Carbon Chemist

Tag: Development of the nervous system

  • Examining the role of common variants in rare neurodevelopmental conditions

    Examining the role of common variants in rare neurodevelopmental conditions

    [ad_1]

    Cohort descriptions and phenotypes

    DDD

    The aim of the DDD study is to find molecular diagnoses for families and patients affected by previously genetically undiagnosed, severe developmental conditions. Recruitment was conducted from 2011 to 2015 across 24 clinical genetics services in the United Kingdom and Ireland58. The clinical inclusion criteria included neurodevelopmental conditions, congenital, growth or behavioural abnormalities and dysmorphic features. Probands were systematically phenotyped through DECIPHER59 using Human Phenotype Ontology (HPO)60 terms and a bespoke online questionnaire that collected information on developmental milestones, growth measurements, number of affected relatives, prematurity, maternal diabetes, and other clinically relevant parameters. The cohort has been described extensively1,50,58,61.

    We focused on probands in the DDD cohort who had neurodevelopmental conditions, which were defined previously by Niemi et al.2 Briefly, these were probands who had at least one of the following neurodevelopmental HPO terms or their descendent terms: abnormality of higher mental function (HP:0011446), neurodevelopmental abnormality (HP:0012759), abnormality of the nervous system morphology (HP:0012639), behavioural abnormality (HP:0000708), seizures (HP:0001250), encephalopathy (HP:001298), abnormal synaptic transmission (HP:0012535), or abnormal nervous system electrophysiology (HP:0001311).

    GEL project

    The 100,000 Genomes project is an initiative by the UK Department of Health and Social Care to sequence the whole genomes of individuals with rare conditions or cancer in the National Health Service62,63. The rare disease branch of the project consists of sequencing data from roughly 72,000 patients with rare conditions and their relatives, in roughly 34,000 families with a variety of structures. There are more than 190 rare conditions represented in the cohort, and about 23% of the patients have neurodevelopmental conditions. The cohort was sequenced at around 35 times coverage, and variant calling and quality control (QC) were performed by Genomics England63,64.

    Patients from GEL with neurodevelopmental conditions were defined as those recruited under the ‘Neurodevelopmental disorders’ disease subcategory, or with more than one HPO term that was a descendant of ‘Neurodevelopmental Abnormality’ (HP:0012759). We removed probands whose age of onset was above 16 years or who had neurodegenerative conditions.

    The set of unrelated GEL controls included patients with cancer above 30 years old (N = 10,469) and unaffected relatives (N = 3,198) of probands with rare conditions who were not in the neurodevelopmental condition set and did not have phenotypes similar to probands from DDD (‘DDD-like’). The DDD-like probands were defined as those who:

    1. 1.

      were recruited into a disease model that was also used to recruit probands who had previously been recruited into DDD (section below on identifying probands overlapping between the two cohorts), or

    2. 2.

      had one the top five HPO terms used in DDD and their descendants, namely HP:0000729 (autistic behaviour), HP:0001250 (seizure), HP:0000252 (microcephaly), HP:0000750 (delayed speech and language development), and HP:0001263 (global developmental delay).

    Probands recruited into the neurodegenerative disorders subcategory or with an age of onset greater than 16 years were removed from the DDD-like set, as were probands recruited into a disease subcategory for which the average age of probands was older than 16 years.

    To define relatedness, we used a file generated by GEL consisting of a pairwise kinship matrix produced using the PLINK2 (refs. 65,66) implementation of the KING robust algorithm67 and a –king-cutoff of 0.0442 (that is, 1/24.5).

    Control cohorts

    The UK Household Longitudinal Study (UKHLS) cohort consists of a continuation of the British Household Panel Survey of individuals living in the United Kingdom68,69. The Avon Longitudinal Study of Parents and Children (ALSPAC) is a birth cohort study of children born in Avon, England with expected dates of delivery between 1 April 1991 and 31 December 1992 (ref. 70). Eligible pregnant women (N = 13,761) were recruited and their children have been phenotyped extensively over the past 30 years. Please note that the study website (http://www.bristol.ac.uk/alspac/researchers/our-data/) contains details of all the data that are available through a fully searchable data dictionary and variable search tool. The MCS is a birth cohort study of children born across the UK during 2000 and 2001 from 18,552 families71,72. Further information about recruitment of these cohorts is given in Supplementary Note 4.

    Ethics

    The DDD study has UK Research Ethics Committee approval (10/H0305/83, granted by the Cambridge South Research Ethics Committee and GEN/284/12, granted by the Republic of Ireland Research Ethics Committee). The 100,000 Genomes project was approved by the East of England—Cambridge Central Research Ethics Committee (REF 20/EE/0035). Ethical approval for ALSPAC was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Ethical approval for each sweep of MCS was obtained from NHS Research Ethics Committees (MREC). Ethical approval for the sixth MCS sweep, which included the collection of saliva samples from children and biological resident parents, was obtained from London-Central REC (MREC; 13/LO/1786).

    Preparation of genetic data

    Individuals from DDD, UKHLS, ALSPAC and MCS were genotyped on various arrays, whereas GEL individuals were whole-genome sequenced. The available data are summarized here briefly:

    A subset of the DDD cohort (all children and several thousand parents) was genotyped on three genotype array chips: the Illumina HumanCoreExome chip (CoreExome), the Illumina OmniChipExpress (OmniChip) and the Illumina Infinium Global Screening Array (GSA). Some probands were genotyped on more than one chip, as shown in Supplementary Fig. 9. In downstream analysis, we used the CoreExome and OmniChip data for analyses of probands, and the GSA and OmniChip data for analyses of trios. QC of CoreExome (including DDD patients and 9,270 UKHLS controls genotyped on the same chip) and OmniChip data were performed by Niemi et al.2 and we performed QC in the GSA data specifically for this paper (Supplementary Tables 13 and 14). The DDD cohort was also exome sequenced, and those data were used for the analyses involving rare variants.

    GEL individuals were whole-genome sequenced with 150 bp paired-end reads using Illumina HiSeqX. Variant calling and QC were performed by Genomics England. We used 78,195 post-QC germline genomes from the Aggregated Variant Calls (aggV2) prepared by the GEL team. We kept variants that passed the QC filters shown in Supplementary Table 15.

    Data we received from ALSPAC were processed in two batches69. In the first batch, we received post-QC array data for G0 mothers (N = 8,884) who were genotyped on the Illumina Human 660W chip and G1 children (N = 8,932) genotyped on the HumanHap550 quad chip. In the second batch, we received another 2,198 parents (G0 mothers and G0 partners73) who were genotyped on the CoreExome array.

    We received data for 21,181 MCS samples who were genotyped using the GSA array chip74.

    We applied standard QC filters in each dataset separately, described further in Supplementary Methods. We used the maximum subset of unrelated individuals that passed QC. We did not use any statistical methods to predetermine sample sizes.

    Genetically predicted ancestry

    To avoid spurious results due to population stratification, all genetic analyses were conducted in a genetically homogeneous subset of individuals with genetic similarity to British individuals from the 1,000 Genomes Project75, henceforth referred to as having GBR ancestry. The Supplementary Methods provide detailed information on ancestry inference, but we summarize it briefly here. The identification of GBR-ancestry samples from the DDD CoreExome and OmniChip data was described previously2. To identify individuals of genetically inferred GBR ancestry in DDD GSA samples, we first projected post-QC samples onto 1,000 Genomes phase 3 individuals75 (Supplementary Fig. 10). We then performed another principal component analysis (PCA) within the loosely defined European ancestry subset and identified a homogeneous subgroup (Supplementary Fig. 11) using uniform manifold approximation and projection (UMAP)76. As we merged parent–offspring trios genotyped on GSA and OmniChip array chips in downstream analysis, we kept GSA individuals who were similar to OmniChip individuals in terms of genetic ancestry in PCA space (Supplementary Fig. 12). In GEL, we used individuals with genetically inferred European ancestry, which were identified by the GEL bioinformatics team. We further restricted to a homogeneous subset (N = 56,249) that represents White British individuals (Supplementary Fig. 13). Array data received from the ALSPAC all had genetically predicted European ancestry, so we did not perform any filtering based on genetic ancestry. We performed similar PCA and UMAP clustering to identify individuals of GBR ancestry in MCS (Supplementary Figs. 14 and 15), and further filtered to individuals who self-reported as being of White ethnicity.

    Relatives within and across cohorts

    Within each dataset, we identified up to third-degree relatives (kinship coefficient greater than 0.0442 by KING v.2.2.4 (ref. 67) using post-QC genotyped array data or WGS data. We always used a subset of unrelated individuals (that is, more distant than third-degree relatives) in downstream analysis. In analyses using trios, we made sure probands in trios were unrelated and parents were unrelated with parents from other families.

    In analyses combining DDD and GEL, we removed from GEL any participants who were also recruited into DDD and/or who were related to DDD participants, and also removed Scottish samples from DDD as we were unable to check whether GEL samples were related to them (Supplementary Methods). We removed individuals from the two birth cohorts who were related to each other or to DDD participants, which left 1,434 and 2,498 trios from ALSPAC and MCS, respectively (Supplementary Methods).

    Imputation and post-imputation QC

    Imputation of array data was performed in each genotyped cohort separately using the maximum number of variants available after QC. Before imputation, we removed palindromic SNPs, SNPs that were not in the imputation reference panel, and SNPs with mismatched alleles. DDD samples and UKHLS controls who were genotyped on the CoreExome array were imputed with the HRC r1.1 reference panel by Niemi et al.2 DDD GSA and OmniChip samples and ALSPAC samples were imputed to the TOPMed r2 reference panel using the TOPMed imputation server, and the MCS samples to the HRC r1.1 reference panel77,78,79. We kept well-imputed common variants with Minimac4 R2 > 0.8 and MAF > 1%. For polygenic score analyses, we subsequently restricted to common variants that passed these QC filters in all genotyped cohorts and also passed QC in the GEL WGS data.

    Extraction and QC of rare variants

    QC of DDD exome sequencing data and extraction of rare single-nucleotide variants, and insertion and deletions (indels) is summarized in Supplementary Table 16. Indels in the same gene and sample were removed (4% of indels with MAF < 1%), as these were often part of complex mutational events that would require haplotype-aware annotation.

    For GEL, details of the QC of single-nucleotide variants and indels in the WGS data are provided by the GEL team63,64 and variant QC is summarized in Supplementary Table 15. We use a custom python script to extract rare variants from GEL aggregated WGS variant call format files (aggV2). We filtered genotypes to those with genotype quality (GQ) ≥ 20 and read depth (DP) ≥ 10. We removed heterozygous genotypes that did not pass a binomial test of balanced REF and ALT alleles (P < 1 × 10−3) or for which ALT/(REF + ALT) (AB ratio) was not between 0.2 and 0.8. We further removed variants with missing high-quality genotypes in more than 5% of all samples in aggV2. We removed indels in the same gene and sample for the same reason described above for DDD.

