Functional inference of gene regulation using single-cell multi-omics

MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents major advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) modules for inferring temporal gene regulatory networks from longitudinal data. These advances, in addition to open chromatin analyses, provide a robust framework after quality control and cell labeling to study gene regulatory programs in human disease. We benchmark MOCHA with four state-of-the-art tools to demonstrate its advances. We also construct cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data.

Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping

The ability to spatially map multiple layers of the omics information over different time points allows for exploring the mechanisms driving brain development, differentiation, arealization, and alterations in disease. Herein we developed and applied spatial tri-omic sequencing technologies, DBiT ARP-seq (spatial ATAC-RNA-Protein-seq) and DBiT CTRP-seq (spatial CUT&Tag-RNA-Protein-seq) together with multiplexed immunofluorescence imaging (CODEX) to map spatial dynamic remodeling in brain development and neuroinflammation. A spatiotemporal tri-omic atlas of the mouse brain was obtained at different stages from postnatal day P0 to P21, and compared to the regions of interest in the human developing brains. Specifically, in the cortical area, we discovered temporal persistence and spatial spreading of chromatin accessibility for the layer-defining transcription factors. In corpus callosum, we observed dynamic chromatin priming of myelin genes across the subregions. Together, it suggests a role for layer specific projection neurons to coordinate axonogenesis and myelination. We further mapped the brain of a lysolecithin (LPC) neuroinflammation mouse model and observed common molecular programs in development and neuroinflammation. Microglia, exhibiting both conserved and distinct programs for inflammation and resolution, are transiently activated not only at the core of the LPC lesion, but also at distal locations presumably through neuronal circuitry. Thus, this work unveiled common and differential mechanisms in brain development and neuroinflammation, resulting in a valuable data resource to investigate brain development, function and disease.

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

Single-cell and spatial multiomic inference of gene regulatory networks using SCRIPro

MOTIVATION

The burgeoning generation of single-cell or spatial multiomic data allows for the characterization of gene regulation networks (GRNs) at an unprecedented resolution. However, the accurate reconstruction of GRNs from sparse and noisy single-cell or spatial multiomic data remains challenging.

RESULTS

Here, we present SCRIPro, a comprehensive computational framework that robustly infers GRNs for both single-cell and spatial multi-omics data. SCRIPro first improves sample coverage through a density clustering approach based on multiomic and spatial similarities. Additionally, SCRIPro scans transcriptional regulator (TR) importance by performing chromatin reconstruction and in silico deletion analyses using a comprehensive reference covering 1,292 human and 994 mouse TRs. Finally, SCRIPro combines TR-target importance scores derived from multiomic data with TR-target expression levels to ensure precise GRN reconstruction. We benchmarked SCRIPro on various datasets, including single-cell multiomic data from human B-cell lymphoma, mouse hair follicle development, Stereo-seq of mouse embryos, and Spatial-ATAC-RNA from mouse brain. SCRIPro outperforms existing motif-based methods and accurately reconstructs cell type-specific, stage-specific, and region-specific GRNs. Overall, SCRIPro emerges as a streamlined and fast method capable of reconstructing TR activities and GRNs for both single-cell and spatial multi-omic data.

AVAILABILITY

SCRIPro is available at https://github.com/wanglabtongji/SCRIPro.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Controlled Noise: Evidence of epigenetic regulation of Single-Cell expression variability

MOTIVATION

Understanding single-cell expression variability (scEV) or gene expression noise among cells of the same type and state is crucial for delineating population-level cellular function. While epigenetic mechanisms are widely implicated in gene expression regulation, a definitive link between chromatin accessibility and scEV remains elusive. Recent advances in single-cell techniques enable the study of single-cell multiomics data that include the simultaneous measurement of scATAC-seq and scRNA-seq within individual cells, presenting an unprecedented opportunity to address this gap.

RESULTS

This paper introduces an innovative testing pipeline to investigate the association between chromatin accessibility and scEV. With single-cell multiomics data of scATAC-seq and scRNA-seq, the pipeline hinges on comparing the prediction performance of scATAC-seq data on gene expression levels between highly variable genes (HVGs) and non-highly variable genes (non-HVGs). Applying this pipeline to paired scATAC-seq and scRNA-seq data from human hematopoietic stem and progenitor cells, we observed a significantly superior prediction performance of scATAC-seq data for HVGs compared to non-HVGs. Notably, there was substantial overlap between well-predicted genes and HVGs. The gene pathways enriched from well-predicted genes are highly pertinent to cell type-specific functions. Our findings support the notion that scEV largely stems from cell-to-cell variability in chromatin accessibility, providing compelling evidence for the epigenetic regulation of scEV and offering promising avenues for investigating gene regulation mechanisms at the single-cell level.

AVAILABILITY

The source code and data used in this paper can be found at https://github.com/SiweiCui/EpigeneticControlOfSingle-CellExpressionVariability.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Supervised discovery of interpretable gene programs from single-cell data

Factor analysis decomposes single-cell gene expression data into a minimal set of gene programs that correspond to processes executed by cells in a sample. However, matrix factorization methods are prone to technical artifacts and poor factor interpretability. We address these concerns with Spectra, an algorithm that combines user-provided gene programs with the detection of novel programs that together best explain expression covariation. Spectra incorporates existing gene sets and cell-type labels as prior biological information, explicitly models cell type and represents input gene sets as a gene-gene knowledge graph using a penalty function to guide factorization toward the input graph. We show that Spectra outperforms existing approaches in challenging tumor immune contexts, as it finds factors that change under immune checkpoint therapy, disentangles the highly correlated features of CD8+ T cell tumor reactivity and exhaustion, finds a program that explains continuous macrophage state changes under therapy and identifies cell-type-specific immune metabolic programs.

REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data

MOTIVATION

Profiling of gene expression and chromatin accessibility by single-cell multi-omics approaches can help to systematically decipher how transcription factors (TFs) regulate target gene expression via cis-region interactions. However, integrating information from different modalities to discover regulatory associations is challenging, in part because motif scanning approaches miss many likely TF binding sites.

RESULTS

We develop REUNION, a framework for predicting genome-wide TF binding and cis-region-TF-gene "triplet" regulatory associations using single-cell multi-omics data. The first component of REUNION, Unify, utilizes information theory-inspired complementary score functions that incorporate TF expression, chromatin accessibility, and target gene expression to identify regulatory associations. The second component, Rediscover, takes Unify estimates as input for pseudo semi-supervised learning to predict TF binding in accessible genomic regions that may or may not include detected TF motifs. Rediscover leverages latent chromatin accessibility and sequence feature spaces of the genomic regions, without requiring chromatin immunoprecipitation data for model training. Applied to peripheral blood mononuclear cell data, REUNION outperforms alternative methods in TF binding prediction on average performance. In particular, it recovers missing region-TF associations from regions lacking detected motifs, which circumvents the reliance on motif scanning and facilitates discovery of novel associations involving potential co-binding transcriptional regulators. Newly identified region-TF associations, even in regions lacking a detected motif, improve the prediction of target gene expression in regulatory triplets, and are thus likely to genuinely participate in the regulation.

AVAILABILITY AND IMPLEMENTATION

All source code is available at https://github.com/yangymargaret/REUNION.

A Novel Macrophage Subpopulation Conveys Increased Genetic Risk of Coronary Artery Disease

BACKGROUND

Coronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. Although over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types such as macrophages.

METHODS

We utilized single-cell RNA-seq and single-cell multiomics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis-the formation of lipid-laden foam cells. The relative genetic contribution to CAD was assessed by partitioning disease heritability across different macrophage subpopulations. Meta-analysis of single-cell RNA-seq data sets from 38 human atherosclerotic samples was conducted to provide high-resolution cross-referencing to macrophage subpopulations in vivo.

RESULTS

We identified 18 782 cis-regulatory elements by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD genome-wide association study data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of a novel CD52-hi lipid-handling macrophage subpopulation. These CD52-hi macrophages displayed significantly less lipoprotein accumulation and were also found in human atherosclerotic plaques. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-β in the causal mechanisms at this locus.

CONCLUSIONS

Our results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key subpopulation through which genetic variants operate to influence disease. These findings provide an unbiased framework for functional fine-mapping of genome-wide association study results using single-cell multiomics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.

Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes

Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage-tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics. However, reliance on transcriptional profiling limits adaptation to other single-cell assays. With CellTag-multi, we present an approach that enables direct capture of heritable random barcodes expressed as polyadenylated transcripts, in both single-cell RNA sequencing and single-cell Assay for Transposase Accessible Chromatin using sequencing assays, allowing for independent clonal tracking of transcriptional and epigenomic cell states. We validate CellTag-multi to characterize progenitor cell lineage priming during mouse hematopoiesis. Additionally, in direct reprogramming of fibroblasts to endoderm progenitors, we identify core regulatory programs underlying on-target and off-target fates. Furthermore, we reveal the transcription factor Zfp281 as a regulator of reprogramming outcome, biasing cells toward an off-target mesenchymal fate. Our results establish CellTag-multi as a lineage-tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.

Single-Cell Sequencing Technology in Ruminant Livestock: Challenges and Opportunities

Advancements in single-cell sequencing have transformed the genomics field by allowing researchers to delve into the intricate cellular heterogeneity within tissues at greater resolution. While single-cell omics are more widely applied in model organisms and humans, their use in livestock species is just beginning. Studies in cattle, sheep, and goats have already leveraged single-cell and single-nuclei RNA-seq as well as single-cell and single-nuclei ATAC-seq to delineate cellular diversity in tissues, track changes in cell populations and gene expression over developmental stages, and characterize immune cell populations important for disease resistance and resilience. Although challenges exist for the use of this technology in ruminant livestock, such as the precise annotation of unique cell populations and spatial resolution of cells within a tissue, there is vast potential to enhance our understanding of the cellular and molecular mechanisms underpinning traits essential for healthy and productive livestock. This review intends to highlight the insights gained from published single-cell omics studies in cattle, sheep, and goats, particularly those with publicly accessible data. Further, this manuscript will discuss the challenges and opportunities of this technology in ruminant livestock and how it may contribute to enhanced profitability and sustainability of animal agriculture in the future.

Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance

Methods that analyze single-cell paired RNA-seq and ATAC-seq multiome data have shown great promise in linking regulatory elements to genes. However, existing methods differ in their modeling assumptions and approaches to account for biological and technical noise-leading to low concordance in their linking scores-and do not capture the effects of genomic distance. We propose pgBoost, an integrative modeling framework that trains a non-linear combination of existing linking strategies (including genomic distance) on fine-mapped eQTL data to assign a probabilistic score to each candidate SNP-gene link. We applied pgBoost to single-cell multiome data from 85k cells representing 6 major immune/blood cell types. pgBoost attained higher enrichment for fine-mapped eSNP-eGene pairs (e.g. 21x at distance >10kb) than existing methods (1.2-10x; p-value for difference = 5e-13 vs. distance-based method and < 4e-35 for each other method), with larger improvements at larger distances (e.g. 35x vs. 0.89-6.6x at distance >100kb; p-value for difference < 0.002 vs. each other method). pgBoost also outperformed existing methods in enrichment for CRISPR-validated links (e.g. 4.8x vs. 1.6-4.1x at distance >10kb; p-value for difference = 0.25 vs. distance-based method and < 2e-5 for each other method), with larger improvements at larger distances (e.g. 15x vs. 1.6-2.5x at distance >100kb; p-value for difference < 0.009 for each other method). Similar improvements in enrichment were observed for links derived from Activity-By-Contact (ABC) scores and GWAS data. We further determined that restricting pgBoost to features from a focal cell type improved the identification of SNP-gene links relevant to that cell type. We highlight several examples where pgBoost linked fine-mapped GWAS variants to experimentally validated or biologically plausible target genes that were not implicated by other methods. In conclusion, a non-linear combination of linking strategies, including genomic distance, improves power to identify target genes underlying GWAS associations.

Cell of origin epigenetic priming determines susceptibility to Tet2 mutation

Hematopoietic stem cell (HSC) mutations can result in clonal hematopoiesis (CH) with heterogeneous clinical outcomes. Here, we investigate how the cell state preceding Tet2 mutation impacts the pre-malignant phenotype. Using an inducible system for clonal analysis of myeloid progenitors, we find that the epigenetic features of clones at similar differentiation status are highly heterogeneous and functionally respond differently to Tet2 mutation. Cell differentiation stage also influences Tet2 mutation response indicating that the cell of origin's epigenome modulates clone-specific behaviors in CH. Molecular features associated with higher risk outcomes include Sox4 that sensitizes cells to Tet2 inactivation, inducing dedifferentiation, altered metabolism and increasing the in vivo clonal output of mutant cells, as confirmed in primary GMP and HSC models. Our findings validate the hypothesis that epigenetic features can predispose specific clones for dominance, explaining why identical genetic mutations can result in different phenotypes.

CellWalker2: multi-omic discovery of hierarchical cell type relationships and their associations with genomic annotations

CellWalker2 is a graph diffusion-based method for single-cell genomics data integration. It extends the CellWalker model by incorporating hierarchical relationships between cell types, providing estimates of statistical significance, and adding data structures for analyzing multi-omics data so that gene expression and open chromatin can be jointly modeled. Our open-source software enables users to annotate cells using existing ontologies and to probabilistically match cell types between two or more contexts, including across species. CellWalker2 can also map genomic regions to cell ontologies, enabling precise annotation of elements derived from bulk data, such as enhancers, genetic variants, and sequence motifs. Through simulation studies, we show that CellWalker2 performs better than existing methods in cell type annotation and mapping. We then use data from the brain and immune system to demonstrate CellWalker2's ability to discover cell type-specific regulatory programs and both conserved and divergent cell type relationships in complex tissues.

GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks

We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based causal implicit generative model for simulating single-cell RNA-seq data, in silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on six experimental reference datasets, we show that our model captures non-linear TF-gene dependencies and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. GRouNdGAN can synthesize cells under new conditions to perform in silico TF knockout experiments. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest.

Decoding the principle of cell-fate determination for its reverse control

Understanding and manipulating cell fate determination is pivotal in biology. Cell fate is determined by intricate and nonlinear interactions among molecules, making mathematical model-based quantitative analysis indispensable for its elucidation. Nevertheless, obtaining the essential dynamic experimental data for model development has been a significant obstacle. However, recent advancements in large-scale omics data technology are providing the necessary foundation for developing such models. Based on accumulated experimental evidence, we can postulate that cell fate is governed by a limited number of core regulatory circuits. Following this concept, we present a conceptual control framework that leverages single-cell RNA-seq data for dynamic molecular regulatory network modeling, aiming to identify and manipulate core regulatory circuits and their master regulators to drive desired cellular state transitions. We illustrate the proposed framework by applying it to the reversion of lung cancer cell states, although it is more broadly applicable to understanding and controlling a wide range of cell-fate determination processes.

Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS

MOTIVATION

The molecular identity of a cell results from a complex interplay between heterogeneous molecular layers. Recent advances in single-cell sequencing technologies have opened the possibility to measure such molecular layers of regulation.

RESULTS

Here, we present HuMMuS, a new method for inferring regulatory mechanisms from single-cell multi-omics data. Differently from the state-of-the-art, HuMMuS captures cooperation between biological macromolecules and can easily include additional layers of molecular regulation. We benchmarked HuMMuS with respect to the state-of-the-art on both paired and unpaired multi-omics datasets. Our results proved the improvements provided by HuMMuS in terms of transcription factor (TF) targets, TF binding motifs and regulatory regions prediction. Finally, once applied to snmC-seq, scATAC-seq and scRNA-seq data from mouse brain cortex, HuMMuS enabled to accurately cluster scRNA profiles and to identify potential driver TFs.

