scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

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.

SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has emerged as a crucial tool for studying cellular heterogeneity. However, dropouts are inherent to the sequencing process, known as dropout events, posing challenges in downstream analysis and interpretation. Imputing dropout data becomes a critical concern in scRNA-seq data analysis. Present imputation methods predominantly rely on statistical or machine learning approaches, often overlooking inter-sample correlations. RESULTS: To address this limitation, We introduced SAE-Impute, a new computational method for imputing single-cell data by combining subspace regression and auto-encoders for enhancing the accuracy and reliability of the imputation process. Specifically, SAE-Impute assesses sample correlations via subspace regression, predicts potential dropout values, and then leverages these predictions within an autoencoder framework for interpolation. To validate the performance of SAE-Impute, we systematically conducted experiments on both simulated and real scRNA-seq datasets. These results highlight that SAE-Impute effectively reduces false negative signals in single-cell data and enhances the retrieval of dropout values, gene-gene and cell-cell correlations. Finally, We also conducted several downstream analyses on the imputed single-cell RNA sequencing (scRNA-seq) data, including the identification of differential gene expression, cell clustering and visualization, and cell trajectory construction. CONCLUSIONS: These results once again demonstrate that SAE-Impute is able to effectively reduce the droupouts in single-cell dataset, thereby improving the functional interpretability of the data.

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.

Dissecting the Single-Cell Diversity and Heterogeneity Underlying Cervical Precancerous Lesions and Cancer Tissues.

The underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.

Dynamics of alternative polyadenylation in single root cells of Arabidopsis thaliana.

Introduction: Single-cell RNA-seq (scRNA-seq) technologies have been widely used to reveal the diversity and complexity of cells, and pioneering studies on scRNA-seq in plants began to emerge since 2019. However, existing studies on plants utilized scRNA-seq focused only on the gene expression regulation. As an essential post-transcriptional mechanism for regulating gene expression, alternative polyadenylation (APA) generates diverse mRNA isoforms with distinct 3' ends through the selective use of different polyadenylation sites in a gene. APA plays important roles in regulating multiple developmental processes in plants, such as flowering time and stress response. Methods: In this study, we developed a pipeline to identify and integrate APA sites from different scRNA-seq data and analyze APA dynamics in single cells. First, high-confidence poly(A) sites in single root cells were identified and quantified. Second, three kinds of APA markers were identified for exploring APA dynamics in single cells, including differentially expressed poly(A) sites based on APA site expression, APA markers based on APA usages, and APA switching genes based on 3' UTR (untranslated region) length change. Moreover, cell type annotations of single root cells were refined by integrating both the APA information and the gene expression profile. Results: We comprehensively compiled a single-cell APA atlas from five scRNA-seq studies, covering over 150,000 cells spanning four major tissue branches, twelve cell types, and three developmental stages. Moreover, we quantified the dynamic APA usages in single cells and identified APA markers across tissues and cell types. Further, we integrated complementary information of gene expression and APA profiles to annotate cell types and reveal subtle differences between cell types. Discussion: This study reveals that APA provides an additional layer of information for determining cell identity and provides a landscape of APA dynamics during Arabidopsis root development.

Leveraging explainable deep learning methodologies to elucidate the biological underpinnings of Huntington's disease using single-cell RNA sequencing data.

BACKGROUND: Huntington's disease (HD) is a hereditary neurological disorder caused by mutations in HTT, leading to neuronal degeneration. Traditionally, HD is associated with the misfolding and aggregation of mutant huntingtin due to an extended polyglutamine domain encoded by an expanded CAG tract. However, recent research has also highlighted the role of global transcriptional dysregulation in HD pathology. However, understanding the intricate relationship between mRNA expression and HD at the cellular level remains challenging. Our study aimed to elucidate the underlying mechanisms of HD pathology using single-cell sequencing data. RESULTS: We used single-cell RNA sequencing analysis to determine differential gene expression patterns between healthy and HD cells. HD cells were effectively modeled using a residual neural network (ResNet), which outperformed traditional and convolutional neural networks. Despite the efficacy of our approach, the F1 score for the test set was 96.53%. Using the SHapley Additive exPlanations (SHAP) algorithm, we identified genes influencing HD prediction and revealed their roles in HD pathobiology, such as in the regulation of cellular iron metabolism and mitochondrial function. SHAP analysis also revealed low-abundance genes that were overlooked by traditional differential expression analysis, emphasizing its effectiveness in identifying biologically relevant genes for distinguishing between healthy and HD cells. Overall, the integration of single-cell RNA sequencing data and deep learning models provides valuable insights into HD pathology. CONCLUSION: We developed the model capable of analyzing HD at single-cell transcriptomic level.

