Abstract

   Autism Spectrum Disorder (ASD) affects 1 percent of the world’s
   population with an increased prevalence of 178 percent since 2000.
   Although altered synaptic function putatively accounts for many of the
   abnormalities seen in ASD, the specific molecular mechanisms underlying
   this disorder remain poorly defined. A growing body of evidence
   suggests that extracellular vesicles (EVs), specifically exosomes, play
   a critical role in cellular communication within the brain. While they
   have been implicated in various types of diseases from cancer to
   neurodegeneration, their involvement in ASD remains largely unexplored.
   In this study, we utilized patient-derived cortical organoid models to
   characterize EVs secreted by human three-dimensional (3D) tissue and
   defined their cargo. Our study reports, for the first time, alterations
   in ASD organoid-derived EVs in comparison to healthy control cortical
   EVs. By utilizing small RNA sequencing, proteomics, nanoparticle
   tracking and microscopy, we provide a comprehensive characterization of
   the cargo carried by EVs secreted from human 3D forebrain models. Our
   findings reveal substantial differences both in the RNA and protein
   content of ASD-derived EVs, providing insight into disease mechanisms
   as well as highlighting the potential of exosome-based diagnostics and
   therapies for ASD.

   Subject terms: Autism spectrum disorders, Molecular neuroscience

Introduction

   Autism Spectrum Disorder (ASD) is a developmental disability
   characterized by impaired social communication, restrictive repetitive
   behaviors, and quite often, cognitive deficits [[37]1]. ASD affects 1
   in 36 children alone in the US, with a gender ratio of 4:1 affecting
   boys- a more than fourfold increase since 2000 [[38]2]. ASD is
   diagnosed using standardized assessment tools such as the Autism
   Diagnostic Observation Schedule (ADOS) in accordance with the criteria
   outlined in the Diagnostic and Statistical Manual of Mental Disorders
   (DSM-5) [[39]1, [40]2]. After half a century of studying ASD, we have
   yet to fully understand the initial insults, progression and
   pathophysiology of this disease, and therefore lack an effective
   treatment. Approximately 95% of these diagnoses are idiopathic,
   indicating the absence of a known genetic anomaly [[41]3, [42]4].
   Genome-wide association and animal-model studies from syndromic ASDs
   suggest that dysregulated synaptic function accounts for most of the
   abnormalities found in ASD. A combination of factors - genetic
   abnormalities [[43]2, [44]3], epigenetics [[45]4, [46]5], immune system
   dysfunction [[47]6, [48]7], maternal immune activation [[49]8–[50]10],
   and electrophysiological changes [[51]11, [52]12] have been implicated
   in the etiology of ASD [[53]13–[54]15]. Current treatments ranging from
   stem cell therapies to behavioral interventions, are only helpful in a
   portion of patients [[55]2], and typically consist of a combination of
   behavioral therapies and interventions aimed to reduce symptoms that
   interfere with the quality of life [[56]16]. Given the brain’s cellular
   diversity and complexity, manipulating a single cell type is unlikely
   to yield effective therapeutic outcomes.

   The majority of ASD cases exhibit polygenic susceptibility and involve
   coordinated cell communication amongst multiple cell types
   [[57]17–[58]20]. Over the last decade, extracellular vesicles (EVs)
   have been identified as critical players in cell-to-cell communication
   [[59]21] where they are produced by virtually all cell types and are
   released into the extracellular environment [[60]21–[61]23]. To date,
   three main types of vesicles have been characterized: apoptotic bodies
   (500–2000 nm), microvesicles (50–1000 nm), and exosomes (40–200 nm).
   Although EVs differ in size, function, and biogenesis [[62]21–[63]23],
   all are secreted, carry molecules, and are involved in both short and
   long-distance cellular communication [[64]24, [65]25]. Additionally,
   EVs can cross the blood–brain barrier (BBB), and be detected
   peripherally, making them intriguing candidates in neurodevelopmental
   biomarker discovery [[66]25], with recent evidence implicating a role
   for EVs during critical periods of brain development [[67]20–[68]25].

   The smallest class of EVs are exosomes ranging from 40–200 nm in size
   and carry cargo composed of mainly proteins and micro-RNAs [[69]21].
   Exosomes stimulate recipient cells through endocytosis and endosome
   fusion, direct release of cargo content, and signaling through cell
   surface receptors [[70]23]. Virtually all cell types in the brain
   release exosomes, including neurons, astrocytes, microglia,
   oligodendrocytes, and endothelial cells [[71]25–[72]32]. Neurons and
   glia have been reported to release exosomes in vitro and in vivo
   [[73]33–[74]37]. Trophic support of neurons from oligodendrocytes and
   astrocytes is thought to be conveyed by exosomes [[75]35]. Current
   evidence for exosome signaling in the brain points toward their role in
   transcriptional regulation [[76]38], neurogenesis [[77]39, [78]40],
   synaptic plasticity [[79]41, [80]42], and neuroinflammation [[81]43,
   [82]44].

   While studies found that cargo composition of exosomes is altered in
   diseases such as cancer and neurodegenerative disorders [[83]22,
   [84]45, [85]46] exosome pathology in neurodevelopmental disorders is
   yet to be defined. A recent study examined exosomes from frozen
   postmortem prefrontal cortex and detected miRNA alterations in
   Schizophrenia and bipolar disorder [[86]46]. Exosomes have been shown
   to both carry hyperphosphorylated Tau between neurons and could seed
   amyloid-β aggregation in Alzheimer’s Disease [[87]47, [88]48]. In vivo
   studies demonstrated that the transmission of Tau via microglia-derived
   exosomes was more efficient than the transmission of the naked form of
   Tau [[89]49]. Recent studies have also used exosomal alpha-synuclein as
   a novel biomarker of early-onset Parkinson’s disease [[90]50].

   Although exosome research in neurodevelopmental disorders is still in
   its early stages, studies have implicated exosome dysfunction in ASD.
   In a 2D human cell culture model of Rett syndrome generated by
   MECP2-knockdown, it was shown that exosomes of MECP2-deficient cells
   lack proteins crucial for neuronal circuit development, while control
   exosomes were significantly enriched in proteins important for
   proliferation, neuronal development, and synaptic maturation [[91]51].
   Treatment of MECP2-knockdown cultures with control exosomes rescued the
   defects in neuronal growth and differentiation [[92]51]. A similar
   study evaluated the efficacy of stem cell-derived exosomes on
   ameliorating autism-related behaviors in a BTB3 mouse model of
   idiopathic autism [[93]52]. Mice treated with exosomes either
   intranasally or intravenously showed increased social interactions and
   decreased repetitive behaviors 3 weeks after treatment. A later study
   administered stem cell-derived exosomes intranasally to a ShankB3 mouse
   model of autism and observed improvements in vocalization and
   repetitive behavior phenotypes, as well as increases in GABA receptor
   GABARB1 expression [[94]53]. While different studies highlight the
   ability of exosomes to improve autism-related behavioral phenotypes
   both in idiopathic and syndromic mouse models of ASD, the exact EV
   pathology and how it may contribute to disease initiation or
   progression are ill defined.

