Biology subjects

Rohani, N.

Publications and source records attributed to Rohani, N..

9 recordsLinked to original sources

bioRxiv2026

Spatial transcriptomics implicates the thalamus and cortex in autism and schizophrenia

The past decade has seen tremendous progress in the identification of genes associated with complex neuropsychiatric disorders, including autism spectrum disorder (ASD) and schizophrenia. Expression patterns of these genes in single cell data strongly implicate excitatory and inhibitory neurons; however, there are limited data on the brain regions involved - a critical question for neurobiology. Spatial transcriptomics provide an opportunity to perform systematic multiregional analyses to provide insights into this question. Here, we have generated a spatial transcriptomics dataset encompassing the diverse anatomical territories of the adult mouse brain sagittal midsection. We compare neuropsychiatric gene enrichment by applying Gene Fraction Enrichment Score (GFES), a novel statistic method that controls for differing neuronal proportions across regions. ASD-associated genes identified by exome sequencing were most enriched in the thalamus followed by the cortex. Schizophrenia genes from genome-wide association studies were also enriched in the thalamus, along with the hippocampus and cortex. These findings add to the evidence that the thalamus plays a major role in neuropsychiatric disorders whilst supporting roles for the cortex and hippocampus. The results highlight shared and distinct patterns for pleiotropic brain disorders that could elucidate common underlying mechanisms and circuitry.

neuroscience↗

bioRxiv2026

Spatiotemporal analysis of autism gene enrichment implicates cortex, thalamus, and hypothalamus

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder. Sequencing analyses have identified 185 ASD-associated genes, which implicate neurons, but the specific brain regions through which these neurons influence neurodevelopment remain unclear. Here, we integrate over one million single-cell RNA sequencing profiles from 20 regions of the developing human brain (4-23 post-conceptual weeks) using a new framework, STARMAPS (Sparse Task-specific Analysis for Revealing Molecular Associations in Particular Single-cell datasets). STARMAPS accounts for coordinated regional and developmental perturbations in gene expression, enabling robust cross-region comparison. We replicate prior findings that ASD-associated genes are enriched in excitatory neurons during mid-fetal development, and we extend these results to reveal distinct spatial signatures. Across 26 excitatory neuron subtypes, six clusters showed significant enrichment for ASD-associated genes. These clusters localize to both cortical and subcortical regions, including the motor, temporal, and visual cortex, as well as the thalamus and hypothalamus. Our findings support a major role for excitatory neurons across distributed brain circuits, implicating previously underappreciated subcortical structures in ASD etiology. By providing a statistically rigorous framework for spatiotemporal integration of single-cell data, STARMAPS enables refined mapping of molecular vulnerability across the developing human brain.

genomics↗

bioRxiv2026

Molecular dynamics of Brodmann Area 22 in development and autism

Challenges in verbal communication are a prominent feature of autism. However, gene regulatory programs in speech-related cortical regions remain poorly characterized. In parallel, it remains unclear whether the heterogeneous genetic factors underlying autism converge on shared neurobiological mechanisms. To address these gaps, we generated paired transcriptomic and epigenomic data from post-mortem human brain tissue across 100 donors. Here, we show that transcriptional differences in the speech-related Brodmann Area 22 in individuals with neurodevelopmental conditions, including autism, are strongest among those with a known genetic diagnosis. A similar but attenuated signature is observed in those without a genetic diagnosis. These transcriptional differences are most pronounced in neurons, with glutamatergic L4/5 intratelencephalic neurons affected across multiple modalities. Finally, multimodal analysis implicates altered RFX3-dependent networks as a central hub in autism, particularly among L4/5 intratelencephalic neurons in non-verbal individuals. Together, our study identifies regulatory architecture linking chromatin state, transcriptional output, and variation in verbal ability in autism.

neuroscience↗

bioRxiv2026

Genome-scale functional mapping of the mammalian whole brain with in vivo Perturb-seq

