Single-cell RNA-seq Analysis Research Articles

Single-cell RNA-Seq analysis reveals cell subsets and gene signatures associated with rheumatoid arthritis disease activity.

Rheumatoid arthritis (RA) management leans toward achieving remission or low disease activity. In this study, we conducted single-cell RNA sequencing (scRNA-Seq) of peripheral blood mononuclear cells (PBMCs) from 36 individuals (18 patients with RA and 18 matched controls, accounting for age, sex, race, and ethnicity), to identify disease-relevant cell subsets and cell type-specific signatures associated with disease activity. Our analysis revealed 18 distinct PBMC subsets, including an IFN-induced transmembrane 3-overexpressing (IFITM3-overexpressing) IFN-activated monocyte subset. We observed an increase in CD4+ T effector memory cells in patients with moderate-high disease activity (DAS28-CRP ≥ 3.2) and a decrease in nonclassical monocytes in patients with low disease activity or remission (DAS28-CRP < 3.2). Pseudobulk analysis by cell type identified 168 differentially expressed genes between RA and matched controls, with a downregulation of proinflammatory genes in the γδ T cell subset, alteration of genes associated with RA predisposition in the IFN-activated subset, and nonclassical monocytes. Additionally, we identified a gene signature associated with moderate-high disease activity, characterized by upregulation of proinflammatory genes such as TNF, JUN, EGR1, IFIT2, MAFB, and G0S2 and downregulation of genes including HLA-DQB1, HLA-DRB5, and TNFSF13B. Notably, cell-cell communication analysis revealed an upregulation of signaling pathways, including VISTA, in both moderate-high and remission-low disease activity contexts. Our findings provide valuable insights into the systemic cellular and molecular mechanisms underlying RA disease activity.

Comprehensive single-cell and bulk RNA-seq analyses reveal a novel CD8+ T cell-associated prognostic signature in ovarian cancer.

CD8+ T cells play pivotal roles in combating intracellular pathogens and eliminating malignant cells in cancer. However, the prognostic role of CD8+ T cells in ovarian carcinoma is insufficiently exploited. Herein, through univariate Cox regression along with least absolute shrinkage and selection operator (LASSO) regression analyses, we developed a novel prognostic model based on CD8+ T cell markers identified by single-cell sequencing (scRNA-seq) analyses. Patient grouping by the median risk score reveals an excellent prognostic efficacy of this model in both training and validation cohorts. Of note, patients classified as low-risk group exhibit a dramatically improved prognosis. In addition, higher enrichment level of immune-related pathways and increased infiltration level of multiple immune cells are found in patients with lower risk score. Importantly, low-risk patients also exhibited higher response rate to immunotherapies. Summarily, this developed CD8+ T cell-associated prognostic model serves as an excellent predictor for clinical outcomes and aids in guiding therapeutic strategy choices for ovarian cancer patients.

Correlation of LLT-1 and NLRC4 inflammasome and its effect on glioblastoma prognosis.

LLT-1 is a well-known ligand for the natural killer (NK) cell inhibitory receptor NKRP1A. Here, we examined NLRC4 inflammasome components and LLT-1 expression in glioblastoma (GBM) tissues to elucidate potential associations and interactions between these factors. GBM tissues were collected for RNA sequencing (RNA-seq) and Immunofluorescent experiments. Colocalization of LLT-1 and other proteins was assessed by immunofluorescence. Computational analyses utilized RNA-seq data from 296 to 52 patients from the Chinese Glioma Genome Atlas and CHA medical records, respectively. These data were subjected to survival, non-negative matrix factorization clustering, Gene Ontology enrichment, and protein-protein interaction analyses. Receptor-ligand interactions between tumor and immune cells were confirmed by single-cell RNA-seq analysis. In GBM tissues, LLT-1 was predominantly colocalized with glial fibrillary acidic protein (GFAP)-expressing astrocytes, but not with microglial markers like Iba-1. Additionally, LLT-1 and activated NLRC4 inflammasomes were mainly co-expressed in intratumoral astrocytes, suggesting an association between LLT-1, NLRC4, and glioma malignancy. High LLT-1 expression correlates with poor prognosis, particularly in the mesenchymal subtype, and is associated with TNF and NOD-like receptor signaling pathway enrichment, indicating a potential role in tumor inflammation and progression. At the single-cell level, mesenchymal-like malignant cells showed high NF, NLR, and IL-1 signaling pathway enrichment compared to other malignant cell types. We revealed an association between NLRC4 inflammasome activity and LLT-1 expression, suggesting a novel regulatory pathway involving TNF, inflammasomes, and IL-1, potentially offering new NK-cell-mediated anti-glioma approaches.

