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Meningiomas, although mostly benign, can be recurrent and fatal. World Health Organization (WHO) grading of the tumor does not always identify high-risk meningioma, and better characterizations of their aggressive biology are needed. To approach this problem, we combined 13 bulk RNA sequencing (RNA-seq) datasets to create a dimension-reduced reference landscape of 1,298 meningiomas. The clinical and genomic metadata effectively correlated with landscape regions, which led to the identification of meningioma subtypes with specific biological signatures. The time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape, where the nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcomes. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.

Aims: To explore the associations between cuprotosis‐related genes (CRGs) across different stages of liver disease in metabolic dysfunction‐associated fatty liver disease (MAFLD), including hepatocellular carcinoma (HCC). Materials and Methods: We analysed several bulk RNA sequencing datasets from patients with MAFLD (n = 331) and MAFLD‐related HCC (n = 271) and two MAFLD single‐cell RNA sequencing datasets. To investigate the associations between CRGs and MAFLD, we performed differential correlation, logistic regression and functional enrichment analyses. We also validated the findings in an independent Wenzhou PERSONS cohort of MAFLD patients (n = 656) used for a genome‐wide association study (GWAS). Results: GLS, GCSH and ATP7B genes showed significant differences across the MAFLD spectrum and were significantly associated with liver fibrosis stages. GLS was closely associated with fibrosis stages in patients with MAFLD and those with MAFLD‐related HCC. GLS is predominantly expressed in monocytes and T cells in MAFLD. During the progression of metabolic dysfunction‐associated fatty liver to metabolic‐associated steatohepatitis, GLS expression in T cells decreased. GWAS revealed that multiple single nucleotide polymorphisms in GLS were associated with clinical indicators of MAFLD. Conclusions: GLS may contribute to liver inflammation and fibrosis in MAFLD mainly through cuprotosis and T‐cell activation, promoting the progression of MAFLD to HCC. These findings suggest that cuprotosis may play a role in MAFLD progression, potentially providing new insights into MAFLD pathogenesis. [ABSTRACT FROM AUTHOR]

Background: Glioma is the predominant malignant brain tumor that lacks effective treatment options due to its shielding by the blood-brain barrier (BBB). Astrocytes play a role in the development of glioma, yet the diverse cellular composition of astrocytoma has not been thoroughly researched. Methods: We examined the internal diversity of seven distinct astrocytoma subgroups through single-cell RNA sequencing (scRNA-seq), pinpointed crucial subgroups using CytoTRACE, monocle2 pseudotime analysis, and slingshot pseudotime analysis, employed various techniques to identify critical subgroups, and delved into cellular communication analysis. Then, we combined the clinical information of GBM patients and used bulk RNA sequencing (bulk RNA-seq) to analyze the prognostic impact of the relevant molecules on GBM patients, and we performed in vitro experiments for validation. Results: The analysis of the current study revealed that C0 IGFBP7+ Glioma cells were a noteworthy subpopulation of astrocytoma, influencing the differentiation and progression of astrocytoma. A predictive model was developed to categorize patients into high- and low-scoring groups based on the IGFBP7 Risk Score (IGRS), with survival analysis revealing a poorer prognosis for the high-IGRS group. Analysis of immune cell infiltration, identification of genes with differential expression, various enrichment analyses, assessment of copy number variations, and evaluation of drug susceptibility were conducted, all of which highlighted their significant influence on the prognosis of astrocytoma. Conclusion: This research enhances comprehension of the diverse cell composition of astrocytoma, delves into the various factors impacting the prognosis of astrocytoma, and offers fresh perspectives on treating glioma. [ABSTRACT FROM AUTHOR]

Pain sensitivity is a significant factor in knee osteoarthritis (KOA), influencing patient outcomes and complicating treatment. Genetic differences, particularly in pain-sensing genes (PSRGs), are known to contribute to the variability in pain experiences among KOA patients. This study aims to systematically analyze PSRGs in KOA to better understand their role and potential as therapeutic targets. We utilized bulk RNA-seq data from the GSE114007 and GSE169077 datasets to identify differentially expressed genes, with 20 genes found to be significantly altered. Key PSRGs, including PENK, NGF, HOXD1, and TRPA1, were identified using LASSO, SVM, and random forest algorithms. Further, KEGG and GO enrichment analyses revealed pathways such as "Neuroactive ligand-receptor interaction" and "ECM-receptor interaction," which were validated through external datasets. Single-cell RNA-seq analysis from GSE152805, GSE133449, and GSE104782 datasets demonstrated the heterogeneity and dynamic expression of PSRGs across different cell subpopulations in synovium, meniscus, and cartilage samples. UMAP and pseudotime analyses were used to visualize spatial distribution and developmental trajectories of these genes. The findings emphasize the critical roles of PSRGs in KOA, highlighting their potential as therapeutic targets and suggesting that integrating genetic information into clinical practice could significantly improve pain management and treatment strategies for KOA. [ABSTRACT FROM AUTHOR]

Mi Tang,1,2,* Liling Xiong,3,* Jianghui Cai,2,4,* Xuejia Gong,2 Li Fan,1 Xiaoyu Zhou,1 Shasha Xing,1 Xiao Yang3 1Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 2School of medicine, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China; 3Obstetrics Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 4Department of Pharmacy, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shasha Xing; Xiao Yang, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China, Email xingshasha1230@126.com; wcchgcptg@163.comPurpose: Intrahepatic cholestasis of pregnancy (ICP) is a disorder that characterized by maternal pruritus, abnormal liver function, and an elevation in total bile acid concentrations during pregnancy. Immune factors have been recognized as playing a vital role in the mechanism of ICP. However, the underlying mechanisms regulating dysfunctional immune cells and immune genes remain to be fully elucidated.Patients and Methods: Single-cell RNA sequencing and bulk RNA sequencing data of the placenta were downloaded from the SRA database. The AUCell package, Monocle package and SCENIC package were utilized to explored immune cell activity, cell trajectory and transcription factor, respectively. GO, KEGG, and GSEA were employed to explore potential biological mechanisms. Cell-cell communications were further investigated using the CellChat package. RT-PCR, and Western blot were used to verify the gene expression in placenta.Results: In placenta cells, macrophages were found to be significantly increased in ICP. Additionally, macrophages exhibited the highest immune gene score and were divided into four subclusters (MF1-4). Our analysis revealed significant elevations in MF2, associated with LPS response and antigen presentation, and MF4, associated with TNF and cytokine production. MF3 displayed an anti-inflammatory phenotype. MF1, closely related to ribosomes and proteins, exhibited a sharp decrease. Although ICP maintained an anti-inflammatory state, macrophage trajectories showed a gradual progression toward inflammation. Subsequently, we confirmed that cytokine- and chemokine-related signaling pathways were emphasized in macrophages. Within the CXCL signaling pathway, the increased expression of CXCL1 in macrophages can interact with CXCR2 in neutrophils, potentially inducing macrophage infiltration, stimulating neutrophil chemotaxis, and leading to an inflammatory response and cellular damage.Conclusion: In conclusion, we firstly revealed the transcriptional signatures of macrophages in ICP and discovered a tendency toward an inflammatory state. This study also provides new evidence that the CXCL1-CXCR2 axis may play an important role in the pathogenesis of ICP.Keywords: intrahepatic cholestasis of pregnancy, scRNA-Seq, bulk RNA-Seq, immune gene, macrophage

