Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia

Single-cell transcriptomic atlas of the chicken cecum reveals cellular responses and state shifts during Eimeria tenella infection

Eimeria tenella (E. tenella) infection is a major cause of coccidiosis in chickens, leading to significant economic losses in the poultry industry due to its impact on the cecum. This study presents a comprehensive single-cell atlas of the chicken cecal epithelium by generating 7,394 cells using 10X Genomics single-cell RNA sequencing (scRNA-seq). We identified 13 distinct cell types, including key immune and epithelial populations, and characterized their gene expression profiles and cell-cell communication networks. Integration of this single-cell data with bulk RNA-seq data from E. tenella-infected chickens revealed significant alterations in cell type composition and state, particularly a marked decrease in APOB+ enterocytes and an increase in cycling T cells during infection. Trajectory analysis of APOB+ enterocytes uncovered shifts toward cellular states associated with cell death and a reduction in those linked to mitochondrial and cytoplasmic protection when infected with E. tenella. These findings highlight the substantial impact of E. tenella on epithelial integrity and immune responses, emphasizing the parasite's role in disrupting nutrient absorption and energy metabolism. Our single-cell atlas serves as a critical resource for understanding the cellular architecture of the chicken cecum and provides a valuable framework for future investigations into cecal diseases and metabolic functions, with potential applications in enhancing poultry health and productivity.

Defining the regulatory logic of breast cancer using single-cell epigenetic and transcriptome profiling

Annotation of cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to understanding tumor biology. Herein, we present matched chromatin accessibility (single-cell assay for transposase-accessible chromatin by sequencing [scATAC-seq]) and transcriptome (single-cell RNA sequencing [scRNA-seq]) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell of origin for subtype-specific breast tumors and implement linear mixed-effects modeling to quantify associations between regulatory elements and gene expression in malignant versus normal cells. These data unveil cancer-specific regulatory elements and putative silencer-to-enhancer switching events in cells that lead to the upregulation of clinically relevant oncogenes. In addition, we generate matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing a conserved oncogenic gene expression program between in vitro and in vivo cells. This work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of cancer cells.

A multi-omic single-cell landscape of the aging mouse ovary

The ovary is one of the first organs in humans to exhibit age-related functional impairments. As an organ composed of diverse heterogeneous cell types, the ovary exhibits cell-type-specific changes during the aging process, ultimately leading to a decline in female fertility. Investigating the molecular mechanisms of ovarian aging is crucial for understanding age-related fertility dysfunction in females. In this study, we combine scRNA-seq and scATAC-seq from mouse young/aged ovaries to characterize molecular features during ovarian aging. Using the single-cell multi-omic data, we revealed the cell-type-specific transcriptional changes during the aging process in seven major ovarian cell types and identified the cis/trans-regulatory elements governing these transcriptional changes. Specifically, we uncovered the transcriptional alterations of TGF-beta signaling in mesenchymal cells and endoplasmic reticulum stress in granulosa cells of aged mouse ovaries and further identified the potential corresponding cis/trans-regulatory elements. These molecular alterations may contribute to aging-induced functional impairments in mouse ovaries. In summary, this work provides transcriptome and chromatin accessibility landscape of ovarian aging in mice, which serve as a resource for identifying the cell-type-specific molecular mechanisms underlying ovarian aging, aiding in the identification of potential diagnostic biomarkers and treatment strategies.

Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood

Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole-genome sequencing (WGS) of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. We show that haplotype-based calling methods can be used with WGS data to successfully identify mLOY events. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European (EUR) ancestry group compared to other ancestries. We identify multiple loci associated with mLOY susceptibility and show that subsets of human hematopoietic stem cells are enriched for the activity of mLOY susceptibility variants. Finally, we found that certain alleles on chromosome Y are more likely to be lost than others in detectable mLOY clones.

Mapping the developmental trajectory of human astrocytes reveals divergence in glioblastoma

Glioblastoma (GBM) is defined by heterogeneous and resilient cell populations that closely reflect neurodevelopmental cell types. Although it is clear that GBM echoes early and immature cell states, identifying the specific developmental programmes disrupted in these tumours has been hindered by a lack of high-resolution trajectories of glial and neuronal lineages. Here we delineate the course of human astrocyte maturation to uncover discrete developmental stages and attributes mirrored by GBM. We generated a transcriptomic and epigenomic map of human astrocyte maturation using cortical organoids maintained in culture for nearly 2 years. Through this approach, we chronicled a multiphase developmental process. Our time course of human astrocyte maturation includes a molecularly distinct intermediate period that serves as a lineage commitment checkpoint upstream of mature quiescence. This intermediate stage acts as a site of developmental deviation separating IDH-wild-type neoplastic astrocyte-lineage cells from quiescent astrocyte populations. Interestingly, IDH1-mutant tumour astrocyte-lineage cells are the exception to this developmental perturbation, where immature properties are suppressed as a result of D-2-hydroxyglutarate oncometabolite exposure. We propose that this defiance is a consequence of IDH1-mutant-associated epigenetic dysregulation, and we identified biased DNA hydroxymethylation (5hmC) in maturation genes as a possible mechanism. Together, this study illustrates a distinct cellular state aberration in GBM astrocyte-lineage cells and presents developmental targets for experimental and therapeutic exploration.

The dynamics of hematopoiesis over the human lifespan

Over a lifetime, hematopoietic stem cells (HSCs) adjust their lineage output to support age-aligned physiology. In model organisms, stereotypic waves of hematopoiesis have been observed corresponding to defined age-biased HSC hallmarks. However, how the properties of hematopoietic stem and progenitor cells change over the human lifespan remains unclear. To address this gap, we profiled individual transcriptome states of human hematopoietic stem and progenitor cells spanning gestation, maturation and aging. Here we define the gene expression networks dictating age-specific differentiation of HSCs and the dynamics of fate decisions and lineage priming throughout life. We additionally identifiy and functionally validate a fetal-specific HSC state with robust engraftment and multilineage capacity. Furthermore, we observe that classification of acute myeloid leukemia against defined transcriptional age states demonstrates that utilization of early life transcriptional programs associates with poor prognosis. Overall, we provide a disease-relevant framework for heterochronic orientation of stem cell ontogeny along the real time axis of the human lifespan.

Single-cell chromatin accessibility landscape profiling reveals the diversity of epigenetic regulation in the rat nervous system

The mammalian nervous system controls complex functions through highly specialized and interacting structures. Single-cell sequencing can provide information on cell-type-specific chromatin structure and regulatory elements, revealing differences in chromatin organization between different cell types and their potential roles of these differences in brain function. Here, we generated a chromatin accessibility dataset through single-cell ATAC-seq of 174,593 high-quality nuclei from 16 adult rat brain regions. We identified cell subtypes of both neuronal and non-neuronal cells with highly specific distributions and characterized gene regulatory elements associated with cell type-specific regions. To further investigate the gene regulatory network involved in spinal cord regeneration, we integrated our scATAC-seq data with published single-nucleus RNA-seq data from the spinal cord, and we identified more detailed regeneration related elements by drawing GRNs centered on the transcription factor Jun in the OPC. We also performed similar integration analyses in the midbrain. Our findings provide a solid foundation for the comprehensive dissection of the molecular architecture of the mammalian nervous system.

