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.

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.

Potential molecular metabolic mechanisms underlying the effects of cimifugin in gastric cancer through single-cell and bulk RNA sequencing combined with network pharmacology.

Background: Gastric cancer (GC) is a leading cause of cancer-related mortality worldwide, posing a significant clinical challenge due to its complex tumor microenvironment (TME) and metabolic heterogeneity. Despite continuous improvements in treatment strategies including surgery, chemotherapy, and targeted therapies, the metabolic reprogramming in GC continues to impede treatment efficacy, highlighting an urgent need for the development of novel therapeutic strategies. This persistent issue underscores the urgent need for novel therapeutic approaches that can effectively address the diverse and dynamic characteristics of GC. Cimifugin, a traditional Chinese medicine (TCM), has garnered attention for its potential role in alleviating inflammation, neurological disorders, pain, and metabolic disorders. Its multi-targeting properties and minimal side effects suggest a broad potential for cancer management, which is currently being explored. This study aims to delineate the molecular mechanisms that cimifugin may impact within the TME and metabolic pathways of GC, with the expectation of contributing to a deeper understanding of GC and the development of innovative treatment strategies. Methods: We identified the GC-related TME cell types and metabolic profiles and pathways by using relevant data from the single-cell RNA sequencing (scRNA-seq) database GSE134520 and the stomach adenocarcinoma (STAD) data set from The Cancer Genome Atlas (TCGA). We also assessed the effects of cimifugin on MKN28 cell proliferation, invasion, and migration. By using six public platforms, we comprehensively predicted the potential biological targets of cimifugin. Clinical prognosis and immunohistochemistry (IHC), molecular docking, and dynamics simulations were used to confirm the clinical relevance and stability of the aforementioned targets. Results: Cimifugin inhibited MKN28 cell proliferation, migration, and invasion. Cimifugin may potentially act on various metabolic pathways in GC, including folate biosynthesis, xenobiotic metabolism via cytochrome P450 (CYP), glutathione metabolism, steroid hormone biosynthesis, and tryptophan metabolism. Cimifugin was noted to stably bind to three significant core targets associated with metabolic reprogramming in GC: AKR1C2, MAOB, and PDE2A; all three targets were strongly expressed in endocrince cells, pit mucous cells (PMCs), and common myeloid progenitors (CMPs). Conclusions: We verified the pharmacological effects of cimifugin on GC cell proliferation, invasion, and migration. AKR1C2, MAOB, and PDE2A were identified as the key targets of cimifugin in GC-related metabolic reprogramming and pathogenesis. Our research provides preliminary insights into the potential therapeutic effects of cimifugin, which could be considered for future exploration in the context of GC treatment.

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.

Single-cell RNA-Seq analysis of molecular changes during radiation-induced skin injury: the involvement of Nur77.

Introduction: Ionizing radiation has been widely used in industry, medicine, military and agriculture. Radiation-induced skin injury is a significant concern in the context of radiotherapy and accidental exposure to radiation. The molecular changes at the single-cell level and intercellular communications during radiation-induced skin injury are not well understood. Methods: This study aims to illustrate this information in a murine model and human skin samples from a radiation accident using single-cell RNA sequencing (scRNA-Seq). We further characterize the functional significance of key molecule, which may provide a potential therapeutic target. ScRNA-Seq was performed on skin samples from a nuclear accident patient and rats exposed to ionizing radiation. Bioinformatic tools were used to analyze the cellular heterogeneity and preferential mRNAs. Comparative analysis was performed to identify dysregulated pathways, regulators, and ligand-receptor interactions in fibroblasts. The function of key molecule was validated in skin cells and in three mouse models of radiation-induced skin injury. Results: 11 clusters in human skin and 13 clusters of cells in rat skin were depicted respectively. Exposure to ionizing radiation caused changes in the cellular population (upregulation of fibroblasts and endothelial cells, downregulation of keratinocytes). Fibroblasts and keratinocytes possessed the most interaction pairs with other cell lineages. Among the five DEGs common to human and rat skins, Nur77 was highly expressed in fibroblasts, which mediated radiosensitivity by cell apoptosis and modulated crosstalk between macrophages, keratinocytes and endothelial cells in radiation-induced skin injury. In animal models, Nur77 knock-out mice (Nur77 -/-) showed more severe injury after radiation exposure than wild-type counterparts in three models of radiation-induced skin injury with complex mechanisms. Conclusion: The study reveals a single-cell transcriptional framework during radiation-induced skin injury, which provides a useful resource to uncover key events in its progression. Nur77 is a novel target in radiation-induced skin injury, which provides a potential therapeutic strategy against this disease.

