Unlocking disease associations during prefrontal cortex development with scRNAseq

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Chen, Timo Lassmann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4948061/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The brain consists of tissue comprising billions of neurons intricately connected through trillions of synapses. Throughout human development, from conception to adulthood, cells in the brain undergo significant changes, assembling functional circuitry over time orchestrated by finely tuned programs of gene expression. Understanding the spatiotemporal signalling that governs brain development and the impact of gene mutations on developmental programs remains a significant challenge. Here we identify the role of genetic variants in brain development to advance the development of therapeutic options. Results This computational study leverages developmental prefrontal cortex single-cell transcriptomic data to associate specific cell types with disease states. Using un-supervised techniques, we identify temporal gene expression patterns and gene co-expression networks enriched for genes associated with neurological disorders. By systematically applying these approaches we identify key cell types and developmental stages associated with disease emergence and progression Conclusions Our approach identifies important cell types and developmental stages relevant to diseases directly from single cell data. By pinpointing cells, genes, and their neighbourhoods, our approach has the potential to contribute to the discovery of new targets and treatment avenues. We hope our work will advance the management of brain-related disorders. single-cell RNAseq prefrontal cortex brain development disease associations Figures Figure 1 Figure 2 Figure 3 Background The brain is a complex organ comprised of functional layers, containing a vast range of different cell types and cellular connections ( 1 ). There are several distinct stages of brain development in humans, from conception through childhood to adulthood. Neuronal cells of the fetal brain are different to adult neuronal cells ( 2 , 3 ). Development of the brain is controlled by highly precise and complex spatiotemporal signalling ( 4 ). Predicting the impact of gene variants on brain development and downstream phenotype is challenging. To date, we have a broad understanding of the cellular composition of the brain, how the early environment shapes development and what role aberrant gene expression plays within the brain ( 5 – 11 ). However, translating this knowledge into therapeutic strategies remains a significant challenge. Brain-related disorders are a heterogeneous group of disorders affecting many aspects of life, and can be grouped into three broad categories; neurological, psychiatric, and neurodevelopmental disorders ( 12 ). We will briefly discuss these three disorders in turn below. Neurological disorders Neurological disorders encompass any disorder effecting the structure and function of the central nervous system (CNS) or peripheral nervous system (PNS);( 13 ). Due to the involvement of the nervous system in the majority of bodily functions, neurological disease symptoms vary greatly. Symptoms include pain, muscle malfunction, changes in sensation, senses, consciousness and cognition, and sleep-related problems. These disorders can have a variety of origins including infection, environmental influences, autoimmune responses, degenerative processes, and genetic factors ( 14 , 15 ). Psychiatric disorders Psychiatric disorders are one of the main causes of morbidity and mortality worldwide and represent a huge burden on both individuals and society ( 16 , 17 ). These disorders are complex, and their etiology and treatment remains enigmatic. The American Psychiatric Association defines psychiatric disorders as being disorders with clinically significant disturbances in mood, thinking and behaviour, and are often associated with distress and or disability in social, occupational and other activities ( 18 ). Diagnosis of psychiatric disorders remains primarily based on signs and symptoms, and there is a distinct lack of predictive biomarkers. This can make diagnosis difficult, given the high heterogeneity and comorbidity of disorders and shared symptoms across different disorders ( 19 , 20 ). This results in a high variability in diagnosis, in turn leading to variable treatment options, at times to the detriment of patient outcomes due to misdiagnosis and the use of inappropriate treatment modalities ( 21 , 22 ). Further, there is a need for better therapeutic options- there have been limited advances in psychiatric pharmacology in the past decade ( 23 ). This has informed the need for a better understanding of the biological mechanisms underpinning psychiatric disorders. All major psychiatric disorders have high heritability ( 24 ), and as such a large proportion of the risk of developing a psychiatric disorder is attributable to genetic factors. Heritability varies between disorders, from 30 to 80% in sibling studies ( 25 , 26 ). However, the penetrance of these genetic factors is highly variable ( 27 , 28 ). In addition, genome-wide association studies (GWAS) have identified common variants associated with psychiatric disorders. Individually these variants have a small effect and are often associated with multiple conditions ( 29 – 32 ). Neurodevelopmental disorders Neurodevelopmental disorders are a subset of psychiatric disorders which result in impairments in cognition, communication, behaviour and/or motor skills resulting from abnormal development. This includes disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, intellectual disability and schizophrenia ( 33 , 34 ). As with other psychiatric disorders, such as major depressive disorder, anxiety disorders, and personality disorders, neurodevelopmental disorders share a high degree of phenotypic and genetic overlap ( 35 – 39 ). Further, there is significant symptomatic overlap among neurological disorders, as well as psychiatric and neurodevelopmental disorders ( 30 , 40 – 45 ). Biomarkers and causative genetic mutations are well established for some conditions ( 46 – 48 ), and in some cases specific cell types have been implicated ( 49 , 50 ). However, better knowledge of the etiology of disorders is needed before translation to therapeutic options is fully realised ( 51 ).Consequently, the Psychiatric Genomics Consortium highlighted the need for high-quality functional data including single-cell transcriptomic profiles across development and brain tissues ( 31 ). The recent emergence of single-cell and single-nucleus RNA sequencing (scRNAseq, snRNAseq) technologies has enabled the interrogation of organs and tissues at the level of a single cell ( 52 , 53 ). This has allowed for the identification of rare cell populations and the characterization of cellular states and their dynamics. The Human Cell Atlas project has spearheaded the effort to create comprehensive reference maps of all human cells using scRNAseq, with the aim to provide insights into health and disease ( 54 ). Concurrently, researchers have mapped the development of cell types over time. As such, there is now a wealth of data in large-scale single cell brain developmental atlases available ( 55 , 56 ). We integrated existing computational approaches into a workflow allowing us to systematically analyse a developmental prefrontal cortex snRNAseq atlas for information relevant to diseases. Gene lists were curated and tailored to specific disorder subtypes. Concurrently, we associated specific cell types in the prefrontal cortex with disease states by identifying key temporal gene expression patterns and gene co-expression networks. Additionally, our analysis pinpoints where and when genes associated with major disorders are most likely to have the biggest impact on phenotype. This integrated approach not only aids in identifying developmental stages critical to disease manifestation but also highlights potential additional targets for pharmaceutical interventions, paving the way for targeted therapies in brain disorder management. Results Curation of disorder-type specific gene lists We retrieved variants associated with neurological, psychiatric, and neurodevelopmental disorders from the NHGRI-EBI GWAS and ClinVar databases. Intronic and exonic variants were used to create disorder-specific gene lists. In total, 2680 unique genes were found to be associated with disorders of the brain and nervous system. 1390 genes related to neurodevelopmental disorders (i.e., intellectual disability, autism spectrum disorders), 593 to neurological disorders (i.e. Alzheimer’s disease, cognitive decline, epilepsy), and 998 genes related to psychiatric disorders (i.e. major depressive disorder, bipolar disorder, anxiety). There was limited overlap between genes derived from GWAS and ClinVar (Supplementary Table 1). For neurodevelopmental genes, 19 genes were common to both GWAS and ClinVar disease gene lists. Similarly, only 9 genes were present in both neurological gene lists, and there was no overlap between the psychiatric disorder gene lists. This indicates that the ClinVar and NHGRI-EBI GWAS databases capture different sets of genes associated with these conditions, highlighting the importance of integrating multiple data sources for a comprehensive understanding. Enriched terms in curated disorder-type specific gene lists align with disorder gene list type To assess whether our curated disorder-type specific gene lists align with known disorders we performed standard enrichment analysis for HPO and DisGeNET terms. Enrichments confirmed over-representation of matching disease terms as expected. For example, the HPO term “delayed speech and language development” was enriched in the neurodevelopmental disorder list, while terms such as “abnormal nervous system electrophysiology” and “mental deterioration” were enriched in the neurological disorder list (Fig. 1 A, B, Supplementary Table 2, 3, 4). Overall, our curated gene lists were enriched for matching disease and phenotypic terms. Interestingly, several disease terms relating to facial abnormalities were found, particularly in the neurodevelopmental disease gene lists. Many disorders involving neurodevelopmental phenotypes also have symptoms involving facial abnormalities, and this has been a clinical research focus in recent years, given the fact that craniofacial and nervous system development are linked ( 62 , 63 ). Altogether, these results show that integrating data from ClinVar and the EMBL-GWAS database results in gene lists enriched for known disease signatures. Although the GWAS and ClinVar disorder-type specific gene lists show limited overlap to one another, each contains key genes associated with their associated disorder and are there therefore complementary. We combined the two gene lists associated with each disorder to capture as many known disease genes as possible, creating a unified, disorder-specific gene list for subsequent analysis. To further investigate the properties of our curated gene lists, we conducted a complementary analysis. We took the genes associated with the significantly enriched terms found in Fig. 1 A and 1 B and compared their similarity to the three curated gene lists using Jaccard indices. Unsurprisingly, we observe that terms found to have a significant enrichment have correspondingly high Jaccard index in the relevant list (Supplementary Table 5). Based on this simple similarity measure, we observe substantial overlaps between the representation of HPO terms between the neurological and neurodevelopmental gene list. For example, genes associated with abnormal communication, a term significantly enriched in the neurodevelopmental list are also present in the neurological list. On the contrary, terms enriched in the neurological list are more specific and have lower similarity to the other two gene lists (Fig. 1 C). This pattern is repeated in DisGeNET terms, although there are terms including “Intelligence” and “Ataxia” that are more specific to one gene list (Fig. 1 D). Our list of genes associated with psychiatric disorders is enriched for fewer HPO and in DisGeNET terms with their underlying genes being under-represented in the other two custom gene lists. This analysis highlights the potential value of our curated gene lists in uncovering previously unrecognized connections between disease genes from ClinVar and GWAS studies and related disease categories. The significant overlaps also suggest that these genes may play roles in multiple related diseases, and additional work is required to disentangle the complex gene – phenotype relationships. For our present study, we will utilize our curated gene lists, along with the HPO and DisGeNET terms, to test for potential disease associations in the subsequent analysis. a HPO phenotypic enrichments across SNV and GWAS gene lists for neurological, neurodevelopmental, and psychiatric disorders, demonstrating distinct enrichment patterns by disease category. b Comparison of DisGeNET phenotypic enrichments in SNV and GWAS gene lists, highlighting significant overlaps in enrichment patterns across neurological and neurodevelopmental