muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

Roles of Adam8 in Neuroinflammation in experimental ischemic Stroke: Insights from single-cell and ribosome-bound mRNA sequencing

Stroke remains a leading cause of global mortality, with neuroinflammation significantly exacerbating clinical outcomes. Microglia serve as key mediators of post-stroke neuroinflammation, though the mechanisms driving their migration to injury sites remain poorly understood. In this study, using publicly available single-cell sequencing data (GSE234052), we identified a migration-associated microglial subtype in a murine model of distal middle cerebral artery occlusion (dMCAO). Additionally, ribosome-bound mRNA sequencing data (GSE225110) from microglia isolated from peri-infarct cortical tissue uncovered dMCAO-induced alterations in microglial mRNA translation. By integrating these datasets, we identified A Disintegrin And Metalloproteinase 8 (Adam8) as a key gene upregulated at both the transcriptional and translational levels post-dMCAO. Protein analysis revealed that both the precursor and active forms of Adam8 were predominantly expressed in microglia and significantly upregulated in peri-infarct regions following dMCAO. Notably, Adam8 inhibition with BK-1361 significantly reduced Adam8 cleavage, M1 microglial migration, inflammation, infarct size, and improved neurological outcomes. Bioinformatics analysis further identified Myo1e as a potential interacting partner of Adam8, a finding validated through immunofluorescence co-localization. These findings highlight Adam8 as a promising therapeutic target for mitigating post-stroke neuroinflammation and offer new insights into the mechanisms of microglial migration.

A dynamic and multimodal framework to define microglial states

The widespread use of single-cell RNA sequencing has generated numerous purportedly distinct and novel subsets of microglia. Here, we challenge this fragmented paradigm by proposing that microglia exist along a continuum rather than as discrete entities. We identify a methodological over-reliance on computational clustering algorithms as the fundamental issue, with arbitrary cluster numbers being interpreted as biological reality. Evidence suggests that the observed transcriptional diversity stems from a combination of microglial plasticity and technical noise, resulting in terminology describing largely overlapping cellular states. We introduce a continuous model of microglial states, where cell positioning along the continuum is determined by biological aging and cell-specific molecular contexts. The model accommodates the dynamic nature of microglia. We advocate for a parsimonious approach toward classification and terminology that acknowledges the continuous spectrum of microglial states, toward a robust framework for understanding these essential immune cells of the CNS.

Bulk transcriptome and single-nucleus RNA sequencing analyses highlight the role of recombination activating 1 in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic condition with an incompletely understood pathogenesis. In this study, five candidate genes-RAG1, CKAP2, CENPK, TYMS, and BUB1-were identified as being associated with NAFLD progression through integrative bioinformatics analyses. A predictive model incorporating these genes demonstrated strong robustness and diagnostic accuracy. Single-nucleus RNA sequencing analysis further revealed that RAG1 plays a potential role in hepatocytes of NAFLD patients. Functional experiments using RNA interference to suppress RAG1 expression in HepG2 cells treated with oleic and palmitic acids showed reduced total glyceride and cholesterol levels, mitigated lipid accumulation, and alterations in pathways related to lipid metabolism, inflammation, and fibrosis. Furthermore, adeno-associated virus-specific knockdown of RAG1 in hepatocytes attenuated hepatic steatosis in high-fat diet-fed mice. These findings suggest that investigating the molecular mechanisms of hub genes like RAG1 may advance our understanding of NAFLD pathogenesis and inform therapeutic development.

Integrating Spatially-Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. The lowering cost of SRT data generation presents an unprecedented opportunity to create large-scale spatial atlases and enable population-level investigation, integrating SRT data across multiple tissues, individuals, species, or phenotypes. Here, unique challenges are described in the SRT data integration, where the analytic impact of varying spatial and biological resolutions is characterized and explored. A succinct review of spatially-aware integration methods and computational strategies is provided. Exciting opportunities to advance computational algorithms amenable to atlas-scale datasets along with standardized preprocessing methods, leading to improved sensitivity and reproducibility in the future are further highlighted.

Exploring genetic and immune cell dynamics in systemic lupus erythematosus patients with Epstein-Barr virus infection via machine learning

OBJECTIVES

EBV is a widespread virus implicated in various diseases, including SLE. However, the specific genes and pathways altered in SLE patients with EBV infection remain unclear. We aimed to identify key genes and immune cells in SLE patients with EBV infection.

METHODS

The datasets of SLE (GSE50772 and GSE81622) and EBV infection (GSE85599 and GSE45918) were obtained from the Gene Expression Omnibus (GEO) database. Next, differential gene expression (DEGs) analyses were conducted to identify overlapping DEGs, and then enrichment analysis was performed. Machine learning was applied to identify key genes. Validation was conducted using receiver operating characteristic (ROC) curve analysis and expression level verification in test datasets and single-cell RNA sequencing. Immune cell infiltration patterns were analysed using CIBERSORTx, and clinical data were reviewed for SLE patients.

RESULTS

We identified 58 overlapping DEGs enriched in IFN-related pathways. Five overlapping DEGs (IFI27, TXK, RAPGEF6, PIK3IP1 and PSENEN) were selected as key genes by machine-learning algorithms, with IFI27 showing the highest diagnostic performance. The expression level of IFI27 was found to be higher in CD4 CTL, CD8-naïve and various B cell subsets of SLE patients with EBV infection. IFI27 showed significant correlation with B intermediate and CD4 CTL cells. Clinical data showed lower CD4 T cell proportions in SLE patients with EBV infection.

CONCLUSION

This study identified IFI27 as a key gene for SLE patients with EBV infection, influencing CD4 CTL and B cell subtypes. These findings enhance the understanding of the molecular mechanisms linking SLE and EBV infection, providing potential targets for diagnostic and therapeutic strategies.

Single-cell transcriptomics reveal how root tissues adapt to soil stress

Land plants thrive in soils showing vastly different properties and environmental stresses1. Root systems can adapt to contrasting soil conditions and stresses, yet how their responses are programmed at the individual cell scale remains unclear. Using single-cell RNA sequencing and spatial transcriptomic approaches, we showed major expression changes in outer root cell types when comparing the single-cell transcriptomes of rice roots grown in gel versus soil conditions. These tissue-specific transcriptional responses are related to nutrient homeostasis, cell wall integrity and defence in response to heterogeneous soil versus homogeneous gel growth conditions. We also demonstrate how the model soil stress, termed compaction, triggers expression changes in cell wall remodelling and barrier formation in outer and inner root tissues, regulated by abscisic acid released from phloem cells. Our study reveals how root tissues communicate and adapt to contrasting soil conditions at single-cell resolution.

Transcriptomic and morphologic vascular aberrations underlying FCDIIb etiology

Focal cortical dysplasia type II (FCDII) is a major cause of drug-resistant epilepsy, but genetic factors explain only some cases, suggesting other mechanisms. In this study, we conduct a molecular analysis of brain lesions and adjacent areas in FCDIIb patients. By analyzing over 217,506 single-nucleus transcriptional profiles from 15 individuals, we find significant changes in smooth muscle cells (SMCs) and astrocytes. We identify abnormal vascular malformations and a unique type of SMC that we call "Firework cells", which migrate from blood vessels into the brain parenchyma and associate with VIM+ cells. These abnormalities create localized ischemic-hypoxic (I/H) microenvironments, as confirmed by clinical data, further impairing astrocyte function, activating the HIF-1α/mTOR/S6 pathway, and causing neuronal loss. Using zebrafish models, we demonstrate that vascular abnormalities resulting in I/H environments promote seizures. Our results highlight vascular malformations as a factor in FCDIIb pathogenesis, suggesting potential therapeutic avenues.

