A single-cell multiomic analysis identifies molecular and gene-regulatory mechanisms dysregulated in developing Down syndrome neocortex.

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Published in final edited form as: Science. 2026 Apr 23;392(6796):eaea1259. doi: 10.1126/science.aea1259 A single cell multiomic analysis identifies molecular and gene-regulatory mechanisms dysregulated in developing Down syndrome neocortex Celine K Vuong 1, 2, 3, † , Alexis Weber 1, 2, 3, 4, 5, † , Patrick Seong 1, 2, 3, ‡ , Nana Matoba 6, 7, ‡ , Yu-Jen Chen 1, 2, 3 , Jordan Peyer 1, 2, 3, 8 , Shahab Younesi 1, 2, 3 , Angelo Salinda 1, 2, 3 , Daniel Gomez 9 , Gabriella Rivas 9 , Abril Morales 9, 10 , Beck Shafie 1, 2, 3 , Pan Zhang 1, 2, 3, § , Susanne Nichterwitz 1, 2, 3 , Le Qi 4, 11 , Nolan T Fernandez 1, 2, 3 , Emily Friedman 1, 2, 3 , Michael I Love 12, 13 , Michael J Gandal 14, 15 , Daniel H Geschwind 1, 4, 11, 16 , William E Lowry 9, 10 , Jason L Stein 6, 7, 17 , Luis de la Torre-Ubieta 1, 2, 3, * Author information Article notes Copyright and License information 1 Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA 90095, USA. 2 Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles; Los Angeles, CA 90095, USA. 3 Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles; Los Angeles, CA 90095, USA. 4 Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA 90095, USA. 5 Genetics and Genomics Graduate Program, University of California, Los Angeles; Los Angeles, CA 90095, USA. 6 Department of Genetics, University of North Carolina at Chapel Hill; Chapel Hill, NC 27599, USA. 7 UNC Neuroscience Center, University of North Carolina at Chapel Hill; Chapel Hill, NC 27599, USA. 8 Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA 90095, USA 9 Department of Molecular Cell and Developmental Biology, University of California Los Angeles; Los Angeles, CA 90095, USA. 10 Molecular Biology Institute, University of California Los Angeles; Los Angeles, CA 90095, USA. 11 Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles; Los Angeles, CA 90095, USA. 12 Department of Genetics, University of North Carolina at Chapel Hill; Chapel Hill, NC 27599. 13 Department of Biostatistics, University of North Carolina at Chapel Hill; Chapel Hill, NC 27599. 14 Department of Psychiatry and Department of Genetics, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA. 15 Lifespan Brain Institute at Penn Med and the Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA. 16 Program in Neurobehavioral Genetics and Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA 90095, USA. 17 Carolina Institute for Developmental Disabilities; Carrboro, NC 27510, USA. † These authors contributed equally to this work ‡ These authors contributed equally to this work § Present address: Institute of Medical Genetics and Genomics, Fudan University; Shanghai, China Author Contributions: CKV, AW and LTU conceived and designed the study. CKV, AW, PS, BS, AS, SN, SY, and LTU collected and processed the tissue specimens. AS and CKV cultured phNPCs. CKV generated single-nuclei multi-ome libraries. CKV and AW processed the single-cell raw data. PZ, LQ, DHG, MJG and JLS assisted with cell demultiplexing analysis and single-cell analysis pipelines. CKV performed demultiplexing, single-cell clustering and annotation, cell composition analysis, and differential gene expression analysis for cortex samples. AW performed the same analyses for phNPCs. CKV performed gene co-expression network and gene-regulatory network analyses. AW and CKV performed trajectory inference analysis. AW performed digital karyotypic analysis, cell and neuron communication analyses, and disorder enrichment analysis. PS developed the automated image quantification analysis. CKV, PS, JP, SY and NTF performed IHC and image quantification. NM and JLS conducted the LDSC analysis. YJC performed reference mapping for quality control. DG, GB, AM and WEL cultured hIPSC-derived NPCs/neurons. CKV, DG, GB, AM and WEL performed metabolomic analyses. SN, YJC and EF supported single-cell analyses. JLS and MIL advised on statistical analyses. CKV, AW and LTU interpreted the data. CKV, AW, PS, NM, JP and LTU wrote the manuscript. * Corresponding author. ldelatorreubieta@mednet.ucla.edu Issue date 2026 Apr 23. PMC Copyright notice PMCID: PMC13225313  NIHMSID: NIHMS2170031  PMID: 42024758 The publisher's version of this article is available at Science Previous version available: This article is based on a previously available preprint posted on bioRxiv on July 4, 2025: " A single cell multi-omic analysis identifies molecular and gene-regulatory mechanisms dysregulated in the developing Down syndrome neocortex ". Abstract Down syndrome (DS) is the most common genetic cause of intellectual disability, yet the cellular and molecular mechanisms driving this developmental disorder remain unclear. In this study, we profiled human mid-gestation neocortex with snMultiomics across 26 donors. We observed a reduction in neural progenitors and corticothalamic neurons and an increase of intratelencephalic neurons, accompanied by accelerated neuronal specification. We uncovered widespread changes in gene expression, chromatin accessibility and cell interaction networks impacting neurogenesis, specification and maturation; and gene-regulatory networks directing these processes, including those downstream of human chromosome 21 (HSA21)-encoded genes. We identified cell-specific molecular pathways shared with other neurodevelopmental disorders and enrichment of genome-wide association study signals in DS-altered chromatin. Together, our data revealed a cascade of molecular dysregulation outlining the earliest steps in DS, providing a foundation for future therapeutic targets. Graphical Abstract Defining molecular mechanisms regulating altered neurogenesis and specification in the developing Ts21 neocortex . We uncovered an accelerated neurogenic timeline and altered excitatory neuron specification leading to increased intratelencephalic (IT) neurons at the expense of corticothalamic (CT) neurons in the developing Ts21 neocortex (top). We used systematic analyses of paired gene expression and chromatin accessibility to define changes in differentiation trajectory, gene expression programs and regulatory networks driving neurodevelopmental changes and identified genetic mechanisms shared among neurodevelopmental disorders (NDDs) (bottom). ASD, autism spectrum disorder; OxPhos, oxidative phosphorylation; RG-vasc., radial glia-vasculature signaling. Main text: Down syndrome (DS) is the most common genetic cause of intellectual disability (ID), affecting ~1/700 live births, and is caused by trisomy of human chromosome 21 (Ts21 of HSA21) ( 1 ). Individuals with DS show deficits in learning, memory and attention; delayed language and motor development; and atypical sensory processing ( 1 ). A subset also have autism spectrum disorder (ASD, 10–15%), attention-deficit-hyperactivity disorder (ADHD, 20–44%) and/or epilepsy (8%), and virtually all are diagnosed with early-onset Alzheimer’s disease (AD) ( 1 – 3 ). Cognitive defects appear as early as 6 months of age, and brain structural changes including reduced whole brain volume, gyrification and sulcal depth in the temporal and frontal lobes, areas important for cognition, are already apparent during mid-gestation ( 4 – 7 ), suggesting that corticogenesis is likely disrupted in DS. Previous studies primarily used model systems and revealed numerous impairments, but produced conflicting results ( 8 ). These discrepancies likely reflect limitations in modeling the protracted length and complexity of human neurogenesis ( 9 – 12 ), species-specific differences in development ( 13 ) and incomplete construct validity of animal models ( 14 , 15 ), altogether highlighting the need for human in vivo studies. Transcriptome profiling of postmortem brains has advanced our understanding of molecular mechanisms driving DS ( 16 – 19 ) in postnatal to adult periods but a prominent gap in understanding the earliest steps remains. The cellular complexity, cell interactions ( 20 ), and precise cell-specific molecular programs ( 21 – 23 ), coordinated by gene-regulatory networks (GRNs) directing the developing neocortex ( 22 , 24 – 26 ), necessitate single-cell resolution approaches. To address this gap in connecting molecular and gene-regulatory processes dysregulated in DS to changes in neurodevelopment, we undertook single-nucleus, multi-omic profiling of gene expression and chromatin accessibility (snMultiome) in developing Ts21 neocortex. We uncovered changes in cell composition resulting from an acceleration of the neurogenic timeline and altered excitatory neuron specification. We leveraged paired multi-omic data to define gene expression programs and regulatory networks dysregulated in Ts21 and to identify convergent risk genes and molecular pathways shared between DS and other neurodevelopmental disorders (NDDs). Altogether, our work reveals neurodevelopmental changes occurring as early as mid-gestation, defines gene-regulatory mechanisms altered in Ts21, and provides a resource for future studies of DS neuropathology and gene regulation. Results A snMultiome analysis of the Ts21 neocortex at mid-gestation reveals widespread dysregulation To understand how Ts21 impacts neurodevelopmental programs, we performed snMultiome on neocortex across a 26-donor cohort spanning gestational weeks (GW) 13 to 23 ( Fig. 1A , fig. S1 , and table S1A ). Control and Ts21 samples had similar mean ages and distributions of sex, sample source, area and ancestry ( Fig. 1B , C and fig. S2A , B ). We retained 113,801 high-quality nuclei ( fig. S2C ) which were clustered and annotated by enrichment analysis to previously published datasets of developing neocortex ( 21 , 22 ) and by expression of canonical marker genes ( Fig. 1D , E and fig. S2D , E ; table S1B ), demonstrating high overlap of gene expression signatures across independent studies. Clustering was primarily defined by cell type identity, rather than donor, sex, or other covariates ( Fig. 1B - D , fig. S2B ) and followed the expected change in cell type composition across developmental time - with radial glia (RG) and deep layer subplate (SP) and corticothalamic (CT) neurons more abundant early followed by upper layer maturing intratelencephalic (IT) neurons and inhibitory neurons (INs) ( Fig. 1C , D ) ( 9 ). Chromatin accessibility profiles closely reflected transcriptomic changes ( Fig. 1D , F and fig. S2F , G ) and accessible peaks were enriched in functional gene-regulatory loci, including promoters and enhancers, rather than in heterochromatin ( fig. S2H ) as expected ( 22 , 24 – 26 ). Fig. 1. A multiomic map of DS developing neocortex. Open in a new tab (A) Experimental design. Major histogenetic processes occurring during profiled ages are listed along with their peak timing. (B) Uniform manifold approximation and projection (UMAP) showing distribution of nuclei from male (blue) and female (pink) samples. (C) Distribution of nuclei colored by gestation week. (D) RNA-based UMAP of cell types in this dataset. Clusters colored by major cell type – radial glia (RG), orange; newborn neurons, blue; upper layer excitatory neurons (EN), dark green; deep layer EN, light green; inhibitory neurons (IN), purple; other cell types, grey. vRG, ventricular-RG; oRG, outer-RG; PgS, S-phase-progenitor; PgG2M, G2M-progenitor; IPC, intermediate progenitor cells; CT, corticothalamic; IT,intratelencephalic; SP, subplate; MGE, medial ganglionic eminence; CGE, caudal ganglionic eminence; OPC, oligodendrocyte precursor cell. (E) Canonical markers for each major cell type. Dot size indicates percent cells expressing the gene. Colorbar, average normalized expression. Individual colorbars denote major cell types as shown in (D). (F) Clustering by chromatin accessibility. Nuclei are colored by RNA-based annotation as in (D). (G) Median HSA21 DEG overexpression in major cell classes. Dotted line indicates 1.5 fold change. (H) DEGs as a percent of all genes encoded on each chromosome. (I) DEGs from each chromosome as a percent of all DEGs. We aggregated differential expression or chromatin accessibility signatures across cell types and compared their chromosomal distribution. Human chromosome 21 (HSA21)-encoded genes were exclusively upregulated at a 3:2 ratio ( Fig. 1G ; table S2 ), which is consistent with previous brain gene expression studies ( 16 – 18 ). Approximately 25% of all HSA21-encoded genes were detected as significantly upregulated, [false discovery rate (FDR) P<0.05, Materials and methods ; Fig. 1H ], but encompassed ~5% of all differentially expressed genes (DEGs), indicating that Ts21 caused widespread transcriptional changes ( Fig. 1I ). Similarly to DEGs, the vast majority of the detected HSA21 peaks (~80%) were more accessible, which is consistent with an extra copy of the chromosome, yet comprised only ~40% of all differentially accessible (DA) peaks ( fig. S2F , G ). These results validate prior observations using bulk or single-cell gene expression in the adult brain ( 16 , 18 ) and extend them to the developing neocortex, along with matching chromatin architecture changes. Altered neurogenesis and excitatory neuron specification in Ts21 neocortex As histology and brain imaging studies have reported gross cytoarchitectural abnormalities in Ts21 ( 5 , 27 , 28 ), we conducted cell-composition analysis. Control and Ts21 samples co-clustered and were composed of the same major cell classes and types ( Fig. 2A , fig. S3A ). At the cluster level, vRG-oRG-PgS (vRG, ventricular RG; oRG, outer RG; PgS, S-phase progenitor) and deep layer excitatory neurons (ENs) (EN-L6-CT_2, EN-L5-L6) were significantly reduced, whereas EN-L2-L3-L4-IT were increased. IN-dLGEs (dLGE, dorso laterial ganglionic eminence), migrating INs settling at the olfactory bulb, were also increased (FDR P<0.05, Materials and methods ; Fig. 2B , table S3 ). To understand longitudinal cell composition dynamics, we quantified cells across early (GW13 to 15), mid (GW16 to 18) and late (GW19 to 23) periods ( Fig. 2C ). Ts21 apical progenitors (vRG-tRG and vRG-oRG-PgS) (tRG, truncated RG) were reduced over time whereas basal (oRG) and intermediate progenitor cells (IPC-EN and IPC-EN-Newborn), which normally expand later and primarily give rise to upper layer neurons ( 9 ), increased at early to mid timepoints and decreased prematurely in Ts21. Their neuronal progeny followed a similar pattern: Deep layer CTs (EN-L6-CT_2) decreased early in Ts21 whereas upper layer ITs (EN-L2-L3-L4-IT) steadily increased during mid to late periods. These changes are consistent with an acceleration of the neurogenic timeline, which stereotypically first produces deep layer neurons followed by upper layer neurons. Fig. 2. Altered cell composition and excitatory neuron specification in DS. Open in a new tab (A) (Top) UMAP distribution of control (Ctrl) and Ts21 nuclei. (Bottom) Major cell classes. (B) Cluster-level cell type composition. (C) Select composition changes over time. Points indicate average composition per donor. (D and E) Immunostaining of NEUROD2 (magenta) and SOX2 (green) in GW15 Ctrl and GW16 Ts21 cortices. 