1. Genetics and Genomics
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CNTN5-/+or EHMT2-/+human iPSC-derived neurons from individuals with autism develop hyperactive neuronal networks

  1. Eric Deneault
  2. Muhammad Faheem
  3. Sean H White
  4. Deivid C Rodrigues
  5. Song Sun
  6. Wei Wei
  7. Alina Piekna
  8. Tadeo Thompson
  9. Jennifer L Howe
  10. Leon Chalil
  11. Vickie Kwan
  12. Susan Walker
  13. Peter Pasceri
  14. Frederick P Roth
  15. Ryan KC Yuen
  16. Karun K Singh  Is a corresponding author
  17. James Ellis  Is a corresponding author
  18. Stephen W Scherer  Is a corresponding author
  1. The Hospital for Sick Children, Canada
  2. The Centre for Applied Genomics, The Hospital for Sick Children, Canada
  3. Stem Cell and Cancer Research Institute, McMaster University, Canada
  4. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Canada
  5. University of Toronto, Canada
  6. Canadian Institute for Advanced Research (CIFAR), Canada
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Cite this article as: eLife 2019;8:e40092 doi: 10.7554/eLife.40092

Abstract

Induced pluripotent stem cell (iPSC)-derived neurons are increasingly used to model Autism Spectrum Disorder (ASD), which is clinically and genetically heterogeneous. To study the complex relationship of penetrant and weaker polygenic risk variants to ASD, ‘isogenic’ iPSC-derived neurons are critical. We developed a set of procedures to control for heterogeneity in reprogramming and differentiation, and generated 53 different iPSC-derived glutamatergic neuronal lines from 25 participants from 12 unrelated families with ASD. Heterozygous de novo and rare-inherited presumed-damaging variants were characterized in ASD risk genes/loci. Combinations of putative etiologic variants (GLI3/KIF21A or EHMT2/UBE2I) in separate families were modeled. We used a multi-electrode array, with patch-clamp recordings, to determine a reproducible synaptic phenotype in 25% of the individuals with ASD (other relevant data on the remaining lines was collected). Our most compelling new results revealed a consistent spontaneous network hyperactivity in neurons deficient for CNTN5 or EHMT2. The biobank of iPSC-derived neurons and accompanying genomic data are available to accelerate ASD research.

Editorial note: This article has been through an editorial process in which authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

https://doi.org/10.7554/eLife.40092.001

Introduction

The past two decades of research has determined autism spectrum disorders (ASD) to be clinically (Fernandez and Scherer, 2017; Jones and Lord, 2013; Mahdi et al., 2018) and genetically (De Rubeis et al., 2014; Gilman et al., 2011; Pinto et al., 2014; Tammimies et al., 2015; C Yuen et al., 2017) heterogeneous. Phenotypically, the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) combines autistic disorder, Asperger disorder, childhood disintegrative disorder and pervasive developmental disorder not otherwise specified into the single grouping of ASD (DSM-5, 2013). There are also syndromic forms of ASD (Carter and Scherer, 2013), and now more than 100 other disorders carrying different names (Betancur, 2011), that in a proportion of subjects can also present the necessary symptoms for an ASD diagnosis.

From the perspective of genetics, heritability estimates and family studies definitely demonstrate genes to be involved (Ronald and Hoekstra, 2011). Single high-penetrance genes and copy number variation (CNV)-affected loci, have now been implicated as bona fide autism-susceptibility (or risk) genes, although none of them show specificity for ASD alone (Malhotra and Sebat, 2012). These genetic alterations are rare in the population (<1% population frequency), and in some individuals, combinations of rare genetic variants affecting different genes can be involved (Devlin and Scherer, 2012), including more complex structural alterations of chromosomes (Brandler et al., 2018; Marshall et al., 2008). Recent research studying common genetic variants indicates that polygenic contributors may be involved, and these can also influence the clinical severity of rare penetrant variants in ASD risk genes (Weiner et al., 2017).

Nearly 1000 putative ASD risk loci are catalogued, with ~100 already being used in the clinical diagnostic setting (Hoang et al., 2018a; Winden et al., 2018). There are some genotype-phenotype associations emerging, including general trends considering medical complications and IQ (Bishop et al., 2017; Sanders et al., 2015; Tammimies et al., 2015), sibling variability depending on the ASD gene variant they carry (Yuen et al., 2015), and lower adaptive ability in those carrying variants compared to affected siblings without the same genetic change (C Yuen et al., 2017). Many of the ASD risk genes identified are connected into gene networks including those involved in synaptic transmission, transcriptional regulation, and RNA processing functions (Bourgeron, 2015; De Rubeis et al., 2014; Geschwind and State, 2015; Pinto et al., 2014; Sahin and Sur, 2015; C Yuen et al., 2017; Yuen et al., 2016), with the impacted genes being involved in all of prenatal, region-specific, or broader brain development (Uddin et al., 2014). Perhaps, a general unifying theme that is emerging from neurophysiologic studies is an increased ratio of excitation and inhibition in key neural systems that can be perturbed by variants in the ASD risk genes, or by environmental variables affecting the same targets (Canitano and Pallagrosi, 2017).

The advent of the induced pluripotent stem cell (iPSC) technology (Takahashi et al., 2007; Yu et al., 2007), followed by cellular re-programming to forebrain glutamatergic neurons (Habela et al., 2016), allows accessible cellular models to be developed for the highly heterogeneous ASD (Beltrão-Braga and Muotri, 2017; Dolmetsch and Geschwind, 2011; Durak and Tsai, 2014; Karmacharya and Haggarty, 2016; Marchetto et al., 2017; Yoon et al., 2014; Zhang et al., 2013). Carrying the same precise repertoire of rare and common genetic variants as the donor proband, iPSC-derived neurons represent the best genetic mimic of proband neurons for functional and mechanistic studies. Induced differentiation can be achieved with high efficiency and consistency using transient ectopic expression of the transcription factor NGN2 (Ho et al., 2016; Zhang et al., 2013), and this has been shown to be useful in diverse phenotyping projects (Deneault et al., 2018; Pak et al., 2015; Yi et al., 2016). Proband-specific iPSC-derived neuronal cells indeed provide a useful model to study disease pathology, and response to drugs, but throughput (both iPSC-derived neurons and phenotyping) is low, with costs still high. As a result, so far, only a few iPSC-derived neuronal lines are typically tested in a single study.

Here, we develop a resource of 53 different iPSC lines derived from 25 individuals with ASD carrying a wide-range of rare genetic variants, and from unaffected family members. We also used clustered regularly interspaced short palindromic repeats (CRISPR) editing (Jinek et al., 2012; Ran et al., 2013) to create four ‘isogenic’ pairs of lines with or without mutation, to better assess mutational impacts. Upon differentiation into excitatory neurons, we investigated synaptic and electrophysiological properties using the large-scale multi-electrode array (MEA), as well as more traditional patch-clamp recordings. Numerous interesting associations were observed between the genetic variants and the neuronal phenotypes analyzed. We share our general experiences and the bioresource with the community. We also highlight one of our more robust findings—an increased neuronal activity in glutamatergic neurons deficient in one copy of CNTN5 or EHMT2—which could be responsible for ASD-related phenotypes.

Results

Selection and collection of tissue samples for reprogramming

Participants were enrolled in the Autism Speaks MSSNG whole-genome sequencing (WGS) project (C Yuen et al., 2017). All ASD and related control-participants were initially consented for WGS and upon return of genetic results, then consented for the iPSC study, using approved protocols through the Research Ethics Board at the Hospital for Sick Children (see Materials and methods section for details) (Hoang et al., 2018b). Some families were also examined by whole exome sequencing. The study took place over a 5 year period and used incrementally developing ASD gene lists from the following papers (Jiang et al., 2013; Marshall et al., 2008; Tammimies et al., 2015; Yuen et al., 2015) (Table 1). These primarily considered data from the Autism Speaks MSSNG project, the Autism Sequencing Consortium (De Rubeis et al., 2014), and the Simons Foundation Autism Research Initiative (SFARI) gene list (discussion below). A diversity of different ASD-risk variants was targeted ranging in size from single nucleotide variants (SNV) to an 823 kb CNV (Figure 1 and Table 1; corresponding genomic coordinates in Supplementary file 1). Typically, one ASD-affected and one sex-matched unaffected member (control) per family were included (Figure 1). In total, 14 ASD-affected and 11 controls participated, of which 21 were males and four were females (Figure 1 and Table 1). Cells from either skin fibroblasts or CD34 +blood cells were collected for reprogramming into iPSCs (Figure 2A and Table 1).

Genetic pedigrees of the participant families with identified genetic variants.

One ASD-affected (black arrow) and one sex-matched unaffected (black star) members were typically selected for iPSC reprogramming. ASD-affected children are represented with a black box; note that line 1-0019-002 (19-2) in A) was used as a control and was described previously (Deneault et al., 2018).

https://doi.org/10.7554/eLife.40092.002
Figure 2 with 1 supplement see all
Generation of iPSCs and neurons.

(A) Schematic representation of the experimental procedure to find specific electrophysiological signatures associated with genetic variants of clinical significance to autism spectrum disorder (ASD). Fibroblasts or blood cells were reprogrammed into iPSCs from a cohort of 25 probands and unaffected family members. Differentiation of iPSCs into glutamatergic neurons was achieved with NGN2 7 day transient overexpression, and electrophysiological properties were monitored using a multi-electrode array (MEA) device. (B) Flow cytometry and (C) Immunohistochemistry revealing expression of the pluripotency markers NANOG, SSEA4, OCT4 and TRA-1–60 in a representative iPSC line. (D) Representative normal male karyotype in iPSC; 20 cells were examined.

https://doi.org/10.7554/eLife.40092.003
Table 1
List of participants with ASD or unaffected controls, with the genetic variant(s) involved, and the different iPSC lines derived.

*The 1 bp deletion in EHMT2 would result in a frameshift 47 codons before the end of the protein and disruption of the stop-codon, potentially leading to the inclusion of a total of 221 incorrect amino acids; more information corresponding to the different genetic variants are presented in Supplementary file 1; MZ, monozygotic; Retro, retrovirus; N/A, not available

https://doi.org/10.7554/eLife.40092.005
Family IDMSSNG IdStatusPrimary genetic
variant(s)
SexAge at
reprogramming
(year)
Cell of originReprogramming
method
iPSC IDReference
ASD Candidate Gene - CNVs
A1-0019-002Unaffected fatherFamily and study controlM44SkinRetro19–2Deneault et al., 2018
1-0019-004ASD-affected16p11.2 deletion/+M15SkinRetro19-4Marshall et al., 2008
B3-0368-000ASD-affectedNRXN1 430 kb deletion/+M8SkinRetroNR3Tammimies et al., 2015
C1-0262-002Unaffected fatherFamily controlM49SkinSendai16K, 16N---
1-0262-003ASD-affectedDLGAP2 791 kb duplication/+M10SkinSendai15E, 15GMarshall et al., 2008
1-0262-004Affected brotherFamily controlM14SkinSendai17E, 17G---
D1-0582-002Unaffected fatherFamily controlM37SkinSendai26E, 26J---
1-0582-003ASD-affectedCNTN5 676 kb deletionM9SkinSendai27H, 27NN/A
E7-0058-003ASD-affectedAGBL4 323 kb deletion/+M4SkinSendai36O, 36PN/A
ASD Candidate Gene – SNVs
F2-1305-005Unaffected brotherFamily controlM7SkinSendai21H, 21P---
2-1305-003ASD-affectedCAPRIN1 p.Q399X/+M12SkinSendai20C, 20E, 75G, 75HJiang et al., 2013
G2-1186-002Unaffected fatherFamily controlM43BloodSendai54E, 54G---
2-1186-003ASD-affectedVIP p.Y73X/+M12BloodSendai53G, 53HJiang et al., 2013
H2-1303-004Unaffected brotherFamily controlM13SkinSendai19A---
2-1303-003ASD-affectedANOS1 p.R423XM19SkinSendai18C, 18EJiang et al., 2013
2-1303-003Corrected ASD-affectedCRISPR-corrected ANOS1 p.X423RM19SkinSendai18CW---
I1-0273-002Unaffected fatherFamily controlM45BloodSendai51C, 51E---
1-0273-003ASD-affectedTHRA p.R384C/+M14BloodSendai52A, 52CYuen et al., 2015
Functional ASD Candidate Genes - SNVs
J1-0494-005Unaffected brotherFamily controlM12BloodSendai50A, 50B, 50H---
1-0494-003ASD-affected MZ twinSET c.112 + 1G>C/+M9BloodSendai48K, 48NN/A
1-0494-004ASD-affected MZ twinSET c.112 + 1G>C/+M9BloodSendai49H, 49GN/A
K7-0254-001Unaffected motherGLI3 p.G727R/+F37BloodSendai64N, 64Q---
7-0254-002Unaffected fatherGLI3 p.G465R/+M41BloodSendai63Q, 63T---
7-0254-003ASD-affectedGLI3 p.G727R/+, mat
GLI3 pG465R/+, pat
KIF21A p.R1156G/+ (mosaic 23%)
F7BloodSendai62M, 62XN/A
7-0254-004Affected brotherGLI3 p.G727R/+
GLI3 pG465R/+
M9BloodSendai61I, 61KN/A
L6-0393-001Unaffected motherFamily controlF54SkinSendai37E---
6-0393-003ASD-affected*EHMT2 p.K1164Nfs/+
UBE2I p.E78K/+
F18SkinSendai38B, 38EN/A

Derivation of iPSC lines

Two different viral approaches were used for cell reprogramming. For historical reasons, the first three cell lines in Table 1, namely iPSC IDs 19–2, 19-4 and NR3, were reprogrammed using retroviruses expressing OCT4/POU5F1, SOX2, KLF4 and MYC, and a lentiviral vector that encoded the pluripotency reporter EOS-GFP/PuroR (Hotta et al., 2009). Then, we moved to non-integrative Sendai virus for all the other tested lines (Table 1). Emerging iPSC colonies were selected for activated endogenous human pluripotency markers, differentiation potential into three germ layer cells after embryoid body formation in vitro, and normal karyotype (Figure 2B–D and Supplementary file 2). Two separate pluripotent and karyotypically normal iPSC lines were typically selected per participant for neuronal differentiation and phenotyping experiments (Table 1).

Transient induction of neuronal differentiation

We induced differentiation of newly generated iPSCs into glutamatergic neurons to test their electrophysiological properties (Figure 2A). We used the NGN2 ectopic expression approach since highly-enriched populations of glutamatergic neurons can be obtained within a week, and they exhibit robust synaptic activity when co-cultured with glial cells (Zhang et al., 2013). Importantly, we determined that this strategy offers highly uniform differentiation levels between cell lines derived from different participants (Deneault et al., 2018). This consistency was necessary to perform suitable phenotyping assays such as network electrophysiology recordings of several different lines in the same experimental batch. The resulting glutamatergic neurons were all subjected to electrophysiological phenotyping.

