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Longitudinal scRNA-seq analysis in mouse and human informs optimization of rapid mouse astrocyte differentiation protocols

Nature Neuroscience volume 26, pages 1726–1738 (2023)Cite this article

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An Author Correction to this article was published on 23 November 2023

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Abstract

Macroglia (astrocytes and oligodendrocytes) are required for normal development and function of the central nervous system, yet many questions remain about their emergence during the development of the brain and spinal cord. Here we used single-cell/single-nucleus RNA sequencing (scRNA-seq/snRNA-seq) to analyze over 298,000 cells and nuclei during macroglia differentiation from mouse embryonic and human-induced pluripotent stem cells. We computationally identify candidate genes involved in the fate specification of glia in both species and report heterogeneous expression of astrocyte surface markers across differentiating cells. We then used our transcriptomic data to optimize a previous mouse astrocyte differentiation protocol, decreasing the overall protocol length and complexity. Finally, we used multi-omic, dual single-nuclei (sn)RNA-seq/snATAC-seq analysis to uncover potential genomic regulatory sites mediating glial differentiation. These datasets will enable future optimization of glial differentiation protocols and provide insight into human glial differentiation.

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Fig. 1: scRNA-seq of human glial differentiation uncovers lineage-specific precursors and transient states.
Fig. 2: scRNA-seq of mouse glial differentiation uncovers lineage-specific precursors and transient states.
Fig. 3: Analysis of subsets of early mouse astrocyte differentiation.
Fig. 4: Comparison of putative cell surface marker gene expression patterns in differentiated and primary cells.
Fig. 5: Multi-omic analysis of two different astrocyte differentiation time series.
Fig. 6: Data integration and functional analyses of differentiated immature astrocytes.

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Data availability

All raw sequencing data generated in this study are available at the GEO archive under accession GSE211140. Gene expression and pseudobulk data for all timepoints from both species are directly searchable via a custom web app, https://liddelowlab.shinyapps.io/frazel_astro_diff_final/. Previously published datasets are available from the NCBI GEO under accession GSE152223, and from the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under accession PRJNA637987. Source data are provided with this paper.

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Acknowledgements

The computational requirements for this work were supported in part by the NYU Langone High Performance Computing (HPC) Core’s resources and personnel. Funding for this work was provided by NIH/NEI (R01EY033353), the Cure Alzheimer’s Fund, Anderson Neurodegeneration Consortium, Anonymous Donors, the Blas Frangione Foundation, The Alzheimer’s Association (to S.A.L.), the Dark Matter Project funded by an NIH Center for Excellence in Genome Science (grants 5RM1HG009491, 3RM1HG009491-03S1 and 3RM1HG009491-03S2 to J.D.B.), National Institute of Neurological Disorders and Stroke (NINDS; grant 1R21NS111186 to V.F.) and NINDS T32 (NYU/Dasen) 5T32NS086750 (to P.W.F.). This investigation was supported (in part) by a grant from the National Multiple Sclerosis Society to S.A.L. S.A.L. also acknowledges the generous support of the Parekh Center for Interdisciplinary Neurology at NYU Grossman School of Medicine, and of P. Slavick. We thank the study participants in ROSMAP and David Bennett and Rush University for the use of ROSMAP iPSC lines. ROSMAP is supported by P30AG10161, P30AG72975, R01AG15819, R01AG17917. U01AG46152, U01AG61356. ROSMAP resources can be requested at https://www.radc.rush.edu.

Author information

Authors and Affiliations

  1. Neuroscience Institute, NYU Grossman School of Medicine, New York City, NY, USA

    Paul W. Frazel, Theodore Fisher, Anne Marchildon & Shane A. Liddelow

  2. The New York Stem Cell Foundation Research Institute, New York City, NY, USA

    David Labib, Nicolette Pirjanian & Valentina Fossati

  3. Institute for Systems Genetics, NYU Grossman School of Medicine, New York City, NY, USA

    Ran Brosh & Jef D. Boeke

  4. Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York City, NY, USA

    Jef D. Boeke

  5. Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA

    Jef D. Boeke

  6. Department of Neuroscience and Physiology, NYU Grossman School of Medicine, New York City, NY, USA

    Shane A. Liddelow

  7. Department of Ophthalmology, NYU Grossman School of Medicine, New York City, NY, USA

    Shane A. Liddelow

  8. Parekh Center for Interdisciplinary Neurology, NYU Grossman School of Medicine, New York City, NY, USA

    Shane A. Liddelow

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Contributions

P.W.F., V.F. and S.A.L. planned the experiments. P.W.F., D.L., T.F., A.M., N.P. and R.B. completed the experiments. P.W.F. and T.F. helped with data analysis. J.D.B., V.F. and S.A.L. obtained funding for this study. P.W.F. and S.A.L. wrote the manuscript. All authors contributed to discussions and critical review of the manuscript.

