Studying the function of common genetic variants in primary human tissues and during development is challenging. To address this, we use an efficient multiplexing strategy to differentiate 215 human induced pluripotent stem cell (iPSC) lines toward a midbrain neural fate, including dopaminergic neurons, and use single-cell RNA sequencing (scRNA-seq) to profile over 1 million cells across three differentiation time points. The proportion of neurons produced by each cell line is highly reproducible and is predictable by robust molecular markers expressed in pluripotent cells. Expression quantitative trait loci (eQTL) were characterized at different stages of neuronal development and in response to rotenone-induced oxidative stress. Of these, 1,284 eQTL colocalize with known neurological trait risk loci, and 46% are not found in the Genotype–Tissue Expression (GTEx) catalog. Our study illustrates how coupling scRNA-seq with long-term iPSC differentiation enables mechanistic studies of human trait-associated genetic variants in otherwise inaccessible cell states.
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Managed access data from scRNA-seq are accessible in the European Genome–phenome Archive (EGA, https://www.dev.ebi.ac.uk/ega/) under the study number EGAS00001002885 (dataset EGAD00001006157). Open access scRNA-seq data are available in the European Nucleotide Archive (ENA) under the study ERP121676 (https://www.ebi.ac.uk/ena/browser/view/PRJEB38269). Processed single-cell count data and eQTL and colocalization summary statistics are available on Zenodo at https://zenodo.org/record/4333872. The two iPSC single-cell datasets are available on Zenodo (https://zenodo.org/record/3625024) and GEO (GSE118723) for the datasets described in Cuomo et al.2 and Sarkar et al.38, respectively. iPSC bulk RNA-seq data from Bonder et al.37 are available on the EGA (study ID, EGAS00001000593, https://www.ebi.ac.uk/ega/studies/EGAS00001000593) and the ENA (ERP007111, https://www.ebi.ac.uk/ena/browser/view/PRJEB7388). Chip genotypes for HipSci lines are available from the EGA (EGAS00001000866) and the NCBI (PRJEB11750).
All scripts are available in the following github repository: https://github.com/single-cell-genetics/singlecell_neuroseq_paper/. The standalone predictor for neuronal differentiation capacity is available at https://github.com/single-cell-genetics/singlecell_neuroseq_paper/tree/master/differentiation_prediction_model/. The eQTL mapping pipeline is available at https://github.com/single-cell-genetics/limix_qtl/.
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All data for this study were generated under the Open Targets project OTAR039. J.J. was supported by a postdoctoral fellowship from Open Targets, A.S.E.C. was supported by a PhD fellowship from the EMBL International PhD Programme, and D.D.S. was supported by a postdoctoral fellowship from the EMBL Interdisciplinary Postdoctoral Programme. M.A.L. was funded by the Medical Research Council (MC_UP_1201/9). N.K. and D.J.G. were funded by the Wellcome Trust grant WT206194. F.T.M. is a New York Stem Cell Foundation, Robertson Investigator and is supported by the New York Stem Cell Foundation (NYSCF-R-156), the Wellcome Trust and Royal Society (211221/Z/18/Z) and the Chan Zuckerberg Initiative (191942) and by the NIHR Cambridge BRC. J.C.M. acknowledges core support from EMBL and Cancer Research UK (C9545/A29580). O.S. is supported by core funding from EMBL and the DKFZ, as well as the BMBF, the Volkswagen Foundation and the EU (810296). We thank the MRC Metabolic Diseases Unit Imaging Core Facility for assistance with imaging. We thank the staff at the Cellular Generation and Phenotyping and Sequencing core facilities at the Wellcome Sanger Institute and the imaging core facility of the Wellcome–MRC Institute of Metabolic Science. We thank H. Kilpinen and P. Puigdevall Costa for useful discussions regarding data analysis.
D.J.G. and E.M. were employees of Genomics PLC, and D.D.S. was an employee of GSK at the time the manuscript was submitted.
Peer review information Nature Genetics thanks Kristen Brennand 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.
Cells at each time point pooled across lines were clustered using Louvain clustering, after normalization and batch correction using Harmony (Methods). Subsequently, clusters were annotated to cell types using known marker genes; when two clusters showed the same gene set enrichment they were assigned to the same cell type identity (Methods). a, UMAPs of cells sampled at each time point and coloured by cluster identity. b, Same UMAPs as in a, with cells this time coloured by cell type annotation. c, Heatmap showing average expression profiles of canonical marker genes across the identified cell types (as in b, excluded dopaminergic neuronal markers; expression scaled between 0 and 1 for each gene). d,e, Heatmaps similar to c, showing average expression profile of marker genes across the identified cell types for d) dopaminergic neurons using literature-curated markers e) cortical hem/Cajal retzius cells using markers (Methods). Legend: Astro: Astrocyte-like, DA: Dopaminergic neurons, Epen1/2: Ependymal-like 1/2, FPP: Floor Plate Progenitors, NB: Neuroblasts, P_FPP: Proliferating Floor Plate Progenitors, P_Sert: Proliferating serotonergic-like neurons, Sert: Serotonergic-like neurons, U_Neur: Unknown Neurons.