Bayesian-frequentist hybrid inference framework for single cell RNA-seq analyses

BackgroundSingle cell RNA sequencing technology (scRNA-seq) has been proven useful in understanding cell-specific disease mechanisms. However, identifying genes of interest remains a key challenge. Pseudo-bulk methods that pool scRNA-seq counts in the same biological replicates have been commonly used to identify differentially expressed genes. However, such methods may lack power due to the limited sample size of scRNA-seq datasets, which can be prohibitively expensive.ResultsMotivated by this, we proposed to use the Bayesian-frequentist hybrid (BFH) framework to increase the power and we showed in simulated scenario, the proposed BFH would be an optimal method when compared with other popular single cell differential expression methods if both FDR and power were considered. As an example, the method was applied to an idiopathic pulmonary fibrosis (IPF) case study.ConclusionIn our IPF example, we demonstrated that with a proper informative prior, the BFH approach identified more genes of interest. Furthermore, these genes were reasonable based on the current knowledge of IPF. Thus, the BFH offers a unique and flexible framework for future scRNA-seq analyses.

Understanding mechanisms of JAK1 inhibition on synovial fibroblasts using combinatorial approaches of bulk and single cell RNAseq analyses.

The aim of these studies was to characterise the molecular effects of a tool JAK1 inhibitor on cultured primary fibroblast-like synoviocytes (FLS) from patients with rheumatoid arthritis (RA) through both total and individual cell analysis. RA-FLS cultures from 6 (Bulk RNA-seq) or 4 (ScRNA-seq) donors were pre-treated with various concentrations (100 nM and 1μM) of ABT-317 with/without exposure to 25% SEB-conditioned PBMC medium to mimic the RA inflammatory milieu. Cells were subjected to both bulk RNA-seq (36 libraries) and single cell RNA-seq (scRNA-seq; 24 libraries) to identify biological processes impacted by CM and ABT-317 treatments. In our bulk RNA-seq analysis, a total of 2,605 differentially expressed genes (DEGs) were identified between CM-stimulation and unstimulated groups, while 1,122 DEGs were found between ABT-317 1μM and DMSO in CM-stimulated groups using thresholds of log2 (fold change) ≥ |0.58| and FDR ≤ 10%. Both bulk and single cell mRNA analysis of RA-FLS treated with a combination of CM and ABT-317 demonstrated the expected changes in inflammatory pathways such as interferon and IL-6 signalling. However, other non-inflammation associated pathways were also altered by ABT-317. In addition, the single cell analysis highlighted that FLS segregate into distinctive clusters upon combination CM and ABT-317 treatment, suggesting JAK inhibition can drive RA-FLS into multiple heterogenous cell populations. Interestingly, one of the unique RA-FLS clusters that emerged from the CM and ABT-317 treatment showed matrix metalloproteinase-3 (MMP3)high expression as well as several gene signatures that are not found in any other ABT-317 derived clusters. JAK inhibition with ABT-317 is effective at globally inhibiting CM-induced pro- and non-inflammatory pathways in FLS cultures, but also results in several distinct fibroblast populations with unique gene-associated pathways. This study advances the molecular understanding of JAK1 inhibitor effects on fibroblasts that may contribute to clinical efficacy.

Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy. scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features. By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients. Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.