Multi-Electrode array analysis of iPSC-derived neurons

MEA phenotyping was predominantly used in order to monitor the excitability of several independent cultured neuron populations in parallel, and in an unbiased manner, as we previously adapted with different NGN2-neuron lines (Deneault et al., 2018). We sought to determine if any selected ASD-risk variants would interfere with spontaneous spiking and synchronized bursting activity in a whole network of interconnected glutamatergic neurons. We ensured that the duration and amplitude of detected spikes were similar to typical mammalian neurons, that is, action potential widths of around 1–2 milliseconds (ms) and peak amplitudes of approximately 20–150 µV (Figure 2—figure supplement 1A). We measured the glutamatergic/GABAergic nature of our cultured neurons produced using NGN2 ectopic expression, which is known to repress GABAergic differentiation at the advantage of glutamatergic (Roybon et al., 2010). Mean firing rate (MFR) and network bursting activity were measured upon treatment with different receptor inhibitors. No substantial change was observed after addition of the GABA receptor inhibitor PTX (Figure 2—figure supplement 1B), indicating that GABAergic neurons are not appreciably present in our cultures. However, the MFR was significantly reduced in the presence of the AMPA receptor inhibitor CNQX while unchanged in untreated cells, with a comparable profile across each selected line (Figure 2—figure supplement 1B). This further suggests that most of the cultures were composed of glutamatergic neurons, and that our induction protocol was consistent across different cultures. All activity was abolished after addition of the sodium channel blocker TTX (Figure 2—figure supplement 1B), indicating that our human neurons were expressing functional sodium channels.

The weighted MFR (wMFR), which represents the MFR per active electrode, was used as a primary read-out for all tested iPSC-derived neurons, at one-week intervals from week 4 to 8 post-NGN2-induction (PNI) (Figure 3). To identify a preferred timepoint for this screen, we first pooled the data of all the independent control lines. Since the highest wMFR value for this pool of ‘all controls’ (~1.8 Hz) was detected at week 6 (Figure 3A), we initially used that timepoint to compare the activity of ASD variant and control lines for each family. In two different families, that is, CNTN5 and EHMT2, a significant higher wMFR was recorded in ASD variant neurons at week 6 compared with their corresponding familial control neurons (Figure 3B). EHMT2 had a strikingly increased wMFR at all timepoints, whereas CNTN5 at other timepoints was equivalent to its controls. We therefore ranked these two genes as high priorities for further study. In contrast, no significant differences were observed for DLGAP2, CAPRIN1, SET or GLI3 (Figure 3C), suggesting that these variants do not differ from control neuronal activity in our MEA assays, and therefore were not studied further. Different dynamics of altered wMFR were observed for ANOS1 and VIP at week 4 (Figure 3D), and the ANOS1 nonsense variant was ranked as an example of a candidate for further study. Conversely, a significant lower wMFR was recorded at weeks 7 and 8 for THRA (Figure 3D). No unaffected family members were available as controls for the single NR3 line (NRXN1) nor for 36O-36P (AGBL4), thus they were not chosen for further study. When we compared their values to the pooled values recorded from all the different familial controls available, no difference was found for NRXN1 and a significantly lower wMFR was observed at weeks 5 and 7 for AGBL4 (Figure 3E).

Figure 3 with 1 supplement see all
Multi-electrode array monitoring of iPSC-derived glutamatergic neurons.

(A–E) Weighted mean firing rate (wMFR) of pooled cell lines from control and KO neurons for each family from week 4 to 8 PNI. (F) Dot plots showing wMFR of each cell line from week 4 to 8 PNI; each dot represents the wMFR of one well, and the color reflects independent experiments. Values are presented as mean ± SEM of several technical and biological replicates, as presented in Supplementary file 3; ‘all controls’ represents the pool of 311 different control wells from 17 independent experiments; *p<0.05 from multiple t test comparison with Holm-Sidak correction (B), and without correction (C–E), and one-way ANOVA Tukey test pointing to intra- or inter-individual variability per family (F).

https://doi.org/10.7554/eLife.40092.006

To explore intra-individual (different lines from the same individual) and inter-individual (different individuals with the same mutation) variability, we plotted all the values obtained from each single well, independent experiment, cell line and individual, at each of the five reading timepoints (Figure 3F). Most lines from an individual were not significantly different from each other, and reassuringly low inter-individual variability was observed with different siblings bearing the same mutation(s), for example 48K and 48N versus 49G and 49H (SET), or 61I and 61K versus 62M and 62X (GLI3), at different timepoints (Figure 3F). A few lines showed a significant intra-individual variability, for example lines 52A and 52C (THRA) at week 4, or lines 75G and 75H (CAPRIN1) at week 8 (Figure 3F). We also noted some inter-independent experiment variability for a given line, for example line 38E (EHMT2) at weeks 4 and 5 (dots with different colours do not overlap in Figure 3F). Note that similar profiles were monitored in terms of MFR (Figure 3—figure supplement 1), indicating that these differences were not due to having more or less active electrodes in different lines. While consistent activity across lines was generally observed, the presence of variability prompted us to interrogate independent variants created by genome editing of CNTN5, ANOS1 and EHMT2.

CNTN5 isogenic pair to control for genetic background contribution

To further characterize the heterozygous CNTN5-mutant neuron lines 27H and 27N, we first showed a significantly higher network burst frequency at weeks 5, 6 and 8 (Figure 4A), indicating a more synchronized neuronal activity across each well. Importantly, CNTN5 protein levels overall were reduced by at least 33% in CNTN5-/+ neurons (Figure 4A, right panel), suggesting that the 676 kb heterozygous loss in CNTN5 interferes with the production of CNTN5 protein, but also that the non-deleted allele may be more active transcriptionally than in controls.

Validation of CNTN5-mutant neuron hyperactivity.

(A) The network burst frequency was recorded from the CNTN5 family from weeks 4–8 PNI, with corresponding protein levels by western blot on the right panel; *p<0.05 from multiple t test comparison with Holm-Sidak correction at weeks 6 and 8. (B) Both wMFR and network burst frequency were recorded from the 19–2-CNTN5 isogenic pair from weeks 4–11 PNI, with protein levels. The iPSC IDs and genotypes are indicated above each graph; values are presented as mean ± SEM of different lines per participant, and of several technical and biological replicates, as presented in Supplementary file 3; actin beta (ACTB) was used as a loading control for the western blots and the relative intensity of each band is indicated below the blots; *p<0.05 from multiple t test comparison with Holm-Sidak correction.

https://doi.org/10.7554/eLife.40092.010

Unaffected sex-matched family members are genetically similar to their related probands, but still present substantial genetic differences that can contribute to a given phenotype. Isogenic cell pairs represent better control of the genetic background contribution (Hoffman et al., 2019). CRISPR editing provides the possibility to engineer such isogenic controls (Miyaoka et al., 2014; Powell et al., 2017). Since editing large CNVs, such as the 676 kb deletion in CNTN5, is currently difficult using existing technology, we elected to introduce a set of nonsense mutations, previously described as ‘StopTag’ (Deneault et al., 2018), to knock out (KO) the expression of this gene in an unrelated iPSC line that was previously generated from a non-ASD and non-carrier individual. This parental line ‘19–2’ was also exploited in similar isogenic KO approaches (Woodbury-Smith et al., 2017) (Ross et al., in revision; Zaslavsky et al., in press), allowing assessment in a different and unrelated genetic background. For technical reasons, we targeted exon 5 of the transcript ENST00000524871.5 of CNTN5 in order to disrupt its expression. A heterozygous iPSC line was isolated to better mimic the heterozygous status of the CNTN5 deletion in the proband lines 27H and 27N. Intriguingly, the new isogenic iPSC-derived neuron line 19–2-CNTN5StopTag/+ did not show significant differences in terms of wMFR or network burst frequency at week 6 (Figure 4B). However, the wMFR of line 19–2 increased up to nearly 3 Hz at week 8 (Figure 4B) while the CNTN5 family controls stayed around 0.5 Hz (Figure 3B). In this context of a more active cell line, we extended the recordings until week 11, and the hyperactive wMFR of 19–2-CNTN5StopTag/+ was only evident from week 10 (Figure 4B). Moreover, CNTN5 protein levels were clearly decreased in this isogenic mutant line (Figure 4B, right panel), implying that StopTag insertion efficiently disrupted gene expression. These results indicate that loss of CNTN5 function is responsible for increased neuronal activity in vitro.

Repair of ANOS1 rescues defective membrane currents

In a complementary approach to minimize the confounding effect of genetic background from familial and unrelated controls, and its impact on phenotype, we sought to edit our proband-specific variants using CRISPR in order to create matching isogenic controls. We prioritized the nonsense variant R423X found in ANOS1 in participant 2-1303-003 and successfully corrected the corresponding iPSC line 18C (Figure 5A–C). Indeed, after detecting 7% edited cells using droplet digital PCR (ddPCR) in well G08 in the primary 96-well culture plate post-nucleofection, two subsequent limiting-dilution enrichment steps were necessary to isolate a 100% corrected iPSC line (Figure 5B). Sanger sequencing confirmed the properly corrected genomic DNA sequence (Figure 5C). This newly corrected line was named ‘18CW’ (see iPSC line ID ‘18CW’ in Table 1 and Figure 5C).

Correction of point mutation in ANOS1 in iPSCs using CRISPR editing.

(A) Design of gRNAs, ssODNs and ddPCR probes for correction of R423X in ANOS1; one sgRNA for each genomic DNA strand, that is, gRNA- in blue and gRNA +in yellow, was devised in close proximity for the double-nicking system using Cas9D10A; the non-sense mutations in ANOS1 is depicted in bold red; a silent mutation was introduced in ssODN (in blue) for ddPCR probe (underlined) specificity and to prevent nicking. (B) ddPCR absolute quantification coupled with two consecutive limiting-dilution enrichment steps were necessary to isolate a 100% corrected line, that is, 100% VIC signal. (C) Sanger sequencing confirmed proper correction of non-sense mutation R423X in line 18C back to wt; this newly corrected line was named 18CW. (D) Outward and inward membrane current detected by patch-clamp recordings; total number of recorded neurons was 15 for both 18C and 18CW; values are presented as mean ± SEM of three independent differentiation experiments, recorded at day 21–25 PNI. *p<0.05 from multiple t test comparison with Holm-Sidak correction.

https://doi.org/10.7554/eLife.40092.013

The CRISPR-corrected line 18CW exhibited a significant difference in wMFR compared with its isogenic counterpart 18C at 4 week, and no difference from the familial control line 19A (Figure 3D). Moreover, the availability of such isogenic set prompted us to explore more detailed electrophysiological properties using patch-clamp recordings of single neurons in order to reveal any phenotype not detected using MEA. While the advantage of MEA experiments is that continuous live monitoring of neural activity can be measured over multiple weeks, we used patch-clamp electrophysiology on NGN2 neurons between days 21–25 PNI, which provides robust recordings to detect phenotypes, as shown in previous studies (Yi et al., 2016). Furthermore, the increased density of neuronal processes appearing beyond 4 weeks PNI can preclude consistent clean patch-clamp recordings, but this is not an issue with MEA. Using this protocol, we detected significantly lower outward membrane current at 40 mV in the mutant line 18C compared to its isogenic control 18CW (Figure 5D). A significantly higher inward current was also observed in mutant neurons between −40 and 0 mV (Figure 5D). No overt off-target mutations were detectable using our previously-described WGS strategy (Deneault et al., 2018). These results indicate that ANOS1-null iPSC-derived glutamatergic neurons present abnormal sodium and potassium membrane currents that might contribute to ASD development. Notably, these observations underline that some specific electrophysiological phenotypes at the single cell level, for example membrane currents, may not be captured when using MEA monitoring at the cell population level.

Neuronal hyperactivity in EHMT2/UBE2I Complex-Variant neurons

Lines 38B and 38E from participant 6-0393-003 carry two ASD-relevant variants; a de novo missense E78K in UBE2I and a de novo frameshift variant K1164Nfs in EHMT2 (Figure 1L and Table 1). MEA recordings showed a significantly higher wMFR (Figure 3B) and network burst frequency (Figure 6A) from week 4 to 8 PNI compared to their related control line 37E. Interestingly, the profile of the wMFR curve (Figure 3B) was similar to that of the MFR curve (Figure 3—figure supplement 1A), indicating that cell survival or expansion is not a major contributor to the difference observed in neuronal activity. To ensure that this hyperactivity was synaptic and not only intrinsic to the neurons, we performed patch-clamp recordings, at day 21–25 PNI to avoid the increased density of neuronal processes that impacts the ability to obtain clean recordings, as stated previously. Intrinsic properties, for example capacitance and resistance, did not vary significantly (Figure 6B), indicating comparable maturity levels between lines 37E and 38E. While spontaneous excitatory post-synaptic current (sEPSC) amplitude was unchanged, sEPSC frequency was significantly higher in mutant neurons compared to controls (Figure 6B). These observations suggest that a potential loss-of-function of UBE2I and/or EHMT2 is involved in ASD-related neuronal dysfunction.

Figure 6 with 2 supplements see all
Electrophysiological and protein level variations in EHMT2-deficient neurons.

(A) Network burst frequency was recorded using MEA from the EHMT2/UBE2I family from weeks 4–8 PNI; values are presented as mean ± SEM of several technical and biological replicates, as presented in Supplementary file 3; *p<0.05 from multiple t test comparison with Holm-Sidak correction. (B) Patch-clamp recordings of two selected lines, that is, 37E (control) and 38E (mutant); values are presented as mean ± SEM of 14 different neurons from two independent differentiation experiments; *p<0.05 from from unpaired t test two-tailed. (C) Western blot showing a decrease in EHMT2 protein levels in mutant neurons (38B and 38E) compared to their respective control neurons (37E). (D) MEA recordings of the isogenic pair 19–2 and 19–2-EHMT2StopTag/+ iPSC-derived neurons from weeks 4–11 PNI; values are presented as mean ± SEM of eight different wells for each three independent differentiation experiments; †note that the same data for control 19–2 was used in Figure 4B since it was generated within the same experiments, that is, plates 26, 33 and 37 (see Supplementary file 3); *p<0.05 from multiple t test comparison with Holm-Sidak correction at week 11 (weighted mean firing rate) and weeks 9–11 (network burst frequency). (E) Western blot showing a decrease in EHMT2 protein levels in mutant neurons 19–2-EHMT2StopTag/+ compared to their respective control (ctrl) neurons 19–2; actin-beta (ACTB) was used as a loading control and the relative intensity of each band is indicated below the blots; pF, picofarad; MΩ, megaohm; Hz, hertz; pA, picoampere.

https://doi.org/10.7554/eLife.40092.015

Evidence of functional impact of EHMT2, but not UBE2l variants

Since our attempts to edit the variants E78K in UBE2I and K1164Nfs in EHMT2 had not been successful, we sought to determine the potential contribution of E78K in UBE2I to the observed synaptic hyperactivity. To estimate the damaging potential of this missense variant on the function of UBE2I protein, we utilized a Saccharomyces cerevisiae complementation assay that was previously developed as a validated surrogate genetic system to predict the pathogenicity of diverse human variants (Sun et al., 2016). In this assay, lethality of a temperature-sensitive allele of the yeast UBC9 gene (ortholog of human UBE2I) is rescued by expressing a functional version of human UBE2I. Several missense variants in UBE2I have been accurately predicted as deleterious at conserved positions, or benign at other positions (Zhang et al., 2017). Therefore, we used this complementation assay to test the consequence of our variant E78K, and found no effect of this variant on the function of human UBE2I (Figure 6—figure supplement 1). Because these results disfavor involvement of the UBE2I variant E78K in the neuronal hyperactivity observed in Figures 3B,F and and 6A–B, we excluded UBE2I from subsequent experiments and further explored a potential causal link between EHMT2 and synaptic activity.

