Single-cell chromatin state analysis with Signac

A multi-omic single-cell landscape of cellular diversification in the developing human cerebral cortex

The vast neuronal diversity in the human neocortex is vital for high-order brain functions, necessitating elucidation of the regulatory mechanisms underlying such unparalleled diversity. However, recent studies have yet to comprehensively reveal the diversity of neurons and the molecular logic of neocortical origin in humans at single-cell resolution through profiling transcriptomic or epigenomic landscapes, owing to the application of unimodal data alone to depict exceedingly heterogeneous populations of neurons. In this study, we generated a comprehensive compendium of the developing human neocortex by simultaneously profiling gene expression and open chromatin from the same cell. We computationally reconstructed the differentiation trajectories of excitatory projection neurons of cortical origin and inferred the regulatory logic governing lineage bifurcation decisions for neuronal diversification. We demonstrated that neuronal diversity arises from progenitor cell lineage specificity and postmitotic differentiation at distinct stages. Our data paves the way for understanding the primarily coordinated regulatory logic for neuronal diversification in the neocortex.

Identification of key genes and immune infiltration in peripheral blood biomarker analysis of delayed cerebral ischemia: Valproic acid as a potential therapeutic drug

BACKGROUND

Delayed cerebral ischemia (DCI) is a common and serious complication of subarachnoid hemorrhage (SAH). Its pathogenesis is not fully understood. Here, we developed a predictive model based on peripheral blood biomarkers and validated the model using several bioinformatic multi-analysis methods.

METHODS

Six datasets were obtained from the GEO database. Characteristic genes were screened using weighted correlation network analysis (WGCNA) and differentially expressed genes. Three machine learning algorithms, elastic networks-LASSO, support vector machines (SVM-RFE) and random forests (RF), were also used to construct diagnostic prediction models for key genes. To further evaluate the performance and predictive value of the diagnostic models, nomogram model were constructed, and the clinical value of the models was assessed using Decision Curve Analysis (DCA), Area Under the Check Curve (AUC), Clinical Impact Curve (CIC), and validated in the mouse single-cell RNA-seq dataset. Mendelian randomization(MR) analysis explored the causal relationship between SAH and stroke, and the intermediate influencing factors. We validated this by retrospectively analyzing the qPCR levels of the most relevant genes in SAH and SAH-DCI patients. This experiment demonstrated a statistically significant difference between SAH and SAH-DCI and normal group controls. Finally, potential small molecule compounds interacting with the selected features were screened from the Comparative Toxicogenomics Database (CTD).

RESULTS

The fGSEA results showed that activation of Toll-like receptor signaling and leukocyte transendothelial cell migration pathways were positively correlated with the DCI phenotype, whereas cytokine signaling pathways and natural killer cell-mediated cytotoxicity were negatively correlated. Consensus feature selection of DEG genes using WGCNA and three machine learning algorithms resulted in the identification of six genes (SPOCK2, TRRAP, CIB1, BCL11B, PDZD8 and LAT), which were used to predict DCI diagnosis with high accuracy. Three external datasets and the mouse single-cell dataset showed high accuracy of the diagnostic model, in addition to high performance and predictive value of the diagnostic model in DCA and CIC. MR analysis looked at stroke after SAH independent of SAH, but associated with multiple intermediate factors including Hypertensive diseases, Total triglycerides levels in medium HDL and Platelet count. qPCR confirmed that significant differences in DCI signature genes were observed between the SAH and SAH-DCI groups. Finally, valproic acid became a potential therapeutic agent for DCI based on the results of target prediction and molecular docking of the characterized genes.

CONCLUSION

This diagnostic model can identify SAH patients at high risk for DCI and may provide potential mechanisms and therapeutic targets for DCI. Valproic acid may be an important future drug for the treatment of DCI.

Progenitors of distinct lineages shape the diversity of mature type 2 conventional dendritic cells

Conventional dendritic cells (cDC) are antigen-presenting cells comprising cDC1 and cDC2, responsible for priming naive CD8+ and CD4+ T cells, respectively. Recent studies have unveiled cDC2 heterogeneity and identified various cDC2 progenitors beyond the common DC progenitor (CDP), hinting at distinct cDC2 lineages. By generating Cd300ciCre-hCD2R26tdTomato reporter mice, we identified a bone marrow pro-cDC2 progenitor exclusively generating cDC2 in vitro and in vivo. Single-cell analyses and multiparametric flow cytometry demonstrated that pro-cDC2 encompasses myeloid-derived pre-cDC2 and lymphoid-derived plasmacytoid DC (pDC)-like precursors differentiating into a transcriptionally convergent cDC2 phenotype. Cd300c-traced cDC2 had distinct transcriptomic profiles, phenotypes, and tissue distributions compared with Ms4a3CreR26tdTomato lineage-traced DC3, a monocyte-DC progenitor (MDP)-derived subset that bypasses CDP. Mice with reduced Cd300c-traced cDC2 showed impaired humoral responses to T cell-dependent antigens. We conclude that progenitors of distinct lineages shape the diversity of mature cDC2 across tissues. Thus, ontogenesis may impact tissue immune responses.

scMultiome analysis identifies a single caudal hindbrain compartment in the developing zebrafish nervous system

BACKGROUND

A key step in nervous system development involves the coordinated control of neural progenitor specification and positioning. A long-standing model for the vertebrate CNS postulates that transient anatomical compartments - known as neuromeres - function to position neural progenitors along the embryonic anteroposterior neuraxis. Such neuromeres are apparent in the embryonic hindbrain - that contains six rhombomeres with morphologically apparent boundaries - but other neuromeres lack clear morphological boundaries and have instead been defined by different criteria, such as differences in gene expression patterns and the outcomes of transplantation experiments. Accordingly, the caudal hindbrain (CHB) posterior to rhombomere (r) 6 has been variably proposed to contain from two to five 'pseudo-rhombomeres', but the lack of comprehensive molecular data has precluded a detailed definition of such structures.

METHODS

We used single-cell Multiome analysis, which allows simultaneous characterization of gene expression and chromatin state of individual cell nuclei, to identify and characterize CHB progenitors in the developing zebrafish CNS.

RESULTS

We identified CHB progenitors as a transcriptionally distinct population, that also possesses a unique profile of accessible transcription factor binding motifs, relative to both r6 and the spinal cord. This CHB population can be subdivided along its dorsoventral axis based on molecular characteristics, but we do not find any molecular evidence that it contains multiple pseudo-rhombomeres. We further observe that the CHB is closely related to r6 at the earliest embryonic stages, but becomes more divergent over time, and that it is defined by a unique gene regulatory network.

