Modelling synaptic dysfunction in childhood dementia using human iPSC-derived cortical networks

Domain: neuroscience
Published: 2026-04-07
Editorial outcome: major_revision

Abstract
Alterations in synaptic homeostasis are linked to cognitive and behavioural impairments in brain disorders. However, synaptic dysfunction in childhood dementia is poorly understood. Here, we generate human cortical circuits from induced pluripotent stem cells (iPSCs) derived from donors with Mucopolysaccharidosis Type IIIA (MPS IIIA), also known as Sanfilippo syndrome, a common form of childhood-onset dementia. Action potential firing capacity and morphology of MPS IIIA patient neurons in culture are similar to those of neurons from neurotypical donors. However, long-term neural maturation reveals excitation/inhibition imbalances caused by hyperactive excitatory synapses, disrupted network dynamics, and dysregulated gene expression linked to synaptic homeostasis. This study validates in vitro human neural models to detect neurophysiological phenotypes in childhood dementias and supports drug discovery strategies that target synaptic dysfunction to improve cognition in MPS IIIA and related brain disorders.

Introduction
Synaptic circuits are essential for information processing in the brain and learning and memory1–5. The formation of neurotypical brain circuits requires the fine tuning of synaptic excitation and inhibition (E/I)6–8. If impaired, the disruption of E/I balance can lead to severe brain disorders9–11. In particular, alterations in synaptic communication have been shown to play key roles in neurodegeneration, disease progression and cognitive decline,11–14. Despite evidences between synaptic dysfunction and several brain disorders, few studies have explored how E/I synaptic imbalances contribute to pediatric neurodegenerative disorders15–18. Investigating the mechanisms that govern synaptic excitation and inhibition in childhood dementia could provide mechanistic insights into the underlying cellular and molecular pathogenic processes and inform future symptomatic and disease-modifying treatments19.

Induced pluripotent stem cell (iPSC) models have emerged as a promising surrogate approach to study synaptic function in patient neurons20. They enable long-term cultures of live human neurons capable of forming functional synaptic networks reflective of those in the developing brain and may reveal early changes that precede neurodegeneration15,21–23. While these models have shown promise in mechanistic studies of common adult neurodegenerative disease21,23–29, iPSC-derived models of childhood dementias remain less advanced30–37. However, unlike adult neurodegenerative diseases, which typically result from a complex interaction of polygenic and environmental factors that are challenging to replicate through in vitro modelling38–42, childhood dementias are largely monogenic with highly penetrant mutations that manifest early during brain development. Therefore, childhood dementias are well suited for accelerating the development of patient-derived models in vitro.

Childhood dementias typically demonstrate common underlying pathophysiology with progressive neurodegeneration, astrogliosis and dysregulated gene pathways43–48, and reflect similar mechanisms to those found in many of the adult dementias45,49. Modelling childhood dementia in vitro using an iPSC-derived neuronal model allows for robust electrophysiological profiling that may reveal early alterations to neuronal structure and function.

Herein, we focused on one of the most prevalent forms of childhood dementia, Sanfilippo Syndrome Type A (also known as Mucopolysaccharidosis type IIIA; MPS IIIA)50. It is characterised by rapid neurodegeneration with symptoms including prominent neurobehavioural dysregulation, motor regression and seizures, with premature death in early adolescence51–57. Clinical trials investigating enzyme and gene replacement therapies have yielded promising outcomes in children under 30 months of age, but this relies on early diagnosis, which has proved limiting in the absence of universal newborn screening programs54,58–63. In addition, gene and enzyme therapies (if approved) will be expensive, limited in their geographic availability, and will not be available for all patients (e.g., those with pre-existing antibodies to a given viral vector). Therefore, additional therapeutic strategies are needed. Consequently, identifying targetable pathways with the potential to ease cognitive symptoms, slow disease progression and improve quality of life, irrespective of timing, remains a critical unmet need. Here, we generated iPSC-derived neuronal cultures from MPS IIIA patients to model electrophysiological properties in individual neurons and neural networks over neurodevelopment. Our analysis revealed a progressive synaptic E/I imbalance in MPS IIIA when compared to neurotypical neurons, which drives network hyperexcitability, and is accompanied by dysregulated expression of glutamatergic postsynaptic genes relating to synaptic structure and function. These findings define a robust, clinically relevant and pharmacologically targetable electrophysiological signature of MPS IIIA that likely contributes to neurodegeneration and cognitive decline in affected children.

Results
Generating functional cortical neuron cultures
To study synaptic function in childhood-onset dementia, we generated mature cortical neural cultures from patient iPSCs. IPSCs were reprogrammed from pure dermal fibroblasts obtained from skin biopsies from five patients with MPS IIIA and five age-matched neurotypical donors. Dermal fibroblasts were then reprogrammed into iPSCs using an episomal non-integrative Sendai viral vector with Yamanaka factors before undergoing cortical patterning, neural differentiation, and up to 120 days of neural maturation to generate functional cortical neurons and astrocytes (Fig. 1A and Supplementary Fig. 1A, B) This transition was confirmed by examining genes (real-time qPCR) associated with each developmental cell stage and demonstrating a cell identity switch from fibroblast to cortical neuron (Fig. 1B and Supplementary Figs. 1C and 2A, B). This protocol was based on previous optimization from our group to increase time in neural maturation medium (BrainPhys) and improve electrophysiological and synaptic maturity20,64,65. Single-nuclei RNA sequencing of 120-day cultures showed the presence of glial, excitatory and inhibitory neuronal populations indicative of physiologically relevant neural cultures (Fig. 1C and Supplementary Fig. 3A). These cell types were classified using four published gene sets and assigned based on the majority match among these66–69.

We generated two neural lines for each patient and neurotypical donor, derived from two separate neural progenitor clones. We characterised each neural line individually. For imaging and MEA assays, both neural lines per donor were profiled separately; for whole-cell patch-clamping, one neural line per patient was analysed (with the exception of the neurotypical donor BIAD12, where both neural lines were included). For single-nuclei transcriptomics, neurotypical and MPS IIIA playgrounds (two clones per donor) were sequenced and computationally demultiplexed to enable donor-level analysis.

To further strengthen reproducibility and increase scalability, for all assays, including imaging, MEA, Patch-clamping, and single-cell transcriptomics, we profiled what we called playground neural cultures in reference to the children who donated their cells for this study, where all neurotypical or disease neural clones were mixed prior to analysis. Our playground neural model is implemented from what is typically called village in a dish for adult patient lines70,71 (Fig. 1D), but with a key modification: clones from different donors were mixed only after 14 days of maturation, when most neural progenitors had already transitioned into post-mitotic cells in BrainPhys-based maturation medium (<6% Ki67+ cells; Supplementary Fig. 3B–E). By day 30, proliferating cells (Ki67+) fell below 1%, with no differences between neurotypical and disease cohorts. Single-nuclei transcriptomics at day 120 confirmed this low-proliferative state (Supplementary Fig. 3F–G). This approach limited the risk of potentially faster proliferative clones dominating the cultures. Most importantly, this also allowed us to establish a robust and scalable model suitable for labour-intensive electrophysiological drug screening.

Across both individual cell lines and the playground model, we patch-clamped 997 neurons (Supplementary Fig. 4), analysed 5,168 MEA electrodes from 323 wells recording the activity of neurons weekly over three months (Supplementary Fig. 5) and imaged 2,565,549 neurons from 304 wells with a high-content confocal microscope (Supplementary Fig. 6).

MPS IIIA patient-derived iPSCs and NPCs showed no difference in phenotypic quality compared to neurotypical donors
Our primary interest was to investigate the pathological consequences of MPS IIIA within mature cortical neural cultures. To do this effectively, we first needed to ensure that any potential differences in mature neural cultures between neurotypical and MPS IIIA were not a result of low quality or incomplete cellular reprogramming or neural differentiation. Here, we assessed the quality and stemness of iPSCs and NPCs to ensure a similar high standard in neurotypical and MPS IIIA lines (Supplementary Fig. 9A). Given the potential for chromosomal abnormalities to be introduced during reprogramming, we first established that the iPSCs used in this study had no karyotypic abnormalities and displayed pluripotency potential to differentiate into the three germ layers (Supplementary Figs. 7 and 8)72. To further investigate the pluripotency quality of the iPSC lines and explore the effect of MPS IIIA on iPSCs, we evaluated protein expression of pluripotency markers in MPS IIIA iPSCs compared to neurotypical donors. We found no differences in expression of SOX2, OCT4 or KLF4 between neurotypical donors and MPS IIIA, with both groups having on average ~80% of cells triple positive for all markers (Fig. 2A, B and Supplementary Figs. 9B and 10B). IPSCs showed double-positive expression of stem cell surface markers SSEA-4 and TRA-1-60 in over 90% of cells in both neurotypical and MPS IIIA groups, with less than 1.5% of cells double-negative for SSEA and TRA-1-60 (Supplementary Fig. 9C–F). There was no significant difference in cells expressing SSEA-4 and TRA-1-60 between neurotypical and MPS IIIA groups (Supplementary Fig. 9D). These results suggest a robust iPSC identity in both groups. Protein expression data is supported by similarly high gene expression of stemness marker OCT4 between groups, with no differences seen in expression of SOX2 or NANOG in neurotypical donors compared to MPS IIIA iPSCs (Fig. 2G and Supplementary Fig. 10C–E). Morphology is a defining feature of iPSCs, with typical iPSCs displaying a small and round shape with a large nucleus compared to cytoplasm. We used phalloidin, an actin stain, to stain iPSCs and quantify morphological traits. Our iPSC cohort revealed no change in cell area or shape, with both groups displaying round cells with minimal cytoplasm (Fig. 2D–F and Supplementary Fig. 10F, G). Growth rate is also an important characteristic of stem cells, with a complex relationship between growth and differentiation. We examined the growth rate of our iPSC cohort by measuring confluency after passaging and immediately prior to the following passage, and then calculated the population doubling rate over time. Similar growth rates were seen between neurotypical and MPS IIIA groups (Fig. 2C and Supplementary Fig. 10H). Based on all the parameters examined, iPSC lines were allocated a quality score that combined protein expression, gene expression, morphology and growth rate (Supplementary Fig. 9K). Overall, there was no difference in the quality of the iPSCs used in this study, with both disease and neurotypical groups displaying characteristics of high-quality iPSCs and each line passing quality control (Fig. 2H and Supplementary Fig. 10I).

Cortically patterned neural progenitor cells from each patient were also examined for quality and expression of developmentally relevant genes. Protein analysis revealed expression of progenitor markers Nestin and SOX2 in over 85% of cells in both neurotypical and MPS IIIA NPCs with no overall difference between MPS IIIA and neurotypical donors (Fig. 2I, J and Supplementary Fig. 11B). To establish if these NPCs were patterned for cortical identity, expression of the cortical marker FOXG1 was assessed. FOXG1, Nestin and SOX2 expression was not significantly different between neurotypical and MPS IIIA groups, and these markers were found to be co-expressed in 76% and 71% of cells in MPS IIIA and neurotypical donors, respectively (Fig. 2K and Supplementary Figs. 9G–J and 11C). NPC morphology is considered indicative of cell quality, with cells typically displaying a cell body with 1–2 short projections. Overly complex cells have been associated with premature differentiation, and cells displaying no projections have potentially not completed their differentiation. Morphological analysis of NPCs revealed no changes in cell area or neurite length and similar cell complexity, with both groups on average having between 1 and 2 extremities per cell (Fig. 2L–O and Supplementary Fig. 11D–F). A previous study by Lehmann et al. showed reduced growth rate in iPSC-derived MPS IIIA NPCs, which was hypothesized to result from loss of sulfamidase34, hence, we evaluated the growth rate of our NPC cohort. NPC growth rate was calculated by changes in cell number over time. Contrary to what was previously shown, although some MPS IIIA NPC lines showed higher population doubling rates, overall NPC growth rates did not significantly differ between groups (Fig. 2P and Supplementary Fig. 11G). Similar to the iPSCs, NPCs were assigned a multimodal quality score based on the examined parameters (Supplementary Fig. 9L). There was overall no difference in the quality of the NPCs used in this study, with both groups displaying characteristics of high-quality NPCs and each line included in this study passed standard quality control (Fig. 2Q and Supplementary Fig. 11H).