Functional genomics studies have provided critical insights into cell type-specific gene regulatory programs, but to date most have been conducted in wild-type tissues or cell cultures. Here, we present a gene expression functional atlas across the mouse brain. We use an enhanced in vivo Perturb-seq platform to analyze transcriptome-wide responses to loss of 1,947 disease-associated genes, profiling over 7.7 million cells spanning major brain regions and neuronal populations. We find striking cell-type-specific essentiality and transcriptional programs and show that closely related disease genes such as two NMDA receptor subunits can drive opposing transcriptional programs. Together, this work reveals insights into the genetics and mechanisms of neurodevelopmental, psychiatric, and neurodegenerative diseases in vivo, paving the way for the design of future genetic medicine.

genomics↗

bioRxiv2025

A Retinoic Acid Autoregulatory Loop Governing Prefrontal-Motor Arealization

The frontal lobe comprises the prefrontal association cortex (PFC), which supports complex cognition and goal-directed behavior, and the motor cortex (MC), which executes movement. A hallmark of primate brain evolution is PFC expansion accompanied by a posterior displacement of the MC. Retinoic acid (RA) signaling has emerged as a key regulator of PFC specification and expansion. However, the mechanisms that spatially confine RA signaling within the developing PFC, and the downstream RA-responsive gene networks, remain poorly understood. Here we defined an RA-associated gene regulatory network (RA-GRN) in the developing human PFC and identified MEIS2, which encodes a transcription factor linked to intellectual disability and autism spectrum disorder (ASD), as its key hub of this network. Conditional deletion of Meis2 in postmitotic cortical excitatory neurons in mice results in a partial respecification of prospective prefrontal association territories toward motor-like molecular and connectional features, highlighting a critical role of postmitotic neurons in establishing and maintaining cortical areal identities. Concomitant with Meis2 loss, the population of excitatory neurons expressing the RA-synthesizing enzyme ALDH1A3, and consequently RA signaling itself, is markedly reduced in the developing medial prefrontal cortex (mPFC). These findings revealed a conserved autoregulatory loop: RA [->] MEIS2 [->] ALDH1A3 [->] RA that reinforces a PFC-enriched RA gradient and organizes the MC-PFC axis. Together, our findings reveal a postmitotic mechanism by which specific features of neuronal identity reinforce RA signaling to define key features of prefrontal and motor cortical territories, linking a classic morphogen to transcriptional identity, neural circuit formation and function, and potentially to psychiatric disorders.

neuroscience↗

bioRxiv2025

Modulating splicing in five prime untranslated regions to treat rare haploinsufficient disease

Rare genetic disorders collectively impact over 300 million people worldwide, yet around 95% have no specific treatments. For the many rare disorders caused by haploinsufficiency, effective therapies need to upregulate protein expression. However, therapeutic upregulation is often not straightforward. Increasing protein translation of the wildtype allele through inhibiting repressive upstream open reading frames (uORFs) has been proposed as a therapeutic approach for a few specific genes. The widespread success of steric-block antisense oligonucleotides (ASOs) for this purpose is, however, debated. Here, we explore an alternative approach, using splice-switching to exclude uORF-containing exons from the mRNA. Through a genome-wide computational screening approach, we identified 79 uORF-containing 5UTR exons in haploinsufficient monogenic disease genes as candidate exon skipping targets. We demonstrate that removing the target exon significantly increased protein translation (between 1.4-5.5 fold) in a luciferase reporter assay for four of six prioritised target 5UTR exons in neurodevelopmental disorder genes (CTCF, GRIN2B, KRIT1, and TSC1). Overall, this work supports the widespread application of 5UTR exon skipping to boost translation of clinically relevant haploinsufficient genes.

genomics↗

bioRxiv2025

Disruption of Cell-Type-Specific Molecular Programs of Medium Spiny Neurons in Autism