CONCLUSIONS

We conclude that the early CHB represents a single neuromere compartment that cannot be molecularly subdivided into pseudo-rhombomeres and that it may share an embryonic origin with r6.

An atlas of transcribed enhancers across helper T cell diversity for decoding human diseases

Transcribed enhancer maps can reveal nuclear interactions underpinning each cell type and connect specific cell types to diseases. Using a 5' single-cell RNA sequencing approach, we defined transcription start sites of enhancer RNAs and other classes of coding and noncoding RNAs in human CD4+ T cells, revealing cellular heterogeneity and differentiation trajectories. Integration of these datasets with single-cell chromatin profiles showed that active enhancers with bidirectional RNA transcription are highly cell type-specific and that disease heritability is strongly enriched in these enhancers. The resulting cell type-resolved multimodal atlas of bidirectionally transcribed enhancers, which we linked with promoters using fine-scale chromatin contact maps, enabled us to systematically interpret genetic variants associated with a range of immune-mediated diseases.

Transcription factor dynamics, oscillation, and functions in human enteroendocrine cell differentiation

Enteroendocrine cells (EECs) secrete serotonin (enterochromaffin [EC] cells) or specific peptide hormones (non-EC cells) that serve vital metabolic functions. The basis for terminal EEC diversity remains obscure. By forcing activity of the transcription factor (TF) NEUROG3 in 2D cultures of human intestinal stem cells, we replicated physiologic EEC differentiation and examined transcriptional and cis-regulatory dynamics that culminate in discrete cell types. Abundant EEC precursors expressed stage-specific genes and TFs. Before expressing pre-terminal NEUROD1, post-mitotic precursors oscillated between transcriptionally distinct ASCL1+ and HES6hi cell states. Loss of either factor accelerated EEC differentiation substantially and disrupted EEC individuality; ASCL1 or NEUROD1 deficiency had opposing consequences on EC and non-EC cell features. These TFs mainly bind cis-elements that are accessible in undifferentiated stem cells, and they tailor subsequent expression of TF combinations that underlie discrete EEC identities. Thus, early TF oscillations retard EEC maturation to enable accurate diversity within a medically important cell lineage.

ezSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists

ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell's streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application ( https://immunesinglecell.org/ezsc/ ) or a software package with a shinyApp interface ( https://github.com/JinmiaoChenLab/ezSingleCell2 ) for offline analysis.

Single-nucleus multi-omics of Parkinson's disease reveals a glutamatergic neuronal subtype susceptible to gene dysregulation via alteration of transcriptional networks

The genetic architecture of Parkinson's disease (PD) is complex and multiple brain cell subtypes are involved in the neuropathological progression of the disease. Here we aimed to advance our understanding of PD genetic complexity at a cell subtype precision level. Using parallel single-nucleus (sn)RNA-seq and snATAC-seq analyses we simultaneously profiled the transcriptomic and chromatin accessibility landscapes in temporal cortex tissues from 12 PD compared to 12 control subjects at a granular single cell resolution. An integrative bioinformatic pipeline was developed and applied for the analyses of these snMulti-omics datasets. The results identified a subpopulation of cortical glutamatergic excitatory neurons with remarkably altered gene expression in PD, including differentially-expressed genes within PD risk loci identified in genome-wide association studies (GWAS). This was the only neuronal subtype showing significant and robust overexpression of SNCA. Further characterization of this neuronal-subpopulation showed upregulation of specific pathways related to axon guidance, neurite outgrowth and post-synaptic structure, and downregulated pathways involved in presynaptic organization and calcium response. Additionally, we characterized the roles of three molecular mechanisms in governing PD-associated cell subtype-specific dysregulation of gene expression: (1) changes in cis-regulatory element accessibility to transcriptional machinery; (2) changes in the abundance of master transcriptional regulators, including YY1, SP3, and KLF16; (3) candidate regulatory variants in high linkage disequilibrium with PD-GWAS genomic variants impacting transcription factor binding affinities. To our knowledge, this study is the first and the most comprehensive interrogation of the multi-omics landscape of PD at a cell-subtype resolution. Our findings provide new insights into a precise glutamatergic neuronal cell subtype, causal genes, and non-coding regulatory variants underlying the neuropathological progression of PD, paving the way for the development of cell- and gene-targeted therapeutics to halt disease progression as well as genetic biomarkers for early preclinical diagnosis.

Direct neuronal reprogramming of mouse astrocytes is associated with multiscale epigenome remodeling and requires Yy1

Direct neuronal reprogramming is a promising approach to regenerate neurons from local glial cells. However, mechanisms of epigenome remodeling and co-factors facilitating this process are unclear. In this study, we combined single-cell multiomics with genome-wide profiling of three-dimensional nuclear architecture and DNA methylation in mouse astrocyte-to-neuron reprogramming mediated by Neurogenin2 (Ngn2) and its phosphorylation-resistant form (PmutNgn2), respectively. We show that Ngn2 drives multilayered chromatin remodeling at dynamic enhancer-gene interaction sites. PmutNgn2 leads to higher reprogramming efficiency and enhances epigenetic remodeling associated with neuronal maturation. However, the differences in binding sites or downstream gene activation cannot fully explain this effect. Instead, we identified Yy1, a transcriptional co-factor recruited by direct interaction with Ngn2 to its target sites. Upon deletion of Yy1, activation of neuronal enhancers, genes and ultimately reprogramming are impaired without affecting Ngn2 binding. Thus, our work highlights the key role of interactors of proneural factors in direct neuronal reprogramming.

A unified model-based framework for doublet or multiplet detection in single-cell multiomics data

Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data-a task at which the benchmarked single-omics methods proved inadequate.

ID factors regulate the ability of Müller glia to become proliferating neurogenic progenitor-like cells

The purpose of this study was to investigate how ID factors regulate the ability of Müller glia (MG) to reprogram into proliferating MG-derived progenitor cells (MGPCs) in the chick retina. We found that ID1 is transiently expressed by maturing MG (mMG), whereas ID4 is maintained in mMG in embryonic retinas. In mature retinas, ID4 was prominently expressed by resting MG, but following retinal damage ID4 was rapidly upregulated and then downregulated in MGPCs. By contrast, ID1, ID2, and ID3 were low in resting MG and then upregulated in MGPCs. Inhibition of ID factors following retinal damage decreased numbers of proliferating MGPCs. Inhibition of IDs, after MGPC proliferation, significantly increased numbers of progeny that differentiated as neurons. In damaged or undamaged retinas inhibition of IDs increased levels of p21Cip1 in MG. In response to damage or insulin+FGF2 levels of CDKN1A message and p21Cip1 protein were decreased, absent in proliferating MGPCs, and elevated in MG returning to a resting phenotype. Inhibition of notch- or gp130/Jak/Stat-signaling in damaged retinas increased levels of ID4 but not p21Cip1 in MG. Although ID4 is the predominant isoform expressed by MG in the chick retina, id1 and id2a are predominantly expressed by resting MG and downregulated in activated MG and MGPCs in zebrafish retinas. We conclude that ID factors have a significant impact on regulating the responses of MG to retinal damage, controlling the ability of MG to proliferate by regulating levels of p21Cip1, and suppressing the neurogenic potential of MGPCs.

