Identification of functional heterogeneity of immune cells and tubular-immune cellular interplay action in diabetic kidney disease.

Background: Renal inflammation plays key roles in the pathogenesis of diabetic kidney disease (DKD). Immune cell infiltration is the main pathological feature in the progression of DKD. Sodium glucose cotransporter 2 inhibitor (SGLT2i) were reported to have antiinflammatory effects on DKD. While the heterogeneity and molecular basis of the pathogenesis and treatment with SGLT2i in DKD remains poorly understood. Methods: To address this question, we performed a single-cell transcriptomics data analysis and cell cross-talk analysis based on the database (GSE181382). The single-cell transcriptome analysis findings were validated using multiplex immunostaining. Results: A total of 58760 cells are categorized into 25 distinct cell types. A subset of macrophages with anti-inflammatory potential was identified. We found that Ccl3+ (S100a8/a9 high) macrophages with anti-inflammatory and antimicrobial in the pathogenesis of DKD decreased and reversed the dapagliflozin treatment. Besides, dapagliflozin treatment enhanced the accumulation of Pck1+ macrophage, characterized by gluconeogenesis signaling pathway. Cell-cross talk analysis showed the GRN/SORT1 pair and CD74 related signaling pathways were enriched in the interactions between tubular epithelial cells and immune cells. Conclusions: Our study depicts the heterogeneity of macrophages and clarifies a new possible explanation of dapagliflozin treatment, showing the metabolism shifts toward gluconeogenesis in macrophages, fueling the anti-inflammatory function of M2 macrophages, highlighting the new molecular features and signaling pathways and potential therapeutic targets, which has provided an important reference for the study of immune-related mechanisms in the progression of the disease.

Integrated Single-cell RNA-seq and Bulk RNA-seq Identify Diagnostic Biomarkers for Postmenopausal Osteoporosis.

AIM: We aimed to explore diagnostic biomarkers of postmenopausal osteoporosis (PMOP). BACKGROUND: PMOP brings enormous physical and economic burden to elderly women. OBJECTIVES: This study aims to screen new biomarkers for osteoporosis, providing insights for early diagnosis and therapeutic targets of osteoporosis. METHODS: Weighted gene co-expression network analysis (WGCNA) was applied to identify osteoporosis-related hub genes. Single-cell transcriptomic atlas of osteoporosis was depicted and the heterogeneity of monocytes was analyzed, based on which the biomarkers for osteoporosis were screened. Gene set enrichment analysis (GSEA) was conducted on the biomarkers. The diagnostic model (nomogram) was established and evaluated based on the expression levels of biomarkers. Additionally, the transcription factor (TF) regulatory network was constructed to predict the potential TF and targeted miRNA of biomarkers. The drugs with significant correlation with biomarkers were identified by Spearman correlation analysis. RESULTS: We obtained 30 osteoporosis-associated hub genes. 9 cell types were identified, and the monocytes were subdivided to 4 subtypes. Three biomarkers, DHX29, LSM5, and UBE2V2, were screened. DHX29 and UBE2V2 were highly expressed in non-classical monocytes, while LSM5 exhibited the highest expression in other monocytes, followed by non-classical monocytes. GSEA indicated that osteoporosis may be correlated with vascular calcification and the biomarkers may be involved in the formation of immune cells. Then, nomogram was constructed and exhibited good robustness. In addition, MYC and SETDB1 were the shared IF in three biomarkers, which may play critical regulatory roles in the progression of osteoporosis. Moreover, 41, 49, and 68 drugs appeared significant correlations with DHX29, LSM5, and UBE2V2, respectively. CONCLUSION: This study provided a basis for early diagnosis and targeted treatment of osteoporosis.

HPV-driven heterogeneity in cervical cancer: study on the role of epithelial cells and myofibroblasts in the tumor progression based on single-cell RNA sequencing analysis.

