Single-cell Multiomics Research Articles

VI-VS: calibrated identification of feature dependencies in single-cell multiomics.

Unveiling functional relationships between various molecular cell phenotypes from data using machine learning models is a key promise of multiomics. Existing methods either use flexible but hard-to-interpret models or simpler, misspecified models. VI-VS(Variational Inference for Variable Selection) balances flexibility and interpretability to identify relevant feature relationships in multiomic data. It uses deep generative models to identify conditionally dependent features, with false discovery rate control. VI-VS is available as an open-source Python package, providing a robust solution to identify features more likely representing genuine causal relationships.

Single-cell multiomics analysis of chronic myeloid leukemia links cellular heterogeneity to therapy response.

The advent of tyrosine kinase inhibitors (TKIs) as treatment of chronic myeloid leukemia (CML) is a paradigm in molecularly targeted cancer therapy. Nonetheless, TKI-insensitive leukemia stem cells (LSCs) persist in most patients even after years of treatment and are imperative for disease progression as well as recurrence during treatment-free remission (TFR). Here, we have generated high-resolution single-cell multiomics maps from CML patients at diagnosis, retrospectively stratified by BCR::ABL1IS (%) following 12 months of TKI therapy. Simultaneous measurement of global gene expression profiles together with >40 surface markers from the same cells revealed that each patient harbored a unique composition of stem and progenitor cells at diagnosis. The patients with treatment failure after 12 months of therapy had a markedly higher abundance of molecularly defined primitive cells at diagnosis compared to the optimal responders. The multiomic feature landscape enabled visualization of the primitive fraction as a mixture of molecularly distinct BCR::ABL1+ LSCs and BCR::ABL1-hematopoietic stem cells (HSCs) in variable ratio across patients, and guided their prospective isolation by a combination of CD26 and CD35 cell surface markers. We for the first time show that BCR::ABL1+ LSCs and BCR::ABL1- HSCs can be distinctly separated as CD26+CD35- and CD26-CD35+, respectively. In addition, we found the ratio of LSC/HSC to be higher in patients with prospective treatment failure compared to optimal responders, at diagnosis as well as following 3 months of TKI therapy. Collectively, this data builds a framework for understanding therapy response and adapting treatment by devising strategies to extinguish or suppress TKI-insensitive LSCs.

Single-cell multiomics analysis of chronic myeloid leukemia links cellular heterogeneity to therapy response

The advent of tyrosine kinase inhibitors (TKIs) as treatment of chronic myeloid leukemia (CML) is a paradigm in molecularly targeted cancer therapy. Nonetheless, TKI-insensitive leukemia stem cells (LSCs) persist in most patients even after years of treatment and are imperative for disease progression as well as recurrence during treatment-free remission (TFR). Here, we have generated high-resolution single-cell multiomics maps from CML patients at diagnosis, retrospectively stratified by BCR::ABL1IS (%) following 12 months of TKI therapy. Simultaneous measurement of global gene expression profiles together with >40 surface markers from the same cells revealed that each patient harbored a unique composition of stem and progenitor cells at diagnosis. The patients with treatment failure after 12 months of therapy had a markedly higher abundance of molecularly defined primitive cells at diagnosis compared to the optimal responders. The multiomic feature landscape enabled visualization of the primitive fraction as a mixture of molecularly distinct BCR::ABL1+ LSCs and BCR::ABL1-hematopoietic stem cells (HSCs) in variable ratio across patients, and guided their prospective isolation by a combination of CD26 and CD35 cell surface markers. We for the first time show that BCR::ABL1+ LSCs and BCR::ABL1- HSCs can be distinctly separated as CD26+CD35- and CD26-CD35+, respectively. In addition, we found the ratio of LSC/HSC to be higher in patients with prospective treatment failure compared to optimal responders, at diagnosis as well as following 3 months of TKI therapy. Collectively, this data builds a framework for understanding therapy response and adapting treatment by devising strategies to extinguish or suppress TKI-insensitive LSCs.

Single-Cell Multiomics Profiling Reveals Heterogeneity of Müller Cells in the Oxygen-Induced Retinopathy Model.