Xiaoyan Huang,1,2 Xiangrong Zhao,1,2 Yaping Li,1,2 Yangmeng Feng,1,2 Guoan Zhang,3 Qiyu Wang,4 Cuixiang Xu1,2 1Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China; 2Shaanxi Engineering Research Center of Cell Immunology, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China; 3Department of Cardiovascular Surgery, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China; 4Department of Graduate School, Yan’an University, Yan’an, People’s Republic of ChinaCorrespondence: Cuixiang Xu, Email xucuixiang1129@163.comBackground: Dilated cardiomyopathy (DCM) is the second leading cause of heart failure, with intricate pathophysiological underpinnings. In order to shed fresh light on the mechanistic research of DCM, we combined bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data to examine significant cells and genes implicated in the disease.Methods: This analysis employed publicly accessible bulk RNA-seq and scRNA-seq DCM datasets. The scRNA-seq data underwent normalization, principal component, and t-distribution stochastic neighbor embedding analysis. Cell-to-cell communication networks and activity analysis were conducted using CellChat. Utilizing enrichment analysis, the marker genes’ role in the active cells was evaluated. After screening by limma software and weighted gene co-expression network analysis, the differentially expressed genes (DEGs) served as hub genes. Furthermore, these hub genes were subjected to immunological studies, transcription factor expression, and gene set enrichment. Lastly, the expression of the four hub genes and their connection to DCM were verified using the rat models.Results: Fibroblasts and monocytes were chosen as hub cells from among the eight identified cell clusters; their marker genes intersected with DEGs to yield six hub genes. In addition, the six hub genes and the essential module genes intersected to yield four essential genes (ASPN, SFRP4, LUM, and FRZB) that were connected to the Wnt signaling pathway and highly expressed in fibroblast. The four hub DEGs had an expression pattern in the DCM rat model experiment results that was in line with the findings of the bioinformatics study. Additionally, there was a strong correlation between decreased cardiac function and the up-regulation of ASPN, SFRP4, LUM, and FRZB.Conclusion: Ultimately, bulk RNA-seq and scRNA-seq data identified fibroblasts and monocytes as the main cell types implicated in DCM. The highly expressed genes ASPN, FRZB, LUM, and SFRP4 in fibroblasts may aid in the mechanistic investigation of DCM.Keywords: dilated cardiomyopathy, single cell RNA-seq, bulk RNA-seq, weighted gene co-expression network analysis, wnt signaling pathway, fibroblasts

Abstract Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package “ConsensusClusterPlus” on the bulk RNA-seq data. Hub genes were discerned through the application of the R package “limma” and univariate regression analysis, with the calculation of risk scores carried out using the R package “survminer”. To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan–Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages “ESTIMATE” and “GSVA” were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis.

Abstract Background 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. Methods 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. Results 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. Conclusion 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.

Colorectal cancer (CRC) is a relatively common malignancy clinically and the second leading cause of cancer‐related deaths. Recent studies have identified T‐cell exhaustion as playing a crucial role in the pathogenesis of CRC. A long‐standing challenge in the clinical management of CRC is to understand how T cells function during its progression and metastasis, and whether potential therapeutic targets for CRC treatment can be predicted through T cells. Here, we propose DeepTEX, a multi‐omics deep learning approach that integrates cross‐model data to investigate the heterogeneity of T‐cell exhaustion in CRC. DeepTEX uses a domain adaptation model to align the data distributions from two different modalities and applies a cross‐modal knowledge distillation model to predict the heterogeneity of T‐cell exhaustion across diverse patients, identifying key functional pathways and genes. DeepTEX offers valuable insights into the application of deep learning in multi‐omics, providing crucial data for exploring the stages of T‐cell exhaustion associated with CRC and relevant therapeutic targets. [ABSTRACT FROM AUTHOR]

Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package "ConsensusClusterPlus" on the bulk RNA-seq data. Hub genes were discerned through the application of the R package "limma" and univariate regression analysis, with the calculation of risk scores carried out using the R package "survminer". To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan–Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages "ESTIMATE" and "GSVA" were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis. [ABSTRACT FROM AUTHOR]

Skin cutaneous melanoma (SKCM), a form of skin cancer, ranks among the most formidable and lethal malignancies. Exploring tumor microenvironment (TME)-based prognostic indicators would help improve the efficacy of immunotherapy for SKCM patients. This study analyzed SKCM scRNA-seq data to cluster non-malignant cells that could be used to explore the TME into nine immune/stromal cell types, including B cells, CD4 T cells, CD8 T cells, dendritic cells, endothelial cells, Fibroblasts, macrophages, neurons, and natural killer (NK) cells. Using data from The Cancer Genome Atlas (TCGA), we employed SKCM expression profiling to identify differentially expressed immune-associated genes (DEIAGs), which were then incorporated into weighted gene co-expression network analysis (WGCNA) to investigate TME-associated hub genes. Discover candidate small molecule drugs based on pivotal genes. Tumor immune microenvironment-associated genes (TIMAGs) for constructing TIMAS were identified and validated. Finally, the characteristics of TIAMS subgroups and the ability of TIMAS to predict immunotherapy outcomes were analyzed. We identified five TIMAGs (CD86, CD80, SEMA4D, C1QA, and IRF1) and used them to construct TIMAS. In addition, five potential SKCM drugs were identified. The results showed that TIMAS-low patients were associated with immune-related signaling pathways, high MUC16 mutation frequency, high T cell infiltration, and M1 macrophages, and were more favorable for immunotherapy. Collectively, TIMAS constructed by comprehensive analysis of scRNA-seq and bulk RNA-seq data is a promising marker for predicting ICI treatment outcomes and improving individualized therapy for SKCM patients. [ABSTRACT FROM AUTHOR]

Background: 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. Methods: 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. Results: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]

In studying the molecular underpinning of spermatogenesis, we expect to understand the fundamental biological processes better and potentially identify genes that may lead to novel diagnostic and therapeutic strategies toward precision medicine in male infertility. In this review, we emphasized our perspective that the path forward necessitates integrative studies that rely on complementary approaches and types of data. To comprehensively analyze spermatogenesis, this review proposes four axes of integration. First, spanning the analysis of spermatogenesis in the healthy state alongside pathologies. Second, the experimental analysis of model systems (in which we can deploy treatments and perturbations) alongside human data. Third, the phenotype is measured alongside its underlying molecular profiles using known markers augmented with unbiased profiles. Finally, the testicular cells are studied as ecosystems, analyzing the germ cells alongside the states observed in the supporting somatic cells. Recently, the study of spermatogenesis has been advancing using single-cell RNA sequencing, where scientists have uncovered the unique stages of germ cell development in mice, revealing new regulators of spermatogenesis and previously unknown cell subtypes in the testis. An in-depth analysis of meiotic and postmeiotic stages led to the discovery of marker genes for spermatogonia, Sertoli and Leydig cells and further elucidated all the other germline and somatic cells in the testis microenvironment in normal and pathogenic conditions. The outcome of an integrative analysis of spermatogenesis using advanced molecular profiling technologies such as scRNA-seq has already propelled our biological understanding, with additional studies expected to have clinical implications for the study of male fertility. By uncovering new genes and pathways involved in abnormal spermatogenesis, we may gain insights into subfertility or sterility. [ABSTRACT FROM AUTHOR]

Abstract Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson’s disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .

Abstract Background Wilms’ tumor (WT) is the most common renal tumor in childhood. Pyroptosis, a type of inflammation-characterized and immune-related programmed cell death, has been extensively studied in multiple tumors. In the current study, we aim to construct a pyroptosis-related gene signature for predicting the prognosis of Wilms’ tumor. Methods We acquired RNA-seq data from TARGET kidney tumor projects for constructing a gene signature, and snRNA-seq data from GEO database for validating signature-constructing genes. Pyroptosis-related genes (PRGs) were collected from three online databases. We constructed the gene signature by Lasso Cox regression and then established a nomogram. Underlying mechanisms by which gene signature is related to overall survival states of patients were explored by immune cell infiltration analysis, differential expression analysis, and functional enrichment analysis. Results A pyroptosis-related gene signature was constructed with 14 PRGs, which has a moderate to high predicting capacity with 1-, 3-, and 5-year area under the curve (AUC) values of 0.78, 0.80, and 0.83, respectively. A prognosis-predicting nomogram was established by gender, stage, and risk score. Tumor-infiltrating immune cells were quantified by seven algorithms, and the expression of CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages are positively or negatively correlated with risk score. Two single nuclear RNA-seq samples of different histology were harnessed for validation. The distribution of signature genes was identified in various cell types. Conclusions We have established a pyroptosis-related 14-gene signature in WT. Moreover, the inherent roles of immune cells (CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages), functions of differentially expressed genes (tissue/organ development and intercellular communication), and status of signaling pathways (proteoglycans in cancer, signaling pathways regulating pluripotent of stem cells, and Wnt signaling pathway) have been elucidated, which might be employed as therapeutic targets in the future.

Background: In spite of its high mortality rate and poor prognosis, the pathogenesis of sepsis is still incompletely understood. This study established a cuproptosis-based risk model to diagnose and predict the risk of sepsis. In addition, the cuproptosis-related genes were identified for targeted therapy. Methods: Single-cell sequencing analyses were used to characterize the cuproptosis activity score (CuAS) and intercellular communications in sepsis. Differential cuproptosis-related genes (CRGs) were identified in conjunction with single-cell and bulk RNA sequencing. LASSO and Cox regression analyses were employed to develop a risk model. Three external cohorts were conducted to assess the model's accuracy. Differences in immune infiltration, immune cell subtypes, pathway enrichment, and the expression of immunomodulators were further evaluated in distinct groups. Finally, various in-vitro experiments, such as flow cytometry, Western blot, and ELISA, were used to explore the role of LST1 in sepsis. Results: ScRNA-seq analysis demonstrated that CuAS was highly enriched in monocytes and was closely related to the poor prognosis of sepsis patients. Patients with higher CuAS exhibited prominent strength and numbers of cell-cell interactions. A total of five CRGs were identified based on the LASSO and Cox regression analyses, and a CRG-based risk model was established. The lower riskScore cohort exhibited enhanced immune cell infiltration, elevated immune scores, and increased expression of immune modulators, indicating the activation of an antibacterial response. Ultimately, in-vitro experiments demonstrated that LST1, a key gene in the risk model, was enhanced in the macrophage in response to LPS, which was closely related to the decrease of macrophage survival rate, the enhancement of apoptosis and oxidative stress injury, and the imbalance of the M1/M2 phenotype. Conclusions: This study constructed a cuproptosis-related risk model to accurately predict the prognosis of sepsis. We further characterized the cuproptosis-related gene LST1 to provide a theoretical framework for sepsis therapy. [ABSTRACT FROM AUTHOR]

For sequencing-based spatial transcriptomics data, the gene-spot count matrix is highly sparse. This feature is similar to scRNA-seq. The goal of this paper is to identify whether there exist genes that are frequently under-detected in Visium compared to bulk RNA-seq, and the underlying potential mechanism of under-detection in Visium. We collected paired Visium and bulk RNA-seq data for 28 human samples and 19 mouse samples, which covered diverse tissue sources. We compared the two data types and observed that there indeed exists a collection of genes frequently under-detected in Visium compared to bulk RNA-seq. We performed a motif search to examine the last 350 bp of the frequently under-detected genes, and we observed that the poly (T) motif was significantly enriched in genes identified from both human and mouse data, which matches with our previous finding about frequently under-detected genes in scRNA-seq. We hypothesized that the poly (T) motif may be able to form a hairpin structure with the poly (A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during Visium library preparation. [ABSTRACT FROM AUTHOR]

Circadian rhythms are essential regulators of a multitude of physiological and behavioral processes, such as the metabolism and function of the liver. Circadian rhythms are crucial to liver homeostasis, as the liver is a key metabolic organ accountable for the systemic equilibrium of the body. Circadian rhythm disruption alone is sufficient to cause liver cancer through the maintenance of hepatic metabolic disorder. Although there is evidence linking CRD to hepatocarcinogenesis, the precise cellular and molecular mechanisms that underlie the circadian crosstalk that leads to hepatocellular carcinoma remain unknown. The expression of CRD-related genes in HCC was investigated in this study via bulk RNA transcriptomic analysis and single-cell sequencing. Dysregulated CRD-related genes are predominantly found in hepatocytes and fibroblasts, according to the findings. By using a combination of single-cell RNA sequencing and bulk RNA sequencing analyses, the dysregulated CRD-related genes ADAMTS13, BIRC5, IGFBP3, MARCO, MT2A, NNMT, and PGLYRP2 were identified. The survival analysis using the Kaplan–Meier method revealed a significant correlation between the expression levels of BIRC5 and IGFBP3 and the survival of patients diagnosed with HCC. [ABSTRACT FROM AUTHOR]

Inferring and characterizing gene co-expression networks has led to important insights on the molecular mechanisms of complex diseases. Most co-expression analyses to date have been performed on gene expression data collected from bulk tissues with different cell type compositions across samples. As a result, the co-expression estimates only offer an aggregated view of the underlying gene regulations and can be confounded by heterogeneity in cell type compositions, failing to reveal gene coordination that may be distinct across different cell types. In this article, we introduce a flexible framework for estimating cell-type-specific gene co-expression networks from bulk sample data, without making specific assumptions on the distributions of gene expression profiles in different cell types. We develop a novel sparse least squares estimator, referred to as CSNet, that is efficient to implement and has good theoretical properties. Using CSNet, we analyzed the bulk gene expression data from a cohort study on Alzheimer's disease and identified previously unknown cell-type-specific co-expressions among Alzheimer's disease risk genes, suggesting cell-type-specific disease mechanisms. for this article are available online. [ABSTRACT FROM AUTHOR]