Background: Cervical cancer (CC) is a neoplasia with a high heterogeneity. We aimed to explore the characteristics of tumor microenvironment (TME) for CC treatment. Methods: HPV positive (+) and negative (-) samples from cervical cancer (CC) patients were sourced from the Gene Expression Omnibus (GEO) database. The single-cell RNA sequencing (scRNA-seq) data were processed and annotated for cell types utilizing the Seurat package. Following this, the expression levels and biological roles of the marker genes were analyzed applying real-time PCR (RT-PCR) and transwell assays. Furthermore, the enrichment of genes with significantly differential expressions and copy number variations was assessed by the ClusterProlifer and inferCNV software packages. Results: Seven main cell clusters were classified based on a total of 12,431 cells. The HPV- CC samples exhibited a higher immune cell infiltration level, while epithelial cells and myofibroblasts had higher proportion in the HPV+ CC samples with extensive heterogeneity. Immune pathways including antigen treatment and presentation, immunoglobulin production and T cell mediated immunity were significantly activated in the HPV- CC group with lower cell cycle and proliferation activity. However, the anti-tumor immunity of these cells was inhibited in HPV+ CC group with higher cell proliferation activity. Moreover, the amplification and loss of CNVs also supported that these cells in HPV- CC samples were prone to anti-tumor activation. Further cell validation results showed that except GZMA, the levels of APOC1, CEACAM6, FOXP3, SFRP4 and TFF3 were all higher in CC cells Hela, and that silencing TFF3 could inhibit the migration and invasion of CC cells in-vitro. Conclusion: This study highlighted the critical role of HPV infection in CC progression, providing a novel molecular basis for optimizing the current preventive screening and personalized treatment for the cancer.

Upregulation of CKS2 in immunosuppressive cells is associated with metastasis and poor prognosis in prostate cancer: a single-cell RNA-sequencing analysis.

Background: Metastasis worsens prostate cancer (PCa) prognosis, with the immunosuppressive microenvironment playing a key role in bone metastasis. This study aimed to investigate how an immunosuppressive environment promotes PCa metastasis and worsens prognosis of patients with PCa. Methods: Candidate oncogenes were identified through analysis of the Gene Expression Omnibus (GEO) database. A prognostic model was developed for the purpose of identifying target genes. A single-cell RNA sequencing data from GEO database was used to analyze the localization of target genes in the tumor microenvironment. A pan-cancer analysis was conducted to study the cancer-causing potential of target genes across different types of tumors. Results: Fifty-one genes were found to be differentially expressed in bone metastasis compared to non-metastatic PCa, with CKS2 identified as the most significant gene associated with poor prognosis. CKS2 was shown to be linked to an immunosuppressive microenvironment and osteoclastic bone metastases, as shown by its negative correlation with immune cell infiltration and osteoblast-related gene expression. Moreover, CKS2 was found in immunosuppressive cells and was linked to bone metastasis in PCa. It was also overexpressed in different types of tumors, making it as an oncogenic gene. Conclusions: This research offers a new perspective on the potential utility of CKS2 as a therapeutic target for the prevention of metastatic PCa.

scRNA-Explorer: An End-user Online Tool for Single Cell RNA-seq Data Analysis Featuring Gene Correlation and Data Filtering.

In the majority of downstream analysis pipelines for single-cell RNA sequencing (scRNA-seq), techniques like dimensionality reduction and feature selection are employed to address the problem of high-dimensional nature of the data. These approaches involve mapping the data onto a lower-dimensional space, eliminating less informative genes, and pinpointing the most pertinent features. This process ultimately leads to a reduction in the number of dimensions used for downstream analysis, which in turn speeds up the computation of large-scale scRNA-seq data. Most approaches are directed to isolate from biological background the genes characterizing different cells and or the condition under study by establishing lists of differentially expressed or coexpressed genes. Herein, we present scRNA-Explorer an open-source online tool for simplified and rapid scRNA-seq analysis designed with the end user in mind. scRNA-Explorer utilizes: (i) Filtering out uninformative cells in an interactive manner via a web interface, (ii) Gene correlation analysis coupled with an extra step of evaluating the biological importance of these correlations, and (iii) Gene enrichment analysis of correlated genes in order to find gene implication in specific functions. We developed a pipeline to address the above problem. The scRNA-Explorer pipeline allows users to interrogate in an interactive manner scRNA-sequencing data sets to explore via gene expression correlations possible function(s) of a gene of interest. scRNA-Explorer can be accessed at https://bioinformatics.med.uoc.gr/shinyapps/app/scrnaexplorer.

Pain sensitivity genes as therapeutic targets in knee osteoarthritis: A comprehensive analysis.

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.

A Low Expression of NRF2 Enhances Oxidative Stress and Autophagy in Myofibroblasts, Promoting Progression of Chronic Obstructive Pulmonary Disease.