AVAILABILITY AND IMPLEMENTATION

HuMMuS is available at https://github.com/cantinilab/HuMMuS.

Untangling the gordian knot: The intertwining interactions between developmental hormone signaling and epigenetic mechanisms in insects

Hormones control developmental and physiological processes, often by regulating the expression of multiple genes simultaneously or sequentially. Crosstalk between hormones and epigenetics is pivotal to dynamically coordinate this process. Hormonal signals can guide the addition and removal of epigenetic marks, steering gene expression. Conversely, DNA methylation, histone modifications and non-coding RNAs can modulate regional chromatin structure and accessibility and regulate the expression of numerous (hormone-related) genes. Here, we provide a review of the interplay between the classical insect hormones, ecdysteroids and juvenile hormones, and epigenetics. We summarize the mode-of-action and roles of these hormones in post-embryonic development, and provide a general overview of epigenetic mechanisms. We then highlight recent advances on the interactions between these hormonal pathways and epigenetics, and their involvement in development. Furthermore, we give an overview of several 'omics techniques employed in the field. Finally, we discuss which questions remain unanswered and possible avenues for future research.

Multimodal cell atlas of the ageing human skeletal muscle

Muscle atrophy and functional decline (sarcopenia) are common manifestations of frailty and are critical contributors to morbidity and mortality in older people1. Deciphering the molecular mechanisms underlying sarcopenia has major implications for understanding human ageing2. Yet, progress has been slow, partly due to the difficulties of characterizing skeletal muscle niche heterogeneity (whereby myofibres are the most abundant) and obtaining well-characterized human samples3,4. Here we generate a single-cell/single-nucleus transcriptomic and chromatin accessibility map of human limb skeletal muscles encompassing over 387,000 cells/nuclei from individuals aged 15 to 99 years with distinct fitness and frailty levels. We describe how cell populations change during ageing, including the emergence of new populations in older people, and the cell-specific and multicellular network features (at the transcriptomic and epigenetic levels) associated with these changes. On the basis of cross-comparison with genetic data, we also identify key elements of chromatin architecture that mark susceptibility to sarcopenia. Our study provides a basis for identifying targets in the skeletal muscle that are amenable to medical, pharmacological and lifestyle interventions in late life.

TIM-3+ CD8 T cells with a terminally exhausted phenotype retain functional capacity in hematological malignancies

Chronic antigen stimulation is thought to generate dysfunctional CD8 T cells. Here, we identify a CD8 T cell subset in the bone marrow tumor microenvironment that, despite an apparent terminally exhausted phenotype (TPHEX), expressed granzymes, perforin, and IFN-γ. Concurrent gene expression and DNA accessibility revealed that genes encoding these functional proteins correlated with BATF expression and motif accessibility. IFN-γ+ TPHEX effectively killed myeloma with comparable efficacy to transitory effectors, and disease progression correlated with numerical deficits in IFN-γ+ TPHEX. We also observed IFN-γ+ TPHEX within CD19-targeted chimeric antigen receptor T cells, which killed CD19+ leukemia cells. An IFN-γ+ TPHEX gene signature was recapitulated in TEX cells from human cancers, including myeloma and lymphoma. Here, we characterize a TEX subset in hematological malignancies that paradoxically retains function and is distinct from dysfunctional TEX found in chronic viral infections. Thus, IFN-γ+ TPHEX represent a potential target for immunotherapy of blood cancers.

Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

We present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and scATAC-seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene-peak correlations and dependence on peak calling. SCARlink outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across high-coverage multi-ome datasets while giving comparable to improved performance on low-coverage datasets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 11× to 15× and 5× to 12× enriched in fine-mapped eQTLs and fine-mapped genome-wide association study (GWAS) variants, respectively. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.

PACS allows comprehensive dissection of multiple factors governing chromatin accessibility from snATAC-seq data

Single nucleus ATAC-seq (snATAC-seq) experimental designs have become increasingly complex with multiple factors that might affect chromatin accessibility, including genotype, cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing methods. In addition, current snATAC-seq data present statistical difficulties due to their sparsity and variations in individual sequence capture. To address these problems, we present a zero-adjusted statistical model, Probability model of Accessible Chromatin of Single cells (PACS), that can allow complex hypothesis testing of factors that affect accessibility while accounting for sparse and incomplete data. For differential accessibility analysis, PACS controls the false positive rate and achieves on average a 17% to 122% higher power than existing tools. We demonstrate the effectiveness of PACS through several analysis tasks including supervised cell type annotation, compound hypothesis testing, batch effect correction, and spatiotemporal modeling. We apply PACS to several datasets from a variety of tissues and show its ability to reveal previously undiscovered insights in snATAC-seq data.

txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility

We develop a large-scale single-cell ATAC-seq method by combining Tn5-based pre-indexing with 10× Genomics barcoding, enabling the indexing of up to 200,000 nuclei across multiple samples in a single reaction. We profile 449,953 nuclei across diverse tissues, including the human cortex, mouse brain, human lung, mouse lung, mouse liver, and lung tissue from a club cell secretory protein knockout (CC16-/-) model. Our study of CC16-/- nuclei uncovers previously underappreciated technical artifacts derived from remnant 129 mouse strain genetic material, which cause profound cell-type-specific changes in regulatory elements near many genes, thereby confounding the interpretation of this commonly referenced mouse model.