Detecting expressed genes in cell populations at the single-cell level with scGeneXpress.

Determining whether genes are expressed or not remains a challenge in single-cell RNAseq experiments due to their different expression spectra, which are influenced by genetics, the microenvironment and gene length. Current approaches for addressing this issue fail to provide a comprehensive landscape of expressed genes, since they neglect the inherent differences in the expression ranges and distributions of genes. Here, we present scGeneXpress, a method for detecting expressed genes in cell populations of single-cell RNAseq samples based on gene-specific reference distributions. We demonstrate that scGeneXpress accurately detects expressed cell markers and identity genes in 34 human and mouse tissues and can be employed to improve differential expression analysis of single-cell RNAseq data.

Single-cell transcriptome analysis of the mouse lungs during the injury and recovery periods after lipopolysaccharide administration.

OBJECTIVE: This study sought to investigate the cellular and molecular alterations during the injury and recovery periods of ALI and develop effective treatments for ALI. METHODS: Pulmonary histology at 1, 3, 6, and 9 days after lipopolysaccharide administration mice were assessed. An unbiased single-cell RNA sequencing was performed in alveoli tissues from injury (day 3) and recovery (day 6) mice after lipopolysaccharide administration. The roles of Fpr2 and Dpp4 in ALI were assessed. RESULTS: The most severe lung injury occurred on day 3, followed by recovery entirely on day 9 after lipopolysaccharide administration. The numbers of Il1a+ neutrophils, monocytes/macrophages, and Cd4+ and Cd8+ T cells significantly increased at day 3 after LPS administration; subsequently, the number of Il1a+ neutrophils greatly decreased, the numbers of monocytes/macrophages and Cd4+ and Cd8+ T cells continuously increased, and the number of resident alveolar macrophages significantly increased at day 6. The interactions between monocytes/macrophages and pneumocytes during the injury period were enhanced by the Cxcl10/Dpp4 pair, and inhibiting Dpp4 improved ALI significantly, while inhibiting Fpr2 did not. CONCLUSIONS: Our results offer valuable insights into the cellular and molecular mechanisms underlying its progression and identify Dpp4 as an effective therapeutic target for ALI.

Germinal center B-cell subgroups in the tumor microenvironment cannot be overlooked: Their involvement in prognosis, immunotherapy response, and treatment resistance in head and neck squamous carcinoma.

Background: More than 60 % of patients with head and neck squamous carcinoma (HNSCC) are diagnosed at advanced stages and miss radical treatment. This has prompted the need to find new biomarkers to achieve early diagnosis and predict early recurrence and metastasis of tumors. Methods: Single-cell RNA sequencing (scRNA-seq) data from HNSCC tissues and peripheral blood samples were obtained through the Gene Expression Omnibus (GEO) database (GSE164690) to characterize the B-cell subgroups, differentiation trajectories, and intercellular communication networks in HNSCC and to construct a prognostic model of the associated risks. In addition, this study analyzed the differences in clinical features, immune cell infiltration, functional enrichment, tumor mutational burden (TMB), and drug sensitivity between the high- and low-risk groups. Results: Using scRNA-seq of HNSCC, we classified B and plasma cells into a total of four subgroups: naive B cells (NBs), germinal center B cells (GCBs), memory B cells (MBs), and plasma cells (PCs). Pseudotemporal trajectory analysis revealed that NBs and GCBs were at the early stage of B cell differentiation, while MBs and PCs were at the end. Cellular communication revealed that GCBs acted on tumor cells through the CD99 and SEMA4 signaling pathways. The independent prognostic value, immune cell infiltration, TMB and drug sensitivity assays were validated for the MEF2B+ GCB score groups. Conclusions: We identified GCBs as B cell-specific prognostic biomarkers for the first time. The MEF2B+ GCB score fills the research gap in the genetic prognostic prediction model of HNSCC and is expected to provide a theoretical basis for finding new therapeutic targets for HNSCC.

Identification and validation of SHC1 and FGFR1 as novel immune-related oxidative stress biomarkers of non-obstructive azoospermia.

