Single-cell chromatin state analysis with Signac

Thalamocortical organoids enable in vitro modeling of 22q11.2 microdeletion associated with neuropsychiatric disorders

Thalamic dysfunction has been implicated in multiple psychiatric disorders. We sought to study the mechanisms by which abnormalities emerge in the context of the 22q11.2 microdeletion, which confers significant genetic risk for psychiatric disorders. We investigated early stages of human thalamus development using human pluripotent stem cell-derived organoids and show that the 22q11.2 microdeletion underlies widespread transcriptional dysregulation associated with psychiatric disorders in thalamic neurons and glia, including elevated expression of FOXP2. Using an organoid co-culture model, we demonstrate that the 22q11.2 microdeletion mediates an overgrowth of thalamic axons in a FOXP2-dependent manner. Finally, we identify ROBO2 as a candidate molecular mediator of the effects of FOXP2 overexpression on thalamic axon overgrowth. Together, our study suggests that early steps in thalamic development are dysregulated in a model of genetic risk for schizophrenia and contribute to neural phenotypes in 22q11.2 deletion syndrome.

Ex vivo expansion potential of murine hematopoietic stem cells is a rare property only partially predicted by phenotype

The scarcity of hematopoietic stem cells (HSCs) restricts their use in both clinical settings and experimental research. Here, we examined a recently developed method for expanding rigorously purified murine HSCs ex vivo. After 3 weeks of culture, only 0.1% of cells exhibited the input HSC phenotype, but these accounted for almost all functional long-term HSC activity. Input HSCs displayed varying potential for ex vivo self-renewal, with alternative outcomes revealed by single-cell multimodal RNA and ATAC sequencing profiling. While most HSC progeny offered only transient in vivo reconstitution, these cells efficiently rescued mice from lethal myeloablation. The amplification of functional HSC activity allowed for long-term multilineage engraftment in unconditioned hosts that associated with a return of HSCs to quiescence. Thereby, our findings identify several key considerations for ex vivo HSC expansion, with major implications also for assessment of normal HSC activity.

Safety, efficacy and determinants of response of allogeneic CD19-specific CAR-NK cells in CD19+ B cell tumors: a phase 1/2 trial

There is a pressing need for allogeneic chimeric antigen receptor (CAR)-immune cell therapies that are safe, effective and affordable. We conducted a phase 1/2 trial of cord blood-derived natural killer (NK) cells expressing anti-CD19 chimeric antigen receptor and interleukin-15 (CAR19/IL-15) in 37 patients with CD19+ B cell malignancies. The primary objectives were safety and efficacy, defined as day 30 overall response (OR). Secondary objectives included day 100 response, progression-free survival, overall survival and CAR19/IL-15 NK cell persistence. No notable toxicities such as cytokine release syndrome, neurotoxicity or graft-versus-host disease were observed. The day 30 and day 100 OR rates were 48.6% for both. The 1-year overall survival and progression-free survival were 68% and 32%, respectively. Patients who achieved OR had higher levels and longer persistence of CAR-NK cells. Receiving CAR-NK cells from a cord blood unit (CBU) with nucleated red blood cells ≤ 8 × 107 and a collection-to-cryopreservation time ≤ 24 h was the most significant predictor for superior outcome. NK cells from these optimal CBUs were highly functional and enriched in effector-related genes. In contrast, NK cells from suboptimal CBUs had upregulation of inflammation, hypoxia and cellular stress programs. Finally, using multiple mouse models, we confirmed the superior antitumor activity of CAR/IL-15 NK cells from optimal CBUs in vivo. These findings uncover new features of CAR-NK cell biology and underscore the importance of donor selection for allogeneic cell therapies. ClinicalTrials.gov identifier: NCT03056339 .

Nano-CUT&Tag for multimodal chromatin profiling at single-cell resolution

The ability to comprehensively analyze the chromatin state with single-cell resolution is crucial for understanding gene regulatory principles in heterogenous tissues or during development. Recently, we developed a nanobody-based single-cell CUT&Tag (nano-CT) protocol to simultaneously profile three epigenetic modalities-two histone marks and open chromatin state-from the same single cell. Nano-CT implements a new set of secondary nanobody-Tn5 fusion proteins to direct barcoded tagmentation by Tn5 transposase to genomic targets labeled by primary antibodies raised in different species. Such nanobody-Tn5 fusion proteins are currently not commercially available, and their in-house production and purification can be completed in 3-4 d by following our detailed protocol. The single-cell indexing in nano-CT is performed on a commercially available platform, making it widely accessible to the community. In comparison to other multimodal methods, nano-CT stands out in data complexity, low sample requirements and the flexibility to choose two of the three modalities. In addition, nano-CT works efficiently with fresh brain samples, generating multimodal epigenomic profiles for thousands of brain cells at single-cell resolution. The nano-CT protocol can be completed in just 3 d by users with basic skills in standard molecular biology and bioinformatics, although previous experience with single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is beneficial for more in-depth data analysis. As a multimodal assay, nano-CT holds immense potential to reveal interactions of various chromatin modalities, to explore epigenetic heterogeneity and to increase our understanding of the role and interplay that chromatin dynamics has in cellular development.

Identification of a distal enhancer regulating hedgehog interacting protein gene in human lung epithelial cells

BACKGROUND

An intergenic region at chromosome 4q31 is one of the most significant regions associated with COPD susceptibility and lung function in GWAS. In this region, the implicated causal gene HHIP has a unique epithelial expression pattern in adult human lungs, in contrast to dominant expression in fibroblasts in murine lungs. However, the mechanism underlying the species-dependent cell type-specific regulation of HHIP remains largely unknown.

