Early developmental origins of cortical disorders modeled in human neural stem cells

Abstract

The implications of the early phases of human telencephalic development, involving neural stem cells (NSCs), in the etiology of cortical disorders remain elusive. Here, we explore the expression dynamics of cortical and neuropsychiatric disorder-associated genes in datasets generated from human NSCs across telencephalic fate transitions in vitro and in vivo. We identify risk genes expressed in brain organizers and sequential gene regulatory networks throughout corticogenesis, revealing disease-specific critical phases when NSCs may be more vulnerable to gene dysfunction and converging signaling across multiple diseases. Further, we simulate the impact of risk transcription factor (TF) depletions on neural cell trajectories traversing human corticogenesis and observe a spatiotemporal-dependent effect for each perturbation. Finally, single-cell transcriptomics of autism-affected patient-derived NSCs in vitro reveals recurrent expression alteration of TFs orchestrating brain patterning and NSC lineage commitment. This work opens perspectives to explore human brain dysfunction at early phases of development.

Fig. 1. Expression dynamics of risk genes across cortical neurogenesis.

a Expression enrichment of disease gene sets in cell types of the first trimester human brain from Braun et al.; Significance shown as bootstrap BH-adjusted q-value. Inhibitory (Inh) and Excitatory (Ex) neurons (Neu); inIPC and enIPC: Inh and Ex neuronal IPCs; OPC + COP: oligodendrocyte precursors and committed OPCs; vascular smooth muscle cells (VSMC). b (i) Fraction of genes expressed in human RG from Braun et al. and (ii) in hNSCs in vitro from Micali et al.. c (i) Selected GWCoGAPS patterns across hNSC passages and FGF2 doses. (ii) Schema of hNSC identity progression in vitro. d Enrichment of disease gene sets in the GWCoGAPS patterns. Wilcoxon rank-sum test (i-iii) and MAGMA (iv); n.s.: not significant; p: uncorrected p-values at p < 0.05; p adj.Dis: significance corrected independently for each disease using the Benjamini–Hochberg (BH) method; p adj. AllTest: significance after BH correction using the whole dataset. e (i) Expression levels of microcephaly-associated risk genes in FGF2-regulated hNSC progression, ordered by temporal peak (left column colored by passage and FGF2 dose). (ii-iii) Slope of gene expression change through (ii) neuronal differentiation of age-specific RG cells and (iii) maturation of neuron subtypes from developing mouse cortex. (iv) Disease associations of each gene (left panel), and log10 p-value of the MAGMA gene-level test of association with each GWAS dataset (right panel). Black dots indicate a top-hit gene in the GWAS publication, based on genome-wide significant loci. f Proportion of genes for each disease with expression peaks in hNSCs at each passage and FGF2 condition. Additional categories: all genes in the dataset; genes with average expression of >1 log2 RPKM (RPKM > 1); 1000 genes at the top and bottom ranks of expression, respectively (top and bottom 1000). Categories are ordered as: PS2 and PS3 high to low, PS4 and PS8 low to high. g (i-ii) Proportion of genes classified in different bins of expression change in (i) differentiating NSCs (sorted by E12 NSC proportion) and (ii) maturing neurons. Coefficient = slope of expression change as described in (c, d). Ne neuron.

Fig. 2. Expression of risk genes in brain organizers.

a (i) Neuronal differentiation protocol. (ii) Proportion of disease genes exhibiting dorsal or ventral bias across differentiation of the 6 hNSC lines from Micali et al.. Binned fold change between dorsal and ventral expression for all disease genes and additional categories, as in Fig. 1f, g. b (i) Filtered PC markers with significant (black dots, Wald test; BH-adjusted p-value < 0.05) dorsal or ventral expression bias in the 6 hNSC lines at DIV 8-30, and expression in (ii) PC clusters and (iii) other cell subtypes from the macaque dataset. Disease association on the left. ZLI: zona limitans intrathalamica. c–e RNAscope on sagittal sections of macaque fetal brains of one E40 and one E52 fetus. Scale bar: 500 µm (panoramic), 100 µm (zoom-in). A anterior, P posterior, D dorsal, LV lateral ventricle, FR frontal, Occ occipital, VZ ventricular zone, SVZ subventricular zone, CP cortical plate, r region.

