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Plant-microbe symbioses require intense interaction and genetic coordination to successfully establish in specific cell types of the host and symbiont. Traditional RNA-seq methodologies lack the cellular resolution to fully capture these complexities, but single-cell and spatial transcriptomics (ST) are now allowing scientists to probe symbiotic interactions at an unprecedented level of detail. Here, we discuss the advantages that novel spatial and single-cell transcriptomic technologies provide in studying plant-microbe endosymbioses and highlight key recent studies. Finally, we consider the remaining limitations of applying these approaches to symbiosis research, which are mainly related to the simultaneous capture of both plant and microbial transcripts within the same cells.

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function.

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. [ABSTRACT FROM AUTHOR]

Rationale: Kidney inflammation plays a crucial role in the pathogenesis of IgA nephropathy (IgAN), yet the specific phenotypes of immune cells involved in disease progression remain incompletely understood. Utilizing joint profiling through longitudinal single-cell RNA-sequencing (scRNAseq) and single-cell assay for transposase-accessible chromatin sequencing (scATACseq) can provide a comprehensive framework for elucidating the development of cell subset diversity and how chromatin accessibility regulates transcription. Objective: We aimed to characterize the dynamic immune cellular landscape at a high resolution in an early IgAN mouse model with acute kidney injury (AKI). Methods and results: A murine model was utilized to mimic 3 immunological states -"immune stability (IS), immune activation (IA) and immune remission (IR)" in early human IgAN-associated glomerulopathy during AKI, achieved through lipopolysaccharide (LPS) injection. Urinary albumin to creatinine ratio (UACR) was measured to further validate the exacerbation and resolution of kidney inflammation during this course. Paired scRNAseq and scATACseq analysis was performed on CD45+ immune cells isolated from kidney tissues obtained from CTRL (healthy vehicle), IS, IA and IR (4 or 5 mice each). The analyses revealed 7 major cell types and 24 clusters based on 72304 single-cell transcriptomes, allowing for the identification and characterization of various immune cell types within each cluster. Our data offer an impartial depiction of the immunological characteristics, as the proportions of immune cell types fluctuated throughout different stages of the disease. Specifically, these analyses also revealed novel subpopulations, such as a macrophage subset (Nlrp1b Mac) with distinct epigenetic features and a unique transcription factor motif profile, potentially exerting immunoregulatory effects, as well as an early subset of Tex distinguished by their effector and cytolytic potential (CX3CR1-transTeff). Furthermore, in order to investigate the potential interaction between immune cells and renal resident cells, we conducted single-cell RNA sequencing on kidney cells obtained from a separate cohort of IS and IA mice without isolating immune cells. These findings underscored the diverse roles played by macrophages and CD8+ T cells in maintaining homeostasis of endothelial cells (ECs) under stress. Conclusions: This study presents a comprehensive analysis of the dynamic changes in immune cell profiles in a model of IgAN, identifying key cell types and their roles and interactions. These findings significantly contribute to the understanding of the pathogenesis of IgAN and may provide potential targets for therapeutic intervention. [ABSTRACT FROM AUTHOR]

Comparing transcriptome profiling between younger and older samples reveals genes related to aging and provides insight into the biological functions affected by aging. Recent research has identified sex, tissue, and cell type-specific age-related changes in gene expression. This study reports the overall picture of the opposite aging effect, in which aging increases gene expression in one cell subset and decreases it in another cell subset. Using the Tabula Muris Senis dataset, a large public single-cell RNA sequencing dataset from mice, we compared the effects of aging in different cell subsets. As a result, the opposite aging effect was observed widely in the genes, particularly enriched in genes related to ribosomal function and translation. The opposite aging effect was observed in the known aging-related genes. Furthermore, the opposite aging effect was observed in the transcriptome diversity quantified by the number of expressed genes and the Shannon entropy. This study highlights the importance of considering the cell subset when intervening with aging-related genes. [ABSTRACT FROM AUTHOR]

Summary In plants, the biosynthetic pathways of some specialized metabolites are partitioned into specialized or rare cell types, as exemplified by the monoterpenoid indole alkaloid (MIA) pathway of Catharanthus roseus (Madagascar Periwinkle), the source of the anticancer compounds vinblastine and vincristine. In the leaf, the C. roseus MIA biosynthetic pathway is partitioned into three cell types with the final known steps of the pathway expressed in the rare cell type termed idioblast. How cell‐type specificity of MIA biosynthesis is achieved is poorly understood. We generated single‐cell multi‐omics data from C. roseus leaves. Integrating gene expression and chromatin accessibility profiles across single cells, as well as transcription factor (TF)‐binding site profiles, we constructed a cell‐type‐aware gene regulatory network for MIA biosynthesis. We showcased cell‐type‐specific TFs as well as cell‐type‐specific cis‐regulatory elements. Using motif enrichment analysis, co‐expression across cell types, and functional validation approaches, we discovered a novel idioblast‐specific TF (Idioblast MYB1, CrIDM1) that activates expression of late‐stage MIA biosynthetic genes in the idioblast. These analyses not only led to the discovery of the first documented cell‐type‐specific TF that regulates the expression of two idioblast‐specific biosynthetic genes within an idioblast metabolic regulon but also provides insights into cell‐type‐specific metabolic regulation. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the sixth most prevalent cancer and a leading cause of cancer-related mortality globally, with most patients diagnosed at advanced stages and facing limited early treatment options. This study aimed to identify characteristic genes associated with T-cell exhaustion due to senescence in hepatocellular carcinoma patients, elucidating the interplay between senescence and T-cell exhaustion. We constructed prognostic models based on five signature genes (ENO1, STMN1, PRDX1, RAN, and RANBP1) linked to T-cell exhaustion, utilizing elastic net regression. The findings indicate that increased expression of ENO1 in T cells may contribute to T-cell exhaustion and Treg infiltration in hepatocellular carcinoma. Furthermore, molecular docking was employed to screen small molecule compounds that target the anti-tumor effects of these exhaustion-related genes. This study provides crucial insights into the diagnosis and treatment of hepatocellular carcinoma, establishing a strong foundation for the development of predictive biomarkers and therapeutic targets for affected patients. [ABSTRACT FROM AUTHOR]