Retinal neovascularization poses heightened risks of vision loss and blindness. Despite its clinical significance, the molecular mechanisms underlying the pathogenesis of retinal neovascularization remain elusive. This study utilized single-cell multiomics profiling in an oxygen-induced retinopathy (OIR) model to comprehensively investigate the intricate molecular landscape of retinal neovascularization. Mice were exposed to hyperoxia to induce the OIR model, and retinas were isolated for nucleus isolation. The cellular landscape of the single-nucleus suspensions was extensively characterized through single-cell multiomics sequencing. Single-cell data were integrated with genome-wide association study (GWAS) data to identify correlations between ocular cell types and diabetic retinopathy. Cell communication analysis among cells was conducted to unravel crucial ligand-receptor signals. Trajectory analysis and dynamic characterization of Müller cells were performed, followed by integration with human retinal data for pathway analysis. The multiomics dataset revealed six major ocular cell classes, with Müller cells/astrocytes showing significant associations with proliferative diabetic retinopathy (PDR). Cell communication analysis highlighted pathways that are associated with vascular proliferation and neurodevelopment, such as Vegfa-Vegfr2, Igf1-Igf1r, Nrxn3-Nlgn1, and Efna5-Epha4. Trajectory analysis identified a subset of Müller cells expressing genes linked to photoreceptor degeneration. Multiomics data integration further unveiled positively regulated genes in OIR Müller cells/astrocytes associated with axon development and neurotransmitter transmission. This study significantly advances our understanding of the intricate cellular and molecular mechanisms underlying retinal neovascularization, emphasizing the pivotal role of Müller cells. The identified pathways provide valuable insights into potential therapeutic targets for PDR, offering promising directions for further research and clinical interventions.

The frontier of precision medicine: application of single-cell multi-omics in preimplantation genetic diagnosis.

The advent of single-cell multi-omics technologies has revolutionized the landscape of preimplantation genetic diagnosis (PGD), offering unprecedented insights into the genetic, transcriptomic, and proteomic profiles of individual cells in early-stage embryos. This breakthrough holds the promise of enhancing the accuracy, efficiency, and scope of PGD, thereby significantly improving outcomes in assisted reproductive technologies (ARTs) and genetic disease prevention. This review provides a comprehensive overview of the importance of PGD in the context of precision medicine and elucidates how single-cell multi-omics technologies have transformed this field. We begin with a brief history of PGD, highlighting its evolution and application in detecting genetic disorders and facilitating ART. Subsequently, we delve into the principles, methodologies, and applications of single-cell genomics, transcriptomics, and proteomics in PGD, emphasizing their role in improving diagnostic precision and efficiency. Furthermore, we review significant recent advances within this domain, including key experimental designs, findings, and their implications for PGD practices. The advantages and limitations of these studies are analyzed to assess their potential impact on the future development of PGD technologies. Looking forward, we discuss the emerging research directions and challenges, focusing on technological advancements, new application areas, and strategies to overcome existing limitations. In conclusion, this review underscores the pivotal role of single-cell multi-omics in PGD, highlighting its potential to drive the progress of precision medicine and personalized treatment strategies, thereby marking a new era in reproductive genetics and healthcare.

Pharmacological activation of STAT1-GSDME pyroptotic circuitry reinforces epigenetic immunotherapy for hepatocellular carcinoma.