Acute myeloid leukemia with mixed phenotype is characterized by RUNX1 mutations, stemness features and limited lineage plasticity

Mixed phenotype (MP) in acute leukemias poses unique classification and management dilemmas and can be seen in entities other than de novo mixed phenotype acute leukemia (MPAL). Although WHO classification empirically recommends excluding AML with myelodysplasia related changes (AML-MRC) and therapy related AML (t-AML) with mixed phenotype (referred to as "AML-MP") from MPAL, there is lack of studies investigating the clinical, genetic, and biologic features of AML-MP. We report the first cohort of AML-MP integrating their clinical, immunophenotypic, genomic and transcriptomic features with comparison to MPAL and AML without MP. Patients with AML-MP share similar clinical and genetic features to its AML counterpart but differs from MPAL. AML-MP harbors more frequent RUNX1 mutations than AML without MP and MPAL. RUNX1 mutations or complex karyotypes did not impact the survival of MPAL patients. Unsupervised hierarchal clustering based on immunophenotype identified biologically distinct clusters with phenotype/genotype correlation and outcome differences. Furthermore, transcriptomic analysis showed an enrichment for stemness signature in AML-MP and AML without MP as compared to MPAL. Lastly, MPAL but not AML-MP often switched to lymphoid only immunophenotype after treatment. Expression of transcription factors critical for lymphoid differentiation were upregulated only in MPAL, but not in AML-MP. Our study for the first time demonstrates that AML- MP clinically and biologically resembles its AML counterpart without MP and differs from MPAL, supporting the recommendation to exclude these patients from the diagnosis of MPAL. Future studies are needed to elucidate the molecular mechanism of mixed phenotype in AML.

Key points

AML-MP clinically and biologically differs from MPAL but resembles AML. AML-MP shows RUNX1 mutations, stemness and limited lineage plasticity.

Single-cell sequencing in diabetic retinopathy: progress and prospects

Diabetic retinopathy is a major ocular complication of diabetes, characterized by progressive retinal microvascular damage and significant visual impairment in working-age adults. Traditional bulk RNA sequencing offers overall gene expression profiles but does not account for cellular heterogeneity. Single-cell RNA sequencing overcomes this limitation by providing transcriptomic data at the individual cell level and distinguishing novel cell subtypes, developmental trajectories, and intercellular communications. Researchers can use single-cell sequencing to draw retinal cell atlases and identify the transcriptomic features of retinal cells, enhancing our understanding of the pathogenesis and pathological changes in diabetic retinopathy. Additionally, single-cell sequencing is widely employed to analyze retinal organoids and single extracellular vesicles. Single-cell multi-omics sequencing integrates omics information, whereas stereo-sequencing analyzes gene expression and spatiotemporal data simultaneously. This review discusses the protocols of single-cell sequencing for obtaining single cells from retina and accurate sequencing data. It highlights the applications and advancements of single-cell sequencing in the study of normal retinas and the pathological changes associated with diabetic retinopathy. This underscores the potential of these technologies to deepen our understanding of the pathogenesis of diabetic retinopathy that may lead to the introduction of new therapeutic strategies.

Modular organization of enhancer network provides transcriptional robustness in mammalian development

Enhancer clusters, pivotal in mammalian development and diseases, can organize as enhancer networks to control cell identity and disease genes; however, the underlying mechanism remains largely unexplored. Here, we introduce eNet 2.0, a comprehensive tool for enhancer networks analysis during development and diseases based on single-cell chromatin accessibility data. eNet 2.0 extends our previous work eNet 1.0 by adding network topology, comparison and dynamics analyses to its network construction function. We reveal modularly organized enhancer networks, where inter-module interactions synergistically affect gene expression. Moreover, network alterations correlate with abnormal and dynamic gene expression in disease and development. eNet 2.0 is robust across diverse datasets. To facilitate application, we introduce eNetDB (https://enetdb.huanglabxmu.com), an enhancer network database leveraging extensive scATAC-seq (single-cell assay for transposase-accessible chromatin sequencing) datasets from human and mouse tissues. Together, our work provides a powerful computational tool and reveals that modularly organized enhancer networks contribute to gene expression robustness in mammalian development and diseases.

scCancerExplorer: a comprehensive database for interactively exploring single-cell multi-omics data of human pan-cancer

Genomic, epigenomic and transcriptomic alterations are hallmarks of cancer cells, and are closely connected. Especially, epigenetic regulation plays a critical role in tumorigenesis and progression. The growing single-cell epigenome data in cancer research provide new opportunities for data mining from a more comprehensive perspective. However, there is still a lack of databases designed for interactively exploring the single-cell multi-omics data of human pan-cancer, especially for the single-cell epigenome data. To fill in the gap, we developed scCancerExplorer, a comprehensive and user-friendly database to facilitate the exploration of the single-cell genome, epigenome (chromatin accessibility and DNA methylation), and transcriptome data of 50 cancer types. Five major modules were provided to explore those data interactively, including 'Integrated multi-omics analysis', 'Single-cell transcriptome', 'Single-cell epigenome', 'Single-cell genome' and 'TCGA analysis'. By simple clicking, users can easily investigate gene expression features, chromatin accessibility patterns, transcription factor activities, DNA methylation states, copy number variations and TCGA survival analysis results. Taken together, scCancerExplorer is distinguished from previous databases with rich and interactive functions for exploring the single-cell multi-omics data of human pan-cancer. It bridges the gap between single-cell multi-omics data and the end-users, and will facilitate progress in the field of cancer research. scCancerExplorer is freely accessible via https://bianlab.cn/scCancerExplorer.

Identification of human cranio-maxillofacial skeletal stem cells for mandibular development

Compared with long bone that arises from the mesoderm, the major portion of the maxillofacial bones and the front bone of the skull are derived from cranial neural crest cells and undergo intramembranous ossification. Human skeletal stem cells have been identified in embryonic and fetal long bones. Here, we describe a single-cell atlas of the human embryonic mandible and identify a population of cranio-maxillofacial skeletal stem cells (CMSSCs). These CMSSCs are marked by interferon-induced transmembrane protein 5 (IFITM5) and are specifically located around the periosteum of the jawbone and frontal bone. Additionally, these CMSSCs exhibit strong self-renewal and osteogenic differentiation capacities but lower chondrogenic differentiation potency, mediating intramembranous bone formation without cartilage formation. IFITM5+ cells are also observed in the adult jawbone and exhibit functions similar to those of embryonic CMSSCs. Thus, this study identifies CMSSCs that orchestrate the intramembranous ossification of cranio-maxillofacial bones, providing a deeper understanding of cranio-maxillofacial skeletal development and promising seed cells for bone repair.