MDB-99. SPATIAL AND TEMPORAL DEVELOPMENT OF GLI2-DRIVEN MEDULLOBLASTOMA

Abstract BACKGROUND Medulloblastoma (MB) is a highly malignant tumor of the cerebellum predominantly affecting children. It can be classified into four major molecular subgroups: WNT, SHH, Group 3, and Group 4. Among these subgroups, the SHH subgroup MB, characterized by GLI2 amplification, is associated with a poor prognosis. However, while GLI2 alterations have been linked to SHH-MB, evidence demonstrating its role as an oncogenic driver in an animal model is lacking. Understanding the mechanisms underlying GLI2-amplified MB initiation and progression is crucial for developing improved therapeutic strategies. METHODS By utilizing transgenic mouse models, our study aims to elucidate the role of GLI2 in SHH-MB tumorigenesis and uncover the underlying mechanisms driving tumor formation. This research effort seeks to identify novel therapeutic targets for the treatment of this disease. RESULTS We discovered that overexpression of GLI2 in embryonic Math1+ granule neuron precursors (GNPs) led to tumorigenesis within the cerebellum, resulting in 100% mortality in the mice. Importantly, the cellular and molecular characteristics of these tumors faithfully recapitulated those found in human SHH-MB. Further analysis revealed that only Math1+ GNPs specifically between E13.5 and E15.5 are susceptible to GLI2-induced tumorigenesis, while overexpression of GLI2 in embryonic GFAP+ stem cell/progenitors failed to induce tumorigenesis. Additionally, our single-cell RNA-seq analysis unveiled the heterogeneity of Math1+ cells during cerebellar development. Furthermore, we demonstrated that the MAPK signaling pathway was involved in the tumorigenesis of embryonic Math1+ GNPs by preventing cell apoptosis. CONCLUSIONS Our findings establish a pivotal oncogenic role of GLI2 in SHH-MB tumorigenesis, with GLI2-driven tumors developing within a specific spatial-temporal window from a distinct cell population during cerebellar development. Targeting the MAPK signaling pathway holds promise in preventing tumorigenesis and progression of GLI2-amplified MB.

Single-cell analysis identifies PLK1 as a driver of immunosuppressive tumor microenvironment in LUAD.

PLK1 (Polo-like kinase 1) plays a critical role in the progression of lung adenocarcinoma (LUAD). Recent studies have unveiled that targeting PLK1 improves the efficacy of immunotherapy, highlighting its important role in the regulation of tumor immunity. Nevertheless, our understanding of the intricate interplay between PLK1 and the tumor microenvironment (TME) remains incomplete. Here, using genetically engineered mouse model and single-cell RNA-seq analysis, we report that PLK1 promotes an immunosuppressive TME in LUAD, characterized with enhanced M2 polarization of tumor associated macrophages (TAM) and dampened antigen presentation process. Mechanistically, elevated PLK1 coincides with increased secretion of CXCL2 cytokine, which promotes M2 polarization of TAM and diminishes expression of class II major histocompatibility complex (MHC-II) in professional antigen-presenting cells. Furthermore, PLK1 negatively regulates MHC-II expression in cancer cells, which has been shown to be associated with compromised tumor immunity and unfavorable patient outcomes. Taken together, our results reveal PLK1 as a novel modulator of TME in LUAD and provide possible therapeutic interventions.

Exploring the immune landscape and drug prediction of an M2 tumor-associated macrophage-related gene signature in EGFR-negative lung adenocarcinoma.

Improving immunotherapy efficacy for EGFR-negative lung adenocarcinoma (LUAD) patients remains a critical challenge, and the therapeutic effect of immunotherapy is largely determined by the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are the top-ranked immune infiltrating cells in the TME, and M2-TAMs exert potent roles in tumor promotion and chemotherapy resistance. An M2-TAM-based prognostic signature was constructed by integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to reveal the immune landscape and select drugs in EGFR-negative LUAD. M2-TAM-based biomarkers were obtained from the intersection of bulk RNA-seq data and scRNA-seq data. After consensus clustering of EGFR-negative LUAD into different clusters based on M2-TAM-based genes, we compared the prognosis, clinical features, estimate scores, immune infiltration, and checkpoint genes among the clusters. Next, we combined univariate Cox and LASSO regression analyses to establish an M2-TAM-based prognostic signature. CCL20, HLA-DMA, HLA-DRB5, KLF4, and TMSB4X were verified as prognostic M2-like TAM-related genes by univariate Cox and LASSO regression analyses. IPS and TMB analyses revealed that the high-risk group responded better to common immunotherapy. The study shows the potential of the M2-like TAM-related gene signature in EGFR-negative LUAD, explores the immune landscape based on M2-like TAM-related genes, and predict immunotherapy response of patients with EGFR-negative LUAD, providing a new insight for individualized treatment.