4′,6-diamidino-2-phenylindole (DAPI), blue. Scalebar, 500μm. (F and G) Insets as shown in (D) and (E) encompassing VZ to oSVZ. NEUROD2 + /SOX2 + cells, white. Scalebar, 150μm. (H) Proportion of SOX2 + and NEUROD2 + cells across all timepoints and germinal layers. (I) Cell proportions per timepoint. (J and K) Immunostaining of SATB2 (magenta) and BCL11B (green) from the same samples as shown in (D) and (E). DAPI, blue. Scalebar, 500μm. (L and M) Insets as shown in (J) and (K) encompassing SP to CP. SATB2+/BCL11B+ cells, white. Scalebar, 100μm. (N) Proportion of BCL11B + and SATB2 + ENs (NEUROD2 + , not shown) across all time points and cortical layers. (O) Cell proportions per time point. FDR-corrected *P<0.05, **P<0.01, ****P<0.0001, binomial generalized linear mixed-effects model [(H) and (N)]; n=3 to 4 donors percondition (points indicate average per donor), 3 images per donor (9 to 12 total, whisker plots); GW13 to 20, [(H) to (O)]. VZ, ventricular zone; iSVZ, inner subventricular zone; oSVZ, outer subventricular zone; IZ, intermediate zone; SP, subplate; CP, cortical plate. To validate these findings, we performed immunohistochemistry (IHC) in developmentally-matched cortices and quantified samples at scale using a semi-automated pipeline ( fig. S3B - E ). Across all timepoints, SOX2 + progenitors were significantly decreased (FDR P=0.031) in proliferative layers [ventricular zone (VZ), inner subventricular zone (iSVZ), and outer SVZ (oSVZ)], whereas both NEUROD2 + neurons (FDR P=2.86 × 10 −6 ) and SOX2 + /NEUROD2 + cells (FDR P=8.94 × 10 −15 ) were increased (binomial linear mixed-effects model; Fig. 2D - I ), which is consistent with cell composition analyses. Next, we assessed EN specification within neuronal layers [intermediate zone (IZ), SP, and cortical plate (CP)], finding that deep layer BCL11B+ neurons were significantly reduced (FDR P=2.41 × 10 −5 ) whereas upper layer SATB2 + (FDR P=0.022) and mixed SATB2 + /BCL11B + (FDR P=8.21 × 10 −28 ) neurons increased (binomial linear mixed-effects model; Fig. 2J - N ). At early timepoints, BCL11B + neurons were reduced and BCL11B + /SATB2 + double positive cells increased (GW13 to 16; Fig. 2O ) indicating potential mis-specification of CT versus IT neurons in Ts21. We compared the expression of canonical programs establishing and maintaining IT ( SATB2 , CUX2 , and RORB ) and CT ( BCL11B , TLE4 , and ZFPM2 ) identity ( 29 , 30 ). IT TFs were significantly upregulated across all CT and SP cell types, while CT TFs were increased in multiple IT cell types (FDR P<0.05, linear mixed-effects model; fig. S3F , G ). Together our observations point to an acceleration of the neurogenic timeline in Ts21 leading to reduction of the progenitor pool, early production of IT neurons at the expense of CT neurons, and IT/CT mis-specification. Heterochrony in the Ts21 neocortex leads to an accelerated neurogenic timeline and neuronal maturation To investigate acceleration of the neurogenic timeline, we applied trajectory inference analysis within the EN lineage ( table S4 , Fig. 3A ). Control cells accumulated into four pseudotime bins: 1, RG; 2, IPCs and newborn neurons; 3, immature neurons; and 4, maturing deep and upper layer neurons corresponding to the expected neurodevelopmental progression ( Fig. 3B ; table S4 ). This distribution was significantly different in Ts21 (P<2.2e-16, Kolmogorov-Smirnov test; Fig. 3B ), where cell density was qualitatively lower in Bin 2 and higher in Bin 3, whereas Bins 1 and 4 showed increased pseudotime. Cell composition analyses showed significant changes in all bins (FDR P<0.05, binomial linear mixed-effects model; Fig. 3C , D ), matching previous results ( Fig. 2B ). EN-Newborn-1 was reduced in Bin 2 whereas EN-Newborn-IT-Immature significantly increased in Bin 3 (FDR P=0.002), reflecting an acceleration through Bin 2, where cells do not yet express neuronal subtype markers, and accumulation in Bin 3, where SATB2 and NKAIN2 expression indicate commitment to the IT lineage. Separating ENs at the CT and IT lineage branchpoint ( Fig. 3 A , E , F ), we found that this ratio was significantly altered in favor of IT neurons in Ts21 (39% deep layer, 61% upper layer; P=5.9 × 10 −160 , binomial linear mixed-effects model; Fig. 3G ). Fig. 3. Accelerated neurogenic timeline and neuronal maturation in DS. Open in a new tab (A) (Left) RNA-based UMAP of EN pseudotime trajectory. Root node labeled in green and deep/upper layer neuron branchpoint in yellow. (Right) Major cell types. (B) (Top) Density plot of cells over pseudotime (Kolmogorov-Smirnov test). Dotted lines denote pseudotime bins. (Bottom) Cell type distributions over pseudotime. (C and D) Differential cell composition of each pseudotime bin. (E and F) Trajectory of upper layer (E) and deep (F) layer EN lineages. (G) Deep versus upper layer cell composition. (H and I) Differential pseudotime of each bin (H) or cell type (I). (J and K) NeuronChat analysis. Number (J) and strength (K) of neuron-neuron interactions. (L) Differential communication probability between neurons. (M) Neuronal communication pathways significantly increased (red) or decreased (blue) in Ts21; non-significant indicated in black (FDR-corrected P<0.05). In (D) and (G), binomial generalized linear mixed-effects model. In (H) and (I), linear mixed-effects model. FDR-corrected ▪P<0.1, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. Cell composition differences in Bins 2 and 3 suggest altered rates of neuronal commitment and differentiation. To test this, we calculated differential pseudotime, finding a significant increase in Bin 4 (FDR P=2.03 × 10 −7 , linear mixed-effects model; Fig. 3H ). Deep layer CT neurons (EN-L6-CT_1/2; EN-L6b-SP) showed significantly increased pseudotime, indicating increased neuronal maturity (FDR P<0.01). EN-Newborn-2 and EN-Newborn-IT-Immature pseudotime was decreased, which is consistent with an accelerated neurogenic timeline and increased numbers of newly committed neurons with immature transcriptional profiles (FDR P<0.001, linear mixed-effects model; Fig. 3I ). Reduced pseudotime, with accumulation of Ts21 cells in Bin-3 ( Fig. 3B ) and increased EN-Newborn-IT-Immature cells at the same stage ( Fig. 3C , D ) suggests that the progenitor-newborn neuron transition is accelerated in Ts21. HSA21 genes were widely expressed along pseudotime, consistent with changes in both progenitors and neurons. HSA21-encoded TFs BACH1 and GABPA peaked at the progenitor-neuron transition and preceded neuronal subtype-specific TFs such as CUX2 , SATB2 and BCL11B , suggesting an additional mechanism affecting CT and IT specification ( fig. S4 ). We validated heterochrony by multiple orthogonal measures. First, we used a transfer learning strategy to estimate nuclei age within a larger timespan ( 22 ) ( fig. S5A - D , table S5 ). We found that unlike controls, predicted Ts21 age was significantly higher than annotated age (P=0.03, linear model; fig. S5E ), and that this was driven by EN-L5-L6, EN-L6-CT, and EN-ITs (FDR P<0.05, linear mixed-effects model; fig. S5F ), which is consistent with accelerated development. Similarly, neuron-neuron communication analysis revealed that interaction number and strength were increased in Ts21 ( Fig. 3J , K ; table S6A , B ), as well as EN-EN and EN-IN communication probability ( table S6C ). Differential EN interactions were most increased among mature neurons, ( Fig. 3L ) and significantly increased pathways included well-established synaptic signaling (FDR P<0.05, Materials and methods ; NRXN-NLGN, Glu-GRIA, and Glu-GRM; Fig. 3M , table S6D ) ( 31 – 33 ). As synaptogenesis is emerging and highly dynamic at these timepoints ( 9 , 34 ), increased interactions are consistent with an earlier transition to neurogenesis and neuronal maturity rather than altered circuit connectivity. Together, these changes in trajectory and neuronal signaling demonstrate an accelerated neurogenic timeline and neuronal maturity that is consistent with proposed heterochrony in DS ( 8 ) and altered specification of IT and CT neurons. Molecular mechanisms controlling metabolism, neurogenesis and specification are altered in Ts21 We identified 3,464 cell type specific DEGs, primarily in EN and their progenitors, of which 2,065 (59%) and 1,399 (41%) were significantly upregulated and downregulated, respectively (FDR P<0.05, Materials and methods ; Fig. 4A , table S2 ). Of 278 detected HSA21 genes, 145 (52%) were upregulated and none were downregulated; these 145 genes comprised ~25% of DEGs identified per cell type ( fig. S6A ), which is consistent with previous studies ( 16 , 18 ). To identify molecular pathways affected, we first pooled cell type specific DEGs by class and performed gene ontology (GO) analysis. Fig. 4. Pro-neurogenic and altered neuronal specification programs in Ts21. Open in a new tab ( A ) Number of DEGs per cell type. ( B to G ) GO of upregulated cell type-specific DEGs grouped by cell class. [( B ), ( D ) and ( F )] Top 50 GO terms ranked by P-value and colored by category, with specific terms highlighted. ( C , E , G ) DEGs associated with processes upregulated in RGs, IPCs and ENs, respectively. In (B) to (G), HSA21-encoded genes are colored in red. ( H ) Cell type-specific hdWGCNA modules. (Top) Module expression across major cell types. Average fold change in Ts21 is shown below dotplot. ( I ) Alluvial plot grouping modules into five major categories. Normalized expression of grouped modules over developmental time is shown below. ( J to M ) Select module network plots showing hub genes (top left), average expression score (top right), and top five biological process (BP) (bottom left) and cell compartment (CC) (bottom right) GO terms enriched in each module. Vertical dashed line indicates FDR-corrected P<0.05. In (H) and (J) to (M), linear mixed-effects model, FDR-corrected *P<0.05, **P<0.01, ***P<0.001. Ts21 RG DEGs were enriched in ubiquitination, metabolism and cytoskeletal dynamics, among other processes ( Fig. 4B ), driven in part by HSA21 genes. The E3 ligase TTC3 , which reduces proliferation through Akt ( 35 ), was upregulated along with two metabolic genes - NDUFV3 , a component of oxidative phosphorylation (OxPhos), and NRIP1 , part of the glycolytic pathway — likely contributing to metabolic imbalance ( Fig. 4C ). Although mitochondrial dysfunction has been documented in Ts21 ( 8 , 36 , 37 ), we found more mitochondrial genes upregulated in RGs overall, which is consistent with an accelerated neurogenic timeline as neural progenitors switch from glycolysis to OxPhos during differentiation ( 38 ). RG genes associated with the Arp2/3 complex ( NCKIPSD and WASF3 ; Fig. 4C ) promoting neurogenic asymmetric division ( 39 ) were also upregulated. Pro-neurogenic changes including fatty acid OxPhos metabolism ( Fig. 4D , E ) ( 38 , 40 , 41 ) and axonogenesis through HSA21-encoded TIAM1 ( Fig. 4E ) ( 42 ) were upregulated in IPCs, whereas synaptogenesis and maturation driven by HSA21-encoded genes including APP , PDE9A and SYNJ1 ( 43 , 44 ) were increased in ENs ( Fig. 4F , G ). OxPhos and fatty acid metabolism were similarly upregulated in INs ( NDUFV3 and FAR2 ; fig. S6B , C ). GO of downregulated DEGs revealed processes disrupted in progenitor maintenance, neuronal connectivity and chromatin remodeling ( fig. S6D ), including lysosomal autophagy influencing asymmetric division ( 45 ) in RGs ( fig. S6E ), axo-dendritic polarization and axonogenesis in IPCs and ENs ( fig. S6F , G ) consistent with reported polarization defects in Ts21 neurons ( 17 ), and chromatin remodeling in EN-L6-IT neurons in which BAZ1A — a member of the imitation switch complex — and NIPBL were reduced ( table S2 ). Dendritic and post-synaptic development were reduced in INs ( fig. S6D ), which is consistent with DS mouse models ( 46 ) and reduced incoming neuronal communication probability ( Fig. 3L ). We next used a systems-level approach — high-dimensional weighted gene co-expression network analysis (hdWGCNA) — to identify dysregulated programs, finding that 18/32 (56%) cell-specific modules were differentially active in Ts21 (FDR P<0.05, linear mixed-effects model; Fig. 4H - I , fig. S6H - N , table S7 ). Consistent with differential gene expression (DGE) analyses, Group 2 modules regulating RG pro-proliferative pathways including cell cycle ( 47 , 48 ) (RG-M7, Fig. 4J ) and ECM-mediated signaling ( 20 , 49 , 50 ) (RG-M5, Fig. 4K ) were downregulated, whereas pro-angiogenic signaling, a process associated with differentiation ( 51 ), was upregulated (RG-M2, fig. S6J ). Nearly all (7/8) programs related to synaptogenesis (Group 1) were upregulated across deep and upper layer neurons (Newborn-M6, EN-Upper-M5/M6, EN-Deep-M2/M5/M7 and IN-MGE-M1) (MGE, medial ganglionic eminence) ( Fig. 4H , I and fig. S6K ). OxPhos processes were upregulated in ENs (EN-Upper-M4, fig. S6L ), which is consistent with an accelerated neurogenic timeline ( 52 , 53 ) and DGE analyses. We also uncovered dysregulation of molecular programs controlling the establishment and refinement of EN identity. In Group 3, modules associated with L6-CT identity, selectively expressed in deep layer neurons ( 29 ) (Newborn-M3 – ZFPM2 , Deep-M3 – TLE4 and ZFPM2 ; Fig. 4L , fig. S6M ), were downregulated. Conversely, an IT-associated module ( 54 , 55 ) (EN-Upper-M5 – RORA , LRRC4C , FOXP1 ; Fig. 4M ) was expressed in both CT and IT neurons and upregulated in Ts21. These changes are consistent with the increased IT/CT neuron ratio and mis-expression of key IT and CT TFs ( fig. S3F , G ), resulting in the mixed-identity state that we observed ( Fig. 2J - O ). Loss of CT TFs, which suppress a related corticofugal subtype, subcerebral projection neurons (SCPN), results in mixed corticofugal identity and mistargeting of L6 axons ( 30 , 56 ). Concordantly, a module containing SP and SCPN hub genes expressed across all deep layer cell types ( 21 , 57 – 59 ) (EN-Deep-M5 – PCP4 , CELF4 ; fig. S6N ) was upregulated. Refinement of L6 identity and targeting also requires downregulation of key TFs at later periods. Consistent with the mixed CT-SCPN identity observed here, L6 CT neurons in postnatal Ts21 neocortex show downregulation of the CT-promoting TF SOX5 and upregulation of SCPN-promoting FEZF2 ( 60 ). EN-Deep-M5 is also enriched in post-synaptic signaling genes ( IGSF2 , GABRB1 and 2 , and RYR3 ) ( 32 , 61 ), suggesting that dysregulation of corticofugal programs could affect L6 neuronal connectivity and circuit function in Ts21. Overall, Ts21 dysregulated molecular programs govern the balance between proliferation and neurogenesis and affect key aspects of neuronal differentiation that are important for proper connectivity and function. An in vitro model of Ts21 recapitulates accelerated differentiation and increased OxPhos To provide orthogonal validation of our in vivo findings, we derived primary human neural progenitor cells (phNPCs), an in vitro model of neurogenesis ( 10 , 62 ), from Ctrl and Ts21 donors ( table S8A ) and performed snMultiome profiling after 4 weeks of differentiation (45,282 nuclei), capturing the major cell types found in vivo ( fig. S7A - E , table S8B ). Ts21 phNPCs showed similar HSA21 overexpression and widespread gene dysregulation as in neocortex ( fig. S7 F - H ). Similarly to in vivo , we found significant reduction of neural progenitors and increase in neurons including EN-Upper-IT (FDR P<0.05, Materials and methods ; fig. S7I , table S9 ) and validated these composition changes by immunocytochemistry ( fig. S7 J - L ). Trajectory analyses ( fig. S8A ) revealed that pseudotime distribution was significantly altered in Ts21 (P<2.2e −16 , Kolmogorov-Smirnov test; fig. S8B ) and increased in neurons, including EN-Upper-IT ( fig. S8C ), which is consistent with an accelerated neurogenic timeline and increased neuronal maturation. DGE and pathway analyses confirmed concordance with in vivo mechanisms (FDR P<0.05, Materials and methods ; fig. S8D - J , table S10 ). In RGs, genes involved in cytoskeletal dynamics and extracellular matrix (ECM) remodeling were upregulated ( ITGB1 , ADAMTS1 , and Arp2/3 complex), whereas those involved in synaptic development and function were upregulated in IPCs and neurons ( fig. S8 E - J ). Metabolic genes were upregulated in all cell types as in vivo , including HSA21-encoded NDUFV3 . We therefore assessed metabolic function in phNPCs, finding significantly increased adenosine triphosphate (ATP) production through OxPhos over glycolysis in Ts21 (P<0.05, two-sided t test; fig. S9A - C ). To disambiguate progenitor versus neuron contribution, we assayed an independent pair of isogenic di- and trisomic human fibroblast-derived induced pluripotent stem cell (hiPSC) lines differentiated into neural progenitor cells (NPCs) or neurons. Similarly to what was observed in phNPCs, OxPhos ATP production was increased in both trisomic NPCs and neurons (P<0.05, two-sided t test; fig. S9D - I ). Total ATP production and glycolytic output also increased, which is consistent with upregulation of glycolysis genes such as HSA21-encoded NRIP1 ( Fig. 4C ) and PFKL ( fig. S8H ). Together, these results demonstrate that an accelerated neurogenic timeline and neuronal maturation in Ts21 is preserved in vitro and validate the increased OxPhos in Ts21. Altered vascular endothelial-RG interactions indicate a pro-neurogenic environment in Ts21 Disrupted ECM-mediated signaling and increased angiogenic signaling in Ts21 RGs suggested that inter-cellular interactions important in the neurogenic niche ( 20 , 51 ) could be impaired. To explore this, we performed cell-cell communication analysis, identifying 67 ligand-receptor pathways ( table S11 ) of which 50/67 (82%) showed significantly differential activity (FDR P<0.05, Materials and methods ; Fig. 5A ). Synaptic signaling interactions in ENs and INs (glutamate, GABA-A and -B; Fig. 5A , fig. S10A ) were upregulated, as was observed in NeuronChat results ( Fig. 3M ), as well as those important for neurite outgrowth and patterning (SEMA6, CDH, CNTN, EPHB, Netrin, CADM, SLIT and SLITRK; Fig. 5A ) ( 63 , 64 ), which is consistent with an accelerated neurogenic timeline and neuronal maturation. Fig. 5. Pro-angiogenic and inflammatory microglial signaling are increased in Ts21. Open in a new tab (A) CellChat pathways down-regulated (blue) and up-regulated (red) (FDR-corrected P<0.05). (B) Vascular, microglia enriched pathways (>25%, P=0.0018), colored as in (A). (C) Intravascular interaction strengths increased (red) and decreased (blue). (D) RG-vascular pro-angiogenic interactions. (E to H) Differential chord plots of RG-vascular ECM interactions. Sender is indicated by arrow origin; receiver is indicated by arrowhead; colored by differential communication probability (blue, lower; red, higher in Ts21). (E and F) Collagen and fibronectin/FN1 signaling in Ts21. (G) Laminin-α2–integrin (left) and laminin-α4–integrin signaling (right) and (H) RG-vascular VEGFA-VEFGR signaling. (I) Pro-inflammatory microglia signaling. (J to M) Differential chord plots of microglia interactions. (J) Pro-inflammatory SPP1 signaling from microglia to multiple cell types and (K) glutamate signaling from microglia to neurons. (L) Anti-inflammatory IGF1 signaling. (M) Pro-angiogenic adenosine signaling via CD39-ADORA2A. (N) Pro-inflammatory microglia module; average expression score (left), hub genes (center) and top five biological processes (BP) and cell compartment (CC) GO enriched terms (right). (O) IBA1 (green) and SPP1 (magenta) immunostaining in neocortex. Red arrows IBA1 + -only, white arrows IBA1 + /SPP1 + microglia. Scalebar, 25μm. (P) IBA + /SPP1 + proportion increased in Ts21. n=4 donors/condition (points); 2 to 3 images per donor (barplot, error bars, SEM). FDR-corrected *P<0.05, ***P<0.001. We noted that in 16/50 (32%) of differential pathways, vascular cells and microglia made up 25% or more of the ligand-receptor interactions ( Fig. 5B ), despite comprising a minority of the cells in the dataset, suggesting disrupted vascular signaling in Ts21 ( Fig. 5B and fig. S10A ). Neurogenesis is closely tied to vascular development, required to oxygenate the developing brain ( 51 , 65 ). We found upregulation in tight junction proteins (OCLN, PECAM1, ESAM, and CDH5; Fig. 5C ) important for blood vessel growth and integrity ( 66 ) and other intra-vascular pro-angiogenic signaling [ANGPT2-TEK ( 67 ), Jagged1-Notch ( 68 ), PDGFB-PDGFRB ( 69 ); Fig. 5C ]. We also observed increased vascular ECM signaling, including collagen and fibronectin with RG integrin-β1 and laminin-ɑ4-integrin signaling ( Fig. 5D - G ), which is important for asymmetric RG division and differentiation ( 70 – 73 ), whereas laminin-ɑ2–integrin vascular-RG interactions important for vRG apical attachment ( 74 ) were downregulated ( Fig. 5D , G ). Vascular endothelial growth factor (VEGF) signaling between RG and vascular cells ( Fig. 5D , H ), a well-established pro-angiogenic pathway promoting vessel ingression towards the VZ ( 51 ), was also increased, which is consistent with co-expression analyses ( fig. S6J ). IHC using endothelial (CD31) and pericyte (PDGFR-B and NG2) markers in neocortices ( fig. S10 B , C ) showed that CD31+ blood vessel density was significantly increased in the Ts21 VZ (FDR P=0.035, linear mixed-effects model; fig. S10D - F ), which is consistent with increased pro-angiogenic RG-vascular signaling. Altogether, intra-vascular and RG-vascular signaling in Ts21 indicate a pro-neurogenic and pro-angiogenic environment that is consistent with increased OxPhos gene programs, differentiation and overall accelerated neurogenic timeline. Pro-inflammatory microglia signaling is increased in Ts21 Increased microglial activation in postmortem studies and DS models has been linked to early-onset neurodegeneration and excessive synaptic pruning ( 75 , 76 ) but has not been described in developing brain. We observed significant increases in microglia-enriched inflammatory signaling pathways that are consistent with an activated state including PTN ( 77 ), MK and SPP1 ( 78 ), among others (FDR P<0.05, Materials and methods ; Fig. 5A , B ). Ts21 microglia increased SPP1 signaling ( Fig. 5I , J ), which promotes phagocytic states ( 79 ), as well as glutamate release ( Fig. 5I , K ), an indicator of an activated state ( 80 ) whereas anti-inflammatory insulin-like growth factor 1 (IGF1) secretion ( Fig. 5I , L ) ( 81 ) was downregulated. Microglia-vascular signaling via CD39 and ADORA2A ( Fig. 5I , M ) which could promote vascular proliferation and angiogenesis was also upregulated ( 82 , 83 ). Consistently, a microglia-expressed module ( table S7J , K ) containing pro-inflammatory hub genes (Microglia-M2 — SPP1 , SRGN , LGMN and HIF1A ) ( 84 – 87 ) and enriched in phagocytic and activation processes was significantly upregulated (P=6.96 × 10 −4 , linear model; Fig. 5N ). IHC confirmed that IBA1 + /SPP1 + microglia were significantly increased (P=0.011, binomial linear mixed-effects model; Fig. 5 O , P ), which is consistent with an activated state. Together these changes suggest that microglial activation may begin as early as prenatal stages in Ts21. Widespread chromatin dysregulation in Ts21 To characterize gene-regulatory dynamics in Ts21, we integrated gene expression and chromatin accessibility, identifying 27 cell types ( Fig. 6A , fig. S11 ) similar to those found by gene expression ( Fig. 1D ). We observed strong concordance between gene expression, gene activity and motif activity of cell type-specific TFs ( fig. S11B ), as expected. Trajectory inference using the joint integration confirmed acceleration of the Ts21 neurogenic timeline ( fig. S12 , table S12 ). Fig. 6. Widespread chromatin dysregulation and upregulation of pro-neurogenic and pro-IT GRNs in Ts21. Open in a new tab (A) Weighted nearest neighbor-based UMAP of joint RNA/ATAC (ATAC, assay for transposes accessible chromatin) clustering, colored by major cell type (B) Cell type-specific DA peaks. (C) GREAT enrichment of annotated gene-regulatory regions in DA peaks. Colorbar indicates fold change. Binomial test, FDR-corrected P<0.05. (D) Motif enrichment of HSA21-encoded TFs in DA peaks. (E) HSA21-encoded DEG epigenetic regulators. Only significant changes (FDR-corrected P<0.05) shown. Colorbar indicates fold enrichment (D) or average log fold-change (E). (F to H) Select progenitor-neuron transition eRegulons. (F) (Top) eRegulon activity (AUC, area under the curve) by target gene expression (colorbar) and chromatin accessibility (dot size). (Bottom) Differential eRegulon gene activity. Colorbar indicates differential activity z-score; two-sided t-test. (G) eRegulon target genes and regions. Primary edges from TFs indicate significant TF-to-region connections; inner green diamonds, chromatin regions. Secondary edges from regions indicate significant region-to-gene connections; darker colors and thicker edges indicate stronger score. Outer diamonds indicate genes targeted by each region; red indicates increased expression in Ts21. (H) Top five BP, CC and molecular function (MF) GO terms enriched in target genes. Red dotted line indicates FDR-corrected P<0.05. (I to K) Select CT and IT eRegulons. HSA21-encoded genes are colored red (I). FDR-corrected *P<0.05, **P<0.01, ***P<0.001. We next identified cell type-specific Ts21-DA peaks, observing a similar pattern to DGE with ENs and RGs showing the most DA peaks ( Fig. 6B and fig. S11C ; table S13A ). Ts21-DA peaks were enriched in promoters and enhancers and under-enriched in heterochromatin, indicating a role