Epigenetic signature and key transcriptional regulators of human antigen-specific type 1 regulatory T cells

Human adaptive immunity is orchestrated by effector and regulatory T (Treg) cells. Natural Tregs arise in the thymus where they are shaped to recognize self-antigens, while type 1 Tregs or Tr1 cells are induced from conventional peripheral CD4 + T cells in response to peripheral antigens, such as alloantigens and allergens. Tr1 cells have been developed as a potential therapy for inducing antigen-specific tolerance, because they can be rapidly differentiated in vitro in response to a target antigen. However, the epigenetic landscape and the identity of transcription factors (TFs) that regulate differentiation, phenotype, and functions of human antigen-specific Tr1 cells is largely unknown, hindering Tr1 research and broader clinical development. Here, we reveal the unique epigenetic signature of antigen-specific Tr1 cells, and TFs that regulate their differentiation, phenotype and function. We showed that in vitro induced antigen-specific Tr1 cells are distinct both clonally and transcriptionally from natural Tregs and other conventional CD4 + T cells on a single-cell level. An integrative analysis of Tr1 cell epigenome and transcriptome identified a TF signature unique to antigen-specific Tr1 cells, and predicted that IRF4, BATF, and MAF act as their transcriptional regulators. Using functional genomics, we showed that each of these TFs play a non-redundant role in regulating Tr1 cell differentiation, suppressive function, and expression of co-inhibitory and cytotoxic proteins. By using the Tr1-specific TF signature as a molecular fingerprint, we tracked Tr1 cells in peripheral blood of recipients of allogeneic hematopoietic stem cell transplantation treated with adoptive Tr1 cell therapy. Furthermore, the same signature identified Tr1 cells in resident CD4 + T cells in solid tumors. Altogether, these results reveal the epigenetic signature and the key transcriptional regulators of human Tr1 cells. These data will guide mechanistic studies of human Tr1 cell biology and the development and optimization of adoptive Tr1 cell therapies.

Complex regulatory networks influence pluripotent cell state transitions in human iPSCs

Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Analyzing hundreds of hiPSCs derived from different individuals, we show the proportions of these pluripotent states vary considerably across lines. We discover 13 gene network modules (GNMs) and 13 regulatory network modules (RNMs), which are highly correlated with each other suggesting that the coordinated co-accessibility of regulatory elements in the RNMs likely underlie the coordinated expression of genes in the GNMs. Epigenetic analyses reveal that regulatory networks underlying self-renewal and pluripotency are more complex than previously realized. Genetic analyses identify thousands of regulatory variants that overlapped predicted transcription factor binding sites and are associated with chromatin accessibility in the hiPSCs. We show that the master regulator of pluripotency, the NANOG-OCT4 Complex, and its associated network are significantly enriched for regulatory variants with large effects, suggesting that they play a role in the varying cellular proportions of pluripotency states between hiPSCs. Our work bins tens of thousands of regulatory elements in hiPSCs into discrete regulatory networks, shows that pluripotency and self-renewal processes have a surprising level of regulatory complexity, and suggests that genetic factors may contribute to cell state transitions in human iPSC lines.

Signaling via a CD27-TRAF2-SHP-1 axis during naive T cell activation promotes memory-associated gene regulatory networks

The interaction of the tumor necrosis factor receptor (TNFR) family member CD27 on naive CD8+ T (Tn) cells with homotrimeric CD70 on antigen-presenting cells (APCs) is necessary for T cell memory fate determination. Here, we examined CD27 signaling during Tn cell activation and differentiation. In conjunction with T cell receptor (TCR) stimulation, ligation of CD27 by a synthetic trimeric CD70 ligand triggered CD27 internalization and degradation, suggesting active regulation of this signaling axis. Internalized CD27 recruited the signaling adaptor TRAF2 and the phosphatase SHP-1, thereby modulating TCR and CD28 signals. CD27-mediated modulation of TCR signals promoted transcription factor circuits that induced memory rather than effector associated gene programs, which are induced by CD28 costimulation. CD27-costimulated chimeric antigen receptor (CAR)-engineered T cells exhibited improved tumor control compared with CD28-costimulated CAR-T cells. Thus, CD27 signaling during Tn cell activation promotes memory properties with relevance to T cell immunotherapy.

Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing

Renal proximal tubule epithelial cells have considerable intrinsic repair capacity following injury. However, a fraction of injured proximal tubule cells fails to undergo normal repair and assumes a proinflammatory and profibrotic phenotype that may promote fibrosis and chronic kidney disease. The healthy to failed repair change is marked by cell state-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq sequencing offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We develop a regularized regression approach to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generate a single nucleus multiomic dataset from seven adult human kidney samples and apply our method to study drivers of a failed injury response associated with kidney disease. We demonstrate that our approach is a highly effective tool for predicting key cis- and trans-regulatory elements underpinning the healthy to failed repair transition and use it to identify NFAT5 as a driver of the maladaptive proximal tubule state.

Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space by using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal data sets, we show scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome data set we generated from differentiating mouse embryonic stem cells over time, we show scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

Studying temporal dynamics of single cells: expression, lineage and regulatory networks

Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-023-01090-5.

Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects

The Single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) has gained increasing popularity in recent years, allowing for chromatin accessibility to be deciphered and gene regulatory networks (GRNs) to be inferred at single-cell resolution. This cutting-edge technology now enables the genome-wide profiling of chromatin accessibility at the cellular level and the capturing of cell-type-specific cis-regulatory elements (CREs) that are masked by cellular heterogeneity in bulk assays. Additionally, it can also facilitate the identification of rare and new cell types based on differences in chromatin accessibility and the charting of cellular developmental trajectories within lineage-related cell clusters. Due to technical challenges and limitations, the data generated from scATAC-seq exhibit unique features, often characterized by high sparsity and noise, even within the same cell type. To address these challenges, various bioinformatic tools have been developed. Furthermore, the application of scATAC-seq in plant science is still in its infancy, with most research focusing on root tissues and model plant species. In this review, we provide an overview of recent progress in scATAC-seq and its application across various fields. We first conduct scATAC-seq in plant science. Next, we highlight the current challenges of scATAC-seq in plant science and major strategies for cell type annotation. Finally, we outline several future directions to exploit scATAC-seq technologies to address critical challenges in plant science, ranging from plant ENCODE(The Encyclopedia of DNA Elements) project construction to GRN inference, to deepen our understanding of the roles of CREs in plant biology.

MICA: a multi-omics method to predict gene regulatory networks in early human embryos

Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.

Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation

In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulatory code. Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types. We found that zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types. These states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors, such as HNF4A, CEBPA, FOXA1 and ONECUT1. To examine the architecture of the enhancers driving these cell states, we trained a hierarchical deep learning model called DeepLiver. Our study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state that can be used to engineer enhancers with specific activity levels and zonation patterns.

Integrated single-cell multiomics uncovers foundational regulatory mechanisms of lens development and pathology

Ocular lens development entails epithelial to fiber cell differentiation, defects in which cause congenital cataracts. We report the first single-cell multiomic atlas of lens development, leveraging snRNA-seq, snATAC-seq and CUT&RUN-seq to discover previously unreported mechanisms of cell fate determination and cataract-linked regulatory networks. A comprehensive profile of cis- and trans-regulatory interactions, including for the cataract-linked transcription factor MAF, is established across a temporal trajectory of fiber cell differentiation. Furthermore, we identify an epigenetic paradigm of cellular differentiation, defined by progressive loss of the H3K27 methylation writer Polycomb repressive complex 2 (PRC2). PRC2 localizes to heterochromatin domains across master-regulator transcription factor gene bodies, suggesting it safeguards epithelial cell fate. Moreover, we demonstrate that FGF hyper-stimulation in vivo leads to MAF network activation and the emergence of novel lens cell states. Collectively, these data depict a comprehensive portrait of lens fiber cell differentiation, while defining regulatory effectors of cell identity and cataract formation.

Mild dehydration effects on the murine kidney single-nucleus transcriptome and chromatin accessibility

Daily, we may experience mild dehydration with a rise in plasma osmolality that triggers the release of vasopressin. Although the effect of dehydration is well characterized in collecting duct principal cells (CDPCs), we hypothesized that mild dehydration (<12 h) results in many kidney cell-specific changes in transcriptomes and chromatin accessibility. Single-nucleus (sn) multiome (RNA-assay for transposase-accessible chromatin) sequencing and bulk RNA sequencing of kidneys from male and female mice that were mildly water deprived or not were compared. Water-deprived mice had a significant increase in plasma osmolality. sn-multiome-seq resulted in 19,837 nuclei that were annotated into 33 clusters. In CDPCs, aquaporin 2 (Aqp2) and aquaporin 3 (Apq3) were greater in dehydrated mice, but there were novel genes like gremlin 2 (Grem2; a cytokine) that were increased compared with ad libitum mice. The transcription factor cAMP-responsive element modulator (Crem) was greater in CDPCs of dehydrated mice, and the Crem DNA motif was more accessible. There were hundreds of sex- and dehydration-specific differentially expressed genes (DEGs) throughout the kidney, especially in the proximal tubules and thin limbs. In male mice, DEGs were enriched in pathways related to lipid metabolism, whereas female DEGs were enriched in organic acid metabolism. Many highly expressed genes had a positive correlation with increased chromatin accessibility, and mild dehydration exerted many transcriptional changes that we detected at the chromatin level. Even with a rise in plasma osmolality, male and female kidneys have distinct transcriptomes suggesting that there may be diverse mechanisms used to remain in fluid balance.NEW & NOTEWORTHY The kidney consists of >30 cell types that work collectively to maintain fluid-electrolyte balance. Kidney single-nucleus transcriptomes and chromatin accessibility profiles from male and female control (ad libitum water and food) or mildly dehydrated mice (ad libitum food, water deprivation) were determined. Mild dehydration caused hundreds of cell- and sex-specific transcriptomic changes, even though the kidney function to conserve water was the same.

Multiomic analysis reveals cellular and epigenetic plasticity in intestinal pouches of ulcerative colitis patients

Background & Aims

Total proctocolectomy with ileal pouch anal anastomosis (IPAA) is the standard of care for patients with severe treatment resistant ulcerative colitis (UC). Despite improvements in patient outcomes, about 50% of patients will develop inflammation of the pouch within 1-2 years following surgery. Establishment of UC pouches is associated with profound histological changes of the mucosa. A detailed characterization of these changes on a cellular and molecular level is crucial for an improved understanding of pouch physiology and diseases management.

