Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data

Comprehensive integration of single-cell RNA and transcriptome RNA sequencing to establish a pyroptosis-related signature for improving prognostic prediction of gastric cancer

Cell pyroptosis, a Gasdermin-dependent programmed cell death characterized by inflammasome, plays a complex and dynamic role in Gastric cancer (GC), a serious threat to human health. Therefore, the value of pyroptosis-related genes (PRGs) as prognostic biomarkers and therapeutic indicators for patients needs to be exploited in GC. This study integrates single-cell RNA sequencing (scRNA-seq) dataset GSE183904 with GC transcriptome data from the TCGA database, focusing on the expression and distribution of PRGs in GC at the single-cell level. The prognostic signature of PRGs was established by using Cox and LASSO analyses. The differences in long-term prognosis, immune infiltration, mutation profile, CD274 and response to chemotherapeutic drugs between the two groups were analyzed and evaluated. A tissue array was used to verify the expression of six PRGs, CD274, CD163 and FoxP3. C12orf75, VCAN, RGS2, MKNK2, SOCS3 and TNFAIP2 were successfully screened out to establish a signature to potently predict the survival time of GC patients. A webserver (https://pumc.shinyapps.io/GastricCancer/) for prognostic prediction in GC patients was developed based on this signature. High-risk score patients typically had worse prognoses, resistance to classical chemotherapy, and a more immunosuppressive tumor microenvironment. VCAN, TNFAIP2 and SOCS3 were greatly elevated in the GC while RGS2 and MKNK2 were decreased in the tumor samples. Further, VCAN was positively related to the infiltrations of Tregs and M2 TAMs in GC TME and the CD274 in tumor cells. In summary, a potent pyroptosis-related signature was established to accurately forecast the survival time and treatment responsiveness of GC patients.

RNA-binding protein AZGP1 inhibits epithelial cell proliferation by regulating the genes of alternative splicing in COPD

BACKGROUND

Chronic Obstructive Pulmonary Disease (COPD) is characterized by high morbidity, disability, and mortality rates worldwide. RNA-binding proteins (RBPs) might regulate genes involved in oxidative stress and inflammation in COPD patients. Single-cell transcriptome sequencing (scRNA-seq) offers an accurate tool for identifying intercellular heterogeneity and the diversity of immune cells. However, the role of RBPs in the regulation of various cells, especially AT2 cells, remains elusive.

MATERIALS AND METHODS

A scRNA-seq dataset (GSE173896) and a bulk RNA-seq dataset acquired from airway tissues (GSE124180) were employed for data mining. Next, RNA-seq analysis was performed in both COPD and control patients. Differentially expressed genes (DEGs) were identified using criteria of fold change (FC ≥ 1.5 or ≤ 1.5) and P value ≤ 0.05. Lastly, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and alternative splicing identification analyses were carried out.

RESULTS

RBP genes exhibited specific expression patterns across different cell groups and participated in cell proliferation and mitochondrial dysfunction in AT2 cells. As an RBP, AZGP1 expression was upregulated in both the scRNA-seq and RNA-seq datasets. It might potentially be a candidate immune biomarker that regulates COPD progression by modulating AT2 cell proliferation and adhesion by regulating the expression of SAMD5, DNER, DPYSL3, GBP5, GBP3, and KCNJ2. Moreover, AZGP1 regulated alternative splicing events in COPD, particularly DDAH1 and SFRP1, holding significant implications in COPD.

CONCLUSION

RBP gene AZGP1 inhibits epithelial cell proliferation by regulating genes participating in alternative splicing in COPD.

RelA-mediated signaling connects adaptation to chronic cardiomyocyte stress with myocardial and systemic inflammation in the ADCY8 model of accelerated aging

Mice with cardiac-specific overexpression of adenylyl cyclase (AC) type 8 (TGAC8) are under a constant state of severe myocardial stress. They have a remarkable ability to adapt to this stress, but they eventually develop accelerated cardiac aging and experience reduced longevity. We have previously demonstrated through bioinformatics that constitutive adenylyl cyclase activation in TGAC8 mice is associated with the activation of inflammation-related signaling pathways. However, the immune response associated with chronic myocardial stress in the TGAC8 mouse remains unexplored. Here we demonstrate that chronic activation of adenylyl cyclase in cardiomyocytes of TGAC8 mice results in activation of cell-autonomous RelA-mediated NF-κB signaling. This is associated with non-cell-autonomous activation of proinflammatory and age-associated signaling in myocardial endothelial cells and myocardial smooth muscle cells, expansion of myocardial immune cells, increase in serum levels of inflammatory cytokines, and changes in the size or composition of lymphoid organs. All these changes precede the appearance of cardiac fibrosis. We provide evidence indicating that RelA activation in cardiomyocytes with chronic activation of adenylyl cyclase is mediated by calcium-protein Kinase A (PKA) signaling. Using a model of chronic cardiomyocyte stress and accelerated aging, we highlight a novel, calcium/PKA/RelA-dependent connection between cardiomyocyte stress, myocardial inflammation, and systemic inflammation. These findings suggest that RelA-mediated signaling in cardiomyocytes might be an adaptive response to stress that, when chronically activated, ultimately contributes to both cardiac and systemic aging.

Expansion of effector regulatory T cells in steroid responders of severe alcohol-associated hepatitis

While steroid therapy is the preferred treatment for severe alcohol-associated hepatitis, the role of effector regulatory T (eTreg) cells and their association with steroid response and clinical outcomes in these patients remains to be elucidated. We prospectively enrolled 47 consecutive patients with alcohol-associated hepatitis, consisting of severe alcohol-associated hepatitis treated with steroids (n=18; steroid-treated group) and mild alcohol-associated hepatitis (n=29; nontreated group). After isolating peripheral blood mononuclear cells from the patients at enrollment and again 7 days later, the frequency of eTreg cells was examined using flow cytometry. Single-cell RNA sequencing analysis was conducted using paired peripheral blood mononuclear cells. In vitro experiments were also performed to assess phenotype changes and the suppressive function of Treg cells following steroid treatment. The steroid-treated group exhibited significantly higher Model for End-Stage Liver Disease scores than the nontreated group ( p < 0.01). Within the steroid-treated group, the proportion of eTreg cells significantly expanded in the steroid responders (n=13; p = 0.01). Furthermore, a significant positive correlation was observed between the decrease in the Model for End-Stage Liver Disease score and the increase in eTreg cells ( p < 0.05). Single-cell RNA sequencing using paired peripheral blood mononuclear cells (pre-steroid and post-steroid therapy) from a steroid responder revealed gene expression changes in T cells and monocytes, suggesting enhancement of Treg cell function. In vitro results showed an elevation in the proportion of eTreg cells after steroid therapy. In conclusion, our findings suggest that the efficacy of steroid therapy in patients with severe alcohol-associated hepatitis is mediated by an increase in the number of eTreg cells.