Interestingly, evaluation of EHMT2 protein abundance revealed a clear decrease in the mutant lines 38B and 38E, as compared to the control 37E (Figure 6C). This suggests that a reduced expression of EHMT2 increases spontaneous spiking activity and sEPSC frequency of glutamatergic neurons.

EHMT2-/+ CRISPR-isogenic pair confirms neuronal hyperactivity

Since the prediction of damage extent of the frameshift variant K1164Nfs on the function of EHMT2 may not be accurate, we used our StopTag insertion strategy in iPSC line 19–2, and targeted exon 20 of the transcript ENST00000375537.8 of EHMT2 in order to disrupt its expression. In this new isogenic line, wMFR and network burst frequency were also increased in iPSC-derived 19–2-EHMT2StopTag/+ neurons compared to control 19–2, around week 10 PNI and beyond (Figure 6D and Figure 6—figure supplement 2A). This increased activity in mutant neurons occurred later than that observed in the familial lines 38B/E, possibly due to the more active 19–2 line. Accordingly, EHMT2 protein levels were reduced by half in mutant cells (Figure 6E). We also performed patch-clamp recordings on these neurons at day 21–25 PNI, as above. We did not detect any significant change in sEPSC frequency and amplitude at this earlier timepoint, similar to the MEA experiment. However, intrinsic properties showed a significant increase in capacitance and decrease in input resistance in mutant cells (Figure 6—figure supplement 2B). These observations suggest that the mutant neurons at 3–4 weeks PNI potentially have a faster maturation rate, however, this phenotype is most pronounced in the hyperactivity recorded by MEAs later at 9–11 weeks PNI. These results support the conclusion that the inactivation of one allele of EHMT2 significantly increases spontaneous network activity of excitatory neurons, with possible effects on the neuronal maturation process.

Discussion

In order to establish a scalable iPSC-derived neuron paradigm to study ASD, we selected 12 well-characterized families bearing assumed etiologic variants in ASD-relevant genes, and CNV loci. Per family, we established one to four different fully-characterized and normal iPSC lines from typically one individual with ASD, and one unaffected (non-ASD) sex-matched member. Simultaneous multi-line electrophysiological evaluation revealed hyperactivity of the simple-variant CNTN5-/+ iPSC-derived glutamatergic neurons in two independent genetic backgrounds. Moreover, isogenic-MEA and patch-clamp recordings confirmed synaptic hyperactivity of iPSC-derived neurons with disruptive mutations in EHMT2, also in two different genetic backgrounds.

To increase the modeling scalability of complex genetic disorders such as ASD while optimizing statistical power, several parameters require careful consideration. Given substantial variation in reprogramming and neuronal differentiation efficiencies, sample size is important to control. It was recently proposed that inter-individual variation, that is the number of probands with similar genetic variants, is more important to consider than intra-individual variation, that is the number of iPSC clones derived from the same individual (Hoffman et al., 2019). Aiming at multi-variant phenotyping, we tested one or two probands per deficient gene, however, we were able to create an isogenic pair in a different genetic background for the two highly relevant genes, that is, CNTN5 (Lionel et al., 2011; Mercati et al., 2017; van Daalen et al., 2011) and EHMT2 (Deimling et al., 2017; Kleefstra et al., 2005; Zylicz et al., 2015), thereby controlling inter-individual variation. We derived two independent iPSC clones per participants to regulate intra-individual variation.

Another important parameter to consider is the cellular homogeneity of neuronal cultures. We preferred to use the NGN2 system over classic dual-SMAD inhibition protocols because in our experience it represents an advantage in terms of cellular homogeneity. It is also faster than other protocols and produces much higher proportion of glutamatergic neurons that can be studied for ASD (Canitano and Pallagrosi, 2017; Habela et al., 2016) or other neurological disorders (Lin et al., 2018). We assume that most NGN2-neurons are glutamatergic based on the data presented in the original publication establishing this technique (Zhang et al., 2013), and on our previous publication using high-cell density RNAseq assessment (Deneault et al., 2018). Moreover, we have treated several of our cultures at the end of the MEA experimentation with different neurotoxins (CNQX, PTX, TTX) to show that most neurons are glutamatergic and not GABAergic, for different lines. In addition, our patch-clamp recordings have demonstrated that these neurons exhibit the properties of excitatory neurons.

Characterization of neuronal composition and survival when MEA is performed is difficult to achieve with high accuracy. Our strategy involved using several technical (3 to 12 per independent experiment) and biological (up to 4) replicates to best compensate for inter-well and inter-iPSC neuronal induction variations (Supplementary File 3). It is possible that some phenotypes were missed, for example in our families without MEA phenotypes, since we cannot exclude the possibility that differences in cell number or composition across individuals in a family actually masked potential MEA phenotypes. MEA phenotypes may not always predict electrophysiological deficits and vice versa, as evidenced for ASTN2 in our recent publication (Deneault et al., 2018). Since the familial controls are often less active, this screen might be biased towards the identification of hyperactive phenotypes rather than hypoactive. However, we have previously detected hypoactive phenotypes in isogenic KO line 19–2 at week 8 and before (Deneault et al., 2018). Line 19–2 is generally more active than most other familial lines, and this may have delayed emergence of the hyperactive phenotype in isogenic cells until week 10. We suggest a developmental time course covering several timepoints for each family moving forward using MEA. For specific lines, different timepoints may be sufficient.

An increased neuronal activity, for example MFR in mutant lines 38B/E, might indicate alterations in synaptic function and/or maturation. We have presented the MFR for all tested cell lines in Figure 3—figure supplement 1, in support of the wMFR in Figure 3. The wMFR is defined as the MFR divided by the number of active electrodes per well. If there is significant failure of electrode activation in a well, for example due to differences in cell survival, dispersion or adhesion, those data are excluded. For example, the increased MFR observed in EHMT2-/+ lines could be due to a better capacity to survive, disperse or adhere than EHMT2+/+ cells, without affecting synaptic activity. However, excluding all inactive electrodes would then result in a comparable wMFR between mutant and control cells, which was not the case. Indeed, both MFR and wMFR were significantly higher in EHMT2-/+ cells. That does not exclude the possibility of a better survival, dispersion or adhesion, but it is likely not the only reason for the observed increase in spiking activity, suggesting greater synaptic activity, as supported by patch-clamp recordings. Indeed, we used patch-clamp recordings to show that sEPSC frequency was significantly increased in mutant line 38E compared to control line 37E. We believe these results directly support synaptic alteration as one of the possible causes for the increased neuronal activity measured by MEA. However, a detailed analysis of cell maturation will be required for each different cell line involved in this study to clarify this issue. It will be interesting in the future to investigate the possible mechanisms involved in the decreased activity observed in THRA-mutant neurons (Figure 3D), after validation by patch-clamp recordings.

ANOS1 (Anosmin 1) is a glycoprotein of the extracellular matrix including four consecutive fibronectin type III domains. Loss-of-function variants in ANOS1 were shown to cause the Kallmann syndrome, which is characterized by congenital hypogonadotropic hypogonadism associated with anosmia, delayed puberty and infertility (Dodé and Hardelin, 2009). Defects in the migration of gonadotropin-releasing hormone (GnRH) neurons were observed during embryonic development, as well as morphological changes in the basal forebrain cortex (Manara et al., 2014). In human, a proband carrying the nonsense variant R423X in ANOS1, and presenting clinical hypogonadotropic hypogonadism, was also diagnosed with ASD (Jiang et al., 2013), suggesting a link between ANOS1 and ASD. Despite the absence of significant MEA results at late recordings, neuronal membrane current defects were validated using patch-clamp recordings in an isogenic pair (Figure 5D). These results indicate that glutamatergic neuron activity is also influenced by ANOS1, which represents a risk gene for ASD.

CNTN5 (Contactin 5) is an immunoglobulin cell adhesion molecule, with four fibronectin type III domains, involved in neurite outgrowth and axon connection in cortical neurons, and was associated with ASD (van Daalen et al., 2011). Different CNVs affecting CNTN5 have been associated with ASD and ADHD, with increased occurrence of hyperacusis (Lionel et al., 2011; Mercati et al., 2017). The molecular mechanisms through which heterozygous loss of CNTN5 increases neuronal activity in vitro (Figure 4A–B) remains to be elucidated. Gene editing of the 676 kb deletion, as found in lines 27H and 27N (Figure 1D and Table 1), to obtain isogenic controls may be challenging due to the size, but this approach might eventually be applied.

Using a yeast complementation assay (Figure 6—figure supplement 1), we estimated that the de novo missense variant E78K in UBE3I was not responsible for the electrophysiological phenotypes observed in participant 6-0393-003 (Figure 6A–B). We were then prompted to investigate further the potential role of the frameshift variant K1164Nfs in EHMT2. EHMT2 (G9a) is a histone methyltransferase (HMTase) that forms a complex with EHMT1 (GLP) to catalyze mono- and dimethylation of lysine nine on histone H3 (H3K9me1/2) (Rice et al., 2003). Of note, EHMT1 protein sequence is highly similar to EHMT2 (Deimling et al., 2017). Actually, EHMT1 haploinsufficiency is involved in intellectual disability (ID) and ASD as part of the Kleefstra syndrome (Kleefstra et al., 2005). EHMT2 represses pluripotency genes in embryonic stem cells (Zylicz et al., 2015) and potentially acts as both repressor of neural progenitor genes and activator of neuronal differentiation (Deimling et al., 2017). The impact of the single base deletion in EHMT2 (K1164Nfs) on the protein function remains to be determined (see Table 1 for details). The frameshift is computationally predicted to extend the protein rather than truncating it, by utilizing sequence in the 3’UTR. However, it is located exactly at the beginning of the post-SET domain, that is at position 1164 of EHMT2. The resulting change in the downstream protein sequence completely disrupts three conserved cysteine residues in the post-SET domain that normally form a zinc-binding site with a fourth conserved cysteine close to the SET domain (Zhang et al., 2003). Since these three conserved cysteine residues are essential for HMTase activity, as replacement with serine abolished HMTase activity (Zhang et al., 2002), we suspect that this HMTase activity of EHMT2 is defective in our mutant glutamatergic neurons and potentially related to the observed hyperactivity (Figure 6). Upon our further validation experiment using a CRISPR-derived isogenic system and an unrelated genetic background (Figure 6D), we propose that EHMT2 impacts the synaptic function of glutamatergic neurons through H3K9me1/2 catalyzing ability. Further experiments might clarify this possibility, such as CRISPR-correction of the K1164Nfs point mutation in lines 38B and 38E to obtain isogenic controls.

Overall, this study highlights a way to improve the scalability of testing multiple iPSC-derived neuronal lines with various ASD-risk variants. Furthermore, our work demonstrates that for future studies to capture and characterize the electrophysiological impact of ASD variants on human iPSC-NGN2 neurons, it is most beneficial to include both MEA and patch-clamp experiments, across multiple timepoints. Analyzing multiple mutations and genes at once can lead to the identification of potential endophenotypes, in this case neuronal hyperactivity. This work revealed that inactivation of at least one allele of CNTN5 or EHMT2 significantly intensifies excitatory neuron synaptic activity in vitro. Such phenotype offers the possibility to implement NGN2-based high-throughput drug screening strategies (Cheng et al., 2017) combining MEA (Tukker et al., 2018) and lines 38B/38E for instance, to discover molecules that may compensate for neuronal hyperactivity.

Materials and methods

Ethics for human experiments

Under the approval of the Canadian Institutes of Health Research Stem Cell Oversight Committee and the Research Ethics Board (REB) at the Hospital for Sick Children, Toronto, Canada, iPSCs were generated from dermal fibroblasts or CD34 +blood cells. Three different informed consent forms for iPSC derivation and publication were obtained: i) Research Consent Form for Parent/Legal Guardians (of an individual with a neurologic condition); ii) Research Consent Form for Unaffected Individuals; iii) Assent form (for individuals with a neurologic condition). These consent forms describe in details the purpose of the research, the description of the research, the potential harms, the potential benefits, confidentiality, storage of the research samples, participation, reimbursement, sponsorship, and declaration of conflict of interest; REB approval file 1000012015.

Skin fibroblasts culture

Skin-punch biopsies were obtained from the upper back area by a clinician at The Hospital for Sick Children. Samples were immersed in 14 ml of ice-cold Alpha-MEM (Wisent Bioproducts) supplemented with penicillin 100 Units/ml and streptomycin 100 μg/ml (ThermoFisher), and transferred immediately to the laboratory at The Centre for Applied Genomics (TCAG). Each biopsy was cut into ~1 mm3 pieces with disposable scalpel in a 60 mm dish. 5 ml of collagenase 1 mg/ml (Sigma, Canada) was added and the dish was placed in 37°C incubator for 1:45 hr. Skin pieces and collagenase were then transferred to a 15 ml tube, and centrifuged at 300 g for 10 min. Supernatant was removed, 5 ml of trypsin 0.05%/EDTA 0.53 mM (Wisent Bioproducts) was added, and the mix was pipetted up and down several times to break up tissue and placed in 37°C incubator for 30 min. After incubation, the mix was centrifuged at 300 g for 10 min, and supernatant was removed leaving 1 ml. The pellet was pipetted up and down vigorously to break to the pieces without creating bubbles. The mix was transferred in a T-12.5 flask along with 5 ml of Alpha-MEM, 15% Fetal Bovine Serum (FBS; Wisent Bioproducts), penicillin 100 Units/ml and streptomycin 100 μg/ml (ThermoFisher), and placed in 37°C incubator for about a week until 100% confluence. Cultured cells were fed every 5–7 days if not confluent. Once confluent, cells were passed into three 100 mm dishes to expand, and frozen in liquid nitrogen.

Reprogramming fibroblasts using integrative virus

Reprogramming of skin fibroblasts was performed using retroviral and lentiviral vectors. Retroviral vectors encoding POU5F1, SOX2, KLF4, MYC, and lentiviral vectors encoding the pluripotency reporter EOS-GFP/PuroR were used and obtained as described (Hotta et al., 2009).