Background: Non-obstructive azoospermia (NOA) is a major contributor of male infertility. Herein, we used existing datasets to identify novel biomarkers for the diagnosis and prognosis of NOA, which could have great significance in the field of male infertility. Methods: NOA datasets were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT was utilized to analyze the distributions of 22 immune cell populations. Hub genes were identified by applying weighted gene co-expression network analysis (WGCNA), machine learning methods, and protein-protein interaction (PPI) network analysis. The expression of hub genes was verified in external datasets and was assessed by receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis (GSEA) was applied to explore the important functions and pathways of hub genes. The mRNA-microRNA (miRNA)-transcription factors (TFs) regulatory network and potential drugs were predicted based on hub genes. Single-cell RNA sequencing data from the testes of patients with NOA were applied for analyzing the distribution of hub genes in single-cell clusters. Furthermore, testis tissue samples were obtained from patients with NOA and obstructive azoospermia (OA) who underwent testicular biopsy. RT-PCR and Western blot were used to validate hub gene expression. Results: Two immune-related oxidative stress hub genes (SHC1 and FGFR1) were identified. Both hub genes were highly expressed in NOA samples compared to control samples. ROC curve analysis showed a remarkable prediction ability (AUCs > 0.8). GSEA revealed that hub genes were predominantly enriched in toll-like receptor and Wnt signaling pathways. A total of 24 TFs, 82 miRNAs, and 111 potential drugs were predicted based on two hub genes. Single-cell RNA sequencing data in NOA patients indicated that SHC1 and FGFR1 were highly expressed in endothelial cells and Leydig cells, respectively. RT-PCR and Western blot results showed that mRNA and protein levels of both hub genes were significantly upregulated in NOA testis tissue samples, which agree with the findings from analysis of the microarray data. Conclusion: It appears that SHC1 and FGFR1 could be significant immune-related oxidative stress biomarkers for detecting and managing patients with NOA. Our findings provide a novel viewpoint for illustrating potential pathogenesis in men suffering from infertility.

A tumor cornification and immune-infiltration-based scheme for anti-PD-1 plus chemotherapy response in advanced squamous cell lung carcinoma.

BACKGROUND: Anti-PD-1 immunotherapy plus chemotherapy (combo) exhibits significantly prolonged survival for squamous cell lung cancer (LUSC). An exploration of predictive biomarkers is still needed. METHODS: High-throughput RNA sequencing (RNA-seq) of 349 LUSC samples from the randomized, multi-center, phase 3 trial ORIENT-12 (ClinicalTrials.gov: NCT03629925) was conducted for biomarker discovery, followed by flow cytometry and multiplex immunohistochemistry (mIHC) in additional clinical cohorts, and in vitro experiments were performed for verification. RESULTS: A high abundance of activated CD8+ T and CD56bright natural killer (NK) cells benefited patients' outcomes (progression-free survival [PFS]; overall survival [OS]) with combo treatment. Tumor cornification level remarkably affected the infiltration of the two crucial immune cells. Thus, a novel scheme of LUSC immune infiltration and cornification characterization-based classification (LICC) was established for combo efficacy prediction. Patients who received combo treatment achieved significant PFS improvements in LICC1 (hazard ratio [HR] = 0.43, 95% confidence interval [CI]: 0.25-0.75, p = 0.0029) and LICC2 (HR = 0.32, 95% CI: 0.17-0.58, p = 0.0002) subtypes but not in the LICC3 subtype (HR = 0.86, 95% CI: 0.60-1.23, p = 0.4053). Via single-cell RNA-seq analysis, the tumor cornification signal was mainly mapped to SPRR3+ tumor cells, whose relationships with activated CD8+ T or CD56bright NK cells were verified using flow cytometry and mIHC. Our data suggest that SPRR3+ tumor cells might evade immune surveillance via the CD24-SIGLEC10 (M2 macrophage) axis to maintain a suppressive tumor microenvironment. CONCLUSIONS: Tumor cornification greatly impacts immune infiltration, and the LICC scheme may guide clinical medication of anti-PD-1+chemo treatment in patients with LUSC. FUNDING: The study was funded by the National Key R&D Program of China, the National Natural Science Foundation of China, Shanghia Multidisplinary Cooperation Building Project for Diagnosis and Treatment of Major Disease, and Innovent Biologics, Inc.

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.

Network-Constrained Eigen-Single-Cell Profile Estimation for Uncovering Crucial Immunogene Regulatory Systems in Human Bone Marrow.