in gene regulation (FDR P<0.05, binomial test; Fig. 6C ). Motifs from TF families with established roles in neurogenesis (C2H2, homeodomain, and BHLH) ( 24 ) were most enriched (FDR P<0.05, hypergeometric test; fig. S11D , table S13B ), suggesting altered neurogenesis gene-regulatory programs. Of known HSA21-encoded TF motifs 7/9 were enriched in Ts21-DA peaks ( Fig. 6D ; table S13B ) in all cell types (PRDM15, PKNOX1, GABPA, ETS2, and ERG) and two were specifically enriched in excitatory neurons (RUNX1 and BACH1) ( Fig. 6D ). RUNX1 enrichment in excitatory neurons is consistent with its known roles promoting neuronal differentiation, whereas BACH1 has not been studied in neurogenesis. We also observed that multiple HSA21-encoded chromatin modifiers including BRWD1 , HMGN1 and PRMT2 were significantly upregulated across many cell types (FDR P<0.05, Fig. 6E ) suggesting that transcriptional dysregulation in Ts21 could also be driven by widespread altered chromatin accessibility leading to differential availability of cis -regulatory elements (CREs). Increased activity of GRNs promoting differentiation and IT specification We next conducted a systematic analysis of GRNs impacted in Ts21, identifying 358 enhancer-driven regulons (eRegulons; 196 activators, 162 repressors; table S14A ) capturing well-established cell type-specific TF activity ( fig. S11E ). HSA21-encoded eRegulons OLIG2, ETS2 and ERG showed highly specific expression in oligodendrocyte precursor cells, microglia and vascular cells, whereas GABPA and BACH1 were active in RG, newborn neurons and INs ( fig. S11E ). Focusing on activators, we identified 90 eRegulons with significantly differential activity across conditions in at least one cell type (FDR P<0.05, Materials and methods ; table S14B , fig. S13 and fig. S14 ), of which 27 control progenitor-neuron transition and CT or IT specification of excitatory neurons. Multiple RG eRegulons promoting stemness over differentiation were downregulated ( Fig. 6F - H ). These included those promoting RG identity (PAX6 and TFAP2C) ( 25 , 88 ) ( Fig. 6G ), proliferation (NR2E1, E2F3 and E2F7) ( 89 , 90 ), REST/Nrsf activity (CHD2) ( 91 ), ( Fig. 6F ) pro-proliferative Wnt and cilia signaling (TCF7L1, TEAD1 and RFX2; fig. S14A ) ( 92 – 94 ) and ECM-mediated RG apico-basal transition (PLAGL1) ( 95 ), which is consistent with observed dysregulation of ECM expression programs ( Fig. 4K ) and accelerated neurogenic timeline ( Fig. 2B , Fig. 3B - D ). Upregulated eRegulons included those promoting vRG-oRG and later IT differentiation via ECM signaling or cholesterol biosynthesis (SOX9 and SREBF2)( 49 , 96 ), or differentiation timing (POU2F1) ( 97 ) ( Fig. 6F , fig. S14B ). We next sought to define GRNs driving cell specification phenotypes observed in the Ts21 excitatory lineage. Previous studies identified key TFs and eRegulons influencing the ordered progression of ENs into first CT then IT neurons ( 22 , 29 , 30 ). We found that 27/33 (81%) CT or IT eRegulons ( table S14C ) were active during mid-gestation, with 14/27 (52%) dysregulated in Ts21 ( Fig. 6I - K ). These reflected the increased IT/CT proportion ( Fig. 2 ), with 7/7 CT eRegulons decreased in newborn and CT neurons, and 6/7 IT eRegulons increased in newborn and IT neurons and mis-expressed in CT and non-IT neurons ( Fig. 6I , fig. S14C ). Lastly, we observed that the HSA21-encoded BACH1 eRegulon was significantly upregulated in EN-Newborn-2 (FDR P = 1.96 × 10 −4 , Fig. 6I ), one of the earliest clusters showing commitment to the IT lineage ( Fig. 3 ). Target genes of BACH1 included pro-IT TFs CUX2 and BHLHE22 ; consistent with this, BACH1 peak expression preceded that of CUX2 in pseudotime ( fig. S4A ), suggesting that altered neuron specification is due in part to direct activity of this HSA21-encoded gene. BACH1 motifs were also enriched within Ts21 DA peaks in ENs in the companion study ( 60 ), suggesting that this dysregulation extends to postnatal periods. We validated GRNs by overlapping differential eRegulons and modules ( Fig. 4 ), finding strong enrichment within those regulating similar processes (FDR P<0.05, Fisher’s exact test; fig. S13C , D ). To provide additional orthogonal validation, we integrated gene expression and chromatin accessibility in phNPCs ( fig. S15A ) and generated cell type specific GRNs, identifying 195 eRegulons (116 activators, 79 repressors; fig. S15B and table S15A ). We identified 72 cell-specific activators with differential activity in Ts21 ( fig. S15 C , D and table S15B ). The same and/or similar proliferative eRegulons were downregulated, whereas pro-neurogenic ( fig. S16 A - C ) and pro-IT ( fig. S16 D - F ) eRegulons were upregulated as in vivo . We again identified HSA21-encoded BACH1 as an activator of pro-IT TFs such as CUX2 and showing increased activity in Ts21 neurons including EN-Upper-IT ( fig. S16 D , E ). Together with our in vivo findings, these results demonstrate that GRNs altered in Ts21 are consistent with a pro-neurogenic state, with HSA21-encoded BACH1 contributing to a pro-IT identity. Convergence of neurodevelopmental pathways altered in Ts21 with rare and common genetic signals of neuropsychiatric disorders Ts21 provides a model for interrogating pathways shared across conditions with cognitive impairment. We first tested the overlap of rare-variant association signals from large-scale whole-exome and genome sequencing studies across ASD, NDD, developmental disorders (DD), and epilepsy ( table S16A ), as well as SFARI syndromic and non-syndromic ASD variants with Ts21 cell-specific DEGs and modules, observing significant overlap across most disorders (FDR P<0.05, Fisher’s exact test; Fig. 7A ). Because enrichments with upregulated Ts21 signatures reflected neuronal enrichment of rare variants overlapping those of accelerated neurogenesis rather than shared mechanisms, we focused on downregulated signals matching expected haploinsufficiency of de novo rare variants. EN-L6-CT_2 and EN-L6-IT DEGs were enriched across multiple disorders including NDD, DD, ASD, and syndromic ASD ( table S16B ), implicating deep-layer ENs as a point of vulnerability across DDs ( Fig. 7B ). Fig. 7. Convergent dysregulation of Ts21-altered neurodevelopmental pathways with rare and common genetic signals of neuropsychiatric disorders. Open in a new tab (A) Rare variant enrichment across Ts21-dysregulated transcriptomic signatures. Enrichment of cell-specific upregulated (red) and downregulated (blue) cell type-specific DEGs and gene co-expression modules with NDD, DD, ASD, and epilepsy-associated gene sets. Fisher’s exact test; dotted red line indicates FDR-corrected P<0.05. Cell type and module colors: RG, orange; IPC and Newborn, blue; EN, green; IN, purple. (B) Shared EN-L6-CT and EN-L6-IT downregulated genes enriched in disease gene sets. (C) Rare variant enrichment in differential eRegulon target genes (Fisher’s exact test, FDR-corrected P<0.05). CT and IT-associated eRegulons are colored in red. Bars are colored by cell type as above. Color scale: –log10(FDR P-value). (D) Ts21 DA peaks or differential eRegulon CRE contributions to heritability across brain traits and disorders (linkage disequilibrium score regression). Traits include ADHD, ASD, educational attainment (EA), epilepsy, AD, cortical thickness and total surface area (SA). Color: FDR <0.05 (purple) to <0.0001 (yellow). Whereas enriched DEGs were mostly cell type specific, a majority (19/35) have established roles in chromatin and transcriptional regulation important for neuronal differentiation and maturation, including KMT2C, POGZ, KAT6A , and EZH2 ( 98 – 101 ) ( Fig. 7B ). Two genes, GATAD2B and SPTBN1, were downregulated in both EN-L6-CT_2 and EN-L6-IT. SPTBN1 has been implicated in axon growth and synapse formation ( 102 , 103 ), whereas GATAD2B is a component of the NuRD chromatin remodeling complex regulating CT and IT lineage TFs SATB2 and BCL11B to fine-tune neuronal specification ( 104 , 105 ) which is consistent with mixed CT and IT identities in Ts21 ( Fig. 2J - O and fig. S3F , G ). We next investigated whether GRNs disrupted in Ts21 overlapped the same genetic signals. Of 90 differential eRegulons, 54 (60%) targeted genes associated with at least one of the tested disorders ( Fig. 7C , table S16C ). Those shared across ASD, NDD, and DD were primarily active in IPCs, newborn neurons and ENs, and were associated with CT or IT specification. Of 14 differential CT or IT eRegulons ( Fig. 6I ), 12 (86%) were enriched in at least one disorder ( Fig. 7C ). Altogether, these findings support that molecular and gene-regulatory programs dysregulated in Ts21 are shared across rare-variant genetic signals of ID and ASD and point to altered neuronal specification. We then asked whether Ts21-DA peaks ( table S13A ) or differential GRNs ( table S14B ) contributed to heritability of neuropsychiatric disorders or other DS-relevant brain traits by stratified linkage disequilibrium score regression across genome wide association study (GWAS) variants ( Fig. 7D , table S16A , D ). Ts21-DA peaks and eRegulons significantly contributed to heritability of intelligence and educational attainment, suggesting that shared programs influence cognition and ID (FDR P<0.05, Materials and methods ). Consistent with reported reduced brain volume in Ts21 and our findings of decreased progenitors, total surface area and average cortical thickness were also enriched in differential eRegulons. Heritability of ASD and ADHD, conditions co-occurring with DS at high rates, was also enriched, and ASD risk was localized to EN-L6-IT and EN-L6-CT neurons, which is consistent with rare-variant enrichments ( Fig. 7A , B ). Lastly, we did not observe heritability of AD risk enriched in Ts21-altered chromatin or GRNs, which is in line with previous studies finding that accessible chromatin in developing brain does not overlap with genetic signals of neurodegeneration ( 21 , 24 , 106 ) and demonstrating the specificity of this approach. Discussion We identified changes in neurogenesis, metabolism, EN specification and cell interactions, and we defined molecular and gene-regulatory programs altered in Ts21 at mid-gestation, addressing longstanding questions of DS pathophysiology and connecting them to HSA21 gene dosage. Structural changes in brain volume and gyrification are thought to contribute to the cognitive deficits observed in DS ( 4 – 7 ), yet previous studies have not agreed on whether the underlying cause is altered neurogenesis or cell death. We found accelerated advancement through the neurogenic timeline and reduced progenitor expansion in the Ts21 developing neocortex, which is consistent with proposed models of heterochrony in DS explaining reduced cortical thickness and brain volume ( 8 ). This finding is supported by reduced RG proportions, earlier generation of IT neurons and at greater proportions, and increased maturation and connectivity of Ts21 neurons. In addition, increased OxPhos and fatty acid oxidation aligns with involvement of mitochondria in setting the tempo of neurogenesis, IT generation, and neuronal maturation ( 52 , 53 ), processes regulating human neoteny that when disrupted could lead to NDDs ( 107 , 108 ). Whereas the increase in IT/CT neuron ratio can be explained in part by acceleration, we found that other genetic factors promote IT over CT identity. First, deep layer neurons misexpress key TFs that establish and maintain IT identity, whereas upper layer neurons conversely misexpress CT-identity TFs. This mixed identity can be observed most clearly during early mid-gestation (GW13 to 16), when a larger proportion of ENs are BCL11B + /SATB2 + . Second, regulation of IT identity is directly tied to Ts21 through the HSA21-encoded BACH1 eRegulon, which drives expression of IT TFs CUX2 and BHLHE22 in newborn neurons. BACH1 peaks just before CUX2 in pseudotime, which is consistent with its potential upstream regulatory function. Lastly, CT identity may be broadly affected in Ts21 because we find upregulation of IT TFs in CT neurons, as well as mixed corticofugal gene co-expression. Together these observations suggest that subtle changes in the relative expression of TFs specifying neuron identity, originating from HSA21, may be sufficient to drive large changes. We found that rare and common variant signals of neuropsychiatric disorders, cognitive function and brain structure overlapped programs altered in Ts21, in particular those associated with CT and IT specification. Chromatin remodelers and TFs associated with ASD, NDD and DD are dysregulated in Ts21 L6-CT and L6-IT, the most mature neurons at this stage of cortical development. Because genetic risk for these disorders has been previously localized to both deep and upper layer neurons, the processes shared with Ts21 are also likely to be found in both subtypes with further maturation. Common variants contributing to disease risk shared between Ts21 and ADHD and ASD were enriched in regulatory elements within deep layer neurons. These disorders share, among others, deficits in sensory processing, which is strongly influenced by L6-CT projections to sensory thalamic nuclei ( 109 ). Mis-specification CT, IT and corticofugal identity may broadly impact sensory processing, potentially leading to delayed motor and language development of individuals with DS. Our study provides a foundation to interpret Ts21 alterations across model systems and through the developmental timeline to adulthood. Single-nuclei molecular profiling in postnatal ( 60 ) and adult ( 16 ) Ts21 neocortex showed compositional changes