Reprogramming fibroblasts using non-integrative Sendai virus

Reprogramming of fibroblasts via Sendai virus was performed at the Centre for Commercialization of Regenerative Medicine (CCRM) using CytoTune-iPS 2.0 Sendai Reprogramming Kit (ThermoFisher). Fibroblasts were cultured in fibroblast expansion media (Advanced DMEM; 10% FBS; 1X L-Glutamine; 1X pen/strep – Thermo Fisher). The desired number of wells for reprogramming from a 24-well plate was coated with 0.1% gelatin. Fibroblasts were dissociated using Trypsin (ThermoFisher) and allowed to settle overnight. Virus multiplicity of infection (MOI) was calculated and viruses combined according to number of cells available for reprogramming and manufacturer’s protocol. 24 hr after transduction, media was changed to wash away viruses. Media was additionally changed on day 3 and 5 after transduction. 6 days after transduction, 6-well plates were coated with Matrigel(Corning). Cells were removed from the 24-well plate using Accutase (ThermoFisher) and plated on Matrigel in expansion media. 24 hr later, media was replaced with E7 media (StemCellTechnologies). Cells were monitored and fed daily with E7. Once colonies were of an adequate size and morphology to pick, individual colonies were picked and plated into E8 media (StemCellTechnologies). Clones growing well were further expanded and characterized using standard assays for pluripotency, karyotyping, genotyping and mycoplasma testing. Directed differentiation was performed using kits for definitive endoderm, neural and cardiac lineages (all ThermoFisher).

Peripheral blood mononuclear sells (PBMCs) isolation from peripheral blood and enrichment of CD34 +cells

Whole peripheral blood was processed at CCRM using Lymphoprep (StemCellTechnologies) in a SepMate tube (StemCellTechnologies) according to manufacturer’s instructions. The sample was centrifuged (10 min at 1200 g). The top layer containing PBMCs was collected and mixed with 10 mL of the PBS/FBS mixture and centrifuged (8 min at 300 g). The PBMC’s collected at the bottom of the tube were washed, counted and resuspended in PBS/FBS mixture. CD34 +cells were then isolated using the Human Whole Blood/Buffy Coat CD34 +Selection kit according to manufacturer’s instructions (StemCellTechnologies). Isolated cells were expanded in StemSpan SFEM II media (StemCellTechnologies) and StemSpan CD34 +Expansion Supplements (StemCellTechnologies) prior to reprogramming.

Reprogramming PBMC using non-Integrative Sendai virus

Reprogramming of CD34 +PBMCs was performed at CCRM using CytoTune-iPS 2.0 Sendai Reprogramming Kit. Expanded cells were spun down and resuspended in StemSpan SFEM II media and StemSpan CD34 +Expansion Supplements, and placed in a single well of a 24-well dish. Virus MOI was calculated and viruses combined according to number of cells available for reprogramming and manufacturer’s protocol. The virus mixture was added to cells, and washed off 24 hr after infection. 48 hr after viral delivery, cells were plated in 6-well plates in SFII and transitioned to ReproTESR for the duration of reprogramming. Once colonies were of an adequate size and morphology to pick, individual colonies were picked and plated into E8. Clones growing well were further expanded and characterized as explained above.

iPSC maintenance

All iPSC lines were maintained on matrigel (Corning) coating, with complete media change every day in mTeSR (StemCellTechnologies). ReLeSR (StemCellTechnologies) was used for passaging. Accutase (InnovativeCellTechnologies) and 10 μM Rho-associated kinase (ROCK) inhibitor (Y-27632; StemCellTechnologies) were used for single-cell dissociation purposes.

Gene editing

For point mutation correction in 18C line, we used the type II CRISPR/Cas9 double-nicking (Cas9D10A) system with two guide RNA (gRNAs) to reduce off-target activity. We devised the gRNA sequences using tools available at http://crispr.mit.edu/. We designed a HDR-based method using a synthesized single-stranded oligonucleotide (ssODN) template to replace the point mutation with the reference nucleotide. To prevent damage to the correct sequence, a silent mutation was introduced in the ssODN close to the proto-adjacent motif (PAM) of the reverse gRNA (gRNA-), which commands Cas9D10A to nick the plus strand, given that ssODN was synthesized as plus strand. All the CRISPR machinery was introduced into iPSC by nucleofection. Screening for correction of the appropriate base pair was based on absolute quantification of allele frequency using droplet digital PCR (ddPCR). Enrichment of corrected cells was obtained through sib-selection step cultures in 96-well plate format, as adapted from (Miyaoka et al., 2014), until a well containing 100% of corrected alleles was identified. For insertion of premature stop codon in 19–2 cells, ribonucleoprotein (RNP) complex was used as a vector to deliver the CRISPR machinery, along with one sgRNA and Cas9 nuclease, for each target gene. Design of sgRNA and ssODN for HDR, nucleofection and isolation of edited lines were described (Deneault et al., 2018).

Lentivirus production

7.5 × 106 HEK293T cells were seeded in a T-75 flask, grown in 10% fetal bovine serum in DMEM (Gibco). The next day, cells were transfected using Lipofectamine 2000 with plasmids for gag-pol (10 μg), rev (10 μg), VSV-G (5 μg), and the target constructs FUW-TetO-Ng2-P2A-EGFP-T2A-puromycin or FUW-rtTA (15 μg; gift from T.C. Südhof laboratory) (Zhang et al., 2013). Next day, the media was changed. The day after that, the media was spun down in a high-speed centrifuge at 30,000 g at 4°C for 2 hr. The supernatant was discarded and 50 μl PBS was added to the pellet and left overnight at 4°C. The next day, the solution was triturated, aliquoted and frozen at −80°C.

Differentiation into glutamatergic neurons

5 × 105 iPSCs/well were seeded in a matrigel-coated 6-well plate in 2 ml of mTeSR supplemented with 10 μM Y-27632. Next day, media in each well was replaced with 2 ml fresh media plus 10 μM Y-27632, 0.8 μg/ml polybrene (Sigma), and the minimal amount of NGN2 and rtTA lentiviruses necessary to generate 100% GFP +cells upon doxycycline induction, depending on prior titration of a given virus batch. The day after, virus-containing media were replaced with fresh mTeSR, and cells were expanded until near-confluency. Newly generated ‘NGN2-iPSCs’ were detached using accutase, and seeded in a new matrigel-coated 6-well plate at a density of 5 × 105 cells per well in 2 ml of mTeSR supplemented with 10 μM Y-27632 (day 0 of differentiation). Next day (day 1), media in each well was changed for 2 ml of CM1 [DMEM-F12 (Gibco), 1x N2 (Gibco), 1x NEAA (Gibco), 1x pen/strep (Gibco), laminin (1 μg/ml; Sigma), BDNF (10 ng/μl; Peprotech) and GDNF (10 ng/μl; Peprotech) supplemented with fresh doxycycline hyclate (2 μg/ml; Sigma) and 10 μM Y-27632. The day after (day 2), media was replaced with 2 ml of CM2 [Neurobasal media (Gibco), 1x B27 (Gibco), 1x glutamax (Gibco), 1x pen/strep, laminin (1 μg/ml), BDNF (10 ng/μl) and GDNF (10 ng/μl)] supplemented with fresh doxycycline hyclate (2 μg/ml) and puromycin (5 μg/ml for 19–2-derived cells, and 2 μg/ml for 50B-derived cells; Sigma). Media was replaced with CM2 supplemented with fresh doxycycline hyclate (2 μg/ml). The same media change was repeated at day 4. At day 6, media was replaced with CM2 supplemented with fresh doxycycline hyclate (2 μg/ml) and araC (10 μM; Sigma). Two days later, these day eight post-NGN2-induction (PNI) neurons were detached using accutase and ready to seed for subsequent experiments, as described below.

Multi-electrode array (MEA)

48-well opaque-bottom MEA plates (Axion Biosystems, M768-KAP-48), 16 electrodes per well, were coated with filter-sterilized 0.1% polyethyleneimine solution in borate buffer pH 8.4 for 1 hr at room temperature, washed four times with water, and dried overnight. 120,000 ‘day8-dox’ neurons/well were seeded in a 5 ul drop of CM2 media at the centre of each well, then covered with 250 μl CM2 media after one hour in the incubator. The day after, 5,000 mouse astrocytes/well were seeded on top of neurons in 50 μl/well CM2 media. Astrocytes were prepared from postnatal day 1 CD-1 mice as described (Kim and Magrané, 2011). Media was half-changed once a week with CM2 media. Every week post-seeding, the electrical activity of the MEA plates was recorded using the Axion Maestro MEA reader (Axion Biosystems). The heater control was set to warm up the reader at 37°C. Each plate was first incubated for 5 min on the pre-warmed reader, then real-time spontaneous neural activity was recorded for 5 min using AxIS 2.0 software (Axion Biosystems). A bandpass filter from 200 Hz to 3 kHz was applied. Spikes were detected using a threshold of 6 times the standard deviation of noise signal on electrodes.

Offline advanced metrics were re-recorded and analysed using Axion Biosystems Neural Metric Tool. An electrode was considered active if at least five spikes were detected per minute. Single electrode bursts were identified as a minimum of five spikes with a maximum interspike interval (ISI) of 100 milliseconds. Network bursts were identified as a minimum of 10 spikes with a maximum ISI of 100 milliseconds covered by at least 25% of electrodes in each well. No non-active well was excluded in the analysis. After the last reading, each well was treated with three synaptic antagonists: GABAA receptor antagonist picrotoxin (PTX; Sigma) at 100 μM, AMPA receptor antagonist 6-cyano-7-nitroquinoxaline-2,3-dion (CNQX; Sigma) at 60 μM, and sodium ion channel antagonist tetrodotoxin (TTX; Alomone labs) at 1 μM. The plates were recorded consecutively, 5–10 min after addition of the antagonists. A 60 min recovery period was allowed in the incubator at 37°C between each antagonist treatment and plate recording.

Patch-clamp recordings

Day 3 PNI neurons were replated at a density of 100,000/well of a poly-ornithin/laminin coated coverslips in a 24-well plate with CM2 media. On day 4, 50,000 mouse astrocytes were added to the plates and cultured until day 21–28 PNI for recording. At day 10, CM2 was supplemented with 2.5% FBS in accordance with (Zhang et al., 2013). Whole-cell recordings (BX51WI; Olympus) were performed at room temperature using an Axoclamp 700B amplifier (Molecular Devices) from borosilicate patch electrodes (P-97 puller; Sutter Instruments) containing a potassium-based intracellular solution (in mM): 123 K-gluconate, 10 KCL, 10 HEPES; 1 EGTA, 2 MgCl2, 0.1 CaCl2, 1 Mg-ATP, and 0.2 Na4GTP (pH 7.2). 0.06% sulpharhodamine dye was added to select neurons for visual confirmation of multipolar neurons. Composition of extracellular solution was (in mM): 140 NaCl, 2.5 KCl, 1 1.25 NaH2PO4, 1 MgCl2, 10 glucose, and 2 CaCl2 (pH 7.4). Whole cell recordings were clamped at −70 mV using Clampex 10.6 (Molecular Devices), corrected for a calculated −10 mV junction potential and analyzed using the Template Search function from Clampfit 10.6 (Molecular Devices). Following initial breakthrough and current stabilization in voltage clamp, the cell was switched to current clamp to monitor initial spiking activity and record the membrane potential (cc = 0,~1 min post-breakthrough). Bias current was applied to bring the cell to ~70 mV whereby increasing 5 pA current steps were applied (starting at −20 pA) to generate the whole cell resistance and to elicit action potentials. Data were digitized at 10 kHz and low-pass filtered at 2 kHz. Inward and outward currents were recorded in whole-cell voltage clamp in response to consecutive 10 mV steps from −90 mV to +40 mV.

Yeast complementation assay

The method for the yeast complementation assay was described previously (Sun et al., 2016).

Antibodies and western blotting

Cells were washed in ice-cold PBS and total protein was extracted in RIPA supplemented with proteinase inhibitor cocktail, and homogenized. Equivalent protein mass was loaded on gradient SDS-PAGE (4–12%) and transferred to Nitrocellulose membrane Hybond ECL (GE HealthCare). Primary antibodies used were rabbit anti-CNTN5 (Novus, NBP1-83243) and rabbit anti-EHMT2/G9A (Abcam, ab185050). HRP-conjugated secondary antibodies (Invitrogen) were used and the membranes were developed with SuperSignal West Pico Chemiluminescent Substrate (Pierce). Images acquired using ChemiDoc MP (BioRad) and quantified using software Imagelab v4.1 (BioRad). Western Blots were repeated at least twice for each biological replicate.

Mycoplasma testing

All cell lines were regularly tested for presence of mycoplasma using a standard method (Otto et al., 1996).

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Decision letter

  1. Huda Y Zoghbi
    Senior Editor; Texas Children's Hospital, United States
  2. Moses V Chao
    Reviewing Editor; New York University Langone Medical Center, United States

In the interests of transparency, eLife includes the editorial decision letter, peer reviews, and accompanying author responses.

[Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed.]

Thank you for submitting your article "CNTN5-/+ or EHMT2-/+ iPSC-derived neurons from individuals with autism develop hyperactive neuronal networks" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Huda Zoghbi as the Senior Editor. The reviewers have opted to remain anonymous.

The Reviewing Editor has highlighted the concerns that require revision and/or responses, and we have included the separate reviews below for your consideration. If you have any questions, please do not hesitate to contact us.

Summary:

This report of IPSC derived neurons with autism-risk gene mutations was highly regarded and represents a novel investigation that many groups have sought to achieve. The finding of hyperactive neuronal networks is consistent with current views of the disorder and is a strong contribution to the field.

Major concerns:

The two referees carried out an extensive evaluation of the manuscript and raised multiple questions about the data acquisition and the mechanism of action. It was felt the multi-electrode array (MEA) protocols need more explanation and assurance of reliability. In summary, both reviewers requested the following additions to strengthen the manuscript-

1) Inclusion of existing MEA data across all 12 donors

2) Consideration of the efficiency and yield of NGN2 donors across donors and experiments.

The MEA data should be a full figure. A full data set will be key in understanding line developmental time course, and variability between wells, lines, and individuals. It was also recommended that there is a need to discuss the apparent limitations of their strategy and implementation.

Separate reviews (please respond to each point):

Reviewer #1:

In their manuscript entitled "CNTN5-/+ or EHMT2-/+ iPSC-Derived Neurons from Individuals with Autism Develop Hyperactive Neuronal Networks," Deneault et al. describe a collection of patient-specific hiPSC lines with heterozygous mutations in 12 different ASD associated loci (15 cases, each with at least one related unaffected relative and/or an CRISPR-edited hiPSC line as control; 27 hiPSC lines total). hiPSCs were NGN2-induced to excitatory neurons and then compared by multielectrode array; key findings for CNTN5-/+ and EHMT2-/+ were evaluated by electrophysiology. This collection of ASD and related control derived hiPSCs represents a valuable resource to the community, worthy of describing in eLife. The value of such a collection would be enhanced by making publically available genotype data from each donor, all hiPSC lines and all MEA data. While an impressive degree of work is described, no insights into why some patient neurons, but not others, showed activity deficits or the mechanism linking genotype to neuronal function is included. With major revisions, this report will be suitable for publication at eLife.

Major detail:

1) ASD neuronal phenotypes – The authors conducted MEA across all 25 hiPSC lines (Supplementary file 3) but do not seem to comprehensively present this data anywhere, focusing instead on CNTN5-/+ and EHMT2-/+. The findings (even if null) should be aggregated and clearly presented as a main figure. Additionally, the authors should make all raw MEA data and all hiPSC lines widely available upon publication.