Morphological profiles of MPS IIIA and neurotypical donor neurons are similar over the course of neurodevelopment in vitro
Alterations in neurite (axon and dendrites) properties contribute to synaptic dysfunction in neurodevelopmental disorders15,73–75. Previous studies have shown conflicting results regarding neurite morphology in MPS III, with one showing an increase in neurite length in MPS IIIB mouse cortical neurons in vitro76 and another showing a decrease in MPS IIIC mouse cortical neurons in vitro and in vivo77. Notably, only Parà and colleagues investigated synaptic communication in a mouse model of MPS IIIC, reporting reduced synaptic neurotransmission accompanying a decrease in neurite length77. Despite these insights from MPS IIIB-C mouse models, neurite complexity and its influence on synaptic communication in human MPS III remains unclear and has not been studied in MPS IIIA. We imaged 846 patch-clamped neurons that were filled with a rhodamine dye via the micropipette to visualise their processes. We then reconstructed their morphology (Supplementary Fig. 12A) and analysed various cell body and neurite features. Neurons were first classified into different morphological types (multipolar, bipolar, fusiform or pyramidal) based on a previous benchmark study64 (Supplementary Fig. 12B). This showed that MPS IIIA and neurotypical cultures could generate neurons of diverse morphological types, with similar proportions of Multipolar/Pyramidal neurons and Bipolar/Fusiform neurons in both genotypes (Fig. 3A, B). Sholl analysis of these morphological types revealed that MPS IIIA neurons generate neurites that are as complex as their neurotypical counterparts over neurodevelopment and across maturation in vitro (Fig. 3C). We then examined the capacitance of neurons as it relates to the intracellular volume of the cell and how the membrane responds to the alteration of current78. While MPS IIIA neurons had a significantly lower capacitance at 30–60-days, long-term maturation showed that neurotypical and MPS IIIA neurons have comparable capacitance values, indicating they are similar in size and have equivalent capabilities to respond to current variation (Fig. 3D and Supplementary Fig. 12C). This is further supported by their similarity in cell body surface area (Fig. 3E and Supplementary Fig. 12D). Analysis of additional cell body and neurite properties indicate that neurotypical and MPS IIIA neurons have similar morphological profiles over the course of neurodevelopment, despite a few relatively minor differences (Fig. 3F). Indeed, when we reduced the dimensionality in a principal component analysis, MPS IIIA and neurotypical neurons cluster together (Fig. 3G). Overall, these findings indicate that MPS IIIA does not affect the capacity of these neurons to grow complex neurite arborisations in vitro (Fig. 3 and Supplementary Fig. 13).

MPS IIIA neurons have action potential firing capabilities and kinetics similar to those of neurotypical neurons
Action potentials play a crucial role in neurotransmission, with their arrival at presynaptic axon terminals triggering the release of neurotransmitters that initiate, modulate or prevent biological responses in postsynaptic neurons79. Abnormalities in neuronal firing that may alter excitability and neurotransmission have been described in other neurodevelopmental disorders80,81. However, action potential firing in MPS IIIA and their potential downstream effects on neurotransmission are unclear. Using whole-cell patch clamping, we incrementally depolarised neurons in current-clamp mode and recorded their evoked action potentials (Supplementary Fig. 14A). Cells were first classified into action potential types using previously described methods that rely on their maximal firing frequency over a threshold of −10 mV64. Within these five action potential types, type 1 is considered the least functionally mature and type 5 is the most functional64 (Supplementary Fig. 14B, C). Over neurodevelopment in vitro, neurotypical and MPS IIIA cultures exhibited similar proportions of these action potential types. They also had a similar increase in the proportion of type 5 neurons from the 30–60-day to 90–120-day time points, indicating further functional maturation during this period (Fig. 4A, B). When examining their firing capabilities, MPS IIIA and neurotypical neurons required comparable amounts of injected current to elicit equivalent responses in action potential firing (Fig. 4C). The kinetics of action potentials evoked by MPS IIIA neurons showed no significant differences to neurotypical neurons, with their amplitude and trough-voltage consistent across conditions, and only minor differences found in their upstroke:downstroke ratio and duration. This is evident in the phase plots, which represent the average kinetics of a single action potential for all cells patched (Fig. 4D, E and Supplementary Fig. 14D). Further, the resting membrane potential of MPS IIIA and neurotypical neurons appeared relatively similar, although slightly more negative in MPS IIIA at later maturation stages (Supplementary Fig. 14E, F).

For further validation, we then investigated the mechanisms that underlie action potential generation: the activation and deactivation of voltage-gated sodium (Na+) and potassium (K+) channels that depolarise and hyperpolarise neuronal membranes (Fig. 4F and Supplementary Fig. 15A). As the action potential firing, kinetics and resting membrane potential of MPS IIIA neurons were unaffected, we expected the function of these channels would also be unaltered. Indeed, the behaviour of voltage-gated sodium channels in MPS IIIA neurons closely resembled their neurotypical counterparts, with similar activation and deactivation membrane voltages over maturation (Fig. 4G). Further supporting this, we saw no change in the maximal sodium peak across neurodevelopment nor any differences in the membrane depolarisation potential at which these peaks are evoked (Fig. 4H and Supplementary Fig. 15B). We also saw no changes in the activation or deactivation of voltage-gated potassium channels over neurodevelopment as shown by the activation curve (Fig. 4I). When we isolated the potassium channels, MPS IIIA neurons appeared to have slightly larger maximal peaks at the 90–120-day time point, potentially indicative of greater potassium efflux following neuronal depolarisation. Despite this, there were no differences in the membrane hyperpolarisation voltage at which these maximal potassium peaks occurred (Fig. 4J and Supplementary Fig. 15C). Overall, MPS IIIA does not appear to affect the intrinsic function of voltage-gated sodium and potassium channels required for successful action potential generation. Altogether, our findings indicate that MPS IIIA pathophysiology does not directly affect the firing capabilities or the membrane potential properties of the neurons and, therefore, is not likely to be an underlying cause of impaired neural communication and cognition.

MPS IIIA neurons show an increased PSD95/Gephyrin synaptic ratio
MPS IIIA patients present with a range of progressive neurological symptoms, including cognitive impairment, motor deterioration, hyperactivity and sleep disturbances. Despite remaining uncertainty regarding the mechanisms underlying cognitive impairment in MPS IIIA, evidence from other neurodevelopmental disorders9,15,16,82,83 suggests that altered excitatory and inhibitory synaptic communication may play a key role. Therefore, we assessed whether synapses were affected in MPS IIIA.

We first examined whether MPS IIIA iPSC-derived neurons displayed differences in the density of excitatory and inhibitory synapses. MPS IIIA playground cultures were stained with markers against neuronal soma and dendrites (MAP2), pre-synaptic terminals (Synapsin-1), glutamatergic postsynaptic sites (postsynaptic density 95; PSD95) (Fig. 5A, B and Supplementary Fig. 16A), and GABAergic postsynaptic sites (postsynaptic protein Gephyrin) (Fig. 5C and Supplementary Fig. 16B). PSD95 is a scaffolding protein that helps organise and stabilise synaptic signalling and is typically localised within excitatory synapses84. Gephyrin is the main scaffolding protein in inhibitory synapses, which anchors glycine and gamma-aminobutyric acid (GABA) type A receptors to postsynaptic sites85. We found that excitatory synapse (PSD95+SYN+) density on neuronal somas and dendrites (MAP2) was significantly increased in MPS IIIA iPSC-derived neural cultures from 30 days of maturation onwards compared to neurotypical donors (Fig. 5A). Similarly, we saw a small but significant increase in the proportion of excitatory synapses over total synapses (SYN puncta) in MPS IIIA cultures when compared to neurotypical donors over maturation (Fig. 5B). In contrast, although both neurotypical and MPS IIIA cultures showed a gradual increase in inhibitory synapse density over 90 days of maturation, the density increased consistently faster in neurotypical cultures, resulting in significantly fewer inhibitory synapses at 90 days in MPS IIIA (Fig. 5C).

Altogether, the fact that PSD95 and Gephyrin puncta fluctuated in the opposite direction indicates an uncoupled influence of MPS IIIA pathophysiology on excitatory and inhibitory synapses and suggests an altered ratio of excitatory to inhibitory postsynaptic terminals. These structural changes may contribute to a disrupted functional E/I balance.

Mild cellular stress exacerbates a shift towards a higher proportion of excitatory PSD95 synapses in MPS IIIA neurons
Chronic stress has been shown to play a key role in neurodegenerative disease by accelerating ageing, cognitive decline and disease progression39,40,86,87. Previous studies have highlighted an increased vulnerability of neurodegenerative models to cellular stressors, further exacerbating disease phenotypes88,89. To determine the effect of cellular stress on MPS IIIA synapses, we transiently removed trophic factors and antioxidants from cell culture media. There was no effect of these mild cellular stress condition on the neurotypical donors (Fig. 5D). However, this same cellular stress exacerbated the increase in density and proportion of excitatory synapses in MPS IIIA playground cultures (Fig. 5D), demonstrating a heightened susceptibility of MPS IIIA neuron synapses to cellular stress.

Altogether, when comparing MPS IIIA and neurotypical neurons in normal tissue culture conditions, the increases in MPS IIIA dendritic density of PSD95 synaptic puncta were relatively small (<15%). However, mild external cellular stress, which was innocuous on neurotypical synapses, further exacerbated the excess of PSD95+ synapses in MPS IIIA by >40%, reinforcing the disease relevance of this synaptic phenotype.

Long-term maturation of MPS IIIA neurons reveals an imbalance between synaptic excitation and inhibition
To further investigate our hypothesis that E/I balance is disrupted in MPS IIIA neurons, we examined postsynaptic communication using whole-cell patch clamping in MPS IIIA and neurotypical neural cultures. We recorded spontaneous and miniature excitatory and inhibitory postsynaptic currents on single neurons (Supplementary Fig. 17). Over maturation, MPS IIIA and neurotypical cultures had similar proportions of neurons receiving spontaneously active excitatory glutamatergic synaptic inputs, mediated by alpha-amino-3-hydroxy-5-methyl-4-isooxazole-propionic acid (AMPA) receptors (Fig. 5E, F). However, the frequency of AMPA-mediated spontaneous synaptic events in MPS IIIA neurons doubled that of neurotypical neurons at 90–120-days (Fig. 5G and Supplementary Fig. 18A). The amplitude of events evoked from these neurons were also significantly larger than their neurotypical counterparts at later time points (Fig. 5E, H and Supplementary Fig. 18B). In contrast, spontaneous GABA-mediated synaptic activity in MPS IIIA neurons was not affected, with no significant differences observed in the frequency, amplitude, or distribution of these events (Fig. 5I–L and Supplementary Fig. 18C, D). As a result, MPS IIIA neurons have a significantly larger shift towards more synaptic excitation than inhibition, disrupting the physiological homeostatic E/I balance within these cultures (Fig. 5M).

When we repeated these experiments in the presence of tetrodotoxin (TTX), a voltage-gated sodium channel antagonist that blocks action potentials, we observed similar shifts in synaptic excitation and inhibition (Fig. 5N–R). Specifically, the frequency of miniature AMPA-mediated events showed an increase in MPS IIIA (Fig. 5N), although it only became significant when analysed as a cumulative distribution. In contrast, the frequency of miniature GABA-mediated events showed a trend towards a decrease in MPS IIIA (Fig. 5P). We found no significant changes to the amplitude of miniature AMPA and GABA events, indicating that the machinery responsible for stochastic postsynaptic release of single neurotransmitter vesicles remains functional in MPS IIIA (Fig. 5O, Q). The opposing polarity of changes in AMPA- vs. GABA-mediated miniature synaptic event frequencies in MPS IIIA, without alterations in their amplitudes, caused a shift in E/I balance that was statistically significant when assessed via cumulative distribution (Fig. 5R). Overall, whether or not TTX was present, these results indicate altered synaptic homeostasis and hyperactive excitatory synaptic transmission in MPS IIIA neurons (Fig. 5).

