Abnormalities in the functional activity of neural networks in a human iPSC model of Dravet syndrome

Abnormalities in the functional activity of neural networks in a human iPSC model of Dravet syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Abnormalities in the functional activity of neural networks in a human iPSC model of Dravet syndrome Ropafadzo Mzezewa, Tanja Hyvärinen, Andrey Vinogradov, Emma Pesu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5615262/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dravet syndrome (DS) is a severe pediatric epilepsy with a limited response to current antiseizure medications. Majority of DS cases are caused by a de novo mutation in the SCN1A gene, encoding the alpha subunit of the voltage-gated sodium channel. While early in vivo studies have shown that DS pathology results from the disinhibition of GABAergic inhibitory neurons, recent studies report alterations in sodium currents in both excitatory and inhibitory neurons. Investigating the excitatory-inhibitory interplay is essential for elucidating the functional alterations caused by SCN1A mutations. Here, the aim was to study how different SCN1A gene pathogenic variants affect the functional phenotype of DS human induced pluripotent stem cell-derived neuronal networks in enriched GABAergic cultures and heterogeneous glutamatergic and GABAergic cultures, using microelectrode arrays (MEAs). We report functional differences in patient-derived GABAergic cultures. In heterogeneous cultures, DS patient-derived neurons displayed altered activity with prominent network bursts and overall, the altered functional activity correlated with the clinical severity of the disease. Principal component analysis revealed distinct clustering between the DS patient and control heterogeneous cultures. Thus, pathogenic SCN1A variants alter the neuronal network functionality suggesting that heterogeneous cultures are competent physiological models for characterizing disease phenotype alterations in DS using MEAs. Biological sciences/Neuroscience/Cellular neuroscience Biological sciences/Neuroscience/Diseases of the nervous system/Epilepsy Biological sciences/Biotechnology/Stem cell biotechnology Human excitatory neurons human inhibitory neurons in vitro microelectrode arrays SCN1A Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Dravet syndrome (DS), also known as severe myoclonic epilepsy of infants (SMEI), is an early-onset epilepsy refractory to most available antiseizure medications 1 – 3 . The disorder falls under the developmental and epileptic encephalopathies (DEE) with major hallmarks of both febrile and afebrile seizures, along with an increased frequency of complex seizures that progresses throughout childhood 1 , 4 . DS patients often develop comorbidities involving autism-like behavior, intellectual disability, ataxia, and an increased incidence of sudden death (SUDEP) 1 , 4 , 5 . The reported incidence is approximately 1 in 15 000 to 1 in 41 000 live births 6 . Over 80% of cases are caused by a de novo mutation in the SCN1A gene, which encodes the a-subunit of Na V 1.1, a voltage-gated sodium channel that is essential for initiating and propagating action potentials 7 , 8 . Haploinsufficiency of the SCN1A gene is sufficient to give rise to the disease, originating from either missense, deletion, insertion, or frameshift pathogenic variants that occur de novo 9 . However, mutation severity is not entirely predictive of seizure outcomes 1 . The pathogenic mechanism underlying DS is the disinhibition of GABAergic neurons, where the loss of function results in a lack of network inhibition, causing increased network excitability 10 . The pathophysiology was first investigated in DS Scn1a knockout mouse models in which DS phenotypes such as seizures, cognitive decline, and hyperactivity were observed 11 . Studies with DS animal models have shown that the functional impairment of sodium currents primarily affects GABAergic interneurons, not pyramidal excitatory neurons 10 – 12 . However, recent in vivo studies have revealed a more complex mechanism in which alterations in sodium channels are observed in both inhibitory and pyramidal excitatory neurons 13 , proposing a need to elucidate the overlooked role of excitatory neurons in DS pathology. Human induced pluripotent stem cell (hiPSC) models have become an integral part of studying the pathophysiology of DS, as neurons can be generated directly from patients carrying specific variants of the mutation associated with the disease 14 . Interestingly, in vitro hiPSC-derived neuronal models have revealed contradictory findings on the underlying functional impairments of DS. Early hiPSC studies have described functional perturbations in sodium current density and action potential frequency in inhibitory neurons 15 – 17 , while other studies have detected deficits in both excitatory and inhibitory neuronal subtypes 18 – 21 . Despite the above findings, the functionality of hiPSC-derived neural models has been predominantly assessed at the single-neuron level using the patch clamp technique 15 , 17 , 19 , 20 . More recently, the microelectrode array (MEA) technique has been utilized to study functional deficits in DS neurons at the network-wide level 22 , 23 . The use of MEAs has increased substantially for drug screening applications and, more importantly, for identifying patient-specific network signatures in disease models 24 – 29 . Fundamentally, epileptic seizures are a consequence of an imbalanced inhibitory and excitatory system; therefore, establishing excitatory and inhibitory neuron cultures is essential for constructing a more physiologically relevant model that only a few studies have presented at the single-neuron level 30 . Therefore, the aim of this study was to determine how different pathogenic SCN1A variants affect the functional phenotype of DS hiPSC-derived networks in a physiologically relevant model using MEAs. We first assessed the functional phenotype in enriched GABAergic cultures. We then extended the approach to study the effect of the two pathogenic variants in a heterogeneous culture system consisting of both excitatory and inhibitory neurons. We report that DS patient-derived networks display prominent altered phenotypes at both the single-channel level and network levels and show distinct clustering with principal component analysis (PCA). This study aimed to further elucidate the pathogenesis of DS by analyzing excitatory and inhibitory neuron cultures with MEAs, and the findings highlight the proficiency of patient-derived human stem cell models to characterize DS functional alterations. Materials and methods Human pluripotent stem cells The study was conducted with neuronal cells derived from the human induced pluripotent stem cell (hiPSC) lines 04511WTs.EURCCs (control 1) 31 , 10902.EURCCs (control 2) 32 , a human embryonic stem cell (hESC) line 08017 (control 3) 33 , and a commercial AICS0012 Mono-allelic mEGFP-tagged TUBA1B WTC iPSC line (Coriell Institute, USA, control 4). The Faculty of Medicine and Health Technology has supportive statement from the Ethics Committee of the Expert Responsibility area of Tampere University Hospital for the derivation; culture and differentiation of hiPSCs (R20159). Informed consent was obtained from the subjects (legally authorized representatives) who donated the cell samples. All experiments were performed in accordance with relevant guidelines and regulations. Patient characteristics and human pluripotent stem cells of DS lines DS patient lines were obtained from Uppsala University, Sweden with ethical approval (D-numbers 319/2009 and 209/2016) under material transfer agreement. DS patient 1 was diagnosed with DS, along with severe developmental delay, and ataxia. The patient had a de novo frameshift variant c.5502-5509dupGCTTGAAC (p.Pro1837Argfs24) in the intracellular COOH-terminal domain of Nav1.1. DS patient 5 was diagnosed with a less severe DS phenotype with mild cognitive decline and had a history of both febrile and non-febrile seizures. The patient had a pathogenic missense variant c.651C > G (p.Thr217Arg; domain I segment 4, voltage sensor of Nav1.1) which was inherited from her mother with febrile seizures 34 . All procedures were performed in accordance with the Helsinki Convention and written informed consent was obtained from all patients or their legal guardians. All experiments were performed in accordance with relevant guidelines and regulations. Differentiation of forebrain GABAergic interneurons The GABAergic differentiation of human pluripotent stem cells (hPSCs) was carried out according to previous publication 34 , 35 with minor modifications. Briefly, two days before neural induction, hPSCs were harvested and plated in Essential-E8™medium supplemented with 10 µM Rho-kinase inhibitor Y27632 (Stem Cell Technologies) onto plastic 24-well plates (Thermo Fisher Scientific) coated with 100 µg/ml poly-L-ornithine (PLO) and 15 µg/ml human recombinant laminin, LN521 (Biolamina, Sweden), to obtain > 80% confluence the next day. Upon 90–100% confluence, neural differentiation was induced on both control hPSCs and DS patient-derived hiPSCs (Days in vitro, DIV 0) using dual SMAD inhibition protocol. Cells were refreshed with neural induction medium (NIM; [DMEM-KO, 15% KnockOut Serum Replacement, 1× GlutaMax, 1× non-essential amino acids, 1% penicillin/streptomycin (all from Thermo Fischer Scientific). NIM was supplemented with 2 µM tankyrase inhibitor XAV939 (Sigma-Aldrich), 100 nM ALK2/3 inhibitor LDN193189 (Stem Cell Technologies) and 10 µM ALK4/5/7 inhibitor SB431542 (Sigma-Aldrich). On DIV 2, cells were plated onto 24 and 48-well cultured plates coated with PLO and LN521 at a density of 100 000 cells/cm 2 . On DIV 4, the medium was gradually replaced from NIM to NBN medium [Neurobasal medium, N2 (1:100), B27 without Vitamin A (1:200), 1% penicillin/streptomycin (Thermo Fischer Scientific) in a ratio of 3:1, supplemented with the dual SMAD inhibitors as used in NIM. On DIV 6, the medium was changed at a 1:1 ratio (NIM: NBN) and by DIV 8 at a 3:1 ratio (NIM: NBN). On DIV 10 of differentiation cells were patterned towards ventral telencephalic fate using NBN medium supplemented with 5 nM recombinant mouse sonic hedgehog (SHH C25II; R&D Systems), 1 µM Purmorphamine (Miltenyi biotech), 10 ng/ml recombinant human brain-derived neurotrophic factor (BDNF; R&D Systems) 200 µM ascorbic acid (AA, Sigma-Aldrich) and 100 µM 2′-O-Di-butyryladenosine 3′,5′-cyclic monophosphate (cAMP; Sigma-Aldrich). NBN media was changed on DIV 11, 13, and on DIV 16. On DIV 17, which was considered the final plating date and the start of the experiments (Fig. 1 a), cells were plated onto PLO and LN521 coated 48-well plates at a density of 100 000 cells/cm 2 as well as on MEA plates 48 array format (Axion BioSystems, Atlanta, GA USA). MEA plates were pretreated first with a 10 µl droplet of 0.1% polyethylenimine (PEI, Sigma-Aldrich-Aldrich) in 0.1 M borate buffer and then with a 10 µl droplet of 50 mg/ml LN521 (Biolamina). Cells were plated at 80 000 cells in a 10 µl droplet (density 635 000 cells/cm 2 ) on MEAs in NBN medium supplemented with brain-derived neurotrophic factor, BDNF, ascorbic acid, and dibutyryl cyclic adenosine monophosphate, db-cAMP (all from Sigma-Aldrich). The following day, cultures were maintained in either NBN media or BrainPhys™ neuronal media (Stemcell Technologies) and the medium was changed three times a week from this point onwards. All samples were cultured at + 37 ◦C in a 5% CO2 humidified incubator). Differentiation of heterogeneous cortical neurons hiPSCs were expanded and differentiated into cortical neurons as previously described 36 . Experiments were started at DIV 32 which was considered the start of experiments when cells were plated for culture and MEA plates. At DIV 32 neurons were plated at a density of 50 000 cells/cm 2 in PLO and LN521-treated well plates. MEA plates were pretreated first with a 10 µl droplet of 0.1% PEI in 0.1 M borate buffer and then with a 10 µl droplet of 50 mg/ml LN521. Thereafter, cells were plated in MEAs at 80 000 cells in a 10 µl droplet (cell density 635 000 cells/cm 2 ). The cultures were kept in