ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

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.

Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression

Clear cell renal cell carcinoma (ccRCC) is a complex disease with remarkable immune and metabolic heterogeneity. Here we perform genomic, transcriptomic, proteomic, metabolomic and spatial transcriptomic and metabolomic analyses on 100 patients with ccRCC from the Tongji Hospital RCC (TJ-RCC) cohort. Our analysis identifies four ccRCC subtypes including De-clear cell differentiated (DCCD)-ccRCC, a subtype with distinctive metabolic features. DCCD cancer cells are characterized by fewer lipid droplets, reduced metabolic activity, enhanced nutrient uptake capability and a high proliferation rate, leading to poor prognosis. Using single-cell and spatial trajectory analysis, we demonstrate that DCCD is a common mode of ccRCC progression. Even among stage I patients, DCCD is associated with worse outcomes and higher recurrence rate, suggesting that it cannot be cured by nephrectomy alone. Our study also suggests a treatment strategy based on subtype-specific immune cell infiltration that could guide the clinical management of ccRCC.

Quantification and visualization of cis-regulatory dynamics in single-cell multi-omics data with TREASMO

Recent advances in single-cell multi-omics technologies have provided unprecedented insights into regulatory processes. We introduce TREASMO, a versatile Python package designed to quantify and visualize transcriptional regulatory dynamics in single-cell multi-omics datasets. TREASMO has four modules, spanning data preparation, correlation quantification, downstream analysis and visualization, enabling comprehensive dataset exploration. By introducing a novel single-cell gene-peak correlation strength index, TREASMO facilitates accurate identification of regulatory changes at single-cell resolution. Validation on a hematopoietic stem and progenitor cell dataset showcases TREASMO's capacity in quantifying the gene-peak correlation strength at the single-cell level, identifying regulatory markers and discovering temporal regulatory patterns along the trajectory.

scAuto as a comprehensive framework for single-cell chromatin accessibility data analysis

Interpreting single-cell chromatin accessibility data is crucial for understanding intercellular heterogeneity regulation. Despite the progress in computational methods for analyzing this data, there is still a lack of a comprehensive analytical framework and a user-friendly online analysis tool. To fill this gap, we developed a pre-trained deep learning-based framework, single-cell auto-correlation transformers (scAuto), to overcome the challenge. Following DNABERT's methodology of pre-training and fine-tuning, scAuto learns a general understanding of DNA sequence's grammar by being pre-trained on unlabeled human genome via self-supervision; it is then transferred to the single-cell chromatin accessibility analysis task of scATAC-seq data for supervised fine-tuning. We extensively validated scAuto on the Buenrostro2018 dataset, demonstrating its superior performance on chromatin accessibility prediction, single-cell clustering, and data denoising. Based on scAuto, we further developed an interactive web server for single-cell chromatin accessibility data analysis. It integrates tutorial-style interfaces for those with limited programming skills. The platform is accessible at http://zhanglab.icaup.cn. To our knowledge, this work is expected to help analyze single-cell chromatin accessibility data and facilitate the development of precision medicine.

Single-cell multi-omics analysis of lineage development and spatial organization in the human fetal cerebellum

Human cerebellum encompasses numerous neurons, exhibiting a distinct developmental paradigm from cerebrum. Here we conducted scRNA-seq, scATAC-seq and spatial transcriptomic analyses of fetal samples from gestational week (GW) 13 to 18 to explore the emergence of cellular diversity and developmental programs in the developing human cerebellum. We identified transitory granule cell progenitors that are conserved across species. Special patterns in both granule cells and Purkinje cells were dissected multidimensionally. Species-specific gene expression patterns of cerebellar lobes were characterized and we found that PARM1 exhibited inconsistent distribution in human and mouse granule cells. A novel cluster of potential neuroepithelium at the rhombic lip was identified. We also resolved various subtypes of Purkinje cells and unipolar brush cells and revealed gene regulatory networks controlling their diversification. Therefore, our study offers a valuable multi-omics landscape of human fetal cerebellum and advances our understanding of development and spatial organization of human cerebellum.

The gene regulatory basis of bystander activation in CD8+ T cells

CD8+ T cells are classically recognized as adaptive lymphocytes based on their ability to recognize specific foreign antigens and mount memory responses. However, recent studies indicate that some antigen-inexperienced CD8+ T cells can respond to innate cytokines alone in the absence of cognate T cell receptor stimulation, a phenomenon referred to as bystander activation. Here, we demonstrate that neonatal CD8+ T cells undergo a robust and diverse program of bystander activation, which corresponds to enhanced innate-like protection against unrelated pathogens. Using a multi-omics approach, we found that the ability of neonatal CD8+ T cells to respond to innate cytokines derives from their capacity to undergo rapid chromatin remodeling, resulting in the usage of a distinct set of enhancers and transcription factors typically found in innate-like T cells. We observed that the switch between innate and adaptive functions in the CD8+ T cell compartment is mediated by changes in the abundance of distinct subsets of cells. The innate CD8+ T cell subset that predominates in early life was also present in adult mice and humans. Our findings provide support for the layered immune hypothesis and indicate that the CD8+ T cell compartment is more functionally diverse than previously thought.