    For MCS, details of the QC of exome sequencing data are in Supplementary Methods.

    Defining monogenic diagnoses in patients

    DDD

    The DDD study identified clinically relevant rare variants from exome sequencing and microarray data using a filtering procedure described in ref. 58. The procedure focuses on identifying rare damaging variants that fit an appropriate inheritance mode in a set of genes that cause developmental disorders (DDG2P, https://www.deciphergenomics.org/ddd/ddgenes). Variants that pass clinical filtering are uploaded to DECIPHER59, where the patients’ clinicians are asked to classify them as definitely pathogenic, likely pathogenic, uncertain, likely benign or benign. We defined ‘diagnosed’ probands as those with one or more variants either annotated as pathogenic or likely pathogenic in DECIPHER by their referring clinician, or predicted as pathogenic or likely pathogenic using diagnoses autocoded following the American College of Medical Genetics and Genomics guidelines as described in ref. 1. All remaining probands were classed as ‘undiagnosed’. Probands with a de novo diagnosis are those with a de novo mutation in a monoallelic or X-linked DDG2P gene that was either annotated or predicted as pathogenic or likely pathogenic.

    GEL

    The probands assigned diagnostic status were those included in the Genomic Medicine Service exit questionnaire, in which a clinician evaluated the pathogenicity of variants of interest identified through GEL’s custom pipeline. We defined diagnosed probands as those that had a pathogenic or likely pathogenic variant that is annotated as partially or fully explaining their phenotype in this exit questionnaire. Probands with a de novo diagnosis are those whose pathogenic or likely pathogenic variants from the exit questionnaire were annotated as de novo protein-truncating or missense variants in DDG2P monoallelic or X-linked genes. We defined undiagnosed probands as those that were present in the exit questionnaire but not annotated as having a pathogenic or likely pathogenic variant and not annotated as ‘yes’ or ‘partially’ in the ‘case_solved_family’ column. We further removed from this undiagnosed set any probands who have potential diagnoses in the Diagnostic Discovery data in GEL, which is a list of variants submitted by researchers that are thought probably to be pathogenic by the GEL clinical team.

    Defining trio sample sets in DDD and GEL

    The procedure used for filtering trios used in DDD and GEL is shown in Supplementary Fig. 16. Briefly, in DDD, we combined data across GSA and OmniChip arrays and kept trios in which all three members had GBR ancestry and the proband had a neurodevelopmental condition. We excluded trios recruited from Scottish centres and kept unrelated trios. We then split trios into those with both parents unaffected and those with one or both parents affected. These were then categorized as genetically diagnosed or undiagnosed. We applied similar filtering in GEL trios. See Supplementary Methods for more information.

    GWAS of neurodevelopmental conditions

    We used PLINK v.1.9 to conduct a GWAS comparing individuals with neurodevelopmental conditions (N = 3,618) to controls (N = 13,667) in GEL, controlling for 20 genetic principal components, age and sex. Before running the GWAS, we removed variants with MAF < 1%, missingness > 2% or Hardy–Weinberg equilibrium P < 1 × 10−5, and performed a differential missingness test between the patients with neurodevelopmental conditions and controls and removed variants with P < 1 × 10−5. We repeated the GWAS comparing DDD patients with neurodevelopmental conditions on the CoreExome array (N = 6,397) to UKHLS controls (N = 9,270) using PLINK v.1.9, after excluding DDD patients recruited from Scottish centres.

    We used METAL80 to conduct an inverse-variance-weighted GWAS meta-analysis between the DDD-UKHLS and GEL GWASs. We removed palindromic SNPs with MAF > 0.4 as the strand could not be easily inferred using MAF. We also excluded SNPs with discordant allele frequency (difference > 0.05) between the two cohorts. This left 5,451,801 overlapping SNPs in the meta-analysis.

    Heritability

    We used several methods to estimate the SNP heritability (the fraction of phenotypic variance explained by genome-wide common variants) on the liability scale assuming a cumulative population prevalence of 1% for rare neurodevelopmental conditions2. First, we applied two methods to estimate SNP heritability using individual-level data in DDD and GEL separately. We performed GREML-LDMS81 stratified by linkage disequilibrium (LD; two bins of equal size) and MAF (three bins: 1–5%, 5–10%, >10%). We also ran phenotype correlation–genotype correlation (PCGC) regression82, using the LDAK-Thin Model to compute the kinship matrix using the direct method. We corrected for sex, and ten genetic principal components as covariates in both methods. We then meta-analysed the SNP heritability estimates from DDD and GEL using an inverse-variance-weighted method. We also used linkage disequilibrium score regression (LDSC)83 to estimate SNP heritability using summary statistics from the GWAS of neurodevelopmental conditions in DDD, in GEL, and a meta-analysis of the two cohorts. We used roughly 1 million common SNPs from HapMap3 with precomputed LD scores. We used the effective sample size (4/(1/Ncases + 1/Ncontrols)) or the sum of two effective sample sizes for the meta-analysis and a sample prevalence of 50% in LDSC, as recommended previously84. We presented the GREML-LDMS estimate in the results, because the estimates were similar to PCGC, and LDSC estimates are known to be under-estimated, especially at low sample size. All estimates are reported in Supplementary Table 3.

    Genetic correlations

    We used LDSC to estimate genetic correlations between the DDD GWAS or the meta-analysed GWAS for neurodevelopmental conditions and various brain-related traits and conditions listed in Supplementary Table 17. We did not use the GEL GWAS to calculate genetic correlations as the SNP heritability was not significantly different from zero according to LDSC.

    To estimate the genetic correlations between neurodevelopmental conditions and various brain-related traits or conditions independent of cognitive performance or educational attainment signals, we used genomic structural equation modelling (GenomicSEM)35,85. We estimated the genetic correlation between the target trait and a latent variable representing the non-cognitive component of neurodevelopmental conditions, which was genetic influences on neurodevelopmental conditions that were not captured in the GWAS for cognitive performance31. We applied the GenomicSEM model without SNP effects. We also estimated genetic correlation with the ‘non-educational attainment’ latent variable, which represented genetic influences on neurodevelopmental conditions that were not accounted for by the educational attainment latent variable. We also used GenomicSEM to estimate the percentage of the genetic correlation between neurodevelopmental conditions and the target trait that was explained by latent variables, namely the cognitive and non-cognitive components of neurodevelopmental conditions when conditioning on the cognitive performance GWAS, or EA and non-EA components of neurodevelopmental conditions when conditioning on the educational attainment GWAS (Supplementary Fig. 1 and Extended Data Fig. 9bc). The GenomicSEM model and formulae used to estimate these percentages can be found in Supplementary Fig. 17 and Supplementary Methods.

    Calculating polygenic scores

    For calculating polygenic scores, we used the set of SNPs that were well imputed in all array cohorts (Minimac4 R2 > 0.8), passed QC in GEL aggV2 samples, and had MAF > 1% in all cohorts. We used LDPred86 to estimate weights for calculating polygenic scores and an LD reference panel composed of HapMap3 (ref. 87) common variants based on 5,000 unrelated individuals of genetically inferred White British ancestry from the UK Biobank88 (Supplementary Methods). GWAS summary statistics for years of schooling (a measure for EA)31, the non-cognitive component of educational attainment (NonCogEA)35, cognitive performance (CP)31, schizophrenia (SCZ)32 and neurodevelopmental conditions2 were matched with the list of overlapping SNPs (Supplementary Table 17). PGSNDC,DDD was evaluated in the DDD OmniChip samples and the GEL samples that were not in the DDD GWAS. To make polygenic scores comparable across cohorts (DDD, GEL, UKHLS, MCS and ALSPAC), we performed a joint PCA across all cohorts and adjusted the raw scores for 20 principal components. For most analyses and unless noted otherwise, residuals were scaled so that the combined set of unrelated control samples from GEL and UKHLS (or GEL controls only for PGSNDC,DDD) had mean of 0 and s.d. of 1, and the resultant scores were used for all analyses unless otherwise indicated. In Fig. 3b and Extended Data Fig. 5, we instead show principal component-adjusted polygenic scores that were standardized using weighted MCS average polygenic scores that should represent an unbiased estimate representative of the background population (Supplementary Methods). We also constructed composite polygenic scores combining individual polygenic scores (Supplementary Methods).

    Analyses of polygenic scores

    Evaluating variance explained by polygenic score

    We evaluated how much variance in risk of neurodevelopmental conditions was explained by the polygenic score on the liability scale82,89,90. We compared 6,397 probands with neurodevelopmental conditions from DDD to 9,270 controls from UKHLS, and 3,618 probands with neurodevelopmental conditions from GEL to 13,667 GEL controls defined as described above. We assumed the population prevalence of neurodevelopmental conditions to be 1% (ref. 2).

    Comparing polygenic scores between different subsets

    We used two-sided t-tests to compare polygenic scores between different groups of probands, parents and controls seen in Figs. 2a and 3b, Extended Data Figs. 5 and 6 and Supplementary Tables 5–7. We report the mean difference in principal component-corrected polygenic scores between groups. Groups who were compared with each other include:

    • Combined set of controls from GEL and UKHLS

    • Control individuals from UK birth cohorts, ALSPAC and MCS

    • Undiagnosed neurodevelopmental condition (NDC) probands regardless of trio status

    • Diagnosed NDC probands regardless of trio status

    • Undiagnosed NDC probands for whom both parents are unaffected

    • Unaffected parents of undiagnosed NDC probands

    • Undiagnosed NDC probands with one or both parents affected

    • Affected parents of undiagnosed NDC probands

    • Diagnosed NDC probands for whom both parents are unaffected

    • Unaffected parents of diagnosed NDC probands

    • NDC probands with de novo diagnoses for whom both parents are unaffected

    • Unaffected parents of NDC probands with de novo diagnoses

    • Diagnosed NDC probands with one or both parents affected

    • Affected parents of diagnosed NDC probands.

    Note that ‘undiagnosed’ and ‘diagnosed’ here indicate whether the patient has a monogenic diagnosis. The sample size of each subset is listed in Supplementary Table 1. We excluded controls from UKHLS as well as DDD CoreExome and GSA probands when testing the DDD-derived polygenic score for neurodevelopmental conditions (as these had been included in the original GWAS, whereas the individuals genotyped on the OmniChip had not). All the t-tests involving probands with a neurodevelopmental condition or their parents were performed in samples from DDD and GEL combined.