BrainPhys media and cultured at + 37 ◦C in a 5% CO2 humidified incubator. Media was changed every other day. MEA recordings Neuronal network activity was recorded with an Axion Maestro system controlled by AxIS Software (Axion Biosystems) with a 12.5 kHz sampling rate. Recordings were obtained under a controlled temperature of 37◦C. The development of spontaneous activity was measured twice a week with 10-minute recordings up to 11 weeks in GABAergic networks and up to 8.5 weeks in heterogeneous cortical networks. Prior recordings, MEA plates were allowed to equilibrate for 3–5 min. Refreshment of BrainPhys medium was always performed one day before the MEA recordings. Pharmacological experiments were performed at DIV 94 with heterogeneous cortical networks. Pharmacological baseline recording was performed for 10 minutes followed by the application of one of the following compounds: CNQX (50µM), kainic acid (KA, 10 µM, Sigma-Aldrich), gabazine (30 µM, Sigma-Aldrich), and GABA (10 µM, Sigma-Aldrich) which responses were then recorded for 10 minutes. MEA analysis for single channel level and network burst level, is described in supplementary material. Neuronal network development was studied from single electrode spiking activity to burst organization, and to network synchronization by expression of network bursts. Networks were considered as mature when they expressed robust bursting behavior organized into networks burst as earlier defined in several studies 27 , 28 , 37 . Statistical analysis Statistical analyses were performed using SPSS Statistics, Version 25.0 (IBM, Armonk, New York, USA) (IBM). The Shapiro-Wilk test was used to evaluate the distribution and determine if the data was normally distributed or not. Data not normally distributed was analyzed using non-parametric tests. Statistical analysis was performed with Kruskal-Wallis tests for multiple comparisons of cell lines or groups or using mixed-effects model with Tukey’s multiple comparisons accounting for both fixed and random effects to analyze repeated measures over time. Mann-Whitney U test was used to determine statistical differences between two cell lines, in gene expression, quantification of puncta, and MEA data. Pharmacological data was normally distributed determined by Shapiro-wilk test and analyzed using one-way ANOVA for multiple comparisons. Unpaired t-test was used to compare two independent samples and paired t-test was used to compare related samples over the two pharmacological timepoints. Pharmacological data for MEA graphs are calculated as a percent from baseline, thus normalized to baseline. Statistical significances are denoted as *p < 0.05, **p < 0.01, ***p < 0.001, ****p ≤ 0.0001. Results hPSC-derived neurons express typical progenitor and GABAergic neuron markers. To obtain a GABAergic neuron-enriched cell population, control and DS patient hPSCs were differentiated into GABAergic inhibitory neurons using the dual SMAD inhibition method according to a previously described protocol 35 , 38 with minor modifications (Fig. 1 a). By days in vitro (DIV) 12, both the control and DS patient-derived neurons expressed the neural ectodermal markers FOXG1, SOX2, and PAX6, while the pluripotency marker OCT4 was lost (Fig. 1 b). NKX2.1, LHX6, DLX2 and SIX6 are key ventral specification transcription factor markers of the medial ganglion eminence (MGE) region, which forms part of the ventral telencephalon region 39 . Control and DS patient derived neurons showed transient upregulation of NKX2.1 by DIV 19, which decreased by DIV 37, and further reduced in DS patient 5 derived neurons at DIV 37. LHX6 expression was upregulated in control 3 neurons at later timepoints compared to all DS patient-derived neurons, while DLX2 was upregulated by DIV 19 (Fig. 1 c). The gene expression of SIX6 was relatively stable in most cultures beginning at 12 DIV but was increased in control 2 neurons (Fig. 1 c). Neuronal progenitors began to mature into early GABAergic inhibitory cells, as assessed by the gene and protein expression of GAD67 (Fig. 1 d, e) and the protein expression of βTUBIII (Fig. 1 d). The levels of the co-chloride transporters NKCC1 and KCC2, which regulate the intracellular Cl − concentration 40 were determined at DIV 97. The expression of NKCC1 was considerably upregulated compared with that of KCC2 in all analyzed cultures, indicating a delayed GABA functional switch, and that early neurons did not fully mature into GABAergic phenotype (Supplementary Fig. S2). In addition, the SCN1A gene was more upregulated in control neurons and DS patient 5 neurons than in DS patient 1 neurons. The encoding protein Nav1.1 was present in both the control and diseased cultures (Supplementary Fig. S2). Furthermore, the secretion of GABA from early GABAergic enriched neurons was verified with ELISA assay (Supplementary Fig. S3). Differential functional activity between DS patient-derived GABAergic networks. Next, we determined how pathogenic variants of the SCN1A gene in GABAergic neurons derived from patients with DS correlated with functional activity development. For this reason, the spontaneous activity of the GABAergic neuronal networks was assessed between DIV 19 and 89 (before end point pharmacology at DIV 95) by MEAs. Networks derived from all 5 cell lines exhibited spontaneous activity. The criteria to include the networks in the MEA were 1) bursting activity at the single-channel level and 2) network bursting (NB) activity showing synchronization at the network level. Three control hPSC lines (controls 1–3) alongside two patient lines, DS patient 1 and DS patient 5, were included in the analysis. Single channel-level analysis revealed significant differences in temporal activity development between the lines in mean firing rate (MFR, p < 0.0001, Fig. 2 a), number of bursts (nburst, p < 0.0001, Fig. 2 b) and percentage of spikes in bursts (p < 0.0001, Fig. 2 c). Neurons derived from DS patient 1, together with those derived from controls 1 and 2, displayed an earlier increase in the MFR activity from DIV 47 onward, while neurons from DS patient 5 and control 3 had slower spiking activity development (Fig. 2 a). Similarly, the bursting activity pattern illustrated that neurons derived from DS patient 1 and neurons derived from controls 1 and 2, exhibited higher nbursts from DIV 47 onward, while overall plateau in nbursts differed between all lines (Fig. 2 b). In addition, the percentage of spikes in bursts was greater in DS patient 1 and control 1 and 2 neurons from DIV 54-DIV 75 than in DS patient 5 and control 3 neurons (Fig. 2 c). When comparing all measurements between DIV 19–89, the burst duration (Supplementary Fig. S2) and bursts per minute (Supplementary Fig. S2) between the lines was not significantly different. However, when examining individual time points neurons derived from DS patient 1 and controls 1 and 2 displayed longer bursts duration than those from DS patient 5 and control 3 between DIV 54-DIV 64 (Supplementary Fig. S2). When comparing functional activity between patient lines, neurons derived from DS patient 1 on average had greater spiking and bursting activity than those derived from DS patient 5. Detailed statistical differences between the lines are presented in Supplementary Table S7 and S8. Next, we evaluated the NB features. Representative raster plots show NB phenotypes among the DS patient and control networks at 82 DIV (Fig. 2 d). At this point cultures had reached their stable peak activity according to single channel-level analysis (Fig. 2 a-c). Compared with all the other networks, the DS patient 1 network also had the highest average NB count (p < 0.01, Fig. 2 e). Furthermore, the IBIs were overall shortest in the DS patient 1 network compared to those in all the control networks (p < 0.001), but there were no observable differences between the patient-derived networks (Fig. 2 f). The duration of bursts was however shorter in the control 2 and DS patient 5 networks than in all the other networks (p < 0.05, Fig. 2 g). Next, a pairwise comparison of NB properties was conducted between DIV 68 and DIV 82, which revealed increased and stable activity, respectively. When evaluating the change in activity pattern between the two designated time points, both DS patient-derived networks showed an increase in NB counts, though there was only a significant increase in DS patient 1 (p < 0.001, Fig. 2 h). In contrast, the controls displayed either no change or a decrease in NB counts (Fig. 2 h). Moreover, the network of DS patient 1 had reduced IBIs and durations of NBs at DIV 82 as compared to those at DIV 67 (Fig. 2 i-j). NB durations seemed to shorten overall at DIV87 in all networks. Taken together, two distinctive behaviors were observed: first, the networks of controls 1 and 2 and DS patient 1 displayed increased spiking and bursting activity, and second, the networks of control 3 and DS patient 5 displayed delayed and decreased spiking and bursting activity at the single-channel level. Intriguingly, DS patient 1 displayed the most robust phenotype at the network level, with a high number of NBs and reduced intervals between NBs, suggesting a hyperactive phenotype. DS patient 5, however, did not express a clear phenotype that was distinct from the control networks. PCA was performed to obtain a comprehensive analysis using all MEA features (Fig. 2 k). PCA was performed on 17 MEA features and identified clusters associated with each cell line. The highest % Overlap values of the patient lines were indicated by a % Overlap DSpatient1 and Control3 of 28.57% and a % Overlap DSpatient5 and DSpatient1 of 36.4% (Fig. 2 k, Supplementary Table S5). In summary, this clustering analysis demonstrated that partial segregation between the diseased and control GABAergic networks was visible. Cortical networks contain heterogeneous neuronal populations. GABAergic neurons play an irrefutable role in the pathophysiology of DS 11 , 17 . However, we were interested in how pathogenic SCN1A variants impacted the function of a neural network consisting of both excitatory and inhibitory neurons, thus presenting better mimicry of the cortex. DS patient-derived and control hiPSCs were therefore differentiated into excitatory and inhibitory cortical neurons and endogenous astrocytes using a well-established protocol 36 (Fig. 3 a). At DIV 67, the heterogeneous nature of the population was confirmed by the positive protein expression and quantification of the VGLUT1 and GAD67 puncta in both the control and patient-derived networks (Fig. 3 b and c). Similarly, the gene expression of both excitatory (AMPA, kainate, and NMDA) and inhibitory (GAD67 and GAD65) markers was verified in control networks up to 6 weeks (DIV 74) after differentiation (Fig. 3 d). Furthermore, the presence of both excitatory presynaptic (synaptophysin) and postsynaptic (PSD95) markers in the neurons (Fig. 3 e, f) and their colocalization (Fig. 3 g) were confirmed through ICC staining and quantification. The DS patient-derived cells also expressed the Nav1.1 protein (Supplementary Fig. S4). Taken together, these cortical cultures comprise a heterogeneous population of glutamatergic-excitatory and GABAergic-inhibitory neurons. DS patient-derived cortical networks display a distinctive hyperactive network burst phenotype. The functional phenotype of the heterogeneous cortical networks was assessed until DIV 90 using MEAs. The same inclusion criteria used for GABAergic cultures were used for cortical cultures. Since control 1 failed to meet one of the functional criteria, as it did not display NBs at any given time point, it was excluded from the analysis. Thus, the recordings of 4 out of 5 cultured cell line-derived networks were included in the functional analysis. Single channel-level analysis revealed significant differences in temporal activity development between the lines in MFR (p < 0.001, Fig. 4 a), nburst, (p < 0.001, Fig. 4 b) and percentage of spikes in bursts (p < 0.001, Fig. 4 c). In contrast to GABAergic enriched cultures, the DS patient 1 and 5 -derived neurons formed a clear distinction with a significantly lower MFR (Fig. 4 a), nbursts (Fig. 4 b) and percentages of spikes in bursts (Fig. 4 c) from DIV 48 onwards