AB091. Establishment and application of brainstem glioma organoids.

BACKGROUND: Traditional preclinical experiments on brainstem gliomas mainly rely on patient-derived primary cell lines, but there are problems such as low success rate in establishment and inability to preserve tumor heterogeneity, which limit the clinical transformation. As a new type of in vitro tumor model, organoids have similar structure and function to the original tumor, requiring less tissue for cultivation, with short cycle and high success rate, which is particularly suitable for brainstem glioma biopsy. There is currently no precedent for the successful construction of brainstem glioma organoid models. This new established organoid provides us a more robust preclinical tool for comprehending the pathogenesis and conducting drug screening for this kind of disease. METHODS: Cultivate patient-derived brainstem glioma organoids in vitro, verify the genetic fidelity and consistency of the organoids through morphological experiments as well as sequencing technology. Then explore the evolutionary direction of multiple types of brainstem gliomas through pseudo-time series analysis. Complete drug screening, natural killer (NK) cell co-culture, oncolytic virus therapy, and other treatments based on organoids in vitro, and evaluate the efficacy. Complete co-culture of organoids and Institute of Cancer Research (ICR) mouse brain slices in vitro. Establish patient-derived organoid xenograft (PDOX) mouse models derived from organoids in vivo. RESULTS: The establishment of organoids of all types of brainstem gliomas was completed for the first time in the world, with a total of 41/48 organoid models derived from patients, with a success rate of 85.4%, covering all segments and pathological types. The results of morphological experiments and sequencing showed that the genetic characteristics of organoids were highly consistent with those of tumor tissues. Drug screening tests for temozolomide and panobinostat were completed in vitro, and NK cell co-culture and oncolytic virus therapy testing were achieved. Co-culture of brainstem glioma organoids and mouse brain slices was achieved in vitro. Furthermore, a PDOX model of brainstem glioma was established. CONCLUSIONS: Brainstem glioma organoids can be established maturely, stably, and reliably, and can be used for preclinical drug testing for patients. Animal models derived from brainstem glioma organoids have broad preclinical experimental value.

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.

LineageVAE: Reconstructing Historical Cell States and Transcriptomes toward Unobserved Progenitors.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the cell state. However, its destructive nature prohibits measuring gene expression changes during dynamic processes such as embryogenesis. Although recent studies integrating scRNA-seq with lineage tracing have provided clonal insights between progenitor and mature cells, challenges remain. Because of their experimental nature, observations are sparse, and cells observed in the early state are not the exact progenitors of cells observed at later time points. To overcome these limitations, we developed LineageVAE, a novel computational methodology that utilizes deep learning based on the property that cells sharing barcodes have identical progenitors. RESULTS: LineageVAE is a deep generative model that transforms scRNA-seq observations with identical lineage barcodes into sequential trajectories toward a common progenitor in a latent cell state space. This method enables the reconstruction of unobservable cell state transitions, historical transcriptomes, and regulatory dynamics at a single-cell resolution. Applied to hematopoiesis and reprogrammed fibroblast datasets, LineageVAE demonstrated its ability to restore backward cell state transitions and infer progenitor heterogeneity and transcription factor activity along differentiation trajectories. AVAILABILITY AND IMPLEMENTATION: The LineageVAE model was implemented in Python using the PyTorch deep learning library. The code is available on GitHub at https://github.com/LzrRacer/LineageVAE/. SUPPLEMENTARY INFORMATION: Available at Bioinformatics online.

Exploring the Influence of T Cell Marker Gene Expression on the Pathobiology and Clinical Prognostic Outcomes in Intestinal-Type Gastric Carcinoma.