Single-cell transcriptome analyses of PBMCs reveal the immunological characteristics of individuals with phlegm-dampness constitution

Ancient traditional Chinese medicine (TCM) doctrine says "The superior doctor prevents illnesses," pointing out preventative medicine as the ultimate goal for medical care. TCM recognizes that genetic predisposition and environmental and lifestyle influences contribute to diseases. It divides people into eight constitutions in addition to one normal/healthy kind. People with one of the eight subhealth constitutions are prone to develop different kinds of corresponding illnesses. The goal for this type of categorization is to help people take preemptive measures to prevent or delay disease onset. As the peripheral immune system through surveying the body, it can capture information from essentially all organs and reflect anomalies occurring in each organ. Thus, the detailed profiling of the peripheral immune-system function can generally reflect a person's overall heath state. In this study, we performed the single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) from individuals with Tanshi (phlegm dampness) constitution. They were prone to develop metabolic disorders including diabetes. scRNA-seq revealed greatly reduced mucosal-associated invariable T cell content and heightened TNFα-NFκB, JAK-STAT, and interferon signaling. These findings indicated heightened chronic inflammation, as well as increased hypoxia/apoptosis responses, likely resulting from frequent sleep apnea that Tanshi individuals experienced. Altogether, this pilot study demonstrated effectiveness in using scRNA-seq to reveal molecular-immunological bases for constitution categorization, thereby substantiating that preventative medicine originated from TCM.

Expansion of OSMR expression and signaling in the human dorsal root ganglion links OSM to neuropathic pain

RNA sequencing studies on human dorsal root ganglion (hDRG) from patients suffering from neuropathic pain show upregulation of OSM, linking this IL-6 family cytokine to pain disorders. In mice, however, OSM signaling causes itch behaviors through a direct effect on its cognate receptor expressed uniquely by pruriceptive sensory neurons. We hypothesized that an expansion in function of OSM-OSM receptor (OSMR) in sensory disorders in humans could be explained by species differences in receptor expression and signaling. Our in situ hybridization and immunohistochemical findings demonstrate broad expression of OSMR in DRG nociceptors and afferent fibers innervating the superficial and deep skin of humans. In patch-clamp electrophysiology, OSM directly activates human sensory neurons engaging MAPK signaling to promote action potential firing. Using CRISPR editing we show that OSM activation of MAPK signaling is dependent on OSMR and not LIFR in hDRG. Bulk, single-nuclei, and single-cell RNA-seq of OSM-treated hDRG cultures reveal expansive similarities in the transcriptomic signature observed in pain DRGs from neuropathic patients, indicating that OSM alone can orchestrate transcriptomic signatures associated with pain. We conclude that OSM-OSMR signaling via MAPKs is a critical signaling factor for DRG plasticity that may underlie neuropathic pain in patients.

Myelin regulatory factor (MYRF) is a critical early regulator of retinal pigment epithelial development

Myelin regulatory factor (Myrf) is a critical transcription factor in early retinal and retinal pigment epithelial development, and human variants in MYRF are a cause for nanophthalmos. Single cell RNA sequencing (scRNAseq) was performed on Myrf conditional knockout mice (Rx > Cre Myrffl/fl) at 3 developmental timepoints. Myrf was expressed specifically in the RPE, and expression was abrogated in Rx > Cre Myrffl/fl eyes. scRNAseq analysis revealed a loss of RPE cells at all timepoints resulting from cell death. GO-term analysis in the RPE revealed downregulation of melanogenesis and anatomic structure morphogenesis pathways, which were supported by electron microscopy and histologic analysis. Novel structural target genes including Ermn and Upk3b, along with macular degeneration and inherited retinal disease genes were identified as downregulated, and a strong upregulation of TGFß/BMP signaling and effectors was observed. Regulon analysis placed Myrf downstream or parallel to Pax6 and Mitf and upstream of Sox10 in RPE differentiation. Together, these results suggest a strong role for MYRF in the RPE maturation by regulating melanogenesis, cell survival, and cell structure, in part acting through suppression of TGFß signaling and activation of Sox10.

How to view the female reproductive tract through single-cell looking glasses

Single-cell technologies have emerged as an unprecedented tool for biologists and clinicians, allowing them to assess organs and tissues at the level of individual cells. In the field of women's reproductive biology, single-cell studies have provided insights into the cellular and molecular processes that regulate reproductive and obstetrical functions in health and disease. The knowledge that these studies generate is helping clinicians to improve the understanding and diagnosis of infertility related issues or pregnancy complications and to find new avenues for their treatment. However, navigating the expansive landscape of this type of transcriptomic data analysis represents a pivotal challenge in current research. Single cell RNA sequencing involves isolating cells into droplets, reverse transcribing RNA to generate complementary DNA, with each droplet content uniquely labeled by a barcode. Upon sequencing the complementary DNAs, the barcodes enable the reassignment of sequencing reads to individual droplets, facilitating the reconstruction of the cellular landscape of the sample obtained from a tissue or organ and beyond. Researchers, equipped with the metaphorical 'single-cell glasses,' must adequately choose from a plethora of strategies to dissect and interpret cellular information. Sophisticated algorithms and the decision-making process are often underestimated, resulting in artefactual or cumbersome interpreted results. Computational biologists apply and innovate computational tools designed to process, model, and interpret expansive datasets. The ramifications of their work extend far beyond the realm of data processing; they give shape to the outcome of analyses, playing a pivotal role in drawing meaningful conclusions from the wealth of information garnered. In this review, we describe the wide variety of approaches and analytical steps available with enough detail to gain a concise picture of what a complete examination of a single-cell dataset would be. We commence with a discussion on key points in experimental design, highlighting crucial questions one should consider. Following this, we delve into the various preprocessing and quality control steps essential for any single-cell dataset. The subsequent section offers a detailed guide on constructing a single-cell atlas, exploring nuances such as differential characteristics in visualization and clustering techniques, as well as strategies for assigning identity to cell populations through gene marker annotations. Moving beyond the creation of an atlas, we explore methods for investigating pathological conditions. This involves conducting cell population comparison tests between conditions and analyzing specific cell-to-cell communications and cellular differentiation trajectories in both health and disease scenarios. This work aims to furnish a newcomer researcher and/or clinician with essential guidelines to embark on a single-cell adventure without succumbing to common pitfalls. By bridging the gap between theory and practice, it facilitates the translation of single-cell technologies into clinically relevant applications. Throughout the manuscript, practical examples of its usage in women's reproductive health studies are provided. Various sections delve into specific clinical scenarios, demonstrating how these guidelines can be instrumental in unraveling the molecular landscapes of diseases and physiological processes related to women's reproduction.