Alcohol-induced epigenetic changes prevent fibrosis resolution after alcohol cessation in miceresolution

BACKGROUND AND AIMS

Alcohol-associated liver disease is a major cause of alcohol-associated mortality. Recently, we identified hepatic demethylases lysine demethylase (KDM)5B and KDM5C as important epigenetic regulators of alcohol response in the liver. In this study, we aimed to investigate the role of KDM5 demethylases in alcohol-associated liver disease resolution.

APPROACH AND RESULTS

We showed that alcohol-induced liver steatosis rapidly resolved after alcohol cessation. In contrast, fibrosis persisted in the liver for up to 8 weeks after the end of alcohol exposure. Defects in fibrosis resolution were in part due to alcohol-induced KDM5B and KDM5C-dependent epigenetic changes in hepatocytes. Using cell-type-specific knockout mice, we found that adeno-associated virus-mediated knockout of KDM5B and KDM5C demethylases in hepatocytes at the time of alcohol withdrawal promoted fibrosis resolution. Single-cell ATAC sequencing analysis showed that during alcohol-associated liver disease resolution epigenetic cell states largely reverted to control conditions. In addition, we found unique epigenetic cell states distinct from both control and alcohol states and identified associated transcriptional regulators, including liver X receptor (LXR) alpha (α). In vitro and in vivo analysis confirmed that knockout of KDM5B and KDM5C demethylases promoted LXRα activity, likely through regulation of oxysterol biosynthesis, and this activity was critical for the fibrosis resolution process. Reduced LXR activity by small molecule inhibitors prevented fibrosis resolution in KDM5-deficient mice.

CONCLUSIONS

In summary, KDM5B and KDM5C demethylases prevent liver fibrosis resolution after alcohol cessation in part through suppression of LXR activity.

Similarity Network Analysis of the Adaptive Immune Response in the Proximal Airway

OBJECTIVES

Recent immunologic study of the adaptive immune repertoire in the subglottic airway demonstrated high-frequency T cell clones that do not overlap between individuals. However, the anatomic distribution and antigenic target of the T cell repertoire in the proximal airway mucosa remain unresolved.

METHODS

Single-cell RNA sequencing of matched scar and unaffected mucosa from idiopathic subglottic stenosis patients (iSGS, n = 32) was performed and compared with airway mucosa from healthy controls (n = 10). T cell receptor (TCR) sequences were interrogated via similarity network analysis to explore antigenic targets using the published algorithm: Grouping of Lymphocyte Interactions by Paratope Hotspots (GLIPH2).

RESULTS

The mucosal T cell repertoire in healthy control airways consisted of highly expressed T cell clones conserved across anatomic subsites (trachea, bronchi, bronchioles, and lung). In iSGS, high-frequency clones were equally represented in both scar and adjacent non-scar tissue. Significant differences in repertoire structure between iSGS scar and unaffected mucosa was observed, driven by unique low-frequency clones. GLIPH2 results suggest low-frequency clones share targets between multiple iSGS patients.

CONCLUSION

Healthy airway mucosa has a highly conserved T cell repertoire across multiple anatomic subsites. Similarly, iSGS patients have highly expressed T cell clones present in both scar and unaffected mucosa. iSGS airway scar possesses an abundance of less highly expanded clones with predicted antigen targets shared between patients. Interrogation of these shared motifs suggests abundant adaptive immunity to viral targets in iSGS airway scar. These results provide insight into disease pathogenesis and illuminate new treatment strategies in iSGS.

LEVEL OF EVIDENCE

NA Laryngoscope, 134:3245-3252, 2024.

Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease

Sporadic early-onset Alzheimer's disease (sEOAD) represents a significant but less-studied subtype of Alzheimer's disease (AD). Here, we generated a single-nucleus multiome atlas derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of nine individuals with or without sEOAD. Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate cis- regulatory elements (cCREs) across brain regions. We prioritized seven conservative transcription factors in glial cells in multiple brain regions, including RFX4 in astrocytes and IKZF1 in microglia, which are implicated in regulating sEOAD-associated genes. Moreover, we identified the top 25 altered intercellular signaling between glial cells and neurons, highlighting their regulatory potential on gene expression in receiver cells. We reported 38 cCREs linked to sEOAD-associated genes overlapped with late-onset AD risk loci, and sEOAD cCREs enriched in neuropsychiatric disorder risk loci. This atlas helps dissect transcriptional and chromatin dynamics in sEOAD, providing a key resource for AD research.

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.

The activity of early-life gene regulatory elements is hijacked in aging through pervasive AP-1-linked chromatin opening

A mechanistic connection between aging and development is largely unexplored. Through profiling age-related chromatin and transcriptional changes across 22 murine cell types, analyzed alongside previous mouse and human organismal maturation datasets, we uncovered a transcription factor binding site (TFBS) signature common to both processes. Early-life candidate cis-regulatory elements (cCREs), progressively losing accessibility during maturation and aging, are enriched for cell-type identity TFBSs. Conversely, cCREs gaining accessibility throughout life have a lower abundance of cell identity TFBSs but elevated activator protein 1 (AP-1) levels. We implicate TF redistribution toward these AP-1 TFBS-rich cCREs, in synergy with mild downregulation of cell identity TFs, as driving early-life cCRE accessibility loss and altering developmental and metabolic gene expression. Such remodeling can be triggered by elevating AP-1 or depleting repressive H3K27me3. We propose that AP-1-linked chromatin opening drives organismal maturation by disrupting cell identity TFBS-rich cCREs, thereby reprogramming transcriptome and cell function, a mechanism hijacked in aging through ongoing chromatin opening.

Systematic single-cell analysis reveals dynamic control of transposable element activity orchestrating the endothelial-to-hematopoietic transition

BACKGROUND

The endothelial-to-hematopoietic transition (EHT) process during definitive hematopoiesis is highly conserved in vertebrates. Stage-specific expression of transposable elements (TEs) has been detected during zebrafish EHT and may promote hematopoietic stem cell (HSC) formation by activating inflammatory signaling. However, little is known about how TEs contribute to the EHT process in human and mouse.

