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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Probabilistic association of differentially expressed genes with cis-regulatory elements

Differential gene expression in response to perturbations is mediated at least in part by changes in binding of transcription factors (TFs) and other proteins at specific genomic regions. Association of these cis-regulatory elements (CREs) with their target genes is a challenging task that is essential to address many biological and mechanistic questions. Many current approaches rely on chromatin conformation capture techniques or single-cell correlational methods to establish CRE-to-gene associations. These methods can be effective but have limitations, including resolution, gaps in detectable association distances, and cost. As an alternative, we have developed DegCre, a nonparametric method that evaluates correlations between measurements of perturbation-induced differential gene expression and differential regulatory signal at CREs to score possible CRE-to-gene associations. It has several unique features, including the ability to use any type of CRE activity measurement, yield probabilistic scores for CRE-to-gene pairs, and assess CRE-to-gene pairings across a wide range of sequence distances. We apply DegCre to six data sets, each using different perturbations and containing a variety of regulatory signal measurements, including chromatin openness, histone modifications, and TF occupancy. To test their efficacy, we compare DegCre associations to Hi-C loop calls and CRISPR-validated CRE-to-gene associations, establishing good performance by DegCre that is comparable or superior to competing methods. DegCre is a novel approach to the association of CREs to genes from a perturbation-differential perspective, with strengths that are complementary to existing approaches and allow for new insights into gene regulation.

Transcription factor Tox2 is required for metabolic adaptation and tissue residency of ILC3 in the gut

Group 3 innate lymphoid cells (ILC3) are the major subset of gut-resident ILC with essential roles in infections and tissue repair, but how they adapt to the gut environment to maintain tissue residency is unclear. We report that Tox2 is critical for gut ILC3 maintenance and function. Gut ILC3 highly expressed Tox2, and depletion of Tox2 markedly decreased ILC3 in gut but not at central sites, resulting in defective control of Citrobacter rodentium infection. Single-cell transcriptional profiling revealed decreased expression of Hexokinase-2 in Tox2-deficient gut ILC3. Consistent with the requirement for hexokinases in glycolysis, Tox2-/- ILC3 displayed decreased ability to utilize glycolysis for protein translation. Ectopic expression of Hexokinase-2 rescued Tox2-/- gut ILC3 defects. Hypoxia and interleukin (IL)-17A each induced Tox2 expression in ILC3, suggesting a mechanism by which ILC3 adjusts to fluctuating environments by programming glycolytic metabolism. Our results reveal the requirement for Tox2 to support the metabolic adaptation of ILC3 within the gastrointestinal tract.

Type I interferons induce an epigenetically distinct memory B cell subset in chronic viral infection

Memory B cells (MBCs) are key providers of long-lived immunity against infectious disease, yet in chronic viral infection, they do not produce effective protection. How chronic viral infection disrupts MBC development and whether such changes are reversible remain unknown. Through single-cell (sc)ATAC-seq and scRNA-seq during acute versus chronic lymphocytic choriomeningitis viral infection, we identified a memory subset enriched for interferon (IFN)-stimulated genes (ISGs) during chronic infection that was distinct from the T-bet+ subset normally associated with chronic infection. Blockade of IFNAR-1 early in infection transformed the chromatin landscape of chronic MBCs, decreasing accessibility at ISG-inducing transcription factor binding motifs and inducing phenotypic changes in the dominating MBC subset, with a decrease in the ISG subset and an increase in CD11c+CD80+ cells. However, timing was critical, with MBCs resistant to intervention at 4 weeks post-infection. Together, our research identifies a key mechanism to instruct MBC identity during viral infection.

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases

Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.

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.

Single-Cell multiomics reveals ENL mutation perturbs kidney developmental trajectory by rewiring gene regulatory landscape

