Copy number variations (CNVs) play a role in the etiology of various neuropsychiatric disorders including intellectual disability (Malhotra and Sebat, 2012), developmental delay (Malhotra and Sebat, 2012), congenital malformations (Cooper et al., 2011), autism spectrum disorder (ASD) (Sebat et al., 2007; Pinto et al., 2010), schizophrenia (SCZ) (Walsh et al., 2008; Levinson et al., 2011), bipolar disorder (BD) (Rucker et al., 2013), and recurrent depression (Malhotra et al., 2011). Microdeletions and microduplications of a 593 kb region of chromosome 16p11.2 have been implicated as a penetrant risk factor in the onset of neurodevelopmental disorders including ASD and SCZ (Weiss et al., 2008; McCarthy et al., 2009). This chromosomal region spans 29.4–32.2 Mb in the reference genome (GRCh37/hg19) and encompasses 29 genes, of which 25 are protein coding (Weiss et al., 2008; Jacquemont et al., 2011; Blumenthal et al., 2014). While estimates of prevalence vary, multiple studies report the presence of a deletion or duplication at the 16p11.2 chromosomal locus in approximately 1% of individuals with ASD (Sebat et al., 2007; Weiss et al., 2008; Jacquemont et al., 2011; Stefansson et al., 2014) and between 0.01% and 0.1% of the general population (Weiss et al., 2008; Jacquemont et al., 2011; Stefansson et al., 2014). A meta-analysis of seven studies suggests an overall prevalence of 0.76% 16p11.2 CNVs among idiopathic ASD probands (Walsh and Bracken, 2011).

The behavioral and physiological phenotypes associated with the 16p11.2 CNV include reciprocal, shared, and gene dosage-dependent abnormalities [Figure 1A]. Both microdeletions and microduplications of 16p11.2 are associated with ASD, although ASD represents a greater proportion of the diagnoses associated with 16p11.2 deletion (Walsh and Bracken, 2011; Glessner et al., 2009; Shen et al., 2010). Conversely, the risk of SCZ is greater in 16p11.2 microduplication carriers (Walsh et al., 2008; McCarthy et al., 2009). Reciprocal neuroanatomical phenotypes of the 16p11.2 CNV include differences in head size and brain volume. Specifically, individuals with a microdeletion of the 16p11.2 locus present with macrocephaly, increased overall gray and white matter volumes, increased cortical surface area, and increased axial diffusivity of white matter tracts such as the anterior corpus callosum and the internal and external capsules (Shinawi et al., 2010; Qureshi et al., 2014; Owen et al., 2014). Individuals with a microduplication of 16p11.2 present with microcephaly and corresponding decreases in gray matter, white matter, and cortical surface area (Shinawi et al., 2010; Qureshi et al., 2014). Recent studies have characterized independent and reciprocal abnormalities in both auditory processing delays and the amplitude of visual evoked potentials among individuals with the 16p11.2 CNVs (Jenkins et al., 2016; LeBlanc and Nelson, 2016). Finally, obesity and hyperphagia are observed among deletion carriers, while low body mass index (BMI) is observed in duplication carriers (Jacquemont et al., 2011; Walters et al., 2010).

Figure 1

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The underlying cellular and molecular mechanisms by which neuropsychiatric disorders develop remain largely unknown. Investigators probing the etiology of neurodevelopmental disorders acquired a powerful new tool with the discovery that human somatic cells can be reprogrammed into human induced pluripotent stem cells (hiPSCs) which, in turn, can be differentiated into ectodermal, endodermal, and mesodermal derivatives (Takahashi et al., 2007; Yu et al., 2007). In the years that followed, the field of hiPSC-mediated neurodevelopmental disease modeling, while hampered by intra- and inter-patient variability (Brennand et al., 2012), has capitalized on the medium’s unique ability to provide: (1) a model of human development at pathologically-relevant time points, (2) an unlimited source of cells for examination, and (3) tissue-specific cell types which share the genetic background of the somatic cell donor. While initial efforts were focused on monogenic neuropsychiatric disorders (Marchetto et al., 2010; Urbach et al., 2010; Paşca et al., 2011), recent work has expanded to include early investigations of gene-environment interactions (Hogberg et al., 2013) and complex polygenic disorders (Brennand, 2011; Shcheglovitov et al., 2013; Yoon et al., 2014; Mariani et al., 2015; Madison et al., 2015).

