Isolation and Preparation of Embryonic Zebrafish Retinal Cells for Single-Cell RNA Sequencing.

Recent technological advances in single-cell RNA sequencing (scRNA-Seq) have enabled scientists to answer novel questions in biology with unparalleled precision. Indeed, in the field of ocular development and regeneration, scRNA-Seq studies have resulted in a number of exciting discoveries that have begun to revolutionize the way we think about these processes. Despite the widespread success of scRNA-Seq, many scientists are wary to perform scRNA-Seq experiments due to the uncertainty of obtaining high-quality viable cell populations that are necessary for the generation of usable data that enable rigorous computational analyses. Here, we describe methodology to reproducibility generate high-quality single-cell suspensions from embryonic zebrafish eyes. These single-cell suspensions served as inputs to the 10× Genomics v3.1 system and yielded high-quality scRNA-Seq data in proof-of-principle studies. In describing methodology to quantitatively assess cell yields, cell viability, and other critical quality control parameters, this protocol can serve as a useful starting point for others in designing their scRNA-Seq experiments in the zebrafish eye and in other developing or regenerating tissues in zebrafish or other model systems.

Experimental Framework for Assessing Mouse Retinal Regeneration Through Single-Cell RNA-Sequencing.

Retinal degenerative diseases including age-related macular degeneration and glaucoma are estimated to currently affect more than 14 million people in the United States, with an increased prevalence of retinal degenerations in aged individuals. An expanding aged population who are living longer forecasts an increased prevalence and economic burden of visual impairments. Improvements to visual health and treatment paradigms for progressive retinal degenerations slow vision loss. However, current treatments fail to remedy the root cause of visual impairments caused by retinal degenerations-loss of retinal neurons. Stimulation of retinal regeneration from endogenous cellular sources presents an exciting treatment avenue for replacement of lost retinal cells. In multiple species including zebrafish and Xenopus, Müller glial cells maintain a highly efficient regenerative ability to reconstitute lost cells throughout the organism's lifespan, highlighting potential therapeutic avenues for stimulation of retinal regeneration in humans. Here, we describe how the application of single-cell RNA-sequencing (scRNA-seq) has enhanced our understanding of Müller glial cell-derived retinal regeneration, including the characterization of gene regulatory networks that facilitate/inhibit regenerative responses. Additionally, we provide a validated experimental framework for cellular preparation of mouse retinal cells as input into scRNA-seq experiments, including insights into experimental design and analyses of resulting data.

Identification of novel protein biomarkers and therapeutic targets for ankylosing spondylitis using human circulating plasma proteomics and genome analysis.

The proteome serves as the primary basis for identifying targets for treatment. This study conducted proteomic range two-sample Mendelian randomization (MR) analysis to pinpoint potential protein markers and treatment targets for ankylosing spondylitis (AS). A total of 4907 data points on circulating protein expression were collected from a large-scale protein quantitative trait locus investigation involving 35,559 individuals. Using data from a Finnish study on AS as the outcome, the dataset comprised 166,144 individuals of European ancestry (1462 cases and 164,682 controls), and causal relationships were determined through bidirectional Mendelian randomization of two samples. Proteins were further validated and identified through single-cell expression analysis, certain cells showing enriched expression levels were detected, and possible treatment targets were optimized. Increased HERC5 expression predicted by genes was related to increased AS risk, whereas the expression of the remaining five circulating proteins, AIF1, CREB3L4, MLN, MRPL55, and SPAG11B, was negatively correlated with AS risk. For each increase in gene-predicted protein levels, the ORs of AS were 2.11 (95% CI 1.44-3.09) for HERC5, 0.14 (95% CI 0.05-0.41) for AIF1, 0.48 (95% CI 0.34-0.68) for CREB3L4, 0.54 (95% CI 0.42-0.68) for MLN, 0.23 (95% CI 0.13-0.38) for MRPL55, and 0.26 (95% CI 0.17-0.39) for SPAG11B. The hypothesis of a reverse causal relationship between these six circulating proteins and AS is not supported. Three of the six protein-coding genes were expressed in both the AS and healthy control groups, while CREB3L4, MLN, and SPAG11B were not detected. Increased levels of HERC5 predicted by genes are related to increased AS risk, whereas the levels of the remaining five circulating proteins, AIF1, CREB3L4, MLN, MRPL55, and SPAG11B, negatively correlate with AS risk. HERC5, AIF1, and MRPL55 are potential therapeutic targets for AS. This study advanced the field by employing a novel combination of proteomic range two-sample MR analysis and single-cell expression analysis to identify potential protein markers and therapeutic targets for AS. This approach enabled a comprehensive understanding of the causal relationships between circulating proteins and AS, which has not been extensively explored in previous studies.

DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates.

Messenger RNA splicing and degradation are critical for gene expression regulation, the abnormality of which leads to diseases. Previous methods for estimating kinetic rates have limitations, assuming uniform rates across cells. DeepKINET is a deep generative model that estimates splicing and degradation rates at single-cell resolution from scRNA-seq data. DeepKINET outperforms existing methods on simulated and metabolic labeling datasets. Applied to forebrain and breast cancer data, it identifies RNA-binding proteins responsible for kinetic rate diversity. DeepKINET also analyzes the effects of splicing factor mutations on target genes in erythroid lineage cells. DeepKINET effectively reveals cellular heterogeneity in post-transcriptional regulation.

Construction and validation of a TAMRGs prognostic signature for gliomas by integrated analysis of scRNA and bulk RNA sequencing data.

BACKGROUND: This study aimed to construct and validate a prognostic model based on tumor associated macrophage-related genes (TAMRGs) by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data. METHODS: The scRNA-seq data of three inhouse glioma tissues were used to identify the tumor-associated macrophages (TAMs) marker genes, the DEGs from the The Cancer Genome Atlas (TCGA) - Genotype-Tissue Expression (GTEx) dataset were used to further select TAMs marker genes. Subsequently, a TAMRG-score was constructed by Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis in the TCGA dataset and validated in the Chinese Glioma Genome Atlas (CGGA) dataset. RESULTS: We identified 186 TAMs marker genes, and a total of 6 optimal prognostic genes including CKS2, LITAF, CTSB, TWISTNB, PPIF and G0S2 were selected to construct a TAMRG-score. The high TAMRG-score was significantly associated with worse prognosis (log-rank test, P<0.001). Moreover, the TAMRG-score outperformed the other three models with AUC of 0.808. Immune cell infiltration, TME scores, immune checkpoints, TMB and drug susceptibility were significantly different between TAMRG-score groups. In addition, a nomogram were constructed by combing the TAMRG-score and clinical information (Age, Grade, IDH mutation and 1p19q codeletion) to predict the survival of glioma patients with AUC of 0.909 for 1-year survival. CONCLUSION: The high TAMRG-score group was associated with a poor prognosis. A nomogram by incorporating TMARG-score could precisely predict glioma survival, and provide evidence for personalized treatment of glioma.

scBubbletree: computational approach for visualization of single cell RNA-seq data.

BACKGROUND: Visualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space. RESULTS: We present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as "bubbles" at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. We demonstrate this with large scRNA-seq data sets, including one with over 1.2 million cells. CONCLUSIONS: To facilitate coherent quantification and visualization of scRNA-seq data we developed the R-package scBubbletree, which is freely available as part of the Bioconductor repository at: https://bioconductor.org/packages/scBubbletree/.

Unveiling osmoregulation and immunological adaptations in Eleutheronema tetradactylum gills through high-throughput single-cell transcriptome sequencing.