disorders. c Jaccard scores of HPO terms significantly enriched in curated disorder-type specific gene lists, with terms subset according to the curated gene list they are enriched in. d Jaccard scores of DisGeNET terms significantly enriched in curated disorder-type specific gene lists, with terms subset according to the curated gene list they are enriched in. Contrasting our curated gene lists to gene lists in HPO and DisGeNET Given our curated gene lists contain more and different sets of genes, we were curious about potential overlaps with known disease gene lists. Genes unique to our lists but reported alongside known disease genes could potentially represent additional disease genes. A significant overlap between the two types of gene lists could allow us to identify such genes. There are notable overlaps between our gene lists and known disease genes in HPO and DisGeNET, including 499 genes from the psychiatric gene list being present in the DisGeNET gene list. 263, 151 and 28 genes are unique to the curated neurodevelopmental, psychiatric, and neurological gene lists respectively, with a total of 456 genes not being present in the HPO and DisGeNET gene lists (Supplementary Fig. 1). These findings suggest that our curated gene lists may harbor novel disease-associated genes warranting further investigation. Temporal clusters are enriched for disease terms We generated clusters of genes sharing common gene expression patterns over time within each of the 18 major cell types as defined by Herring et al. (for principal neurons; PN-dev, L2-3-CUX2, L4-RORB, L5-6-THEMIS and L5-6-TLE4, for inhibitory neurons; early developing MGE-dev, CGE-dev, ID2, VIP, SST, PV, PV_SCUBE3, other cell types; Astro, Oligo, OPCs, Micro, Vas);(Supplementary Table 6). For each cell type we generated 12 temporal clusters. Of the resulting 216 clusters, 7 contained less than 200 genes (Supplementary Table 7). To assess whether temporal clusters are made up of genes sharing a common function, we performed enrichment analysis using gene ontology terms. We identified 152 unique enriched terms across 17 cell types and 57 clusters, including 52 brain and nervous system specific terms (Supplementary Table 8). These results demonstrate that we can use an un-supervised approach to discover temporal clusters of functionally related genes within individual cell types. The enrichment of brain and nervous system-specific terms indicates that our temporal clusters capture gene expression patterns of potential importance to disease onset and progression. To test this, we performed overrepresentation analysis (ORA) using DisGeNET and HPO terms. We found 139 unique disease term enrichments, of which, 60 were related to the brain and nervous system (Fig. 2 A, Supplementary Table 9, 10). Further, we tested whether our manually curated disorder-type specific gene lists were over-represented in specific temporal gene clusters. Genes transiently upregulated during the neonatal stage in SST interneurons were associated with dystonia and parkinsonian disorders (Fig. 2 A, C). Additionally, this temporal gene cluster shows a higher Jaccard score for genes present in our manually curated neurological gene list compared to other clusters (Fig. 2 B). This points to early developmental disturbances having an important role in the emergence of neurological disorders in adulthood. Further, the DisGeNET terms “progressive supranuclear palsy” and “Drug dependence” are enriched in genes upregulated through development in SST interneurons (Fig. 2 A, C). In this cluster of temporally expressed genes, the Jaccard score for genes belonging to the curated psychiatric disease gene list is high relative to other SST correlation clusters (Fig. 2 B). This suggests that the upregulation of these genes may play a crucial role in the onset and progression of psychiatric disorders, particularly those relating to substance abuse. Further, these genes are of interest for studies relating to neurodegenerative disorders, such as progressive supranuclear palsy, which is currently poorly understood. Temporal clusters in specific cell types also align with the observed HPO enrichments. For example, the HPO term “Microcephaly” is enriched in genes upregulated in the fetal stage followed by a decline and subsequent plateau in remaining developmental stages for ID2 inhibitory interneurons (Fig. 2 D). Microcephaly is an early developmental condition wherein an infant’s head circumference is more than 2 standard deviations below the mean for their age and sex ( 64 – 67 ). This is typically indicative of an infant’s brain not developing properly during pregnancy ( 68 , 69 ). As such, the temporal expression patterns align well with enriched disease terms and the stages expected to be important in disease. A further example of interest is temporal cluster 11 in PN-dev neurons. This cluster is transiently upregulated during the childhood developmental stage and shows enrichment for ten neurodevelopmental-associated HPO terms (Fig. 2 D, F, Supplementary Table 10). In this cluster, the curated neurodevelopmental disease gene list has the highest Jaccard score among curated gene list (Fig. 2 E). Given neurodevelopmental disorders typically manifest in childhood ( 18 ), this is to be expected. A similar pattern is seen in cluster six in PN-dev cells, where genes are transiently upregulated during childhood, and the curated neurodevelopmental disease gene list has a high Jaccard score relative to other temporal gene clusters (Fig. 2 C). a Enrichment of DisGeNET terms in temporal gene clusters in the prefrontal cortex identifies disease associations. b Comparison of Jaccard scores between temporal gene clusters in SST cells and curated disorder-type specific gene lists, and temporal gene clusters in SST cells and DisGeNET terms enriched in SST cells. The x-axis represents different temporal gene clusters in SST cells, while the y-axis shows the Jaccard scores, indicating the degree of similarity. Each point corresponds to a Jaccard score between a temporal gene cluster and either a curated disorder-type specific gene list or a DisGeNET term. Of particular note are temporal clusters six, seven and twelve. c Overview of disease-relevant temporal gene expression patterns in clusters showing significance to specific disease classifications. d Enrichment of HPO terms identifies disease-associated terms in temporal clusters in the prefrontal cortex e Comparison of Jaccard scores between temporal gene clusters in PN-dev cells and curated disorder-type specific gene lists, and temporal clusters in PN-dev cells and HPO terms enriched in PN-dev cells. The x-axis represents different temporal gene clusters in PN-dev cells, while the y-axis shows the Jaccard scores, indicating the degree of similarity. Each point corresponds to a Jaccard score between a cluster and either a curated disorder-type specific gene list or a HPO term. f Temporal gene expression patterns of clusters that appear important to specific disease classifications. Enrichments relating to structural abnormalities are associated with a pattern of gene expression by which genes are highly expressed in the fetal stage, followed by a decline in expression through to adulthood. Meanwhile, enrichments associated to neurodevelopmental disease terms are associated with a pattern of gene expression by which gene expression transiently increases during childhood before decreasing. In summary, we clustered genes based on their temporal gene expression patterns in each cell type. We found known disease term associated genes over-represented in these clusters. Our analysis demonstrates that our curated disorder-type specific gene lists and overrepresented disease terms match the expected temporal expression patterns of various disease phenotypes. Given the overrepresentation of known disease genes in our temporal clusters, we hypothesize that other genes in these clusters could also be linked to diseases, potentially providing an avenue to uncover new biological pathways or mechanisms critical to disease progression. These observations could contribute to the development of novel therapeutic targets. Moreover, their association with established disease genes suggests these genes might play as of yet unrecognized roles in disease etiology and severity, providing opportunities for early diagnosis or personalized treatment strategies respectively. Network derived gene modules provide further insight into disease associations Using hdWGCNA we performed weighted gene co-expression network analysis for each cell type independently to obtain gene modules. Mirroring the approach for the temporal clusters, we performed enrichment analysis on these modules. We derived between 3 and 17 modules for each cell type and identified 101 enriched disease terms, of which 40 were brain-specific (Supplementary Tables 11,12). Three gene modules were responsible for all brain-related HPO term enrichments: two modules in L2-3 CUX2 cells, and another one in VIP cells. One of the L2-3 CUX2 module appeared to be specific to neurological and morphological abnormalities, while the other L2-3 CUX2 module and VIP module were primarily psychiatric enrichment driven (Fig. 3 A). Gene modules in eight cell types (Astro, ID2, L2-3 CUX2, L4-RORB, L5-6 THEMIS, LAMP5 NOS1, PV SCUBE3 and VIP cells) were enriched for brain-related DisGeNET terms. Interestingly, ventriculomegaly appeared in both HPO and DisGeNET enrichments for the same L2-3 CUX2 module (Fig. 3 A, B). While enrichments between HPO and DisGeNET have followed similar “themes”, there has been limited overlap in the specific enrichments observed in DisGeNET and HPO terms thus far in the analysis. DisGeNET enrichments were varied, with one L5-6 THEMIS module producing the majority of the enrichments, including “Specific Learning Disability” and “Frontotemporal Lobar Degeneration”. By combining module eigengene values with sample metadata, we calculated the average module eigengene value for each developmental stage. A high value in a stage indicates that the co-expression module is highly active during that specific developmental stage, suggesting that the underlying genes are likely playing an important role. We examined how curated disorder-type specific disease gene list genes are represented in these modules and paired this with their activity at the different the developmental stages. We found that while Jaccard scores are relatively low, the stages contributing to the module reflect the known trajectory of presentation for these curated disease gene lists. For example, in the L2-3 CUX2 turquoise module, the highest scoring curated disorder-type specific gene list is the neurodevelopmental list. In this co-expression module, infancy and adolescence are the developmental stages contributing (Fig. 3 C, D). Interestingly, the majority of enrichments reaching significance arise from L2-3 CUX2 modules (Fig. 3 A). One co-expression module contains enrichments pertaining to neurological and morphological enrichments such as “abnormal PNS morphology”, “abnormal cerebral cortex morphology”, and “peripheral neuropathy”. Further, the only stage contributing to the co-expression module is infancy (Fig. 3 C). Given these disease terms relate to the structure of the CNS and PNS, it follows that an early developmental stage would be an important timepoint. In the second co-expression module, enrichments such as “depression”, “impairment in personality functioning”, and “dystonia” are present (Fig. 3 A). The Jaccard scores are similar for the curated neurological and psychiatric disease gene lists, and the stages contributing to the co-expression module are adolescence and adulthood (Fig. 3 C, D). Psychiatric disorders, particularly mood disorders such as depression typically have an onset during adolescence and adulthood, with a quarter of individuals having their first symptoms before the age of 17, and three quarters before 34 ( 70 ), aligning with adolescence and adulthood being the major stage contributors in the co-expression module. a Enrichment of HPO terms in gene co-expression modules b Enrichment of DisGeNET terms in gene co-expression modules c Jaccard scores in L2-3 CUX2 gene co-expression modules for enriched HPO and DisGeNET terms, and curated disorder-type specific gene lists. Scores emphasize the variable presence of curated disorder-type specific gene lists in co-expression modules . d Importance of developmental stages in gene co-expression modules for L2-3 CUX2 cells. In summary, while network derived gene modules overall do not show numerous associations to either our curated or HPO and DisGenNET gene lists, activity of the network module at particular stages is relevant. The difference in enrichments between the temporal clusters and network derived gene modules underscores the importance of interrogating single cell datasets with multiple methods to discover novel gene-disease associations. Presence of manually curated disease gene lists in co-expression modules and temporal expression clusters In the context of the developing brain, studying gene expression patterns can contribute to our understanding of the molecular mechanisms underlying brain functions, development, and disorders. We wanted