BACKGROUND: The inflammation phenotypes are often closely related to oxidative stress and autophagy pathway activation, which could be developed as a treatment target. AIMS: The aim of this study was to explore the underlying mechanism of inflammation in chronic obstructive pulmonary disease (COPD). METHODS: The lung tissue single-cell RNA-seq (scRNA-seq) dataset of GSE171541 was downloaded from the Gene Expression Omnibus (GEO) database. The marker genes were obtained from the CellMarker database. "Seurat" and "harmony" R packages were used for single-cell profiling analysis. Then, the "AUCell" R package was employed to calculate the reactive oxygen species (ROS) and autophagy pathway scores. Gene regulation network analysis was performed by applying the "SCENIC" package, followed by conducting correlation analysis with Spearman's rank correlation method. The cigarettes were used to develop a traumatic model in mice, and the expression of relevant genes was measured by qRT-PCR. RESULTS: The scRNA-seq analysis classified 12 cell subgroups in which the contractility of myofibroblasts was closely associated with the progression of COPD. Further analysis showed that ROS and autophagy pathways were significantly activated in myofibroblasts and that the nuclear factor erythroid 2-related factor 2 (NRF2) and its mediated oxidative stress pathway were inhibited in myofibroblasts. In addition, the downregulated NRF2 gene was negatively correlated with the expression of autophagy and ROS activation. In the traumatic mice model, NRF2 was downregulated in COPD mice but further elevated in the COPD+NRF2 mice group. Interestingly, the mRNA levels of Kelchlike ECH-associated protein 1 (Keap1), NADPH oxidase (NOX), and Cathepsin B (CTSB) were upregulated in COPD group in comparison to the control group but they were downregulated by NRF2. These results suggested that low-expressed NFR2 promoted autophagy and ROS pathway activation in myofibroblasts for COPD progression. CONCLUSION: We identified a cell myofibroblast cluster closely associated with COPD progression using the scRNA-seq analysis. The downregulated NFR2, as a key risk factor, mediated myofibroblast death by activating the oxidative stress and autophagy pathway for COPD progression.

Integrative bioinformatics approach yields a novel gene expression risk model for prognosis and progression prediction in prostate cancer.

Prostate cancer (PCa), a prevalent malignancy among elderly males, exhibits a notable rate of advancement, even when subjected to conventional androgen deprivation therapy or chemotherapy. An effective progression prediction model would prove invaluable in identifying patients with a higher progression risk. Using bioinformatics strategies, we integrated diverse data sets of PCa to construct a novel risk model predicated on gene expression and progression-free survival (PFS). The accuracy of the model was assessed through validation using an independent data set. Eight genes were discerned as independent prognostic factors and included in the prediction model. Patients assigned to the high-risk cohort demonstrated a diminished PFS, and the areas under the curve of our model in the validation set for 1-year, 3-year, and 5-year PFS were 0.9325, 0.9041 and 0.9070, respectively. Additionally, through the application of single-cell RNA sequencing to two castration-related prostate cancer (CRPC) samples and two hormone-related prostate cancer (HSPC) samples, we discovered that luminal cells within CRPC exhibited an elevated risk score. Subsequent molecular biology experiments corroborated our findings, illustrating heightened SYK expression levels within tumour tissues and its contribution to cancer cell migration. We found that the knockdown of SYK could inhibit migration in PCa cells. Our progression-related risk model demonstrated the potential prognostic value of SYK and indicated its potential as a target for future diagnosis and treatment strategies in PCa management.

Investigating the basis of lineage decisions and developmental trajectories in the dorsal spinal cord through pseudotime analyses.

Dorsal interneurons (dIs) in the spinal cord encode the perception of touch, pain, heat, itchiness and proprioception. Previous studies using genetic strategies in animal models have revealed important insights into dI development, but the molecular details of how dIs arise as distinct populations of neurons remain incomplete. We have developed a resource to investigate dI fate specification by combining a single-cell RNA-Seq atlas of mouse embryonic stem cell-derived dIs with pseudotime analyses. To validate this in silico resource as a useful tool, we used it to first identify genes that are candidates for directing the transition states that lead to distinct dI lineage trajectories, and then validated them using in situ hybridization analyses in the developing mouse spinal cord in vivo. We have also identified an endpoint of the dI5 lineage trajectory and found that dIs become more transcriptionally homogeneous during terminal differentiation. This study introduces a valuable tool for further discovery about the timing of gene expression during dI differentiation and demonstrates its utility in clarifying dI lineage relationships.