RESULTS

We reconstructed the single-cell EHT trajectories of human and mouse and resolved the dynamic expression patterns of TEs during EHT. Most TEs presented a transient co-upregulation pattern along the conserved EHT trajectories, coinciding with the temporal relaxation of epigenetic silencing systems. TE products can be sensed by multiple pattern recognition receptors, triggering inflammatory signaling to facilitate HSC emergence. Interestingly, we observed that hypoxia-related signals were enriched in cells with higher TE expression. Furthermore, we constructed the hematopoietic cis-regulatory network of accessible TEs and identified potential TE-derived enhancers that may boost the expression of specific EHT marker genes.

CONCLUSIONS

Our study provides a systematic vision of how TEs are dynamically controlled to promote the hematopoietic fate decisions through transcriptional and cis-regulatory networks, and pre-train the immunity of nascent HSCs.

A cis-regulatory module underlies retinal ganglion cell genesis and axonogenesis

Atoh7 is transiently expressed in retinal progenitor cells (RPCs) and is required for retinal ganglion cell (RGC) differentiation. In humans, a deletion in a distal non-coding regulatory region upstream of ATOH7 is associated with optic nerve atrophy and blindness. Here, we functionally interrogate the significance of the Atoh7 regulatory landscape to retinogenesis in mice. Deletion of the Atoh7 enhancer structure leads to RGC deficiency, optic nerve hypoplasia, and retinal blood vascular abnormalities, phenocopying inactivation of Atoh7. Further, loss of the Atoh7 remote enhancer impacts ipsilaterally projecting RGCs and disrupts proper axonal projections to the visual thalamus. Deletion of the Atoh7 remote enhancer is also associated with the dysregulation of axonogenesis genes, including the derepression of the axon repulsive cue Robo3. Our data provide insights into how Atoh7 enhancer elements function to promote RGC development and optic nerve formation and highlight a key role of Atoh7 in the transcriptional control of axon guidance molecules.

B cells modulate lung antiviral inflammatory responses via the neurotransmitter acetylcholine

The rapid onset of innate immune defenses is critical for early control of viral replication in an infected host, yet it can also lead to irreversible tissue damage, especially in the respiratory tract. Intricate regulatory mechanisms must exist that modulate inflammation, while controlling the infection. Here, B cells expressing choline acetyl transferase (ChAT), an enzyme required for production of the metabolite and neurotransmitter acetylcholine (ACh) are identified as such regulators of the immediate early response to influenza A virus. Lung tissue ChAT + B cells are shown to interact with a7 nicotinic Ach receptor-expressing lung interstitial macrophages in mice within 24h of infection to control their production of TNFa, shifting the balance towards reduced inflammation at the cost of enhanced viral replication. Thus, innate-stimulated B cells are key participants of an immediate-early regulatory cascade that controls lung tissue damage after viral infection.

Single-cell epigenomic reconstruction of developmental trajectories from pluripotency in human neural organoid systems

Cell fate progression of pluripotent progenitors is strictly regulated, resulting in high human cell diversity. Epigenetic modifications also orchestrate cell fate restriction. Unveiling the epigenetic mechanisms underlying human cell diversity has been difficult. In this study, we use human brain and retina organoid models and present single-cell profiling of H3K27ac, H3K27me3 and H3K4me3 histone modifications from progenitor to differentiated neural fates to reconstruct the epigenomic trajectories regulating cell identity acquisition. We capture transitions from pluripotency through neuroepithelium to retinal and brain region and cell type specification. Switching of repressive and activating epigenetic modifications can precede and predict cell fate decisions at each stage, providing a temporal census of gene regulatory elements and transcription factors. Removing H3K27me3 at the neuroectoderm stage disrupts fate restriction, resulting in aberrant cell identity acquisition. Our single-cell epigenome-wide map of human neural organoid development serves as a blueprint to explore human cell fate determination.

Differential impact of quiescent non-coding loci on chromatin entropy

Non-coding regions of the human genome are important for functional regulations, but their mechanisms remain elusive. We used machine learning to guide a CRISPR screening on hubs (i.e. non-coding loci forming many 3D contacts) and significantly increased the discovery rate of hubs essential for cell growth. We found no clear genetic or epigenetic differences between essential and nonessential hubs, but we observed that some neighboring hubs in the linear genome have distinct spatial contacts and opposite effects on cell growth. One such pair in an epigenetically quiescent region showed different impacts on gene expression, chromatin accessibility and chromatin organization. We also found that deleting the essential hub altered the genetic network activity and increased the entropy of chromatin accessibility, more severe than that caused by deletion of the nonessential hub, suggesting that they are critical for maintaining an ordered chromatin structure. Our study reveals new insights into the system-level roles of non-coding regions in the human genome.

SARS-CoV-2 inflammation durably imprints memory CD4 T cells

Memory CD4 T cells are critical to human immunity, yet it is unclear whether viral inflammation during memory formation has long-term consequences. Here, we compared transcriptional and epigenetic landscapes of Spike (S)-specific memory CD4 T cells in 24 individuals whose first exposure to S was via SARS-CoV-2 infection or mRNA vaccination. Nearly 2 years after memory formation, S-specific CD4 T cells established by infection remained enriched for transcripts related to cytotoxicity and for interferon-stimulated genes, likely because of a chromatin accessibility landscape altered by inflammation. Moreover, S-specific CD4 T cells primed by infection had reduced proliferative capacity in vitro relative to vaccine-primed cells. Furthermore, the transcriptional state of S-specific memory CD4 T cells was minimally altered by booster immunization and/or breakthrough infection. Thus, infection-associated inflammation durably imprints CD4 T cell memory, which affects the function of these cells and may have consequences for long-term immunity.

Single-cell multi-omics analysis reveals cooperative transcription factors for gene regulation in oligodendrocytes

Oligodendrocytes are the myelinating cells within the central nervous system. Many oligodendrocyte genes have been associated with brain disorders. However, how transcription factors (TFs) cooperate for gene regulation in oligodendrocytes remains largely uncharacterized. To address this, we integrated scRNA-seq and scATAC-seq data to identify the cooperative TFs that co-regulate the target gene (TG) expression in oligodendrocytes. First, we identified co- binding TF pairs whose binding sites overlapped in oligodendrocyte-specific regulatory regions. Second, we trained a deep learning model to predict the expression level of each TG using the expression levels of co-binding TFs. Third, using the trained models, we computed the TF importance and TF-TF interaction scores for predicting TG expression by the Shapley interaction scores. We found that the co-binding TF pairs involving known important TF pairs for oligodendrocyte differentiation, such as SOX10-TCF12, SOX10-MYRF, and SOX10-OLIG2, exhibited significantly higher Shapley scores than others (t-test, p-value < 1e-4). Furthermore, we identified 153 oligodendrocyte-associated eQTLs that reside in oligodendrocyte-specific enhancers or promoters where their eGenes (TGs) are regulated by cooperative TFs, suggesting potential novel regulatory roles from genetic variants. We also experimentally validated some identified TF pairs such as SOX10-OLIG2 and SOX10-NKX2.2 by co-enrichment analysis, using ChIP-seq data from rat peripheral nerve.