SOX9 switch links regeneration to fibrosis at the single-cell level in mammalian kidneys

The steps governing healing with or without fibrosis within the same microenvironment are unclear. After acute kidney injury (AKI), injured proximal tubular epithelial cells activate SOX9 for self-restoration. Using a multimodal approach for a head-to-head comparison of injury-induced SOX9 lineages, we identified a dynamic SOX9 switch in repairing epithelia. Lineages that regenerated epithelia silenced SOX9 and healed without fibrosis (SOX9on-off). By contrast, lineages with unrestored apicobasal polarity maintained SOX9 activity in sustained efforts to regenerate, which were identified as a SOX9on-on Cadherin6pos cell state. These reprogrammed cells generated substantial single-cell WNT activity to provoke a fibroproliferative response in adjacent fibroblasts, driving AKI to chronic kidney disease. Transplanted human kidneys displayed similar SOX9/CDH6/WNT2B responses. Thus, we have uncovered a sensor of epithelial repair status, the activity of which determines regeneration with or without fibrosis.

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.

Massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing

Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called single-cell combinatorial fluidic indexing ATAC-sequencing ("scifi-ATAC-seq"), which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using a widely commercialized microfluidics platform (10X Genomics). With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples in a single emulsion reaction can be indexed, representing a ~20-fold increase in throughput compared to the standard 10X Genomics workflow.

YY1 mutations disrupt corticogenesis through a cell-type specific rewiring of cell-autonomous and non-cell-autonomous transcriptional programs

Germline mutations of YY1 cause Gabriele-de Vries syndrome (GADEVS), a neurodevelopmental disorder featuring intellectual disability and a wide range of systemic manifestations. To dissect the cellular and molecular mechanisms underlying GADEVS, we combined large-scale imaging, single-cell multiomics and gene regulatory network reconstruction in 2D and 3D patient-derived physiopathologically relevant cell lineages. YY1 haploinsufficiency causes a pervasive alteration of cell type specific transcriptional networks, disrupting corticogenesis at the level of neural progenitors and terminally differentiated neurons, including cytoarchitectural defects reminiscent of GADEVS clinical features. Transcriptional alterations in neurons propagated to neighboring astrocytes through a major non-cell autonomous pro-inflammatory effect that grounds the rationale for modulatory interventions. Together, neurodevelopmental trajectories, synaptic formation and neuronal-astrocyte cross talk emerged as salient domains of YY1 dosage-dependent vulnerability. Mechanistically, cell-type resolved reconstruction of gene regulatory networks uncovered the regulatory interplay between YY1, NEUROG2 and ETV5 and its aberrant rewiring in GADEVS. Our findings underscore the reach of advanced in vitro models in capturing developmental antecedents of clinical features and exposing their underlying mechanisms to guide the search for targeted interventions.

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.

Accessible chromatin maps of inflammatory bowel disease intestine nominate cell-type mediators of genetic disease risk

Inflammatory Bowel Disease ( IBD ) is a chronic and often debilitating autoinflammatory condition, with an increasing incidence in children. Standard-of-care therapies lead to sustained transmural healing and clinical remission in fewer than one-third of patients. For children, TNFα inhibition remains the only FDA-approved biologic therapy, providing an even greater urgency to understanding mechanisms of response. Genome-wide association studies ( GWAS ) have identified 418 independent genetic risk loci contributing to IBD, yet the majority are noncoding and their mechanisms of action are difficult to decipher. If causal, they likely alter transcription factor ( TF ) binding and downstream gene expression in particular cell types and contexts. To bridge this knowledge gap, we built a novel resource: multiome-seq (tandem single-nuclei ( sn )RNA-seq and chromatin accessibility ( snATAC )-seq) of intestinal tissue from pediatric IBD patients, where anti-TNF response was defined by endoscopic healing. From the snATAC-seq data, we generated a first-time atlas of chromatin accessibility (putative regulatory elements) for diverse intestinal cell types in the context of IBD. For cell types/contexts mediating genetic risk, we reasoned that accessible chromatin will co-localize with genetic disease risk loci. We systematically tested for significant co-localization of our chromatin accessibility maps and risk variants for 758 GWAS traits. Globally, genetic risk variants for IBD, autoimmune and inflammatory diseases are enriched in accessible chromatin of immune populations, while other traits (e.g., colorectal cancer, metabolic) are enriched in epithelial and stromal populations. This resource opens new avenues to uncover the complex molecular and cellular mechanisms mediating genetic disease risk.

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

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

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.

Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting Calling Cards data

MOTIVATION

Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors' (TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding events at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure TF binding and mRNA expression from single-cell CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data.

RESULTS

We introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. Pycallingcards introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type-specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF functions via CC data.

AVAILABILITY AND IMPLEMENTATION

Pycallingcards can be accessed on the GitHub repository: https://github.com/The-Mitra-Lab/pycallingcards.