Single-cell sequencing uncovers a high ESM1-expression endothelial cell subpopulation associated with bladder cancer progression and the immunosuppressive microenvironment

Despite remarkable advancements in therapeutic strategies, a considerable proportion of patients with bladder cancer (BC) still experience disease progression and unfavorable prognosis. The heterogeneity and biological functions of tumor endothelial cells (ECs) during BC progression remain poorly understood. We collected scRNA-seq data from BC samples and identified two EC subpopulations through hierarchical clustering analysis. The activity of signaling pathways in distinct EC subpopulations was assessed utilizing AUCell analysis. Gene regulatory networks (GRN) were constructed and analyzed for different EC subpopulations using the pySCENIC algorithm. Additionally, we investigated the association between the abundance of EC subpopulations and both clinical prognosis and immune cell infiltration. The biological effects of ESM1 protein on BC cells were further validated through EdU and Transwell assays. We analyzed 7,519 CD45-negative single cells from BC tissues and discerned two distinct EC subpopulations. The two subpopulations were characterized by high expression of ESM1 (S1 ECs) and CXCL2 (S2 ECs), respectively. In S1 ECs, we observed significant activation of signaling pathways involved in tumor promotion, including angiogenesis and cell proliferation. Additionally, our GRN analysis uncovered notable differences in transcription factor activity between S1 and S2 ECs. Moreover, ESM1 protein promoted proliferation and migration of BC cells. Patients with higher abundance of the S1 EC subpopulation exhibited more unfavorable clinical outcomes and increased infiltration of inhibitory immune cells. Our findings elucidate the transcriptional profiles and biological roles of the high ESM1-expression endothelial cell subpopulation in BC. This subpopulation is associated with poor prognosis and immunosuppressive tumor microenvironment. Accordingly, targeting endothelial cells with high ESM1 expression may offer a novel therapeutic strategy for patients with BC.

Integrative single-cell analysis reveals transcriptional and epigenetic regulatory features of human developmental dysplasia of the hip

OBJECTIVE

Developmental dysplasia of the hip (DDH) is a developmental disorder that has long-term chronic pain and limited hip joint mobility. The aim of the current study is to understand the specific chondrocyte composition involved in DDH development, identify effective biomarkers for DDH prediction, and elucidate the gene regulatory elements driving DDH progression.

METHOD

In this study, we performed an integrated analysis combining single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin sequencing to investigate the molecular programs and epigenetic changes governing human DDH pathogenesis. Validation of marker genes for distinct chondrocyte populations was performed via immunohistochemical assays, alongside characterization of regulatory elements specific to DDH.

RESULTS

Our analysis identified seven molecularly distinct chondrocyte populations in DDH cartilage, including a novel inflammatory chondrocyte population with unique molecular signatures. Furthermore, we reconstructed the differentiation trajectory of chondrocytes, shedding light on their roles in DDH pathogenesis. Integrative analyses of transcriptomic and chromatin accessibility profiles highlighted shared regulatory features and transcriptional programs among chondrocyte subtypes, with several regulatory elements linked to DDH progression. Immunohistochemical validation corroborated the presence of key marker genes in distinct chondrocyte subsets.

CONCLUSION

Our findings contribute to clarifying the cellular heterogeneity of DDH and offer insights into potential early diagnostic and therapeutic strategies for this condition.

Defining the molecular identity and morphology of glia limitans superficialis astrocytes in vertebrates

Astrocytes are a highly abundant glial cell type and perform critical homeostatic functions in the central nervous system. Like neurons, astrocytes have many discrete heterogeneous subtypes. The subtype identity and functions are, at least in part, associated with their anatomical location and can be highly restricted to strategically important anatomical domains. Here, we report that astrocytes forming the glia limitans superficialis, the outermost border of the brain and spinal cord, are a highly specialized astrocyte subtype and can be identified by a single marker: myocilin (Myoc). We show that glia limitans superficialis astrocytes cover the entire brain and spinal cord surface, exhibit an atypical morphology, and are evolutionarily conserved from zebrafish, rodents, and non-human primates to humans. Identification of this highly specialized astrocyte subtype will advance our understanding of CNS homeostasis and potentially be targeted for therapeutic intervention to combat peripheral inflammatory effects on the CNS.

Single-cell RNA sequencing reveals a new mechanism of endothelial cell heterogeneity and healing in diabetic foot ulcers

Diabetic foot ulcers (DFU) are a common and severe complication among diabetic patients, posing a significant burden on patients' quality of life and healthcare systems due to their high incidence, amputation rates, and mortality. This study utilized single-cell RNA sequencing technology to deeply analyze the cellular heterogeneity of the skin on the feet ofDFU patients and the transcriptomic characteristics of endothelial cells, aiming to identify key cell populations and genes associated with the healing and progression of DFU. The study found that endothelial cells from DFU patients exhibited significant transcriptomic differences under various conditions, particularly in signaling pathways related to inflammatory responses and angiogenesis. Through trajectory analysis and cell communication research, we revealed the key role of endothelial cell subsets in the development of DFU and identified multiple important gene modules associated with the progression of DFU. Notably, the promoting effect of the SH3BGRL3 gene on endothelial cell proliferation, migration, and angiogenic capabilities under high glucose conditions was experimentally verified, providing a new potential target and theoretical basis for the treatment of DFU. This study not only enhances the understanding of the pathogenesis ofDFU but also provides a scientific basis for the development ofnew therapeutic strategies.

T-bet+ CXCR3+ B cells drive hyperreactive B-T cell interactions in multiple sclerosis

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS). Self-peptide-dependent autoproliferation (AP) of B and T cells is a key mechanism in MS. Here, we show that pro-inflammatory B-T cell-enriched cell clusters (BTECs) form during AP and mirror features of a germinal center reaction. T-bet+CXCR3+ B cells are the main cell subset amplifying and sustaining their counterpart Th1 cells via interferon (IFN)-γ and are present in highly inflamed meningeal tissue. The underlying B cell activation signature is reflected by epigenetic modifications and receptor-ligand interactions with self-reactive T cells. AP+ CXCR3+ B cells show marked clonal evolution from memory to somatically hypermutated plasmablasts and upregulation of IFN-γ-related genes. Our data underscore a key role of T-bet+CXCR3+ B cells in the pathogenesis of MS in both the peripheral immune system and the CNS compartment, and thus they appear to be involved in both early relapsing-remitting disease and the chronic stage.

Exploring and mitigating shortcomings in single-cell differential expression analysis with a new statistical paradigm

BACKGROUND

Differential expression analysis is pivotal in single-cell transcriptomics for unraveling cell-type-specific responses to stimuli. While numerous methods are available to identify differentially expressed genes in single-cell data, recent evaluations of both single-cell-specific methods and methods adapted from bulk studies have revealed significant shortcomings in performance. In this paper, we dissect the four major challenges in single-cell differential expression analysis: excessive zeros, normalization, donor effects, and cumulative biases. These "curses" underscore the limitations and conceptual pitfalls in existing workflows.