that are consistent with those presented here with increased proportions of upper layer IT neurons. Similarly, neuroinflammatory cascades are broadly recognized in the adult DS brain ( 19 , 75 ) and were detected at early postnatal periods in the companion study ( 60 ), a parallel of which we began to observe during mid-gestation in the form of activated microglia transcriptomes. These observations suggest alterations during development are maintained throughout life and could pinpoint opportunities for early intervention. In the future, it will be important to conduct functional studies leveraging systems-level screening approaches to identify the most impactful therapeutic strategies for DS. Materials and methods Tissue acquisition, dissection and single-nuclei isolation Sample acquisition De-identified human midgestation brain tissue was obtained from the UCLA Gene and Cell Therapy Core, the UCLA Translational Pathology Core Laboratory, and the NIH NeuroBioBank repository according to IRB guidelines and to the legal and institutional ethical regulations of the UCLA Office of Human Research Protection. Full informed consent was obtained from all of the parent donors. For tissue obtained at UCLA, gestational age was estimated following the guidelines of the American College of Obstetricians & Gynecologists. Briefly, for second trimester estimations, age based on the date of last menstrual period was revised using ultrasonographic dating incorporating head circumference, femur length and abdominal circumference. Excluding the trisomy of HSA21 in Ts21 samples, no known major pathogenic CNVs implicated in neuropsychiatric disorders were found in any donors. Ctrl and Ts21 tissue samples obtained at UCLA were stored in ice-cold Leibovitz’s L-15 (Corning 10–045-CV) medium supplemented with 1x Penicillin-Streptomycin (Gibco 15140122) for initial dissection to obtain brain tissue. Cortical tissue was then sectioned under a dissection microscope in ice-cold Dissection Media [HBSS w/o Ca2 + and Mg2 + (Gibco 14170120), supplemented with 100mM HEPES (Gibco 15630080), 10mM Sodium Pyruvate (Gibco 11360070) and 1x Pen-Strep] to obtain neocortex. Dissected samples were immediately flash-frozen in liquid nitrogen (for snMultiome); fixed overnight (16 hrs) in 4% PFA, then equilibrated overnight in 30% sucrose and embedded in OCT medium (for IHC); or used to generate primary human neural progenitor cells (phNPCs) lines as previously described( 10 ) (see “ Cell culture and in vitro assays ” section). Flash frozen and fixed, embedded tissue were stored at –80°C. All samples were processed within 1 hr of receipt, with an average and maximum post-mortem interval (PMI) of 3 and 4 hrs, respectively. The PMI is defined as the number of hours from start of the surgery to when the tissue was frozen or fixed. Cortical area annotations are listed in table S1 as reported from the NIH NBB. When anatomical landmarking was not possible, we reported the region as “Cortex.” We note that reference mapping and areal prediction using a spatial atlas with annotated cortical areas ( 110 ) predicted the majority of cells originate from the frontal cortex, with no substantial difference between Ctrl and Ts21 cohorts. Nuclei isolation and snMultiome capture As manufacturer-provided protocols produced low quality nuclei, we optimized a previously described ( 111 ) two-step nuclei isolation protocol preserving nuclear integrity and molecular and chromatin signatures. Briefly, frozen neocortical sections (~10 to 20mg) were homogenized in RNAase-free conditions in 1–2ml of low-detergent lysis buffer (250mM Sucrose, 25mM KCl, 5mM MgCl2, 10mM Tris-HCL pH7.4, 1% BSA, 0.05% Triton-X, 1mM DTT, 1x EDTA-free protease inhibitor, 0.04U/μl RNase inhibitor) using a 2mL dounce homogenizer on ice. Nuclei were pelleted at 600 g for 8 minutes at 4°C, washed twice in wash buffer (lysis buffer without Triton-X) and resuspended in 1x PBS supplemented with 1% BSA, 1mM DTT, 1x protease inhibitor and 0.05U/μl RNase inhibitor. Nuclei were filtered through a 40μM tip strainer, and an aliquot was stained with Trypan Blue to confirm nuclei quality and counted on a hemocytometer. Nuclei (1 M per pool) were further permeabilized in preparation for DNA tagmentation by the addition of 5x OMNI buffer (50mM Tris-HCl, 50mM NaCl, 15mM MgCl2, 0.5% Tween-20, 0.5% IGEPAL, 0.05% Digitonin, 5% BSA, 1X PBS, 5mM DTT, 1U/μl RNase Inhibitor, 5x Protease Inhibitor) for 1 minute, pelleted at 600 g for 8 min at 4°C and washed twice in OMNI wash buffer (10mM Tris-HCL, 10mM NaCl, 3mM MgCl2, 0.1% Tween-20, 1% BSA, 1mM DTT, 1U/μL RNase Inhibitor, 1x Protease Inhibitor). The final nuclei pellet was resuspended in 1x Nuclei Buffer (10X Genomics). An aliquot was stained with Trypan Blue to assess nuclei quality and single nuclei suspension and counted again before pooling and loading onto a 10X multi-ome flowcell. To minimize potential effects of technical batches, nuclei from multiple donor samples were pooled (3–4 samples/pool) into a single suspension, loaded onto the same 10X multi-ome flowcell and processed according to the 10X Genomics Next GEM Single Cell Multiome ATAC + Gene Expression kit to capture paired single-nucleus, multi-omic RNA and assay for transposase-accessible chromatin (ATAC) samples. Approximately 5,000 nuclei per donor sample were targeted for capture. Libraries were sequenced on a NovaSeq 6000 to a targeted average depth of 25,000 read pairs per nucleus each for RNA and ATAC modalities. To obtain high quality nuclei from differentiated phNPCs (described below), plated cells were first gently washed with 1x PBS then released by incubation with Papain (Worthington Biochemical LK003176 ) and DNAse I (Worthington Biochemical LK003170 ) for 20 minutes at 37C in a 5% CO2 incubator with gentle shaking. Lifted cells were gently dissociated by trituration with a P1000 and pelleted, then washed once with and resuspended in 1x PBS with 0.5% BSA. The concentration and quality of cell suspensions were determined using Trypan Blue, then 2–3 M cells were resuspended in ice cold low-detergent lysis buffer, transferred to a 2ml dounce on ice and processed for nuclei isolation and snMultiome as described above. All samples were randomized and blinded to the experimenter performing papain digestion and nuclei isolation. Samples were unblinded prior to pooling and loading onto the 10X flowcell. Sample genotyping To confirm sample ploidy and sex, genomic DNA was isolated from flash frozen neocortical samples using the Qiagen DNeasy Blood and Tissue kit (Qiagen, 69504) following the manufacturer’s instructions. Genomic DNA was RNAse-treated (RNAse I, Ambion AM2294) for 30 min at 37°C followed by heat inactivation at 70°C for 15 min and subsequently purified using the Genomic DNA Clean & Concentrator kit (Zymo Research, D4064). DNA was also isolated from an aliquot of the same phNPCS used for snMultiome profiling and IHC to confirm ploidy. Single nucleotide polymorphism (SNP) genotyping was performed using the Illumina Infinium CoreExome-24 BeadChip at the UCSD IGM Genomics Center. Genotype calling and calculations for B-allele frequencies (BAF), and Log-R-Ratio (LRR) per sample were performed using Illumina GenomeStudio software (v2.0.5). Each sample was assessed for Ts21 using BAF by the absence of SNV distribution at 0.5 (AB alleles) and presence of SNV distributions at 0.33 (AAB alleles) and 0.66 (ABB alleles). SNV BAFs were plotted along all chromosomes to confirm Ts21 and exclude additional aneuploidies in Ts21 and control samples. To assess the degree of trisomy, we used copy number (CN) analysis of SNP genotypes to estimate contiguous segments of HSA21 and the average segment LRR, using circular binary segmentation and maximal t-test assessment with the DNAcopy package( 112 ). Over 80% of HSA21 was assigned CN=3 (LRR > 0.2) for Ts21 samples, indicative of full meiotic Ts21 rather than mitotic segmental duplication. HSA21 segment maps were utilized to estimate mosaicism with the assumption that the proportion of probe B allele signal intensity of the total allelic signal intensity centers at 0.5 (AB) in disomic heterozygotes and clusters at 0.33 (AAB) and 0.67 (ABB) in trisomic heterozygotes. Thus, in mosaic mixtures, the fraction of trisomic cells is a linear combination of disomic (0.5) and trisomic (0.67 or 0.33) BAF( 113 , 114 ): BAFupper = (1– m )0.5+ m 0.67. Simplified: m = 5.88( BAFupper –0.5). Validated: BAF(lower) = (1 – m)(0.5) + m(0.33) . This estimation accounts for probe-variability and BAF compression caused by technical standardization. The average HSA21 segment trisomic fraction was m > 0.9 in all Ts21 samples, consistent with full non-mosaic trisomy. To infer ancestry, donor genotype bedfiles were filtered to remove missing genotypes (geno >0.05), rare variants (maf >0.01), genotypes that deviate from the Hardy Weinberg Equilibrium (hwe >1e −6 ) and genotypes with a high SNP missingness rate (mind >0.01). Ancestry was estimated from donor genotype data using PlinkQC ( 115 ) (v0.3.4), according to the provided vignette ( https://meyer-lab-cshl.github.io/plinkQC/articles/AncestryCheck.html ). Non-AT or -GC SNPs were removed from both donor and reference and the bedfile was filtered for variants in linkage disequilibrium with r 2 > 0.2 in a 50kb window. To ensure genotype bedfiles matched the reference, chromosome and position mismatches were identified and corrected. Allele flips were also identified and either corrected or removed in the absence of a match. Once both were prepared, the bedfile and population reference were merged and principal component analysis (PCA) was run with Plink. The resulting eigenvalues were plotted and analyzed for clustering and association using ggplot2 in R. Single-nuclei multi-omic analyses Data pre-processing and sample demultiplexing CellRanger ARC (v2.02, 10X Genomics) was used to generate fastq files from raw BCL sequencing files for the ATAC modality (cellranger-arc mkfastq), and the cellranger-arc count pipeline was used to process both gene expression and chromatin accessibility fastq files for read mapping, cell barcode calling and generation of quality control metrics according to standard 10X Genomics guidelines using default settings. Reads were aligned to the 10X Genomics pre-built human reference genome (GRCh38 2024-A-2.0.0). SNP-based demultiplexing was used to identify and remove multiplets and assign nuclei to donors. Cellsnp-lite (v1.2.3) ( 116 ) was used to generate single-nuclei SNP profiles from reads mapped to both RNA and ATAC modalities for each sequencing library. Reference donor SNP profiles imputed from microarray genotyping of exonic variants (Infinium Core Exome-24, see above) were generated using the TOPMed imputation server ( https://imputation.biodatacatalyst.nhlbi.nih.gov ) and the TOPMed reference panel (v.R3 on GRCh38) ( 117 ). RNA and ATAC nuclei SNP profiles were matched to reference donor profiles using vireoSNP (v0.5.8) ( 118 ). For tissue samples, multiplets – nuclei assigned to more than one donor (~16%) – and those with mismatched donor assignments between modalities (~1%) were removed. Nuclei with a donor assignment in only one modality (<1%) were retained and assigned using that modality. The majority (~83%) of nuclei were singlets and assigned to the same donor in both modalities. For phNPC samples, multiplet nuclei assigned to more than one donor (~15%) and nuclei with mismatched donor assignments between modalities (0.03%) were also removed, and those with donor assignment in only one modality (6%) were kept. The majority of phNPC nuclei (82%) were singlets and assigned to the same donor in both modalities. These were used for all subsequent analyses. Following donor assignment, to minimize effects of cell-free RNA in the medium and ensure accurate transcript quantification, ambient RNA removal was performed on the RNA modality using CellBender (v0.3.0) ( 119 ), and the resulting nucleus × gene matrix was used for further analyses. Quality control, integration, clustering and cell type annotation After pre-processing, we applied the following filters to retain high quality nuclei: (i) gene expression count (nCount_RNA) >200 and <3 standard deviations from the donor mean, (ii) ATAC fragment count (nCount_ATAC) >100 and (iii) percent mitochondrial genes <5% (tissue) or <10% (phNPCs, to account for increased proliferative state of in vitro models). The sctransform normalization method in Seurat (v4.4.0) ( 120 ) was used to correct technical variance in read depth and gene detection in individual donor samples. Differences in cell health were corrected for by regressing out mitochondrial gene percentage using var.to.regress in sctransform . To control for inter-donor variability and other technical effects, samples were then integrated across all donors and timepoints using the RNA modality by reciprocal PCA projection method with 3000 features (genes). Dimensionality reduction by PCA analysis was used to minimize technical noise in clustering, and the first 30 PCs were selected for UMAP embedding and clustering. Nuclei were clustered using the Louvain algorithm at a resolution of 0.5. Cluster-enriched genes were identified for each cluster using the FindAllMarkers function (min.pct=0.25 and logfc.threshold=0.25) in Seurat. Clusters comprised of all major cell types of the developing neocortex were annotated by enrichment of cell type markers from previously published datasets of mid-gestation neocortex ( 21 , 22 ) (Fisher’s exact test, FDR-corrected P<0.05, with all detected genes in each dataset as background), and