scRNA-seq Analysis Research Articles

Integrating single-cell RNA-seq and bulk RNA-seq to explore prognostic value and immune landscapes of methionine metabolism-related signature in breast cancer

BackgroundNeoadjuvant, endocrine, and targeted therapies have significantly improved the prognosis of breast cancer (BC). However, due to the high heterogeneity of cancer, some patients cannot benefit from existing treatments. Increasing evidence suggests that amino acids and their metabolites can alter the tumor malignant behavior through reshaping tumor microenvironment and regulation of immune cell function. Breast cancer cell lines have been identified as methionine-dependent, and methionine restriction has been proposed as a potential cancer treatment strategy.MethodsWe integrated transcriptomic and single-cell RNA sequencing (ScRNA-seq) analyses based on The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. Then we applied weighted gene co-expression network analysis (WGCNA) and Cox regression to evaluate methionine metabolism-related genes (MRGs) in BC, constructing and validating a prognostic model for BC patients. Immune landscapes and immunotherapy were further explored. Finally, in vitro experiments were conducted to assess the expression and function of key genes APOC1.ResultsIn this study, we established and validated a prognostic signature based on eight methionine-related genes to predict overall survival (OS) in BC patients. Patients were further stratified into high-risk and low-risk groups according to prognostic risk score. Further analysis revealed significant differences between two groups in terms of pathway alterations, immune microenvironment characteristics, and immune checkpoint expression. Our study shed light on the relationship between methionine metabolism and immune infiltration in BC. APOC1, a key gene in the prognostic signature, was found to be upregulated in BC and closely associated with immune cell infiltration. Notably, APOC1 was primarily expressed in macrophages. Subsequent in vitro experiments demonstrated that silencing APOC1 reduced the generation of tumor-associated macrophages (TAMs) with an M2 phenotype while significantly decreasing the proliferation, invasion, and migration of MDA-MB-231 and MDA-MB-468 breast cancer cell lines.ConclusionWe established a prognostic risk score consisting of genes associated with methionine metabolism, which helps predict prognosis and response to treatment in BC. The function of APOC1 in regulating macrophage polarization was explored.

Lipid nanoparticles deliver DNA-encoded biologics and induce potent protective immunity

Lipid nanoparticles (LNPs) for mRNA delivery have advanced significantly, but LNP-mediated DNA delivery still faces clinical challenges. This study compared various LNP formulations for delivering DNA-encoded biologics, assessing their expression efficacy and the protective immunity generated by LNP-encapsulated DNA in different models. The LNP formulation used in Moderna’s Spikevax mRNA vaccine (LNP-M) demonstrated a stable nanoparticle structure, high expression efficiency, and low toxicity. Notably, a DNA vaccine encoding the spike protein, delivered via LNP-M, induced stronger antigen-specific antibody and T cell immune responses compared to electroporation. Single-cell RNA sequencing (scRNA-seq) analysis revealed that the LNP-M/pSpike vaccine enhanced CD80 activation signaling in CD8+ T cells, NK cells, macrophages, and DCs, while reducing the immunosuppressive signals. The enrichment of TCR and BCR by LNP-M/pSpike suggested an increase in immune response specificity and diversity. Additionally, LNP-M effectively delivered DNA-encoded antigens, such as mouse PD-L1 and p53R172H, or monoclonal antibodies targeting mouse PD1 and human p53R282W. This approach inhibited tumor growth or metastasis in several mouse models. The long-term anti-tumor effects of LNP-M-delivered anti-p53R282W antibody relied on memory CD8+ T cell responses and enhanced MHC-I signaling from APCs to CD8+ T cells. These results highlight LNP-M as a promising and effective platform for delivering DNA-based vaccines and cancer immunotherapies.

