Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Functional specialisation and coordination of myonuclei

Myofibres serve as the functional unit for locomotion, with the sarcomere as fundamental subunit. Running the entire length of this structure are hundreds of myonuclei, located at the periphery of the myofibre, juxtaposed to the plasma membrane. Myonuclear specialisation and clustering at the centre and ends of the fibre are known to be essential for muscle contraction, yet the molecular basis of this regionalisation has remained unclear. While the 'myonuclear domain hypothesis' helped explain how myonuclei can independently govern large cytoplasmic territories, novel technologies have provided granularity on the diverse transcriptional programs running simultaneously within the syncytia and added a new perspective on how myonuclei communicate. Building upon this, we explore the critical cellular and molecular sources of transcriptional and functional heterogeneity within myofibres, discussing the impact of intrinsic and extrinsic factors on myonuclear programs. This knowledge provides new insights for understanding muscle development, repair, and disease, but also opens avenues for the development of novel and precise therapeutic approaches.

AAV5 Delivery of CRISPR/Cas9 Mediates Genome Editing in the Lungs of Young Rhesus Monkeys

Genome editing has the potential to treat genetic diseases in a variety of tissues, including the lung. We have previously developed and validated a dual adeno-associated virus (AAV) CRISPR platform that supports effective editing in the airways of mice. To validate this delivery vehicle in a large animal model, we have shown that intratracheal instillation of CRISPR/Cas9 in AAV5 can edit a housekeeping gene or a disease-related gene in the lungs of young rhesus monkeys. We observed up to 8% editing of angiotensin-converting enzyme 2 (ACE2) in lung lobes after single-dose administration. Single-nuclear RNA sequencing revealed that AAV5 transduces multiple cell types in the caudal lung lobes, including alveolar cells, macrophages, fibroblasts, endothelial cells, and B cells. These results demonstrate that AAV5 is efficient in the delivery of CRISPR/Cas9 in the lung lobes of young rhesus monkeys.

CellMag-CARWash: A High Throughput Droplet Microfluidic Device for Live Cell Isolation and Single Cell Applications

The recent push toward understanding an individual cell's behavior and identifying cellular heterogeneity has created an unmet need for technologies that can probe live cells at the single-cell level. Cells within a population are known to exhibit heterogeneous responses to environmental cues. These differences can lead to varied cellular states, behavior, and responses to therapeutics. Techniques are needed that are not only capable of processing and analyzing cellular populations at the single cell level, but also have the ability to isolate specific cell populations from a complex sample at high throughputs. The new CellMag-Coalesce-Attract-Resegment Wash (CellMag-CARWash) system combines positive magnetic selection with droplet microfluidic devices to isolate cells of interest from a mixture with >93% purity and incorporate treatments within individual droplets to observe single cell biological responses. This workflow is shown to be capable of probing the single cell extracellular vesicle (EV) secretion of MCF7 GFP cells. This article reports the first measurement of β-Estradiol's effect on EV secretion from MCF7 cells at the single cell level. Single cell processing revealed that MCF7 GFP cells possess a heterogeneous response to β-Estradiol stimulation with a 1.8-fold increase relative to the control.

Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding

Single-cell transcriptome sequencing (scRNA-seq) is a high-throughput technique used to study gene expression at the single-cell level. Clustering analysis is a commonly used method in scRNA-seq data analysis, helping researchers identify cell types and uncover interactions between cells. However, the choice of a robust similarity metric in the clustering procedure is still an open challenge due to the complex underlying structures of the data and the inherent noise in data acquisition. Here, we propose a deep clustering method for scRNA-seq data called scRISE (scRNA-seq Iterative Smoothing and self-supervised discriminative Embedding model) to resolve this challenge. The model consists of two main modules: an iterative smoothing module based on graph autoencoders designed to denoise the data and refine the pairwise similarity in turn to gradually incorporate cell structural features and enrich the data information; and a self-supervised discriminative embedding module with adaptive similarity threshold for partitioning samples into correct clusters. Our approach has shown improved quality of data representation and clustering on seventeen scRNA-seq datasets against a number of state-of-the-art deep learning clustering methods. Furthermore, utilizing the scRISE method in biological analysis against the HNSCC dataset has unveiled 62 informative genes, highlighting their potential roles as therapeutic targets and biomarkers.

Discovering mechanisms of human genetic variation and controlling cell states at scale

Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.

Single-cell analysis in rheumatic and allergic diseases: insights for clinical practice

Since the advent of single-cell RNA sequencing (scRNA-seq) methodology, single-cell analysis has become a powerful tool for exploration of cellular networks and dysregulated immune responses in disease pathogenesis. Advanced bioinformatics tools have enabled the combined analysis of scRNA-seq data and information on various cell properties, such as cell surface molecular profiles, chromatin accessibility and spatial information, leading to a deeper understanding of pathology. This Review provides an overview of the achievements in single-cell analysis applied to clinical samples of rheumatic and allergic diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, allergic airway diseases and atopic dermatitis, with an expanded scope beyond peripheral blood cells to include local diseased tissues. Despite the valuable insights that single-cell analysis has provided into disease pathogenesis, challenges remain in translating single-cell findings into clinical practice and developing personalized treatment strategies. Beyond understanding the atlas of cellular diversity, we discuss the application of data obtained in each study to clinical practice, with a focus on identifying biomarkers and therapeutic targets.