by expression of canonical cell type markers ( Fig. 1D , E and fig. S2D , E ). To integrate RNA and ATAC modalities, ATAC read fragments from individual donor samples were first merged, keeping unique cell barcode and donor information, and narrow peaks were called on the merged fragments using MACS2 (v2.2.9.1) ( 121 ) with default parameters. Regions overlapping ENCODE blacklisted regions were excluded. The resulting nucleus × peaks and nucleus × genes matrices were then integrated together using the latent semantic indexing (LSI) method in Signac (v1.12.0) ( 122 ) to integrate peak and gene embeddings. Nuclei were then re-clustered using the weighted nearest neighbor (WNN) algorithm ( 120 ) (1 to 30 PCs and 2 to 40 LSI components), joint UMAP embedding of the resulting nearest-neighbor graph and Louvain clustering (resolution=1.1). Cell type annotation of resulting clusters was performed as described above for gene expression clusters. Differential cell composition To quantitatively assess cell composition changes across conditions, we employed sccomp ( 123 ), a Bayesian framework based on sum-constrained Beta-binomial distributions designed for single-cell composition analysis of complex data. For in vivo samples, sccomp was run with condition (Ctrl or Ts21) as a predictor and gestational age and sex as fixed effects, and counts were grouped by donor and replicate. Three outlier replicates were identified and removed from composition analyses. For phNPCs, sccomp was run with condition as a predictor, and counts were grouped by donor and replicate. For both in vivo tissue and phNPCs we required a FDR threshold of <0.05 and cell cluster size >500 cells or >1% of the cells in at least one condition to consider composition changes statistically significant. Three outlier replicates were identified and removed from composition analyses. For pseudotime composition analyses, we employed a binomial generalized linear mixed-effects model [using the glmmTMB function from the glmmTMB R package (v1.1.9)] which was fit across all cell types present in each bin, with cell counts as a function of condition and cell type, gestational age and sex as fixed effects and donor as a random effect. A pairwise comparison of the estimated marginal means [emmeans, from the emmeans R package (v1.10.7)] was then performed across conditions for each cell type. Resulting P-values were FDR-corrected across all cell types tested in the pseudotime bin. Deep/upper lineage cell composition was also assessed using a binomial generalized linear mixed-effects model as for pseudotime bins, with gestational age and sex as fixed effects and donor as a random effect. Differential gene expression analysis Differentially expressed genes were identified per cell type using a negative binomial gamma mixed model from the NEBULA package ( 124 ) (v1.5.5). This model properly accounts for the hierarchical structure of the dataset to mitigate pseudoreplication bias that can inflate significance in scRNA-seq. For in vivo samples, NEBULA was run with condition (control or Ts21) as a predictor, donor as a random effect and sex and gestational age as fixed effects. For phNPC samples, NEBULA was run with condition as a predictor, and donor and replicate as random effects. The number of genes detected in each cell was used as the scaling factor for that cell. Resulting P-values were FDR-corrected across all cells of the respective cluster. All fold changes are reported as the natural log transformed value as output by NEBULA. Genes were considered significantly upregulated if FDR-corrected P-value<0.05; downregulated genes were considered significant if FDR-corrected P-value<0.1. Differential program and module expression Differential expression of CT and IT TFs were calculated for fig. S3 , and differential expression of hdWGCNA modules (see below) were calculated for Figs. 4 and 5 and fig. S6 . The UCell package ( 125 ) (v2.8.0) was used to calculate the mean expression of sets of genes of interest in each cell type. For comparison of CT and IT programs, cells within the EN lineage were selected and mean expression of gene sets was calculated from RNA counts using the AddModuleScore_UCell function. Differential TF activity across cell types was calculated by fitting a linear mixed-effects model [using the lmer function from the lme4 R package (v1.1–35.5)] with expression score as a function of condition and cell type, gestation week and sex as fixed effects and donor as a random effect. The estimated marginal means was used to perform pairwise comparisons between conditions in each cell type, and resulting P-values were FDR adjusted across all cell types. For comparison of hdWGCNA modules, the ModuleExprScore wrapper function for UCell from the hdWGCNA package was used to calculate mean expression of the top 25 genes by module eigengene connectivity (kME, see below) for each module. Differential module expression was calculated per network by fitting a linear mixed-effects model with expression score as a function of condition and module, with gestation week and sex as fixed effects and donor as a random effect. Emmeans was used to perform pairwise comparisons between conditions in each module, and resulting P-values were FDR adjusted across all modules in the network. For the microglia network, a linear model was fit to each module, with activity score as the output, condition as the predictor, and sex and gestation week as fixed effects; resulting P-values were FDR adjusted. Trajectory inference analysis Trajectory inference analysis in the EN lineage was performed using Monocle3 ( 126 ). The UMAP coordinates and dimensions calculated for the RNA or WNN integrated Seurat objects ( Fig. 1A ) were used to calculate inter-cell Euclidean distances and establish an unordered continuum, or principal graph, with nodes, along which pseudotime was determined. For in vivo samples, the root was assigned to the node within the highest density of cells that were vRG-oRG-PgS of GW 13 donors for both RNA and WNN trajectories. Cells that do not differentiate from RGs (interneurons, microglia, vascular cells) were excluded from the in vivo pseudotime analysis. Pseudotime was estimated along the trajectory by each cell’s expression profile age relative to the root cells. As expected, RGs had the lowest pseudotime and highest transcriptional similarity to the root, with progressively increased pseudotime in IPCs and newborn neurons, followed by deep and upper layer neurons, which diverged into two lineages. The significance of the difference in the global trajectory distribution between control and Ts21 cells was determined using the Kolmogorov-Smirnov test. Within the cell distribution over pseudotime, temporal boundaries or “pseudotime bins” were delimited as the intervals spanning peaks of cell density over pseudotime in control samples. Lineages of EN-Deep and EN-Upper cells were identified by naturally occurring branches in the principal graph and corresponding similarities in pseudotime estimation. Cell type-specific differential pseudotime was calculated by fitting a linear mixed-effects model with pseudotime as a function of condition and cell type, gestation week and sex as fixed effects and donor as a random effect. Emmeans was used to perform pairwise comparisons between conditions in each cell type, and resulting P-values were FDR adjusted across all cell types. For phNPC samples, all cells were used to infer a pseudotime trajectory from the RNA modality. The root was assigned to the node within the highest density of RG-1 cells, and cell type-specific differential pseudotime was calculated as above. Reference integration and age estimation Age estimation was performed by integration with a published reference dataset spanning the first trimester to adolescence ( 22 ) using the RPCA method in Seurat (v4.4.0) and the same parameters described above (quality control, integration, clustering and cell type annotation). Cells from prenatal timepoints (GW8 to 38) in the reference dataset were used for age estimation. To estimate age, a KNN regression was performed using the integrated UMAP coordinates and 10 nearest neighbors via the knn.reg function from the FNN R package (v1.1.4). To validate the accuracy of the method, we confirmed that our samples clustered with the second trimester reference ( fig. S5A - C ), and that predicted age of the reference sample was not significantly different from its annotated age ( fig. S5D ). To compare Ctrl and Ts21 donor-wise age distributions, a line was fitted to the mean actual and mean predicted donor ages. The slopes of the fitted lines were compared using a linear model with condition as an interaction term and significance was evaluated by ANOVA. Differential predicted age was assessed by fitting a linear mixed-effects model to the data, with predicted age as a function of condition and celltype, sex as a fixed effect and donor as a random effect. The estimated marginal means was used to perform pairwise comparisons between conditions in each cell type and resulting P-values were FDR adjusted across all cell types. Gene expression over pseudotime Changes in gene expression for all HSA21-encoded genes and neurodevelopmental TFs were calculated as a function of pseudotime. Normalized expression was extracted for the HSA21-encoded genes and neurodevelopmental TFs per cell, ordered by pseudotime. A smoothing spline was applied to each gene, with 3 degrees of freedom. The resulting gene expression matrix smoothed over pseudotime was normalized to the center of the data by z-score and scaled. Neuron-neuron communication network estimation and contrast Neuron-neuron interactions were estimated from the normalized RNA matrix, using NeuronChat (v.1.0.0) ( 127 ). With the default parameter settings, the inter-neural communication probabilities and strengths and communication network were estimated based on the expression of ligand-receptor pairs and their corresponding weights in the NeuronChat database, per cell type. These ligand-receptor pairs were aggregated into specific neural pathways and their functional similarities were estimated. The resulting individual control and Ts21 NeuronChat networks were contrasted for cell-specific and ligand-receptor specific differential inter-neural communication (FDR corrected <0.05). Cell-cell communication network estimation and contrast Using CellChat (v2.1.2) ( 128 ), the expression of ligand-receptor gene pairs from the CellChat database (CellChatDB v2) was aggregated across the normalized RNA matrix per condition and identified as interactions if sufficiently expressed in sending and receiving cell groups, with default parameters. The expression of ligand-receptor pairs was weighted by network-based smoothing with a PPI adjacency matrix (PPI.human). The interaction strength for each ligand-receptor pair was calculated by trimmed mean summaries of the adjusted expression. The strengths of interactions present in at least 10 cells were aggregated by pathway and then by cell type to generate an interaction network. The centrality, or the contribution, of each cell type to the overall communication network was then calculated. Control and Ts21 CellChat objects were then merged for cross-condition comparison. Within the merged CellChat object, pairwise similarity testing, manifold learning and classification learning were implemented to contrast each condition’s network. The difference in network contribution by pathways was calculated by aggregating pathway signals across cell pairs per condition and assessed by paired Wilcoxon test. Communication probabilities for network pathways were pooled by contributing cell type and transformed by negative reciprocal logarithm [–1/log(p)] to identify cell type-specific differences in network contribution. To assess differences in specific interactions, the merged network was subset by ligand-receptor pair and a differential communication probability was calculated by the scaled difference of interaction strengths between conditions by sending and receiving cell type pairs. High-dimensional gene co-expression network analyses (hdWGCNA) Single-nucleus hdWGCNA analyses were performed using the hdWGCNA package (v0.3.03) ( 129 ). hdWGCNA is a systems-level approach which uses unsupervised learning methods to identify groups of co-expressed genes (modules) with differential activity across conditions. It is intrinsically more sensitive and powered to ascertain relevant mechanisms compared to differential expression, as sub-threshold changes in gene expression can be summed at the module level to reveal dysregulation across conditions. To counter gene drop off inherent in snRNAseq, individual cell type clusters were grouped into 8 major cell types – RG (vRG-tRG, oRG, vRG-oRG-PgS), Pg-Cycling (PgG2M, PgG2M-PgS), IPC (IPC-EN, IPC-EN-Newborn), EN-Newborn (EN-Newborn-1/2, EN-Newborn-IT-Immature), EN-Upper (EN-IT-Immature, EN-L2-L3-L4-IT) EN-Deep (EN-L6-IT, EN-L5-L6, EN-L6-CT_1/2, EN-L6b, EN-L6b-SP), IN-CGE (IN-dLGE-Immature, IN-CGE-Immature) and IN-MGE (IN-MGE-Immature, IN-MGE-SST). Individual co-expression networks were calculated for each major cell type. For each network, the top 2000 most variable genes were selected and retained for network analysis using the variance stabilizing transformation (vst) method in the Seurat FindVariableFeatures function. We also required that selected genes were expressed in at least 5% of cells in each major cell type annotation. Soft-power thresholds were tested using the TestSoftPowers function and the lowest threshold to