Co-differential genes between DKD and aging: implications for a diagnostic model of DKD.

Objective: Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus (DM) that is closely related to aging. In this study, we found co-differential genes between DKD and aging and established a diagnostic model of DKD based on these genes. Methods: Differentially expressed genes (DEGs) in DKD were screened using GEO datasets. The intersection of the DEGs of DKD and aging-related genes revealed DKD and aging co-differential genes. Based on this, a genetic diagnostic model for DKD was constructed using LASSO regression. The characteristics of these genes were investigated using consensus clustering, WGCNA, functional enrichment, and immune cell infiltration. Finally, the expression of diagnostic model genes was analyzed using single-cell RNA sequencing (scRNA-seq) in DKD mice (model constructed by streptozotocin (STZ) injection and confirmed by tissue section staining). Results: First, there were 159 common differential genes between DKD and aging, 15 of which were significant. These co-differential genes were involved in stress, glucolipid metabolism, and immunological functions. Second, a genetic diagnostic model (including IGF1, CETP, PCK1, FOS, and HSPA1A) was developed based on these genes. Validation of these model genes in scRNA-seq data revealed statistically significant variations in FOS, HSPA1A, and PCK1 gene expression between the early DKD and control groups. Validation of these model genes in the kidneys of DKD mice revealed that Igf1, Fos, Pck1, and Hspa1a had lower expression in DKD mice, with Igf1 expression being statistically significant. Conclusion: Our findings suggest that DKD and aging co-differential genes are significant in DKD diagnosis, providing a theoretical basis for novel research directions on DKD.

scRNA-seq revealed high stemness epithelial malignant cell clusters and prognostic models of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the sole causes of death in lung cancer patients. This study combined with single-cell RNA-seq analysis to identify tumor stem-related prognostic models to predict the prognosis of lung adenocarcinoma, chemotherapy agents, and immunotherapy efficacy. mRNA expression-based stemness index (mRNAsi) was determined by One Class Linear Regression (OCLR). Differentially expressed genes (DEGs) were detected by limma package. Single-cell RNA-seq analysis in GSE123902 dataset was performed using Seurat package. Weighted Co-Expression Network Analysis (WGCNA) was built by rms package. Cell differentiation ability was determined by CytoTRACE. Cell communication analysis was performed by CellCall and CellChat package. Prognosis model was constructed by 10 machine learning and 101 combinations. Drug predictive analysis was conducted by pRRophetic package. Immune microenvironment landscape was determined by ESTIMATE, MCP-Counter, ssGSEA analysis. Tumor samples have higher mRNAsi, and the high mRNAsi group presents a worse prognosis. Turquoise module was highly correlated with mRNAsi in TCGA-LUAD dataset. scRNA analysis showed that 22 epithelial cell clusters were obtained, and higher CSCs malignant epithelial cells have more complex cellular communication with other cells and presented dedifferentiation phenomenon. Cellular senescence and Hippo signaling pathway are the major difference pathways between high- and low CSCs malignant epithelial cells. The pseudo-temporal analysis shows that cluster1, 2, high CSC epithelial cells, are concentrated at the end of the differentiation trajectory. Finally, 13 genes were obtained by intersecting genes in turquoise module, Top200 genes in hdWGCNA, DEGs in high- and low- mRNAsi group as well as DEGs in tumor samples vs. normal group. Among 101 prognostic models, average c-index (0.71) was highest in CoxBoost + RSF model. The high-risk group samples had immunosuppressive status, higher tumor malignancy and low benefit from immunotherapy. This work found that malignant tumors and malignant epithelial cells have high CSC characteristics, and identified a model that could predict the prognosis, immune microenvironment, and immunotherapy of LUAD, based on CSC-related genes. These results provided reference value for the clinical diagnosis and treatment of LUAD.

Tumor-specific activation of folate receptor beta enables reprogramming of immune cells in the tumor microenvironment.

