Mid-gestational cell-type-specific transcriptomic signatures in the prefrontal and superior temporal cortex in Down syndrome

Domain: neuroscience
Published: 2025-12-11
Editorial outcome: minor_revision

Abstract
The cellular and molecular mechanisms underlying cortical alterations during early fetal development in Down syndrome (DS) remain largely unexplored. Here, we perform single-nucleus RNA sequencing (snRNA-seq) analysis on mid-gestational DS and control brain samples, including prefrontal cortex (PFC) and superior temporal plane cortex (STP). Through comparative spatiotemporal analyses, we decode cell-type- and region-specific transcriptional alterations associated with chr21 abnormalities, including a disrupted inhibitory-to-excitatory balance during mid-gestational development. RUNX1 and APP emerge as the most significantly dysregulated chromosome 21 genes in the PFC and STP, respectively. Abnormal cortical distribution of excitatory neurons in both regions is potentially driven by dysregulated neuronal migration genes and impaired lactylation metabolism. Moreover, glial cells modulate the differentiation and migration of excitatory neurons through multiple intercellular signaling pathways. These findings provide critical insights into the pathogenesis of DS-related mid-gestational cortical abnormalities and offer valuable resources for disease modeling and development of spatiotemporally targeted therapeutic strategies.

Introduction
Down syndrome (DS) is a genetic condition caused by trisomy of human chromosome 21 (Ts21)1,2, with a global birth prevalence of approximately 1 in 779–1023 live births. This persistent prevalence underscores the complex personal, medical, and societal factors1,3. Individuals with DS experience a wide range of clinical features, including variable neurodevelopmental delays, cognitive impairments, and craniofacial abnormalities, along with an increased risk of congenital heart defects, autoimmune disorders, and diverse neurological conditions, such as Alzheimer’s disease (AD)4. Among these, the most profound abnormalities observed in DS are the disruption of cortical development that fundamentally alters the structure and function of the cerebral cortex, impairing cognition and behavior5.

The human cerebral cortex, a highly specialized and evolutionarily advanced brain structure, develops through a series of intricate cellular and molecular processes, including neurogenesis, neuronal migration, and cortical layering6. These processes underlie the functional specialization of regions such as the prefrontal cortex (PFC), which governs higher-order cognitive functions like memory, decision-making, and social behavior, and the superior temporal plane (STP), which is critical for auditory processing and language comprehension7,8. Dysfunction in these regions contributes significantly to the cognitive and neurodevelopmental deficits in DS.

Despite progress in understanding the general mechanisms of cortical development, the specific cellular and molecular disruptions in DS, particularly during the fetal stage, remain poorly understood due to the scarcity and limited availability of DS fetal brain samples.

Cortical neurogenesis in vertebrates occurs in an inside-to-outside manner, with neuronal migration playing a pivotal role in ensuring proper positioning and cortical layer formation9. Aberrant neuronal migration in DS has been linked to cortical thinning, abnormal layering, and the cognitive and motor impairments characteristic of the disorder10,11. The molecular mechanisms underlying these developmental alterations, particularly during early fetal development, remain unclear. Recent advances in single-cell transcriptomics have provided a powerful approach to systematically decipher the cellular heterogeneity and intricate molecular events of regulating neuronal migration and development, allowing for more precise insights into the pathophysiology of DS12. In this study, we utilized single-nucleus RNA sequencing (snRNA-seq) to decode spatiotemporal gene expression dynamics and cellular heterogeneity in the developing cerebral cortex of fetuses with DS. By analyzing 22 mid-gestational brain samples, including the PFC and STP, from DS and control cases, we constructed a comparative cellular atlas of cortical development in DS. Our findings reveal profound alterations in neuronal migration, cortical layering, and cell-cell communication, shedding light on the molecular mechanisms underlying DS-associated cortical abnormalities during the second trimester.

Results
Transcriptional atlas of prefrontal and superior temporal plane cortex in fetuses with DS
To explore the cellular and molecular alterations underlying DS during fetal brain development in the second trimester, we performed snRNA-seq on 22 postmortem cortical samples (PFC and STP) from 4 fetuses with DS and 11 controls (Fig. 1a, Supplementary Data 1), with groups comparable in postmortem interval (PMI) (Supplementary Data 1). The study included 4 fetuses with DS (2 cases providing both PFC and STP samples, 1 case providing only PFC, and 1 case providing only STP), and 11 control fetuses (5 cases providing both PFC and STP samples, 4 cases providing only PFC, and 2 cases providing only STP) (Supplementary Data 1).

Although the gestational ages of our collected samples were all within the mid-gestation period, the relatively broad age range and sex imbalance between groups necessitated both global and stratified analyses to ensure data reliability (Supplementary Fig. 1). We first performed global analysis using all available samples to maximize statistical power. To account for potential confounding effects, we further conducted stratified analyses based on gestational age and sex where feasible. After QC, we retained 378,232 nuclei (156,090 from PFC; 222,142 from STP), with comparable gene and transcript counts in each cell nucleus between the two groups (Fig. 1b–d, Supplementary Fig. 2a–c). Uniform manifold approximation and projection (UMAP) indicated similar cellular architecture across brain regions (Fig. 1b, c).

Based on clustering, marker gene analysis, and comparisons with previously published data, we annotated 17 high-resolution cell types (Supplementary Fig. 2d, Supplementary Data 2), including 10 excitatory neuron subtypes (ExNs, 142,134 cells, 37.6%), inhibitory neuron subtypes (InNs, 45,182, 11.9%), neuroblast (NB, 61,403, 16.2%), oligodendrocyte precursor cells (OPC, 4767, 1.3%), astrocyte subtypes (Astro, 4743, 1.3%), microglia (Micro, 395, 0.1%), and several vascular cell types (Vas, 627, 0.2%) (Fig. 1b–d, Supplementary Data 3). Cell types were classified according to established transcriptional signatures and nomenclature consistent with recent publications8,13,14. Cell-type identities were well-conserved across donors. ExNs subtypes were subclassified based on expression of cortical-specific marker genes13,15,16 and neurodevelopmental genes (DCX and STMN1, important marker genes for immature neurons during brain development)17–19 (Supplementary Fig. 2d). Both ImN and NB exhibit high levels of CEP170 and tubulin family genes (Supplementary Fig. 2d, Supplementary Data 4), regulating the stability and dynamic changes of microtubules to control the migration of ImN and axonal growth20,21. NB cells express higher levels of DCX and tubulin genes, while lacking the expression of cortex-specific genes. InNs were further subclassified by ganglionic eminence origin (LHX6 for medial ganglionic eminence (MGE); NR2F2 for caudal ganglionic eminence (CGE))22.

A global comparative analysis of cell populations between DS and control samples revealed significant alterations in both the PFC and STP regions, with more pronounced changes observed in the PFC (Fig. 1e–h). Stratified analyses based on gestational age further validated these findings, demonstrating at least 70% consistency with the global results (Supplementary Fig. 2e, f), thereby supporting the robustness and generalizability of our observations. Consistent with findings from the DS mouse model and studies involving the adult human PFC14,23,24, both regions exhibited an increased InN/ExN ratio (Fig. 1f, Supplementary Data 3), with CGE-derived InNs driving this shift in PFC, and MGE-derived InNs in STP (Fig. 1g, h). Except for ExN_5/6 and ImN-L5, the cells with differential changes showed opposite variations in the two neocortices (Fig. 1g, h). In addition, case-control analysis (cacoa)25 confirmed regional differences in cellular changes in the DS brain (Supplementary Fig. 3a). Moreover, we observed an increased transcriptional heterogeneity in the PFC of fetuses with DS, which exhibited significantly higher inter-individual expression variability compared to the STP region (Supplementary Fig. 3b, c), suggesting early region-specific dysregulation. This variability may underlie phenotypic heterogeneity in DS, as the PFC governs higher-order functions such as cognition and behavior26.

Aggregate analysis of 621 chromosome 21 (chr21) genes showed significant expression elevation in fetuses with DS (mean fold change = 1.48 for STP and 0.27 for PFC, Padj < 0.01; Fig. 1i), with stratified analyses showing great consistency of variation trends with the global findings (Supplementary Fig. 3d). While our bulk analysis confirmed significant upregulation of chr21 gene sets (Fig. 1i), we observed the established pattern of partial dosage compensation: 12.1% (STP) and 0% (PFC) of genes showed ≥2-fold increase, 39.03% and 0.45% exhibited >1-fold change, and 54.77% and 19.82% were variably expressed–aligning with prior reports of chr21 regulation with variable dosage sensitivity in DS1,27.

Systematic differential analysis of gene expression
We identified differentially expressed genes (DEGs) between DS and control samples across the major cell types (Fig. 2a, b, Supplementary Data 5). Notably, STP cells exhibited widespread upregulation (>90% of DEGs), contrasting with the PFC. Among these cell types, ExN L5/6 and ExN L3-5 populations demonstrated the highest susceptibility to disease-related transcriptomical alterations, followed by NB (Fig. 2b, c). Only a minor fraction of DEGs overlapped between PFC and STP (Fig. 2c). To explore sex-related effects, we stratified the DEGs by sex. Given the limited number of male samples, subgroup analyses were restricted to female fetuses (2 DS females vs. 4 controls). These female-only results showed strong concordance (>73% in PFC cell types and >62% in most STP cell types, except for Astro) with the full dataset (Supplementary Data 6). In the PFC, 60% of cell types showed at least 60% DEG overlap between males and females (Supplementary Fig. 4a), while the STP exhibited more pronounced sex-related differences, with DEG consistency between sexes dropping to as low as 49.7% (Supplementary Fig. 4b). When compared with bulk RNA-seq data from DS PFC tissues28, 69.4% of genes showed consistent expression change trends (Supplementary Fig. 4c). Further analyses across stratified datasets revealed that the extent to which cell types are influenced by gestational age varied (Supplementary Data 7). Nevertheless, disease-sensitive populations such as ExN L5/6, ExN L3-5, and NB exhibited high consistency across global and stratified analyses — ≥82% in PFC (Supplementary Data 7). In contrast, non-neuronal populations showed a much smaller number of DEGs, potentially due to lower statistical power associated with their low abundance. These comparative observations on the number and dominant directionality of DEGs highlight the heterogeneous responses of different brain regions and cell types to DS pathology. Further analysis revealed that the STP transcriptome was more susceptible to chromosomal abnormalities than the PFC, with a larger number of DEGs detected in at least 10 STP cell types (192 genes) compared to the PFC (30 genes) (Supplementary Data 5). Key DEGs included RUNX1, ANKRD36B, and ANKRD36C in the PFC, and APP, CD24, and STMN2 in the STP (Supplementary Data 5). These findings suggest that different brain regions exhibit distinct molecular vulnerabilities to DS pathology.

We examined the chromosomal distribution of DEGs and found that most were localized on chr1-8, with a smaller subset on chr21 (Supplementary Fig. 5a, b). Unlike in adult individuals with DS14, chr21 DEGs were predominantly upregulated in neurons of fetuses with DS, rather than in microglia or endothelial cells (Fig. 2d). Notably, stratified analyses based on gestational age revealed a high degree of concordance with the global dataset—at least 80% of chr21 DEGs were consistently identified across datasets (Supplementary Data 8). This suggests robust differential expression of chr21 genes in DS, minimally influenced by gestational age variability. Nine shared chr21 DEGs, including DSCAM, APP, CXADR, SON, KCNJ6, TIAM1, GRIK1, MIR99AHG, and NCAM2, showed significant changes in both the PFC and STP (Fig. 2d). RUNX1 and APP were the most significantly altered genes in the PFC and STP, respectively. Virtual knockout analysis implicated RUNX1 in regulating multiple tubulin family genes and STMN1/2, which are involved in neuronal development and axon regeneration, while APP-targeted genes were primarily linked to dendritic transport and neuroepithelial cell differentiation (Supplementary Fig. 5c, d, Supplementary Data 9).

To contextualize fetal findings in later development, we compared PFC data from young and aged individuals with DS14 (Supplementary Data 10), where 8 chr21 DEGs in ExNs and 5 in InNs persist across development stages. DEG overlap declined with age, with ExN DEGs showing early disruption, while InN changes increased later (Fig. 2e).

A previous study indicated that brain organoids derived from patients with DS can partially model abnormal neurogenesis in DS29. We compared fetal signatures with in vitro cortical organoid differentiation trajectories and found that the organoids shared more overlapping DEGs with the STP, supporting their use as a model for DS STP research (Fig. 2f). Organoids cultured for 30 days showed more similarity to the brain than those cultured for 70 days. Shared genes TIAM1 and APP showed significant changes in both the brain and organoids, suggesting their critical role in DS brain function. Further integration of snRNA-eq data from organoids indicated that DS-derived organoids more accurately simulate the neuronal differentiation status of the STP, such as the consistent cell count of InN1-3 in both fetal brain and organoids (Fig. 2g, h). However, current organoid models only simulate a small fraction of the abnormalities found in the PFC or STP of individuals with DS.

Beyond the brain, DS pathology extends to peripheral tissues. Transcriptomic analysis of peripheral blood from a large clinical cohort30, comprising 304 individuals with DS (163 males, 141 females) and 96 euploid controls (44 males, 52 females), revealed significant alterations in the transcriptome of peripheral blood in individuals with DS, accompanied by upregulation of most genes encoded on chr21. Notably, peripheral blood exhibited a significant upregulation of chr21 genes with a greater number of DEGs compared to the neocortices (Fig. 2i, Supplementary Data 11), suggesting that peripheral blood is more susceptible to chr21 gene triplication than brain tissue. Nevertheless, there was minimal overlap between blood and brain DEGs (Fig. 2i), reflecting the tissue-specific nature of DS pathology. One notable exception was SON, a chr21 DEG significantly changed in both the neocortices and peripheral blood (Fig. 2i). Research indicates that the de novo mutations of SON can affect the key genes (TUBG1 and HDAC6) crucial for neuronal migration and cortical organization, disrupt the RNA splicing of genes essential for brain development and metabolism, and lead to intellectual disability syndrome31. These findings suggest that peripheral blood may serve as a useful proxy for studying certain molecular aspects of DS and identifying potential biomarkers.

Disrupted development of ExN subtypes in the neocortices of fetuses with DS
Perturbations in neuronal migration and differentiation are thought to contribute to cortical abnormalities in DS, but their effects in the human fetal brain remain poorly defined. Previous studies in mice have shown that gain-of-function (GOF) mutations in DSCAM and DSCAML1 impair forebrain cortex development and neuronal migration during fetal stages32. To investigate these processes in humans, we performed subclustering of ExNs in the PFC, identifying layer-specific subtypes (Fig. 3a, Supplementary Fig. 2d), marked by CUX2 (L2), RORB (L4), and PCP4 (L5) expression15,16. Analysis of cell-type proportions across late second trimester revealed broadly similar developmental trajectories between control and DS groups in both PFC and STP, but several subpopulations showed altered abundance, particularly around GW23 (Fig. 3b). Immature ExNs expressing DCX and tubulin, as well as mature CUX2+ L2 and L2–4 neurons, were differentially represented in DS (Fig. 3b). CUX2-enriched L2 neurons have been implicated in neurodevelopmental and neuropsychiatric disorders such as AD, Multiple Sclerosis (MS), and Schizophrenia (SCZ)25,33.