The fourfinger threadfin fish (Eleutheronema tetradactylum) is an economically significant species renowned for its ability to adapt to varying salinity environments, with gills serving as their primary organs for osmoregulation and immune defense. Previous studies focused on tissue and morphological levels, whereas ignored the cellular heterogeneity and the crucial gene information related to core cell subsets within E. tetradactylum gills. In this study, we utilized high-throughput single-cell RNA sequencing (scRNA-seq) to analyze the gills of E. tetradactylum, characterizing 16 distinct cell types and identifying unique gene markers and enriched functions associated within each cell type. Additionally, we subdivided ionocyte cells into four distinct subpopulations for the first time in E. tetradactylum gills. By employing weighted gene co-expression network analysis (WGCNA), we further investigated the cellular heterogeneity and specific response mechanisms to salinity fluctuant. Our findings revealed the intricate osmoregulation and immune functions of gill cells, highlighting their crucial roles in maintaining homeostasis and adapting to fluctuating salinity levels. This comprehensive cell-type atlas provides valuable insights into the species adaptive strategies, contributing to the conservation and management of this commercially significant fish as well as other euryhaline species.

A natural language processing system for the efficient extraction of cell markers.

Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal tool for exploring cellular landscapes across diverse species and tissues. Precise annotation of cell types is essential for understanding these landscapes, relying heavily on empirical knowledge and curated cell marker databases. In this study, we introduce MarkerGeneBERT, a natural language processing (NLP) system designed to extract critical information from the literature regarding species, tissues, cell types, and cell marker genes in the context of single-cell sequencing studies. Leveraging MarkerGeneBERT, we systematically parsed full-text articles from 3702 single-cell sequencing-related studies, yielding a comprehensive collection of 7901 cell markers representing 1606 cell types across 425 human tissues/subtissues, and 8223 cell markers representing 1674 cell types across 482 mouse tissues/subtissues. Comparative analysis against manually curated databases demonstrated that our approach achieved 76% completeness and 75% accuracy, while also unveiling 89 cell types and 183 marker genes absent from existing databases. Furthermore, we successfully applied the compiled brain tissue marker gene list from MarkerGeneBERT to annotate scRNA-seq data, yielding results consistent with original studies. Conclusions: Our findings underscore the efficacy of NLP-based methods in expediting and augmenting the annotation and interpretation of scRNA-seq data, providing a systematic demonstration of the transformative potential of this approach. The 27323 manual reviewed sentences for training MarkerGeneBERT and the source code are hosted at https://github.com/chengpeng1116/MarkerGeneBERT .

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.

Background: Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction. Methods: Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts. Results: We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions. Conclusions: This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.

PGRMC2 influences the onset of postmenopausal osteoporosis through disulfidptosis in monocytes: Evidence from experimental validation and Mendelian randomization.

This study explores the role of disulfidptosis in monocytes and its relation to postmenopausal osteoporosis (PMOP). Using single-cell RNA sequencing and microarray assays, we identified key genes: LONRF1, ACAP2, IPO9, and PGRMC2. Through differential analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning, these genes were linked to PMOP. Functional enrichment and ROC curve analysis demonstrated their effectiveness in distinguishing postmenopausal fracture patients from healthy individuals. Notably, PGRMC2 exhibited significant expression differences, highlighted by a notable Area Under the Curve (AUC) value of 0.665. Further investigation involved Western blotting and immunohistochemical assays, revealing decreased PGRMC2 expression in ovariectomized (OVX) mice. This decrease was consistent across both experimental methods, emphasizing PGRMC2's role in PMOP. Moreover, PGRMC2 was predominantly present in macrophages compared to monocytes within bone tissue and was significantly located in bone marrow mesenchymal stem cells (BM-MSCs) in PMOP patients. It was also abundantly found in osteoblasts and adipocytes. Additionally, a Mendelian randomization analysis using the TwoSampleMR R package, with data from decode and GWAS databases, was conducted. This analysis showed a significant impact of PGRMC2 on osteoporosis risk (p = 0.0048, OR = 0.6836), suggesting a potential protective effect against the disease. Our results suggest that PGRMC2 may facilitate the differentiation of monocytes into macrophages in bone tissue, influencing the behavior of BM-MSCs. This, in turn, could impact the progression and severity of PMOP. The study provides new insights into the molecular mechanisms underlying postmenopausal osteoporosis and highlights the potential of PGRMC2 as a therapeutic target or biomarker for this condition.