The transition from acute kidney injury (AKI) to chronic kidney disease (CKD) is a critical clinical issue. Although previous studies have suggested macrophages as a key player in promoting inflammation and fibrosis during this transition, the heterogeneity and dynamic characterization of macrophages are still poorly understood. Here, we used integrated single‐cell RNA sequencing and spatial transcriptomic to characterize the spatiotemporal heterogeneity of macrophages in murine AKI‐to‐CKD model of unilateral ischemia‐reperfusion injury. A marked increase in macrophage infiltration at day 1 was followed by a second peak at day 14 post AKI. Spatiotemporal profiling revealed that injured tubules and macrophages co‐localized early after AKI, whereas in late chronic stages had spatial proximity to fibroblasts. Further pseudotime analysis revealed two distinct lineages of macrophages in this transition: renal resident macrophages differentiated into the pro‐repair subsets, whereas infiltrating monocyte‐derived macrophages contributed to chronic inflammation and fibrosis. A novel macrophage subset, extracellular matrix remodeling‐associated macrophages (EAMs) originating from monocytes, linked to renal fibrogenesis and communicated with fibroblasts via insulin‐like growth factors (IGF) signalling. In sum, our study identified the spatiotemporal dynamics of macrophage heterogeneity with a unique subset of EAMs in AKI‐to‐CKD transition, which could be a potential therapeutic target for preventing CKD development. [ABSTRACT FROM AUTHOR]

In this phase II study, 47 patients with locally advanced, resectable esophageal squamous cell carcinoma (ESCC) received three cycles of pembrolizumab plus chemotherapy, followed by Da Vinci robot-assisted surgery. The primary endpoints were safety and major pathological response (MPR). Key secondary endpoints included complete pathological response (pCR) and survival. No grade ≥3 adverse events or surgical delays occurred during neoadjuvant therapy. Among 46 patients studied for efficacy, the MPR and pCR rates were 72% and 41%, respectively. After a median follow-up of 27.2 months, the 2-year overall survival (OS) and disease-free survival (DFS) rates were 91% and 89%, respectively. Expansion of TRGC2+ NKT cells in peripheral blood correlated with neoadjuvant treatment effectiveness, which was validated by in vitro organoid experiments and external cancer datasets, and its functional classification and mechanism of action were further explored. These findings show preoperative pembrolizumab plus chemotherapy is a promising therapeutic strategy for resectable ESCC. [Display omitted] • Neoadjuvant pembrolizumab + chemotherapy was explored in resectable stage III ESCC • Quality of life and nutritional status improved following neoadjuvant therapy • After a median follow-up of 27.2 months, the 2-year overall survival rate was 91% • TRGC2+ NKT cells in peripheral blood are a potential response biomarker Shang et al. demonstrate the efficacy and safety of neoadjuvant pembrolizumab plus chemotherapy for patients with locally advanced, resectable esophageal squamous cell carcinoma in a phase II clinical trial. They identify TRGC2+ NKT cells as potential biomarkers of response using single-cell RNA-seq in peripheral blood. [ABSTRACT FROM AUTHOR]

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 tolllike 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 immunerelated 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives: PD‐1 plays a crucial role in the immune dysregulation of rheumatoid arthritis (RA), but the specific characteristics of PD‐1+CD4+ T cells remain unclear and require further investigation. Methods: Circulating PD‐1+CD4+ T cells from RA patients were analysed using flow cytometry. Plasma levels of soluble PD‐1 (sPD‐1) were measured using enzyme‐linked immunosorbent assay (ELISA). Single‐cell RNA sequence data from peripheral blood mononuclear cells (PBMCs) and synovial tissue of patients were obtained from the GEO and the ImmPort databases. Bioinformatics analyses were performed in the R studio to characterise PD‐1+CD4+ T cells. Expression of CCR7, KLF2 and IL32 in PD‐1+CD4+ T cells was validated by flow cytometry. Results: RA patients showed an elevated proportion of PD‐1+CD4+ T cells in peripheral blood, along with increased plasma sPD‐1 levels, which positively correlated with TNF‐α and erythrocyte sedimentation rate. Bioinformatic analysis revealed PD‐1 expression on CCR7+CD4+ T cells in PBMCs, and on both CCR7+CD4+ T cells and CXCL13+CD4+ T cells in RA synovium. PD‐1 was co‐expressed with CCR7, KLF2, and IL32 in peripheral CD4+ T cells. In synovium, PD‐1+CCR7+CD4+ T cells had higher expression of TNF and LCP2, while PD‐1+CXCL13+CD4+ T cells showed elevated levels of ARID5A and DUSP2. PD‐1+CD4+ T cells in synovium also appeared to interact with B cells and fibroblasts through BTLA and TNFSF signalling pathways. Conclusion: This study highlights the increased proportion of PD‐1+CD4+ T cells and elevated sPD‐1 levels in RA. The transcriptomic profiles and signalling networks of PD‐1+CD4+ T cells offer new insights into their role in RA pathogenesis. [ABSTRACT FROM AUTHOR]

Organogenesis, the phase of embryonic development that starts at the end of gastrulation and continues until birth is the critical process for understanding cellular differentiation and maturation during organ development. The rapid development of single‐cell transcriptomics technology has led to many novel discoveries in understanding organogenesis while also accumulating a large quantity of data. To fill this gap, OrganogenesisDB (http://organogenesisdb.com/), which is a comprehensive database dedicated to exploring cell‐type identification and gene expression dynamics during organogenesis, is developed. OrganogenesisDB contains single‐cell RNA sequencing data for more than 1.4 million cells from 49 published datasets spanning various developmental stages. Additionally, 3324 cell markers are manually curated for 1120 cell types across 9 human organs and 4 mouse organs. OrganogenesisDB leverages various analysis tools to assist users in annotating and understanding cell types at different developmental stages and helps in mining and presenting genes that exhibit specific patterns and play key regulatory roles during cell maturation and differentiation. This work provides a critical resource and useful tool for deciphering cell lineage determination and uncovering the mechanisms underlying organogenesis. [ABSTRACT FROM AUTHOR]