MPS IIIA patient-derived cultures reveal hyperactive electrical neuronal circuits
Behavioural and cognitive deficits in children are ultimately caused by changes in electrical activity at the neural circuit level. Therefore, we next sought to examine how the E/I synaptic imbalance, as revealed by whole-cell patch clamping and imaging, might impact neural circuit function. Multielectrode arrays (MEA) are more scalable electrophysiological assays than patch-clamping and can provide complementary insights into neural network dynamics by recording multiple neurons at once. Therefore, we utilised MEA to compare MPS IIIA neural network activity to neurotypical tissue in vitro (Fig. 6A). We averaged recordings from 5,148 electrodes across 138 wells from 10 neurotypical clones and 185 wells from 11 MPS IIIA clones (Supplementary Fig. 19A). Consistent with the patch-clamping data, we found that action potential firing rates and burst frequency were similar between MPS IIIA and neurotypical single neurons (Fig. 6B, C and Supplementary Fig. 19B, C, E, F). This further confirms that the intrinsic capacity of single MPS IIIA neurons to fire action potentials is neurotypical. In addition, the spike-sorted synchrony index, a measure of how precisely neurons fire together within a short time window, was comparable between conditions (Fig. 6D and Supplementary Fig. 19D, G). However, at the network level, MPS IIIA cultures were hyperactive compared to neurotypical cultures. This was supported by a significantly higher number of active and bursting neurons and twice the amount of synchronized network events (Fig. 6A, E–G and Supplementary Fig. 19B–H). Overall, these results suggest that the neuronal E/I imbalance observed in individual MPS IIIA patient-derived neurons may contribute to large-scale circuit dysfunction and hyperactivity at a neural network level. Such neuronal hyperactivity may underlie pathophysiological mechanisms leading to cognitive impairments observed in MPS IIIA patients.

MPS IIIA neurons reveal dysregulated gene expression implicated in synaptic homeostasis
Previous studies have suggested that genes associated with synaptic structure and function are implicated in E/I imbalances of neurological disorders9. To explore the molecular mechanisms that may underlie the hyperactivity and E/I imbalances in our cultures, we performed single-nuclei RNA (snRNA) sequencing at 120 days in vitro, focusing on excitatory and inhibitory neurons that were identified by canonical markers (Fig. 7A and Supplementary Fig. 20B–E). We found 1,251 significantly differentially expressed genes (DEGs) in MPS IIIA excitatory neurons, and 880 in inhibitory neurons (Fig. 7B). Upregulated DEGs were found in both neuronal types (882 DEGs in excitatory and 867 DEGs in inhibitory neurons). In contrast, most downregulated DEGs were found in excitatory neurons (369 DEGs), and few in inhibitory neurons (13 DEGs). Comparative analysis also revealed substantial overlap in genes dysregulated across both neuronal types (464 common upregulated and 12 downregulated DEGs) (Fig. 7C). For example, several genes, such as SATB2 and NEUROD6 have been implicated in synaptic plasticity and neurodevelopmental disorders90–92.

Gene ontology analysis of the top 15 up or down regulated DEGs for excitatory and inhibitory neurons revealed that most of these genes have a role in Signalling pathways, Cellular metabolism, Ions and transport, and Cellular differentiation and development (Fig. 7D and Supplementary Fig. 20F). We then specifically examined the expression of synaptic genes in MPS IIIA excitatory and inhibitory neurons to identify possible mechanisms underlying synaptic hyperactivity. To this, we mapped genes significantly up or down regulated in MPS IIIA excitatory or inhibitory neurons to the SynGO 20231201 list of synaptic genes93. This showed significant enrichment of genes associated with pre- and post-synaptic structure, regulation and function in MPS IIIA excitatory and inhibitory neurons, suggesting their dysregulated expression may underlie the phenotype we observe (Fig. 7E). More specifically, glutamatergic postsynaptic genes NLGN1, SHANK2, DISC1 and CDKL5 were either significant or trending towards significantly upregulated in MPS IIIA excitatory and inhibitory neurons (Fig. 7F). These four genes were identified via a literature search exploring genes that are known to be implicated in E/I imbalances in neurological disorders9, and each play a role in organising, stabilising and maintaining glutamatergic signalling9. Aligning with our electrophysiological phenotypes, we do not see dysregulation of key GABAergic postsynaptic genes in MPS IIIA neurons (Fig. 7F). These findings indicate that our synaptic imbalance phenotype correlates with dysregulated expression of genes associated with glutamatergic synapse structure, maintenance and function, rather than altered expression of GABAergic synapse genes.

Methods
Experimental model and subject details
Study ethics. This study was approved by the Women’s and Children’s Health Network Human Research Ethics Committee (South Australia) under ethical approval number HREC/18/WCHN/147. All research was conducted in accordance with relevant guidelines and regulations. All participants (or their legal guardians) provided written informed consent prior to tissue extraction, which included consent to publish non-identifiable data. Participants were not compensated for their involvement in this study.

Patient recruitment and collection. Fibroblasts used in this study were obtained from skin biopsies of 5 children with MPS IIIA (3 female, 2 male, aged 4–11 years) and 5 age and sex matched neurotypical control donors (2 female, 3 male, aged 4–12 years) by Dr. Nicholas Smith (Women’s and Children’s Hospital, South Australia). MPS IIIA patients were clinically confirmed to have SGSH mutations and symptoms; controls were defined as clinically and genotypically neurotypical at the time of biopsy.
Participant sex was recorded based on clinical assignment at the time of enrolment. While sex was considered in the study design through donor matching, data were not analyzed disaggregated by sex due to the small sample size.

IPSC reprogramming. Patient fibroblasts were reprogrammed using CytotuneTM-iPS 2.0 Sendai Reprogramming Kit (Invitrogen) according to manufacturer’s instructions. Briefly, fibroblasts were maintained in media containing high glucose DMEM (Gibco), 1% GlutaMAX (Gibco), 10% FBS (Gibco), 1% MEM Non-Essential Amino Acids Solution (Gibco) and passaged using TryPLE express (Gibco) for 4 passages. Passage 4 fibroblasts were plated on 6-well hESC-qualified Matrigel (Corning) -coated plates at a density of 5 × 10e4 cells per well. Once cells reached ~50% confluency, they were transfected with a CytotuneTM-iPSC 2.0 Sendai virus. After transfection, cells were maintained in fibroblast media for 7 days and fed daily. On day 7, transduced cells, now forming iPSC colonies, were seeded onto 6-well hESC-qualified Matrigel (Corning) -coated plates and switched to mTeSRTM Plus media (STEMCELL Technologies). iPSC colonies were fed daily until colonies were an appropriate size for picking. Prior to picking, cells were treated with Collagenase Type IV (STEMCELL Technologies) and iPSC colonies were manually picked. Each picked iPSC colony was grown in an individual well and treated as a separate iPSC clone. iPSC clones were expanded, and qPCR was performed to confirm the downregulation of Sendai virus expression. The top iPSC clone from each line was selected based on the results of genetic, morphological and protein analyses.

Maintenance of hPSC lines. WA09 (H9) ESCs (WiCell, Wisconsin, USA) and iPSCs were maintained in mTeSRTM Plus (STEMCELL technology) on hESC-qualified Matrigel (Corning)-coated culture ware. The colonies were passaged using 1 U/mL of Dispase treatment (STEMCELL Technologies) or Gentle Cell Dissociation Reagent (STEMCELL Technologies) and mechanically scraped before being re-plated onto fresh Matrigel-coated plates every 3–5 days.

Generating H9 SGSH KO using CRISPR/Cas9. SGSH knockout (SGSH KO) ESC line was generated using CRISPR/Cas9 gene-editing of WA09 ESC cells, resulting in the insertion of a single nucleotide in exon 6, introducing a frameshift mutation in the SGSH gene. CRISPR/Cas9 protocols were performed according to Turan et al. 2019 with modifications (Supplementary Fig. 21)118. Briefly, WA09 (H9) ESCs (WiCell, Wisconsin, USA) were dissociated to single cells and nucleofected with PX459 V2.0-eSPCas9(1.1)-T2A-GFP with guide sequence (guide sequence: GTACACCACCGTCGGCCGCA). After 24 h, GFP expression was confirmed, and GFP+ cells were sorted into 96-well plates in mTeSRTM Plus with CloneR supplement (STEMCELL technology). Cells in 96-well plate were cultured in mTeSRTM Plus (STEMCELL technology) for 14 days with half media changes daily. Once clones were large enough, they were split into 48-well plates. Screening was then performed to identify edited clones. DNA was extracted from clones and underwent PCR amplification on target locus under the following conditions: (1) initial denaturation at 98 °C for 30 s, (2) 34 cycles of denaturation and annealing/extension at 98 °C for 10 s, 67 °C for 30 s and 72 °C for 40 s, (3) Final extension at 72 °C for 2 min. The PCR products were run on agarose gel in TAE buffer (44.5 mM Tris, 20 mM glacial acetic acid, 1 mM Ethylenediaminetetraacetic acid (EDTA) disodium salt dihydrate) before being extracted and sent for Sanger sequencing. Sequencing was analysed using ICE CRISPR Analysis Tool (Synthego Performance Analysis, ICE Analysis. 2019. v3.0. Synthego; 05.05.2020) to confirm CRISPR edits. Edited clones were now considered SGSH KOs and were expanded and frozen for future use.

Trilineage differentiation. Patient iPSCs were differentiated into the 3 germ layers (mesoderm, endoderm, and ectoderm) with the STEMdiff™ Trilineage Differentiation Kit (STEMCELL Technologies) according to the manufacturer’s instructions. Briefly, iPSCs were dissociated as single cells and plated in mTeSR Plus (STEMCELL Technologies) and rock inhibitor Y-27632 (STEMCELL Technologies) at a density of 2 × 105 cells/well for mesodermal differentiation, and 8 × 105 cells/well for endodermal and ectodermal differentiation. Over the following 5–7 days, cells underwent a full media change daily with 1 mL/well of respective differentiation media. After differentiation, cells were dissociated with TrypLE Express (Gibco) for 5 min and snap-frozen.

Assessing trilineage differentiation to germ layers. RNA was extracted from snap-frozen cells using Invitrogen TRIzol Plus RNA Purification Kit (Invitrogen) according to manufacturer’s instructions. 500 µg of RNA was converted to cDNA using SuperScript IV VILO master mix (Invitrogen). Confirmation of trilineage differentiation was performed with 384-well plate Trilineage qPCR Array Kit (STEMCELL Technologies) according to manufacturer’s instructions. Briefly, qPCR master mix kit (STEMCELL Technologies) was used to prepare a master mix with 20 ng of cDNA from each condition, including iPSC control. The Trilineage qPCR plate reaction was performed under the following conditions (1) polymerase activation at 95 °C for 20 s, (2) 40 cycles of denaturation and annealing/extension at 95 °C for 1 s and 60 °C for 20 s. Ct data were analysed using the online qPCR analysis tool v1.2 (STEMCELL Technologies: www.stemcell.com/qPCRanalysis).

Karyotyping. hPSCs and NPCs were cultured in T25 flasks and transferred to Women’s and Children’s Hospital (SA Pathology—Cytogenetics, Genetics and Molecular Pathology) for G-band karyotyping. Briefly, cells at 70–80% confluency were treated with colchicine (0.1 mg/L) for 2–3 h to arrest mitosis at the metaphase. After incubation, cells were washed, dissociated, and pelleted. The cell pellet was incubated with 8 mL hypotonic solution (0.075 M KCl/0.8% Tri-Sodium Citrate) for 25 min, then fixed with Carnoy’s fixative (3:1 Methanol/Acetic Acid). Fixed cells were spread onto a slide and analysed. A minimum of 20 metaphases were analysed for each cell line.

Surface marker analysis on hPSCs using FACS. hPSC cultures at 60–80% confluency were washed and dissociated as single cells using Accutase (STEMCELL Technologies) at 37 °C for 5 min. Dissociated cells were resuspended and diluted in sorting media containing mTESR plus + 10% CloneR (STEMCELL Technologies). Cells were centrifuged at 300 × g for 5 min and resuspended in sorting media. Cell suspension containing 1 × 106 cells was put into a fresh tube and centrifuged for 300 × g for 5 min. The cell pellet was resuspended in 100 µL of sorting media and 5 µL of conjugated antibodies SSEA-4 and TRA-1-60 (STEMCELLS Technologies) were added before being incubated at 4 °C for 45 min. Towards the end of the incubation, 10 µL of diluted DRAQ5 (1:100) was added to samples to identify cell nuclei. Cells were centrifuged at 100 × g for 5 min at 4 °C. The supernatant was removed, and cells were resuspended in 500 µL of cold PBS. The cells suspension was filtered in 40 µm Nylon mesh cell strainers to remove any clumping, and 20 µL of DAPI (1:10) was added to samples before the run to isolate live cells. All hPSC samples were analysed on the FortessaTM Cell Analyzer (BD Biosciences), and voltage gating was adjusted based on single stain and unstained controls prepared in parallel. Post-acquisition analysis was performed with FlowJo software (BD Biosciences).