METHODS

We employed snATAC-seq analysis to identify open chromatin regions within the COPD GWAS region in various human lung cell types. ChIP-quantitative PCR, reporter assays, chromatin conformation capture assays and Hi-C assays were conducted to characterize the regulatory element in this region. CRISPR/Cas9-editing was performed in BEAS-2B cells to generate single colonies with stable knockout of the regulatory element. RT-PCR and Western blot assays were used to evaluate expression of HHIP and epithelial-mesenchymal transition (EMT)-related marker genes.

FINDINGS

We identified a distal enhancer within the COPD 4q31 GWAS locus that regulates HHIP transcription at baseline and after TGFβ treatment in a SMAD3-dependent, but Hedgehog-independent manner in human bronchial epithelial cells. The distal enhancer also maintains chromatin topological domains near 4q31 locus and HHIP gene. Reduced HHIP expression led to increased EMT induced by TGFβ in human bronchial epithelial cells.

INTERPRETATION

A distal enhancer regulates HHIP expression both under homeostatic condition and upon TGFβ treatment in human bronchial epithelial cells. The interaction between HHIP and TGFβ signalling possibly contributes to COPD pathogenesis.

FUNDING

Supported by NIH grants R01HL127200, R01HL148667 and R01HL162783 (to X. Z).

A single-nuclei paired multiomic analysis of the human midbrain reveals age- and Parkinson's disease-associated glial changes

Age is the primary risk factor for Parkinson's disease (PD), but how aging changes the expression and regulatory landscape of the brain remains unclear. Here we present a single-nuclei multiomic study profiling shared gene expression and chromatin accessibility of young, aged and PD postmortem midbrain samples. Combined multiomic analysis along a pseudopathogenesis trajectory reveals that all glial cell types are affected by age, but microglia and oligodendrocytes are further altered in PD. We present evidence for a disease-associated oligodendrocyte subtype and identify genes lost over the aging and disease process, including CARNS1, that may predispose healthy cells to develop a disease-associated phenotype. Surprisingly, we found that chromatin accessibility changed little over aging or PD within the same cell types. Peak-gene association patterns, however, are substantially altered during aging and PD, identifying cell-type-specific chromosomal loci that contain PD-associated single-nucleotide polymorphisms. Our study suggests a previously undescribed role for oligodendrocytes in aging and PD.

Mechanisms of action and resistance in histone methylation-targeted therapy

Epigenomes enable the rectification of disordered cancer gene expression, thereby providing new targets for pharmacological interventions. The clinical utility of targeting histone H3 lysine trimethylation (H3K27me3) as an epigenetic hallmark has been demonstrated1-7. However, in actual therapeutic settings, the mechanism by which H3K27me3-targeting therapies exert their effects and the response of tumour cells remain unclear. Here we show the potency and mechanisms of action and resistance of the EZH1-EZH2 dual inhibitor valemetostat in clinical trials of patients with adult T cell leukaemia/lymphoma. Administration of valemetostat reduced tumour size and demonstrated durable clinical response in aggressive lymphomas with multiple genetic mutations. Integrative single-cell analyses showed that valemetostat abolishes the highly condensed chromatin structure formed by the plastic H3K27me3 and neutralizes multiple gene loci, including tumour suppressor genes. Nevertheless, subsequent long-term treatment encounters the emergence of resistant clones with reconstructed aggregate chromatin that closely resemble the pre-dose state. Acquired mutations at the PRC2-compound interface result in the propagation of clones with increased H3K27me3 expression. In patients free of PRC2 mutations, TET2 mutation or elevated DNMT3A expression causes similar chromatin recondensation through de novo DNA methylation in the H3K27me3-associated regions. We identified subpopulations with distinct metabolic and gene translation characteristics implicated in primary susceptibility until the acquisition of the heritable (epi)mutations. Targeting epigenetic drivers and chromatin homeostasis may provide opportunities for further sustained epigenetic cancer therapies.

ScISOr-ATAC reveals convergent and divergent splicing and chromatin specificities between matched cell types across cortical regions, evolution, and in Alzheimer's Disease

Multimodal measurements have become widespread in genomics, however measuring open chromatin accessibility and splicing simultaneously in frozen brain tissues remains unconquered. Hence, we devised Single-Cell-ISOform-RNA sequencing coupled with the Assay-for-Transposase-Accessible-Chromatin (ScISOr-ATAC). We utilized ScISOr-ATAC to assess whether chromatin and splicing alterations in the brain convergently affect the same cell types or divergently different ones. We applied ScISOr-ATAC to three major conditions: comparing (i) the Rhesus macaque (Macaca mulatta) prefrontal cortex (PFC) and visual cortex (VIS), (ii) cross species divergence of Rhesus macaque versus human PFC, as well as (iii) dysregulation in Alzheimer's disease in human PFC. We found that among cortical-layer biased excitatory neuron subtypes, splicing is highly brain-region specific for L3-5/L6 IT_RORB neurons, moderately specific in L2-3 IT_CUX2.RORB neurons and unspecific in L2-3 IT_CUX2 neurons. In contrast, at the chromatin level, L2-3 IT_CUX2.RORB neurons show the highest brain-region specificity compared to other subtypes. Likewise, when comparing human and macaque PFC, strong evolutionary divergence on one molecular modality does not necessarily imply strong such divergence on another molecular level in the same cell type. Finally, in Alzheimer's disease, oligodendrocytes show convergently high dysregulation in both chromatin and splicing. However, chromatin and splicing dysregulation most strongly affect distinct oligodendrocyte subtypes. Overall, these results indicate that chromatin and splicing can show convergent or divergent results depending on the performed comparison, justifying the need for their concurrent measurement to investigate complex systems. Taken together, ScISOr-ATAC allows for the characterization of single-cell splicing and chromatin patterns and the comparison of sample groups in frozen brain samples.