RESULTS

To address the limitations of current single-cell differential expression analysis methods, we propose GLIMES, a statistical framework that leverages UMI counts and zero proportions within a generalized Poisson/Binomial mixed-effects model to account for batch effects and within-sample variation. We rigorously benchmarked GLIMES against six existing differential expression methods using three case studies and simulations across different experimental scenarios, including comparisons across cell types, tissue regions, and cell states. Our results demonstrate that GLIMES is more adaptable to diverse experimental designs in single-cell studies and effectively mitigates key shortcomings of current approaches, particularly those related to normalization procedures. By preserving biologically meaningful signals, GLIMES offers improved performance in detecting differentially expressed genes.

CONCLUSIONS

By using absolute RNA expression rather than relative abundance, GLIMES improves sensitivity, reduces false discoveries, and enhances biological interpretability. This paradigm shift challenges existing workflows and highlights the need for careful consideration of normalization strategies, ultimately paving the way for more accurate and robust single-cell transcriptomic analyses.

Distinct gene regulatory dynamics drive skeletogenic cell fate convergence during vertebrate embryogenesis

Cell type repertoires have expanded extensively in metazoan animals, with some clade-specific cells being crucial to evolutionary success. A prime example are the skeletogenic cells of vertebrates. Depending on anatomical location, these cells originate from three different precursor lineages, yet they converge developmentally towards similar cellular phenotypes. Furthermore, their 'skeletogenic competency' arose at distinct evolutionary timepoints, thus questioning to what extent different skeletal body parts rely on truly homologous cell types. Here, we investigate how lineage-specific molecular properties are integrated at the gene regulatory level, to allow for skeletogenic cell fate convergence. Using single-cell functional genomics, we find that distinct transcription factor profiles are inherited from the three precursor states and incorporated at lineage-specific enhancer elements. This lineage-specific regulatory logic suggests that these regionalized skeletogenic cells are distinct cell types, rendering them amenable to individualized selection, to define adaptive morphologies and biomaterial properties in different parts of the vertebrate skeleton.

Analysis of multi-condition single-cell data with latent embedding multivariate regression

Identifying gene expression differences in heterogeneous tissues across conditions is a fundamental biological task, enabled by multi-condition single-cell RNA sequencing (RNA-seq). Current data analysis approaches divide the constituent cells into clusters meant to represent cell types, but such discrete categorization tends to be an unsatisfactory model of the underlying biology. Here, we introduce latent embedding multivariate regression (LEMUR), a model that operates without, or before, commitment to discrete categorization. LEMUR (1) integrates data from different conditions, (2) predicts each cell's gene expression changes as a function of the conditions and its position in latent space and (3) for each gene, identifies a compact neighborhood of cells with consistent differential expression. We apply LEMUR to cancer, zebrafish development and spatial gradients in Alzheimer's disease, demonstrating its broad applicability.

A multiplex single-cell RNA-Seq pharmacotranscriptomics pipeline for drug discovery

The gene-regulatory dynamics governing drug responses in cancer are yet to be fully understood. Here, we report a pipeline capable of producing high-throughput pharmacotranscriptomic profiling through live-cell barcoding using antibody-oligonucleotide conjugates. This pipeline combines drug screening with 96-plex single-cell RNA sequencing. We show the potential of this approach by exploring the heterogeneous transcriptional landscape of primary high-grade serous ovarian cancer (HGSOC) cells after treatment with 45 drugs, with 13 distinct classes of mechanisms of action. A subset of phosphatidylinositol 3-OH kinase (PI3K), protein kinase B (AKT) and mammalian target of rapamycin (mTOR) inhibitors induced the activation of receptor tyrosine kinases, such as the epithelial growth factor receptor (EGFR), and this was mediated by the upregulation of caveolin 1 (CAV1). This drug resistance feedback loop could be mitigated by the synergistic action of agents targeting PI3K-AKT-mTOR and EGFR for HGSOC with CAV1 and EGFR expression. Using this workflow could enable the personalized testing of patient-derived tumor samples at single-cell resolution.

Decoding the corneal immune microenvironment in healthy and diabetic mice during corneal wound healing

Diabetic keratopathy (DK) is an underdiagnosed ocular complication of diabetes mellitus. The changes of ocular immune microenvironment contribute to the pathogenesis of DK, while precise mechanisms remain inadequately understood. Here, we employed single-cell RNA sequencing (scRNA-seq) to elucidate the transcriptional alterations of immune cells from diabetic and healthy control mouse corneas during homeostasis and wound healing. Unbiased clustering analysis unveiled 3 major cell subsets and 11 subdivided cell clusters, including T cells, monocyte lineages, and neutrophil subpopulations. The further sub-clustering analysis demonstrated that T cells exhibited cytotoxicity characteristics in both homeostasis and wound healing of diabetic cornea. Moreover, dendritic cells preferred the migratory and maturation phenotype and may recruit and maintain cytotoxic T cells. Macrophages in diabetic cornea preferred the pro-inflammatory M1 phenotype. Under injury conditions, diabetic corneal neutrophils exhibited a more mature and functional possession of neutrophil extracellular traps (NETs). Furthermore, cell-cell communication revealed that the immune cells exhibited hyperactivation and pro-inflammatory responses, while the monocyte lineages exhibited the activating effect on T cells in diabetic cornea. This study represents the inaugural effort to establish a comprehensive scRNA-Seq transcriptomic profile of corneal immune cells during wound healing in healthy and diabetic mice, which offers a valuable reference for subsequent investigations into the pathological roles of immune cells in DK.

A single-cell atlas to map sex-specific gene-expression changes in blood upon neurodegeneration

The clinical course and treatment of neurodegenerative disease are complicated by immune-system interference and chronic inflammatory processes, which remain incompletely understood. Mapping immune signatures in larger human cohorts through single-cell gene expression profiling supports our understanding of observed peripheral changes in neurodegeneration. Here, we employ single-cell gene expression profiling of over 909k peripheral blood mononuclear cells (PBMCs) from 121 healthy individuals, 48 patients with mild cognitive impairment (MCI), 46 with Parkinson's disease (PD), 27 with Alzheimer's disease (AD), and 15 with both PD and MCI. The dataset is interactively accessible through a freely available website ( https://www.ccb.uni-saarland.de/adrcsc ). In this work, we identify disease-associated changes in blood cell type composition and the gene expression in a sex-specific manner, offering insights into peripheral and solid tissue signatures in AD and PD.

Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution

Deciphering the features, structure, and functions of the cell niche in tissues remains a major challenge. Here, we present scNiche, a computational framework to identify and characterize cell niches from spatial omics data at single-cell resolution. We benchmark scNiche with both simulated and biological datasets, and demonstrate that scNiche can effectively and robustly identify cell niches while outperforming other existing methods. In spatial proteomics data from human triple-negative breast cancer, scNiche reveals the influence of the microenvironment on cellular phenotypes, and further dissects patient-specific niches with distinct cellular compositions or phenotypic characteristics. By analyzing mouse liver spatial transcriptomics data across normal and early-onset liver failure donors, scNiche uncovers disease-specific liver injury niches, and further delineates the niche remodeling from normal liver to liver failure. Overall, scNiche enables decoding the cellular microenvironment in tissues from single-cell spatial omics data.