Cell differentiation during organogenesis relies on precise epigenetic and transcriptional control. Disruptions to this regulation can result in developmental abnormalities and malignancies, yet the underlying mechanisms are not well understood. Wilms tumors, a type of embryonal tumor closely linked to disrupted organogenesis, harbor mutations in epigenetic regulators in 30-50% of cases. However, the role of these regulators in kidney development and pathogenesis remains unexplored. By integrating mouse modeling, histological characterizations, and single-cell transcriptomics and chromatin accessibility profiling, we show that a Wilms tumor-associated mutation in the chromatin reader protein ENL disrupts kidney development trajectory by rewiring the gene regulatory landscape. Specifically, the mutant ENL promotes the commitment of nephron progenitors while simultaneously restricting their differentiation by dysregulating key transcription factor regulons, particularly the HOX clusters. It also induces the emergence of abnormal progenitor cells that lose their chromatin identity associated with kidney specification. Furthermore, the mutant ENL might modulate stroma-nephron interactions via paracrine Wnt signaling. These multifaceted effects caused by the mutation result in severe developmental defects in the kidney and early postnatal mortality in mice. Notably, transient inhibition of the histone acetylation binding activity of mutant ENL with a small molecule displaces transcriptional condensates formed by mutant ENL from target genes, abolishes its gene activation function, and restores developmental defects in mice. This work provides new insights into how mutations in epigenetic regulators can alter the gene regulatory landscape to disrupt kidney developmental programs at single-cell resolution in vivo . It also offers a proof-of-concept for the use of epigenetics-targeted agents to rectify developmental defects.

Interneuron loss and microglia activation by transcriptome analyses in the basal ganglia of Tourette syndrome

Tourette syndrome (TS) is a disorder of high-order integration of sensory, motor, and cognitive functions afflicting as many as 1 in 150 children and characterized by motor hyperactivity and tics. Despite high familial recurrence rates, a few risk genes and no biomarkers have emerged as causative or predisposing factors. The syndrome is believed to originate in basal ganglia, where patterns of motor programs are encoded. Postmortem immunocytochemical analyses of brains with severe TS revealed decreases in cholinergic, fast-spiking parvalbumin, and somatostatin interneurons within the striatum (caudate and putamen nuclei). Here, we performed single cell transcriptomic and chromatin accessibility analyses of the caudate nucleus from 6 adult TS and 6 control post-mortem brains. The data reproduced the known cellular composition of the adult human striatum, including a majority of medium spiny neurons (MSN) and small populations of GABAergic and cholinergic interneurons. Comparative analysis revealed that interneurons were decreased by roughly 50% in TS brains, while no difference was observed for other cell types. Differential gene expression analysis suggested that mitochondrial function, and specifically oxidative metabolism, in MSN and synaptic function in interneurons are both impaired in TS subjects. Furthermore, such an impairment was coupled with activation of immune response pathways in microglia. Also, our data explicitly link gene expression changes to changes in cis-regulatory activity in the corresponding cell types, suggesting de-regulation as a factor for the etiology of TS. These findings expand on previous research and suggest that impaired modulation of striatal function by interneurons may be the origin of TS symptoms.

Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation

Despite the success of antiretroviral therapy, human immunodeficiency virus (HIV) cannot be cured because of a reservoir of latently infected cells that evades therapy. To understand the mechanisms of HIV latency, we employed an integrated single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) approach to simultaneously profile the transcriptomic and epigenomic characteristics of ∼ 125,000 latently infected primary CD4+ T cells after reactivation using three different latency reversing agents. Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor (TF) activities across the cell population. We identified cellular transcripts and TFs whose expression/activity was correlated with viral reactivation and demonstrated that a machine learning model trained on these data was 75%-79% accurate at predicting viral reactivation. Finally, we validated the role of two candidate HIV-regulating factors, FOXP1 and GATA3, in viral transcription. These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.

Complementation testing identifies genes mediating effects at quantitative trait loci underlying fear-related behavior

Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3' UTR of FAIM2

The ch12q13 locus is among the most significant childhood obesity loci identified in genome-wide association studies. This locus resides in a non-coding region within FAIM2; thus, the underlying causal variant(s) presumably influence disease susceptibility via cis-regulation. We implicated rs7132908 as a putative causal variant by leveraging our in-house 3D genomic data and public domain datasets. Using a luciferase reporter assay, we observed allele-specific cis-regulatory activity of the immediate region harboring rs7132908. We generated isogenic human embryonic stem cell lines homozygous for either rs7132908 allele to assess changes in gene expression and chromatin accessibility throughout a differentiation to hypothalamic neurons, a key cell type known to regulate feeding behavior. The rs7132908 obesity risk allele influenced expression of FAIM2 and other genes and decreased the proportion of neurons produced by differentiation. We have functionally validated rs7132908 as a causal obesity variant that temporally regulates nearby effector genes and influences neurodevelopment and survival.

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.