Maturation and persistence of CAR T cells derived from human pluripotent stem cells via chemical inhibition of G9a/GLP

Elucidating mechanisms of T cell development can guide in vitro T cell differentiation from induced pluripotent stem cells (iPSCs) and facilitate off-the-shelf T cell-based immunotherapies. Using a stroma-free human iPSC-T cell differentiation platform, we screened for epigenetic modulators that influence T cell specification and identified the H3K9-directed histone methyltransferases G9a/GLP as repressors of T cell fate. We show that G9a/GLP inhibition during specific time windows of differentiation of hematopoietic stem and progenitor cells (HSPCs) skews cell fates toward lymphoid lineages. Inhibition of G9a/GLP promotes the production of lymphoid cells during zebrafish embryonic hematopoiesis, demonstrating the evolutionary conservation of G9a/GLP function. Importantly, chemical inhibition of G9a/GLP facilitates the generation of mature iPSC-T cells that bear transcriptional similarity to peripheral blood αβ T cells. When engineered to express chimeric antigen receptors, the epigenetically engineered iPSC-T cells exhibit enhanced effector functions in vitro and durable, persistent antitumor activity in a xenograft tumor-rechallenge model.

A multiomic atlas identifies a treatment-resistant, bone marrow progenitor-like cell population in T cell acute lymphoblastic leukemia

Refractoriness to initial chemotherapy and relapse after remission are the main obstacles to curing T cell acute lymphoblastic leukemia (T-ALL). While tumor heterogeneity has been implicated in treatment failure, the cellular and genetic factors contributing to resistance and relapse remain unknown. Here we linked tumor subpopulations with clinical outcome, created an atlas of healthy pediatric hematopoiesis and applied single-cell multiomic analysis to a diverse cohort of 40 T-ALL cases. We identified a bone marrow progenitor (BMP)-like leukemia subpopulation associated with treatment failure and poor overall survival. The single-cell-derived molecular signature of BMP-like blasts predicted poor outcome across multiple subtypes of T-ALL and revealed that NOTCH1 mutations additively drive T-ALL blasts away from the BMP-like state. Through in silico and in vitro drug screenings, we identified a therapeutic vulnerability of BMP-like blasts to apoptosis-inducing agents including venetoclax. Collectively, our study establishes multiomic signatures for rapid risk stratification and targeted treatment of high-risk T-ALL.

Dissection of Gene Expression at the Single-Cell Level: scRNA-seq

Sequencing approaches that allowed for a better resolution of the transcriptome have been a major goal in the transcriptomics field since the development of RNA-seq techniques. While RNA-seq provides gene expression data from one entire sample in bulk, single-cell analysis allows for a better characterization of gene expression associated to specific cell types. Single-cell RNA-seq (scRNA-seq) is a reliable technique to unravel transcriptomic features of the tissues of interest dissociated at a single-cell level. The main feature of the single-cell technique is its ability to generate barcoded individual cells that allow for tracking the origin of thousands to millions of transcripts and reveal new cell types associated to diseases and different cell types and states. In this chapter, we discuss how scRNA-seq has become the gold standard to deepen the understanding of the gene expression with single-cell resolution.

Elucidating the Role of Estrogen Effects in Leukemia: Insights from Single-Cell RNA Sequencing and Mendelian Randomization

Background: Epidemiological studies have confirmed the potential role of estrogen effects in influencing the development and outcome of leukemia. Estrogen effects are increasingly attracting research interest for their potential antitumor effects beyond gynecological tumors. However, their causal relationship remains unclear. Methods: In a novel approach, this study integrates single-cell RNA sequencing (scRNA-seq) with Mendelian randomization (MR) to explore the relationship between estrogen (and its receptor) and leukemia (and its related proteins). This integration showcases the uniqueness of our methodology and provides a new perspective for understanding the molecular relationship between them. Secondary analyses using genetic risk scores (GRS) were performed to further verify the robustness of the results. Results: Our scRNA-seq analysis identified 14 BMMC mononuclear cell subsets, and the result showed that the estrogen receptor was implicated in leukemia. The MR results showed that there was a relationship between estradiol and leukemia inhibitory factor (β = 0.0621; P = 0.0229), and leukemia inhibitory factor receptor (β = 0.0665; P = 0.0218). The result of GRS analysis verified the MR analysis. Conclusions: While both scRNA-seq and MR have yielded intriguing results, inconsistencies between these methodologies hint at a more elaborate underlying mechanism. The observed discrepancies underscore the complexity of the estrogen effects-leukemia relationship, suggesting that elucidating these interactions demands larger cohorts and enhanced sequencing depth in future studies. This research paves the way for a more nuanced understanding of the role of estrogen effects in leukemia and sets the stage for targeted therapeutic interventions.

Single-cell landscape of alternative polyadenylation in human lymphoid hematopoiesis

Alternative polyadenylation (APA) is an essential post-transcriptional process that produces mature mRNA isoforms by regulating the usage of polyadenylation sites (PASs). APA is involved in lymphocyte activation; however, its role throughout the entire differentiation trajectory remains elusive. Here, we analyzed single-cell 3'-end transcriptome data from healthy subjects to construct a dynamic-APA landscape from hematopoietic stem and progenitor cells (HSPCs) to terminally differentiated lymphocytes. This analysis covered 19973 cells of 12 clusters from five lineages (B cells, CD4+ T cells, CD8+ T cells, natural killer cells, and plasmacytoid dendritic cells). A total of 2364 genes exhibited differential 3'-untranslated region (3'UTR) PAS usage, and 3021 genes displayed differential intronic cleavage during lymphoid differentiation. We observed a global trend of 3'UTR shortening during lymphoid differentiation. Nevertheless, specific events of both 3'UTR shortening and lengthening were also identified within each cluster. The APA patterns delineated three differentiation stages: HSPCs, precursor cells, and mature cells. Moreover, we demonstrated that the conversion of naïve T cells to memory T cells was accompanied by dynamic APA in transcription factor-encoding genes (TCF7 and NFATC2IP), immune function-related genes (BCL2, CD5, CD28, GOLT1B, and TMEM59), and protein ubiquitination-related genes (UBE2G1, YPEL5, and SUMO3). These findings expand our understanding of the underlying molecular mechanisms of APA and facilitate studies on the regulatory role of APA in lymphoid hematopoiesis.