2) Inter-individual and intra-individual variability – The authors have generated perhaps the largest systematic MEA dataset across unrelated controls and ASD patients with different rare mutations. While they aggregate all control data in SI Figure 2 and 3, it would be tremendously valuable to the field if they might plot their results by donor as a main figure. Being able to show how MEA activity changes over time, and between donors, would greatly inform our understanding of inter-individual variation in MEA. Similarly, in those cases where the same line was tested on multiple MEA plates, again, it would be useful to present variability.

3) Inter-well and inter-hiPSC neuronal induction efficiency – It is incredibly difficult to assess and adjust for differences in total cell number and cell type composition in MEA experiments, both of which are expected to impact MEA recordings. What was the authors' strategy? It would be helpful if in the discussion they might inform the readers how best to learn from and extend their analyses.

4) CRISPR-engineering – How did the authors rule out off-target effects following CRISPR-editing to produce isogenic pairs?

5) Phenotypic characterization – Increased neuronal activity might indicate alterations in synaptic function and/or maturation. For the two genes focused on within the manuscript, CNTN5-/+ or EHMT2-/+, the authors should provide attempt to resolve this question (even a time course of transcriptomic changes over 6-weeks of NGN2-induction).

6) Mechanism – Can the authors speculate as to the mechanism through which CNTN5-/+ or EHMT2-/+ increase neuronal activity? Possibly by linking their results to existing animal models? Perhaps RNAseq analyses might provide insights into the downstream changes in patient-derive neurons?

7) Data sharing – The authors have generated a great resource that is likely to be of great interest to the community. The value of such a collection would be enhanced by making publically available the inclusion of SNP and exome genotype data from each donor and clarification of the specific hiPSC lines available (at which repository). How many validated hiPSCs per donor are being made available? Through which repository? Will the MEA data be made available?

Minor detail:

1) Clarification of terminology – NGN2-overexpression is best described as "induction" not "differentiation." The authors are cautioned against describing the neurons as "cortical" without clear evidence of patterning.

2) Can the authors clarify in the main text (not just in the Materials and methods) that astrocyte co-culture was used with NGN2-neurons to improve maturation for MEA and electrophysiological.

Additional data files and statistical comments:

Please make available all MEA raw data to the community.

Reviewer #2:

This study uses important new tools, specifically human iPSC-derived neurons in models of developing neurons in culture, to define mechanisms that may underlie ASD pathogenesis. Further, they correctly state that: "Proband-specific iPSC-derived neuronal cells indeed provide a useful model to study disease pathology, and response to drugs, but throughput (both iPSC-derived neurons and phenotyping) is low, with costs still high. As a result, so far, only a few iPSC-derived neuronal lines are typically tested in a single study." This indeed has been a limitation, so that evidence of new technologies and approaches would be a welcome advance to speed development of the field. Furthermore, they correctly point out that iPSC-derived neurons carry "the precise repertoire of rare and common genetic variants as the donor proband,".… and "represent the best genetic mimic of proband neurons for functional and mechanistic studies." This approach does overcome many limitations of more narrow, experimentally designed lines where only known genetic factors can be specifically manipulated. So the field needs to move in this direction.

The authors have performed a tremendous amount of experimental work on numerous genetically altered human iPSC-derived neuronal lines, and these lines remain as a resource for the broader community, which may benefit. The large numbers of lines and experiments provide some level of reproducibility and do identify some very specific abnormalities in neuronal function. However there are a number of deficiencies in the manuscript that diminish enthusiasm. There are major concerns regarding inadequate descriptions of methods and data analysis, making it difficult for another investigator to reproduce this approach as presented. Without this information, it is difficult to assess the utility of the recommended multi-electrode array (MEA) protocols and analytic techniques, since the MEA was informative for only 3 of the 12 families. It is not clear that this low success rate represents an absence of circuit abnormalities in the other 9 families, or alternatively, a technical and analytic design that does not distinguish changes in circuit activity, from abnormalities in cell survival, cell aggregation, cell distribution, electrode contact, or cell type composition. The absence of any cellular analysis that underlies the MEA outcomes seems to place this work in the domain of preliminary studies. Specific comments follow:

Abstract: It seems to this reviewer that the Abstract communicates a more positive result than was actually achieved, as regards the "standardized set of procedures," that "used a large-scale multi-electrode array (MEA).…" These statements seem to suggest the value of this approach to the general field. Yet, of the 12 families with genetic variants, only 3 of the 12 MEA studies revealed any differences, which does not necessarily recommend this as a mainstay approach. The alternative approach, using single cell electrophysiology, defined only one additional pathology. The Abstract should be modified to change the emphasis, and consider stating the success rate of 33%.

Materials and methods:

The authors do not mention performing a power analysis in designing the study, and power analysis is not discussed in the statistical methods. There is concern that the inherent variability in the MEA data is not well appreciated nor well controlled, and without this analysis, it cannot be transparently reported.

There is no peer-reviewed, published characterization by the authors of their results of using NEUROG2 expression to induce glutamatergic neurons, characterizations that other cited authors have performed. While the authors state that; "Importantly, we determined that this strategy offers highly uniform differentiation levels between cell lines derived from different participants (Deneault et al., 2018);" that reference is to a manuscript submitted to bioRxiv that has not undergone peer review, and for the current article to be published, analysis of the NEUROG2 induction procedure needs to be presented. The statement that "differentiation levels" were uniform between cell lines, is quite a claim, and it is not at all clear what this means. For example what endpoints were assessed among multiple lines to indicate uniformity? One can imagine, for example, that failure to observe a specific differentiation feature, could reflect that the marker is not expressed by a cell, versus, that it is expressed but at a time point not examined. Such an outcome could reflect changes in i) cell fate, ii) induction of differentiation, iii) survival of a specific cell class, or simply, iv) a delay in its expression in suboptimal culture conditions. There is no characterization of the surviving cell population when MEA analysis of the cultures is performed, so that specific experimental outcomes cannot be related to the cell population under study. Even though the culture bottoms are opaque, immunocytochemistry using fluorescent labeling would allow visualization of cell numbers (DAPI) and cell type (NeuN, MAP2, vGlut, vGAD, etc) by use of the epifluorescence microscope to understand how cell composition at the time of MEA recording impacted the outcomes. This seems a reasonable request since the majority of MEA studies did not reveal group genetic differences, with only 3 of the 12 families demonstrating changes in firing rate and network burst frquencies.

The MEA methods are incomplete as reported, and extensive review of Supplementary file 3 still leaves uncertainties for this reviewer. The authors employ MEA as the single major measure of outcome, but they do not reference their previous use of this technology. It is not adequate to cite other laboratories. The authors should describe more fully the nature of the MEA technology, how the cultures appear in them, the data that is generated, and how it was analyzed. To start, the authors indicate that 48-well opaque-bottom MEA plates (Axion Biosystems) were used. But going to the website there are four (4) "Classic" varieties of this plate, as well as many other formats. Please provide exact catalogue number or numbers. How many electrodes are there in each well? It may be helpful to show a layout/picture for the electrode array, and also pictures of fixed and stained neuronal cultures to define the variability in cell contact to multiple electrodes, and perhaps the extremes of electrode contact they observe. Doing a brief PubMed search, I found two recent 2018 publications that provide images that show large variability of cell growth around fixed electrode arrays (DeRosa et al., Sci Rep 2018; Odawara et al., Sci Rep 2018; also see Hammond et al., BMC Neurosci, 2013). Please state the range of individual MEA wells used in these types of studies: Supplementary file 3 seems to indicate a range from 3 wells to as many as 12. How many independent MEA assay experiments are performed for each line? Supplementary file 3 is confusing, specifically the column labeled "Total nb independent plates." This number coincides with the reported number of different MEA plates (MEA plate IDs are 1-37, with only 21 involved here). Is an independent plate a separate experiment? Usually in iPSC studies, within a single clone (as reported here), independent experiments imply different passages of the same clone, analyzed in subsequent weeks, or after thawing an earlier passage and newly plated. But after NEUROG2 induction, it seems the cells by design are non-proliferative (anti-mitotic drug AraC is applied, among other strategies). But without a proliferative stem cell culture, how are independent experiments performed? Is the NEUROG2 induction performed multiple times on the same clone (a true biological replication) to yield 3 independent plates? Or are glutamatergic-induced cells frozen and plated at multiple times, another valid approach? Or alternatively, is a single induction plated at the same time to multiple plates? The issue of reproducibility is critical in this kind of study, and it is surprising that there is no mention of these issues that are currently central to the field.

Results:

Line 19-2 is used as a control for one family and is also the line for all isogenic lines produced. It is the only line shown that was reprogrammed by retrovirus (all other lines were induced using non-integrating Sendai virus vectors), so what is known about virus integration site mutations, and does this interfere or alternatively facilitate outcomes, and impact its use as a control? To make it easier for the reader to understand this report, the authors need to provide a description of line 19-2 here, rather than refer the reader to the un-reviewed manuscript in bioRxiv.

Supplementary file 2 indicates that for some families, only a single line was assessed for pluripotency markers and surprisingly, this seems to include 2 important control lines, 19-2 and NR3. It seems a concern that a control line used for many experiments was analyzed using only a single clone; please address.

The authors indicate they "sought to determine if any selected ASD-risk variants would interfere with spontaneous spiking and synchronized bursting activity in a whole network of interconnected glutamatergic neurons." To determine when to make MEA comparative analysis they pooled the N=341 control line wells from the 12 families, and observed that the mean firing rate (MFR) was the highest (~1.7 Hz) at week six (6) post-NEUROG2-induction (PNI). Thus they used this time point to compare all cell lines for the 1) weighted mean firing rate (MFR) and the 2) network burst frequency. This approach seems reasonable and seems to establish a standard set of conditions and protocols, one of their goals, as stated in the Abstract.

However, to this reviewer, the choice of a single time to make assessments seems to diminish the likelihood of finding differences between control and disease related genetic variants. I think it is not obvious nor expected that control lines from each family would exhibit the same maturation profile, and while the authors do perform intra-familial comparisons, the advantages of this type of analysis seem to be thwarted in this design. Given genetic heterogeneity, one could easily imagine different time frames. With all the expenses in cost, media, and time to set up these complex models, why not make recording every week for example? The current protocol as presented seems to be an inefficient use of resources. And perhaps in support of this criticism, the authors themselves seem to agree, as shown in Figure 3! Because the Family D mutant line #27 that carries CNTN5+/- revealed differences in MEA at 6 weeks, they created an isogenic line 19-2 control line by inserting the CNTN5 Stop-Tag. However they report they did not find any effects on MEA parameters at 6 weeks. Thus they extended analysis to 11 weeks, and now found statistically significant differences at 10 weeks. However, it seems that at week 7, the outcome appears to be the opposite, with StopTag showing 50% less activity (Figure 3B, left; would likely be significant with a t-test). It seems the authors cannot have it both ways, we use 6 weeks, but if we do not find an expected outcome, we wait longer. Further, maybe many of the other negative MEA results would appear different if assessed at other time points.

There is also concern that cell survival plays an important role. In CNTN5+/-, for example, perhaps cells with lower firing rates die off with progressive incubation. This would result in survival of only cells with increased MFR. However, because there is no report of the number of negative electrodes in each plate (which might increase with time), the results at 11 weeks appear to be the reverse of that seen at 7 weeks, yet this outcome may have little to do with synaptic functions. To this reviewer, since time plays an important role in genetic variant effects (Figure 3B), and the authors seem to tentatively conclude that changes in MEA measured activity depend on changes at the synapse, it would strengthen their case if single cell electrophysiology of these cells at this or an earlier time were correlated with the MEA outcomes, to demonstrate that CNTN5 effects are mediated by changes in synaptic currents, and not in cell survival, distribution, or adhesion. Cellular analysis at MEA culture termination would address this unexplored mechanism.

The definition of weighted mean firing rate is not clear; the authors state, "We monitored the weighted mean firing rate (MFR), which represents the MFR per active electrode,.…". If understood correctly, this measure only averages active electrodes in any MEA well. Thus if there is significant (and currently un-described) failure of electrode activation in a well, that data is excluded. A major feature of a cell line may be that in one case only 20-30% of electrodes have signal due to differences in cell survival or dispersion, whereas in another line, it may routinely be 80-100% coverage. If this is the case, this type of information may impact the kinds of cellular mechanisms that one considers to explain the outcomes. This kind of information should be assessed, if not already done, and should be reported transparently in a Table that includes all families, perhaps in the supplement. One might find a correlation of decreasing/increasing cell-electrode contact (appearing as active/inactive electrodes/well) to the outcomes reported.

There is concern about the sole use of retrovirally-induced line 19-2 for all isogenic experiments. In contrast to control lines from the 10 families (Families C-L, Supplementary file 3), whose study led to selecting 6 weeks for MEA analysis, line 19-2 with introduced CRISPR gene variant only reveal differences at 8-12 weeks. There raises two issues. Firstly, MEA analysis of, for example, EHMT2+/-, demonstrates abnormal MFR and burst frequency only from weeks 4 to 7 when compared to family controls (Figure 5A), a result that is lost by week 8. In contrast, the 19-2 isogenic line only shows differences at week 9 or later. Similar differences are observed for CNTN5+/- in Figure 3. Second, the MFR in line 19-2 is 4 Hz or more, whereas analysis of all the other 10 family control lines (Sendai based) examined at 6 weeks (sometimes 4-8 weeks) are mostly less than 1 Hz, and always below 2 Hz. These consistent differences raise concerns that the 19-2 line brings a special test system in which perhaps most of the analyzed variants could/should be assessed. But this may also bias results because only this single line is used for isogenic studies. Would this same effect occur in a Sendai non-integrating virus-induced iPSC model for isogenic line production?

Subsection “EHMT2-/+ CRISPR-Isogenic Pair Confirms Neuronal Hyperactivity”: The authors should reserve interpretations of differences in expression of MEA in the isogenic line compared to the family lines, 38B/E. They state: "This increased activity in mutant neurons occurred later than that observed in the familial lines 38B/E, possibly due to different genetic backgrounds." Alternatively, there are changes in cell survival, aggregation, electrode contact, or distribution that have not been assessed. It is premature to raise the issue of genetic background as explanation. Furthermore, the authors seem to be mistaken, reporting there is "lower resting membrane potential," but that is not what is shown in Figure S5 where the mean values appear identical; please correct this results error.

The discussion fails to mention any limitations to the approach of MEA at 6 weeks, the absence of cell characterization, and possible alternative mechanisms to the currently pre-conceived notions of the gene's function. The authors might in fact suggest that a full time course analysis would be beneficial. They fail to discuss the very different behavior of retrovirally created 19-2 line used at 9-12 weeks that show outcomes not observed in the Sendai virus created lines assessed at 6 weeks. They fail to address the possibility of explanatory mechanisms in their studies such as changes in cell survival, composition, distribution, or electrode contact. In all, these studies seem preliminary in nature and set a stage for further analysis before we recommend this specific set of procedures to be adopted by the field.