Genomic screening uncovered interferon-gamma (IFNγ) pathway defects in tumours refractory to immune checkpoint blockade (ICB). However, its non-mutational regulation and reversibility for therapeutic development remain less understood. We aimed to identify ICB resistance-associated druggable histone deacetylases (HDACs) and develop a readily translatable combination approach for patients with hepatocellular carcinoma (HCC). We correlated the prognostic outcomes of HCC patients from a pembrolizumab trial (NCT03419481) with tumourous cell expressions of all HDAC isoforms by single-cell RNA sequencing. We investigated the therapeutic efficacy and mechanism of action of selective HDAC inhibition in 4 ICB-resistant orthotopic and spontaneous models using immune profiling, single-cell multiomics and chromatin immunoprecipitation-sequencing and verified by genetic modulations and co-culture systems. HCC patients showing higher HDAC1/2/3 expressions exhibited deficient IFNγ signalling and poorer survival on ICB therapy. Transient treatment of a selective class-I HDAC inhibitor CXD101 resensitised HDAC1/2/3high tumours to ICB therapies, resulting in CD8+T cell-dependent antitumour and memory T cell responses. Mechanistically, CXD101 synergised with ICB to stimulate STAT1-driven antitumour immunity through enhanced chromatin accessibility and H3K27 hyperacetylation of IFNγ-responsive genes. Intratumoural recruitment of IFNγ+GZMB+cytotoxic lymphocytes further promoted cleavage of CXD101-induced Gasdermin E (GSDME) to trigger pyroptosis in a STAT1-dependent manner. Notably, deletion of GSDME mimicked STAT1 knockout in abolishing the antitumour efficacy and survival benefit of CXD101-ICB combination therapy by thwarting both pyroptotic and IFNγ responses. Our immunoepigenetic strategy harnesses IFNγ-mediated network to augment the cancer-immunity cycle, revealing a self-reinforcing STAT1-GSDME pyroptotic circuitry as the mechanistic basis for an ongoing phase-II trial to tackle ICB resistance (NCT05873244).

Decoding the human prenatal immune system with single-cell multi-omics.

The human immune system is made up of a huge variety of cell types each with unique functions. Local networks of resident immune cells are poised to sense and protect against pathogen entry, whereas more widespread innate and adaptive immune networks provide first rapid, then long-lasting and targeted responses. However, how we develop such a diverse and complex system remains unknown. Studying human development directly has been challenging in the past, but recent advances in single-cell and spatial genomics, together with the co-ordinated efforts of the Human Cell Atlas and other initiatives, have led to new studies that map the development of the human immune system in unprecedented detail. In this Review, we consider the timings, transitions, cell types and tissue microenvironments that are crucial for building the human immune system. We also compare and contrast the human system with model species and in vitro systems, and discuss how an understanding of prenatal immune system development will improve our knowledge of human disease.

Single-cell multiomics: a new frontier in drug research and development

Single-cell multiomics (sc-multiomics) is a burgeoning field that simultaneously integrates multiple layers of molecular information, enabling the characterization of dynamic cell states and activities in development and disease as well as treatment response. Studying drug actions and responses using sc-multiomics technologies has revolutionized our understanding of how small molecules intervene for specific cell types in cancer treatment and how they are linked with disease etiology and progression. Here, we summarize recent advances in sc-multiomics technologies that have been adapted and improved in drug research and development, with a focus on genome-wide examination of drug-chromatin engagement and the applications in drug response and the mechanisms of drug resistance. Furthermore, we discuss how state-of-the-art technologies can be taken forward to devise innovative personalized treatment modalities in biomedical research.

Bibliometric and visual analysis of single-cell multiomics in neurodegenerative disease arrest studies.

Neurodegenerative diseases are progressive disorders that severely diminish the quality of life of patients. However, research on neurodegenerative diseases needs to be refined and deepened. Single-cell polyomics is a technique for obtaining transcriptomic, proteomic, and other information from a single cell. In recent years, the heat of single-cell multiomics as an emerging research tool for brain science has gradually increased. Therefore, the aim of this study was to analyze the current status and trends of studies related to the application of single-cell multiomics in neurodegenerative diseases through bibliometrics. A total of 596 publications were included in the bibliometric analysis. Between 2015 and 2022, the number of publications increased annually, with the total number of citations increasing significantly, exhibiting the fastest rate of growth between 2019 and 2022. The country/region collaboration map shows that the United States has the most publications and cumulative citations, and that China and the United States have the most collaborations. The institutions that produced the greatest number of articles were Harvard Medical School, Skupin, Alexander, and Wiendl. Among the authors, Heinz had the highest output. Mathys, H accumulated the most citations and was the authoritative author in the field. The journal Nature Communications has published the most literature in this field. A keyword analysis reveals that neurodegenerative diseases and lesions (e.g., Alzheimer's disease, amyloid beta) are the core and foundation of the field. Conversely, single-cell multiomics related research (e.g., single-cell RNA sequencing, bioinformatics) and brain nerve cells (e.g., microglia, astrocytes, neural stem cells) are the hot frontiers of this specialty. Among the references, the article "Single-cell transcriptomic analysis of Alzheimer's disease" is the most frequently cited (1,146 citations), and the article "Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq" was the most cited article in the field. The objective of this study is to employ bibliometric methods to visualize studies related to single-cell multiomics in neurodegenerative diseases. This will enable us to summarize the current state of research and to reveal key trends and emerging hotspots in the field.