To validate these results, we examined the cortical distribution of upper-layer (SATB2+FOXP2−) and deep-layer (SATB2+FOXP2+) neurons at GW23 using immunofluorescence (Fig. 3c, d). In controls, FOXP2+ cells predominantly localized to the deep cortical layers, and SATB2+ cells to middle-upper cortical layers (Fig. 3c), consistent with known inside-out cortical layering8. In DS fetal brains, a reduction in upper-layer neurons and increased deep-layer neurons abundance were observed in PFC and STP regions (Fig. 3c, d). Bin-based counting analysis showed that FOXP2+ cells and SATB2+ cells exhibited differential distributions between control and DS in various cortical layers, which aligns with the results from snRNA-seq (Fig. 3e, f). These findings support the impaired inside-out migration pattern of neocortical neurons in DS, a hallmark of normal cortical development.

Further analysis identified 52 DEGs in PFC and 65 DEGs in STP related to cell migration (Fig. 4a, Supplementary Data 12). To evaluate the robustness of these findings, we performed Jaccard similarity analyses of overlapped migration-related DEGs between each stratified subgroup and the full GW17–28 dataset, revealing consistency rates of at least 73% in PFC and 66% in STP (Supplementary Fig. 6a). The protein-protein interaction (PPI) network indicated enrichment in pathways involved in neuronal migration, cell dynamics, cell positioning, and axon development (Supplementary Fig. 6b–e). Cytotrace analysis and Monocle analysis revealed that DS neurons were transcriptionally less mature and exhibited delayed differentiation and migration (Fig. 4b, Supplementary Fig. 7a, b). In controls, migration-related genes displayed distinct temporal expression patterns in the late second trimester, whereas DS brains exhibited enrichment of neuronal migration genes around GW23, suggesting dysregulated timing (Supplementary Fig. 7c, d). This phenomenon was observed in both the PFC and STP. Additionally, genes associated with immune cell migration were highly expressed in control brains at GW23, suggesting synchronized but distinct migration processes between neurons and immune cells during cortical development. This synchronization appeared disrupted in DS, where dysregulated migration genes may interfere with the interaction between innate immune cells and brain parenchymal cells, potentially contributing to cortical development abnormalities.

To evaluate the diagnostic potential of migration-related DEGs, we constructed machine learning models using these genes as feature variables. We found that these genes were able to accurately distinguish the neuron states across different conditions, achieving 78% and 71% accuracy in the PFC and STP, respectively (Fig. 4c–e). Besides, our predictive model maintained about 70% accuracy when applied to stratified data (Supplementary Fig. 8a). Furthermore, by integrating the data from two normal PFC datasets (GSE168408 includes one sample at GW22 and one at GW24; GSE204684 includes one sample at GW22 and one at GW24), we found that the prediction accuracy of our model for normal cells could reach 99% (Supplementary Fig. 8a). In these models, TUBA1A, TUBB2B, and TUBB2A were the highly contributing genes in both PFC and STP (Fig. 4c–e). These genes exhibited significant expression differences during cortical development (Fig. 4f), and immunostaining confirmed abnormal distribution of TUBB2B+NeuN+SATB2+ neurons in DS cortex at GW23 (Fig. 4g–i, Supplementary Fig. 8b). Finally, virtual knockout analysis of DS-associated DEGs identified dysregulated chr21 genes in both PFC and STP, which could regulate tubulin-mediated neuronal microtubule dynamics and axon formation (Fig. 4j, Supplementary Data 13, Supplementary Data 14). These findings emphasize the critical role of microtubule-associated dysregulation in disrupted cortical development in the late second trimester of DS.

DS neocortical cells exhibit significant metabolic alterations
Studies have shown that abnormal levels of glycolytic and tricarboxylic acid (TCA) cycle metabolites, as well as altered insulin signaling, in the brain tissue of patients with DS suggest their potential role in cognitive deficits34. Through single-cell transcriptomic metabolic analysis, we observed widespread and significant metabolic changes across cell types within the PFC and STP of DS (Fig. 5a). Notably, disruptions in neurotransmitter-related metabolites, such as GABA, glutamate, and glutamine, form the basis for impaired signaling and synaptic transmission between neurons (Fig. 5a). These metabolic abnormalities were further associated with dysfunctions in neuronal development, migration, neurotransmitter synthesis, and synaptic plasticity.

Research has indicated that ribosome stalling—harmful to the organism—tends to favor the accumulation of proline, arginine, and lysine35. Consistently, we found significant upregulation of proline and arginine in all cell types in the PFC NB and STP (Fig. 5a). These findings suggest that, beyond transcriptomic changes, alterations in ribosome-associated metabolites further exacerbate cortical developmental abnormalities in DS. Such metabolic disruptions likely interfere with the translation of key functional proteins or enzymes, thereby contributing to the pathogenesis of DS.

Recent research has highlighted the role of protein lactylation, a post-translational modification of lysine residues in histones and non-histone proteins, as a critical regulator of neuronal function36,37. Increased hypoxia and glycolysis have been shown to enhance protein lactylation levels in various cell types38. It has been reported that HDAC6 catalyzes the lactylation of α-tubulin in a concentration-dependent manner, promoting the dynamics of neuronal microtubules39. In our analysis, both global and hierarchical analyses demonstrated widespread changes in lactate levels in cells of the PFC and STP (Fig. 5b, Supplementary Fig. 9a–c, Supplementary Fig. 10a, b), providing a basis for abnormal protein lactylation modifications in neurons. Mass spectrometry analysis identified 285 peptides in cortical neurons subject to lactylation modifications39. Among these, we observed significant changes in 48 and 44 lactylation-regulated molecules in the PFC and STP, respectively, including downregulation of α-tubulin (Fig. 5c, Supplementary Data 15). The PPI network showed strong interconnections between these genes, which are primarily associated with neuronal migration and microtubule dynamics (Fig. 5d). Jaccard similarity analyses compared overlapped lactylation-regulated molecules among stratified subgroups to the full GW17–28 dataset, revealing molecular consistency rates of at least 67% in PFC and 58% in STP (Supplementary Fig. 10c). These findings establish a potential link between DS-associated cortical abnormalities and extensive lactylation dysregulation, particularly involving α-tubulin (Fig. 5e).

Dysplasia of InNs in DS affects ExN migration through cellular communication
Abnormal proportions and subtypes of InNs and ExNs in the PFC of adult patients with DS have been well-documented14. Through subpopulation analysis of InNs in the PFC and STP, we identified 10 distinct subtypes (Fig. 6a). InN1, InN5, and InN8 express NR2F1 and NR2F2, which are derived from the caudal ganglionic eminence (CGE). InN3, InN4, InN6, InN9, and InN10 express LHX6 and SOX6, originating from the MGE, while InN2 and InN7 express MEIS2, FOXP1, and FOXP2, derived from the lateral ganglionic eminence (LGE) (Fig. 6b). Consistent with previous studies15,40, the cell populations enriched with CCK, CALB2, and VIP are derived from the CGE, while SATB1 and SST-enriched cells originate from the MGE. Studies have shown that Reelin (RELN) plays a crucial role in neuronal migration and cortical lamination, and its reduced expression may impair neuronal migration in the hippocampus, leading to neural circuit abnormalities that impact cognitive functions such as memory, learning, and language comprehension41. Our normalized cell counts showed a reduction in the RELN+ (InN6 and InN9) cell populations (Fig. 6c), suggesting a potential link to DS-related cognitive dysfunction (Fig. 6d). Cytotrace analysis revealed that all InNs are differentiated cells, with only subtle differentiation differences (Fig. 6e). Monocle pseudotime analysis indicated significant differentiation differences between InN subtypes across fetal ages, cell types, brain regions, and experimental groups (Fig. 6f, g). Notably, transcription factors ARX, CUX1, and SOX11 exhibited significant differential expression between control and DS InNs, highlighting potential regulatory dysfunctions (Fig. 6h).

To explore how InNs regulate ExNs, we employed CellChat, NicheNet, and NeuroChat to analyze cellular communication. CellChat and NeuroChat analyses revealed opposing changes in cellular communication within the PFC and STP in DS compared to the controls (Supplementary Fig. 11a, b). CellChat analysis indicated that NRG, PTN, and SEMA3 were the most abundant and intense forms of cellular communication (Supplementary Fig. 11c). NeuroChat, a neuron-specific cellular communication analysis, identified NLGN1-related signaling as the most intense and significantly altered form of communication (Supplementary Fig. 11d). Further investigation showed that cellular communication between InNs and ExNs was reduced in the PFC but increased in the STP (Fig. 6i). Specifically, we observed significant reduction in NRXN1-NLGN1, NRXN2-NLGN1, and NRXN3-NLGN1 communication in the PFC, whereas these interactions were significantly elevated in the STP (Supplementary Fig. 11e). This discrepancy underscores the region-specific impacts of DS pathology on cortical development. Ligand-receptor analysis further identified NLGN2 and PTN from InNs as key regulators of ExN development and migration in DS. These ligands target multiple ExN genes, including RUNX1, STMN1, TUBB, and TUBB2B, which are critical for microtubule dynamics and neuronal migration (Fig. 6j). Together, these findings suggest that intrinsic differentiation abnormalities in InNs, combined with altered cellular communication pathways, disrupt ExN migration and cortical development in DS (Fig. 6k).

Glial dysfunction in DS affects neuronal development and migration
The differentiation of OPCs into oligodendrocytes is critical for neuronal developmental processes, such as migration, proliferation, and differentiation42. In this study, subpopulation analysis identified four distinct OPC subtypes in the PFC and STP of fetuses with DS (Fig. 7a). OPC3, expressing OLIG1 and OLIG2, represents the major source of mature oligodendrocytes in adults (Fig. 7b). OPC4, which expresses BCAS1, MPB, and PLP1, corresponds to immature oligodendrocytes (Fig. 7b). Notably, the numbers of OPC1 and OPC2 were found to be oppositely regulated in the PFC and STP of DS, while both OPC3 and OPC4 populations were significantly reduced in the DS neocortices (Fig. 7c). Immunofluorescence staining confirmed these findings, with a notable decrease of BCAS1+OLIG2+ double-positive cells in the DS neocortex (Fig. 7d, e).

Analysis of Astro in the PFC and STP identified four distinct subtypes (Fig. 7f). Astro1 and Astro2, which express GFAP, AQP4, ID2, and GPC5, DPP10, represent the main Astro populations (Fig. 7g). Astro3, expressing SOX2 and DCX, potentially consisting of neural progenitor cells43 (Fig. 7g). Astro4, expressing ASCL1, CDK1, TOP2A, and HMGB2, consists of potential intermediate neural progenitors44,45 (Fig. 7g). In DS, Astro1 and Astro3 populations were increased in both the PFC and STP, while Astro2 and Astro4 populations are decreased (Fig. 7h). The molecules GPC5 and DPP10, which are expressed in Astro, are known to promote neuronal growth and synapse formation46,47. Immunofluorescence staining also validated these snRNA-seq results, showing a significant increase in ID2+GFAP+ double-positive cells in the DS neocortices (Fig. 7i, j). These shifts in cell population and dynamics and gene expression among neural and intermediate progenitor cells suggest abnormalities in the differentiation of these cells in fetuses with DS.

To investigate how glial dysfunction affects ExN development, we utilized NicheNet analysis to identify ligand-receptor interactions between OPCs, Astro, and ExNs. Consistent with the results from CellChat and NeuroChat, both OPCs and Astro were found to regulate the expression of downstream target genes in ExNs by targeting receptors such as NLGN1 and ERBB4 (Fig. 7k, l). Further analysis of the downstream target genes of NLGN1 and ERBB4 revealed that OPCs and Astro in the PFC affect the expression of RUNX1 in ExNs, which subsequently influences tubulin expression (Fig. 7m, Supplementary Data 16). In contrast, in the STP, OPCs and Astro predominantly affect the expression of DSCAM in ExNs, a gene involved in neuronal differentiation and recognition (Fig. 7m). These findings emphasize the region-specific roles of glial cells in modulating ExN development in DS.

The abnormality of the DS transcriptome is associated with neural function and related disease susceptibility
Using Expression-Weighted Cell-type Enrichment (EWCE) analysis, we assessed the enrichment of 24 neuropsychiatric disease risk genes in the DS snRNA-seq data (Supplementary Data 17). Compared with PFC, STP exhibited a higher association with disease risk genes (Fig. 8a, Supplementary Fig. 12a). Neuropsychiatric disorders demonstrate regional specificity. Risk genes of alcohol dependence (ALD) and multiple sclerosis (MS) are mainly enriched in the PFC, while those of Tourette syndrome (TS), AD, and Parkinson’s disease (PD) are predominantly enriched in the STP. Bipolar disorder (BIP) and schizophrenia (SCZ) showed significant enrichment in both the PFC and STP, implying the complexity of the mechanisms and the heterogeneity of these diseases (Supplementary Fig. 12a). When analyzing disease by cell type, we found that STP cells are enriched with a greater number of disease-associated genes (Supplementary Fig. 12b). Stratified analyses based on gestational age yielded a high level of concordance (≥75%) in disease susceptibility compared with the global dataset (Supplementary Figs. 13–15). These outcomes underscore heightened disease susceptibility of the STP region in DS brains. Prior work has documented this region susceptibility pattern in neuropathological studies, showing a progressive volume reduction of STP in patients with schizophrenia48–51. In the DS transcriptome, genes associated with mental disorders such as schizophrenia, depression, and autism were abnormally expressed, potentially predisposing individuals to these conditions in later life52. As anticipated, genes related to aging and memory disorders were significantly enriched in the cells of the DS group in STP (Supplementary Figs. 13–15), supporting the symptoms of premature aging in DS53–55. Further application of single-cell Disease Risk Score (scDRS) analysis evaluated the degree of association between common mental disorders and neurofunctions in different cell types of the neocortices. We observed significant reductions in intelligence (INT), educational attainment (ECOL and EY), and verbal numeric reasoning (VNR) abilities in cells of the DS group (Fig. 8b). Conversely, cells associated with AD and MS were significantly increased in the DS group (Fig. 8b).

To further explore the connection between differential cell migration genes in ExNs and neuropsychiatric disorders, we conducted virtual knockout analysis to identify the regulatory genes associated with each migration-related gene. The resulting gene module, comprising cell migration genes and their corresponding regulatory genes, was designated as the DS-related cell migration gene module (DSMM). By examining the associations between these DSMM and various diseases, we found that STP exhibited a correlation with a greater number of disorders (Fig. 8c, Supplementary Data 18). In the PFC, the most prominent associations with aging-related genes were found, followed by those related to anxiety, ALD, SCZ, and major depressive disorder (MDD) (Fig. 8c). In the STP, the majority of associated genes were linked to aging and narcolepsy, followed by anxiety, SCZ, and MDD (Fig. 8d). These results emphasize the differential roles of PFC and STP cells in disease susceptibility, providing a framework for understanding the distinct contributions of different brain regions and cell types to DS pathology.

