatives and reveals novel lineages within the brainstem and cerebellum. Neuron 48, 31–43 (2005).

    Article  CAS  PubMed  Google Scholar 

  26. Englund, C. et al. Unipolar brush cells of the cerebellum are produced in the rhombic lip and migrate through developing white matter. J. Neurosci. 26, 9184–9195 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Fink, A. J. et al. Development of the deep cerebellar nuclei: transcription factors and cell migration from the rhombic lip. J. Neurosci. 26, 3066–3076 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zecevic, N. & Rakic, P. Differentiation of Purkinje cells and their relationship to other components of developing cerebellar cortex in man. J. Comp. Neurol. 167, 27–47 (1976).

    Article  CAS  PubMed  Google Scholar 

  30. Dastjerdi, F. V., Consalez, G. G. & Hawkes, R. Pattern formation during development of the embryonic cerebellum. Front. Neuroanat. 6, 10 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Emmert-Buck, M. R. et al. Laser capture microdissection. Science 274, 998–1001 (1996).

    Article  CAS  PubMed  Google Scholar 

  32. Espina, V. et al. Laser-capture microdissection. Nat. Protoc. 1, 586–603 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Liu, J. et al. Jointly defining cell types from multiple single-cell datasets using LIGER. Nat. Protoc. 15, 3632–3662 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Vladoiu, M. C. et al. Childhood cerebellar tumours mirror conserved fetal transcriptional programs. Nature 572, 67–73 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Van De Weghe, J. C. et al. Mutations in ARMC9, which encodes a basal body protein, cause Joubert syndrome in humans and ciliopathy phenotypes in zebrafish. Am. J. Hum. Genet. 101, 23–36 (2017).

    Article  CAS  Google Scholar 

  38. Feliciano, P. et al. Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes. NPJ Genom. Med. 4, 19 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. RK, C. Y. et al. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat. Neurosci. 20, 602–611 (2017).

    Article  CAS  Google Scholar 

  40. Ruzzo, E. K. et al. Inherited and de novo genetic risk for autism impacts shared networks. Cell 178, 850–866 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Willsey, A. J. et al. The Psychiatric Cell Map Initiative: a convergent systems biological approach to illuminating key molecular pathways in neuropsychiatric disorders. Cell 174, 505–520 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Yuen, R. K. et al. Whole-genome sequencing of quartet families with autism spectrum disorder. Nat. Med. 21, 185–191 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. De Strooper, B. & Karran, E. The cellular phase of Alzheimer’s disease. Cell 164, 603–615 (2016).

    Article  PubMed  CAS  Google Scholar 

  44. Bis, J. C. et al. Whole exome sequencing study identifies novel rare and common Alzheimer’s-Associated variants involved in immune response and transcriptional regulation. Mol. Psychiatry 25, 1859–1875 (2018).

  45. Wizeman, J. W., Guo, Q., Wilion, E. M. & Li, J. Y. Specification of diverse cell types during early neurogenesis of the mouse cerebellum. eLife 8, e42388 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Hovestadt, V. et al. Resolving medulloblastoma cellular architecture by single-cell genomics. Nature 572, 74–79 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Carter, R. A. et al. A single-cell transcriptional atlas of the developing murine cerebellum. Curr. Biol. 28, 2910–2920 (2018).

    Article  CAS  PubMed  Google Scholar 

  48. 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  PubMed  Google Scholar 

  49. Nakatani, T., Minaki, Y., Kumai, M., Nitta, C. & Ono, Y. The c-Ski family member and transcriptional regulator Corl2/Skor2 promotes early differentiation of cerebellar Purkinje cells. Dev. Biol. 388, 68–80 (2014).

    Article  CAS  PubMed  Google Scholar 

  50. Haldipur, P. et al. Preterm delivery disrupts the developmental program of the cerebellum. PLoS ONE 6, e23449 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Gerrelli, D., Lisgo, S., Copp, A. J. & Lindsay, S. Enabling research with human embryonic and fetal tissue resources. Development 142, 3073–3076 (2015).

    Article  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  53. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  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  PubMed  PubMed Central  CAS  Google Scholar 

  55. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    Article  CAS  PubMed  Google Scholar 

  56. DeLuca, D. S. et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530–1532 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Mirzaa, G. M. et al. De novo and inherited variants in ZNF292 underlie a neurodevelopmental disorder with features of autism spectrum disorder. Genet. Med. 22, 538–546 (2020).

    Article  CAS  PubMed  Google Scholar 

  60. Epting, D. et al. Loss of CBY1 results in a ciliopathy characterized by features of Joubert syndrome. Hum. Mutat. 41, 2179–2194 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Latour, B. L. et al. Dysfunction of the ciliary ARMC9/TOGARAM1 protein module causes Joubert syndrome. J. Clin. Invest. 130, 4423–4439 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Luo, M. et al. Disrupted intraflagellar transport due to IFT74 variants causes Joubert syndrome. Genet. Med. https://doi.org/10.1038/s41436-021-01106-z (2021).