to determine whether co-expression modules in which brain-related disease terms were enriched had a corresponding “signature” for a specific curated disease gene list. We found that co-expression modules enriched with significant brain-related disease terms did not consistently align with our curated disorder-type specific gene lists (Fig. 3 C). This is in contrast to the results using temporal clustering, in which an increase in the Jaccard score of a curated disorder-type specific gene list contributing to a temporal gene cluster corresponded directly with the type of disease term that was enriched (Fig. 2 B, E). This inconsistency in the relationship between enrichments and curated disorder-type specific disease gene lists in co-expression modules highlights a distinct difference between how disease terms and curated disorder-type specific gene lists are represented in clusters obtained by temporal clustering or network-based identification of gene modules. Discussion We sought to better elucidate the contribution of cell types to disorders of the brain using snRNAseq data from the prefrontal cortex during development. While Herring et al. completed a comprehensive analysis of their data, their analysis of disease associations within the brain was limited in scope- namely, for each cell type, four general gene trends are presented (up, down, transiently up, and transiently down). Further, only a selection of neurological and psychiatric disorders were examined in a subset of the major cell types using DisGeNET but not HPO. Our study builds on this foundation by incorporating all major cell types and systematically querying both DisGeNET and HPO databases, aiming to uncover more subtle disease associations. Furthermore, we tested associations between temporal genes and manually curated gene lists from ClinVar and NHGRI-EBI GWAS. We utilised both unsupervised temporal clustering and network derived gene modules to explore how known disease associations can be mapped onto single cell data. Given that both approaches take advantage of gene expression data over time, there was considerable overlap between temporal clusters and the gene network modules (Supplementary Table 13). When examining the genes driving significant DisGeNET enrichments in each cell types’ gene co-expression modules versus temporal clusters, there was overlap in three cell types: PV_SCUBE3, SST, and VIP cells (Supplementary Table 14). PV_SCUBE3 and VIP cells had one overlapping gene each: IL15 and CRYAB respectively. IL15 is an important cytokine in the brain, with its receptors playing significant roles in neuronal activity and synaptic plasticity, and modulating GABA and serotonin transmission ( 71 , 72 ). Additionally, IL15 has been found to have a role in alcohol dependence ( 73 ) and schizophrenia ( 74 ). CRYAB protein is a molecular chaperone that primarily binds misfolded proteins to prevent protein aggregation and is associated with processes such as cell apoptosis. CRYAB has a biological role in neurodegenerative diseases such as Parkinson’s and Huntington’s disease ( 75 , 76 ). SST interneurons had the largest overlap- 20 genes. Within this set of 20 genes, there were genes with associations to disorders including Alzheimer’s disease ( 77 ). Similarly, when comparing the genes driving significant HPO term enrichments in cell types for co-expression modules and temporal clusters, there was only overlap in a single cell type: ID2 cells (Supplementary Table 15), in which the overlapping genes were GTPBP2 and IFT140. GTPBP2 has been associated with neurodevelopmental disorder ( 78 ), while IFT140 is associated with cranioectodermal dysplasia ( 79 ). Overall, while there was limited overlap between temporal clusters and network modules, using both approaches resulted in meaningful disease term enrichments on their own, highlighting the importance of interrogating the single cell data in multiple analytical ways. Analysis of network derived gene modules yielded fewer clear disease associations compared to our analysis based on temporal clusters. Terms that were enriched overlapped with our curated gene lists and included terms such as "Delayed speech and language development" and "Abnormal repetitive mannerisms", "Microcephaly", "Micrognathia", and "Anteverted nares" are enriched in both the curated disorder-type specific gene list enrichments, and the gene co-expression module enrichments. One possible explanation for enrichments of facial abnormality-related terms is that HPO terms are designed to be useful for classifying a patient’s symptoms, and many terms may occur in a patient at once. It is possible that we are observing a co-morbidity between morphological and structural abnormalities and disorders of the brain. This is especially possible given that ClinVar was used in the curation of the curated disorder-type specific gene lists. ClinVar entries commonly include HPO terms. When looking at the disorder-type specific gene lists contributing to these common morphological disease term enrichments, the gene lists derived from ClinVar are responsible for all of the enrichments. This may be an avenue for further exploration in the diagnosis of neurodevelopmental disorders. Specifically, identification of craniofacial abnormalities can aid in early identification of neurodevelopmental disorders ( 80 , 81 ) and rare diseases ( 82 ). Several genes consistently appear in our analysis: ERCC1, KATNB1, HRAS, KRAS, SMC3, DCC and TNF (Supplementary Tables 16–19). HRAS and KRAS belong to the RAS gene family, which plays a major role in cell division, growth, and differentiation. Mutations in the RAS pathway are associated with various syndromes which include various facial abnormalities in their symptoms. DCC has been associated to eight psychiatric disorders ( 31 ), and TNF, a pro-inflammatory cytokine involved in immune regulation and inflammation, is associated with various brain-related disorders including Parkinson’s, Alzheimer’s, major depressive disorder, and schizophrenia ( 83 , 84 ). Further, ERCC1 deficiency has been associated with cerebro-oculo-facio-skeletal syndrome ( 85 ), and SMC3 has been associated with Cornelia De Lange Syndrome ( 86 ). While brain specific DisGeNET enrichments in temporal clusters were limited, SST cells exhibited a wide range of enrichments for disorders such as major affective disorder, generalized anxiety disorder, and dystonia. The substantial enrichments observed in SST interneurons underscore the importance of SST and SST-expressing cells in psychiatric and neurological disorders ( 87 – 89 ). The association of these cells to conditions such as major affective disorders and dystonia suggests a fundamental role in the modulation of emotional and motor functions. SST interneurons are pivotal in maintaining the balance between excitation and inhibition in the brain, a balance essential for normal physiological function ( 90 , 91 ). Given the neurophysiological roles of SST cells, which include regulating synaptic activity and maintaining inhibitory neurotransmission ( 92 ), our findings point to critical developmental stages where these cells may exert significant influence on disease pathology. There is limited literature currently available relating specific cell types and time points to specific disease terms. However, many disease enrichments align with what is already known from animal models. For example, the DisGeNET term “substance withdrawal syndrome” is enriched in astrocytes (Fig. 3 B). It has been found that astrocytes are important in alcohol withdrawal in mice ( 93 ). This underscores the potential role of astrocytes not only in the neuropathology of addiction but also in the development of therapeutic strategies targeting specific cell types. Additionally, our study extends this understanding by providing cell-type-timepoint-disease associations, highlighting critical developmental stages where specific cell types may exert significant influence on disease pathology. This dual perspective enhances our ability to identify windows of vulnerability and potential therapeutic intervention points in brain-related disorders. L2-3 CUX2 cells exhibited brain-specific disease enrichments in network modules for both HPO and DisGeNET enrichments, including “abnormal cerebral cortex morphology”, “depression”, and “Atrophy/degeneration affecting the CNS”. CUX2 is involved in the regulation of neuron formation and differentiation in the cortex, with layer 2–3 neurons of the cortex being integral to sensory perception, decision making and memory. Dysfunction in these neurons is associated to various cognitive deficits and disorders such as multiple sclerosis ( 94 ). Moreover, CUX2 was found to be a susceptibility gene for major depressive disorder due to its increased expression in both the blood and post-mortem prefrontal cortex samples from major depressive disorder patients ( 95 ). Additionally, CUX-2 expressing neurons in the superior temporal gyrus are susceptible to Alzheimer’s disease ( 96 ). These findings highlight the importance of CUX2 in brain-related disorders. Conclusion The results across astrocytes, SST, and L2-3 CUX2 cells call for a deeper investigation into cell type-specific pathways. Such research could lead to more targeted and effective treatments for a variety of disorders, enhancing our ability to manipulate pathological processes at the cellular level. Continued exploration of the molecular mechanisms within these cells could provide critical insights into their contributions to these disorders, offering new targets for therapeutic interventions and potentially reshaping approaches to diagnosis and therapy in neurological and psychiatric conditions. We aimed to identify cells, genes and timepoints important to disease in the brain by utilising temporal expression cluster and gene co-expression analysis. Our analysis facilitates the discovery of novel associations between genes and human diseases. When comparing curated disorder-type specific gene lists and the genes in disease terms enriched in co-expression modules and temporal clusters, we find a low level of similarity between the two. This discrepancy suggests that the genes identified in our computationally derived modules and clusters may belong to novel or less characterized pathways not yet captured by current disease annotations. Therefore, we provide three sources of genes that warrant further examination for association with brain-related disorders. Curated disorder-type specific gene lists are especially promising given their strong association with genes in HPO and DisGeNET terms. However, temporal clusters and co-expression modules offer insights into cell and time-specific disease associations which are informative for ties to novel therapeutic timings and targets. Consequently, we report potentially new disease-gene associations based on our data. It is possible that these genes are involved in biologically distinct pathways that share a similar activation pattern, or that there is an as of yet undiscovered biological link between the two. The confidence of these disease-gene associations could be improved with further validation. In conclusion, this study has provided a view of how cell-specific gene expression and co-expression patterns relate to brain disorders, emphasizing the complexity of the brain and the relationships between genetic factors and disease phenotypes. As datasets with more timepoints become available, these analysis strategies can be utilised to make more precise associations of timepoints to disease. Additionally, as annotation resources such as HPO and ClinVar evolve, this analysis will become more powerful. The associations and insights gained in this study pave the way for further research into the molecular mechanisms underlying these associations, which could eventually lead to better diagnostic and therapeutic strategies. Methods Manual curation of disorder-type specific gene lists from ClinVar and GWAS data GWAS variants were acquired from the NHGRI-EBI GWAS Catalog association data (12th March 2023)( 57 ). Variants associated with HP:0000707 (abnormality of nervous system) and MONDO_0002025 (psychiatric disorders) with child traits were downloaded. Variants relating to non-direct measurements, such as those assessing treatment efficacy in a disorder, were excluded. The tabular data was filtered for genic entries associated with psychiatric, neurological, or neurodevelopmental disorders (Supplementary Table 20), resulting in three gene lists based on the reported genes (Supplementary Fig. 2). ClinVar variant summary data was downloaded (data accessed 12th April 2023). Similar to NHGRI-EBI GWAS data, data was in a tabular format, which included variant name based on Human Genome Variation Society (HGVS) format, Gene ID and symbol, clinical significance, and phenotype. Variants were filtered to retain pathogenic single nucleotide variants with four or more confidence stars. Now defunct or withdrawn HGNC IDs were removed, and variants associated with neurological, neurodevelopmental, and psychiatric phenotypes retained (Supplementary Table 20). SNVs were classified as being psychiatric, neurological, or neurodevelopmental, and gene symbol and disorder type were