Growth rate analysis for hPSCs and NPCs. Population doubling level per day was used to assess growth rate for both hPSCs and NPCs. For hPSCs, growth rate was determined by live cell imaging at 2 time points in brightfield at 4× magnification (Olympus IX73P1F): (1) one day after passaging (x), and (2) when confluent (colony borders begin to touch) before passaging (y). Using a custom macro on ImageJ software119, a binary mask was generated for regions containing hPSC colonies, and the area was quantified. The growth rate was calculated using the following formula: population doubling level = 3.322 × ((y) - (x)). The value of 3.322 represents the exponential nature of cellular growth. For NPCs, cell counts were used in place of confluency. For both hPSCs and NPCs, population doubling level was divided per days between timepoints.

iPSC multimodal scoring. Custom multimodal scoring R scripts were used to determine quality of iPSCs. Using the “DeSIR” package on R, 16 parameters were categorized into four major scores: Morphology, Gene expression, Protein expression, and Growth rate. For each major score, parameters were weighted to desirability functions on a scale between 0.01 and 1 (Supplementary Table 2). The major 4 scores where then weighted (Morphology = 1, gene expression = 1, protein expression = 1, and growth rate = 0.5) into a final overall desirability score between 0.01 and 1 with 1 being the highest and 0.01 being the lowest (Supplementary Fig. 9K).

NPC multimodal scoring. Custom multimodal scoring R scripts were used to determine quality of NPCs. Using the “DeSIR” package on R, 19 parameters were categorized into four major scores: Morphology, Gene expression, Protein expression, and Growth rate. For each major score, parameters were weighted to desirability functions on a scale between 0.01 and 1 (Supplementary Table 3). The major 4 scores where then weighted (Morphology = 0.9, gene expression = 0.7, protein expression = 0.9, and growth rate = 0.3) into a final overall desirability score between 0.01 and 1 with 1 being the highest and 0.01 being the lowest (Supplementary Fig. 9L).

Quantitative real-time qPCR analysis. RNA was extracted from fresh or snap-frozen cells using Invitrogen TRIzol Plus RNA Purification Kit (Invitrogen) according to manufacturer’s instructions. RNA was converted to cDNA using SuperScript IV VILO master mix (Invitrogen). Reactions were prepared using TaqMan™ Gene Expression Master Mix (Applied Biosystems) as per manufacturer’s protocol with custom TaqMan® Gene Expression Assays (Applied Biosystems) (see Supplementary Table 6) in a 384-well plate. For the analysis of mRNA levels, each gene was analysed in triplicate and water was used as a no-template control. Gene expression was normalized against ACTB (β-actin) and GAPDH expression. Comparative Ct method was used to analyse qPCR data120, where average CT was calculated for each gene and delta CT was calculated as follows: deltaCT = AverageCt target gene - AverageCt of ACTB and GAPDH combined. Relative mRNA gene expression was presented as 2-deltaCt.

Human pluripotent stem cell–derived cortical neuron generation. All neuron generation and cultures were conducted according to previously established protocols20. Briefly, NPCs were generated from hESCs and hiPSCs via dual SMAD inhibition neural induction. Embryoid bodies (EBs) were formed by culturing ESCs or hiPSCs in low-attachment 10-cm dishes with neural induction medium (DMEM/F12 with 15 mM HEPES (STEMCELL Technologies), NeuroCult™ SM1 with vitamin A (STEMCELL Technologies), N2 Supplement A (STEMCELL Technologies), 0.1 µM LDN193189 (STEMCELL Technologies), 10 µM SB431542 (STEMCELL Technologies)) for 7 days with media changes every second day. On day 7, EBs were transferred to laminin (Gibco)-coated 10 cm-dishes in neural induction media with laminin (1 µg/ml; Gibco). EB’s were cultured for 7 more days with media changes every second day until neural rosettes became visible around the EB core. Rosettes were manually selected and transferred onto fresh Matrigel-coated plates with neural progenitor medium (DMEM/F12 + GlutaMAX (Gibco), NeuroCult™ SM1 with vitamin A (STEMCELL Technologies), N2 Supplement A (STEMCELL Technologies), FGF2 (10 ng/ml; STEMCELL Technologies), 200 nM ascorbic acid, and laminin (1 µg/ml)), with cells now considered NPCs. Cultured NPCs were maintained at high densities, and medium changes were conducted every other day. Once cells reached confluency, cells were dissociated using Accutase (STEMCELL Technologies) and plated at 1.58 × 105 cells/cm2 into Matrigel (Corning)-coated plates. NPCs were cryopreserved at passage 4 for all subsequent experiments.
For neural maturation, passage 6 NPCs were dissociated and seeded onto tissue culture plates coated with laminin (5 µg/ml; Gibco) in cortical neural maturation medium (NMM). NMM consisted of BrainPhys neuronal medium supplemented with N2 Supplement A (STEMCELL Technologies), SM1 without vitamin A (STEMCELL Technologies), 200 nM ascorbic acid, 1.2 nM laminin (1 µg/ml; Gibco), 20 ng of BDNF (STEMCELL Technologies), 20 ng of GDNF (STEMCELL Technologies), 10 ng/mL IGF (STEMCELL Technologies), and 0.5 mM dibutyryl cyclic adenosine 50-monophosphate. Neurons were maintained for 14 days following differentiation with half medium changes performed every 2 to 3 days. At day 14, neurons were detached and strained 5 times through a 40 µm nylon mesh cell strainer before being re-plated on poly-L-ornithine (10 µg/ml; Sigma-Aldrich) and laminin (5 µg/ml)–coated tissue culture plates in cortical NMM containing half concentrations of growth factors. To generate the playground cultures, where neurotypical donor and MPS IIIA neuronal lines were combined, respectively, cell lines underwent neural induction and differentiation separately. Neuronal lines were combined 14 days after differentiation during replating when neurons were post-mitotic. Plates were maintained at 37 °C with 5% CO2 in standard tissue culture incubators. Neuronal cultures were functionally matured for 30, 60, 90 or 120 days in NMM before experiments.

Cellular stress. Trophic factors and antioxidants were removed from the neuronal maturation media in order to induce cellular stress. For cellular stress experiments, neurons were grown in neuronal maturation media (NMM) until Day 15 of maturation. At Day 15, neurons were switched to stress NMM (BrainPhys neuronal medium supplemented with N2A, SM1 without vitamin A and 1.2 nM laminin (1 µg/ml)) and cultured for 15 days before being fixed at day 30.

Immunofluorescence staining and high-content imaging. Cells were seeded on 384-well imaging plates (PerkinElmer) at a density of 13,000 cells. After the experiment cells were washed with BrainPhys imaging media (STEMCELL Technologies) to remove cellular debris. Washing media was removed and cells were fixed for 10 min in 4% paraformaldehyde (PFA). After fixing cells were washed three times with PBS. For staining, fixed cells were washed with TBS prior to being blocked and permeabilized with 0.1% Triton X-100 with 3% donkey serum in 0.1 M Tris-buffered saline for 60 min. Cells were incubated with primary antibodies (see Table S1) diluted in blocking solution at 4 °C overnight. Cells were washed 3× with TBS and incubated for 1 h at room temperature in the dark with secondary Alexa conjugated (488, 568 or 647 nm) antibodies (see Table S1). After incubation, cells were counterstained with DAPI (1:1000, cat# D9642-1MG, Sigma-Aldrich) before washing and storage in PBS. Cell images were acquired with an Operetta CLS high-content imaging system (PerkinElmer). Whole wells (25 fields of view) were imaged using the 20× water confocal imaging lens. Images were analysed with Harmony 4.9 software (PerkinElmer) using custom designed pipelines.

Immunofluorescence quality control. Cellular detachment and clumping can alter the accuracy of neuronal morphology analysis pipelines. Each imaged well is composed of 25 fields of view (FOV). Each FOV underwent a quality control assessment before being included in further dendritic and synaptic analyses. FOVs were filtered to exclude fields with a total clumping area of ≥104000 µm2 (25% of the total FOV), ≥4 clumping events (clumping with an area of more than 5000 µm2) or a neuronal density (MAP2+DAPI+ cells) of less than 1 (Supplementary Figs. 22 and 23, and Supplementary Information Tables 4 and 5).

Plating using multielectrode array. Cortical neuron cultures for neurotypical and MPS IIIA individual patient lines and playgrounds were replated on poly-L-ornithine (10 µg/ml; Sigma-Aldrich) and laminin (5 µg/ml)–coated Lumos 48-well, 16 electrode plates (Biotrend) at day 14 maturation. Ten microliters of cell suspension at 30,000–40,000 cells per uL was added directly to the centre of the electrode. Cells were settled for 45 min before 400 µL of cortical NMM containing half concentrations of growth factors was added to the well. Plates were settled for 48 h before being recorded on the Maestro Pro (Axion Biosystems) twice a week. Cells received a half media change every 3–4 days over maturation.

Multielectrode array data acquisition and analysis. MEA recordings were taken by a Maestro Pro MEA system (Axion Biosystems). Cell cultures in Lumos MEA 48-well plates (M768-tMEA-48OPT, Axion Biosystems) were maintained at 37 °C with 5% CO2 environment during recordings, and were allowed to settle for ten minutes prior to recording. Version 3.10 AxIS Navigator acquisition software (Axion Biosystems) was used to sample voltage potentials simultaneously across 16 electrodes per well. The sampling frequency was 12.5 kHz. The threshold for detecting spikes was set on a per-electrode basis and defined as the voltage exceeding 6 standard deviations away from the mean background noise. Electrodes with a mean firing rate ≥1 spike per minute (0.017 Hz) were considered active. Neurons with a mean spike frequency of ≥5 spikes per minute were considered active. All wells were included in firing (Firing rate per active neuron (Hz) and Number of active neurons per well), bursting (Burst frequency (Hz) and Number of active bursting cells) and network (Spike-sorted synchrony index) metric analyses. Wells with a network event frequency of 0 Hz were excluded from the Network event frequency (Hz) metric only (Supplementary Fig. 24).. raw recordings were converted in AxIS to .spk files.. spk files were converted to .nex files in Axion Data Export Tool version 3.5. .nex files were single unit spike sorted in Plexon Offline Sorter version 4.5 (Plexon Inc) by a 3DT-DistE-M algorithm using a degrees of freedom (D.O.F) Multiplier of 10 and Initial Number of Units of 8. Short interspike interval (ISI) waveforms, defined as spikes occurring within the neuron’s refractory period (<2 ms), were excluded during spike sorting using the Automatic Short ISI Spike Removal tool. Single unit spike sorted files (.nex and .mat) were processed in Neural Metric Tool version 3.1.7 to output metrics into .csv files. Further data processing was performed in Python 3.12.2 (Python Software Foundation) using custom scripts for collation and graphing to produce time series and raster plots. For time series, wells were first averaged over 7-day bins. To ensure equal weighting of individual lines, wells were averaged by plate and cell line and then averaged to their respective cohorts, MPS IIIA and neurotypical, over the maturation period. Following this, a rolling average was applied over two binned periods. Time series binned data (Supplementary Fig. 19B–D) was analysed using a Wilcoxon matched-pairs signed rank t-test. Values for each timepoint were then compared using multiple unpaired nonparametric t-tests to determine significance in GraphPad Prism 10. 7-day binned data was also averaged per cell line for timepoints within the 30–50 and 55–70-day windows to produce histograms. Histogram significance was determined using a mixed-effects model (REML) with fixed effects for time, cell line, and their interaction, and random effects for genotype. Šídák’s multiple comparisons test was used to compare genotypes at each timepoint window. For raster plots, the cumulative spike frequency was calculated using 100 ms bins, followed by applying a rolling average over a 1 s period. This processed data was used to determine the network’s firing activity. A 60- or 120-second window was then selected by identifying the period that maximised the cumulative firing within that interval.

Replating neurons onto coverslips for patch-clamping. Cortical neuron cultures for individual neurotypical and MPS IIIA cell lines and the playgrounds were replated onto DAP-treated65, poly-L-ornithine (10 µg/ml; Sigma-Aldrich) and laminin (5 µg/ml)–coated standard glass coverslips at day 14 maturation. Cells received a half media change every 2–3 days with cortical NMM.