Xist ribonucleoproteins promote female sex-biased autoimmunity

Autoimmune diseases disproportionately affect females more than males. The XX sex chromosome complement is strongly associated with susceptibility to autoimmunity. Xist long non-coding RNA (lncRNA) is expressed only in females to randomly inactivate one of the two X chromosomes to achieve gene dosage compensation. Here, we show that the Xist ribonucleoprotein (RNP) complex comprising numerous autoantigenic components is an important driver of sex-biased autoimmunity. Inducible transgenic expression of a non-silencing form of Xist in male mice introduced Xist RNP complexes and sufficed to produce autoantibodies. Male SJL/J mice expressing transgenic Xist developed more severe multi-organ pathology in a pristane-induced lupus model than wild-type males. Xist expression in males reprogrammed T and B cell populations and chromatin states to more resemble wild-type females. Human patients with autoimmune diseases displayed significant autoantibodies to multiple components of XIST RNP. Thus, a sex-specific lncRNA scaffolds ubiquitous RNP components to drive sex-biased immunity.

Single-nucleus multi-omic profiling of human placental syncytiotrophoblasts identifies cellular trajectories during pregnancy

The human placenta has a vital role in ensuring a successful pregnancy. Despite the growing body of knowledge about its cellular compositions and functions, there has been limited research on the heterogeneity of the billions of nuclei within the syncytiotrophoblast (STB), a multinucleated entity primarily responsible for placental function. Here we conducted integrated single-nucleus RNA sequencing and single-nucleus ATAC sequencing analyses of human placentas from early and late pregnancy. Our findings demonstrate the dynamic heterogeneity and developmental trajectories of STB nuclei and their correspondence with human trophoblast stem cell (hTSC)-derived STB. Furthermore, we identified transcription factors associated with diverse STB nuclear lineages through their gene regulatory networks and experimentally confirmed their function in hTSC and trophoblast organoid-derived STBs. Together, our data provide insights into the heterogeneity of human STB and represent a valuable resource for interpreting associated pregnancy complications.

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.

MMR vaccination induces trained immunity via functional and metabolic reprogramming of γδ T cells

The measles, mumps, and rubella (MMR) vaccine protects against all-cause mortality in children, but the immunological mechanisms mediating these effects are poorly known. We systematically investigated whether MMR can induce long-term functional changes in innate immune cells, a process termed trained immunity, that could at least partially mediate this heterologous protection. In a randomized, placebo-controlled trial, 39 healthy adults received either the MMR vaccine or a placebo. Using single-cell RNA-Seq, we found that MMR caused transcriptomic changes in CD14+ monocytes and NK cells, but most profoundly in γδ T cells. Monocyte function was not altered by MMR vaccination. In contrast, the function of γδ T cells was markedly enhanced by MMR vaccination, with higher production of TNF and IFN-γ, as well as upregulation of cellular metabolic pathways. In conclusion, we describe a trained immunity program characterized by modulation of γδ T cell function induced by MMR vaccination.

CDX2 dose-dependently influences the gene regulatory network underlying human extraembryonic mesoderm development

Proper regulation of gene dosage is critical for the development of the early embryo and the extraembryonic tissues that support it. Specifically, loss of Cdx2 in vivo leads to stunted development of the allantois, an extraembryonic mesoderm-derived structure critical for nutrient delivery and waste removal in the early embryo. In this study, we investigate how CDX2 dose-dependently influences the gene regulatory network underlying extraembryonic mesoderm development. We generate an allelic series for CDX2 in human induced pluripotent stem cells (hiPSCs) consisting of WT, heterozygous, and homozygous null CDX2 genotypes, differentiate these cells in a 2D gastruloid model, and subject these cells to multiomic single nucleus RNA and ATAC sequencing. We identify several genes that CDX2 dose-dependently regulate cytoskeletal integrity and adhesiveness in the extraembryonic mesoderm population, including regulators of the VEGF, canonical WNT, and non-canonical WNT signaling pathways. Despite these dose-dependent gene expression patterns, snATAC-seq reveals that heterozygous CDX2 expression is capable of inducing a WT-like chromatin accessibility profile, suggesting accessibility is not sufficient to drive gene expression when the CDX2 dosage is reduced. Finally, because the loss of CDX2 or TBXT phenocopy one another in vivo, we compare differentially expressed genes in our CDX2 knock-out model to those from TBXT knock-out hiPSCs differentiated in an analogous experiment. This comparison identifies several communally misregulated genes that are critical for cytoskeletal integrity and tissue permeability, including ANK3 and ANGPT1. Together, these results clarify how CDX2 dose-dependently regulates gene expression in the extraembryonic mesoderm and suggest these genes may underlie the defects in vascular development and allantoic elongation seen in the absence or reduction of CDX2 in vivo.

Single cell multi-omics of fibrotic kidney reveal epigenetic regulation of antioxidation and apoptosis within proximal tubule

BACKGROUND

Until now, there has been no particularly effective treatment for chronic kidney disease (CKD). Fibrosis is a common pathological change that exist in CKD.

METHODS

To better understand the transcriptional dynamics in fibrotic kidney, we make use of single-nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq) and single-cell RNA sequencing (scRNA-seq) from GEO datasets and perform scRNA-seq of human biopsy to seek possible transcription factors (TFs) regulating target genes in the progress of kidney fibrosis across mouse and human kidneys.