Folate receptors can perform folate transport, cell adhesion, and/or transcription factor functions. The beta isoform of the folate receptor (FRß) has attracted considerable attention as a biomarker for immunosuppressive macrophages and myeloid-derived suppressor cells, however, its role in immunosuppression remains uncharacterized. We demonstrate here that FRß cannot bind folate on healthy tissue macrophages, but does bind folate after macrophage incubation in anti-inflammatory cytokines or cancer cell-conditioned media. We further show that FRß becomes functionally active following macrophage infiltration into solid tumors, and we exploit this tumor-induced activation to target a toll-like receptor 7 agonist specifically to immunosuppressive myeloid cells in solid tumors without altering myeloid cells in healthy tissues. We then use single-cell RNA-seq to characterize the changes in gene expression induced by the targeted repolarization of tumor-associated macrophages and finally show that their repolarization not only changes their own phenotype, but also induces a proinflammatory shift in all other immune cells of the same tumor mass, leading to potent suppression of tumor growth. Because this selective reprogramming of tumor myeloid cells is accompanied by no systemic toxicity, we propose that it should constitute a safe method to reprogram the tumor microenvironment.

Scellpam: an R package/C++ library to perform parallel partitioning around medoids on scRNAseq data sets.

BACKGROUND: Partitioning around medoids (PAM) is one of the most widely used and successful clustering method in many fields. One of its key advantages is that it only requires a distance or a dissimilarity between the individuals, and the fact that cluster centers are actual points in the data set means they can be taken as reliable representatives of their classes. However, its wider application is hampered by the large amount of memory needed to store the distance matrix (quadratic on the number of individuals) and also by the high computational cost of computing such distance matrix and, less importantly, by the cost of the clustering algorithm itself. RESULTS: Therefore, new software has been provided that addresses these issues. This software, provided under GPL license and usable as either an R package or a C++ library, calculates in parallel the distance matrix for different distances/dissimilarities ([Formula: see text], [Formula: see text], Pearson, cosine and weighted Euclidean) and also implements a parallel fast version of PAM (FASTPAM1) using any data type to reduce memory usage. Moreover, the parallel implementation uses all the cores available in modern computers which greatly reduces the execution time. Besides its general application, the software is especially useful for processing data of single cell experiments. It has been tested in problems including clustering of single cell experiments with up to 289,000 cells with the expression of about 29,000 genes per cell. CONCLUSIONS: Comparisons with other current packages in terms of execution time have been made. The method greatly outperforms the available R packages for distance matrix calculation and also improves the packages that implement the PAM itself. The software is available as an R package at https://CRAN.R-project.org/package=scellpam and as C++ libraries at https://github.com/JdMDE/jmatlib and https://github.com/JdMDE/ppamlib The package is useful for single cell RNA-seq studies but it is also applicable in other contexts where clustering of large data sets is required.

Single-cell transcriptomic analysis reveals crucial oncogenic signatures and its associative cell types involved in gastric cancer.

The intricate association of oncogenic markers negatively impacts accurate gastric cancer diagnosis and leads to the proliferation of mortality rate. Molecular heterogeneity is inevitable in determining gastric cancer's progression state with multiple cell types involved. Identification of pathogenic gene signatures is imperative to understand the disease's etiology. This study demonstrates a systematic approach to identifying oncogenic gastric cancer genes linked with different cell types. The raw counts of adjacent normal and gastric cancer samples are subjected to a quality control step. The dimensionality reduction and multidimensional clustering are performed using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) techniques. The adjacent normal and gastric cancer sample cell clusters are annotated with the Human Primary Cell Atlas database using the "SingleR." Cellular state transition between the distinct groups is characterized using trajectory analysis. The ligand-receptor interaction between Vascular Endothelial Growth Factor (VEGF) and cell clusters unveils crucial molecular pathways in gastric cancer progression. Chondrocytes, Smooth muscle cells, and fibroblast cell clusters contain genes contributing to poor survival rates based on hazard ratio during survival analysis. The GC-related oncogenic signatures are isolated by comparing the gene set with the DisGeNET database. Twelve gastric cancer biomarkers (SPARC, KLF5, HLA-DRB1, IGFBP3, TIMP3, LGALS1, IGFBP6, COL18A1, F3, COL4A1, PDGFRB, COL5A2) are linked with gastric cancer and further validated through gene set enrichment analysis. Drug-gene interaction found PDGFRB, interacting with various anti-cancer drugs, as a potential inhibitor for gastric cancer. Further investigations on these molecular signatures will assist the development of precision therapeutics, promising longevity among gastric cancer patients.

Synovium is a sensitive tissue for mapping the negative effects of systemic iron overload in osteoarthritis: identification and validation of two potential targets.