Purpose: Ovarian metastasis of gastric cancer (GC), commonly referred to as Krukenberg tumors, leads to a poor prognosis. However, the cause of metastasis remains unknown. Here, we present an integrated single-cell RNA sequencing (scRNA-Seq) analysis of the immunological microenvironment of two paired clinical specimens with ovarian metastasis of GC. Methods: scRNA-Seq was performed to determine the immunological microenvironment in ovarian metastasis of gastric cancer. CellChat was employed to analyze cell-cell communications across different cell types. Functional enrichment analysis was done by enrichKEGG in clusterProfiler. GEPIA2 was used to assess the influence of certain genes and gene signatures on prognosis. Results: The ovarian metastasis tissues exhibit a heterogenous immunological microenvironment compared to the primary tumors. Exhaustion of T and B cells is observed in the ovarian metastasis tissues. Compared to the paired adjacent non-tumoral and primary tumors, the ratio of endothelial cells and fibroblasts is high in the ovarian metastasis tissues. Compared to primary ovarian cancers, we identify a specific group of tumor-associated fibroblasts with MFAP4 and CAPNS1 expression in the ovarian metastatic tissues of GC. We further define metastasis-related-endothelial and metastasis-related-fibroblast signatures and indicate that patients with these high signature scores have a poor prognosis. In addition, the ovarian metastasis tissue has a lower level of intercellular communications compared to the primary tumor. Conclusion: Our findings reveal the immunological microenvironment of ovarian metastasis of gastric cancer and will promote the discovery of new therapeutic strategies for ovarian metastasis in gastric cancer. [ABSTRACT FROM AUTHOR]

Purpose: Osteosarcoma, a highly malignant primary bone tumor primarily affecting adolescents, frequently develops resistance to initial chemotherapy, leading to metastasis and limited treatment options. Our study aims to uncover novel therapeutic targets for metastatic and recurrent osteosarcoma. Methods: In this study, we proved the potential of modulating the YAP1-regulated glutamine metabolic pathway to augment the response of OS to DFMO. We initially employed single-cell transcriptomic data to gauge the activation level of polyamine metabolism in MTAP-deleted OS patients. This was further substantiated by transcriptome sequencing data from recurrent and non-recurrent patient tissues, confirming the activation of polyamine metabolism in progressive OS. Through high-throughput drug screening, we pinpointed CIL56, a YAP1 inhibitor, as a promising candidate for a combined therapeutic strategy with DFMO. In vivo, we utilized PDX and CDX models to validate the therapeutic efficacy of this drug combination. In vitro, we conducted western blot analysis, qPCR analysis, immunofluorescence staining, and PuMA experiments to monitor alterations in molecular expression, distribution, and tumor metastasis capability. We employed CCK-8 and colony formation assays to assess the proliferative capacity of cells in the experimental group. We used flow cytometry and reactive oxygen probes to observe changes in ROS and glutamine metabolism within the cells. Finally, we applied RNA-seq in tandem with metabolomics to identify metabolic alterations in OS cells treated with a DFMO and CIL56 combination. This enabled us to intervene and validate the role of the YAP1-mediated glutamine metabolic pathway in DFMO resistance. Results: Through single-cell RNA-seq data analysis, we pinpointed a subset of late-stage OS cells with significantly upregulated polyamine metabolism. This upregulation was further substantiated by transcriptomic profiling of recurrent and non-recurrent OS tissues. High-throughput drug screening revealed a promising combination strategy involving DFMO and CIL56. DFMO treatment curbs the phosphorylation of YAP1 protein in OS cells, promoting nuclear entry and initiating the YAP1-mediated glutamine metabolic pathway. This reduces intracellular ROS levels, countering DFMO's anticancer effect. The therapeutic efficacy of DFMO can be amplified both in vivo and in vitro by combining it with the YAP1 inhibitor CIL56 or the glutaminase inhibitor CB-839. This underscores the significant potential of targeting the YAP1-mediated glutamine metabolic pathway to enhance efficacy of DFMO. Conclusion: Our findings elucidate YAP1-mediated glutamine metabolism as a crucial bypass mechanism against DFMO, following the inhibition of polyamine metabolism. Our study provides valuable insights into the potential role of DFMO in an "One-two Punch" therapy of metastatic and recurrent osteosarcoma. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Evidence from observational studies indicates that inflammatory proteins play a vital role in Guillain-Barre Syndrome (GBS). Nevertheless, it is unclear how circulating inflammatory proteins are causally associated with GBS. Herein, we conducted a two-sample Mendelian randomization (MR) analysis to systematically explore the causal links of genetically determined systemic inflammatory proteins on GBS. Methods: A total of 8,293 participants of European ancestry were included in a genome-wide association study of 41 inflammatory proteins as instrumental variables. Five MR approaches, encompassing inverse-variance weighted, weighted median, MR-Egger, simple model, and weighted model were employed to explore the causal links between inflammatory proteins and GBS. MR-Egger regression was utilized to explore the pleiotropy. Cochran's Q statistic was implemented to quantify the heterogeneity. Furthermore, we performed single-cell RNA sequencing analysis and predicted potential drug targets through molecular docking technology. Results: By applying MR analysis, four inflammatory proteins causally associated with GBS were identified, encompassing IFN-g (OR:1.96, 95%CI: 1.02-3.78, PIVW=0.045), IL-7 (OR:1.86, 95%CI: 1.07-3.23, PIVW=0.029), SCGF-b (OR:1.56, 95%CI: 1.11-2.19, PIVW=0.011), and Eotaxin (OR:1.99, 95%CI: 1.01-3.90, PIVW=0.046). The sensitivity analysis revealed no evidence of pleiotropy or heterogeneity. Additionally, significant genes were found through single-cell RNA sequencing analysis and several anti-inflammatory or neuroprotective small molecular compounds were identified by utilizing molecular docking technology. Conclusions: Our MR analysis suggested that IFN-g, IL-7, SCGF-b, and Eotaxin were causally linked to the occurrence and development of GBS. These findings elucidated potential causal associations and highlighted the significance of these inflammatory proteins in the pathogenesis and prospective therapeutic targets for GBS. [ABSTRACT FROM AUTHOR]