Transcriptomic, epigenomic, and spatial metabolomic cell profiling redefines regional human kidney anatomy

A large-scale multimodal atlas that includes major kidney regions is lacking. Here, we employed simultaneous high-throughput single-cell ATAC/RNA sequencing (SHARE-seq) and spatially resolved metabolomics to profile 54 human samples from distinct kidney anatomical regions. We generated transcriptomes of 446,267 cells and chromatin accessibility profiles of 401,875 cells and developed a package to analyze 408,218 spatially resolved metabolomes. We find that the same cell type, including thin limb, thick ascending limb loop of Henle and principal cells, display distinct transcriptomic, chromatin accessibility, and metabolomic signatures, depending on anatomic location. Surveying metabolism-associated gene profiles revealed non-overlapping metabolic signatures between nephron segments and dysregulated lipid metabolism in diseased proximal tubule (PT) cells. Integrating multimodal omics with clinical data identified PLEKHA1 as a disease marker, and its in vitro knockdown increased gene expression in PT differentiation, suggesting possible pathogenic roles. This study highlights previously underrepresented cellular heterogeneity underlying the human kidney anatomy.

Deciphering Müller cell heterogeneity signatures in diabetic retinopathy across species: an integrative single-cell analysis

Diabetic retinopathy (DR), a leading cause of visual impairment, demands a profound comprehension of its cellular mechanisms to formulate effective therapeutic strategies. Our study presentes a comprehensive single-cell analysis elucidating the intricate landscape of Müller cells within DR, emphasizing their nuanced involvement. Utilizing scRNA-seq data from both Sprague-Dawley rat models and human patients, we delineated distinct Müller cell clusters and their corresponding gene expression profiles. These findings were further validated through differential gene expression analysis utilizing human transcriptomic data. Notably, certain Müller cell clusters displayed upregulation of the Rho gene, implying a phagocytic response to damaged photoreceptors within the DR microenvironment. This phenomenon was consistently observed across species. Additionally, the co-expression patterns of RHO and PDE6G within Müller cell clusters provided compelling evidence supporting their potential role in maintaining retinal integrity during DR. Our results offer novel insights into the cellular dynamics of DR and underscore Müller cells as promising therapeutic targets for preserving vision in retinal disorders induced by diabetes.

Single-Cell Multi-Omics Map of Cell Type-Specific Mechanistic Drivers of Multiple Sclerosis Lesions

BACKGROUND AND OBJECTIVES

In progressive multiple sclerosis (MS), compartmentalized inflammation plays a pivotal role in the complex pathology of tissue damage. The interplay between epigenetic regulation, transcriptional modifications, and location-specific alterations within white matter (WM) lesions at the single-cell level remains underexplored.

METHODS

We examined intracellular and intercellular pathways in the MS brain WM using a novel dataset obtained by integrated single-cell multi-omics techniques from 3 active lesions, 3 chronic active lesions, 3 remyelinating lesions, and 3 control WM of 6 patients with progressive MS and 3 non-neurologic controls. Single-nucleus RNA-seq and ATAC-seq were combined and additionally enriched with newly conducted spatial transcriptomics from 1 chronic active lesion. Functional gene modules were then validated in our previously published bulk tissue transcriptome data obtained from 73 WM lesions of patients with progressive MS and 25 WM of non-neurologic disease controls.

RESULTS

Our analysis uncovered an MS-specific oligodendrocyte genetic signature influenced by the KLF/SP gene family. This modulation has potential associations with the autocrine iron uptake signaling observed in transcripts of transferrin and its receptor LRP2. In addition, an inflammatory profile emerged within these oligodendrocytes. We observed unique cellular endophenotypes both at the periphery and within the chronic active lesion. These include a distinct metabolic astrocyte phenotype, the importance of FGF signaling among astrocytes and neurons, and a notable enrichment of mitochondrial genes at the lesion edge populated predominantly by astrocytes. Our study also identified B-cell coexpression networks indicating different functional B-cell subsets with differential location and specific tendencies toward certain lesion types.

DISCUSSION

The use of single-cell multi-omics has offered a detailed perspective into the cellular dynamics and interactions in MS. These nuanced findings might pave the way for deeper insights into lesion pathogenesis in progressive MS.

Multi-omics reveals the switch role of abnormal methylation in the regulation of decidual macrophages function in recurrent spontaneous abortion

RSA, recurrent spontaneous abortion, often causes serious physical damage and psychological pressure in reproductive women with unclarified pathogenesis. Abnormal function of decidual cells and aberrant DNA methylation have been reported to cause RSA, but their association remains unclear. Here, we integrated transcriptome, DNA methylome, and scRNA-seq to clarify the regulatory relationship between DNA methylation and decidual cells in RSA. We found that DNA methylation mainly influenced the function of decidual macrophages (DMs), of which four hub genes, HLA-A, HLA-F, SQSTM1/P62, and Interferon regulatory factor 7 (IRF7), related to 22 hypomethylated CpG sites, regulated 16 hub pathways to participate in RSA pathogenesis. In particular, using transcription factor analysis, it is suggested that the upregulation of IRF7 transcription was associated with enhanced recruitment of the transcription factor STAT1 by the hypomethylated promoter region of IRF7. As the current research on DNA methylation of macrophages in the uterine microenvironment of RSA is still blank, our systematic picture of abnormal DNA methylation in regulating DM function provides new insights into the role of DNA methylation in RSA occurrence, which may aid in further prevention and treatment of RSA.