in contrast to control 2 and 4 neurons forming their own cluster. The burst duration in DS patient 1 were significantly lower compared to control 2 from DIV 55-DIV 83 and to control 4 from DIV 55-DIV90 (Supplementary Fig. S4, p < 0.0001). Furthermore, DS patient 5 neurons were significantly lower compared to control 2 and 4 neurons from DIV 55-DIV 62 thereafter matching the controls (Supplementary Fig. S4). Bursts per minute counts were also significant different between lines. (Supplementary Fig. S4, p < 0.0001). The detailed statistical differences between the lines are presented in Supplementary Table S9, S10 and S11. Next, the NB phenotype was assessed. Representative raster plots at DIV 83 showed prominent differences in the NBs between the DS patient-derived and control networks (Fig. 4 d). The number of NBs at DIV 83 was greater in DS patient 1 than in all other networks, and DS patient 1 neurons had the shortest duration of NBs (Fig. 4 e-f). The IBIs in NBs were different among all cultures except between control 2 and DS patient 1 (Fig. 4 g). Since DIV 67 was the timepoint at which network activity matured, the changes in NB counts revealed that all networks, excluding control 2, displayed a significant increase in NB activity between DIV 67 (the common peak of activity) and DIV 83 (close to the experimental endpoint) (Fig. 4 h). The duration of NBs was significantly decreased in the DS patient 1 and control 4 networks between the two timepoints, while IBIs in NBs were decreased in all networks except in control 2 (Fig. 4 i-j). Interestingly, the change in the mean spike frequency within NBs was significantly lower in DS patient 1, in contrast to all the other networks, as similarly observed at the single-channel level at late timepoints (Fig. 4 k, Supplementary Fig. S4). The number of electrodes participating in single-channel bursts and NBs was lower in the diseased networks than in the controls (Supplementary Fig. 4i-j). This observed outcome suggested that the strength of NBs was decreased in the DS patient-derived networks compared to control networks. To obtain a comprehensive analysis of all MEA features, PCA was once again applied (Fig. 4 l). PCA was determined from 17 MEA features and revealed clusters associated with each cell line. The highest % Overlap values of the patient lines were indicated by the % Overlap DSpatient1 and DSpatient5 and the % Overlap DSpatient5 and DSpatient1 (26.3% and 23.6%, respectively) (Fig. 4 l, Supplementary Table S6). In summary, the results revealed distinctive phenotypic patterns in the DS patient-derived networks. Dravet patient-derived heterogenous cortical networks display increased sensitivity to pharmacological responses. Since a more well-defined separation in spontaneous activity patterns was visible in the heterogeneous cortical networks, we evaluated whether there was a specific neuronal type driving the elicited phenotype. We performed pharmacological experiments and analyzed the response of DS cortical networks to two GABA-specific modulators, a GABA agonist, GABA (γ-aminobutyric acid), and a GABA A receptor antagonist, gabazine, as well as two excitatory modulators, kainic acid (a glutamate agonist) and CNQX (a NMDA antagonist). The pharmacological effects on the cortical networks were tested at DIV 94. Seeing that we observed striking differences between the diseased and control networks in MFR and nbursts (determined from PCA1, Supplementary Table S4) and NB features (determined from PCA2, Supplementary Table S4), we focused on these features to understand how chemical modulations alter the phenotypes of the networks. The application of GABA did not alter spiking activity in DS patient 1; however, activity in both controls was significantly decreased compared to that in DS patient 1 (p < 0.001, Fig. 5 a). The nbursts were markedly lower in the diseased networks compared to the control networks (p < 0.05, Fig. 5 b). The application of gabazine did not cause any obvious changes in the MFR or nburst features across the cultures (Fig. 5 c-d). At the network-wide level, the number of NBs following GABA treatment was completely lost in all the cultures except those in the control 4 network (Fig. 5 e). Gabazine appeared to increase NBs in the diseased networks, while no significant change in the controls was detected (Fig. 5 f). When examining the effect of excitatory modulators, we observed that following treatment with kainic acid, a seizure-inducing component, DS patient 1 showed a significant increase in spiking activity in relation to the control networks, which, in contrast, showed a decrease in spiking activity (p < 0.01, Fig. 5 g). Compared to those in the control networks, the nbursts at the single-channel level decreased in the DS patient-derived networks (Fig. 5 h). When cultures were treated with CNQX, spiking and burst patterns decreased relative to those at baseline, with no observable differences among the cultures (Fig. 5 i-j). NBs were completely disrupted following kainic acid treatment in the disease-derived networks but were only reduced in the control networks (Fig. 5 k). Similarly, CNQX treatment abolished the formation of NBs in all networks except for the control 4 network (Fig. 5 l). Taken together, the DS-derived networks exhibited more sensitive responses to GABA and kainic acid. Discussion In this study, we demonstrate how the impact of two pathogenic variants in the SCN1A gene alters neuronal functionality at the network level in two culture types; enriched GABAergic and heterogeneous excitatory and inhibitory cultures, using MEAs. The DS patient-derived GABAergic networks develop similarly to those of controls; however, differences between the pathogenic variants were detectable in certain features of functional activity. The heterogeneous culture set up containing the appropriate cell types revealed more prominent alterations in the functional patterns between DS patient-derived and control networks, indicating the physiological relevance of the model. GABAergic neurons have been reported to be the primary cell type affected in DS 11 . Discrepancies have, however, been reported in several hiPSC models in that not only are GABAergic neurons defective by disease-causing mutations but also excitatory neurons 16 , 22 . Here, gene expression and immunocytochemistry confirmed GABAergic identity in the networks. Differential network bursting activity with an increased number of NBs over time was identified in patient-derived networks, while in control networks, the NBs either remained the same or decreased at late time points. Strikingly, DS patient 1 neurons exhibited greater network bursting activity at the later phase of the study. Previous studies that have used MEAs to analyze hiPSC-derived GABAergic neurons have shown that activity gradually decreases over time as neurons become functionally inhibitory 41 . Here, the persistent NB activity in the diseased networks may be indicative of functional immaturity of the GABAergic neurons. We observed that the GABA switch was delayed in all cultures, as NKCC1 expression remained higher than KCC2 expression suggesting that these GABAergic cultures were still in the immature excitatory state 40 . In the postmortem brain of DS patients, the expression of NKCC1 is increased, while there is a prominent reduction in the expression of KCC2 42 . These findings suggest that the GABA shift is compromised in these patients and other patients with neurodevelopmental disorders (Talos et al. 2012; Ruffolo et al. 2018). In this study, we show for the first time using MEAs that early neuron DS-derived GABAergic cultures have an altered network phenotype. DS patient 1, displayed an increase in NB hyperactivity. Intriguingly, previous studies that have modeled DS using hiPSC-derived GABAergic enriched cultures have shown deficits in action potential generation as well as sodium currents when assessed with patch clamp 15 , 17 . Furthermore, Schuster and colleagues (2019) demonstrated with the same patient lines used in this study that action potential frequency was decreased in comparison to that of their matched controls 38 . When extended to the network level, we observed NB hyperactivity. These findings imply that functional impairment is prominent in DS GABAergic networks, correlating with the pathophysiology of the disease. In the second model, which contained physiologically relevant neuronal subtypes (excitatory, inhibitory, and endogenous astrocytes), we detected more apparent differences in the functional phenotype between the DS patient-derived and control networks. To be able to appropriately mimic epilepsy-related disorders in vitro , which often manifests as an imbalance between excitation and inhibition, the combined use of excitatory and inhibitory neuronal subtypes is fundamental. Using our differentiation protocol 36 , we established a heterogeneous population characterized by the presence of VGLTU1-positive and GAD67-positive neurons with immunocytochemistry and gene expression. No observable differences were observed in the morphology of the control and diseased neurons or with the colocalized synaptic puncta. This indicates that patient and control cultures were similar throughout their maturation. Apparent functional differences were, however, observed as DS patient-derived networks showed decreased spiking and bursting activity patterns at the single-channel level. This phenomenon was similarly observed in a recent study in which cultures contained excitatory neurons only 22 , 23 . In contrast, the altered firing activity detected at the single-channel level was accompanied by a greater number of NBs, suggesting hyperactive network activity. Patient-specific differences were observed, as DS patient 1 exhibited the greatest number of NBs, with the shortest duration and intervals between NBs. Interestingly, DS patient 1, who was clinically diagnosed with a more severe DS phenotype, demonstrated the greatest hyperactivity (highest number of NBs) in both GABAergic and heterogeneous cortical cultures. Our results are similar to a previous patient-derived hiPSC study that reported the same phenomenon in a DS patient line, with most electrophysiological alterations corresponding to the severity of the patients' diagnosis 21 . The hyperactive bursting phenotype has been identified as a type of seizure prediction pattern in vitro following the application of seizure-liability compounds 24 , implying that the basal functional activity seen in the patient-specific network depicts patterns of seizure-like behavior in vitro . The presence of Nav1.1 was verified in heterogeneous cortical cultures, and it colocalized with both excitatory and inhibitory-specific markers. In rodents, Nav1.1 is known to be predominantly expressed in GABAergic neurons, while in humans, Nav1.1 is expressed in both excitatory and inhibitory neurons 44 , 45 . To determine whether there was a driving cell type exhibiting altered activity in heterogeneous cortical networks, pharmacological manipulation was performed using excitatory and inhibitory compounds, and phenotypic changes in DS network activity were assessed. Striking differences in spiking activity following GABA and kainic acid treatment were prominent in the DS patient 1 culture compared to all other cultures. Both modulators decreased spiking activity in most cultures, while in DS patient 1, the effect of GABA did not change to baseline levels, and kainic acid increased spiking activity. Efforts have been made in previous hiPSC studies involving both cell types in culture to understand how each cell type contributes to disease phenotypes. In a study by J. Liu et al. (2016), the effect of a genome-edited SCN1A mutation in hiPSC-derived mixed cultures showed that changes in sodium currents were specific to GABAergic fluorescently labeled neurons. In our study, although we could not isolate the driving cell type at the network level, we observed altered susceptibility in DS patient cultures following treatment with specific modulators. These findings suggest the potential of this model to study functional changes induced by anti-seizure medication in line with recent in vitro DS models 46 . Notably, KA treatment