Diabetic foot ulcer (DFU) is a chronic and serious complication of diabetes mellitus. It is mainly caused by hyperglycaemia, diabetic peripheral vasculopathy and diabetic peripheral neuropathy. These conditions result in ulceration of foot tissues and chronic wounds. If left untreated, DFU can lead to amputation or even endanger the patient's life. Single‐cell RNA sequencing (scRNA‐seq) is a technique used to identify and characterise transcriptional subpopulations at the single‐cell level. It provides insight into cellular function and the molecular drivers of disease. The objective of this paper is to examine the subpopulations, genes and molecules of cells associated with chronic wounds of diabetic foot by using scRNA‐seq. The paper aims to explore the wound‐healing mechanism of DFU from three aspects: inflammation, angiogenesis and extracellular matrix remodelling. The goal is to gain a better understanding of the mechanism of DFU wound healing and identify possible DFU therapeutic targets, providing new insights for the application of DFU personalised therapy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Although the patient survival rate for many malignancies has been improved with immune checkpoint inhibitors (ICIs), some patients experience various immune-related adverse events (irAEs). IrAEs impact several organ systems, including the kidney. With anti-programmed cell death protein 1 (PD-1) therapy (pembrolizumab), kidney-related adverse events occur relatively rarely compared with other irAEs. However, the occurrence of AKI usually leads to anti-PD-1 therapy interruption or discontinuation. Therefore, there is an urgent need to clarify the mechanisms of renal irAEs (R-irAEs) to facilitate early management. This study aimed to analyse the characteristics of peripheral blood mononuclear cells (PBMCs) in R-irAEs. Methods: PBMCs were collected from three patients who developed R-irAEs after anti-PD-1 therapy and three patients who did not. The PBMCs were subjected to scRNA-seq to identify cell clusters and differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses were performed to investigate the most active biological processes in immune cells. Results: Fifteen cell clusters were identified across the two groups. FOS, RPS26, and JUN were the top three upregulated genes in CD4+ T cells. The DEGs in CD4+ T cells were enriched in Th17 differentiation, Th1 and Th2 cell differentiation, NF-kappa B, Nod-like receptor, TNF, IL-17, apoptosis, and NK cell-mediated cytotoxicity signaling pathways. RPS26, TRBV25-1, and JUN were the top three upregulated genes in CD8+ T cells. The DEGs in CD8+ T cells were enriched in Th17 cell differentiation, antigen processing and presentation, natural killer cell-mediated cytotoxicity, the intestinal immune network for IgA production, the T-cell receptor signalling pathway, Th1 and Th2 cell differentiation, the phagosome, and cell adhesion molecules. Conclusions: In conclusion, R-irAEs are associated with immune cell dysfunction. DEGs and their enriched pathways identified in CD4+ T cells and CD8+ T cells play important roles in the development of renal irAEs related to anti-PD-1 therapy. These findings offer fresh perspectives on the pathogenesis of renal damage caused by anti-PD-1 therapy. [ABSTRACT FROM AUTHOR]

Understanding the liver stem cells (LSCs) holds great promise for new insights into liver diseases and liver regeneration. However, the heterogenicity and plasticity of liver cells have made it controversial. Here, by employing single‐cell RNA‐sequencing technology, transcriptome features of Krt19+ bile duct lineage cells isolated from Krt19CreERT; Rosa26R‐GFP reporter mouse livers are examined. Distinct biliary epithelial cells which include adult LSCs, as well as their downstream hepatocytes and cholangiocytes are identified. Importantly, a novel cell surface LSCs marker, CD63, as well as CD56, which distinguished active and quiescent LSCs are discovered. Cell expansion and bi‐potential differentiation in culture demonstrate the stemness ability of CD63+ cells in vitro. Transplantation and lineage tracing of CD63+ cells confirm their contribution to liver cell mass in vivo upon injury. Moreover, CD63+CD56+ cells are proved to be activated LSCs with vigorous proliferation ability. Further studies confirm that CD63+CD56− quiescent LSCs express VEGFR2 and FGFR1, and they can be activated to proliferation and differentiation through combination of growth factors: VEGF‐A and bFGF. These findings define an authentic adult liver stem cells compartment, make a further understanding of fate regulation on LSCs, and highlight its contribution to liver during pathophysiologic processes. [ABSTRACT FROM AUTHOR]

Therapeutics targeting tumor endothelial cells (TECs) have been explored for decades, with only suboptimal efficacy achieved, partly due to an insufficient understanding of the TEC heterogeneity across cancer patients. We integrated single-cell RNA-seq data of 575 cancer patients from 19 solid tumor types, comprehensively charting the TEC phenotypic diversities. Our analyses uncovered underappreciated compositional and functional heterogeneity in TECs from a pan-cancer perspective. Two subsets, CXCR4 + tip cells and SELE + veins, represented the prominent angiogenic and proinflammatory phenotypes of TECs, respectively. They exhibited distinct spatial organization patterns, and compared to adjacent non-tumor tissues, tumor tissue showed an increased prevalence of CXCR4 + tip cells, yet with SELE + veins depleted. Such functional and spatial characteristics underlie their differential associations with the response of anti-angiogenic therapies and immunotherapies. Our integrative resources and findings open new avenues to understand and clinically intervene in the tumor vasculature. [ABSTRACT FROM AUTHOR]