Integrated bioinformatics analysis and experimental validation reveal the relationship between ALOX5AP and the prognosis and immune microenvironment in glioma

BACKGROUND

Treatment of gliomas, the most prevalent primary malignant neoplasm of the central nervous system, is challenging. Arachidonate 5-lipoxygenase activating protein (ALOX5AP) is crucial for converting arachidonic acid into leukotrienes and is associated with poor prognosis in multiple cancers. Nevertheless, its relationship with the prognosis and the immune microenvironment of gliomas remains incompletely understood.

METHODS

The differential expression of ALOX5AP was evaluated based on public Databases. Kaplan-Meier, multivariate Cox proportional hazards regression analysis, time-dependent receiver operating characteristic, and nomogram were used to estimate the prognostic value of ALOX5AP. The relationship between ALOX5AP and immune infiltration was calculated using ESTIMATE and CIBERSORT algorithms. Relationships between ALOX5AP and human leukocyte antigen molecules, immune checkpoints, tumor mutation burden, TIDE score, and immunophenoscore were calculated to evaluate glioma immunotherapy response. Single gene GSEA and co-expression network-based GO and KEGG enrichment analysis were performed to explore the potential function of ALOX5AP. ALOX5AP expression was verified using multiplex immunofluorescence staining and its prognostic effects were confirmed using a glioma tissue microarray.

RESULT

ALOX5AP was highly expressed in gliomas, and the expression level was related to World Health Organization (WHO) grade, age, sex, IDH mutation status, 1p19q co-deletion status, MGMTp methylation status, and poor prognosis. Single-cell RNA sequencing showed that ALOX5AP was expressed in macrophages, monocytes, and T cells but not in tumor cells. ALOX5AP expression positively correlated with M2 macrophage infiltration and poor immunotherapy response. Immunofluorescence staining demonstrated that ALOX5AP was upregulated in WHO higher-grade gliomas, localizing to M2 macrophages. Glioma tissue microarray confirmed the adverse effect of ALOX5AP in the prognosis of glioma.

CONCLUSION

ALOX5AP is highly expressed in M2 macrophages and may act as a potential biomarker for predicting prognosis and immunotherapy response in patients with glioma.

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.

Automated spatial omics landscape analysis approach reveals novel tissue architectures in ulcerative colitis

The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.

Single-Cell Cortical Transcriptomics Reveals Common and Distinct Changes in Cell-Cell Communication in Alzheimer's and Parkinson's Disease

Alzheimer's disease (AD) and Parkinson's disease (PD) cause significant neuronal loss and severely impair daily living. Despite different clinical manifestations, these disorders share common pathological molecular hallmarks, including mitochondrial dysfunction and synaptic degeneration. A detailed comparison of molecular changes at single-cell resolution in the cortex, as one of the main brain regions affected in both disorders, may reveal common susceptibility factors and disease mechanisms. We performed single-cell transcriptomic analyses of post-mortem cortical tissue from AD and PD subjects and controls to identify common and distinct disease-associated changes in individual genes, cellular pathways, molecular networks, and cell-cell communication events, and to investigate common mechanisms. The results revealed significant disease-specific, shared, and opposing gene expression changes, including cell type-specific signatures for both diseases. Hypoxia signaling and lipid metabolism emerged as significantly modulated cellular processes in both AD and PD, with contrasting expression alterations between the two diseases. Furthermore, both pathway and cell-cell communication analyses highlighted shared significant alterations involving the JAK-STAT signaling pathway, which has been implicated in the inflammatory response in several neurodegenerative disorders. Overall, the analyses revealed common and distinct alterations in gene signatures, pathway activities, and gene regulatory subnetworks in AD and PD. The results provide insights into coordinated changes in pathway activity and cell-cell communication that may guide future diagnostics and therapeutics.

Adaptive immunity to retroelements promotes barrier integrity

Maintenance of tissue integrity is a requirement of host survival. This mandate is of prime importance at barrier sites that are constitutively exposed to the environment. Here, we show that exposure of the skin to non-inflammatory xenobiotics promotes tissue repair; more specifically, mild detergent exposure promotes the reactivation of defined retroelements leading to the induction of retroelement-specific CD8 + T cells. These T cell responses are Langerhans cell dependent and establish tissue residency within the skin. Upon injury, retroelement-specific CD8 + T cells significantly accelerate wound repair via IL-17A. Collectively, this work demonstrates that tonic environmental exposures and associated adaptive responses to retroelements can be coopted to preemptively set the tissue for maximal resilience to injury.

Multisensory gamma stimulation mitigates the effects of demyelination induced by cuprizone in male mice

Demyelination is a common pathological feature in a wide range of diseases, characterized by the loss of myelin sheath and myelin-supporting oligodendrocytes. These losses lead to impaired axonal function, increased vulnerability of axons to damage, and result in significant brain atrophy and neuro-axonal degeneration. Multiple pathomolecular processes contribute to neuroinflammation, oligodendrocyte cell death, and progressive neuronal dysfunction. In this study, we use the cuprizone mouse model of demyelination to investigate long-term non-invasive gamma entrainment using sensory stimulation as a potential therapeutic intervention for promoting myelination and reducing neuroinflammation in male mice. Here, we show that multisensory gamma stimulation mitigates demyelination, promotes oligodendrogenesis, preserves functional integrity and synaptic plasticity, attenuates oligodendrocyte ferroptosis-induced cell death, and reduces brain inflammation. Thus, the protective effects of multisensory gamma stimulation on myelin and anti-neuroinflammatory properties support its potential as a therapeutic approach for demyelinating disorders.

scPriorGraph: constructing biosemantic cell-cell graphs with prior gene set selection for cell type identification from scRNA-seq data

Cell type identification is an indispensable analytical step in single-cell data analyses. To address the high noise stemming from gene expression data, existing computational methods often overlook the biologically meaningful relationships between genes, opting to reduce all genes to a unified data space. We assume that such relationships can aid in characterizing cell type features and improving cell type recognition accuracy. To this end, we introduce scPriorGraph, a dual-channel graph neural network that integrates multi-level gene biosemantics. Experimental results demonstrate that scPriorGraph effectively aggregates feature values of similar cells using high-quality graphs, achieving state-of-the-art performance in cell type identification.