   The emergence of induced pluripotent stem cell (iPSC) technology to
   derive monolayer brain cell types and 3D brain organoids from human
   donors offers a unique opportunity to study exosome pathology in ASD
   [[95]54]. Previously, iPSCs-derived cerebral organoids were used to
   study the neuropathologies of ASD and schizophrenia during early brain
   development [[96]55–[97]58]. In the present study, we demonstrate
   successful isolation and characterization of EVs from ASD
   patient-derived forebrain organoids. We have identified significant
   alterations in the RNA and protein cargo of ASD EVs in comparison to
   healthy control EVs. Altered gene expression in EVs pointed towards
   disruptions in polyol pathways, protein synthesis, ubiquitination, and
   chromatin remodeling, all of which are linked to synaptic plasticity,
   cellular signaling, and protein homeostasis. Intriguingly, we
   identified several known ASD risk genes among the differentially
   regulated RNAs and proteins in EVs derived from ASD forebrain
   organoids. Our findings provide novel insights into the molecular
   mechanisms of ASD and supports the potential of EVs as therapeutic
   targets for the disorder.

Results

EVs secreted from CTRL and ASD organoids display similar density and
morphology

   To characterize EVs in early ASD brain development, we generated 3D
   dorsal forebrain organoids from human iPSCs. We sampled organoids from
   16 unique human donors, comprising n = 8 healthy controls (CTRL) and
   n = 8 individuals with ASD (Fig. [98]1a). Briefly, iPSCs from human
   donors were grown as monolayer cultures atop vitronectin-coated plates
   before being dissociated with Accutase to yield single-cell iPSC
   suspensions. Stem cell suspensions were correspondingly cultured into
   3D embryoid bodies before being subjected to Cortical Differentiation
   Medium (CDM) and maintained as previously described [[99]59]. All 16
   lines employed in the study formed organoids of similar morphologies
   (Fig. [100]1b), but ASD-derived organoids were significantly larger in
   diameter than CTRLs (Fig. [101]1b, c, Fig. [102]S1a). The ASD forebrain
   organoids exhibited similar phenotypes of increased progenitor pool and
   reduced neurogenesis as previously reported in this model system as
   well as in patients (Fig. [103]1d, e, Fig. [104]S1b-c) (see
   Discussion).

Fig. 1. Forebrain organoids generated from iPSC lines derived from healthy
controls and individuals with ASD.

   [105]Fig. 1
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   a Schematic depicting experimental design and timeline. Briefly,
   forebrain organoids were generated using a previously described
   protocol [[107]59]. At 60 DIV, media was collected, and EVs were
   isolated, followed by subsequent analysis. b Light microscopy images of
   representative brain organoids at 60 DIV from all 16 lines used in the
   study. c Bar graph depicts average size of organoids derived from CTRL
   and ASD lines (n = 8 lines per group; n = 3 organoids per line;
   Student’s t-test). d Representative images of SOX2 (green),
   β-III-tubulin (red) and DAPI (blue) immunostaining in CTRL and ASD
   organoids to assess proliferative and neurogenic zones. e
   Quantifications of ventricular-like zone density and thickness across
   16 organoid lines used in the study (n = 8 lines per group; n = 3–10
   organoids per line). Scale bar = 0.25 mm in b; 500 µm in d. Data is
   represented as mean ± SEM, p < 0.05, Student’s t-test.

   We next sought to determine whether EVs in ASD organoids are altered
   compared to EVs secreted by CTRL organoids. The schematic of
   experimental design and timeline is depicted in Fig. [108]1a. To study
   ASD organoid-derived EVs, we generated CTRL and ASD organoids and
   collected the supernatant at day 60 of in vitro differentiation (60
   DIV). Ten days prior to supernatant collection, FBS was removed from
   media to prevent contamination with FBS-derived vesicles (Fig.
   [109]1a). EVs were isolated from culture media using a combination of
   commercially available kits (ThermoFisher) and ultracentrifugation. To
   validate EV purification, we followed the guidelines of the
   International Society for Extracellular Vesicles that recommends
   nanoparticle tracking analysis (NTA) for biophysical features,
   transmission electron microscopy (TEM) for high resolution imaging, and
   Western blot for validation of EV-specific proteins [[110]60, [111]61]
   (Fig. [112]2a). Western blot analysis verified the presence of exosomal
   markers CD9 and CD63 in the EV pellet post kit precipitation, but not
   in the processed supernatant sample, pointing to efficient EV isolation
   (Fig. [113]2b). NTA revealed that across 16 lines, the majority of
   particles were < 400 nm (Fig. [114]2c), with average particle sizes in
   the 90–250 nm range, representative of exosome size. NTA also allowed
   us to measure EV concentrations across samples. Intriguingly, ASD EVs
   and CTRL EVs did not differ in either size or density (Fig. [115]2d).
   To visualize organoid-derived EVs, we employed TEM as previously
   described [[116]61, [117]62]. TEM images revealed vesicles exhibiting
   cup/rose-like morphologies, commonly associated with exosomes (Fig.
   [118]2e). Taken together, we have used three techniques that
   independently assessed the size, morphology, or biochemistry of
   vesicles isolated from ASD and CTRL forebrain organoids. Although these
   analyses collectively suggest that the isolated EVs are mainly composed
   of exosomes, we cannot exclude the possibility that we have also
   isolated other types of EVs along with exosomes. Therefore, we simply
   refer to the purified vesicles as EVs.

Fig. 2. Exosome abundance in organoid-derived EVs.

   [119]Fig. 2
   [120]Open in a new tab

   a Schematic of exosome validation methods. EVs were isolated from
   maintenance media of CTRL and ASD dorsal forebrain organoids at 60 DIV
   using a combination of Exo-isolation kit and centrifugation. EVs were
   then resuspended in either RIPA or PBS buffers, depending on downstream
   analysis. b Representative Western blot examples in pellet and
   supernatant samples. Only pellets, not supernatant, were positive for
   exosome markers (CD9, CD63) indicating high efficiency in EV isolation.
   c Representative graphs of nanoparticle tracking analysis (NTA). The
   x-axis represents concentration (particles/mL), and the y-axis
   represents particle size (nm). The top four are representative plots
   from CTRL lines while the bottom four plots are representative examples
   from ASD lines. The peaks typically appeared in the < 200 nm range,
   representative of exosome size. d Bar graphs depicting no significant
   difference in either average particle size or concentration between
   CTRL and ASD EVs at the NTA analysis (Student’s t-test). Each point on
   the graph represents an independent cell line. Data are represented as
   mean ± SEM. e Representative TEM images of EVs isolated from CTRL and
   ASD organoid media. Vesicles typically displayed a canonical exosome
   cup/rose shape and size. Scale bar = 200 nm.