Methods

We generated cell-type-resolved transcriptional and epigenetic atlases of UC pouches using scRNA-seq and scATAC-seq data from paired biopsy samples from the ileal pouch and ileal segment above the pouch (pre-pouch) of UC-IPAA patients (n=6, female=2) without symptoms. We also collected data from paired biopsies of the terminal ileum (TI) and ascending colon (AC) from healthy controls (n=6, female=3).

Results

We identified novel populations of colon-like absorptive and secretory epithelial cells, constituting a significant proportion of the epithelial cell fraction in the pouch but not in matched pre-pouch samples. Pouch-specific enterocytes expressed colon-specific genes, including CEACAM5, CA2. However, in contrast to normal colonic epithelium, these cells also expressed a range of inflammatory and secretory genes, similar to previously detected gene expression signatures in IBD patients. Comparison to longitudinal bulk RNA-seq data from UC pouches demonstrated that colon-like epithelial cells are present early after pouch functionalization and independently of subsequent pouchitis. Finally, single cell chromatin accessibility revealed activation colonic transcriptional regulators, including CDX1, NFIA, and EHF.

Conclusion

UC pouches are characterized by partial colonic metaplasia of the epithelium. These data constitute a resource of transcriptomic and epigenetic signatures of cell populations in the pouch and provide an anchor for understanding the underlying molecular mechanisms of pouchitis.

Alterations in gut microbiota and host transcriptome of patients with coronary artery disease

BACKGROUND

Coronary artery disease (CAD) is a widespread heart condition caused by atherosclerosis and influences millions of people worldwide. Early detection of CAD is challenging due to the lack of specific biomarkers. The gut microbiota and host-microbiota interactions have been well documented to affect human health. However, investigation that reveals the role of gut microbes in CAD is still limited. This study aims to uncover the synergistic effects of host genes and gut microbes associated with CAD through integrative genomic analyses.

RESULTS

Herein, we collected 52 fecal and 50 blood samples from CAD patients and matched controls, and performed amplicon and transcriptomic sequencing on these samples, respectively. By comparing CAD patients with health controls, we found that dysregulated gut microbes were significantly associated with CAD. By leveraging the Random Forest method, we found that combining 20 bacteria and 30 gene biomarkers could distinguish CAD patients from health controls with a high performance (AUC = 0.92). We observed that there existed prominent associations of gut microbes with several clinical indices relevant to heart functions. Integration analysis revealed that CAD-relevant gut microbe genus Fusicatenibacter was associated with expression of CAD-risk genes, such as GBP2, MLKL, and CPR65, which is in line with previous evidence (Tang et al., Nat Rev Cardiol 16:137-154, 2019; Kummen et al., J Am Coll Cardiol 71:1184-1186, 2018). In addition, the upregulation of immune-related pathways in CAD patients were identified to be primarily associated with higher abundance of genus Blautia, Eubacterium, Fusicatenibacter, and Monoglobus.

CONCLUSIONS

Our results highlight that dysregulated gut microbes contribute risk to CAD by interacting with host genes. These identified microbes and interacted risk genes may have high potentials as biomarkers for CAD.

Gene regulatory network inference in the era of single-cell multi-omics

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data

BACKGROUND

Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) quantifies chromatin accessibility in single nuclei. These two data types provide complementary information for deciphering cell types and states. However, when analyzed individually, they sometimes produce conflicting results regarding cell type/state assignment. The power is compromised since the two modalities reflect the same underlying biology. Recently, it has become possible to measure both gene expression and chromatin accessibility from the same nucleus. Such paired data enable the direct modeling of the relationships between the two modalities. Given the availability of the vast amount of single-modality data, it is desirable to integrate the paired and unpaired single-modality datasets to gain a comprehensive view of the cellular complexity.

RESULTS

We benchmark nine existing single-cell multi-omic data integration methods. Specifically, we evaluate to what extent the multiome data provide additional guidance for analyzing the existing single-modality data, and whether these methods uncover peak-gene associations from single-modality data. Our results indicate that multiome data are helpful for annotating single-modality data. However, we emphasize that the availability of an adequate number of nuclei in the multiome dataset is crucial for achieving accurate cell type annotation. Insufficient representation of nuclei may compromise the reliability of the annotations. Additionally, when generating a multiome dataset, the number of cells is more important than sequencing depth for cell type annotation.

CONCLUSIONS

Seurat v4 is the best currently available platform for integrating scRNA-seq, snATAC-seq, and multiome data even in the presence of complex batch effects.

Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data

Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

Single-cell chromatin accessibility and transcriptomic characterization of Behcet's disease

Behect's disease is a chronic vasculitis characterized by complex multi-organ immune aberrations. However, a comprehensive understanding of the gene-regulatory profile of peripheral autoimmunity and the diverse immune responses across distinct cell types in Behcet's disease (BD) is still lacking. Here, we present a multi-omic single-cell study of 424,817 cells in BD patients and non-BD individuals. This study maps chromatin accessibility and gene expression in the same biological samples, unraveling vast cellular heterogeneity. We identify widespread cell-type-specific, disease-associated active and pro-inflammatory immunity in both transcript and epigenomic aspects. Notably, integrative multi-omic analysis reveals putative TF regulators that might contribute to chromatin accessibility and gene expression in BD. Moreover, we predicted gene-regulatory networks within nominated TF activators, including AP-1, NF-kB, and ETS transcript factor families, which may regulate cellular interaction and govern inflammation. Our study illustrates the epigenetic and transcriptional landscape in BD peripheral blood and expands understanding of potential epigenomic immunopathology in this disease.