We focus on characterizing cell lines from young and aged-healthy and -AML (acute myeloid leukemia) cell lines, and our goal is to identify the key markers associated with the progression of AML. To characterize the age-related phenotypes in AML cell lines, we consider eigenCell analysis that effectively encapsulates the primary expression level patterns across the cell lines. However, earlier investigations utilizing eigenGenes and eigenCells analysis were based on linear combination of all features, leading to the disturbance from noise features. Moreover, the analysis based on a fully dense loading matrix makes it challenging to interpret the results of eigenCells analysis. In order to address these challenges, we develop a novel computational approach termed network-constrained eigenCells profile estimation, which employs a sparse learning strategy. The proposed method estimates eigenCell based on not only the lasso but also network constrained penalization. The use of the network-constrained penalization enables us to simultaneously select neighborhood genes. Furthermore, the hub genes and their regulator/target genes are easily selected as crucial markers for eigenCells estimation. That is, our method can incorporate insights from network biology into the process of sparse loading estimation. Through our methodology, we estimate sparse eigenCells profiles, where only critical markers exhibit expression levels. This allows us to identify the key markers associated with a specific phenotype. Monte Carlo simulations demonstrate the efficacy of our method in reconstructing the sparse structure of eigenCells profiles. We employed our approach to unveil the regulatory system of immunogenes in both young/aged-healthy and -AML cell lines. The markers we have identified for the age-related phenotype in both healthy and AML cell lines have garnered strong support from previous studies. Specifically, our findings, in conjunction with the existing literature, indicate that the activities within this subnetwork of CD79A could be pivotal in elucidating the mechanism driving AML progression, particularly noting the significant role played by the diminished activities in the CD79A subnetwork. We expect that the proposed method will be a useful tool for characterizing disease-related subsets of cell lines, encompassing phenotypes and clones.

Single-cell RNA sequencing reveals microenvironmental infiltration in non-small cell lung cancer with different responses to immunotherapy.

BACKGROUND: Immunotherapy represents a groundbreaking and monumental achievement in the field of cancer therapy, marking a significant advancement in fighting against this devastating disease. Lung cancer has showed consistent clinical improvements in response to immunotherapy treatments, yet, it is undeniable that challenges such as limited response rates acquire resistance, and the unclear fundamental mechanisms were inevitable problems. METHODS: The cellular composition was defined and distinguished through single-cell RNA sequencing (scRNA-seq) analysis of MPR (major pathologic response) and NMPR (non-major pathologic response) samples in GSE207422, including four primary MPR samples and eight primary NMPR samples. RESULTS: We found obvious difference in CD8+ T cell population between MPR and NMPR samples, with high expression of TYMS, RRM2, and BIRC5 in NPMR samples. Meanwhile, the proportion of macrophages and tumor epithelial cells infiltration increased in the NMPR samples. We discovered biomarkers (ACTN4, ATF3, BRD2, CDKN1A, and CHMP4B) in epithelial cells which were potentially represented worse outcomes. CONCLUSIONS: By exploring the difference of tumor microenvironment (TME) in samples with different corresponding degrees of neoadjuvant immunotherapy, this research introduces a number of novel biomarkers for predicting the response of treatment and a theoretical basis for overcoming immunotherapy resistance.

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.

Identification and Characterization of Multiple Paneth Cell Types in the Mouse Small Intestine.

The small intestinal crypts harbor secretory Paneth cells (PCs) which express bactericidal peptides that are crucial for maintaining intestinal homeostasis. Considering the diverse environmental conditions throughout the course of the small intestine, multiple subtypes of PCs are expected to exist. We applied single-cell RNA-sequencing of PCs combined with deep bulk RNA-sequencing on PC populations of different small intestinal locations and discovered several expression-based PC clusters. Some of these are discrete and resemble tuft cell-like PCs, goblet cell (GC)-like PCs, PCs expressing stem cell markers, and atypical PCs. Other clusters are less discrete but appear to be derived from different locations along the intestinal tract and have environment-dictated functions such as food digestion and antimicrobial peptide production. A comprehensive spatial analysis using Resolve Bioscience was conducted, leading to the identification of different PC's transcriptomic identities along the different compartments of the intestine, but not between PCs in the crypts themselves.

Olfr2-positive macrophages originate from monocytes proliferate in situ and present a pro-inflammatory foamy-like phenotype.