hiPSCs have offered a novel window into human-specific alterations in neurodevelopment for a number of disorders. Previous work has shown that neurons derived from 16p11.2 patient hiPSCs exhibit abnormal somatic size and dendritic morphology (Deshpande et al., 2017). Interestingly, transcriptional and phenotypic abnormalities have also been observed in hiPSC-derived cortical neural stem cells derived from individuals with idiopathic ASD (Marchetto et al., 2017; Schafer et al., 2019). The 16p11.2 deletion has widespread effects on signaling pathways that underpin critical neural progenitor functions, including proliferation and fate choice (Pucilowska et al., 2018), yet 16p11.2 CNV-related alterations in gene expression patterns have not been examined in early neuroepithelial precursors (radial glia), the stem and progenitor cells of the developing cortex.

Here, we report the derivation, neural differentiation, and transcriptomic characterization of hiPSCs reprogrammed from donors with the 16p11.2 CNV. In total, 65 hiPSC clones were generated from 13 donors. Ten donors harbor a microdeletion at the 16p11.2 chromosomal locus and three donors carry a microduplication. All lines reported here are available through the Simons Foundation Autism Research Initiative (SFARI) (https://sfari.org/resources/autism-models/ips-cells). As a cautionary note, we also report on the relatively high frequency at which hiPSC clones were found to contain randomly integrated reprogramming vectors, in spite of the use of non-integrating episomal reprogramming strategies. In many clones, this led to the inappropriate expression of reprogramming genes in neural progenitor cells (NPCs), which had a more penetrant impact on genome-wide gene expression patterns than the 16p11.2 CNVs. Moreover, we report that only a subset of genes within the 16p11.2 CNV locus are expressed in early NPCs, and 14 of these genes show significant reduction in RNA abundance in clones that carry the 16p11.2 deletion. We also show that these alterations are accompanied by changes in the expression of 93 additional genes that are not located within the 16p11.2 deletion interval, including genes with known relevance in neurodevelopment. These genes impinge on signaling pathways relevant to recently described phenotypic abnormalities in neurons in vitro (Deshpande et al., 2017) and in human carriers in vivo (Shinawi et al., 2010; Qureshi et al., 2014). Finally, we expand upon these findings by exploring the correlation between gene networks and clinical traits from donors and identify cellular metabolism as a compelling avenue for continued investigation.

Current investigations into the etiology of neurodevelopmental disorders have been limited by an inability to recapitulate the developmental trajectory of the human brain under controlled laboratory conditions. hiPSCs offer a unique opportunity to study these early developmental mechanisms. Here, we report the generation and banking of publicly available resource of 65 hiPSC lines with CNVs at the 16p11.2 chromosomal locus. Furthermore, we employ this resource to identify transcriptional alterations that accompany deletion of the 16p11.2 locus in hiPSC-derived NPCs. 16p11.2 CNVs remain one of the most commonly identified variants associated with ASD and, in addition to the previously described links with ASD and SCZ, 16p11.2 CNVs are implicated in intellectual disability, language disorders, ADHD, motor disorders, and epilepsy (Shinawi et al., 2010; Rosenfeld et al., 2010; Hanson et al., 2015).

The hiPSC lines examined here were generated from biospecimens in the Simons Foundation VIP Collection and represent lines from 10 deletion donors and three duplication donors. These 65 lines are a significant addition to the 14 hiPSC clones recently described by Deshpande et al., 2017 and include donors that are age and sex matched across genotypes. 16p11.2 CNVs are implicated in a spectrum of non-CNS phenotypes, including those of the heart, kidneys, digestive tract, genitals, and bones (Zufferey et al., 2012). The recruited individuals for this study encompass a broad spectrum of physiological attributes and psychiatric diagnoses. A substantial number of donors is required to verify gene-specific phenotypes in hiPSC models due to intra- and inter-individual variability between clones (Brennand et al., 2012). Clone-to-clone variability, in part due to incomplete epigenetic remodeling and the resultant influence of epigenetic memory (Polo et al., 2010; Kim et al., 2010; Lister et al., 2011), may confound conclusions built upon line-to-line comparisons (Onder and Daley, 2012). Additionally, generating large genomic rearrangements in isogenic control lines through genetic engineering strategies such as the CRISPR/Cas9 system is technically challenging (Grobarczyk et al., 2015; Tai et al., 2016). The availability of a large number of clones provides an opportunity to explore phenotypes that may be relevant to both CNV-specific effects and the influence of an individual’s genetic background on the penetrance of a given phenotype. Importantly, this hiPSC collection contains at least three independently derived clones for most donors. It follows that this resource may significantly improve the field’s ability to identify morphological, physiological, and circuit-level characteristics at different stages of development that contribute to the etiology of 16p11.2-related phenotypes.