Integrative analysis of single-cell RNA-seq and gut microbiome metabarcoding data elucidates macrophage dysfunction in mice with DSS-induced ulcerative colitis

Ulcerative colitis (UC) is a significant inflammatory bowel disease caused by an abnormal immune response to gut microbes. However, there are still gaps in our understanding of how immune and metabolic changes specifically contribute to this disease. Our research aims to address this gap by examining mouse colons after inducing ulcerative colitis-like symptoms. Employing single-cell RNA-seq and 16 s rRNA amplicon sequencing to analyze distinct cell clusters and microbiomes in the mouse colon at different time points after induction with dextran sodium sulfate. We observe a significant reduction in epithelial populations during acute colitis, indicating tissue damage, with a partial recovery observed in chronic inflammation. Analyses of cell-cell interactions demonstrate shifts in networking patterns among different cell types during disease progression. Notably, macrophage phenotypes exhibit diversity, with a pronounced polarization towards the pro-inflammatory M1 phenotype in chronic conditions, suggesting the role of macrophage heterogeneity in disease severity. Increased expression of Nampt and NOX2 complex subunits in chronic UC macrophages contributes to the inflammatory processes. The chronic UC microbiome exhibits reduced taxonomic diversity compared to healthy conditions and acute UC. The study also highlights the role of T cell differentiation in the context of dysbiosis and its implications in colitis progression, emphasizing the need for targeted interventions to modulate the inflammatory response and immune balance in colitis.

An attenuated lymphocytic choriomeningitis virus vector enhances tumor control in mice partly via IFN-I.

Viral vectors are being used for the treatment of cancer. Yet, their efficacy varies among tumors and their use poses challenges in immunosuppressed patients, underscoring the need for alternatives. We report striking antitumoral effects by a nonlytic viral vector based on attenuated lymphocytic choriomeningitis virus (r3LCMV). We show in multiple tumor models that injection of tumor-bearing mice with this vector results in improved tumor control and survival. Importantly, r3LCMV improved tumor control in immunodeficient Rag1-/- mice and MyD88-/- mice, suggesting that multiple pathways contributed to the antitumoral effects. The antitumoral effects of r3LCMV were also observed when this vector was administered several weeks before tumor challenges, suggesting the induction of trained immunity. Single-cell RNA sequencing analyses, antibody blockade experiments, and knockout models revealed a critical role for host-intrinsic IFN-I in the antitumoral efficacy of r3LCMV vectors. Collectively, these data demonstrate potent antitumoral effects by r3LCMV vectors and unveil multiple mechanisms underlying their antitumoral efficacy.

Single-cell and bulk RNA-seq unveils the immune infiltration landscape associated with cuproptosis in cerebral cavernous malformations

BackgroundCerebral cavernous malformations (CCMs) are vascular abnormalities associated with deregulated angiogenesis. Their pathogenesis and optimal treatment remain unclear. This study aims to investigate the molecular signatures of cuproptosis, a newly identified type of cell death, associated with CCMs development.MethodsBulk RNA sequencing (RNA-seq) from 15 CCM and 6 control samples were performed with consensus clustering and clustered to two subtypes based on expression levels of cuproptosis-related genes (CRGs). Differentially expressed genes and immune infiltration between subtypes were then identified. Machine learning algorithms including the least absolute shrinkage and selection operator and random forest were employed to screen for hub genes for CCMs associated with cuproptosis. Furthermore, Pathway enrichment and correlation analysis were used to explore the functions of hub genes and their association with immune phenotypes in CCMs. An external dataset was then employed for validation. Finally, employing the Cellchat algorithm on a single-cell RNA-seq dataset, we explored potential mechanisms underlying the participation of these hub genes in cell-cell communication in CCMs.ResultsOur study revealed two distinct CCM subtypes with differential pattern of CRG expression and immune infiltration. Three hub genes (BTBD10, PFDN4, and CEMIP) were identified and validated, which may significantly associate with CCM pathogenesis. These genes were found to be significantly upregulated in CCM endothelial cells (ECs) and were validated through immunofluorescence and western blot analysis. Single-cell RNA-seq analysis revealed the cellular co-expression patterns of these hub genes, particularly highlighting the high expression of BTBD10 and PFDN4 in ECs. Additionally, a significant co-localization was also observed between BTBD10 and the pivotal cuproptosis gene FDX1 in Mki67+ tip cells, indicating the crucial role of cuproptosis for angiogenesis in CCMs. The study also explored the cell-cell communication between subcluster of ECs expressing these hub genes and immune cells, particularly M2 macrophages, suggesting a role for these interactions in CCM pathogenesis.ConclusionThis study identifies molecular signatures linking cuproptosis to CCMs pathogenesis. Three hub genes—PFDN4, CEMIP, and BTBD10—may influence disease progression by modulating immunity. Further research is needed to understand their precise disease mechanisms and evaluate their potential as biomarkers or therapeutic targets for CCMs.