RESULTS

Our analysis has displayed chromatin accessibility, gene expression pattern and cell-cell communications at single-cell level in kidneys suffering from unilateral ureteral obstruction (UUO) or chronic interstitial nephritis (CIN). Using multimodal data, there exists epigenetic regulation producing less Sod1 and Sod2 mRNA within the proximal tubule which is hard to withstand oxidative stress during fibrosis. Meanwhile, a transcription factor Nfix promoting the apoptosis-related gene Ifi27 expression found by multimodal data was validated by an in vitro study. And the gene Ifi27 upregulated by in situ AAV injection within the kidney cortex aggravates kidney fibrosis.

CONCLUSIONS

In conclusion, as we know oxidation and apoptosis are traumatic factors during fibrosis, thus enhancing antioxidation and inhibiting the Nfix-Ifi27 pathway to inhibit apoptosis could be a potential treatment for kidney fibrosis.

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.

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.

Single-cell sequencing analysis within biologically relevant dimensions

The currently predominant approach to transcriptomic and epigenomic single-cell analysis depends on a rigid perspective constrained by reduced dimensions and algorithmically derived and annotated clusters. Here, we developed Seqtometry (sequencing-to-measurement), a single-cell analytical strategy based on biologically relevant dimensions enabled by advanced scoring with multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems. By utilizing information only in the form of specific signatures, Seqtometry bypasses unsupervised clustering and individual annotations of clusters. Instead, Seqtometry combines qualitative and quantitative cell-type identification with specific characterization of diverse biological processes under experimental or disease conditions. Comprehensive analysis by Seqtometry of various immune cells as well as other cells from different organs and disease-induced states, including multiple myeloma and Alzheimer's disease, surpasses corresponding cluster-based analytical output. We propose Seqtometry as a single-cell sequencing analysis approach applicable for both basic and clinical research.

Single-cell multi-omic analysis of the vestibular schwannoma ecosystem uncovers a nerve injury-like state

Vestibular schwannomas (VS) are benign tumors that lead to significant neurologic and otologic morbidity. How VS heterogeneity and the tumor microenvironment (TME) contribute to VS pathogenesis remains poorly understood. In this study, we perform scRNA-seq on 15 VS, with paired scATAC-seq (n = 6) and exome sequencing (n = 12). We identify diverse Schwann cell (SC), stromal, and immune populations in the VS TME and find that repair-like and MHC-II antigen-presenting SCs are associated with myeloid cell infiltrate, implicating a nerve injury-like process. Deconvolution analysis of RNA-expression data from 175 tumors reveals Injury-like tumors are associated with larger tumor size, and scATAC-seq identifies transcription factors associated with nerve repair SCs from Injury-like tumors. Ligand-receptor analysis and in vitro experiments suggest that Injury-like VS-SCs recruit myeloid cells via CSF1 signaling. Our study indicates that Injury-like SCs may cause tumor growth via myeloid cell recruitment and identifies molecular pathways that may be therapeutically targeted.

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.

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.

TargetGene: a comprehensive database of cell-type-specific target genes for genetic variants

Annotating genetic variants to their target genes is of great importance in unraveling the causal variants and genetic mechanisms that underlie complex diseases. However, disease-associated genetic variants are often located in non-coding regions and manifest context-specific effects, making it challenging to accurately identify the target genes and regulatory mechanisms. Here, we present TargetGene (https://ngdc.cncb.ac.cn/targetgene/), a comprehensive database reporting target genes for human genetic variants from various aspects. Specifically, we collected a comprehensive catalog of multi-omics data at the single-cell and bulk levels and from various human tissues, cell types and developmental stages. To facilitate the identification of Single Nucleotide Polymorphism (SNP)-to-gene connections, we have implemented multiple analytical tools based on chromatin co-accessibility, 3D interaction, enhancer activities and quantitative trait loci, among others. We applied the pipeline to evaluate variants from nearly 1300 Genome-wide association studies (GWAS) and assembled a comprehensive atlas of multiscale regulation of genetic variants. TargetGene is equipped with user-friendly web interfaces that enable intuitive searching, navigation and browsing through the results. Overall, TargetGene provides a unique resource to empower researchers to study the regulatory mechanisms of genetic variants in complex human traits.

Histone lactylation couples cellular metabolism with developmental gene regulatory networks

Embryonic cells exhibit diverse metabolic states. Recent studies have demonstrated that metabolic reprogramming drives changes in cell identity by affecting gene expression. However, the connection between cellular metabolism and gene expression remains poorly understood. Here we report that glycolysis-regulated histone lactylation couples the metabolic state of embryonic cells with chromatin organization and gene regulatory network (GRN) activation. We found that lactylation marks genomic regions of glycolytic embryonic tissues, like the neural crest (NC) and pre-somitic mesoderm. Histone lactylation occurs in the loci of NC genes as these cells upregulate glycolysis. This process promotes the accessibility of active enhancers and the deployment of the NC GRN. Reducing the deposition of the mark by targeting LDHA/B leads to the downregulation of NC genes and the impairment of cell migration. The deposition of lactyl-CoA on histones at NC enhancers is supported by a mechanism that involves transcription factors SOX9 and YAP/TEAD. These findings define an epigenetic mechanism that integrates cellular metabolism with the GRNs that orchestrate embryonic development.

