Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Nova proteins direct synaptic integration of somatostatin interneurons through activity-dependent alternative splicing

Somatostatin interneurons are the earliest born population of cortical inhibitory cells. They are crucial to support normal brain development and function; however, the mechanisms underlying their integration into nascent cortical circuitry are not well understood. In this study, we begin by demonstrating that the maturation of somatostatin interneurons in mouse somatosensory cortex is activity dependent. We then investigated the relationship between activity, alternative splicing, and synapse formation within this population. Specifically, we discovered that the Nova family of RNA-binding proteins are activity-dependent and are essential for the maturation of somatostatin interneurons, as well as their afferent and efferent connectivity. Within this population, Nova2 preferentially mediates the alternative splicing of genes required for axonal formation and synaptic function independently from its effect on gene expression. Hence, our work demonstrates that the Nova family of proteins through alternative splicing are centrally involved in coupling developmental neuronal activity to cortical circuit formation.

Molecular cascades and cell-type specific signatures in ASD revealed by single cell genomics

Understanding how genetic variation exerts its effects on the human brain in health and disease has been greatly informed by functional genomic characterization. Studies over the last decade have demonstrated robust evidence of convergent transcriptional and epigenetic profiles in post-mortem cerebral cortex from individuals with Autism Spectrum Disorder (ASD). Here, we perform deep single nuclear (sn) RNAseq to elucidate changes in cell composition, cellular transcriptomes and putative candidate drivers associated with ASD, which we corroborate using snATAC-seq and spatial profiling. We find changes in cell state composition representing transitions from homeostatic to reactive profiles in microglia and astrocytes, a pattern extending to oligodendrocytes and blood brain barrier cells. We identify profound changes in differential expression involving thousands of genes across neuronal and glial subtypes, of which a substantial portion can be accounted for by specific transcription factor networks that are significantly enriched in common and rare genetic risk for ASD. These data, which are available as part of the PsychENCODE consortium, provide robust causal anchors and resultant molecular phenotypes for understanding ASD changes in human brain.

Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD

Neuropsychiatric disorders classically lack defining brain pathologies, but recent work has demonstrated dysregulation at the molecular level, characterized by transcriptomic and epigenetic alterations1-3. In autism spectrum disorder (ASD), this molecular pathology involves the upregulation of microglial, astrocyte and neural-immune genes, the downregulation of synaptic genes, and attenuation of gene-expression gradients in cortex1,2,4-6. However, whether these changes are limited to cortical association regions or are more widespread remains unknown. To address this issue, we performed RNA-sequencing analysis of 725 brain samples spanning 11 cortical areas from 112 post-mortem samples from individuals with ASD and neurotypical controls. We find widespread transcriptomic changes across the cortex in ASD, exhibiting an anterior-to-posterior gradient, with the greatest differences in primary visual cortex, coincident with an attenuation of the typical transcriptomic differences between cortical regions. Single-nucleus RNA-sequencing and methylation profiling demonstrate that this robust molecular signature reflects changes in cell-type-specific gene expression, particularly affecting excitatory neurons and glia. Both rare and common ASD-associated genetic variation converge within a downregulated co-expression module involving synaptic signalling, and common variation alone is enriched within a module of upregulated protein chaperone genes. These results highlight widespread molecular changes across the cerebral cortex in ASD, extending beyond association cortex to broadly involve primary sensory regions.

Rare coding variation provides insight into the genetic architecture and phenotypic context of autism

Some individuals with autism spectrum disorder (ASD) carry functional mutations rarely observed in the general population. We explored the genes disrupted by these variants from joint analysis of protein-truncating variants (PTVs), missense variants and copy number variants (CNVs) in a cohort of 63,237 individuals. We discovered 72 genes associated with ASD at false discovery rate (FDR) ≤ 0.001 (185 at FDR ≤ 0.05). De novo PTVs, damaging missense variants and CNVs represented 57.5%, 21.1% and 8.44% of association evidence, while CNVs conferred greatest relative risk. Meta-analysis with cohorts ascertained for developmental delay (DD) (n = 91,605) yielded 373 genes associated with ASD/DD at FDR ≤ 0.001 (664 at FDR ≤ 0.05), some of which differed in relative frequency of mutation between ASD and DD cohorts. The DD-associated genes were enriched in transcriptomes of progenitor and immature neuronal cells, whereas genes showing stronger evidence in ASD were more enriched in maturing neurons and overlapped with schizophrenia-associated genes, emphasizing that these neuropsychiatric disorders may share common pathways to risk.

Cell type hierarchy reconstruction via reconciliation of multi-resolution cluster tree

A wealth of clustering algorithms are available for single-cell RNA sequencing (scRNA-seq) data to enable the identification of functionally distinct subpopulations that each possess a different pattern of gene expression activity. Implementation of these methods requires a choice of resolution parameter to determine the number of clusters, and critical judgment from the researchers is required to determine the desired resolution. This supervised process takes significant time and effort. Moreover, it can be difficult to compare and characterize the evolution of cell clusters from results obtained at one single resolution. To overcome these challenges, we built Multi-resolution Reconciled Tree (MRtree), a highly flexible tree-construction algorithm that generates a cluster hierarchy from flat clustering results attained for a range of resolutions. Because MRtree can be coupled with most scRNA-seq clustering algorithms, it inherits the robustness and versatility of a flat clustering approach, while maintaining the hierarchical structure of cells. The constructed trees from multiple scRNA-seq datasets effectively reflect the extent of transcriptional distinctions among cell groups and align well with levels of functional specializations among cells. Importantly, application to fetal brain cells identified subtypes of cells determined mainly by maturation states, spatial location and terminal specification.