  63. Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Deciphering Developmental Disorders Study. Prevalence and architecture of de novo mutations in developmental disorders. Nature 542, 433–438 (2017).

  65. Rauch, A. et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380, 1674–1682 (2012).

    Article  CAS  PubMed  Google Scholar 

  66. Irizarry, R. A., Wang, C., Zhou, Y. & Speed, T. P. Gene set enrichment analysis made simple. Stat. Methods Med. Res. 18, 565–575 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was funded by the National Institutes of Health under National Institute of Neurological Disorders and Stroke, National Institute of Child Health and Human Development and National Institute of Mental Health grant numbers NS095733 to K.J.M., HD000836 to I.A.G., NS050375 to W.B.D. and MH110926, MH116488 and MH106934 to N.S. The project that gave rise to these results received the support of a fellowship from ‘la Caixa’ Foundation (ID 100010434) to G. Santpere. The fellowship code is LCF/BQ/PI19/11690010. K.A.A. received a Parental Leave Grant from Life Science Editors and would like to thank C. Lilliehook for editorial assistance. This publication is part of the Human Cell Atlas (www.humancellatlas.org/publications).

Author information

Authors and Affiliations

Authors

Contributions

K.A.A. conceived the project, designed experiments, analyzed data and wrote the manuscript. Z.T. performed experiments, analyzed data and contributed to manuscript preparation. I.G.P. analyzed data and contributed to manuscript preparation. P.H. performed experiments and contributed to data interpretation and manuscript preparation. M.D., M.H. and L.M.O. performed experiments. M.H., C.R., A.B.R. and G. Seelig provided SPLiT-seq expertise and experimental support. I.G.P., A.E.T., G. Santpere and B.L.G. analyzed data. F.O.G., D.O. and P.A. provided experimental and/or analysis support. S.N.L., N.S., W.B.D., D.D. and I.A.G. supervised experiments and/or data analysis. K.J.M. provided general oversight and contributed to data interpretation and manuscript preparation.

Corresponding authors

Correspondence to Kimberly A. Aldinger or Kathleen J. Millen.

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Competing interests

C.R., A.B.R. and G. Seelig are shareholders of Parse Biosciences. The remaining authors declare no competing financial interests.

Additional information

Peer review information Nature Neuroscience thanks Mary Hatten, Fenna Krienen, and the other, anonymous, reviewer(s) 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.

Extended data

Extended Data Fig. 1 Quality control related analyses of LCM RNA-seq data.

a, Example of cerebellum section stained with cresyl violet (purple) and anti-calbindin antibody (brown). Section before and after LCM and images of Purkinje cell (PC) and external granule cell layer (EGL) tissue captured into collection tubes are shown. Example shown is representative of 11 specimens. Scale bars: 200 um (white), 400 um (black). b, Boxplots of gene expression for established markers showing highest expression in the expected samples (box: 25-75th percentiles, whiskers: 10-90th percentiles, horizontal line in box: median). Dots indicate outliers. RNA-seq sample numbers per region: n = 13 for bulk; 17 for EGL; 18 for PCL; 9 for RL. c, Expression of the female-specific non-coding RNA XIST and the chromosome Y specific gene DDX3Y show correct sex assignment for female (pink) and male (blue) samples. RNA-seq sample numbers: n = 13 for bulk; 17 for EGL; 18 for PCL; 9 for RL. RNA-seq sample numbers per sex: n = 44 female; 13 male.

Extended Data Fig. 2 Co-expression modules in the developing human cerebellum.

Weighted gene co-expression network analysis (WGCNA) dendrogram identified 21 modules comprised of 6,336 expressed genes (row 1). M0 (grey) comprised of nonclustered genes was not analyzed further. Rows 2-4 show differential expression relationships between module genes and LCM-enriched region compared to bulk expression. EGL, external granule cell layer; PCL, Purkinje cell layer; RL, rhombic lip.

Extended Data Fig. 3 Co-expression modules in the developing human cerebellum by region.

Boxplots of gene expression per WGCNA module for bulk and spatial regions (box: 25-75th percentiles, whiskers: 10-90th percentiles, horizontal line in box: median). Number of genes per module: n = 48 for M1; 81 for M2; 88 for M3; 40 for M4; 149 for M5; 79 for M6; 253 for M7; 283 for M8; 288 for M9; 102 for M10; 121 for M11; 87 for M12; 401 for M13; 136 for M14; 139 for M15; 317 for M16; 367 for M17; 182 for M18; 395 for M19; 327 for M20. EGL, external granule cell layer; PCL, Purkinje cell layer; RPKM, reads per kilobase of transcript per million mapped reads; RL, rhombic lip.