in control cultures resulted in a decreased spiking rate, which coincides with the basal phenotype displayed in diseased networks 27 , 29 . This observed effect supports the seizure-like pattern displayed by DS patient-derived culture. Future studies would require isogenic lines, to validate the direct impact of the functional changes caused by the disease-causing variants. In summary, this study reports a viable model showing prominent changes in the functional activity of DS networks in heterogeneous cortical neuronal cultures. We report that patient-specific differences exhibiting a more prominent seizure-like phenotype corresponded with the severity of the clinical diagnosis. The presented model recapitulates key disease phenotypes of individual patients and thus provides a relevant platform in facilitating pathogenic assessment, geared towards drug screening and potentially advancing the development of personalized therapies for genetic epilepsies. Data Availability Statement Data is provided within the manuscript or supplementary information files or can be accessed from the corresponding author with a reasonable request. Declarations Additional Information Authors report no competing interests. Author Contribution R.M. T.H and S.N. designed the study; J.S and N.D provided the patients cell lines and consulted with GABAergic differentiation; R.M, E.P, L.I, O.K performed experiments; R.M, T.H, A.V, L.I, O.K, V.V and F.E.K analyzed the data; R.M and O.K produced the figures, R.M wrote the first draft of the paper with T.H and S.N guidance and all authors contributes to final manuscript. Acknowledgement The work was supported by the Imaging Facility, Facility of Electrophysiological Measurements and iPS Cells Facility (Faculty of Medicine and Health Technology, Tampere University). The authors also thank Biocenter Finland for the support of Imaging, Electrophysiological Measurements and iPS cells facilities. We thank Hanna Mäkelä and Eija Hannuksela for their technical assistance with cell maintenance and analyses. We kindly thank MSc Satu Jäntti and the students Eveliina Taimela and Anna-Mari Moilanen, for assisting in cell culture maintenance and pharmacology experiments, performing ICC and qPCR experiments. This work was supported by Research Council of Finland (grant number 348517 to SN), Hjärnfonden (grant number FO2022-0042 to ND) T.H. was supported by Tampere Institute for Advanced Study. R.M was supported by Finnish Cultural Foundation, Brain research foundation and Tampere University MET doctoral research. Data Availability Data is provided within the manuscript or supplementary information files or can be accessed from the corresponding author with a reasonable request. References Dravet, C. The core Dravet syndrome phenotype. Epilepsia 52 (Suppl 2), 3–9. 10.1111/j.1528-1167.2011.02994.x (2011). Ding, J. et al. SCN1A Mutation—Beyond Dravet Syndrome: A Systematic Review and Narrative Synthesis. Front. Neurol. 12 , 743726. 10.3389/fneur.2021.743726 (2021). Chilcott, E., Díaz, J. 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09:38:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5615262/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5615262/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75655323,"identity":"416853d2-2573-440a-a2e1-35111afd83ff","added_by":"auto","created_at":"2025-02-06 19:52:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12104048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of GABAergic neuronal cultures.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) Timeline of the GABAergic differentiation protocol. (\u003cstrong\u003eb\u003c/strong\u003e) Representative images showing positive expression of the neuroectodermal markers FOXG1, SOX2, and PAX6 in hPSC-derived control and DS patient neurons by DIV 12, (\u003cstrong\u003ec\u003c/strong\u003e) Changes in theexpression of genes encoding ventral identity markers from DIV 12 to DIV 37 (data shown as the mean±SEM). Samples were ran in triplicates with 1-2 technical repeats. Data was normalized to DIV 12 values. (\u003cstrong\u003ed\u003c/strong\u003e) Immunocytochemistry showing maturation and GABAergic neuron identity by DIV 37. Nuclei were counterstained with DAPI (blue), axons were counterstained with bTUBIII and GABAergic neurons with GAD67. The staining for individual channels can be found in \u003cstrong\u003eSupplementary Fig. S1\u003c/strong\u003e. The scale bar is 200 µm. (\u003cstrong\u003ee\u003c/strong\u003e) Gene expression of the mature GABA-specific marker GAD67 and the SCN1A gene. Control 1 (hiPSCs), Control 2 (hiPSCs), Control 3 (hESCs). The illustrations werecreated using Biorender.com.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-5615262/v1/0e1ae27aa0c77a6085341438.png"},{"id":75655154,"identity":"a0261b94-cbac-49a0-a3cd-6f7c4403190a","added_by":"auto","created_at":"2025-02-06 19:44:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1944214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional characterization of GABAergic neuronal networks\u003c/strong\u003e. Single electrode level MEA data analysis showing (\u003cstrong\u003ea\u003c/strong\u003e) mean firing rate (spike rate, Hz), (\u003cstrong\u003eb\u003c/strong\u003e) number of bursts per well, and (\u003cstrong\u003ec\u003c/strong\u003e) percentage of spikes in bursts. (\u003cstrong\u003ed\u003c/strong\u003e) Representative raster plots of 5-minute segments (300 sec) showing the overall activity of networks at DIV 82, with representative single traces displayed on top of the raster blots. Network-level MEA data showing (\u003cstrong\u003ee\u003c/strong\u003e) the number of NBs, (\u003cstrong\u003ef\u003c/strong\u003e) the mean IBI in NBs, and (\u003cstrong\u003eg\u003c/strong\u003e) the mean NB duration, all at DIV 82. (\u003cstrong\u003eh\u003c/strong\u003e) Pairwise analysis depicting the NB counts, (\u003cstrong\u003ei\u003c/strong\u003e) mean IBIs in NBs and (\u003cstrong\u003ej\u003c/strong\u003e) mean NB duration from DIV 68 to DIV 82 across all networks. (\u003cstrong\u003ek\u003c/strong\u003e) Multiparametric analysis of17 MEA features is presented forthe first 3 principal components (PCA 1-3). For all MEA data shown, \u003cem\u003en=\u003c/em\u003enumber of wells/independent differentiation rounds: Control 1, \u003cem\u003en\u003c/em\u003e=44/3; Control 2, \u003cem\u003en\u003c/em\u003e=16/1; Control 3, \u003cem\u003en\u003c/em\u003e=28/2; DS patient 1, \u003cem\u003en\u003c/em\u003e=30/2; and DS patient 5, \u003cem\u003en\u003c/em\u003e=52/3. The data in the graphs \u003cstrong\u003ea-c\u003c/strong\u003e are presented as mean±95% confidence interval (CI) and the Tukey box plots (\u003cstrong\u003ee-g)\u003c/strong\u003e are presented as medians. Statistical analyses for graphs \u003cstrong\u003ea-c\u003c/strong\u003e were conducted with mixed-effects model with Tukey’s multiple comparisons over time and with Kruskal‒Wallis tests for multiple comparisons of networks (cell lines), as shown in graphs \u003cstrong\u003ee‒g\u003c/strong\u003e. The Mann‒Whitney U test was used to determine differences between two cell lines, as shown in graphs \u003cstrong\u003ee‒g\u003c/strong\u003e. The Wilcoxon test was used for repeated measures over time, as shown in graphs \u003cstrong\u003eh‒j\u003c/strong\u003e (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p ≤ 0.0001).\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-5615262/v1/56fec817a5ddd9683683bfd4.png"},{"id":75655177,"identity":"cf611993-61bf-47e8-bd08-05b48afd1f98","added_by":"auto","created_at":"2025-02-06 19:44:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10612890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCortical networks consist of heterogeneous subpopulations of neurons.\u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Timeline of the cortical differentiation protocol. (\u003cstrong\u003eb\u003c/strong\u003e) Representative images showing VGLUT1- and GAD67-positive staining. The reconstructed 3D images show altered coloring of protein markers for ease of visualization. (\u003cstrong\u003ec\u003c/strong\u003e) Computational analysis showing the quantification of VGLUT1 and GAD67 punctaper neurite length in controland DS patient networks. (\u003cstrong\u003ed\u003c/strong\u003e) Gene expression showing the fold change in the expression of the glutamate-specific receptors AMPA, kainateand NMDA, as well as the GABA-specific genes GAD67 (GAD1) and GAD65 (GAD2), over the course of differentiation until DIV 74. (\u003cstrong\u003ee\u003c/strong\u003e) Immunocytochemistry showing the number of presynaptic and postsynaptic (PSD95) punctaper neurite length. Reconstructed 3D images are color contrasted for ease of visualization. The scalebar is 5 µm. (\u003cstrong\u003ef\u003c/strong\u003e) Computational analysis showing quantification of pre- and postsynaptic punctaper neurite length. (\u003cstrong\u003eg\u003c/strong\u003e) Quantification of colocalized puncta per neurite length. The data are presented as mean±SEM in graph \u003cstrong\u003ed\u003c/strong\u003e and as Tukey box plots in graphs \u003cstrong\u003ec\u003c/strong\u003e, \u003cstrong\u003ef\u003c/strong\u003eand \u003cstrong\u003eg\u003c/strong\u003e. Illustrations were made with Biorender.com.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-5615262/v1/0d2b19f39304113d999dd569.png"},{"id":75655159,"identity":"58020feb-e2bb-47d0-807f-ca1d48057ace","added_by":"auto","created_at":"2025-02-06 19:44:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2066669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional characterization of DS patient-derived heterogeneous cortical networks. \u003c/strong\u003eSingle-channel analysis showing the (\u003cstrong\u003ea\u003c/strong\u003e) MFR (Hz), (\u003cstrong\u003eb\u003c/strong\u003e) number of bursts per well, and (\u003cstrong\u003ec\u003c/strong\u003e) percentage of spikes in bursts. (\u003cstrong\u003ed\u003c/strong\u003e) Representative raster plots of 5-minute segments (300 sec) showing network activity by DIV 83, with representative single traces above the raster plots. Network-level detection showing (\u003cstrong\u003ee\u003c/strong\u003e) the number of NBs, (\u003cstrong\u003ef\u003c/strong\u003e) the mean duration of NBs, and (\u003cstrong\u003eg\u003c/strong\u003e) mean IBIs in NBs at DIV 83. (\u003cstrong\u003eh\u003c/strong\u003e) Changes in NB counts analyzed between DIV 67 and DIV83. (\u003cstrong\u003ei\u003c/strong\u003e) Changes in the mean duration of NBs, (\u003cstrong\u003ej\u003c/strong\u003e) IBIs in NBs and (\u003cstrong\u003ek\u003c/strong\u003e) mean spike frequency in NBs. (\u003cstrong\u003el\u003c/strong\u003e) Multiparametric analysis of 16 MEA features is presented forthe first 3 principal components (PCA 1-3). For all MEA data shown, \u003cem\u003en=\u003c/em\u003enumber of wells/separate differentiation rounds: Control 2, \u003cem\u003en\u003c/em\u003e=28/2; Control 4, \u003cem\u003en\u003c/em\u003e=40/3; DS patient 1, \u003cem\u003en\u003c/em\u003e=60/4; and DS patient 5, \u003cem\u003en\u003c/em\u003e=52/4. The data in graphs (\u003cstrong\u003ea-c\u003c/strong\u003e) is presented as mean±95% CIand graphs \u003cstrong\u003ee-g \u003c/strong\u003eas Tukey box plots. Statistical analyses for graphs \u003cstrong\u003ea-c\u003c/strong\u003ewere conducted with mixed-effects model with Tukey’s multiple comparisons over time and with Kruskal‒Wallistests for multiple comparisons of networks (cell lines), as shown in graphs \u003cstrong\u003ee‒g\u003c/strong\u003e. Mann-Whitney U test, was used to determine differences between two cell lines, graphs \u003cstrong\u003ee-g\u003c/strong\u003e. The Wilcoxon test was used for repeated measures over time, as shown in graphs \u003cstrong\u003eh‒k\u003c/strong\u003e (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p ≤ 0.0001).\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-5615262/v1/1959ca903c6e476586b72e61.png"},{"id":75655173,"identity":"e0533d90-4601-4850-a264-d17dc43b55da","added_by":"auto","created_at":"2025-02-06 19:44:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1681009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePharmacological responses in DS patient-derived cortical networks.