    We also compared female probands versus male probands without a monogenic diagnosis regardless of trio status (2,427 and 1,574 male probands from DDD and GEL, and 1,426 and 918 female probands from DDD and GEL), and unaffected mothers versus unaffected fathers (1,523 trios from DDD and 1,343 trios from GEL) using two-sided t-tests (Extended Data Fig. 8ab).

    Polygenic score and diagnostic status

    We compared average polygenic scores in probands with a neurodevelopmental condition with and without a monogenic diagnosis using two-sided t-tests, combining probands from DDD and GEL regardless of whether they were in a trio or not. We compared subgroups from families affected by neurodevelopmental conditions to the combined control set from UKHLS and GEL, as well as to unrelated children from the MCS cohort who were reweighted using available sociodemographic data to make them more representative of the general UK population (Supplementary Note 4).

    Within DDD (N = 7,549 without excluding Scottish samples or samples who were related to GEL participants), we tested whether the proband’s PGSEA was associated with factors affecting getting a diagnosis in linear regression models:

    $${\rm{PGS}}\sim {\rm{factor}}$$

    Note that we use the tilde symbol to indicate that the variable before the tilde was regressed on the variable(s) after the tilde. We investigated the following binary factors: trio status (N = 5,507 with both parents exome sequenced but not necessarily genotyped), proband sex (N = 4,421 male probands), whether the proband had any affected first-degree relatives (N = 1,623), whether the proband was born preterm (N = 1,098 with gestation <37 weeks), whether the mother had diabetes (N = 242) and whether the proband had severe intellectual disability or developmental delay (ID/DD; N = 941) versus mild or moderate ID/DD (N = 1,887). We compared probands with the above-mentioned characteristics to all other probands, except when comparing probands with severe versus mild or moderate ID/DD for which we excluded probands without ID/DD or with ID/DD of unknown severity. We also investigated a continuous factor, the degree of consanguinity, quantified by the fraction of the genome in runs of homozygosity (FROH) divided by 0.0625, which is the expected fraction given a first-cousin marriage.

    We also tested whether the mother’s or father’s PGSEA was associated with the above factors, in a total of 2,497 samples; we did not test for association with trio status as parental genotype data were only available for full trios anyway.

    To assess how the association between the above-mentioned factors and diagnostic status changed after correcting for proband’s PGSEA, as well as how the association between proband’s PGSEA and diagnostic status changed after controlling for these factors, we fitted the following logistic regression models:

    $${\rm{Diagnostic\; status}}\sim {\rm{factor}}$$

    $${\rm{Diagnostic\; status}}\sim {\rm{PGS}}$$

    $${\rm{Diagnostic\; status}}\sim {\rm{PGS}}+{\rm{factor}}$$

    We also fitted a joint model to assess the effect of PGSEA on diagnostic status controlling for both trio status and prematurity, which showed significant associations with both PGSEA and diagnostic status. We excluded from this joint model factors that were not associated with PGSEA or diagnostic status within the DDD samples with European ancestry (sex, maternal diabetes and FROH), and factors that are likely to be the consequence of having or not having a monogenic diagnosis, rather than a cause of getting a diagnosis (severity of ID/DD and having affected family members).

    See the Supplementary Methods for a description of estimation of the odds ratio of diagnosis for different configurations of affected relatives shown in Extended Data Fig. 7a.

    Evaluating over-transmission of polygenic scores

    We conducted polygenic transmission disequilibrium tests (pTDTs) in undiagnosed and diagnosed probands from DDD (N = 1,523 undiagnosed, 443 diagnosed) and GEL (N = 1,343 undiagnosed, 507 diagnosed) combined. We also conducted pTDTs in these trios excluding autistic probands.

    The pTDT is a two-sided one-sample t-test of the probands’ polygenic score deviation from expectation, which is their parents’ mean polygenic score. The pTDT deviation is defined as:

    $${\rm{pTDT}}\;{\rm{deviation}}={{\rm{PGS}}}_{{\rm{child}}}-({{\rm{PGS}}}_{{\rm{mother}}}+{{\rm{PGS}}}_{{\rm{father}}})/2$$

    To evaluate whether the pTDT deviation is significantly different from 0, the pTDT test statistic (tpTDT) is defined as:

    $${t}_{{\rm{pTDT}}}=\frac{{\rm{mean}}({\rm{pTDT}}\;{\rm{deviation}})}{\frac{{\rm{s.d.}}({\rm{pTDT}}\;{\rm{deviation}})}{\sqrt{n}}}$$

    Association with non-transmitted alleles

    Alleles in parents that are not transmitted to the child can still influence the child’s phenotype by affecting the parents’ behaviour. This phenomenon is called genetic nurture or indirect genetic effects4,26,30. Alleles that are transmitted to the child can influence the child’s phenotype both directly (direct genetic effects) and indirectly through other relatives who carry the same alleles (indirect genetic effects) and whose behaviour is influenced by those alleles. Kong et al. proposed to estimate the direct genetic effect as δ = θT − θNT, where θT indicates the effect of parental transmitted alleles and θNT indicates the effect of parental non-transmitted alleles, which capture both the indirect genetic effects and potential confounding factors4,91. We can estimate θT and θNT of a given polygenic score in the following regression model:

    $${{\rm{c}}{\rm{h}}{\rm{i}}{\rm{l}}{\rm{d}}}^{{\prime} }\,{\rm{s}}\,{\rm{p}}{\rm{h}}{\rm{e}}{\rm{n}}{\rm{o}}{\rm{t}}{\rm{y}}{\rm{p}}{\rm{e}}\sim {\hat{\theta }}_{{\rm{T}}}\times {{\rm{P}}{\rm{G}}{\rm{S}}}_{{\rm{T}}}+{\hat{\theta }}_{{\rm{N}}{\rm{T}}}\times {{\rm{P}}{\rm{G}}{\rm{S}}}_{{\rm{N}}{\rm{T}}}$$

    where PGST is a polygenic score calculated using transmitted alleles (which is the child’s polygenic score), and PGSNT is a polygenic score calculated using parental non-transmitted alleles, which is equivalent to the difference between the sum of parents’ polygenic scores and the child’s polygenic score. This model can also be rewritten as:

    $$\begin{array}{l}{{\rm{child}}}^{{\prime} }\,{\rm{s}}\,{\rm{phenotype}}\sim ({\widehat{\theta }}_{{\rm{T}}}-{\widehat{\theta }}_{{\rm{NT}}})\,\times \,{{\rm{PGS}}}_{{\rm{child}}}\\ \,\,\,\,\,\,\,\,\,+\,{\widehat{\theta }}_{{\rm{NT}}}\times ({{\rm{PGS}}}_{{\rm{mother}}}+{{\rm{PGS}}}_{{\rm{father}}})\end{array}$$

    Therefore, in a regression model in which the child’s polygenic score and parents’ polygenic scores are both fitted, the coefficient on the child’s polygenic score captures the direct genetic effect, and the coefficient on parents’ polygenic scores captures the association between non-transmitted alleles and the child’s phenotype. The latter may reflect true indirect genetic effects as well as confounding effects such as uncorrected population stratification and parental assortment29. Thus, we refer to the coefficients on parents’ polygenic scores in this model as ‘non-transmitted coefficients’ rather than simply ‘indirect genetic effects’, following Young et al.24, as they are mathematically equivalent to the coefficients on the polygenic score constructed from the non-transmitted alleles in a joint regression with the proband’s polygenic score.

    We evaluated direct genetic effects (\(\hat{\delta }\)) and effects of maternal and paternal non-transmitted common alleles (\({\widehat{\theta }}_{m,{\rm{NT}}}\) and \({\widehat{\theta }}_{f,{\rm{NT}}}\)) on case status in the following trio model using logistic regression on polygenic scores:

    $${1}_{{\rm{N}}{\rm{D}}{\rm{C}}{\rm{s}}{\rm{t}}{\rm{a}}{\rm{t}}{\rm{u}}{\rm{s}}}\sim \hat{\delta }\times {{\rm{P}}{\rm{G}}{\rm{S}}}_{{\rm{c}}{\rm{h}}{\rm{i}}{\rm{l}}{\rm{d}}}+{\hat{\theta }}_{m,{\rm{N}}{\rm{T}}}\times {{\rm{P}}{\rm{G}}{\rm{S}}}_{{\rm{m}}{\rm{o}}{\rm{t}}{\rm{h}}{\rm{e}}{\rm{r}}}+{\hat{\theta }}_{f,{\rm{N}}{\rm{T}}}\times {{\rm{P}}{\rm{G}}{\rm{S}}}_{{\rm{f}}{\rm{a}}{\rm{t}}{\rm{h}}{\rm{e}}{\rm{r}}}$$

    where 1NDC status is an indicator variable for whether the individual is a case with a neurodevelopmental condition (1) or control (0). We also ran the regression without correcting for parents’ polygenic scores (proband-only model) in the same samples for comparison:

    $${1}_{{\rm{N}}{\rm{D}}{\rm{C}}{\rm{s}}{\rm{t}}{\rm{a}}{\rm{t}}{\rm{u}}{\rm{s}}}\sim {\hat{\theta }}_{{\rm{T}}}\times {{\rm{P}}{\rm{G}}{\rm{S}}}_{{\rm{c}}{\rm{h}}{\rm{i}}{\rm{l}}{\rm{d}}}$$

    Probands with a neurodevelopmental condition were from DDD and GEL trios where the proband was undiagnosed and both parents were unaffected (N = 2,866 trios). Control samples were trios from the two birth cohorts (ALSPAC and MCS, N = 1,434 and N = 2,498, respectively) as well as trios from GEL where the proband did not have DDD-like developmental disorders or neurodevelopmental conditions (N = 872).

    We verified that the polygenic scores in the trio model did not show excessive collinearity (Supplementary Methods).