Methods
Ethics statement
This study has been clinically registered (Trial registration: ChiCTR, ChiCTR2300070041), and human sample collection and research analysis were approved by the Affiliated Hospital of Zunyi Medical University (Approval No. KLL-2022-446). The informed consent was designed as recommended by the ISSCR guidelines for fetal tissue donation. Informed consent for fetal tissue procurement and research was obtained from the patient after her decision to legally terminate her pregnancy but before the abortive procedure. Participants in this study were not compensated for tissue donation; however, they received free diagnostic testing related to the experiment and were reimbursed for transportation costs incurred during participation in the study, in accordance with the ethical guidelines set by the Affiliated Hospital of Zunyi Medical University. Fetal brain tissue samples were collected after the donor signed an informed consent document that was in strict observance of the legal and institutional ethical regulations for elective pregnancy termination specimens at Affiliated Hospital of Zunyi Medical University. Ts21 was confirmed in all fetuses with DS through karyotype testing. All samples used in these studies had not been involved in any other procedures. All the protocols were in compliance with the ‘Interim Measures for the Administration of Human Genetic Resources’ administered by the Chinese Ministry of Science and Technology.

Tissue sample collection and dissection
The study included 4 fetuses with DS (2 cases providing both PFC and STP samples, 1 case providing only PFC, and 1 case providing only STP), and 11 control fetuses (5 cases providing both PFC and STP samples, 4 cases providing only PFC, and 2 cases providing only STP) (Supplementary Data 1). Fetal brains were collected in ice-cold artificial cerebrospinal fluid containing 125.0 mM NaCl, 26.0 mM NaHCO3, 2.5 mM KCl, 2.0 mM CaCl2, 1.0 mM MgCl2, 1.25 mM NaH2PO4 at a pH of 7.4 when oxygenated (95% O2 and 5% CO2). The fresh brain tissue was collected including 2 different regions: PFC and STP with 0.5 g pieces for each region which was cut with a scalpel according to the human fetal brain map of Gray’s Anatomy. The digestion was performed at 37°C for 30 min in 20 mL digestion buffer containing 10 mg/mL collagenase IV, 15 μg/mL DNase I, followed by gently shaking once every 5 min. Subsequently, the digestion reaction was stopped by using Magnetic-Activated Cell Sorting (MACS) buffer and filtered with a 40 μm cell strainer. After centrifugation and resuspension, the cells were stained for flow cytometry analysis.

Nucleus extraction for snRNA-seq
The approaches for nuclei extraction for snRNA-seq libraries generation in this study were the same as in a previous study79. For the nuclei extraction process, the tissues were thawed and cut into small pieces, then transferred into a homogenization buffer containing 20 mM Tris pH 8.0, 1× protease inhibitor cocktail, 500 mM sucrose, 0.1% NP-40, 0.2 U/μL RNase inhibitor, 1% bovine serum albumin (BSA), and 0.1 mM DTT. The tissue pieces were ground 15 times with tight pestles and filtered with a 30 μm cell strainer. The supernatants were carefully discarded after being centrifuged at 500×g for 5 min at 4°C. The pellets (nuclei) were resuspended in PBS containing 1% BSA and 20 U/μL RNase inhibitor for later snRNA-seq library construction.

Single-nuclei library construction and sequencing
Briefly, single-cell suspensions were used to generate barcoded snRNA-seq libraries by droplet generation, emulsion breakage, bead collection, reverse transcription, as well as cDNA amplification. The mRNA capture was performed on a DNBelab C4 device (MGITech Co., Ltd.). cDNA amplification and library construction were generated using the DNBelab C series reagent kit (MGITech Co., Ltd.) following the manufacturer’s instructions. All the libraries were sequenced on the DNBSEQ-T7 platform.

Pre-processing and quality control of snRNA-seq data
Cell Ranger 7.0.1 (10x Genomics) was used to process the raw sequencing data. The sequencing data from snRNA-seq was filtered, and the gene expression matrix was obtained using DNBelab C Series scRNA analysis software. To further ensure the quality of our dataset, we only retained cells with the number of detected genes greater than 200 and the percentage of detected mitochondrial genes less than 5%. Doublets were detected using DoubletFinder (v.2.0.3). After sample integration and clustering, clusters lacking specific marker genes, with relatively low gene content and high mitochondrial ratios, were discarded.

Identification of cell clusters
Briefly, UMIs from each valid cell barcode were retained for all downstream analyses and analyzed using the R package Seurat (v.4.2.2). After filtering, datasets from different sequencing libraries underwent normalization (using “NormalizeData()” function with parameters “normalization.method=“LogNormalize”, scale.factor = 10,000”) and identification of highly variable genes (HVGs) (using “FindVariableFeatures()” function with the options “selection.method=“vst”, nfeatures=3000”). Then, we applied “FindIntegrationAnchors” and “IntegrateData” functions to integrate all the sequencing libraries with the top 30 significant principal components (PCs) (dim=1:30). The top 3000 HVGs of each dataset were used for downstream PC analysis (PCA). The top 30 significant PCs were selected for clustering and visualization using UMAP.

Identification of cell-type-specific marker genes
“FindMarkers()” function implemented in Seurat v.4.2.280 was used to identify cell-type-specific marker genes with the options “logfc.threshold=0.25, min.pct=0.25”. P-value was corrected using the Bonferroni method, and 0.05 was set as a threshold to define significance.

Gene ontology (GO) term enrichment analysis
The “enrichGO()” function of the clusterProfiler R package81 was used for enrichment analysis, and the Benjamini-Hochberg (BH) method was employed for multiple test correction (OrgDb=org.Hs.eg.db, pAdjustMethod=“BH”, pvalueCutoff=0.05). A GO term with an adjusted p-value lower than 0.05 was considered significantly enriched.

Cell composition analysis
To quantify the impact of aging on cell differentiation dynamics, we employed two distinct analytical methods for inter-group cell composition difference analysis. These included a cell composition analysis utilizing the Cacoa package25 and a differential cell density analysis conducted with the miloR package82. The estimateCellLoadings() function from the Cacoa package (v.0.4.0) was utilized for analyzing cellular components. Specifically, logarithmic ratio transformation was applied to the fractions of different cell types, followed by typical discriminant analysis using the candisc software package to derive weighted comparisons between the two sample groups. These partition coefficient was assessed through random sub-sampling of cells, and its robustness, along with statistical significance, was thoroughly evaluated. Then, we perform 1000 resamplings, during which 1000 cells are randomly selected from each group to assess the robustness of the test. BH procedure is employed for multiple comparison correction. Cacoa package’s estimateCellDensity function is utilized to conduct a differential cell density analysis. To assess the differences in cell density between sample groups, we first employ the ks R package to compute the kernel density for each sample within the joint embedding space. Subsequently, we normalize the resulting density matrix across samples using quantile normalization techniques. To quantify the differences in cell density between sample groups, we perform t-test on samples located within each grid box. To mitigate background noise, we filtered out boxes containing at least one cell, and the z-scores were represented as a heatmap. The sample labels were randomly shuffled 200 times to assess the robustness of the test. For single-cell differential abundance analysis, we utilized the Milo function to create a miloR object, followed by employing the buildGraph function to construct a K-nearest neighbors (KNN) graph in UMAP space. Subsequently, the makeNhoods function was applied to define cellular neighborhoods, while the countCells function quantified the number of cells within each neighborhood across all samples. The testNhood function was employed to evaluate neighborhood differential abundance with a spatial false discovery rate (FDR) significance threshold set at 0.05. Visualization of differential abundance neighborhoods was achieved using the plotNhoodGraphDA function.

Pseudotime trajectory analysis
Monocle2 package was used to discover the cell state transitions. Genes expressed in less than 5 cells were filtered out. DEGs were computed by the function “differentialGeneTest” in monocle2. Genes with a qvalue less than 0.01 were regarded as DEGs and sorted by qvalue using the “setOrderingFilter” function. The pseudotime trajectory was constructed by the “DDRTree” algorithm with default parameters. The dynamical expression changes of selected marker genes by pseudotime were visualized by the “plot_genes_in_pseudotime” and “plot_pseudotime_heatmap” functions. CytoTRACE analysis was carried out using the CytoTRACE R package (v.0.3.3). Following the generation of the gene-cell expression matrix, the trajectory was then inferred using default parameters and visualized using the “plotCytoTRACE” function.

Disease prediction of cells using machine learning
The snRNA-seq data of ExNs or InNs were selected for disease classification. We selected DEGs or genes associated with cell migration as input and disease state as output for machine learning. The pipeline and functions were implemented in the automated machine learning R package h2o (v.3.44.0.2). For data splitting, 25% of nuclei were first split into the testing set, and the rest 75% were further split into training and validation sets using 10-fold cross-validation and 20 max models. The best model was extracted using the function “h2o.get_best_model”, evaluated on the test and validation sets with “h2o.predict”, and the permutation-based variable importance was obtained using “h2o.permutation_importance_plot”.

Gene set score analysis
Gene set scores were acquired by analyzing the transcriptome of each input cell against the aforementioned gene sets by the Seurat function “AddModuleScore”. Changes in the scores between groups were analyzed using the ggpubr R package via the Wilcoxon test.

Expression-weighted cell-type enrichment analysis
Expression-weighted cell-type enrichment (EWCE)79 was employed for the analysis of disease gene enrichment using default parameters. This analysis was conducted separately for each species dataset. This analysis was performed separately for each species dataset. To mitigate the bias effect, we initially employ the “vst()” function to conduct a variance-stabilizing transformation on the unique molecular identifier count matrix. Subsequently, we utilize the “fix_bad_mgi_symbols()” function to rectify gene nomenclature. The “drop_uninformative_genes()” function is then applied to eliminate non-informative genes, thereby reducing computational time and minimizing noise in subsequent analyses. Following this, we implement the “generate.celltype.data()” function to compute the specificity matrix for each dataset. We perform 100 bootstrap resamplings with replacement on all detected genes within each species dataset as a background reference, and p-values are adjusted using the BH method. A significance threshold of 0.05 is established.

Gene-regulatory network
To identify organ-specific gene-regulatory networks, we performed Single-cell Regulatory Network Inference and Clustering (v.0.12.1; a Python implementation of PySCENIC)83. Firstly, data was subsampled by randomly selecting 800 cells from each tissue/organ. The original expression data were normalized and multiplied by 10,000, followed by a log1p transformation. Next, normalized counts were used to generate the co-expression module with the GRNboost2 algorithm implemented in the arboreto package (v.0.1.3). Finally, we used pySCENIC with its default parameters to infer co-expression modules using the above-created RcisTarget database. An AUCell value matrix was generated to represent the activity of regulators in each cell. GRNs were visualized by the igraph package in R and Cytoscape (v.3.9.0).

Single-cell disease relevance score (scDRS)
ScDRS was used to evaluate polygenic disease enrichment of individual cells in snRNA-seq data84. GWAS summary statistics for 17 diseases/characteristics (average N=346K) are downloaded directly from the GitHub page of scDRS. First, to determine statistical significance, scDRS generates 1000 sets of cell-specific raw control scores at Monte Carlo samples of matched control gene sets (matching gene set size, mean expression, and expression variance of the putative disease genes). Next, scDRS normalizes the raw disease score and raw control scores for each cell (producing the normalized disease score and normalized control scores), and then computes cell-level p-values based on the empirical distribution of the pooled normalized control scores across all control gene sets and all cells.

Identification of DEGs
To identify DEGs in aging or sex, p-values were calculated and FDR-corrected using Model-based Analysis of Single-cell Transcriptomics (MAST)33. All nuclei from different group samples for corresponding cell types were used. MAST was used to perform zero-inflated regression analysis by fitting a linear mixed model. To exclude gene expression changes stemming from confounders, such as age, sex, fractions of ribosomal and mitochondrial transcripts, the following model for aging and sex was fit with MAST:
zlm(~condition + nCountRNA + percent:mt + Sex, sca, method = glmer, ebayes=T)
zlm(~condition + nCountRNA + percent:mt + Age, sca, method = glmer, ebayes=T)
Where percent.mt is mitochondrial RNA fraction.
To identify genes differentially expressed due to the age effect, a likelihood ratio test was performed by comparing the model with and without the diagnosis factor. Genes with at least 25% increase or decrease in expression in a group versus another group and a FDR-corrected p < 0.05 were selected as differentially expressed.

Virtual knockout of the gene of interest
To elucidate the effect of DEGs knockout on cell-specific function, we extracted the snRNA-seq data of the cell type and used the expression matrix of genes×cells as the input for scTenifoldKnk85. The virtual knockout perturbed genes with FDR-corrected p < 0.05 were selected as differentially expressed. The interaction enrichment analysis was provided and based on the STRING protein-protein interaction database. The R package Enrichr (v.3.2) was used for functional enrichment analysis. In the scTenifoldKnk analysis, the perturbation effect of a virtual gene knockout was quantified by computing the Euclidean distance between the network embeddings of each gene before and after the knockout. To assess statistical significance, p-values were computed using a Chi-squared distribution with one degree of freedom, based on the fold change relative to expected values. These p-values were subsequently adjusted for multiple testing using the BH method.

Integrated data source
To provide a developmental and systemic context for our fetal cortical findings, we incorporated three orthogonal datasets with proper attribution. Age-dependent comparisons utilized published PFC data from a previous study on DS brain aging14. This cohort included 23 postmortem samples: Aged group (56–65 years): 6 neurotypical controls, 4 individuals with DS; Young group (12–39 years): 8 neurotypical controls, 5 individuals with DS. Reference transcriptional trajectories during in vitro corticogenesis were obtained from Tang et al. 29, encompassing single-cell RNA-seq of iPSC-derived cortical organoids across differentiation stages. Peripheral blood transcriptome analyses utilized published data from Waugh et al. 30, comprising whole-blood RNA-seq from 304 individuals with DS and 96 euploid controls. All external datasets underwent identical normalization and batch correction procedures as our primary fetal snRNA-seq data.