The large number of genes within the 16p11.2 region, and their wide-ranging functions, make it challenging to pinpoint specific genes relevant to neuropsychiatric disease. One of the goals of this work was to determine which 16p11.2 genes are expressed in early cortical development, as well as which show altered expression following the deletion of one allele. Clones that were free of integrated reprogramming vectors represented genomes from six individuals carrying 16p11.2 deletions. Of these, the deletion intervals were known for five subjects [Figure 5A]. We found that all 14 DE genes within the canonical 16p11.2 interval were located within a shorter interval of the chromosome where deletions overlapped for the five subjects with known breakpoints. This clustering to a sub-interval of the locus most likely reflects the fact that the majority of clones analyzed were missing one allele of each DE gene in this region. It is likely that clones with larger deletions may also show copy-number-dependent expression for genes outside of the shared interval. For example, it is reasonable to suspect that genes unique to the much larger deletion present in DEL_6_7 are downregulated, but that the difference is diluted in the current analysis by clones with normal copy number in this region.

The data presented can be used to confidently identify genes within the 16p11.2 locus that are expressed in early NPCs. Of 56 genes within and flanking the deletion intervals of the clones evaluated, 15 are expressed at or below the limits of detection. Although these genes may play roles at later stages of brain development, they seem unlikely to be relevant for the early patterning and initial establishment of neuronal subtype identity. Conversely, it also stands to reason that the remaining 41 genes are likely to have roles in early cortical development that may be affected when copy number is altered. Of these, 14 are differentially expressed and all are downregulated in DEL: MAZ, DOC2A, PRRT2, PAGR1, TAOK2, KIF22, HIRIP3, ALDOA, C16orf92, INO80E, MAPK3, KCTD13, SEZ6L2, and FAM57B. Their statistically significant downregulation suggests that these 16p11.2 loci are worthy of increased scrutiny as genes that may commonly influence developmental outcomes across individuals that share this deletion interval. Of particular note is the 16p11.2 region gene MAPK3, as its protein product Erk1 is known to interact with several other observed DE genes (e.g. SELE, CALML6, RAF1, SEMA4A). This finding supports existing work suggesting that the Erk1 MAPK pathway may drive phenotypic abnormalities associated with the 16p11.2 CNV (Pucilowska et al., 2018; Pucilowska et al., 2015). Although we detected a decrease in MAPK3 gene expression in all the 16p11.2 deletion lines [Figure 5—figure supplement 1A], we did not observe any changes in NPC proliferation between wild type and deletion lines indicating that MAPK3 may not influence NPC proliferation in these 16p11.2 patient lines [Figure 4—figure supplement 2A]. This is consistent with prior work that also found no significant difference in the proliferation of DEL cells at the NPC stage (Deshpande et al., 2017).

In addition to the 14 DE genes within the 16p11.2 locus, there are 93 additional genes that are differentially expressed. Fifteen of these genes are identified as being generally related to psychiatric disease, and nine are more specifically identified as related to ASD [Figure 5—figure supplement 3A]. All these genes are listed in the SFARI Gene Scoring Module database (Abrahams et al., 2013) and are associated with at least five published reports supporting their role as a candidate gene in ASD. Of these genes, CNTNAP2, a neurexin family member that is important excitatory synapse formation and function, is one of the strongest known candidate-genes for ASD risk (Abrahams et al., 2013). Several additional DE genes are also relevant to excitatory synapses in mature neurons (e.g. PRRT2, CALML6, C1QL3, DOC2A, GABRE, C1QL3, CNTNAP2), and may reflect early perturbations in gene networks relevant to cortical excitatory networks. Previous work reports changes in neurite branching and synapse development in more mature neurons (Deshpande et al., 2017), and here, we find genes that may be related to these later changes are differentially expressed well before such neurons emerge in culture.