BACKGROUND: Gastric cancer (GC) poses a significant global health challenge. This study is aimed at elucidating the role of the immune system, particularly T cells and their subtypes, in the pathogenesis and progression of intestinal-type gastric carcinoma (GC), and at evaluating the predictive utility of a T cell marker gene-based risk score for overall survival. METHODS: We performed an extensive analysis using single-cell RNA sequencing data to map the diversity of immune cells and identify specific T cell marker genes within GC. Pseudotime trajectory analysis was employed to observe the expression patterns of tumor-related pathways and transcription factors (TFs) at various disease stages. We developed a risk score using data from The Cancer Genome Atlas (TCGA) as a training set and validated it with the GSE15459 dataset. RESULTS: Our analysis revealed distinct patterns of T cell marker gene expression associated with different stages of GC. The risk score, based on these markers, successfully stratified patients into high-risk and low-risk groups with significantly different overall survival prospects. High-risk patients exhibited poorer survival outcomes compared to low-risk patients (p < 0.05). Additionally, the risk score was capable of identifying patients across a spectrum from chronic atrophic gastritis to early GC. CONCLUSION: The findings enhance the understanding of the tumor immune microenvironment in GC and propose new immunotherapeutic targets. The T cell marker gene-based risk score offers a potential tool for gastroenterologists to tailor treatment plans more precisely according to the cancer's severity.

Single-cell transcriptomics reveals tumor landscape in ovarian carcinosarcoma. / 单细胞转录组学揭示卵巢癌肉瘤的肿瘤特征.

OBJECTIVES: The present study used single-cell RNA sequencing (scRNA-seq) to characterize the cellular composition of ovarian carcinosarcoma (OCS) and identify its molecular characteristics. METHODS: scRNA-seq was performed in resected primary OCS for an in-depth analysis of tumor cells and the tumor microenvironment. Immunohistochemistry staining was used for validation. The scRNA-seq data of OCS were compared with those of high-grade serous ovarian carcinoma (HGSOC) tumors and other OCS tumors. RESULTS: Both malignant epithelial and malignant mesenchymal cells were observed in the OCS patient of this study. We identified four epithelial cell subclusters with different biological roles. Among them, epithelial subcluster 4 presented high levels of breast cancer type 1 susceptibility protein homolog (BRCA1) and DNA topoisomerase 2-α (TOP2A) expression and was related to drug resistance and cell cycle. We analyzed the interaction between epithelial and mesenchymal cells and found that fibroblast growth factor (FGF) and pleiotrophin (PTN) signalings were the main pathways contributing to communication between these cells. Moreover, we compared the malignant epithelial and mesenchymal cells of this OCS tumor with our previous published HGSOC scRNA-seq data and OCS data. All the epithelial subclusters in the OCS tumor could be found in the HGSOC samples. Notably, the mesenchymal subcluster C14 exhibited specific expression patterns in the OCS tumor, characterized by elevated expression of cytochrome P450 family 24 subfamily A member 1 (CYP24A1), collagen type XXIII α1 chain (COL23A1), cholecystokinin (CCK), bone morphogenetic protein 7 (BMP7), PTN, Wnt inhibitory factor 1 (WIF1), and insulin-like growth factor 2 (IGF2). Moreover, this subcluster showed distinct characteristics when compared with both another previously published OCS tumor and normal ovarian tissue. CONCLUSIONS: This study provides the single-cell transcriptomics signature of human OCS, which constitutes a new resource for elucidating OCS diversity.

