Siwei Xu

Regulation of gene expression is a highly coordinated process in both the healthy and pathological brain with unique patterns across a multitude of cell types. Here we present a multi-omic single nucleus study of ~175,000 nuclei from 50 donors with alcohol use disorder (AUD) and control donors without AUD, profiling cell type specific gene expression and chromatin accessibility in the human central amygdala. We identify all major CNS cell types and neuronal subtypes and find inhibitory neurons are particularly affected by AUD. We find high numbers of differentially expressed genes (DEGs) including GABRA2 , GRM8 , and NCAM1 and show significant enrichment for AUD risk genes within these DEGs. We identified 51,431 cell type-specific, disease associated candidate cis -regulatory elements including an interneuron-associated set of chromatin loops at the AUD risk gene CALN1 . Transcription factor footprinting identified Kruppel-like factors upstream of AUD GWAS genes and DEGs. Finally, we also perform cell type-specific fine mapping for AUD GWAS to prioritize variants within functional genomic elements.

The single-cell sequencing revolution enables simultaneous molecular profiling of various modalities across thousands of individual cells, allowing scientists to investigate the diverse functions of complex tissues. Among all the analysis steps, assigning individual cells to specific types is fundamental for understanding cellular heterogeneity. However, this process is labor-intensive and requires extensive expert knowledge. Recent advances in large language models (LLMs) have demonstrated their ability to automatically extract biological knowledge, such as marker genes, promoting efficient, and automated cell-type annotations. To evaluate the capability of modern LLMs in automating the cell-type identification process, we first introduce an automated cell-type annotation method with comprehensive benchmark: Single-cell Omics Arena). Specifically, we began by compiling 11 publicly available single-cell RNA sequencing (scRNA-seq) datasets and evaluating eight LLMs across 1226 cell-type annotation-related tasks. This effort established a foundation for automated cell-type annotation from scRNA-seq data using interpretable features such as gene names. Building upon this benchmark, we introduced domain-specific chain-of-thought prompting techniques to enhance the accuracy of cell-type annotation and facilitate the extraction of relevant biological insights. Finally, to accommodate non-interpretable features, we proposed to leverage a pretrained VAE-based cross-modality translation module to convert features such as epigenetic marks into interpretable representations, which enables the seamless extension of LLM-based cell-type annotation to non-RNA-based sequencing technologies. In summary, our benchmark provides key insights into automated cell-type annotation from scRNA-seq data and demonstrates the potential of cross-modality translation for handling non-interpretable features.

Measuring splicing and chromatin accessibility simultaneously in frozen tissues remains challenging. Here we combined single-cell isoform RNA sequencing and assay for transposase accessible chromatin (ScISOr–ATAC) to interrogate the correlation between these modalities in single cells in human and rhesus macaque frozen cortical tissue samples. Applying a previous definition of four ‘cell states’ in which the transcriptome and chromatin accessibility are coupled or decoupled for each gene, we demonstrate that splicing patterns in one cell state can differ from those of another state within the same cell type. We also use ScISOr–ATAC to measure the correlation of chromatin and splicing across brain cell types, cortical regions and species (macaque and human) and in Alzheimer’s disease. In macaques, some excitatory neuron subtypes show brain-region-specific splicing and chromatin accessibility. In human and macaque prefrontal cortex, strong evolutionary divergence in one molecular modality does not necessarily imply strong divergence in another modality. Finally, in Alzheimer’s disease, oligodendrocytes show high dysregulation in both chromatin and splicing.