Additional data files and statistical comments:

A major feature of a cell line may be that in one case only 20-30% of electrodes have signal due to differences in cell survival or dispersion, whereas in another line, it may routinely be 80-100% coverage. If this is the case, this type of information may impact the kinds of cellular mechanisms that one considers to explain the outcomes. This kind of information should be assessed, if not already done, and should be reported transparently in a Table that includes all families, perhaps in the supplement. One might find a correlation of decreasing/increasing cell-electrode contact (appearing as active/inactive electrodes/well) to the outcomes reported.

[Editors’ note: further revisions were suggested before publication.]

The revised manuscript has been evaluated by a previous referee, who found the majority of the concerns have been adequately addressed by reorganizing the data and presenting the findings in a consistent manner. The Reviewing Editor's assessment is that several minor issues require attention. There is a need to acknowledge alternative explanations for the results and for further clarification of the MEA phenotypes. The editors feel that these issues can be dealt with by small revisions and additions to the text. Please make all the edits as outlined below.

Reviewer #1:

The authors have done a good job responding to my criticisms. The manuscript now more transparently details the variability between hiPSCs/experiments. The authors present phenotypic MEA and electrophysiological differences in CNTN5+/- and EHMT2+/- hiPSC-neurons from ASD. While they now clarify in the abstract that they identified synaptic phenotypes in 25% of the individuals tested, they should more clearly note that they cannot rule out synaptic phenotypes in the other 75% of ASD cases. For example, secondary differences in cellular replication, survival or patterning could mask activity differences by MEA. Moreover, the authors should consider in the discussion that MEA phenotypes need not predict electrophysiological deficits and vice versa, as evidenced for ASTN2 in their recent Stem Cell Reports publication. Finally, the authors still fail to resolve whether deficits in CNTN5 and EHMT2 result from impaired maturation rather than impaired synaptic function. With modest and mostly textual revisions, this report will be suitable for publication at eLife.

Minor Comments:

The nomenclature used in Figure 2–supplement 1 is very unclear. Can the authors add the relevant family ID and gene name such that the reader does not need to constantly refer back to Table 1?

https://doi.org/10.7554/eLife.40092.026

Author response

Reviewer #1:

[…] Major detail:

1) ASD neuronal phenotypes – The authors conducted MEA across all 25 hiPSC lines (Supplementary file 3) but do not seem to comprehensively present this data anywhere, focusing instead on CNTN5-/+ and EHMT2-/+. The findings (even if null) should be aggregated and clearly presented as a main figure. Additionally, the authors should make all raw MEA data and all hiPSC lines widely available upon publication.

We thank the reviewer for these helpful suggestions. Accordingly, we have aggregated all the null data (previously presented as Figures S2 and S3) along with the positive data into a single new Figure 3. All raw MEA data and all iPSC lines will be publicly available upon request at the time of publication. We are also open to submitting the iPSC cell lines to an appropriate repository if one is available. We are in discussions with Autism Speaks who have expressed interest in supporting this. As is the case for all of our bioresources described in publications, they will be available upon request. We adhere closely to standard academic publishing principles.

2) Inter-individual and intra-individual variability – The authors have generated perhaps the largest systematic MEA dataset across unrelated controls and ASD patients with different rare mutations. While they aggregate all control data in SI Figure 2 and 3, it would be tremendously valuable to the field if they might plot their results by donor as a main figure. Being able to show how MEA activity changes over time, and between donors, would greatly inform our understanding of inter-individual variation in MEA. Similarly, in those cases where the same line was tested on multiple MEA plates, again, it would be useful to present variability.

We agree that plotting all the results by donor as a main figure would help to appreciate the inter-individual and intra-individual variability. Hence, we have aggregated all the data in a new Figure 3 showing how the activity changes over time, and between donors, and even between different biological replicates for each donor. Correspondingly, we have added the following (framed) paragraphs to the section “Multi-Electrode Array Analysis of iPSC-Derived Neurons” of the Results:

“The weighted MFR (wMFR), which represents the MFR per active electrode, was used as a primary read-out for all tested iPSC-derived neurons, at one-week intervals from week 4 to 8 post-NGN2-induction (PNI) (Figure 3). To identify a preferred timepoint for this screen, we first pooled the data of all the independent control lines. Since the highest wMFR value for this pool of ‘all controls’ (~1.8 Hz) was detected at week 6 (Figure 3A), we initially used that timepoint to compare the activity of ASD variant and control lines for each family. In two different families, i.e., CNTN5 and EHMT2, a significant higher wMFR was recorded in ASD variant neurons at week 6 compared with their corresponding familial control neurons (Figure 3B). EHMT2 had a strikingly increased wMFR at all timepoints, whereas CNTN5 at other timepoints was equivalent to its controls. We therefore ranked these two genes as high priorities for further study. In contrast, no significant differences were observed for DLGAP2, CAPRIN1, SET or GLI3 (Figure 3C), suggesting that these variants do not differ from control neuronal activity in our MEA assays, and therefore were not studied further. Different dynamics of altered wMFR were observed for ANOS1 and VIP at week 4 (Figure 3D), and the ANOS1 nonsense variant was ranked as an example of a candidate for further study. Conversely, a significant lower wMFR was recorded at weeks 7 and 8 for THRA (Figure 3D). No unaffected family members were available as controls for the single NR3 line (NRXN1) nor for 36O-36P (AGBL4), thus they were not chosen for further study. When we compared their values to the pooled values recorded from all the different familial controls available, no difference was found for NRXN1 and a significantly lower wMFR was observed at weeks 5 and 7 for AGBL4 (Figure 3E).

To explore intra-individual (different lines from the same individual) and inter-individual (different individuals with the same mutation) variability, we plotted all the values obtained from each single well, independent experiment, cell line and individual, at each of the five reading timepoints (Figure 3F). Most lines from an individual were not significantly different from each other, and reassuringly low inter-individual variability was observed with different siblings bearing the same mutation(s), e.g., 48K and 48N versus 49G and 49H (SET), or 61I and 61K versus 62M and 62X (GLI3), at different timepoints (Figure 3F). A few lines showed a significant intra-individual variability, e.g., lines 52A and 52C (THRA) at week 4, or lines 75G and 75H (CAPRIN1) at week 8 (Figure 3F). We also noted some inter-independent experiment variability for a given line, e.g., line 38E (EHMT2) at weeks 4 and 5 (dots with different colours do not overlap in Figure 3F). Note that similar profiles were monitored in terms of MFR (Figure 3—figure supplement 1), indicating that these differences were not due to having more or less active electrodes in different lines. While consistent activity across lines was generally observed, the presence of variability prompted us to interrogate independent variants created by genome editing of CNTN5, ANOS1 and EHMT2.”

3) Inter-well and inter-hiPSC neuronal induction efficiency – It is incredibly difficult to assess and adjust for differences in total cell number and cell type composition in MEA experiments, both of which are expected to impact MEA recordings. What was the authors' strategy? It would be helpful if in the discussion they might inform the readers how best to learn from and extend their analyses.

The third and fourth paragraphs of the Discussion, as follow, now discuss this issue:

“We assume that most NGN2-neurons are glutamatergic based on the data presented in the original publication establishing this technique (Zhang et al., 2013), and on our previous publication using high-cell density RNAseq assessment (Deneault et al., 2018). Moreover, we have treated several of our cultures at the end of the MEA experimentation with different neurotoxins (CNQX, PTX, TTX) to show that most neurons are glutamatergic and not GABAergic, for different lines. In addition, our patch-clamp recordings have demonstrated that these neurons exhibit the properties of excitatory neurons.

Characterization of neuronal composition and survival when MEA is performed is difficult to achieve with high accuracy. Our strategy involved using several technical (3 to 12 per independent experiment) and biological (up to 4) replicates to best compensate for inter-well and inter-iPSC neuronal induction variations (Supplementary file 3). It is possible that some phenotypes were missed since we cannot exclude the possibility that differences in cell number or composition across individuals in a family actually masked potential phenotypes. However, this issue was ruled out for CNTN5 and EHMT2 because the phenotypes were confirmed in unrelated isogenic pairs, and by patch-clamp recordings.”

4) CRISPR-engineering – How did the authors rule out off-target effects following CRISPR-editing to produce isogenic pairs?

In our recent publication, which is now out in the peer-reviewed literature (Deneault et al., Stem Cell Reports, 2018), we have designed a new approach to detect any off-target mutations based on whole-genome sequencing of a series of isogenic KO lines. We have used the same strategy here and inserted the following (framed) sentence within the second paragraph of the “Repair of ANOS1 Rescues Defective Membrane Currents” section of the Results:

“No overt off-target mutations were detectable using our previously-described WGS strategy (Deneault et al., 2018).”

This was expected since we used the double-nicking approach. Regarding the isogenic pairs made using ribonucleoprotein (RNP) complexes in line 19-2, i.e., 19-2-CNTN5 and 19-2-EHMT2, we have not investigated any potential off-target mutations since this method was previously described with very low probability of off-target mutations by several different groups. Moreover, finding similar significant phenotypes as those observed in the patient lines further dissociated any potential off-target mutations to such phenotypes.

5) Phenotypic characterization – Increased neuronal activity might indicate alterations in synaptic function and/or maturation. For the two genes focused on within the manuscript, CNTN5-/+ or EHMT2-/+, the authors should provide attempt to resolve this question (even a time course of transcriptomic changes over 6-weeks of NGN2-induction).

We agree that an increased neuronal activity might indicate alterations in synaptic function and/or maturation. We now discuss this in the fifth paragraph of the Discussion, as following:

“An increased neuronal activity, e.g., MFR in mutant lines 38B/E, might indicate alterations in synaptic function and/or maturation. We have presented the MFR for all tested cell lines in Figure 3—figure supplement 1, in support of the wMFR in Figure 3. The wMFR is defined as the MFR divided by the number of active electrodes per well. If there is significant failure of electrode activation in a well, for example due to differences in cell survival, dispersion or adhesion, those data are excluded. For example, the increased MFR observed in EHMT2-/+ lines could be due to a better capacity to survive, disperse or adhere than EHMT2+/+ cells, without affecting synaptic activity. However, excluding all inactive electrodes would then result in a comparable wMFR between mutant and control cells, which was not the case. Indeed, both MFR and wMFR were significantly higher in EHMT2-/+ cells. That does not exclude the possibility of a better survival, dispersion or adhesion, but it is likely not the only reason for the observed increase in spiking activity, suggesting greater synaptic activity, as supported by patch-clamp recordings. Indeed, we used patch-clamp recordings to show that sEPSC frequency was significantly increased in mutant line 38E compared to control line 37E. We believe these results directly support synaptic alteration as one of the possible causes for the increased neuronal activity measured by MEA. However, a detailed analysis of cell maturation will be required for each different cell line involved in this study to clarify this issue.”

Moreover, as explained in the last paragraph of the Results, we also performed patch-clamp recordings on 19-2-EHMT2 neurons at day 21-25 PNI. We did not detect any significant change in sEPSC frequency and amplitude at this earlier timepoint, similarly to the MEA experiment. However, intrinsic properties showed a significant increase in capacitance and decrease in input resistance in mutant cells (Figure 6—figure supplement 2). These observations suggest that the mutant neurons at 3-4 weeks PNI potentially have a faster maturation rate, however, this phenotype is most pronounced in the hyperactivity recorded by MEAs later at 9-11 weeks PNI. These results support the conclusion that the inactivation of one allele of EHMT2 significantly increases spontaneous network activity of excitatory neurons, with possible effects on the neuronal maturation process. We also agree that a time course of RNAseq on the isogenic pairs could be informative regarding synaptic function and/or maturation pathways in the future. However, the presence of mouse astrocytes in the electrophysiological experiments could potentially limit the full understanding of what is happening at the two relevant timepoints.

6) Mechanism – Can the authors speculate as to the mechanism through which CNTN5-/+ or EHMT2-/+ increase neuronal activity? Possibly by linking their results to existing animal models? Perhaps RNAseq analyses might provide insights into the downstream changes in patient-derive neurons?

Speculation about potential mechanisms and linking to existing animal models are part of the Discussion. We have used RNAseq analyses in a previous publication (Deneault et al., 2018) to provide insights into the possible downstream changes in several different isogenic pairs involving KO of other ASD genes, and revealed interesting convergence of DEGs and networks. In the future, this tool will certainly help to shed light into the downstream genes and pathways affected by the absence of EHMT2, for example, which is mostly located in the nucleus and could be impacting activity-dependent transcription of synaptic signaling genes.

7) Data sharing – The authors have generated a great resource that is likely to be of great interest to the community. The value of such a collection would be enhanced by making publically available the inclusion of SNP and exome genotype data from each donor and clarification of the specific hiPSC lines available (at which repository). How many validated hiPSCs per donor are being made available? Through which repository? Will the MEA data be made available?

All raw MEA data, as well as all iPSC lines, will be publicly available upon request. Genotype data, in many different forms, from each donor and the relevant controls described in the paper is already available through the Autism Speaks MSSNG WGS project (Yuen et al., 2017). Of course, as our group always does, we will follow standard academic publishing rules and provide any information/resource needed for others to replicate our experiments.

Minor detail:

1) Clarification of terminology – NGN2-overexpression is best described as "induction" not "differentiation." The authors are cautioned against describing the neurons as "cortical" without clear evidence of patterning.

We agree with this comment and we have made appropriate modifications throughout the manuscript where applicable.

2) Can the authors clarify in the main text (not just in the Materials and methods) that astrocyte co-culture was used with NGN2-neurons to improve maturation for MEA and electrophysiological.

We have modified the main text accordingly.

Additional data files and statistical comments:

Please make available all MEA raw data to the community.

All raw MEA data will be publicly available upon request at the time of publication. We are also actively searching for a public repository to accept it (including the National Database for Autism Research).

Reviewer #2:

[…] Specific comments follow:

Abstract: It seems to this reviewer that the Abstract communicates a more positive result than was actually achieved, as regards the "standardized set of procedures," that "used a large-scale multi-electrode array (MEA).…" These statements seem to suggest the value of this approach to the general field. Yet, of the 12 families with genetic variants, only 3 of the 12 MEA studies revealed any differences, which does not necessarily recommend this as a mainstay approach. The alternative approach, using single cell electrophysiology, defined only one additional pathology. The Abstract should be modified to change the emphasis, and consider stating the success rate of 33%.

We have modified the Abstract accordingly:

“We used a multi-electrode array, with patch-clamp recordings, to determine a synaptic phenotype in 25% of the individuals with ASD.”

Materials and methods:

The authors do not mention performing a power analysis in designing the study, and power analysis is not discussed in the statistical methods. There is concern that the inherent variability in the MEA data is not well appreciated nor well controlled, and without this analysis, it cannot be transparently reported.