Connectivity Network Feature Sharing in Single-Cell RNA Sequencing Data Identifies Rare Cells

Single-cell RNA sequencing is a valuable technique for identifying diverse cell subtypes. A key challenge in this process is that the detection of rare cells is often missed by conventional methods due to low abundance and subtle features of these cells. To overcome this, we developed SCLCNF (Local Connectivity Network Feature Sharing in Single-Cell RNA sequencing), a novel approach that identifies rare cells by analyzing features uniquely expressed in these cells. SCLCNF creates a cellular connectivity network, considering how each cell relates to its neighbors. This network helps to pinpoint coexpression patterns unique to rare cells, utilizing a rarity score to confirm their presence. Our method performs better in detecting rare cells than existing techniques, offering enhanced robustness. It has proven to be effective in human gastrula data sets for accurately pinpointing rare cells, and in sepsis data sets where it uncovers previously unidentified rare cell populations.

Single-cell RNA-binding protein pattern-mediated molecular subtypes depict the hallmarks of the tumor microenvironment in bladder urothelial carcinoma

Objectives

Bladder carcinoma (BC) is a common malignancy of the urinary tract. As a new hallmark of cancer for drug therapy, RNA-binding proteins (RBPs) are key regulatory factors in alternative splicing events. This work is to uncover the relationship between BC and RBP in order to find drug targets in BC.

Methods

In this work, data from single-cell RNA-seq GSE1355337, PRJNA662018, and the TCGA-Bladder urothelial carcinoma (BLCA) cohorts are integrated to identify their relationships. A scoring system is constructed according to RBPs gene expression and patients’ survival. A network is constructed to analyze the alternative splicing events and RBP genes.

Results

A scoring system identified 321 RBPs significantly associated with the prognosis of patients. Subsequent typing of these RBP genes in two single-cell datasets demonstrated that most of the RBP genes had variable copy numbers. Three RBP clusters were identified. Using RBP genes as a signature in BC epithelial cells allows for differentiation between different grades of BC samples. The novel RBP genes-based subtype system reflects BC clinical staging. Notably, CellChat analysis revealed that the RBP genes-associated cell subtypes of T cells had extensive interactions with epithelial cells. Further analysis showed that the ligand-receptor pair MIF-CXCR4 mediated the communication between RBP-associated subtypes of BC epithelial cells and T cells.

Conclusions

Taken together, RBP genes are associated with BC progress and offer new indicators for precision medicine in BC.

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.

Identification of Regulatory RNA-Binding Proteins Associated with Immune Infiltration in Laryngeal Squamous Cell Carcinoma

We analyzed bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) data to identify alternative splicing (AS) events and regulatory RNA-binding proteins (RBPs) associated with immune infiltration in human laryngeal squamous cell carcinoma (LSCC). Whole-transcriptome sequencing data of 20 human laryngeal cancer and paracancerous tissues were downloaded from the Gene Expression Omnibus public database, using newly published splicing-site usage variation analysis software to obtain highly conserved regulated AS (RAS) events, and scientific reverse convolution algorithm analysis was used to identify significantly different immune cells and perform a correlation analysis between the two. The software package edgeR was used to identify differentially expressed RBPs and the immune infiltration-related LSCC-RAS they may regulate. Finally, we present the expression profiles and survival curves of 117 human laryngeal cancer samples from The Cancer Genome Atlas dataset for the identified RBPs and LSCC-RAS. We also downloaded the gene set enrichment 150321 scRNA-seq data for two human LSCC tissue samples. The RBP expression pattern and the expression of prophase RBP genes were analyzed in different LSCC cell populations. RNA-binding motif protein 47 (RBM47) and filamin A, as well as the RBP-RAS events that were screened in both the fibulin 2 and fibronectin 1 genes, were all significantly associated with the prognosis, and the RBM47 gene was upregulated in myeloid cells. Because the prognosis was significantly associated with two RBP regulators and two LSCC-RAS events, they may be critical regulators of immune cell survival during laryngeal cancer progression, and RBM47 may regulate macrophage-associated AS and affect immunity.

Thymic stromal lymphopoietin induces IL-4/IL-13 from T cells to promote sebum secretion and adipose loss

BACKGROUND

The cytokine TSLP promotes type 2 immune responses and can induce adipose loss by stimulating lipid loss from the skin through sebum secretion by sebaceous glands, which enhances the skin barrier. However, the mechanism by which TSLP upregulates sebaceous gland function is unknown.

OBJECTIVES

This study investigated the mechanism by which TSLP stimulates sebum secretion and adipose loss.

METHODS

RNA-sequencing analysis was performed on sebaceous glands isolated by laser capture microdissection and single-cell RNA-sequencing analysis was performed on sorted skin T cells. Sebocyte function was analyzed by histological analysis and sebum secretion in vivo and by measuring lipogenesis and proliferation in vitro.

RESULTS

This study found that TSLP sequentially stimulated the expression of lipogenesis genes followed by cell death genes in sebaceous glands to induce holocrine secretion of sebum. TSLP did not affect sebaceous gland activity directly. Rather, single-cell RNA-sequencing revealed that TSLP recruited distinct T-cell clusters that produce IL-4 and IL-13, which were necessary for TSLP-induced adipose loss and sebum secretion. Moreover, IL-13 was sufficient to cause sebum secretion and adipose loss in vivo and to induce lipogenesis and proliferation of a human sebocyte cell line in vitro.

CONCLUSIONS

This study proposes that TSLP stimulates T cells to deliver IL-4 and IL-13 to sebaceous glands, which enhances sebaceous gland function, turnover, and subsequent adipose loss.

A practical handbook on single-cell RNA sequencing data quality control and downstream analysis

Advancements in single-cell analysis have facilitated high-resolution observation of the transcriptome in individual cells. However, standards for obtaining high-quality cells and data analysis pipelines remain variable. Here, we provide the groundwork for improving the quality of single-cell analysis by delineating guidelines for selecting high-quality cells and considerations throughout the analysis. This review will streamline researchers' access to single-cell analysis and serve as a valuable guide for analysis.

Single-cell profiling identifies a CD8bright CD244bright Natural Killer cell subset that reflects disease activity in HLA-A29-positive birdshot chorioretinopathy

Birdshot chorioretinopathy is an inflammatory eye condition strongly associated with MHC-I allele HLA-A29. The striking association with MHC-I suggests involvement of T cells, whereas natural killer (NK) cell involvement remains largely unstudied. Here we show that HLA-A29-positive birdshot chorioretinopathy patients have a skewed NK cell pool containing expanded CD16 positive NK cells which produce more proinflammatory cytokines. These NK cells contain populations that express CD8A which is involved in MHC-I recognition on target cells, display gene signatures indicative of high cytotoxic activity (GZMB, PRF1 and ISG15), and signaling through NK cell receptor CD244 (SH2D1B). Long-term monitoring of a cohort of birdshot chorioretinopathy patients with active disease identifies a population of CD8bright CD244bright NK cells, which rapidly declines to normal levels upon clinical remission following successful treatment. Collectively, these studies implicate CD8bright CD244bright NK cells in birdshot chorioretinopathy.

