Developmental convergence and divergence in human stem cell models of autism

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TL;DR

This study uses human induced pluripotent stem cells to model autism spectrum disorder (ASD), finding that diverse genetic mutations converge on shared transcriptional changes during cortical organoid development. It identifies a network enriched in ASD risk genes driving these changes, validated by CRISPR screening.

Key Takeaways

  • ASD involves over 100 rare genetic mutations, but transcriptomic analyses show convergent dysregulation in the brain.
  • Human cortical organoids from stem cells reveal mutation-specific changes early, converging on shared pathways as development progresses.
  • A shared RNA/protein interaction network, enriched in ASD risk genes, drives downstream gene expression changes.
  • CRISPR–Cas9 screening validates transcriptional regulators affecting convergent molecular effects in neural progenitors.
  • The study provides insights into how genetic risk propagates through transcriptional regulation to impact neurodevelopment in ASD.

Tags

Cellular neuroscienceDisease modelScienceHumanities and Social Sciencesmultidisciplinary

Abstract

Two decades of genetic studies in autism spectrum disorder (ASD) have identified more than 100 genes harbouring rare risk mutations1,2,3,4,5,6,7,8,9,10,11,12,13. Despite this substantial heterogeneity, transcriptomic and epigenetic analyses have identified convergent patterns of dysregulation across the ASD postmortem brain14,15,16,17. To identify shared and distinct mechanisms of ASD-linked mutations, we assembled a large patient collection of human induced pluripotent stem (hiPS) cells, consisting of 70 hiPS cell lines after stringent quality control representing 8 ASD-associated mutations, idiopathic ASD, and 20 lines from non-affected control individuals. Here we used these hiPS cell lines to generate human cortical organoids, profiling by RNA sequencing at four distinct time points up to 100 days after in vitro differentiation. Early time points harboured the largest mutation-specific changes, but different mutations converged on shared transcriptional changes as development progressed. We identified a shared RNA and protein interaction network, which was enriched in ASD risk genes and predicted to drive the observed downstream changes in gene expression. CRISPR–Cas9 screening of these candidate transcriptional regulators in induced human neural progenitors validated their downstream convergent molecular effects. These data illustrate how risk associated with genetically defined forms of ASD can propagate by means of transcriptional regulation to affect convergently dysregulated pathways, providing new insight into the convergent impact of ASD genetic risk on human neurodevelopment.

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Main

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder (NDD), with a childhood prevalence of close to 2%18. The last decade of genetic studies has yielded hundreds of risk genes, consistent with extraordinary aetiological heterogeneity1,2,3,4. More than 100 high-confidence mutations have been associated with ASD in genetic studies2,5,6,7,8,9,10,11,12,13. These rare, usually de novo mutations with large effect sizes are expected to account for 10–15% of ASD cases19,20, whereas common genetic variation is predicted to explain at least 50% of genetic risk21,22,23. Overall, ASD shows a complex genetic architecture, with a substantial component derived from a collection of distinct, rare disorders with overlapping clinical features.

Despite its genetic heterogeneity, postmortem transcriptome analysis has revealed consistent changes in most individuals with idiopathic ASD, as well as individuals with a specific syndromic form of ASD, (dup)15q11–13 (refs. 14,15,16,17). However, the mechanisms by which distinct mutations can lead to convergent molecular pathology, and whether convergence occurs across rare forms of ASD remains unknown. Understanding how these processes develop is complicated by the fact that the expression of most ASD risk genes peaks during fetal development, yet gene expression studies in human brain are conducted after this critical developmental window has ended16,24,25,26,27. Several lines of evidence, including genetic28, genomic29,30, neuroimaging31 and neuropathology32, indicate that early neurodevelopment has an essential role in the development of ASD.

The advent of stem cell-based in vitro systems enables high-fidelity modelling of human brain development in NDDs33,34,35,36,37,38,39,40,41. Most studies have investigated relatively small numbers of lines from individuals with idiopathic ASD42, or focused on individual mutations, which have demonstrated the utility of human induced pluripotent stem (hiPS) cell-based systems to study the impact of ASD genetic risk on neurodevelopment43,44,45,46. In addition, recent work has highlighted the power of studying many genes in parallel, identifying evidence for molecular convergence using CRISPR-based perturbations of ASD risk genes on control genetic backgrounds47,48,49. However, studies of lines derived from affected individuals are a major gap in the field.

Here we profile a large cohort of hiPS cell lines, starting from 96 hiPS cell lines ascertained from individuals with 8 different mutations associated with ASD and 11 individuals with idiopathic ASD and 30 lines derived from 25 matched control participants. From each of these lines, we derived neural organoids using a guided differentiation approach to create human cortical organoids (hCOs)50,51. Using orthogonal analytic approaches, we find evidence for convergence during early neuronal differentiation in those with genetically defined forms, but no significant shared signal across the idiopathic cases. We identify and characterize a downregulated, chromatin and transcriptional network that contains several ASD risk genes, including members of the SWI–SNF complex. This network is predicted to drive the observed changes in downstream gene expression associated with these ASD susceptibility mutations. We use CRISPRi to validate the effects of many putative network drivers on downstream gene expression, which includes downregulation of important neurodevelopmental pathways including many ASD susceptibility genes.

Generation of hCOs from hiPS cell lines

We reprogrammed somatic cells (Methods) to generate hiPS cells from a cohort of individuals with eight different mutations associated with ASD: (1) 22q11.2 deletion, a 1.5–3-megabase (Mb) deletion that leads to a constellation of variably present symptoms including heart defects, craniofacial features, intellectual disability, ASD (roughly 20%) and psychosis (roughly 25%)52,53 (n = 18)44; (2) 22q13.3 deletion, known as Phelan–McDermid syndrome, a deletion spanning 130 kilobases (kb) to 9 Mb that includes SHANK3, among other genes and presents with developmental delay, hypotonia and impaired social interactions54 (n = 4); (3) 15q13.3 deletion, a 1.5-Mb deletion that presents variably with intellectual disability and epilepsy55 (n = 3); (4) 16p11.2 deletion, a roughly 600-kb deletion presenting variably with intellectual disability, motor impairments, communication deficits and ASD56 (n = 4); (5) 16p11.2 duplication, which can share intellectual disability and ASD phenotypes with the reciprocal deletion57 (n = 4); (6) Timothy syndrome, which is characterized by variants within the CACNA1C gene and presents with syndactyly, prolonged QT interval, ASD and intellectual disability58,59 (n = 2)45,60,61; (7) PCDH19-related disorder, which is associated with epilepsy and can also include intellectual disability and ASD62,63 (n = 2) and (8) the SHANK3 R522W mutation (n = 1), a point mutation associated with neurodevelopmental risk64; mutations in SHANK3 share behavioural phenotypes with the larger 22q13.3 deletion61,65,66. We also profiled individuals with idiopathic ASD with no known pathogenic variants (n = 11) and unaffected individuals (n = 25) (Fig. 1a and Supplementary Table 1) for a total of 74 individuals. For several individuals we used several hiPS cell lines to assess reproducibility, amounting to a total of 96 hiPS cell lines (Fig. 1a,b and Extended Data Fig. 1).

Fig. 1: Experimental workflow and validation.

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