Cell–cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive global network view of multilayered communications. In this context, we propose scHyper, a new method that can infer intercellular communications from a global network perspective and identify the potential impact of all cells, ligand, and receptor expression on the communication score. scHyper designed a new way to represent tripartite relationships, by extracting a heterogeneous hypergraph that includes the source (ligand expression), the target (receptor expression), and the relevant ligand–receptor (L-R) pairs. scHyper is based on hypergraph representation learning, which measures the degree of match between the intrinsic attributes (static embeddings) of nodes and their observed behaviors (dynamic embeddings) in the context (hyperedges), quantifies the probability of forming hyperedges, and thus reconstructs the cell–cell communication score. Additionally, to effectively mine the key mechanisms of signal transmission, we collect a rich dataset of multisubunit complex L-R pairs and propose a nonparametric test to determine significant intercellular communications. Comparing with other tools indicates that scHyper exhibits superior performance and functionality. Experimental results on the human tumor microenvironment and immune cells demonstrate that scHyper offers reliable and unique capabilities for analyzing intercellular communication networks. Therefore, we introduced an effective strategy that can build high-order interaction patterns, surpassing the limitations of most methods that can only handle low-order interactions, thus more accurately interpreting the complexity of intercellular communications. [ABSTRACT FROM AUTHOR]

LMNA-related dilated cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C (LMNA) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. The molecular mechanisms of the disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA-related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA-mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (four from Patients and eight from Controls) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for cardiac progenitors to cardiomyocytes (CMs) and epicardium-derived cells (EPDCs). Data integration and comparative analyses of Patient and Control cells found cell type and lineage-specific differentially expressed genes (DEGs) with enrichment, supporting pathway dysregulation. Top DEGs and enriched pathways included 10 ZNF genes and RNA polymerase II transcription in pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CMs; LMNA and epigenetic regulation, as well as DDIT4 and mTORC1 signaling in EPDCs. Top DEGs also included XIST and other X-linked genes, six imprinted genes (SNRPN, PWAR6, NDN, PEG10, MEG3, MEG8), and enriched gene sets related to metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs, as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Spermatogenesis is a continuous process in which functional sperm are produced through a series of mitotic and meiotic divisions and morphological changes in germ cells. The aberrant development and fate transitions of spermatogenic cells cause hybrid sterility in mammals. Cattle-yak, a hybrid animal between taurine cattle (Bos taurus) and yak (Bos grunniens), exhibits male-specific sterility due to spermatogenic failure. In the present study, we performed single-cell RNA sequencing analysis to identify differences in testicular cell composition and the developmental trajectory of spermatogenic cells between yak and cattle-yak. The composition and molecular signatures of spermatogonial subtypes were dramatically different between these 2 animals, and the expression of genes associated with stem cell maintenance, cell differentiation and meiotic entry was altered in cattle-yak, indicating the impairment of undifferentiated spermatogonial fate decisions. Cell communication analysis revealed that signaling within different spermatogenic cell subpopulations was weakened, and progenitor spermatogonia were unable to or delayed receiving and sending signals for transformation to the next stage in cattle-yak. Simultaneously, the communication between niche cells and germ cells was also abnormal. Collectively, we obtained the expression profiles of transcriptome signatures of different germ cells and testicular somatic cell populations at the single-cell level and identified critical regulators of spermatogonial differentiation and meiosis in yak and sterile cattle-yak. The findings of this study shed light on the genetic mechanisms that lead to hybrid sterility and speciation in bovid species. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.

Abstract 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.

Abstract 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.

Abstract Clustering is widely used for single-cell analysis, but current methods are limited in accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method that outperforms other methods at hard clustering without the need for hyperparameter tuning. It also performs soft clustering to characterize continuum-like regions and quantify clustering uncertainty, demonstrated here by mapping the connectivity and intermediate transitions between MNIST handwritten digits and between hypothalamic tanycyte subpopulations. This hyperparameter-randomized ensemble approach improves the accuracy, robustness, ease of use, and interpretability of single-cell clustering, and may prove useful in other fields as well.

Abstract Background Although the patient survival rate for many malignancies has been improved with immune checkpoint inhibitors (ICIs), some patients experience various immune-related adverse events (irAEs). IrAEs impact several organ systems, including the kidney. With anti-programmed cell death protein 1 (PD-1) therapy (pembrolizumab), kidney-related adverse events occur relatively rarely compared with other irAEs. However, the occurrence of AKI usually leads to anti-PD-1 therapy interruption or discontinuation. Therefore, there is an urgent need to clarify the mechanisms of renal irAEs (R-irAEs) to facilitate early management. This study aimed to analyse the characteristics of peripheral blood mononuclear cells (PBMCs) in R-irAEs. Methods PBMCs were collected from three patients who developed R-irAEs after anti-PD-1 therapy and three patients who did not. The PBMCs were subjected to scRNA-seq to identify cell clusters and differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses were performed to investigate the most active biological processes in immune cells. Results Fifteen cell clusters were identified across the two groups. FOS, RPS26, and JUN were the top three upregulated genes in CD4+ T cells. The DEGs in CD4+ T cells were enriched in Th17 differentiation, Th1 and Th2 cell differentiation, NF-kappa B, Nod-like receptor, TNF, IL-17, apoptosis, and NK cell-mediated cytotoxicity signaling pathways. RPS26, TRBV25-1, and JUN were the top three upregulated genes in CD8+ T cells. The DEGs in CD8+ T cells were enriched in Th17 cell differentiation, antigen processing and presentation, natural killer cell-mediated cytotoxicity, the intestinal immune network for IgA production, the T-cell receptor signalling pathway, Th1 and Th2 cell differentiation, the phagosome, and cell adhesion molecules. Conclusions In conclusion, R-irAEs are associated with immune cell dysfunction. DEGs and their enriched pathways identified in CD4+ T cells and CD8+ T cells play important roles in the development of renal irAEs related to anti-PD-1 therapy. These findings offer fresh perspectives on the pathogenesis of renal damage caused by anti-PD-1 therapy.

Abstract 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.