Data stratification and cross-validation
The collected samples were concentrated in the mid-gestational period. Given the extreme rarity of such specimens, we initially prioritized using all available samples for global analyses while controlling for potential confounding factors such as sex and gestational age where possible. However, due to the broad gestational range and sex imbalance between groups, we also performed stratified analyses based on gestational age and sex. For gestational age, we generated three additional stratified datasets: GW21–23, GW21–28, and GW21–28 matched. The GW21–28 dataset consisted of PFC samples matched by propensity scores, and the GW21–28 matched dataset included both PFC and STP samples following propensity score matching.
For sex-based stratification, due to the limited number of male fetuses with DS (n=1), we performed an independent differential expression analysis focusing on female samples (n=2 DS vs. 4 controls), where sufficient statistical power could be achieved. Although formal testing on the male sample was not feasible, we conducted an exploratory evaluation of expression patterns in the male fetus with DS.
We employed the following formula to calculate the consistency between stratified and global results:
Consistency Rate (%) = (N_consistent / N_total) * 100
N_consistent: The number of items showing consistent results between global and stratified analyses. In cell proportion analysis, N_consistent is the number of cell types whose direction of change (increase or decrease) is the same in both global and stratified analyses. In DGE analysis, it is the number of genes that are significantly differentially expressed (e.g., adjusted p-value < 0.05) in both analyses and share the same direction of change (e.g., both upregulated or downregulated). In gene set enrichment analysis (e.g., chr21 genes), it refers to gene sets that are significantly enriched in both analyses with consistent enrichment direction. In EWCE disease enrichment, N_consistent represents the number of cell types showing significant enrichment and the same direction of association (positive or negative Z-score) with the disease gene set in both analyses. N_total: The total number of items compared (e.g., cell types, genes, gene sets, or disease associations).
The interpretation framework was defined: High consistency (>70%) indicates disease susceptibility and generality of the gene set across subgroups. Moderate consistency (50–70%) suggests partial confounding effects, with some genes potentially influenced by sex or gestational age. Low consistency (<50%) implies strong subgroup-specific effects, likely reflecting significant gestational age or sex-related variability. Additionally, to validate the robustness and biological relevance of our findings, we performed cross-validation with previously published datasets, including GSE168408, GSE204684, and GSE59630.

Multiplexed immunofluorescence staining
Brain tissues of the GW23 fetus from the control and DS were used for multiplexed immunofluorescence staining. The cortical tissues were dissected and fixed with 4% paraformaldehyde (BL539A, Biosharp) for up to 24 h and sequentially placed in 10%, 20%, and 30% sucrose (21164955, Biosharp) at 4°C until the tissues were completely immersed in each solution. The tissue samples were frozen with optimal cutting temperature compound (4583, Tissue-Tek) at −40°C for 30 min and sectioned at a thickness of 15 μm using a microtome (CM1950, Leica). Sections were then stored at −40°C. After antigen repair with citrate-EDTA antigen retrieval solution (1×) (BL55A, Biosharp), the sections were incubated with 3% hydrogen peroxide (Jiangxi Grass Coral Disinfection Products Co. Ltd.) for 15 min and blocked with 5% goat serum (SL038, Solarbio) and 0.3% Triton X-100 (1139ML100, BioFROX) for 2 h. Subsequently, the sections were supplemented with primary antibodies for 18 h at 4°C. This was followed by rinsing in PBS with Tween 20 (PBST) 5 times (5 min each). Secondary antibodies (kit-5020, MaxVision-HRP, mouse/rabbit) were incubated at room temperature for 15 min. This was followed by washing with PBST and incubation for 10 min with TSA Plus fluorescent enhancement dye (iF488-Tyramide, iF555-Tyramide, or iF647-Tyramide) (GB1236, Servicebio). For double/triple-label staining, these sections were subjected to antigen repair again with citrate-EDTA antigen retrieval solution and were incubated with 3% hydrogen peroxide for 15 min, followed by blocking with 5% goat serum and 0.3% Triton X-100. Incubation with primary and secondary antibodies was performed. Detailed primary antibody information is provided in Supplementary Data 19. Finally, after DAPI staining for 10 min, sections were sealed with an anti-fluorescent quenching agent (Po126, Beyotime). Fluorescence images were captured with a Nikon AX confocal microscope and NIS-Elements AX software (image size 2048×2048 pixels).
Cortices were immunostained for FOXP2, SATB2, TUBB2B, and NEUN, and random cortical structures were imaged on a confocal microscope. From the pial surface, radial columns covering the full thickness of the cortical plate (CP) were cropped for analyses. The positions of all positive nuclei were separately marked using the “Cell Counter” plugin in ImageJ. Their y-coordinates on the image were recorded and normalized to the full thickness of the CP to measure their relative laminar positions in the CP. The frequency distributions of the relative vertical positions in 10 evenly divided bins for each marker were calculated and plotted in Prism software (GraphPad). The relative locations of all marker+ nuclei were used to plot the cumulative distribution curve and calculate the P-values.

