Human cytomegalovirus (HCMV) is a common herpesvirus that persistently infects a large portion of the world\'s population. Despite the robust host immune response, HCMV is able to replicate, evade host defenses, and establish latency throughout the lifespan by developing multiple immunomodulatory strategies, making the studies on the interaction between HCMV infection and host response particularly important. HCMV has a strict host specificity that specifically infects humans. Therefore, most of the in vivo researches of HCMV rely on clinical samples. Fortunately, the establishment of humanized mouse models allows for convenient in-lab animal experiments involving HCMV infection. Single-cell RNA sequencing enables the study of the relationship between viral and host gene expressions at the single-cell level within host cells. In this study, we assessed the gene expression alterations of PBMCs at the single-cell level within HCMV-infected humanized mice, which sheds light onto the virus-host interactions in the context of HCMV infection of humanized mice and provides a valuable dataset for the related researches.

We aimed to explore the aberrant expression status of hsa-miR-141-3p and dual-specificity protein phosphatase 1 (DUSP1) and their relative mechanisms in uterine cervical carcinoma (UCC).Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) was conducted to detect the expression of hsa-miR-141-3p. Immunohistochemical (IHC) staining was performed to examine the expression of DUSP1 in UCC. Gene chips and RNA-seq datasets were also obtained to assess the expression level. Integrated standardized mean difference (SMD) was calculated to evaluate the expression status of hsa-miR-141-3p in UCC tissues comprehensively. DUSP1-overexpression and hsa-miR-141-3p-inhibition HeLa cells were established, and CCK-8, transwell, wound healing, cell cycle, and apoptosis assays were implemented. The targets of hsa-miR-141-3p were obtained with online tools, and the combination of hsa-miR-141-3p and DUSP1 was validated via dual-luciferase reporter assay. Single-cell RNA-seq data were analyzed to explore hsa-miR-141-3p and DUSP1 in different cells. An integrated SMD of 1.41 (95% CI[0.45, 2.38], p = 0.0041) with 558 samples revealed the overexpression of hsa-miR-141-3p in UCC tissues. And the pooled SMD of -1.06 (95% CI[-1.45, -0.66], p < 0.0001) with 1,268 samples indicated the downregulation of DUSP1. Inhibition of hsa-miR-141-3p could upregulate DUSP1 expression and suppress invasiveness and metastasis of HeLa cells. Overexpression of DUSP1 could hamper proliferation, invasion, and migration and boost apoptosis and distribution of G1 phase. The dual-luciferase reporter assay validated the combination of hsa-miR-141-3p and DUSP1. Moreover, the targets of hsa-miR-141-3p were mainly enriched in the MAPK signaling pathway and activated in fibroblasts and endothelial cells. The current study illustrated the upregulation of hsa-miR-141-3p and the downregulation of DUSP1 in UCC tissues. Hsa-miR-141-3p could promote UCC progression by targeting DUSP1.

UNASSIGNED: Fibroblasts play an important role in the development of idiopathic pulmonary fibrosis (IPF).
UNASSIGNED: We employed single-cell RNA-sequencing data obtained from the Gene Expression Omnibus database to perform cell clustering and annotation analyses. We then performed secondary clustering of fibroblasts and conducted functional enrichment and cell trajectory analyses of the two newly defined fibroblast subtypes. Bulk RNA-sequencing data were used to perform consensus clustering and weighted gene co-expression network analysis. We constructed a fibroblast-related prognostic model using least absolute shrinkage, selection operator regression, and Cox regression analysis. The prognostic model was validated using a validation dataset. Immune infiltration and functional enrichment analyses were conducted for patients in the high- and low-risk IPF groups.
UNASSIGNED: We characterized two fibroblast subtypes that are active in IPF (F3+ and ROBO2+). Using fibroblast-related genes, we identified five genes (CXCL14, TM4SF1, CYTL1, SOD3, and MMP10) for the prognostic model. The area under the curve values of our prognostic model were 0.852, 0.859, and 0.844 at one, two, and three years in the training set, and 0.837, 0.758, and 0.821 at one, two, and three years in the validation set, respectively.
UNASSIGNED: This study annotates and characterizes different subtypes of fibroblasts in IPF.