extracted to create gene lists (Supplementary Fig. 3). For both ClinVar (SNV) and GWAS, gene lists were curated for the three disorder types, for a total of six gene lists; SNV_psych, SNV_neurol and SNV_neurodev, and GWAS_psych, GWAS_neurol, and GWAS_neurodev (Supplementary Table 1). snRNAseq The developmental prefrontal cortex single cell atlas generated by Herring et. al. ( 55 ) was downloaded. Gene counts and metadata were loaded in R version 4.3.2 to create a Seurat (v5.0.1) ( 58 ) object. SCTransform was applied for normalization. Cells with no genes detected were removed, and genes expressed in at least five percent of a cell type were retained for downstream analysis. Temporal clustering Temporal gene expression patterns were determined using TCseq (v1.24.3) ( 59 ). TCseq enables analysis of sequencing data collected at multiple timepoints and can be used to study how gene expression changes over time. Genes are grouped into clusters which have similar temporal expression patterns. Here, the processed Seurat object was used as input. For each cell type, average expression was calculated for each developmental stage (fetal, neonatal, infancy, childhood, adolescence, adulthood) using the Seurat function “AverageExpression”. Genes were then grouped into twelve temporal clusters for each major cell type (Supplementary Fig. 4), with time interval set to the six developmental stages. For each major cell type, the twelve clusters were retained to test for disease associations. Identification of gene modules in co-expression modules High-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA);( 60 ), is a method for network inference and identification of core gene modules tailored to single cell data. We used hdWGCNA (v0.2.26) to construct gene networks for each cell type independently and subsequently identified core gene-expression modules. Single cell data stored as Seurat objects was normalized, and variable genes were identified using the “vst” selection method. The data was then scaled, and principal component analysis (PCA) was performed on the variable genes. Uniform Manifold Approximation and Projection (UMAP) was executed using 30 dimensions, followed by the identification of nearest neighbours and clustering with a resolution of 1.5. Cells annotated as being poor quality by Herring et al. were removed from the analysis. To analyse each cell type, the data were subset by major_clust, and hdWGCNA was run separately for each subset. Metacells, aggregates of small groups of similar cells coming from the same biological sample, were created by specifying developmental stage as the grouping variable. During construction of metacells, the maximum number of shared cells was set to 30, with remaining hdWGCNA steps run using default parameters. Each co-expression module was named after a colour, and from this, gene lists were saved for each cell type. This approach allows for the identification of cell type-specific co-expression networks, providing insights into the unique regulatory mechanisms and functional pathways active in different cell types. HPO, DisGeNET, and GO term enrichment The curated gene lists were analysed for overlap with existing disease gene lists using the clusterProfiler (v4.10.0)( 61 ) “compareCluster” function with the background being a gene list combining all SNV and GWAS gene lists. In other words, we ask specifically for enrichments within one of the gene lists not present in the other lists. We tested our lists for overlap to Human Phenotype Ontology (HPO), gene ontology (GO), and DisGeNET terms. Enrichments were considered significant when gene counts were greater than five and adjusted p-values were less than 0.05. Results were visualised using heatmaps and the emapplot() function. To identify associations within our temporal clusters and gene network modules, enrichment analysis was completed as described above, with background being set to all genes reliably detected (n = 7186). DisGeNET, HPO, and GO gene sets were queried. Enrichments were considered significant when gene counts were greater than five, and adjusted p-values were less than 0.2. Declarations Ethics approval and consent to participate Not Applicable. Consent for publication Not Applicable. Competing interests The authors declare they have no competing interests. Funding TL is supported by a Fellowship from the Feilman Foundation and the Stan Perron foundation. KF is in receipt of an Australian Government Research Training Program Fees offset Scholarship at The University of Western Australia (UWA) and is supported by The Stan and Jean Perron Award for Excellence and a philanthropic postgraduate scholarship administered through the UWA Graduate Research School. Author Contribution T.L. and K.O.F. designed the research. K.O.F. analysed the data in conjunction with K.G.C. K.O.F. interpreted the data. K.O.F. wrote the manuscript in conjunction with T.L. All authors have read and approved the final manuscript. Data Availability 10.5281/zenodo.12065339 contains all code to reproduce the analysis and figures in the paper. References Bota M, Dong HW, Swanson LW. From gene networks to brain networks. Nat Neurosci. 2003;6(8):795–9. Darmanis S, Sloan SA, Zhang Y, Enge M, Caneda C, Shuer LM, et al. 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Chen","email":"","orcid":"","institution":"Computational Biology, Precision Health, Telethon Kids Institute, Perth Children’s Hospital, Nedlands, Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"G.","lastName":"Chen","suffix":""},{"id":353057359,"identity":"92e24d84-9508-43d1-a2cd-182a79ef467b","order_by":2,"name":"Timo Lassmann","email":"","orcid":"","institution":"Computational Biology, Precision Health, Telethon Kids Institute, Perth Children’s Hospital, Nedlands, Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Timo","middleName":"","lastName":"Lassmann","suffix":""}],"badges":[],"createdAt":"2024-08-21 02:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4948061/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4948061/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64867573,"identity":"005f75d8-cfc3-48be-9d89-c1edb6d970a1","added_by":"auto","created_at":"2024-09-19 19:12:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eValidation of disorder-type specific gene lists across brain-related disorder subtypes.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eHPO phenotypic enrichments across SNV and GWAS gene lists for neurological, neurodevelopmental, and psychiatric disorders, demonstrating distinct enrichment patterns by disease category.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eComparison of DisGeNET phenotypic enrichments in SNV and GWAS gene lists, highlighting significant overlaps in enrichment patterns across neurological and neurodevelopmental disorders.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ec \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eJaccard scores of HPO terms significantly enriched in curated disorder-type specific gene lists, with terms subset according to the curated gene list they are enriched in.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ed \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eJaccard scores of DisGeNET terms significantly enriched in curated disorder-type specific gene lists, with terms subset according to the curated gene list they are enriched in.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4948061/v1/71b32e418bf0290d2168ad40.png"},{"id":64867572,"identity":"e85f9ec9-83b2-482f-940a-2b57271b3e4b","added_by":"auto","created_at":"2024-09-19 19:12:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUtilising temporal clusters of genes to identify disease associations \u003cbr\u003e\na \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eEnrichment of DisGeNET terms in temporal gene clusters in the prefrontal cortex identifies disease associations.\u003cbr\u003e\n \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eb \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eComparison of Jaccard scores between temporal gene clusters in SST cells and curated disorder-type specific gene lists, and temporal gene clusters in SST cells and DisGeNET terms enriched in SST cells. The x-axis represents different temporal gene clusters in SST cells, while the y-axis shows the Jaccard scores, indicating the degree of similarity. Each point corresponds to a Jaccard score between a temporal gene cluster and either a curated disorder-type specific gene list or a DisGeNET term. Of particular note are temporal clusters six, seven and twelve.\u003cbr\u003e\n \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ec Overview of disease-relevant temporal gene expression patterns in clusters showing significance to specific disease classifications.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ed \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eEnrichment of HPO terms identifies disease-associated terms in temporal clusters in the prefrontal cortex\u003cbr\u003e\n \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ee \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eComparison of Jaccard scores between temporal gene clusters in PN-dev cells and curated disorder-type specific gene lists, and temporal clusters in PN-dev cells and HPO terms enriched in PN-dev cells. The x-axis represents different temporal gene clusters in PN-dev cells, while the y-axis shows the Jaccard scores, indicating the degree of similarity. Each point corresponds to a Jaccard score between a cluster and either a curated disorder-type specific gene list or a HPO term.\u003cbr\u003e\n \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ef \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eTemporal gene expression patterns of clusters that appear important to specific disease classifications. Enrichments relating to structural abnormalities are associated with a pattern of gene expression by which genes are highly expressed in the fetal stage, followed by a decline in expression through to adulthood. Meanwhile, enrichments associated to neurodevelopmental disease terms are associated with a pattern of gene expression by which gene expression transiently increases during childhood before decreasing.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4948061/v1/396c443f569c179a1c3f1bed.png"},{"id":64867571,"identity":"034d42b3-d6a7-4d6f-bc02-a7cecded2ed1","added_by":"auto","created_at":"2024-09-19 19:12:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":23333,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentifying associations with HPO and DisGeNET terms and disease gene lists in co-expression modules\u003c/strong\u003e\u003cbr\u003e\n \u003cem\u003e\u003cstrong\u003ea \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eEnrichment of HPO terms in gene co-expression modules\u003cbr\u003e\n \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eb \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eEnrichment of DisGeNET terms in gene co-expression modules \u003cbr\u003e\n \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ec \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eJaccard scores in L2-3 CUX2 gene co-expression modules for enriched HPO and DisGeNET terms, and curated disorder-type specific gene lists. Scores emphasize the variable presence of curated disorder-type specific gene lists in co-expression modules\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ed \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eImportance of developmental stages in gene co-expression modules for L2-3 CUX2 cells.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4948061/v1/2e021b82ab0d1ea16194713c.png"},{"id":108181514,"identity":"03b17a4d-5a39-4b7f-a6fa-8721ff1e791f","added_by":"auto","created_at":"2026-04-30 08:58:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":493260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4948061/v1/d8172ef7-8664-40a1-abeb-3fd96905f8b3.pdf"},{"id":64867749,"identity":"5c92526f-a089-491d-95d5-e8f0f821e048","added_by":"auto","created_at":"2024-09-19 19:20:10","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":174375,"visible":true,"origin":"","legend":"","description":"","filename":"SF13.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4948061/v1/884747fb6979414ae741430d.pdf"},{"id":64867576,"identity":"09b344a4-b557-4bec-afc3-9b8356181f3d","added_by":"auto","created_at":"2024-09-19 19:12:10","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":3318215,"visible":true,"origin":"","legend":"","description":"","filename":"SF4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4948061/v1/8f6dcb8598ffc1c3d83c5fe8.pdf"},{"id":64867574,"identity":"41021f0c-4a34-4aa5-bdaf-a261350a4547","added_by":"auto","created_at":"2024-09-19 19:12:10","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1440505,"visible":true,"origin":"","legend":"","description":"","filename":"ST1ST20.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4948061/v1/2f7e3976308c94dfb63221cc.