    We performed various sensitivity analyses in the following subsets (Supplementary Fig. 4): patients versus controls from GEL trios only, and patients from GEL and DDD versus each of the three control cohorts separately (GEL, MCS or ALSPAC). We also conducted the analysis while controlling for the rare variant burden score (RVBS) in GEL trios (Extended Data Fig. 10b; section below on ‘Analyses of polygenic scores and rare coding variants’).

    $$\begin{array}{c}{1}_{{\rm{N}}{\rm{D}}{\rm{C}}{\rm{s}}{\rm{t}}{\rm{a}}{\rm{t}}{\rm{u}}{\rm{s}}}\sim {{\rm{P}}{\rm{G}}{\rm{S}}}_{{\rm{c}}{\rm{h}}{\rm{i}}{\rm{l}}{\rm{d}}}+{{\rm{R}}{\rm{V}}{\rm{B}}{\rm{S}}}_{{\rm{c}}{\rm{h}}{\rm{i}}{\rm{l}}{\rm{d}}}+{{\rm{P}}{\rm{G}}{\rm{S}}}_{{\rm{m}}{\rm{o}}{\rm{t}}{\rm{h}}{\rm{e}}{\rm{r}}}\\ \,\,\,\,\,+\,{{\rm{R}}{\rm{V}}{\rm{B}}{\rm{S}}}_{{\rm{m}}{\rm{o}}{\rm{t}}{\rm{h}}{\rm{e}}{\rm{r}}}+\,{{\rm{P}}{\rm{G}}{\rm{S}}}_{{\rm{f}}{\rm{a}}{\rm{t}}{\rm{h}}{\rm{e}}{\rm{r}}}+{{\rm{R}}{\rm{V}}{\rm{B}}{\rm{S}}}_{{\rm{f}}{\rm{a}}{\rm{t}}{\rm{h}}{\rm{e}}{\rm{r}}}\end{array}$$

    We restricted this latter analysis to GEL trios to minimize artefactual differences in rare variant calling and QC between cases and controls, which could otherwise create spurious associations.

    See the Supplementary Methods for a description of how we modified the running of this trio model to investigate the hypothesis that the effects of non-transmitted alleles associated with educational attainment and cognition might be mediated by prematurity.

    Analyses of polygenic scores and rare coding variants

    Sequence data from DDD, GEL and MCS were annotated with the Ensembl Variant Effect Predictor (VEP)92. We kept the ‘worst consequence’ annotation across transcripts. From parents and probands, we extracted autosomal heterozygous PTVs (transcript ablation, frameshift, splice acceptor, splice donor and stop gained) annotated as high-confidence by LOFTEE93 (HC PTVs), as well as variants in the following classes that we grouped as ‘missense’: missense, stop lost, start lost, inframe insertion, inframe deletion and loss-of-function variants annotated as low-confidence by LOFTEE93. We retained rare variants with MAF < 1 × 10−5 in each gnomAD super-population and MAF < 1 × 10−4 in the respective cohorts.

    We considered four (non-mutually exclusive) groups of damaging rare variants:

    1. 1.

      HC PTVs in constrained genes (pLI > 0.9)94

    2. 2.

      HC PTVs and missense variants (MPC ≥ 2)95 in constrained genes (pLI > 0.9)

    3. 3.

      HC PTVs in monoallelic DDG2P genes with a loss-of-function mechanism (that is, ‘absent gene product’)

    4. 4.

      HC PTVs and missense variants (MPC ≥ 2) in monoallelic DDG2P genes with a loss-of-function mechanism.

    We retained probands and parents who were heterozygous for these variants. We required the variants in the children to have been inherited from a parent.

    To investigate whether parental assortment leads to correlated rare and common variant burden, we calculated rare variant burden scores as the number of rare variants in the classes described above, then calculated the Pearson’s correlation coefficients between rare variant burden scores and polygenic scores using the ‘cor’ function in R. We used trios in which both parents were unaffected in this analysis. Rare variant burden scores were corrected for 20 genetic principal components using linear regression models. We then calculated the correlation coefficients between the principal component-adjusted rare variant burden scores in parents and the principal component-adjusted polygenic scores in their partners. We also assessed the correlation within the same person among either children or parents. We repeated the analysis in subsets of trios in which the proband was undiagnosed as well as in trios in which the proband had a monogenic de novo diagnosis (Supplementary Fig. 6). The main analysis in Fig. 5 and the sensitivity analysis in Extended Data Fig. 10b is based on group 2 above, whereas Supplementary Figs. 6–8 show the results for all four groups of variants. To investigate whether the results were affected by uncorrected population structure, we also calculated rare variant burden scores using rare synonymous variants in either monoallelic DDG2P genes with a loss-of-function mechanism or constrained genes, and assessed their correlation with polygenic score.

    To assess whether polygenic scores modify penetrance of rare inherited variants, we conducted one-sided paired t-tests comparing the polygenic score between unaffected parents transmitting a damaging variant to their affected offspring who inherited the variant (Supplementary Fig. 8). We hypothesized that the unaffected parents would have a more protective polygenic background than their affected offspring (indicated by higher PGSEA, PGSCP, PGSNonCogEA and lower PGSSCZ, PGSNDC,DDD). If more than one parent transmitted a variant to a proband, one parent–child pair was chosen at random from the trio. We used trios in which the proband was undiagnosed and both parents were unaffected in this analysis.

    Construction and use of weights for MCS

    We were concerned that control cohorts might not be random samples of the population with respect to educational attainment, and that this might bias our effect sizes for the difference in polygenic scores between cases and controls (Supplementary Note 4). We decided to use MCS, for which extensive sociodemographic data are available, to calculate a mean polygenic score that would be representative of the general population, using inverse-probability weighting. MCS deliberately oversampled minority ethnic and disadvantaged individuals in the United Kingdom96 (sampling bias), and they provide sampling weights to account for this. Furthermore, missingness in each wave of data collection, including the collection of DNA for genotyping, was non-random (non-response bias). To correct for non-response bias, we produced non-response weights per individual using the inverse of the probability of being genotyped estimated from a logistic regression, considering covariates collected at the first study sweep, as previously described96,97 (Supplementary Methods). We fitted the model to predict who was in the sample of unrelated children of GBR ancestry with genotype data (N = 5,884 of 6,036 children who had complete data for these covariates), and separately to predict who was in the subset of these that also had genotype data on both parents (N = 2,445 of 2,498 trio children who had no missingness). To produce weights that account for both sampling bias and non-response bias, we multiplied the non-response weight from regression models by the sampling weights provided by MCS. These weights were then used to calculate adjusted polygenic scores shown in Fig. 3b and Extended Data Figs. 5 and 6c and adjusted correlation between polygenic score and rare variant burden score shown in Supplementary Fig. 7.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    [ad_2]

    Source link

  • Author Correction: Restoration of vision after transplantation of photoreceptors

    [ad_1]

    a, Schematic of water-maze apparatus (adapted from ref. 22; see Supplementary Information). Mice were trained to associate striped grating with escape from water by a hidden platform. An animal ‘passes’ a trial by crossing the red line (decision point) on the side of the divider with the striped grating. b, Pass rate of Nrl-GFP-treated (black), sham-injected (dark grey) and non-injected (mid grey) Gnat1−/− and non-injected wild-type (light grey) mice. Nrl-GFP-treated animals with a pass-rate of at least 70% are shown in green throughout. Mouse numbers in red refer to mice shown in Supplementary Movie. c, Average performance rate of all groups. d, Visual acuity and e, contrast sensitivity measurements for responders from Nrl-GFP-treated (green) and wild-type (light grey) groups. f, Swim-time latencies (time-to-platform) for all (light grey) and correct choice-only (dark grey) trials. g, Ability to solve water-maze task plotted against integrated Nrl-GFP photoreceptor number. h, Examples of integration in animals that successfully (top; Nrl-GFP-treated, number 6) or unsuccessfully (bottom; Nrl-GFP-treated, number 5) solved the task, as indicated in g (circled, red). These panel images are cropped from montages composed of multiple smaller images manually assembled across overlapping areas. Scale bar, 100 µm. i–k, Pass rate (i), visual acuity (j) and contrast sensitivity (k) for Nrl-GFP-treated (light grey bars) and sham-injected (dark grey bars) Gnat1−/− mice before and after transplantation under photopic conditions. Means ± s.e.m.; ANOVA; n, number of animals.

    [ad_2]

    Source link

  • Antisense oligonucleotide therapeutic approach for Timothy syndrome

    [ad_1]

    Culture of hiPS and HEK293T cells

    The hiPS cells in this study were previously described and validated2,3. A total of six hiPS cell lines were derived from fibroblasts collected from three healthy individuals and three with TS. Approval for this study was obtained from the Stanford IRB panel, and informed consent was obtained from all participants. The isogenic TS1 (G406R) line was derived in the KOLF2.1 hiPS cell line via nucleofection using the guide RNA-targeting GGTGTGCTTAGCGG and the homologous repair template ssODN, aggaatagcagaaagaataaataaaaataaatggaaaaatcaagacctttttccttggtcctgcttacCTGCTAAGCACACCGAGAACCAAGTTAAGTAC33. The CW30293 hiPS cell line was obtained from CIRM. The presence of the heterozygous mutation was confirmed by Sanger sequencing. hiPS cells were cultured in feeder-free essential 8 medium (E8, Thermo Fisher Scientific, catalogue no. A1517001) without antibiotics and kept in the wells of six-well plates (Corning, catalogue no. 3506) coated for 1 h at room temperature with vitronectin recombinant human protein (VTN-N, Thermo Fisher Scientific, no. A14700) diluted 1:100 to a final concentration of 5 ng ml−1 in Dulbecco’s PBS (DPBS), with neither calcium nor magnesium (Thermo Fisher Scientific, catalogue no. 14190136). To facilitate passaging, hiPS cells were first washed with DPBS and then incubated with 0.5 mM EDTA (Invitrogen, catalogue no. 15575020) in DPBS at room temperature for 7 min. Following removal of EDTA solution, cells were seeded in fresh wells of six-well plates coated with VTN-N and containing E8 medium. The hiPS cells used in this study were maintained free of Mycoplasma at 37 °C in a humidified-air atmosphere with 5% CO2. The lenti-X 293T cell line, a subclone of HEK293T cells, was obtained from Takara Bio (catalogue no. 632180) and cultured in DMEM (Gibco, catalogue no. 10313021) supplemented with 10% fetal bovine serum (Corning, catalogue no. 35016CV) and 1× GlutaMAX (Thermo Fisher Scientific, catalogue no. 35050061). This cell line was chosen because it is compatible with robust plasmid overexpression.