RNA and protein profiles are significantly different between ASD and CTRL EVs

   To compare the small RNA profiles of EVs derived from CTRL organoids
   and ASD organoids, we employed small RNA sequencing using the NEXTFLEX
   Small RNA Sequencing kit v4 which is designed to capture short RNAs
   (17–65 nucleotides) with 5´ monophosphorylated and 3´ hydroxylated
   ends. However, the kit also allows for the capture of larger molecules
   ( < 200 bp) by using the no-size selection protocol. We were able to
   detect RNAs from all 16 lines used in the study with dispersion
   estimate signifying low read count bias (Fig. [121]S2a). Of note, one
   line (CTRL line 3) showed low levels of RNA abundance (Fig. [122]S2b)
   and thus was excluded from downstream analysis. We first assessed RNA
   types present in the purified EVs and determined that organoid-derived
   EVs carry a large RNA heterogeneity. We incorporated the cut-off of 50
   base pairs early in our pipeline and into the alignment steps.
   Intriguingly, in addition to non-coding RNAs, the analysis revealed
   that many of the fragments consistently aligned to protein coding RNAs
   as well as long RNAs both in CTRL and ASD EVs (Fig. [123]3a). Given the
   heterogeneity of ASD, no RNAs were commonly altered across all ASD
   lines. However, several RNAs showed recurrent alterations across
   multiple ASD lines when compared to CTRL lines. We collectively
   identified 177 RNAs that were differentially regulated in ASD EVs
   compared to CTRL EVs. Of those 177 RNAs, 113 were protein-coding, with
   58 upregulated and 55 downregulated. The remaining 64 differentially
   regulated RNAs were comprised of miRNAs, lincRNAs, snoRNAs,
   pseudogenes, and some uncharacterized transcripts (Fig. [124]3b, c).
   The top 20 differentially regulated protein-coding RNAs in ASD are also
   shown (Fig. [125]S2c). To gain more insight into the function of
   differentially regulated RNAs, we grouped them based on their
   associated biological process, molecular functions, cellular
   components, and pathways (Fig. [126]3d). Molecular function analysis
   predicted PIP2 phosphatase activity as the most differentially
   regulated molecular pathway. Among the 177 differentially regulated
   RNAs, ubiquitin ligase complex and synaptic vesicle membranes were the
   most enriched cellular components. In the differentially regulated
   non-coding RNA pool (64 RNAs), there were 31 miRNAs significant at the
   raw p-value level (Fig. [127]3e). While some of these miRNAs were
   previously shown to be EV transported [[128]63–[129]65], most of the
   differentially regulated miRNAs were novel as EV cargo (Fig. [130]S2d).
   To predict the functional impact of varied miRNA profiles in EVs across
   all ASD lines, we performed a miRNA–target gene network topology
   analysis. The network revealed a highly interconnected regulatory
   architecture, with several hub genes targeted by multiple EV-enriched
   miRNAs (Fig. [131]3f). Ubiquitin C (UBC) emerged as the most central
   node (degree = 99, betweenness = 4835.98), suggesting a pivotal role in
   EV-mediated signaling. Other top-ranking genes included heat shock
   proteins (HSP90AA1, HSP90AB1), transcription factors (TP53, HNF4A), and
   synaptic regulators (AKT1, CTNNB1). These hubs are implicated in stress
   response, proteostasis, chromatin remodeling, and synaptic
   plasticity—processes increasingly recognized as dysregulated in ASD.
   Our findings reveal that human forebrain organoid-derived EVs carry
   various RNA subtypes and that RNA content of ASD EVs is vastly
   different than CTRL EVs.

Fig. 3. Differentially regulated RNAs point to translational control and
ubiquitin activity in ASD EVs.

   [132]Fig. 3
   [133]Open in a new tab

   a RNA counts by category per ASD and CTRL sample (n = 16 samples). CTRL
   samples CS1 and CS6 are 2 CTRL lines of female origin, while all the
   other CTRL lines are of male origin. b Volcano plot displaying
   significantly expressed genes. Thresholds of significance used included
   Log2 FC > +/− 1.5 and adjusted log10 Benjamini-Hochberg corrected
   p < +/− 0.05. c Hierarchical and K-means heatmap of the top 50
   differentially expressed RNAs in each sample (see [134]supplementary
   materials for the full list of differentially regulated RNAs). d Bar
   graph of GSEA enrichment terms from the respective pathway database.
   Terms ordered by descending odds ratio. Significant GSEA categories
   include i) Gene Ontology: Biological Processes (BP), ii) KEGG Kyoto
   Encyclopedia of Genes and Genomes, iii) Gene Ontology: Molecular
   Function (MF), iv) Gene Ontology: Cellular Component (CC). e
   Hierarchical and K-means heatmap of the top 31 differentially expressed
   miRNAs in each sample; p < 0.05. f miRNA–gene interaction network and
   hub gene analysis. Node size corresponds to the degree of connectivity,
   with larger nodes indicating higher numbers of interactions.

   To compare protein profiles of EVs secreted from CTRL organoids and ASD
   organoids, we employed label-free mass spectrometry and successfully
   captured a large pool of proteins from all 16 lines (Fig. [135]4a, b)
   with comparable variability (Fig. [136]4c). Of note, two ASD lines
   showed lower protein capture than average. Similar to the RNA analysis,
   we did not find proteins that were commonly altered across all ASD
   samples. Collectively, 362 proteins were differentially regulated in
   ASD EVs in comparison to CTRL EVs with the majority exhibiting
   downregulation (279 downregulated and 83 upregulated) (Fig. [137]4b,
   d). We grouped the differentially regulated proteins based on
   biological process, molecular function, cellular component, and pathway
   represented (Fig. [138]4e). The gene ontology (GO) analysis predicted
   cytoplasmic translation and mRNA stabilization as the most altered
   biological processes. While molecular function analysis determined
   ubiquitin ligase and ubiquitin transferase inhibitor activity as the
   most differentially regulated molecular pathways, the top categories
   included ribosomal subunits and the B-WICH chromatin remodeling complex
   in cellular compartment analysis.

Fig. 4. Altered protein expression comprises translation and protein
degradation related categories in ASD EVs.

   [139]Fig. 4
   [140]Open in a new tab

   a Protein counts by category per ASD and CTRL sample (n = 16 samples).
   CTRL samples CS1 and CS6 are 2 CTRL lines of female origin, while all
   the other CTRL lines are of male origin. b Volcano plot displaying
   proteins expressed significantly. Thresholds of significance used
   included Log2 FC > +/− 1.5 and adjusted log10 Benjamini-Hochberg
   corrected p < 0.05. c Coefficient of variance in CV and ASD lines,
   (n = 16 samples), non-significant. d Clustered heatmap of 362
   significant proteins based on a student’s t-test analysis (p < 0.05).
   Values based on z-score normalization. e Gene ontology analysis of
   significant proteins using enrichR. Top 10 categories selected
   (p < 0.05); odds ratio observed between ASD and CTRLs. Terms ordered by
   descending odds ratio. Significant GSEA categories include i) Gene
   Ontology: Biological Processes (BP), ii) KEGG Kyoto Encyclopedia of
   Genes and Genomes, iii) Gene Ontology: Molecular Function (MF), iv)
   Gene Ontology: Cellular Component (CC).