Whole-cell patch clamping setup and data acquisition. Individual coverslips containing cortical neurons were transfected with a Synapsin:GFP lentiviral vector generated using previously described methods121 before being transferred to a recording chamber that was perfused with ACSF at room temperature (21–23 °C) using the Minipuls 3 (Gilson). ACSF contained: 121 mM NaCl, 4.2 mM KCL, 1 mM MgSO4, 29 mM NaHCO3, 0.45 mM NaHPO4, 0.5 mM Na2HPO4, 1.1 mM CaCl2 and 20 mM glucose (all chemicals from Sigma) and was continuously bubbled with 95% O2 and 5% CO2 and had an osmolality similar to human CSF (300–310 mOsm). For tetrodotoxin (TTX, Abcam, ab120054) experiments, a final concentration of 1 µM TTX in ACSF was used. Recordings were performed underneath an upright Olympus BX51 microscope, a 40× water-immersion objective lens and a PCO.Panda 4.2 camera. A cool-LED pE300 illumination unit at 460 nm was used to visualise neurons expressing Synapsin:GFP. Borosilicate glass filaments (BF100-50-7.5, Sutter Instrument) were pulled with an open resistance of 3–6 MΩ using a P-1000 micropipette puller (Sutter Instruments). Patch pipettes were filled with an internal solution that has a pH and osmolarity similar to regular physiological conditions (pH 7.3, 290–300 mOsmol) and contained: 130 mM K-gluconate, 6 mM KCl, 4 mM NaCl, 10 mM Na-HEPES, 0.2 mM K-EGTA, 0.3 mM Na-GTP, 2 mM Mg-ATP, 0.2 mM cAMP, 10 mM D-glucose, 0.15% biocytin and 0.06% rhodamine (all chemicals from Sigma).
Recordings were amplified using a Digidata 1550B/Multiclamp series 700B, digitised and sampled using PClamp software (v10.7 Molecular Devices) at 50 kHz for voltage-clamp recordings and 100 kHz for current-clamp recordings. Prior to the formation of a giga-Ohm seal the pipette was offset so that the baseline sat at ~0 pA. Electrode capacitance was accounted for in cell-attached mode. Neurons were held at −70 mV in voltage-clamp and a brief test pulse was administered to assess patch quality and passive membrane properties immediately after membrane rupture. To record voltage-gated sodium (Na+) and potassium (K+) currents, cells were held at −70 mV in voltage-clamp mode and injected with +5 mV current steps across 20 sweeps after an initial hyperpolarising step of −5 mV. Cells were held in C=0 to record the resting membrane potential. Action potential firing and kinetics were obtained by using a sufficient amount of current to hold the neuron at −70 mV in current-clamp mode. If holding current exceeded −100 pA that patch was considered poor quality and aborted. Neurons were subjected to a 500 ms depolarising current step for 15–20 sweeps to elicit the maximal response from the cell. AMPA-mediated events were recorded at −70 mV in voltage-clamp mode and GABA-mediated events were recorded at 0 mV in voltage-clamp mode (at the reversal potentials for sodium and chloride). Synaptic activity recordings were recorded over 3 min with a gap-free run, with patch quality assessed prior to recording data. All experimenters were blinded to the genotypes during data acquisition and analysis.

Whole-cell patch clamping recording exclusion criteria. To exclude unhealthy neurons and suboptimal recording conditions, only neurons with (1) >1 GΩ seal and (2) access resistance <20 MΩ were included in the final analysis stages. If patch quality or cell health deteriorated such that quality control checks were failed, the patch was aborted and only recordings acquired prior to this were included.

Electrophysiological feature analysis
Action potential analysis. abf files were loaded into the custom-built script using a loader class module. To remove high-frequency noise a Bessel filter (5000 Hz cut-off) was applied to each recording. In addition, root mean square high-frequency noise was calculated across 1-s pre-stimulus windows and set at <2 mV. If exceeded, the sweep was considered “unstable” and was excluded from further analyses. Action potentials (AP) were detected by first identifying positions where the membrane potential derivative (dV/dt) exceeded 5 mV/ms. This identified a putative spike timestamp, which was refined based on threshold-to-peak voltage, time differences and absolute peak height. A spike was detected if it met the following criteria: (1) minimum height of 2 mV, (2) >−10 mV absolute peak amplitude, (3) not coinciding with the current step start and (4) <10 ms delay between start of spike and time of peak. A minimum absolute peak amplitude of −10 mV was used as it is closest to the reversal potential of cations (0 mV). The maximum firing frequency of APs >−10 mV was determined by isolating the sweep of the recording that contained the most spikes over a −10 mV threshold that also met the requirements of the above criterion of a spike. All kinetics properties were calculated on the first action potential spike within the maximal firing sweep. Features upstroke:downstroke ratio, AP duration and trough voltage were only calculated for type 4 and 5 neurons as these calculations relied on a spike train. The junction potential (~10 mV) was corrected for post analysis.

Action potential type classification. Neurons were classified into types 1 through to 5 according to previous methods64. Type 1 neurons express small sodium currents but cannot fire an action potential over −10 mV. These are considered “aborted” APs. Type 2 neurons only fire one action potential over −10 mV that is followed by a plateau. Type 3 neurons fire one action potential over −10 mV and are typically followed by one or more aborted APs. Type 4 neurons fire more than one action potential above −10 mV, however they do not fire greater than 10 Hz. Type 5 neurons fire APs greater than 10 Hz.

Resting membrane potential. abf files were loaded using a custom-built metadata and data loading function to screen for recordings with a resting membrane potential protocol tag. Recordings were passed through a Bessel filter (5000 Hz cut-off, 5th filter-order) to remove low-frequency noise. The baseline was averaged across the recording to find the resting membrane potential.

Voltage-gated sodium and potassium current analysis. abf were passed into a custom-built Python script, where a low pass filter was immediately applied to filter out high-frequency noise at a cutoff of 2000 Hz. A rolling window was also applied to each sweep of the recording to prevent the stimulus artefacts at the start and end of the recording from being included in downstream analysis. Passive membrane properties for each recording were calculated using a linear regression model on all sweeps prior to the detection of the first Nav depolarisation. Passive inputs were calculated to be the mean current response to stimulus across the last 40 ms of the trace. These passive correction values were then applied to values obtained to Kv currents during the analysis. To remove poor quality recordings, those with a baseline <−200 pA were excluded from further analysis. Recordings were also excluded if a sodium depolarisation occurred within the first sweep as a linear prediction could not be calculated. The maximum Nav current was determined by isolating the largest negative depolarisation within 30 ms of the start of the stimulus. The Kv peak was calculated by identifying the largest difference between fast-inactivating Kv currents and slow-inactivating Kv currents.

Spontaneous and miniature excitatory and inhibitory postsynaptic current analysis. abf files were loaded into the script using custom-built metadata and data loading functions. All recordings underwent a median baseline subtraction and a signal drop exclusion as such that if a recording dropped −75 pA from its starting position for more than 10 s for AMPA-mediated events, and −150 pA for 10 s for GABA-mediated events, the recording was cut off from this point and excluded from further analysis. A Butterworth lowpass filter with a cutoff of 1000 Hz was used to remove high-frequency noise from recordings. Recordings were exposed to a high-pass Butterworth filter with a cutoff of 0.1 Hz to remove slow electrode drift for cleaner event detection. A linear convolution was then used to remove any additional noise from recordings.
To detect AMPA and GABA events, a root mean squared method was used to automatically place high and low thresholds that were catered to each recording. For AMPA events, amplitude cutoffs of 15 and 600 pA were used for regular patch conditions and 10–500 pA for miniature event analysis. Duration cutoffs of 1–30 ms were used for individual cell lines and miniature event analysis, and 1 ms to 1 s for the playground model to account for more bursting activity. For GABA events, amplitude thresholds of 20 and 2000 pA and duration cutoffs of 10 ms to 2 s were used for both regular conditions and miniature event analysis. Additionally, to ensure large baseline fluctuations were not included as a slope exclusion criterion was used. This was calculated as the slope between the peak of the GABA event and baseline, and a threshold of 0.055 or greater was detected as an event (given it satisfied all other detection parameters).

Neuron morphological analysis. Neuronal morphology tracing was completed on Neurolucida 360 (MBF Bioscience) using rhodamine images taken during whole-cell patch clamping experiments. High-quality rhodamine images were selected and aligned to create a montage of the neuron. A bright field image was used to trace the size of the soma to control for changes that can be induced by the patch pipette. The soma trace was then overlaid onto the montage of the neuron and neurite tracing was completed. To classify branches as neurites or either immature spines or filopodia, branches were measured and those greater than 10 mm were included as neurites. Reconstructions were saved as a .dat file and loaded into Neurolucida Explorer (MBF Biosciences) for analysis. Cells that were swollen or had very low rhodamine perfusion through the neurites were excluded from analysis. Neurons were categorised into multipolar, fusiform, unipolar, bipolar or pyramidal cells during analysis.

Unsupervised clustering analysis. Hierarchical clustering was performed on 22 morphological features extracted from Neurolucida Explorer (MBF Bioscience). Each feature was scaled using the “StandardScaler” function from the sklearn (scikit-learn version 1.2.2) package and the resulting array was then filtered to remove infinite values from the data. Data was then subjected to a PCA fit using sklearn’s “PCA” function to find the explained variance ratio between the 22 features, where the random_state argument was set to 42. An elbow plot was then generated using the explained variance ratio to pick the appropriate number of principal components for further analysis. The “IncrementalPCA” function was used to centre the data, with the n_components argument being set to where the deflection point of the elbow plot was observed. Matplotlib (version 3.7.2) was used to visualise the distribution of the cells.

Genomic DNA extraction and quantification for SNP genotyping. Genomic DNA was extracted and purified using the PureLink Genomic DNA Mini Kit (Thermo Fisher Scientific, Cat. # K182002) following the manufacturer’s protocol. Briefly, neural progenitor cultures were dissociated using Accutase (Stemcell Technologies, Cat. # 07920) and resuspended in 200 µL of PBS. The cell suspension was treated with Proteinase K and RNase A provided in the kit, followed by lysis with Genomic Lysis Buffer. Samples were incubated at 55 °C for 10 min, then mixed with 100% ethanol to achieve a homogeneous solution. The lysate was transferred to a spin column and centrifuged at 10,000 × g for 1 min. The column was washed sequentially with 500 µL of Wash Buffer 1 (centrifuged at 10,000 × g for 1 min) and 500 µL of Wash Buffer 2 (centrifuged at maximum speed for 3 min). DNA was eluted by adding 35 µL of Genomic Elution Buffer, incubating at room temperature for 1 min, and centrifuging at maximum speed for 1 min. A second elution step was performed with an additional 35 µL of Elution Buffer, followed by centrifugation at maximum speed for 1.5 min, yielding a final volume of 70 µL of purified DNA.
DNA concentration was determined using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Cat. # Q32854) according to the manufacturer’s instructions. The Qubit working solution was prepared by diluting the dsDNA HS Reagent 1:200 in the HS Buffer. For each measurement, 2 µL of DNA sample was mixed with 198 µL of working solution in Qubit assay tubes, vortexed, briefly centrifuged, and incubated for at least 2 min before quantification using the Qubit 4 Fluorometer.

SNP genotyping. SNP Genotyping was performed by Dr Chris O’Keeffe at Genomics platform at the Garvan Institute using the Axiom UK Biobank Array. Briefly, SNP genotypes were processed through Axion suite with default settings. Variants were removed if they did not pass Hardy–Weinburg equilibrium or were missing more than 5% of genotypes across all samples. Variants were phased with eagle before imputing with minimac4. Variants with at least 5% minor allele frequency and imputation R2 score greater than 0.3 were retained for downstream analyses. For demultiplexing, only variants overlapping genes were used.

