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PubMed2026

Altered ECM deposition and cell adhesion signaling in a human cortical organoid model of fragile X syndrome.

Fragile X Syndrome (FXS) is the most common inherited intellectual disability, and the most common monogenic cause of autism spectrum disorder (ASD). It is caused by epigenetic silencing of the FMR1 gene leading to the loss of FMRP, an RNA-binding protein that regulates local mRNA translation in neuronal dendrites, crucial for synapse development. Three-dimensional (3D) brain organoid models derived through in vitro differentiation of pluripotent stem cells offer a powerful tool to dissect the underlying mechanisms of neurodevelopmental disorders. Here, we generated human FXS and control organoids using isogenic human embryonic stem cell clones with and without the FXS mutation. Our results show that mature FXS cortical brain organoids can be derived by inhibiting the TGFβ and Wnt pathways. Moreover, expression analyses including immunofluorescence, qRT-PCR, proteomics and western blotting reveal altered levels of neuronal markers and ECM deposition along with modulated downstream signaling molecules. Interestingly, in silico analysis of proteomics revealed several altered pathways, such as cell adhesion, regulation of neurogenesis and cell cycle that are implicated in FXS. Collectively, our unique FXS-organoids derived from isogenic hESC lines may serve as a model for studying the pathology of FXS disorder and for developing therapeutical intervention.

Humans↗

PubMed2023

Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis.

Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked the capacity to accurately annotate cells, particularly in complex datasets. Here we present SIMS (Scalable, Interpretable Machine Learning for Single-Cell), an end-to-end data-efficient machine learning pipeline for discrete classification of single-cell data that can be applied to new datasets with minimal coding. We benchmarked SIMS against common single-cell label transfer tools and demonstrated that it performs as well or better than state of the art algorithms. We then use SIMS to classify cells in one of the most complex tissues: the brain. We show that SIMS classifies cells of the adult cerebral cortex and hippocampus at a remarkably high accuracy. This accuracy is maintained in trans-sample label transfers of the adult human cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal subtypes, even in periods of fate refinement, shedding light on genetic changes affecting specific cell types across development. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities and unveil genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. When cell types are obscured by stress signals, label transfer from primary tissue improves the accuracy of cortical organoid annotations, serving as a reliable ground truth. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

Brain organoids↗

PubMed2026

Metabolic atlas of early human cortex reveals glycolytic remodeling and pentose phosphate pathway control of cell fate transitions.

Cortical development involves rapid progenitor expansion and cell diversification supported by tightly regulated metabolic programs, yet these programs remain largely uncharacterized in human development. Here, we generated a metabolic atlas of the early human cortex using primary tissue and stem cell-derived cortical organoids. We observed dynamic changes in core metabolic functions, including an unexpected increase in glycolysis and pentose phosphate pathway (PPP) activity during late neurogenesis. Manipulation of glucose availability in cortical organoids altered cell-type composition, increasing outer radial glia (oRG) and inhibitory neuron populations. Pharmacological and genetic inhibition of PPP enzymes recapitulated these cell fate changes. Ribose was sufficient to rescue radial glia (RG) gene expression changes, revert organoid cell-type composition, and restore levels of ATP and hypotaurine. These data identify a critical role for the PPP in modulating RG cell fate specification and generate a resource for future exploration of additional metabolic pathways in human cortical development.

cell fate↗

PubMed2024

SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

Single-Cell Analysis↗

PubMed2026

Cross-tissue immune profiling of APOE ε4 reveals early dysregulation in Alzheimer's disease.

INTRODUCTION: Apolipoprotein E (APOE) ε4 is the strongest genetic risk factor for late-onset Alzheimer's disease (AD), but its contribution to disease pathogenesis remains incompletely understood. METHODS: Here, we integrate proteomic profiling of plasma (n = 9028), cerebrospinal fluid (n = 1099), dorsolateral prefrontal cortex (n = 720), and superior temporal gyrus (n = 105) to define the immune phenotype associated with APOE ε4. RESULTS: We identify a conserved, allele dose-dependent pro-inflammatory immune protein signature across peripheral and central tissues independent of AD diagnosis. This signature also emerges in patient-derived cortical organoids prior to amyloid beta and tau pathology, supporting a genotype-driven mechanism. Cross-tissue comparisons reveal shared innate and antiviral responses alongside tissue-specific immune signaling. Notably, a 12-week medical ketogenic diet partially reversed the APOE ε4 immune signature. DISCUSSION: These findings position immune dysregulation as an early and tractable driver of AD risk in APOE ε4 carriers with direct implications for targeted prevention strategies.

Humans↗

PubMed2025

Single-cell analysis of dup15q syndrome reveals developmental and postnatal molecular changes in autism.