Statistical and reproducibility
If not specified, all statistical analyses and data visualization were done in R (v.4.2.2). The number of replicates and statistical analysis results are detailed in the corresponding figure legends. Data from multiplexed immunofluorescence staining were collected from at least 3 independent experiments and presented as the mean ± SEM. Statistical analysis was performed using Prism 10.3.1 software (GraphPad).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study by Niu et al. is aimed at detecting possible gene expression abnormalities in the developing cortex of fetuses with Down syndrome (DS). To this end, they carried out single-nucleus RNA sequencing using samples from the prefrontal cortex and superior temporal plane of four fetuses with Down syndrome and eleven control fetuses, aged 17-28 GW. Gene expression was analyzed in each cell type (e.g., excitatory neurons, inhibitory neurons, glia) populating the developing cortex which were recognized based on their transcriptome. The study shows cell-specific and region-specific gene expression changes in fetuses with DS vs. controls. In addition, the study reports a relative increase in the number of inhibitory vs. excitatory neurons and abnormalities in their cortical distribution in fetuses with DS vs. controls. These abnormalities appear to change across different gestational times. The study is well designed and provides very useful information on gene expression abnormalities during cortical development in DS, a field scarcely explored due to the limited availability of fetal material. The article is altogether well-written and well-discussed. I have some suggestions that the authors may wish to address.\nLine 2. The title is too ambitious because the study is not ”a comprehensive transcription atlas” of cortical development, being focused on two brain regions only. I suggest changing the title in a more realistic manner.\nLines 24 and 28. Here, as well as in other parts of the text (lines 94, 119, 122, 136, 241, 242), the authors use the term “embryonic” referring to the brains they have analyzed in this study. In humans the embryonic period finishes at GW8. Thus, the term “embryonic” should be replaced with “fetal”.\nLine 34. The expression “comprehensive spatiotemporal analysis” is too ambitious because the gene expression analysis is limited to two cortical regions and because in the case of fetuses with DS the number of cases per gestational age is very small (2 fetuses at GW21, 1 fetus at GW23 and 1 fetus at GW28).\nLine 56. “This process is particularly pronounced”. It is not clear what is meant here with “process”. If it does refer to corticogenesis I don’t think that corticogenesis is more pronounced in the regions examined in this study.\nLines 89 and 89. The statement “with groups matched per GW, gender and postmortem interval” is misleading. The brain samples of fetuses with DS derive from 3 females and 1 male only, so I do not understand the statement “gender matched”. Regarding gestational ages, the samples of fetuses with DS consist in 2 cases at GW21, 1 case at GW23, and 1 case at GW28. Thus, I do not understand the comparisons that are reported in Supplementary Fig. 1A. Although it is not specified, it seems that the first graph from left (gestational age) refers to the samples used for the analysis of the prefrontal cortex and the second graph refers to the cases used for the analysis of the superior temporal plane. In fact, there are 3 data points on the column labeled DS and 3 data points on the column labeled DS in the first and second graph from left, respectively. The same holds for the third and fourth graphs from left (postmortem interval). If so, this is not correct. The comparisons of age and postmortem intervals should include all the fetuses with DS (n=4) and controls (n=11).\nLines 98-99. Regarding cell taxonomy and nomenclature of cell types, it is important to specify the studies from which they are derived.\nLines 127-128. The statement “leading to consistent overexpression of chr21 related genes” is not completely true because not all the genes on HSA21 are over expressed (Antonarakis SE et al.. Nat Rev Dis Primers. 2020 Feb 6;6(1):9). Indeed, an analysis of DS lymphoblastoid cells showed that only 29% of HSA21 genes are sensitive to the gene-dosage effect (expression close to the expected value of 1.5) or are amplified (expression >1.5), 56% are compensated (expression <1.5), and 15% are highly variable among individuals (Aït Yahya-Graison E, et al. Am J Hum Genet. 2007 Sep;81(3):475-91). Moreover, the sentence “analysis of 621 chr21 genes revealed elevated expression across all cell types in DS fetuses compared to controls” is not clear. It seems that all the 621 analyzed genes were overexpressed in fetuses with DS, which is unlikely.\nLine 131 “DS fetuses” The authors should use person-first language here and throughout their report (e.g., \"fetuses with DS\" instead of \"DS fetuses” or “individuals with DS” instead of “DS individuals”).\nLines 186-187. “To elucidate age-dependent transcriptional changes we compared the PFC of young and aged DS individuals (Supplementary Table 8)”. In the Methods, however, there is no mention regarding these cases (young and adults). Perhaps these data derive from cases used in previous studies? If so, this should be specified. Please note that data of young and adult cases are reported in a Table that is headed as “Supplementary Table 7” and not as “Supplementary Table 8”.\nLine 195. Again, I suppose that the data regarding organoids reported in Fig. 2 derive from a previous study. Which study? This point should be clarified.\nLine 210. “Transcriptomic analysis of peripheral blood…”. Same comment as above,\nLines 238-256. The analysis of changes in the abundance of certain subpopulations of neurons in the examined regions across ages has a major flaw because in the case of fetuses with DS it is based on 2 cases at GW21, 1 case at GW23, and 1 case at GW28. Moreover, while the cases at GW21 and GW23 were females, the case at GW28 was a male. Thus, interpretation of results across ages seems hazardous. The same holds for the data presented in the following part of the section “Abnormal alterations of ExN subtypes in the neocortex of DS fetal brain”. It is true that it is difficult to get access to fetal samples, but data needs to be strengthened by more cases to be convincing. Thus, I suggest shortening the whole section and interpreting results. with more caution.\nLines 445-446. The sentence “The differential changes….” needs to be clarified.\nLine 487. “The findings underline the higher disease susceptibility of the STP region…”. Citations that this region is more susceptible to damage should be provided.\nMinor\nLine 52. “… abnormalities observed in DS is the disruption of cortical development, fundamentally alters…), I think that a “that” is missing between “development” and “alters”.-\nLine 64. “Neurogenesis”. It should be specified that it is “cortical neurogenesis” that is characterized by an inward-outward pattern.\nLine 86. Quotation [16] seems to be wrong here.\nLine 118. “huan cortex” should be “human cortex”.-\nLine 124. “two neocortex” should be “two neocortices”.-\nLine 195. “The study” should be “A study”.\nLine 237. “Abnormal alterations…”. An alteration is abnormal, by definition. Please reword the title,\nLine 244. “Supplementary Figure 1D does nor report data of neurons at different differentiation stages.\nLine 307. The cases are not 4 per group in the case of fetuses with DS, since 3 of the 4 cases were used for the PFC and 3 of the 4 cases were used for the temporal plane cortex.\nLine 433. “of DS” should be “of fetuses with DS”.\nLines 482-483. Please specify the meaning of the acronyms.\nLine 736. “FC” should be defined.\nLine 741. The hippocampus has not been used in this study,\nLine 744. “were followed” should be “were the same as in…”.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The manuscript by Niu et al “A Comprehensive Transcriptomic Atlas of Cortical Development in Down Syndrome Fetuses” examines differences between development in midgestational fetuses with and without Trisomy 21 (Ts21). This work performed single nucleus RNA sequencing (snRNAseq) on cells from the prefrontal cortex (PFC) and superior temporal plane cortex (STP). For the PFC, 3 (2F 1M) and 9 (5M and 4F) fetuses with and without Ts21 were used; and for the STP 3 (2F 1M) and 7 (4M 3 F) fetuses with and without Ts21 were used. From these samples, this study purports to find alterations inhibitory-to-excitatory neuron ratio, dysregulated gene expression, differential cell development and distribution, and cellular pathways that are disrupted in these tissues in those fetuses with Ts21. Because Trisomy 21 is such a heterogeneous disorder with a wide variability in manifestation, it is difficult to believe the results presented from such a small number of fetuses, over a wide developmental stage, with no potential sex differences considered. There may be differences as outlined in the manuscript, but these are likely not based on enough samples to have the power to detect real differences between fetuses with and without Ts21.\nThere are only 3 fetuses with Ts21 used for sampling the STP and PFC. The gestational age for the fetuses with Ts21 were 21-28 weeks for STP and PFC, and without Ts21 were 17-25 weeks. Even though there were not statistically significant differences between the age ranges of the groups (small sample sizes hinder detecting such differences!), 1) there is a wide age range between fetuses of the study where a lot of temporal fetal developmental differences are found in brain tissue, and 2) the developmental age range of fetuses with and without Ts21 is different (not age matched as indicated in the manuscript). Furthermore, there is a wide range of differences between developing embryos and fetuses with Ts21, with up to 75% and 50% of fetuses spontaneously lost in the first and second trimesters, respectively. These data indicate a wide range of developmental differences in fetuses with Ts21; for example, 1 or more of the 3 fetuses with Ts21 sampled in this study may have had so much genetic or cellular dysregulation that it would have been spontaneously lost before the third trimester (if allowed to continue development), and the results given would not relate to data mentioned in the manuscript comparing them to live born individuals with Ts21. Thus, due to the high variability in development associated with Ts21, the sample sizes are likely not large enough and occur at too wide of a developmental age to detect correct potential differences for Ts21 development and support the conclusions of the manuscript.\nThe manuscript (supplemental information) lists the sex of each fetus, but no attempt is made in this study to account for sex specific differences in fetal development that would lead to sex differences in cognitive development that are becoming more recognized in individuals with Down syndrome (DS). Furthermore, there is only 1 male fetus with Ts21 at one gestational age (28 W) that makes it hard to believe any extension of these data to other male fetuses with Ts21. Based on these points, and the points made in the previous paragraph, these data do not seem generalizable to developing individuals with Ts21 in the ways that have been written in the manuscript. Additional data or analyses of some of these data in different ways would need to be done to make the results seem plausible, support the conclusions, and applicable to past and future and decrease the significance of these results.\nOther concerns:\nPlease use person first language when referring to individuals with DS. Individuals with DS do not like to be referred to as “patients” and this reviewer would argue that a fetus does not have DS, but only exhibits trisomy 21. Therefore, it is person with DS, not DS patients or DS fetuses, and likely should be fetuses with Ts21 throughout the manuscript.\nThe phrase “due to individual choice or other objective reasons” (line 47) seems offensive to parents with children with DS and should be eliminated."}, "author_response": "Q1: Line 2. The title is too ambitious because the study is not “a comprehensive transcription atlas” of cortical development, being focused on two brain regions only. I suggest changing the title in a more realistic manner.\nResponse: We sincerely appreciate the reviewer’s constructive suggestion. We agreed that the original title overstated the scope of our study, as our analysis was indeed restricted to two cortical regions (prefrontal cortex and superior temporal plane). To better reflect the focus of our work, we have revised the title to: \"Cell-type specific transcriptomic signatures in the prefrontal and superior temporal cortex of mid-gestational fetuses with Down syndrome\". We believe this new title more accurately represents the regional and cell-type-specific nature of our findings while avoiding overgeneralization.\nResponse: We sincerely thank the reviewer for catching this important oversight. We fully agree that \"fetal\" is the correct term for the developmental stage of our samples (GW17-28). We have now replaced all instances of \"embryonic\" with \"fetal\" throughout the manuscript, including in the abstract, results, and discussion sections (lines 24, 79, 175, 180, 206, 208, 220, 571, 588). This revision ensures accurate terminology consistent with established human developmental staging.\nQ3: Line 34. The expression “comprehensive spatiotemporal analysis” is too ambitious because the gene expression analysis is limited to two cortical regions and because in the case of fetuses with DS the number of cases per gestational age is very small (2 fetuses at GW21, 1 fetus at GW23 and 1 fetus at GW28).\nResponse: We thank the reviewer for their helpful suggestion to improve the precision of our language. To better reflect the more targeted nature of our work, we have revised the phrase to “a comparative analysis of gene expression across gestational ages” and corrected other similar phrases throughout the revised manuscript (lines 26, 72, 110, 438, 562, 1076). This revision removes any unintended overstatement while still accurately describing our approach to examining developmental changes in DS versus control fetuses.\nQ4: Line 56. “This process is particularly pronounced”. It is not clear what is meant here with “process”. If it does refer to corticogenesis I don’t think that corticogenesis is more pronounced in the regions examined in this study.\nResponse: We thank the reviewer for raising this important point. Upon revisiting the phrasing, we agree that the original wording was ambiguous and could be misinterpreted. We did not intend to suggest that corticogenesis itself is more pronounced in the prefrontal cortex (PFC) or superior temporal plane (STP). Rather, we meant that the functional specialization of these regions (e.g., higher-order cognition, auditory processing) reflects the complexity of the underlying cellular and molecular processes (e.g., neurogenesis, migration, layering). We have revised the sentence (lines 53-56) as follows:\nRevised: These processes underlie the functional specialization of regions such as the prefrontal\n2\ncortex (PFC), which governs higher-order cognitive functions like memory, decision-making, and social behavior, and the superior temporal plane (STP), which is critical for auditory processing and language comprehension.\nQ5: Lines 89 and 89. The statement “with groups matched per GW, gender and postmortem interval” is misleading. The brain samples of fetuses with DS derive from 3 females and 1 male only, so I do not understand the statement “gender matched”. Regarding gestational ages, the samples of fetuses with DS consist in 2 cases at GW21, 1 case at GW23, and 1 case at GW28. Thus, I do not understand the comparisons that are reported in Supplementary Fig. 1A. Although it is not specified, it seems that the first graph from left (gestational age) refers to the samples used for the analysis of the prefrontal cortex and the second graph refers to the cases used for the analysis of the superior temporal plane. In fact, there are 3 data points on the column labeled DS and 3 data points on the column labeled DS in the first and second graph from left, respectively. The same holds for the third and fourth graphs from left (postmortem interval). If so, this is not correct. The comparisons of age and postmortem intervals should include all the fetuses with DS (n=4) and controls (n=11).\nResponse: We sincerely appreciate the reviewer's careful scrutiny of our cohort description and supplementary figures. We agree these points require clarification and have made the careful revisions.\nGender matching statement: We have removed the claim of gender matching from original manuscript. The revised text now reads: \"we performed snRNA-seq on 22 postmortem cortical samples (PFC and STP) from 4 fetuses with DS and 11 controls (Fig. 1a, Supplementary Table 1), with groups comparable in postmortem interval (PMI).\" (lines 80-82)\nSample composition clarification: We have added explicit details about our sample composition in the Results section (lines 82-85): \"The study included 4 DS fetuses (2 cases providing both PFC and STP samples, 1 case providing only PFC, and 1 case providing only STP), and 11 control fetuses (5 cases providing both PFC and STP samples, 4 case providing only PFC, and 2 case providing only\nSTP) (Supplementary Table 1).\" Thus, there are totally 3 DS PFC samples, 3 DS STP samples, 9 PFC control samples and 7 STP control samples used for snRNA-seq analysis (details seen in Revised Supplementary Table 1). Supplementary Fig.1a showed the intergroup differences in gestational age and PMI of the samples, exhibiting correct sample composition. We acknowledge the confusion\n3\ncaused by the original presentation, and have supplemented more details to distinguish PFC and STP samples (Revised Supplementary Fig.1a).\nRevised Supplementary Fig.1a The intergroup differences in gestational age and PMI of the samples.\nPart of Revised Supplementary Table 1 Demographic information and summary statistics for\nsingle nucleus libraries\nResponse: We thank the reviewer for this important suggestion. In the revised manuscript, we have now explicitly referenced the foundational studies that informed our cell type classification and system (lines 99-102). Cell types were classified according to established transcriptional signatures, with nomenclature consistent with recent publications [1-3].\nReferences\n1. Fan, X., et al., Single-cell transcriptome analysis reveals cell lineage specification in temporal-spatial patterns in human cortical development. Sci Adv, 2020. 6(34): p. eaaz2978.\n2. Zhong, S., et al., A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature, 2018. 555(7697): p. 524-528.\n3. Palmer, C.R., et al., Altered cell and RNA isoform diversity in aging Down syndrome brains. Proc Natl Acad Sci U S A, 2021. 118(47).\nQ7: Lines 127-128. The statement “leading to consistent overexpression of chr21 related genes” is not completely true because not all the genes on HSA21 are over expressed (Antonarakis SE et al.. Nat Rev Dis Primers. 2020 Feb 6;6(1):9). Indeed, an analysis of DS lymphoblastoid cells showed that only 29% of HSA21 genes are sensitive to the gene-dosage effect (expression close to the expected value of 1.5) or are amplified (expression >1.5), 56% are compensated (expression <1.5), and 15% are highly variable among individuals (Aït Yahya-Graison E, et al. Am J Hum Genet. 