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.

Scalable single-cell profiling of chromatin modifications with sciCUT&Tag

Cleavage under targets and tagmentation (CUT&Tag) is an antibody-directed in situ chromatin profiling strategy that is rapidly replacing immune precipitation-based methods, such as chromatin immunoprecipitation-sequencing. The efficiency of the method enables chromatin profiling in single cells but is limited by the numbers of cells that can be profiled. Here, we describe a combinatorial barcoding strategy for CUT&Tag that harnesses a nanowell dispenser for simple, high-resolution, high-throughput, single-cell chromatin profiling. In this single-cell combinatorial indexing CUT&Tag (sciCUT&Tag) protocol, lightly cross-linked nuclei are bound to magnetic beads and incubated with primary and secondary antibodies in bulk and then arrayed in a 96-well plate for a first round of cellular indexing by antibody-directed Tn5 tagmentation. The sample is then repooled, mixed and arrayed across 5,184 nanowells at a density of 12-24 nuclei per well for a second round of cellular indexing during PCR amplification of the sequencing-ready library. This protocol can be completed in 1.5 days by a research technician, and we illustrate the optimized protocol by profiling histone modifications associated with developmental gene repression (H3K27me3) as well as transcriptional activation (H3K4me1-2-3) in human peripheral blood mononuclear cells and use single-nucleotide polymorphisms to facilitate collision removal. We have also used sciCUT&Tag for simultaneous profiling of multiple chromatin epitopes in single cells. The reduced cost, improved resolution and scalability of sciCUT&Tag make it an attractive platform to profile chromatin features in single cells.

Uniform quantification of single-nucleus ATAC-seq data with Paired-Insertion Counting (PIC) and a model-based insertion rate estimator

Existing approaches to scoring single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) feature matrices from sequencing reads are inconsistent, affecting downstream analyses and displaying artifacts. We show that, even with sparse single-cell data, quantitative counts are informative for estimating the regulatory state of a cell, which calls for a consistent treatment. We propose Paired-Insertion Counting as a uniform method for snATAC-seq feature characterization and provide a probability model for inferring latent insertion dynamics from snATAC-seq count matrices.

Slide-tags enables single-nucleus barcoding for multimodal spatial genomics

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed1-6. However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as an input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 μm spatial resolution and delivered whole-transcriptome data that are indistinguishable in quality from ordinary single-nucleus RNA-sequencing data. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualized receptor-ligand interactions driving B cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to almost any single-cell measurement technology. As a proof of principle, we performed multiomic measurements of open chromatin, RNA and T cell receptor (TCR) sequences in the same cells from metastatic melanoma, identifying transcription factor motifs driving cancer cell state transitions in spatially distinct microenvironments. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.

Modeling fragment counts improves single-cell ATAC-seq analysis

Single-cell ATAC sequencing coverage in regulatory regions is typically binarized as an indicator of open chromatin. Here we show that binarization is an unnecessary step that neither improves goodness of fit, clustering, cell type identification nor batch integration. Fragment counts, but not read counts, should instead be modeled, which preserves quantitative regulatory information. These results have immediate implications for single-cell ATAC sequencing analysis.

Antibody dependent cell-mediated cytotoxicity selection pressure induces diverse mechanisms of resistance

Targeted monoclonal antibody therapy has emerged as a powerful therapeutic strategy for cancer. However, only a minority of patients have durable responses and the development of resistance remains a major clinical obstacle. Antibody-dependent cell-mediated cytotoxicity (ADCC) represents a crucial therapeutic mechanism of action; however, few studies have explored ADCC resistance. Using multiple in vitro models of ADCC selection pressure, we have uncovered both shared and distinct resistance mechanisms. Persistent ADCC selection pressure yielded ADCC-resistant cells that are characterized by a loss of NK cell conjugation and this shared resistance phenotype is associated with cell-line dependent modulation of cell surface proteins that contribute to immune synapse formation and NK cell function. We employed single-cell RNA sequencing and proteomic screens to interrogate molecular mechanisms of resistance. We demonstrate that ADCC resistance involves upregulation of interferon/STAT1 and DNA damage response signaling as well as activation of the immunoproteasome. Here, we identify pathways that modulate ADCC sensitivity and report strategies to enhance ADCC-mediated elimination of cancer cells. ADCC resistance could not be reversed with combinatorial treatment approaches. Hence, our findings indicate that tumor cells utilize multiple strategies to inhibit NK cell mediated-ADCC. Future research and development of NK cell-based immunotherapies must incorporate plans to address or potentially prevent the induction of resistance.