ABSTRACT: Spermatogenesis is a continuous process in which functional sperm are produced through a series of mitotic and meiotic divisions and morphological changes in germ cells. The aberrant development and fate transitions of spermatogenic cells cause hybrid sterility in mammals. Cattle-yak, a hybrid animal between taurine cattle (Bos taurus) and yak (Bos grunniens), exhibits male-specific sterility due to spermatogenic failure. In the present study, we performed single-cell RNA sequencing analysis to identify differences in testicular cell composition and the developmental trajectory of spermatogenic cells between yak and cattle-yak. The composition and molecular signatures of spermatogonial subtypes were dramatically different between these 2 animals, and the expression of genes associated with stem cell maintenance, cell differentiation and meiotic entry was altered in cattle-yak, indicating the impairment of undifferentiated spermatogonial fate decisions. Cell communication analysis revealed that signaling within different spermatogenic cell subpopulations was weakened, and progenitor spermatogonia were unable to or delayed receiving and sending signals for transformation to the next stage in cattle-yak. Simultaneously, the communication between niche cells and germ cells was also abnormal. Collectively, we obtained the expression profiles of transcriptome signatures of different germ cells and testicular somatic cell populations at the single-cell level and identified critical regulators of spermatogonial differentiation and meiosis in yak and sterile cattle-yak. The findings of this study shed light on the genetic mechanisms that lead to hybrid sterility and speciation in bovid species.

Single-cell RNA sequencing technologies have been pivotal in advancing the development of algorithms for clustering heterogeneous cell populations. Existing methods for utilizing scRNA-seq data to identify cell types tend to neglect the beneficial impact of dropout events and perform clustering focusing solely on quantitative perspective. Here, we introduce a novel method named scQA, notable for its ability to concurrently identify cell types and cell type-specific key genes from both qualitative and quantitative perspectives. In contrast to other methods, scQA not only identifies cell types but also extracts key genes associated with these cell types, enabling bidirectional clustering for scRNA-seq data. Through an iterative process, our approach aims to minimize the number of landmarks to approximately a dozen while maximizing the inclusion of quasi-trend-preserved genes with dropouts both qualitatively and quantitatively. It then clusters cells by employing an ingenious label propagation strategy, obviating the requirement for a predetermined number of cell types. Validated on 20 publicly available scRNA-seq datasets, scQA consistently outperforms other salient tools. Furthermore, we confirm the effectiveness and potential biological significance of the identified key genes through both external and internal validation. In conclusion, scQA emerges as a valuable tool for investigating cell heterogeneity due to its distinctive fusion of qualitative and quantitative facets, along with bidirectional clustering capabilities. Furthermore, it can be seamlessly integrated into border scRNA-seq analyses. The source codes are publicly available at https://github.com/LD-Lyndee/scQA.

Increased vulnerability to stress is a major risk factor for several mood disorders, including major depressive disorder. Although cellular and molecular mechanisms associated with depressive behaviors following stress have been identified, little is known about the mechanisms that confer the vulnerability that predisposes individuals to future damage from chronic stress. We used multisite in vivo neurophysiology in freely behaving male and female C57BL/6 mice (n = 12) to measure electrical brain network activity previously identified as indicating a latent stress vulnerability brain state. We combined this neurophysiological approach with single-cell RNA sequencing of the prefrontal cortex to identify distinct transcriptomic differences between groups of mice with inherent high and low stress vulnerability. We identified hundreds of differentially expressed genes (p adjusted <.05) across 5 major cell types in animals with high and low stress vulnerability brain network activity. This unique analysis revealed that GABAergic (gamma-aminobutyric acidergic) neuron gene expression contributed most to the network activity of the stress vulnerability brain state. Upregulation of mitochondrial and metabolic pathways also distinguished high and low vulnerability brain states, especially in inhibitory neurons. Importantly, genes that were differentially regulated with vulnerability network activity significantly overlapped (above chance) with those identified by genome-wide association studies as having single nucleotide polymorphisms significantly associated with depression as well as genes more highly expressed in postmortem prefrontal cortex of patients with major depressive disorder. This is the first study to identify cell types and genes involved in a latent stress vulnerability state in the brain. [ABSTRACT FROM AUTHOR]

Abstract Bone marrow-derived mesenchymal stem cells (BMSCs) exhibit multi-lineage differentiation potential and robust proliferative capacity. The late stage of differentiation signifies the functional maturation and characterization of specific cell lineages, which is crucial for studying lineage-specific differentiation mechanisms. However, the molecular processes governing late-stage BMSC differentiation remain poorly understood. This study aimed to elucidate the key biological processes involved in late-stage BMSC differentiation. Publicly available transcriptomic data from human BMSCs were analyzed after approximately 14 days of osteogenic, adipogenic, and chondrogenic differentiation. Thirty-one differentially expressed genes (DEGs) associated with differentiation were identified. Pathway enrichment analysis indicated that the DEGs were involved in extracellular matrix (ECM)-receptor interactions, focal adhesion, and glycolipid biosynthesis, a ganglion series process. Subsequently, the target genes were validated using publicly available single-cell RNA-seq data from mouse BMSCs. Lamc1 exhibited predominant distribution in adipocytes and osteoblasts, primarily during the G2/M phase. Tln2 and Hexb were expressed in chondroblasts, osteoblasts, and adipocytes, while St3gal5 was abundantly distributed in stem cells. Cell communication analysis identified two receptors that interact with LAMCI. q-PCR results confirmed the upregulation of Lamc1, Tln2, Hexb, and St3gal5 during osteogenic differentiation and their downregulation during adipogenic differentiation. Knockdown of Lamc1 inhibited adipogenic and osteogenic differentiation. In conclusion, this study identified four genes, Lamc1, Tln2, Hexb, and St3gal5, that may play important roles in the late-stage differentiation of BMSCs. It elucidated their interactions and the pathways they influence, providing a foundation for further research on BMSC differentiation.

Disorders associated with the immune system burden multiple organs, although the shared biology exists across the diseases. Preceding family-based studies reveal that immune diseases are heritable to varying degrees, providing the basis for immunogenomics. The recent cost reduction in genetic analysis intensively promotes biobank-scale studies and the development of frameworks for statistical genetics. The accumulating multi-layer omics data, including genome-wide association studies (GWAS) and RNA-sequencing at single-cell resolution, enable us to dissect the genetic backgrounds of immune-related disorders. Although autoimmune and allergic diseases are generally categorized into different disease categories, epidemiological studies reveal the high incidence of autoimmune and allergic disease complications, suggesting the shared genetics and biology between the disease categories. Biobank resources and consortia cover multiple immune-related disorders to accumulate phenome-wide associations of genetic variants and enhance researchers to analyze the shared and heterogeneous genetic backgrounds. The emerging post-GWAS and integrative multi-omics analyses provide genetic and biological insights into the multicategorical disease associations.