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unlocking disease associations during prefrontal cortex development with scRNAseq","fulltext":[{"header":"Background","content":"\u003cp\u003eThe brain is a complex organ comprised of functional layers, containing a vast range of different cell types and cellular connections (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). There are several distinct stages of brain development in humans, from conception through childhood to adulthood. Neuronal cells of the fetal brain are different to adult neuronal cells (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Development of the brain is controlled by highly precise and complex spatiotemporal signalling (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Predicting the impact of gene variants on brain development and downstream phenotype is challenging.\u003c/p\u003e \u003cp\u003eTo date, we have a broad understanding of the cellular composition of the brain, how the early environment shapes development and what role aberrant gene expression plays within the brain (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, translating this knowledge into therapeutic strategies remains a significant challenge. Brain-related disorders are a heterogeneous group of disorders affecting many aspects of life, and can be grouped into three broad categories; neurological, psychiatric, and neurodevelopmental disorders (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). We will briefly discuss these three disorders in turn below.\u003c/p\u003e"},{"header":"Neurological disorders","content":"\u003cp\u003eNeurological disorders encompass any disorder effecting the structure and function of the central nervous system (CNS) or peripheral nervous system (PNS);(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Due to the involvement of the nervous system in the majority of bodily functions, neurological disease symptoms vary greatly. Symptoms include pain, muscle malfunction, changes in sensation, senses, consciousness and cognition, and sleep-related problems. These disorders can have a variety of origins including infection, environmental influences, autoimmune responses, degenerative processes, and genetic factors (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePsychiatric disorders\u003c/h2\u003e \u003cp\u003ePsychiatric disorders are one of the main causes of morbidity and mortality worldwide and represent a huge burden on both individuals and society (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). These disorders are complex, and their etiology and treatment remains enigmatic. The American Psychiatric Association defines psychiatric disorders as being disorders with clinically significant disturbances in mood, thinking and behaviour, and are often associated with distress and or disability in social, occupational and other activities (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiagnosis of psychiatric disorders remains primarily based on signs and symptoms, and there is a distinct lack of predictive biomarkers. This can make diagnosis difficult, given the high heterogeneity and comorbidity of disorders and shared symptoms across different disorders (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This results in a high variability in diagnosis, in turn leading to variable treatment options, at times to the detriment of patient outcomes due to misdiagnosis and the use of inappropriate treatment modalities (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Further, there is a need for better therapeutic options- there have been limited advances in psychiatric pharmacology in the past decade (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This has informed the need for a better understanding of the biological mechanisms underpinning psychiatric disorders.\u003c/p\u003e \u003cp\u003eAll major psychiatric disorders have high heritability (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), and as such a large proportion of the risk of developing a psychiatric disorder is attributable to genetic factors. Heritability varies between disorders, from 30 to 80% in sibling studies (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, the penetrance of these genetic factors is highly variable (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In addition, genome-wide association studies (GWAS) have identified common variants associated with psychiatric disorders. Individually these variants have a small effect and are often associated with multiple conditions (\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eNeurodevelopmental disorders\u003c/h2\u003e \u003cp\u003eNeurodevelopmental disorders are a subset of psychiatric disorders which result in impairments in cognition, communication, behaviour and/or motor skills resulting from abnormal development. This includes disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, intellectual disability and schizophrenia (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs with other psychiatric disorders, such as major depressive disorder, anxiety disorders, and personality disorders, neurodevelopmental disorders share a high degree of phenotypic and genetic overlap (\u003cspan additionalcitationids=\"CR36 CR37 CR38\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Further, there is significant symptomatic overlap among neurological disorders, as well as psychiatric and neurodevelopmental disorders (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41 CR42 CR43 CR44\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Biomarkers and causative genetic mutations are well established for some conditions (\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), and in some cases specific cell types have been implicated (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). However, better knowledge of the etiology of disorders is needed before translation to therapeutic options is fully realised (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).Consequently, the Psychiatric Genomics Consortium highlighted the need for high-quality functional data including single-cell transcriptomic profiles across development and brain tissues (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe recent emergence of single-cell and single-nucleus RNA sequencing (scRNAseq, snRNAseq) technologies has enabled the interrogation of organs and tissues at the level of a single cell (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). This has allowed for the identification of rare cell populations and the characterization of cellular states and their dynamics. The Human Cell Atlas project has spearheaded the effort to create comprehensive reference maps of all human cells using scRNAseq, with the aim to provide insights into health and disease (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Concurrently, researchers have mapped the development of cell types over time. As such, there is now a wealth of data in large-scale single cell brain developmental atlases available (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe integrated existing computational approaches into a workflow allowing us to systematically analyse a developmental prefrontal cortex snRNAseq atlas for information relevant to diseases. Gene lists were curated and tailored to specific disorder subtypes. Concurrently, we associated specific cell types in the prefrontal cortex with disease states by identifying key temporal gene expression patterns and gene co-expression networks. Additionally, our analysis pinpoints where and when genes associated with major disorders are most likely to have the biggest impact on phenotype. This integrated approach not only aids in identifying developmental stages critical to disease manifestation but also highlights potential additional targets for pharmaceutical interventions, paving the way for targeted therapies in brain disorder management.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCuration of disorder-type specific gene lists\u003c/h2\u003e \u003cp\u003eWe retrieved variants associated with neurological, psychiatric, and neurodevelopmental disorders from the NHGRI-EBI GWAS and ClinVar databases. Intronic and exonic variants were used to create disorder-specific gene lists. In total, 2680 unique genes were found to be associated with disorders of the brain and nervous system. 1390 genes related to neurodevelopmental disorders (i.e., intellectual disability, autism spectrum disorders), 593 to neurological disorders (i.e. Alzheimer\u0026rsquo;s disease, cognitive decline, epilepsy), and 998 genes related to psychiatric disorders (i.e. major depressive disorder, bipolar disorder, anxiety). There was limited overlap between genes derived from GWAS and ClinVar (Supplementary Table\u0026nbsp;1). For neurodevelopmental genes, 19 genes were common to both GWAS and ClinVar disease gene lists. Similarly, only 9 genes were present in both neurological gene lists, and there was no overlap between the psychiatric disorder gene lists. This indicates that the ClinVar and NHGRI-EBI GWAS databases capture different sets of genes associated with these conditions, highlighting the importance of integrating multiple data sources for a comprehensive understanding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEnriched terms in curated disorder-type specific gene lists align with disorder gene list type\u003c/h2\u003e \u003cp\u003eTo assess whether our curated disorder-type specific gene lists align with known disorders we performed standard enrichment analysis for HPO and DisGeNET terms. Enrichments confirmed over-representation of matching disease terms as expected. For example, the HPO term \u0026ldquo;delayed speech and language development\u0026rdquo; was enriched in the neurodevelopmental disorder list, while terms such as \u0026ldquo;abnormal nervous system electrophysiology\u0026rdquo; and \u0026ldquo;mental deterioration\u0026rdquo; were enriched in the neurological disorder list (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B, Supplementary Table\u0026nbsp;2, 3, 4). Overall, our curated gene lists were enriched for matching disease and phenotypic terms.\u003c/p\u003e \u003cp\u003eInterestingly, several disease terms relating to facial abnormalities were found, particularly in the neurodevelopmental disease gene lists. Many disorders involving neurodevelopmental phenotypes also have symptoms involving facial abnormalities, and this has been a clinical research focus in recent years, given the fact that craniofacial and nervous system development are linked (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAltogether, these results show that integrating data from ClinVar and the EMBL-GWAS database results in gene lists enriched for known disease signatures. Although the GWAS and ClinVar disorder-type specific gene lists show limited overlap to one another, each contains key genes associated with their associated disorder and are there therefore complementary. We combined the two gene lists associated with each disorder to capture as many known disease genes as possible, creating a unified, disorder-specific gene list for subsequent analysis.\u003c/p\u003e \u003cp\u003eTo further investigate the properties of our curated gene lists, we conducted a complementary analysis. We took the genes associated with the significantly enriched terms found in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and compared their similarity to the three curated gene lists using Jaccard indices. Unsurprisingly, we observe that terms found to have a significant enrichment have correspondingly high Jaccard index in the relevant list (Supplementary Table\u0026nbsp;5). Based on this simple similarity measure, we observe substantial overlaps between the representation of HPO terms between the neurological and neurodevelopmental gene list. For example, genes associated with abnormal communication, a term significantly enriched in the neurodevelopmental list are also present in the neurological list. On the contrary, terms enriched in the neurological list are more specific and have lower similarity to the other two gene lists (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). This pattern is repeated in DisGeNET terms, although there are terms including \u0026ldquo;Intelligence\u0026rdquo; and \u0026ldquo;Ataxia\u0026rdquo; that are more specific to one gene list (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Our list of genes associated with psychiatric disorders is enriched for fewer HPO and in DisGeNET terms with their underlying genes being under-represented in the other two custom gene lists. This analysis highlights the potential value of our curated gene lists in uncovering previously unrecognized connections between disease genes from ClinVar and GWAS studies and related disease categories. The significant overlaps also suggest that these genes may play roles in multiple related diseases, and additional work is required to disentangle the complex gene \u0026ndash; phenotype relationships. For our present study, we will utilize our curated gene lists, along with the HPO and DisGeNET terms, to test for potential disease associations in the subsequent analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ea\u003c/b\u003e \u003cem\u003eHPO phenotypic enrichments across SNV and GWAS gene lists for neurological, neurodevelopmental, and psychiatric disorders, demonstrating distinct enrichment patterns by disease category.