We agree that variability in the MEA data could not have been well appreciated in the previous version of the manuscript. In a two-sample comparison setting, a power analysis estimates the sample size required to likely detect an effect of a given size. In a screening approach, the sensitivity and specificity of the screen are also influenced by the sample size. Here, we elected to use MEA as a screening tool because we had previously evaluated the sensitivity of our protocol. For example, MEA was used to confirm a hypoactive neuronal phenotype, previously detected using patch-clamp recordings, in 4 out of 5 KO lines, in which 6-8 MEA wells were recorded per line per experiment, in mostly 4 independent experiments (Deneault et al., Stem Cell Reports, 2018). With such a high level of sensitivity, plus testing usually 2 different lines per individual, we assumed that aiming at 6 technical and 3 biological replicates per line would support enough sensitivity for our screen. In addition, we have aggregated all the data in a new Figure 3 showing how the activity changes over time, and between donors, and even between different biological replicates for each donor. Correspondingly, we added the following (framed) paragraphs to the section “Multi-Electrode Array Analysis of iPSC-Derived Neurons” of the Results:

“The weighted MFR (wMFR), which represents the MFR per active electrode, was used as a primary read-out for all tested iPSC-derived neurons, at one-week intervals from week 4 to 8 post-NGN2-induction (PNI) (Figure 3). To identify a preferred timepoint for this screen, we first pooled the data of all the independent control lines. Since the highest wMFR value for this pool of ‘all controls’ (~1.8 Hz) was detected at week 6 (Figure 3A), we initially used that timepoint to compare the activity of ASD variant and control lines for each family. In two different families, i.e., CNTN5 and EHMT2, a significant higher wMFR was recorded in ASD variant neurons at week 6 compared with their corresponding familial control neurons (Figure 3B). EHMT2 had a strikingly increased wMFR at all timepoints, whereas CNTN5 at other timepoints was equivalent to its controls. We therefore ranked these two genes as high priorities for further study. In contrast, no significant differences were observed for DLGAP2, CAPRIN1, SET or GLI3 (Figure 3C), suggesting that these variants do not differ from control neuronal activity in our MEA assays, and therefore were not studied further. Different dynamics of altered wMFR were observed for ANOS1 and VIP at week 4 (Figure 3D), and the ANOS1 nonsense variant was ranked as an example of a candidate for further study. Conversely, a significant lower wMFR was recorded at weeks 7 and 8 for THRA (Figure 3D). No unaffected family members were available as controls for the single NR3 line (NRXN1) nor for 36O-36P (AGBL4), thus they were not chosen for further study. When we compared their values to the pooled values recorded from all the different familial controls available, no difference was found for NRXN1 and a significantly lower wMFR was observed at weeks 5 and 7 for AGBL4 (Figure 3E).

To explore intra-individual (different lines from the same individual) and inter-individual (different individuals with the same mutation) variability, we plotted all the values obtained from each single well, independent experiment, cell line and individual, at each of the five reading timepoints (Figure 3F). Most lines from an individual were not significantly different from each other, and reassuringly low inter-individual variability was observed with different siblings bearing the same mutation(s), e.g., 48K and 48N versus 49G and 49H (SET), or 61I and 61K versus 62M and 62X (GLI3), at different timepoints (Figure 3F). A few lines showed a significant intra-individual variability, e.g., lines 52A and 52C (THRA) at week 4, or lines 75G and 75H (CAPRIN1) at week 8 (Figure 3F). We also noted some inter-independent experiment variability for a given line, e.g., line 38E (EHMT2) at weeks 4 and 5 (dots with different colours do not overlap in Figure 3F). Note that similar profiles were monitored in terms of MFR (Figure 3—figure supplement 1), indicating that these differences were not due to having more or less active electrodes in different lines. While consistent activity across lines was generally observed, the presence of variability prompted us to interrogate independent variants created by genome editing of CNTN5, ANOS1 and EHMT2

There is no peer-reviewed, published characterization by the authors of their results of using NEUROG2 expression to induce glutamatergic neurons, characterizations that other cited authors have performed. While the authors state that; "Importantly, we determined that this strategy offers highly uniform differentiation levels between cell lines derived from different participants (Deneault et al., 2018);" that reference is to a manuscript submitted to bioRxiv that has not undergone peer review, and for the current article to be published, analysis of the NEUROG2 induction procedure needs to be presented. The statement that "differentiation levels" were uniform between cell lines, is quite a claim, and it is not at all clear what this means. For example what endpoints were assessed among multiple lines to indicate uniformity? One can imagine, for example, that failure to observe a specific differentiation feature, could reflect that the marker is not expressed by a cell, versus, that it is expressed but at a time point not examined. Such an outcome could reflect changes in i) cell fate, ii) induction of differentiation, iii) survival of a specific cell class, or simply, iv) a delay in its expression in suboptimal culture conditions. There is no characterization of the surviving cell population when MEA analysis of the cultures is performed, so that specific experimental outcomes cannot be related to the cell population under study. Even though the culture bottoms are opaque, immunocytochemistry using fluorescent labeling would allow visualization of cell numbers (DAPI) and cell type (NeuN, MAP2, vGlut, vGAD, etc) by use of the epifluorescence microscope to understand how cell composition at the time of MEA recording impacted the outcomes. This seems a reasonable request since the majority of MEA studies did not reveal group genetic differences, with only 3 of the 12 families demonstrating changes in firing rate and network burst frequencies.

We have used the NEUROG2 overexpression strategy for its high consistency in differentiation levels between several different lines. The reference to our bioRxiv paper was made for expediency and openness, and the paper has now undergone stringent and full peer-review and is published (Deneault et al., Stem Cell Reports, 2018). The third and fourth paragraphs of the Discussion, as followed, now discuss this issue:

“We assume that most NGN2-neurons are glutamatergic based on the data presented in the original publication establishing this technique (Zhang et al., 2013), and on our previous publication using high-cell density RNAseq assessment (Deneault et al., 2018). Moreover, we have treated several of our cultures at the end of the MEA experimentation with different neurotoxins (CNQX, PTX, TTX) to show that most neurons are glutamatergic and not GABAergic, for different lines. In addition, our patch-clamp recordings have demonstrated that these neurons exhibit the properties of excitatory neurons.

Characterization of neuronal composition and survival when MEA is performed is difficult to achieve with high accuracy. Our strategy involved using several technical (3 to 12 per independent experiment) and biological (up to 4) replicates to best compensate for inter-well and inter-iPSC neuronal induction variations (Supplementary file 3). It is possible that some phenotypes were missed since we cannot exclude the possibility that differences in cell number or composition across individuals in a family actually masked potential phenotypes. However, this issue was ruled out for CNTN5 and EHMT2 because the phenotypes were confirmed in unrelated isogenic pairs, and by patch-clamp recordings.”

The MEA methods are incomplete as reported, and extensive review of Supplementary file 3 still leaves uncertainties for this reviewer. The authors employ MEA as the single major measure of outcome, but they do not reference their previous use of this technology. It is not adequate to cite other laboratories. The authors should describe more fully the nature of the MEA technology, how the cultures appear in them, the data that is generated, and how it was analyzed. To start, the authors indicate that 48-well opaque-bottom MEA plates (Axion Biosystems) were used. But going to the website there are four (4) "Classic" varieties of this plate, as well as many other formats. Please provide exact catalogue number or numbers. How many electrodes are there in each well? It may be helpful to show a layout/picture for the electrode array, and also pictures of fixed and stained neuronal cultures to define the variability in cell contact to multiple electrodes, and perhaps the extremes of electrode contact they observe. Doing a brief PubMed search, I found two recent 2018 publications that provide images that show large variability of cell growth around fixed electrode arrays (DeRosa et al., Sci Rep 2018; Odawara et al., Sci Rep 2018; also see Hammond et al., BMC Neurosci, 2013). Please state the range of individual MEA wells used in these types of studies: Supplementary file 3 seems to indicate a range from 3 wells to as many as 12. How many independent MEA assay experiments are performed for each line? Supplementary file 3 is confusing, specifically the column labeled "Total nb independent plates." This number coincides with the reported number of different MEA plates (MEA plate IDs are 1-37, with only 21 involved here). Is an independent plate a separate experiment? Usually in iPSC studies, within a single clone (as reported here), independent experiments imply different passages of the same clone, analyzed in subsequent weeks, or after thawing an earlier passage and newly plated. But after NEUROG2 induction, it seems the cells by design are non-proliferative (anti-mitotic drug AraC is applied, among other strategies). But without a proliferative stem cell culture, how are independent experiments performed? Is the NEUROG2 induction performed multiple times on the same clone (a true biological replication) to yield 3 independent plates? Or are glutamatergic-induced cells frozen and plated at multiple times, another valid approach? Or alternatively, is a single induction plated at the same time to multiple plates? The issue of reproducibility is critical in this kind of study, and it is surprising that there is no mention of these issues that are currently central to the field.

We agree that the MEA technology is still in its infancy, but it is still important data to use, and in doing so we will help to move the field forward. We have now referenced our previous use of the MEA technology in the first sentence of the “Multi-Electrode Array Analysis of iPSC-Derived Neurons” section of the Results. Data generation and analysis is explained in Materials and methods. We have now provided the exact catalogue number of the MEA plates used, as well as the number of electrodes per well in Materials and methods. Since only opaque-bottom 48-well MEA plates were commercially available at the time this study was performed, it was not possible to take clear pictures of cell-electrode contact. We apologize for the confusion regarding Supplementary file 3, and have now modified it to reflect the reviewer’s requests. For example, we have replaced the column label "Total nb independent plates" for “Total nb independent experiments", as an independent plate meant independent experiment. Here, independent experiments imply independent infections with NGN2 viruses of iPSCs at different passages, entailing completely independent inductions. So, NGN2 induction was performed multiple times on the same clone (a true biological replication) to yield 3 independent plates. We have clarified this issue in the legend of Supplementary file 3.

Results:

Line 19-2 is used as a control for one family and is also the line for all isogenic lines produced. It is the only line shown that was reprogrammed by retrovirus (all other lines were induced using non-integrating Sendai virus vectors), so what is known about virus integration site mutations, and does this interfere or alternatively facilitate outcomes, and impact its use as a control? To make it easier for the reader to understand this report, the authors need to provide a description of line 19-2 here, rather than refer the reader to the un-reviewed manuscript in bioRxiv.

To help us stay within length restrictions, the reader is now referred to our peer-reviewed and published paper (Deneault et al., 2018), where line 19-2 was extensively characterized.

Supplementary file 2 indicates that for some families, only a single line was assessed for pluripotency markers and surprisingly, this seems to include 2 important control lines, 19-2 and NR3. It seems a concern that a control line used for many experiments was analyzed using only a single clone; please address.

The purpose of using the line 19-2 here was to validate any finding in an independent genetic background while providing an isogenic control via CRISPR editing. Since two different clones, e.g., 38B and 38E, have replicated an increased neuronal activity (Figures 3 and 6), and that properly editing one clone requires extensive time and costs, we thought that one genetically-independent and isogenic clone would be sufficient to support our primary findings. We have tried to accompany all our findings by using conservative wording throughout the manuscript.

The authors indicate they "sought to determine if any selected ASD-risk variants would interfere with spontaneous spiking and synchronized bursting activity in a whole network of interconnected glutamatergic neurons." To determine when to make MEA comparative analysis they pooled the N=341 control line wells from the 12 families, and observed that the mean firing rate (MFR) was the highest (~1.7 Hz) at week six (6) post-NEUROG2-induction (PNI). Thus they used this time point to compare all cell lines for the 1) weighted mean firing rate (MFR) and the 2) network burst frequency. This approach seems reasonable and seems to establish a standard set of conditions and protocols, one of their goals, as stated in the Abstract.

However, to this reviewer, the choice of a single time to make assessments seems to diminish the likelihood of finding differences between control and disease related genetic variants. I think it is not obvious nor expected that control lines from each family would exhibit the same maturation profile, and while the authors do perform intra-familial comparisons, the advantages of this type of analysis seem to be thwarted in this design. Given genetic heterogeneity, one could easily imagine different time frames. With all the expenses in cost, media, and time to set up these complex models, why not make recording every week for example? The current protocol as presented seems to be an inefficient use of resources. And perhaps in support of this criticism, the authors themselves seem to agree, as shown in Figure 3! Because the Family D mutant line #27 that carries CNTN5+/- revealed differences in MEA at 6 weeks, they created an isogenic line 19-2 control line by inserting the CNTN5 Stop-Tag. However they report they did not find any effects on MEA parameters at 6 weeks. Thus they extended analysis to 11 weeks, and now found statistically significant differences at 10 weeks. However, it seems that at week 7, the outcome appears to be the opposite, with StopTag showing 50% less activity (Figure 3B, left; would likely be significant with a t-test). It seems the authors cannot have it both ways, we use 6 weeks, but if we do not find an expected outcome, we wait longer. Further, maybe many of the other negative MEA results would appear different if assessed at other time points.

We thank the reviewer for the constructive critique of our design. These are early days in performing this type of analysis and with standards of data presentation in place, we try to present the most relevant data to the experiments at hand. In the previous version of the manuscript, we assumed that it would be clearer for the reader if we show only one timepoint, i.e., the most active in control cells (week 6). However, as suggested by both reviewers, we decided to aggregate all the data and build a new Figure 3, showing how the activity changes over time, and between donors, and even between different biological replicates for each donor. Now, we think this is a better way to appreciate the variability of MEA, and consider potential difference in maturation rate of various lines. Line 19-2 is generally more active than most other familial lines, and this may have delayed emergence of the hyperactive phenotype in isogenic cells until week 10, as now discussed in the new version. Moreover, we have previously detected hypoactive phenotypes in line 19-2 at week 8 and before (Deneault et al., Stem Cell Reports, 2018).

There is also concern that cell survival plays an important role. In CNTN5+/-, for example, perhaps cells with lower firing rates die off with progressive incubation. This would result in survival of only cells with increased MFR. However, because there is no report of the number of negative electrodes in each plate (which might increase with time), the results at 11 weeks appear to be the reverse of that seen at 7 weeks, yet this outcome may have little to do with synaptic functions. To this reviewer, since time plays an important role in genetic variant effects (Figure 3B), and the authors seem to tentatively conclude that changes in MEA measured activity depend on changes at the synapse, it would strengthen their case if single cell electrophysiology of these cells at this or an earlier time were correlated with the MEA outcomes, to demonstrate that CNTN5 effects are mediated by changes in synaptic currents, and not in cell survival, distribution, or adhesion. Cellular analysis at MEA culture termination would address this unexplored mechanism.

We agree that patch-clamp recordings of CNTN5+/- neurons should help to support our MEA findings in the future. However, we have used weighted MFR (wMFR) (Figure 3), in addition to MFR (Figure 3—figure supplement 1), to take into account electrodes that might be inactive due to cell survival, distribution or adhesion.

The definition of weighted mean firing rate is not clear; the authors state, "We monitored the weighted mean firing rate (MFR), which represents the MFR per active electrode,.…". If understood correctly, this measure only averages active electrodes in any MEA well. Thus if there is significant (and currently un-described) failure of electrode activation in a well, that data is excluded. A major feature of a cell line may be that in one case only 20-30% of electrodes have signal due to differences in cell survival or dispersion, whereas in another line, it may routinely be 80-100% coverage. If this is the case, this type of information may impact the kinds of cellular mechanisms that one considers to explain the outcomes. This kind of information should be assessed, if not already done, and should be reported transparently in a Table that includes all families, perhaps in the supplement. One might find a correlation of decreasing/increasing cell-electrode contact (appearing as active/inactive electrodes/well) to the outcomes reported.