Dissection of the cell communication interactions in lung adenocarcinoma identified a prognostic model with immunotherapy efficacy assessment and a potential therapeutic candidate gene ITGB1.

Background: The tumor microenvironment (TME) in lung adenocarcinoma (LUAD) influences tumor progression and immunosuppressive phenotypes through cell communication. We aimed to decipher cellular communication and molecular patterns in LUAD. Methods: We analyzed scRNA-seq data from LUAD patients in multiple cohorts, revealing complex cell communication networks within the TME. Using cell chat analysis and COSmap technology, we inferred LUAD's spatial organization. Employing the NMF algorithm and survival screening, we identified a cell communication interactions (CCIs) model and validated it across various datasets. Results: We uncovered intricate cell communication interactions within the TME, identifying three LUAD patient subtypes with distinct prognosis, clinical characteristics, mutation status, expression patterns, and immune infiltration. Our CCI model exhibited robust performance in prognosis and immunotherapy response prediction. Several potential therapeutic targets and agents for high CCI score patients with immunosuppressive profiles were identified. Machine learning algorithms pinpointed the novel candidate gene ITGB1 and validated its role in LUAD tumor phenotype in vitro. Conclusion: Our study elucidates molecular patterns and cell communication interactions in LUAD as effective biomarkers and predictors of immunotherapy response. Targeting cell communication interactions offers novel avenues for LUAD immunotherapy and prognostic evaluations, with ITGB1 emerging as a promising therapeutic target.

Single-cell multi-omics reveal stage of differentiation and trajectory-dependent immunity-related gene expression patterns in human erythroid cells.

The role of Erythroid cells in immune regulation and immunosuppression is one of the emerging topics in modern immunology that still requires further clarification as Erythroid cells from different tissues and different species express different immunoregulatory molecules. In this study, we performed a thorough investigation of human bone marrow Erythroid cells from adult healthy donors and adult acute lymphoblastic leukemia patients using the state-of-the-art single-cell targeted proteomics and transcriptomics via BD Rhapsody and cancer-related gene copy number variation analysis via NanoString Sprint Profiler. We found that human bone marrow Erythroid cells express the ARG1, LGALS1, LGALS3, LGALS9, and C10orf54 (VISTA) immunosuppressive genes, CXCL5, CXCL8, and VEGFA cytokine genes, as well as the genes involved in antimicrobial immunity and MHC Class II antigen presentation. We also found that ARG1 gene expression was restricted to the single erythroid cell cluster that we termed ARG1-positive Orthochromatic erythroblasts and that late Erythroid cells lose S100A9 and gain MZB1 gene expression in case of acute lymphoblastic leukemia. These findings show that steady-state erythropoiesis bone marrow Erythroid cells express myeloid signature genes even without any transdifferentiating stimulus like cancer.

Exploring the molecular mechanisms of macrophages in islet transplantation using single-cell analysis.

