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Spatial and cell type transcriptional landscape of human cerebellar development

Nature Neuroscience volume 24, pages 1163–1175 (2021)Cite this article

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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.

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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.

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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

  1. Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA, USA

    Kimberly A. Aldinger, Zachary Thomson, Parthiv Haldipur, Dan Doherty, William B. Dobyns, Ian A. Glass & Kathleen J. Millen

  2. Brotman Baty Institute for Precision Medicine, Seattle, WA, USA

    Kimberly A. Aldinger, Dan Doherty & Ian A. Glass

  3. Department of Pediatrics, University of Washington, Seattle, WA, USA

    Ian G. Phelps, Mei Deng, Diana O’Day, Dan Doherty, William B. Dobyns, Ian A. Glass & Kathleen J. Millen

  4. Center for Developmental Biology and Regenerative Medicine, Seattle Children’s Research Institute, Seattle, WA, USA

    Andrew E. Timms

  5. Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA

    Matthew Hirano, Alexander B. Rosenberg & Georg Seelig

  6. Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA

    Gabriel Santpere, Belen Lorente-Galdos, Forrest O. Gulden & Nenad Sestan

  7. Neurogenomics Group, Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain

    Gabriel Santpere

  8. Department of Bioengineering, University of Washington, Seattle, WA, USA

    Charles Roco

  9. Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK

    Lynne M. Overman & Steven N. Lisgo

  10. University College London Great Ormond Street Institute of Child Health, London, UK

    Paula Alexandre

  11. Department of Neurology, University of Washington, Seattle, WA, USA

    William B. Dobyns

  12. School of Computer Science and Engineering, University of Washington, Seattle, WA, USA

    Georg Seelig

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  1. Kimberly A. Aldinger
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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.

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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.

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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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Supplementary Figs. 1–4.

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Supplementary Tables 1–12.

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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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  • DOI: https://doi.org/10.1038/s41593-021-00872-y

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