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

Foundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs in single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. By comparing ten different single-cell FMs with task-specific methods, we found that single-cell FMs may not consistently excel in all tasks than task-specific methods. However, the emergent abilities and the successful applications of cross-species/cross-modality transfer learning of FMs are promising. In addition, we present a systematic evaluation of the effects of hyper-parameters, initial settings, and stability for training single-cell FMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning. Our work summarizes the current state of single-cell FMs and points to their constraints and avenues for future development.

SHARE-Topic: Bayesian interpretable modeling of single-cell multi-omic data

Multi-omic single-cell technologies, which simultaneously measure the transcriptional and epigenomic state of the same cell, enable understanding epigenetic mechanisms of gene regulation. However, noisy and sparse data pose fundamental statistical challenges to extract biological knowledge from complex datasets. SHARE-Topic, a Bayesian generative model of multi-omic single cell data using topic models, aims to address these challenges. SHARE-Topic identifies common patterns of co-variation between different omic layers, providing interpretable explanations for the data complexity. Tested on data from different technological platforms, SHARE-Topic provides low dimensional representations recapitulating known biology and defines associations between genes and distal regulators in individual cells.

Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation

It is well known how sequencing technologies propelled cellular biology research in recent years, providing incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to simultaneously perform the mentioned sequencing modalities on the same cells. Yet, there still needs to be a clear and dedicated way to analyze these multi-modal data. One of the current methods is to calculate the Gene Activity Matrix (GAM), which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well defined, and it is unclear how various accessible regions impact the expression of the genes. Moreover, the transcription process is highly regulated by the transcription factors that bind to the different DNA regions. Therefore, this work presents a continuation of the meta-analysis of Genomic-Annotated Gene Activity Matrix (GAGAM) contributions, aiming to investigate the correlation between the TF expression and motif information in the different functional genomic regions to understand the different Transcription Factors (TFs) dynamics involved in different cell types.

Integrating single-cell multimodal epigenomic data using 1D-convolutional neural networks

Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportunities to investigate how epigenomic modalities vary together across cell types and states. A pivotal step in using this type of data is integrating the epigenomic modalities to learn a unified representation of each cell, but existing approaches are not designed to model the unique nature of this data type. Our key insight is to model single-cell multimodal epigenome data as a multi-channel sequential signal. Based on this insight, we developed ConvNet-VAEs, a novel framework that uses 1D-convolutional variational autoencoders (VAEs) for single-cell multimodal epigenomic data integration. We evaluated ConvNet-VAEs on nano-CT and scNTT-seq data generated from juvenile mouse brain and human bone marrow. We found that ConvNet-VAEs can perform dimension reduction and batch correction better than previous architectures while using significantly fewer parameters. Furthermore, the performance gap between convolutional and fully-connected architectures increases with the number of modalities, and deeper convolutional architectures can increase performance while performance degrades for deeper fully-connected architectures. Our results indicate that convolutional autoencoders are a promising method for integrating current and future single-cell multimodal epigenomic datasets.

Loss of Ezh2 in the medial ganglionic eminence alters interneuron fate, cell morphology and gene expression profiles

Introduction

Enhancer of zeste homolog 2 (Ezh2) is responsible for trimethylation of histone 3 at lysine 27 (H3K27me3), resulting in repression of gene expression. Here, we explore the role of Ezh2 in forebrain GABAergic interneuron development.

Methods

We removed Ezh2 in the MGE by generating Nkx2-1Cre;Ezh2 conditional knockout mice. We then characterized changes in MGE-derived interneuron fate and electrophysiological properties in juvenile mice, as well as alterations in gene expression, chromatin accessibility and histone modifications in the MGE.

Results

Loss of Ezh2 increases somatostatin-expressing (SST+) and decreases parvalbumin-expressing (PV+) interneurons in the forebrain. We observe fewer MGE-derived interneurons in the first postnatal week, indicating reduced interneuron production. Intrinsic electrophysiological properties in SST+ and PV+ interneurons are normal, but PV+ interneurons display increased axonal complexity in Ezh2 mutant mice. Single nuclei multiome analysis revealed differential gene expression patterns in the embryonic MGE that are predictive of these cell fate changes. Lastly, CUT&Tag analysis revealed that some genomic loci are particularly resistant or susceptible to shifts in H3K27me3 levels in the absence of Ezh2, indicating differential selectivity to epigenetic perturbation.

Discussion

Thus, loss of Ezh2 in the MGE alters interneuron fate, morphology, and gene expression and regulation. These findings have important implications for both normal development and potentially in disease etiologies.

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

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

An atlas of cells in the human tonsil

Palatine tonsils are secondary lymphoid organs (SLOs) representing the first line of immunological defense against inhaled or ingested pathogens. We generated an atlas of the human tonsil composed of >556,000 cells profiled across five different data modalities, including single-cell transcriptome, epigenome, proteome, and immune repertoire sequencing, as well as spatial transcriptomics. This census identified 121 cell types and states, defined developmental trajectories, and enabled an understanding of the functional units of the tonsil. Exemplarily, we stratified myeloid slan-like subtypes, established a BCL6 enhancer as locally active in follicle-associated T and B cells, and identified SIX5 as putative transcriptional regulator of plasma cell maturation. Analyses of a validation cohort confirmed the presence, annotation, and markers of tonsillar cell types and provided evidence of age-related compositional shifts. We demonstrate the value of this resource by annotating cells from B cell-derived mantle cell lymphomas, linking transcriptional heterogeneity to normal B cell differentiation states of the human tonsil.