A Novel Macrophage Subpopulation Conveys Increased Genetic Risk of Coronary Artery Disease

BACKGROUND

Coronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. Although over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types such as macrophages.

METHODS

We utilized single-cell RNA-seq and single-cell multiomics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis-the formation of lipid-laden foam cells. The relative genetic contribution to CAD was assessed by partitioning disease heritability across different macrophage subpopulations. Meta-analysis of single-cell RNA-seq data sets from 38 human atherosclerotic samples was conducted to provide high-resolution cross-referencing to macrophage subpopulations in vivo.

RESULTS

We identified 18 782 cis-regulatory elements by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD genome-wide association study data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of a novel CD52-hi lipid-handling macrophage subpopulation. These CD52-hi macrophages displayed significantly less lipoprotein accumulation and were also found in human atherosclerotic plaques. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-β in the causal mechanisms at this locus.

CONCLUSIONS

Our results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key subpopulation through which genetic variants operate to influence disease. These findings provide an unbiased framework for functional fine-mapping of genome-wide association study results using single-cell multiomics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.

The Gene Expression Landscape of Disease Genes

Fine-mapping and gene-prioritisation techniques applied to the latest Genome-Wide Association Study (GWAS) results have prioritised hundreds of genes as causally associated with disease. Here we leverage these recently compiled lists of high-confidence causal genes to interrogate where in the body disease genes operate. Specifically, we combine GWAS summary statistics, gene prioritisation results and gene expression RNA-seq data from 46 tissues and 204 cell types in relation to 16 major diseases (including 8 cancers). In tissues and cell types with well-established relevance to the disease, the prioritised genes typically have higher absolute and relative (i.e. tissue/cell specific) expression compared to non-prioritised 'control' genes. Examples include brain tissues in psychiatric disorders (P-value < 1×10-7), microglia cells in Alzheimer's Disease (P-value = 9.8×10-3) and colon mucosa in colorectal cancer (P-value < 1×10-3). We also observe significantly higher expression for disease genes in multiple tissues and cell types with no established links to the corresponding disease. While some of these results may be explained by cell types that span multiple tissues, such as macrophages in brain, blood, lung and spleen in relation to Alzheimer's disease (P-values < 1×10-3), the cause for others is unclear and motivates further investigation that may provide novel insights into disease etiology. For example, mammary tissue in Type 2 Diabetes (P-value < 1×10-7); reproductive tissues such as breast, uterus, vagina, and prostate in Coronary Artery Disease (P-value < 1×10-4); and motor neurons in psychiatric disorders (P-value < 3×10-4). In the GTEx dataset, tissue type is the major predictor of gene expression but the contribution of each predictor (tissue, sample, subject, batch) varies widely among disease-associated genes. Finally, we highlight genes with the highest levels of gene expression in relevant tissues to guide functional follow-up studies. Our results could offer novel insights into the tissues and cells involved in disease initiation, inform drug target and delivery strategies, highlighting potential off-target effects, and exemplify the relative performance of different statistical tests for linking disease genes with tissue and cell type gene expression.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

Leveraging Multi-Tissue, Single-Cell Atlases as Tools to Elucidate Shared Mechanisms of Immune-Mediated Inflammatory Diseases

The observation that certain therapeutic strategies for targeting inflammation benefit patients with distinct immune-mediated inflammatory diseases (IMIDs) is exemplified by the success of TNF blockade in conditions including rheumatoid arthritis, ulcerative colitis, and skin psoriasis, albeit only for subsets of individuals with each condition. This suggests intersecting "nodes" in inflammatory networks at a molecular and cellular level may drive and/or maintain IMIDs, being "shared" between traditionally distinct diagnoses without mapping neatly to a single clinical phenotype. In line with this proposition, integrative tumour tissue analyses in oncology have highlighted novel cell states acting across diverse cancers, with important implications for precision medicine. Drawing upon advances in the oncology field, this narrative review will first summarise learnings from the Human Cell Atlas in health as a platform for interrogating IMID tissues. It will then review cross-disease studies to date that inform this endeavour before considering future directions in the field.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

Myoferlin: a potential marker of response to radiotherapy and survival in locally advanced rectal cancer

PURPOSE

Patients with locally advanced rectal cancer often require neo-adjuvant chemoradiotherapy to downstage the disease, but the response is variable with no predictive biomarkers. We have previously revealed through proteomic profiling that myoferlin is associated with response to radiotherapy. The aims of this study were to further validate this finding and explore the potential for myoferlin to act as a prognostic and/or therapeutic target.

MATERIALS AND METHODS

Immunohistochemical analysis of a tissue microarray for 111 patients was used to validate the initial proteomic findings. Manipulation of myoferlin was achieved using siRNA, a small molecular inhibitor (wj460) and a CRISPR-Cas9 knockout cell line. Radiosensitisation following treatment was assessed using 2D clonogenic assays, 3D spheroid models and patient derived organoids. Underlying mechanisms were investigated using electrophoresis, immunofluorescence and immunoblotting.