Multi-omic profiling of the developing human cerebral cortex at the single-cell level

The cellular complexity of the human brain is established via dynamic changes in gene expression throughout development that is mediated, in part, by the spatiotemporal activity of cis-regulatory elements (CREs). We simultaneously profiled gene expression and chromatin accessibility in 45,549 cortical nuclei across six broad developmental time points from fetus to adult. We identified cell type-specific domains in which chromatin accessibility is highly correlated with gene expression. Differentiation pseudotime trajectory analysis indicates that chromatin accessibility at CREs precedes transcription and that dynamic changes in chromatin structure play a critical role in neuronal lineage commitment. In addition, we mapped cell type-specific and temporally specific genetic loci implicated in neuropsychiatric traits, including schizophrenia and bipolar disorder. Together, our results describe the complex regulation of cell composition at critical stages in lineage determination and shed light on the impact of spatiotemporal alterations in gene expression on neuropsychiatric disease.

Craniofacial developmental biology in the single-cell era

The evolution of a unique craniofacial complex in vertebrates made possible new ways of breathing, eating, communicating and sensing the environment. The head and face develop through interactions of all three germ layers, the endoderm, ectoderm and mesoderm, as well as the so-called fourth germ layer, the cranial neural crest. Over a century of experimental embryology and genetics have revealed an incredible diversity of cell types derived from each germ layer, signaling pathways and genes that coordinate craniofacial development, and how changes to these underlie human disease and vertebrate evolution. Yet for many diseases and congenital anomalies, we have an incomplete picture of the causative genomic changes, in particular how alterations to the non-coding genome might affect craniofacial gene expression. Emerging genomics and single-cell technologies provide an opportunity to obtain a more holistic view of the genes and gene regulatory elements orchestrating craniofacial development across vertebrates. These single-cell studies generate novel hypotheses that can be experimentally validated in vivo. In this Review, we highlight recent advances in single-cell studies of diverse craniofacial structures, as well as potential pitfalls and the need for extensive in vivo validation. We discuss how these studies inform the developmental sources and regulation of head structures, bringing new insights into the etiology of structural birth anomalies that affect the vertebrate head.

Gene-regulation modules in nonalcoholic fatty liver disease revealed by single-nucleus ATAC-seq

We investigated the progression of nonalcoholic fatty liver disease from fatty liver to steatohepatitis using single-nucleus and bulk ATAC-seq on the livers of rats fed a high-fat diet (HFD). Rats fed HFD for 4 wk developed fatty liver, and those fed HFD for 8 wk further progressed to steatohepatitis. We observed an increase in the proportion of inflammatory macrophages, consistent with the pathological progression. Utilizing machine learning, we divided global gene regulation into modules, wherein transcription factors within a module could regulate genes within the same module, reaffirming known regulatory relationships between transcription factors and biological processes. We identified core genes-central to co-expression and protein-protein interaction-for the biological processes discovered. Notably, a large part of the core genes overlapped with genes previously implicated in nonalcoholic fatty liver disease. Single-nucleus ATAC-seq, combined with data-driven statistical analysis, offers insight into in vivo global gene regulation as a combination of modules and assists in identifying core genes of relevant biological processes.

The technological landscape and applications of single-cell multi-omics

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data

Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.

Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics

Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference, including dynamic rewiring, causal inference, feedback loop modeling and context specificity. To address these challenges, we develop Dictys, a dynamic GRN inference and analysis method that leverages multiomic single-cell assays of chromatin accessibility and gene expression, context-specific transcription factor footprinting, stochastic process network and efficient probabilistic modeling of single-cell RNA-sequencing read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context-specific and dynamic GRNs across developmental contexts. Dictys' network analyses recover unique insights in human blood and mouse skin development with cell-type-specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver transcription factors and their regulated targets. Dictys is available as a free, open-source and user-friendly Python package.

SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) and links these enhancers to candidate target genes. To improve both recall and precision of TF identification, we curated and clustered a motif collection with more than 30,000 motifs. We benchmarked SCENIC+ on diverse datasets from different species, including human peripheral blood mononuclear cells, ENCODE cell lines, melanoma cell states and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers and GRNs between human and mouse cell types in the cerebral cortex. Finally, we use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state. SCENIC+ is available at scenicplus.readthedocs.io .

Epigenetic memory of coronavirus infection in innate immune cells and their progenitors

Inflammation can trigger lasting phenotypes in immune and non-immune cells. Whether and how human infections and associated inflammation can form innate immune memory in hematopoietic stem and progenitor cells (HSPC) has remained unclear. We found that circulating HSPC, enriched from peripheral blood, captured the diversity of bone marrow HSPC, enabling investigation of their epigenomic reprogramming following coronavirus disease 2019 (COVID-19). Alterations in innate immune phenotypes and epigenetic programs of HSPC persisted for months to 1 year following severe COVID-19 and were associated with distinct transcription factor (TF) activities, altered regulation of inflammatory programs, and durable increases in myelopoiesis. HSPC epigenomic alterations were conveyed, through differentiation, to progeny innate immune cells. Early activity of IL-6 contributed to these persistent phenotypes in human COVID-19 and a mouse coronavirus infection model. Epigenetic reprogramming of HSPC may underlie altered immune function following infection and be broadly relevant, especially for millions of COVID-19 survivors.