Duplication 15q (dup15q) syndrome is a leading genetic cause of autism spectrum disorder, offering a key model for studying autism-related mechanisms. Using single-cell and single-nucleus RNA sequencing of cortical organoids from dup15q patient-derived iPSCs and post-mortem brain samples, we identify increased glycolysis, disrupted layer-specific marker expression, and aberrant morphology in deep-layer neurons during fetal-stage organoid development. In adolescent-adult postmortem brains, upper-layer neurons exhibit heightened transcriptional burden related to synaptic signaling, a pattern shared with idiopathic autism. Using spatial transcriptomics, we confirm these cell-type-specific disruptions in brain tissue. By gene co-expression network analysis, we reveal disease-associated modules that are well preserved between postmortem and organoid samples, suggesting metabolic dysregulation that may lead to altered neuron projection, synaptic dysfunction, and neuron hyperexcitability in dup15q syndrome.

Humans↗

PubMed2026

Thalamic NRXN1-mediated input to human cortical progenitors drives excitatory neurogenesis.

The human cerebral cortex develops through coordinated signals from within the cortex and from other brain regions, including the thalamus. However, how thalamic neuronal projections influence early human cortical development remains less well-understood. In this study, we fused cortical and thalamic organoids to investigate how thalamic input shapes the maturation of human cortical cells. Using single-nuclei RNA-sequencing and cellular imaging, we found that thalamic input increases the production of cortical excitatory neurons. We identify neurexin-1 (NRXN1) as a mediator of physical contact between thalamic axons and cortical outer radial glia. Genetic knockout of thalamic NRXN1 reduced these contacts and attenuated the production of upper-layer excitatory neurons. These findings reveal a mechanism by which thalamic input regulates human cortical progenitors and shapes excitatory neuron production during development.

Animals↗

PubMed2025

A mouse organoid platform for modeling cerebral cortex development and cis-regulatory evolution in vitro.

Natural selection has shaped the gene regulatory networks that orchestrate cortical development, leading to structural and functional variation across mammals, but the molecular and cellular mechanisms underpinning these changes have only begun to be characterized. Here, we develop a reproducible protocol for cerebral cortex organoid generation from mouse epiblast stem cells (EpiSCs), which recapitulates the timing and cellular differentiation programs of the embryonic cortex. We generated cortical organoids from F1 hybrid EpiSCs derived from crosses between laboratory mice (C57BL/6J) and four wild-derived inbred strains spanning ∼1 M years of evolutionary divergence to comprehensively map cis-acting transcriptional regulatory variation across developing cortical cell types, using single-cell RNA sequencing (scRNA-seq). We identify hundreds of genes that exhibit dynamic allelic imbalances, providing the first insight into the developmental mechanisms underpinning changes in cortical structure and function between subspecies. These experimental methods and cellular resources represent a powerful platform for investigating gene regulation in the developing cerebral cortex.

Organoids↗

PubMed2026

CRISPR-Enabled functional genomics in hPSCs-derived neural models for autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a genetically heterogeneous neurodevelopmental condition in which hundreds of individually rare risk variants converge on a small number of shared biological pathways, including synaptic scaffolding, chromatin remodeling, excitation-inhibition balance, and cellular energy metabolism. Translating this genetic heterogeneity into mechanistic insight requires experimental systems capable of interrogating individual gene functions in human-relevant neural contexts at scale. CRISPR-enabled functional genomics in human pluripotent stem cell (hPSC)-derived neural models, spanning neural progenitors, cortical and inhibitory neurons, astrocytes, microglia, and brain organoids, provides precisely this capability. By integrating pooled perturbation screens with multimodal readouts including single-cell and spatial transcriptomics, chromatin accessibility profiling, proximity labeling proteomics, multi-electrode array electrophysiology, and metabolic flux analysis, these platforms enable systematic, causal mapping of ASD gene function at system resolution. Early applications have already revealed convergent mechanisms: BAF complex disruption expands the ventral progenitor pool and biases its fate toward oligodendrocyte and interneuron lineages; ADNP loss impairs microglial synaptic pruning through altered endocytic trafficking; and mTOR pathway dysregulation in PTEN- and TSC2-perturbed models links genetic risk directly to metabolic and mitochondrial dysfunction. Computational frameworks including MIMOSCA and SCEPTRE enable causal network reconstruction and pseudotime inference from these datasets, moving the field from gene lists toward pathway-level models of ASD pathobiology. Translational applications leverage isogenic iPSC panels and variant-level base and prime editing to stratify ASD variants by functional impact, informing gene therapy design for haploinsufficient targets such as CHD8 and SCN2A via AAV or antisense oligonucleotide delivery. Remaining challenges, including model developmental immaturity, batch variability, and the difficulty of modeling polygenic risk, are addressed by a roadmap integrating spatial perturbomics, AI-driven causal inference, and population-scale standardized biobanks. This review synthesizes the current state of CRISPR-based functional genomics in human stem cell neural models as a coherent experimental framework for converting ASD genetic associations into mechanistic understanding and therapeutic opportunity.

Humans↗