\u003c/strong\u003e(\u003cstrong\u003ea-d\u003c/strong\u003e) Graphs showing normalized data from baseline, following treatment with GABA, in (\u003cstrong\u003ea\u003c/strong\u003e) MFR, (\u003cstrong\u003eb\u003c/strong\u003e) nburst counts and following treatment with gabazine in (\u003cstrong\u003ec\u003c/strong\u003e) MFR, (\u003cstrong\u003ed\u003c/strong\u003e) nburst counts. Changes in NB counts from baseline (gray) to after treatment with pharmacological compounds (pink). (\u003cstrong\u003ee\u003c/strong\u003e) GABA (\u003cstrong\u003ef\u003c/strong\u003e) gabazine. Graphs showing normalized data from baseline, following treatment with kainic acid, in (\u003cstrong\u003eg\u003c/strong\u003e) MFR, (\u003cstrong\u003eh\u003c/strong\u003e) nburst counts, and following treatment with CNQX in (\u003cstrong\u003ei\u003c/strong\u003e) MFR, (\u003cstrong\u003ej\u003c/strong\u003e) nburst counts. Changes in NB counts from baseline (gray) to after treatment with pharmacological compounds (pink). (\u003cstrong\u003ek\u003c/strong\u003e) Kainic acid (\u003cstrong\u003el\u003c/strong\u003e) CNQX. The data are presented as the mean±SEM. As the data were normally distributed, statistical analysis was performed using one-way analysis of variance (ANOVA) for multiple comparisons. Unpaired t tests were used to determine differences between two cell lines (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-5615262/v1/c40dd1dbfc1096ec5ef0fc92.png"},{"id":82021782,"identity":"0f7f1b56-e785-4463-b8cf-e9ebd0fb539c","added_by":"auto","created_at":"2025-05-06 05:40:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":34297274,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5615262/v1/32da3ac1-1488-4728-a61f-9f739fc1d2ea.pdf"},{"id":75655155,"identity":"fc35146a-a687-472b-acc4-5ed093a15e8a","added_by":"auto","created_at":"2025-02-06 19:44:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1573772,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5615262/v1/1f9f92fa618b1cb138d7a0ab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Abnormalities in the functional activity of neural networks in a human iPSC model of Dravet syndrome","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDravet syndrome (DS), also known as severe myoclonic epilepsy of infants (SMEI), is an early-onset epilepsy refractory to most available antiseizure medications\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The disorder falls under the developmental and epileptic encephalopathies (DEE) with major hallmarks of both febrile and afebrile seizures, along with an increased frequency of complex seizures that progresses throughout childhood \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. DS patients often develop comorbidities involving autism-like behavior, intellectual disability, ataxia, and an increased incidence of sudden death (SUDEP) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The reported incidence is approximately 1 in 15 000 to 1 in 41 000 live births \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Over 80% of cases are caused by a \u003cem\u003ede novo\u003c/em\u003e mutation in the \u003cem\u003eSCN1A\u003c/em\u003e gene, which encodes the a-subunit of Na\u003csub\u003eV\u003c/sub\u003e1.1, a voltage-gated sodium channel that is essential for initiating and propagating action potentials \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Haploinsufficiency of the \u003cem\u003eSCN1A\u003c/em\u003e gene is sufficient to give rise to the disease, originating from either missense, deletion, insertion, or frameshift pathogenic variants that occur \u003cem\u003ede novo\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, mutation severity is not entirely predictive of seizure outcomes \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe pathogenic mechanism underlying DS is the disinhibition of GABAergic neurons, where the loss of function results in a lack of network inhibition, causing increased network excitability \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The pathophysiology was first investigated in DS \u003cem\u003eScn1a\u003c/em\u003e knockout mouse models in which DS phenotypes such as seizures, cognitive decline, and hyperactivity were observed \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Studies with DS animal models have shown that the functional impairment of sodium currents primarily affects GABAergic interneurons, not pyramidal excitatory neurons \u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, recent \u003cem\u003ein vivo\u003c/em\u003e studies have revealed a more complex mechanism in which alterations in sodium channels are observed in both inhibitory and pyramidal excitatory neurons \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, proposing a need to elucidate the overlooked role of excitatory neurons in DS pathology.\u003c/p\u003e \u003cp\u003eHuman induced pluripotent stem cell (hiPSC) models have become an integral part of studying the pathophysiology of DS, as neurons can be generated directly from patients carrying specific variants of the mutation associated with the disease \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Interestingly, \u003cem\u003ein vitro\u003c/em\u003e hiPSC-derived neuronal models have revealed contradictory findings on the underlying functional impairments of DS. Early hiPSC studies have described functional perturbations in sodium current density and action potential frequency in inhibitory neurons \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, while other studies have detected deficits in both excitatory and inhibitory neuronal subtypes \u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Despite the above findings, the functionality of hiPSC-derived neural models has been predominantly assessed at the single-neuron level using the patch clamp technique \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. More recently, the microelectrode array (MEA) technique has been utilized to study functional deficits in DS neurons at the network-wide level \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The use of MEAs has increased substantially for drug screening applications and, more importantly, for identifying patient-specific network signatures in disease models \u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFundamentally, epileptic seizures are a consequence of an imbalanced inhibitory and excitatory system; therefore, establishing excitatory and inhibitory neuron cultures is essential for constructing a more physiologically relevant model that only a few studies have presented at the single-neuron level \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Therefore, the aim of this study was to determine how different pathogenic \u003cem\u003eSCN1A\u003c/em\u003e variants affect the functional phenotype of DS hiPSC-derived networks in a physiologically relevant model using MEAs. We first assessed the functional phenotype in enriched GABAergic cultures. We then extended the approach to study the effect of the two pathogenic variants in a heterogeneous culture system consisting of both excitatory and inhibitory neurons. We report that DS patient-derived networks display prominent altered phenotypes at both the single-channel level and network levels and show distinct clustering with principal component analysis (PCA). This study aimed to further elucidate the pathogenesis of DS by analyzing excitatory and inhibitory neuron cultures with MEAs, and the findings highlight the proficiency of patient-derived human stem cell models to characterize DS functional alterations.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHuman pluripotent stem cells\u003c/h2\u003e \u003cp\u003eThe study was conducted with neuronal cells derived from the human induced pluripotent stem cell (hiPSC) lines 04511WTs.EURCCs (control 1) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, 10902.EURCCs (control 2) \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, a human embryonic stem cell (hESC) line 08017 (control 3) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and a commercial AICS0012 Mono-allelic mEGFP-tagged TUBA1B WTC iPSC line (Coriell Institute, USA, control 4). The Faculty of Medicine and Health Technology has supportive statement from the Ethics Committee of the Expert Responsibility area of Tampere University Hospital for the derivation; culture and differentiation of hiPSCs (R20159). Informed consent was obtained from the subjects (legally authorized representatives) who donated the cell samples. All experiments were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient characteristics and human pluripotent stem cells of DS lines\u003c/h3\u003e\n\u003cp\u003eDS patient lines were obtained from Uppsala University, Sweden with ethical approval (D-numbers 319/2009 and 209/2016) under material transfer agreement. DS patient 1 was diagnosed with DS, along with severe developmental delay, and ataxia. The patient had a de novo frameshift variant c.5502-5509dupGCTTGAAC (p.Pro1837Argfs24) in the intracellular COOH-terminal domain of Nav1.1. DS patient 5 was diagnosed with a less severe DS phenotype with mild cognitive decline and had a history of both febrile and non-febrile seizures. The patient had a pathogenic missense variant c.651C\u0026thinsp;\u0026gt;\u0026thinsp;G (p.Thr217Arg; domain I segment 4, voltage sensor of Nav1.1) which was inherited from her mother with febrile seizures \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. All procedures were performed in accordance with the Helsinki Convention and written informed consent was obtained from all patients or their legal guardians. All experiments were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003ch3\u003eDifferentiation of forebrain GABAergic interneurons\u003c/h3\u003e\n\u003cp\u003eThe GABAergic differentiation of human pluripotent stem cells (hPSCs) was carried out according to previous publication \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e with minor modifications. Briefly, two days before neural induction, hPSCs were harvested and plated in Essential-E8\u0026trade;medium supplemented with 10 \u0026micro;M Rho-kinase inhibitor Y27632 (Stem Cell Technologies) onto plastic 24-well plates (Thermo Fisher Scientific) coated with 100 \u0026micro;g/ml poly-L-ornithine (PLO) and 15 \u0026micro;g/ml human recombinant laminin, LN521 (Biolamina, Sweden), to obtain\u0026thinsp;\u0026gt;\u0026thinsp;80% confluence the next day. Upon 90\u0026ndash;100% confluence, neural differentiation was induced on both control hPSCs and DS patient-derived hiPSCs (Days in vitro, DIV 0) using dual SMAD inhibition protocol. Cells were refreshed with neural induction medium (NIM; [DMEM-KO, 15% KnockOut Serum Replacement, 1\u0026times; GlutaMax, 1\u0026times; non-essential amino acids, 1% penicillin/streptomycin (all from Thermo Fischer Scientific). NIM was supplemented with 2 \u0026micro;M tankyrase inhibitor XAV939 (Sigma-Aldrich), 100 nM ALK2/3 inhibitor LDN193189 (Stem Cell Technologies) and 10 \u0026micro;M ALK4/5/7 inhibitor SB431542 (Sigma-Aldrich). On DIV 2, cells were plated onto 24 and 48-well cultured plates coated with PLO and LN521 at a density of 100 000 cells/cm\u003csup\u003e2\u003c/sup\u003e. On DIV 4, the medium was gradually replaced from NIM to NBN medium [Neurobasal medium, N2 (1:100), B27 without Vitamin A (1:200), 1% penicillin/streptomycin (Thermo Fischer Scientific) in a ratio of 3:1, supplemented with the dual SMAD inhibitors as used in NIM. On DIV 6, the medium was changed at a 1:1 ratio (NIM: NBN) and by DIV 8 at a 3:1 ratio (NIM: NBN). On DIV 10 of differentiation cells were patterned towards ventral telencephalic fate using NBN medium supplemented with 5 nM recombinant mouse sonic hedgehog (SHH C25II; R\u0026amp;D Systems), 1 \u0026micro;M Purmorphamine (Miltenyi biotech), 10 ng/ml recombinant human brain-derived neurotrophic factor (BDNF; R\u0026amp;D Systems) 200 \u0026micro;M ascorbic acid (AA, Sigma-Aldrich) and 100 \u0026micro;M 2\u0026prime;-O-Di-butyryladenosine 3\u0026prime;,5\u0026prime;-cyclic monophosphate (cAMP; Sigma-Aldrich). NBN media was changed on DIV 11, 13, and on DIV 16. On DIV 17, which was considered the final plating date and the start of the experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), cells were plated onto PLO and LN521 coated 48-well plates at a density of 100 000 cells/cm\u003csup\u003e2\u003c/sup\u003e as well as on MEA plates 48 array format (Axion BioSystems, Atlanta, GA USA). MEA plates were pretreated first with a 10 \u0026micro;l droplet of 0.1% polyethylenimine (PEI, Sigma-Aldrich-Aldrich) in 0.1 M borate buffer and then with a 10 \u0026micro;l droplet of 50 mg/ml LN521 (Biolamina). Cells were plated at 80 000 cells in a 10 \u0026micro;l droplet (density 635 000 cells/cm\u003csup\u003e2\u003c/sup\u003e) on MEAs in NBN medium supplemented with brain-derived neurotrophic factor, BDNF, ascorbic acid, and dibutyryl cyclic adenosine monophosphate, db-cAMP (all from Sigma-Aldrich). The following day, cultures were maintained in either NBN media or BrainPhys\u0026trade; neuronal media (Stemcell Technologies) and the medium was changed three times a week from this point onwards. All samples were cultured at +\u0026thinsp;37 ◦C in a 5% CO2 humidified incubator).