Single-cell analysis of bidirectional reprogramming between early embryonic states identify mechanisms of differential lineage plasticities in mice

Two distinct lineages, pluripotent epiblast (EPI) and primitive (extra-embryonic) endoderm (PrE), arise from common inner cell mass (ICM) progenitors in mammalian embryos. To study how these sister identities are forged, we leveraged mouse embryonic stem (ES) cells and extra-embryonic endoderm (XEN) stem cells-in vitro counterparts of the EPI and PrE. Bidirectional reprogramming between ES and XEN coupled with single-cell RNA and ATAC-seq analyses showed distinct rates, efficiencies, and trajectories of state conversions, identifying drivers and roadblocks of reciprocal conversions. While GATA4-mediated ES-to-iXEN conversion was rapid and nearly deterministic, OCT4-, KLF4-, and SOX2-induced XEN-to-induced pluripotent stem (iPS) reprogramming progressed with diminished efficiency and kinetics. A dominant PrE transcriptional program, safeguarded by GATA4, alongside elevated chromatin accessibility and reduced DNA methylation of the EPI underscored the differential plasticities of the two states. Mapping in vitro to embryo trajectories tracked reprogramming cells in either direction along EPI and PrE in vivo states, without transitioning through the ICM.

EpiCHAOS: a metric to quantify epigenomic heterogeneity in single-cell data

Epigenetic heterogeneity is a fundamental property of biological systems and is recognized as a potential driver of tumor plasticity and therapy resistance. Single-cell epigenomics technologies have been widely employed to study epigenetic variation between-but not within-cellular clusters. We introduce epiCHAOS: a quantitative metric of cell-to-cell heterogeneity, applicable to any single-cell epigenomics data type. After validation in synthetic datasets, we apply epiCHAOS to investigate global and region-specific patterns of epigenetic heterogeneity across diverse biological systems. EpiCHAOS provides an excellent approximation of stemness and plasticity in development and malignancy, making it a valuable addition to single-cell cancer epigenomics analyses.

Normal breast tissues harbour rare populations of aneuploid epithelial cells

Aneuploid epithelial cells are common in breast cancer1,2; however, their presence in normal breast tissues is not well understood. To address this question, we applied single-cell DNA sequencing to profile copy number alterations in 83,206 epithelial cells from the breast tissues of 49 healthy women, and we applied single-cell DNA and assay for transposase-accessible chromatin sequencing co-assays to the samples of 19 women. Our data show that all women harboured rare aneuploid epithelial cells (median 3.19%) that increased with age. Many aneuploid epithelial cells (median 82.22%) in normal breast tissues underwent clonal expansions and harboured copy number alterations reminiscent of invasive breast cancers (gains of 1q; losses of 10q, 16q and 22q). Co-assay profiling showed that the aneuploid cells were mainly associated with the two luminal epithelial lineages, and spatial mapping showed that they localized in ductal and lobular structures with normal histopathology. Collectively, these data show that even healthy women have clonal expansions of rare aneuploid epithelial cells in their breast tissues.

Uncovering cell-type-specific immunomodulatory variants and molecular phenotypes in COVID-19 using structurally resolved protein networks

Immunomodulatory variants that lead to the loss or gain of specific protein interactions often manifest only as organismal phenotypes in infectious disease. Here, we propose a network-based approach to integrate genetic variation with a structurally resolved human protein interactome network to prioritize immunomodulatory variants in COVID-19. We find that, in addition to variants that pass genome-wide significance thresholds, variants at the interface of specific protein-protein interactions, even though they do not meet genome-wide thresholds, are equally immunomodulatory. The integration of these variants with single-cell epigenomic and transcriptomic data prioritizes myeloid and T cell subsets as the most affected by these variants across both the peripheral blood and the lung compartments. Of particular interest is a common coding variant that disrupts the OAS1-PRMT6 interaction and affects downstream interferon signaling. Critically, our framework is generalizable across infectious disease contexts and can be used to implicate immunomodulatory variants that do not meet genome-wide significance thresholds.

CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis

MOTIVATION

Selecting representative genes or marker genes to distinguish cell types is an important task in single-cell sequencing analysis. Although many methods have been proposed to select marker genes, the genes selected may have redundancy and/or do not show cell-type-specific expression patterns to distinguish cell types.

RESULTS

Here, we present a novel model, named CosGeneGate, to select marker genes for more effective marker selections. CosGeneGate is inspired by combining the advantages of selecting marker genes based on both cell-type classification accuracy and marker gene specific expression patterns. We demonstrate the better performance of the marker genes selected by CosGeneGate for various downstream analyses than the existing methods with both public datasets and newly sequenced datasets. The non-redundant marker genes identified by CosGeneGate for major cell types and tissues in human can be found at the website as follows: https://github.com/VivLon/CosGeneGate/blob/main/marker gene list.xlsx.

DECA: harnessing interpretable transformer model for cellular deconvolution of chromatin accessibility profile

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) identifies chromatin accessibility across the genome, crucial for gene expression regulating. However, bulk ATAC-seq obscures cellular heterogeneity, while single-cell ATAC-seq suffers from issues such as sparsity and costliness. To this end, we introduce DECA, a sophisticated deep learning model based on vision transformer to deconvolve cell type information from bulk chromatin accessibility profiles, utilizing single-cell ATAC-seq datasets as reference for enhanced precision and resolution. Notably, patch attention generated by DECA's multi-head attention mechanism aligns with chromatin interactions detected by Hi-C. Additionally, DECA predicted lineage-specific cell composition changes due to genetic perturbation. The chromatin accessibility signatures predicted by DECA are enriched with cell-type specific genetic variations. Ultimately, we applied DECA on pan-cancer ATAC-seq datasets and demonstrated its capability to deconvolve cell type proportions with clinical significance. Taken together, DECA deconvolves cellular proportions and predicts their chromatin accessibility profiles from bulk chromatin accessibility data, which enable exploring the gene regulatory programs in development and diseases.

multiDGD: A versatile deep generative model for multi-omics data

Recent technological advancements in single-cell genomics have enabled joint profiling of gene expression and alternative modalities at unprecedented scale. Consequently, the complexity of multi-omics data sets is increasing massively. Existing models for multi-modal data are typically limited in functionality or scalability, making data integration and downstream analysis cumbersome. We present multiDGD, a scalable deep generative model providing a probabilistic framework to learn shared representations of transcriptome and chromatin accessibility. It shows outstanding performance on data reconstruction without feature selection. We demonstrate on several data sets from human and mouse that multiDGD learns well-clustered joint representations. We further find that probabilistic modeling of sample covariates enables post-hoc data integration without the need for fine-tuning. Additionally, we show that multiDGD can detect statistical associations between genes and regulatory regions conditioned on the learned representations. multiDGD is available as an scverse-compatible package on GitHub.