RESULTS

Analysis of both the diagnostic biopsy and tumour resection samples confirmed that low myoferlin expression correlated with a good response to neoadjuvant LCRT. High myoferlin expression was associated with spread to local lymph nodes and worse 5-year survival (p = 0.01, HR 3.5, 95%CI [1.27, 10.04]). This was externally validated using the S:CORT database. Quantification of myoferlin using immunoblotting in immortalised colorectal cancer cell lines and organoids demonstrated that high myoferlin expression was associated with increased radioresistance. Biological and pharmacological manipulation of myoferlin resulted in significantly increased radiosensitivity across all cell lines in 2D and 3D models. Following irradiation, myoferlin knockdown cells had a significantly impaired ability to repair DNA double strand breaks. This appeared to be mediated via non-homologous end-joining.

CONCLUSIONS

We have confirmed that high expression of myoferlin in rectal cancer is associated with poor response to neoadjuvant therapy and worse long-term survival. Furthermore, the manipulation of myoferlin led to increased radiosensitivity in vitro. This suggests that myoferlin could be targeted to enhance the sensitivity of rectal cancer patients to radiotherapy and further work is required.

Identifying Mechanisms and Therapeutic Targets in Muscle using Bayesian Parameter Estimation with Conditional Variational Autoencoders

Cardiomyopathies, often caused by mutations in genes encoding muscle proteins, are traditionally treated by phenotyping hearts and addressing symptoms post irreversible damage. With advancements in genotyping, early diagnosis is now possible, potentially preventing such damage. However, the intricate structure of muscle and its myriad proteins make treatment predictions challenging. Here we approach the problem of estimating therapeutic targets for a mutation in mouse muscle using a spatially explicit half sarcomere muscle model. We selected 9 rate parameters in our model linked to both small molecules and cardiomyopathy-causing mutations. We then randomly varied these rate parameters and simulated an isometric twitch for each combination to generate a large training dataset. We used this dataset to train a Conditional Variational Autoencoder (CVAE), a technique used in Bayesian parameter estimation. Given simulated or experimental isometric twitches, this machine learning model is able to then predict the set of rate parameters which are most likely to yield that result. We then predict the set of rate parameters associated with both control and the cardiac Troponin C (cTnC) I61Q variant in mouse trabeculae and model parameters that recover the abnormal I61Q cTnC twitches.

SIGNIFICANCE

Machine learning techniques have potential to accelerate discoveries in biologically complex systems. However, they require large data sets and can be challenging in high dimensional systems such as cardiac muscle. In this study, we combined experimental measures of cardiac muscle twitch forces with mechanistic simulations and a newly developed mixture of Bayesian inference with neural networks (in autoencoders) to solve the inverse problem of determining the underlying kinetics for observed force generation by cardiac muscle. The autoencoders are trained on millions of simulations spanning parameter spaces that correspond to the mechanochemistry of cardiac sarcomeres. We apply the trained model to experimental data in order to infer parameters that can explain a diseased twitch and ways to recover it.

Publication, funding, and experimental data in support of Human Reference Atlas construction and usage

Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts, published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemas and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, named HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 4,279 anatomical structures, 1,210 cell types, 2,089 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit has 22 tables with 20,939,937 records including 6 junction tables with 13,170,651 relationships. The HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage.

Single nucleus and spatial transcriptomic profiling of healthy human hamstring tendon

The molecular and cellular basis of health in human tendons remains poorly understood. Among human tendons, hamstring tendon has markedly low pathology and can provide a prototypic healthy tendon reference. The aim of this study was to determine the transcriptomes and location of all cell types in healthy hamstring tendon. Using single nucleus RNA sequencing, we profiled the transcriptomes of 10 533 nuclei from four healthy donors and identified 12 distinct cell types. We confirmed the presence of two fibroblast cell types, endothelial cells, mural cells, and immune cells, and identified cell types previously unreported in tendons, including different skeletal muscle cell types, satellite cells, adipocytes, and undefined nervous system cells. The location of these cell types within tendon was defined using spatial transcriptomics and imaging, and potential transcriptional networks and cell-cell interactions were analyzed. We demonstrate that fibroblasts have the highest number of potential cell-cell interactions in our dataset, are present throughout the tendon, and play an important role in the production and organization of extracellular matrix, thus confirming their role as key regulators of hamstring tendon homeostasis. Overall, our findings underscore the complexity of the cellular networks that underpin healthy human tendon function and the central role of fibroblasts as key regulators of hamstring tendon tissue homeostasis.