Single nuclei extraction and RNA sequencing. Cortical neuronal cultures for neurotypical and MPS IIIA playgrounds were cultured for 30 and 120 days in neuronal maturation media before undergoing single nuclei extraction. Single nuclei extraction was performed according to 10× single nuclei extraction protocols with modifications. Cells were washed with BrainPhys (STEMCELL Technologies) and detached with Accutase (STEMCELL Technologies) for 15 min. Cells were collected into Falcon tubes and diluted with BrainPhys (STEMCELL technologies). Cells were spun at 400 × g for 5 min at 4 °C before supernatant was carefully removed. Cells were gently resuspended, using a wide-bore tip, in ice-cold lysis buffer consisting of 10 mM Tris-HCl (pH 7.4), 10 mM NaCl, 3 mM MgCl2 and 0.1% IGEPAL (Sigma) in PBS and lysed for 1 min on ice. After cell lysis, cells were washed 3× with wash buffer consisting of 1% BSA and 0.2 U/ul RNase inhibitor in PBS. Extracted nuclei were strained with a 40 µm nylon mesh cell strainer and the quality of nuclei was assessed. A total of 30,000 cells (30-days) and 49,500 cells (120-days) per condition were loaded to the wells of an eight-channel microfluidic chip and transferred to a Chromium Controller. Single-nuclei RNA library preparation and sequencing were performed by the South Australian Genomics Centre and the Australian Genome Research Facility. Single-nuclei suspensions were loaded without delay onto a GemCode Single Cell Instrument (10× Genomics) to generate single-nuclei gel beads in emulsion (GEM). snRNA-seq libraries were prepared from GEMs using Chromium Next GEM Single Cell 3′ Reagent Kits v3.1 (Dual Index, 10× Genomics, Cat. # CG000388 Rev B), Library Construction Kit (10× Genomics, Cat. # 10000196), Chromium Next GEM Single Cell 3ʹ v3.1 Gel Beads kit (10× Genomics, Cat. # 2000164) and Chromium Next GEM Chip G Single Cell Kit (10× Genomics, Cat. # 10000196) according to the manufacturer’s instructions. Briefly, GEM reverse-transcription incubation was performed in a 96-deep-well reaction module at 53 °C for 45 min, 85 °C for 5 min and ending at 4 °C. Next, GEMs were broken, and complementary DNA (cDNA) was cleaned using DynaBeads MyOne Silane Beads (10× Genomics, Cat. # 2000048) according to the manufacturer’s instructions. Full-length, barcoded cDNA originating from the mRNA was PCR amplified with a 96-deep-well reaction module at 98 °C for 3 min, 11 cycles at 98 °C for 15 s, 63 °C for 20 s and 72 °C for 1 min, 1 cycle at 72 °C for 1 min and ending at 4 °C. Size selection via a SPRIselect Reagent kit was used to separate the amplified cDNA molecules for 3′ gene expression library construction. Gene expression library construction to generate Illumina-ready sequencing libraries was performed after clean-up using a SPRIselect Reagent kit (Beckman Coulter, Cat. # B23318) and enzymatic fragmentation by adding R1 (read 1 primer), P5, P7, i7 sample index and R2 (read 2 primer sequence) via end-repair, A-tailing, adapter ligation, post-ligation SPRIselect clean-up/size selection and sample index PCR. The cDNA content of pre-fragmentation and post-sample index PCR samples was analysed using a 2100 BioAnalyzer (Agilent). Sequencing libraries were loaded on Illumina NovaSeq flow cell, with sequencing settings according to the recommendations of 10× Genomics. For 3′ gene expression: read 1: 28 cycles; read 2: 90 cycles; index i7: 10 cycles; index i5: 10 cycles.

Single nuclei data processing. Single nuclei sequencing (snRNAseq) reads from 5 patient-derived iPSC cell lines were demultiplexed and aligned to the GRCh38-2020-A reference using Cell Ranger 7.0 or 9.0 (10× Genomics) with default settings. Counts were generated using Cell Ranger’s “count” function. Total number of sequenced cells at 30-days was 17,476 for neurotypical donors and 11,012 for MPS IIIA; and at 120-days it was 33,560 for neurotypical donors and 41,785 cells for MPS IIIA. Digital gene expression matrices were generated from sequencing data using CellRanger. Demultiplexing of donors, ambient RNA and doublet detection was conducted using Demuxafy 2.0.1122. Souporcell and Vireo wrapper scripts from Demuxafy were used for demultiplexing and ambient RNA detection, respectively123–127. Vireo was specifically used to conduct SNP genotyping-based demultiplexing. Solo, ScDblFinder and Scds wrapper scripts were used for doublet detection using default methods. Demultiplexing, ambientRNA and doublet assignments were combined using Demuxafy’s Combine Results with method Majority Singlet to determine donor, doublet or singlet assignments based on all methods. Downstream quality control procedures were performed using Seurat v5 in RStudio and conducted separately for neurotypical and MPS IIIA datasets. Count matrices, features, and barcodes from CellRanger were imported using Read10×() and a pre-filtering step was performed to keep cells expressing a minimum of 200 genes (to get rid of any empty droplets) and genes expressed in at least three cells. Outlier cells were then identified based on several metrics (number of reads, number of expressed genes, doublets, proportion of mitochondrial genes as well as ambient RNA); cells were tagged as outliers when they were either doublets or had ≥50,000 or ≤1000 reads, ≤1000 genes, >5% mitochondrial genes and if the log likelihood ratio of ambient RNA was ≥0. Raw counts were corrected (normalised and scaled) using SCTransform() and LogNormalise() for the RNA counts. Principal component analysis was conducted, and the most significant PCs were used to embed cells in a k-nearest neighbour graph using FindNeighbors(). The default Louvain algorithm in FindClusters() was used to generate cell clusters. Uniform manifold approximation and projection (UMAP) plots, using the most significant principal components, were generated to visualise the distribution of cells and cell types. This method was applied for individual neurotypical and MPS IIIA datasets as well as merged datasets. Methods for SCTransform(), RunUMAP(), FindNeighbors() and FindClusters() were repeated following subsetting for cell populations downstream. The above methods were performed for Heathy and MPS IIIA samples at 30- and 120-days.

Integration with neurotypical, human cortical biopsies. Neurotypical and MPS IIIA cortical cultures datasets at 30-days and 120-days were integrated separately on to neurotypical, human cortical biopsies datasets collected from children aged 6, 8, 10, 12 and 14. Biopsy datasets were downloaded from data published by Herring et al., 202266. Briefly, Neurotypical or MPS IIIA cultures were first merged with the Herring biopsy dataset. Merged datasets were normalised and scaled using SCTransform(). Principal component analyses were performed using RunPCA(). To perform PCA, we extracted the 500 most DEGs in excitatory/inhibitory neurons and non-neuronal populations (OPC, Oligo, Astro, Vas and Micro) from the Herring dataset. We then filtered these features to keep only those highly variable features prevalent within the scaled data of the SCT assay. Following this, we integrated the iPSC cortical dataset with the Herring dataset using RunHarmony().

Cell type classification. Cell type classifications for the datasets were performed using three different classification methods. Briefly, this included (1) UMAP and Seurat cluster-based classifications, (2) cell type scoring using published gene lists from Nowakowski68 and Liddelow67 and (3) scMayoMap, an R package developed for cell annotations69. Following classification, a custom function was used to assign the final cell types. Each method is detailed below.

UMAP and Seurat cluster-based cell classification. Following integration, FindNeighbors(), FindClusters() and RunUMAP() were performed as before using the new harmony reduction. Neurotypical or MPS IIIA cells within clusters of Herring were named according to the major cell type from the Herring dataset that was prevalent within that cluster. For example, if Seurat cluster 1 contained Excitatory neurons from the Herring dataset, then Neurotypical or MPS IIIA cells also within this cluster were named accordingly.

scMayoMap cell annotations. To perform scMayoMap cell annotations, we first identified conserved markers across both datasets in each Seurat cluster using the FindConservedMarkers() function. We then used the provided scMayoMap database and filtered to keep brain cell types and those within either the Herring or Nowakowski dataset as these are also cell types that are most likely prevalent within our cortical cultures. We then tested our conserved markers against this database. For each cluster, scMayoMap returns a top and a second top scoring cell type. We extracted these first and second top scores to assign cell types as cluster_type1 for the top score, and cluster_type2 for the second top score for each cell.

Calculation of cell type scores. Relative cell type scores for neuronal and non-neuronal cells were calculated using gene sets made available by Nowakowski and Liddelow et al.68 and the AddModuleScore() function in Seurat. To define cell types, the maximum score across all analysed cell types was determined using a custom function. Cells were defined as a particular cell type if the maximum score of a cell type was >0.1. Cells with a maximum score <0.1 were defined as belonging to other cell types (termed; Other). Given that the Nowakowski dataset contained subclassifications of neurons and non-neuronal cells, we grouped cells into broad categories of Neurons (Excitatory/Inhibitory), Astrocytes, OPCs, RGs, Immature Neurons and Other (Microglia, Pericytes).

Final cell type assignments. Once cell type classifications were performed from all three methods, we identified the final cell type by using a custom function. Briefly, priority was given to the Seurat cluster assignments, and if the cluster assignment appeared in the top-scoring scMayoMap assignment (cluster_type1) or the Nowakowski assignments, the final cell type was assigned accordingly. For example, if the Seurat cluster type was Excitatory neurons, the scMayoMap cluster_type1 was Excitatory, but the Nowakowski type was Inhibitory, then the final cell type was Excitatory.
In the instance that no matches were observed, if majority of the cells were a combination of non-neuronal cells such as Astrocytes, OPCs or radial glia, we assigned the final cell type as radial glia, as these cells were most likely indistinguishable through the methods due to being immature and in the process of undergoing differentiation. If the cells were a combination of non-neuronal cell types and neuronal cell types, we assigned them as Immature Neurons.
In the instance of cell types such as microglia, mature oligodendrocytes or vasculature/endothelial cells, prevalent within the Herring dataset but not present within our cortical cultures, we used the Nowakowski assignments to assign the cell type.

Classification of cycling cells. Relative cell cycle scores for cells were calculated using gene sets made available by Tirosh et al.128 and the AddModuleScore() function in Seurat. To define cell cycle, the maximum score across all analysed cell types was determined using a custom function. Cells were defined as belonging to G1S or G2M, if the maximum score of a cell cycle phase was >0.1. Cells with a maximum score <0.1 were defined as Non Cycling. This was performed for only for the iPSC cortical cells.

Differential expression analysis. For differential expression analysis, neurotypical and MPS IIIA Seurat objects were merged, RNA count matrices were joined using JoinLayers() and log normalised using NormalizeData(). Differential expression between neurotypical and MPS IIIA, using the Wilcoxon rank-sum test of the FindMarkers() function, was performed on the log normalised RNA counts for excitatory neurons or inhibitory neurons. P value adjustments were made using Bonferroni correction. Additional parameters used in FindMarkers() include; logfc.threshold (threshold for log fold change) = 0; min.pct (minimum percent of cells expressing markers) = 0.05; min.diff.pct (minimum percentage difference of cells expressing markers between the two groups) = 0.1. Genes were ranked using a custom function that uses the following formula: (average fold change) × (-log10(adjusted p value)).

Gene set enrichment analysis (GSEA). GSEA was performed using the fgsea() function to assess the presence of synapse-associated genes in differentially expressed gene lists. A log fold change of synaptic genes from the SynGO release 20231201 list93 was calculated using normalised count data. These were ordered by decreasing log fold change and used as the stat argument. Significantly up or downregulated genes in excitatory or inhibitory neurons were used as the pathways argument of fgsea(). Enrichment analysis was performed using the fgsea() function with the following parameters, gseaParam: 1; minSize; 1; maxSize; 500; nPermSimple: 10000.
Gene ontology analysis was conducted using the enrichGO() function from the clusterProfiler package, where the significantly up or downregulated (adjusted p-value of <0.05 & avg log2FC > or < 0) genes for MPS IIIA excitatory and inhibitory neurons made up the vector of gene IDs. All gene names expressed in the RNA assay were used for the universe argument. GO IDs underwent similarity scoring using GO_similarity() and simplifyGO() with louvain for the clustering method. Clusters were then assigned to broad groups based on common words in each cluster, which were Signalling pathways, Cellular metabolism, Ions and transport, and Cellular differentiation and development. Chord plots were selected for top 15 up and downregulated genes, except for inhibitory neurons, which only had 4 significantly downregulated genes. The following parameters were used for the enrichGO() function: OrgDB = org.Hs.eg.db, ont = BP, pvalueCutoff = 0.05, pAdjustMethods = BH, qvalueCutoff = 0.1, minGSSize = 10, maxGSSize = 500.