Extended Data Fig. 2 Cell type proportions across time points and definition of neuronal differentiation efficiency at day 52.
a, Heatmap of the cell proportion matrix. The colour code in the first bar indicates the assignment of lines to pools. Rows (that is cell line, pool combinations) were hierarchically clustered according to their Euclidean distance (as in Fig. 2b). Cell proportions were estimated for each cell type and time point for all combinations of cell lines, considering 10 pools with at least 10 cells at all time points (138 lines). b, Proportion of variance explained by each principal component calculated from the cell proportions matrix from a. c, Comparison of the first principal component (PC1) with the sum of fractions of dopaminergic and serotonergic-like neurons present on day 52. d, UMAP of the scRNA-seq profiles used for the cell proportions matrix from a, with cells coloured by the loading of PC1 (left) and PC2 (right) of the cell proportion matrix. Legend: Astro: Astrocyte-like, DA: Dopaminergic neurons, Epen1: Ependymal-like 1, FPP: Floor Plate Progenitors, NB: Neuroblasts, P_FPP: Proliferating Floor Plate Progenitors, Sert: Serotonergic-like neurons, U_Neur1: Unknown Neurons 1.
a, Histogram of neuronal differentiation efficiencies across cell lines. The dashed line denotes the threshold to define differentiation success or failure (that is efficiency=0.2). b, Effect of pooling on neuronal differentiation efficiency. Shown is a scatterplot of neuronal differentiation efficiency, estimated from independent single-line differentiations (x-axis) vs differentiation efficiency defined from pooled data from the corresponding lines (y-axis). Bars connecting cyan and blue points indicate differentiation efficiencies for replicates of the same cell line in different pools (cyan points), and the average of those replicates (blue points). For cell lines differentiated in multiple pools, average differentiation efficiencies are shown by blue points. The Pearson R and p-value were computed from the average values (blue points) only. c, Precision-recall curve for a logistic regression model trained to predict differentiation failure from iPSC gene expression data (Methods). Shown is precision versus recall, as assessed using leave-one-out cross validation. d, Area under the precision-recall curve (AUPR) for models as presented in c, when considering alternative threshold values to define differentiation failure. e, Histogram of the predicted differentiation based on iPSC gene expression for 812 HipSci cell lines. The threshold used to define potent differentiators corresponds to 35% recall, 100% precision, when using 0.2 as threshold (as in a, b). f, Cross validation of differentiation outcome prediction. The dataset was split in half (pools 1–8, 9–17) to define independent training and test fractions. All processing steps (merging, clustering, batch correction, etc.) were performed separately for these two fractions, following identical steps and parameter settings to those used for the main analysis. We trained a predictive model using data from pools 1–8, and assessed its performance on pools 9–17. Only cell lines not contained in pools 1–8 were considered for performance assessment.
Extended Data Fig. 4 Predicted neuronal differentiation capacity across replicate iPSC lines derived from the same donor.
a,b, Variance component analysis of neuronal differentiation efficiency. a, Variance component breakdown of a model that explains neuronal differentiation efficiency as a function of cell line, pool, sex, age and noise (n = 230; fitted using lme4). b, In order to assess the effect of XCI status, we fit an analogous variance component model as in a, however considering only female lines (n = 115), and explaining neuronal differentiation efficiency as a function of cell line, pool, XCI status, age, and noise. c, Histogram of predicted neuronal differentiation efficiency based on iPSC gene expression for 812 HipSci cell lines. The vertical line indicates the prediction threshold that corresponds to 35% recall, 100% precision (c.f. Extended Data Fig. 3). d, Scatter plot of predicted neuronal differentiation efficiency for two replicate lines from the same donor. Shown are data from n = 271 donors contained in HipSci with RNA-seq data from two independent reprogramming events. Replicate 1 is chosen as the line with the lower predicted score. Colours indicate three categories of donors, according to the concordance of predicted neuronal differentiation capacity: both lines predicted to fail (blue, n = 13), both lines predicted to be potent differentiators (green, n = 209), discordant predictions, with one potent and one failing differentiator (yellow, n = 49). To assess whether this was significantly different from what we would expect for any two lines taken by chance, we performed a chi square test comparing the expected frequencies for any two given lines (based on the overall results) and the observed frequencies for pairs of lines from the same donor, obtaining a non-significant result (p = 0.1991). e, Bulk RNA-seq expression of UTF1 and TAC3 for the two replicate lines for the same donor, stratified by the categorisation as in d. In the box plots, the middle line is the median and the lower and upper edges of the box denote the first and third quartiles.