    Generation of hCO and hSO from hiPS cells

    The generation of hCO, hSO and hFA was performed as previously described3,34,35. In brief, hiPS cells were incubated with Accutase (Innovate Cell Technologies, no. AT-104) at 37 °C for 7–8 min and dissociated into single hiPS cells. Single-cell suspensions were collected in a 50 ml Falcon tube and cell pellets obtained via centrifugation at 300g for 3 min. Cell numbers were counted following resuspension of cell pellets. Approximately 3 × 106 cells in 2 ml of E8 medium supplemented with ROCK inhibitor Y-27632 (10 μM, Selleckchem, catalogue no. S1049) were added per well of an AggreWell 800 plate (STEMCELL Technologies, catalogue no. 34815). The plates were then centrifuged at 100g for 3 min to allow cells to sink to the bottom of the wells (day 0). Twenty-four hours following cell aggregation (day 1), spheroids were dislodged by pipetting (with a P1000 tip cut at the end) and transferred to ultralow-attachment plastic dishes (Corning, no. 3262) in essential 6 medium (E6, Life Technologies, no. A1516401) supplemented with 2.5 μM dorsomorphin (Sigma-Aldrich, catalogue no. P5499) and 10 μM SB-431542 (Tocris, catalogue no. 1614). From days 2 to 6, E6 medium was changed daily and supplemented with dorsomorphin and SB-431542. In addition the Wnt pathway inhibitor XAV-939 (XAV, 1.25 μM, Tocris, catalogue no. 3748) was added, together with dorsomorphin and SB-431542. On the seventh day in suspension, basal medium was switched to neural medium consisting of Neurobasal A (Life Technologies, catalogue no. 10888), B-27 supplement without vitamin A (B-27, Life Technologies, catalogue no. 12587), GlutaMAX (1:100, Life Technologies, catalogue no. 35050) and 10 U ml−1 penicillin-streptomycin (Gibco, catalogue no. 15140122). From days 6 to 24 the neural medium was supplemented with 20 ng ml−1 epidermal growth factor (EGF, R&D Systems, catalogue no. 236-EG) and 20 ng ml−1 basic fibroblast growth factor (FGF, R&D Systems, catalogue no. 233-FB) for 19 days (until day 24), with medium changed daily from days 7–18 and every other day until day 24. From days 25–42 the neural medium contained 20 ng ml−1 brain-derived neurotrophic factor (Peprotech, catalogue no. 450-02) and 20 ng ml−1 NT3 (Peprotech, catalogue no. 450-03), with medium change every other day. From day 43, hCO were cultured with only neural medium without growth factors. The generation of hSO differs from that of hCO in that, from days 7–12, the neural medium was supplemented with XAV (1.25 μM) in addition to EGF and FGF; from days 13–24 the neural medium was supplemented with XAV (1.25 μM) and SAG (100 nM, EMD Millipore, catalogue no. 566660) in addition to EGF and FGF.

    ASOs

    ASOs were 20-nt-long synthesized using the phosphorothioate backbone and with a MOE modification. 5-Methylcytosine was used during synthesis rather than cytosine. ASOs tested on hiPS cell-derived forebrain organoids were purified by standard desalting followed by Na+ salt exchange. These ASOs were reconstituted in nuclease-free water at a concentration of 1 mM and stored at −20 °C thereafter for in vitro experiments. For in vivo injection, ASO.14 was reconstituted at a concentration of 10 μg μl−1 in DPBS for injection of 30 μl of 300 μg ASO into rat cisterna magna. All ASOs used in this study were manufactured by Integrated DNA Technologies. Cy5-labelled ASOs were synthesized by the addition of Cy5 to the 5′ of the ASO (Integrated DNA Technologies) followed by HPLC purification and Na+ salt exchange.

    Recombinant DNA and viruses

    pDup4-1 was obtained from Addgene (plasmid no. 23022) and was used as the backbone for the minigene splicing reporter. pDup4-1 was digested with ApaI and BglII (New England Biolabs) and the resulting 4,595 bp fragment was purified following loading on a 1% agarose gel using the QIAquick PCR Purification Kit (Qiagen, catalogue no. 28106). Genomic DNA from TS hiPS cells was purified with the DNeasy Blood & Tissue Kit (Qiagen, catalogue no. 69506). Amplicons encompassing exons 8 and 8A of CACNA1C were amplified with GoTaq Long PCR Master Mix (Promega, catalogue no. M4021). Primer sequences and cycling conditions are listed in Supplementary Tables 1 and 2. Purified PCR products were digested with ApaI and BglII. Following one further round of purification, DNA was dephosphorylated with FastAP thermosensitive alkaline phosphatase (Thermo Fisher Scientific, catalogue no. EF0654) then ligated to the pDup4-1 backbone using T4 DNA ligase (Thermo Fisher Scientific, catalogue no. EL0011). Following transformation (One Shot Stbl3 Chemically Competent E. coli, Thermo Fisher Scientific, catalogue no. C737303), colonies were picked for sequence verification. The human PTBP1 ORF plasmid was obtained from Genscript (clone ID OHu15891D, accession no. NM_002819.5). Plasmids encoding WT and TS CaV1.2 were synthesized by VectorBuilder based on transcript ENST00000399655.6 under a CAG promoter into a lentivirus backbone. An HA tag was placed in between Q683 and T684. The GCaMP plasmid was obtained from Addgene (plasmid no. 111543). Plasmids encoding the β1b and a2δ subunits of the L-type calcium channel were described previously5. The maps and sequences of minigene splicing reporters and human CaV1.2 expression plasmids are included in Supplementary Figs. 3–6 (generated by SnapGene 5.1.4.1, SnapGene software from Dotmatics).

    RNA extraction and qPCR

    For all samples, RNA was extracted using the RNeasy Plus Mini Kit (Qiagen, catalogue no. 74136). Unless otherwise noted, reverse transcription was performed using the SuperScript III First-Strand Synthesis SuperMix for qRT-PCR (Invitrogen, catalogue no. 11752050) according to the manufacturer’s instructions. qPCR was performed on a QuantStudio 6 Flex Real-Time PCR system (Thermo Fisher Scientific, catalogue no. 4485689) using SYBR Green PCR Master Mix (Thermo Fisher Scientific, catalogue no. 4312704). Primers for qPCR are listed in Supplementary Tables 1 and 2.

    Transcript analysis of CACNA1C exons 8 and 8A

    Restriction fragment-length polymorphism analysis of CACNA1C exons 8 and 8A was performed on PCR fragments amplified from cDNA. DNA was purified using AMPure XP beads (Beckman Coulter, catalogue no. A63881) according to the manufacturer’s instructions. Purified DNA was digested with BamHI (Thermo Fisher Scientific, catalogue no.ER0055) at 37 °C for 3 h and loaded on 2% agarose gel. Gel images were taken on a Gel Doc XR+ imager (Bio-Rad, catalogue no. 1708195). For next-generation sequencing analysis of transcripts, primers with the Illumina adaptor were used to amplify the region encompassing exons 7–9. Following bead purification, DNA was eluted in water and sent for sequencing using the Genewiz Amplicon-EZ module. Next-generation sequencing analysis of the minigene splicing reporter was performed similarly by amplifying minigene transcripts from the cDNA of transfected HEK cells 3 days post transfection. Primers and cycling conditions are listed in Supplementary Tables 1 and 2.

    Transfection of HEK cells

    Approximately 30,000–75,000 HEK cells were seeded per well of a 24-well plate (Corning, catalogue no. 353047). The following day, plasmids were mixed with 1 mg ml−1 PEI MAX (Polysciences, catalogue no. 24765-1) in 50 μl of a 150 mM NaCl solution. Following about 10 s of vigorous vortexing, plasmid mixtures were incubated for 15 min at room temperature and then added to the wells (Supplementary Tables 3–5).

    Dissociation for monolayer culture

    Dissociation of hCO for monolayer culture was performed as previously described, with minor optimizations4. Coverslips were coated with approximately 0.001875% polyethylenimine (PEI, Sigma-Aldrich, catalogue no. 03880) for 1 h at 37 °C, washed four times with water and dried. On the day of dissociation, betweeen four and six hCO per hiPS cell line were transferred to wells in six-well plates (Corning, catalogue no. 3506) and incubated for 45–60 min at 37 °C with 3 ml of enzymatic dissociation solution. This solution consisted of 30 U ml−1 papain (Worthington Biochemical, catalogue no. LS003127), 1× EBSS (Millipore Sigma, catalogue no. E7150), 0.46% d(+)-glucose, 0.5 mM EDTA, 26 mM NaHCO3, 10 μM Y-27632, 125 U ml−1 deoxyribonuclease I (Worthington Biochemical, catalogue no. LS002007) and 6.1 mM l-cysteine (Millipore Sigma, catalogue no. C7880). Following papain incubation, samples were collected in a 15 ml Falcon tube and centrifuged at 1,200 rpm for 1 min. Following removal of the supernatant, samples were washed with 1 ml of inhibitor solution with 2% trypsin inhibitor (Worthington Biochemical, catalogue no.LS00308) and resuspended in 1 ml of the same solution for trituration. Following trituration, 1 ml of inhibitor solution with 4% trypsin inhibitor was added slowly beneath the cell suspension to create a gradient layer; the gradient solution was then centrifuged at 1,200 rpm for 5 min. Cell pellets were resuspended in culture medium consisting of Neurobasal A supplemented with B-27 and 10 μM Y-27632. Undissociated tissue was removed by passing the cell suspension through a 40 μm cell strainer (Corning, catalogue no. 352340). Finally, dissociated cells were seeded on the coverslip at a density of 50,000 cells per coverslip in 1 ml of culture medium. The inhibitor solution differs from the enzyme solution in that it contains neither papain nor EDTA. All centrifugation steps were performed at room temperature.

    Calcium imaging

    Fura-2 calcium imaging on monolayer hCO cells was performed as previously described26. In brief, cells were loaded with 1 mM Fura-2 acetoxymethyl ester (Fura-2 AM, Invitrogen, no. F1221) for 30 min at 37 °C in NM medium, washed with NM medium for 5 min and then transferred to a perfusion chamber (RC-20, Warner instruments) in low-potassium Tyrode’s solution (5 mM KCl, 129 mM NaCl, 2 mM CaCl2, 1 mM MgCl2, 30 mM glucose, 25 mM HEPES pH 7.4) on the stage of an inverted fluorescence microscope (Eclipse TE2000U, Nikon). Following 0.5 min of baseline imaging, high-potassium Tyrode’s solution was perfused for 1 min. Imaging was performed at room temperature (25 °C) on an epifluorescence microscope equipped with an excitation filter wheel and an automated stage. Openlab software (PerkinElmer) and IGOR Pro (v.5.1, WaveMetrics) were used to collect and quantify time-lapse excitation 340:380-nm-ratio images at an imaging rate of approximately 1 Hz, as previously described20. Residual calcium was calculated as (C − A)/(B − A), where A is the baseline value (fifth frame), B is the peak value following depolarization (manually determined) and C is the decay value (B + 25th frame).