Extended Data Fig. 4 Co-expression modules in the developing human cerebellum by age.

LOESS expression values across development are shown with 95% CIs per module. Spatial regions are distinguished by colors: bulk (salmon); EGL (green); PCL (turquoise); RL (purple). EGL, external granule cell layer; PCW, postconceptional week; PCL, Purkinje cell layer; RPKM, reads per kilobase of transcript per million mapped reads; RL, rhombic lip.

Extended Data Fig. 5 Quality control related analyses of snRNA-seq data.

a, UMAP visualization of 69,174 human cerebellar nuclei colored by dataset (n = 1,076 for 01k; 3,530 for 05k; 4,960 for 10k; 59,608 for 80k). Rhombic lip (RL) is circled. UMAP visualization of 1,018 RL nuclei colored by dataset at right (nuclei numbers: n = 41 for 01k; 88 for 05k; 67 for 10k; 822 for 80k). b, The same UMAP as in a with nuclei colored by type (n = 4,462 cells; 64,712 nuclei). c, The same UMAP as in a and b showing nuclei from each dataset. Nuclei are colored by cell type. d, The same UMAP as in a-c showing nuclei sampled from same age biological and technical replicates (n = 11,213 for 14 PCW; 8,453 nuclei for 13334; 2,098 cells for 27588 Exp1; 662 cells for 27588 Exp2; n = 15,556 for 17 PCW; 524 cells for 13377; 8,540 nuclei for 14104; 3,364 nuclei for 14104 h; 3,128 nuclei for 14104 v). e, Stacked bar chart shows the percentage of age sampled in each of the 21 cell types. Bar colors represent age sampled in postconceptional weeks (9-20 PCW). f, Expression of the female-specific non-coding RNA XIST and the chromosome Y specific gene DDX3Y show correct sex assignment for female (salmon) and male (turquoise) samples (n = 14 female; 12 male).

Extended Data Fig. 6 Cell-type-specific marker genes.

Dot plot showing expression of the top 5 most differentially expressed genes for each of the 21 cell types identified in early and mid-gestation fetal cerebellum. The size of the dot represents the percentage of cells within a cell type in which that gene was detected and its color represents the average expression level. Statistics are presented in Supplementary Table 9.

Extended Data Fig. 7 Distribution of major cell types.

a-c, Stacked bar charts show the percentage of the four major cell types from each dataset (a), developmental age (b), and specimen (c). Dataset 01k and 05k from experiment (Exp) 1 represent deep and shallow sequencing runs, respectively, from the same 6 samples (one per age). Dataset 10k from Exp 2 represents 11 samples (7 for a single age and 4 for 17 PCW), including 5 replicates from Exp 1. Dataset 80k from Exp 3 represents 9 samples (6 for a single age and 3 for 17 PCW), including 6 replicates from Exp 2. Sample and experiment characteristics are presented in Supplementary Tables 2 and 7.

Extended Data Fig. 8 Co-expression of marker genes in eCN/UBC.

a, The same UMAP visualization of cell types that originate from the RL as in Fig. 5a with nuclei colored by expression level for LMX1A (red), EOMES (green), and co-expression (yellow). b, The same UMAP visualization the eCN/UBC subcluster as in Fig. 5e with nuclei colored by expression level for LMX1A (red), EOMES (green), and co-expression (yellow).

Extended Data Fig. 9 Cell type heterogeneity in LCM-isolated regions of the cerebellum.

Box plots (box: 25-75th percentiles, whiskers: 10-90th percentiles, horizontal line in box: median) with data points (dots) showing the proportion of each of the 21 cell types from the Developmental Cell Atlas of the Human Cerebellum represented in the LCM RNA-seq data, grouped by LCM-isolated region. RL, rhombic lip; EGL, external granule cell layer; PCL, Purkinje cell layer.

Extended Data Fig. 10 Cerebellar cell type enrichment in Joubert syndrome and Alzheimer’s disease.

Heatmaps of mean expression per fetal cerebellar cell type for genes associated with Joubert syndrome (a) or Alzheimer’s disease (b). Color scheme is based on Z-score distribution. In the heatmaps, each row represents one gene and each column represents a single cell type. Horizontal white lines indicate branch divisions in the clustering dendrograms (not shown). The full list of genes is provided in Supplementary Table 11. Enrichment P values (-Log10 P value) for each cell type are shown in the bottom bar plots. Significance determined by one-sample Z-test, two-tailed P value. The dashed line is the Bonferroni significance threshold (P < 0.05); no gene enrichment was detected among the 21 cerebellar cell types.

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Aldinger, K.A., Thomson, Z., Phelps, I.G. et al. Spatial and cell type transcriptional landscape of human cerebellar development. Nat Neurosci 24, 1163–1175 (2021). https://doi.org/10.1038/s41593-021-00872-y

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