Background: Islet transplantation is a promising treatment for type 1 diabetes that aims to restore insulin production and improve glucose control, but long-term graft survival remains a challenge due to immune rejection. Methods: ScRNA-seq data from syngeneic and allogeneic islet transplantation grafts were obtained from GSE198865. Seurat was used for filtering and clustering, and UMAP was used for dimension reduction. Differentially expressed genes were analyzed between syngeneic and allogeneic islet transplantation grafts. Gene set variation analysis (GSVA) was performed on the HALLMARK gene sets from MSigDB. Monocle 2 was used to reconstruct differentiation trajectories, and cytokine signature enrichment analysis was used to compare cytokine responses between syngeneic and allogeneic grafts. Results: Three distinct macrophage clusters (Mø-C1, Mø-C2, and Mø-C3) were identified, revealing complex interactions and regulatory mechanisms within macrophage populations. The significant activation of macrophages in allogeneic transplants was marked by the upregulation of allograft rejection-related genes and pathways involved in inflammatory and interferon responses. GSVA revealed eight pathways significantly upregulated in the Mø-C2 cluster. Trajectory analysis revealed that Mø-C3 serves as a common progenitor, branching into Mø-C1 and Mø-C2. Cytokine signature enrichment analysis revealed significant differences in cytokine responses, highlighting the distinct immunological environments created by syngeneic and allogeneic grafts. Conclusion: This study significantly advances the understanding of macrophage roles within the context of islet transplantation by revealing the interactions between immune pathways and cellular fate processes. The findings highlight potential therapeutic targets for enhancing graft survival and function, emphasizing the importance of understanding the immunological aspects of transplant acceptance and longevity.

Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis.

Lupus nephritis (LN) is a challenging condition with limited diagnostic and treatment options. In this study, we applied 12 distinct machine learning algorithms along with Non-negative Matrix Factorization (NMF) to analyze single-cell datasets from kidney biopsies, aiming to provide a comprehensive profile of LN. Through this analysis, we identified various immune cell populations and their roles in LN progression and constructed 102 machine learning-based immune-related gene (IRG) predictive models. The most effective models demonstrated high predictive accuracy, evidenced by Area Under the Curve (AUC) values, and were further validated in external cohorts. These models highlight six hub IRGs (CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR) as key diagnostic markers for LN, showing remarkable diagnostic performance in both renal and peripheral blood cohorts, thus offering a novel approach for noninvasive LN diagnosis. Further clinical correlation analysis revealed that expressions of IFNGR1, PLAUR, and CYBB were negatively correlated with the glomerular filtration rate (GFR), while CYBB also positively correlated with proteinuria and serum creatinine levels, highlighting their roles in LN pathophysiology. Additionally, protein-protein interaction (PPI) analysis revealed significant networks involving hub IRGs, emphasizing the importance of the interleukin family and chemokines in LN pathogenesis. This study highlights the potential of integrating advanced genomic tools and machine learning algorithms to improve diagnosis and personalize management of complex autoimmune diseases like LN.

SCIntRuler: Guiding the integration of multiple single-cell RNA-seq datasets with a novel statistical metric.

MOTIVATION: The growing number of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets, such as augmenting sample sizes and enhancing analytical robustness. Inherent diversity and batch discrepancies within samples or across studies continue to pose significant challenges for computational analyses. Questions persist in practice, lacking definitive answers: Should we use a specific integration method or opt for simply merging the datasets during joint analysis? Among all the existing data integration methods, which one is more suitable in specific scenarios? RESULT: To fill the gap, we introduce SCIntRuler, a novel statistical metric for guiding the integration of multiple scRNA-seq datasets. SCIntRuler helps researchers make informed decisions regarding the necessity of data integration and the selection of an appropriate integration method. Our simulations and real data applications demonstrate that SCIntRuler streamlines decision-making processes and facilitates the analysis of diverse scRNA-seq datasets under varying contexts, thereby alleviating the complexities associated with the integration of heterogeneous scRNA-seq datasets. AVAILABILITY: The implementation of our method is available on CRAN as an open-source R package with a user- friendly manual available: https://cloud.r-project.org/web/packages/SCIntRuler/index.html.

Effects of Leydig cell elimination on testicular interstitial cell populations: characterization by scRNA-seq and immunocytochemical techniques.