Abstract Background Pancreatic fibrosis is an early diagnostic feature of the common inherited disorder cystic fibrosis (CF). Many people with CF (pwCF) are pancreatic insufficient from birth and the replacement of acinar tissue with cystic lesions and fibrosis is a progressive phenotype that may later lead to diabetes. Little is known about the initiating events in the fibrotic process though it may be a sequela of inflammation in the pancreatic ducts resulting from loss of CFTR impairing normal fluid secretion. Here we use a sheep model of CF (CFTR −/− ) to examine the evolution of pancreatic disease through gestation. Methods Fetal pancreas was collected at six time points from 50-days of gestation through to term, which is equivalent to ~ 13 weeks to term in human. RNA was extracted from tissue for bulk RNA-seq and single cells were prepared from 80-day, 120-day and term samples for scRNA-seq. Data were validated by immunochemistry. Results Transcriptomic evidence from bulk RNA-seq showed alterations in the CFTR −/− pancreas by 65-days of gestation, which are accompanied by marked pathological changes by 80-days of gestation. These include a fibrotic response, confirmed by immunostaining for COL1A1, αSMA and SPARC, together with acinar loss. Moreover, using scRNA-seq we identify a unique cell population that is significantly overrepresented in the CFTR −/− animals at 80- and 120-days gestation, as are stellate cells at term. Conclusion The transcriptomic changes and cellular imbalance that we observe likely have pivotal roles in the evolution of CF pancreatic disease and may provide therapeutic opportunities to delay or prevent pancreatic destruction in CF.

Abstract Background Muscle satellite cells (MuSCs) exert essential roles in skeletal muscle adaptation to growth, injury and ageing, and their functions are extensively modulated by microenvironmental factors. However, the current knowledge about the interaction of MuSCs with niche cells is quite limited. Methods A 10× single‐cell RNA sequencing (scRNA‐seq) was performed on porcine longissimus dorsi and soleus (SOL) muscles to generate a single‐cell transcriptomic dataset of myogenic cells and other cell types. Sophisticated bioinformatic analyses, including unsupervised clustering analysis, marker gene, gene set variation analysis (GSVA), AUCell, pseudotime analysis and RNA velocity analysis, were performed to explore the heterogeneity of myogenic cells. CellChat analysis was used to demonstrate cell–cell communications across myogenic cell subpopulations and niche cells, especially fibro‐adipogenic progenitors (FAPs). Integrated analysis with human and mice datasets was performed to verify the expression of FGF7 across diverse species. The role of FGF7 on MuSC proliferation was evaluated through administering recombinant FGF7 to porcine MuSCs, C2C12, cardiotoxin (CTX)‐injured muscle and d‐galactose (d‐gal)‐induced ageing model. Results ScRNA‐seq totally figured out five cell types including myo‐lineage cells and FAPs, and myo‐lineage cells were further classified into six subpopulations, termed as RCN3+, S100A4+, ID3+, cycling (MKI67+), MYF6+ and MYMK+ satellite cells, respectively. There was a higher proportion of cycling and MYF6+ cells in the SOL population. CellChat analysis uncovered a particular impact of FAPs on myogenic cells mediated by FGF7, which was relatively highly expressed in SOL samples. Administration of FGF7 (10 ng/mL) significantly increased the proportion of EdU+ porcine MuSCs and C2C12 by 4.03 ± 0.81% (P

Abstract Background Breast cancer (BC) is a heterogeneous disease, with the ductal subtype exhibiting significant cellular diversity that influences prognosis and response to treatment. Single-cell RNA sequencing data from the GEO database were utilized in this study to investigate the underlying mechanisms of cellular heterogeneity and to identify potential prognostic markers and therapeutic targets. Methods Bioinformatics analysis was conducted using R packages to analyze the single-cell sequencing data. The presence of highly variable genes and differences in malignant potency within the same BC samples were examined. Differential gene expression and biological function between Type 1 and Type 2 ductal epithelial cells were identified. Lasso regression and Cox proportional hazards regression analyses were employed to identify genes associated with patient prognosis. Experimental validation was performed in vitro and in vivo to confirm the functional relevance of the identified genes. Results The analysis revealed notable heterogeneity among BC cells, with the presence of highly variable genes and differences in malignant behavior within the same samples. Significant disparities in gene expression and biological function were identified between Type 1 and Type 2 ductal epithelial cells. Through regression analyses, CYP24A1 and TFPI2 were identified as pivotal genes associated with patient prognosis. Kaplan-Meier curves demonstrated their prognostic significance, and experimental validation confirmed their inhibitory effects on malignant behaviors of ductal BC cells. Conclusion This study highlights the cellular heterogeneity in ductal subtype breast cancer and delineates the differential gene expressions and biological functions between Type 1 and Type 2 ductal epithelial cells. The genes CYP24A1 and TFPI2 emerged as promising prognostic markers and therapeutic targets, exhibiting inhibitory effects on BC cell malignancy in vitro and in vivo. These findings offer the potential for improved BC management and the development of targeted treatment strategies.

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.

Abstract Idiopathic pulmonary fibrosis (IPF) is the most predominant type of idiopathic interstitial pneumonia and has an increasing incidence, poor prognosis, and unclear pathogenesis. In order to investigate the molecular mechanisms underlying IPF further, we performed single-cell RNA sequencing analysis on three healthy controls and five IPF lung tissue samples. The results revealed a significant shift in epithelial cells (ECs) phenotypes in IPF, which may be attributed to the differentiation of alveolar type 2 cells to basal cells. In addition, several previously unrecognized basal cell subtypes were preliminarily identified, including extracellular matrix basal cells, which were increased in the IPF group. We identified a special population of fibroblasts that highly expressed extracellular matrix-related genes, POSTN, CTHRC1, COL3A1, COL5A2, and COL12A1. We propose that the close interaction between ECs and fibroblasts through ligand–receptor pairs may have a critical function in IPF development. Collectively, these outcomes provide innovative perspectives on the complexity and diversity of basal cells and fibroblasts in IPF and contribute to the understanding of possible mechanisms in pathological lung fibrosis.