Single-nucleotide variant calling in single-cell sequencing data with Monopogen

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

Mapping genotypes to chromatin accessibility profiles in single cells

In somatic tissue differentiation, chromatin accessibility changes govern priming and precursor commitment towards cellular fates1-3. Therefore, somatic mutations are likely to alter chromatin accessibility patterns, as they disrupt differentiation topologies leading to abnormal clonal outgrowth. However, defining the impact of somatic mutations on the epigenome in human samples is challenging due to admixed mutated and wild-type cells. Here, to chart how somatic mutations disrupt epigenetic landscapes in human clonal outgrowths, we developed genotyping of targeted loci with single-cell chromatin accessibility (GoT-ChA). This high-throughput platform links genotypes to chromatin accessibility at single-cell resolution across thousands of cells within a single assay. We applied GoT-ChA to CD34+ cells from patients with myeloproliferative neoplasms with JAK2V617F-mutated haematopoiesis. Differential accessibility analysis between wild-type and JAK2V617F-mutant progenitors revealed both cell-intrinsic and cell-state-specific shifts within mutant haematopoietic precursors, including cell-intrinsic pro-inflammatory signatures in haematopoietic stem cells, and a distinct profibrotic inflammatory chromatin landscape in megakaryocytic progenitors. Integration of mitochondrial genome profiling and cell-surface protein expression measurement allowed expansion of genotyping onto DOGMA-seq through imputation, enabling single-cell capture of genotypes, chromatin accessibility, RNA expression and cell-surface protein expression. Collectively, we show that the JAK2V617F mutation leads to epigenetic rewiring in a cell-intrinsic and cell type-specific manner, influencing inflammation states and differentiation trajectories. We envision that GoT-ChA will empower broad future investigations of the critical link between somatic mutations and epigenetic alterations across clonal populations in malignant and non-malignant contexts.

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity

Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models-especially variational autoencoders-have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE's capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively.

Projection-TAGs enable multiplex projection tracing and multi-modal profiling of projection neurons

Single-cell multiomic techniques have sparked immense interest in developing a comprehensive multi-modal map of diverse neuronal cell types and their brain wide projections. However, investigating the spatial organization, transcriptional and epigenetic landscapes of brain wide projection neurons is hampered by the lack of efficient and easily adoptable tools. Here we introduce Projection-TAGs, a retrograde AAV platform that allows multiplex tagging of projection neurons using RNA barcodes. By using Projection-TAGs, we performed multiplex projection tracing of the mouse cortex and high-throughput single-cell profiling of the transcriptional and epigenetic landscapes of the cortical projection neurons. Projection-TAGs can be leveraged to obtain a snapshot of activity-dependent recruitment of distinct projection neurons and their molecular features in the context of a specific stimulus. Given its flexibility, usability, and compatibility, we envision that Projection-TAGs can be readily applied to build a comprehensive multi-modal map of brain neuronal cell types and their projections.

RAG suppresses group 2 innate lymphoid cells

Antigen specificity is the central trait distinguishing adaptive from innate immune function. Assembly of antigen-specific T cell and B cell receptors occurs through V(D)J recombination mediated by the Recombinase Activating Gene endonucleases RAG1 and RAG2 (collectively called RAG). In the absence of RAG, mature T and B cells do not develop and thus RAG is critically associated with adaptive immune function. In addition to adaptive T helper 2 (Th2) cells, group 2 innate lymphoid cells (ILC2s) contribute to type 2 immune responses by producing cytokines like Interleukin-5 (IL-5) and IL-13. Although it has been reported that RAG expression modulates the function of innate natural killer (NK) cells, whether other innate immune cells such as ILC2s are affected by RAG remains unclear. We find that in RAG-deficient mice, ILC2 populations expand and produce increased IL-5 and IL-13 at steady state and contribute to increased inflammation in atopic dermatitis (AD)-like disease. Further, we show that RAG modulates ILC2 function in a cell-intrinsic manner independent of the absence or presence of adaptive T and B lymphocytes. Lastly, employing multiomic single cell analyses of RAG1 lineage-traced cells, we identify key transcriptional and epigenomic ILC2 functional programs that are suppressed by a history of RAG expression. Collectively, our data reveal a novel role for RAG in modulating innate type 2 immunity through suppression of ILC2s.