Ephrin Forward Signaling Controls Interspecies Cell Competition in Pluripotent Stem Cells.

In the animal kingdom, evolutionarily conserved mechanisms known as cell competition eliminate unfit cells during development. Interestingly, cell competition also leads to apoptosis of donor cells upon direct contact with host cells from a different species during interspecies chimera formation. The mechanisms underlying how host animal cells recognize and transmit cell death signals to adjacent xenogeneic human cells remain incompletely understood. In this study, we developed an interspecies cell contact reporter system to dissect the mechanisms underlying competitive interactions between mouse and human pluripotent stem cells (PSCs). Through single-cell RNA-seq analyses, we discovered that Ephrin A ligands in mouse cells play a crucial role in signaling cell death to adjacent human cells that express EPHA receptors during interspecies PSC co-culture. We also demonstrated that blocking the Ephrin A-EPHA receptor interaction pharmacologically, and inhibiting Ephrin forward signaling genetically in the mouse cells, enhances the survival of human PSCs and promotes chimera formation both in vitro and in vivo . Our findings elucidate key mechanisms of interspecies PSC competition during early embryogenesis and open new avenues for generating humanized tissues or organs in animals, potentially revolutionizing regenerative medicine.

Phase I dose escalation trial of the first-in-class TNFR2 agonist monoclonal antibody, HFB200301, in monotherapy and in combination with tislelizumab, an anti-PD-1 monoclonal antibody, in adult patients with advanced solid tumors.

2526 Background: Tumor necrosis factor receptor-2 (TNFR2) is expressed on effector CD8+, CD4+, and regulatory T (Treg) cells, natural killer (NK) cells, and myeloid cells. Activation of TNFR2 is anticipated to yield effective anti-tumor immunity by stimulating T-cell and NK-cell activation and proliferation in the tumor microenvironment. HFB200301 is a first-in-class anti-TNFR2 agonistic monoclonal antibody that triggers both innate and adaptive immune stimulation by binding to a specific epitope on TNFR2. We report initial results of an ongoing Phase I dose escalation, multicenter study (NCT05238883) of HFB200301 in monotherapy and in combination with tislelizumab (TIS) in patients (pts) with advanced refractory solid tumors. Methods: A Phase I trial designed to evaluate the safety, pharmacokinetics (PK), pharmacodynamics (PD), and initial efficacy of HFB200301 in monotherapy and in combination with TIS. Eligible pts, ≥18 years with an ECOG PS 0-1 and advanced solid tumors, will be enrolled in dose escalation at 5 different dose cohorts in monotherapy (Q4W), 3 different dose cohorts in combination with TIS (Q4W), or in dose expansion following determination of safety. Radiographic response is assessed every 8 weeks. Results: A total of 39 pts have been enrolled, 27 pts in monotherapy and 12 pts in combination. All pts received prior systemic therapy (median 2, range 1-4); 23 pts (59%) had prior checkpoint inhibitor treatment. No dose limiting toxicities were observed in either the monotherapy or combination arms. Treatment-related adverse events (TRAEs) occurred in 44% of monotherapy and in 50% of combination pts, with pruritis (11%), tremor (7%), fatigue (7%), asthenia (7%) and nausea (7%) being the most common TRAEs. No ≥ Grade 3 TRAEs were reported, and there were no drug discontinuations due to TRAEs. Preliminary HFB200301 PK analysis demonstrated non-linear clearance of HFB200301, with dose-proportional maximum concentration [Cmax], achieving sufficient exposures for peripheral target engagement in all tested doses. Emerging PD data demonstrates evidence of immune activation and expansion of CD8+ T cells, but not Tregs, in the tumor microenvironment and peripheral blood. Single cell RNAseq analysis of peripheral blood mononuclear cells demonstrated PD modulation in TNFR2 positive immune cell types post-HFB200301 treatment. Early response evaluation reveals ongoing durable clinical responses (&gt; 6 months) in a PD-L1+ pleural mesothelioma pt and an EBV+ gastric cancer pt from combination cohorts. Conclusions: HFB200301 demonstrates a favorable safety profile, dose-dependent PK, with PD and preliminary clinical activity in monotherapy and in combination with TIS in pts with heavily pre-treated refractory solid tumors. Clinical trial information: NCT05238883 .