Tropism for ciliated cells is the dominant driver of influenza viral burst size in the human airway

Influenza viruses pose a significant burden on global human health. Influenza has a broad cellular tropism in the airway, but how infection of different epithelial cell types impacts replication kinetics and burden in the airways is not fully understood. Using primary human airway cultures, which recapitulate the diverse epithelial cell landscape of the human airways, we investigated the impact of cell type composition on virus tropism and replication kinetics. Cultures were highly diverse across multiple donors and 30 independent differentiation conditions and supported a range of influenza replication. Although many cell types were susceptible to influenza, ciliated and secretory cells were predominantly infected. Despite the strong tropism preference for secretory and ciliated cells, which consistently make up 75% or more of infected cells, only ciliated cells were associated with increased virus production. Surprisingly, infected secretory cells were associated with overall reduced virus output. The disparate response and contribution to influenza virus production could be due to different pro- and antiviral interferon-stimulated gene signatures between ciliated and secretory populations, which were interrogated with single-cell RNA sequencing. These data highlight the heterogeneous outcomes of influenza virus infections in the complex cellular environment of the human airway and the disparate impacts of infected cell identity on multiround burst size, even among preferentially infected cell types.

Directed differentiation of pancreatic δ cells from human pluripotent stem cells

Dysfunction of pancreatic δ cells contributes to the etiology of diabetes. Despite their important role, human δ cells are scarce, limiting physiological studies and drug discovery targeting δ cells. To date, no directed δ-cell differentiation method has been established. Here, we demonstrate that fibroblast growth factor (FGF) 7 promotes pancreatic endoderm/progenitor differentiation, whereas FGF2 biases cells towards the pancreatic δ-cell lineage via FGF receptor 1. We develop a differentiation method to generate δ cells from human stem cells by combining FGF2 with FGF7, which synergistically directs pancreatic lineage differentiation and modulates the expression of transcription factors and SST activators during endoderm/endocrine precursor induction. These δ cells display mature RNA profiles and fine secretory granules, secrete somatostatin in response to various stimuli, and suppress insulin secretion from in vitro co-cultured β cells and mouse β cells upon transplantation. The generation of human pancreatic δ cells from stem cells in vitro would provide an unprecedented cell source for drug discovery and cell transplantation studies in diabetes.

Hepatocyte-macrophage crosstalk via the PGRN-EGFR axis modulates ADAR1-mediated immunity in the liver

ADAR1-mediated RNA editing establishes immune tolerance to endogenous double-stranded RNA (dsRNA) by preventing its sensing, primarily by MDA5. Although deleting Ifih1 (encoding MDA5) rescues embryonic lethality in ADAR1-deficient mice, they still experience early postnatal death, and removing other MDA5 signaling proteins does not yield the same rescue. Here, we show that ablation of MDA5 in a liver-specific Adar knockout (KO) murine model fails to rescue hepatic abnormalities caused by ADAR1 loss. Ifih1;Adar double KO (dKO) hepatocytes accumulate endogenous dsRNAs, leading to aberrant transition to a highly inflammatory state and recruitment of macrophages into dKO livers. Mechanistically, progranulin (PGRN) appears to mediate ADAR1 deficiency-induced liver pathology, promoting interferon signaling and attracting epidermal growth factor receptor (EGFR)+ macrophages into dKO liver, exacerbating hepatic inflammation. Notably, the PGRN-EGFR crosstalk communication and consequent immune responses are significantly repressed in ADAR1high tumors, revealing that pre-neoplastic or neoplastic cells can exploit ADAR1-dependent immune tolerance to facilitate immune evasion.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Synergic activity of FGFR2 and MEK inhibitors in the treatment of FGFR2-amplified cancers of unknown primary

Patients with cancer of unknown primary (CUP) carry the double burden of an aggressive disease and reduced access to therapies. Experimental models are pivotal for CUP biology investigation and drug testing. We derived two CUP cell lines (CUP#55 and #96) and corresponding patient-derived xenografts (PDXs), from ascites tumor cells. CUP cell lines and PDXs underwent histological, immune-phenotypical, molecular, and genomic characterization confirming the features of the original tumor. The tissue-of-origin prediction was obtained from the tumor microRNA expression profile and confirmed by single-cell transcriptomics. Genomic testing and fluorescence in situ hybridization analysis identified FGFR2 gene amplification in both models, in the form of homogeneously staining region (HSR) in CUP#55 and double minutes in CUP#96. FGFR2 was recognized as the main oncogenic driver and therapeutic target. FGFR2-targeting drug BGJ398 (infigratinib) in combination with the MEK inhibitor trametinib proved to be synergic and exceptionally active, both in vitro and in vivo. The effects of the combined treatment by single-cell gene expression analysis revealed a remarkable plasticity of tumor cells and the greater sensitivity of cells with epithelial phenotype. This study brings personalized therapy closer to CUP patients and provides the rationale for FGFR2 and MEK targeting in metastatic tumors with FGFR2 pathway activation.

Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning

Cell type identification is an important task for single-cell RNA-sequencing (scRNA-seq) data analysis. Many prediction methods have recently been proposed, but the predictive accuracy of difficult cell type identification tasks is still low. In this work, we proposed a novel Gaussian noise augmentation-based scRNA-seq contrastive learning method (GsRCL) to learn a type of discriminative feature representations for cell type identification tasks. A large-scale computational evaluation suggests that GsRCL successfully outperformed other state-of-the-art predictive methods on difficult cell type identification tasks, while the conventional random genes masking augmentation-based contrastive learning method also improved the accuracy of easy cell type identification tasks in general.

Timing of whole genome duplication is associated with tumor-specific MHC-II depletion in serous ovarian cancer

Whole genome duplication is frequently observed in cancer, and its prevalence in our prior analysis of end-stage, homologous recombination deficient high grade serous ovarian cancer (almost 80% of samples) supports the notion that whole genome duplication provides a fitness advantage under the selection pressure of therapy. Here, we therefore aim to identify potential therapeutic vulnerabilities in primary high grade serous ovarian cancer with whole genome duplication by assessing differentially expressed genes and pathways in 79 samples. We observe that MHC-II expression is lowest in tumors which have acquired whole genome duplication early in tumor evolution, and further demonstrate that reduced MHC-II expression occurs in subsets of tumor cells rather than in canonical antigen-presenting cells. Early whole genome duplication is also associated with worse patient survival outcomes. Our results suggest an association between the timing of whole genome duplication, MHC-II expression and clinical outcome in high grade serous ovarian cancer that warrants further investigation for therapeutic targeting.