We apologize if the definition of wMFR was not made clear in the previous manuscript. It is defined as the MFR divided by the number of active electrodes per well. An electrode was considered active if it detected at least five spikes per minute, as explained in the section “Multi-electrode array (MEA)” of the Materials and methods. If there is significant failure of electrode activation in a well, for example, due to differences in cell survival, dispersion or adhesion, that data is indeed excluded. However, a detailed analysis of cell survival, dispersion and adhesion, will be required for each different cell line involved in this study to clarify this issue in the future. This is one of the reasons why we decided in the new version of the manuscript to present the MFR (Figure 3—figure supplement 1) in order to support the wMFR to take into account potential issue with cell survival, dispersion or adhesion. For example, the increased MFR observed in EHMT2-/+ lines in could be due to a better capacity to survive, disperse or adhere than EHMT2+/+ cells, without affecting synaptic activity. However, excluding all inactive electrodes would then result in a comparable wMFR, which is not the case (Figure 3B). Indeed, both MFR and wMFR are significantly higher in EHMT2-/+. That does not exclude the possibility of a better survival, dispersion or adhesion, but it is likely not the only reason for the observed increase in spiking activity, suggesting greater synaptic activity, as supported by patch-clamp recordings in Figure 6B. We now discuss this in the fifth paragraph of the Discussion.

There is concern about the sole use of retrovirally-induced line 19-2 for all isogenic experiments. In contrast to control lines from the 10 families (Families C-L, Supplementary file 3), whose study led to selecting 6 weeks for MEA analysis, line 19-2 with introduced CRISPR gene variant only reveal differences at 8-12 weeks. There raises two issues. Firstly, MEA analysis of, for example, EHMT2+/-, demonstrates abnormal MFR and burst frequency only from weeks 4 to 7 when compared to family controls (Figure 5A), a result that is lost by week 8. In contrast, the 19-2 isogenic line only shows differences at week 9 or later. Similar differences are observed for CNTN5+/- in Figure 3. Second, the MFR in line 19-2 is 4 Hz or more, whereas analysis of all the other 10 family control lines (Sendai based) examined at 6 weeks (sometimes 4-8 weeks) are mostly less than 1 Hz, and always below 2 Hz. These consistent differences raise concerns that the 19-2 line brings a special test system in which perhaps most of the analyzed variants could/should be assessed. But this may also bias results because only this single line is used for isogenic studies. Would this same effect occur in a Sendai non-integrating virus-induced iPSC model for isogenic line production?

We agree that line 19-2 is generally more active than other tested lines in our hands, and we cannot exclude at this stage the possibility that it could be due to the use of retrovirus for reprogramming. However, we don’t think that these differences in intensity and time window should preclude the use of this line for validation/isogenic experiments. Indeed, we have previously shown that a completely independent line, i.e., 50B (Sendai-reprogrammed), was able to reproduce significant electrophysiological and RNAseq results obtained in isogenic KO studies performed in line 19-2 (Deneault et al., 2018). Line 19-2 has also been extensively used in other projects to validate different results obtained with familial lines defective in SHANK2 (Zaslavsky et al., Nature Neuroscience, accepted manuscript), and PTCHD1-AS (Ross et al., Biological Psychiatry, in revision). Furthermore, a pattern of outward/inward currents comparable to that found in 18C/18CW lines (Figure 5D) was monitored in a different and unrelated isogenic pair, in which our StopTag fragment was previously inserted within the ANOS1 coding sequence in line 19-2 (Deneault et al., 2018). This result was found on the right panel of Figure 5D in the original manuscript. However, we prefer now to exclude this panel from the revised manuscript and put this profile only in the response to reviewers (see Author response image 1) since the trace of the control 19-2 was previously reported in (Deneault et al., 2018). This isogenic pair 19-2/19-2-ANOS1StopTag/+ phenocopies the genetically-independent isogenic pair 18C/18CW (Figure 5D). Since line 19-2 could be used to validate both hypoactive and hyperactive electrophysiological phenotypes, we don’t think that this line create any significant bias on its own.

Author response image 1
Outward and inward membrane current detected by patch-clamp recordings; total number of recorded neurons was 20 for 19-2-ANOS-/y and 33 for control 19-2; values are presented as mean+SEM of three independent differentiation experiments, recorded at day 21-25 PNI.

*p < 0.05 from multiple t test comparison; ** note that control 19-2 profile was previously reported in Deneault et al., 2018

Subsection “EHMT2-/+ CRISPR-Isogenic Pair Confirms Neuronal Hyperactivity”: The authors should reserve interpretations of differences in expression of MEA in the isogenic line compared to the family lines, 38B/E. They state: "This increased activity in mutant neurons occurred later than that observed in the familial lines 38B/E, possibly due to different genetic backgrounds." Alternatively, there are changes in cell survival, aggregation, electrode contact, or distribution that have not been assessed. It is premature to raise the issue of genetic background as explanation. Furthermore, the authors seem to be mistaken, reporting there is "lower resting membrane potential," but that is not what is shown in Figure S5 where the mean values appear identical; please correct this results error.

We agree that it is probably premature to raise the issue of genetic background as explanation. Accordingly, we have modified “possibly due to different genetic backgrounds" for “possibly due to the more active 19-2 line”. As now explained earlier in the second paragraph of the “CNTN5 Isogenic Pair to Control for Genetic Background Contribution” section of the Results: “However, the wMFR of line 19-2 increased up to nearly 3 Hz at week 8 (Figure 4B) while the CNTN5 family controls stayed around 0.5 Hz (Figure 3B). In this context of a more active cell line, we extended the recordings until week 11, and the hyperactive wMFR of 19-2-CNTN5StopTag/+ was only evident from week 10 (Figure 4B).”

Moreover, based on our response to one of the other points raised above by this reviewer, we think that the issue of cell survival and electrode contact is not a major contributor to the different time window observed in the activity in lines 38B/E and 19-2. We apologize for the confusion regarding the lower “resting membrane potential”, we have now changed it for lower “input resistance” in the main text.

The discussion fails to mention any limitations to the approach of MEA at 6 weeks, the absence of cell characterization, and possible alternative mechanisms to the currently pre-conceived notions of the gene's function. The authors might in fact suggest that a full time course analysis would be beneficial. They fail to discuss the very different behavior of retrovirally created 19-2 line used at 9-12 weeks that show outcomes not observed in the Sendai virus created lines assessed at 6 weeks. They fail to address the possibility of explanatory mechanisms in their studies such as changes in cell survival, composition, distribution, or electrode contact. In all, these studies seem preliminary in nature and set a stage for further analysis before we recommend this specific set of procedures to be adopted by the field.

In the new version of the manuscript, we present a time course of the wMFR and MFR from week 4 to 8 PNI, which addresses the reviewer’s concern about the use of only week 6 as a timepoint. We now discuss the different behavior of line 19-2 at the end of the second paragraph in the Discussion. Moreover, we discuss some limitations of using MEA as a primary read-out, e.g., “It is possible that some phenotypes were missed since we cannot exclude the possibility that differences in cell number or composition across individuals in a family actually masked potential phenotypes.” We also discuss that since the familial controls are often not very active, this screen might be biased towards the identification of hyperactive phenotypes rather than hypoactive. Better MEA controls will need to be developed in the future.

Additional data files and statistical comments:

A major feature of a cell line may be that in one case only 20-30% of electrodes have signal due to differences in cell survival or dispersion, whereas in another line, it may routinely be 80-100% coverage. If this is the case, this type of information may impact the kinds of cellular mechanisms that one considers to explain the outcomes. This kind of information should be assessed, if not already done, and should be reported transparently in a Table that includes all families, perhaps in the supplement. One might find a correlation of decreasing/increasing cell-electrode contact (appearing as active/inactive electrodes/well) to the outcomes reported.

This point was addressed above.

[Editors’ note: further revisions were suggested before publication.]

· We added a few new words to the Abstract (it now slightly exceeds 150 words). This additional text was added to address the reviewer's suggestion about the 75% of lines without detectable phenotypes. If you need to have the Abstract at 150 words please just remove the additions (the previous presentation was fine with us too);

· We made a few new modifications to the 4th paragraph of the Discussion to respond to the reviewer's first two requests;

· We clarified the nomenclature of Figure 2–figure supplement 1, and modified the corresponding legend;

· We modified the last box of the transparent reporting file to include the new Source data files that were recently uploaded;

· We brought more precision on the type of multiple t-test comparisons that were used in the legend of Figures 3-6;

· Regarding the last request of the reviewer, we acknowledge that we did not completely resolve whether deficits (or phenotypes) in CNTN5 and EHMT2 result from impaired maturation rather than impaired synaptic function (we will never be able to do everything), however, we feel that we have already explained this extensively in the 5th paragraph of the Discussion.

https://doi.org/10.7554/eLife.40092.027

Article and author information

Author details

  1. Eric Deneault

    1. Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
    2. The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6875-0380
  2. Muhammad Faheem

    1. Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
    2. The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada
    Contribution
    Formal analysis, Validation, Investigation, Methodology
    Competing interests
    No competing interests declared
  3. Sean H White

    Department of Biochemistry and Biomedical Sciences, Stem Cell and Cancer Research Institute, McMaster University, Hamilton, Canada
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
  4. Deivid C Rodrigues

    Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Canada
    Contribution
    Formal analysis, Validation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
  5. Song Sun

    1. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada
    2. The Donnelly Centre, University of Toronto, Toronto, Canada
    3. Department of Molecular Genetics, University of Toronto, Toronto, Canada
    Contribution
    Data curation, Formal analysis, Validation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
  6. Wei Wei

    Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Canada
    Contribution
    Validation, Investigation, Methodology
    Competing interests
    No competing interests declared
  7. Alina Piekna

    Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Canada
    Contribution
    Validation, Investigation, Methodology
    Competing interests
    No competing interests declared
  8. Tadeo Thompson

    Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Canada
    Contribution
    Validation, Investigation, Methodology
    Competing interests
    No competing interests declared
  9. Jennifer L Howe

    1. Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
    2. The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
  10. Leon Chalil

    Department of Biochemistry and Biomedical Sciences, Stem Cell and Cancer Research Institute, McMaster University, Hamilton, Canada
    Contribution
    Data curation, Formal analysis, Validation, Methodology
    Competing interests
    No competing interests declared
  11. Vickie Kwan

    Department of Biochemistry and Biomedical Sciences, Stem Cell and Cancer Research Institute, McMaster University, Hamilton, Canada
    Contribution
    Data curation, Formal analysis, Validation, Methodology
    Competing interests
    No competing interests declared
  12. Susan Walker

    1. Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
    2. The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
  13. Peter Pasceri

    Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Canada
    Contribution
    Validation, Methodology, Project administration
    Competing interests
    No competing interests declared
  14. Frederick P Roth

    1. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada
    2. The Donnelly Centre, University of Toronto, Toronto, Canada
    3. Department of Molecular Genetics, University of Toronto, Toronto, Canada
    4. Department of Computer Science, University of Toronto, Toronto, Canada
    5. Canadian Institute for Advanced Research (CIFAR), Toronto, Canada
    Contribution
    Resources, Data curation, Supervision, Funding acquisition, Validation, Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  15. Ryan KC Yuen

    1. Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
    2. The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
  16. Karun K Singh

    Department of Biochemistry and Biomedical Sciences, Stem Cell and Cancer Research Institute, McMaster University, Hamilton, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Visualization, Writing—original draft, Writing—review and editing
    For correspondence
    singhk2@mcmaster.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8301-3533
  17. James Ellis

    Department of Molecular Genetics, University of Toronto, Toronto, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Visualization, Writing—original draft, Writing—review and editing
    For correspondence
    jellis@sickkids.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4400-0091
  18. Stephen W Scherer

    1. Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
    2. The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada
    3. Department of Molecular Genetics, University of Toronto, Toronto, Canada
    4. McLaughlin Centre, University of Toronto, Toronto, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Visualization, Writing—original draft, Writing—review and editing
    For correspondence
    stephen.scherer@sickkids.ca
    Competing interests
    Serves on the Scientific Advisory Committees of Population Bio and Deep Genomics, and intellectual property originating from his research and held at the Hospital for Sick Children is licensed to Lineagen, and separately Athena Diagnostics.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8326-1999

Funding

Canadian Institutes of Health Research

  • Fritz Roth
  • Karun K Singh
  • James Ellis
  • Stephen W Scherer

Canadian Institute for Advanced Research

  • Fritz Roth
  • Stephen W Scherer

Canada Foundation for Innovation

  • Fritz Roth
  • James Ellis
  • Stephen W Scherer

National Institutes of Health

  • Fritz Roth
  • James Ellis
  • Stephen W Scherer

Ontario Brain Institute

  • Karun K Singh
  • James Ellis
  • Stephen W Scherer

Natural Sciences and Engineering Research Council of Canada

  • Karun K Singh

Province of Ontario Neurodevelopmental Disorders

  • Karun K Singh
  • James Ellis

Ontario Research Fund

  • James Ellis
  • Stephen W Scherer

Genome Canada

  • Stephen W Scherer

University of Toronto McLaughlin Centre

  • Stephen W Scherer

Autism Speaks

  • Stephen W Scherer

Hospital for Sick Children

  • Stephen W Scherer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors wish to acknowledge the resources of MSSNG (www.mss.ng), Autism Speaks and The Centre for Applied Genomics at The Hospital for Sick Children, Toronto, Canada. We also thank the participating families for their time and contributions to this database, as well as the generosity of the donors who supported this program. We also thank Drs. Melissa Carter, Wendy Roberts, Brian Chung and Rosanna Weksberg for obtaining skin biopsies and blood work, and the families for volunteering. We also thank Tara Paton, Guillermo Casallo, Barbara Kellam, Ny Hoang and Sylvia Lamoureux for technical help; TC Südhof for the NGN2/rtTA lentiviral constructs.

Ethics

Human subjects: All ASD and related control-participants were initially consented for WGS and upon return of genetic results, then consented for the iPSC study, using approved protocols through the Research Ethics Board at the Hospital for Sick Children. Under the approval of the Canadian Institutes of Health Research Stem Cell Oversight Committee and the Research Ethics Board (REB) at the Hospital for Sick Children, Toronto, Canada, iPSCs were generated from dermal fibroblasts or CD34+ blood cells. Three different informed consent forms for iPSC derivation and publication were obtained: i) Research Consent Form for Parent/Legal Guardians (of an individual with a neurologic condition); ii) Research Consent Form for Unaffected Individuals; iii) Assent form (for individuals with a neurologic condition). These consent forms describe in details the purpose of the research, the description of the research, the potential harms, the potential benefits, confidentiality, storage of the research samples, participation, reimbursement, sponsorship, and declaration of conflict of interest; REB approval file 1000012015.

Senior Editor

  1. Huda Y Zoghbi, Texas Children's Hospital, United States

Reviewing Editor

  1. Moses V Chao, New York University Langone Medical Center, United States

Publication history

  1. Received: July 14, 2018
  2. Accepted: January 19, 2019
  3. Version of Record published: February 12, 2019 (version 1)

Copyright

© 2019, Deneault et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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