AIMS: Olfactory receptor 2 (Olfr2) has been identified in a minimum of 30% of vascular macrophages, and its depletion was shown to reduce atherosclerosis progression. Mononuclear phagocytes, including monocytes and macrophages within the vessel wall, are major players in atherosclerosis. Single-cell RNA sequencing studies revealed that atherosclerotic artery walls encompass several monocytes and vascular macrophages, defining at least nine distinct subsets potentially serving diverse functions in disease progression. This study investigates the functional phenotype and ontogeny of Olfr2-expressing vascular macrophages in atherosclerosis. METHODS AND RESULTS: Olfr2+ macrophages rapidly increase in Apoe-/- mice's aorta when fed a Western diet (WD). Mass cytometry showed that Olfr2+ cells are clustered within the CD64 high population and enriched for CD11c and Ccr2 markers. Olfr2+ macrophages express many pro-inflammatory cytokines, including Il1b, Il6, Il12, and Il23, and chemokines, including Ccl5, Cx3cl1, Cxcl9, and Ccl22. By extracting differentially expressed genes from bulk RNA sequencing (RNA-seq) of Olfr2+ vs. Olfr2- macrophages, we defined a signature that significantly mapped to single-cell data of plaque myeloid cells, including monocytes, subendothelial MacAir, and Trem2Gpnmb foamy macrophages. By adoptive transfer experiments, we identified that Olfr2 competent monocytes from CD45.1Apoe-/-Olfr2+/+ mice transferred into CD45.2Apoe-/-Olfr2-/- recipient mice fed WD for 12 weeks, accumulate in the atherosclerotic aorta wall already at 72 h, and differentiate in macrophages. Olfr2+ macrophages showed significantly increased BrdU incorporation compared to Olfr2- macrophages. Flow cytometry confirmed that at least 50% of aortic Olfr2+ macrophages are positive for BODIPY staining and have increased expression of both tumour necrosis factor and interleukin 6 compared to Olfr2- macrophages. Gene set enrichment analysis of the Olfr2+ macrophage signature revealed a similar enrichment pattern in human atherosclerotic plaques, particularly within foamy/TREM2hi-Mφ and monocytes. CONCLUSIONS: In summary, we conclude that Olfr2+ macrophages in the aorta originate from monocytes and can accumulate at the early stages of disease progression. These cells can undergo differentiation into MacAir and Trem2Gpnmb foamy macrophages, exhibiting proliferative and pro-inflammatory potentials. This dynamic behaviour positions them as key influencers in shaping the myeloid landscape within the atherosclerotic plaque.

Identification of neutrophil extracellular trap-related biomarkers in non-alcoholic fatty liver disease through machine learning and single-cell analysis.

Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver condition globally. Simultaneously, the annual disease burden, particularly liver cirrhosis caused by NAFLD, has increased significantly. Neutrophil Extracellular Traps (NETs) play a crucial role in the progression of this disease and are key to the pathogenesis of NAFLD. However, research into the specific roles of NETs-related genes in NAFLD is still a field requiring thorough investigation. Utilizing techniques like AddModuleScore, ssGSEA, and WGCNA, our team conducted gene screening to identify the genes linked to NETs in both single-cell and bulk transcriptomics. Using algorithms including Random Forest, Support Vector Machine, Least Absolute Shrinkage, and Selection Operator, we identified ZFP36L2 and PHLDA1 as key hub genes. The pivotal role of these genes in NAFLD diagnosis was confirmed using the training dataset GSE164760. This study identified 116 genes linked to NETs across single-cell and bulk transcriptomic analyses. These genes demonstrated enrichment in immune and metabolic pathways. Additionally, two NETs-related hub genes, PHLDA1 and ZFP36L2, were selected through machine learning for integration into a prognostic model. These hub genes play roles in inflammatory and metabolic processes. scRNA-seq results showed variations in cellular communication among cells with different expression patterns of these key genes. In conclusion, this study explored the molecular characteristics of NETs-associated genes in NAFLD. It identified two potential biomarkers and analyzed their roles in the hepatic microenvironment. These discoveries could aid in NAFLD diagnosis and management, with the ultimate goal of enhancing patient outcomes.

The microenvironmental factors induced invasive tumor cells in glioblastoma.

Glioblastoma (GBM) cells have the potential to switch from being "proliferative cells" to peritumoral "invasive cells". Peritumoral GBM cells have highly invasive properties that allow them to survive surgery, leading to recurrence. The mechanisms underlying the manner in which the tumor microenvironment (TME) regulates the invasiveness of GBM remain unclear. Single-cell RNA sequencing analysis revealed heterogeneity in GBM cells, microglia and macrophages. In this study, the Oncostatin M receptor (OSMR) and leukemia inhibitory factor receptor (LIFR) expression indicated higher invasiveness in core GBM cells. Under environmental stress, the expression of OSMR and LIFR were up-regulated with the effect of hypoxic, acidic, and low-glucose conditions in vitro. Functional experiments revealed that TME stress significantly influences the proliferation, migration and invasion of GBM cells. The differences in core/peripheral TMEs in GBM affected the invasive properties, indicating the significant role of OSMR expression within the TME in tumor progression and postoperative therapy.