Protocol for the isolation and single-nuclei multiomic analyses of the human fetal epicardium

The characterization of cell populations that reside in the outer layer of the heart has been hindered by difficulties in their isolation. Here, we present a protocol for isolation and single-nuclei multiomic analyses of the human fetal epicardium. We describe steps for microdissection, isolation, and enrichment of epicardial cells by mechanical dissociations and direct lysis. We then detail procedures for integrating transcriptome and chromatin accessibility datasets. This approach allows the analysis of diverse cell populations, marked by unique cis-regulatory elements. For complete details on the use and execution of this protocol, please refer to Travisano et al.1.

Non-stem cell lineages as an alternative origin of intestinal tumorigenesis in the context of inflammation

According to conventional views, colon cancer originates from stem cells. However, inflammation, a key risk factor for colon cancer, has been shown to suppress intestinal stemness. Here, we used Paneth cells as a model to assess the capacity of differentiated lineages to trigger tumorigenesis in the context of inflammation in mice. Upon inflammation, Paneth cell-specific Apc mutations led to intestinal tumors reminiscent not only of those arising in patients with inflammatory bowel disease, but also of a larger fraction of human sporadic colon cancers. The latter is possibly because of the inflammatory consequences of western-style dietary habits, a major colon cancer risk factor. Machine learning methods designed to predict the cell-of-origin of cancer from patient-derived tumor samples confirmed that, in a substantial fraction of sporadic cases, the origins of colon cancer reside in secretory lineages and not in stem cells.

Multiplexed single-cell characterization of alternative polyadenylation regulators

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice, we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle, including elongation, splicing, termination, and surveillance. We train and validate a deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis-regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation.

Massively parallel in vivo Perturb-seq reveals cell-type-specific transcriptional networks in cortical development

Leveraging AAVs' versatile tropism and labeling capacity, we expanded the scale of in vivo CRISPR screening with single-cell transcriptomic phenotyping across embryonic to adult brains and peripheral nervous systems. Through extensive tests of 86 vectors across AAV serotypes combined with a transposon system, we substantially amplified labeling efficacy and accelerated in vivo gene delivery from weeks to days. Our proof-of-principle in utero screen identified the pleiotropic effects of Foxg1, highlighting its tight regulation of distinct networks essential for cell fate specification of Layer 6 corticothalamic neurons. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% by lentivirus, to achieve analysis of over 30,000 cells in one experiment and enable massively parallel in vivo Perturb-seq. Compatible with various phenotypic measurements (single-cell or spatial multi-omics), it presents a flexible approach to interrogate gene function across cell types in vivo, translating gene variants to their causal function.

Identification of endothelial and mesenchymal FOXF1 enhancers involved in alveolar capillary dysplasia

Mutations in the FOXF1 gene, a key transcriptional regulator of pulmonary vascular development, cause Alveolar Capillary Dysplasia with Misalignment of Pulmonary Veins, a lethal lung disease affecting newborns and infants. Identification of new FOXF1 upstream regulatory elements is critical to explain why frequent non-coding FOXF1 deletions are linked to the disease. Herein, we use multiome single-nuclei RNA and ATAC sequencing of mouse and human patient lungs to identify four conserved endothelial and mesenchymal FOXF1 enhancers. We demonstrate that endothelial FOXF1 enhancers are autoactivated, whereas mesenchymal FOXF1 enhancers are regulated by EBF1 and GLI1. The cell-specificity of FOXF1 enhancers is validated by disrupting these enhancers in mouse embryonic stem cells using CRISPR/Cpf1 genome editing followed by lineage-tracing of mutant embryonic stem cells in mouse embryos using blastocyst complementation. This study resolves an important clinical question why frequent non-coding FOXF1 deletions that interfere with endothelial and mesenchymal enhancers can lead to the disease.

GATA2 heterozygosity causes an epigenetic feedback mechanism resulting in myeloid and erythroid dysplasia

The transcription factor GATA2 has a pivotal role in haematopoiesis. Heterozygous germline GATA2 mutations result in a syndrome characterized by immunodeficiency, bone marrow failure and predispositions to myelodysplastic syndrome (MDS) and acute myeloid leukaemia. Clinical symptoms in these patients are diverse and mechanisms driving GATA2-related phenotypes are largely unknown. To explore the impact of GATA2 haploinsufficiency on haematopoiesis, we generated a zebrafish model carrying a heterozygous mutation of gata2b (gata2b+/-), an orthologue of GATA2. Morphological analysis revealed myeloid and erythroid dysplasia in gata2b+/- kidney marrow. Because Gata2b could affect both transcription and chromatin accessibility during lineage differentiation, this was assessed by single-cell (sc) RNA-seq and single-nucleus (sn) ATAC-seq. Sn-ATAC-seq showed that the co-accessibility between the transcription start site (TSS) and a -3.5-4.1 kb putative enhancer was more robust in gata2b+/- zebrafish HSPCs compared to wild type, increasing gata2b expression and resulting in higher genome-wide Gata2b motif use in HSPCs. As a result of increased accessibility of the gata2b locus, gata2b+/- chromatin was also more accessible during lineage differentiation. scRNA-seq data revealed myeloid differentiation defects, that is, impaired cell cycle progression, reduced expression of cebpa and cebpb and increased signatures of ribosome biogenesis. These data also revealed a differentiation delay in erythroid progenitors, aberrant proliferative signatures and down-regulation of Gata1a, a master regulator of erythropoiesis, which worsened with age. These findings suggest that cell-intrinsic compensatory mechanisms, needed to obtain normal levels of Gata2b in heterozygous HSPCs to maintain their integrity, result in aberrant lineage differentiation, thereby representing a critical step in the predisposition to MDS.

A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling

Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.

CAraCAl: CAMML with the integration of chromatin accessibility

BACKGROUND

A vital step in analyzing single-cell data is ascertaining which cell types are present in a dataset, and at what abundance. In many diseases, the proportions of varying cell types can have important implications for health and prognosis. Most approaches for cell type annotation have centered around cell typing for single-cell RNA-sequencing (scRNA-seq) and have had promising success. However, reliable methods are lacking for many other single-cell modalities such as single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), which quantifies the extent to which genes of interest in each cell are epigenetically "open" for expression.