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDifferentiation of heterogeneous cortical neurons\u003c/h3\u003e\n\u003cp\u003ehiPSCs were expanded and differentiated into cortical neurons as previously described\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Experiments were started at DIV 32 which was considered the start of experiments when cells were plated for culture and MEA plates. At DIV 32 neurons were plated at a density of 50 000 cells/cm\u003csup\u003e2\u003c/sup\u003e in PLO and LN521-treated well plates. MEA plates were pretreated first with a 10 \u0026micro;l droplet of 0.1% PEI in 0.1 M borate buffer and then with a 10 \u0026micro;l droplet of 50 mg/ml LN521. Thereafter, cells were plated in MEAs at 80 000 cells in a 10 \u0026micro;l droplet (cell density 635 000 cells/cm\u003csup\u003e2\u003c/sup\u003e). The cultures were kept in BrainPhys media and cultured at +\u0026thinsp;37 ◦C in a 5% CO2 humidified incubator. Media was changed every other day.\u003c/p\u003e\n\u003ch3\u003eMEA recordings\u003c/h3\u003e\n\u003cp\u003eNeuronal network activity was recorded with an Axion Maestro system controlled by AxIS Software (Axion Biosystems) with a 12.5 kHz sampling rate. Recordings were obtained under a controlled temperature of 37◦C. The development of spontaneous activity was measured twice a week with 10-minute recordings up to 11 weeks in GABAergic networks and up to 8.5 weeks in heterogeneous cortical networks. Prior recordings, MEA plates were allowed to equilibrate for 3\u0026ndash;5 min. Refreshment of BrainPhys medium was always performed one day before the MEA recordings. Pharmacological experiments were performed at DIV 94 with heterogeneous cortical networks. Pharmacological baseline recording was performed for 10 minutes followed by the application of one of the following compounds: CNQX (50\u0026micro;M), kainic acid (KA, 10 \u0026micro;M, Sigma-Aldrich), gabazine (30 \u0026micro;M, Sigma-Aldrich), and GABA (10 \u0026micro;M, Sigma-Aldrich) which responses were then recorded for 10 minutes. MEA analysis for single channel level and network burst level, is described in supplementary material. Neuronal network development was studied from single electrode spiking activity to burst organization, and to network synchronization by expression of network bursts. Networks were considered as mature when they expressed robust bursting behavior organized into networks burst as earlier defined in several studies\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS Statistics, Version 25.0 (IBM, Armonk, New York, USA) (IBM). The Shapiro-Wilk test was used to evaluate the distribution and determine if the data was normally distributed or not. Data not normally distributed was analyzed using non-parametric tests. Statistical analysis was performed with Kruskal-Wallis tests for multiple comparisons of cell lines or groups or using mixed-effects model with Tukey\u0026rsquo;s multiple comparisons accounting for both fixed and random effects to analyze repeated measures over time. Mann-Whitney U test was used to determine statistical differences between two cell lines, in gene expression, quantification of puncta, and MEA data. Pharmacological data was normally distributed determined by Shapiro-wilk test and analyzed using one-way ANOVA for multiple comparisons. Unpaired t-test was used to compare two independent samples and paired t-test was used to compare related samples over the two pharmacological timepoints. Pharmacological data for MEA graphs are calculated as a percent from baseline, thus normalized to baseline. Statistical significances are denoted as *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****p\u0026thinsp;\u0026le;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003ehPSC-derived neurons express typical progenitor and GABAergic neuron markers.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo obtain a GABAergic neuron-enriched cell population, control and DS patient hPSCs were differentiated into GABAergic inhibitory neurons using the dual SMAD inhibition method according to a previously described protocol\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e with minor modifications (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). By days in vitro (DIV) 12, both the control and DS patient-derived neurons expressed the neural ectodermal markers FOXG1, SOX2, and PAX6, while the pluripotency marker OCT4 was lost (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). \u003cem\u003eNKX2.1, LHX6, DLX2\u003c/em\u003e and \u003cem\u003eSIX6\u003c/em\u003e are key ventral specification transcription factor markers of the medial ganglion eminence (MGE) region, which forms part of the ventral telencephalon region\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Control and DS patient derived neurons showed transient upregulation of \u003cem\u003eNKX2.1\u003c/em\u003e by DIV 19, which decreased by DIV 37, and further reduced in DS patient 5 derived neurons at DIV 37. \u003cem\u003eLHX6\u003c/em\u003e expression was upregulated in control 3 neurons at later timepoints compared to all DS patient-derived neurons, while \u003cem\u003eDLX2\u003c/em\u003e was upregulated by DIV 19 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). The gene expression of \u003cem\u003eSIX6\u003c/em\u003e was relatively stable in most cultures beginning at 12 DIV but was increased in control 2 neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Neuronal progenitors began to mature into early GABAergic inhibitory cells, as assessed by the gene and protein expression of GAD67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, e) and the protein expression of βTUBIII (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The levels of the co-chloride transporters NKCC1 and KCC2, which regulate the intracellular Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e concentration\u003csup\u003e40\u003c/sup\u003e were determined at DIV 97. The expression of NKCC1 was considerably upregulated compared with that of KCC2 in all analyzed cultures, indicating a delayed GABA functional switch, and that early neurons did not fully mature into GABAergic phenotype (Supplementary Fig. S2). In addition, the SCN1A gene was more upregulated in control neurons and DS patient 5 neurons than in DS patient 1 neurons. The encoding protein Nav1.1 was present in both the control and diseased cultures (Supplementary Fig. S2). Furthermore, the secretion of GABA from early GABAergic enriched neurons was verified with ELISA assay (Supplementary Fig. S3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferential functional activity between DS patient-derived GABAergic networks.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNext, we determined how pathogenic variants of the \u003cem\u003eSCN1A\u003c/em\u003e gene in GABAergic neurons derived from patients with DS correlated with functional activity development. For this reason, the spontaneous activity of the GABAergic neuronal networks was assessed between DIV 19 and 89 (before end point pharmacology at DIV 95) by MEAs. Networks derived from all 5 cell lines exhibited spontaneous activity. The criteria to include the networks in the MEA were 1) bursting activity at the single-channel level and 2) network bursting (NB) activity showing synchronization at the network level. Three control hPSC lines (controls 1\u0026ndash;3) alongside two patient lines, DS patient 1 and DS patient 5, were included in the analysis.\u003c/p\u003e \u003cp\u003eSingle channel-level analysis revealed significant differences in temporal activity development between the lines in mean firing rate (MFR, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), number of bursts (nburst, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) and percentage of spikes in bursts (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Neurons derived from DS patient 1, together with those derived from controls 1 and 2, displayed an earlier increase in the MFR activity from DIV 47 onward, while neurons from DS patient 5 and control 3 had slower spiking activity development (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Similarly, the bursting activity pattern illustrated that neurons derived from DS patient 1 and neurons derived from controls 1 and 2, exhibited higher nbursts from DIV 47 onward, while overall plateau in nbursts differed between all lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In addition, the percentage of spikes in bursts was greater in DS patient 1 and control 1 and 2 neurons from DIV 54-DIV 75 than in DS patient 5 and control 3 neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). When comparing all measurements between DIV 19\u0026ndash;89, the burst duration (Supplementary Fig. S2) and bursts per minute (Supplementary Fig. S2) between the lines was not significantly different. However, when examining individual time points neurons derived from DS patient 1 and controls 1 and 2 displayed longer bursts duration than those from DS patient 5 and control 3 between DIV 54-DIV 64 (Supplementary Fig. S2). When comparing functional activity between patient lines, neurons derived from DS patient 1 on average had greater spiking and bursting activity than those derived from DS patient 5. Detailed statistical differences between the lines are presented in Supplementary Table S7 and S8.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we evaluated the NB features. Representative raster plots show NB phenotypes among the DS patient and control networks at 82 DIV (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). At this point cultures had reached their stable peak activity according to single channel-level analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-c). Compared with all the other networks, the DS patient 1 network also had the highest average NB count (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Furthermore, the IBIs were overall shortest in the DS patient 1 network compared to those in all the control networks (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but there were no observable differences between the patient-derived networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). The duration of bursts was however shorter in the control 2 and DS patient 5 networks than in all the other networks (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). Next, a pairwise comparison of NB properties was conducted between DIV 68 and DIV 82, which revealed increased and stable activity, respectively. When evaluating the change in activity pattern between the two designated time points, both DS patient-derived networks showed an increase in NB counts, though there was only a significant increase in DS patient 1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). In contrast, the controls displayed either no change or a decrease in NB counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). Moreover, the network of DS patient 1 had reduced IBIs and durations of NBs at DIV 82 as compared to those at DIV 67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei-j). NB durations seemed to shorten overall at DIV87 in all networks.