BACKGROUND: Single-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions.
RESULTS: In this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources.
CONCLUSIONS: COMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

UNASSIGNED: Natural Killer (NK) cells are vital components of the innate immune system, crucial for combating infections and tumor growth, making them pivotal in cancer prognosis and immunotherapy. We sought to understand the diverse characteristics of NK cells within lung adenocarcinoma (LUAD) by conducting single-cell RNA sequencing analyses.
UNASSIGNED: Using the scRNA-seq dataset for multiple primary lung cancers (MPLCs), we examined two major NK cell groups, NK1 and NK2, comparing the expression profiles of 422 differentially expressed NK signature genes. We identified eight genes (SPON2, PLEKHG3, CAMK2N1, RAB27B, CTBP2, EFHD2, GOLM1, and PLOD1) that distinguish NK1 from NK2 cells. A prognostic signature, the NK gene signature (NKGS) score, was established through LASSO Cox regression. High NKGS scores were linked to poorer overall survival in TCGA-LUAD patients and consistently validated in other datasets (GSE31210 and GSE14814).
UNASSIGNED: Functional analysis revealed an enrichment of genes related to the TGF-β signaling pathway in the high NKGS score group. Moreover, a high NKGS score correlated with an immunosuppressive tumor microenvironment (TME) driven by immune evasion mechanisms. We also observed reduced T-cell receptor (TCR) repertoire diversity in the high-risk NKGS group, indicating a negative association between inflammation and risk score.
UNASSIGNED: This study introduced the innovative NKGS score, differentiating NK1 from NK2 cells. High NKGS scores were associated with the TGF-β pathway and provided insights into LUAD prognosis and immune activities.

Cancer-associated fibroblasts (CAFs), integral components of the tumor microenvironment, play a pivotal role in tumor proliferation, metastasis, and clinical outcomes. However, its specific roles in Kidney Renal Clear Cell Carcinoma (KIRC) remain poorly understood. Employing the established Seurat single-cell analysis pipeline, we identified 21 CAFs marker genes. Subsequently, a prognostic signature consisting of 6 CAFs marker genes (RGS5, PGF, TPM2, GJA4, SEPT4, and PLXDC1) was developed in a cohort through univariate and LASSO Cox regression analyses. The model\'s efficacy was then validated in an external cohort, with a remarkable predictive performance in 1-, 3-, and 5-year. Patients in the high-risk group exhibited significantly inferior survival outcomes (p < 0.001), and the risk score was an independent prognostic factor (p < 0.05). Distinct differences in immune cell profiles and drug susceptibility were observed between the two risk groups. In KIRC, the PGF-VEGFR1 signaling pathway displayed a notable increase. PGF expression was significantly elevated in tumor tissues, as demonstrated by quantitative real-time polymerase chain reaction. In vitro, transwell assays and CCK8 revealed that recombinant-PGF could enhance the capability of cell proliferation, migration, and invasion in 769P and 786-O cells. This study firstly developed a novel predictive model based on 6 CAFs genes for KIRC. Additionally, PGF may present a potential therapeutic target to enhance KIRC treatment.

UNASSIGNED: Macrophages play a crucial role in the progression of AF, closely linked to atrial inflammation and myocardial fibrosis. However, the functions and molecular mechanisms of different phenotypic macrophages in AF are not well understood. This study aims to analyze the infiltration characteristics of atrial immune cells in AF patients and further explore the role and molecular expression patterns of M2 macrophage-related genes in AF.
UNASSIGNED: This study integrates single-cell and large-scale sequencing data to analyze immune cell infiltration and molecular characterization of the LAA in patients with AF, using SR as a control group. CIBERSORT assesses immune cell types in LAA tissues; WGCNA identifies signature genes; cell clustering analyzes cell types and subpopulations; cell communication explores macrophage interactions; hdWGCNA identifies M2 macrophage gene modules in AF. AF biomarkers are identified using LASSO and Random Forest, validated with ROC curves and RT-qPCR. Potential molecular mechanisms are inferred through TF-miRNA-mRNA networks and single-gene enrichment analyses.
UNASSIGNED: Myeloid cell subsets varied considerably between the AF and SR groups, with a significant increase in M2 macrophages in the AF group. Signals of inflammation and matrix remodeling were observed in AF. M2 macrophage-related genes IGF1, PDK4, RAB13, and TMEM176B were identified as AF biomarkers, with RAB13 and TMEM176B being novel markers. A TF-miRNA-mRNA network was constructed using target genes, which are enriched in the PPAR signaling pathway and fatty acid metabolism.
UNASSIGNED: Over infiltration of M2 macrophages may be an important factor in the progression of AF. The M2 macrophage-related genes IGF1, RAB13, TMEM176B and PDK4 may regulate the progression of AF through the PPAR signaling pathway and fatty acid metabolism.