Jiachen Ge,1,&ast; Ming Tao,2,&ast; Gaolei Zhang,1 Jianping Cai,1 Deyu Li,1 Lianyuan Tao1 1Department of Hepatobiliary Surgery, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 2Department of General Surgery, Peking University Third Hospital, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Deyu Li; Lianyuan Tao, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, No. 7 Weiwu Road, Zhengzhou City, Henan Province, People’s Republic of China, Email lideyu19@hotmail.com; tly2007tly@hotmail.comPurpose: Hepatocellular carcinoma has become one of the severe diseases threatening human health. T cell exhaustion is deemed as a reason for immunotherapy resistance. However, little is known about the roles of CD8 Tex-related lncRNAs in HCC.Materials and Methods: We processed single-cell RNA sequencing to identify CD8 Tex-related genes. CD8 Tex-related lncRNAs were identified based on their correlations with mRNAs. Unsupervised clustering approach was used to identify molecular clusters of CD8 Tex-related lncRNAs. Differences in prognosis and immune infiltration between the clusters were explored. Machine learning algorithms were used to construct a prognostic signature. Samples were classified as low- and high-risk groups based on their risk scores. We identified prognosis-related lncRNAs and constructed a ceRNA network. In vitro experiments were conducted to investigate the impacts of CD8 Tex-related lncRNAs on proliferation and apoptosis of HCC cells.Results: We clarified cell types within two HCC single-cell datasets. We identified specific markers of CD8 Tex cells and analyzed their potential functions. Twenty-eight lncRNAs were identified as CD8 Tex-related. Based on CD8 Tex-related lncRNAs, samples were categorized into two distinct clusters, which exhibited significant differences in survival rates and immune infiltration. Ninety-six algorithm combinations were employed to establish a prognostic signature. RSF emerged as the one with the highest C-index. Patients in high- and low-risk groups exhibited marked differences in prognosis, enriched pathways, mutations and drug sensitivities. MCM3AP-AS1, MAPKAPK5-AS1 and PART1 were regarded as prognosis-related lncRNAs. A ceRNA network was constructed based on CD8 Tex-related lncRNAs and mRNAs. Experiments on cell lines and organoids indicated that downregulation of MCM3AP-AS1, MAPKAPK5-AS1 and PART1 suppressed cell proliferation and induced apoptosis.Conclusion: CD8 Tex-related lncRNAs played crucial roles in HCC progression. Our findings provided new insights into the regulatory mechanisms of CD8 Tex-related lncRNAs in HCC.Keywords: hepatocellular carcinoma, lncRNA, T cell exhaustion, single-cell RNA-seq, machine learning, prognostic signature

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

Abstract Metric multidimensional scaling is one of the classical methods for embedding data into low-dimensional Euclidean space. It creates the low-dimensional embedding by approximately preserving the pairwise distances between the input points. However, current state-of-the-art approaches only scale to a few thousand data points. For larger data sets such as those occurring in single-cell RNA sequencing experiments, the running time becomes prohibitively large and thus alternative methods such as PCA are widely used instead. Here, we propose a simple neural network-based approach for solving the metric multidimensional scaling problem that is orders of magnitude faster than previous state-of-the-art approaches, and hence scales to data sets with up to a few million cells. At the same time, it provides a non-linear mapping between high- and low-dimensional space that can place previously unseen cells in the same embedding.

Abstract Genetic variations in the ovine ovulation rate, which are associated with the FecB mutation, provide useful models by which to explore the mechanisms regulating the development of mammalian antral follicles. In order to study the effects of the FecB mutation on cumulus cell differentiation, preovulatory follicles were aspirated and cumulus cells were isolated from three FecB genotypes (homozygous, heterozygous and wild type) of Small Tail Han (STH) sheep superstimulated with FSH. Transcriptome information from tens of thousands of cumulus cells was determined with the 10 × Genomics single-cell RNA-seq technology. Under the superovulation treatment, the observed number of preovulatory follicles in the ovaries of FecB carriers was still significantly higher than that in the wild-type (P

Abstract Background Depression is a complex mood disorder whose pathogenesis involves multiple cell types and molecular pathways. The prefrontal cortex, as a key brain region for emotional regulation, plays a crucial role in depression. Microglia, as immune cells of the central nervous system, have been closely linked to the development and progression of depression through their dysfunctional states. This study aims to utilize single-cell RNA-seq technology to reveal the pathogenic mechanism of YAP1 in prefrontal cortex microglia in depression. Methods Firstly, we performed cell type identification and differential analysis on normal and depressed prefrontal cortex tissues by mining single-cell RNA-seq datasets from public databases. Focusing on microglia, we conducted sub-clustering, differential gene KEGG enrichment analysis, intercellular interaction analysis, and pseudotime analysis. Additionally, a cross-species analysis was performed to explore the similarities and differences between human and rhesus monkey prefrontal cortex microglia. To validate our findings, we combined bulk RNA-Seq and WGCNA analysis to reveal key genes associated with depression and verified the relationship between YAP1 and depression using clinical samples. Results Our study found significant changes in the proportion and transcriptional profiles of microglia in depressed prefrontal cortex tissues. Further analysis revealed multiple subpopulations of microglia and their associated differential genes and signaling pathways related to depression. YAP1 was identified as a key molecule contributing to the development of depression and was significantly elevated in depression patients. Moreover, the expression level of YAP1 was positively correlated with HAMD scores, suggesting its potential as a biomarker for predicting the onset of depression. Conclusion This study utilized single-cell RNA-seq technology to reveal the pathogenic mechanism of YAP1 in prefrontal cortex microglia in depression, providing a new perspective for a deeper understanding of the pathophysiology of depression and identifying potential targets for developing novel treatment strategies.