   To determine if varied molecule expression in EVs across all ASD lines
   converge upon known biological pathways, we performed topology-based
   enrichment network analysis (Fig. [141]5). For RNA cargo, enriched gene
   clusters were predominantly associated with apoptosis, metabolic
   regulation, and vesicular trafficking—highlighted by categories such as
   “intrinsic apoptotic signaling,” “GDP biosynthesis,” and
   “clathrin-coated endocytosis”. These findings suggest that ASD EVs may
   influence recipient cells by modulating survival pathways and
   intracellular transport. In parallel, analysis of differentially
   expressed proteins revealed a striking convergence on pathways related
   to translation and RNA processing. Highly enriched terms included
   “cytoplasmic translation”, “RNA processing”, “peptide metabolic
   process”, and “gene expression”, underscoring a coordinated disruption
   in protein synthesis machinery in ASD EVs.

Fig. 5. Altered pathways predicted by network analysis in ASD EVs.

   [142]Fig. 5
   [143]Open in a new tab

   Topology-based network enrichment analysis of differentially expressed
   genes and proteins in ASD-derived EVs. Top: Gene network analysis
   revealed 10 significantly enriched categories, including apoptosis, GDP
   metabolism, and clathrin-mediated endocytosis (p < 0.01). Bottom:
   Protein network analysis identified highly interconnected enrichment in
   translation, RNA processing, and biosynthetic pathways (p < 1×10⁻¹⁵).
   Node size reflects the degree of connectivity within the GO network.

Differentially regulated molecules include ASD risk genes in ASD
organoid-derived EVs

   Lastly, we sought to determine whether differentially regulated
   molecules in ASD EVs include any known ASD risk genes. For this, we
   used the Simons Foundation Autism Research Initiative (SFARI) gene
   database, which focuses on genes implicated in ASD susceptibility.
   SFARI gene comparison pointed to 7 ASD risk genes in the differentially
   regulated RNA pool (Fig. [144]6a). At the protein level, we found a
   significant abundance of differentially regulated SFARI risk genes in
   ASD EVs compared to CTRL EVs (Fig. [145]6b). Notably, a majority of
   these proteins were downregulated in ASD and mostly involved in
   transcription, DNA damage, and chromatin structure (Fig. [146]6c). In
   contrast, upregulated proteins were associated with neuronal
   excitability and calcium signaling. Collectively, these findings
   suggest that differential regulation of ASD risk genes in ASD-derived
   EVs reflects key disruptions in synaptic function, RNA processing, DNA
   stability, and synaptic integrity.

Fig. 6. Differentially regulated RNAs and proteins in ASD EVs include ASD
risk genes.

   Fig. 6
   [147]Open in a new tab

   a Heatmap of the significant SFARI genes among the differentially
   expressed mRNAs in each group. Values based on z-score normalization
   (p < 0.05). b Heatmap of the significant SFARI genes among the
   differentially expressed proteins. Values based on z-score
   normalization (p < 0.05). c enrichR gene ontology analysis of the
   significant SFARI risk genes among the differentially expressed
   proteins in ASD EVs. Top categories selected (p < 0.05); odds ratio
   observed between ASD and CTRL.

Discussion

   In ASD, neural circuits involved in social interaction, communication,
   and repetitive behaviors are disrupted, potentially due to imbalances
   in excitatory and inhibitory neurotransmission and altered
   connectivity. Development of the neuronal circuit and its function
   relies on a continuous crosstalk between neurons and non-neuronal
   cells. In the last decade, EVs have gained attention for their role in
   facilitating intercellular communication by delivering cargo such as
   RNA and proteins, which can influence a variety of cellular processes
   in recipient cells [[148]66–[149]71]. The cargo within EVs is dynamic
   and can vary depending on the cell type of origin, suggesting that EVs
   may play a central role in modulating neurodevelopmental processes.
   While recent studies implicate EVs in neuronal phenotypes seen in ASD
   models with potential use of exosomes as a novel therapeutic tool, the
   pathology of EVs and its exact impact on the initial insults of the
   disorder are yet to be determined. Our study takes a critical step in
   understanding this dynamic by providing the first comprehensive
   characterization of EVs secreted by multicellular brain organoids
   derived from both ASD patients and neurotypical controls. By profiling
   the RNA and proteomic cargo of patient-derived EVs during
   neurodevelopment, we postulate how EV-mediated intercellular
   communication may be altered in ASD.

   Adopting a protocol for the generation of human dorsal forebrain
   organoids [[150]59], we generated hundreds of reproducible organoids
   across 16 different iPSC donors, 8 of which were derived from
   individuals with idiopathic ASD (Fig. [151]1, see also [152]Supp
   Table). The morphological phenotype in ASD-derived brain organoids,
   specifically the increased size, align with well-documented
   neurodevelopmental phenotypes observed both in in vitro organoid models
   of ASD and some patients [[153]72, [154]73]. This phenotype is
   consistent with the increased brain volume seen in a fraction of ASD
   patients, particularly during early childhood [[155]74], supporting
   that brain organoid models could serve as a relevant representation of
   the abnormal developmental trajectories observed in ASD. The increased
   thickness of the ventricular zone in ASD organoids further supports
   this, as it aligns with similar findings reported by other groups in
   ASD organoids [[156]75] and in patients, where abnormal changes in the
   subependymal zone have been linked to neurodevelopmental delays and
   irregular brain development [[157]74]. Despite the genetic variability
   in iPSC lines, our model system consistently recapitulates key aspects
   of early brain pathology in neurodevelopmental disorders
   [[158]54–[159]58]. This reinforces the potential of brain organoids as
   a platform for studying ASD and exploring therapeutic avenues for
   modulating early neurodevelopmental disruptions.