Machine learning-based integration of CD8 T cell-related gene signatures for comprehensive prognostic assessment in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) stands as the most prevalent histological subtype of lung cancer, exhibiting heterogeneity in outcomes and diverse responses to therapy. CD8 T cells are consistently present throughout all stages of tumor development and play a pivotal role within the tumor microenvironment (TME). Our objective was to investigate the expression profiles of CD8 T cell marker genes, establish a prognostic risk model based on these genes in LUAD, and explore its relationship with immunotherapy response. Methods: By leveraging the expression data and clinical records from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, we identified 23 consensus prognostic genes. Employing ten machine-learning algorithms, we generated 101 combinations, ultimately selecting the optimal algorithm to construct an artificial intelligence-derived prognostic signature named riskScore. This selection was based on the average concordance index (C-index) across three testing cohorts. Results: RiskScore emerged as an independent risk factor for overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) in LUAD. Notably, riskScore exhibited notably superior predictive accuracy compared to traditional clinical variables. Furthermore, we observed a positive correlation between the high-risk riskScore group and tumor-promoting biological functions, lower tumor mutational burden (TMB), lower neoantigen (NEO) load, and lower microsatellite instability (MSI) scores, as well as reduced immune cell infiltration and an increased probability of immune evasion within the TME. Of significance, the immunophenoscore (IPS) score displayed significant differences among risk subgroups, and riskScore effectively stratified patients in the IMvigor210 and GSE135222 immunotherapy cohort based on their survival outcomes. Additionally, we identified potential drugs that could target specific risk subgroups. Conclusions: In summary, riskScore demonstrates its potential as a robust and promising tool for guiding clinical management and tailoring individualized treatments for LUAD patients.

Quantitative comparison of single-cell RNA sequencing versus single-molecule RNA imaging for quantifying transcriptional noise.

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome-wide noise, remains unclear. Here we utilize a small-molecule perturbation (IdU) to amplify noise and assess noise quantification from numerous scRNA-seq algorithms on human and mouse datasets, and then compare to noise quantification from single-molecule RNA FISH (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise, without altered mean-expression levels, for ~90% of genes and that smFISH analysis verifies noise amplification for the vast majority of genes tested. Collectively, the analyses suggest that most scRNA-seq algorithms are appropriate for quantifying noise including a simple normalization approach, although all of these systematically underestimate noise compared to smFISH. From a practical standpoint, this analysis argues that IdU is a globally penetrant noise-enhancer molecule-amplifying noise without altering mean-expression levels-which could enable investigations of the physiological impacts of transcriptional noise.

A Comparison of Tools That Identify Tumor Cells by Inferring Copy Number Variations from Single-Cell Experiments in Pancreatic Ductal Adenocarcinoma.

Single-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must be performed, including identification of the tumor cells by markers and algorithms that infer copy number variations (CNVs). We compared the performance of sciCNV, InferCNV, CopyKAT and SCEVAN tools that identify tumor cells by inferring CNVs from scRNA-seq data. Sequencing data from Pancreatic Ductal Adenocarcinoma (PDAC) patients, adjacent and healthy tissues were analyzed, and the predicted tumor cells were compared to those identified by well-assessed PDAC markers. Results from InferCNV, CopyKAT and SCEVAN overlapped by less than 30% with InferCNV showing the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). We show that the predictions are highly dependent on the sample and the software used, and that they return so many false positives hence are of little use in verifying or filtering predictions made via tumor biomarkers. We highlight how critical this processing can be, warn against the blind use of these software and point out the great need for more reliable algorithms.

Thyroid hormone T4 mitigates traumatic brain injury in mice by dynamically remodeling cell type specific genes, pathways, and networks in hippocampus and frontal cortex.

The complex pathology of mild traumatic brain injury (mTBI) is a main contributor to the difficulties in achieving a successful therapeutic regimen. Thyroxine (T4) administration has been shown to prevent the cognitive impairments induced by mTBI in mice but the mechanism is poorly understood. To understand the underlying mechanism, we carried out a single cell transcriptomic study to investigate the spatiotemporal effects of T4 on individual cell types in the hippocampus and frontal cortex at three post-injury stages in a mouse model of mTBI. We found that T4 treatment altered the proportions and transcriptomes of numerous cell types across tissues and timepoints, particularly oligodendrocytes, astrocytes, and microglia, which are crucial for injury repair. T4 also reversed the expression of mTBI-affected genes such as Ttr, mt-Rnr2, Ggn12, Malat1, Gnaq, and Myo3a, as well as numerous pathways such as cell/energy/iron metabolism, immune response, nervous system, and cytoskeleton-related pathways. Cell-type specific network modeling revealed that T4 mitigated select mTBI-perturbed dynamic shifts in subnetworks related to cell cycle, stress response, and RNA processing in oligodendrocytes. Cross cell-type ligand-receptor networks revealed the roles of App, Hmgb1, Fn1, and Tnf in mTBI, with the latter two ligands having been previously identified as TBI network hubs. mTBI and/or T4 signature genes were enriched for human genome-wide association study (GWAS) candidate genes for cognitive, psychiatric and neurodegenerative disorders related to mTBI. Our systems-level single cell analysis elucidated the temporal and spatial dynamic reprogramming of cell-type specific genes, pathways, and networks, as well as cell-cell communications as the mechanisms through which T4 mitigates cognitive dysfunction induced by mTBI.