Extended Data Fig. 5 Analysis of iPSC scRNA-seq data reveals a subpopulation characterised by expression of predictive marker genes associated with lower differentiation efficiency.
a, UMAP overview of the dataset. iPSC scRNA-seq data from (Cuomo et al. 2020) were analysed following the analogous batch adjustment and clustering steps as applied to the neuronal differentiation data, identifying 5 clusters. b, UMAP as in a, coloured by the squared correlation coefficient R2 between correlation of bulk expression and differentiation efficiency (R values are indicated, as in Fig. 3b) and log fold change between one cluster and all others. c, Violin plots of gene expression for selected pluripotency genes (NANOG, SOX2, POU5F1) as well as marker genes that are upregulated and downregulated respectively in cluster 2 (UTF1, TAC3, from Fig. 3). d, Scatter plot of the proportion of cluster 2 cells between replicate experiments (based on n = 23 lines differentiated in two separate pools in Cuomo et al. paper 2020). LOESS curve and 95% confidence interval are included. e, Scatter plot between neuronal differentiation efficiency (x-axis) and the proportion of cells assigned to cluster 2 (y-axis) analogous to Fig. 3f, however using computational estimates of the proportions of cluster 2 cells based for a larger set of HipSci lines (using Decon-cell, based on bulk RNA-seq, n = 182; Methods).
a, Distribution of the number of cells per cell line with scRNA-seq available for eQTL mapping for each context (cell type-condition). Dots correspond to individual cell lines (number of cell line per context ranging between 104 and 173). b, Number of genes with at least one eQTL (that is eGenes) for each context (cell type-condition) detected using either a traditional linear model (coral) or using a linear mixed model that accounts for heterostochastic noise due to variation in the number of cells assayed for each line (seagreen; Methods). c, Sharing of eQTL signal between 14 eQTL maps across all contexts (cell type-conditions), as estimated using MASHR (Methods). d, Distribution of the number of contexts (cell type-conditions) in which a given eQTL is identified (from 1 to 14, lfsr < 0.05, quantified using MASHR). Legend: Astro: Astrocytes-like; DA: Dopaminergic neurons, Epen1: Ependymal-like1, FPP: Floor Plate Progenitors, P_FPP: Proliferating Floor Plate Progenitors, Sert: Serotonergic-like neurons.
a, Scatter plot of the first two principal components of the kinship matrix, revealing no evidence for pronounced population structure or relatedness between lines. b, Genomic location of eQTL lead variants relative to normalized gene coordinates, considering 1,024 eQTL identified in DA day 52 untreated cells (using Model 0, see below). c, Scatter plot of effect size estimates (left) and negative log p-values (right) for eQTL lead variants (FDR < 5%), comparing the model considered in this study (Model 0, x-axis) versus 4 alternative eQTL models (Model 1–4, y-axis). Inlined is Pearson’ R. Shown are results obtained on day 52 untreated DA cells, comparing the following models: Model 0: y = PC1:15 + SNP + 1/n + noise (1,024 eGenes), Model 1: y = pool + sex + SNP + 1/n + noise (1 pool per line selected; (608 eGenes, 574 of which also in Model 0), Model 2: y = pool + sex + SNP + K + noise (320 eGenes, 312 of which shared with Model 0), Model 3: y = PCs + K + noise (471 eGenes, 457 of which shared with Model 0), Model 4: y = pool + SNP + K + 1/n + noise (856 eGenes, 734 of which shared with Model 0).
a, Fraction of GTEx brain eGenes that could be assessed in each of the considered contexts (cell type-conditions; Methods). b, Fraction of GTEx brain eQTL that were replicated in this study (nominal p < 0.5; fraction relative to the set of assessed genes from a). c, Figure analogous to main text Fig. 4c, additionally including eQTL counts from a pseudobulk eQTL analysis (top red dot on the left, red square on the right; calculated using cells from all day 52 cells untreated pooled). d, Figure analogous to main text Fig. 5a, additionally including colocalisation results from a pseudobulk eQTL analysis (using cells from all day 52 cells untreated pooled). In the box plots, the middle line is the median and the lower and upper edges of the box denote the first and third quartiles, while the violin plots show the distribution. Legend: Astro: Astrocytes-like; DA: Dopaminergic neurons, Epen1: Ependymal-like1, FPP: Floor Plate Progenitors, P_FPP: Proliferating Floor Plate Progenitors, Sert: Serotonergic-like neurons.
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Jerber, J., Seaton, D.D., Cuomo, A.S.E. et al. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat Genet 53, 304–312 (2021). https://doi.org/10.1038/s41588-021-00801-6
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