    For GCaMP imaging, HEK293T cells were seeded in 24-well plates. The following day, cells were transfected with a mixture of plasmids including subunits CaV1.2 β1b, α2δ and α1 and GCaMP6-X (Supplementary Table 3). Three days post transfection, imaging was performed with an SP8 confocal microscope (Leica Microsystems) at a frame interval of 1.2875 s. Before imaging, cell culture medium was replaced with 500 μl of 5 mM Tyrode’s solution. Following 30 s of baseline imaging, 500 μl of 129 mM Tyrode’s solution (final concentration 67 mM KCl) was added.

    Similarly, for GCaMP imaging in two-dimensional neurons, TS and WT hCO were dissociated into 24-well imaging plates (Cellvis P24-0-N) and infected with AAV-DJ-hSYN1::GCaMP6f (Gene Vector and Virus Core, Wu Tsai Neurosciences Institute, Stanford University). Various concentrations of ASOs (ASO.14, ASO.17, ASO.18 and ASO.Scr) were applied to dissociated neurons. After 10 days, GCaMP imaging was carried out with an SP8 confocal microscope using the 20× objective at 1.2875 s per frame). Before imaging, culture medium was replaced with 500 μl of 5 mM Tyrode’s solution. Following 30 s of baseline imaging, 500 μl of 129 mM Tyrode’s solution (final concentration 67 mM KCl) was added. Imaging was acquired over a total time of 8 min.

    For GCaMP imaging analysis of HEK293T cells, regions of interest (ROIs) corresponding to cell somas were identified semiautomatically using a custom-written ImageJ segmentation macro. ROIs were detected in the frame following depolarization (fifth or sixth frame following KCl administration) by applying a mask, watershedding and using the ‘Analyze particles’ function (size 10–1,000, circularity 0.4–1.0). A minority of ROIs were manually excluded due to either cell drift, off-target detection of background or detection of more than a single soma within the same ROI. For GCaMP analysis in neurons, ROIs corresponding to cell somas were manually annotated. Downstream analyses for both HEK293T cells and neurons were performed using custom-written R codes. Mean grey values were transformed to relative changes in fluorescence: dF/F(t) = (F(t) − F0)/F0, where F0 represents average grey values of the time series of each ROI. Cells were excluded if their amplitude was lower than the baseline mean or more than 20× baseline mean. Residual calcium values were calculated as described above, with B representing peak value, A baseline value (20 frames upstream of the peak-value frame) and C decay value (200 frames after the peak-value frame). Extreme residual calcium values (lower than −5 or higher than +5) were excluded.

    Patch-clamp recordings

    Patch-clamp recordings were performed on cortical neurons dissociated from hCO, as previously described4. hCO were dissociated at days 100–150. A few days following dissociation, cells were infected with AAV-DJ-SYN1::eYFP and 1 μM ASO was added 1 week following dissociation. Recordings were typically made around 3–4 weeks following dissociation. Cells were identified as eYFP+ with an upright slice scope microscope (Scientifica) equipped with an Infinity2 CCD camera and Infinity Capture software (Teledyne Lumenera). Recordings were performed with borosilicate glass electrodes with a resistance of 7–10 MΩ. For barium current recordings the external solution contained 100 mM NaCl, 3 mM KCl, 2 mM MgCl2, 20 mM BaCl2, 25 mM TEA-Cl, 4 mM 4-AP, 10 mM HEPES and 20 mM glucose pH 7.4, with NaOH and 300 mOsm. The internal solution contained 110 mM CsMethylSO3, 30 mM TEA-Cl, 10 mM EGTA, 4 mM MgATP, 0.3 mM Na2GTP, 10 mM HEPES and 5 mM QX314-Cl pH 7.2, with CsOH and 290 mOsm. Data were acquired with a MultiClamp 700B Amplifier (Molecular Devices) and a Digidata 1550B Digitizer (Molecular Devices), low-pass filtered at 2 kHz, digitized at 20 kHz and analysed with pCLAMP (v.10.6, Molecular Devices). Cells were subjected to −10 mV hyperpolarization (100 ms) every 10 s to monitor input and access resistance. Cells were excluded for analysis if they showed a change of over 30%. Liquid junction potential was not corrected in this study.

    For barium current recordings, cells were recorded in the presence of tetrodotoxin (TTX) (0.5 μM) to block sodium currents and were held at −70 mV in voltage-clamp and depolarizing voltage steps (5 s for the majority of cells, from −70 to +20 mV) in increments of 5 mV. Inactivation of barium current was calculated from cells subjected to 5 s or 2–3-s depolarization steps at 2 s under maximal current (−20 to 0 mV for the majority). For some cells, recordings with a prestep of −110 mV (or −100 mV) hyperpolarization were also included for inactivation at 2 s. Leak subtraction was used to minimize the artefact of membrane resistance in MultiClamp 700B. I–V curves were fitted in Origin (OriginPro 2021b, OriginLab) with a Boltzmann exponential function: I = Gmax × (V − EBa)/{1 + exp[(V0.5 − V)/K]}, where Gmax is the maximal conductance of calcium channels, EBa is the reversal potential of barium estimated by the curve-fitting programme, V0.5 is the potential for half-maximal, steady-state activation of barium current and K is a voltage-dependent slope factor.

    For voltage-dependent barium current inactivation, cells were held at −70 mV. A series of prepulse voltage steps (3 s) were administered, from −110 or −100 to +40 mV, in increments of 10 mV. Testing of the voltage step (−10 or 0 mV, where maximal current was recorded) was then carried out for a further 1–3 s. Barium current inactivation was calculated as relative current normalized to current amplitude from the first test pulse. Voltage-dependent inactivation curves were fitted with exponential functions in Origin.

    Immunostaining

    Dissociated cells from TS hCO at 100–120 days of differentiation were plated on precoated coverslips and placed in wells of a 12-well plate; different concentrations of Cy5-ASO.14 were then added. After 3 days the coverslips were first fixed for 10 min at room temperature with a solution containing one volume each of culture medium and fixation buffer comprising 4% paraformaldehyde (PFA) and 4% sucrose in DPBS. Next, two volumes of fixation buffer were added for an extra 20 min to finalize the fixation step. Following two rounds of washing with DPBS, coverslips were incubated for 1 h with blocking buffer consisting of 0.3% Triton X-100 and 10% normal donkey serum prepared with PBS. Following removal of the blocking buffer, primary antibodies were added for overnight incubation at 4 °C. Antibodies CTIP2 (abcam, catalogue no. ab18465) and SATB2 (abcam, catalogue no. ab51502) were diluted in blocking buffer at 1:300. Coverslips were washed twice with DPBS then incubated with secondary antibody (1:1,000 in blocking buffer; donkey anti-rat Alexa 488, Thermo Fisher Scientific, catalogue no. A-21208; and donkey anti-mouse Alexa 568, Thermo Fisher Scientific, catalogue no. A10037) at room temperature for 1 h. Following a further two rounds of washing with DPBS, Hoechst 33258 (Thermo Fisher Scientific, catalogue no. H3569) was added to coverslips for 10 min followed by a final round of washing with DPBS. Finally, coverslips were mounted on slides (Fisherbrand Superfrost Plus Microscope Slides, Fisher Scientific, catalogue no. 12-550-15) using Aqua-Poly/Mount (Polysciences, catalogue no. 18606). Images were acquired with a confocal SP8 (Leica Microsystems) using a 20× objective.

    The TUNEL assay was performed using the in situ cell death detection kit (Roche, catalogue no. 12156792910). In brief, hCO were dissociated and exposed to either 1 μM ASO or scrambled control for 48 h. Cells were then fixed in 4% PFA, permeabilized in Triton X-100 and incubated with TUNEL reaction solution for 1 h at 37 °C. Samples pretreated with DNase1 for 10 min were used as positive control. Following rinsing and counterstaining with Hoechst, coverslips were imaged with a Stellaris microscope using the 20× objective. Images were stitched in Fiji and a custom macro was used to split channels, set thresholds for detection of nuclei via Hoechst and determine Cy3+ nuclei via thresholds set blindly on control samples.

    For c-Cas3, immunostaining was performed as for Cy5 samples except that rabbit anti-c-Cas3 (Asp175) (1:300, CST, catalogue no. 9661S) and mouse anti-MAP2 antibody (1:100, Sigma-Aldrich, catalogue no. M4403) were used as primary antibodies and donkey anti-rabbit 568 (1:1,000, Thermo Fisher Scientific, catalogue no. A10042) and donkey anti-mouse Alexa:568 (1:1,000, Thermofisher Scientific, catalogue no. A10037) as secondary antibodies. Coverslips were imaged with a confocal SP8 microscope using the 40× objective. Three to four fields were acquired per coverslip. Images were analysed using Fiji with maximal projection, standardized thresholding and circularization to identify cells (via Hoechst nuclear staining) and then c-Cas3+ cells (via Cas3 staining).

    For staining of t-hCO, following slicing of fresh rat brain containing t-hCO, slices were postfixed in 4% PFA overnight at 4 °C and then washed three times with PBS. Next, slices were incubated with blocking buffer at room temperature for 1 h with 10% normal donkey serum and 0.3% (vol/vol) Triton X-100 in DPBS then incubated with primary antibody diluted in blocking buffer overnight at 4 °C (anti-HNA, mouse, 1:200, abcam, catalogue no. ab191181). Washing steps, staining with secondary antibody and staining of nuclei are described above.

    Flow cytometry

    TS hCO were incubated with 1 μM Cy5.ASO.14 in wells of 24-well, ultralow-attachment plate (Corning, catalogue no. 3473) for 2 days. hCO were then dissociated and resuspended in 200 μl of staining buffer containing 3% bovine serum albumin and 0.5 mM EDTA. Cells were incubated either with or without PE Mouse Anti-Human CD90 (BD Biosciences, catalogue no. 555596, dilution 1:100) for 30 min at 4 °C. Next, three rounds of washing steps were performed using the staining buffer and cells were resuspended in 200 μl of staining buffer and passed through a 40 μm cell strainer. Non-treated hCO not stained with CD90 served as a control for setting up the gate during cell acquisition. G575 and R670 were used for measurement of PE and Cy5 signal, respectively. Flow cytometry was performed on a BD Aria cell sorter at the Stanford Shared FACS Facility according to the Facility’s calibration instructions. Data were processed using FlowJo 10.7.1 software (BD).