   Here, we demonstrate the successful isolation of EVs from CTRL and ASD
   forebrain organoids using kit-based precipitation of EVs. Although
   recent studies have isolated EVs from CTRL organoids using varying
   protocols [[160]76, [161]77] standardized methods for EV isolation
   remain elusive. Several isolation techniques are employed, such as
   ultracentrifugation and precipitation-based kits, each with its own
   advantages and applications [[162]78–[163]81]. Given the limited
   culture size and heterogeneity of our iPSC lines, we opted for
   kit-based precipitation to maximize EV yield. We confirmed successful
   isolation of EVs by 3 different methods- NTA, TEM and Western blotting,
   ensuring consistent EV isolation across all lines. While we observed no
   major morphological differences in EVs between ASD and CTRL organoids,
   the overlap in size and surface markers between exosomes and other
   micro-vesicles made it challenging to distinguish specific EV subtypes
   with certainty. Thus, for the scope of this study, we focused on a
   deeper analysis of the RNA and proteomic cargo itself, rather than
   outer differences of EVs, aiming to uncover potential molecular
   differences in ASD. In our analysis of the RNA cargo in EVs, we
   observed significant differences both in coding and non-coding RNA
   content between ASD and CTRL samples (Fig. [164]3). Although most of
   the EV studies focus on miRNA content, EVs were also shown to carry
   mRNAs [[165]82]. In fact, we found that 58 coding RNAs were upregulated
   and 55 coding RNAs were downregulated in ASD EVs compared to CTRLs
   (Fig. [166]3c). While there was no specific coding RNA commonly altered
   across all ASD samples, some transcripts, such as FOXP2, SP140, and
   MARCHF3 exhibited upregulation in more than 50% of ASD samples compared
   to all CTRL samples. Intriguingly, these genes have commonly been
   implicated in the regulation of gene expression. While FOXP2 is a
   transcription factor, SP140 influences chromatin accessibility to
   regulate expression of genes involved in neuronal development.
   Similarly, MARCHF3 functions as an E3 ubiquitin ligase, inducing
   protein degradation, and thereby regulating protein expression. These
   alterations are also reflected in our enrichment analysis (see below).
   Biological process analysis of DEGs also pointed to clinically
   significant pathways such as polyols and phosphoinositide metabolism
   (Fig. [167]3d). Recent findings have suggested that reducing polyol
   consumption can alleviate gastrointestinal and behavioral symptoms in
   ASD individuals [[168]83]. Similarly, phosphoinositides, particularly
   phosphatidylinositol 4,5-bisphosphate (PIP2) and its metabolites, play
   an important role in regulating cellular signaling pathways involved in
   neuronal development, synaptic plasticity, and neurotransmitter release
   [[169]84, [170]85]. Disruptions in phosphoinositide signaling could
   affect neuronal connectivity and synaptic function, both of which are
   central to the pathophysiology of ASD. In addition to coding genes, we
   also identified a range of upregulated noncoding RNA elements,
   particularly miRNAs, in ASD-derived EVs (Fig. [171]3e, Fig. [172]S2d).
   Specifically, exosomal miRNAs were shown to play a pivotal role in
   regulating gene expression by modulating mRNA stability and translation
   [[173]86–[174]88]. miRNA370 was commonly downregulated in all ASD lines
   compared to the majority of CTRL lines. miRNA370 was previously shown
   to increase permeability of the BBB [[175]88]. Indeed, altered BBB
   permeability and associated inflammation have been observed in ASD
   [[176]89]. On the contrary, miR3943 was upregulated in 6 out of 8 ASD
   samples compared to all CTRL samples. Intriguingly, this human-specific
   microRNA has been implicated in the regulation of excitatory
   synaptogenesis timing [[177]90]. Our study also identified novel miRNAs
   in the context of EV cargo such as miRNA6724–1, miRNA6724–2,
   miRNA6724–3, and miRNA6724–4. Of note, miRNA4286 overexpression was
   identified as an unfavorable prognostic marker in non-small cell lung
   cancer [[178]91].

   We also detected altered expressions of proteins involved in key
   cellular processes such as ubiquitination, translation, and ribosome
   biogenesis in ASD EVs (Fig. [179]4). Perturbations in the
   ubiquitin-proteasome system, a critical pathway for protein turnover
   and the degradation of damaged or misfolded proteins, can lead to the
   accumulation of faulty proteins, triggering neuroinflammatory
   responses. This disruption is particularly relevant in the context of
   ASD, where chronic inflammation and immune dysregulation are
   increasingly recognized as contributing factors to neurodevelopmental
   impairments [[180]92–[181]94]. Several lines of evidence suggest that
   alterations in the ubiquitin-proteasome system and other
   ubiquitin-related pathways may contribute to the onset or severity of
   ASD [[182]95–[183]100]. Of note, proteins of the B-WICH complex, a
   regulator of chromatin remodeling [[184]101–[185]103], along with
   proteins involved in translation and ribosome regulation, compromised
   the top categories in GO analysis of altered EV proteome in ASD. In
   fact, whole exome sequencing studies employing large patient cohorts
   have revealed that the majority of ASD risk genes are chromatin and
   gene expression regulators [[186]104]. Similarly, altered translation
   has been implicated in various diverse types of ASD models
   [[187]105–[188]107]. Most importantly, our topology-based enrichment
   network analysis of varied molecule expression found in EVs across
   patient lines pointed to limited biological pathways most of which have
   been linked to ASD (Fig. [189]5). For RNA cargo, enriched gene clusters
   were predominantly associated with apoptosis, metabolic regulation, and
   vesicular trafficking, as observed by top categories in the topology
   map (GDP biosynthesis, clathrin-endocytosis, apoptotic signaling).
   These findings suggest that ASD EVs may influence recipient cells by
   modulating cell survival pathways and intracellular transport. The
   network analysis of proteins with varied expressions across ASD EVs
   found enrichment in translational and gene expression regulation
   categories. This network analysis aligns with the findings from the GO
   analyses of differentially regulated EV proteins from individual lines,
   which collectively pointed to gene regulation pathways (Fig. [190]4).
   Taken together, these findings suggest that the most in ASD EVs are
   linked to both EV formation and transport (endocytotic), and regulation
   of translational control in recipient cells (translational regulation,
   ribosomal enrichments). Our findings support the hypothesis that
   dysregulation of translational control is a converging mechanism, not
   only in syndromic, but also in idiopathic forms of ASD [[191]107].

   Lastly, among the differentially regulated coding RNAs and proteins in
   ASD-derived EVs, several SFARI-based ASD risk genes were identified.
   Notably, the RNA of FOXP2, a transcription factor implicated in
   neurodevelopment and speech disorders [[192]108], was differentially
   regulated- upregulated in some ASD EV lines and downregulated in
   others. Similarly, the RNA of the epigenetic regulator MBD1, whose
   deficiency is linked to autism-related behaviors in mice [[193]109],
   was also among the altered ASD risk genes. MBD1 was upregulated in the
   majority of ASD lines compared to the majority of CTRL lines. In
   contrast, RNAs of certain ASD risk genes that were downregulated in ASD
   EVs are linked to axon guidance (i.e., ROBO2). Similarly, the CLASP1
   protein, linked to cell polarization and axon guidance, was upregulated
   in EVs from the majority of ASD lines compared to CTRL EVs. However,
   most of differentially expressed EV proteins that are recognized as ASD
   risk genes were downregulated in ASD EVs. These proteins are primarily
   involved in RNA splicing, RNA processing, and protein synthesis.
   Together, these categories represented 75% of the downregulated ASD
   risk genes in the ASD EV proteome, further supporting the hypothesis
   that dysregulation of translational control is a converging mechanism
   in ASD.