Quantification and statistical analysis. Statistical analyses were performed using GraphPad Prism 10.0 (GraphPad Software, Inc., CA, USA) using unpaired nonparametric two-tailed t-test, Mann–Whitney unless otherwise stated. P values <0.05 were considered statistically significant and non-significant results are denoted as ns. Generally, data has been normalised to the average of the neurotypical control unless otherwise stated. Python and R scripts were used for data processing of large datasets. All data was processed using application-relevant software, custom scripts or algorithms to treat each condition equally, and where manual analysis was performed, experimenters were blinded.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Mazzachi et al., have utilised 5 MPSIIIA patient and 5 healthy control iPSC lines which they differentiated in parallel into neural stem cells for prolonged maturation into cortical cultures for functional study. MPSIIIA is one type of lysosomal storage disorder caused by dysfunction in one of the genes encoding a lysosomal enzyme involved in the degradation of heparan sulfate. Investigating functional deficits in human models is highly important as MPSIIIA patients present severe and early-onset neurodegeneration. In these derived cortical cultures analysed at different time points (30-60 and 90-120 days of differentiation), authors have identified functional changes present in the MPSIIIA cultures. Interestingly, authors do not find any differences in the electrophysiological properties of individual neurons but do identify clear changes at the network level of functionality. The authors also achieve fantastic morphological reconstructions of the cells to assess the structure of mature Synapsin:GFP neurons. Finally, authors use this cortical model in combination with approved pharmacological treatments to investigate whether they can achieve amelioration of the functional changes. The work covers a wide range of functional analyses, and the techniques utilized are of a high standard within the field. Although of interest, the manuscript presents some weaknesses that could be addressed to improve the study and support the conclusions. Major concerns: 1. The authors claim in Line 114 & Figure 1 that the cultures were formed of post-mitotic neurons at Day 14 – however there is no clear support of this claim in protein immunohistochemistry or other analyses. The single cell RNAseq data found in Figure 1B counters this with the presence of radial glia, astrocytes and immature neurons at Day 120 in culture. Overall neurons represented < 35% of the culture and only 15% of the total cells were mature neurons. 2. Individual cell lines present a high variability in their final fate in playground cultures assessed by scRNAseq at Day 120. In Figure S10-A it is clear in the MPSIIIA culture that many lines are high underpresented e.g. 2 lines appear to make up >60% of the culture, with 1 line making up more than >60% of the total excitatory (MPSIIIA7) or inhibitory (MPSIIIA10) neuron population. These cells will be vastly over-represented in the excitatory or inhibitory analyses performed and will provide the majority input to network level activity. Unlike the ‘Village in a dish’ model proposed by Neavin et al., (Nat. Comm., 2023, DOI: 10.1038/s41467-023-38704-1) the authors have not demonstrated high reproducibility of the fraction of cells that were used between preparations of the ‘Playground cultures’. Likewise, this brings into question the changes in excitatory and interneuron post-synaptic currents as they could be due to shifts in excitatory or interneurons proportions within the cultures. 3. With this inherent variability and the majority of cells at 120 days being astrocytic, it is suggested to provide individual panels of the cell types in Figure 1B within the panel that identify whether the clustering has been successful, and split by genotype to demonstrate that similar populations are present in healthy control and MPSIIIA patients. 4. The manuscript shows limited biological replicates. As shown in figure S2 the authors only independently differentiate N=2, or in many cases N=1, biological replicates of neural progenitor cells. There is also concern regarding the N=1 synaptic staining despite the technical replicates as initial plating density or other variabilities in cell culture could have influenced the distribution of these markers. Likewise, regarding concern 2 there is a high inherent variability in the cultures, and this may be more evident with multiple preparations from each line. 5. In Line 919 the authors note that they discard inactive wells prior to full analysis, the reviewers suggest that the total number of inactive wells should be described in the supplementary data to identify whether there is a particular phenotype based on the total number of active/inactive wells and to demonstrate the actual number of electrodes used per condition 6. Throughout, there is an odd statistical comparison, given that many of the figures depend on data generated from multiple lines, an ANOVA should be performed instead of a presumed t-test. 7. The reviewers primary concern with the interpretation of the data presented is that there is a high possibility that by selectively patching the ‘healthiest’ cells in the MPSIIIA condition (e.g. selected for good morphology, good neuronal properties, etc.) then the authors could be artificially selecting for newer-born neurons wherein the disorder has not had time to significantly progress. There are evident progenitors identified by the scRNAseq and a high proportion of immature neurons in the total neuron population. There is no evidence presented of accumulation of heparan sulfate to support that these neurons are under progressive load. Therefore, by not attempting to include any sick or dying cells, or at least address the proportion of these cells between culture conditions, there could be a bias for healthy cell types that does not reflect conditions when examining the whole network function, as shown by the altered MEA function. In particular, the type of tonic firing observed during the MEA recordings is a newborn neuron phenotype that gives rise to synchronised burst firing in more mature neurons, as shown in Kirwan et al. (Development, 2015, DOI: 10.1242/dev.123851). This could imply that the continuous renewal of the MPSIIIA cultures leads to a continuously reset network development, hence the more immature or hypoxia-like phenotype. For example, the capacitance is lower in early timepoints, then recovers to a higher point, is this due to the presence of newly-formed cells that are being patched. There are also more active neurons in MPSIIIA despite reported neuronal death in the accompanying manuscript. 8. Instead of barplots, boxplots or violin plots should be used when examining data with very high numbers of individual dataplots. Datapoints should also always be shown if they will be used e.g. in Figure 4 many barplots (A-D) show individual datapoints but then the authors switch to reporting only the N number. It would be better in this scenario to show all the datapoints so the reader can assess if there are many outliers. Minor concerns: 1. The term ‘isogenic’ is used inappropriately to describe the cultures at Line 110, this does not fit with the established use of isogenic in iPSC culture to describe lines which have the same genotype except for a single or multiple disease-causative genes that have been introduced or corrected. 2. Figure S4 and the Cell stress mentioned in the methods but not referred to in the main text other than to state a number of cells imaged. 3. In figure S10 percentages should be provided for each of the conditions in the pie-charts to determine actual change. 4. There is reference to ‘STAR methods’ if this is the case for this manuscript then a workflow of the pipelines for the image analysis should be included 5. Clumping covering 104263um2 is quite a large clumping area, there is no number for the clumping events, it would be ideal if the authors could indicate discarded data amounts in the total number of imaged regions 6. Significant differences in the capacitance at 30 days (Figure 2D) are not mentioned in the text, whereas non-significant trends are mentioned in the text at Line 253 (Figure 4N/P). 7. Examples of synaptic colocalization should be shown as part of Figure 4, as the current figures it is difficult to clearly identify due to the scale. A XY-XZ 3D colocalization plot would also greatly benefit the manuscript.] 8. Inhibitory staining was only performed in one timepoint as shown in figure 7, and at this point there are high variations in patient-neuron fate ratios. As such the changes seen could be dependent on an individual patient. 9. In figure 4 it is mentioned that TTX shows disproportionate hyperactivity in the MPSIIIA cultures but then the figure shows zero significance. 10. Authors should comment on the role of glial support and glial neurotransmitter uptake. 11. The decrease in PSD95 may be just a consequence and not a disease mechanism since at day 30 neurodegeneration is already reported.", "reviewer_2": "This manuscript , focuses on studying the excitation/inhibition imbalances in patient-derived induced pluripotent stem cells (iPSC) from children Mucopolysaccharidosis Type IIIA (MPS IIIA). Authors describe MPS IIIA phenotypes based on hyperactive excitatory synapses and dysregulated gene expression linked to synaptic homeostasis. A drug screen with whole-cell patch-clamping, multi-electrode arrays and high- content confocal imaging identified a cocktail of repurposed drugs effective in restoring synaptic and neural network activity to healthy levels. Similarly to the accompanying Greenberg et al., manuscript, the use of neuronal ‘playgrounds’ composed of 5 patient/healthy subject lines with 2 clones per line contributes to a high rigor of this study. The diversity in experiment design on neuronal synaptic integrity and neuronal activity are of high value, with multiple detailed aspects of these phenotypes being evaluated in parallel. Little is known regarding the molecular mechanisms of the MPS IIIA, and no drugs are currently available for these patients. This study is impactful and paves a path towards further in vivo and pre-clinical screen to identify FDA-approved therapeutics. That being said, I do have some concerns to bring to the authors’ attention below: Major comments: • Authors describe to generate cortical neuronal cultures, dominating with MAP2 multipolar/pyramidal neurons. It is therefore puzzling that they detect GABAergic synaptic Gephryn+ staining in these cells. It would be of high benefit to include a better characterization of these neuronal-astrocytic cultures, with specifications on the inhibitory/excitatory inputs. In addition to a detailed characterization of their cell culture system, it is suggested to move results from Fig6A to Figure 1. • Why are the treatment timelines different in the high content image-based drug screening assay (treatments at DIV40-55), the neuronal activity assays (treatments start at DIV65-DIV80), and the patch clamp assays (treatments at DIV85-99)? Since the drugs do rescue synaptic changes already at DIV40-55, one would expect to observe neuronal activity changes from those early treatments. Authors should elaborate why they performed three different experimental set up for these linked and complementary experiments. • Similarly to the accompanying Greenberg et al., manuscript, one concert that needs to be discussed is the heterogenicity of the ‘playground’ samples, with fact that MPSIIIA 10 and 7 being the most prominent cell lines in the MPSIIA ‘playground’, with MPSIIIA 8 lines to compose the least of ‘playground’. This is a common issue with such village-based approaches. As different lines show variable response to neuronal and astrocytic pathologies (Suppl. Fig 8), there may be a variance in cellular responses to the tested drug, highlighting ‘responders’ and nonresponders’. Authors should deeply analyze their scRNAseq data, to 1. Determine if the drug effects are not driven by the dominant patient lines, and 2. Determine which lines may be more ‘astrogliotic’ that could potentially skew the neuronal activity analysis • It is currently standard in the field to work with isogenic gene-corrected controls, especially relatively easily generated for a point mutations. The authors might want to validate their key findings and their drug selection process in an isogenic gene-edited pair as a ‘gold standard’, which would be much cleaner system accounting for other genetic variance in the specific patient line, further elevating the ‘playground’ approach. • As the authors identify combination #2 to be the most efficacious therapeutic strategy impacting neuronal activity, they also discuss the potential of this drug combination on other preclinical disease phenotypes, as identified by the companion manuscript Greenberg et al.,. The companion article is not exploring the impact of combination #2. As the authors strongly suggest for this therapeutic strategy to be clinically relevant, these MPSIIIA phenotype rescue experiments should be performed here, as described in Greenberg et al., • Authors observe that the combination #2 shows sustained effect on the number of active neurons after treatment ceased. Yet they do not comment on the other two MEA parameters which went back to the MPSIIIA - untreated levels. These differences in readouts should be elaborated, as the current conclusion that combination #2 has long term benefits is not fully clear. • The authors did not state the number of differentiations/ biological replicates their results consist of. Drug screening assays, when established, are acceptable to be performed on one differentiation, but all of the proof of concept/phenotype assays (Fig. 2,3,4) should be done in at least 2-3 independent differentiations per current standards. Also, the single cell RNAseq data should be at least followed up with target validation in an independent differentiation. • It is unclear how the compound doses were determined prior to the drug screen (Fig 7), and there is no information on how the drug combination #1 and combination #2 were designed. Minor comments: • Typo line 168, should be Figure S7", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We sincerely thank the reviewers and editor for their detailed, and constructive feedback, which we have carefully considered throughout the revision process. In response, we undertook a comprehensive series of new experiments, including single-cell genotyping to accurately demultiplex donor identities, immunocytochemistry analyses assessing cell proliferation in astrocytes and neurons (Ki67, GFAP, DAPI, MAP2) and detailed synapse density profiling (excitatory and inhibitory markers) at multiple key developmental timepoints (15, 30, and 60 days), as well as a evaluating the effects of APAT (formerly Combination #2) on neurodegeneration and astrogliosis at 30 days. To enhance data transparency and robustness, we generated several new supplementary figures showing representative synaptic co-localisation images, breakdowns of synaptic activity by individual iPSC-derived neuronal lines and playground models, and detailed tables and histograms documenting data exclusion and quality control across all assays. Major figures were updated to include these new experimental replicates, improved statistical analyses, clearer data presentation with added data points and box plots replacing histograms where appropriate. Additionally, multiple supplementary figures now include metadata on independent neuronal differentiations and batch information for greater reproducibility and clarity. Throughout the manuscript, we have revised text sections in Results, Methods, Discussion and Limitations to comprehensively reflect these updates, clarify methodological details such as field-of-view filtering criteria and drug dosing rationales, discuss potential biases and limitations candidly, and refine terminology for more precise scientific communication. Changes in the text are highlighted in red in the revised manuscript and responses to each specific comment are outlined below in blue. This extensive revision demonstrates our commitment to addressing every reviewer concern thoroughly, improving the rigor and transparency of our study, and ultimately strengthening the overall impact and clarity of the manuscript. We truly appreciate the opportunity to improve our work and hope these revisions meet the high standards expected. A summary of the figure changes is outlined below: ● Figure 1B: Updated to include separate UMAP panels for healthy and MPSIIIA samples, demonstrating comparable cell type proportions between genotypes. ● Figure 2D-E, Figure 3E, H, J and Figure 4H, L, G, K: Updated statistical analysis to a Kruskal-Wallis one-way ANOVA with Dunn’s correction for multiple comparisons. ● Figure 4A-C: New data from an independent neuronal differentiation for Synapsin, Gephyrin and PSD-95. ● Figure 4F, J: Updated formatting includes dot points to represent individual donors and playground models. ● Figure 4-R: Updated formatting including dot points to represent individual neurons patched in the playground model. ● Figure 5C-G: Updated statistical analysis to a mixed-effects model (REML), with cell line and time as fixed effects and condition as a random effect, followed by Šídák’s multiple comparisons test. ● Figure 6A, C: Updated cell type classification. ● Figure 6F: New panel examining the expression of glutamatergic and GABAergic postsynaptic genes in excitatory and inhibitory neurons. ● Figure 7: Updated formatting to align with the colour scheme used in companion manuscript. ● Figure 7F: Updated analysis of MEA data using the average recordings from 45 to 64 days as pretreatment baseline (previously 14 to 64 days). ● Supplementary Figure 1B-H: New data showing the distribution of various cell types among healthy and MPSIIIA donors and proliferation/cell cycling analysis of our playground cultures. ● Supplementary Figures 2-5A, 7A, 8A, 10, and 12A: New data summarising the number of independent neuronal differentiations for each cell line, and the total number of neurons, wells and differentiations per healthy and MPSIIIA conditions. ● Supplementary Figure 5C-D: Data for capacitance and cell body surface area data represented as boxplots to highlight data distribution for Figure 2D-E. ● Supplementary Figure 7D-F: Data for maximum AP amplitude, upstroke:downstroke ratio, AP duration, trough voltage and resting membrane potential represented as boxplots to highlight data distribution for Figure 3E. ● Supplementary Figure 8B-C: Data for voltage-gated potassium channel analysis represented as box plots to highlight data distribution for Figure 3H & 3J. ● Supplementary Figure 9: New figure highlighting synaptic co-localisation in high-content imaging. ● Supplementary Figure 11: New figure showing AMPA- and GABA-mediated spontaneous synaptic activity over neurodevelopment split by ESC lines, iPSC cell lines and playground model. ● Supplementary Figure 13A: New data from a 30-day snRNAseq experiment and updating formatting to include percentage contribution per donor and labelling of donors after genotyping and demultiplexing. ● Supplementary Figure 16: Updated analysis of MEA data using the average recordings from 45 to 64 days as pretreatment baseline (previously 14 to 64 days). ● Supplementary Figure 17: New figure and data validating efficacy of APAT on phenotypes from companion manuscript. ● Supplementary Figure 18-20: New figures indicating the amounts of data discarded during quality control assessment of high-content imaging data. ● Supplementary Figure 21: New figure indicating the number of inactive wells and electrodes discarded from network event frequency metric analysis of MEA data."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I would like to congratulate the authors for the extensive and excellent work performed. The addition of new replicates for synaptic stainings, new data presentation, and the new scRNAseq data are clearly improving the quality of the work and clarity of the manuscript. My only remaining concern is about the BIAD03 line for several reasons. First, this line shows a quite higher expression of SOX2 at the iPSC level (companion manuscript), which could have an impact in the differentiation from NSC to neurons since SOX2 needs to be downregulated during neuronal differentiation. In fact, this is the line with lower excitatory postsynaptic communication, and interestingly, in the scRNAseq data most of excitatory neurons generated from this line cluster separately from all other excitatory neurons generated by disease or healthy lines (Fig 1B, Supplementary Fig 1B and Fig 6A). What are the differences in between these two populations and what can be the functional impacts of these differences? In Supplementary Fig. 12, it actually seems that for some of the measurements, 1 or 2 individual patient lines could be driving some of the differences and one wonders if BIAD03 is that line and the driver of the functional changes in the playground networks since it is clearly dominating it at the later time points.", "reviewer_2": "The authors have adequately addressed all of my previous comments and concerns. They have conducted additional experiments and provided detailed information on the biological replicates for improved clarity. These additions enhance the reproducibility of the reported phenotypes and compound rescue, significantly strengthening the overall rigor of the study.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We thank the reviewer for their positive assessment of our revisions and for highlighting the improvements in data quality and clarity, which was the result of insightful reviewers’ suggestions in the first round of review. We also appreciate their detailed attention to the BIAD03 line. Below, we address BIAD03 concerns and questions using electrophysiological and transcriptomic datasets, which show that BIAD03 undergoes typical neural differentiation and only begins to diverge at a later time in culture, likely reflecting donor-specific disease variability rather than impaired lineage commitment. We acknowledge the reviewer’s concern that elevated SOX2 mRNA levels in the BIAD03 iPSC line could potentially influence downstream neuronal differentiation and function. We agree that appropriate downregulation of SOX2 is an important feature of neural differentiation. However, published work (Cell Stem Cell 2007, DOI: 10.1016/j.stem.2007.09.002) indicates that moderate elevation of SOX2 at the iPSC stage does not impair differentiation, provided SOX2 is correctly regulated at the NPC stage. Consistent with this, BIAD03 NPCs display comparable proportions of NES+/SOX2+ and NES+/SOX2+FOXG1+ cells to the other MPS IIIA lines (Figure R1A-B), supporting appropriate lineage commitment. While BIAD03 shows slightly higher SOX2 mRNA levels at the iPSC stage (Figure R1C), the absolute expression level (2-ΔCt = 0.08) is similar to that observed in BIAD10 and ESC lines and lies within the reported range for human iPSCs. BIAD03 also meets standard pluripotency and quality control criteria, including OCT4/SOX2/KLF4 co-expression (Figure R1D), typical morphology, and comparable proliferation rates. In addition, grouped analysis of SOX2 protein expression reveals no significant difference between healthy and MPS IIIA iPSC lines (Figure R1E). Most importantly, BIAD03 neurons exhibited electrophysiological properties similar to other MPS IIIA lines, confirming successful neural differentiation (Figure R1F-H). They showed fast, repetitive action potential firing (Figure R1F), supported by functional voltage-gated channels (Figure R1G). Consistent with early synapse formation, some cells across all lines lacked AMPA inputs at the earliest time point (30-60 days). AMPA activity in BIAD03 at 30-60 days was indeed relatively low, but differing significantly only from BIAD01 (Figure R1H). BIAD03 GABAergic activity matched BIAD01 and BIAD11, while BIAD02 and BIAD10 were lower (Figure R1H). Furthermore, 60-day snRNA-seq data clustered BIAD03 excitatory neurons with other MPS IIIA patients (Figure R1I-J), supporting early similarities, and further excluding the possibility of BIAD03 not differentiating correctly. We agree with the reviewer that the distinct transcriptomic clustering of BIAD03 excitatory neurons at 120 days is intriguing (Manuscript Figures 1B and 6A; Supplementary Figure 3B). To explore this further, we performed differential expression and gene ontology analyses comparing BIAD03 excitatory neurons with those from the other MPS IIIA lines. Genes upregulated in BIAD03 were enriched for pathways related to axonal and dendritic projection development (Figure R2A), which aligns with our morphological analysis showing increased cellular complexity in BIAD03 neurons at both time points, including greater cell body surface area, branching (node number), and total neurite length (Figure R2B). Conversely, genes downregulated in BIAD03 were enriched for synaptic-related pathways, including glutamatergic synaptic transmission (Figure R2C). This finding is consistent with our patch-clamp data at both early and later stages, where BIAD03 exhibits lower AMPA drive relative to other patient lines (Figure R1H; Supplementary Figure 18). Importantly, these transcriptional differences do not translate into deficits in other key functional measures, such as action potential firing capacity (Figure R2D) or voltage-gated sodium and potassium channel function (Figure R2E), which are comparable across all patient lines. Similarly, in MEA assays, BIAD03 closely follows the cohort average across maturation stages and readouts (Figure R2D), consistent with the lower AMPA activity detected by patch-clamping. Together, these data indicate that BIAD03 alone does not drive the synaptic or network phenotypes observed across the cohort. Rather than reflecting impaired neural differentiation, we interpret these findings as an example of possible inter-patient heterogeneity in MPS IIIA, a condition known to exhibit variability in disease onset, progression, and severity despite shared genetic causes. Although this study was not designed to detect with confidence patient-specific differences, capturing population heterogeneity is a strength in this multi-donor model, as it more closely reflects the clinical spectrum of the disease than isogenic or single-donor approaches. BIAD03 exhibits characteristics suggesting that it falls on the milder side of the MPS IIIA disease spectrum, as evidenced by reduced lysosomal burden, less pronounced reactive astrogliosis, and a slower rate of neurodegeneration. As a result, BIAD03 neurons tend to persist longer in the neural culture and may contribute a relatively higher proportion of excitatory neurons at later time points in the playground (e.g., 120 days), but did not bias the conclusions. In summary, although BIAD03 shows modestly elevated SOX2 mRNA levels at the iPSC stage, this is appropriately regulated during neural differentiation, resulting in normal neuronal identity and function (at the early neural maturation timepoint; 30-60 days). The late-stage (120 day) transcriptomic divergence of BIAD03 may reflect donor-specific biological variability, including relatively increased neuromorphological complexity and reduced synaptic drive, rather than a neural differentiation issue. Importantly, electrophysiological and MEA data demonstrates that BIAD03 does not disproportionately influence the functional phenotypes observed across the cohort. We believe the inclusion of BIAD03 may strengthen the model by representing the milder end of the MPS IIIA spectrum, therefore enhancing the overall generalisability of our findings."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I thank the authors for their thorough characterization of the BIAD03 line differentiation to address my previous concern and agree that some of the differences could be due to inter-individual specific features."}, "author_response": ""}]