CRISPR Screening of Transcribed Super-Enhancers Identifies Drivers of Triple-Negative Breast Cancer Progression

Triple-negative breast cancer (TNBC) is the most therapeutically recalcitrant form of breast cancer, which is due in part to the paucity of targeted therapies. A systematic analysis of regulatory elements that extend beyond protein-coding genes could uncover avenues for therapeutic intervention. To this end, we analyzed the regulatory mechanisms of TNBC-specific transcriptional enhancers together with their noncoding enhancer RNA (eRNA) transcripts. The functions of the top 30 eRNA-producing super-enhancers were systematically probed using high-throughput CRISPR-interference assays coupled to RNA sequencing that enabled unbiased detection of target genes genome-wide. Generation of high-resolution Hi-C chromatin interaction maps enabled annotation of the direct target genes for each super-enhancer, which highlighted their proclivity for genes that portend worse clinical outcomes in patients with TNBC. Illustrating the utility of this dataset, deletion of an identified super-enhancer controlling the nearby PODXL gene or specific degradation of its eRNAs led to profound inhibitory effects on target gene expression, cell proliferation, and migration. Furthermore, loss of this super-enhancer suppressed tumor growth and metastasis in TNBC mouse xenograft models. Single-cell RNA sequencing and assay for transposase-accessible chromatin with high-throughput sequencing analyses demonstrated the enhanced activity of this super-enhancer within the malignant cells of TNBC tumor specimens compared with nonmalignant cell types. Collectively, this work examines several fundamental questions about how regulatory information encoded into eRNA-producing super-enhancers drives gene expression networks that underlie the biology of TNBC. Significance: Integrative analysis of eRNA-producing super-enhancers defines molecular mechanisms controlling global patterns of gene expression that regulate clinical outcomes in breast cancer, highlighting the potential of enhancers as biomarkers and therapeutic targets.

Single-cell transcriptional mapping reveals genetic and non-genetic determinants of aberrant differentiation in AML

In acute myeloid leukemia (AML), genetic mutations distort hematopoietic differentiation, resulting in the accumulation of leukemic blasts. Yet, it remains unclear how these mutations intersect with cellular origins and whether they converge upon similar differentiation patterns. Single-cell RNA sequencing (scRNA-seq) has enabled high-resolution mapping of the relationship between leukemia and normal cell states, yet this application is hampered by imprecise reference maps of normal hematopoiesis and small sample sizes among patient cohorts. As a first step we constructed a reference atlas of human bone marrow hematopoiesis from 263,519 single-cell transcriptomes spanning 55 cellular states, that was benchmarked against independent datasets of immunophenotypically pure hematopoietic stem and progenitor cells. Using this reference atlas, we mapped over 1.2 million single-cell transcriptomes spanning 318 AML, mixed phenotype acute leukemia (MPAL), and acute erythroid leukemia (AEL) samples. This large-scale analysis, together with systematic mapping of genotype-to-phenotype associations between driver mutations and differentiation landscapes, revealed convergence of diverse genetic alterations on twelve recurrent patterns of aberrant differentiation in AML. This included unconventional lymphoid and erythroid priming linked to RUNX1 and TP53 mutations, respectively. We also identified non-genetic determinants of AML differentiation such as two subgroups of KMT2A-rearranged AML that differ in the identity of their leukemic stem cells (LSCs), likely reflecting distinct cellular origins. Furthermore, distinct LSC-driven hierarchies can co-exist within individual patients, providing insights into AML evolution. Together, precise mapping of normal and malignant cell states provides a framework for advancing the study and disease classification of hematologic malignancies thereby informing therapy development.

Chromatin structure and 3D architecture define the differential functions of PU.1 regulatory elements in blood cell lineages

The precise spatiotemporal expression of the hematopoietic ETS transcription factor PU.1, a key determinant of hematopoietic cell fates, is tightly regulated at the chromatin level. However, how chromatin signatures are linked to this dynamic expression pattern across different blood cell lineages remains uncharacterized. Here, we performed an in-depth analysis of the relationships between gene expression, chromatin structure, 3D architecture, and trans-acting factors at PU.1 cis-regulatory elements (PCREs). By identifying phylogenetically conserved DNA elements within chromatin-accessible regions in primary human blood lineages, we discovered multiple novel candidate PCREs within the upstream region of the human PU.1 locus. A subset of these elements localizes within an 8-kb-wide cluster exhibiting enhancer features, including open chromatin, demethylated DNA, enriched enhancer histone marks, present enhancer RNAs, and PU.1 occupation, presumably mediating PU.1 autoregulation. Importantly, we revealed the presence of a common 35-kb-wide CTCF-flanked insulated neighborhood that contains the PCRE cluster (PCREC), forming a chromatin territory for lineage-specific and PCRE-mediated chromatin interactions. These include functional PCRE-promoter interactions in myeloid and B cells that are absent in erythroid and T cells. By correlating chromatin structure and 3D architecture with PU.1 expression in various lineages, we were able to attribute enhancer versus silencer functions to individual elements. Our findings provide mechanistic insights into the interplay between dynamic chromatin structure and 3D architecture in the chromatin regulation of PU.1 expression. This study lays crucial groundwork for additional experimental studies that validate and dissect the role of PCREs in epigenetic regulation of normal and malignant hematopoiesis.

Unlocking the Complexity: Exploration of Acute Lymphoblastic Leukemia at the Single Cell Level

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. ALL originates from precursor lymphocytes that acquire multiple genomic changes over time, including chromosomal rearrangements and point mutations. While a large variety of genomic defects was identified and characterized in ALL over the past 30 years, it was only in recent years that the clonal heterogeneity was recognized. Thanks to the latest advancements in single-cell sequencing techniques, which have evolved from the analysis of a few hundred cells to the analysis of thousands of cells simultaneously, the study of tumor heterogeneity now becomes possible. Different modalities can be explored at the single-cell level: DNA, RNA, epigenetic modifications, and intracellular and cell surface proteins. In this review, we describe these techniques and highlight their advantages and limitations in the study of ALL biology. Moreover, multiomics technologies and the incorporation of the spatial dimension can provide insight into intercellular communication. We describe how the different single-cell sequencing technologies help to unravel the molecular complexity of ALL, shedding light on its development, its heterogeneity, its interaction with the leukemia microenvironment and possible relapse mechanisms.

Integrated temporal transcriptional and epigenetic single-cell analysis reveals the intrarenal immune characteristics in an early-stage model of IgA nephropathy during its acute injury

Rationale

Kidney inflammation plays a crucial role in the pathogenesis of IgA nephropathy (IgAN), yet the specific phenotypes of immune cells involved in disease progression remain incompletely understood. Utilizing joint profiling through longitudinal single-cell RNA-sequencing (scRNAseq) and single-cell assay for transposase-accessible chromatin sequencing (scATACseq) can provide a comprehensive framework for elucidating the development of cell subset diversity and how chromatin accessibility regulates transcription.