Fig. 3. Sequential gene regulatory networks across hNSC progression.

a Proportion of target genes for each disease regulon with expression peak in hNSCs across passages and FGF2 conditions. Each core TF (top axis) is colored by its expression peak with p-value associated. The category “Any geneset/Any disease” includes all the genes of the disease regulons. Number of targets per regulon indicated on the top bar. b Distribution of expression correlations between core TFs and their targets, positively and negatively correlated, for each disease. Number of correlated targets is shown on the top and bottom summary bar plots, respectively (biweight midcorrelation, Student p-value < 0.05). The color of the bars and TF labeling represent the expression peaks. Significantly high number of positively or negatively correlated targets are marked as ‘*’: p-value < 0.05, or ‘**’: adjusted p-value < 0.05 (permutation test and BH correction). c Predicted gene regulatory network of the core TFs in the disease regulons in hNSCs across passages and FGF2 conditions, with nodes representing genes colored by disease. For a TF found in multiple disease regulons, the thicker stroke indicates the disease in which it is a core TF; the node size indicates the connections to other TFs. Background colors indicate gene expression peak in the in vitro hNSCs, same colors as in (a). Edge colors represent the relationship between expression peak of TFs and their targets, mutual regulation, and association with the same or different diseases. d Temporal regulons ordered by core TF peak expression over hNSC progression, same colors as in (a). TFs associated with diseases, colored as in (c). (i) Number of targets for each regulon. (ii) Overlap of temporal regulons with disease shown as odds ratio of the regulon gene enrichment in each disease (color of the grid), fraction of disease genes present in a regulon (dot size), and enrichment significance (dot color) (one-sided Fisher’s exact test, BH-corrected p-value).

Fig. 4. In silico perturbation of human gene regulatory networks.

a Trajectories of fetal human neural cells from Trevino et al. analyzed in CellOracle: RG progression and gliogenesis for all donors (top); neurogenesis (bottom, only PCW20 is shown). b Network centrality of disease-associated core TFs across RG progression/gliogenesis and neurogenesis. The eigenvector centrality of a TF in the GRN of a cell type is shown by dots representing the influence of a gene in the network. Cell types (y axes) and TFs (x axes) are colored by the trajectory tested. The disease association of each TF is on the top bar. “*”: genes highlighted in the text. c, d Partition-based graph abstraction (PAGA) map of RG progression and gliogenesis (ci), and potential of heat diffusion for affinity-based transition embedding (PHATE) map of neurogenesis (di). KO simulation of (c) KLF6 and (d) MEF2C. (ii) Trajectory perturbation: arrows simulate cell flow after KO perturbation with promoted (green) or depleted (red) trajectory changes. (iii) Cell transitions from original cell identities (left) and after KO simulation (right). e, f KO simulation of disease-associated TFs and/or temporal regulon core TFs in each cell type across (e) RG progression/gliogenesis and (f) neurogenesis. Expression peak throughout in vitro hNSC progression is next to each TF. Next column shows core TFs in the temporal regulons. (i) Perturbation score indicating gain or depletion of a given cell type. (ii) Individual cell-type transitions after KO simulations. Grids represent the fraction of the original cell type before perturbation (labeled red on the top) and their final identity. (iii) Regulatory role of every TF in each cell type. (iv) Disease association of a TF and role in disease (dis.) regulons.

Fig. 5. Analysis of Control and ASD patient-derived NSC lines.

a ScRNA-seq of control- and ASD in vitro NSCs. UMAP of cell subtypes and density of the cell cycle phases. b DEGs between grouped ASD versus grouped control RG_Early cell pseudo-bulk. Fold change (FC) expression ratio between ASD and control cells (x axis) versus the significance of the differential expression (y axis, Wald’s test, BH-adjusted p-value). Blue and red dots mean downregulated or upregulated in ASD, respectively. Top DEGs are labeled. c DEGs in RG_Early in individual ASD samples versus grouped controls. d (i) Expression level (color gradient) and percentage of cells (dot size) expressing organizer genes from Micali et al. in RG_Early of each of our lines, and (ii) differential expression of the same genes across ASD-control pair organoids in the RG cluster at TD0 from Jourdon et al.. e Cumulative fraction of DEGs identified in our Control and ASD lines (y-axis, grouped into different subsets by color) found to be differentially expressed in varying frequencies among the ASD-control pairs in the RG cluster at TD0, from Jourdon et al. (x-axis). Distribution for all genes/TFs differentially expressed in Jourdon et al. is given as baseline reference (black/gray lines). f TFs differentially expressed in RG_Early in individual ASD samples from panel c, also found significantly perturbed (upregulated or downregulated) in at least 1 ASD-control pair in the DEG data from Jourdon et al. in different organoid NSC subtypes and stages (dot size shows number of ASD pairs significantly perturbed, dot color shows frequency among total number of pairs tested), sorted by recurrence in RG cluster at TD0. g IHC on developing macaque telencephalon for selected TFs from panel c. Qualitative analysis of one fetus. PAX6 marks VZ RG cells and its panoramic (inset) serves as acquisition control. Scale bar: 500 µm (panoramics); 200 µm (zoom-in). h, i KO perturbation of TFs from (c) across (h) RG progression/gliogenesis (20 TFs tested) and (i) neurogenesis (19 TFs tested in PCW20). Perturbation score (i), cell type transitions (ii), role of a TF in each cell type (iii) as in Fig. 4.