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eb\u003c/b\u003e \u003cem\u003eComparison of DisGeNET phenotypic enrichments in SNV and GWAS gene lists, highlighting significant overlaps in enrichment patterns across neurological and neurodevelopmental disorders.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003ec\u003c/b\u003e \u003cem\u003eJaccard scores of HPO terms significantly enriched in curated disorder-type specific gene lists, with terms subset according to the curated gene list they are enriched in.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003ed\u003c/b\u003e \u003cem\u003eJaccard scores of DisGeNET terms significantly enriched in curated disorder-type specific gene lists, with terms subset according to the curated gene list they are enriched in.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eContrasting our curated gene lists to gene lists in HPO and DisGeNET\u003c/h2\u003e \u003cp\u003eGiven our curated gene lists contain more and different sets of genes, we were curious about potential overlaps with known disease gene lists. Genes unique to our lists but reported alongside known disease genes could potentially represent additional disease genes. A significant overlap between the two types of gene lists could allow us to identify such genes. There are notable overlaps between our gene lists and known disease genes in HPO and DisGeNET, including 499 genes from the psychiatric gene list being present in the DisGeNET gene list. 263, 151 and 28 genes are unique to the curated neurodevelopmental, psychiatric, and neurological gene lists respectively, with a total of 456 genes not being present in the HPO and DisGeNET gene lists (Supplementary Fig.\u0026nbsp;1). These findings suggest that our curated gene lists may harbor novel disease-associated genes warranting further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTemporal clusters are enriched for disease terms\u003c/h2\u003e \u003cp\u003eWe generated clusters of genes sharing common gene expression patterns over time within each of the 18 major cell types as defined by Herring \u003cem\u003eet al.\u003c/em\u003e (for principal neurons; PN-dev, L2-3-CUX2, L4-RORB, L5-6-THEMIS and L5-6-TLE4, for inhibitory neurons; early developing MGE-dev, CGE-dev, ID2, VIP, SST, PV, PV_SCUBE3, other cell types; Astro, Oligo, OPCs, Micro, Vas);(Supplementary Table\u0026nbsp;6). For each cell type we generated 12 temporal clusters. Of the resulting 216 clusters, 7 contained less than 200 genes (Supplementary Table\u0026nbsp;7). To assess whether temporal clusters are made up of genes sharing a common function, we performed enrichment analysis using gene ontology terms. We identified 152 unique enriched terms across 17 cell types and 57 clusters, including 52 brain and nervous system specific terms (Supplementary Table\u0026nbsp;8). These results demonstrate that we can use an un-supervised approach to discover temporal clusters of functionally related genes within individual cell types. The enrichment of brain and nervous system-specific terms indicates that our temporal clusters capture gene expression patterns of potential importance to disease onset and progression. To test this, we performed overrepresentation analysis (ORA) using DisGeNET and HPO terms. We found 139 unique disease term enrichments, of which, 60 were related to the brain and nervous system (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Supplementary Table\u0026nbsp;9, 10). Further, we tested whether our manually curated disorder-type specific gene lists were over-represented in specific temporal gene clusters.\u003c/p\u003e \u003cp\u003eGenes transiently upregulated during the neonatal stage in SST interneurons were associated with dystonia and parkinsonian disorders (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, C). Additionally, this temporal gene cluster shows a higher Jaccard score for genes present in our manually curated neurological gene list compared to other clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). This points to early developmental disturbances having an important role in the emergence of neurological disorders in adulthood.\u003c/p\u003e \u003cp\u003eFurther, the DisGeNET terms \u0026ldquo;progressive supranuclear palsy\u0026rdquo; and \u0026ldquo;Drug dependence\u0026rdquo; are enriched in genes upregulated through development in SST interneurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, C). In this cluster of temporally expressed genes, the Jaccard score for genes belonging to the curated psychiatric disease gene list is high relative to other SST correlation clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). This suggests that the upregulation of these genes may play a crucial role in the onset and progression of psychiatric disorders, particularly those relating to substance abuse. Further, these genes are of interest for studies relating to neurodegenerative disorders, such as progressive supranuclear palsy, which is currently poorly understood.\u003c/p\u003e \u003cp\u003eTemporal clusters in specific cell types also align with the observed HPO enrichments. For example, the HPO term \u0026ldquo;Microcephaly\u0026rdquo; is enriched in genes upregulated in the fetal stage followed by a decline and subsequent plateau in remaining developmental stages for ID2 inhibitory interneurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Microcephaly is an early developmental condition wherein an infant\u0026rsquo;s head circumference is more than 2 standard deviations below the mean for their age and sex (\u003cspan additionalcitationids=\"CR65 CR66\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). This is typically indicative of an infant\u0026rsquo;s brain not developing properly during pregnancy (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). As such, the temporal expression patterns align well with enriched disease terms and the stages expected to be important in disease.\u003c/p\u003e \u003cp\u003eA further example of interest is temporal cluster 11 in PN-dev neurons. This cluster is transiently upregulated during the childhood developmental stage and shows enrichment for ten neurodevelopmental-associated HPO terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, F, Supplementary Table\u0026nbsp;10). In this cluster, the curated neurodevelopmental disease gene list has the highest Jaccard score among curated gene list (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Given neurodevelopmental disorders typically manifest in childhood (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), this is to be expected. A similar pattern is seen in cluster six in PN-dev cells, where genes are transiently upregulated during childhood, and the curated neurodevelopmental disease gene list has a high Jaccard score relative to other temporal gene clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ea\u003c/b\u003e \u003cem\u003eEnrichment of DisGeNET terms in temporal gene clusters in the prefrontal cortex identifies disease associations.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eb\u003c/b\u003e \u003cem\u003eComparison of Jaccard scores between temporal gene clusters in SST cells and curated disorder-type specific gene lists, and temporal gene clusters in SST cells and DisGeNET terms enriched in SST cells. The x-axis represents different temporal gene clusters in SST cells, while the y-axis shows the Jaccard scores, indicating the degree of similarity. Each point corresponds to a Jaccard score between a temporal gene cluster and either a curated disorder-type specific gene list or a DisGeNET term. Of particular note are temporal clusters six, seven and twelve.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003ec\u003c/b\u003e \u003cem\u003eOverview of disease-relevant temporal gene expression patterns in clusters showing significance to specific disease classifications.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003ed\u003c/b\u003e \u003cem\u003eEnrichment of HPO terms identifies disease-associated terms in temporal clusters in the prefrontal cortex\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003ee\u003c/b\u003e \u003cem\u003eComparison of Jaccard scores between temporal gene clusters in PN-dev cells and curated disorder-type specific gene lists, and temporal clusters in PN-dev cells and HPO terms enriched in PN-dev cells. The x-axis represents different temporal gene clusters in PN-dev cells, while the y-axis shows the Jaccard scores, indicating the degree of similarity. Each point corresponds to a Jaccard score between a cluster and either a curated disorder-type specific gene list or a HPO term.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003ef\u003c/b\u003e \u003cem\u003eTemporal gene expression patterns of clusters that appear important to specific disease classifications. Enrichments relating to structural abnormalities are associated with a pattern of gene expression by which genes are highly expressed in the fetal stage, followed by a decline in expression through to adulthood. Meanwhile, enrichments associated to neurodevelopmental disease terms are associated with a pattern of gene expression by which gene expression transiently increases during childhood before decreasing.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eIn summary, we clustered genes based on their temporal gene expression patterns in each cell type. We found known disease term associated genes over-represented in these clusters. Our analysis demonstrates that our curated disorder-type specific gene lists and overrepresented disease terms match the expected temporal expression patterns of various disease phenotypes. Given the overrepresentation of known disease genes in our temporal clusters, we hypothesize that other genes in these clusters could also be linked to diseases, potentially providing an avenue to uncover new biological pathways or mechanisms critical to disease progression. These observations could contribute to the development of novel therapeutic targets. Moreover, their association with established disease genes suggests these genes might play as of yet unrecognized roles in disease etiology and severity, providing opportunities for early diagnosis or personalized treatment strategies respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eNetwork derived gene modules provide further insight into disease associations\u003c/h2\u003e \u003cp\u003eUsing hdWGCNA we performed weighted gene co-expression network analysis for each cell type independently to obtain gene modules. Mirroring the approach for the temporal clusters, we performed enrichment analysis on these modules. We derived between 3 and 17 modules for each cell type and identified 101 enriched disease terms, of which 40 were brain-specific (Supplementary Tables\u0026nbsp;11,12).\u003c/p\u003e \u003cp\u003eThree gene modules were responsible for all brain-related HPO term enrichments: two modules in L2-3 CUX2 cells, and another one in VIP cells. One of the L2-3 CUX2 module appeared to be specific to neurological and morphological abnormalities, while the other L2-3 CUX2 module and VIP module were primarily psychiatric enrichment driven (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eGene modules in eight cell types (Astro, ID2, L2-3 CUX2, L4-RORB, L5-6 THEMIS, LAMP5 NOS1, PV SCUBE3 and VIP cells) were enriched for brain-related DisGeNET terms. Interestingly, ventriculomegaly appeared in both HPO and DisGeNET enrichments for the same L2-3 CUX2 module (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). While enrichments between HPO and DisGeNET have followed similar \u0026ldquo;themes\u0026rdquo;, there has been limited overlap in the specific enrichments observed in DisGeNET and HPO terms thus far in the analysis. DisGeNET enrichments were varied, with one L5-6 THEMIS module producing the majority of the enrichments, including \u0026ldquo;Specific Learning Disability\u0026rdquo; and \u0026ldquo;Frontotemporal Lobar Degeneration\u0026rdquo;.\u003c/p\u003e \u003cp\u003eBy combining module eigengene values with sample metadata, we calculated the average module eigengene value for each developmental stage. A high value in a stage indicates that the co-expression module is highly active during that specific developmental stage, suggesting that the underlying genes are likely playing an important role. We examined how curated disorder-type specific disease gene list genes are represented in these modules and paired this with their activity at the different the developmental stages. We found that while Jaccard scores are relatively low, the stages contributing to the module reflect the known trajectory of presentation for these curated disease gene lists. For example, in the L2-3 CUX2 turquoise module, the highest scoring curated disorder-type specific gene list is the neurodevelopmental list. In this co-expression module, infancy and adolescence are the developmental stages contributing (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003eInterestingly, the majority of enrichments reaching significance arise from L2-3 CUX2 modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). One co-expression module contains enrichments pertaining to neurological and morphological enrichments such as \u0026ldquo;abnormal PNS morphology\u0026rdquo;, \u0026ldquo;abnormal cerebral cortex morphology\u0026rdquo;, and \u0026ldquo;peripheral neuropathy\u0026rdquo;. Further, the only stage contributing to the co-expression module is infancy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Given these disease terms relate to the structure of the CNS and PNS, it follows that an early developmental stage would be an important timepoint.