RESULTS

To leverage the informative potential of scATAC-seq data, we developed CAMML with the integration of chromatin accessibility (CAraCAl), a bioinformatic method that performs cell typing on scATAC-seq data. CAraCAl performs cell typing by scoring each cell for its enrichment of cell type-specific gene sets. These gene sets are composed of the most upregulated or downregulated genes present in each cell type according to projected gene activity.

CONCLUSIONS

We found that CAraCAl does not improve performance beyond CAMML when scRNA-seq is present, but if only scATAC-seq is available, CAraCAl performs cell typing relatively successfully. As such, we also discuss best practices for cell typing and the strengths and weaknesses of various cell annotation options.

Leveraging single cell multiomic analyses to identify factors that drive human chondrocyte cell fate

Cartilage plays a crucial role in skeletal development and function, and abnormal development contributes to genetic and age-related skeletal disease. To better understand how human cartilage develops in vivo , we jointly profiled the transcriptome and open chromatin regions in individual nuclei recovered from distal femurs at 2 fetal timepoints. We used these multiomic data to identify transcription factors expressed in distinct chondrocyte subtypes, link accessible regulatory elements with gene expression, and predict transcription factor-based regulatory networks that are important for growth plate or epiphyseal chondrocyte differentiation. We developed a human pluripotent stem cell platform for interrogating the function of predicted transcription factors during chondrocyte differentiation and used it to test NFATC2 . We expect new regulatory networks we uncovered using multiomic data to be important for promoting cartilage health and treating disease, and our platform to be a useful tool for studying cartilage development in vitro .

Statement of Significance

The identity and integrity of the articular cartilage lining our joints are crucial to pain-free activities of daily living. Here we identified a gene regulatory landscape of human chondrogenesis at single cell resolution, which is expected to open new avenues of research aimed at mitigating cartilage diseases that affect hundreds of millions of individuals world-wide.

Single cell regulatory architecture of human pancreatic islets suggests sex differences in β cell function and the pathogenesis of type 2 diabetes

Biological sex affects the pathogenesis of type 2 and type 1 diabetes (T2D, T1D) including the development of β cell failure observed more often in males. The mechanisms that drive sex differences in β cell failure is unknown. Studying sex differences in islet regulation and function represent a unique avenue to understand the sex-specific heterogeneity in β cell failure in diabetes. Here, we examined sex and race differences in human pancreatic islets from up to 52 donors with and without T2D (including 37 donors from the Human Pancreas Analysis Program [HPAP] dataset) using an orthogonal series of experiments including single cell RNA-seq (scRNA-seq), single nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq), dynamic hormone secretion, and bioenergetics. In cultured islets from nondiabetic (ND) donors, in the absence of the in vivo hormonal environment, sex differences in islet cell type gene accessibility and expression predominantly involved sex chromosomes. Of particular interest were sex differences in the X-linked KDM6A and Y-linked KDM5D chromatin remodelers in female and male islet cells respectively. Islets from T2D donors exhibited similar sex differences in differentially expressed genes (DEGs) from sex chromosomes. However, in contrast to islets from ND donors, islets from T2D donors exhibited major sex differences in DEGs from autosomes. Comparing β cells from T2D and ND donors revealed that females had more DEGs from autosomes compared to male β cells. Gene set enrichment analysis of female β cell DEGs showed a suppression of oxidative phosphorylation and electron transport chain pathways, while male β cell had suppressed insulin secretion pathways. Thus, although sex-specific differences in gene accessibility and expression of cultured ND human islets predominantly affect sex chromosome genes, major differences in autosomal gene expression between sexes appear during the transition to T2D and which highlight mitochondrial failure in female β cells.

Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans

Atherosclerosis (AS), a leading cause of cardio-cerebrovascular disease worldwide, is driven by the accumulation of lipid contents and chronic inflammation. Traditional strategies primarily focus on lipid reduction to control AS progression, leaving residual inflammatory risks for major adverse cardiovascular events (MACEs). While anti-inflammatory therapies targeting innate immunity have reduced MACEs, many patients continue to face significant risks. Another key component in AS progression is adaptive immunity, but its potential role in preventing AS remains unclear. To investigate this, we conducted a retrospective cohort study on tumor patients with AS plaques. We found that anti-programmed cell death protein 1 (PD-1) monoclonal antibody (mAb) significantly reduces AS plaque size. With multi-omics single-cell analyses, we comprehensively characterized AS plaque-specific PD-1+ T cells, which are activated and pro-inflammatory. We demonstrated that anti-PD-1 mAb, when captured by myeloid-expressed Fc gamma receptors (FcγRs), interacts with PD-1 expressed on T cells. This interaction turns the anti-PD-1 mAb into a substitute PD-1 ligand, suppressing T-cell functions in the PD-1 ligands-deficient context of AS plaques. Further, we conducted a prospective cohort study on tumor patients treated with anti-PD-1 mAb with or without Fc-binding capability. Our analysis shows that anti-PD-1 mAb with Fc-binding capability effectively reduces AS plaque size, while anti-PD-1 mAb without Fc-binding capability does not. Our work suggests that T cell-targeting immunotherapy can be an effective strategy to resolve AS in humans.

Multiome in the Same Cell Reveals the Impact of Osmotic Stress on Arabidopsis Root Tip Development at Single-Cell Level

Cell-specific transcriptional regulatory networks (TRNs) play vital roles in plant development and response to environmental stresses. However, traditional single-cell mono-omics techniques are unable to directly capture the relationships and dynamics between different layers of molecular information within the same cells. While advanced algorithm facilitates merging scRNA-seq and scATAC-seq datasets, accurate data integration remains a challenge, particularly when investigating cell-type-specific TRNs. By examining gene expression and chromatin accessibility simultaneously in 16,670 Arabidopsis root tip nuclei, the TRNs are reconstructed that govern root tip development under osmotic stress. In contrast to commonly used computational integration at cell-type level, 12,968 peak-to-gene linkage is captured at the bona fide single-cell level and construct TRNs at an unprecedented resolution. Furthermore, the unprecedented datasets allow to more accurately reconstruct the coordinated changes of gene expression and chromatin states during cellular state transition. During root tip development, chromatin accessibility of initial cells precedes gene expression, suggesting that changes in chromatin accessibility may prime cells for subsequent differentiation steps. Pseudo-time trajectory analysis reveal that osmotic stress can shift the functional differentiation of trichoblast. Candidate stress-related gene-linked cis-regulatory elements (gl-cCREs) as well as potential target genes are also identified, and uncovered large cellular heterogeneity under osmotic stress.