Eed controls craniofacial osteoblast differentiation and mesenchymal proliferation from the neural crest

The histone methyltransferase Polycomb repressive complex 2 (PRC2) is required for specification of the neural crest, and mis-regulation of neural crest development can cause severe congenital malformations. PRC2 is necessary for neural crest induction, but the embryonic, cellular, and molecular consequences of PRC2 activity after neural crest induction are incompletely understood. Here we show that Eed, a core subunit of PRC2, is required for craniofacial osteoblast differentiation and mesenchymal proliferation after induction of the neural crest. Integrating mouse genetics with single-cell RNA sequencing, our results reveal that conditional knockout of Eed after neural crest cell induction causes severe craniofacial hypoplasia, impaired craniofacial osteogenesis, and attenuated craniofacial mesenchymal cell proliferation that is first evident in post-migratory neural crest cell populations. We show that Eed drives mesenchymal differentiation and proliferation in vivo and in primary craniofacial cell cultures by regulating diverse transcription factor programs that are required for specification of post-migratory neural crest cells. These data enhance understanding of epigenetic mechanisms that underlie craniofacial development, and shed light on the embryonic, cellular, and molecular drivers of rare congenital syndromes in humans.

Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

mosaicMPI: a framework for modular data integration across cohorts and -omics modalities

Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.

Construction of multilayered small intestine-like tissue by reproducing interstitial flow

Recent advances have made modeling human small intestines in vitro possible, but it remains a challenge to recapitulate fully their structural and functional characteristics. We suspected interstitial flow within the intestine, powered by circulating blood plasma during embryonic organogenesis, to be a vital factor. We aimed to construct an in vivo-like multilayered small intestinal tissue by incorporating interstitial flow into the system and, in turn, developed the micro-small intestine system by differentiating definitive endoderm and mesoderm cells from human pluripotent stem cells simultaneously on a microfluidic device capable of replicating interstitial flow. This approach enhanced cell maturation and led to the development of a three-dimensional small intestine-like tissue with villi-like epithelium and an aligned mesenchymal layer. Our micro-small intestine system not only overcomes the limitations of conventional intestine models but also offers a unique opportunity to gain insights into the detailed mechanisms underlying intestinal tissue development.

ScType enables fast and accurate cell type identification from spatial transcriptomics data

SUMMARY

The limited resolution of spatial transcriptomics (ST) assays in the past has led to the development of cell type annotation methods that separate the convolved signal based on available external atlas data. In light of the rapidly increasing resolution of the ST assay technologies, we made available and investigated the performance of a deconvolution-free marker-based cell annotation method called scType. In contrast to existing methods, the spatial application of scType does not require computationally strenuous deconvolution, nor large single-cell reference atlases. We show that scType enables ultra-fast and accurate identification of abundant cell types from ST data, especially when a large enough panel of genes is detected. Examples of such assays are Visium and Slide-seq, which currently offer the best trade-off between high resolution and number of genes detected by the assay for cell type annotation.

AVAILABILITY AND IMPLEMENTATION

scType source R and python codes for spatial data are openly available in GitHub (https://github.com/kris-nader/sp-type or https://github.com/kris-nader/sc-type-py). Step-by-step tutorials for R and python spatial data analysis can be found in https://github.com/kris-nader/sp-type and https://github.com/kris-nader/sc-type-py/blob/main/spatial_tutorial.md, respectively.

Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing

BACKGROUND

It appears that tumour-infiltrating neoantigen-reactive CD8 + T (Neo T) cells are the primary driver of immune responses to gastrointestinal cancer in patients. However, the conventional method is very time-consuming and complex for identifying Neo T cells and their corresponding T cell receptors (TCRs).

METHODS

By mapping neoantigen-reactive T cells from the single-cell transcriptomes of thousands of tumour-infiltrating lymphocytes, we developed a 26-gene machine learning model for the identification of neoantigen-reactive T cells.

RESULTS

In both training and validation sets, the model performed admirably. We discovered that the majority of Neo T cells exhibited notable differences in the biological processes of amide-related signal pathways. The analysis of potential cell-to-cell interactions, in conjunction with spatial transcriptomic and multiplex immunohistochemistry data, has revealed that Neo T cells possess potent signalling molecules, including LTA, which can potentially engage with tumour cells within the tumour microenvironment, thereby exerting anti-tumour effects. By sequencing CD8 + T cells in tumour samples of patients undergoing neoadjuvant immunotherapy, we determined that the fraction of Neo T cells was significantly and positively linked with the clinical benefit and overall survival rate of patients.

CONCLUSION

This method expedites the identification of neoantigen-reactive TCRs and the engineering of neoantigen-reactive T cells for therapy.

CTCF mutation at R567 causes developmental disorders via 3D genome rearrangement and abnormal neurodevelopment

The three-dimensional genome structure organized by CTCF is required for development. Clinically identified mutations in CTCF have been linked to adverse developmental outcomes. Nevertheless, the underlying mechanism remains elusive. In this investigation, we explore the regulatory roles of a clinically relevant R567W point mutation, located within the 11th zinc finger of CTCF, by introducing this mutation into both murine models and human embryonic stem cell-derived cortical organoid models. Mice with homozygous CTCFR567W mutation exhibit growth impediments, resulting in postnatal mortality, and deviations in brain, heart, and lung development at the pathological and single-cell transcriptome levels. This mutation induces premature stem-like cell exhaustion, accelerates the maturation of GABAergic neurons, and disrupts neurodevelopmental and synaptic pathways. Additionally, it specifically hinders CTCF binding to peripheral motifs upstream to the core consensus site, causing alterations in local chromatin structure and gene expression, particularly at the clustered protocadherin locus. Comparative analysis using human cortical organoids mirrors the consequences induced by this mutation. In summary, this study elucidates the influence of the CTCFR567W mutation on human neurodevelopmental disorders, paving the way for potential therapeutic interventions.