Objective

We aimed to characterize the dynamic immune cellular landscape at a high resolution in an early IgAN mouse model with acute kidney injury (AKI).

Methods and results

A murine model was utilized to mimic 3 immunological states -"immune stability (IS), immune activation (IA) and immune remission (IR)" in early human IgAN-associated glomerulopathy during AKI, achieved through lipopolysaccharide (LPS) injection. Urinary albumin to creatinine ratio (UACR) was measured to further validate the exacerbation and resolution of kidney inflammation during this course. Paired scRNAseq and scATACseq analysis was performed on CD45+ immune cells isolated from kidney tissues obtained from CTRL (healthy vehicle), IS, IA and IR (4 or 5 mice each). The analyses revealed 7 major cell types and 24 clusters based on 72304 single-cell transcriptomes, allowing for the identification and characterization of various immune cell types within each cluster. Our data offer an impartial depiction of the immunological characteristics, as the proportions of immune cell types fluctuated throughout different stages of the disease. Specifically, these analyses also revealed novel subpopulations, such as a macrophage subset (Nlrp1b Mac) with distinct epigenetic features and a unique transcription factor motif profile, potentially exerting immunoregulatory effects, as well as an early subset of Tex distinguished by their effector and cytolytic potential (CX3CR1-transTeff). Furthermore, in order to investigate the potential interaction between immune cells and renal resident cells, we conducted single-cell RNA sequencing on kidney cells obtained from a separate cohort of IS and IA mice without isolating immune cells. These findings underscored the diverse roles played by macrophages and CD8+ T cells in maintaining homeostasis of endothelial cells (ECs) under stress.

Conclusions

This study presents a comprehensive analysis of the dynamic changes in immune cell profiles in a model of IgAN, identifying key cell types and their roles and interactions. These findings significantly contribute to the understanding of the pathogenesis of IgAN and may provide potential targets for therapeutic intervention.

Implementing distinct spatial proteogenomic technologies: opportunities, challenges, and key considerations

Our ability to understand the cellular complexity of tissues has been revolutionized in recent years with significant advances in proteogenomic technologies including those enabling spatial analyses. This has led to numerous consortium efforts, such as the human cell atlas initiative which aims to profile all cells in the human body in healthy and diseased contexts. The availability of such information will subsequently lead to the identification of novel biomarkers of disease and of course therapeutic avenues. However, before such an atlas of any given healthy or diseased tissue can be generated, several factors should be considered including which specific techniques are optimal for the biological question at hand. In this review, we aim to highlight some of the considerations we believe to be important in the experimental design and analysis process, with the goal of helping to navigate the rapidly changing landscape of technologies available.

Best practices for differential accessibility analysis in single-cell epigenomics

Differential accessibility (DA) analysis of single-cell epigenomics data enables the discovery of regulatory programs that establish cell type identity and steer responses to physiological and pathophysiological perturbations. While many statistical methods to identify DA regions have been developed, the principles that determine the performance of these methods remain unclear. As a result, there is no consensus on the most appropriate statistical methods for DA analysis of single-cell epigenomics data. Here, we present a systematic evaluation of statistical methods that have been applied to identify DA regions in single-cell ATAC-seq (scATAC-seq) data. We leverage a compendium of scATAC-seq experiments with matching bulk ATAC-seq or scRNA-seq in order to assess the accuracy, bias, robustness, and scalability of each statistical method. The structure of our experiments also provides the opportunity to define best practices for the analysis of scATAC-seq data beyond DA itself. We leverage this understanding to develop an R package implementing these best practices.

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data

Assay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) is a widely used technique to explore gene regulatory mechanisms. For most ATAC-Seq data from healthy and diseased tissues such as tumors, chromatin accessibility measurement represents a mixed signal from multiple cell types. In this work, we derive reliable chromatin accessibility marker peaks and reference profiles for most non-malignant cell types frequently observed in the microenvironment of human tumors. We then integrate these data into the EPIC deconvolution framework (Racle et al., 2017) to quantify cell-type heterogeneity in bulk ATAC-Seq data. Our EPIC-ATAC tool accurately predicts non-malignant and malignant cell fractions in tumor samples. When applied to a human breast cancer cohort, EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes.

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 generate 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. We evaluate enhancer activity for 59 elements using an in vivo transgenic assay and validate 44 (75%), demonstrating that single cell accessibility can be a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieve significant reduction in our variant search space and nominate candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work delivers 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.

Navigating Change in Tumor Naming: Exploring the Complexities and Considerations of Shifting Toward Molecular Classifications

Navigating change in tumor naming. Balance organ-based and molecular classifications for optimal treatment.

Multiomic single cell sequencing identifies stemlike nature of mixed phenotype acute leukemia

Despite recent work linking mixed phenotype acute leukemia (MPAL) to certain genetic lesions, specific driver mutations remain undefined for a significant proportion of patients and no genetic subtype is predictive of clinical outcomes. Moreover, therapeutic strategy for MPAL remains unclear, and prognosis is overall poor. We performed multiomic single cell profiling of 14 newly diagnosed adult MPAL patients to characterize the inter- and intra-tumoral transcriptional, immunophenotypic, and genetic landscapes of MPAL. We show that neither genetic profile nor transcriptome reliably correlate with specific MPAL immunophenotypes. Despite this, we find that MPAL blasts express a shared stem cell-like transcriptional profile indicative of high differentiation potential. Patients with the highest differentiation potential demonstrate inferior survival in our dataset. A gene set score, MPAL95, derived from genes highly enriched in the most stem-like MPAL cells, is applicable to bulk RNA sequencing data and is predictive of survival in an independent patient cohort, suggesting a potential strategy for clinical risk stratification.

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures.

DOGMA-seq and multimodal, single-cell analysis in acute myeloid leukemia

Acute myeloid leukemia (AML) is a complex cancer, yet advances in recent years from integrated genomics methods have helped improve diagnosis, treatment, and means of patient stratification. A recent example of a powerful, multimodal method is DOGMA-seq, which can measure chromatin accessibility, gene expression, and cell-surface protein levels from the same individual cell simultaneously. Previous bimodal single-cell techniques, such as CITE-seq (Cellular indexing of transcriptomes and epitopes), have only permitted the transcriptome and cell-surface protein expression measurement. DOGMA-seq, however, builds on this foundation and has implications for examining epigenomic, transcriptomic, and proteomic interactions between various cell types. This technique has the potential to be particularly useful in the study of cancers such as AML. This is because the cellular mechanisms that drive AML are rather heterogeneous and require a more complete understanding of the interplay between the genetic mutations, disruptions in RNA transcription and translation, and surface protein expression that cause these cancers to develop and evolve. This technique will hopefully contribute to a more clear and complete understanding of the growth and progression of complex cancers.