BACKGROUND: The prevention and treatment of osteoarthritis (OA) pose a major challenge in its research. The synovium is a critical tissue in the systematic treatment of OA. The present study aimed to investigate potential target genes and their correlation with iron overload in OA patients. METHODS: The internal datasets for analysis included the microarray datasets GSE46750, GSE55457, and GSE56409, while the external datasets for validation included GSE12021 and GSE55235. The GSE176308 dataset was used to generate single-cell RNA sequencing profiles. To investigate the expression of the target genes in synovial samples, quantitative reverse transcription-PCR, western blotting, and immunohistochemical assay were conducted. ELISA was used to detect the levels of ferritin and Fe2+ in both serum and synovium. RESULTS: JUN and ZFP36 were screened from the differentially expressed genes, and their mRNA were significantly reduced in the OA synovium compared to that in normal synovium. Subsequently, complex and dynamically evolving cellular components were observed in the OA synovium. The mRNA level of JUN and ZFP36 differed across various cell clusters of OA synovium and correlated with immune cell infiltration. Moreover, ferritin and Fe2+ were significantly increased in the serum and synovium of OA patients. Further, we found that JUN elevated and ZFP36 decreased at protein level. CONCLUSIONS: The synovium is a sensitive tissue for mapping the adverse effects of systemic iron overload in OA. JUN and ZFP36 represent potential target genes for attenuating iron overload during OA treatment. Some discrepancies between the transcription and protein levels of JUN suggest that post-transcriptional modifications may be implicated. Future studies should also focus on the roles of JUN and ZFP36 in inducing changes in cellular components in the synovium during OA pathogenesis.

Single-cell RNA-seq analysis reveals that immune cells induce human nucleus pulposus ossification and degeneration.

Background and aims: Determining the transcriptomes and molecular mechanism underlying human degenerative nucleus pulposus (NP) is of critical importance for treating intervertebral disc degeneration (IDD). Here, we aimed to elucidate the detailed molecular mechanism of NP ossification and IDD using single-cell RNA sequencing. Methods: Single-cell RNA-seq and bioinformatic analysis were performed to identify NP cell populations with gene signatures, biological processes and pathways, and subpopulation analysis, RNA velocity analysis, and cell-to-cell communication analysis were performed in four IDD patients. We also verified the effects of immune cells on NP ossification using cultured NP cells and a well-established rat IDD model. Results: We identified five cell populations with gene expression profiles in degenerative NP at single-cell resolution. GO database analysis showed that degenerative NP-associated genes were mainly enriched in extracellular matrix organization, immune response, and ossification. Gene set enrichment analysis showed that rheumatoid arthritis signaling, antigen processing and presentation signaling were activated in the blood cell cluster. We revealed that stromal cells, which are progenitor cells, differentiated toward an ossification phenotype and delineated interactions between immune cells (macrophages and T cells) and stromal cells. Immune factors such as TNF-α, CD74 and CCL-3 promoted the differentiation of stromal cells toward an ossification phenotype in vitro. Blocking TNF-α with a specific inhibitor successfully reversed NP ossification and modified NP morphology in vivo. Conclusion: Our study revealed an increase in macrophages and T cells in degenerative NP, which induced stromal cell differentiation toward an ossification phenotype, and contributed to the identification of a novel therapeutic target to delay IDD.

Transcriptomic evidence for Brachyury expression in the caudal tip region of adult Ptychodera flava (Hemichordata).

Most metazoans have a single copy of the T-box transcription factor gene Brachyury. This gene is expressed in cells of the blastopore of late blastulae and the archenteron invagination region of gastrulae. It appears to be crucial for gastrulation and mesoderm differentiation of embryos. Although this expression pattern is shared by most deuterostomes, Brachyury expression has not been reported in adult stages. Here we show that Brachyury of an indirect developer, the hemichordate acorn worm Ptychodera flava, is expressed not only in embryonic cells, but also in cells of the caudal tip (anus) region of adults. This spatially restricted expression, shown by whole-mount in situ hybridization, was confirmed by Iso-Seq RNA sequencing and single-cell RNA-seq (scRNA-seq) analysis. Iso-Seq analysis showed that gene expression occurs only in the caudal region of adults, but not in anterior regions, including the stomochord. scRNA-seq analysis showed a cluster that contained Brachyury-expressing cells comprising epidermis- and mesoderm-related cells, but which is unlikely to be associated with the nervous system or muscle. Although further investigation is required to examine the roles of Brachyury in adults, this study provides important clues for extending studies on Brachyury expression involved in development of the most posterior region of deuterostomes.