Sequential drug treatment targeting cell cycle and cell fate regulatory programs blocks non-genetic cancer evolution in acute lymphoblastic leukemia

BACKGROUND

Targeted therapies exploiting vulnerabilities of cancer cells hold promise for improving patient outcome and reducing side-effects of chemotherapy. However, efficacy of precision therapies is limited in part because of tumor cell heterogeneity. A better mechanistic understanding of how drug effect is linked to cancer cell state diversity is crucial for identifying effective combination therapies that can prevent disease recurrence.

RESULTS

Here, we characterize the effect of G2/M checkpoint inhibition in acute lymphoblastic leukemia (ALL) and demonstrate that WEE1 targeted therapy impinges on cell fate decision regulatory circuits. We find the highest inhibition of recovery of proliferation in ALL cells with KMT2A-rearrangements. Single-cell RNA-seq and ATAC-seq of RS4;11 cells harboring KMT2A::AFF1, treated with the WEE1 inhibitor AZD1775, reveal diversification of cell states, with a fraction of cells exhibiting strong activation of p53-driven processes linked to apoptosis and senescence, and disruption of a core KMT2A-RUNX1-MYC regulatory network. In this cell state diversification induced by WEE1 inhibition, a subpopulation transitions to a drug tolerant cell state characterized by activation of transcription factors regulating pre-B cell fate, lipid metabolism, and pre-BCR signaling in a reversible manner. Sequential treatment with BCR-signaling inhibitors dasatinib, ibrutinib, or perturbing metabolism by fatostatin or AZD2014 effectively counteracts drug tolerance by inducing cell death and repressing stemness markers.

CONCLUSIONS

Collectively, our findings provide new insights into the tight connectivity of gene regulatory programs associated with cell cycle and cell fate regulation, and a rationale for sequential administration of WEE1 inhibitors with low toxicity inhibitors of pre-BCR signaling or metabolism.

ARID1B maintains mesenchymal stem cell quiescence via inhibition of BCL11B-mediated non-canonical Activin signaling

ARID1B haploinsufficiency in humans causes Coffin-Siris syndrome, associated with developmental delay, facial dysmorphism, and intellectual disability. The role of ARID1B has been widely studied in neuronal development, but whether it also regulates stem cells remains unknown. Here, we employ scRNA-seq and scATAC-seq to dissect the regulatory functions and mechanisms of ARID1B within mesenchymal stem cells (MSCs) using the mouse incisor model. We reveal that loss of Arid1b in the GLI1+ MSC lineage disturbs MSCs' quiescence and leads to their proliferation due to the ectopic activation of non-canonical Activin signaling via p-ERK. Furthermore, loss of Arid1b upregulates Bcl11b, which encodes a BAF complex subunit that modulates non-canonical Activin signaling by directly regulating the expression of activin A subunit, Inhba. Reduction of Bcl11b or non-canonical Activin signaling restores the MSC population in Arid1b mutant mice. Notably, we have identified that ARID1B suppresses Bcl11b expression via specific binding to its third intron, unveiling the direct inter-regulatory interactions among BAF subunits in MSCs. Our results demonstrate the vital role of ARID1B as an epigenetic modifier in maintaining MSC homeostasis and reveal its intricate mechanistic regulatory network in vivo, providing novel insights into the linkage between chromatin remodeling and stem cell fate determination.

Flexible modeling of regulatory networks improves transcription factor activity estimation

Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.

Compensation between FOXP transcription factors maintains proper striatal function

Spiny projection neurons (SPNs) of the striatum are critical in integrating neurochemical information to coordinate motor and reward-based behavior. Mutations in the regulatory transcription factors expressed in SPNs can result in neurodevelopmental disorders (NDDs). Paralogous transcription factors Foxp1 and Foxp2, which are both expressed in the dopamine receptor 1 (D1) expressing SPNs, are known to have variants implicated in NDDs. Utilizing mice with a D1-SPN-specific loss of Foxp1, Foxp2, or both and a combination of behavior, electrophysiology, and cell-type-specific genomic analysis, loss of both genes results in impaired motor and social behavior as well as increased firing of the D1-SPNs. Differential gene expression analysis implicates genes involved in autism risk, electrophysiological properties, and neuronal development and function. Viral-mediated re-expression of Foxp1 into the double knockouts is sufficient to restore electrophysiological and behavioral deficits. These data indicate complementary roles between Foxp1 and Foxp2 in the D1-SPNs.

Dynamic Single Cell Transcriptomics Defines Kidney FGF23/KL Bioactivity and Novel Segment-Specific Inflammatory Targets

FGF23 via its coreceptor αKlotho (KL) provides critical control of phosphate metabolism, which is altered in rare and very common syndromes, however the spatial-temporal mechanisms dictating renal FGF23 functions remain poorly understood. Thus, developing approaches to modify specific FGF23-dictated pathways has proven problematic. Herein, wild type mice were injected with rFGF23 for 1, 4 and 12h and renal FGF23 bioactivity was determined at single cell resolution. Computational analysis identified distinct epithelial, endothelial, stromal, and immune cell clusters, with differential expressional analysis uniquely tracking FGF23 bioactivity at each time point. FGF23 actions were sex independent but critically relied upon constitutive KL expression mapped within proximal tubule (S1-S3) and distal tubule (DCT/CNT) cell sub-populations. Temporal KL-dependent FGF23 responses drove unique and transient cellular identities, including genes in key MAPK- and vitamin D-metabolic pathways via early- (AP-1-related) and late-phase (EIF2 signaling) transcriptional regulons. Combining ATACseq/RNAseq data from a cell line stably expressing KL with the in vivo scRNAseq pinpointed genomic accessibility changes in MAPK-dependent genes, including the identification of FGF23-dependent EGR1 distal enhancers. Finally, we isolated unexpected crosstalk between FGF23-mediated MAPK signaling and pro-inflammatory TNF receptor activation via NF-κB, which blocked FGF23 bioactivity in vitro and in vivo . Collectively, our findings have uncovered novel pathways at the single cell level that likely influence FGF23-dependent disease mechanisms.

Translational statement

Inflammation and elevated FGF23 in chronic kidney disease (CKD) are both associated with poor patient outcomes and mortality. However, the links between these manifestations and the effects of inflammation on FGF23-mediated mineral metabolism within specific nephron segments remain unclear. Herein, we isolated an inflammatory pathway driven by TNF/NF-κB associated with regulating FGF23 bioactivity. The findings from this study could be important in designing future therapeutic approaches for chronic mineral diseases, including potential combination therapies or early intervention strategies. We also suggest that further studies could explore these pathways at the single cell level in CKD models, as well as test translation of our findings to interactions of chronic inflammation and elevated FGF23 in human CKD kidney datasets.

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.