Mapping cis- and trans-regulatory target genes of human-specific deletions

Deletion of functional sequence is predicted to represent a fundamental mechanism of molecular evolution1,2. Comparative genetic studies of primates2,3 have identified thousands of human-specific deletions (hDels), and the cis-regulatory potential of short (≤31 base pairs) hDels has been assessed using reporter assays4. However, how structural variant-sized (≥50 base pairs) hDels influence molecular and cellular processes in their native genomic contexts remains unexplored. Here, we design genome-scale libraries of single-guide RNAs targeting 7.2 megabases of sequence in 6,358 hDels and present a systematic CRISPR interference (CRISPRi) screening approach to identify hDels that modify cellular proliferation in chimpanzee pluripotent stem cells. By intersecting hDels with chromatin state features and performing single-cell CRISPRi (Perturb-seq) to identify their cis- and trans-regulatory target genes, we discovered 19 hDels controlling gene expression. We highlight two hDels, hDel_2247 and hDel_585, with tissue-specific activity in the liver and brain, respectively. Our findings reveal a molecular and cellular role for sequences lost in the human lineage and establish a framework for functionally interrogating human-specific genetic variants.

A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders

Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generated single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. Seventy-five percent of elements (44 of 59) validated in an in vivo transgenic reporter assay, demonstrating that single cell accessibility is a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieved significant reduction in our variant search space and nominated candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as new candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work provides novel non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.

A distinct human cell type expressing MHCII and RORγt with dual characteristics of dendritic cells and type 3 innate lymphoid cells

Recent studies have characterized various mouse antigen-presenting cells (APCs) expressing the lymphoid-lineage transcription factor RORγt (Retinoid-related orphan receptor gamma t), which exhibit distinct phenotypic features and are implicated in the induction of peripheral regulatory T cells (Tregs) and immune tolerance to microbiota and self-antigens. These APCs encompass Janus cells and Thetis cell subsets, some of which express the AutoImmune REgulator (AIRE). RORγt+ MHCII+ type 3 innate lymphoid cells (ILC3) have also been implicated in the instruction of microbiota-specific Tregs. While RORγt+ APCs have been actively investigated in mice, the identity and function of these cell subsets in humans remain elusive. Herein, we identify a rare subset of RORγt+ cells with dendritic cell (DC) features through integrated single-cell RNA sequencing and single-cell ATAC sequencing. These cells, which we term RORγt+ DC-like cells (R-DC-like), exhibit DC morphology, express the MHC class II machinery, and are distinct from all previously reported DC and ILC3 subsets, but share transcriptional and epigenetic similarities with DC2 and ILC3. We have developed procedures to isolate and expand them in vitro, enabling their functional characterization. R-DC-like cells proliferate in vitro, continue to express RORγt, and differentiate into CD1c+ DC2-like cells. They stimulate the proliferation of allogeneic T cells. The identification of human R-DC-like cells with proliferative potential and plasticity toward CD1c+ DC2-like cells will prompt further investigation into their impact on immune homeostasis, inflammation, and autoimmunity.

Single-nucleus multiomic atlas of frontal cortex in amyotrophic lateral sclerosis with a deep learning-based decoding of alternative polyadenylation mechanisms

The understanding of how different cell types contribute to amyotrophic lateral sclerosis (ALS) pathogenesis is limited. Here we generated a single-nucleus transcriptomic and epigenomic atlas of the frontal cortex of ALS cases with C9orf72 (C9) hexanucleotide repeat expansions and sporadic ALS (sALS). Our findings reveal shared pathways in C9-ALS and sALS, characterized by synaptic dysfunction in excitatory neurons and a disease-associated state in microglia. The disease subtypes diverge with loss of astrocyte homeostasis in C9-ALS, and a more substantial disturbance of inhibitory neurons in sALS. Leveraging high depth 3'-end sequencing, we found a widespread switch towards distal polyadenylation (PA) site usage across ALS subtypes relative to controls. To explore this differential alternative PA (APA), we developed APA-Net, a deep neural network model that uses transcript sequence and expression levels of RNA-binding proteins (RBPs) to predict cell-type specific APA usage and RBP interactions likely to regulate APA across disease subtypes.

Single-cell multi-omics analysis of human testicular germ cell tumor reveals its molecular features and microenvironment

Seminoma is the most common malignant solid tumor in 14 to 44 year-old men. However, its molecular features and tumor microenvironment (TME) is largely unexplored. Here, we perform a series of studies via genomics profiling (single cell multi-omics and spatial transcriptomics) and functional examination using seminoma samples and a seminoma cell line. We identify key gene expression programs share between seminoma and primordial germ cells, and further characterize the functions of TFAP2C in promoting tumor invasion and migration. We also identify 15 immune cell subtypes in TME, and find that subtypes with exhaustion features were located closer to the tumor region through combined spatial transcriptome analysis. Furthermore, we identify key pathways and genes that may facilitate seminoma disseminating beyond the seminiferous tubules. These findings advance our knowledge of seminoma tumorigenesis and produce a multi-omics atlas of in situ human seminoma microenvironment, which could help discover potential therapy targets for seminoma.

Common and divergent gene regulatory networks control injury-induced and developmental neurogenesis in zebrafish retina

Following acute retinal damage, zebrafish possess the ability to regenerate all neuronal subtypes through Müller glia (MG) reprogramming and asymmetric cell division that produces a multipotent Müller glia-derived neuronal progenitor cell (MGPC). This raises three key questions. First, do MG reprogram to a developmental retinal progenitor cell (RPC) state? Second, to what extent does regeneration recapitulate retinal development? And finally, does loss of different retinal cell subtypes induce unique MG regeneration responses? We examined these questions by performing single-nuclear and single-cell RNA-Seq and ATAC-Seq in both developing and regenerating retinas. Here we show that injury induces MG to reprogram to a state similar to late-stage RPCs. However, there are major transcriptional differences between MGPCs and RPCs, as well as major transcriptional differences between activated MG and MGPCs when different retinal cell subtypes are damaged. Validation of candidate genes confirmed that loss of different subtypes induces differences in transcription factor gene expression and regeneration outcomes.