B cell-mediated antigen presentation promotes adverse cardiac remodeling in chronic heart failure

Cardiovascular disease remains the leading cause of death worldwide. A primary driver of cardiovascular mortality is ischemic heart failure, a form of cardiac dysfunction that can develop in patients who survive myocardial infarction. Acute cardiac damage triggers robust changes in the spleen with rapid migration of immune cells from the spleen to the heart. Activating this "cardio-splenic" axis contributes to progressive cardiac dysfunction. The cardio-splenic axis has, therefore, been identified as a promising therapeutic target to prevent or treat heart failure. However, our understanding of the precise mechanisms by which specific immune cells contribute to adverse cardiac remodeling within the cardio-splenic axis remains limited. Here, we show that splenic B cells contribute to the development of heart failure via MHC II-mediated antigen presentation. We found that the adoptive transfer of splenic B cells from mice with ischemic heart failure promoted adverse cardiac remodeling and splenic inflammatory changes in naïve recipient mice. Based on single-cell RNA sequencing analysis of splenic B cells from mice with ischemic heart failure, we hypothesized that B cells contributed to adverse cardiac remodeling through antigen presentation by MHC II molecules. This mechanism was confirmed using transgenic mice with B cell-specific MHC II deletion, and by analyzing circulating B cells from humans who experienced myocardial infarction. Our results broaden our understanding of B lymphocyte biology, reshape current models of immune activation in response to myocardial injury, and point towards MHC II-mediated signaling in B cells as a novel and specific therapeutic target in chronic heart failure.

Protocol to identify defined reprogramming factor expression using a factor-indexing single-nuclei multiome sequencing approach

Ectopic expression of lineage-specific transcription factors (TFs) of another cell type can induce cell fate reprogramming. However, the heterogeneity of reprogramming cells has been a challenge for data interpretation and model evaluation. Here, we present a protocol to characterize cells expressing defined factors during direct cell reprogramming using a factor-indexing approach based on single-nuclei multiome sequencing (FI-snMultiome-seq). We describe the steps for barcoding TFs, converting human fibroblasts to pancreatic ductal-like cells using defined TFs, and preparing library for FI-snMultiome-seq analysis. For complete details on the use and execution of this protocol, please refer to Fei et al.1.

Automated single-cell omics end-to-end framework with data-driven batch inference

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

Gliovascular transcriptional perturbations in Alzheimer's disease reveal molecular mechanisms of blood brain barrier dysfunction

To uncover molecular changes underlying blood-brain-barrier dysfunction in Alzheimer's disease, we performed single nucleus RNA sequencing in 24 Alzheimer's disease and control brains and focused on vascular and astrocyte clusters as main cell types of blood-brain-barrier gliovascular-unit. The majority of the vascular transcriptional changes were in pericytes. Of the vascular molecular targets predicted to interact with astrocytic ligands, SMAD3, upregulated in Alzheimer's disease pericytes, has the highest number of ligands including VEGFA, downregulated in Alzheimer's disease astrocytes. We validated these findings with external datasets comprising 4,730 pericyte and 150,664 astrocyte nuclei. Blood SMAD3 levels are associated with Alzheimer's disease-related neuroimaging outcomes. We determined inverse relationships between pericytic SMAD3 and astrocytic VEGFA in human iPSC and zebrafish models. Here, we detect vast transcriptome changes in Alzheimer's disease at the gliovascular-unit, prioritize perturbed pericytic SMAD3-astrocytic VEGFA interactions, and validate these in cross-species models to provide a molecular mechanism of blood-brain-barrier disintegrity in Alzheimer's disease.

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

Background

Angiogenesis plays a pivotal role in colorectal cancer (CRC), yet its underlying mechanisms demand further exploration. This study aimed to elucidate the significance of angiogenesis-related genes (ARGs) in CRC through comprehensive multi-omics analysis.

Methods

CRC patients were categorized according to ARGs expression to form angiogenesis-related clusters (ARCs). We investigated the correlation between ARCs and patient survival, clinical features, consensus molecular subtypes (CMS), cancer stem cell (CSC) index, tumor microenvironment (TME), gene mutations, and response to immunotherapy. Utilizing three machine learning algorithms (LASSO, Xgboost, and Decision Tree), we screen key ARGs associated with ARCs, further validated in independent cohorts. A prognostic signature based on key ARGs was developed and analyzed at the scRNA-seq level. Validation of gene expression in external cohorts, clinical tissues, and blood samples was conducted via RT-PCR assay.

Results

Two distinct ARC subtypes were identified and were significantly associated with patient survival, clinical features, CMS, CSC index, and TME, but not with gene mutations. Four genes (S100A4, COL3A1, TIMP1, and APP) were identified as key ARCs, capable of distinguishing ARC subtypes. The prognostic signature based on these genes effectively stratified patients into high- or low-risk categories. scRNA-seq analysis showed that these genes were predominantly expressed in immune cells rather than in cancer cells. Validation in two external cohorts and through clinical samples confirmed significant expression differences between CRC and controls.

Conclusion

This study identified two ARG subtypes in CRC and highlighted four key genes associated with these subtypes, offering new insights into personalized CRC treatment strategies.

Single-nucleus multiomic analysis of Beckwith-Wiedemann syndrome liver reveals PPARA signaling enrichment and metabolic dysfunction

Beckwith-Wiedemann Syndrome (BWS) is an epigenetic overgrowth syndrome caused by methylation changes in the human 11p15 chromosomal locus. Patients with BWS exhibit tissue overgrowth, as well as an increased risk of childhood neoplasms in the liver and kidney. To understand the impact of these 11p15 changes, specifically in the liver, we performed single-nucleus RNA sequencing (snRNA-seq) and single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) to generate paired, cell-type-specific transcriptional and chromatin accessibility profiles of both BWS-liver and nonBWS-liver nontumorous tissue. Our integrated RNA+ATACseq multiomic approach uncovered hepatocyte-specific enrichment and activation of the peroxisome proliferator-activated receptor α (PPARA) - a liver metabolic regulator. To confirm our findings, we utilized a BWS-induced pluripotent stem cell (iPSC) model, where cells were differentiated into hepatocytes. Our data demonstrates the dysregulation of lipid metabolism in BWS-liver, which coincided with observed upregulation of PPARA during hepatocyte differentiation. BWS liver cells exhibited decreased neutral lipids and increased fatty acid β-oxidation, relative to controls. We also observed increased reactive oxygen species (ROS) byproducts in the form of peroxidated lipids in BWS hepatocytes, which coincided with increased oxidative DNA damage. This study proposes a putative mechanism for overgrowth and cancer predisposition in BWS liver due to perturbed metabolism.