Future studies might utilize these early transcriptional features to test whether related functions are affected in mature neurons. For example, the 16p11.2 region gene KCTD13 is important for the maintenance of synaptic transmission in the CA1 region of the hippocampus, with its loss resulting in decreased fEPSP slope and the frequency of mEPSP currents (Escamilla et al., 2017). Interestingly, the 16p11.2 region gene TAOK2 has been shown to influence the differentiation, basal dendrite formation, and axonal projection of pyramidal neurons in the cortex with its deletion resulting in abnormal dendrite formation and impaired axonal elongation in mouse brains (de Anda et al., 2012). We found the expression of KCTD13 and TAOK2 to be reduced across all deletion lines suggesting that reduced KCTD13 and TAOK2 expression in cortical progenitors may impact cortical network development and the maintenance of appropriate synaptic tone in the cortex of autistic individuals.

Other features that the DE gene list may predict include alterations related to movement or locomotion of the cell [Figure 5G]. Several DE genes modulate actin and cytoskeletal organization, which could contribute to abnormal cell morphology (e.g. SELE, TEKT4, TUBGCP4, KLHL4). Additionally, three downregulated genes are related to glucose metabolism and glycogen synthesis (e.g. TLCD3B, FBP2, ALDOA), suggesting that changes in cellular metabolism and energy management may influence outcomes. Finally, we observed that the transcript with the highest fold-change and smallest p-value was aligned the microRNA (miRNA) miR-6723. Although our sequencing protocol was not designed to accurately profile mature miRNAs, the library preparation would capture polyadenylated pri-miRNAs that could later be converted into mature miRNAs (Cai et al., 2004). The functions of many of these miRNAs are not well characterized but existing literature supports further investigation. miR-6723 itself is enriched in neurons relative to microglia, astrocytes, and endothelial cells (Spaethling et al., 2017). Interestingly, it is also downregulated in term placentas of women with chronic Chagas disease, suggesting a role in cellular proliferation or immune response (Winn et al., 2007; Juiz et al., 2018). Four other miRNAs appear in the differential expression gene list and have roles in proliferation that may impact progenitor function. Of note, miR-301B impairs cellular senescence and has a role in the spread of certain cancers (Ramalho-Carvalho et al., 2017; Danesh et al., 2018). miR-637 inhibits tumorigenesis, and its downregulation may enhance cell proliferation (Zhang et al., 2011; Zhang et al., 2018).

It is also worth noting that SNP array data show that 4 of the 13 DEL Int- clones contained additional CNVs, including microdeletion in 14q11.1, a microduplication in 14q11.2, and a microdeletion in 7p11.2. All of these CNVs are potentially associated with neurodevelopmental abnormalities (Zahir et al., 2007; Varvagiannis et al., 2014), and it is possible that these additional CNVs contribute to the transcriptional alterations. However, there is no significant difference in the average fold change of DE gene expression in DEL relative to WT between the subset of clones carrying these additional CNVs and the remaining DEL clones [Figure 5—figure supplement 1B]. Although it is important to document all background CNVs for future reference, these CNVs are unlikely to account for the transcriptional changes in the NPCs reported here.

This work also suggests that patient-specific transcriptional abnormalities, in addition to a core 16p11.2 transcript deficiency, may interact to generate a heterogeneous patient phenotype. Our WGCNA analysis identified several modules correlated with phenotypes within 16p11.2 deletion patients [Figure 6A]. Interestingly, these signals were related not only to higher order neurological functions, but to physical attributes and body plan. These modules do not contain genes identified as distinguishing DEL from WT samples. 16p11.2 region genes were most frequently observed in the grey module (n = 11 genes), followed by the blue (n = 3) and turquoise (n = 1) module. The grey module also contained the most DE genes (n = 32) in the DE pipeline presented in Figure 5 and genes identified in the linear mixed model pipeline (n = 16) [Figure 5—figure supplement 4]. This is to be expected given that the contrast examined with WGCNA was within DEL patients, rather than comparing all DEL clones with WT clones as in the DE analysis of Figure 5. However, it is possible that the gene programs accounting for phenotype heterogeneity within DEL are due to transcriptional programs that exist independent of the 16p11.2 deletion region. This further reinforces the possibility that patient-specific etiology may be due to an interaction of a generalized existing liability posed by the presence of the 16p11.2 deletion interacting with patient-specific features.