Integration and transfer learning of single-cell transcriptomes via cFIT

Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets have been generated that allow for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets or transfer knowledge from one to the other to better understand cellular identity and functions. Here, we present a simple yet surprisingly effective method named common factor integration and transfer learning (cFIT) for capturing various batch effects across experiments, technologies, subjects, and even species. The proposed method models the shared information between various datasets by a common factor space while allowing for unique distortions and shifts in genewise expression in each batch. The model parameters are learned under an iterative nonnegative matrix factorization (NMF) framework and then used for synchronized integration from across-domain assays. In addition, the model enables transferring via low-rank matrix from more informative data to allow for precise identification in data of lower quality. Compared with existing approaches, our method imposes weaker assumptions on the cell composition of each individual dataset; however, it is shown to be more reliable in preserving biological variations. We apply cFIT to multiple scRNA-seq datasets of developing brain from human and mouse, varying by technologies and developmental stages. The successful integration and transfer uncover the transcriptional resemblance across systems. The study helps establish a comprehensive landscape of brain cell-type diversity and provides insights into brain development.

Functional genomics links genetic origins to pathophysiology in neurodegenerative and neuropsychiatric disease

Neurodegenerative and neuropsychiatric disorders are pervasive and debilitating conditions characterized by diverse clinical syndromes and comorbidities, whose origins are as complex and heterogeneous as their associated phenotypes. Risk for these disorders involves substantial genetic liability, which has fueled large-scale genetic studies that have led to a flood of discoveries. In turn, these discoveries have exposed substantial gaps in our knowledge with regards to the complicated genetic architecture of each disorder and the substantial amount of genetic overlap among disorders, which implies some degree of shared pathophysiology underlying these clinically distinct, multifactorial disorders. Understanding the role of specific genetic variants will involve resolving the connections between molecular pathways, heterogeneous cell types, specific circuits and disease pathogenesis at the tissue and patient level. We consider the current known genetic basis of these disorders and highlight the utility of molecular systems approaches that establish the function of genetic variation in the context of specific neurobiological networks, cell-types, and life stages. Beyond expanding our knowledge of disease mechanisms, understanding these relationships provides promise for early detection and potential therapeutic interventions.

Neuronal Inactivity Co-opts LTP Machinery to Drive Potassium Channel Splicing and Homeostatic Spike Widening

Homeostasis of neural firing properties is important in stabilizing neuronal circuitry, but how such plasticity might depend on alternative splicing is not known. Here we report that chronic inactivity homeostatically increases action potential duration by changing alternative splicing of BK channels; this requires nuclear export of the splicing factor Nova-2. Inactivity and Nova-2 relocation were connected by a novel synapto-nuclear signaling pathway that surprisingly invoked mechanisms akin to Hebbian plasticity: Ca²⁺-permeable AMPA receptor upregulation, L-type Ca²⁺ channel activation, enhanced spine Ca²⁺ transients, nuclear translocation of a CaM shuttle, and nuclear CaMKIV activation. These findings not only uncover commonalities between homeostatic and Hebbian plasticity but also connect homeostatic regulation of synaptic transmission and neuronal excitability. The signaling cascade provides a full-loop mechanism for a classic autoregulatory feedback loop proposed ∼25 years ago. Each element of the loop has been implicated previously in neuropsychiatric disease.

Rbfox1 Mediates Cell-type-Specific Splicing in Cortical Interneurons

Cortical interneurons display a remarkable diversity in their morphology, physiological properties, and connectivity. Elucidating the molecular determinants underlying this heterogeneity is essential for understanding interneuron development and function. We discovered that alternative splicing differentially regulates the integration of somatostatin- and parvalbumin-expressing interneurons into nascent cortical circuits through the cell-type-specific tailoring of mRNAs. Specifically, we identified a role for the activity-dependent splicing regulator Rbfox1 in the development of cortical interneuron-subtype-specific efferent connectivity. Our work demonstrates that Rbfox1 mediates largely non-overlapping alternative splicing programs within two distinct but related classes of interneurons.

Early Somatostatin Interneuron Connectivity Mediates the Maturation of Deep Layer Cortical Circuits

The precise connectivity of somatostatin and parvalbumin cortical interneurons is generated during development. An understanding of how these interneuron classes incorporate into cortical circuitry is incomplete but essential to elucidate the roles they play during maturation. Here, we report that somatostatin interneurons in infragranular layers receive dense but transient innervation from thalamocortical afferents during the first postnatal week. During this period, parvalbumin interneurons and pyramidal neurons within the same layers receive weaker thalamocortical inputs, yet are strongly innervated by somatostatin interneurons. Further, upon disruption of the early (but not late) somatostatin interneuron network, the synaptic maturation of thalamocortical inputs onto parvalbumin interneurons is perturbed. These results suggest that infragranular somatostatin interneurons exhibit a transient early synaptic connectivity that is essential for the establishment of thalamic feedforward inhibition mediated by parvalbumin interneurons.