Flexible use of conserved motif vocabularies constrains genome access in cell type evolution

Cell types evolve into a hierarchy with related types grouped into families. How cell type diversification is constrained by the stable separation between families over vast evolutionary times remains unknown. Here, integrating single-nucleus multiomic sequencing and deep learning, we show that hundreds of sequence features (motifs) divide into distinct sets associated with accessible genomes of specific cell type families. This division is conserved across highly divergent, early-branching animals including flatworms and cnidarians. While specific interactions between motifs delineate cell type relationships within families, surprisingly, these interactions are not conserved between species. Consistently, while deep learning models trained on one species can predict accessibility of other species' sequences, their predictions frequently rely on distinct, but synonymous, motif combinations. We propose that long-term stability of cell type families is maintained through genome access specified by conserved motif sets, or 'vocabularies', whereas cell types diversify through flexible use of motifs within each set.

Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers

To identify cancer-associated gene regulatory changes, we generated single-cell chromatin accessibility landscapes across eight tumor types as part of The Cancer Genome Atlas. Tumor chromatin accessibility is strongly influenced by copy number alterations that can be used to identify subclones, yet underlying cis-regulatory landscapes retain cancer type-specific features. Using organ-matched healthy tissues, we identified the "nearest healthy" cell types in diverse cancers, demonstrating that the chromatin signature of basal-like-subtype breast cancer is most similar to secretory-type luminal epithelial cells. Neural network models trained to learn regulatory programs in cancer revealed enrichment of model-prioritized somatic noncoding mutations near cancer-associated genes, suggesting that dispersed, nonrecurrent, noncoding mutations in cancer are functional. Overall, these data and interpretable gene regulatory models for cancer and healthy tissue provide a framework for understanding cancer-specific gene regulation.

HA-1-targeted T-cell receptor T-cell therapy for recurrent leukemia after hematopoietic stem cell transplantation

ABSTRACT

Relapse is the leading cause of death after allogeneic hematopoietic stem cell transplantation (HCT) for leukemia. T cells engineered by gene transfer to express T cell receptors (TCR; TCR-T) specific for hematopoietic-restricted minor histocompatibility (H) antigens may provide a potent selective antileukemic effect post-HCT. We conducted a phase 1 clinical trial using a novel TCR-T product targeting the minor H antigen, HA-1, to treat or consolidate treatment of persistent or recurrent leukemia and myeloid neoplasms. The primary objective was to evaluate the feasibility and safety of administration of HA-1 TCR-T after HCT. CD8+ and CD4+ T cells expressing the HA-1 TCR and a CD8 coreceptor were successfully manufactured from HA-1-disparate HCT donors. One or more infusions of HA-1 TCR-T following lymphodepleting chemotherapy were administered to 9 HCT recipients who had developed disease recurrence after HCT. TCR-T cells expanded and persisted in vivo after adoptive transfer. No dose-limiting toxicities occurred. Although the study was not designed to assess efficacy, 4 patients achieved or maintained complete remissions following lymphodepletion and HA-1 TCR-T, with 1 patient still in remission at >2 years. Single-cell RNA sequencing of relapsing/progressive leukemia after TCR-T therapy identified upregulated molecules associated with T-cell dysfunction or cancer cell survival. HA-1 TCR-T therapy appears feasible and safe and shows preliminary signals of efficacy. This clinical trial was registered at ClinicalTrials.gov as #NCT03326921.

New frameworks for hematopoiesis derived from single-cell genomics

ABSTRACT

Recent advancements in single-cell genomics have enriched our understanding of hematopoiesis, providing intricate details about hematopoietic stem cell biology, differentiation, and lineage commitment. Technological advancements have highlighted extensive heterogeneity of cell populations and continuity of differentiation routes. Nevertheless, intermediate "attractor" states signify structure in stem and progenitor populations that link state transition dynamics to fate potential. We discuss how innovative model systems quantify lineage bias and how stress accelerates differentiation, thereby reducing fate plasticity compared with native hematopoiesis. We conclude by offering our perspective on the current model of hematopoiesis and discuss how a more precise understanding can translate to strategies that extend healthy hematopoiesis and prevent disease.

scATAcat: cell-type annotation for scATAC-seq data

Cells whose accessibility landscape has been profiled with scATAC-seq cannot readily be annotated to a particular cell type. In fact, annotating cell-types in scATAC-seq data is a challenging task since, unlike in scRNA-seq data, we lack knowledge of 'marker regions' which could be used for cell-type annotation. Current annotation methods typically translate accessibility to expression space and rely on gene expression patterns. We propose a novel approach, scATAcat, that leverages characterized bulk ATAC-seq data as prototypes to annotate scATAC-seq data. To mitigate the inherent sparsity of single-cell data, we aggregate cells that belong to the same cluster and create pseudobulk. To demonstrate the feasibility of our approach we collected a number of datasets with respective annotations to quantify the results and evaluate performance for scATAcat. scATAcat is available as a python package at https://github.com/aybugealtay/scATAcat.

Mitoclone2: an R package for elucidating clonal structure in single-cell RNA-sequencing data using mitochondrial variants

Clonal cell population dynamics play a critical role in both disease and development. Due to high mitochondrial mutation rates under both healthy and diseased conditions, mitochondrial genomic variability is a particularly useful resource in facilitating the identification of clonal population structure. Here we present mitoClone2, an all-inclusive R package allowing for the identification of clonal populations through integration of mitochondrial heteroplasmic variants discovered from single-cell sequencing experiments. Our package streamlines the investigation of this phenomenon by providing: built-in compatibility with commonly used tools for the delineation of clonal structure, the ability to directly use multiplexed BAM files as input, annotations for both human and mouse mitochondrial genomes, and helper functions for calling, filtering, clustering, and visualizing variants.