2007 Sep;81(3):475-91). Moreover, the sentence “analysis of 621 chr21 genes revealed elevated expression across all cell types in DS fetuses compared to controls” is not clear. It seems that all the 621 analyzed genes were overexpressed in fetuses with DS, which is unlikely.\nResponse: We deeply appreciate the reviewer's expertise in highlighting this crucial nuance. To improve accuracy, we have modified overexpression claims in the revised manuscript.\nRevised: \"Aggregate analysis of 621 chr21 genes showed significant expression elevation in fetuses with DS (mean fold-change = 1.48 for STP and 0.27 for PFC, padj < 0.01; Fig. 1i), with stratified analyses showing greatly consist of variation trends with the global findings (Extended Data Fig. 2c). While our bulk analysis confirmed significant upregulation of chr21 gene sets (Fig. 1i), we observed the established pattern of partial dosage compensation: 12.1 % (STP) and 0 % (PFC) of\n5\ngenes showed ≥ 2-fold increase, 39.03 % and 0.45 % exhibited > 1-fold change, and 54.77 % and 19.82 % were variably expressed – aligning with prior reports of chr21 regulation with variable dosage sensitivity in DS.\" (lines121-128).\nIn this revision, we added methodological nuance in Results, providing the expected pattern of partial dosage compensation, consistent with prior reports of chromosome 21 gene regulation in DS [4, 5]. These two articles have also been cited in the revised manuscript (line 128).\nReferences\n4. Antonarakis, S.E., et al., Down syndrome. Nat Rev Dis Primers, 2020. 6(1): p. 9.\n5. Aït Yahya-Graison, E., et al., Classification of human chromosome 21 gene-expression variations in Down syndrome: impact on disease phenotypes. Am J Hum Genet, 2007. 81(3): p. 475-91.\nQ8: Line 131 “DS fetuses” The authors should use person-first language here and throughout their report (e.g., \"fetuses with DS\" instead of \"DS fetuses” or “individuals with DS” instead of “DS individuals”).\nResponse: We sincerely thank the reviewer for highlighting this important consideration. We have now revised the terminology throughout the manuscript to consistently use person-first language as recommended. This change has been implemented in all relevant sections (abstract, introduction, results, discussion) to align with current standards of respectful and precise scientific communication.\nQ9: Lines 186-187. “To elucidate age-dependent transcriptional changes we compared the PFC of young and aged DS individuals (Supplementary Table 7)”. In the Methods, however, there is no mention regarding these cases (young and adults). Perhaps these data derive from cases used in previous studies? If so, this should be specified. Please note that data of young and adult cases are reported in a Table that is headed as “Supplementary Table 7” and not as “Supplementary Table 8”.\nResponse: We sincerely appreciate the reviewer's careful attention to methodological transparency. We have added explicit data source disclosure in revised Methods section (lines 776-779):\n“Age-dependent comparisons utilized published prefrontal cortex data from previous study on DS brain aging (Reference No.14 in the main text). This cohort included 23 postmortem samples: Aged group (56-65 years): 6 neurotypical controls, 4 individuals with DS; Young group (12-39 years): 8 neurotypical controls, 5 individuals with DS.”\nQ10: Line 195. Again, I suppose that the data regarding organoids reported in Fig. 2 derive from a previous study. Which study? This point should be clarified.\nResponse: We thank the reviewer for requesting this important clarification. The organoid data shown in Fig. 2 were indeed generated in a previous study cited in the methods section of this revised manuscript [6] (lines 779-781).\nReferences\n6. Tang, X.Y., et al., DSCAM/PAK1 pathway suppression reverses neurogenesis deficits in iPSC-derived cerebral organoids from patients with Down syndrome. J Clin Invest, 2021. 131(12).\nQ11: Line 210. “Transcriptomic analysis of peripheral blood…”. Same comment as above,\nResponse: We thank the reviewer for prompting this important methodological clarification. The peripheral blood transcriptome data referencedwere derived from the study of Waugh KA, et al. (2023) [7], analyzing whole-blood transcriptome data from 304 individuals with DS (163 male and 141 female) versus 96 euploid controls (44 male and 52 female) (lines 781-782).\nReferences\n7. Waugh, K.A., et al., Triplication of the interferon receptor locus contributes to hallmarks of Down syndrome in a mouse model. Nat Genet, 2023. 55(6): p. 1034-1047.\nQ12: Lines 238-256. The analysis of changes in the abundance of certain subpopulations of neurons in the examined regions across ages has a major flaw because in the case of fetuses with DS it is based on 2 cases at GW21, 1 case at GW23, and 1 case at GW28. Moreover, while the cases at GW21 and GW23 were females, the case at GW28 was a male. Thus, interpretation of results across ages seems hazardous. The same holds for the data presented in the following part of the section “Abnormal alterations of ExN subtypes in the neocortex of DS fetal brain”. It is true that it is difficult to get access to fetal samples, but data needs to be strengthened by more cases to be convincing. Thus, I suggest shortening the whole section and interpreting results with more caution.\nResponse: We sincerely appreciate the reviewer’s insightful comments regarding the limited sample size and potential confounding effects of sex and gestational age in our analysis of neuronal subpopulations across developmental stages. With regards to our sample limitations, we have shortened relevant sections you’ve mentioned by deleting overstated interpretations of generalizing our results to all individuals with DS and focusing our results on second trimester of DS (lines 204-258).\nAdditionally, for interpreting our results with more caution, we have incorporated additional analyses prompted by Reviewer 3’s similar comments on sex and sample size effects. These supplementary analyses were designed to assess the robustness of our findings in the context of limited sample representation and potential sex-specific differences. Stratified analyses using narrower and better age-matched subsets of samples (GW21–23 subgroup (a narrow, tightly age-matched range); GW21–28 subgroup (a broader mid-gestational range after propensity score matching, PFC only); GW21–28_match subgroup (both PFC and STP samples after propensity score matching)). All these stratified subgroup analyses demonstrated a high concordance with findings obtained from the original full dataset (GW17–28). Furthermore, we conducted exploratory analyses and examined available bulk transcriptomic datasets of fetal brains, which demonstrated that sex differences had a relatively modest effect on global gene expression compared to other biological factors such as gestational age or chromosomal status. The detailed results and corresponding responses can be found under our reply to Reviewer 3 (pages 13-38 below). Together, these revisions and analyses enhance the rigor and interpretability of our conclusions. Furthermore, we have added a dedicated paragraph to the Discussion clearly outlining these limitations and emphasizing that our findings are exploratory in nature (lines 609-637). Although the sample size is limited, the findings are still valuable and provide important reference for future in-depth investigations into the pathogenic mechanisms of DS during embryonic development.\nQ13: Lines 445-446. The sentence “The differential changes….” needs to be clarified.\nResponse: We thank the reviewer for noting this lack of clarity. The sentence has been revised as follows:\nWe believe this version better communicates our intended meaning while maintaining scientific precision (lines 350-352).\nQ14: Line 487. “The findings underline the higher disease susceptibility of the STP region…”. Citations that this region is more susceptible to damage should be provided.\nResponse: We thank the reviewer for prompting us to better support this important claim. We have now added 4 key references documenting STP vulnerability to neurological disorders following this sentence you’ve indicated (lines 378-381), which has been improved as follows:\nRevised: These findings underline the higher disease susceptibility of the STP region in DS brains. Prior work has documented this region susceptibility pattern in neuropathological studies, showing a progressive volume reduction of STP in patients with schizophrenia [8-11] (Reference No. 48-51 in the main text).\nReferences\n8. van Erp, T.G.M., et al., Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium. Biol Psychiatry, 2018. 84(9): p. 644-654.\n9. Kasai, K., et al., Progressive decrease of left superior temporal gyrus gray matter volume in patients with first-episode schizophrenia. Am J Psychiatry, 2003. 160(1): p. 156-64.\n10. Takahashi, T., et al., A follow-up MRI study of the superior temporal subregions in schizotypal disorder and first-episode schizophrenia. Schizophr Res, 2010. 119(1-3): p. 65-74.\n11. Takahashi, T., et al., Volume reduction of the left planum temporale gray matter associated with long duration of untreated psychosis in schizophrenia: a preliminary report. Psychiatry Res, 2007. 154(3): p. 209-19.\nMinor\nQ15: Line 52. “… abnormalities observed in DS is the disruption of cortical development, fundamentally alters…), I think that a “that” is missing between “development” and “alters”.-\nCorrected (line 48).\nCorrected (line 61).\nQ17: Line 86. Quotation [16] seems to be wrong here.\nDeleted (line 81).\nQ18: Line 118. “huan cortex” should be “human cortex”.-\nCorrected (line 115).\nQ19: Line 124. “two neocortex” should be “two neocortices”.-\nCorrected (line 118).\nQ20: Line 195. “The study” should be “A study”.\nCorrected (line 179).\nQ21: Line 237. “Abnormal alterations…”. An alteration is abnormal, by definition. Please reword the title,\nCorrected as “Disrupted development of ExN subtypes in the neocortices of fetuses with DS” (line 204).\nQ22: Line 244. “Supplementary Figure 1D does nor report data of neurons at different differentiation stages.\nResponse: We apologize for any confusion. Supplementary Fig. 1d represents marker genes distinguishing excitatory neuron subtypes, which reflect both laminar position and developmental maturation stages based on known cortical neurogenesis timelines. While not a direct developmental trajectory analysis, the marker patterns reflect neurogenic stages (deep layers → upper layers). We have modified the text to more accurately describe Supplementary Fig. 1d (lines 94-109).\nQ23: Line 307. The cases are not 4 per group in the case of fetuses with DS, since 3 of the 4 cases were used for the PFC and 3 of the 4 cases were used for the temporal plane cortex.\nResponse: As we responded above (Q5), there are totally 3 DS PFC samples, 3 DS STP samples, 9 PFC control samples and 7 STP control samples used for snRNA-seq analysis. As for immunostaining,\n10\nwe stained 4-8 sections from 3 DS samples and from 4 control samples. The sample information has been corrected in the figure legends of Fig.3 you’ve mentioned (lines 1102-1103).\nQ24: Line 433. “of DS” should be “of fetuses with DS”.\nCorrected (line 335).\nQ25: Lines 482-483. Please specify the meaning of the acronyms.\nResponse: We thank the reviewer for highlighting this oversight. We have now ensured all acronyms are defined at first use, and supplemented full name of abbreviations used in the figures throughout the revised manuscript (lines 371-375, 1170-1179).\nQ26: Line 736. “FC” should be defined.\nIt has been corrected as “Flow Cytometry” (line 630).\nQ27: Line 741. The hippocampus has not been used in this study,\nThis word “hippocampus” has been deleted (line 635).\nQ28: Line 744. “were followed” should be “were the same as in…”.\nIt has been corrected as instructed (line 637).\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We would like to sincerely thank you for taking the time to carefully review our manuscript and for your thoughtful and constructive comments. We greatly appreciate your effort as part of the Nature Communications initiative to support Early Career Researchers in the peer review process. In response to your valuable suggestions, we have carefully revised the manuscript to address all the concerns raised. Specifically, we have modified and streamlined several figures to improve clarity, removed or rephrased overstatements to ensure accurate interpretation of the data, and added\n11\nnew stratified analyses to further examine the potential confounding effects of sex and sample size. These additional analyses provide a more nuanced understanding of our findings and reinforce the robustness of our conclusions. All changes have been clearly marked in the revised manuscript, and detailed point-by-point responses are provided in our reply to other reviewers. We are grateful for your input, which has significantly improved the quality and rigor of our work.\nReviewer #3 (Remarks to the Author):\nThe manuscript by Niu et al “A Comprehensive Transcriptomic Atlas of Cortical Development in Down Syndrome Fetuses” examines differences between development in midgestational fetuses with and without Trisomy 21 (Ts21). This work performed single nucleus RNA sequencing (snRNAseq) on cells from the prefrontal cortex (PFC) and superior temporal plane cortex (STP). For the PFC, 3 (2F 1M) and 9 (5M and 4F) fetuses with and without Ts21 were used; and for the STP 3 (2F 1M) and 7 (4M 3 F) fetuses with and without Ts21 were used. From these samples, this study purports to find alterations inhibitory-to-excitatory neuron ratio, dysregulated gene expression, differential cell development and distribution, and cellular pathways that are disrupted in these tissues in those fetuses with Ts21. Because Trisomy 21 is such a heterogeneous disorder with a wide variability in manifestation, it is difficult to believe the results presented from such a small number of fetuses, over a wide developmental stage, with no potential sex differences considered. There may be differences as outlined in the manuscript, but these are likely not based on enough samples to have the power to detect real differences between fetuses with and without Ts21.\nThere are only 3 fetuses with Ts21 used for sampling the STP and PFC. The gestational age for the fetuses with Ts21 were 21-28 weeks for STP and PFC, and without Ts21 were 17-25 weeks. Even though there were not statistically significant differences between the age ranges of the groups (small sample sizes hinder detecting such differences!), 1) there is a wide age range between fetuses of the study where a lot of temporal fetal developmental differences are found in brain tissue, and 2) the developmental age range of fetuses with and without Ts21 is different (not age matched as indicated in the manuscript). Furthermore, there is a wide range of differences between developing embryos\n12\nand fetuses with Ts21, with up to 75% and 50% of fetuses spontaneously lost in the first and second trimesters, respectively. These data indicate a wide range of developmental differences in fetuses with Ts21; for example, 1 or more of the 3 fetuses with Ts21 sampled in this study may have had so much genetic or cellular dysregulation that it would have been spontaneously lost before the third trimester (if allowed to continue development), and the results given would not relate to data mentioned in the manuscript comparing them to live born individuals with Ts21. Thus, due to the high variability in development associated with Ts21, the sample sizes are likely not large enough and occur at too wide of a developmental age to detect correct potential differences for Ts21 development and support the conclusions of the manuscript.\nResponse: We sincerely thank the reviewer for the thoughtful and constructive comments. We carefully considered your concerns regarding our manuscript and summarized your feedback into three major points, as detailed below:\nMajor Comment 1: Sample size consideration “Because Trisomy 21 is such a heterogeneous disorder with a wide variability in manifestation, it is difficult to believe the results presented from such a small number of fetuses, over a wide developmental stage, with no potential sex differences considered. There may be differences as outlined in the manuscript, but these are likely not based on enough samples to have the power to detect real differences between fetuses with and without Ts21.”\nResponse to Major Comment 1: We acknowledge the reviewer’s concerns regarding our limited sample size (only 4 DS cases: two at GW21, one at GW23, and one at GW28), we have explicitly clarified this point in the revised manuscript discussion.\nHowever, we respectfully emphasize that obtaining mid-gestation fetal brain tissues with confirmed trisomy 21 (DS) is globally challenging due to stringent ethical considerations, rigorous informed consent requirements, and significant practical constraints. These samples typically derive from elective pregnancy terminations, and oversight inherently limits both the availability and matching of samples in terms of sex, gestational age, and other biological characteristics. Thus, despite extensive efforts, achieving a fully balanced cohort is practically difficult and represents a well-recognized challenge across developmental neuroscience research.\nWe also wish to highlight that this limitation is not unique to our study. Indeed, similar constraints have been explicitly acknowledged in several recent landmark studies published in top-tier journals.\nAppendix Table 1 Summary of sampling of the human developing cerebellum (HRA006719)\nThis study provides key insights into the developmental landscape of the human cerebellum, particularly for\nUBC, astrocyte, and oligodendrocyte lineages, and it also provides insights into the brain developmental\nabnormality of TS21.\nGroup Control TS21\nGW GW19_1 GW22_2 GW_23_3 GW24_4 GW24_5 GW25_6 GW22_1- GW22_1- GW22_2 GW22_3- GW22_3-\n1 2 1 2\nGender Male Female Male Female Male Female Male Male Female Female Female\nYu H, Liu Y, Xu F, Fu Y, Yang M, Ding L, Wu Y, Tang F, Qiao J, Wen L. A human fetal cerebellar map of the late second trimester\nreveals developmental molecular characteristics and abnormality in trisomy 21. Cell Rep. 2024 Aug 27;43(8):114586.\nAppendix Table 2 Summary of sampling of the human developing hippocampus (GSE119212)\nThis study provides a blueprint for understanding human hippocampal development and a tool for\ninvestigating related diseases.\nGroup GW16-18 GW20-22 GW25-27\nGW/Sample GW16 GW18 GW20 GW22_01 GW22_02 GW25 GW27\nGender Female Female Male Male Male Female Female\nZhong, S., W. Ding, L. Sun, Y. Lu, H. Dong, X. Fan, Z. Liu, R. Chen, S. Zhang, Q. Ma, F. Tang, Q. Wu, and X. Wang, Decoding the\ndevelopment of the human hippocampus. Nature, 2020. 577(7791): p. 531-536.\nThis study provides a blueprint for understanding the development of the human prefrontal cortex in the early\nand mid-gestational stages in order to systematically dissect the cellular basis and molecular regulation of\nprefrontal cortex function in humans.