Corresponding authors

Correspondence to Paul W. Frazel or Shane A. Liddelow.

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Competing interests

S.A.L. is an academic founder and sits on the SAB of AstronauTx Ltd. and is a SAB member of the BioAccess Fund. J.D.B. is a founder and director of CDI Labs Inc., a founder of and consultant to Neochromosome Inc., a founder member of SAB, a consultant to ReOpen Diagnostics LLC and serves or served on the SAB of the following: Logomix, Inc., Sangamo Inc., Modern Meadow Inc., Rome Therapeutics Inc., Sample6 Inc., Tessera Therapeutics Inc. and the Wyss Institute. The remaining authors declare no competing interests. V.F. is listed as inventor on issued and pending patent claims covering glial cell generation methods.

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Extended data

Extended Data Fig. 1 Differentiation overview and scRNAseq analysis metrics.

a) Detailed human differentiation overview, with phase contrast micrographs of cells at the timepoints analyzed via scRNAseq. Representative images from 3 independent differentiations. b) Table of quality control processing statistics for the data produced in this study and for the reanalyzed data from20. c) Number of genes identified for each cell and number of unique molecular identifier data for each cell. d) Harmony integration of two independently harvested and processed samples from the same human timepoint (30day) shows no evidence of batch effect. SB = SB431542, LDN = LDN193189; SAG = smoothened agonist; RA = retinoic acid; AA = ascorbic acid.

Extended Data Fig. 2 Survey of differentiation cell type heterogeneity across 9 iPSC lines.

a) UMAP of 128,839 nuclei from 9 different iPSC lines (see Methods for line information). b) Clustering of nuclei from a based on gene expression. c) Cell type proportion for each cluster identified in b) for each of the 9 iPSC lines tested suggest broad representation of each cell type in each iPSC line. d) Dot plot for the top 2 genes enriched in astrocytes from 9 different iPSC lines; differential expression analysis performed using the Wilcoxon ranked-sum test via Seurat. e) Normalized expression of STMN2 and TTYH1 shows similar expression of both genes across all 9 cell lines. f) One well of differentiated cells from each of the 9 lines were immunostained in parallel for cell type marker genes: GFAP (astrocytes; green), MAP2 (neurons; red), and SOX10 (oligodendrocytes; yellow) plus Hoescht 33342 nuclear stain (blue). Representative image from 2 independent wells.

Extended Data Fig. 3 Cell type feature plots.

a) Feature maps for neuron cell-type marker genes (SNAP25, STMN2, SYT1). b) Feature maps for astrocyte cell-type marker genes (CLU, ID3, SPARC). c) Feature maps for oligodendrocyte cell-type marker genes (MBP, MOG, OLIG1). d) Dot plot of top enriched genes for each cluster as determined by Scanpy (clusters correspond to those numbered in Fig. 1d). e) Number of cells per cell-type at each time point.

Extended Data Fig. 4 Reclustering and analysis of transient states in human glial differentiation.

a) FLE reduction of the same data from Fig. 1, reclustered at higher resolution (1.2). Marker genes for each of the clusters of interest are listed in the table to the right (see Results for further discussion), and the clusters are labeled with arrows on the FLE plot. b) Feature maps for mitotic scores from Scanpy70 (‘Scanpy growth rates’) and from scVelo30 (‘S phase score’ and ‘G2M phase score’). c) Feature maps for genes enriched in shared neuron/astrocyte precursors (NHLH1, DLL3, EGFR). d) Feature maps for genes enriched in astrocyte/oligodendrocyte precursors (OLIG2, ASCL1).

Extended Data Fig. 5 Inferred transcription factor (TF) activity in differentiating human glia detected using BITFAM.

a) Heatmap of BITFAM detected activities for each cluster from Fig. 1c (all human data timepoints). Clusters are numbered and labeled on the x-axis, and detected TFs are on the y-axis. Pink arrowheads: TFs with gene expression plotted below, green arrowheads: TFs with gene expression plotted in earlier supplemental figures. b) gene expression plots for genes highlighted with purple arrowheads in the BITFAM heatmap from a.