Multi-omic Characterization of HIV Effects at Single Cell Level across Human Brain Regions

HIV infection exerts profound and long-lasting neurodegenerative effects on the central nervous system (CNS) that can persist despite antiretroviral therapy (ART). Here, we used single-nucleus multiome sequencing to map the transcriptomic and epigenetic landscapes of postmortem human brains from 13 healthy individuals and 20 individuals with HIV who have a history of treatment with ART. Our study spanned three distinct regions-the prefrontal cortex, insular cortex, and ventral striatum-enabling a comprehensive exploration of region-specific and cross-regional perturbations. We found widespread and persistent HIV-associated transcriptional and epigenetic alterations across multiple cell types. Detailed analyses of microglia revealed state changes marked by immune activation and metabolic dysregulation, while integrative multiomic profiling of astrocytes identified multiple subpopulations, including a reactive subpopulation unique to HIV-infected brains. These findings suggest that cells from people with HIV exhibit molecular shifts that may underlie ongoing neuroinflammation and CNS dysfunction. Furthermore, cell-cell communication analyses uncovered dysregulated and pro-inflammatory interactions among glial populations, underscoring the multifaceted and enduring impact of HIV on the brain milieu. Collectively, our comprehensive atlas of HIV-associated brain changes reveals distinct glial cell states with signatures of proinflammatory signaling and metabolic dysregulation, providing a framework for developing targeted therapies for HIV-associated neurological dysfunction.

Early cell cycle genes in cortical organoid progenitors predict interindividual variability in infant brain growth trajectories

Human induced pluripotent stem cell (iPSC) derived cortical organoids (hCOs) model neurogenesis on an individual's genetic background. The degree to which hCO phenotypes recapitulate the brain growth of the participants from which they were derived is not well established. We generated up to 3 iPSC clones from each of 18 participants in the Infant Brain Imaging Study, who have undergone longitudinal brain imaging during infancy. We identified consistent hCO morphology and cortical cell types across clones from the same participant. hCO cross-sectional area and production of cortical hem cells were associated with in vivo cortical growth rates. Cell cycle associated genes expression in early progenitors at the crux of fate decision trajectories were correlated with cortical growth rate from 6-12 months of age, and were enriched in microcephaly and neurodevelopmental disorder genes. Our data suggest the hCOs capture inter-individual variation in cortical cell types influencing infant cortical surface area expansion.

snRNA-seq stratifies multiple sclerosis patients into distinct white matter glial responses

Poor understanding of the cellular and molecular basis of clinical and genetic heterogeneity in progressive multiple sclerosis (MS) has hindered the search for new effective therapies. To address this gap, we analyzed 632,000 single-nucleus RNA sequencing profiles from 156 brain tissue samples of MS and control donors to examine inter- and intra-donor heterogeneity. We found distinct cell type-specific gene expression changes between MS gray and white matter, highlighting clear pathology differences. MS lesion subtypes had different cellular compositions but surprisingly similar cell-type gene expression patterns both within and across patients, suggesting global changes. Most gene expression variability was instead explained by patient effects, allowing us to stratify patients and describe the different pathological processes occurring between patient subgroups. Future mapping of these brain molecular profiles with blood and/or CSF profiles from living MS patients will allow precision medicine approaches anchored in patient-specific pathological processes.

A Reproducibility Focused Meta-Analysis Method for Single-Cell Transcriptomic Case-Control Studies Uncovers Robust Differentially Expressed Genes

We assessed the reproducibility of differentially expressed genes (DEGs) in previously published Alzheimer's (AD), Parkinson's (PD), Schizophrenia (SCZ), and COVID-19 scRNA-seq studies. While transcriptional scores from DEGs of individual PD and COVID-19 datasets had moderate predictive power for case-control status of other datasets (AUC=0.77 and 0.75), genes from individual AD and SCZ datasets had poor predictive power (AUC=0.68 and 0.55). We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets, and found DEGs with improved predictive power (AUC=0.88, 0.91, 0.78, and 0.62). By multiple other metrics, specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. The DEGs revealed known and novel biological pathways, and we validate BCAT1 as down-regulated in AD mouse oligodendrocytes. Lastly, we evaluate factors influencing reproducibility of individual studies as a prospective guide for experimental design.

uniLIVER: a human liver cell atlas for data-driven cellular state mapping

The liver performs several vital functions such as metabolism, toxin removal, and glucose storage through the coordination of various cell types. With the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER (http://lifeome.net/database/uniliver) by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Moreover, we introduce LiverCT, a novel machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of "variant" cellular states analogy to the sequence variants in genomic analysis. Applying LiverCT on liver cancer datasets, we find that the "deviated" states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma, and the enrichments of tumor cells with the hepatocyte-cholangiocyte "intermediate" states significantly indicate poor prognosis. Besides, we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis. This reference atlas mapping framework can also be extended to any other tissues.

Genome-scale spatial mapping of the Hodgkin lymphoma microenvironment identifies tumor cell survival factors

A key challenge in cancer research is to identify the secreted factors that contribute to tumor cell survival. Nowhere is this more evident than in Hodgkin lymphoma, where malignant Hodgkin Reed Sternberg (HRS) cells comprise only 1-5% of the tumor mass, the remainder being infiltrating immune cells that presumably are required for the survival of the HRS cells. Until now, there has been no way to characterize the complex Hodgkin lymphoma tumor microenvironment at genome scale. Here, we performed genome-wide transcriptional profiling with spatial and single-cell resolution. We show that the neighborhood surrounding HRS cells forms a distinct niche involving 31 immune and stromal cell types and is enriched in CD4+ T cells, myeloid and follicular dendritic cells, while being depleted of plasma cells. Moreover, we used machine learning to nominate ligand-receptor pairs enriched in the HRS cell niche. Specifically, we identified IL13 as a candidate survival factor. In support of this hypothesis, recombinant IL13 augmented the proliferation of HRS cells in vitro. In addition, genome-wide CRISPR/Cas9 loss-of-function studies across more than 1,000 human cancer cell lines showed that IL4R and IL13RA1, the heterodimeric partners that constitute the IL13 receptor, were uniquely required for the survival of HRS cells. Moreover, monoclonal antibodies targeting either IL4R or IL13R phenocopied the genetic loss of function studies. IL13-targeting antibodies are already FDA-approved for atopic dermatitis, suggesting that clinical trials testing such agents should be explored in patients with Hodgkin lymphoma.

Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data

BACKGROUND

Imaging-based spatial transcriptomics technologies allow us to explore spatial gene expression profiles at the cellular level. Cell type annotation of imaging-based spatial data is challenging due to the small gene panel, but it is a crucial step for downstream analyses. Many good reference-based cell type annotation tools have been developed for single-cell RNA sequencing and sequencing-based spatial transcriptomics data. However, the performance of the reference-based cell type annotation tools on imaging-based spatial transcriptomics data has not been well studied yet.

RESULTS

We compared performance of five reference-based methods (SingleR, Azimuth, RCTD, scPred and scmapCell) with the marker-gene-based manual annotation method on an imaging-based Xenium data of human breast cancer. A practical workflow has been demonstrated for preparing a high-quality single-cell RNA reference, evaluating the accuracy, and estimating the running time for reference-based cell type annotation tools.

CONCLUSIONS

SingleR was the best performing reference-based cell type annotation tool for the Xenium platform, being fast, accurate and easy to use, with results closely matching those of manual annotation.

Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data

Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinson's disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.

edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets

edgeR is an R/Bioconductor software package for differential analyses of sequencing data in the form of read counts for genes or genomic features. Over the past 15 years, edgeR has been a popular choice for statistical analysis of data from sequencing technologies such as RNA-seq or ChIP-seq. edgeR pioneered the use of the negative binomial distribution to model read count data with replicates and the use of generalized linear models to analyze complex experimental designs. edgeR implements empirical Bayes moderation methods to allow reliable inference when the number of replicates is small. This article announces edgeR version 4, which includes new developments across a range of application areas. Infrastructure improvements include support for fractional counts, implementation of model fitting in C and a new statistical treatment of the quasi-likelihood pipeline that improves accuracy for small counts. The revised package has new functionality for differential methylation analysis, differential transcript expression, differential transcript and exon usage, testing relative to a fold-change threshold and pathway analysis. This article reviews the statistical framework and computational implementation of edgeR, briefly summarizing all the existing features and functionalities but with special attention to new features and those that have not been described previously.

Detecting gene expression in Caenorhabditis elegans

Reliable methods for detecting and analyzing gene expression are necessary tools for understanding development and investigating biological responses to genetic and environmental perturbation. With its fully sequenced genome, invariant cell lineage, transparent body, wiring diagram, detailed anatomy, and wide array of genetic tools, Caenorhabditis elegans is an exceptionally useful model organism for linking gene expression to cellular phenotypes. The development of new techniques in recent years has greatly expanded our ability to detect gene expression at high resolution. Here, we provide an overview of gene expression methods for C. elegans, including techniques for detecting transcripts and proteins in situ, bulk RNA sequencing of whole worms and specific tissues and cells, single-cell RNA sequencing, and high-throughput proteomics. We discuss important considerations for choosing among these techniques and provide an overview of publicly available online resources for gene expression data.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Gpnmb and Spp1 mark a conserved macrophage injury response masking fibrosis-specific programming in the lung

Macrophages are required for healthy repair of the lungs following injury, but they are also implicated in driving dysregulated repair with fibrosis. How these 2 distinct outcomes of lung injury are mediated by different macrophage subsets is unknown. To assess this, single-cell RNA-Seq was performed on lung macrophages isolated from mice treated with LPS or bleomycin. Macrophages were categorized based on anatomic location (airspace versus interstitium), developmental origin (embryonic versus recruited monocyte derived), time after inflammatory challenge, and injury model. Analysis of the integrated dataset revealed that macrophage subset clustering was driven by macrophage origin and tissue compartment rather than injury model. Gpnmb-expressing recruited macrophages that were enriched for genes typically associated with fibrosis were present in both injury models. Analogous GPNMB-expressing macrophages were identified in datasets from both fibrotic and nonfibrotic lung disease in humans. We conclude that this subset represents a conserved response to tissue injury and is not sufficient to drive fibrosis. Beyond this conserved response, we identified that recruited macrophages failed to gain resident-like programming during fibrotic repair. Overall, fibrotic versus nonfibrotic tissue repair is dictated by dynamic shifts in macrophage subset programming and persistence of recruited macrophages.

HIV-SEQ REVEALS GLOBAL HOST GENE EXPRESSION DIFFERENCES BETWEEN HIV-TRANSCRIBING CELLS FROM VIREMIC AND SUPPRESSED PEOPLE WITH HIV

"Active" reservoir cells transcribing HIV can perpetuate chronic inflammation in virally suppressed people with HIV (PWH) and likely contribute to viral rebound after antiretroviral therapy (ART) interruption, so they represent an important target for new therapies. These cells, however, are difficult to study using single-cell RNA-seq (scRNA-seq) due to their low frequency and low levels of HIV transcripts, which are usually not polyadenylated. Here, we developed "HIV-seq" to enable more efficient capture of HIV transcripts - including non-polyadenylated ones - for scRNA-seq analysis of cells from PWH. By spiking in a set of custom-designed capture sequences targeting conserved regions of the HIV genome during scRNA-seq, we increased our ability to find HIV RNA+ cells from PWH by up to 44%. Implementing HIV-seq in conjunction with surface phenotyping by CITE-seq on paired blood specimens from PWH before vs. after ART suppression, we found that HIV RNA+ cells were enriched among T effector memory (Tem) cells during both viremia and ART suppression, but exhibited a cytotoxic signature during viremia only. By contrast, HIV RNA+ cells from the ART-suppressed timepoints exhibited a distinct anti-inflammatory signature involving elevated TGF-β and diminished IFN signaling. Overall, these findings demonstrate that active reservoir cells exhibit transcriptional features distinct from HIV RNA+ cells during viremia, and underscore HIV-seq as a useful tool to better understand the mechanisms by which HIV-transcribing cells can persist during ART.

Plasticity of Human Microglia and Brain Perivascular Macrophages in Aging and Alzheimer's Disease

The complex roles of myeloid cells, including microglia and perivascular macrophages, are central to the neurobiology of Alzheimer's disease (AD), yet they remain incompletely understood. Here, we profiled 832,505 human myeloid cells from the prefrontal cortex of 1,607 unique donors covering the human lifespan and varying degrees of AD neuropathology. We delineated 13 transcriptionally distinct myeloid subtypes organized into 6 subclasses and identified AD-associated adaptive changes in myeloid cells over aging and disease progression. The GPNMB subtype, linked to phagocytosis, increased significantly with AD burden and correlated with polygenic AD risk scores. By organizing AD-risk genes into a regulatory hierarchy, we identified and validated MITF as an upstream transcriptional activator of GPNMB, critical for maintaining phagocytosis. Through cell-to-cell interaction networks, we prioritized APOE-SORL1 and APOE-TREM2 ligand-receptor pairs, associated with AD progression. In both human and mouse models, TREM2 deficiency disrupted GPNMB expansion and reduced phagocytic function, suggesting that GPNMB's role in neuroprotection was TREM2-dependent. Our findings clarify myeloid subtypes implicated in aging and AD, advancing the mechanistic understanding of their role in AD and aiding therapeutic discovery.

Single-Cell RNA Sequencing of PBMCs Identified Junction Plakoglobin (JUP) as Stratification Biomarker for Endometriosis

This study aimed to identify unique characteristics in the peripheral blood mononuclear cells (PBMCs) of endometriosis patients and develop a non-invasive early diagnostic tool. Using single-cell RNA sequencing (scRNA-seq), we constructed the first single-cell atlas of PBMCs from endometriosis patients based on 107,964 cells and 25,847 genes. Within CD16+ monocytes, we discovered JUP as a dysregulated gene. To assess its diagnostic potential, we measured peritoneal fluid (PF) and serum JUP levels in a large cohort of 199 patients including 20 women with ovarian cancer (OC). JUP was barely detectable in PF but was significantly elevated in the serum of patients with endometriosis and OC, with levels 1.33 and 2.34 times higher than controls, respectively. Additionally, JUP was found in conditioned culture media of CD14+/CD16+ monocytes aligning with our scRNA-seq data. Serum JUP levels correlated with endometriosis severity and endometrioma presence but were unaffected by dysmenorrhea, menstrual cycle, or adenomyosis. When combined with CA125 (cancer antigen 125) JUP enhanced the specificity of endometriosis diagnosis from 89.13% (CA125 measured alone) to 100%. While sensitivity remains a challenge at 19%, our results suggest that JUP's potential to enhance diagnostic accuracy warrants additional investigation. Furthermore, employing serum JUP as a stratification marker unlocked the potential to identify additional endometriosis-related genes, offering novel insights into disease pathogenesis.