Metabolic inflexibility promotes mitochondrial health during liver regeneration

Mitochondria are critical for proper organ function and mechanisms to promote mitochondrial health during regeneration would benefit tissue homeostasis. We report that during liver regeneration, proliferation is suppressed in electron transport chain (ETC)-dysfunctional hepatocytes due to an inability to generate acetyl-CoA from peripheral fatty acids through mitochondrial β-oxidation. Alternative modes for acetyl-CoA production from pyruvate or acetate are suppressed in the setting of ETC dysfunction. This metabolic inflexibility forces a dependence on ETC-functional mitochondria and restoring acetyl-CoA production from pyruvate is sufficient to allow ETC-dysfunctional hepatocytes to proliferate. We propose that metabolic inflexibility within hepatocytes can be advantageous by limiting the expansion of ETC-dysfunctional cells.

Gene regulatory landscape of cerebral cortex folding

Folding of the cerebral cortex is a key aspect of mammalian brain development and evolution, and defects are linked to severe neurological disorders. Primary folding occurs in highly stereotyped patterns that are predefined in the cortical germinal zones by a transcriptomic protomap. The gene regulatory landscape governing the emergence of this folding protomap remains unknown. We characterized the spatiotemporal dynamics of gene expression and active epigenetic landscape (H3K27ac) across prospective folds and fissures in ferret. Our results show that the transcriptomic protomap begins to emerge at early embryonic stages, and it involves cell-fate signaling pathways. The H3K27ac landscape reveals developmental cell-fate restriction and engages known developmental regulators, including the transcription factor Cux2. Manipulating Cux2 expression in cortical progenitors changed their proliferation and the folding pattern in ferret, caused by selective transcriptional changes as revealed by single-cell RNA sequencing analyses. Our findings highlight the key relevance of epigenetic mechanisms in defining the patterns of cerebral cortex folding.

A dynamic in vitro model of Down syndrome neurogenesis with trisomy 21 gene dosage correction

Excess gene dosage from chromosome 21 (chr21) causes Down syndrome (DS), spanning developmental and acute phenotypes in terminal cell types. Which phenotypes remain amenable to intervention after development is unknown. To address this question in a model of DS neurogenesis, we derived trisomy 21 (T21) human induced pluripotent stem cells (iPSCs) alongside, otherwise, isogenic euploid controls from mosaic DS fibroblasts and equipped one chr21 copy with an inducible XIST transgene. Monoallelic chr21 silencing by XIST is near-complete and irreversible in iPSCs. Differential expression reveals that T21 neural lineages and iPSCs share suppressed translation and mitochondrial pathways and activate cellular stress responses. When XIST is induced before the neural progenitor stage, T21 dosage correction suppresses a pronounced skew toward astrogenesis in neural differentiation. Because our transgene remains inducible in postmitotic T21 neurons and astrocytes, we demonstrate that XIST efficiently represses genes even after terminal differentiation, which will empower exploration of cell type-specific T21 phenotypes that remain responsive to chr21 dosage.

CellMarkerPipe: cell marker identification and evaluation pipeline in single cell transcriptomes

Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe ( https://github.com/yao-laboratory/cellMarkerPipe ), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker's overall reliable performance in single marker gene selection, with COSG showing commendable speed and comparable efficacy. Furthermore, we demonstrate the pivotal role of our approach in real-world medical datasets. This general and opensource pipeline stands as a significant advancement in streamlining cell marker gene identification and evaluation, fitting broad applications in the field of cellular biology and medical research.

B cell-mediated antigen presentation promotes adverse cardiac remodeling in chronic heart failure

Cardiovascular disease remains the leading cause of death worldwide. A primary driver of cardiovascular mortality is ischemic heart failure, a form of cardiac dysfunction that can develop in patients who survive myocardial infarction. Acute cardiac damage triggers robust changes in the spleen with rapid migration of immune cells from the spleen to the heart. Activating this "cardio-splenic" axis contributes to progressive cardiac dysfunction. The cardio-splenic axis has, therefore, been identified as a promising therapeutic target to prevent or treat heart failure. However, our understanding of the precise mechanisms by which specific immune cells contribute to adverse cardiac remodeling within the cardio-splenic axis remains limited. Here, we show that splenic B cells contribute to the development of heart failure via MHC II-mediated antigen presentation. We found that the adoptive transfer of splenic B cells from mice with ischemic heart failure promoted adverse cardiac remodeling and splenic inflammatory changes in naïve recipient mice. Based on single-cell RNA sequencing analysis of splenic B cells from mice with ischemic heart failure, we hypothesized that B cells contributed to adverse cardiac remodeling through antigen presentation by MHC II molecules. This mechanism was confirmed using transgenic mice with B cell-specific MHC II deletion, and by analyzing circulating B cells from humans who experienced myocardial infarction. Our results broaden our understanding of B lymphocyte biology, reshape current models of immune activation in response to myocardial injury, and point towards MHC II-mediated signaling in B cells as a novel and specific therapeutic target in chronic heart failure.

ScSNViz: a user-friendly toolset for visualization and analysis of Cell-Specific Expressed SNVs

Motivation

Understanding genetic variation at the single-cell level is crucial for insights into cellular heterogeneity, clonal evolution, and gene expression regulation, but there is a scarcity of tools for visualizing and analyzing cell-level genetic variants.

Results

We introduce scSNViz, a comprehensive R-based toolset for visualization and analysis of cell-specific expressed Single Nucleotide Variants (sceSNVs) within cell-barcoded single-cell RNA-sequencing (scRNA-seq) data. ScSNViz offers 3D sceSNV visualization capabilities for dimensionally reduced scRNA-seq gene expression data, compatibility with popular scRNA-seq processing tools like Seurat, cell-type classification tools such as SingleR and scType, and trajectory inference computation using Slingshot. Furthermore, scSNViz conducts estimation, summary, and graphical representation of statistical metrics pertaining to sceSNVs distribution and expression across individual cells. It also provides support for the analysis of individual sceSNVs as well as sets comprising multiple expressed sceSNVs of interest.

Availability

ScSNViz is implemented as user-friendly R-scripts, freely available on https://horvathlab.github.io/NGS/scSNViz , supported by help utilities, and requiring no specialized bioinformatics skills for use.