Extended Data Fig. 6 Differentiation overview and scRNAseq analysis metrics.

a) Mouse differentiation overview, with comparison across protocols. Quality control and pre/post filtering numbers for each mouse timepoint from the various protocols in table. b–d) Micrographs of cells during the embryoid body (EB), adherent differentiation (AD), and astrocyte growth factor stages. e, f) GFAP staining after protocol including EB stage. As expected based on published results22, (e) minimal GFAP staining is visible following treatment with FGF and BMP4 but (f) robust staining is visible after CNTF treatment. All images are representative from 3 independent differentiations g) Harmony integration of two independently harvested and processed samples from the same mouse timepoint (2 day EB) shows no evidence of batch effect. h) Number of genes identified for each cell and i) number of unique molecular identifier data for each cell from each timepoint.

Extended Data Fig. 7 Cell type feature plots.

a) Feature maps for neuron cell-type marker genes (Dcx, Tubb3, Syt1). b) Feature maps for astrocyte cell-type marker genes (Vim, Sparcl1, Clu). c) Feature maps for putative astrocyte precursor genes (Nhlh1, Nhlh2, Hmga2). d) Scanpy growth were calculated for each cell.

Extended Data Fig. 8 Multiomics quality control and genomic accessibility analyses.

a) Violin plots of genes and counts per nucleus (top row), ATAC counts pre/post filter (middle row), and nucleosome signal and transcription start site enrichment scores (bottom row) for each timepoint. b) Table of each sample included in dataset analyzed for Fig. 5. Nuclei were filtered based on the following cutoffs (see Methods): nCount_ATAC < 120000, nCount_ATAC > 500, nCount_RNA < 50000, nCount_RNA > 500, TSS.enrichment > 1. c) Pseudotime calculated based on the stochastic model of RNA velocity. d) Confidence in RNA velocity calculated for each cell based on local coherence of velocity vectors. e) Annotated coverage plot for Fabp7 locus. See Fig. 5f legend for detailed description of plot. f, g) Transcription factor motifs detected as enriched in peaks that were differentially accessible in BMP4/FGF1 (f) or CNTF (g) conditions. h) Approach for peak deletion for multiomic validation. i) Agarose gel demonstrating successful deletion of peak #2 and replacement with single-stranded oligo-donor nucleotide (ssODN) template. j) No differences in S100a6 gene expression between the unedited (Ai17iCre) and the edited, peak #2 deleted (∆p2) mESC lines as measured by quantitative PCR. N = 6 biologically independent wells from 2 separate differentiations, two-sided t-test with no multiple comparisons, data are plotted as mean ± s.e.m.

Source data

Extended Data Fig. 9 Analysis of gene expression final timepoint from each growth factor time series.

a) (Left) Dimensional reduction of gene expression data from final timepoints. (Right) Top 10 genes positively enriched in cells belonging to one growth factor versus the other, as calculated in Scanpy. b) Louvain clustering of data from (a) identifies multiple clusters of cells based on gene expression that are either shared by both growth factor conditions (clusters 0,4), unique to BMP4/FGF1 condition (clusters 3,6), or unique to the CNTF condition (clusters 1,2). Top 10 marker genes for the above clusters are listed to the right. c–e) Validation of gene expression results with S100A6 immunostaining in cells from the final CNTF timepoint. Representative images from 3 independent differentiations.

Extended Data Fig. 10 Primary atlas integration, marker genes, and functional testing quality controls.

a) Cell class annotations were plotted from cell atlas metadata, and plotted for all 93,894 cells from the atlas. b) Cells were initially clustered at low resolution (0.1); reprint of Fig. 6c for ease of comparison. c) Cells were clustered at higher resolution (0.3). d) Top marker genes for select clusters (dashed lines) from Extended Data Fig. 10b,c that contain both primary cells and differentiated cells. Marker genes for subclusters emerging from either Cluster 3 (left) or Cluster 4 (right) as part of re-clustering at higher resolution. e, f) Pie chart of percentage of cells from the BMP or CNTF 9 day timepoints (BMP: 7445 total, CNTF: 8361 total) that belong to each cluster from (b). g) Feature plots for major marker genes in panel (d). h) Top: Still image from Supplementary Video 1 with example of scratch quantification. Bottom: Graph of scratch closure over time from one well. i) Graphical summary of findings from the human datasets (top) and mouse datasets (bottom).

Supplementary information

Supplementary Information

Supplementary Figs. 1–8.

Reporting Summary

Supplementary Video 1

Scratch repair time-lapse video.

Source data

Source Data Extended Data Fig. 8

Unprocessed genotyping gel.

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Frazel, P.W., Labib, D., Fisher, T. et al. Longitudinal scRNA-seq analysis in mouse and human informs optimization of rapid mouse astrocyte differentiation protocols. Nat Neurosci 26, 1726–1738 (2023). https://doi.org/10.1038/s41593-023-01424-2

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