\nGroup GW8-12 GW13-16 GW19-26\nGW8 GW9 GW10_01 GW10_02 GW10_03 GW12 GW13 GW16 GW19 GW23_01 GW23_02 GW26\nGW/Sample\nFemale Female Male Female Female Male Female Female Female Male Female Female\nGender\nTang, J. Zhang, J. Qiao, and X. Wang, A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.\nNature, 2018. 555(7697): p. 524-528.\nReferences\n2. Zhong, S., et al., A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.\nNature, 2018. 555(7697): p. 524-528.\n12. Yu, H., et al., A human fetal cerebellar map of the late second trimester reveals developmental molecular\ncharacteristics and abnormality in trisomy 21. Cell Rep, 2024. 43(8): p. 114586.\n13. Zhong, S., et al., Decoding the development of the human hippocampus. Nature, 2020. 577(7791): p. 531-536.\nMajor Comment 2: Clarification on gestational age and matching “The gestational age for the\nfetuses with Ts21 were 21-28 weeks for STP and PFC, and without Ts21 were 17-25 weeks. Even\nthough there were not statistically significant differences between the age ranges of the groups (small\nsample sizes hinder detecting such differences!), there is a wide age range between fetuses of the\nstudy where a lot of temporal fetal developmental differences are found in brain tissue, and the\ndevelopmental age range of fetuses with and without Ts21 is different (not age matched as indicated\nin the manuscript).”\nResponse to Major Comment 2: We sincerely appreciate the reviewer’s insightful comments\nregarding potential confounding effects due to gestational age variability and matching between DS\n(Ts21) and control fetal samples. We fully acknowledge that the gestational age range in our study\n(17–28 weeks) presents inherent variability, which is common yet challenging in fetal developmental\n15\nstudies. To address these concerns explicitly and thoroughly, we have adopted a structured and\nrigorous analytical approach as detailed below:\n1. Transparent reporting of gestational ages: We have comprehensively updated Supplementary\nTable 1 and revised Fig. 1a in the revised manuscript to transparently present individual gestational\nages and clarify the distributions clearly. This allows for a straightforward evaluation of the matching\nstatus between Ts21 and control groups.\n2. Statistical adjustment for gestational age: In the revised Methods section, we explicitly clarify\nthat gestational age has been included as a covariate in all statistical models assessing differential\ngene expression and changes in cellular composition. This statistical adjustment ensures that the\nprimary findings are not unduly influenced by variability in gestational age across samples.\n3. Stratified subgroup analyses: To further enhance the robustness and reproducibility of our\nfindings, we conducted additional stratified analyses using narrower and better age-matched subsets\nof samples, including: GW21–23 subgroup (a narrow, tightly age-matched range); GW21–28\nsubgroup (a broader mid-gestational range after propensity score matching, PFC only); GW21–\n28_match subgroup (both PFC and STP samples after propensity score matching).\nThese subgroup analyses encompassed all major results of our manuscript (Appendix Table 4),\nincluding:\n1) Cell type proportion analysis\n2) Chromosome 21 gene enrichment assessment\n3) Differentially expressed gene identification\n4) Identification of chr21-specific DEGs\n5) Gene expression dynamics of neuronal migration pathways\n6) Performance validation of migration-related gene prediction models\n7) Metabolic pathway analysis using single-cell flux estimation analysis (scFEA)\n8) Protein lactylation pathway analysis\n9) Neuropsychiatric disease risk gene enrichment (EWCE-based)\n4. Robustness and consistency of results: Encouragingly, all these stratified subgroup analyses\ndemonstrated a high concordance with findings obtained from the original full dataset (GW17–28)\n(consistency rate provided in Appendix Table 4). For instance, key DEGs, cell-type-specific\n16\ntranscriptomic signatures, metabolic pathway alterations, and disease-related gene enrichments\nremained highly consistent across these carefully matched sub-analyses. Detailed results supporting\nthis consistency are now presented clearly in the supplementary materials (Extended Data Figs. 1–\n9 and Extended Data Tables 1–7).\nAppendix Table 4 Stratified analyses applied to major results in the manuscript and the\nconcordance with the full dataset (GW17-28)\nGestational Age\nGestational Age Stratification\nStratification\nRelated Newly PFC STP\nAnalysis content original added/modified\nGW 21- GW 21-\nfigures figures\nGW 21-23 GW 21-28 GW 21-23\n28_match 28_match\n(vs. GW17- (vs. GW17- (vs. GW17-\n(vs. GW17- (vs. GW17-\n18) 18) 18)\n18) 18)\nExtended Data\nFig.1e 88.24% 94.12% 94.12% 82.35% 70.59%\nFig.2a\n1) Cell type proportion\nanalysis\nExtended Data\nFig.1g,h 88.23% 94.11% 100% 82.35 70.59\nFig.2b\n2) Chr21 gene\nExtended Data\nenrichment Fig.1i 100% 100% 100% 100% 100%\nFig.2c\nassessment\nExtended Data 11.76%- 5.31%- 51.09%-\n3) DEGs identification Fig.2b, c 5.88%-100% 5.88%-100%\nTable 2 100% 84.18% 98.51\nFig.2d,\n4) Identification of Extended Data\nSupplementary 100% 86.36% 95.45% 100% 80%\nchr21-specific DEGs Table 3-7\nFig. 2a, b\n5) Gene expression\nExtended Data\ndynamics of neuronal Fig.4a 94.23% 90.38% 86.54% 84.62% 67.69%\nFig.4a\nmigration pathways\n6) Performance\nvalidation of\nExtended Data\nmigration-related Fig.4c-e 82%/76% 81%/73% 81%/75% 75%/70% 75%/67%\ngene prediction\nmodels\n7) Metabolic pathway Fig.5a-c;\nFig.5b 69% 70% 86.50% 87.10% 91.10%\nanalysis using scFEA Extended Data\nFig.6a, b\n8) Protein lactylation Extended Data\nFig.5c 82.69% 92.31% 98.08% 87.50% 85.42%\npathway analysis Fig.6c\n9) Neuropsychiatric Extended Data\ndisease risk gene Supplementary Fig.5a-c;\n90.3%/96.5% 93.5%/75.4% 93.5%/81.1% 90.3%/96.5% 93.5%/75.4%\nenrichment (EWCE- Fig. 6 Extended Data\nbased) Fig.7-9\nSpecifically:\n1) Cell type proportion analysis: A global comparative analysis of cell populations between DS\nand control samples revealed significant alterations in both the PFC and STP regions, with more\npronounced changes observed in the PFC (Revised Fig. 1e–h). Stratified analyses based on\ngestational age and sex further validated these findings, demonstrating at least 70% consistency\nwith the global results (Extended Data Fig. 2a, b), thereby supporting the robustness and\ngeneralizability of our observations. Cell type proportion changes (GW21-23) showed >70%\ndirectional agreement with full cohort results (GW21-28), demonstrating trans-gestational\nstability during mid-gestation.\nproportions between DS and control groups from different stratified data. b Bar plot showing the comparison\nof various cell types in different stratified data of PFC and STP. Percentages indicate the consistency rate of\neach stratified subgroup compared to the full dataset (GW17–28).\n2) Chromosome 21 gene enrichment assessment: For chromosome 21 genes, aggregate analysis\nof chr21 genes showed significant expression elevation in fetuses with DS (mean fold-change = 1.48 for STP and 0.27 for PFC, padj < 0.01; Fig.1i), with stratified analyses showing great\nconsistency of variation trends with the full dataset findings (Extended Data Fig. 2c).\ngroups from different stratified data. Percentages indicate the consistency rate of each stratified subgroup\ncompared to the full dataset (GW17–28).\n3) Differentially expressed gene identification: In the manuscript, among all cell types, ExN L5/6\nand ExN L3-5 populations demonstrated the highest susceptibility to disease-related\ntranscriptomic alterations, followed by neuroblasts (NB) (Fig. 2b, c). Thus, we compared DEGs\nin different stratified analysis scenarios. It is true that the gestational age has a small effect on the\nresults of the DEGs, but to a large extent they show consistency. Analyses across different\nstratified datasets further revealed that the extent to which cell types are influenced by gestational\nage varies (Extended Data Table 2). Nevertheless, disease-sensitive populations such as ExN\nL5/6, ExN L3-5, and NB exhibited high consistency across global and stratified analyses—≥82%\n20\nin PFC (Extended Data Table 2). In contrast, non-neuronal populations showed a much smaller\nnumber of DEGs, potentially reflecting reduced statistical power due to their low abundance.\n4) Identification of chr21-specific DEGs: We examined the chromosomal distribution of DEGs\nand found that most were localized on chromosome 1-8, with a smaller subset on chromosome\n21 (chr21) (Supplementary Fig. 2a, b). Chr21 DEGs were predominantly upregulated in neurons\nof fetuses with DS, rather than in microglia or endothelial cells (Fig. 2d). Notably, stratified\nanalyses based on gestational age revealed a high degree of concordance with the global dataset—\nat least 80% of chr21 DEGs were consistently identified across datasets (Extended Data Tables\n3–7). This suggests the robustly differential expression of chr21 genes in DS, which was\nminimally influenced by gestational age variability.\n5) Gene expression dynamics of neuronal migration pathways: Further analysis identified 52\nDEGs in PFC and 65 DEGs in STP related to cell migration (Fig. 4a, Supplementary Table 9).\nTo evaluate the robustness of these findings, we performed Jaccard similarity analyses of\noverlapped migration-related DEGs between each stratified subgroup and the full GW17–28\ndataset, revealing consistency rates of at least 73% in PFC and 66% in STP (Extended Data Fig.\n4a).\n6) Performance validation of migration-related gene prediction models: To evaluate the\ndiagnostic potential of migration-related DEGs, we constructed machine learning models using\nthese genes as feature variables. We found that these genes were able to accurately distinguish\nthe neuron states across different conditions, achieving 78% and 71% accuracy in the PFC and\nSTP, respectively (Fig. 4c-e). Besides, our predictive model maintained about 70% accuracy\nwhen applied to stratified data (Extended Data Fig. 4b). Furthermore, by integrating the data\nfrom two normal PFC datasets (GSE168408 includes one sample at GW22 and one at GW24;\nGSE204684 includes one sample at GW22 and one at GW24), we found that the prediction\naccuracy of our model for normal cells could reach 99% (Extended Data Fig. 4b).\nPink represents the differentially expressed cell migration genes in the global analysis. Blue represents the\ndifferentially expressed cell migration genes in different stratified data. Jaccard similarity analyses compared\noverlapped migration-related molecules between stratified subgroups to the full GW17–28 dataset. b Up,\nConfusion matrices of disease prediction performance for different stratified data and two publicly published\ndatasets of PFC. Below, Confusion matrices of disease prediction performance for different stratified data of\nSTP.\n7) Metabolic pathway analysis using scFEA: In global analysis, we observed widespread changes\nin lactate levels in cells of both the PFC and STP (Fig. 5b), laying the foundation for abnormal\nprotein lactylation modifications in neurons. We further validated the possible effect of\n22\ngestational age on different metabolites. The concordance of metabolite changes across tertiles\ncould reach 69 % in PFC and more than 87 % in STP. In addition, lactate metabolism which was\nfocused on in this study, maintained 100 % concordance in all stratification analyses (Extended\nData Fig. 5 and 6a, b).\n8) Protein lactylation pathway analysis: We further focused on the variation of lactated proteins\nunder different stratification analysis scenarios, and Jaccard similarity analyses compared\noverlapped lactylation-regulated molecules among stratified subgroups to the full GW17–28\ndataset, revealing molecular consistency rates of at least 67% in PFC and 58% in STP (Extended\nData Fig. 6c).\ndifferential expression in different cell types from GW21-23 (a), GW21-28 match (b) and GW21-28 (c) data\n(two-sided Wilcoxon rank-sum test, *P < 0.05, **P < 0.01, ***P < 0.001). Red indicates upregulation in DS,\n24\nand blue indicates downregulation. Percentages indicate the consistency rate of each stratified subgroup\ncompared to the full dataset (GW17–28).\ndifferential expression in different cell types from GW21-23 (a) and GW21-28 match data (b) (two-sided\nWilcoxon rank-sum test, *P < 0.05, **P < 0.01, ***P < 0.001). Red indicates upregulation in DS, and blue\nindicates downregulation. c Venn diagram showing DEGs (FDR-adjusted P-value < 0.05, |log2FC| > 0.25)\nassociated with lactylated proteins between stratified subgroups in the PFC and STP. Jaccard similarity\nanalyses compared overlapped lactylation-regulated molecules between stratified subgroups to the full\nGW17–28 dataset. Percentages indicate the consistency rate of each stratified subgroup compared to the full\ndataset (GW17–28).\n9) Neuropsychiatric disease risk gene enrichment (EWCE-based): Using Expression Weighted\nCelltype Enrichment (EWCE) analysis, we evaluated the enrichment of 24 neuropsychiatric\ndisease risk genes in DS single-cell sequencing data (Fig. 8). Stratified analyses based on\ngestational age and sex yielded a high level of concordance with the global dataset, with ≥75%\noverlap in results (Extended Data Figs. 7–9). These findings underline the higher disease\nsusceptibility of the STP region in DS brains.\ndata. a Group enrichment level of 24 gene sets associated with neurological diseases in the two cortex. Bar\n27\nplot showing the standard derivation of a certain disease risk gene set, with region-related group indicated\nrighg the plot. Asterisks denote the BH-corrected P- value < 0.05 calculated using EWCE (Permutation Test).\nb Cell types enrichment level of 24 gene sets associated with neurological diseases in the two cortex.\nPercentages indicate the consistency rate of each stratified subgroup compared to the full dataset (GW17–\n28).\n28\ndata. a Group enrichment level of 24 gene sets associated with neurological diseases in the two cortex. Bar plot\nshowing the standard derivation of a certain disease risk gene set, with region-related group indicated right the\nplot. Asterisks denote the BH-corrected P- value < 0.05 calculated using EWCE (Permutation Test). b Cell types\nenrichment level of 24 gene sets associated with neurological diseases in the two cortex.\n30\na Group enrichment level of 24 gene sets associated with neurological diseases in the two cortex. Bar plot showing\nthe standard derivation of a certain disease risk gene set, with region-related group indicated right the plot.\nAsterisks denote the BH-corrected P- value < 0.05 calculated using EWCE (Permutation Test). b Cell types\nenrichment level of 24 gene sets associated with neurological diseases in the two cortex.\nOverall, despite the inherent challenge posed by the naturally variable gestational ages of fetal\nsamples, our comprehensive analytical strategies—including careful covariate adjustments, rigorous\nstratified subgroup analyses, and transparent reporting—collectively demonstrate that our primary\nfindings are robust and reproducible. These methodological enhancements have been clearly\narticulated in our revised manuscript and supplementary documents to assure reviewers and readers\nof the validity of our key results and interpretations. These new data have been incorporated into the\nrevised Results section (lines 112-114, 121-123, 140-153, 164-168, 227-230, 246-250, 279-282, 287-\n290, 377-381), with corresponding methods updated (lines 774-816). While these validation\napproaches demonstrate robustness, residual gestational age effects remain a study limitation that\nwarrants cautious interpretation of our exploratory findings. In response, we have added a dedicated\nparagraph to the Discussion clearly outlining these limitations and emphasizing that our findings are\nexploratory in nature. Furthermore, we have revised the manuscript to soften any overstatements and\nto avoid generalizing our results to all individuals with DS. Although the sample size is limited, the\nfindings are still valuable and provide important reference for future in-depth investigations into the\npathogenic mechanisms of DS during embryonic development.\nAll text sections (abstract, results, and discussion) have been carefully edited to maintain scientific\nrigor while avoiding overinterpretation of our data. We believe these revisions provide appropriate\ncontext for readers to evaluate the contribution of this work to understanding fetal cortical\ndevelopment in trisomy 21.\nthere is a wide range of differences between developing embryos and fetuses with Ts21, with up to\n75% and 50% of fetuses spontaneously lost in the first and second trimesters, respectively. These data\nindicate a wide range of developmental differences in fetuses with Ts21; for example, 1 or more of\nthe 3 fetuses with Ts21 sampled in this study may have had so much genetic or cellular dysregulation\nthat it would have been spontaneously lost before the third trimester (if allowed to continue\ndevelopment).”\nResponse to Major Comment 3: We sincerely appreciate the reviewer’s insightful comment\nregarding developmental variability and the high rate of spontaneous loss associated with trisomy 21\n(Ts21) pregnancies. Indeed, as highlighted by the reviewer, approximately 75% of trisomy 21\nconceptions result in spontaneous miscarriage during the first trimester, with an additional\napproximately 50% being lost during the second trimester, underscoring significant developmental\nheterogeneity among DS fetuses.\nIn clinical practice, trisomy 21 is typically screened early in pregnancy through standard prenatal\ndiagnostic tests (e.g., non-invasive prenatal testing, chorionic villus sampling, and amniocentesis).\nWhen trisomy 21 is detected, clinical recommendations for elective termination, if chosen, are\ngenerally made during the late first or early second trimester (typically before 24 gestational weeks).\nIt’s hard to distinguish prenatal DS fetuses based on cellular and genetic dysregulation in current\nresearch. In our study, fetal brain tissues obtained from mid-gestation primarily result from elective\nterminations after prenatal diagnosis, rather than spontaneous miscarriages, which usually occur\nearlier. All fetal brain samples were ethically obtained by an experienced clinician under rigorous\nethical approval and informed consent procedures. Each sample underwent detailed karyotyping or\ngenotyping to confirm trisomy 21. We carefully reviewed recent literature for comparison: similar\ntranscriptomic studies on human DS fetal brain tissues—such as the recent work published by Yu et\nal. (Cell Reports, 2024) [12], and previous studies published in prominent journals [14]—likewise\ntypically result from elective terminations of pregnancy after prenatal diagnosis. Although this is\nindeed a common practice shared across the field, the selection deserves more attention on\ndistinguishing criteria in future.\nfindings, since fetuses that survive into mid-gestation may represent a subgroup of DS individuals\nwith comparatively milder neurodevelopmental abnormalities. To address this concern, we applied\nrigorous analytical strategies (including statistical covariate adjustments and stratified subgroup\nanalyses based on gestational age) to carefully mitigate the impact of developmental variability on\nour findings. Additionally, in the revised manuscript, we explicitly state (lines 578-588) that our\nresults should be interpreted within the context of this limitation, and we emphasize that future studies\nshould include a broader and more representative range of DS fetal samples.