Investigating intra-tumoural heterogeneity and microenvironment diversity in primary cardiac angiosarcoma through single-cell RNA sequencing

BACKGROUND

Primary cardiac angiosarcoma (PCAS) is a rare and aggressive heart tumour with limited treatment options and a poor prognosis. Understanding cellular heterogeneity and tumour microenvironment (TME) is crucial for the development of effective therapies. Here, we investigated the intratumoural heterogeneity and TME diversity of PCAS using single-cell RNA sequencing (scRNA-seq).

METHODS

We performed scRNA-seq analysis on tumour samples from four patients with PCAS, supplemented with multicolour immunohistochemistry for identification. We used scRNA-seq data from five normal cardiac tissue samples downloaded from public databases for comparative analyses. Bioinformatic analyses, including Cell Ranger, Seurat, Monocle2, hdWGCNA, SCENIC and NicheNet, were utilized to identify distinct cell populations, transcriptional patterns, and co-regulating gene modules.

RESULTS

Our analysis revealed significant intratumoural heterogeneity in PCAS driven by diverse biological processes such as protein synthesis, degradation, and RIG-I signalling inhibition. The SCENIC analysis identified three primary transcription factors' clusters (CEBPB, MYC and TAL1). T-cell subset analysis showed exhausted antigen-specific T-cells, complicating the efficacy of immune checkpoint blockade. Furthermore, we observed suppressive macrophages (SPP1+ and OLR1+) and reduced mitochondrial gene MT-RNR2 (MTRNR2L12) expression in TME-infiltrating cells, indicating impaired mitochondrial function.

CONCLUSION

This study elucidates the complex cellular landscape and immune microenvironment of PCAS, highlighting potential molecular targets for the development of novel therapies. These findings underscore the importance of a multifaceted therapeutic approach for addressing the challenges posed by PCAS's heterogeneity and immune evasion.

KEY POINTS

Insights into the heterogeneity and transcriptional patterns of sarcoma cells may explain the challenges in treating primary cardiac angiosarcoma (PCAS) using the current therapeutic modalities. Characterization of the immune microenvironment revealed significant immunosuppression mediated by specific myeloid cell populations (SPP1+ and OLR1+ macrophages). Identification of mitochondrial dysfunction in immune cells within the PCAS microenvironment, particularly the notable downregulation of the MTRNR2L12 protein, suggests a new avenue for therapeutic targeting.

An essential role for Cmtr2 in mammalian embryonic development

CMTR2 is an mRNA cap methyltransferase with poorly understood physiological functions. It catalyzes 2'-O-ribose methylation of the second transcribed nucleotide of mRNAs, potentially serving to mark RNAs as "self" to evade the cellular innate immune response. Here we analyze the consequences of Cmtr2 deficiency in mice. We discover that constitutive deletion of Cmtr2 results in mouse embryos that die during mid-gestation, exhibiting defects in embryo size, placental malformation and yolk sac vascularization. Endothelial cell deletion of Cmtr2 in mice results in vascular and hematopoietic defects, and perinatal lethality. Detailed characterization of the constitutive Cmtr2 KO phenotype shows an activation of the p53 pathway and decreased proliferation, but no evidence of interferon pathway activation. In summary, our study reveals the essential roles of Cmtr2 in mammalian cells beyond its immunoregulatory function.

Genome-wide identification of cell type-specific susceptibility genes for Juvenile dermatomyositis through the analysis of N6-methyladenosine-associated SNPs

Genome-wide association studies (GWASs) have pinpointed genetic loci associated with juvenile dermatomyositis (JDM). Functional genes within the GWAS loci may be cell type-specific, but their identity remains largely unknown. N6-methyladenosine (m6A) plays a pivotal role in regulating various cellular processes and is linked to autoimmune diseases. This study aimed to underscore the potential functional genes within the GWAS loci through the analysis of m6A-associated SNPs (m6A-SNPs), specifically within relevant cell types. JDM-associated m6A-SNPs were identified from the GWAS summary dataset. The correlation between m6A-SNPs and gene expression was assessed through bulk tissue and single-cell eQTL analyses. To further investigate the relationship between gene expression and JDM, Mendelian randomization analysis was employed. Additionally, differential expression analyses were conducted on bulk tissues, as well as single-cell transcriptomic data comprising 6 JDM patients and 11 juvenile controls (99,396 cells). Seven m6A-SNPs associated with JDM were identified. Bulk tissue analysis revealed differential expression of HLA-DPA1, HLA-DPB1, MICB, HLA-A, HLA-F, HLA-DQB2, HLA-DRB5, TAP2, PSMB9, MICA, AIF1, and DDX39B influenced by m6A-SNPs, all showing associations with JDM in both differential expression and Mendelian randomization analyses. In single-cell analysis, the six m6A-SNPs within the HLA locus acted as cell-type-specific eQTLs, correlating with the expression of HLA-A, HLA-B, HLA-C, HLA-DPB1, HLA-DQA1, HLA-DQB1 and HLA-DRB1 in myeloid, T or B cells. Notably, these genes displayed abnormal expression in T, B, and myeloid cells of JDM patients. The present study identified m6A-SNPs within JDM susceptibility genes, shedding light on the intricate interplay between m6A-SNPs, gene expression, and JDM.

Cell-specific priors rescue differential gene expression in spatial spot-based technologies

Spatial transcriptomics (ST), a breakthrough technology, captures the complex structure and state of tissues through the spatial profiling of gene expression. A variety of ST technologies have now emerged, most prominently spot-based platforms such as Visium. Despite the widespread use of ST and its distinct data characteristics, the vast majority of studies continue to analyze ST data using algorithms originally designed for older technologies such as single-cell (SC) and bulk RNA-seq-particularly when identifying differentially expressed genes (DEGs). However, it remains unclear whether these algorithms are still valid or appropriate for ST data. Therefore, here, we sought to characterize the performance of these methods by constructing an in silico simulator of ST data with a controllable and known DEG ground truth. Surprisingly, our findings reveal little variation in the performance of classic DEG algorithms-all of which fail to accurately recapture known DEGs to significant levels. We further demonstrate that cellular heterogeneity within spots is a primary cause of this poor performance and propose a simple gene-selection scheme, based on prior knowledge of cell-type specificity, to overcome this. Notably, our approach outperforms existing data-driven methods designed specifically for ST data and offers improved DEG recovery and reliability rates. In summary, our work details a conceptual framework that can be used upstream, agnostically, of any DEG algorithm to improve the accuracy of ST analysis and any downstream findings.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Inferring Cell-Type-Specific Co-Expressed Genes from Single Cell Data

Background

Cell-type-specific gene co-expression networks are widely used to characterize gene relationships. Although many methods have been developed to infer such co-expression networks from single-cell data, the lack of consideration of false positive control in many evaluations may lead to incorrect conclusions because higher reproducibility, higher functional coherence, and a larger overlap with known biological networks may not imply better performance if the false positives are not well controlled.