Forskolin-driven conversion of human somatic cells into induced neurons through regulation of the cAMP-CREB1-JNK signaling

Human somatic cells can be reprogrammed into neuron cell fate through regulation of a single transcription factor or application of small molecule cocktails. Methods: Here, we report that forskolin efficiently induces the conversion of human somatic cells into induced neurons (FiNs). Results: A large population of neuron-like phenotype cells was observed as early as 24-36 h post-induction. There were >90% TUJ1-, >80% MAP2-, and >80% NEUN-positive neurons at 5 days post-induction. Multiple subtypes of neurons were present among TUJ1-positive cells, including >60% cholinergic, >20% glutamatergic, >10% GABAergic, and >5% dopaminergic neurons. FiNs exhibited typical neural electrophysiological activity in vitro and the ability to survive in vitro and in vivo more than 2 months. Mechanistically, forskolin functions in FiN reprogramming by regulating the cAMP-CREB1-JNK signals, which upregulates cAMP-CREB1 expression and downregulates JNK expression. Conclusion: Overall, our studies identify a safer and efficient single-small-molecule-driven reprogramming approach for induced neuron generation and reveal a novel regulatory mechanism of neuronal cell fate acquisition.

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

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

Single-cell multi-omics reveals insights into differentiation of rare cell types in mucinous colorectal cancer

Neuroendocrine cells have been implicated in therapeutic resistance and worse overall survival in many cancer types. Mucinous colorectal cancer (mCRC) is uniquely enriched for enteroendocrine cells (EECs), the neuroendocrine cell of the normal colon epithelium, as compared to non-mucinous CRC. Therefore, targeting EEC differentiation may have clinical value in mCRC. Here, single cell multi-omics was used to uncover epigenetic alterations that accompany EEC differentiation, identify STAT3 as a novel regulator of EEC specification, and discover a rare cancer-specific cell type with enteric neuron-like characteristics. Further experiments demonstrated that lysine-specific demethylase 1 (LSD1) and CoREST2 mediate STAT3 demethylation and regulate STAT3 chromatin binding. Knockdown of CoREST2 in an orthotopic xenograft mouse model resulted in decreased primary tumor growth and lung metastases. In culmination, these results provide rationale for new LSD1 inhibitors that target the interaction between LSD1 with STAT3 or CoREST2, which may improve clinical outcomes for patients with mCRC.

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 we know, cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it's not clear how it will work on the integrated single-cell multi-omics data. Here, we developed a Cell Cycle-Aware Network (CCAN) 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 out-standing 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.

Multimodal profiling reveals site-specific adaptation and tissue residency hallmarks of γδ T cells across organs in mice

γδ T cells perform heterogeneous functions in homeostasis and disease across tissues. However, it is unclear whether these roles correspond to distinct γδ subsets or to a homogeneous population of cells exerting context-dependent functions. Here, by cross-organ multimodal single-cell profiling, we reveal that various mouse tissues harbor unique site-adapted γδ subsets. Epidermal and intestinal intraepithelial γδ T cells are transcriptionally homogeneous and exhibit epigenetic hallmarks of functional diversity. Through parabiosis experiments, we uncovered cellular states associated with cytotoxicity, innate-like rapid interferon-γ production and tissue repair functions displaying tissue residency hallmarks. Notably, our observations add nuance to the link between interleukin-17-producing γδ T cells and tissue residency. Moreover, transcriptional programs associated with tissue-resident γδ T cells are analogous to those of CD8+ tissue-resident memory T cells. Altogether, this study provides a multimodal landscape of tissue-adapted γδ T cells, revealing heterogeneity, lineage relationships and their tissue residency program.

A fast, scalable and versatile tool for analysis of single-cell omics data

Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.

Neuronal MAPT expression is mediated by long-range interactions with cis-regulatory elements

Tauopathies are a group of neurodegenerative diseases defined by abnormal aggregates of tau, a microtubule-associated protein encoded by MAPT. MAPT expression is near absent in neural progenitor cells (NPCs) and increases during differentiation. This temporally dynamic expression pattern suggests that MAPT expression could be controlled by transcription factors and cis-regulatory elements specific to differentiated cell types. Given the relevance of MAPT expression to neurodegeneration pathogenesis, identification of such elements is relevant to understanding disease risk and pathogenesis. Here, we performed chromatin conformation assays (HiC & Capture-C), single-nucleus multiomics (RNA-seq+ATAC-seq), bulk ATAC-seq, and ChIP-seq for H3K27ac and CTCF in NPCs and differentiated neurons to nominate candidate cis-regulatory elements (cCREs). We assayed these cCREs using luciferase assays and CRISPR interference (CRISPRi) experiments to measure their effects on MAPT expression. Finally, we integrated cCRE annotations into an analysis of genetic variation in neurodegeneration-affected individuals and control subjects. We identified both proximal and distal regulatory elements for MAPT and confirmed the regulatory function for several regions, including three regions centromeric to MAPT beyond the H1/H2 haplotype inversion breakpoint. We also found that rare and predicted damaging genetic variation in nominated CREs was nominally depleted in dementia-affected individuals relative to control subjects, consistent with the hypothesis that variants that disrupt MAPT enhancer activity, and thereby reduced MAPT expression, may be protective against neurodegenerative disease. Overall, this study provides compelling evidence for pursuing detailed knowledge of CREs for genes of interest to permit better understanding of disease risk.

scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.