Single-cell insights: pioneering an integrated atlas of chromatin accessibility and transcriptomic landscapes in diabetic cardiomyopathy

BACKGROUND

Diabetic cardiomyopathy (DCM) poses a growing health threat, elevating heart failure risk in diabetic individuals. Understanding DCM is crucial, with fibroblasts and endothelial cells playing pivotal roles in driving myocardial fibrosis and contributing to cardiac dysfunction. Advances in Multimodal single-cell profiling, such as scRNA-seq and scATAC-seq, provide deeper insights into DCM's unique cell states and molecular landscape for targeted therapeutic interventions.

METHODS

Single-cell RNA and ATAC data from 10x Multiome libraries were processed using Cell Ranger ARC v2.0.1. Gene expression and ATAC data underwent Seurat and Signac filtration. Differential gene expression and accessible chromatin regions were identified. Transcription factor activity was estimated with chromVAR, and Cis-coaccessibility networks were calculated using Cicero. Coaccessibility connections were compared to the GeneHancer database. Gene Ontology analysis, biological process scoring, cell-cell communication analysis, and gene-motif correlation was performed to reveal intricate molecular changes. Immunofluorescent staining utilized various antibodies on paraffin-embedded tissues to verify the findings.

RESULTS

This study integrated scRNA-seq and scATAC-seq data obtained from hearts of WT and DCM mice, elucidating molecular changes at the single-cell level throughout the diabetic cardiomyopathy progression. Robust and accurate clustering analysis of the integrated data revealed altered cell proportions, showcasing decreased endothelial cells and macrophages, coupled with increased fibroblasts and myocardial cells in the DCM group, indicating enhanced fibrosis and endothelial damage. Chromatin accessibility analysis unveiled unique patterns in cell types, with heightened transcriptional activity in myocardial cells. Subpopulation analysis highlighted distinct changes in cardiomyocytes and fibroblasts, emphasizing pathways related to fatty acid metabolism and cardiac contraction. Fibroblast-centered communication analysis identified interactions with endothelial cells, implicating VEGF receptors. Endothelial cell subpopulations exhibited altered gene expressions, emphasizing contraction and growth-related pathways. Candidate regulators, including Tcf21, Arnt, Stat5a, and Stat5b, were identified, suggesting their pivotal roles in DCM development. Immunofluorescence staining validated marker genes of cell subpopulations, confirming PDK4, PPARγ and Tpm1 as markers for metabolic pattern-altered cardiomyocytes, activated fibroblasts and endothelial cells with compromised proliferation.

CONCLUSION

Our integrated scRNA-seq and scATAC-seq analysis unveils intricate cell states and molecular alterations in diabetic cardiomyopathy. Identified cell type-specific changes, transcription factors, and marker genes offer valuable insights. The study sheds light on potential therapeutic targets for DCM.

Single-cell multiomics guided mechanistic understanding of Fontan-associated liver disease

The Fontan operation is the current standard of care for single-ventricle congenital heart disease. Individuals with a Fontan circulation (FC) exhibit central venous hypertension and face life-threatening complications of hepatic fibrosis, known as Fontan-associated liver disease (FALD). The fundamental biology and mechanisms of FALD are little understood. Here, we generated a transcriptomic and epigenomic atlas of human FALD at single-cell resolution using multiomic snRNA-ATAC-seq. We found profound cell type-specific transcriptomic and epigenomic changes in FC livers. Central hepatocytes (cHep) exhibited the most substantial changes, featuring profound metabolic reprogramming. These cHep changes preceded substantial activation of hepatic stellate cells and liver fibrosis, suggesting cHep as a potential first "responder" in the pathogenesis of FALD. We also identified a network of ligand-receptor pairs that transmit signals from cHep to hepatic stellate cells, which may promote their activation and liver fibrosis. We further experimentally demonstrated that activins A and B promote fibrotic activation in vitro and identified mechanisms of activin A's transcriptional activation in FALD. Together, our single-cell transcriptomic and epigenomic atlas revealed mechanistic insights into the pathogenesis of FALD and may aid identification of potential therapeutic targets.

Computational tools for plant genomics and breeding

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.