Alveolar epithelial progenitor cells require Nkx2-1 to maintain progenitor-specific epigenomic state during lung homeostasis and regeneration

Lung epithelial regeneration after acute injury requires coordination cellular coordination to pattern the morphologically complex alveolar gas exchange surface. During adult lung regeneration, Wnt-responsive alveolar epithelial progenitor (AEP) cells, a subset of alveolar type 2 (AT2) cells, proliferate and transition to alveolar type 1 (AT1) cells. Here, we report a refined primary murine alveolar organoid, which recapitulates critical aspects of in vivo regeneration. Paired scRNAseq and scATACseq followed by transcriptional regulatory network (TRN) analysis identified two AT1 transition states driven by distinct regulatory networks controlled in part by differential activity of Nkx2-1. Genetic ablation of Nkx2-1 in AEP-derived organoids was sufficient to cause transition to a proliferative stressed Krt8+ state, and AEP-specific deletion of Nkx2-1 in adult mice led to rapid loss of progenitor state and uncontrolled growth of Krt8+ cells. Together, these data implicate dynamic epigenetic maintenance via Nkx2-1 as central to the control of facultative progenitor activity in AEPs.

A reversible epigenetic memory of inflammatory injury controls lineage plasticity and tumor initiation in the mouse pancreas

Inflammation is essential to the disruption of tissue homeostasis and can destabilize the identity of lineage-committed epithelial cells. Here, we employ lineage-traced mouse models, single-cell transcriptomic and chromatin analyses, and CUT&TAG to identify an epigenetic memory of inflammatory injury in the pancreatic acinar cell compartment. Despite resolution of pancreatitis, our data show that acinar cells fail to return to their molecular baseline, with retention of elevated chromatin accessibility and H3K4me1 at metaplasia genes, such that memory represents an incomplete cell fate decision. In vivo, we find this epigenetic memory controls lineage plasticity, with diminished metaplasia in response to a second insult but increased tumorigenesis with an oncogenic Kras mutation. The lowered threshold for oncogenic transformation, in turn, can be restored by blockade of MAPK signaling. Together, we define the chromatin dynamics, molecular encoding, and recall of a prolonged epigenetic memory of inflammatory injury that impacts future responses but remains reversible.

Whole exome sequencing reveals genetic landscape associated with left ventricular outflow tract obstruction in Chinese Han population

Left ventricular outflow tract obstruction (LVOTO), a major form of outflow tract malformation, accounts for a substantial portion of congenital heart defects (CHDs). Unlike its prevalence, the genetic architecture of LVOTO remains largely unknown. To unveil the genetic mutations and risk genes potentially associated with LVOTO, we enrolled a cohort of 106 LVOTO patients and 100 healthy controls and performed a whole-exome sequencing (WES). 71,430 rare deleterious mutations were found in LVOTO patients. By using gene-based burden testing, we further found 32 candidate genes enriched in LVOTO patient including known pathological genes such as GATA5 and GATA6. Most variants of 32 risk genes occur simultaneously rather exclusively suggesting polygenic inherence of LVOTO and 14 genes out of 32 risk genes interact with previously discovered CHD genes. Single cell RNA-seq further revealed dynamic expressions of GATA5, GATA6, FOXD3 and MYO6 in endocardium and neural crest lineage indicating the mutations of these genes lead to LVOTO possibly through different lineages. These findings uncover the genetic architecture of LVOTO which advances the current understanding of LVOTO genetics.

A protocol for simultaneous high-sensitivity genotyping and chromatin accessibility profiling in single cells

Single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) resolves the heterogeneity of epigenetic states across cells but does not typically capture exonic mutations, which limits our knowledge of how somatic mutations alter chromatin landscapes. Here, we present a plate-based approach coupling high-sensitivity genotyping of genomic loci with high-content scATAC-seq libraries from the same single cells. We first describe steps for optimization of genotyping primers, followed by detailed guidance on the preparation of both scATAC-seq and single-cell genotyping libraries, fully automated on high-throughput liquid handling platforms. For complete details on the use and execution of this protocol, please refer to Turkalj, Jakobsen et al.1.

Genetic implication of prenatal GABAergic and cholinergic neuron development in susceptibility to schizophrenia

Background

The ganglionic eminences are fetal-specific structures that give rise to gamma-aminobutyric acid (GABA)- and acetylcholine- releasing neurons of the forebrain. Given evidence for GABAergic and cholinergic disturbances in schizophrenia, as well as an early neurodevelopmental component to the disorder, we tested the potential involvement of developing cells of the ganglionic eminences in mediating genetic risk for the condition.