Representing and extracting knowledge from single-cell data

Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.

Unraveling the epigenetic code: human kidney DNA methylation and chromatin dynamics in renal disease development

Epigenetic changes may fill a critical gap in our understanding of kidney disease development, as they not only reflect metabolic changes but are also preserved and transmitted during cell division. We conducted a genome-wide cytosine methylation analysis of 399 human kidney samples, along with single-nuclear open chromatin analysis on over 60,000 cells from 14 subjects, including controls, and diabetes and hypertension attributed chronic kidney disease (CKD) patients. We identified and validated differentially methylated positions associated with disease states, and discovered that nearly 30% of these alterations were influenced by underlying genetic variations, including variants known to be associated with kidney disease in genome-wide association studies. We also identified regions showing both methylation and open chromatin changes. These changes in methylation and open chromatin significantly associated gene expression changes, most notably those playing role in metabolism and expressed in proximal tubules. Our study further demonstrated that methylation risk scores (MRS) can improve disease state annotation and prediction of kidney disease development. Collectively, our results suggest a causal relationship between epigenetic changes and kidney disease pathogenesis, thereby providing potential pathways for the development of novel risk stratification methods.

Single-cell multiomics decodes regulatory programs for mouse secondary palate development

Perturbations in gene regulation during palatogenesis can lead to cleft palate, which is among the most common congenital birth defects. Here, we perform single-cell multiome sequencing and profile chromatin accessibility and gene expression simultaneously within the same cells (n = 36,154) isolated from mouse secondary palate across embryonic days (E) 12.5, E13.5, E14.0, and E14.5. We construct five trajectories representing continuous differentiation of cranial neural crest-derived multipotent cells into distinct lineages. By linking open chromatin signals to gene expression changes, we characterize the underlying lineage-determining transcription factors. In silico perturbation analysis identifies transcription factors SHOX2 and MEOX2 as important regulators of the development of the anterior and posterior palate, respectively. In conclusion, our study charts epigenetic and transcriptional dynamics in palatogenesis, serving as a valuable resource for further cleft palate research.

Peak-agnostic high-resolution cis-regulatory circuitry mapping using single cell multiome data

Single same cell RNAseq/ATACseq multiome data provide unparalleled potential to develop high resolution maps of the cell-type specific transcriptional regulatory circuitry underlying gene expression. We present CREMA, a framework that recovers the full cis-regulatory circuitry by modeling gene expression and chromatin activity in individual cells without peak-calling or cell type labeling constraints. We demonstrate that CREMA overcomes the limitations of existing methods that fail to identify about half of functional regulatory elements which are outside the called chromatin 'peaks'. These circuit sites outside called peaks are shown to be important cell type specific functional regulatory loci, sufficient to distinguish individual cell types. Analysis of mouse pituitary data identifies a Gata2-circuit for the gonadotrope-enriched disease-associated Pcsk1 gene, which is experimentally validated by reduced gonadotrope expression in a gonadotrope conditional Gata2-knockout model. We present a web accessible human immune cell regulatory circuit resource, and provide CREMA as an R package.

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

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

scaDA: A Novel Statistical Method for Differential Analysis of Single-Cell Chromatin Accessibility Sequencing Data

Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named "scaDA", which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer's disease (AD) study, regions which are most enriched in GO terms related to neurogenesis, the clinical phenotype of AD, and SNPs identified in AD-associated GWAS.

CHOIR improves significance-based detection of cell types and states from single-cell data

Clustering is a critical step in the analysis of single-cell data, as it enables the discovery and characterization of putative cell types and states. However, most popular clustering tools do not subject clustering results to statistical inference testing, leading to risks of overclustering or underclustering data and often resulting in ineffective identification of cell types with widely differing prevalence. To address these challenges, we present CHOIR (clustering hierarchy optimization by iterative random forests), which applies a framework of random forest classifiers and permutation tests across a hierarchical clustering tree to statistically determine which clusters represent distinct populations. We demonstrate the enhanced performance of CHOIR through extensive benchmarking against 14 existing clustering methods across 100 simulated and 4 real single-cell RNA-seq, ATAC-seq, spatial transcriptomic, and multi-omic datasets. CHOIR can be applied to any single-cell data type and provides a flexible, scalable, and robust solution to the important challenge of identifying biologically relevant cell groupings within heterogeneous single-cell data.

Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS

Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas .

Epigenetic reprogramming driving successful and failed repair in acute kidney injury

Acute kidney injury (AKI) causes epithelial damage followed by subsequent repair. While successful repair restores kidney function, this process is often incomplete and can lead to chronic kidney disease (CKD) in a process called failed repair. To better understand the epigenetic reprogramming driving this AKI-to-CKD transition we generated a single nucleus multiomic atlas for the full mouse AKI time course, consisting of ~280,000 single nucleus transcriptomes and epigenomes. We reveal cell-specific dynamic alterations in gene regulatory landscapes reflecting especially activation of proinflammatory pathways. We further generated single nucleus multiomic data from four human AKI samples including validation by genome-wide identification of NF-kB binding sites. A regularized regression analysis identifies key regulators involved in both successful and failed repair cell fate, identifying the transcription factor CREB5 as a regulator of both successful and failed tubular repair that also drives proximal tubule cell proliferation after injury. Our interspecies multiomic approach provides a foundation to comprehensively understand cell states in AKI.

Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion-based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis-regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data.