Limitations and Future Directions

   While our study provides important insights into the differential cargo
   of EVs in ASD-derived organoids, several limitations must be
   acknowledged. One limitation is that the field of EV studies is at its
   early stages, and the lack of standardized protocols for the isolation
   and characterization of EVs from multicellular tissues presents a
   challenge. Currently, both ultracentrifugation and precipitation
   methods are employed for EV isolation, yet no consensus exists on the
   optimal approach for isolating EVs from complex models such as
   organoids. In this study, we opted for precipitation to maximize the
   yield of EVs from a broad range of lines. While ultracentrifugation
   could provide a purer EV population, the significant scaling required
   for organoid cultures would not justify the marginal benefits for our
   study’s scope. Future studies should compare these two isolation
   methods directly to establish clear guidelines for EV research in brain
   organoid models. Another limitation concerns the characterization of
   the isolated EVs. Despite using an “exosome-specific” precipitation
   kit, the vesicles obtained exhibited overlapping size distributions
   between exosomes and microvesicles, as determined by nanoparticle
   tracking analysis (NTA). As a result, it was not possible to
   conclusively separate exosomes from microvesicles, or to determine
   whether the isolated population consisted of pure exosomes,
   microvesicles, or aggregates of smaller exosomes. Therefore, we refer
   to these vesicles as “extracellular vesicles (EVs)” throughout this
   study, despite compelling evidence to suggest that they primarily
   represent exosomes.

   ASD exhibits a significant male bias in prevalence, with males
   diagnosed approximately four times more than females. The differences
   in symptom presentation may contribute to females being underdiagnosed
   or receiving diagnoses later than males. Current diagnostic tools,
   primarily developed based on male-centric data, may not adequately
   capture the female autism-related phenotype. This highlights the need
   for more nuanced research to understand the complexities of sex
   differences in ASD. Because understanding sex differences in ASD is
   crucial for developing personalized medicine approaches. In the present
   study, we first wanted to establish whether there is EV pathology in
   ASD human-derived models, which enables capturing very early
   neurodevelopmental signatures. Our study is not powered to explore sex
   differences (8/8 of our ASD lines and 6/8 of our CTRL are of male
   origin). As such, an interesting future direction would be to
   investigate sex differences in EV pathology using larger cohorts and
   similar organoid-based models. Given the strong male bias in ASD
   diagnosis and differences in symptom presentation between sexes, such
   studies could shed light on sex-specific mechanisms which can allow for
   the development of more personalized diagnostic approaches.
   Additionally, although our sample size was sufficient to detect early
   differences in EV cargo, it was not designed to identify robust
   biomarkers for ASD. Future research with larger, more diverse cohorts
   could explore whether specific RNA or protein expression signatures in
   EVs might serve as reliable, non-invasive approaches for early ASD
   diagnosis and monitoring of neurodevelopmental conditions. Related to
   sample size, heterogeneity of ASD also poses a significant challenge in
   research, as it reflects diverse etiologies and outcomes within the
   spectrum. This variability complicates our efforts to identify cellular
   mechanisms, develop effective interventions, and predict individual
   trajectories. Like most other studies, our work is also on a relatively
   small sample size, limiting the generalizability of findings to the
   broader ASD population. However, by focusing on specific developmental
   time points and a specific cellular process, it provides unique
   insights and contributions to generating novel hypotheses in ASD
   research. It has also been established that the causes of ASD are
   complex and involve a combination of genetic and environmental factors,
   with varying contributions in different individuals. Our study was
   designed to determine inherent deficits in EVs derived from ASD patient
   iPSCs, independent of environmental factors. An interesting future
   extension would be to investigate how environmental exposures alter EV
   densities and content in control models and in models with pre-genetic
   predispositions for ASD.

   Brain organoids recapitulate key aspects of early brain development
   providing a platform to investigate the molecular and cellular
   mechanisms underlying neurodevelopmental disorders. When derived from
   patient iPSCs, they can offer potentially more accurate understanding
   of disease pathogenesis. This also allows to model individual
   variations in disease presentation and response to treatment. Although
   cortical organoids offer a valuable model for studying early
   neurodevelopmental processes, they do not fully recapitulate the
   functional and structural complexity of the mature brain. Consequently,
   the findings from this study are best understood in the context of
   early brain development. To better understand the translational
   relevance of our findings, it will be important to validate these
   results in in vivo models, such as mouse-organoid xenografts. Finally,
   while our study demonstrates significant differences in EV cargo
   between ASD and CTRL organoids, the functional implications of these
   differences remain speculative. Future work should focus on the
   specific biological effects of EV-mediated signaling, particularly how
   the altered cargo influences critical pathways such as protein
   synthesis, RNA splicing, and synaptic function. By employing more
   targeted functional assays, it will be possible to determine the
   precise role of EVs in modulating ASD-related neurodevelopmental
   processes and to explore their potential as biomarkers or therapeutic
   targets.

Conclusions

   Our study, for the first time, reports a comparative analysis between
   EVs in human brain organoids derived from healthy controls and from
   individuals with ASD. EVs are recognized as regulators of brain
   development by facilitating cell-to-cell communication, and influencing
   processes like neurogenesis, synaptogenesis, and myelination,
   potentially playing a role in neurodevelopmental disorders. We
   identified significant alterations in RNA and protein content in ASD
   EVs at relatively earlier time points of brain organoid development.
   Gene ontology and network analysis pointed to RNA regulation,
   translation, DNA damage, and chromatin regulation, suggesting that EV
   pathology in ASD may contribute to impaired gene expression regulation,
   a hallmark of neurodevelopmental disorders. While most of these
   processes have been linked to ASD pathology and etiology, our study
   identifies EV pathology and EV-mediated non-cell-autonomous regulation
   of brain development as a potential factor in ASD etiology.
   Understanding non-cell-autonomous regulation of neuronal activity in
   ASD is crucial in addressing the underlying mechanisms of the disorder
   and for developing targeted therapies. Our findings not only advance
   our understanding of the role of EVs in ASD mechanisms but also sets
   the stage for exploring their potential as biomarkers, or therapeutic
   targets, in the treatment of ASD.

Materials and Methods

iPSC Lines

   iPSC lines were purchased from NIH, CIRM repositories and Coriell
   Institute (Supplementary Table [194]1). Each repository characterized
   and validated cells as pluripotent and performed karyotyping to ensure
   the genomic integrity of each reprogrammed line. ASD lines MH0148698
   and MH0148713 from a NIMH collection were genetically characterized and
   published [[195]110]. A total of 16 different iPSC lines (8 CTRL, 8
   ASD) were utilized. All ASD samples were derived from non-syndromic
   cases and were not associated with any known genetic anomaly (see also
   [196]Supplementary Table). All ASD iPSC lines were derived from males.
   To maintain consistency and based on availability, we also used CTRL
   lines that were derived from males except two lines. All iPSC lines
   were maintained on Vitronectin-coated plates and fed with Essential 8
   (E8) + E8 supplement (ThermoFisher, CAT#: A1517001). All iPSC lines
   were cultured simultaneously to control for idiosyncratic culturing
   conditions. In all experiments, low passages (less than 18) were used,
   and all differentiations were derived from a single clone for each
   line.