yield a model fit >0.8 was chosen. Metacells were generated using the following parameters: minimum module size=50, maximum cells shared between modules=10, number of nearest neighbors k=25 and RNA-based UMAP coordinates. Signed co-expression networks were then constructed using metacells, with a merge cut height of 0.2 for dynamic tree cutting and default settings for remaining parameters, yielding the following number of modules per network, excluding the grey module, – RG, 7; Pg-Cycling, 6; IPC, 6; EN-Newborn, 6; EN-Upper, 7; EN-Deep, 7; IN-CGE, 4; IN-MGE, 3. Summarized module expression values (ME) were calculated across all cells in the dataset using the ModuleEigengenes function and corrected across donors with Harmony. Eigengene-based connectivity (kME) was calculated for each cell using the ModuleConnectivity function. After filtering, we retained 32 cell-specific modules and classified them into five major groups based on their developmental expression pattern and GO, reflecting major corticogenesis processes ( Fig. 4H - I , fig. S6H - Q ). While modules were primarily cell type-specific, we observed some expected overlapping expression between related cell types ( Fig. 4H , fig. S6H ). For microglia, analyses were performed using all genes expressed in at least 5% of cells due to the small cluster size. The soft-power threshold was chosen as described above. Metacells were generated using the following parameters: minimum module size=2, maximum shared cells between modules=10, number of nearest neighbors k=2 and RNA-based UMAP coordinates. A signed co-expression network, ME and kME were calculated as described above with the additional requirement of minModuleSize=100, yielding 2 modules. Modules in all networks were considered expressed above background if the median module expression score was >0.025 in either condition. Differential chromatin accessibility and motif enrichment analysis Differentially accessible (DA) peaks were identified per WNN cell type via the FindMarkers Seurat function using a logistic regression (LR) model with peak read depth (nCount_peaks), sex and gestational age as latent variables. Peaks with a logFC >0.1 and Bonferroni-corrected P-value <0.05 were considered DA. Cell type-specific TF motif enrichment within DA peaks was performed using a hypergeometric test via the FindMotifs Signac function with default parameters and the JASPAR 2020 motif database. Motifs with a Benjamini-Hochberg corrected P-value <0.05 were considered enriched. The RunChromVar wrapper in Signac was used to calculate cell type-specific motif activity scores with default parameters. Enrichment of peaks in epigenetically annotated regions Enrichment of DA peaks within epigenetically annotated regions was performed using the GREAT method ( 130 ) with imputed chromatin states defined by the Roadmap Epigenomics project ( https://egg2.wustl.edu/roadmap/web_portal/ ) as previously described ( 24 ). Briefly, the enrichment of DA peaks within an imputed 25-state chromatin state model derived from prenatal brain tissue (E081 and E082) ( 131 ) was calculated using the ratio between the (#bases in state AND overlap feature)/(#bases in genome) and the [(#bases overlap feature)/(#bases in genome) × (#bases in state)/(#bases in genome)]. Annotated chromatin state region coordinates were lifted over to hg38. Significance was determined using a binomial test and P-values were FDR corrected. Regions were considered enriched if FDR-corrected P<0.05 and –log10 fold-change>0. Gene-regulatory network analyses The SCENIC+ pipeline (v1.0a1) ( 132 ) was used to build GRNs from paired single-nucleus gene expression and chromatin accessibility multi-omic data, linking TFs to their putative enhancers and target genes. As running the pipeline on all nuclei in the dataset was not possible with current method and memory constraints, we used metacells to reduce the number of cells while preserving data structure and retaining rare cell types. The MetacellsbyGroups function in the hdWGCNA ( 129 ) package was used to construct metacells within each WNN cell type using the following parameters: for in vivo tissue - minimum size=1, maximum cells shared=2, number of nearest neighbors k=12 and RNA-based UMAP coordinates; for phNPCs - minimum size=1, maximum cells shared=3, nearest neighbors k=12 and RNA-based UMAP coordinates. The resulting metacell gene expression matrix was used as input into the SCENIC+ pipeline. ATAC fragments were re-assigned to metacells, then MACS2 (v2.2.9.1) was used to call pseudobulked peaks within each cell type. Peaks were extended 250 base pairs (bp) on either side and pycisTopic (v2.0a0) was used to derive consensus peaks. High quality metacells were retained using the following filters: minimum unique fragments ≥10 4 and minimum TSS enrichment ≥7.5. Topic modeling of co-accessible regions (peaks) using the LDA method was then performed for a range of topic numbers and the optimal topic number ( 30 ) was chosen as the lowest number where maximum topic coherence and log likelihood converged. Putative enhancer regions were selected using three methods: Otsu thresholding, selecting the top 3,000 regions per topic or identifying DA regions (Wilcoxon rank sum, logFC >0.5, adjusted P-value <0.05). A custom cistarget database of TF motif scores was generated using Cluster-buster, the Aerts lab motif collection (v10nr_clust_public) and consensus peaks generated above. This custom cistarget database was used for subsequent motif enrichment within enhancer regions. A SCENIC+ wrapper function was then used to generate eRegulons consisting of a TF, regions enriched for the TF motif, and genes both linked to enriched regions and significantly co-expressed with the TF. High quality eRegulons were retained using the following filters: eRegulons with >10 genes, a positive region-gene relationship and extended eRegulons were kept only when the direct one was not available. Differentially active eRegulons were identified within each cell type using gene activity. First, area under the curve (AUC) activity scores were calculated for each eRegulon in each metacell and z-score normalized. The mean eRegulon z-score AUC was then calculated per cell type and condition (control or Ts21). An eRegulon with mean z-score>0 in either condition was considered expressed in that cell type. For each expressed eRegulon in each cell type, the fold change was calculated and significance was assessed using a two-sided t-test with equal variance. P-values were FDR corrected within cell type using the number of expressed eRegulons. For in vivo tissue and phNPCs we required a mean z-scored AUC >0.7 in the control condition and an absolute differential z-score >0.08 to filter out lowly expressed eRegulons and small changes in activity. eRegulons passing these filters and with an FDR-corrected P-value <0.05 were considered differentially active. Pathway enrichment analysis Enrichment analysis was performed using gprofiler2 (v0.2.1) for Gene Ontology (GO) databases against all expressed genes as the background gene set. For hdWGCNA modules, genes were ranked by highest correlation to the eigengene (kME) and an ordered query was run. For cell type- or class-specific DEGs and eRegulon genes, an unordered query was run. An FDR-adjusted P-value cutoff of 0.05 was used to determine significantly enriched GO terms or pathways and term size was set to <1,000 genes. Rare variant enrichment analyses Gene sets of rare variants associated with neuropsychiatric and neurodevelopmental disorders co-occuring with DS including ASD ( 133 ), NDD ( 133 ), DD ( 134 ), and epilepsy ( 135 ), as well as those from the SFARI database for syndromic ASD and non-syndromic ASD (score 1) were compiled ( table S16A ). Altered Ts21 genes and pathways, including cell-specific DEGs and co-expression modules were tested against each disorder. Cell type and module-specific enrichment with disorders was determined by a Fisher’s exact test, using all expressed genes as the background gene set. To rule out spurious associations, we required each list to have at least 30 genes and at least 5 intersecting genes and an FDR-corrected P-value < 0.05. The same enrichment analysis was applied to significantly differential directed activator eRegulon target genes. Partitioned heritability analysis To evaluate whether DA peaks or regions identified in SCENIC+ analysis significantly contribute to heritability in a cell type-specific manner, we performed Stratified Linkage Disequilibrium (LD) Score regression (S-LDSC) ( 136 , 137 ) using summary statistics and the baseline-LD model (v1.2) across seven traits identified by genome-wide association studies (GWAS) and each cell type separately. GWAS traits used were ASD ( 106 ), ADHD ( 138 ), Intelligence ( 139 ), Epilepsy ( 140 ), educational attainment ( 141 ), Alzheimer’s disease ( 142 ), average cortical thickness and total surface area ( 141 ) ( table S16A ). GWAS summary statistics were converted into .sumstats format described as in ( https://github.com/bulik/ldsc/wiki/Partitioned-Heritability ). Multiple testing correction across cell types was performed using the Benjamini-Hochberg false discovery rate (FDR-BH) method. Significance was defined as FDR < 0.05. Immunohistochemistry, imaging and quantification Immunofluorescence labeling of neocortical cryosections was carried out using standard procedures optimized for fixed tissue. Briefly, 15 μm coronal sections were permeabilized in 1x PBS with 0.2% Triton-X (PT) and blocked with 10% normal serum. Sections were incubated overnight at 4°C with primary antibodies followed by three PT washes and incubation with fluorophore-conjugated secondary antibodies for 45 min at room temperature. All secondary antibodies used were conjugated to Alexa Fluor 488 (1:1000), 555 (1:1000) or 647 (1:500) (Invitrogen). Sections were then counterstained with DAPI, mounted in ProLong Gold antifade reagent and cured overnight at room temperature before imaging. 1x TBS rather than 1x PBS was used for vascular and microglia stainings to reduce background reactivity, as SPP1 is a heavily phosphorylated protein. Immunofluorescence of cultured cells was performed as above, with the following exception: Cells were fixed in a solution of 4% PFA + 4% sucrose for 10 min then permeabilized for 10 min exactly in 1x PBS with 0.4% Trixon-X before blocking. All imaging and quantification was performed on randomized and blinded samples. Neurogenesis For neurogenesis stains in vivo , the following primary antibodies were used: Goat α SOX2 (Santa Cruz, sc-17320, 1:500; RRID:AB_2286684), Rabbit α NEUROD2 (Abcam, ab104430, 1:500; RRID:AB_10975628), Mouse α SATB2 (Abcam, ab51502, 1:500; RRID:AB_882455), and Rat α BCL11B (CTIP2) (Abcam, ab18465, 1:250; RRID:AB_2064130). Stained cryosections from 8 age-matched donors (4 Ctrl and 4 Ts21, each condition evenly distributed from GW13 to 20) were imaged on a Leica DMi8 epifluorescence microscope. Three tiled images per donor (24 total images; n=12 Ctrl, n=12 Ts21) spanning the cortical wall were captured at 20x magnification to include the ventricular and subventricular zones, intermediate zone, subplate, and cortical plate. We developed a semi-automated pipeline to minimize technical bias and quantify samples at scale ( fig. S3B - E ). Multi-channel images were compiled for subsequent cell segmentation and intensity-based cell classification. Segmentation masks were generated for each nucleus using CellPose v3.0 ( 142 ). An average of 4,843 and 4,398 nuclei were segmented per image for SOX2/NEUROD2 and BCL11B/SATB2 analyses, respectively. Background signals and large regional pixel intensity differences within images were minimized in ImageJ v2.16.0 using a standardized rolling-ball subtraction algorithm (ball radius=140 pixels), allowing comparable pixel intensity values between nuclei both within and between images. Raw fluorescent intensities were then extracted for each immunolabeled marker. For each channel, marker-positivity of segmented nuclei was assigned through a combination of k-means clustering (SOX2, BCL11B, SATB2) and fixed thresholding (DAPI, NEUROD2). Cortical layer borders were manually defined based on DAPI + nuclei density and output as normalized distances along the ventricular zone to cortical plate axis for each image. Nuclei were assigned to layers by mapping each nucleus to normalized depth coordinates along the cortical wall. As accurate automatic segmentation of VZ nuclei at the earliest time points (GW13 to 14) was not possible, we did not include it in the SOX2/NEUROD2 analysis. For SOX2/NEUROD2 and BCL11B/SATB2 analyses we employed a statistical approach which utilizes individual nuclei counts, accounts for biologically relevant factors such as the expected cell type changes over developmental time and incorporates both technical (3 images/donor) and biological replicates (3 to 4 donors per condition). To assess differential marker proportion we fit a binomial generalized linear mixed-effects model to the data with cell counts as a function of condition and marker, timepoint as a fixed effect and donor as a random effect. Emmeans was used to perform pairwise comparisons between conditions in each marker category and resulting P-values were FDR adjusted across all markers. For BCL11B/SATB2, differential marker proportion was calculated with timepoint as an interaction effect to account for changes at early timepoints. For neurogenesis stains in vitro (phNPCs), the following antibodies were used: Guinea pig α GFAP (Synaptic Systems, 173 005, 1:500; RRID:AB_2109820) and Mouse α TUJ1 (Covance, MMS-435P, 1:1000; RRID:AB_2313773). Two coverslips per line were imaged on a Leica DMi8, with 5 fields of view captured per coverslip at 20x magnification (Ctrl, n=40 images from 4 phNPC lines; Ts21, n=30 images from 3 phNPC lines. GFAP + , TUJ1 + and GFAP + /TUJ + cells were manually quantified in ImageJ. For each marker, a binomial generalized mixed model was used to fit marker counts using the glmer function from the lme4 R package ( 143 ) (v1.1–35.5). Donor, coverslip and image were used as random intercepts and condition (control or Ts21) as the predictor. Significance was assessed by ANOVA using the carData R package (v3.0–5) and P-values were FDR-adjusted across all markers in the set. Vasculature For vascular stains, the following primary antibodies were used: Mouse α CD31 (PECAM-1) (Novus Biologicals, NB600–562SS, 1:100; RRID:AB_10002476), Goat α PDGFR-β (R&D Systems, AF385-SP, 1:100; RRID:AB_355339), and Rabbit α NG2 (CSPG4) (Millipore-Sigma, AB5320, 1:200; RRID:AB_91789). Stained cryosections from 9 donors (4 Ctrl and 5 Ts21, each condition evenly distributed from GW13 to GW23) were imaged using a Leica DMi8. 