STAT3-Mediated Ferroptosis is Involved in Sepsis-Associated Acute Respiratory Distress Syndrome

Sepsis-induced acute respiratory distress syndrome (ARDS) poses a grave danger to life, resulting from sepsis-induced multi-organ failure. Although ferroptosis, a form of iron-dependent lipid peroxidative cell death, has been associated with sepsis-induced ARDS, the specific mechanisms are not fully understood. In this study, we utilized WGCNA, PPI, friends analysis, and six machine learning techniques (Lasso, SVM, RFB, XGBoost, AdaBoost, and LightGBM) to pinpoint STAT3 as a potential diagnostic marker. A significant increase in monocyte and neutrophil levels was observed in patients with sepsis-induced ARDS, as revealed by immune infiltration analyses, when compared to controls. Moreover, there was a positive correlation between STAT3 expression and the level of infiltration. Single-cell analysis uncovered a notable disparity in B-cell expression between sepsis and sepsis-induced ARDS. Furthermore, in vitro experiments using LPS-treated human bronchial epithelial cells (BEAS-2B) and THP1 cells demonstrated a significant increase in STAT3 phosphorylation expression. Additionally, the inhibition of STAT3 phosphorylation by Stattic effectively prevented LPS-induced ferroptosis in both BEAS-2B and THP1 cells. This indicates that the activation of STAT3 phosphorylation promotes ferroptosis in human bronchial epithelial cells in response to LPS. In summary, this research has discovered and confirmed STAT3 as a potential biomarker for the diagnosis and treatment of sepsis-induced ARDS.

Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective

Treatment failure for the lethal brain tumor glioblastoma (GBM) is attributed to intratumoral heterogeneity and tumor evolution. We utilized 3D neuronavigation during surgical resection to acquire samples representing the whole tumor mapped by 3D spatial coordinates. Integrative tissue and single-cell analysis revealed sources of genomic, epigenomic, and microenvironmental intratumoral heterogeneity and their spatial patterning. By distinguishing tumor-wide molecular features from those with regional specificity, we inferred GBM evolutionary trajectories from neurodevelopmental lineage origins and initiating events such as chromothripsis to emergence of genetic subclones and spatially restricted activation of differential tumor and microenvironmental programs in the core, periphery, and contrast-enhancing regions. Our work depicts GBM evolution and heterogeneity from a 3D whole-tumor perspective, highlights potential therapeutic targets that might circumvent heterogeneity-related failures, and establishes an interactive platform enabling 360° visualization and analysis of 3D spatial patterns for user-selected genes, programs, and other features across whole GBM tumors.

Single cell 'omic profiles of human aortic endothelial cells in vitro and human atherosclerotic lesions ex vivo reveals heterogeneity of endothelial subtype and response to activating perturbations

Objective

Endothelial cells (ECs), macrophages, and vascular smooth muscle cells (VSMCs) are major cell types in atherosclerosis progression, and heterogeneity in EC sub-phenotypes are becoming increasingly appreciated. Still, studies quantifying EC heterogeneity across whole transcriptomes and epigenomes in both in vitro and in vivo models are lacking.

Approach and Results

To create an in vitro dataset to study human EC heterogeneity, multiomic profiling concurrently measuring transcriptomes and accessible chromatin in the same single cells was performed on six distinct primary cultures of human aortic ECs (HAECs). To model pro-inflammatory and activating environments characteristic of the atherosclerotic microenvironment in vitro, HAECs from at least three donors were exposed to three distinct perturbations with their respective controls: transforming growth factor beta-2 (TGFB2), interleukin-1 beta (IL1B), and siRNA-mediated knock-down of the endothelial transcription factor ERG (siERG). To form a comprehensive in vivo/ex vivo dataset of human atherosclerotic cell types, meta-analysis of single cell transcriptomes across 17 human arterial specimens was performed. Two computational approaches quantitatively evaluated the similarity in molecular profiles between heterogeneous in vitro and in vivo cell profiles. HAEC cultures were reproducibly populated by 4 major clusters with distinct pathway enrichment profiles: EC1-angiogenic, EC2-proliferative, EC3-activated/mesenchymal-like, and EC4-mesenchymal. Exposure to siERG, IL1B or TGFB2 elicited mostly distinct transcriptional and accessible chromatin responses. EC1 and EC2, the most canonically 'healthy' EC populations, were affected predominantly by siERG; the activated cluster EC3 was most responsive to IL1B; and the mesenchymal population EC4 was most affected by TGFB2. Quantitative comparisons between in vitro and in vivo transcriptomes confirmed EC1 and EC2 as most canonically EC-like, and EC4 as most mesenchymal with minimal effects elicited by siERG and IL1B. Lastly, accessible chromatin regions unique to EC2 and EC4 were most enriched for coronary artery disease (CAD)-associated SNPs from GWAS, suggesting these cell phenotypes harbor CAD-modulating mechanisms.

Conclusion

Primary EC cultures contain markedly heterogeneous cell subtypes defined by their molecular profiles. Surprisingly, the perturbations used here, which have been reported by others to be involved in the pathogenesis of atherosclerosis as well as induce endothelial-to-mesenchymal transition (EndMT), only modestly shifted cells between subpopulations, suggesting relatively stable molecular phenotypes in culture. Identifying consistently heterogeneous EC subpopulations between in vitro and in vivo models should pave the way for improving in vitro systems while enabling the mechanisms governing heterogeneous cell state decisions.

