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, multiomics datasets into a resource comprising >2.8 million 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 >550,000 cell type–specific regulatory elements and >1.4 million 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.

Recent advances in single-cell sequencing technologies have provided unprecedented opportunities to measure the gene expression profile and RNA velocity of individual cells. However, modeling transcriptional dynamics is computationally challenging because of the high-dimensional, sparse nature of the single-cell gene expression measurements and the nonlinear regulatory relationships. Here, we present DeepVelo , a neural network–based ordinary differential equation that can model complex transcriptome dynamics by describing continuous-time gene expression changes within individual cells. We apply DeepVelo to public datasets from different sequencing platforms to (i) formulate transcriptome dynamics on different time scales, (ii) measure the instability of cell states, and (iii) identify developmental driver genes via perturbation analysis. Benchmarking against the state-of-the-art methods shows that DeepVelo can learn a more accurate representation of the velocity field. Furthermore, our perturbation studies reveal that single-cell dynamical systems could exhibit chaotic properties. In summary, DeepVelo allows data-driven discoveries of differential equations that delineate single-cell transcriptome dynamics.

Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations. Therefore, developing efficient and interpretable methods to quantify network changes and pinpoint driver genes across conditions is crucial. We propose a hierarchical graph representation learning method, called iHerd. Given a set of networks, iHerd first hierarchically generates a series of coarsened sub-graphs in a data-driven manner, representing network modules at different resolutions (e.g., the level of signaling pathways). Then, it sequentially learns low-dimensional node representations at all hierarchical levels via efficient graph embedding. Lastly, iHerd projects separate gene embeddings onto the same latent space in its graph alignment module to calculate a rewiring index for driver gene prioritization. To demonstrate its effectiveness, we applied iHerd on a tumor-to-normal GRN rewiring analysis and cell-type-specific GCN analysis using single-cell multiome data of the brain. We showed that iHerd can effectively pinpoint novel and well-known risk genes in different diseases. Distinct from existing models, iHerd’s graph coarsening for hierarchical learning allows us to successfully classify network driver genes into early and late divergent genes (EDGs and LDGs), emphasizing genes with extensive network changes across and within signaling pathway levels. This unique approach for driver gene classification can provide us with deeper molecular insights. The code is freely available at https://github.com/aicb-ZhangLabs/iHerd. All other relevant data are within the manuscript and supporting information files.

Recent advances in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) have allowed simultaneous epigenetic profiling over thousands of individual cells to dissect the cellular heterogeneity and elucidate regulatory mechanisms at the finest possible resolution. However, scATAC-seq is challenging to model computationally due to the ultra-high dimensionality, low signal-to-noise ratio, complex feature interactions, and high vulnerability to various confounding factors. In this study, we present Translator, an efficient transfer learning approach to capture generalizable chromatin interactions from high-quality (HQ) reference scATAC-seq data to obtain robust cell representations in low-to-moderate quality target scATAC-seq data. We applied Translator on various simulated and real scATAC-seq datasets and demonstrated that Translator could learn more biologically meaningful cell representations than other methods by incorporating information learned from the reference data, thus facilitating various downstream analyses such as clustering and motif enrichment measurements. Moreover, Translator's block-wise deep learning framework can handle nonlinear relationships with restricted connections using fewer parameters to boost computational efficiency through Graphics Processing Unit (GPU) parallelism. Finally, we have implemented Translator as a free software package available for the community to leverage large-scale, HQ reference data to study target scATAC-seq data.

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder affecting 44 million people worldwide, leading to cognitive decline, memory loss, and significant impairment in daily functioning. The recent single-cell sequencing technology has revolutionized genetic and genomic resolution by enabling scientists to explore the diversity of gene expression patterns at the finest resolution. Here, we leveraged the large-scale and publicly available single-nucleus RNA sequencing (snRNA-seq) in the human prefrontal cortex (PFC) from 23 AD samples and 13 controls to investigate cell-to-cell communication (C2C) in healthy brains and their perturbations in AD. Specifically, we first performed broad communication pattern analyses and discovered the inter-mixing of cell types and signaling pathways in AD brains. Secondly, we performed cell-type- centric analysis and found that excitatory neurons in AD have significantly increased their communications to inhibitory neurons, while inhibitory neurons and other supporting cells globally decreased theirs to all cells. Then, we delved deeper with a signaling-centric view, showing that canonical signaling pathways CSF, TGFβ, and CX3C are significantly dysregulated in their signaling to the cell type microglia/PVM and WNT pathway is dysregulated in its signaling from endothelial to neuronal cells in AD. Finally, after extracting 23 known AD risk genes, our intracellular communication analysis revealed a strong connection of extracellular ligand genes APP, APOE, and PSEN1 to intracellular AD risk genes TREM2, ABCA1, and APP in the communication from astrocytes and microglia to neurons. In summary, with the novel advances in single-cell sequencing technologies, we show that cellular signaling is regulated in a cell-type- specific manner and that improper regulation of extracellular signaling genes is linked to intracellular risk genes, connecting signaling to genetic differences manifested in AD. Author Summary: Alzheimer’s is a devastating neurodegenerative disorder affecting 44 million people worldwide, leading to cognitive decline, memory loss, and significant impairment in daily functioning. The complex interplay of signaling genes suggests cells act in concert with other cells through cell-to-cell communication. Utilizing the recent advances in single-cell sequencing, we investigated dysregulated ligand-receptor gene pairs in the disease at the cell-type resolution. Specifically, our broad communication pattern analyses revealed the inter-mixing of cell types and signaling pathways in AD brains. Our cell-type-centric analysis found that excitatory neurons in AD have significantly increased their communications with inhibitory neurons, while inhibitory neurons and other supporting cells globally decreased theirs to all cells. With a signaling-centric view, we show that CSF, TGFβ, and CX3C pathways are significantly dysregulated in their signaling to the cell type microglia/PVM while the WNT pathway is dysregulated in its signaling from endothelial to neuronal cells in AD. Finally, our intracellular communication analysis revealed a strong connection of extracellular ligand genes APP, APOE, and PSEN1 to intracellular AD risk genes TREM2, ABCA1, and APP in the communication from astrocytes and microglia to neurons. In summary, performing a cell-to-cell communication analysis better explains the genetic differences manifested in Alzheimer’s.

Genome-wide association studies (GWASs) and expression analyses implicate noncoding regulatory regions as harboring risk factors for psychiatric disease, but functional characterization of these regions remains limited. Here, we perform capture STARR-sequencing of over 70,000 candidate regions to identify active enhancers in primary human neural progenitor cells (phNPCs). We select candidate regions by integrating data from NPCs, prefrontal cortex, developmental timepoints, and GWASs. Over 8000 regions demonstrate enhancer activity in the phNPCs, and we link these regions to over 2200 predicted target genes. These genes are involved in neuronal and psychiatric disease-associated pathways, including neuronal system, nervous system development, and developmental delay. We functionally validate a subset of these enhancers using mutation STARR-sequencing and CRISPR deletions, demonstrating the effects of genetic variation on enhancer activity and enhancer deletion on gene expression. Overall, we identify thousands of highly active enhancers and functionally validated a subset of these enhancers, improving our understanding of regulatory networks underlying brain function and disease.