Housekeeping protein-coding genes interrogated with tissue and individual variations

Housekeeping protein-coding genes are stably expressed genes in cells and tissues that are thought to be engaged in fundamental cellular biological functions. They are often utilized as normalization references in molecular biology research and are especially important in integrated bioinformatic investigations. Prior studies have examined human housekeeping protein-coding genes by analyzing various gene expression datasets. The inclusion of different tissue types significantly impacted the discovery of housekeeping genes. In this report, we investigated particularly individual human subject expression differences in protein-coding genes across different tissue types. We used GTEx V8 gene expression datasets obtained from more than 16,000 human normal tissue samples. Furthermore, the Gini index is utilized to investigate the expression variations of protein-coding genes between tissue and individual donor subjects. Housekeeping protein-coding genes found using Gini index profiles may vary depending on the tissue subtypes investigated, particularly given the diverse sample size collections across the GTEx tissue subtypes. We subsequently selected major tissues and identified subsets of housekeeping genes with stable expression levels among human donors within those tissues. In this work, we provide alternative sets of housekeeping protein-coding genes that show more consistent expression patterns in human subjects across major solid organs. Weblink: https://hpsv.ibms.sinica.edu.tw .

Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy on specific regional brain cells in a mouse dementia model

Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimer's disease (AD), but the lack of methods to examine brain tissues makes it difficult to evaluate therapeutics. Here, we investigated the changes in spatial transcriptomic signatures and brain cell types using the 10x Genomics Visium platform in immune-modulated AD models after various treatments. To proceed with an analysis suitable for barcode-based spatial transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and an anti-CD4 antibody, which ameliorated behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers interpret the real action of drug candidates by simultaneously investigating the dynamics of all transcripts for the development of novel AD therapeutics.

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRA's Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies. In addition, three workflows were developed to map new experimental data into the HRA's CCF. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and demonstrates first atlas usage applications and previews.

The Macrophage Landscape Across the Lifespan of a Human Cardiac Allograft

BACKGROUND

Much of our knowledge of organ rejection after transplantation is derived from rodent models.

METHODS

We used single-nucleus RNA sequencing to investigate the inflammatory myocardial microenvironment in human pediatric cardiac allografts at different stages after transplantation. We distinguished donor- from recipient-derived cells using naturally occurring genetic variants embedded in single-nucleus RNA sequencing data.

RESULTS

Donor-derived tissue resident macrophages, which accompany the allograft into the recipient, are lost over time after transplantation. In contrast, monocyte-derived macrophages from the recipient populate the heart within days after transplantation and form 2 macrophage populations: recipient MP1 and recipient MP2. Recipient MP2s have cell signatures similar to donor-derived resident macrophages; however, they lack signatures of pro-reparative phagocytic activity typical of donor-derived resident macrophages and instead express profibrotic genes. In contrast, recipient MP1s express genes consistent with hallmarks of cellular rejection. Our data suggest that recipient MP1s activate a subset of natural killer cells, turning them into a cytotoxic cell population through feed-forward signaling between recipient MP1s and natural killer cells.

CONCLUSIONS

Our findings reveal an imbalance of donor-derived and recipient-derived macrophages in the pediatric cardiac allograft that contributes to allograft failure.

scAI-SNP: a method for inferring ancestry from single-cell data

Collaborative efforts, such as the Human Cell Atlas, are rapidly accumulating large amounts of single-cell data. To ensure that single-cell atlases are representative of human genetic diversity, we need to determine the ancestry of the donors from whom single-cell data are generated. Self-reporting of race and ethnicity, although important, can be biased and is not always available for the datasets already collected. Here, we introduce scAI-SNP, a tool to infer ancestry directly from single-cell genomics data. To train scAI-SNP, we identified 4.5 million ancestry-informative single-nucleotide polymorphisms (SNPs) in the 1000 Genomes Project dataset across 3201 individuals from 26 population groups. For a query single-cell data set, scAI-SNP uses these ancestry-informative SNPs to compute the contribution of each of the 26 population groups to the ancestry of the donor from whom the cells were obtained. Using diverse single-cell data sets with matched whole-genome sequencing data, we show that scAI-SNP is robust to the sparsity of single-cell data, can accurately and consistently infer ancestry from samples derived from diverse types of tissues and cancer cells, and can be applied to different modalities of single-cell profiling assays, such as single-cell RNA-seq and single-cell ATAC-seq. Finally, we argue that ensuring that single-cell atlases represent diverse ancestry, ideally alongside race and ethnicity, is ultimately important for improved and equitable health outcomes by accounting for human diversity.

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

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

De novo detection of somatic mutations in high-throughput single-cell profiling data sets

Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.

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

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

Multimodal cell atlas of the ageing human skeletal muscle

Muscle atrophy and functional decline (sarcopenia) are common manifestations of frailty and are critical contributors to morbidity and mortality in older people1. Deciphering the molecular mechanisms underlying sarcopenia has major implications for understanding human ageing2. Yet, progress has been slow, partly due to the difficulties of characterizing skeletal muscle niche heterogeneity (whereby myofibres are the most abundant) and obtaining well-characterized human samples3,4. Here we generate a single-cell/single-nucleus transcriptomic and chromatin accessibility map of human limb skeletal muscles encompassing over 387,000 cells/nuclei from individuals aged 15 to 99 years with distinct fitness and frailty levels. We describe how cell populations change during ageing, including the emergence of new populations in older people, and the cell-specific and multicellular network features (at the transcriptomic and epigenetic levels) associated with these changes. On the basis of cross-comparison with genetic data, we also identify key elements of chromatin architecture that mark susceptibility to sarcopenia. Our study provides a basis for identifying targets in the skeletal muscle that are amenable to medical, pharmacological and lifestyle interventions in late life.