Background: The mammalian testicular interstitial cells are not well-defined. The present study characterized the interstitial cell types and their turnover dynamics in adult rats. Additionally, the heterogeneity of the mesenchymal population and the effects of Leydig cell elimination on interstitial homeostasis were further analyzed by scRNA-seq datasets and immunocytochemical techniques. Methods: Interstitial cells were defined at the transcriptomic level by scRNA-seq and then confirmed and quantified with protein markers. The dividing activity of the major cell types was determined by continuous EdU labeling of the animals for one week. Some of the rats were also treated with a dose of ethylenedimethylsulfonate (EDS) to examine how the loss of Leydig cells (LCs) could affect interstitial homeostasis for three weeks. Results: Seven interstitial cell types were identified, including cell types (percentage of the whole interstitial population) as follows: Leydig (44.6%), macrophage and dendritic (19.1%), lymphoid (6.2%), vascular endothelial (7.9%), smooth muscle (10.7%), and mesenchymal (11.5%) cells. The EdU experiment indicated that most cell types were dividing at relatively low levels (<9%) except for the mesenchymal cells (MCs, 17.1%). Further analysis of the transcriptome of MCs revealed 4 subgroups with distinct functions, including 1) glutathione metabolism and xenobiotic detoxification, 2) ROS response and AP-1 signaling, 3) extracellular matrix synthesis and binding, and 4) immune response and regulation. Stem LCs (SLCs) are primarily associated with subgroup 3, expressing ARG1 and GAP43. EDS treatment not only eliminated LCs but also increased subgroup 3 and decreased subgroups 1 and 2 of the mesenchymal population. Moreover, EDS treatment increased the division of immune cells by more than tenfold in one week. Conclusion: Seven interstitial cell types were identified and quantified for rat testis. Many may play more diversified roles than previously realized. The elimination of LCs led to significant changes in MCs and immune cells, indicating the importance of LCs in maintaining testicular interstitial homeostasis.

SRSF2 is essential for maintaining pancreatic beta-cell identity and regulating glucose homeostasis in mice.

Diabetes is characterized by decreased beta-cell mass and islet dysfunction. The splicing factor SRSF2 plays a crucial role in cell survival, yet its impact on pancreatic beta cell survival and glucose homeostasis remains unclear. We observed that the deletion of Srsf2 specifically in beta cells led to time-dependent deterioration in glucose tolerance, impaired insulin secretion, decreased islet mass, an increased number of alpha cells, and the onset of diabetes by the age of 10 months in mice. Single-cell RNA sequencing (scRNA-seq) analyses revealed that, despite an increase in populations of unfolded protein response (UPR)-activated and undifferentiated beta cells within the SRSF2_KO group, there was a notable decrease in the expression of UPR-related and endoplasmic reticulum (ER)-related genes, accompanied by a loss of beta-cell identity. This suggests that beta cells have transitioned from an adaptive phase to a maladaptive phase in islets of 10-month-old SRSF2_KO mice. Further results demonstrated that deletion of SRSF2 caused decreased proliferation in beta cells within 3-month-old islets and Min6 cells. These findings underscore the essential role of SRSF2 in controlling beta-cell proliferation and preserving beta-cell function in mice.

Single-cell RNA-seq analysis of rat molars reveals cell identity and driver genes associated with dental mesenchymal cell differentiation.

BACKGROUND: The molecular mechanisms and signaling pathways involved in tooth morphogenesis have been the research focus in the fields of tooth and bone development. However, the cell population in molars at the late bell stage and the mechanisms of hard tissue formation and mineralization remain limited knowledge. RESULTS: Here, we used the rat mandibular first and second molars as models to perform single-cell RNA sequencing (scRNA-seq) analysis to investigate cell identity and driver genes related to dental mesenchymal cell differentiation during the late bell hard tissue formation stage. We identified seven main cell types and investigated the heterogeneity of mesenchymal cells. Subsequently, we identified novel cell marker genes, including Pclo in dental follicle cells, Wnt10a in pre-odontoblasts, Fst and Igfbp2 in periodontal ligament cells, and validated the expression of Igfbp3 in the apical pulp. The dynamic model revealed three differentiation trajectories within mesenchymal cells, originating from two types of dental follicle cells and apical pulp cells. Apical pulp cell differentiation is associated with the genes Ptn and Satb2, while dental follicle cell differentiation is associated with the genes Tnc, Vim, Slc26a7, and Fgfr1. Cluster-specific regulons were analyzed by pySCENIC. In addition, the odontogenic function of driver gene TNC was verified in the odontoblastic differentiation of human dental pulp stem cells. The expression of osteoclast differentiation factors was found to be increased in macrophages of the mandibular first molar. CONCLUSIONS: Our results revealed the cell heterogeneity of molars in the late bell stage and identified driver genes associated with dental mesenchymal cell differentiation. These findings provide potential targets for diagnosing dental hard tissue diseases and tooth regeneration.