Study Design

We combined data from a recent large-scale genome-wide association study of schizophrenia with single cell RNA sequencing data from the human ganglionic eminences to test enrichment of schizophrenia risk variation in genes with high expression specificity for particular developing cell populations within these structures. We additionally performed the single nuclei Assay for Transposase-Accessible Chromatin with Sequencing (snATAC-Seq) to map potential regulatory genomic regions operating in individual cell populations of the human ganglionic eminences, using these to additionally test for enrichment of schizophrenia common genetic variant liability and to functionally annotate non-coding variants associated with the disorder.

Study Results

Schizophrenia common variant liability was enriched in genes with high expression specificity for developing neuron populations that are predicted to form dopamine D1 and D2 receptor expressing GABAergic medium spiny neurons of the striatum, cortical somatostatin-positive GABAergic interneurons, calretinin-positive GABAergic neurons and cholinergic neurons. Consistent with these findings, schizophrenia genetic risk was also concentrated in predicted regulatory genomic sequence mapped in developing neuronal populations of the ganglionic eminences.

Conclusions

Our study provides evidence for a role of prenatal GABAergic and cholinergic neuron development in later susceptibility to schizophrenia.

Characterization of human pluripotent stem cell differentiation by single-cell dual-omics analyses

In vivo differentiation of human pluripotent stem cells (hPSCs) has unique advantages, such as multilineage differentiation, angiogenesis, and close cell-cell interactions. To systematically investigate multilineage differentiation mechanisms of hPSCs, we constructed the in vivo hPSC differentiation landscape containing 239,670 cells using teratoma models. We identified 43 cell types, inferred 18 cell differentiation trajectories, and characterized common and specific gene regulation patterns during hPSC differentiation at both transcriptional and epigenetic levels. Additionally, we developed the developmental single-cell Basic Local Alignment Search Tool (dscBLAST), an R-based cell identification tool, to simplify the identification processes of developmental cells. Using dscBLAST, we aligned cells in multiple differentiation models to normally developing cells to further understand their differentiation states. Overall, our study offers new insights into stem cell differentiation and human embryonic development; dscBLAST shows favorable cell identification performance, providing a powerful identification tool for developmental cells.

Broad H3K4me3 Domain Is Associated with Spatial Coherence during Mammalian Embryonic Development

It is well known that the chromatin states play a major role in cell-fate decision and cell-identity maintenance; however, the spatial variation of chromatin states in situ remains poorly characterized. Here, by leveraging recently available spatial-CUT&Tag data, we systematically characterized the global spatial organization of the H3K4me3 profiles in a mouse embryo. Our analysis identified a subset of genes with spatially coherent H3K4me3 patterns, which together delineate the tissue boundaries. The spatially coherent genes are strongly enriched with tissue-specific transcriptional regulators. Remarkably, their corresponding genomic loci are marked by broad H3K4me3 domains, which is distinct from the typical H3K4me3 signature. Spatial transition across tissue boundaries is associated with continuous shortening of the broad H3K4me3 domains as well as expansion of H3K27me3 domains. Our analysis reveals a strong connection between the genomic and spatial variation of chromatin states, which may play an important role in embryonic development.

Homeostatic, repertoire and transcriptional relationships between colon T regulatory cell subsets

Foxp3+ regulatory T cells (Tregs) in the colon are key to promoting peaceful coexistence with symbiotic microbes. Differentiated in either thymic or peripheral locations, and modulated by microbes and other cellular influencers, colonic Treg subsets have been identified through key transcription factors (TFs; Helios, Rorγ, Gata3, and cMaf), but their interrelationships are unclear. Applying a multimodal array of immunologic, genomic, and microbiological assays, we find more overlap than expected between populations. The key TFs (Rorγ, Helios, Gata3, and cMaf) play different roles, some essential for subset identity, others driving functional gene signatures. Functional divergence was clearest under challenge. Single-cell genomics revealed a spectrum of phenotypes between the Helios+ and Rorγ+ poles, different Treg-inducing bacteria inducing the same Treg phenotypes to varying degrees, not distinct populations. TCR repertoires in monocolonized mice revealed that Helios+ and Rorγ+ Tregs are related and cannot be uniquely equated to tTreg and pTreg. Comparison of spleen and colon repertoires revealed that 2 to 5% of clonotypes are shared between the locations. We propose that rather than the origin of their differentiation, tissue-specific cues dictate the spectrum of colonic Treg phenotypes.

A longitudinal single-cell atlas of treatment response in pediatric AML

Pediatric acute myeloid leukemia (pAML) is characterized by heterogeneous cellular composition, driver alterations and prognosis. Characterization of this heterogeneity and how it affects treatment response remains understudied in pediatric patients. We used single-cell RNA sequencing and single-cell ATAC sequencing to profile 28 patients representing different pAML subtypes at diagnosis, remission and relapse. At diagnosis, cellular composition differed between genetic subgroups. Upon relapse, cellular hierarchies transitioned toward a more primitive state regardless of subtype. Primitive cells in the relapsed tumor were distinct compared to cells at diagnosis, with under-representation of myeloid transcriptional programs and over-representation of other lineage programs. In some patients, this was accompanied by the appearance of a B-lymphoid-like hierarchy. Our data thus reveal the emergence of apparent subtype-specific plasticity upon treatment and inform on potentially targetable processes.