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

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

An esophagus cell atlas reveals dynamic rewiring during active eosinophilic esophagitis and remission

Coordinated cell interactions within the esophagus maintain homeostasis, and disruption can lead to eosinophilic esophagitis (EoE), a chronic inflammatory disease with poorly understood pathogenesis. We profile 421,312 individual cells from the esophageal mucosa of 7 healthy and 15 EoE participants, revealing 60 cell subsets and functional alterations in cell states, compositions, and interactions that highlight previously unclear features of EoE. Active disease displays enrichment of ALOX15+ macrophages, PRDM16+ dendritic cells expressing the EoE risk gene ATP10A, and cycling mast cells, with concomitant reduction of TH17 cells. Ligand-receptor expression uncovers eosinophil recruitment programs, increased fibroblast interactions in disease, and IL-9+IL-4+IL-13+ TH2 and endothelial cells as potential mast cell interactors. Resolution of inflammation-associated signatures includes mast and CD4+ TRM cell contraction and cell type-specific downregulation of eosinophil chemoattractant, growth, and survival factors. These cellular alterations in EoE and remission advance our understanding of eosinophilic inflammation and opportunities for therapeutic intervention.

The molecular map of CLL and Richter's syndrome

Clonal expansion of B-cells, from the early stages of monoclonal B-cell lymphocytosis through to chronic lymphocytic leukemia (CLL), and then in some cases to Richter's syndrome (RS) provides a comprehensive model of cancer evolution, notable for the marked morphological transformation and distinct clinical phenotypes. High-throughput sequencing of large cohorts of patients and single-cell studies have generated a molecular map of CLL and more recently, of RS, yielding fundamental insights into these diseases and of clonal evolution. A selection of CLL driver genes have been functionally interrogated to yield novel insights into the biology of CLL. Such findings have the potential to impact patient care through risk stratification, treatment selection and drug discovery. However, this molecular map remains incomplete, with extant questions concerning the origin of the B-cell clone, the role of the TME, inter- and intra-compartmental heterogeneity and of therapeutic resistance mechanisms. Through the application of multi-modal single-cell technologies across tissues, disease states and clinical contexts, these questions can now be addressed with the answers holding great promise of generating translatable knowledge to improve patient care.

Multi-organ single-cell transcriptomics of immune cells uncovered organ-specific gene expression and functions

Despite the wealth of publicly available single-cell datasets, our understanding of distinct resident immune cells and their unique features in diverse human organs remains limited. To address this, we compiled a meta-analysis dataset of 114,275 CD45+ immune cells sourced from 14 organs in healthy donors. While the transcriptome of immune cells remains relatively consistent across organs, our analysis has unveiled organ-specific gene expression differences (GTPX3 in kidney, DNTT and ACVR2B in thymus). These alterations are linked to different transcriptional factor activities and pathways including metabolism. TNF-α signaling through the NFkB pathway was found in several organs and immune compartments. The presence of distinct expression profiles for NFkB family genes and their target genes, including cytokines, underscores their pivotal role in cell positioning. Taken together, immune cells serve a dual role: safeguarding the organs and dynamically adjusting to the intricacies of the host organ environment, thereby actively contributing to its functionality and overall homeostasis.

Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis

Here we demonstrate that the large language model GPT-4 can accurately annotate cell types using marker gene information in single-cell RNA sequencing analysis. When evaluated across hundreds of tissue and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations. This capability can considerably reduce the effort and expertise required for cell type annotation. Additionally, we have developed an R software package GPTCelltype for GPT-4's automated cell type annotation.

Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments

A mechanistic understanding of the biological and technical factors that impact transcript measurements is essential to designing and analyzing single-cell and single-nucleus RNA sequencing experiments. Nuclei contain the same pre-mRNA population as cells, but they contain a small subset of the mRNAs. Nonetheless, early studies argued that single-nucleus analysis yielded results comparable to cellular samples if pre-mRNA measurements were included. However, typical workflows do not distinguish between pre-mRNA and mRNA when estimating gene expression, and variation in their relative abundances across cell types has received limited attention. These gaps are especially important given that incorporating pre-mRNA has become commonplace for both assays, despite known gene length bias in pre-mRNA capture. Here, we reanalyze public data sets from mouse and human to describe the mechanisms and contrasting effects of mRNA and pre-mRNA sampling on gene expression and marker gene selection in single-cell and single-nucleus RNA-seq. We show that pre-mRNA levels vary considerably among cell types, which mediates the degree of gene length bias and limits the generalizability of a recently published normalization method intended to correct for this bias. As an alternative, we repurpose an existing post hoc gene length-based correction method from conventional RNA-seq gene set enrichment analysis. Finally, we show that inclusion of pre-mRNA in bioinformatic processing can impart a larger effect than assay choice itself, which is pivotal to the effective reuse of existing data. These analyses advance our understanding of the sources of variation in single-cell and single-nucleus RNA-seq experiments and provide useful guidance for future studies.

Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits

Genetic effects on functionally related 'omic' traits often co-occur in relevant cellular contexts, such as tissues. Motivated by the multi-tissue methylation quantitative trait loci (mQTLs) and expression QTLs (eQTLs) analysis, we propose X-ING (Cross-INtegrative Genomics) for cross-omics and cross-context integrative analysis. X-ING takes as input multiple matrices of association statistics, each obtained from different omics data types across multiple cellular contexts. It models the latent binary association status of each statistic, captures the major association patterns among omics data types and contexts, and outputs the posterior mean and probability for each input statistic. X-ING enables the integration of effects from different omics data with varying effect distributions. In the multi-tissue cis-association analysis, X-ING shows improved detection and replication of mQTLs by integrating eQTL maps. In the trans-association analysis, X-ING reveals an enrichment of trans-associations in many disease/trait-relevant tissues.

Integrated analysis reveals the dysfunction of intercellular communication and metabolic signals in dilated cardiomyopathy

Aims

Dilated cardiomyopathy refers to a heart muscle condition characterized by structural and functional irregularities in the myocardium that are not related to ischemia. Due to diverse etiologies such as genetic mutations, infections, and exposure to toxins, dilated cardiomyopathy can lead to substantial morbidity and mortality despite advances in the management of heart failure in dilated cardiomyopathy patients. We sought to analyze the characteristics of cell-cell communication and the metabolic signaling pathways in dilated cardiomyopathy.

Methods and results

The single-nucleus sequencing data of left ventricle samples were acquired from two donor datasets and two dilated cardiomyopathy datasets. Three dilated cardiomyopathy bulk-sequencing datasets were included to determine the shared dilated cardiomyopathy-specific alterations in differentially expressed genes and signaling pathways. Using "CellChat," we analyzed intercellular communication to grasp how cell clusters interact and to map out the impaired signaling pathways in both donor and dilated cardiomyopathy conditions. Gene set enrichment analysis was applied to compare the metabolic signaling before and after dilated cardiomyopathy. We showcased how cell clusters exhibited abnormal cell-to-cell signaling transduction and how each cell type displayed dysfunctional metabolic signaling pathways through the integration of various datasets. The crucial ligand-receptor signaling contributing to outgoing or incoming signaling of dilated cardiomyopathy was identified in a cell-type dependent way, and the cell-specific metabolic alterations in glucose, lipid and amino acid were determined. The expression of gene pairs in BMP and NOTCH signal, as well as the gene expression in the arginine metabolism was validated.

Conclusions

We reveal the key signals and metabolic pathways for dilated cardiomyopathy adaptation and maintenance, providing potential targets for dilated cardiomyopathy interference.

Comparison of the single-cell and single-nucleus hepatic myeloid landscape within decompensated cirrhosis patients

Background and aims

A complete understanding of disease pathophysiology in advanced liver disease is hampered by the challenges posed by clinical specimen collection. Notably, in these patients, a transjugular liver biopsy (TJB) is the only safe way to obtain liver tissue. However, it remains unclear whether successful sequencing of this extremely small and fragile tissue can be achieved for downstream characterization of the hepatic landscape.

Methods

Here we leveraged in-house available single-cell RNA-sequencing (scRNA-seq) and single-nucleus (snRNA-seq) technologies and accompanying tissue processing protocols and performed an in-patient comparison on TJB's from decompensated cirrhosis patients (n = 3).

Results

We confirmed a high concordance between nuclear and whole cell transcriptomes and captured 31,410 single nuclei and 6,152 single cells, respectively. The two platforms revealed similar diversity since all 8 major cell types could be identified, albeit with different cellular proportions thereof. Most importantly, hepatocytes were most abundant in snRNA-seq, while lymphocyte frequencies were elevated in scRNA-seq. We next focused our attention on hepatic myeloid cells due to their key role in injury and repair during chronic liver disease. Comparison of their transcriptional signatures indicated that these were largely overlapping between the two platforms. However, the scRNA-seq platform failed to recover sufficient Kupffer cell numbers, and other monocytes/macrophages featured elevated expression of stress-related parameters.

Conclusion

Our results indicate that single-nucleus transcriptome sequencing provides an effective means to overcome complications associated with clinical specimen collection and could sufficiently profile all major hepatic cell types including all myeloid cell subsets.

Meta-analyses of mouse and human prostate single-cell transcriptomes reveal widespread epithelial plasticity in tissue regression, regeneration, and cancer

Recent advances in single-cell RNA-sequencing (scRNA-seq) technology have facilitated studies of cell states and plasticity in tissue maintenance and cancer, including in the prostate. Here we present meta-analyses of multiple new and published scRNA-seq datasets to establish reference cell type classifications for the normal mouse and human prostate. Our analyses demonstrate transcriptomic similarities between epithelial cell states in the normal prostate, in the regressed prostate after androgen-deprivation, and in primary prostate tumors. During regression in the mouse prostate, all epithelial cells shift their expression profiles towards a proximal periurethral (PrU) state, demonstrating an androgen-dependent plasticity that is restored to normal during androgen restoration and regeneration. In the human prostate, we find progressive rewiring of transcriptional programs across epithelial cell types in benign prostate hyperplasia and treatment-naïve prostate cancer. Notably, we detect copy number variants predominantly within Luminal Acinar cells in prostate tumors, suggesting a bias in their cell type of origin, as well as a larger field of transcriptomic alterations in non-tumor cells. Finally, we observe that Luminal Acinar tumor cells in treatment-naïve prostate cancer display heterogeneous androgen receptor (AR) signaling activity, including a split between high-AR and low-AR profiles with similarity to PrU-like states. Taken together, our analyses of cellular heterogeneity and plasticity provide important translational insights into the origin and treatment response of prostate cancer.