\u003c/p\u003e \u003cp\u003eTaken together, two distinctive behaviors were observed: first, the networks of controls 1 and 2 and DS patient 1 displayed increased spiking and bursting activity, and second, the networks of control 3 and DS patient 5 displayed delayed and decreased spiking and bursting activity at the single-channel level. Intriguingly, DS patient 1 displayed the most robust phenotype at the network level, with a high number of NBs and reduced intervals between NBs, suggesting a hyperactive phenotype. DS patient 5, however, did not express a clear phenotype that was distinct from the control networks.\u003c/p\u003e \u003cp\u003ePCA was performed to obtain a comprehensive analysis using all MEA features (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ek). PCA was performed on 17 MEA features and identified clusters associated with each cell line. The highest %\u003cem\u003eOverlap\u003c/em\u003e values of the patient lines were indicated by a %\u003cem\u003eOverlap\u003c/em\u003e\u003csub\u003eDSpatient1 and Control3 of\u003c/sub\u003e 28.57% and a %\u003cem\u003eOverlap\u003c/em\u003e\u003csub\u003eDSpatient5 and DSpatient1\u003c/sub\u003e of 36.4% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ek, Supplementary Table S5). In summary, this clustering analysis demonstrated that partial segregation between the diseased and control GABAergic networks was visible.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCortical networks contain heterogeneous neuronal populations.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGABAergic neurons play an irrefutable role in the pathophysiology of DS \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, we were interested in how pathogenic \u003cem\u003eSCN1A\u003c/em\u003e variants impacted the function of a neural network consisting of both excitatory and inhibitory neurons, thus presenting better mimicry of the cortex. DS patient-derived and control hiPSCs were therefore differentiated into excitatory and inhibitory cortical neurons and endogenous astrocytes using a well-established protocol \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). At DIV 67, the heterogeneous nature of the population was confirmed by the positive protein expression and quantification of the VGLUT1 and GAD67 puncta in both the control and patient-derived networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and c). Similarly, the gene expression of both excitatory (AMPA, kainate, and NMDA) and inhibitory (GAD67 and GAD65) markers was verified in control networks up to 6 weeks (DIV 74) after differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Furthermore, the presence of both excitatory presynaptic (synaptophysin) and postsynaptic (PSD95) markers in the neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, f) and their colocalization (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg) were confirmed through ICC staining and quantification. The DS patient-derived cells also expressed the Nav1.1 protein (Supplementary Fig. S4). Taken together, these cortical cultures comprise a heterogeneous population of glutamatergic-excitatory and GABAergic-inhibitory neurons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDS patient-derived cortical networks display a distinctive hyperactive network burst phenotype.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe functional phenotype of the heterogeneous cortical networks was assessed until DIV 90 using MEAs. The same inclusion criteria used for GABAergic cultures were used for cortical cultures. Since control 1 failed to meet one of the functional criteria, as it did not display NBs at any given time point, it was excluded from the analysis. Thus, the recordings of 4 out of 5 cultured cell line-derived networks were included in the functional analysis. Single channel-level analysis revealed significant differences in temporal activity development between the lines in MFR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), nburst, (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) and percentage of spikes in bursts (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). In contrast to GABAergic enriched cultures, the DS patient 1 and 5 -derived neurons formed a clear distinction with a significantly lower MFR (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), nbursts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) and percentages of spikes in bursts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) from DIV 48 onwards in contrast to control 2 and 4 neurons forming their own cluster. The burst duration in DS patient 1 were significantly lower compared to control 2 from DIV 55-DIV 83 and to control 4 from DIV 55-DIV90 (Supplementary Fig. S4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Furthermore, DS patient 5 neurons were significantly lower compared to control 2 and 4 neurons from DIV 55-DIV 62 thereafter matching the controls (Supplementary Fig. S4). Bursts per minute counts were also significant different between lines. (Supplementary Fig. S4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The detailed statistical differences between the lines are presented in Supplementary Table S9, S10 and S11.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, the NB phenotype was assessed. Representative raster plots at DIV 83 showed prominent differences in the NBs between the DS patient-derived and control networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The number of NBs at DIV 83 was greater in DS patient 1 than in all other networks, and DS patient 1 neurons had the shortest duration of NBs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee-f). The IBIs in NBs were different among all cultures except between control 2 and DS patient 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). Since DIV 67 was the timepoint at which network activity matured, the changes in NB counts revealed that all networks, excluding control 2, displayed a significant increase in NB activity between DIV 67 (the common peak of activity) and DIV 83 (close to the experimental endpoint) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh). The duration of NBs was significantly decreased in the DS patient 1 and control 4 networks between the two timepoints, while IBIs in NBs were decreased in all networks except in control 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei-j). Interestingly, the change in the mean spike frequency within NBs was significantly lower in DS patient 1, in contrast to all the other networks, as similarly observed at the single-channel level at late timepoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ek, Supplementary Fig. S4).\u003c/p\u003e \u003cp\u003eThe number of electrodes participating in single-channel bursts and NBs was lower in the diseased networks than in the controls (Supplementary Fig.\u0026nbsp;4i-j). This observed outcome suggested that the strength of NBs was decreased in the DS patient-derived networks compared to control networks. To obtain a comprehensive analysis of all MEA features, PCA was once again applied (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003el). PCA was determined from 17 MEA features and revealed clusters associated with each cell line. The highest %\u003cem\u003eOverlap\u003c/em\u003e values of the patient lines were indicated by the %\u003cem\u003eOverlap\u003c/em\u003e\u003csub\u003eDSpatient1 and DSpatient5\u003c/sub\u003e and the %\u003cem\u003eOverlap\u003c/em\u003e\u003csub\u003eDSpatient5 and DSpatient1\u003c/sub\u003e (26.3% and 23.6%, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003el, Supplementary Table S6). In summary, the results revealed distinctive phenotypic patterns in the DS patient-derived networks.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDravet patient-derived heterogenous cortical networks display increased sensitivity to pharmacological responses.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSince a more well-defined separation in spontaneous activity patterns was visible in the heterogeneous cortical networks, we evaluated whether there was a specific neuronal type driving the elicited phenotype. We performed pharmacological experiments and analyzed the response of DS cortical networks to two GABA-specific modulators, a GABA agonist, GABA (γ-aminobutyric acid), and a GABA\u003csub\u003eA\u003c/sub\u003e receptor antagonist, gabazine, as well as two excitatory modulators, kainic acid (a glutamate agonist) and CNQX (a NMDA antagonist). The pharmacological effects on the cortical networks were tested at DIV 94. Seeing that we observed striking differences between the diseased and control networks in MFR and nbursts (determined from PCA1, Supplementary Table S4) and NB features (determined from PCA2, Supplementary Table S4), we focused on these features to understand how chemical modulations alter the phenotypes of the networks.\u003c/p\u003e \u003cp\u003eThe application of GABA did not alter spiking activity in DS patient 1; however, activity in both controls was significantly decreased compared to that in DS patient 1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The nbursts were markedly lower in the diseased networks compared to the control networks (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The application of gabazine did not cause any obvious changes in the MFR or nburst features across the cultures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-d). At the network-wide level, the number of NBs following GABA treatment was completely lost in all the cultures except those in the control 4 network (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). Gabazine appeared to increase NBs in the diseased networks, while no significant change in the controls was detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen examining the effect of excitatory modulators, we observed that following treatment with kainic acid, a seizure-inducing component, DS patient 1 showed a significant increase in spiking activity in relation to the control networks, which, in contrast, showed a decrease in spiking activity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg). Compared to those in the control networks, the nbursts at the single-channel level decreased in the DS patient-derived networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh). When cultures were treated with CNQX, spiking and burst patterns decreased relative to those at baseline, with no observable differences among the cultures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ei-j). NBs were completely disrupted following kainic acid treatment in the disease-derived networks but were only reduced in the control networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ek). Similarly, CNQX treatment abolished the formation of NBs in all networks except for the control 4 network (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003el). Taken together, the DS-derived networks exhibited more sensitive responses to GABA and kainic acid.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrate how the impact of two pathogenic variants in the \u003cem\u003eSCN1A\u003c/em\u003e gene alters neuronal functionality at the network level in two culture types; enriched GABAergic and heterogeneous excitatory and inhibitory cultures, using MEAs. The DS patient-derived GABAergic networks develop similarly to those of controls; however, differences between the pathogenic variants were detectable in certain features of functional activity. The heterogeneous culture set up containing the appropriate cell types revealed more prominent alterations in the functional patterns between DS patient-derived and control networks, indicating the physiological relevance of the model.