ROR2 regulates cellular plasticity in pancreatic neoplasia and adenocarcinoma

Cellular plasticity is a hallmark of pancreatic ductal adenocarcinoma (PDAC) starting from the conversion of normal cells into precancerous lesions to the progression of carcinoma subtypes associated with aggressiveness and therapeutic response. We discovered that normal acinar cell differentiation, maintained by the transcription factor Pdx1, suppresses a broad gastric cell identity that is maintained in metaplasia, neoplasia, and the classical subtype of PDAC in mouse and human. We have identified the receptor tyrosine kinase Ror2 as marker of a gastric metaplasia (SPEM)-like identity in the pancreas. Ablation of Ror2 in a mouse model of pancreatic tumorigenesis promoted a switch to a gastric pit cell identity that largely persisted through progression to the classical subtype of PDAC. In both human and mouse pancreatic cancer, ROR2 activity continued to antagonize the gastric pit cell identity, strongly promoting an epithelial to mesenchymal transition, conferring resistance to KRAS inhibition, and vulnerability to AKT inhibition.

Epigenetic reprogramming shapes the cellular landscape of schwannoma

Mechanisms specifying cancer cell states and response to therapy are incompletely understood. Here we show epigenetic reprogramming shapes the cellular landscape of schwannomas, the most common tumors of the peripheral nervous system. We find schwannomas are comprised of 2 molecular groups that are distinguished by activation of neural crest or nerve injury pathways that specify tumor cell states and the architecture of the tumor immune microenvironment. Moreover, we find radiotherapy is sufficient for interconversion of neural crest schwannomas to immune-enriched schwannomas through epigenetic and metabolic reprogramming. To define mechanisms underlying schwannoma groups, we develop a technique for simultaneous interrogation of chromatin accessibility and gene expression coupled with genetic and therapeutic perturbations in single-nuclei. Our results elucidate a framework for understanding epigenetic drivers of tumor evolution and establish a paradigm of epigenetic and metabolic reprograming of cancer cells that shapes the immune microenvironment in response to radiotherapy.

scATAnno: Automated Cell Type Annotation for single-cell ATAC Sequencing Data

The recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key task is to determine cell types based on epigenetic profiling. We introduce scATAnno, a workflow designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow can generate scATAC-seq reference atlases from publicly available datasets, and enable accurate cell type annotation by integrating query data with reference atlases, without the aid of scRNA-seq profiling. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect unknown cell populations within the query data. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), basal cell carcinoma (BCC) and Triple Negative Breast Cancer (TNBC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a powerful tool for cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems.

The chromatin landscape of healthy and injured cell types in the human kidney

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

An exhausted-like microglial population accumulates in aged and APOE4 genotype Alzheimer's brains

The dominant risk factors for late-onset Alzheimer's disease (AD) are advanced age and the APOE4 genetic variant. To examine how these factors alter neuroimmune function, we generated an integrative, longitudinal single-cell atlas of brain immune cells in AD model mice bearing the three common human APOE alleles. Transcriptomic and chromatin accessibility analyses identified a reactive microglial population defined by the concomitant expression of inflammatory signals and cell-intrinsic stress markers whose frequency increased with age and APOE4 burden. An analogous population was detectable in the brains of human AD patients, including in the cortical tissue, using multiplexed spatial transcriptomics. This population, which we designate as terminally inflammatory microglia (TIM), exhibited defects in amyloid-β clearance and altered cell-cell communication during aducanumab treatment. TIM may represent an exhausted-like state for inflammatory microglia in the AD milieu that contributes to AD risk and pathology in APOE4 carriers and the elderly, thus presenting a potential therapeutic target for AD.

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

Enteroendocrine cells (EECs), which secrete serotonin (enterochromaffin cells, EC) or a dominant peptide hormone, serve vital physiologic functions. As with any adult human lineage, the basis for terminal cell diversity remains obscure. We replicated human EEC differentiation in vitro , mapped transcriptional and chromatin dynamics that culminate in discrete cell types, and studied abundant EEC precursors expressing selected transcription factors (TFs) and gene programs. Before expressing the pre-terminal factor NEUROD1, non-replicating precursors oscillated between epigenetically similar but transcriptionally distinct ASCL1 + and HES6 hi cell states. Loss of either factor substantially accelerated EEC differentiation and disrupted EEC individuality; ASCL1 or NEUROD1 deficiency had opposing consequences on EC and hormone-producing cell features. Expressed late in EEC differentiation, the latter TFs mainly bind cis -elements that are accessible in undifferentiated stem cells and tailor the subsequent expression of TF combinations that specify EEC types. Thus, TF oscillations retard EEC maturation to enable accurate EEC diversification.

Integrative single-cell characterization of a frugivorous and an insectivorous bat kidney and pancreas

Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we use integrative single-cell sequencing (scRNA-seq and scATAC-seq) on insectivorous (Eptesicus fuscus; big brown bat) and frugivorous (Artibeus jamaicensis; Jamaican fruit bat) bat kidneys and pancreases and identify key cell population, gene expression and regulatory differences associated with the Jamaican fruit bat that also relate to human disease, particularly diabetes. We find a decrease in loop of Henle and an increase in collecting duct cells, and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the Jamaican fruit bat kidney. The Jamaican fruit bat pancreas shows an increase in endocrine and a decrease in exocrine cells, and differences in genes and regulatory elements involved in insulin regulation. We also find that these frugivorous bats share several molecular characteristics with human diabetes. Combined, our work provides insights from a frugivorous mammal that could be leveraged for therapeutic purposes.