Background: Acute Respiratory Distress Syndrome (ARDS) or its earlier stage Acute lung injury (ALI), is a worldwide health concern that jeopardizes human well-being. Currently, the treatment strategies to mitigate the incidence and mortality of ARDS are severely restricted. This limitation can be attributed, at least in part, to the substantial variations in immunity observed in individuals with this syndrome. Methods: Bulk and single cell RNA sequencing from ALI mice and single cell RNA sequencing from ARDS patients were analyzed. We utilized the Seurat program package in R and cellmarker 2.0 to cluster and annotate the data. The differential, enrichment, protein interaction, and cell-cell communication analysis were conducted. Results: The mice with ALI caused by pulmonary and extrapulmonary factors demonstrated differential expression including Clec4e, Retnlg, S100a9, Coro1a, and Lars2. We have determined that inflammatory factors have a greater significance in extrapulmonary ALI, while multiple pathways collaborate in the development of pulmonary ALI. Clustering analysis revealed significant heterogeneity in the relative abundance of immune cells in different ALI models. The autocrine action of neutrophils plays a crucial role in pulmonary ALI. Additionally, there was a significant increase in signaling intensity between B cells and M1 macrophages, NKT cells and M1 macrophages in extrapulmonary ALI. The CXCL, CSF3 and MIF, TGFβ signaling pathways play a vital role in pulmonary and extrapulmonary ALI, respectively. Moreover, the analysis of human single-cell revealed DCs signaling to monocytes and neutrophils in COVID-19-associated ARDS is stronger compared to sepsis-related ARDS. In sepsis-related ARDS, CD8+ T and Th cells exhibit more prominent signaling to Bcell nucleated DCs. Meanwhile, both MIF and CXCL signaling pathways are specific to sepsis-related ARDS. Conclusion: This study has identified specific gene signatures and signaling pathways in animal models and human samples that facilitate the interaction between immune cells, which could be targeted therapeutically in ARDS patients of various etiologies. [ABSTRACT FROM AUTHOR]

Temporal RNA-sequencing (RNA-seq) studies of bulk samples provide an opportunity for improved understanding of gene regulation during dynamic phenomena such as development, tumor progression or response to an incremental dose of a pharmacotherapeutic. Moreover, single-cell RNA-seq (scRNA-seq) data implicitly exhibit temporal characteristics because gene expression values recapitulate dynamic processes such as cellular transitions. Unfortunately, temporal RNA-seq data continue to be analyzed by methods that ignore this ordinal structure and yield results that are often difficult to interpret. Here, we present Error Modelled Gene Expression Analysis (EMOGEA), a framework for analyzing RNA-seq data that incorporates measurement uncertainty, while introducing a special formulation for those acquired to monitor dynamic phenomena. This method is specifically suited for RNA-seq studies in which low-count transcripts with small-fold changes lead to significant biological effects. Such transcripts include genes involved in signaling and non-coding RNAs that inherently exhibit low levels of expression. Using simulation studies, we show that this framework down-weights samples that exhibit extreme responses such as batch effects allowing them to be modeled with the rest of the samples and maintain the degrees of freedom originally envisioned for a study. Using temporal experimental data, we demonstrate the framework by extracting a cascade of gene expression waves from a well-designed RNA-seq study of zebrafish embryogenesis and an scRNA-seq study of mouse pre-implantation and provide unique biological insights into the regulation of genes in each wave. For non-ordinal measurements, we show that EMOGEA has a much higher rate of true positive calls and a vanishingly small rate of false negative discoveries compared to common approaches. Finally, we provide two packages in Python and R that are self-contained and easy to use, including test data. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) has been wreaking havoc for 3 years. PANoptosis, a distinct and physiologically relevant inflammatory programmed cell death, perpetuates cytokine storm and multi-organ injuries in COVID-19. Although PANoptosis performs indispensable roles in host defense, further investigation is needed to elucidate the exact processes through which PANoptosis modulates immunological responses and prognosis in COVID-19. This study conducted a bioinformatics analysis of online single-cell RNA sequence (scRNA-seq) and bulk RNA-seq datasets to explore the potential of PANoptosis as an indicator of COVID-19 severity. The degree of PANoptosis in bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) indicated the severity of COVID-19. Single-cell transcriptomics identified pro-inflammatory monocytes as one of the primary sites of PANoptosis in COVID-19. The study subsequently demonstrated the immune and metabolic characteristics of this group of pro-inflammatory monocytes. In addition, the analysis illustrated that dexamethasone was likely to alleviate inflammation in COVID-19 by mitigating PANoptosis. Finally, the study showed that the PANoptosis-related genes could predict the intensive care unit admission (ICU) and outcomes of COVID-19 patients who are hospitalized. [ABSTRACT FROM AUTHOR]

Spinal cord injury (SCI) is a debilitating condition with an estimated 18,000 new cases annually in the United States. The field has accepted and adopted standardized databases such as the Open Data Commons for Spinal Cord Injury (ODC-SCI) to aid in broader analyses, but these currently lack high-throughput data despite the availability of nearly 6000 samples from over 90 studies available in the Sequence Read Archive. This limits the potential for large datasets to enhance our understanding of SCI-related mechanisms at the molecular and cellular level. Therefore, we have developed a protocol for processing RNA-Seq samples from high-throughput sequencing experiments related to SCI resulting in both raw and normalized data that can be efficiently mined for comparisons across studies, as well as homologous discovery across species. We have processed 1196 publicly available RNA-Seq samples from 50 bulk RNA-Seq studies across nine different species, resulting in an SQLite database that can be used by the SCI research community for further discovery. We provide both the database as well as a web-based front-end that can be used to query the database for genes of interest, differential gene expression, genes with high variance, and gene set enrichments. [ABSTRACT FROM AUTHOR]

Glioma is the most prevalent malignant brain tumour. Currently, reshaping its tumour microenvironment has emerged as an appealing strategy to enhance therapeutic efficacy. As the largest group of transmembrane transport proteins, solute carrier proteins (SLCs) are responsible for the transmembrane transport of various metabolites and ions. They play a crucial role in regulating the metabolism and functions of malignant cells and immune cells within the tumour microenvironment, making them a promising target in cancer therapy. Through multidimensional data analysis and experimental validation, we investigated the genetic landscape of SLCs in glioma. We established a classification system comprising 7‐SLCs to predict the prognosis of glioma patients and their potential responses to immunotherapy and chemotherapy. Our findings unveiled specific SLC expression patterns and their correlation with the immune‐suppressive microenvironment and metabolic status. The 7‐SLC classification system was validated in distinguishing subgroups within the microenvironment, specifically identifying subsets involving malignant cells and tumour‐associated macrophages. Furthermore, the orphan protein SLC43A3, a core member of the 7‐SLC classification system, was identified as a key facilitator of tumour cell proliferation and migration, suggesting its potential as a novel target for cancer therapy. [ABSTRACT FROM AUTHOR]

Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinomas (ccRCCs) epitomize the most formidable clinical subtype among renal neoplasms. While the impact of tumor-associated fibroblasts on ccRCC progression is duly acknowledged, a paucity of literature exists elucidating the intricate mechanisms and signaling pathways operative at the individual cellular level. Methods: Employing single-cell transcriptomic analysis, we meticulously curated UMAP profiles spanning substantial ccRCC populations, delving into the composition and intrinsic signaling pathways of these cohorts. Additionally, Myofibroblasts were fastidiously categorized into discrete subpopulations, with a thorough elucidation of the temporal trajectory relationships between these subpopulations. We further probed the cellular interaction pathways connecting pivotal subpopulations with tumors. Our endeavor also encompassed the identification of prognostic genes associated with these subpopulations through Bulk RNA-seq, subsequently validated through empirical experimentation. Results: A notable escalation in the nFeature and nCount of Myofibroblasts and EPCs within ccRCCs was observed, notably enriched in oxidation-related pathways. This phenomenon is postulated to be closely associated with the heightened metabolic activities of Myofibroblasts and EPCs. The Myofibroblasts subpopulation, denoted as C3 HMGA1+ Myofibroblasts, emerges as a pivotal subset, displaying low differentiation and positioning itself at the terminal point of the temporal trajectory. Intriguingly, these cells exhibit a high degree of interaction with tumor cells through the MPZ signaling pathway network, suggesting that Myofibroblasts may facilitate tumor progression via this pathway. Prognostic genes associated with C3 were identified, among which TUBB3 is implicated in potential resistance to tumor recurrence. Finally, experimental validation revealed that the knockout of the key gene within the MPZ pathway, MPZL1, can inhibit tumor activity, proliferation, invasion, and migration capabilities. Conclusion: This investigation delves into the intricate mechanisms and interaction pathways between Myofibroblasts and ccRCCs at the single-cell level. We propose that targeting MPZL1 and the oxidative phosphorylation pathway could serve as potential key targets for treating the progression and recurrence of ccRCC. This discovery paves the way for new directions in the treatment and prognosis diagnosis of ccRCC in the future. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to investigate the role of BMP2 in hepatocellular carcinoma (HCC) growth and metastasis using a dual approach combining single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq. Methods: scRNA-seq data from the GEO database and bulk RNA-seq data from the TCGA database were analyzed. Differentially expressed marker genes of endothelial cells were identified and analyzed using enrichment analysis, PPI analysis, correlation analysis, and GSEA. In vitro, experiments were conducted using the Huh-7 HCC cell line, and in vivo, models of HCC growth and metastasis were established by knocking down BMP2. Results: The scRNA-seq analysis identified BMP2 as a key marker gene in endothelial cells of HCC samples. Elevated BMP2 expression correlated with poor prognosis in HCC. In vitro experiments showed that silencing BMP2 inhibited the proliferation, migration, and invasion of liver cancer cells. In vivo studies confirmed increased BMP2 expression in HCC tissues, promoting angiogenesis and HCC growth. Conclusion: This study highlights the role of BMP2 in tumor angiogenesis and HCC progression. Targeting BMP2 could be a promising therapeutic strategy against HCC.

Transcriptomic studies are an important tool for understanding the molecular pathways underlying host plant use by agricultural pests, including vectors of damaging plant pathogens. Thus far, bulk RNA-Seq has been the main approach for non-model insects. This method relies on pooling large numbers of whole organisms or hundreds of individually dissected organs. The latter approach is logistically challenging, may introduce artifacts of handling and storage, and is not compatible with biological replication. Here, we tested an approach to generate transcriptomes of individual salivary glands and other low-input body tissues from whiteflies (Bemisia tabaci MEAM1), which are major vectors of plant viruses. By comparing our outputs to published bulk RNA-Seq datasets for whole whitefly bodies and pools of salivary glands, we demonstrate that this approach recovers similar numbers of transcripts relative to bulk RNA-Seq in a tissue-specific manner, and for some metrics, exceeds performance of bulk tissue RNA-Seq. Libraries generated from individual salivary glands also yielded additional novel transcripts not identified in pooled salivary gland datasets, and had hundreds of enriched transcripts when compared with whole head tissues. Overall, our study demonstrates that it is feasible to produce high quality, replicated transcriptomes of whitefly salivary glands and other low-input tissues. We anticipate that our approach will expand hypothesis-driven research on salivary glands of whiteflies and other Hemiptera, thus enabling novel control strategies to disrupt feeding and virus transmission.

Abstract Wilms tumor (WT) is the most common malignancy of the genitourinary system in children. Currently, the Integration of single-cell RNA sequencing (scRNA-Seq) and Bulk RNA sequencing (RNA-Seq) analysis of heterogeneity between different cell types in pediatric WT tissues could more accurately find prognostic markers, but this is lacking. RNA-Seq and clinical data related to WT were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Small nucleolar RNA host gene 15 (SNHG15) was identified as a risk signature from the TARGET dataset by using weighted gene co-expression network analysis, differentially expressed analysis and univariate Cox analysis. After that, the functional mechanisms, immunological and molecular characterization of SNHG15 were investigated at the scRNA-seq, pan-cancer, and RNA-seq levels using Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), ESTIMATE, and CIBERSORT. Based on scRNA-seq data, we identified 20 clusters in WT and annotated 10 cell types. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing M2 macrophages as hubs for intercellular communication. In addition, in vitro cellular experiments showed that siRNAs interfering with SNHG15 significantly inhibited the proliferation and migration of G401 cells and promoted the apoptosis of G401 cells compared with the control group. The effect of siRNAs interfering with SNHG15 on EMT-related protein expression was verified by Western blotting assay. Thus, our findings will improve our current understanding of the pathogenesis of WT, and they are potentially valuable in providing novel prognosis markers for the treatment of WT.

Abstract Background Polycystic ovary syndrome (PCOS) is one of the most common reproductive endocrine disorders in females of childbearing age. Various types of ovarian cells work together to maintain normal reproductive function, whose discordance often takes part in the development and progression of PCOS. Understanding the cellular heterogeneity and compositions of ovarian cells would provide insight into PCOS pathogenesis, but are, however, not well understood. Transcriptomic characterization of cells isolated from PCOS cases have been assessed using bulk RNA-seq but cells isolated contain a mixture of many ovarian cell types. Methods Here we utilized the reference scRNA-seq data from human adult ovaries to deconvolute and estimate cell proportions and dysfunction of ovarian cells in PCOS, by integrating various granulosa cells(GCs) transcriptomic data. Results We successfully defined 22 distinct cell clusters of human ovarian cells. Then after transcriptome integration, we obtained a gene expression matrix with 13,904 genes within 30 samples (15 control vs. 15 PCOS). Subsequent deconvolution analysis revealed decreased proportion of small antral GCs and increased proportion of KRT8 high mural GCs, HTRA1 high cumulus cells in PCOS, especially increased differentiation from small antral GCs to KRT8 high mural GCs. For theca cells, the abundance of internal theca cells (TCs) and external TCs was both increased. Less TCF21 high stroma cells (SCs) and more STAR high SCs were observed. The proportions of NK cells and monocytes were decreased, and T cells occupied more in PCOS and communicated stronger with inTCs and exTCs. In the end, we predicted the candidate drugs which could be used to correct the proportion of ovarian cells in patients with PCOS. Conclusions Taken together, this study provides insights into the molecular alterations and cellular compositions in PCOS ovarian tissue. The findings might contribute to our understanding of PCOS pathophysiology and offer resource for PCOS basic research.