Characterizing Rare Dormant and Cycling Lineages Using the Watermelon System.

Barcode-based lineage tracing approaches enable molecular characterization of clonal cell families. Barcodes that are expressed as mRNA can be used to deconvolve lineage identity from single-cell RNA sequencing transcriptional data. Here we describe the Watermelon system, which facilitates the simultaneous tracing of lineage, transcriptional, and proliferative state at a single cell level.

Characterizing mitochondrial features in osteoarthritis through integrative multi-omics and machine learning analysis.

Purpose: Osteoarthritis (OA) stands as the most prevalent joint disorder. Mitochondrial dysfunction has been linked to the pathogenesis of OA. The main goal of this study is to uncover the pivotal role of mitochondria in the mechanisms driving OA development. Materials and methods: We acquired seven bulk RNA-seq datasets from the Gene Expression Omnibus (GEO) database and examined the expression levels of differentially expressed genes related to mitochondria in OA. We utilized single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) analyses to explore the functional mechanisms associated with these genes. Seven machine learning algorithms were utilized to identify hub mitochondria-related genes and develop a predictive model. Further analyses included pathway enrichment, immune infiltration, gene-disease relationships, and mRNA-miRNA network construction based on these hub mitochondria-related genes. genome-wide association studies (GWAS) analysis was performed using the Gene Atlas database. GSEA, gene set variation analysis (GSVA), protein pathway analysis, and WGCNA were employed to investigate relevant pathways in subtypes. The Harmonizome database was employed to analyze the expression of hub mitochondria-related genes across various human tissues. Single-cell data analysis was conducted to examine patterns of gene expression distribution and pseudo-temporal changes. Additionally, The real-time polymerase chain reaction (RT-PCR) was used to validate the expression of these hub mitochondria-related genes. Results: In OA, the mitochondria-related pathway was significantly activated. Nine hub mitochondria-related genes (SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4) were identified. They constructed predictive models with good ability to predict OA. These genes are primarily associated with macrophages. Unsupervised consensus clustering identified two mitochondria-associated isoforms that are primarily associated with metabolism. Single-cell analysis showed that they were all expressed in single cells and varied with cell differentiation. RT-PCR showed that they were all significantly expressed in OA. Conclusion: SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4 are potential mitochondrial target genes for studying OA. The classification of mitochondria-associated isoforms could help to personalize treatment for OA patients.

Single cell RNA-sequencing of feline peripheral immune cells with V(D)J repertoire and cross species analysis of T lymphocytes.