From bioinformatics to clinical applications: a novel prognostic model of cuproptosis-related genes based on single-cell RNA sequencing data in hepatocellular carcinoma.

OBJECTIVE AND METHODS: To ascertain the connection between cuproptosis-related genes (CRGs) and the prognosis of hepatocellular carcinoma (HCC) via single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data, relevant data were downloaded from the GEO and TCGA databases. The differentially expressed CRGs (DE-CRGs) were filtered by the overlaps in differentially expressed genes (DEGs) between HCC patients and normal controls (NCs) in the scRNA-seq database, DE-CRGs between high- and low-CRG-activity cells, and DEGs between HCC patients and NCs in the TCGA database. RESULTS: Thirty-three DE-CRGs in HCC were identified. A prognostic model (PM) was created employing six survival-related genes (SRGs) (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) via univariate Cox regression analysis and LASSO. The predictive ability of the model was validated via a nomogram and receiver operating characteristic curves. Research has employed tumor immune dysfunction and exclusion as a means to examine the influence of PM on immunological heterogeneity. Macrophage M0 levels were significantly different between the high-risk group (HRG) and the low-risk group (LRG), and a greater macrophage level was linked to a more unfavorable prognosis. The drug sensitivity data indicated a substantial difference in the half-maximal drug-suppressive concentrations of idarubicin and rapamycin between the HRG and the LRG. The model was verified by employing public datasets and our cohort at both the protein and mRNA levels. CONCLUSION: A PM using 6 SRGs (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) was developed via bioinformatics research. This model might provide a fresh perspective for assessing and managing HCC.

Gene Misexpression in a Smoc2+ve/Sox2-Low Population in Juvenile Prop1-Mutant Pituitary Gland.

Mutations in the pituitary-specific transcription factor Prophet of Pit-1 (PROP1) are the most common genetic etiology of combined pituitary hormone deficiency (CPHD). CPHD is associated with short stature, attributable to growth hormone deficiency and/or thyroid-stimulating hormone deficiency, as well as hypothyroidism and infertility. Pathogenic lesions impair pituitary development and differentiation of endocrine cells. We performed single-cell RNA sequencing of pituitary cells from a wild-type and a Prop1-mutant P4 female mouse to elucidate population-specific differential gene expression. We observed a Smoc2+ve population that expressed low Sox2, which trajectory analyses suggest are a transitional cell state as stem cells differentiate into endocrine cells. We also detected ectopic expression of Sox21 in these cells in the Prop1df/df mutant. Prop1-mutant mice are known to overexpress Pou3f4, which we now show to be also enriched in this Smoc2+ve population. We sought to elucidate the role of Pou3f4 during pituitary development and to determine the contributions of Pou3f4 upregulation to pituitary disease by utilizing double-mutant mice lacking both Prop1 and Pou3f4. However, our data showed that Pou3f4 is not required for normal pituitary development and function. Double mutants further demonstrated that the upregulation of Pou3f4 was not causative for the overexpression of Sox21. These data indicate loss of Pou3f4 is not a potential cause of CPHD, and further studies may investigate the functional consequence of upregulation of Pou3f4 and Sox21, if any, in the novel Smoc2+ve cell population.