Abstract Background Low back pain is a leading cause of disability worldwide and is frequently attributed to intervertebral disc (IVD) degeneration. Though the contributions of the adjacent cartilage endplates (CEP) to IVD degeneration are well documented, the phenotype and functions of the resident CEP cells are critically understudied. To better characterize CEP cell phenotype and possible mechanisms of CEP degeneration, bulk and single-cell RNA sequencing of non-degenerated and degenerated CEP cells were performed. Methods Human lumbar CEP cells from degenerated (Thompson grade ≥ 4) and non-degenerated (Thompson grade ≤ 2) discs were expanded for bulk (N=4 non-degenerated, N=4 degenerated) and single-cell (N=1 non-degenerated, N=1 degenerated) RNA sequencing. Genes identified from bulk RNA sequencing were categorized by function and their expression in non-degenerated and degenerated CEP cells were compared. A PubMed literature review was also performed to determine which genes were previously identified and studied in the CEP, IVD, and other cartilaginous tissues. For single-cell RNA sequencing, different cell clusters were resolved using unsupervised clustering and functional annotation. Differential gene expression analysis and Gene Ontology, respectively, were used to compare gene expression and functional enrichment between cell clusters, as well as between non-degenerated and degenerated CEP samples. Results Bulk RNA sequencing revealed 38 genes were significantly upregulated and 15 genes were significantly downregulated in degenerated CEP cells relative to non-degenerated cells (|fold change| ≥ 1.5). Of these, only 2 genes were previously studied in CEP cells, and 31 were previously studied in the IVD and other cartilaginous tissues. Single-cell RNA sequencing revealed 11 unique cell clusters, including multiple chondrocyte and progenitor subpopulations with distinct gene expression and functional profiles. Analysis of genes in the bulk RNA sequencing dataset showed that progenitor cell clusters from both samples were enriched in “non-degenerated” genes but not “degenerated” genes. For both bulk- and single-cell analyses, gene expression and pathway enrichment analyses highlighted several pathways that may regulate CEP degeneration, including transcriptional regulation, translational regulation, intracellular transport, and mitochondrial dysfunction. Conclusions This thorough analysis using RNA sequencing methods highlighted numerous differences between non-degenerated and degenerated CEP cells, the phenotypic heterogeneity of CEP cells, and several pathways of interest that may be relevant in CEP degeneration.

Background: Cervical carcinoma (CC) represents a prevalent gynecological neoplasm, with a discernible rise in prevalence among younger cohorts observed in recent years. Nonetheless, the intrinsic cellular heterogeneity of CC remains inadequately investigated. Methods: We utilized single-cell RNA sequencing (scRNA-seq) transcriptomic analysis to scrutinize the tumor epithelial cells derived from four specimens of cervical carcinoma (CC) patients. This method enabled the identification of pivotal subpopulations of tumor epithelial cells and elucidation of their contributions to CC progression. Subsequently, we assessed the influence of associated molecules in bulk RNA sequencing (Bulk RNA-seq) cohorts and performed cellular experiments for validation purposes. Results: Through our analysis, we have discerned C3 PLP2+ Tumor Epithelial Progenitor Cells as a noteworthy subpopulation in cervical carcinoma (CC), exerting a pivotal influence on the differentiation and progression of CC. We have established an independent prognostic indicator--the PLP2+ Tumor EPCs score. By stratifying patients into high and low score groups based on the median score, we have observed that the high-score group exhibits diminished survival rates compared to the low-score group. The correlations observed between these groups and immune infiltration, enriched pathways, single-nucleotide polymorphisms (SNPs), drug sensitivity, among other factors, further underscore their impact on CC prognosis. Cellular experiments have validated the significant impact of ATF6 on the proliferation and migration of CC cell lines. Conclusion: This study enriches our comprehension of the determinants shaping the progression of CC, elevates cognizance of the tumor microenvironment in CC, and offers valuable insights for prospective CC therapies. These discoveries contribute to the refinement of CC diagnostics and the formulation of optimal therapeutic approaches. [ABSTRACT FROM AUTHOR]

Background: High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice. Methods: We jointly analyzed the transcriptomic profiles of single-cell and bulk datasets of HGSOC to identify cell types associated with chemoresistance. Copy number variation (CNV) inference was performed to identify malignant cells. We subsequently analyzed the expression of candidate biomarkers and their relationship with patients' prognosis. The enrichment analysis and potential biological function of candidate biomarkers were explored. Then, we validated the candidate biomarker using in vitro experiments. Results: We identified 8871 malignant epithelial cells in a single-cell RNA sequencing dataset, of which 861 cells were associated with chemoresistance. Among these malignant epithelial cells, FBXO2 (F-box protein 2) is highly expressed in cells related to chemoresistance. Moreover, FBXO2 expression was found to be higher in epithelial cells from chemoresistance samples compared to those from chemosensitivity samples in a separate single-cell RNA sequencing dataset. Patients exhibiting elevated levels of FBXO2 experienced poorer outcomes in terms of both overall survival (OS) and progression-free survival (PFS). FBXO2 could impact chemoresistance by influencing the PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interactions and regulating tumorigenesis. The 50% maximum inhibitory concentration (IC50) of cisplatin decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2 during an in vitro experiment. Conclusions: We determined that FBXO2 is a potential biomarker linked to chemoresistance in HGSOC by combining single-cell RNA-seq and bulk RNA-seq dataset. Our results suggest that FBXO2 could serve as a valuable prognostic marker and potential target for drug development in HGSOC.

In studying the molecular underpinning of spermatogenesis, we expect to understand the fundamental biological processes better and potentially identify genes that may lead to novel diagnostic and therapeutic strategies toward precision medicine in male infertility. In this review, we emphasized our perspective that the path forward necessitates integrative studies that rely on complementary approaches and types of data. To comprehensively analyze spermatogenesis, this review proposes four axes of integration. First, spanning the analysis of spermatogenesis in the healthy state alongside pathologies. Second, the experimental analysis of model systems (in which we can deploy treatments and perturbations) alongside human data. Third, the phenotype is measured alongside its underlying molecular profiles using known markers augmented with unbiased profiles. Finally, the testicular cells are studied as ecosystems, analyzing the germ cells alongside the states observed in the supporting somatic cells. Recently, the study of spermatogenesis has been advancing using single-cell RNA sequencing, where scientists have uncovered the unique stages of germ cell development in mice, revealing new regulators of spermatogenesis and previously unknown cell subtypes in the testis. An in-depth analysis of meiotic and postmeiotic stages led to the discovery of marker genes for spermatogonia, Sertoli and Leydig cells and further elucidated all the other germline and somatic cells in the testis microenvironment in normal and pathogenic conditions. The outcome of an integrative analysis of spermatogenesis using advanced molecular profiling technologies such as scRNA-seq has already propelled our biological understanding, with additional studies expected to have clinical implications for the study of male fertility. By uncovering new genes and pathways involved in abnormal spermatogenesis, we may gain insights into subfertility or sterility.

Zhiyong Tan,1– 3,* Xiaorong Chen,4,* Jieming Zuo,1– 3,* Shi Fu,1– 3,* Jiansong Wang,1– 3 Haifeng Wang1– 3 1Department of Urology, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 2Urological Disease Clinical Medical Center of Yunnan Province, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 3Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 4Department of Kidney Transplantation, the Third Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jiansong Wang; Haifeng Wang, Department of Urology, the Second Affiliated Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, 650101, People’s Republic of China, Email wangjiansong@kmmu.edu.cn; t13085348360@163.comBackground: As known abnormal sialylation exerts crucial roles in the growth, metastasis, and immune evasion of cancers, but the molecular characteristics and roles in bladder cancer (BLCA) remain unclear. This study intends to establish BLCA risk stratification based on sialylation-related genes and elucidate its role in prognosis, tumor microenvironment, and immunotherapy of BLCA.Methods: Bulk RNA-seq and scRNA-seq data were downloaded from open-access databases. The scRNA-seq data were processed using the R package “Seurat” to identify the core cell types. The tumor sub-typing of BLCA samples was performed by the R package “ConsensusClusterPlus” in the bulk RNA-seq data. Signature genes were identified by the R package “limma” and univariate regression analysis to calculate risk scores using the R package “GSVA” and establish risk stratification of BLCA patients. Finally, the differences in clinicopathological characteristics, tumor microenvironment, and immunotherapy efficacy between the different groups were investigated.Results: 5 core cell types were identified in the scRNA-seq dataset, with monocytes and macrophages presenting the greatest percentage, sialylation-related gene expression, and sialylation scores. The bulk RNA-seq samples were classified into 3 tumor subtypes based on 19 prognosis-related sialylation genes. The 10 differential expressed genes (DEGs) with the smallest p-values were collected as signature genes, and the risk score was calculated, with the samples divided into high and low-risk score groups. The results showed that patients in the high-risk score group exhibited worse survival outcomes, higher tumor grade, more advanced stage, more frequency of gene mutations, higher expression levels of immune checkpoints, and lower immunotherapy response.Conclusion: We established a novel risk stratification of BLCA from a glycomics perspective, which demonstrated good accuracy in determining the prognostic outcome, clinicopathological characteristics, immune microenvironment, and immunotherapy efficacy of patients, and we are proposing to apply it to direct the choice of clinical treatment options for patients.Keywords: bladder cancer, bulk RNA-seq, scRNA-seq, sialylation, tumor microenvironment, immunotherapy