Autism spectrum disorders (ASD) are highly heritable neurodevelopmental conditions with major contributions from rare genetic variants. Most studies have focused on cortical mechanisms; even growing evidence implicates subcortical circuits in ASD etiology. To systematically map developmental and molecular alterations beyond the cortex, we profiled lineage relationships across five brain regions in an ASD mouse model. Most prominent changes emerged in the striatum, a hub for learning and motor control. Furthermore, we performed single-nucleus multiomic profiling of human putamen from ASD and neurotypical donors revealed cell-type-specific transcriptomic and regulatory alterations. Differential expression converged on synaptic and energy metabolic dysfunctions in D1 striosome medium spiny neurons (MSNs), coupled with astrocytic remodeling of synaptic support. Gene regulatory network analysis identified EGR3 and EGR1 as key transcriptional regulators of ASD-associated programs of D1 MSNs. Together, these results establish the striatum as a central node of ASD convergence and provide a multiomic resource for dissecting its subcortical mechanisms.

neuroscience↗

bioRxiv2024

Insights into Health Data Science Education: A Qualitative Content Analysis

BackgroundEarly career researchers in Health Data Science (HDS) struggle to effectively manage their learning process due to the novel and interdisciplinary nature of this field. To date, there is limited understanding about learning strategies in health data science. Therefore, we aim to uncover learning strategies that early career researchers employ to address their educational challenges, as well as shed light on their preferences regarding HDS teaching approach and course design. MethodIn this study, we conducted a qualitative content analysis through semistructured interviews with ten early career researchers, including individuals pursuing masters, PhD, and postdoctoral research programmes in HDS, across two higher education institutions in the United Kingdom. Interviews were carried out in person from June 2023 to August 2023. Data were analysed qualitatively using NVivo software. Descriptive statistics were employed for quantitative analysis. ResultsRegarding learning strategies, we identified ten main categories with 22 codes, including collaboration, information seeking, active learning, focus granularity, elaboration, organisation, order granularity, goal orientation, reviewing, and deep learning strategies. Regarding course design and teaching, we discovered four categories with 14 codes, including course materials, duration and complexity, online discussion, and teaching approaches. ConclusionsEarly career researchers used a range of learning strategies aligned with well-established learning theories, such as peer learning, information seeking, and active learning. It is also evident that learners in HDS favour interactive courses that provide them with hands-on experience and interactive discussion. The insights derived from our findings can enhance the quality of education in HDS.

scientific communication and education↗

bioRxiv2024

Providing Insights into Health Data Science Education through Artificial Intelligence

BackgroundHealth Data Science (HDS) is a novel interdisciplinary field that integrates biological, clinical, and computational sciences with the aim of analysing clinical and biological data through the utilisation of computational methods. Training healthcare specialists who are knowledgeable in both health and data sciences is highly required, important, and challenging. Therefore, it is essential to analyse students learning experiences through artificial intelligence techniques in order to provide both teachers and learners with insights about effective learning strategies and to improve existing HDS course designs. MethodsWe applied artificial intelligence methods to uncover learning tactics and strategies employed by students in an HDS massive open online course with over 3,000 students enrolled. We also used statistical tests to explore students engagement with different resources (such as reading materials and lecture videos) and their level of engagement with various HDS topics. ResultsWe found that students in HDS employed four learning tactics, such as actively connecting new information to their prior knowledge, taking assessments and practising programming to evaluate their understanding, collaborating with their classmates, and repeating information to memorise. Based on the employed tactics, we also found three types of learning strategies, including low engagement (Surface learners), moderate engagement (Strategic learners), and high engagement (Deep learners), which are in line with well-known educational theories. The results indicate that successful students allocate more time to practical topics, such as projects and discussions, make connections among concepts, and employ peer learning. ConclusionsWe applied artificial intelligence techniques to provide new insights into HDS education. Based on the findings, we provide pedagogical suggestions not only for course designers but also for teachers and learners that have the potential to improve the learning experience of HDS students.

scientific communication and education↗