Preliminary Analysis of Aging-Related Genes in Intracerebral Hemorrhage by Integration of Bulk and Single-Cell RNA Sequencing Technology.

Aging is recognized as the key risk for intracerebral hemorrhage (ICH). The detailed mechanisms of aging in ICH warrant exploration. This study aimed to identify potential aging-related genes associated with ICH. ICH-specific aging-related genes were determined by the intersection of differentially expressed genes (DEGs) between perihematomal tissues and corresponding contralateral parts of four patients with ICH (GSE24265) and 349 aging-related genes obtained from the Aging Atlas database. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) analyses were performed to identify the potential biological functions and pathways in which these ICH-specific aging-related genes may be involved. Then, PPI network was established to identify the hub genes of ICH-specific aging-related genes. Meanwhile, miRNA-mRNA and transcription factor (TF)-mRNA regulatory networks were constructed to further explore the ICH-specific aging-related genes regulation. The relationship between these hub genes and immune infiltration was also further explored. Additional single-cell RNA-seq analysis (scRNA-seq, GSE167593) was used to locate the hub genes in different cell types. Besides, expression levels of the hub genes were validated using clinical samples from our institute and another GEO dataset (GSE206971). This study identified 24 ICH-specific aging-related genes, including 22 up-regulated and 2 down-regulated genes. The results of GO and KEGG suggested that the ICH-specific aging-related genes mainly enriched in immunity and inflammation-related pathways, suggesting that aging may affect the ich pathogenesis by regulating inflammatory and immune-related pathways. Our study revealed 24 ICH-specific aging-related genes and their functions highly pertinent to ICH pathogenesis, providing new insights into the impact of aging on ICH.

Her2 amplification, Rel-A, and Bach1 can influence APOBEC3A expression in breast cancer cells.

APOBEC-induced mutations occur in 50% of sequenced human tumors, with APOBEC3A (A3A) being a major contributor to mutagenesis in breast cancer cells. The mechanisms that cause A3A activation and mutagenesis in breast cancers are still unknown. Here, we describe factors that influence basal A3A mRNA transcript levels in breast cancer cells. We found that basal A3A mRNA correlates with A3A protein levels and predicts the amount of APOBEC signature mutations in a panel of breast cancer cell lines, indicating that increased basal transcription may be one mechanism leading to breast cancer mutagenesis. We also show that alteration of ERBB2 expression can drive A3A mRNA levels, suggesting the enrichment of the APOBEC mutation signature in Her2-enriched breast cancer could in part result from elevated A3A transcription. Hierarchical clustering of transcripts in primary breast cancers determined that A3A mRNA was co-expressed with other genes functioning in viral restriction and interferon responses. However, reduction of STAT signaling via inhibitors or shRNA in breast cancer cell lines had only minor impact on A3A abundance. Analysis of single cell RNA-seq from primary tumors indicated that A3A mRNA was highest in infiltrating immune cells within the tumor, indicating that correlations of A3A with STAT signaling in primary tumors may be result from higher immune infiltrates and are not reflective of STAT signaling controlling A3A expression in breast cancer cells. Analysis of ATAC-seq data in multiple breast cancer cell lines identified two transcription factor sites in the APOBEC3A promoter region that could promote A3A transcription. We determined that Rel-A, and Bach1, which have binding sites in these peaks, elevated basal A3A expression. Our findings highlight a complex and variable set of transcriptional activators for A3A in breast cancer cells.