LSD1 and CoREST2 Potentiate STAT3 Activity to Promote Enteroendocrine Cell Differentiation in Mucinous Colorectal Cancer.

Neuroendocrine cells have been implicated in therapeutic resistance and worse overall survival in many cancer types. Mucinous colorectal cancer (mCRC) is uniquely enriched for enteroendocrine cells (EECs), the neuroendocrine cell of the normal colon epithelium, as compared to non-mCRC. Therefore, targeting EEC differentiation may have clinical value in mCRC. Here, single cell multi-omics uncovered epigenetic alterations that accompany EEC differentiation, identified STAT3 as a regulator of EEC specification, and discovered a rare cancer-specific cell type with enteric neuron-like characteristics. Furthermore, LSD1 and CoREST2 mediated STAT3 demethylation and enhanced STAT3 chromatin binding. Knockdown of CoREST2 in an orthotopic xenograft mouse model resulted in decreased primary tumor growth and lung metastases. Collectively, these results provide rationale for developing LSD1 inhibitors that target the interaction between LSD1 and STAT3 or CoREST2, which may improve clinical outcomes for patients with mCRC.

Midkine as a driver of age-related changes and increase in mammary tumorigenesis

Aging is a pivotal risk factor for cancer, yet the underlying mechanisms remain poorly defined. Here, we explore age-related changes in the rat mammary gland by single-cell multiomics. Our findings include increased epithelial proliferation, loss of luminal identity, and decreased naive B and Tcells with age. We discover a luminal progenitor population unique to old rats with profiles reflecting precancerous changes and identify midkine (Mdk) as a gene upregulated with age and a regulator of age-related luminal progenitors. Midkine treatment of young rats mimics age-related changes via activating PI3K-AKT-SREBF1 pathway and promotes nitroso-N-methylurea-induced mammary tumorigenesis. Midkine levels increase with age in human blood and mammary epithelium, and higher MDK in normal breast tissue is associated with higher breast cancer risk in younger women. Our findings reveal a link between aging and susceptibility to tumor initiation and identify midkine as a mediator of age-dependent increase in breast tumorigenesis.

Fibroblast Growth Factor 23 Bioactivity Induces General and Cell Type-Specific Kidney Genomic Reprogramming as Detected by Single-Cell Multiomics

Fibroblast Growth Factor 23 Bioactivity Induces General and Cell Type-Specific Kidney Genomic Reprogramming as Detected by Single-Cell Multiomics

Integrative single-cell multi-omics of CD19-CARpos and CARneg Tcells suggest drivers of immunotherapy response in B cell neoplasias.

The impact of phenotypic, clonal, and functional heterogeneity of chimeric antigen receptor (CAR)-T cells on clinical outcome remains understudied. Here, we integrate clonal kinetics with transcriptomic heterogeneity resolved by single-cell omics to interrogate cellular dynamics of non-transduced (CARneg) and transduced (CARpos) Tcells, in the infusion product (IP) and at the CAR-T cell expansion peak in five B cell acute lymphoblastic leukemia (B-ALL) patients treated with CD19CAR-T cells (varni-cel). We identify significant differences in cellular dynamics in response to therapy. CARpos Tcells at IP of complete response patients exhibit a significantly higher CD4:CD8 ratio, validated in a larger cohort B-ALL patients (n= 47). Conversely, at the expansion peak, there is a clonal expansion of CD8+ effector memory and cytotoxic Tcells. Cytotoxic CARpos γδ-T cells expansion correlates with treatment efficacy validated in a cohort of B-ALL (n= 18) and diffuse large B cell lymphoma (DLBCL) patients (n= 58). Our data provide insights into the complexity of Tcell responses following CAR-T cell therapy and suggest drivers of immunotherapy response.

A-263 Single-cell multiomics identifies formation of immunologic memory in CTCL - transition from blood to skin

A-263 Single-cell multiomics identifies formation of immunologic memory in CTCL - transition from blood to skin

Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease.