Single-cell mitochondrial sequencing reveals low-frequency mitochondrial mutations in naturally aging mice

Mitochondria play a crucial role in numerous biological processes; however, limited methods and research have focused on revealing mitochondrial heterogeneity at the single-cell level. In this study, we optimized the DNBelab C4 single-cell ATAC (assay for transposase-accessible chromatin) sequencing workflow for single-cell mitochondrial sequencing (C4_mtscATAC-seq). We validated the effectiveness of our C4_mtscATAC-seq protocol by sequencing the HEK-293T cell line with two biological replicates, successfully capturing both mitochondrial content (~68% of total sequencing data) and open chromatin status simultaneously. Subsequently, we applied C4_mtscATAC-seq to investigate two mouse tissues, spleen and bone marrow, obtained from two mice aged 2 months and two mice aged 23 months. Our findings revealed higher mitochondrial DNA (mtDNA) content in young tissues compared to more variable mitochondrial content in aged tissues, consistent with higher activity scores of nuclear genes associated with mitochondrial replication and transcription in young tissues. We detected a total of 22, 15, and 21 mtDNA mutations in the young spleen, aged spleen, and bone marrow, respectively, with most variant allele frequencies (VAF) below 1%. Moreover, we observed a higher number of mtDNA mutations with higher VAF in aged tissues compared to young tissues. Importantly, we identified three mtDNA variations (m.9821A>T, m.15219T>C, and m.15984C>T) with the highest VAF in both aged spleen and aged bone marrow. By comparing cells with and without these mtDNA variations, we analyzed differential open chromatin status to identify potential genes associated with these mtDNA variations, including transcription factors such as KLF15 and NRF1. Our study presents an alternative single-cell mitochondrial sequencing method and provides crude insights into age-related single-cell mitochondrial variations.

Non-linear transcriptional responses to gradual modulation of transcription factor dosage

Genomic loci associated with common traits and diseases are typically non-coding and likely impact gene expression, sometimes coinciding with rare loss-of-function variants in the target gene. However, our understanding of how gradual changes in gene dosage affect molecular, cellular, and organismal traits is currently limited. To address this gap, we induced gradual changes in gene expression of four genes using CRISPR activation and inactivation. Downstream transcriptional consequences of dosage modulation of three master trans-regulators associated with blood cell traits (GFI1B, NFE2, and MYB) were examined using targeted single-cell multimodal sequencing. We showed that guide tiling around the TSS is the most effective way to modulate cis gene expression across a wide range of fold-changes, with further effects from chromatin accessibility and histone marks that differ between the inhibition and activation systems. Our single-cell data allowed us to precisely detect subtle to large gene expression changes in dozens of trans genes, revealing that many responses to dosage changes of these three TFs are non-linear, including non-monotonic behaviours, even when constraining the fold-changes of the master regulators to a copy number gain or loss. We found that the dosage properties are linked to gene constraint and that some of these non-linear responses are enriched for disease and GWAS genes. Overall, our study provides a straightforward and scalable method to precisely modulate gene expression and gain insights into its downstream consequences at high resolution.

Deciphering the genetics and mechanisms of predisposition to multiple myeloma

Multiple myeloma (MM) is an incurable malignancy of plasma cells. Epidemiological studies indicate a substantial heritable component, but the underlying mechanisms remain unclear. Here, in a genome-wide association study totaling 10,906 cases and 366,221 controls, we identify 35 MM risk loci, 12 of which are novel. Through functional fine-mapping and Mendelian randomization, we uncover two causal mechanisms for inherited MM risk: longer telomeres; and elevated levels of B-cell maturation antigen (BCMA) and interleukin-5 receptor alpha (IL5RA) in plasma. The largest increase in BCMA and IL5RA levels is mediated by the risk variant rs34562254-A at TNFRSF13B. While individuals with loss-of-function variants in TNFRSF13B develop B-cell immunodeficiency, rs34562254-A exerts a gain-of-function effect, increasing MM risk through amplified B-cell responses. Our results represent an analysis of genetic MM predisposition, highlighting causal mechanisms contributing to MM development.

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

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

Temporally resolved early bone morphogenetic protein-driven transcriptional cascade during human amnion specification

Amniogenesis, a process critical for continuation of healthy pregnancy, is triggered in a collection of pluripotent epiblast cells as the human embryo implants. Previous studies have established that bone morphogenetic protein (BMP) signaling is a major driver of this lineage specifying process, but the downstream BMP-dependent transcriptional networks that lead to successful amniogenesis remain to be identified. This is, in part, due to the current lack of a robust and reproducible model system that enables mechanistic investigations exclusively into amniogenesis. Here, we developed an improved model of early amnion specification, using a human pluripotent stem cell-based platform in which the activation of BMP signaling is controlled and synchronous. Uniform amniogenesis is seen within 48 hr after BMP activation, and the resulting cells share transcriptomic characteristics with amnion cells of a gastrulating human embryo. Using detailed time-course transcriptomic analyses, we established a previously uncharacterized BMP-dependent amniotic transcriptional cascade, and identified markers that represent five distinct stages of amnion fate specification; the expression of selected markers was validated in early post-implantation macaque embryos. Moreover, a cohort of factors that could potentially control specific stages of amniogenesis was identified, including the transcription factor TFAP2A. Functionally, we determined that, once amniogenesis is triggered by the BMP pathway, TFAP2A controls the progression of amniogenesis. This work presents a temporally resolved transcriptomic resource for several previously uncharacterized amniogenesis states and demonstrates a critical intermediate role for TFAP2A during amnion fate specification.

Terminal deoxynucleotidyl transferase and CD84 identify human multi-potent lymphoid progenitors

Lymphoid specification in human hematopoietic progenitors is not fully understood. To better associate lymphoid identity with protein-level cell features, we conduct a highly multiplexed single-cell proteomic screen on human bone marrow progenitors. This screen identifies terminal deoxynucleotidyl transferase (TdT), a specialized DNA polymerase intrinsic to VDJ recombination, broadly expressed within CD34+ progenitors prior to B/T cell emergence. While these TdT+ cells coincide with granulocyte-monocyte progenitor (GMP) immunophenotype, their accessible chromatin regions show enrichment for lymphoid-associated transcription factor (TF) motifs. TdT expression on GMPs is inversely related to the SLAM family member CD84. Prospective isolation of CD84lo GMPs demonstrates robust lymphoid potentials ex vivo, while still retaining significant myeloid differentiation capacity, akin to LMPPs. This multi-omic study identifies human bone marrow lymphoid-primed progenitors, further defining the lympho-myeloid axis in human hematopoiesis.

A single-cell atlas of chromatin accessibility in mouse organogenesis

Organogenesis is a highly complex and precisely regulated process. Here we profiled the chromatin accessibility in >350,000 cells derived from 13 mouse embryos at four developmental stages from embryonic day (E) 10.5 to E13.5 by SPATAC-seq in a single experiment. The resulting atlas revealed the status of 830,873 candidate cis-regulatory elements in 43 major cell types. By integrating the chromatin accessibility atlas with the previous transcriptomic dataset, we characterized cis-regulatory sequences and transcription factors associated with cell fate commitment, such as Nr5a2 in the development of gastrointestinal tract, which was preliminarily supported by the in vivo experiment in zebrafish. Finally, we integrated this atlas with the previous single-cell chromatin accessibility dataset from 13 adult mouse tissues to delineate the developmental stage-specific gene regulatory programmes within and across different cell types and identify potential molecular switches throughout lineage development. This comprehensive dataset provides a foundation for exploring transcriptional regulation in organogenesis.