\nWe agree fully with the reviewer’s suggestion that further studies, ideally integrating clinical\ndocumentation and a wider spectrum of fetal DS samples, will be essential to better capture and\ncharacterize the variability in DS neurodevelopmental outcomes. Nonetheless, our current study\nrepresents an important foundational step, providing crucial preliminary insights into the molecular\nand cellular mechanisms potentially underlying abnormal brain development in DS.\nWe sincerely thank the reviewer for this important comment, which allowed us to clarify the clinical\ncontext and limitations of our work more precisely, ensuring the results are communicated with\nappropriate rigor and transparency.\nReferences\n12. Yu H., et al., A human fetal cerebellar map of the late second trimester reveals developmental molecular\ncharacteristics and abnormality in trisomy 21. Cell Rep, 2024. 43(8): p. 114586.\n14. Marderstein, A.R., et al., Single-cell multi-omics map of human fetal blood in Down syndrome. Nature,\n2024. 634(8032): p. 104-112.\nThe manuscript (supplemental information) lists the sex of each fetus, but no attempt is made in this\nstudy to account for sex specific differences in fetal development that would lead to sex differences\nin cognitive development that are becoming more recognized in individuals with Down syndrome\n(DS). Furthermore, there is only 1 male fetus with Ts21 at one gestational age (28 W) that makes it\nhard to believe any extension of these data to other male fetuses with Ts21. Based on these points,\n34\nand the points made in the previous paragraph, these data do not seem generalizable to developing\nindividuals with Ts21 in the ways that have been written in the manuscript. Additional data or\nanalyses of some of these data in different ways would need to be done to make the results seem\nplausible, support the conclusions, and applicable to past and future and decrease the significance of\nthese results.\nResponse: We sincerely thank the reviewers for raising the important issue of sex differences in fetal\ndevelopment. We fully acknowledge the critical role of sex-specific factors in neurodevelopmental\nresearch. Given the limited sample size and uneven sex distribution in our study, we were unable to\nperform robust sex-stratified analyses. In particular, our cohort included only one male fetus with\nTs21 at GW28, making it statistically challenging to draw reliable conclusions regarding sex\ndifferences. We recognize this limitation and have explicitly addressed it in the revised manuscript\n(lines 570-598). Nevertheless, we conducted exploratory analyses and examined available bulk\ntranscriptomic datasets of fetal brains, which demonstrated that sex differences had a relatively\nmodest effect on global gene expression compared to other biological factors such as gestational age\nor chromosomal status.\nSpecifically:\n(1) Independent statistical analysis of female samples:\nTo further explore sex-related effects, we analyzed DEGs stratified by sex. Considering the limited\nnumber of male Ts21 samples (n = 1), we performed separate differential expression analyses on\nfemale fetuses where sufficient statistical power could be achieved (n=2 Ts21 vs 4 controls). The\nresults revealed that different cell types were affected by sex factors to varying degrees. Although sex\ndifferences were observed, the effects were relatively modest. These female-only results showed high\nconcordance (>73% in PFC cell types and >62% (except for Astro) in most STP cell types) with the\nfull dataset. These findings suggest that our data provide reliable insights for female DS cases\n(Extended Data Table 1).\nExtended Data Table 1 Female DEGs/global DEGs (%)\nAstro 75.0 19.5\nNB 78.2 94.1\nImN_L5_6 97.1 65.3\nImN_L5 100.0 97.1\nImN_L4 82.1 72.3\nImN_L3_5 73.7 62.3\nImN_L2_4 80.3 84.4\nExN_L5_6 76.7 93.8\nExN_L3_5 100.0 97.9\nExN_L3_5_LINC02388 98.0 92.0\nExN_L2_4 100.0 98.0\nExN_L2 84.0 92.4\nCGE_InN 75.5 94.7\nMGE_InN 78.1 89.8\nOPC 100.0 88.9\n(2) Exploratory assessment of male data\nWhile formal statistical testing of male samples was not feasible, we examined female-associated\nDEGs showed expression changes in the same direction in the male Ts21 fetus. In the PFC, 60% of\ncell types showed at least 60% DEG overlap between males and females (Extended Data Fig. 3a),\nwhile the STP exhibited more pronounced sex-related differences, with DEG consistency between\nsexes dropping to as low as 49.7% (Extended Data Fig. 3b). In both PFC and STP, while sex-related\neffects were observable, we attribute these primarily to gestational age differences, given that the sole\nmale DS sample was obtained at GW28.\nPFC (a) and STP (b).\n(3) Consistency with published male data\nWe compared our findings with bulk RNA-seq data from DS fetal PFC cortices [15], revealing 69.4%\nconcordance in directionality of dysregulated genes: downregulated genes showed 79.8%\nconsistency, while upregulated genes demonstrated 58.2% consistency (Extended Data Fig. 3c).\nUndoubtedly, sex differences exist in fetal brain development. In our primary statistical models, we\ntreated sex as a covariate to minimize its influence on global results, and where possible, analyzed\nmale and female data separately. These analyses suggest only modest sex-specific variations.\nEmerging evidence does indicate potential divergence in developmental trajectories between male\nand female individuals with DS [16]. However, compared to neurotypical controls, most molecular\nand clinical phenotypes show consistent trends across sexes in DS, with only minimal sex-dependent\ndifferences in expression levels [16, 17]. Due to our limited sample size (particularly having one 28\nGW male Ts21 fetus), all sex-related findings require cautious interpretation. We maintain that our\nresults likely reflect general phenomena in DS rather than being sex-specific. Following the\nreviewers' guidance, we have substantially tempered sex-related conclusions in the manuscript,\nexplicitly framed findings as preliminary and exploratory, revised language regarding generalizability\nto \"all Ts21-developing individuals\", and added explicit caveats about limited capacity to evaluate\nsex effects. These modifications ensure our conclusions are properly qualified given the study's\nconstraints.\nWe deeply appreciate the reviewers' insights on this crucial issue and have accordingly revised the\nDiscussion section. Their comments have identified key directions for future research. We have added\n38\nstatements emphasizing the need for larger-scale studies with balanced sex representation, adequate\nnumbers of both male and female participants, and comprehensive investigation of potential sex-\nspecific differences in DS neurodevelopment (lines 570-598). We are grateful for the reviewers'\nemphasis on this important consideration, which strengthens both our manuscript and the field's\nresearch agenda.\nReferences\n15. Olmos-Serrano, J.L., et al., Down Syndrome Developmental Brain Transcriptome Reveals Defective\nOligodendrocyte Differentiation and Myelination. Neuron, 2016. 89(6): p. 1208-1222.\n16. Aoki, S., Y. Yamauchi, and K. Hashimoto, Developmental trend of children with Down's\nsyndrome - How do sex and neonatal conditions influence their developmental patterns? Brain Dev, 2018. 40(3): p. 181-187.\n17. Flores-Aguilar, L., et al., Evolution of neuroinflammation across the lifespan of individuals with Down\nsyndrome. Brain, 2020. 143(12): p. 3653-3671.\nOther concerns:\nPlease use person first language when referring to individuals with DS. Individuals with DS do\nnot like to be referred to as “patients” and this reviewer would argue that a fetus does not have DS,\nbut only exhibits trisomy 21. Therefore, it is person with DS, not DS patients or DS fetuses, and\nlikely should be fetuses with Ts21 throughout the manuscript.\nResponse: We sincerely appreciate the reviewer's thoughtful guidance regarding person-first\nlanguage and precise terminology for trisomy 21. We have implemented the language changes\nthroughout the manuscript. Full-text search was performed to ensure all instances were corrected,\nusing \"fetuses with DS\" instead of \"DS fetuses”, “individuals with DS” instead of “DS individuals”.\nThe phrase “due to individual choice or other objective reasons” (line 47) seems offensive to parents\nwith children with DS and should be eliminated.\nResponse:\nWe sincerely appreciate the reviewer's important perspective regarding the phrasing about birth\nprevalence of DS. We recognize how the original wording could be perceived as insensitive and have\nrevised this statement to be both scientifically accurate and respectful (lines 42-44):\nremains approximately 1 per 779–1023 live births, reflecting complex personal, medical and societal\nfactors”.\nReferences\n1. Fan, X., et al., Single-cell transcriptome analysis reveals cell lineage specification in\ntemporal-spatial patterns in human cortical development. Sci Adv, 2020. 6(34): p.\neaaz2978.\n2. Zhong, S., et al., A single-cell RNA-seq survey of the developmental landscape of the\nhuman prefrontal cortex. Nature, 2018. 555(7697): p. 524-528.\n3. Palmer, C.R., et al., Altered cell and RNA isoform diversity in aging Down syndrome\nbrains. Proc Natl Acad Sci U S A, 2021. 118(47).\n4. Antonarakis, S.E., et al., Down syndrome. Nat Rev Dis Primers, 2020. 6(1): p. 9.\n5. Aït Yahya-Graison, E., et al., Classification of human chromosome 21 gene-expression\nvariations in Down syndrome: impact on disease phenotypes. Am J Hum Genet, 2007. 81(3):\np. 475-91.\n6. Tang, X.Y., et al., DSCAM/PAK1 pathway suppression reverses neurogenesis deficits in\niPSC-derived cerebral organoids from patients with Down syndrome. J Clin Invest, 2021.\n131(12).\n7. Waugh, K.A., et al., Triplication of the interferon receptor locus contributes to\nhallmarks of Down syndrome in a mouse model. Nat Genet, 2023. 55(6): p. 1034-1047.\n8. van Erp, T.G.M., et al., Cortical Brain Abnormalities in 4474 Individuals With\nSchizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through\nMeta Analysis (ENIGMA) Consortium. Biol Psychiatry, 2018. 84(9): p. 644-654.\n9. Kasai, K., et al., Progressive decrease of left superior temporal gyrus gray matter\nvolume in patients with first-episode schizophrenia. Am J Psychiatry, 2003. 160(1): p.\n156-64.\n10. Takahashi, T., et al., A follow-up MRI study of the superior temporal subregions in\nschizotypal disorder and first-episode schizophrenia. Schizophr Res, 2010. 119(1-3): p.\n65-74.\n11. Takahashi, T., et al., Volume reduction of the left planum temporale gray matter\nassociated with long duration of untreated psychosis in schizophrenia: a preliminary\nreport. Psychiatry Res, 2007. 154(3): p. 209-19.\n12. Yu, H., et al., A human fetal cerebellar map of the late second trimester reveals\ndevelopmental molecular characteristics and abnormality in trisomy 21. Cell Rep, 2024.\n43(8): p. 114586.\n13. Zhong, S., et al., Decoding the development of the human hippocampus. Nature, 2020.\n577(7791): p. 531-536.\n14. Marderstein, A.R., et al., Single-cell multi-omics map of human fetal blood in Down\nsyndrome. Nature, 2024. 634(8032): p. 104-112.\n40\n15. Olmos-Serrano, J.L., et al., Down Syndrome Developmental Brain Transcriptome Reveals\nDefective Oligodendrocyte Differentiation and Myelination. Neuron, 2016. 89(6): p. 1208-\n1222.\n16. Aoki, S., Y. Yamauchi, and K. Hashimoto, Developmental trend of children with Down's\nsyndrome - How do sex and neonatal conditions influence their developmental patterns?\nBrain Dev, 2018. 40(3): p. 181-187.\n17. Flores-Aguilar, L., et al., Evolution of neuroinflammation across the lifespan of\nindividuals with Down syndrome. Brain, 2020. 143(12): p. 3653-3671.\n41"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed my concerns in an appropriate manner. I have noticed that expressions such as “DS fetus/es” and DS patient/s” still appear in the revised version. Please replace with “Fetus/es with DS” and “people with DS”, respectively. In addition, there are some sentences that need to be amended. For instance:\nLines 305-306. Please add a quotation regarding the role of InN6 and InN9.\nLine 123. “…show greatly consist…”. I suppose that the authors mean “show great consistency”.\nLines 129-129. Something is missing in this sentence.\nLimes 314-315. Something is missing in this sentence.\nLines 371-372. The sentence would be clearer by insertion of “risk genes” after “(MS)” and “(PD)”, respectively.\nLines 377-378. Something is missing in this sentence.\nLines 425-428. The phrasing of these two sentences is somewhat clumsy. Pease, reword them.\nLine 472. “…the PFC and STP of DS fetal” I suppose that the word “brain” is missing after “fetal”.\nLine 488. What is meant with the expression “neuron states”?\nLine 576 “Irreplaceable window”. I think that “the rare cases” provide a “unique opportunity” rather than representing a window.\nLine 634. “…including 3 different regions: PFC and STP”. I suppose that “3” should be replaced by “2”.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The authors have addressed my comments and concerns. I have no further questions or comments for the authors."}, "author_response": "Thank you for your valuable feedback and for pointing out the areas that require further revision. We have carefully addressed each of your comments as follows:\n1. Use of “DS fetus/es” and “DS patient/s”:\nWe appreciate your suggestion to replace “DS fetus/es” and “DS patient/s” with\n“Fetus/es with DS” and “people with DS,” respectively. We have made these\nchanges throughout the manuscript to align with the preferred terminology.\n2. Lines 305-306:\nRegarding the role of InN6 and InN9, we will add a relevant citation to support\nthis point and clarify their role in the context of our findings. We apologize for\nthe misunderstanding in the original description. The statement in Lines 305-\n306 was intended to build on the preceding sentence, which already includes\nrelevant citations. We have now revised the text to clarify this point and avoid\nany ambiguity. The reference supporting the role of InN6 and InN9 is included,\nand we have ensured that the connection between the sentences is clearer (lines\n301-306).\nRevised: “Studies have shown that Reelin (RELN) plays a crucial role in\nneuronal migration and cortical lamination, and its reduced expression may\nimpair neuronal migration in the hippocampus, leading to neural circuit\nabnormalities that impact cognitive functions such as memory, learning, and\nlanguage comprehension.41 Our normalized cell counts showed a reduction in\nthe RELN+ (InN6 and InN9) cell populations (Fig. 6c), suggesting a potential\nlink to DS-related cognitive dysfunction (Fig. 6d).”\n3. Line 123:\nWe have corrected the phrase “showing greatly consist” to “showing great\nconsistency,” as you suggested (line 128).\n4. Lines 129-130:\nWe have reviewed this section and revised this sentence to complete the\nsentence, ensuring it is clear and coherent (lines 121-124).\nRevised: Moreover, we observed an increased transcriptional heterogeneity in\nthe PFC of fetuses with DS, which exhibited significantly higher inter-\nindividual expression variability compared to the STP region (Supplementary\nFig. 3b, c), suggesting early region-specific dysregulation.\n5. Lines 314-315:\nWe have amended this sentence by adding the missing content to improve\nclarity and readability (lines 313-314).\nRevised: CellChat and NeuroChat analyses revealed opposing changes in\ncellular communication within the PFC and STP in DS compared to the controls\n(Supplementary Fig. 11a, b).\n6. Lines 371-372:\nWe have inserted “risk genes” in the indicated place, as recommended, to\nimprove the clarity of this sentence (lines 367-370).\nRevised: Risk genes of alcohol dependence (ALD) and multiple sclerosis (MS)\nare mainly enriched in the PFC, while those of Tourette syndrome (TS), AD and\nParkinson’s disease (PD) are predominantly enriched in the STP.\n7. Lines 377-378:\nWe have reviewed this sentence and added the missing content for better clarity\n(lines 373-376).\nRevised: Stratified analyses based on gestational age yielded a high level of\nconcordance (≥75%) in disease susceptibility compared with the global dataset\n(Supplementary Fig. 13–15). These outcomes underscore heightened disease\nsusceptibility of the STP region in DS brains.\n8. Lines 425-428:\nWe agree that these two sentences could be reworded for clarity. We have\nrephrased them to ensure the meaning is more precise and the flow is smoother\n(lines 418-419).\nRevised: Our results indicated significant transcriptomic differences between\nthe PFC and STP in fetuses with DS.\n9. Line 472:\nWe have added the word “brain” after “fetal” to correct this sentence as you\nsuggested: “the PFC and STP of DS fetal brains” (line 464).\n10. Line 488 – “Neuron states”:\nThank you for pointing this out. By “neuron states,” we refer to the\ndifferentiation stages of neurons (e.g., mature vs. immature). We have revised\nthis expression as “developmental states of neurons” in the manuscript to avoid\nconfusion (line 481).\n11. Line 576 – “Irreplaceable window”:\nWe agree with your comment. We have revised this to indicate that “the rare\ncases” provide a “unique opportunity” rather than using the term “window”\n(lines 568-570).\n12. Line 634 – “3 different regions: PFC and STP”:\nWe have corrected “3” to “2” as you suggested, since we are referring only to\nthe PFC and STP regions (line 629).\nOnce again, we sincerely appreciate your careful review and thoughtful suggestions.\nWe believe these revisions enhance the clarity and quality of the manuscript. If there\nare any further concerns, please feel free to let us know.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nResponse:\nWe would like to sincerely thank you for your thoughtful review and for your valuable\ninput on our manuscript. We greatly appreciate the effort and time you dedicated to\nproviding constructive feedback. It is encouraging to know that this review is part of\nthe Nature Communications initiative to support Early Career Researchers, and we are\ngrateful for the opportunity to benefit from such a collaborative process. As part of the\nrevisions, we have carefully addressed the feedback provided by both reviewers, as well\nas the additional comments from the editorial team. We believe the manuscript has been\nstrengthened as a result of these revisions. Once again, thank you for your invaluable\ncontribution to improving the quality of our work.\nReviewer #3 (Remarks to the Author):\nThe authors have addressed my comments and concerns. I have no further questions or\ncomments for the authors.\nResponse:\nWe would like to express our sincere gratitude for your thoughtful and constructive\nfeedback. We are pleased to hear that our responses to your previous comments have\naddressed your concerns, and we appreciate your time and effort in reviewing our\nmanuscript. As suggested, we have carefully incorporated all necessary revisions in line\nwith the feedback from both the reviewers and the editorial team. These revisions have\nhelped improve the clarity and quality of our manuscript, and we are grateful for your\nsupport in this process. Thank you once again for your valuable contribution to this\nwork."}]

Decision letter