Within the four modules most highly correlated with a spectrum of patient traits, the member genes were most often significantly associated with metabolic functions [Figure 6C]. However, the strongest enrichments fell within a select set of functions related to cell responses to external stimuli [Figure 6D]. This supports the hypothesis that patient phenotypic variance might account for heterogeneous clinical presentation, and that this variance might emerge both in metabolic and signaling functions of the cell. However, the greater enrichment of cell-cell signaling pathways suggest that the phenotype might extend beyond simple metabolic dysfunction and include a breakdown of intercellular communication systems tightly linked to growth and metabolism of the cell. Several modules were significantly associated with one patient flagged with abnormal Behavior scores. However, we do not consider those modules here because Behavior-specific transcriptional associations are confounded with clone-specific associations.

Of the modules most highly associated with patient traits, the pink4 module of genes appeared to have the highest correlation with DEL patient phenotypes, and was one of the few modules highly associated with poor outcomes on a Language metric [Figure 6—figure supplement 2]. The genes most highly associated with this module contain regulators of major developmental cascades in development that could conceivably disrupt gene programs related to both nervous system function and general cellular growth, including Wnt pathway negative regulator WIF1, and Erk1/2 MAPK pathway effector FGF1 [Figure 6E]. Interestingly, increased Weight Z-score in this patient cohort was associated with reduced expression of insulin (INS), but increased expression of fetal growth-regulating insulin-like growth factor 2. Although the present study characterizes neural tissue, if generalized insulin-related signaling abnormalities exist in these patients, this disruption would likely be compounded by established alterations in MAPK signaling, an insulin signaling effector mediating cell growth, leading to a high BMI phenotype. Taken together with the observation made about the larger four-module block, the identification of BMI-associated genes within NPCs could be attributable to the fact that some aspects of the gene program modulated by 16p11.2 deletion may represent a generalized phenotype with tissue type-specific effects.

The pink4 module is similarly associated with two functions that could be conceivably impacted by dysregulation of the developing brain. One of these changes, Head Circumference Z-score, suggests a possible link with abnormal activity within NPCs, as head circumference has been associated with aberrant proliferation in NPCs, and DEL patients have been observed to have decreased head circumference (Qureshi et al., 2014; Vaccarino et al., 2009). Additionally, the association of this module with language deficits suggest a possible link to more mature circuit assembly and function, coincident with previous reports of language disruption associated within the 16p11.2 deletion phenotype (Blackmon et al., 2018; Kim et al., 2020). This could arise through a negative correlation with expression of AMIGO1, a adhesion molecule expressed throughout the nervous system associated with the development of dendrite outgrowth and the formation of fasciculated fiber tracts (Zhao et al., 2014) and dendritic outgrowth (Chen et al., 2012). These features are also negatively associated with calcium-activated potassium channel member KCNMB4, which has been previously associated with high-order disruption of neural function through its association with ASD in SNP association studies (Skafidas et al., 2014). Together, the correlation between these sets of genes and phenotypes associated with abnormal neurodevelopment invites speculation into the heterogeneous nature of language deficits in patients with 16p11.2 CNVs.

It is surprising that the pink4 module is associated not only with neural function and BMI, but also Age. While it is not possible to pinpoint what aspects of the genetic signature might be attributable to this association, the observed relationship between trait and expression of specific genes (e.g. Weight Z-score and INS expression) indicate that the combined correlations are not entirely attributable to normal changes in weight with age. The results of the WGCNA analysis suggest, as a whole, that several gene modules may be associated with a spectrum of clinical outcomes with 16p11.2 microdeletion, that some BMI-associated changes are detected even in a cell type presumably unrelated to BMI (neural progenitors), and that some of these correlations with clinical features involve genes outside of the 16p11.2 microdeletion. While further investigation is needed to untangle the complexity of patient-specific gene programs and generic 16p11.2 deletion effects, this work provides a springboard for further interrogation of hiPSC-derived cell types and features common to 16p11.2 microdeletion relative to features that may arise in individual donors due to other genetic background effects.