Dorsal forebrain organoid generation

   Cerebral organoids were generated using a modified version of a
   previously published protocol [[197]59]. Briefly, on day 0, cultured
   human iPSCs, 80–90% confluent, were dissociated to single cells with
   Accutase (Gibco), and 9000 cells per well were reaggregated in
   ultra-low cell-adhesion 96-well plates in Cortical Differentiation
   Medium (CDM) I, containing Glasgow-MEM (Gibco), 20% Knockout Serum
   Replacement (Gibco), 0.1 mM Minimum Essential Medium non-essential
   amino acids (MEM-NEAA) (Gibco), 1 mM pyruvate (Gibco), 0.1 mM
   2-mercaptoethanol (Gibco), 100 U/mL penicillin, and 100 μg/mL
   streptomycin (Corning). From day 0–day 3, ROCK inhibitor Y–27632
   (Millipore) was added to the media at a final concentration of 20 μM.
   From day 0–day 10, Wnt inhibitor IWR1 (Calbiochem) and TGFβ inhibitor
   SB431542 (Stem Cell Technologies) were added at a concentration of 3 μM
   and 5 μM, respectively. From day 10, the floating aggregates were
   cultured in ultra-low attachment culture dishes (Corning) under orbital
   agitation (50 rpm) in CDM II, containing DMEM/F12 medium (Gibco), 2 mM
   Glutamax (Gibco), 1% N2 (Gibco), 1% Chemically Defined Lipid
   Concentrate (Gibco), 0.25 μg/mL fungizone (Gibco), 100 U/mL penicillin,
   and 100 μg/mL streptomycin. On day 18 cell aggregates were transferred
   to CDM III, consisting of CDM II supplemented with 10% fetal bovine
   serum (FBS) (GE-Healthcare), 5 μg/mL heparin (Sigma), and 1% Matrigel
   (Corning) and maintained at 50 rpm. From day 24 until day 50, organoids
   were cultured in CDM IV, consisting of CDM III supplemented with B27
   supplement (Gibco) and 2% Matrigel.

Confocal microscopy

   To prepare organoids for immunohistochemistry, they were drop-fixed in
   4% paraformaldehyde, dehydrated in 30% sucrose and embedded in
   Tissue-Tek using OCT compound (CAT#: 4583) and biopsy molds. Organoids
   were then serially cryosectioned onto slides at 30 µm. Thus, each slide
   from this sectioning contained 3–4 unique sections/Fields of View (FOV)
   per each organoid studied. Using this approach, we were able to
   robustly assess both independent and focal cell populations in each
   biological and technical organoid replicate. In further preparation,
   all sections underwent heat-mediated antigen retrieval in the citrate
   buffer and primary antibodies were incubated for each section
   overnight. Primary antibodies used were SOX2 (1:500, Abcam, ab92494),
   Beta-III-tubulin (1:500, Abcam, ab78078), Ki67 (1:250, ThermoFisher,
   CAT#: MA5-14520), PHC3 (1:200, ThermoFisher, CAT#: H00080012-B01P).
   Secondary antibodies were incubated for 2 h at room temperature and
   comprised antibodies for rabbit (Fluor 488 CAT#: A11008 & Fluor 633
   CAT#: A21070), mouse (Fluor 488 CAT#: A11001 & Fluor 633 CAT#: A21052)
   and were used at a 1:2000 dilution and sourced from Life Technologies.
   Microscopy was completed on an Olympus IX81 Laser-Scanning Confocal
   Microscope, controlled by proprietary Olympus FluoView software. Images
   were typically acquired at 1200 × 1200-pixel resolution with optical Z
   slices (step sizes) ranging from 1–10 µm depending on the unit of
   analysis. All 16 lines were used for immunofluorescence
   quantifications.

EV isolation

   EVs were isolated from organoid media at 60 DIV. 10 days prior to media
   collection, the organoid media was changed to FBS-depleted CDM IV. CDM
   IV media was ultra-centrifuged at 100,000 g overnight to remove
   FBS-derived vesicles. This ensures that the media collected at 60 DIV
   contains EVs derived solely from organoids. Cells were maintained in
   FBS-depleted CDM IV and at day 60 media was collected and processed
   using the Total Exosome Isolation Reagent (ThermoFisher CAT#: 4478359).
   Briefly, 10 mL of media was mixed with 5 mL of total exosome reagent
   and incubated at 4 C overnight. The next day, samples were centrifuged
   at 10,000 g for 1 h and the pellet was resuspended in 1x PBS or RIPA
   buffer, depending on the downstream analysis. Samples resuspended in 1X
   PBS were used for RNA sequencing, TEM analysis and NTA while samples
   resuspended in RIPA buffer were used for proteomics and Western blot.

Western blot

   For Western blot, EVs were lysed using RIPA buffer and sonication.
   30 µg of total protein of each sample was separated on 10% SDS-PAGE
   under non-reducing conditions and transferred onto a nitrocellulose
   membrane (BioRad CAT#: 1620113). The membranes were blocked overnight
   with 5% milk at 4 °C and incubated with primary antibodies (ABs)
   against CD63 (1:500, ThermoFisher, CAT#: 10628D) and CD9 (1:500,
   ThermoFisher, CAT#: MA5-31980) for 2 h at RT. After washing, the blots
   were incubated with HRP-conjugated anti-mouse and anti-rabbit IgG
   secondary antibody (1:2000, IRDye® 680RD, CAT#: 926-68070 & IRDye®
   800CW CAT#: 926-32210, Licor) for 1 h at RT. Protein expression was
   visualized using the Licor Odyssey® M Imaging system.

Transmission electron microscopy imaging

   For negative staining TEM analysis, 5 μL of EVs in PBS (0.1 μg/μL) were
   placed on a glow-discharge formvar and carbon-coated 400 mesh grid and
   allowed to settle for 1 min. The sample was blotted and negatively
   stained with 50 μL of aqueous uranyl acetate (1.5%). After 1 min, the
   grid was blotted and air-dried for 5 min. Grids were imaged using a
   JEM-1400 transmission electron microscope (JEOL, USA, Inc., Peabody,
   MA) operated at 100 kV and images were captured on a Veleta 2 × 2 K CCD
   camera (EM-SIS, Germany).