\u003c/p\u003e \u003cp\u003eIn the second co-expression module, enrichments such as \u0026ldquo;depression\u0026rdquo;, \u0026ldquo;impairment in personality functioning\u0026rdquo;, and \u0026ldquo;dystonia\u0026rdquo; are present (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The Jaccard scores are similar for the curated neurological and psychiatric disease gene lists, and the stages contributing to the co-expression module are adolescence and adulthood (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D). Psychiatric disorders, particularly mood disorders such as depression typically have an onset during adolescence and adulthood, with a quarter of individuals having their first symptoms before the age of 17, and three quarters before 34 (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e), aligning with adolescence and adulthood being the major stage contributors in the co-expression module.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ea\u003c/b\u003e \u003cem\u003eEnrichment of HPO terms in gene co-expression modules\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eb\u003c/b\u003e \u003cem\u003eEnrichment of DisGeNET terms in gene co-expression modules\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003ec\u003c/b\u003e \u003cem\u003eJaccard scores in L2-3 CUX2 gene co-expression modules for enriched HPO and DisGeNET terms, and curated disorder-type specific gene lists. Scores emphasize the variable presence of curated disorder-type specific gene lists in co-expression modules\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003ed\u003c/b\u003e \u003cem\u003eImportance of developmental stages in gene co-expression modules for L2-3 CUX2 cells.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eIn summary, while network derived gene modules overall do not show numerous associations to either our curated or HPO and DisGenNET gene lists, activity of the network module at particular stages is relevant. The difference in enrichments between the temporal clusters and network derived gene modules underscores the importance of interrogating single cell datasets with multiple methods to discover novel gene-disease associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePresence of manually curated disease gene lists in co-expression modules and temporal expression clusters\u003c/h2\u003e \u003cp\u003eIn the context of the developing brain, studying gene expression patterns can contribute to our understanding of the molecular mechanisms underlying brain functions, development, and disorders. We wanted to determine whether co-expression modules in which brain-related disease terms were enriched had a corresponding \u0026ldquo;signature\u0026rdquo; for a specific curated disease gene list. We found that co-expression modules enriched with significant brain-related disease terms did not consistently align with our curated disorder-type specific gene lists (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). This is in contrast to the results using temporal clustering, in which an increase in the Jaccard score of a curated disorder-type specific gene list contributing to a temporal gene cluster corresponded directly with the type of disease term that was enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, E). This inconsistency in the relationship between enrichments and curated disorder-type specific disease gene lists in co-expression modules highlights a distinct difference between how disease terms and curated disorder-type specific gene lists are represented in clusters obtained by temporal clustering or network-based identification of gene modules.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe sought to better elucidate the contribution of cell types to disorders of the brain using snRNAseq data from the prefrontal cortex during development. While Herring et al. completed a comprehensive analysis of their data, their analysis of disease associations within the brain was limited in scope- namely, for each cell type, four general gene trends are presented (up, down, transiently up, and transiently down). Further, only a selection of neurological and psychiatric disorders were examined in a subset of the major cell types using DisGeNET but not HPO. Our study builds on this foundation by incorporating all major cell types and systematically querying both DisGeNET and HPO databases, aiming to uncover more subtle disease associations. Furthermore, we tested associations between temporal genes and manually curated gene lists from ClinVar and NHGRI-EBI GWAS.\u003c/p\u003e \u003cp\u003eWe utilised both unsupervised temporal clustering and network derived gene modules to explore how known disease associations can be mapped onto single cell data. Given that both approaches take advantage of gene expression data over time, there was considerable overlap between temporal clusters and the gene network modules (Supplementary Table\u0026nbsp;13). When examining the genes driving significant DisGeNET enrichments in each cell types\u0026rsquo; gene co-expression modules versus temporal clusters, there was overlap in three cell types: PV_SCUBE3, SST, and VIP cells (Supplementary Table\u0026nbsp;14). PV_SCUBE3 and VIP cells had one overlapping gene each: IL15 and CRYAB respectively. IL15 is an important cytokine in the brain, with its receptors playing significant roles in neuronal activity and synaptic plasticity, and modulating GABA and serotonin transmission (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Additionally, IL15 has been found to have a role in alcohol dependence (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e) and schizophrenia (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). \u003cem\u003eCRYAB\u003c/em\u003e protein is a molecular chaperone that primarily binds misfolded proteins to prevent protein aggregation and is associated with processes such as cell apoptosis. CRYAB has a biological role in neurodegenerative diseases such as Parkinson\u0026rsquo;s and Huntington\u0026rsquo;s disease (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). SST interneurons had the largest overlap- 20 genes. Within this set of 20 genes, there were genes with associations to disorders including Alzheimer\u0026rsquo;s disease (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, when comparing the genes driving significant HPO term enrichments in cell types for co-expression modules and temporal clusters, there was only overlap in a single cell type: ID2 cells (Supplementary Table\u0026nbsp;15), in which the overlapping genes were GTPBP2 and IFT140. GTPBP2 has been associated with neurodevelopmental disorder (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e), while IFT140 is associated with cranioectodermal dysplasia (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). Overall, while there was limited overlap between temporal clusters and network modules, using both approaches resulted in meaningful disease term enrichments on their own, highlighting the importance of interrogating the single cell data in multiple analytical ways.\u003c/p\u003e \u003cp\u003eAnalysis of network derived gene modules yielded fewer clear disease associations compared to our analysis based on temporal clusters. Terms that were enriched overlapped with our curated gene lists and included terms such as \"Delayed speech and language development\" and \"Abnormal repetitive mannerisms\", \"Microcephaly\", \"Micrognathia\", and \"Anteverted nares\" are enriched in both the curated disorder-type specific gene list enrichments, and the gene co-expression module enrichments.\u003c/p\u003e \u003cp\u003eOne possible explanation for enrichments of facial abnormality-related terms is that HPO terms are designed to be useful for classifying a patient\u0026rsquo;s symptoms, and many terms may occur in a patient at once. It is possible that we are observing a co-morbidity between morphological and structural abnormalities and disorders of the brain. This is especially possible given that ClinVar was used in the curation of the curated disorder-type specific gene lists. ClinVar entries commonly include HPO terms. When looking at the disorder-type specific gene lists contributing to these common morphological disease term enrichments, the gene lists derived from ClinVar are responsible for all of the enrichments. This may be an avenue for further exploration in the diagnosis of neurodevelopmental disorders. Specifically, identification of craniofacial abnormalities can aid in early identification of neurodevelopmental disorders (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e) and rare diseases (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral genes consistently appear in our analysis: ERCC1, KATNB1, HRAS, KRAS, SMC3, DCC and TNF (Supplementary Tables\u0026nbsp;16\u0026ndash;19). HRAS and KRAS belong to the RAS gene family, which plays a major role in cell division, growth, and differentiation. Mutations in the RAS pathway are associated with various syndromes which include various facial abnormalities in their symptoms. DCC has been associated to eight psychiatric disorders (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and TNF, a pro-inflammatory cytokine involved in immune regulation and inflammation, is associated with various brain-related disorders including Parkinson\u0026rsquo;s, Alzheimer\u0026rsquo;s, major depressive disorder, and schizophrenia (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e). Further, ERCC1 deficiency has been associated with cerebro-oculo-facio-skeletal syndrome (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e), and SMC3 has been associated with Cornelia De Lange Syndrome (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile brain specific DisGeNET enrichments in temporal clusters were limited, SST cells exhibited a wide range of enrichments for disorders such as major affective disorder, generalized anxiety disorder, and dystonia. The substantial enrichments observed in SST interneurons underscore the importance of SST and SST-expressing cells in psychiatric and neurological disorders (\u003cspan additionalcitationids=\"CR88\" citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e). The association of these cells to conditions such as major affective disorders and dystonia suggests a fundamental role in the modulation of emotional and motor functions. SST interneurons are pivotal in maintaining the balance between excitation and inhibition in the brain, a balance essential for normal physiological function (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e). Given the neurophysiological roles of SST cells, which include regulating synaptic activity and maintaining inhibitory neurotransmission (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e), our findings point to critical developmental stages where these cells may exert significant influence on disease pathology.\u003c/p\u003e \u003cp\u003eThere is limited literature currently available relating specific cell types and time points to specific disease terms. However, many disease enrichments align with what is already known from animal models. For example, the DisGeNET term \u0026ldquo;substance withdrawal syndrome\u0026rdquo; is enriched in astrocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). It has been found that astrocytes are important in alcohol withdrawal in mice (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). This underscores the potential role of astrocytes not only in the neuropathology of addiction but also in the development of therapeutic strategies targeting specific cell types. Additionally, our study extends this understanding by providing cell-type-timepoint-disease associations, highlighting critical developmental stages where specific cell types may exert significant influence on disease pathology. This dual perspective enhances our ability to identify windows of vulnerability and potential therapeutic intervention points in brain-related disorders.\u003c/p\u003e \u003cp\u003eL2-3 CUX2 cells exhibited brain-specific disease enrichments in network modules for both HPO and DisGeNET enrichments, including \u0026ldquo;abnormal cerebral cortex morphology\u0026rdquo;, \u0026ldquo;depression\u0026rdquo;, and \u0026ldquo;Atrophy/degeneration affecting the CNS\u0026rdquo;. CUX2 is involved in the regulation of neuron formation and differentiation in the cortex, with layer 2\u0026ndash;3 neurons of the cortex being integral to sensory perception, decision making and memory. Dysfunction in these neurons is associated to various cognitive deficits and disorders such as multiple sclerosis (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e). Moreover, CUX2 was found to be a susceptibility gene for major depressive disorder due to its increased expression in both the blood and post-mortem prefrontal cortex samples from major depressive disorder patients (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e). Additionally, CUX-2 expressing neurons in the superior temporal gyrus are susceptible to Alzheimer\u0026rsquo;s disease (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e). These findings highlight the importance of CUX2 in brain-related disorders.