Mapping Cell Atlases at the Single-Cell Level

Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

Pan-Cancer Single-Nucleus Total RNA Sequencing Using snHH-Seq

Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single-cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA-seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high-throughput and high-sensitivity method called snHH-seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full-length RNA-seq data is also established. snHH-seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan-cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full-length RNA at the single-nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.

Activated fibroblasts drive cellular interactions in end-stage pediatric hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a relatively rare but debilitating diagnosis in the pediatric population and patients with end-stage HCM require heart transplantation. In this study, we performed single-nucleus RNA sequencing on pediatric HCM and control myocardium. We identified distinct underling cellular processes in pediatric, end-stage HCM in cardiomyocytes, fibroblasts, endothelial cells, and myeloid cells, compared to controls. Pediatric HCM was enriched in cardiomyocytes exhibiting "stressed" myocardium gene signatures and underlying pathways associated with cardiac hypertrophy. Cardiac fibroblasts exhibited clear activation signatures and heightened downstream processes associated with fibrosis, more so than adult counterparts. There was notable depletion of tissue-resident macrophages, and increased vascular remodeling in endothelial cells. Our analysis provides the first single nuclei analysis focused on end-stage pediatric HCM.

A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity

Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.

Integrating genetic regulation and single-cell expression with GWAS prioritizes causal genes and cell types for glaucoma

Primary open-angle glaucoma (POAG), characterized by retinal ganglion cell death, is a leading cause of irreversible blindness worldwide. However, its molecular and cellular causes are not well understood. Elevated intraocular pressure (IOP) is a major risk factor, but many patients have normal IOP. Colocalization and Mendelian randomization analysis of >240 POAG and IOP genome-wide association study (GWAS) loci and overlapping expression and splicing quantitative trait loci (e/sQTLs) in 49 GTEx tissues and retina prioritizes causal genes for 60% of loci. These genes are enriched in pathways implicated in extracellular matrix organization, cell adhesion, and vascular development. Analysis of single-nucleus RNA-seq of glaucoma-relevant eye tissues reveals that the POAG and IOP colocalizing genes and genome-wide associations are enriched in specific cell types in the aqueous outflow pathways, retina, optic nerve head, peripapillary sclera, and choroid. This study nominates IOP-dependent and independent regulatory mechanisms, genes, and cell types that may contribute to POAG pathogenesis.

Transcriptomic signatures of individual cell types in cerebral cavernous malformation

Cerebral cavernous malformation (CCM) is a hemorrhagic neurovascular disease with no currently available therapeutics. Prior evidence suggests that different cell types may play a role in CCM pathogenesis. The contribution of each cell type to the dysfunctional cellular crosstalk remains unclear. Herein, RNA-seq was performed on fluorescence-activated cell sorted endothelial cells (ECs), pericytes, and neuroglia from CCM lesions and non-lesional brain tissue controls. Differentially Expressed Gene (DEG), pathway and Ligand-Receptor (LR) analyses were performed to characterize the dysfunctional genes of respective cell types within CCMs. Common DEGs among all three cell types were related to inflammation and endothelial-to-mesenchymal transition (EndMT). DEG and pathway analyses supported a role of lesional ECs in dysregulated angiogenesis and increased permeability. VEGFA was particularly upregulated in pericytes. Further pathway and LR analyses identified vascular endothelial growth factor A/ vascular endothelial growth factor receptor 2 signaling in lesional ECs and pericytes that would result in increased angiogenesis. Moreover, lesional pericytes and neuroglia predominantly showed DEGs and pathways mediating the immune response. Further analyses of cell specific gene alterations in CCM endorsed potential contribution to EndMT, coagulation, and a hypoxic microenvironment. Taken together, these findings motivate mechanistic hypotheses regarding non-endothelial contributions to lesion pathobiology and may lead to novel therapeutic targets. Video Abstract.

The UCSC Genome Browser database: 2024 update

The UCSC Genome Browser (https://genome.ucsc.edu) is a web-based genomic visualization and analysis tool that serves data to over 7,000 distinct users per day worldwide. It provides annotation data on thousands of genome assemblies, ranging from human to SARS-CoV2. This year, we have introduced new data from the Human Pangenome Reference Consortium and on viral genomes including SARS-CoV2. We have added 1,200 new genomes to our GenArk genome system, increasing the overall diversity of our genomic representation. We have added support for nine new user-contributed track hubs to our public hub system. Additionally, we have released 29 new tracks on the human genome and 11 new tracks on the mouse genome. Collectively, these new features expand both the breadth and depth of the genomic knowledge that we share publicly with users worldwide.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes

BACKGROUND

Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters.

METHODS

We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships.

RESULTS

We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~ 85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community.