Decision letter
Thank you again for submitting your manuscript "Hyperactive synaptic circuits in a human neuronal model of childhood dementia" to Nature Communications. We have now received reports from 3 reviewers and, on the basis of their comments, we have decided to invite a revision of your work for further consideration in our journal. In particular your revision should: 1. Address the remaining concerns of the reviewers, 2. Ensure that all data in this paper is independent; there can be no overlapping data with the other paper which will not be published at the same time. 3. Since the Greenberg/drug screening paper will not be published simultaneously the we suggest the drug recovery data be removed from Mazzachi/hyperactive circuits paper. 4. Remove all references to the Greenberg/drug screening paper. When resubmitting, you must provide a point-by-point response to the reviewers’ comments. Please show all changes in the manuscript text file with track changes or colour highlighting. If you are unable to address specific reviewer requests or find any points invalid, please explain why in the point-by-point response. We sincerely thank the reviewers for their careful re-evaluation of our manuscript and thank the editors for allowing us the opportunity to submit a revised version for further consideration. We are grateful for the constructive feedback and are delighted that, with these revisions, the work may be suitable for publication. We have made the following amendments to address the reviewers' and editors' latest comments and requests: 1. Addressed the remaining concern of Reviewer #1 with electrophysiological and transcriptomic analysis, which can be found in the two new figures attached below. 2. Ensured all data in this manuscript is independent. We confirm that the revised version has no overlapping data with the other paper. We transferred all iPSC/NPC quality control data and karyotyping from the Greenberg/drug screening paper into this manuscript to support the successful generation of neural cultures from high-quality stem cells. 3. Removed the drug screen data (originally Figure 7) from the paper and all references throughout the manuscript as requested. 4. Removed all references to the Greenberg/drug screening paper throughout the manuscript as requested.