Nanoparticle tracking analysis

   NTA was performed using a previously established protocol [[198]111,
   [199]112]. All samples were diluted in PBS to a final volume of 1 ml.
   Ideal measurement concentrations were found by pre-testing the ideal
   particle per frame value (20–100 particles/frame). Following settings
   were set according to the manufacturer’s software manual (NanoSight
   NS500 User Manual, MAN0513-06-EN-00, 2016): camera level was increased
   until all particles were distinctly visible not exceeding a particle
   signal saturation over 20%, camera level 11, BLUE405, 1498 frames.
   Autofocus was adjusted so that indistinct particles were avoided. For
   each measurement, five 1-min videos were captured under the following
   conditions: cell temperature: 24 °C; Syringe speed: 40 µl/s. After
   capture, the videos have been analyzed by the in-build NanoSight
   Software NTA 3.4 Build 3.4.003 with a detection threshold 5–18.

Small RNA library preparation

   Small RNA libraries were generated using NEXTFlex Small RNA Library
   Prep Kit v4 (Revvity). This kit is designed to capture short RNAs
   (17–65 nucleotides) with 5’ monophosphorylated and 3’ hydroxylated
   ends. The kit also allows for the capture of larger molecules
   ( < 200 bp) by using the no size selection protocol. The manufacturer’s
   instructions were followed through PCR amplification including 3’
   adenylated adapter ligation, 5’ adapter ligation and RT first strand
   synthesis. Following PCR amplification, the libraries were cleaned up
   using the provided magnetic beads and the resulting pellet was
   resuspended in 11 μL of ultra-pure water. Sample concentrations were
   quantified using a NanoDrop Spectrophotometer, QC was evaluated using
   an Agilent Bioanalyzer Sample and ran on a picogel.

RNA sequencing

   The small RNAs from exosomes were isolated using the Total Exosome RNA
   & Protein Isolation Kit (ThermoFisher). Purified small RNA integrity
   was checked using a small RNA QC kit on 2100 Bioanalyzer (Agilent
   Technologies, Santa Clara, CA), and the concentration was measured
   using the Qubit Fluorometer (ThermoFisher Scientific, Inc., CA).
   Preparation of small RNA sample library and sequencing were performed
   by the Genomics Resources Core Facility at Weill Cornell Medicine using
   the NEXTFLEX® Small RNA-Seq Kit v4 with UDIs kit (Revvity Inc.,),
   according to the manufacturer’s instructions. The normalized cDNA
   libraries were pooled and sequenced on Illumina NovaSeq X Plus
   sequencer with pair-end 50 cycles. The raw sequencing reads in BCL
   format were processed through bcl2fastq 2.19 (Illumina) for FASTQ
   conversion and demultiplexing.

Bioinformatics pipeline for RNA sequencing

   Approximately 5–20 million paired-end reads were generated per sample.
   Sequencing adapters and duplicate sequence reads were removed, and
   low-quality reads trimmed using Trimmomatic (version 0.39). Cleaned
   reads were aligned to the reference human genome (version GRCh38.99)
   using STAR alignment (version 2.7.10b). Read counts of genetic features
   were obtained by the SubRead package (version 2.0.3). The DESeq2
   R/Bioconductor package (version 1.44.0) was used to normalize count
   data and identify the differentially expressed genes (DEGs) between
   control and ASD samples. Significant differentially expressed genes
   were defined by a p value < 0.05 and a log[2] fold change > 1.5 as
   thresholds. A minimum threshold of expression was set at 25%,
   representing the number of samples with non-zero expression values that
   would be considered for differential analysis. Employing these
   parameters, 177 differentially expressed genes and RNA elements were
   identified. Gene Ontology and pathway analyses were performed using the
   clusterProfiler package (version 4.12.6) in R. Cutoffs of significance
   were based on p-value and Benjamini-Hochberg (BH) adjusted p-value. All
   statistical analyses were performed in R (version 4.4.1). Figures were
   generated using ggplot2 (version 3.5.1), DESeq2 (version 1.44.0),
   EnhancedVolcano (version 1.22.0), and enrichplot (version 1.24.2).

Sample preparation and label free proteomics

   The protein samples were acetone precipitated and re-suspended in 0.1%
   RapiGest (Waters), 25 mM ammonium bicarbonate. The samples were then
   reduced with DTT, alkylated with iodoacetamide, and digested overnight
   with trypsin at 37 °C. The digests were desalted by C18 Stage-tip
   columns. The digests were analyzed using a Thermo Fisher Scientific
   EASY-nLC 1200 coupled on-line to a Fusion Lumos mass spectrometer
   (ThermoFisher Scientific). Buffer A (0.1% FA in water) and buffer B
   (0.1% FA in 80% ACN) were used as mobile phases for gradient
   separation. A 75 µm × 15 cm chromatography column (ReproSil-Pur C18-AQ,
   3 µm, Dr. Maisch GmbH, Germany) was packed in-house for peptide
   separation. Peptides were separated with a gradient of 5–40% buffer B
   over 30 min, 40–100% B over 10 min at a flow rate of 400 nL/min. The
   Fusion Lumos mass spectrometer was operated in a data independent
   acquisition (DIA) mode. MS1 scans were collected in the Orbitrap mass
   analyzer from 350–1400 m/z at 120 K resolutions. The instrument was set
   to select precursors in 45 × 14 m/z wide windows with 1 m/z overlap
   from 350–975 m/z for HCD fragmentation. The MS/MS scans were collected
   in the orbitrap at 15 K resolution. Data were searched against the
   human Uniprot database (8/7/2021) using DIA-NN v1.8 and filtered for 1%
   false discovery rate for both protein and peptide identifications

Bioinformatics pipeline for proteomics analysis

   Perseus (v2.0.7.0) (PMID: 27348712) was applied for downstream data
   processing and analysis. Data quality thresholds included protein group
   quantification at a 40% inclusion rate in at least one group (n = 8).
   Protein intensities were log2 transformed and normalized through
   quantile normalization, with missing values imputed from normal
   distribution (width 0.3, downshift 1.8). Proteins were subjected to PCA
   and hierarchical clustering using an unpaired student’s t-test using
   Euclidean distance and average linkage clustering (p < 0.05). Proteins
   were Z-score normalized. Gene Ontology (GO) functional enrichment and
   network/pathway analysis were conducted using gProfiler and enrichR
   databases for GO biological processes, cellular components, molecular
   functions, and KEGG pathways. Enrichment analysis was restricted to the
   top 10 categories with the highest statistically significant odds
   ratios comparing ASD and control groups (p < 0.05; PMID: 31066453,
   PMID: 27141961). PCA plots and heatmaps were generated in Perseus. Bar
   and violin plots were created in GraphPad Prism (v10.0.2) or Microsoft
   Excel. Comparative proteome analysis was conducted using the Simons
   Foundation Autism Research Initiative (SFARI) online database (PMID:
   24090431).

Supplementary information

   [200]Supplementary Data^ (4.9MB, docx)
   [201]Excel file- 177 RNA^ (37.9KB, xlsx)
   [202]41398_2025_3607_MOESM3_ESM.xlsx^ (265.4KB, xlsx)

   Excel file-362 differentially regulated proteins
   [203]All significant miRNAs^ (11.8KB, xlsx)

Acknowledgements