\u003c/p\u003e \u003cp\u003eGABAergic neurons have been reported to be the primary cell type affected in DS \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Discrepancies have, however, been reported in several hiPSC models in that not only are GABAergic neurons defective by disease-causing mutations but also excitatory neurons \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Here, gene expression and immunocytochemistry confirmed GABAergic identity in the networks. Differential network bursting activity with an increased number of NBs over time was identified in patient-derived networks, while in control networks, the NBs either remained the same or decreased at late time points. Strikingly, DS patient 1 neurons exhibited greater network bursting activity at the later phase of the study. Previous studies that have used MEAs to analyze hiPSC-derived GABAergic neurons have shown that activity gradually decreases over time as neurons become functionally inhibitory \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Here, the persistent NB activity in the diseased networks may be indicative of functional immaturity of the GABAergic neurons. We observed that the GABA switch was delayed in all cultures, as \u003cem\u003eNKCC1\u003c/em\u003e expression remained higher than \u003cem\u003eKCC2\u003c/em\u003e expression suggesting that these GABAergic cultures were still in the immature excitatory state \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In the postmortem brain of DS patients, the expression of NKCC1 is increased, while there is a prominent reduction in the expression of KCC2 \u003csup\u003e42\u003c/sup\u003e. These findings suggest that the GABA shift is compromised in these patients and other patients with neurodevelopmental disorders (Talos et al. 2012; Ruffolo et al. 2018).\u003c/p\u003e \u003cp\u003eIn this study, we show for the first time using MEAs that early neuron DS-derived GABAergic cultures have an altered network phenotype. DS patient 1, displayed an increase in NB hyperactivity. Intriguingly, previous studies that have modeled DS using hiPSC-derived GABAergic enriched cultures have shown deficits in action potential generation as well as sodium currents when assessed with patch clamp \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Furthermore, Schuster and colleagues (2019) demonstrated with the same patient lines used in this study that action potential frequency was decreased in comparison to that of their matched controls \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. When extended to the network level, we observed NB hyperactivity. These findings imply that functional impairment is prominent in DS GABAergic networks, correlating with the pathophysiology of the disease.\u003c/p\u003e \u003cp\u003eIn the second model, which contained physiologically relevant neuronal subtypes (excitatory, inhibitory, and endogenous astrocytes), we detected more apparent differences in the functional phenotype between the DS patient-derived and control networks. To be able to appropriately mimic epilepsy-related disorders \u003cem\u003ein vitro\u003c/em\u003e, which often manifests as an imbalance between excitation and inhibition, the combined use of excitatory and inhibitory neuronal subtypes is fundamental. Using our differentiation protocol \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, we established a heterogeneous population characterized by the presence of VGLTU1-positive and GAD67-positive neurons with immunocytochemistry and gene expression. No observable differences were observed in the morphology of the control and diseased neurons or with the colocalized synaptic puncta. This indicates that patient and control cultures were similar throughout their maturation.\u003c/p\u003e \u003cp\u003eApparent functional differences were, however, observed as DS patient-derived networks showed decreased spiking and bursting activity patterns at the single-channel level. This phenomenon was similarly observed in a recent study in which cultures contained excitatory neurons only \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In contrast, the altered firing activity detected at the single-channel level was accompanied by a greater number of NBs, suggesting hyperactive network activity. Patient-specific differences were observed, as DS patient 1 exhibited the greatest number of NBs, with the shortest duration and intervals between NBs. Interestingly, DS patient 1, who was clinically diagnosed with a more severe DS phenotype, demonstrated the greatest hyperactivity (highest number of NBs) in both GABAergic and heterogeneous cortical cultures. Our results are similar to a previous patient-derived hiPSC study that reported the same phenomenon in a DS patient line, with most electrophysiological alterations corresponding to the severity of the patients' diagnosis \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The hyperactive bursting phenotype has been identified as a type of seizure prediction pattern \u003cem\u003ein vitro\u003c/em\u003e following the application of seizure-liability compounds \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, implying that the basal functional activity seen in the patient-specific network depicts patterns of seizure-like behavior \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe presence of Nav1.1 was verified in heterogeneous cortical cultures, and it colocalized with both excitatory and inhibitory-specific markers. In rodents, Nav1.1 is known to be predominantly expressed in GABAergic neurons, while in humans, Nav1.1 is expressed in both excitatory and inhibitory neurons \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. To determine whether there was a driving cell type exhibiting altered activity in heterogeneous cortical networks, pharmacological manipulation was performed using excitatory and inhibitory compounds, and phenotypic changes in DS network activity were assessed. Striking differences in spiking activity following GABA and kainic acid treatment were prominent in the DS patient 1 culture compared to all other cultures. Both modulators decreased spiking activity in most cultures, while in DS patient 1, the effect of GABA did not change to baseline levels, and kainic acid increased spiking activity. Efforts have been made in previous hiPSC studies involving both cell types in culture to understand how each cell type contributes to disease phenotypes. In a study by J. Liu et al. (2016), the effect of a genome-edited \u003cem\u003eSCN1A\u003c/em\u003e mutation in hiPSC-derived mixed cultures showed that changes in sodium currents were specific to GABAergic fluorescently labeled neurons. In our study, although we could not isolate the driving cell type at the network level, we observed altered susceptibility in DS patient cultures following treatment with specific modulators. These findings suggest the potential of this model to study functional changes induced by anti-seizure medication in line with recent \u003cem\u003ein vitro\u003c/em\u003e DS models \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Notably, KA treatment in control cultures resulted in a decreased spiking rate, which coincides with the basal phenotype displayed in diseased networks \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This observed effect supports the seizure-like pattern displayed by DS patient-derived culture. Future studies would require isogenic lines, to validate the direct impact of the functional changes caused by the disease-causing variants.\u003c/p\u003e \u003cp\u003eIn summary, this study reports a viable model showing prominent changes in the functional activity of DS networks in heterogeneous cortical neuronal cultures. We report that patient-specific differences exhibiting a more prominent seizure-like phenotype corresponded with the severity of the clinical diagnosis. The presented model recapitulates key disease phenotypes of individual patients and thus provides a relevant platform in facilitating pathogenic assessment, geared towards drug screening and potentially advancing the development of personalized therapies for genetic epilepsies.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eData is provided within the manuscript or supplementary information files or can be accessed from the corresponding author with a reasonable request.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAdditional Information\u003c/h2\u003e \u003cp\u003eAuthors report no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.M. T.H and S.N. designed the study; J.S and N.D provided the patients cell lines and consulted with GABAergic differentiation; R.M, E.P, L.I, O.K performed experiments; R.M, T.H, A.V, L.I, O.K, V.V and F.E.K analyzed the data; R.M and O.K produced the figures, R.M wrote the first draft of the paper with T.H and S.N guidance and all authors contributes to final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe work was supported by the Imaging Facility, Facility of Electrophysiological Measurements and iPS Cells Facility (Faculty of Medicine and Health Technology, Tampere University). The authors also thank Biocenter Finland for the support of Imaging, Electrophysiological Measurements and iPS cells facilities. We thank Hanna M\u0026auml;kel\u0026auml; and Eija Hannuksela for their technical assistance with cell maintenance and analyses. We kindly thank MSc Satu J\u0026auml;ntti and the students Eveliina Taimela and Anna-Mari Moilanen, for assisting in cell culture maintenance and pharmacology experiments, performing ICC and qPCR experiments. This work was supported by Research Council of Finland (grant number 348517 to SN), Hj\u0026auml;rnfonden (grant number FO2022-0042 to ND) T.H. was supported by Tampere Institute for Advanced Study. R.M was supported by Finnish Cultural Foundation, Brain research foundation and Tampere University MET doctoral research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files or can be accessed from the corresponding author with a reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDravet, C. 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Majority of DS cases are caused by a \u003cem\u003ede novo\u003c/em\u003e mutation in the \u003cem\u003eSCN1A\u003c/em\u003e gene, encoding the alpha subunit of the voltage-gated sodium channel. While early \u003cem\u003ein vivo\u003c/em\u003e studies have shown that DS pathology results from the disinhibition of GABAergic inhibitory neurons, recent studies report alterations in sodium currents in both excitatory and inhibitory neurons. Investigating the excitatory-inhibitory interplay is essential for elucidating the functional alterations caused by \u003cem\u003eSCN1A\u003c/em\u003e mutations. Here, the aim was to study how different \u003cem\u003eSCN1A\u003c/em\u003e gene pathogenic variants affect the functional phenotype of DS human induced pluripotent stem cell-derived neuronal networks in enriched GABAergic cultures and heterogeneous glutamatergic and GABAergic cultures, using microelectrode arrays (MEAs). We report functional differences in patient-derived GABAergic cultures. In heterogeneous cultures, DS patient-derived neurons displayed altered activity with prominent network bursts and overall, the altered functional activity correlated with the clinical severity of the disease. Principal component analysis revealed distinct clustering between the DS patient and control heterogeneous cultures. Thus, pathogenic \u003cem\u003eSCN1A\u003c/em\u003e variants alter the neuronal network functionality suggesting that heterogeneous cultures are competent physiological models for characterizing disease phenotype alterations in DS using MEAs.\u003c/p\u003e","manuscriptTitle":"Abnormalities in the functional activity of neural networks in a human iPSC model of Dravet syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-06 19:44:47","doi":"10.21203/rs.3.rs-5615262/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e14090cd-841d-4a1c-802f-61eef5f0971e","owner":[],"postedDate":"February 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43674634,"name":"Biological sciences/Neuroscience/Cellular neuroscience"},{"id":43674635,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Epilepsy"},{"id":43674636,"name":"Biological sciences/Biotechnology/Stem cell biotechnology"}],"tags":[],"updatedAt":"2025-05-06T05:23:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-06 19:44:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5615262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5615262","identity":"rs-5615262","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}