Conserved Cis-Acting Range Extender Element Mediates Extreme Long-Range Enhancer Activity in Mammals

While most mammalian enhancers regulate their cognate promoters over moderate distances of tens of kilobases (kb), some enhancers act over distances in the megabase range. The sequence features enabling such extreme-distance enhancer-promoter interactions remain elusive. Here, we used in vivo enhancer replacement experiments in mice to show that short- and medium-range enhancers cannot initiate gene expression at extreme-distance range. We uncover a novel conserved cis-acting element, Range EXtender (REX), that confers extreme-distance regulatory activity and is located next to a long-range enhancer of Sall1. The REX element itself has no endogenous enhancer activity. However, addition of the REX to other short- and mid-range enhancers substantially increases their genomic interaction range. In the most extreme example observed, addition of the REX increased the range of an enhancer by an order of magnitude, from its native 71kb to 840kb. The REX element contains highly conserved [C/T]AATTA homeodomain motifs. These motifs are enriched around long-range limb enhancers genome-wide, including the ZRS, a benchmark long-range limb enhancer of Shh. Mutating the [C/T]AATTA motifs within the ZRS does not affect its limb-specific enhancer activity at short range, but selectively abolishes its long-range activity, resulting in severe limb reduction in knock-in mice. In summary, we identify a sequence signature globally associated with long-range enhancer-promoter interactions and describe a prototypical REX element that is necessary and sufficient to confer extreme-distance gene activation by remote enhancers.

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.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, 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-nucleus RNA and ATAC sequencing analyses provide molecular insights into early pod development of peanut fruit

Peanut (Arachis hypogaea L.) is an important leguminous oil and economic crop that produces flowers aboveground and fruits belowground. Subterranean fruit-pod development, which significantly affects peanut production, involves complex molecular mechanisms that likely require the coordinated regulation of multiple genes in different tissues. To investigate the molecular mechanisms that underlie peanut fruit-pod development, we characterized the anatomical features of early fruit-pod development and integrated single-nucleus RNA-sequencing (snRNA-seq) and single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) data at the single-cell level. We identified distinct cell types, such as meristem, embryo, vascular tissue, cuticular layer, and stele cells within the shell wall. These specific cell types were used to examine potential molecular changes unique to each cell type during pivotal stages of fruit-pod development. snRNA-seq analyses of differentially expressed genes revealed cell-type-specific insights that were not previously obtainable from transcriptome analyses of bulk RNA. For instance, we identified MADS-box genes that contributes to the formation of parenchyma cells and gravity-related genes that are present in the vascular cells, indicating an essential role for the vascular cells in peg gravitropism. Overall, our single-nucleus analysis provides comprehensive and novel information on specific cell types, gene expression, and chromatin accessibility during the early stages of fruit-pod development. This information will enhance our understanding of the mechanisms that underlie fruit-pod development in peanut and contribute to efforts aimed at improving peanut production.

Dependency-aware deep generative models for multitasking analysis of spatial omics data

Spatially resolved transcriptomics (SRT) technologies have significantly advanced biomedical research, but their data analysis remains challenging due to the discrete nature of the data and the high levels of noise, compounded by complex spatial dependencies. Here, we propose spaVAE, a dependency-aware, deep generative spatial variational autoencoder model that probabilistically characterizes count data while capturing spatial correlations. spaVAE introduces a hybrid embedding combining a Gaussian process prior with a Gaussian prior to explicitly capture spatial correlations among spots. It then optimizes the parameters of deep neural networks to approximate the distributions underlying the SRT data. With the approximated distributions, spaVAE can contribute to several analytical tasks that are essential for SRT data analysis, including dimensionality reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, resolution enhancement and identification of spatially variable genes. Moreover, we have extended spaVAE to spaPeakVAE and spaMultiVAE to characterize spatial ATAC-seq (assay for transposase-accessible chromatin using sequencing) data and spatial multi-omics data, respectively.

CountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq Data

Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. To address this, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences.

Cell-type-specific effects of autism-associated chromosome 15q11.2-13.1 duplications in human brain

Recurrent copy number variation represents one of the most well-established genetic drivers in neurodevelopmental disorders, including autism spectrum disorder (ASD). Duplication of 15q11.2-13.1 (dup15q) is a well-described neurodevelopmental syndrome that increases the risk of ASD by over 40-fold. However, the effects of this duplication on gene expression and chromatin accessibility in specific cell types in the human brain remain unknown. To identify the cell-type-specific transcriptional and epigenetic effects of dup15q in the human frontal cortex we conducted single-nucleus RNA-sequencing and multi-omic sequencing on dup15q cases (n=6) as well as non-dup15q ASD (n=7) and neurotypical controls (n=7). Cell-type-specific differential expression analysis identified significantly regulated genes, critical biological pathways, and differentially accessible genomic regions. Although there was overall increased gene expression across the duplicated genomic region, cellular identity represented an important factor mediating gene expression changes. Neuronal subtypes, showed greater upregulation of gene expression across a critical region within the duplication as compared to other cell types. Genes within the duplicated region that had high baseline expression in control individuals showed only modest changes in dup15q, regardless of cell type. Of note, dup15q and ASD had largely distinct signatures of chromatin accessibility, but shared the majority of transcriptional regulatory motifs, suggesting convergent biological pathways. However, the transcriptional binding factor motifs implicated in each condition implicated distinct biological mechanisms; neuronal JUN/FOS networks in ASD vs. an inflammatory transcriptional network in dup15q microglia. This work provides a cell-type-specific analysis of how dup15q changes gene expression and chromatin accessibility in the human brain and finds evidence of marked cell-type-specific effects of this genetic driver. These findings have implications for guiding therapeutic development in dup15q syndrome, as well as understanding the functional effects CNVs more broadly in neurodevelopmental disorders.

Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach

Single-cell (sc) RNA, ATAC and Multiome sequencing became powerful tools for uncovering biological and disease mechanisms. Unfortunately, manual analysis of sc data presents multiple challenges due to large data volumes and complexity of configuration parameters. This complexity, as well as not being able to reproduce a computational environment, affects the reproducibility of analysis results. The Scientific Data Analysis Platform (https://SciDAP.com) allows biologists without computational expertise to analyze sequencing-based data using portable and reproducible pipelines written in Common Workflow Language (CWL). Our suite of computational pipelines addresses the most common needs in scRNA-Seq, scATAC-Seq and scMultiome data analysis. When executed on SciDAP, it offers a user-friendly alternative to manual data processing, eliminating the need for coding expertise. In this protocol, we describe the use of SciDAP to analyze scMultiome data. Similar approaches can be used for analysis of scRNA-Seq, scATAC-Seq and scVDJ-Seq datasets.