    Immunoblot for measurement of CaV1.2 protein level

    hCO derived from control and TS iPS cell lines were aliquoted to wells of a 24-well, ultralow-attachment plate (Corning, catalogue no. 3473). Each well contained two or three hCO cultured in 2 ml of neural medium, followed by the addition of 1 μM ASO. Medium was 50% replaced following 3 days of ASO exposure and samples collected following 7 days of ASO exposure. Protein lysates for hCO were prepared using the RIPA buffer system (Santa Cruz, catalogue no. sc-24948). Protein lysates of t-hCO were prepared by the brief addition of 50 µl of SDS Buffer (1.5% SDS, 25 mM Tris pH 7.5) in a 1.5 ml tube followed by sonication (Qsonica Q500 sonicator; pulse 3 s on, 3 s off, amplitude 20%). Protein concentrations were quantified using the bicinchoninic acid assay (Pierce, ThermoFisher, catalogue no. 23225): 20 μg of protein per sample per lane was loaded and run on a 4–12% Bis-Tris PAGE gel (Bolt 4–12% Bis-Tris Protein Gel, Invitrogen, no. NW04122BOX) and transferred to a polyvinylidene difluoride membrane (Immobulin-FL, EMD Millipore, catalogue no. IPFL00010). Membranes were blocked with 5% bovine serum albumin in Tris buffered saline with Tween (TBS-T) for 1 h at room temperature and incubated overnight with primary antibodies against GAPDH (mouse, 1:5,000, GeneTex, catalogue no. GTX627408) and CaV1.2 (rabbit, 1:1,000, Alamone labs, catalogue no. ACC-003) for 48 h for hCO samples, and for 96 h for transplanted samples, at 4 °C. Membranes were washed three times with TBS-T and then incubated with near-infrared fluorophore-conjugated species-specific secondary antibodies—either goat anti-mouse IgG polyclonal antibody (IRDye 680RD, 1:10,000, LI-COR Biosciences, catalogue no. 926–68070) or goat anti-rabbit IgG polyclonal antibody (IRDye 800CW, 1:10,000, LI-COR Biosciences, catalogue no. 926–32211), for 1 h at room temperature. Following the application of secondary antibody, membranes were washed three times with TBS-T, once with TBS and then imaged using a LI-COR Odyssey CLx imaging system (LI-COR).

    TLR9 assay for ASO toxicity

    We used the human TLR9 reporter assay (Invivogen, catalogue no. hkb-htlr9) according to the manufacturer’s instructions. In brief, modified HEK293T cells were grown on 100 mm cell culture plates to 50–80% confluency. They were then detached in PBS, resuspended at 450,000 cells ml−1 in HEKBlue solution and replated into a 96-well plate. Positive controls were exposed to ODN2006 (Invivogen, catalogue no. tlrl-2006), and negative controls to sterile water; other samples were exposed to 1 μM ASO for 16–24 h. Following exposure, TLR9 activation was detected by spectrophotometer (620–655 nm absorption) using a monochromator plate reader (Tecan, Infinite M1000) and XFluor 2.0 software.

    Interneuron migration and imaging analysis

    Following 45–50 days of differentiation, hSO were incubated overnight with LV.Dlxi1/2b::eGFP lentiviral particles in an Eppendorf tube and transferred to a 24-well plate. After 3–7 days, hSO were coincubated with an hCO in an Eppendorf tube supplemented with 1 ml of medium to generate hFA, which were then cultured in a single well of an ultralow-attachment 24-well plate (Corning). Baseline imaging of interneuron migration was taken around 3–4 weeks following the formation of hFA. Next, 1 μM ASO was added to hFA followed by reimaging 2 weeks later. All imaging was taken over a period of 20 min for 12–15 h inside a confocal chamber at 37 °C in a humidified-air atmosphere with 5% CO2. Quantification of saltation length and frequency was performed as previously described3. Only mobile cells were included for analysis. ImageJ was used for analysis of interneuron migration. In cases where hFA moved during imaging, linear stack alignment with SIFT was used to correct minor shifts. To estimate the distance of individual saltations, Dlxi1/2b::eGFP cells showing a swelling of the soma were identified and distance (in μm) to the new position of the soma following nucleokinesis was recorded manually. The time necessary for this movement was used to calculate the speed when mobile. Typically, only cells showing two or more saltation movements were included.

    Transplantation into athymic newborn rats

    Animal procedures were performed following animal care guidelines approved by Stanford University’s Administrative Panel on Laboratory Animal Care (APLAC). Pregnant RNU euthymic (rnu/+) rats were either purchased (Charles River Laboratories) or bred in house. Animals were maintained under a 12/12 h light/dark cycle and provided food and water ad libitum. Three-to-seven-day-old athymic (FOXN1−/−) rat pups were identified by immature whisker growth before culling. Pups (both male and female) were anaesthetized with 2–3% isoflurane and mounted on a stereotaxic frame. A craniotomy, of about 2–3 mm in diameter, was performed above S1, preserving the dura intact. Next, the dura mater was punctured using a 30-G needle (approximately 0.3 mm) close to the lateral side of the craniotomy. A hCO was next moved onto a thin, 3 × 3 cm parafilm and excess medium removed. Using a Hamilton syringe connected to a 23-G, 45° needle, the hCO was gently pulled into the distal tip of the needle. The syringe was next mounted on a syringe pump connected to the stereotaxic device. The sharp tip of the needle was positioned above the 0.3-mm-wide prefabricated puncture in the dura mater (z = 0 mm) and the syringe was reduced by 1–2 mm (z = approximately −1.5 mm) until a tight seal between needle and dura mater had formed. Next, the syringe was elevated to the centre of the cortical surface at z = −0.5 mm and the hCO ejected at a speed of 1–2 μl min−1. Following completion of hCo injection, the needle was retracted at a rate of 0.2–0.5 mm min−1, the skin was closed and the pup immediately placed on a warm heat pad until complete recovery.

    MRI of transplanted rats

    All animal procedures followed animal care guidelines approved by Stanford University’s APLAC. Rats (more than 60 days post transplantation) were anaesthetized with 5% isoflurane for induction and 1–3% isoflurane during imaging. For imaging, an actively shielded Bruker 7 Tesla horizontal bore scanner (Bruker Corp.) with International Electric Company gradient drivers, a 120-mm-inner-diameter shielded gradient insert (600 mT m−1, 1,000 T m−1 s−1), AVANCE III electronics, eight-channel multicoil radiofrequency and multinuclear capabilities, and the supporting Paravision 6.0.1 platform, were used. Acquisitions were performed with an 86-mm-inner-diameter actively decouplable volume radiofrequency coil with a four-channel, cryocooled, receive-only radiofrequency coil. Axial two-dimensional Turbo-RARE (TR 2,500 ms, TE 33 ms, two averages) 16-slice acquisitions were performed at 0.6–0.8 mm slice thickness with samples of approximately 256 Å. Signal was received by a 2-cm-inner-diameter quadrature transmit–receive volume radiofrequency coil (Rapid MR International). Successful transplantations were defined as those resulting in a continuous area of T2-weighted MRI signal in the transplanted hemisphere.

    ASO injection into rat cisterna magna

    Rats were anaesthetized with 5% isoflurane for induction and 2–3% isoflurane during ASO injection through the cisterna magna. Animals were placed in the prone position with a small paper roll under the neck to tilt the head downwards. The neck was shaved and wiped clean with ethanol. To target the cisterna magna the foramen magnum was determined by touch and a 27-G needle attached to a syringe (BD, catalogue no. 305620) filled with 300 μg of ASO was percutaneously inserted into the cisterna magna perpendicularly to the neck. The needle was held with the bevel face upwards and 30 μl of ASO was slowly injected into the cisterna magna. The procedure took less than 2 min per rat. Animals recovered from anaesthesia within 10 min of isoflurane induction. ASO injections were performed in rats with t-hCO at 162–258 days and were not blinded. Sample sizes were estimated empirically.

    Processing of ASO-injected rats

    Rats were anaesthetized with isoflurane and brain tissue was removed and placed in cold (approximately 4 °C), oxygenated (95% O2 and 5% CO2) sucrose slicing solution containing 234 mM sucrose, 11 mM glucose, 26 mM NaHCO3, 2.5 mM KCl, 1.25 mM NaH2PO4, 10 mM MgSO4 and 0.5 mM CaCl2 (approximately 310 mOsm). Coronal rat brain slices (300–400 μm) containing t-hCO were sectioned using a Leica VT1200 vibratome as previously described3. t-hCO sections were then moved to a continuously oxygenated slice chamber, at room temperature, which contained aCSF (10 mM glucose, 26 mM NaHCO3, 2.5 mM KCl, 1.25 mM NaHPO4, 1 mM MgSO4, 2 mM CaCl2 and 126 mM NaCl (298 mOsm)).

    Calcium imaging in t-hCO from rats receiving ASO injection

    Following dissection and sectioning of rat brains with t-hCO, slices were incubated with Calbryte 520 AM (AAT Bioquest, catalogue no. 20650) in 1:1 of NPC medium and PBS for 45–60 min at 37 °C. Slices were then transferred to a 24-well imaging plate containing 500 μl of warm, low-potassium Tyrode’s solution (5 mM KCl, 129 mM NaCl, 2 mM CaCl2, 1 mM MgCl2, 30 mM glucose, 25 mM HEPES pH 7.4) and imaged with a confocal microscope (Leica Stellaris) for 30 s at 37 °C, after which medium was replaced by high-potassium Tyrode’s solution (high-KCl, 67 mM KCl: 67 mM NaCl, 2 mM CaCl2, 1 mM MgCl2, 30 mM glucose and 25 mM HEPES pH 7.4) and imaging resumed for 25 min. Mean grey values were collected from ROIs delineating Calbryte+ somas (visualized by standard deviation projection of the entire time series) with Fiji (ImageJ v.2.1.0, NIH). Mean grey values were transformed to relative changes in fluorescence: dF/F(t) = (F(t) − F0)/F0, where F0 represents average grey values of the time series of each ROI. Residual calcium was calculated as (C − A)/(B − A), where B is the peak value following depolarization (maximal peak value determined by custom-written MATLAB routines (v. R2019b and v. R2022b, 9.4.0, MathWorks), A is the baseline value (B − 50th frame) and C is the decay value (B + 150th frame).

    Golgi staining

    Golgi staining was conducted using the FD Rapid GolgiStain Kit (FD Neurotechnologies, catalogue no. PK401) according to the manufacturer’s instructions. In brief, freshly dissected t-hCO were incubated with solution A/B mixture in the dark and then transferred to solution C. After 72 h the tissue was embedded in agarose, the vibratome chamber filled with solution C and tissue sectioned at 100 μm using a Leica VT1200S vibratome. Sections were mounted on gelatin-coated slides, stained in solution D/E, washed, dehydrated, cleared and coverslipped. Images were acquired on a SP8 confocal microscope with brightfield. Cells were counted as neurons based on their morphology; dendrites were manually traced using neuTube. Both tracing and analysis were performed blinded.