CONCLUSIONS

Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

SINGLE-CELL TRANSCRIPTOME ANALYSIS IN HEALTH AND DISEASE

ABSTRACT

The analysis of the single-cell transcriptome has emerged as a powerful tool to gain insights on the basic mechanisms of health and disease. It is widely used to reveal the cellular diversity and complexity of tissues at cellular resolution by RNA sequencing of the whole transcriptome from a single cell. Equally, it is applied to discover an unknown, rare population of cells in the tissue. The prime advantage of single-cell transcriptome analysis is the detection of stochastic nature of gene expression of the cell in tissue. Moreover, the availability of multiple platforms for the single-cell transcriptome has broadened its approaches to using cells of different sizes and shapes, including the capture of short or full-length transcripts, which is helpful in the analysis of challenging biological samples. And with the development of numerous packages in R and Python, new directions in the computational analysis of single-cell transcriptomes can be taken to characterize healthy versus diseased tissues to obtain novel pathological insights. Downstream analysis such as differential gene expression analysis, gene ontology term analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, cell-cell interaction analysis, and trajectory analysis has become standard practice in the workflow of single-cell transcriptome analysis to further examine the biology of different cell types. Here, we provide a broad overview of single-cell transcriptome analysis in health and disease conditions currently applied in various studies.

Exploring the Regulation and Function of Rpl3l in the Development of Early-Onset Dilated Cardiomyopathy and Congestive Heart Failure Using Systems Genetics Approach

BACKGROUND

Cardiomyopathies, diseases affecting the myocardium, are common causes of congestive heart failure (CHF) and sudden cardiac death. Recently, biallelic variants in ribosomal protein L3-like (RPL3L) have been reported to be associated with severe neonatal dilated cardiomyopathy (DCM) and CHF. This study employs a systems genetics approach to gain understanding of the regulatory mechanisms underlying the role of RPL3L in DCM.

METHODS

Genetic correlation, expression quantitative trait loci (eQTL) mapping, differential expression analysis and comparative functional analysis were performed using cardiac gene expression data from the patients and murine genetic reference populations (GRPs) of BXD mice (recombinant inbred strains from a cross of C57BL/6J and DBA/2J mice). Additionally, immune infiltration analysis was performed to understand the relationship between DCM, immune cells and RPL3L expression.

RESULTS

Systems genetics analysis identified high expression of Rpl3l mRNA, which ranged from 11.31 to 12.16 across murine GRPs of BXD mice, with an ~1.8-fold difference. Pathways such as "diabetic cardiomyopathy", "focal adhesion", "oxidative phosphorylation" and "DCM" were significantly associated with Rpl3l. eQTL mapping suggested Myl4 (Chr 11) and Sdha (Chr 13) as the upstream regulators of Rpl3l. The mRNA expression of Rpl3l, Myl4 and Sdha was significantly correlated with multiple echocardiography traits in BXD mice. Immune infiltration analysis revealed a significant association of RPL3L and SDHA with seven immune cells (CD4, CD8-naive T cell, CD8 T cell, macrophages, cytotoxic T cell, gamma delta T cell and exhausted T cell) that were also differentially infiltrated between heart samples obtained from DCM patients and normal individuals.

CONCLUSIONS

RPL3L is highly expressed in the heart tissue of humans and mice. Expression of Rpl3l and its upstream regulators, Myl4 and Sdha, correlate with multiple cardiac function traits in murine GRPs of BXD mice, while RPL3L and SDHA correlate with immune cell infiltration in DCM patient hearts, suggesting important roles for RPL3L in DCM and CHF pathogenesis via immune inflammation, necessitating experimental validations of Myl4 and Sdha in Rpl3l regulation.

High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis

Single-cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical samples. Current commercially available technologies provide whole transcriptome single-cell, whole transcriptome spatial, or targeted in situ gene expression analysis. Here, we combine these technologies to explore tissue heterogeneity in large, FFPE human breast cancer sections. This integrative approach allowed us to explore molecular differences that exist between distinct tumor regions and to identify biomarkers involved in the progression towards invasive carcinoma. Further, we study cell neighborhoods and identify rare boundary cells that sit at the critical myoepithelial border confining the spread of malignant cells. Here, we demonstrate that each technology alone provides information about molecular signatures relevant to understanding cancer heterogeneity; however, it is the integration of these technologies that leads to deeper insights, ushering in discoveries that will progress oncology research and the development of diagnostics and therapeutics.

Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis

Cell type annotation is an essential step in single-cell RNA-seq analysis. However, it is a time-consuming process that often requires expertise in collecting canonical marker genes and manually annotating cell types. Automated cell type annotation methods typically require the acquisition of high-quality reference datasets and the development of additional pipelines. We assessed the performance of GPT-4, a highly potent large language model, for cell type annotation, and demonstrated that it can automatically and accurately annotate cell types by utilizing marker gene information generated from standard single-cell RNA-seq analysis pipelines. Evaluated across hundreds of tissue types and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations and has the potential to considerably reduce the effort and expertise needed in cell type annotation. We also developed GPTCelltype, an open-source R software package to facilitate cell type annotation by GPT-4.