Abstract The two most prevalent subtypes of epithelial ovarian carcinoma (EOC) are ovarian clear cell carcinoma (OCCC) and high-grade serous ovarian carcinoma (HGSC). Patients with OCCC have a poor prognosis than those with HGSC due to chemoresistance, implying the need for novel treatment target. In this study, we applied single-cell RNA sequencing (scRNA-seq) together with bulk RNA-seq data from the GEO (Gene Expression Omnibus) database (the GSE189553 dataset) to characterize and compare tumor heterogeneity and cell-level evolution between OCCC and HGSC samples. To begin, we found that the smaller proportion of an epithelial OCCC cell subset in the G2/M phase might explain OCCC chemoresistance. Second, we identified a possible pathogenic OCCC epithelial cell subcluster that overexpresses LEFTY1. Third, novel biomarkers separating OCCC from HGSC were discovered and subsequently validated on a wide scale using immunohistochemistry. Amine oxidase copper containing 1 (AOC1) was preferentially expressed in OCCC over HGSC, while S100 calcium-binding protein A2 (S100A2) was detected less frequently in OCCC than in HGSC. In addition, we discovered that metabolic pathways were enriched in the epithelial compartment of the OCCC samples. In vitro experiments verified that inhibition of oxidative phosphorylation or glycolysis pathways exerted direct antitumor effects on both OCCC and HGSC cells, while targeting glutamine metabolism or ferroptosis greatly attenuated chemosensitivity only in OCCC cells. Finally, to determine whether there were any variations in immune cell subsets between OCCC and HGSC, data from scRNA-seq and mass cytometry were pooled for analysis. In summary, our work provides the first holistic insights into the cellular and molecular distinctions between OCCC and HGSC and is a valuable source for discovering new targets to leverage in clinical treatments to improve the poor prognosis of patients with OCCC.

Abstract Asthma exacerbations in children are associated with respiratory viral infection and atopy, resulting in systemic immune activation and infiltration of immune cells into the airways. The gene networks driving the immune activation and subsequent migration of immune cells into the airways remains incompletely understood. Cellular and molecular profiling of PBMC was employed on paired samples obtained from atopic asthmatic children (n = 19) during acute virus-associated exacerbations and later during convalescence. Systems level analyses were employed to identify coexpression networks and infer the drivers of these networks, and validation was subsequently obtained via independent samples from asthmatic children. During exacerbations, PBMC exhibited significant changes in immune cell abundance and upregulation of complex interlinked networks of coexpressed genes. These were associated with priming of innate immunity, inflammatory and remodelling functions. We identified activation signatures downstream of bacterial LPS, glucocorticoids and TGFB1. We also confirmed that LPS binding protein was upregulated at the protein-level in plasma. Multiple gene networks known to be involved positively or negatively in asthma pathogenesis, are upregulated in circulating PBMC during acute exacerbations, supporting the hypothesis that systemic pre-programming of potentially pathogenic as well as protective functions of circulating immune cells preceeds migration into the airways. Enhanced sensitivity to LPS is likely to modulate the severity of acute asthma exacerbations through exposure to environmental LPS.

Background: Malignant tumours, particularly non-small cell lung cancer (NSCLC), pose a significant threat to human health due to their prevalence and lethality. Treatment methods for NSCLC vary greatly among individuals, making it crucial to identify predictive markers. Moreover, during tumour initiation and progression, tumour cells can release signaling molecules to induce polarization of macrophages towards a more tumour friendly M2 phenotype, which can promote tumour growth, metastasis, and drug resistance. Methods: We employed a comprehensive approach, combining bulk RNA-seq and single-cell sequencing analysis. Results: In our study, we used bulk RNA-seq and single-cell sequencing methods to analyze differential cells in NSCLC and adjacent tissues, searching for relevant marker genes that can predict prognosis and drug efficacy. We scrutinized biological phenomena such as macrophage-related gene methylation, copy number variation, and alternative splicing. Additionally, we utilized a co-culture technique of immune and tumour cells to explore the role of these genes in macrophage polarization. Our findings revealed distinct differences in macrophages between cancerous and adjacent tissues. We identified ANP32A, CCL20, ERAP2, MYD88, TMEM126B, TUBB6, and ZNF655 as macrophage-related genes that correlate with NSCLC patient prognosis and immunotherapy efficacy. Notably, ERAP2, TUBB6, CCL20, and TMEM126B can induce macrophage M0 to M2 polarization, promoting tumour proliferation. Conclusion: These findings significantly contribute to our understanding of the NSCLC tumour immune microenvironment. They pave the way for further research into the potential of these genes as targets for regulating tumour occurrence and development.

Background: Cuproptosis is a novel mode of cell death, which is strongly related to energy metabolism in mitochondria and regulated by protein lipoylation. Currently, the molecular mechanisms of cuproptosis-related genes (CRGs) involved in systemic lupus erythematosus (SLE) largely remained unclear, our study is aimed to explore the mechanisms of cuproptosis and CRGs involved in SLE. Methods: Bulk RNA-seq datasets were collected to display the expressions of CRGs in peripheral blood mononuclear cells (PBMCs) of SLE and healthy individuals, and then ROC analysis was used to establish the diagnostic models of CRGs. Next, the immune infiltration analyses were applied to reveal the difference of immune cells infiltration in LIAS-low and LIAS-high group. Additionally, WGCNA analysis was performed to find the gene modules significantly correlated with the LIAS expression level. We also performed the functional enrichment analyses for LIAS-related gene modules to determine the potential pathways involved in the development of SLE. Finally, scRNA-seq dataset was used to cluster immune cell subsets, reveal the activated pathways, and study cell-cell interactions in LIAS-low and LIAS-high cells. Result: We found CDKN2A was significantly increased and LIAS was significantly decreased in SLE patients compared with healthy individuals. The AUC score showed that LIAS had a great diagnostic value than other CRGs. Additionally, the results of immune infiltration analyses showed that immune cells proportion were diverse in LIAS-low and LIAS-high samples. The gene sets related to LIAS expression level were involved in dephosphorylation of JAK1 by SHP1, phosphorylation of STAT2, cytokine signaling in immune system, expression of interferon-alpha and beta, inhibition of JAK kinase activity by SOCS1/3, and so on. Finally, the results of cell-cell communication showed that CCL- (CCL5 + CCR1) and ANNEXIN- (ANXA1 + FPR1) might play an essential role in the communication network between LIAS-low and LIAS-high cells. Conclusion: Above findings inferred that LIAS-mediated cuproptosis might involve in a comprehensive cellular and molecular mechanism to cause the occurrence and development of SLE. [ABSTRACT FROM AUTHOR]