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results across astrocytes, SST, and L2-3 CUX2 cells call for a deeper investigation into cell type-specific pathways. Such research could lead to more targeted and effective treatments for a variety of disorders, enhancing our ability to manipulate pathological processes at the cellular level. Continued exploration of the molecular mechanisms within these cells could provide critical insights into their contributions to these disorders, offering new targets for therapeutic interventions and potentially reshaping approaches to diagnosis and therapy in neurological and psychiatric conditions.\u003c/p\u003e \u003cp\u003eWe aimed to identify cells, genes and timepoints important to disease in the brain by utilising temporal expression cluster and gene co-expression analysis. Our analysis facilitates the discovery of novel associations between genes and human diseases. When comparing curated disorder-type specific gene lists and the genes in disease terms enriched in co-expression modules and temporal clusters, we find a low level of similarity between the two. This discrepancy suggests that the genes identified in our computationally derived modules and clusters may belong to novel or less characterized pathways not yet captured by current disease annotations. Therefore, we provide three sources of genes that warrant further examination for association with brain-related disorders. Curated disorder-type specific gene lists are especially promising given their strong association with genes in HPO and DisGeNET terms. However, temporal clusters and co-expression modules offer insights into cell and time-specific disease associations which are informative for ties to novel therapeutic timings and targets. Consequently, we report potentially new disease-gene associations based on our data. It is possible that these genes are involved in biologically distinct pathways that share a similar activation pattern, or that there is an as of yet undiscovered biological link between the two. The confidence of these disease-gene associations could be improved with further validation.\u003c/p\u003e \u003cp\u003eIn conclusion, this study has provided a view of how cell-specific gene expression and co-expression patterns relate to brain disorders, emphasizing the complexity of the brain and the relationships between genetic factors and disease phenotypes. As datasets with more timepoints become available, these analysis strategies can be utilised to make more precise associations of timepoints to disease. Additionally, as annotation resources such as HPO and ClinVar evolve, this analysis will become more powerful. The associations and insights gained in this study pave the way for further research into the molecular mechanisms underlying these associations, which could eventually lead to better diagnostic and therapeutic strategies.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eManual curation of disorder-type specific gene lists from ClinVar and GWAS data\u003c/h2\u003e \u003cp\u003eGWAS variants were acquired from the NHGRI-EBI GWAS Catalog association data (12th March 2023)(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Variants associated with HP:0000707 (abnormality of nervous system) and MONDO_0002025 (psychiatric disorders) with child traits were downloaded. Variants relating to non-direct measurements, such as those assessing treatment efficacy in a disorder, were excluded. The tabular data was filtered for genic entries associated with psychiatric, neurological, or neurodevelopmental disorders (Supplementary Table\u0026nbsp;20), resulting in three gene lists based on the reported genes (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eClinVar variant summary data was downloaded (data accessed 12th April 2023). Similar to NHGRI-EBI GWAS data, data was in a tabular format, which included variant name based on Human Genome Variation Society (HGVS) format, Gene ID and symbol, clinical significance, and phenotype. Variants were filtered to retain pathogenic single nucleotide variants with four or more confidence stars. Now defunct or withdrawn HGNC IDs were removed, and variants associated with neurological, neurodevelopmental, and psychiatric phenotypes retained (Supplementary Table\u0026nbsp;20). SNVs were classified as being psychiatric, neurological, or neurodevelopmental, and gene symbol and disorder type were extracted to create gene lists (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eFor both ClinVar (SNV) and GWAS, gene lists were curated for the three disorder types, for a total of six gene lists; SNV_psych, SNV_neurol and SNV_neurodev, and GWAS_psych, GWAS_neurol, and GWAS_neurodev (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003esnRNAseq\u003c/h2\u003e \u003cp\u003eThe developmental prefrontal cortex single cell atlas generated by Herring et. al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) was downloaded. Gene counts and metadata were loaded in R version 4.3.2 to create a Seurat (v5.0.1) (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) object. SCTransform was applied for normalization. Cells with no genes detected were removed, and genes expressed in at least five percent of a cell type were retained for downstream analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTemporal clustering\u003c/h2\u003e \u003cp\u003eTemporal gene expression patterns were determined using TCseq (v1.24.3) (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). TCseq enables analysis of sequencing data collected at multiple timepoints and can be used to study how gene expression changes over time. Genes are grouped into clusters which have similar temporal expression patterns. Here, the processed Seurat object was used as input. For each cell type, average expression was calculated for each developmental stage (fetal, neonatal, infancy, childhood, adolescence, adulthood) using the Seurat function \u0026ldquo;AverageExpression\u0026rdquo;. Genes were then grouped into twelve temporal clusters for each major cell type (Supplementary Fig.\u0026nbsp;4), with time interval set to the six developmental stages.\u003c/p\u003e \u003cp\u003eFor each major cell type, the twelve clusters were retained to test for disease associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of gene modules in co-expression modules\u003c/h2\u003e \u003cp\u003eHigh-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA);(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), is a method for network inference and identification of core gene modules tailored to single cell data. We used hdWGCNA (v0.2.26) to construct gene networks for each cell type independently and subsequently identified core gene-expression modules.\u003c/p\u003e \u003cp\u003eSingle cell data stored as Seurat objects was normalized, and variable genes were identified using the \u0026ldquo;vst\u0026rdquo; selection method. The data was then scaled, and principal component analysis (PCA) was performed on the variable genes. Uniform Manifold Approximation and Projection (UMAP) was executed using 30 dimensions, followed by the identification of nearest neighbours and clustering with a resolution of 1.5. Cells annotated as being poor quality by Herring \u003cem\u003eet al.\u003c/em\u003e were removed from the analysis.\u003c/p\u003e \u003cp\u003eTo analyse each cell type, the data were subset by major_clust, and hdWGCNA was run separately for each subset. Metacells, aggregates of small groups of similar cells coming from the same biological sample, were created by specifying developmental stage as the grouping variable. During construction of metacells, the maximum number of shared cells was set to 30, with remaining hdWGCNA steps run using default parameters. Each co-expression module was named after a colour, and from this, gene lists were saved for each cell type. This approach allows for the identification of cell type-specific co-expression networks, providing insights into the unique regulatory mechanisms and functional pathways active in different cell types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eHPO, DisGeNET, and GO term enrichment\u003c/h2\u003e \u003cp\u003eThe curated gene lists were analysed for overlap with existing disease gene lists using the clusterProfiler (v4.10.0)(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e) \u0026ldquo;compareCluster\u0026rdquo; function with the background being a gene list combining all SNV and GWAS gene lists. In other words, we ask specifically for enrichments within one of the gene lists not present in the other lists. We tested our lists for overlap to Human Phenotype Ontology (HPO), gene ontology (GO), and DisGeNET terms. Enrichments were considered significant when gene counts were greater than five and adjusted p-values were less than 0.05. Results were visualised using heatmaps and the emapplot() function.\u003c/p\u003e \u003cp\u003eTo identify associations within our temporal clusters and gene network modules, enrichment analysis was completed as described above, with background being set to all genes reliably detected (n\u0026thinsp;=\u0026thinsp;7186). DisGeNET, HPO, and GO gene sets were queried. Enrichments were considered significant when gene counts were greater than five, and adjusted p-values were less than 0.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eTL is supported by a Fellowship from the Feilman Foundation and the Stan Perron foundation. KF is in receipt of an Australian Government Research Training Program Fees offset Scholarship at The University of Western Australia (UWA) and is supported by The Stan and Jean Perron Award for Excellence and a philanthropic postgraduate scholarship administered through the UWA Graduate Research School.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.L. and K.O.F. designed the research. K.O.F. analysed the data in conjunction with K.G.C. K.O.F. interpreted the data. K.O.F. wrote the manuscript in conjunction with T.L. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e10.5281/zenodo.12065339 contains all code to reproduce the analysis and figures in the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBota M, Dong HW, Swanson LW. From gene networks to brain networks. 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Alzheimer\u0026rsquo;s Dement. 2023;19(S13):e080147.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"single-cell RNAseq, prefrontal cortex, brain development, disease associations","lastPublishedDoi":"10.21203/rs.3.rs-4948061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4948061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe brain consists of tissue comprising billions of neurons intricately connected through trillions of synapses. Throughout human development, from conception to adulthood, cells in the brain undergo significant changes, assembling functional circuitry over time orchestrated by finely tuned programs of gene expression. Understanding the spatiotemporal signalling that governs brain development and the impact of gene mutations on developmental programs remains a significant challenge. Here we identify the role of genetic variants in brain development to advance the development of therapeutic options.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis computational study leverages developmental prefrontal cortex single-cell transcriptomic data to associate specific cell types with disease states. Using un-supervised techniques, we identify temporal gene expression patterns and gene co-expression networks enriched for genes associated with neurological disorders. By systematically applying these approaches we identify key cell types and developmental stages associated with disease emergence and progression\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur approach identifies important cell types and developmental stages relevant to diseases directly from single cell data. By pinpointing cells, genes, and their neighbourhoods, our approach has the potential to contribute to the discovery of new targets and treatment avenues. We hope our work will advance the management of brain-related disorders.\u003c/p\u003e","manuscriptTitle":"Unlocking disease associations during prefrontal cortex development with scRNAseq","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-19 19:12:05","doi":"10.21203/rs.3.rs-4948061/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2eb2639-0d99-4a60-847d-cf26a882e2dd","owner":[],"postedDate":"September 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T08:26:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-19 19:12:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4948061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4948061","identity":"rs-4948061","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}