Finally, we emphasize the importance of screening clones for cryptically integrated reprogramming plasmids, especially as published methods highlight the lack of integration when using episomal reprogramming vectors. It is likely that these effects are most relevant for reprogramming methods that utilize DNA expression vectors and less likely for RNA-based delivery methods, such as modified-RNA or Sendai virus-based reprogramming. Many of the clones used in this study and available in the repository contain random integration of reprogramming plasmids that introduce strong transcriptional artifacts. The impact of cryptic reprogramming vector integration is well illustrated by the segregation of Int+ and Int- clones into identifiable clusters within the first two PCs in Figure 4A. These transcriptional perturbations could emerge from two possible sources: (1) an increase in reprogramming factor protein product brought about by the insertion of transcriptionally active transcripts or (2) a disruptive insertion of integrant into a transcriptionally active locus. We note that one Int+ clone (DEL_4_1) has a silenced insertion that contains integrant DNA [Figure 3—figure supplement 1] but did not produce detectable transcript that aligns to the integrated plasmid. Moreover, this Int+ clone has an expression profile that closely resembles those of the Int- clones [Figure 4D]. This is consistent with the observed transcriptional effects being primarily related to expression of the reprogramming factors rather than random insertional mutations. The strongest correlation appears to be due to plasmid-based expression of POU5F1. This is also suggested in our qRT-PCR data, where POU5F1 transcripts are not detected in clone DE_4_2, which is most similar in DE gene transcriptional patterns observed in the Int- clones [Figure 3—figure supplement 1]. The integration and expression of reprogramming vectors is accompanied by the differential expression of more than 3000 genes. Not surprisingly, the integration-associated DE gene list contains 136 genes previously identified as POU5F1 transcriptional targets, representing approximately 16% of genes cataloged in three putative POU5F1 target gene lists (Sharov et al., 2008). Given that such cryptic integrations may also exist in other hiPSC lines that have been reprogrammed using non-integrating episomal systems, we provide a customizable RNA-seq analysis pipeline that can detect expression from cryptic plasmids. It is computationally lightweight and can feasibly be performed on a laptop computer. This method provides users with the means to quickly interrogate their data and detect whether similar integration effects should be considered and addressed.

In summary, the collection of 65 human hiPSC clones from 13 donors with either a microdeletion or microduplication at the 16p11.2 chromosomal locus provides an important resource for studying the early neurodevelopmental alterations that may underlie physiological and behavioral features seen in individuals carrying 16p11.2 CNVs. The data presented herein identify several early transcriptomic network perturbations which may act as priming events for later functional alterations in more mature cell types (Schafer et al., 2019). A majority of the available clones have been thoroughly evaluated for reprogramming success, ploidy, SNPs, and the capacity to differentiate down the cortical neural lineage. The clones have also been screened and sorted for cryptic integration of the ‘non-integrating’ reprogramming vectors, and data from the subset of footprint-free lines provide several novel candidates for future studies of 16p11.2 deletion. The complete demographic and diagnostic information for each fibroblast donor, and instructions for obtaining the cells themselves, are available through SFARI at https://sfari.org/resources/autism-models/ips-cells. The comprehensive information collected from donors makes this hiPSC resource particularly valuable for investigating associations between in vitro phenotypes and clinical diagnoses that may be unique to a given individual or shared among 16p11.2 CNV carriers.

RNAseq data GEO Submission GSE144736 All additional data is included in the manuscript and supporting files.

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Author details

1. Julien G Roth

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Kristin L Muench

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7560-3258

2. Kristin L Muench

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Conceptualization, Formal analysis, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Julien G Roth

   Competing interests

   No competing interests declared

3. Aditya Asokan

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Victoria M Mallett

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Data curation, Validation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

5. Hui Gai

   1. Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
   2. Department of Neurobiology, Stanford University School of Medicine, Stanford, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Yogendra Verma

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Data curation, Formal analysis, Methodology

   Competing interests

   No competing interests declared

7. Stephen Weber

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Data curation, Formal analysis, Methodology

   Competing interests

   No competing interests declared

8. Carol Charlton

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Data curation, Formal analysis, Methodology

   Competing interests

   No competing interests declared

9. Jonas L Fowler

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

10. Kyle M Loh

    Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

    Contribution

    Formal analysis, Investigation, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

11. Ricardo E Dolmetsch

    Department of Neurobiology, Stanford University School of Medicine, Stanford, United States

    Present address

    Department of Neuroscience, Novartis Institutes for BioMedical Research, Cambridge, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2738-8338

12. Theo D Palmer

    Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    tpalmer@stanford.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6266-1862

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