2–3 multi-channel images per donor encompassing entire sections spanning the cortical wall were captured at 10x magnification for a total of 30 images (Ctrl, n=12; Ts21, n=18). Quantitative image analysis was performed in ImageJ using custom macros. Layer boundaries for the ventricular zone (VZ), inner subventricular zone (iSVZ), and outer subventricular zone (oSVZ) were manually drawn based on DAPI + nuclei density. To create regions of interest (ROIs) for stained blood vessels, a binary image was created for each channel by applying automated Otsu thresholding and the ‘Analyze Particles’ function was used to create outlines with a minimum particle area of 4 pixels 2 . These ROIs were used for density and mean fluorescence intensity measurements. Background fluorescence was corrected on each channel using a standardized rolling-ball subtraction algorithm (ball radius=50 pixels). Per layer blood vessel density was calculated as the total ROI area divided by the total layer area. Mean fluorescence intensity within ROIs was measured using the background-subtracted grayscale image. For each marker, we fit a linear mixed-effects model to the data with density or intensity as a function of condition and layer, sex and gestational age as fixed effects and donor as a random effect. Emmeans was used to perform pairwise comparisons across conditions in each layer. Resulting P-values were FDR-corrected across layers. Microglial activation For activated microglia stains, the following primary antibodies were used: Goat α IBA1 (Abcam, ab5076, 1:100; RRID:AB_2224402) and Rabbit α Osteopontin/SPP1 (ProteinTech, 22952–1-AP, 1:100; RRID:AB_2783651). Stained cryosections from 8 age-matched donors (4 Ctrl and 4 Ts21, each condition evenly distributed from GW13 to GW20) were imaged using a Leica DMi8. 2 multi-channel images per donor encompassing entire sections spanning the cortical wall were captured at 20x magnification for a total of 16 images. IBA + only and IBA1 + /SPP1 + microglia were quantified manually using ImageJ. We employed a statistical approach which utilizes individual cell counts, accounts for biologically relevant factors such as the expected cell type changes over developmental time and incorporates both technical (2 images per donor) and biological replicates (4 donors per condition). A binomial generalized linear mixed-effects model was fitted to the data with cell counts as a function of condition and marker, timepoint as a fixed effect and donor as a random effect. Emmeans was used to perform pairwise comparisons between conditions in each marker category and resulting P-values were FDR adjusted across all markers. Cell culture and in vitro assays phNPC maintenance and differentiation Ctrl and Ts21 phNPC lines were derived from midgestation cortex and cultured as previously described ( 10 ). Briefly, midgestation human neocortex was enzymatically dissociated into a single cell suspension and cultured on uncoated 10cm dishes in Harvesting Media [DMEM/F12 with Glutamine (Gibco 11320033), supplemented with 10% KnockOut Serum Replacement (Gibco 10–828-028), B-27 w/o Vitamin A (Gibco 12587010), 1ug/mL heparin, Anti-anti (Gibco 15240062) and 10ug/ml Ciprofloxacin] with freshly added growth factors [EGF (Peprotech AF-100–15), FGF2 (Peprotech 100–18B) and PDGF (Peprotech 10013A); 20ng/ml each] until neurospheres formed (~1 week). Neurospheres were then plated on polyornithine/fibronectin coated plates and cultured in Proliferation Media [Neurobasal A (Gibco 10888022) supplemented with 10% BIT 9500 (StemCell Technologies 09500), Anti-anti, 1x GlutaMax (Gibco 35050061) and 1 μg/ml heparin] with freshly added growth factors [EGF, FGF2 and LIF (Sigma LIF1010); 20 ng/ml each] to establish a monolayer culture and passaged at 70% confluency. Frozen stocks were banked at each passage. For snMultiome, all lines were simultaneously thawed from frozen stocks of similar low passage ( 5 – 6 ) to reduce experimental variability, maintained in Proliferation Media and passaged at 70% confluency twice before differentiation. Two independent differentiations 1–2 passages apart were performed for each line. For differentiation, cells were plated at 4.5×10 4 cells/cm 2 on polyornithine/laminin coated plates or acid-washed glass coverslips in Proliferation Media, then switched to Differentiation Media [Neurobasal A (Gibco 10888022) supplemented with B-27 w/o Vitamin A, Anti-anti and 1x GlutaMax] with freshly added 500ng/ml retinoic acid (Sigma R2625), 10 μM Forskolin (Sigma F6886), 10 ng/ml BDNF (Peprotech 450–02), 10 ng/ml NT-3 (Peprotech 450–03) and 10mM KCl 24 hours post-plating. Cells were maintained in Differentiation Media for 4 weeks until collection for snMultiome or fixation for immunocytochemistry. For Seahorse assays, cells were plated on a polyornithine/laminin-coated XF24 microplate at a density of 13,000 cells per well (Agilent 100777–004) in Proliferation Media, switched to Differentiation Media 24 hours later and cultured for 2 weeks. NPC and neuron differentiation from hIPSCs Isogenic disomic and trisomic hiPSCs were generated by the laboratory of David Russell ( 144 ). HiPSCs were maintained on Matrigel-coated plates (Corning, 354277) in mTeSR Plus (StemCell Technologies, 100–0276) until 80% confluency. CNS type neural progenitor cell (NPC) fate was induced using the STEMdiff SMADi Neural Induction Kit (StemCell Technologies 08581) for 6–9 days, according to the manufacturer’s protocol. In our hands, this approach generates a uniform population of immature neurons, as described previously ( 145 ). To induce neuronal fate, the NPCs were further cultured in DMEMF-12 supplemented with N2 (Thermo Fisher 17504044) and B-27 (Thermo Fisher 17502048). Seahorse assay The oxygen consumption rate (OCR) and the extracellular acidification rate (ECAR) were measured in a Seahorse XFe24 Extracellular Flux Analyzer (Agilent Technologies). On the day of the assay, the cells were washed twice with assay medium [unbuffered DMEM assay medium (Sigma D5030) supplemented with 5 mM HEPES, 8 mM glucose, 2 mM glutamine, and 1 mM sodium pyruvate; pH 7.4]. Compounds were injected sequentially during the assay resulting in final concentrations of 2 μM oligomycin, 1.8 μM FCCP, and 1 μM rotenone and 2 μM antimycin. OCR and ECAR were normalized to the number of nuclei stained with 5 μg/ml Hoechst and counted on an Operetta High-Content Imaging System (PerkinElmer). Respirometry parameters and ATP production rates were calculated as described previously ( 146 , 147 ). For phNPCs, 4 Ctrl and 4 Ts21 lines were assayed (2–3 technical replicates per line). For hIPSC-derived NPCs and neurons, 5–11 technical replicates were assayed per differentiation per line. Significance was assessed using a two-sided t-test with equal variance. Flux assays, cell counting and initial parameter calculations were performed blinded. Supplementary Material Supplementary materials NIHMS2170031-supplement-Supplementary_materials.pdf (45.4MB, pdf) Table S1 NIHMS2170031-supplement-Table_S1.xlsx (15.3MB, xlsx) Table S2 NIHMS2170031-supplement-Table_S2.xlsx (571.5KB, xlsx) Table S3 NIHMS2170031-supplement-Table_S3.xlsx (14.6KB, xlsx) Table S4 NIHMS2170031-supplement-Table_S4.xlsx (7.1MB, xlsx) Table S5 NIHMS2170031-supplement-Table_S5.xlsx (4.4MB, xlsx) Table S6 NIHMS2170031-supplement-Table_S6.xlsx (33.4KB, xlsx) Table S7 NIHMS2170031-supplement-Table_S7.xlsx (888.7KB, xlsx) Table S8 NIHMS2170031-supplement-Table_S8.xlsx (6MB, xlsx) Table S9 NIHMS2170031-supplement-Table_S9.xlsx (13.4KB, xlsx) Table S10 NIHMS2170031-supplement-Table_S10.xlsx (432.9KB, xlsx) Table S11 NIHMS2170031-supplement-Table_S11.xlsx (32KB, xlsx) Table S12 NIHMS2170031-supplement-Table_S12.xlsx (7.5MB, xlsx) Table S13 NIHMS2170031-supplement-Table_S13.xlsx (2MB, xlsx) Table S14 NIHMS2170031-supplement-Table_S14.xlsx (23MB, xlsx) Table S15 NIHMS2170031-supplement-Table_S15.xlsx (10.7MB, xlsx) Table S16 NIHMS2170031-supplement-Table_S16.xlsx (53.8KB, xlsx) Supplementary Materials Figs. S1 to S16 Tables S1 to S16 Acknowledgements: We thank members of the de la Torre-Ubieta, Stein, Lowry, Geschwind, Gandal, Bhattacharyya and Sousa laboratories and V. Arboleda, C. Luo and L. Stiles for helpful discussions and critical reading of the manuscript. Brain tissue was obtained from the UCLA CFAR (5P30 AI028697) and Translational Pathology Core Laboratory (TPCL) and from the NIH Neurobiobank. Illumina sequencing was conducted at the UCLA Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research Sequencing Core, multi-ome library quality control was performed at the Technology Center for Genomics & Bioinformatics (TCGB), imaging was performed at the Intellectual and Developmental Disabilities Research Center (IDDRC) Functional Visualization core, and metabolic flux assays were performed at the Mitochondria and Metabolism Core, University of California, Los Angeles, Los Angeles, CA. Digital karyotyping was conducted at the IGM Genomics Center, University of California, San Diego, La Jolla, CA. Figure 5 was created with BioRender.com . Funding: This work was supported by National Institute of Child Health and Human Development (NICHD) grant R21HD108606 to LTU, National Institute of Mental Health (NIMH) grant R01MH124018 to LTU, NICHD grant P50 HD103557 to LTU and DHG, UCLA Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research Rose Hills Foundation Innovator grant to LTU, UCLA Jonsson Comprehensive Cancer Center and Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research Ablon Scholars Program to LTU, UCLA Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research Innovation grant to LTU, California Institute for Regenerative Medicine DISC0–14519 to WEL, NIMH grant U01MH105991 to DHG, National Institute of Neurological Disorders and NIMH grant T32NS048004 to J.P, UCLA Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research Postdoctoral Training Program to SN, and NIH Biomedical Big Data Training Program grant T32LM012424 to AW. Footnotes Competing Interests : WEL is an inventor on patents (12,559,490;12,227,503; 11,787,804; 11,472,804; 11,312,714; 10,828,289; 11,213,513;12,544,406; 9,005,966) held by The Regents of the University of California that covers hair growth, stroke, and human induced pluripotent stem cells. All other authors declare that they have no competing interests. Data and materials availability: snMultiome data is available at the Neuroscience Multi-omic (NeMO) Archive archive ( http://assets.nemoarchive.org/col-umstjg0 ). Controlled-access data is subject to approval by the NIMH Data Archive. References and Notes 1. Antonarakis SE, Skotko BG, Rafii MS, Strydom A, Pape SE, Bianchi DW, Sherman SL, Reeves RH, Down syndrome. Nat Rev Dis Primers 6, 9 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Fortea J, Zaman SH, Hartley S, Rafii MS, Head E, Carmona-Iragui M, Alzheimer’s disease associated with Down syndrome: a genetic form of dementia. 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Supplementary Materials Supplementary materials NIHMS2170031-supplement-Supplementary_materials.pdf (45.4MB, pdf) Table S1 NIHMS2170031-supplement-Table_S1.xlsx (15.3MB, xlsx) Table S2 NIHMS2170031-supplement-Table_S2.xlsx (571.5KB, xlsx) Table S3 NIHMS2170031-supplement-Table_S3.xlsx (14.6KB, xlsx) Table S4 NIHMS2170031-supplement-Table_S4.xlsx (7.1MB, xlsx) Table S5 NIHMS2170031-supplement-Table_S5.xlsx (4.4MB, xlsx) Table S6 NIHMS2170031-supplement-Table_S6.xlsx (33.4KB, xlsx) Table S7 NIHMS2170031-supplement-Table_S7.xlsx (888.7KB, xlsx) Table S8 NIHMS2170031-supplement-Table_S8.xlsx (6MB, xlsx) Table S9 NIHMS2170031-supplement-Table_S9.xlsx (13.4KB, xlsx) Table S10 NIHMS2170031-supplement-Table_S10.xlsx (432.9KB, xlsx) Table S11 NIHMS2170031-supplement-Table_S11.xlsx (32KB, xlsx) Table S12 NIHMS2170031-supplement-Table_S12.xlsx (7.5MB, xlsx) Table S13 NIHMS2170031-supplement-Table_S13.xlsx (2MB, xlsx) Table S14 NIHMS2170031-supplement-Table_S14.xlsx (23MB, xlsx) Table S15 NIHMS2170031-supplement-Table_S15.xlsx (10.7MB, xlsx) Table S16 NIHMS2170031-supplement-Table_S16.xlsx (53.8KB, xlsx) Data Availability Statement snMultiome data is available at the Neuroscience Multi-omic (NeMO) Archive archive ( http://assets.nemoarchive.org/col-umstjg0 ). Controlled-access data is subject to approval by the NIMH Data Archive. ACTIONS View on publisher site PDF (4.5 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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