Introduction: The domestic cat (Felis catus) is a valued companion animal and a model for virally induced cancers and immunodeficiencies. However, species-specific limitations such as a scarcity of immune cell markers constrain our ability to resolve immune cell subsets at sufficient detail. The goal of this study was to characterize circulating feline T cells and other leukocytes based on their transcriptomic landscape and T-cell receptor repertoire using single cell RNA-sequencing. Methods: Peripheral blood from 4 healthy cats was enriched for T cells by flow cytometry cell sorting using a mouse anti-feline CD5 monoclonal antibody. Libraries for whole transcriptome, alpha/beta T cell receptor transcripts and gamma/delta T cell receptor transcripts were constructed using the 10x Genomics Chromium Next GEM Single Cell 5' reagent kit and the Chromium Single Cell V(D)J Enrichment Kit with custom reverse primers for the feline orthologs. Results: Unsupervised clustering of whole transcriptome data revealed 7 major cell populations - T cells, neutrophils, monocytic cells, B cells, plasmacytoid dendritic cells, mast cells and platelets. Sub cluster analysis of T cells resolved naive (CD4+ and CD8+), CD4+ effector T cells, CD8+ cytotoxic T cells and gamma/delta T cells. Cross species analysis revealed a high conservation of T cell subsets along an effector gradient with equitable representation of veterinary species (horse, dog, pig) and humans with the cat. Our V(D)J repertoire analysis demonstrated a skewed T-cell receptor alpha gene usage and a restricted T-cell receptor gamma junctional length in CD8+ cytotoxic T cells compared to other alpha/beta T cell subsets. Among myeloid cells, we resolved three clusters of classical monocytes with polarization into pro- and anti-inflammatory phenotypes in addition to a cluster of conventional dendritic cells. Lastly, our neutrophil sub clustering revealed a larger mature neutrophil cluster and a smaller exhausted/activated cluster. Discussion: Our study is the first to characterize subsets of circulating T cells utilizing an integrative approach of single cell RNA-sequencing, V(D)J repertoire analysis and cross species analysis. In addition, we characterize the transcriptome of several myeloid cell subsets and demonstrate immune cell relatedness across different species.

Status of single-cell RNA sequencing for reproductive toxicology in zebrafish and the transcriptomic trade-off.

The utilization of transcriptomic studies identifying profiles of gene expression, especially in toxicogenomics, has catapulted next-generation sequencing to the forefront of reproductive toxicology. An innovative yet underutilized RNA sequencing technique emerging into this field is single-cell RNA sequencing (scRNA-seq), which provides sequencing at the individual cellular level of gonad tissue. ScRNA-seq provides a novel and unique perspective for identifying distinct cellular profiles, including identification of rare cell subtypes. The specificity of scRNA-seq is a powerful tool for reproductive toxicity research, especially for translational animal models including zebrafish. Studies to date not only have focused on 'tissue atlassing' or characterizing what cell types make up different tissues but have also begun to include toxicant exposure as a factor that this review aims to explore. Future scRNA-seq studies will contribute to understanding exposure-induced outcomes; however, the trade-offs with traditional methods need to be considered.

TNBC response to paclitaxel phenocopies interferon response which reveals cell cycle-associated resistance mechanisms.

Paclitaxel is a standard of care neoadjuvant therapy for patients with triple negative breast cancer (TNBC); however, it shows limited benefit for locally advanced or metastatic disease. Here we used a coordinated experimental-computational approach to explore the influence of paclitaxel on the cellular and molecular responses of TNBC cells. We found that escalating doses of paclitaxel resulted in multinucleation, promotion of senescence, and initiation of DNA damage induced apoptosis. Single-cell RNA sequencing (scRNA-seq) of TNBC cells after paclitaxel treatment revealed upregulation of innate immune programs canonically associated with interferon response and downregulation of cell cycle progression programs. Systematic exploration of transcriptional responses to paclitaxel and cancer-associated microenvironmental factors revealed common gene programs induced by paclitaxel, IFNB, and IFNG. Transcription factor (TF) enrichment analysis identified 13 TFs that were both enriched based on activity of downstream targets and also significantly upregulated after paclitaxel treatment. Functional assessment with siRNA knockdown confirmed that the TFs FOSL1, NFE2L2 and ELF3 mediate cellular proliferation and also regulate nuclear structure. We further explored the influence of these TFs on paclitaxel-induced cell cycle behavior via live cell imaging, which revealed altered progression rates through G1, S/G2 and M phases. We found that ELF3 knockdown synergized with paclitaxel treatment to lock cells in a G1 state and prevent cell cycle progression. Analysis of publicly available breast cancer patient data showed that high ELF3 expression was associated with poor prognosis and enrichment programs associated with cell cycle progression. Together these analyses disentangle the diverse aspects of paclitaxel response and identify ELF3 upregulation as a putative biomarker of paclitaxel resistance in TNBC.