By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will introduce the latest advances in cardiac health and disease at single-cell resolution; single-cell detection methods that can be used for transcriptome, genome, epigenome, and proteome analysis; single-cell multi-omics; as well as their future application prospects.

DRCTdb: disease-related cell type analysis to decode cell type effect and underlying regulatory mechanisms.

Understanding the molecular mechanisms underlying genetic diseases is challenging due to environmental and genetic factors. Genome-wide association studies (GWAS) have identified numerous genetic loci, but their functional implications are largely unknown. Single-cell multiomics sequencing has emerged as a powerful tool to study disease-specific cell types and their relationship with genetic variants. However, comprehensive databases for exploring these mechanisms across different tissues are lacking. We present the Disease-Related Cell Type database (DRCTdb), integrating GWAS and single-cell multiomics data to identify disease-related cell types and elucidate their regulatory mechanisms. DRCTdb contains well-processed data from 16 studies, covering 4 million cells within 28 tissues. Users can browse relationships and regulatory mechanisms between SNPs of 42 genetic diseases and cell types based on GWAS and single-cell data. DRCTdb also offers data downloads and is available at https://singlecellatlas.top/DRCTDB .

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.

AMAR-seq: Automated Multimodal Sequencing of DNA Methylation, Chromatin Accessibility, and RNA Expression with Single-Cell Resolution.

Parallel single-cell multimodal sequencing is the most intuitive and precise tool for cellular status research. In this study, we propose AMAR-seq to automate methylation, chromatin accessibility, and RNA expression coanalysis with single-cell precision. We validated the accuracy and robustness of AMAR-seq in comparison with standard single-omics methods. The high gene detection rate and genome coverage of AMAR-seq enabled us to establish a genome-wide gene expression regulatory atlas and triple-omics landscape with single base resolution and implement single-cell copy number variation analysis. Applying AMAR-seq to investigate the process of mouse embryonic stem cell differentiation, we revealed the dynamic coupling of the epigenome and transcriptome, which may contribute to unraveling the molecular mechanisms of early embryonic development. Collectively, we propose AMAR-seq for the in-depth and accurate establishment of single-cell multiomics regulatory patterns in a cost-effective, efficient, and automated manner, paving the way for insightful dissection of complex life processes.

Star wars against leukemia: attacking the clones.

Leukemia, although most likely starts as a monoclonal genetic/epigenetic anomaly, is a polyclonal disease at manifestation. This polyclonal nature results from ongoing evolutionary changes in the genome/epigenome of leukemia cells to promote their survival and proliferation advantages. We discuss here how genetic and/or epigenetic aberrations alter intracellular microenvironment in individual leukemia clones and how extracellular microenvironment selects the best fitted clones. This dynamic polyclonal composition of leukemia makes designing an effective therapy a challenging task especially because individual leukemia clones often display substantial differences in response to treatment. Here, we discuss novel therapeutic approach employing single cell multiomics to identify and eradicate all individual clones in a patient.

Learning meaningful representation of single-neuron morphology via large-scale pre-training.

Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine the effects of brain disorders and treatments. Despite the recent surge in the amount of available neuron morphology reconstructions due to advancements in microscopy imaging, existing computational and deep learning methods for modeling neuron morphology have been limited in both scale and accuracy. In this paper, we propose MorphRep, a model for learning meaningful representation of neuron morphology pre-trained with over 250 000 existing neuron morphology data. By encoding the neuron morphology into graph-structured data, using graph transformers for feature encoding and enforcing the consistency between multiple augmented views of neuron morphology, MorphRep achieves the state of the art performance on widely used benchmarking datasets. Meanwhile, MorphRep can accurately characterize the neuron morphology space across neuron morphometrics, fine-grained cell types, brain regions and ages. Furthermore, MorphRep can be applied to distinguish neurons under a wide range of conditions, including genetic perturbation, drug injection, environment change and disease. In summary, MorphRep provides an effective strategy to embed and represent neuron morphology and can be a valuable tool in integrating cell morphology into single-cell multiomics analysis. The codebase has been deposited in https://github.com/YaxuanLi-cn/MorphRep.