Single-cell joint profiling of multiple epigenetic proteins and gene transcription

Sculpting the epigenome with a combination of histone modifications and transcription factor occupancy determines gene transcription and cell fate specification. Here, we first develop uCoTarget, utilizing a split-pool barcoding strategy for realizing ultrahigh-throughput single-cell joint profiling of multiple epigenetic proteins. Through extensive optimization for sensitivity and multimodality resolution, we demonstrate that uCoTarget enables simultaneous detection of five histone modifications (H3K27ac, H3K4me3, H3K4me1, H3K36me3, and H3K27me3) in 19,860 single cells. We applied uCoTarget to the in vitro generation of hematopoietic stem/progenitor cells (HSPCs) from human embryonic stem cells, presenting multimodal epigenomic profiles in 26,418 single cells. uCoTarget reveals establishment of pairing of HSPC enhancers (H3K27ac) and promoters (H3K4me3) and RUNX1 engagement priming for H3K27ac activation along the HSPC path. We then develop uCoTargetX, an expansion of uCoTarget to simultaneously measure transcriptome and multiple epigenome targets. Together, our methods enable generalizable, versatile multimodal profiles for reconstructing comprehensive epigenome and transcriptome landscapes and analyzing the regulatory interplay at single-cell level.

scGRN: a comprehensive single-cell gene regulatory network platform of human and mouse

Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237 051 cell type-specific GRNs (62 999 692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizing and downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.

Complementation testing identifies causal genes at quantitative trait loci underlying fear related behavior

Knowing the genes involved in quantitative traits provides a critical 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. Here we address a key step towards that goal by deploying a test that directly queries whether a gene mediates the effect of a quantitative trait locus (QTL). To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six QTLs, and identified six genes. Four genes, Lsamp, Ptprd, Nptx2 and Sh3gl, have known roles in synapse function; the fifth gene, Psip1, is a transcriptional co-activator not previously implicated in behavior; the sixth is a long non-coding RNA 4933413L06Rik with no known function. Single nucleus transcriptomic and epigenetic analyses implicated excitatory neurons as likely mediating the genetic effects. Surprisingly, variation in transcriptome and epigenetic modalities between inbred strains occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results open a bottleneck in using genetic mapping of QTLs to find novel biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

rworkflows: automating reproducible practices for the R community

Despite calls to improve reproducibility in research, achieving this goal remains elusive even within computational fields. Currently, >50% of R packages are distributed exclusively through GitHub. While the trend towards sharing open-source software has been revolutionary, GitHub does not have any default built-in checks for minimal coding standards or software usability. This makes it difficult to assess the current quality R packages, or to consistently use them over time and across platforms. While GitHub-native solutions are technically possible, they require considerable time and expertise for each developer to write, implement, and maintain. To address this, we develop rworkflows; a suite of tools to make robust continuous integration and deployment ( https://github.com/neurogenomics/rworkflows ). rworkflows can be implemented by developers of all skill levels using a one-time R function call which has both sensible defaults and extensive options for customisation. Once implemented, any updates to the GitHub repository automatically trigger parallel workflows that install all software dependencies, run code checks, generate a dedicated documentation website, and deploy a publicly accessible containerised environment. By making the rworkflows suite free, automated, and simple to use, we aim to promote widespread adoption of reproducible practices across a continually growing R community.

Mapping enhancer and chromatin accessibility landscapes charts the regulatory network of Alzheimer's disease

BACKGROUND

Enhancers are regulatory elements that target and modulate gene expression and play a role in human health and disease. However, the roles of enhancer regulatory circuit abnormalities driven by epigenetic alterations in Alzheimer's disease (AD) are unclear.

METHODS

In this study, a multiomic integrative analysis was performed to map enhancer and chromatin accessibility landscapes and identify regulatory network abnormalities in AD. We identified differentially methylated enhancers and constructed regulatory networks across brain regions using AD brain tissue samples. Through the integration of snATAC-seq and snRNA-seq datasets, we mapped enhancers with DNA methylation alterations (DMA) and chromatin accessibility landscapes. Core regulatory triplets that contributed to AD neuropathology in specific cell types were further prioritized.

RESULTS

We revealed widespread DNA methylation alterations (DMA) in the enhancers of AD patients across different brain regions. In addition, the genome-wide transcription factor (TF) binding profiles showed that enhancers with DMA are pervasively regulated by TFs. The TF-enhancer-gene regulatory network analysis identified core regulatory triplets that are associated with brain and immune cell proportions and play important roles in AD pathogenesis. Enhancer regulatory circuits with DMA exhibited distinct chromatin accessibility patterns, which were further characterized at single-cell resolutions.

CONCLUSIONS

Our study comprehensively investigated DNA methylation-mediated regulatory circuit abnormalities and provided novel insights into the potential pathogenesis of AD.

RNA-mediated symmetry breaking enables singular olfactory receptor choice

Olfactory receptor (OR) choice provides an extreme example of allelic competition for transcriptional dominance, where every olfactory neuron stably transcribes one of approximately 2,000 or more OR alleles1,2. OR gene choice is mediated by a multichromosomal enhancer hub that activates transcription at a single OR3,4, followed by OR-translation-dependent feedback that stabilizes this choice5,6. Here, using single-cell genomics, we show formation of many competing hubs with variable enhancer composition, only one of which retains euchromatic features and transcriptional competence. Furthermore, we provide evidence that OR transcription recruits enhancers and reinforces enhancer hub activity locally, whereas OR RNA inhibits transcription of competing ORs over distance, promoting transition to transcriptional singularity. Whereas OR transcription is sufficient to break the symmetry between equipotent enhancer hubs, OR translation stabilizes transcription at the prevailing hub, indicating that there may be sequential non-coding and coding mechanisms that are implemented by OR alleles for transcriptional prevalence. We propose that coding OR mRNAs possess non-coding functions that influence nuclear architecture, enhance their own transcription and inhibit transcription from their competitors, with generalizable implications for probabilistic cell fate decisions.