Abstract Background Heligmosomoides bakeri (often mistaken for Heligmosomoides polygyrus) is a promising model for parasitic nematodes with the key advantage of being amenable to study and manipulation within a controlled laboratory environment. While draft genome sequences are available for this worm, which allow for comparative genomic analyses between nematodes, there is a notable lack of information on its gene expression. Methods We generated biologically replicated RNA-seq datasets from samples taken throughout the parasitic life of H. bakeri. RNA from tissue-dwelling and lumen-dwelling worms, collected under a dissection microscope, was sequenced on an Illumina platform. Results We find extensive transcriptional sexual dimorphism throughout the fourth larval and adult stages of this parasite and identify alternative splicing, glycosylation, and ubiquitination as particularly important processes for establishing and/or maintaining sex-specific gene expression in this species. We find sex-linked differences in transcription related to aging and oxidative and osmotic stress responses. We observe a starvation-like signature among transcripts whose expression is consistently upregulated in males, which may reflect a higher energy expenditure by male worms. We detect evidence of increased importance for anaerobic respiration among the adult worms, which coincides with the parasite’s migration into the physiologically hypoxic environment of the intestinal lumen. Furthermore, we hypothesize that oxygen concentration may be an important driver of the worms encysting in the intestinal mucosa as larvae, which not only fully exposes the worms to their host’s immune system but also shapes many of the interactions between the host and parasite. We find stage- and sex-specific variation in the expression of immunomodulatory genes and in anthelmintic targets. Conclusions We examine how different the male and female worms are at the molecular level and describe major developmental events that occur in the worm, which extend our understanding of the interactions between this parasite and its host. In addition to generating new hypotheses for follow-up experiments into the worm’s behavior, physiology, and metabolism, our datasets enable future more in-depth comparisons between nematodes to better define the utility of H. bakeri as a model for parasitic nematodes in general. Graphical Abstract

Background: Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM. Methods: We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes. Results: We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors. Conclusions: By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM. [ABSTRACT FROM AUTHOR]

The two most prevalent subtypes of epithelial ovarian carcinoma (EOC) are ovarian clear cell carcinoma (OCCC) and high-grade serous ovarian carcinoma (HGSC). Patients with OCCC have a poor prognosis than those with HGSC due to chemoresistance, implying the need for novel treatment target. In this study, we applied single-cell RNA sequencing (scRNA-seq) together with bulk RNA-seq data from the GEO (Gene Expression Omnibus) database (the GSE189553 dataset) to characterize and compare tumor heterogeneity and cell-level evolution between OCCC and HGSC samples. To begin, we found that the smaller proportion of an epithelial OCCC cell subset in the G2/M phase might explain OCCC chemoresistance. Second, we identified a possible pathogenic OCCC epithelial cell subcluster that overexpresses LEFTY1. Third, novel biomarkers separating OCCC from HGSC were discovered and subsequently validated on a wide scale using immunohistochemistry. Amine oxidase copper containing 1 (AOC1) was preferentially expressed in OCCC over HGSC, while S100 calcium-binding protein A2 (S100A2) was detected less frequently in OCCC than in HGSC. In addition, we discovered that metabolic pathways were enriched in the epithelial compartment of the OCCC samples. In vitro experiments verified that inhibition of oxidative phosphorylation or glycolysis pathways exerted direct antitumor effects on both OCCC and HGSC cells, while targeting glutamine metabolism or ferroptosis greatly attenuated chemosensitivity only in OCCC cells. Finally, to determine whether there were any variations in immune cell subsets between OCCC and HGSC, data from scRNA-seq and mass cytometry were pooled for analysis. In summary, our work provides the first holistic insights into the cellular and molecular distinctions between OCCC and HGSC and is a valuable source for discovering new targets to leverage in clinical treatments to improve the poor prognosis of patients with OCCC. [ABSTRACT FROM AUTHOR]

Asthma exacerbations in children are associated with respiratory viral infection and atopy, resulting in systemic immune activation and infiltration of immune cells into the airways. The gene networks driving the immune activation and subsequent migration of immune cells into the airways remains incompletely understood. Cellular and molecular profiling of PBMC was employed on paired samples obtained from atopic asthmatic children (n = 19) during acute virus-associated exacerbations and later during convalescence. Systems level analyses were employed to identify coexpression networks and infer the drivers of these networks, and validation was subsequently obtained via independent samples from asthmatic children. During exacerbations, PBMC exhibited significant changes in immune cell abundance and upregulation of complex interlinked networks of coexpressed genes. These were associated with priming of innate immunity, inflammatory and remodelling functions. We identified activation signatures downstream of bacterial LPS, glucocorticoids and TGFB1. We also confirmed that LPS binding protein was upregulated at the protein-level in plasma. Multiple gene networks known to be involved positively or negatively in asthma pathogenesis, are upregulated in circulating PBMC during acute exacerbations, supporting the hypothesis that systemic pre-programming of potentially pathogenic as well as protective functions of circulating immune cells preceeds migration into the airways. Enhanced sensitivity to LPS is likely to modulate the severity of acute asthma exacerbations through exposure to environmental LPS. [ABSTRACT FROM AUTHOR]

Abstract Background The prognostic management of bladder cancer (BLCA) remains a great challenge for clinicians. Recently, bulk RNA-seq sequencing data have been used as a prognostic marker for many cancers but do not accurately detect core cellular and molecular functions in tumor cells. In the current study, bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data were combined to construct a prognostic model of BLCA. Methods BLCA scRNA-seq data were downloaded from Gene Expression Omnibus (GEO) database. Bulk RNA-seq data were obtained from the UCSC Xena. The R package "Seurat" was used for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were utilized for downscaling and cluster identification. The FindAllMarkers function was used to identify marker genes for each cluster. The limma package was used to obtain differentially expressed genes (DEGs) affecting overall survival (OS) in BLCA patients. Weighted gene correlation network analysis (WGCNA) was used to identify BLCA key modules. The intersection of marker genes of core cells and genes of BLCA key modules and DEGs was used to construct a prognostic model by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between the high and low-risk groups were also investigated. Results scRNA-seq data were analyzed to identify 19 cell subpopulations and 7 core cell types. The ssGSEA showed that all 7 core cell types were significantly downregulated in tumor samples of BLCA. We identified 474 marker genes from the scRNA-seq dataset, 1556 DEGs from the Bulk RNA-seq dataset, and 2334 genes associated with a key module identified by WGCNA. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 3 signature genes, namely MAP1B, PCOLCE2, and ELN. The feasibility of the model was validated by an internal training set and two external validation sets. Moreover, patients with high-risk scores are predisposed to experience poor OS, a larger prevalence of stage III-IV, a greater TMB, a higher infiltration of immune cells, and a lesser likelihood of responding favorably to immunotherapy. Conclusion By integrating scRNA-seq and bulk RNA-seq data, we constructed a novel prognostic model to predict the survival of BLCA patients. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.