Results

In this study, we have developed an efficient and effective simulation tool to derive empirical p-values in co-expression inference to appropriately control false positives in assessing method performance. We studied the power of the p-value-based approach in inferring cell-type-specific co-expressions from single-cell data using both simulated and real data. We also highlight the need to adjust for random overlaps between the inferred and known networks when the number of selected correlated gene pairs varies substantially across different methods. We further illustrate the expression level bias in known biological networks and the impact of such bias in method assessment.

Conclusion

Our study indicates the importance of controlling false positives in the inference of co-expressed genes to achieve more reliable results and proposes a simulation-based p-value method to achieve this.

Microglia and monocyte-derived macrophages drive progression of pediatric high-grade gliomas and are transcriptionally shaped by histone mutations

Pediatric high-grade gliomas (pHGGs), including hemispheric pHGGs and diffuse midline gliomas (DMGs), harbor mutually exclusive tumor location-specific histone mutations. Using immunocompetent de novo mouse models of pHGGs, we demonstrated that myeloid cells were the predominant infiltrating non-neoplastic cell population. Single-cell RNA sequencing (scRNA-seq), flow cytometry, and immunohistochemistry illustrated the presence of heterogeneous myeloid cell populations shaped by histone mutations and tumor location. Disease-associated myeloid (DAM) cell phenotypes demonstrating immune permissive characteristics were identified in murine and human pHGG samples. H3.3K27M DMGs, the most aggressive DMG, demonstrated enrichment of DAMs. Genetic ablation of chemokines Ccl8 and Ccl12 resulted in a reduction of DAMs and an increase in lymphocyte infiltration, leading to increased survival of tumor-bearing mice. Pharmacologic inhibition of chemokine receptors CCR1 and CCR5 resulted in extended survival and decreased myeloid cell infiltration. This work establishes the tumor-promoting role of myeloid cells in DMG and the potential therapeutic opportunities for targeting them.

Deciphering cell-specific genetic insights: Unraveling the immunogenetic landscape of systemic lupus erythematosus

Functional genes within genomic loci associated with systemic lupus erythematosus (SLE), as identified by genome-wide association studies, exhibit cell-specific characteristics. This study delves into the impact of genetic variants within SLE loci on gene expression in different types of immune cells, unraveling the complex interplay between genetics and immunopathogenesis. Through the integration of genetic association and single-cell transcriptomic sequencing data, we identified potential cell-specific susceptibility genes for SLE across diverse immune cell subsets. The single-cell eQTL analysis revealed 30,409 associations involving 3583 SLE-associated SNPs. These SNPs exhibited associations with expression levels of 147 genes across 14 distinct cell types. The single-cell summary data-based Mendelian randomization (SMR) analysis identified 119 significant associations between the expression levels of 44 genes and SLE. Notably, myeloid cells exhibited associations solely within the MHC region, while T, B, and natural killer cells showed associations with both MHC and non-MHC genes in relation to SLE. Analysis of single-cell transcriptomic data from 33 children SLE cases and 11 match controls (227,303 cells), as well as 7 adult SLE cases and 5 match controls (78,414 cells) highlights differential expression of key genes. Notably, genetic variants within HLA-DRB1, HLA-DRB5, HLA-DQA1, HLA-DQB1, IRF7, IRF5, BLK and HLA-DPA1 play a pivotal role in mediating immune dysregulation in specific immune cell types. Our study contributes to a comprehensive understanding of the intricate relationships between genetics, gene expression and SLE susceptibility. The findings shed light on the cell-specific impacts of genetic variants within SLE-associated genomic loci.

Metformin decelerates aging clock in male monkeys

In a rigorous 40-month study, we evaluated the geroprotective effects of metformin on adult male cynomolgus monkeys, addressing a gap in primate aging research. The study encompassed a comprehensive suite of physiological, imaging, histological, and molecular evaluations, substantiating metformin's influence on delaying age-related phenotypes at the organismal level. Specifically, we leveraged pan-tissue transcriptomics, DNA methylomics, plasma proteomics, and metabolomics to develop innovative monkey aging clocks and applied these to gauge metformin's effects on aging. The results highlighted a significant slowing of aging indicators, notably a roughly 6-year regression in brain aging. Metformin exerts a substantial neuroprotective effect, preserving brain structure and enhancing cognitive ability. The geroprotective effects on primate neurons were partially mediated by the activation of Nrf2, a transcription factor with anti-oxidative capabilities. Our research pioneers the systemic reduction of multi-dimensional biological age in primates through metformin, paving the way for advancing pharmaceutical strategies against human aging.

Ferroptosis-related gene signature for predicting prognosis and identifying potential therapeutic drug in EGFR wild-type lung adenocarcinoma

Epidermal growth factor receptor wild type lung adenocarcinoma (EGFRWT LUAD) still has limited treatment options and unsatisfactory clinical outcomes. Ferroptosis, as a form of cell death, has been reported to play a dual role in regulating tumor cell survival. In this study, we constructed a 3-ferroptosis-gene signature, FeSig, and verified its accuracy and efficacy in predicting EGFRWT LUAD prognosis at both the RNA and protein levels. Patients with higher FeSig scores were found to have worse clinical outcomes. Additionally, we explored the relationship between FeSig and tumor microenvironment, revealing that enhanced interactions between fibroblasts and tumor cells in FeSighigh patients causing tumor resistance to ferroptosis. To address this challenge, we screened potential drugs from NCI-60 (The US National Cancer Institute 60 human tumour cell line anticancer drug screen) and Connectivity map database, ultimately identifying 6-mercatopurine (6-MP) as a promising candidate. Both in vitro and in vivo experiments demonstrated its efficacy in treating FeSighigh EGFRWT LUAD tumor models. In summary, we develop a novel FeSig for predicting prognosis and guiding drug application.

TGF-β-mediated crosstalk between TIGIT+ Tregs and CD226+CD8+ T cells in the progression and remission of type 1 diabetes

Type 1 diabetes (T1D) is a chronic autoimmune condition characterized by hyperglycemia resulting from the destruction of insulin-producing β-cells that is traditionally deemed irreversible, but partial remission (PR) with temporary reversal of hyperglycemia is sometimes observed. Here we use single-cell RNA sequencing to delineate the immune cell landscape across patients in different T1D stages. Together with cohort validation and functional assays, we observe dynamic changes in TIGIT+CCR7- Tregs and CD226+CCR7-CD8+ cytotoxic T cells during the peri-remission phase. Machine learning algorithms further identify TIGIT+CCR7- Tregs and CD226+CD8+ T cells as biomarkers for β-cell function decline in a predictive model, while cell communication analysis and in vitro assays suggest that TIGIT+CCR7- Tregs may inhibit CD226+CCR7-CD8+ T cells via TGF-β signaling. Lastly, in both cyclophosphamide-induced and streptozotocin (STZ)-induced mouse diabetes models, CD226 inhibition postpones insulitis onset and reduces hyperglycemia severity. Our results thus identify two interrelated immune cell subsets that may serve as biomarkers for monitoring disease progression and targets for therapeutic intervention of T1D.

Visualizing scRNA-Seq data at population scale with GloScope

Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organism's phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.