Myelin plasticity in the ventral tegmental area is required for opioid reward

All drugs of abuse induce long-lasting changes in synaptic transmission and neural circuit function that underlie substance-use disorders1,2. Another recently appreciated mechanism of neural circuit plasticity is mediated through activity-regulated changes in myelin that can tune circuit function and influence cognitive behaviour3-7. Here we explore the role of myelin plasticity in dopaminergic circuitry and reward learning. We demonstrate that dopaminergic neuronal activity-regulated myelin plasticity is a key modulator of dopaminergic circuit function and opioid reward. Oligodendroglial lineage cells respond to dopaminergic neuronal activity evoked by optogenetic stimulation of dopaminergic neurons, optogenetic inhibition of GABAergic neurons, or administration of morphine. These oligodendroglial changes are evident selectively within the ventral tegmental area but not along the axonal projections in the medial forebrain bundle nor within the target nucleus accumbens. Genetic blockade of oligodendrogenesis dampens dopamine release dynamics in nucleus accumbens and impairs behavioural conditioning to morphine. Taken together, these findings underscore a critical role for oligodendrogenesis in reward learning and identify dopaminergic neuronal activity-regulated myelin plasticity as an important circuit modification that is required for opioid reward.

A guide to single-cell RNA sequencing analysis using web-based tools for non-bioinformatician

Single-cell RNA sequencing (scRNA-seq) is a technique that has proven to be a powerful tool for a wide range of fields and research studies. However, scRNA-seq data analysis has been dominated by scientists highly trained in bioinformatics or those with extensive computational experience and understanding. Recently, this trend has begun to shift as more user-friendly web-based scRNA-seq analysis tools have been developed that require little computational experience to use. However, barriers persist for nonbioinformaticians in using this technique. Complex, unfamiliar language and scarce comprehensive literature guidance to provide a framework for understanding scRNA-seq analysis outputs are among the obstacles. This work introduces many popular web-based tools for scRNA-seq and provides a general overview of their user interfaces and features. Then, a comprehensive start-to-finish introductory scRNA-seq analysis pipeline is described in detail, which aims to enable researchers to carry out scRNA-seq analysis, regardless of computational experience. Companion video tutorials can be found at "EasyScRNAseqTutorials" on YouTube (https://www.youtube.com/@scrnaseqtutorials). However, as scRNA-seq continues to penetrate new fields and expand in importance, there remains a need for more literature to help overcome barriers to its use by explaining further the highly complex and advanced analyses that are introduced within this paper.

Single-cell investigation of lead toxicity from neurodevelopment to neurodegeneration: Current review and future opportunities

Human exposure to the metal lead (Pb) is prevalent and associated with adverse neurodevelopmental and neurodegenerative outcomes. Pb disrupts normal brain function by inducing oxidative stress and neuroinflammation, altering cellular metabolism, and displacing essential metals. Prior studies on the molecular impacts of Pb have examined bulk tissues, which collapse information across all cell types, or in targeted cells, which are limited to cell autonomous effects. These approaches are unable to represent the complete biological implications of Pb exposure because the brain is a cooperative network of highly heterogeneous cells, with cellular diversity and proportions shifting throughout development, by brain region, and with disease. New technologies are necessary to investigate whether Pb and other environmental exposures alter cell composition in the brain and whether they cause molecular changes in a cell-type-specific manner. Cutting-edge, single-cell approaches now enable research resolving cell-type-specific effects from bulk tissues. This article reviews existing Pb neurotoxicology studies with genome-wide molecular signatures and provides a path forward for the field to implement single-cell approaches with practical recommendations.

Deciphering bat influenza H18N11 infection dynamics in male Jamaican fruit bats on a single-cell level

Jamaican fruit bats (Artibeus jamaicensis) naturally harbor a wide range of viruses of human relevance. These infections are typically mild in bats, suggesting unique features of their immune system. To better understand the immune response to viral infections in bats, we infected male Jamaican fruit bats with the bat-derived influenza A virus (IAV) H18N11. Using comparative single-cell RNA sequencing, we generated single-cell atlases of the Jamaican fruit bat intestine and mesentery. Gene expression profiling showed that H18N11 infection resulted in a moderate induction of interferon-stimulated genes and transcriptional activation of immune cells. H18N11 infection was predominant in various leukocytes, including macrophages, B cells, and NK/T cells. Confirming these findings, human leukocytes, particularly macrophages, were also susceptible to H18N11, highlighting the zoonotic potential of this bat-derived IAV. Our study provides insight into a natural virus-host relationship and thus serves as a fundamental resource for future in-depth characterization of bat immunology.

Single-Cell Sequencing Technology in Ruminant Livestock: Challenges and Opportunities

Advancements in single-cell sequencing have transformed the genomics field by allowing researchers to delve into the intricate cellular heterogeneity within tissues at greater resolution. While single-cell omics are more widely applied in model organisms and humans, their use in livestock species is just beginning. Studies in cattle, sheep, and goats have already leveraged single-cell and single-nuclei RNA-seq as well as single-cell and single-nuclei ATAC-seq to delineate cellular diversity in tissues, track changes in cell populations and gene expression over developmental stages, and characterize immune cell populations important for disease resistance and resilience. Although challenges exist for the use of this technology in ruminant livestock, such as the precise annotation of unique cell populations and spatial resolution of cells within a tissue, there is vast potential to enhance our understanding of the cellular and molecular mechanisms underpinning traits essential for healthy and productive livestock. This review intends to highlight the insights gained from published single-cell omics studies in cattle, sheep, and goats, particularly those with publicly accessible data. Further, this manuscript will discuss the challenges and opportunities of this technology in ruminant livestock and how it may contribute to enhanced profitability and sustainability of animal agriculture in the future.

Decoding the complexity of delayed wound healing following Enterococcus faecalis infection

Wound infections are highly prevalent and can lead to delayed or failed healing, causing significant morbidity and adverse economic impacts. These infections occur in various contexts, including diabetic foot ulcers, burns, and surgical sites. Enterococcus faecalis is often found in persistent non-healing wounds, but its contribution to chronic wounds remains understudied. To address this, we employed single-cell RNA sequencing (scRNA-seq) on infected wounds in comparison to uninfected wounds in a mouse model. Examining over 23,000 cells, we created a comprehensive single-cell atlas that captures the cellular and transcriptomic landscape of these wounds. Our analysis revealed unique transcriptional and metabolic alterations in infected wounds, elucidating the distinct molecular changes associated with bacterial infection compared to the normal wound healing process. We identified dysregulated keratinocyte and fibroblast transcriptomes in response to infection, jointly contributing to an anti-inflammatory environment. Notably, E. faecalis infection prompted a premature, incomplete epithelial-mesenchymal transition in keratinocytes. Additionally, E. faecalis infection modulated M2-like macrophage polarization by inhibiting pro-inflammatory resolution in vitro, in vivo, and in our scRNA-seq atlas. Furthermore, we discovered macrophage crosstalk with neutrophils, which regulates chemokine signaling pathways, while promoting anti-inflammatory interactions with endothelial cells. Overall, our findings offer new insights into the immunosuppressive role of E. faecalis in wound infections.