Post-traumatic stress disorder (PTSD) is a polygenic disorder occurring after extreme trauma exposure. Recent studies have begun to detail the molecular biology of PTSD. However, given the array of PTSD-perturbed molecular pathways identified so far¹, it is implausible that a single cell type is responsible. Here we profile the molecular responses in over two million nuclei from the dorsolateral prefrontal cortex of 111 human brains, collected post-mortem from individuals with and without PTSD and major depressive disorder. We identify neuronal and non-neuronal cell-type clusters, gene expression changes and transcriptional regulators, and map the epigenomic regulome of PTSD in a cell-type-specific manner. Our analysis revealed PTSD-associated gene alterations in inhibitory neurons, endothelial cells and microglia and uncovered genes and pathways associated with glucocorticoid signalling, GABAergic transmission and neuroinflammation. We further validated these findings using cell-type-specific spatial transcriptomics, confirming disruption of key genes such as SST and FKBP5. By integrating genetic, transcriptomic and epigenetic data, we uncovered the regulatory mechanisms of credible variants that disrupt PTSD genes, including ELFN1, MAD1L1 and KCNIP4, in a cell-type-specific context. Together, these findings provide a comprehensive characterization of the cell-specific molecular regulatory mechanisms that underlie the persisting effects of traumatic stress response on the human prefrontal cortex.

Single-cell sequencing technologies have revolutionized genomics by enabling the simultaneous profiling of various molecular modalities within individual cells. Their integration, especially cross-modality translation, offers deep insights into cellular regulatory mechanisms. Many methods have been developed for cross-modality translation, but their reliance on scarce high-quality co-assay data limits their applicability. Addressing this, we introduce scACT, a deep generative model designed to extract cross-modality biological insights from unpaired single-cell data. scACT tackles three major challenges: aligning unpaired multi-modal data via adversarial training, facilitating cross-modality translation without prior knowledge via cycle-consistent training, and enabling interpretable regulatory interconnections explorations via in-silico perturbations. To test its performance, we applied scACT on diverse single-cell datasets and found it outperformed existing methods in all three tasks. Finally, we have developed scACT as an individual open-source software package to advance single-cell omics data processing and analysis within the research community.

Spatial transcriptomics has transformed genomic research by measuring spatially resolved gene expressions, allowing us to investigate how cells adapt to their microenvironment via modulating their expressed genes. This essential process usually starts from cell-cell communication (CCC) via ligand-receptor (LR) interaction, leading to regulatory changes within the receiver cell. However, few methods were developed to connect them to provide biological insights into intercellular regulation. To fill this gap, we propose iMiracle, an iterative multi-view graph neural network that models each cell's intercellular regulation with three key features. Firstly, iMiracle integrates inter- and intra-cellular networks to jointly estimate cell-type- and micro-environment-driven gene expressions. Optionally, it allows prior knowledge of intra-cellular networks as pre-structured masks to maintain biological relevance. Secondly, iMiracle employs iterative learning to overcome the sparsity of spatial transcriptomic data and gradually fill in the missing edges in the CCC network. Thirdly, iMiracle infers a cell-specific ligand-gene regulatory score based on the contributions of different LR pairs to interpret inter-cellular regulation. We applied iMiracle to nine simulated and eight real datasets from three sequencing platforms and demonstrated that iMiracle consistently outperformed ten methods in gene expression imputation and four methods in regulatory score inference. Lastly, we developed iMiracle as an open-source software and anticipate that it can be a powerful tool in decoding the complexities of inter-cellular transcriptional regulation.

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

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.

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. Summary Figure:

Mapping chromatin insulator loops is crucial to investigating genome evolution, elucidating critical biological functions, and ultimately quantifying variant impact in diseases. However, chromatin conformation profiling assays are usually expensive, time-consuming, and may report fuzzy insulator annotations with low resolution. Therefore, we propose a weakly supervised deep learning method, InsuLock, to address these challenges. Specifically, InsuLock first utilizes a Siamese neural network to predict the existence of insulators within a given region (up to 2000 bp). Then, it uses an object detection module for precise insulator boundary localization via gradient-weighted class activation mapping (~40 bp resolution). Finally, it quantifies variant impacts by comparing the insulator score differences between the wild-type and mutant alleles. We applied InsuLock on various bulk and single-cell datasets for performance testing and benchmarking. We showed that it outperformed existing methods with an AUROC of ~0.96 and condensed insulator annotations to ~2.5% of their original size while still demonstrating higher conservation scores and better motif enrichments. Finally, we utilized InsuLock to make cell-type-specific variant impacts from brain scATAC-seq data and identified a schizophrenia GWAS variant disrupting an insulator loop proximal to a known risk gene, indicating a possible new mechanism of action for the disease.