A new integrative analysis of histopathology and single cell RNA-seq reveals the CCL5 mediated T and NK cell interaction with vascular cells in idiopathic pulmonary arterial hypertension

BackgroundInflammation and dysregulated immunity play vital roles in idiopathic pulmonary arterial hypertension (IPAH), while the mechanisms that initiate and promote these processes are unclear.MethodsTranscriptomic data of lung tissues from IPAH patients and controls were obtained from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA), differential expression analysis, protein-protein interaction (PPI) and functional enrichment analysis were combined with a hemodynamically-related histopathological score to identify inflammation-associated hub genes in IPAH. The monocrotaline-induced rat model of pulmonary hypertension was utilized to confirm the expression pattern of these hub genes. Single-cell RNA-sequencing (scRNA-seq) data were used to identify the hub gene-expressing cell types and their intercellular interactions.ResultsThrough an extensive bioinformatics analysis, CXCL9, CCL5, GZMA and GZMK were identified as hub genes that distinguished IPAH patients from controls. Among these genes, pulmonary expression levels of Cxcl9, Ccl5 and Gzma were elevated in monocrotaline-exposed rats. Further investigation revealed that only CCL5 and GZMA were highly expressed in T and NK cells, where CCL5 mediated T and NK cell interaction with endothelial cells, smooth muscle cells, and fibroblasts through multiple receptors.ConclusionsOur study identified a new inflammatory pathway in IPAH, where T and NK cells drove heightened inflammation predominantly via the upregulation of CCL5, providing groundwork for the development of targeted therapeutics.

Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.

Systematic evaluation of single-cell RNA-seq analyses performance based on long-read sequencing platforms

IntroductionThe rapid development of next-generation sequencing (NGS)-based single-cell RNA sequencing (scRNA-seq) allows for detecting and quantifying gene expression in a high-throughput manner, providing a powerful tool for comprehensively understanding cellular function in various biological processes. However, the NGS-based scRNA-seq only quantifies gene expression and cannot reveal the exact transcript structures (isoforms) of each gene due to the limited read length. On the other hand, the long read length of third-generation sequencing (TGS) technologies, including Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), enable direct reading of intact cDNA molecules. ObjectivesBoth ONT and PacBio have been used in conjunction with scRNA-seq, but their performance in single-cell analyses has not been systematically evaluated. MethodsTo address this, we generated ONT and PacBio data from the same single-cell cDNA libraries containing different amount of cells. ResultsUsing NGS as a control, we assessed the performance of each platform in cell type identification. Additionally, the reliability in identifying novel isoforms and allele-specific gene/isoform expression by both platforms was verified, providing a systematic evaluation to design the sequencing strategies in single-cell transcriptome studies. ConclusionBeyond gene expression analysis, which the NGS-based scRNA-seq only affords, TGS-based scRNA-seq achieved gene splicing analyses, identifying novel isoforms. Attribute to higher sequencing quality of PacBio, it outperforms ONT in accuracy of novel transcripts identification and allele-specific gene/isoform expression.

O20 Dissecting junctional epidermolysis bullosa heterogeneity

Abstract Introduction and aims Epidermolysis bullosa (EB) is a family of rare, incurable, inherited disorders that are characterized by extreme skin and mucosal fragility. One of the most severe forms is junctional EB (JEB). JEB is caused by mutations in genes encoding the skin basement membrane proteins laminin 332 (α3, β3, γ2), type XVII collagen or integrin α6β4. Despite similarities between the disease pathology of JEB and other forms of EB there is a dramatic reduction in life expectancy for patients with JEB. Patients with JEB rarely survive beyond the first year of life. To investigate this disparity, we have optimized a previously published conditional tamoxifen induced knockout JEB mouse model (Lama3flx/flxK14CreERT). This mouse model recapitulates all key features of JEB, such as thickening of the epidermis and visible blistering owing to the loss of laminin α3 at the basement membrane. Methods Single-cell (sc)RNA-Seq analysis was conducted on pooled skin samples from a healthy (control) and JEB mouse model (n = 3 per genotype). Immunofluorescent staining was conducted to confirm scRNA-Seq analysis including markers for skin differentiation (K14, transglutaminase 1, loricrin), proliferation (Ki67), basement membrane (laminin α3, laminin β3, laminin γ2, collagen VII) and immune markers (F4/80, Ly6-G/C, CD3, CD79). Results We can see large differences in cellular populations across the two conditions. With increased T- and B-immune-cell subpopulations in the JEB model compared with the healthy control. Utilizing CellChatDB we identified disease-specific signalling pathways that identify activated T- and B-cell populations interacting with JEB macrophage populations via proinflammatory pathways. Conclusions We have utilized an in vivo mouse model to gain insight into the molecular and cellular differences in a JEB diseased phenotype. Identifying changes in cellular pathways could be used as a targeted approach to identify a beneficial therapeutic treatment for JEB.