    Statistics and reproducibility

    Data are presented as either mean ± s.d. or mean ± s.e.m. unless otherwise indicated. Distribution of raw data was tested for normality of distribution; statistical analyses were performed using either two-tailed student’s t-tests, one-way ANOVA with multiple comparisons, two-tailed Mann–Whitney tests or Kruskal–Wallis tests. Statistical analysis was performed in Prism (GraphPad). Data shown for representative experiments were repeated, with similar results, in at least three independent biological replicates, unless otherwise noted. Sample sizes were estimated empirically.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    [ad_2]

    Source link

  • Cell-type-resolved mosaicism reveals clonal dynamics of the human forebrain

    [ad_1]

  • Anderson, S. A., Eisenstat, D. D., Shi, L. & Rubenstein, J. L. Interneuron migration from basal forebrain to neocortex: dependence on Dlx genes. Science 278, 474–476 (1997).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Letinic, K., Zoncu, R. & Rakic, P. Origin of GABAergic neurons in the human neocortex. Nature 417, 645–649 (2002).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Wonders, C. P. & Anderson, S. A. The origin and specification of cortical interneurons. Nat. Rev. Neurosci. 7, 687–696 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Petanjek, Z., Berger, B. & Esclapez, M. Origins of cortical GABAergic neurons in the cynomolgus monkey. Cereb. Cortex 19, 249–262 (2009).

    Article 
    PubMed 

    Google Scholar     

  • Hansen, D. V. et al. Non-epithelial stem cells and cortical interneuron production in the human ganglionic eminences. Nat. Neurosci. 16, 1576–1587 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Delgado, R. N. et al. Individual human cortical progenitors can produce excitatory and inhibitory neurons. Nature 601, 397–403 (2022).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Andrews, M. G. et al. LIF signaling regulates outer radial glial to interneuron fate during human cortical development. Cell Stem Cell 30, 1382–1391.e5 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Bulfone, A. et al. Spatially restricted expression of Dlx-1, Dlx-2 (Tes-1), Gbx-2, and Wnt-3 in the embryonic day 12.5 mouse forebrain defines potential transverse and longitudinal segmental boundaries. J. Neurosci. 13, 3155–3172 (1993).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Puelles, L. & Rubenstein, J. L. Forebrain gene expression domains and the evolving prosomeric model. Trends Neurosci. 26, 469–476 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Furuta, Y., Piston, D. W. & Hogan, B. L. Bone morphogenetic proteins (BMPs) as regulators of dorsal forebrain development. Development 124, 2203–2212 (1997).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Grove, E. A., Tole, S., Limon, J., Yip, L. & Ragsdale, C. W. The hem of the embryonic cerebral cortex is defined by the expression of multiple Wnt genes and is compromised in Gli3-deficient mice. Development 125, 2315–2325 (1998).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Monuki, E. S., Porter, F. D. & Walsh, C. A. Patterning of the dorsal telencephalon and cerebral cortex by a roof plate-Lhx2 pathway. Neuron 32, 591–604 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Bandler, R. C. et al. Single-cell delineation of lineage and genetic identity in the mouse brain. Nature 601, 404–409 (2022).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Ratz, M. et al. Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics. Nat. Neurosci. 25, 285–294 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Dang, H. et al. Monoclonal antibody specific to acid phosphatase isoenzyme 4. Prostate 9, 47–55 (1986).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Bizzotto, S. et al. Landmarks of human embryonic development inscribed in somatic mutations. Science 371, 1249–1253 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Breuss, M. W. et al. Somatic mosaicism reveals clonal distributions of neocortical development. Nature 604, 689–696 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Park, S. et al. Clonal dynamics in early human embryogenesis inferred from somatic mutation. Nature 597, 393–397 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Rakic, P. Mode of cell migration to the superficial layers of fetal monkey neocortex. J. Comp. Neurol. 145, 61–83 (1972).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Kriegstein, A. R. & Noctor, S. C. Patterns of neuronal migration in the embryonic cortex. Trends Neurosci. 27, 392–399 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Rakic, P. Evolution of the neocortex: a perspective from developmental biology. Nat. Rev. Neurosci. 10, 724–735 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Wichterle, H., Turnbull, D. H., Nery, S., Fishell, G. & Alvarez-Buylla, A. In utero fate mapping reveals distinct migratory pathways and fates of neurons born in the mammalian basal forebrain. Development 128, 3759–3771 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Ma, T. et al. Subcortical origins of human and monkey neocortical interneurons. Nat. Neurosci. 16, 1588–1597 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Arshad, A. et al. Extended production of cortical interneurons into the third trimester of human gestation. Cereb. Cortex 26, 2242–2256 (2016).

    Article 
    PubMed 

    Google Scholar     

  • Alzu’bi, A. et al. The transcription factors COUP-TFI and COUP-TFII have distinct roles in arealisation and GABAergic interneuron specification in the early human fetal relencephalon. Cereb. Cortex 27, 4971–4987 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Alzu’bi, A. et al. Distinct cortical and sub-cortical neurogenic domains for GABAergic interneuron precursor transcription factors NKX2.1, OLIG2 and COUP-TFII in early fetal human telencephalon. Brain Struct. Funct. 222, 2309–2328 (2017).

    Article 
    PubMed 

    Google Scholar     

  • Marks, J. R. et al. Unifying comprehensive genomics and transcriptomics in individual cells to illuminate oncogenic and drug resistance mechanisms. Preprint at bioRxiv https://doi.org/10.1101/2022.04.29.489440 (2023).

  • Lodato, M. A. et al. Aging and neurodegeneration are associated with increased mutations in single human neurons. Science 359, 555–559 (2018).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Rodin, R. E. et al. The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing. Nat. Neurosci. 24, 176–185 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Ernst, A. et al. Neurogenesis in the striatum of the adult human brain. Cell 156, 1072–1083 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Ju, Y. S. et al. Somatic mutations reveal asymmetric cellular dynamics in the early human embryo. Nature 543, 714–718 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Fasching, L. et al. Early developmental asymmetries in cell lineage trees in living individuals. Science 371, 1245–1248 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Ginhoux, F. & Garel, S. The mysterious origins of microglia. Nat. Neurosci. 21, 897–899 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Prinz, M., Jung, S. & Priller, J. Microglia biology: one century of evolving concepts. Cell 179, 292–311 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Gilbert, E., Shanmugam, A. & Cavalleri, G. L. Revealing the recent demographic history of Europe via haplotype sharing in the UK Biobank. Proc. Natl Acad. Sci. USA 119, e2119281119 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Diaz-Papkovich, A., Anderson-Trocme, L. & Gravel, S. A review of UMAP in population genetics. J. Hum. Genet. 66, 85–91 (2021).

    Article 
    PubMed 

    Google Scholar     

  • Xu, Q., Cobos, I., De La Cruz, E., Rubenstein, J. L. & Anderson, S. A. Origins of cortical interneuron subtypes. J. Neurosci. 24, 2612–2622 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Miyoshi, G. et al. Genetic fate mapping reveals that the caudal ganglionic eminence produces a large and diverse population of superficial cortical interneurons. J. Neurosci. 30, 1582–1594 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Mayer, C. et al. Clonally related forebrain interneurons disperse broadly across both functional areas and structural boundaries. Neuron 87, 989–998 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Oliver, G. et al. Prox 1, a prospero-related homeobox gene expressed during mouse development. Mech. Dev. 44, 3–16 (1993).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • LaBonne, C. & Bronner-Fraser, M. Neural crest induction in Xenopus: evidence for a two-signal model. Development 125, 2403–2414 (1998).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Saint-Jeannet, J. P., He, X., Varmus, H. E. & Dawid, I. B. Regulation of dorsal fate in the neuraxis by Wnt-1 and Wnt-3a. Proc. Natl Acad. Sci. USA 94, 13713–13718 (1997).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Faure, S., de Santa Barbara, P., Roberts, D. J. & Whitman, M. Endogenous patterns of BMP signaling during early chick development. Dev. Biol. 244, 44–65 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Stuhlmiller, T. J. & Garcia-Castro, M. I. Current perspectives of the signaling pathways directing neural crest induction. Cell. Mol. Life Sci. 69, 3715–3737 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Bingman, V. P., Salas, C. & Rodriguez, F. in Encyclopedia of Neuroscience (eds Binder, M. D., Hirokawa, N. & Windhorst, U.) 1356–1360 (Springer Berlin Heidelberg, 2009).

  • Grillner, S., Robertson, B. & Stephenson-Jones, M. The evolutionary origin of the vertebrate basal ganglia and its role in action selection. J. Physiol. 591, 5425–5431 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Stephenson-Jones, M., Samuelsson, E., Ericsson, J., Robertson, B. & Grillner, S. Evolutionary conservation of the basal ganglia as a common vertebrate mechanism for action selection. Curr. Biol. 21, 1081–1091 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Sepulveda, W., Sepulveda, F., Schonstedt, V., Stern, J. & Diaz-Serani, R. Neuroimaging findings in fetal hemimegalencephaly: case study and review. Fetal Diagn. Ther. https://doi.org/10.1159/000535406 (2023).

  • Chung, C. et al. shishenyxx/Human_Inhibitory_Neurons: 1.0.1. Zenodo https://doi.org/10.5281/ZENODO.10772159 (2024).

  • Huang, A. Y. et al. MosaicHunter: accurate detection of postzygotic single-nucleotide mosaicism through next-generation sequencing of unpaired, trio, and paired samples. Nucleic Acids Res. 45, e76 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Breuss, M. W. et al. Autism risk in offspring can be assessed through quantification of male sperm mosaicism. Nat. Med. 26, 143–150 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Yang, X. et al. Control-independent mosaic single nucleotide variant detection with DeepMosaic. Nat. Biotechnol. 41, 870–877 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Dou, Y. et al. Accurate detection of mosaic variants in sequencing data without matched controls. Nat. Biotechnol. 38, 314–319 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Nott, A. et al. Brain cell type-specific enhancer–promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-seq applications. Bioinformatics 27, 1571–1572 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Gonzalez-Pena, V. et al. Accurate genomic variant detection in single cells with primary template-directed amplification. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2024176118 (2021).

  • Lee, J. et al. Mutalisk: a web-based somatic MUTation AnaLyIS toolKit for genomic, transcriptional and epigenomic signatures. Nucleic Acids Res. 46, W102–W108 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Tamura, K., Stecher, G. & Kumar, S. MEGA11: Molecular Evolutionary Genetics Analysis version 11. Mol. Biol. Evol. 38, 3022–3027 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

[ad_2]

Source link