On the RNA level, viral infections are characterized by perturbations in the host cell transcriptome as well as the development of viral genetic information. Investigating the abundance and dynamic of RNA molecules can provide ample information to understand many aspects of the infection, from viral replication to pathogenesis. A key aspect therein is the resolution of the data, as infections are generally highly heterogeneous. Even in simple model systems such as cell lines, viral infections happen in a very asynchronous way. Quantifying RNAs at single-cell resolution can therefore substantially increase our understanding of these processes.Whereas measuring the RNA in bulk, that is, in samples containing thousands to hundreds of thousands of cells, is established and widely used since many years, methods for studying not only just a few different RNAs in individual cells became widely available only recently. Here, I outline and compare current concepts and methodologies for using single-cell RNA-sequencing to study virus infections. This covers sample preparation, cell preservation, biosafety considerations, and various experimental methods, with a special focus on the aspects that are important for studying virus infections. Since there is not \"the one\" method for doing single-cell RNA-sequencing, I will not provide a detailed protocol. Rather, this chapter should serve as a primer for getting started with single-cell RNA-sequencing experiments of virus infections and discusses the criteria that allow readers to choose the best procedures for their specific research question.

Triple-negative breast cancer (TNBC) is a particularly aggressive mammary neoplasia with a high fatality rate, mainly because of the development of resistance to administered chemotherapy, the standard treatment for this disease. In this study, we employ both bulk RNA-sequencing and single-cell RNA-sequencing (scRNA-seq) to investigate the transcriptional landscape of TNBC cells cultured in two-dimensional monolayers or three-dimensional spheroids, before and after developing resistance to the chemotherapeutic agents paclitaxel and doxorubicin. Our findings reveal significant transcriptional heterogeneity within the TNBC cell populations, with the scRNA-seq identifying rare subsets of cells that express resistance-associated genes not detected by the bulk RNA-seq. Furthermore, we observe a partial shift towards a highly mesenchymal phenotype in chemoresistant cells, suggesting the epithelial-to-mesenchymal transition (EMT) as a prevalent mechanism of resistance in subgroups of these cells. These insights highlight potential therapeutic targets, such as the PDGF signaling pathway mediating EMT, which could be exploited in this setting. Our study underscores the importance of single-cell approaches in understanding tumor heterogeneity and developing more effective, personalized treatment strategies to overcome chemoresistance in TNBC.

UNASSIGNED: Single-cell RNA-sequencing (scRNA-seq) technology has revealed novel cell populations in organs, uncovered regulatory relationships between genes, and allowed for tracking of cell lineage trajectory during development. It demonstrates promise as a method to better understand transplant biology; however, fundamental bioinformatic tools for its use in the context of transplantation have not been developed. One major need has been a robust method to identify cells as being either donor or recipient genotype origin, and ideally without the need to separately sequence the donor and recipient.
UNASSIGNED: We implemented a novel two-stage genotype discovery method (scTx) optimized for transplant samples by being robust to disparities in cell number and cell type. Using both in silico and real-world scRNA-seq transplant data, we benchmarked our method against existing demultiplexing methods to profile their limitations in terms of sequencing depth, donor and recipient cell imbalance, and single nucleotide variant input selection.
UNASSIGNED: Using in silico data, scTx could more accurately separate donor from recipient cells and at much lower genotype ratios than existing methods. This was further validated using solid-organ scRNA-seq data where scTx could more reliably identify when a second genotype was present and at lower numbers of cells from a second genotype.
UNASSIGNED: scTx introduces the capability to accurately segregate donor and recipient gene expression at the single-cell level from scRNA-seq data without the need to separately genotype the donor and recipient. This will facilitate the use of scRNA-seq in the context of transplantation.