Development and validation of cuproptosis-related genes in synovitis during osteoarthritis progress.

Osteoarthritis (OA) is one of the most common refractory degenerative joint diseases worldwide. Synovitis is believed to drive joint cartilage destruction during OA pathogenesis. Cuproptosis is a novel form of copper-induced cell death. However, few studies have examined the correlations between cuproptosis-related genes (CRGs), immune infiltration, and synovitis. Therefore, we analyzed CRGs in synovitis during OA. Microarray datasets (GSE55235, GSE55457, GSE12021, GSE82107 and GSE176308) were downloaded from the Gene Expression Omnibus database. Next, we conducted differential and subtype analyses of CRGs across synovitis. Immune infiltration and correlation analyses were performed to explore the association between CRGs and immune cell abundance in synovitis. Finally, single-cell RNA-seq profiling was performed using the GSE176308 dataset to investigate the expression of CRGs in the various cell clusters. We found that the expression of five CRGs (FDX1, LIPT1, PDHA1, PDHB, and CDKN2A) was significantly increased in the OA synovium. Moreover, abundant and various types of immune cells infiltrated the synovium during OA, which was correlated with the expression of CRGs. Additionally, single-cell RNA-seq profiling revealed that the cellular composition of the synovium was complex and that their proportions varied greatly as OA progressed. The expression of CRGs differed across various cell types in the OA synovium. The current study predicted that cuproptosis may be involved in the pathogenesis of synovitis. The five screened CRGs (FDX1, LIPT1, PDHA1, PDHB, and CDKN2A) could be explored as candidate biomarkers or therapeutic targets for OA synovitis.

Necroptosis activation is associated with greater methylene blue-photodynamic therapy-induced cytotoxicity in human pancreatic ductal adenocarcinoma cells.

Pancreatic ductal adenocarcinomas (PDAC) are the fourth leading cause of death due to neoplasms. In view of the urgent need of effective treatments for PDAC, photodynamic therapy (PDT) appears as a promising alternative. However, its efficacy against PDAC and the mechanisms involved in cell death induction remain unclear. In this study, we set out to evaluate PDT's cytotoxicity using methylene blue (MB) as a photosensitizer (PS) (MB-PDT) and to evaluate the contribution of necroptosis in its effect in human PDAC cells. Our results demonstrated that MB-PDT induced significant death of different human PDAC models presenting two different susceptibility profiles. This effect was independent of MB uptake or its subcellular localization. We found that the ability of triggering necroptosis was determinant to increase the treatment efficiency. Analysis of single cell RNA-seq data from normal and neoplastic human pancreatic tissues showed that specific necroptosis proteins RIPK1, RIPK3 and MLKL presented significant higher expression levels in cells displaying a transformed phenotype providing further support to the use of approaches that activate necroptosis, like MB-PDT, as useful adjunct to surgery of PDAC to tackle the problem of microscopic residual disease as well as to minimize the chance of local and metastatic recurrence.

The clinicopathological characteristics, prognosis and immune microenvironment mapping in MSI-H/MMR-D endometrial carcinomas.

Endometrial cancer had a relatively high prevalence of MMR deficiency. MMR-D/MSI-H endometrial cancer patients are suggested to be potential beneficiaries of PD-1/PD-L1 inhibitor therapy. Here, we explored the prognostic value of MSI subtype in endometrial cancer and its correlation with immune environment. Based on expression and clinical data of 78 POLE, 123 MSI and 299 Other EC samples from the TCGA-UCEC project, we found that the MSI tumors were identified more often in early stage, had a lower age, better patient survival, enriched CD8+ T cells, and regulatory T cells and less M2 macrophages and activated dendritic cells than the Other group, and shared a relatively similar expression profile with POLE group by differential analysis. In addition, we established the immune landscape of an MMR-D endometrial cancer tissue using unbiased single-cell RNA-seq analysis of 3371 cells. By immunohistochemistry analysis, we found that the MMR-D tumors showed a higher trend of CD20+ B cells infiltration. Our study might expand our understanding of the role of immune subsets in MSI endometrial carcinomas and provide guidance of immunotherapy for endometrial cancer.