The microenvironment cell index is a novel indicator for the prognosis and therapeutic regimen selection of cancers

BackgroundIt is worthwhile to establish a prognostic prediction model based on microenvironment cells (MCs) infiltration and explore new treatment strategies for triple-negative breast cancer (TNBC).MethodsThe xCell algorithm was used to quantify the cellular components of the TNBC microenvironment based on bulk RNA sequencing (bulk RNA-seq) data. The MCs index (MCI) was constructed using the least absolute shrinkage and selection operator Cox (LASSO-Cox) regression analysis. Single-cell RNA sequencing (scRNA-seq), spatially resolved transcriptomics (SRT), and multiplex immunofluorescence (mIF) staining analyses verified MCI. The mechanism of action of the MCI was investigated in tumor-bearing mice.ResultsMCI consists of the six types of MCs, which can precisely predict the prognosis of the TNBC patients. scRNA-seq, SRT, and mIF analyses verified the existence and proportions of these cells. Furthermore, combined with the spatial distribution characteristics of the six types of MCs, an MCI-enhanced (MCI-e) model was constructed, which could predict the prognosis of the TNBC patients more accurately. More importantly, inhibition of the insulin signaling pathway activated in the cancer cells of the MCIhigh the TNBC patients significantly prolonged the survival time of tumor-bearing mice.ConclusionsOverall, our results demonstrate that MCs infiltration can be exploited as a novel indicator for the prognosis and therapeutic regimen selection of the TNBC patients.

Immune Gene Expression Profiling in Individuals with Turner Syndrome, Graves’ Disease, and a Healthy Female by Single-Cell RNA Sequencing: A Comparative Study

Turner syndrome (TS) can be determined by karyotype analysis, marked by the loss of one X chromosome in females. However, the genes involved in autoimmunity in TS patients remain unclear. In this study, we aimed to analyze differences in immune gene expression between a patient with TS, a healthy female, and a female patient with Graves’ disease using single-cell RNA sequencing (scRNA-seq) analysis of antigen-specific CD4(+) T cells. We identified 43 differentially expressed genes in the TS patient compared with the healthy female and the female patient with Graves’ disease. Many of these genes have previously been suggested to play a role in immune system regulation. This study provides valuable insights into the differences in immune-related gene expression between TS patients, healthy individuals, and those with autoimmune diseases.

Macrophage heterogeneity and oncogenic mechanisms in lung adenocarcinoma: insights from scRNA-seq analysis and predictive modeling

Macrophage heterogeneity and oncogenic mechanisms in lung adenocarcinoma: insights from scRNA-seq analysis and predictive modeling

TransCAE: Enhancing cell type annotation in single-cell RNA-seq data with transfer learning and convolutional autoencoder.

Single-cell RNA sequencing (scRNA-seq) analysis offers tremendous potential for addressing various biological questions, with one key application being the annotation of query datasets with unknown cell types using well-annotated external reference datasets. However, the performance of existing supervised or semi-supervised methods largely depends on the quality of source data. Furthermore, these methods often struggle with the batch effects arising from different platforms when handling multiple reference or query datasets, making precise annotation challenging. We developed transCAE, a robust transfer learning-based algorithm for single-cell annotation that integrates unsupervised dimensionality reduction with supervised cell type classification. This approach fully leverages information from both reference and query datasets to achieve precise cell classification within the query data. Extensive evaluations show that transCAE significantly enhances classification accuracy and efficiently mitigates batch effects. Compared to other state-of-the-art methods, transCAE demonstrates superior performance in experiments involving multiple reference or query datasets. These strengths position transCAE as an optimal annotation method for scRNA-seq datasets.

Inactivation of GSK3β by Ser389 phosphorylation prevents thymocyte necroptosis and impacts Tcr repertoire diversity.

The assembly of Tcrb and Tcra genes require double negative (DN) thymocytes to undergo multiple rounds of programmed DNA double-strand breaks (DSBs), followed by their efficient repair. However, mechanisms governing cell cycle checkpoints and specific survival pathways during the repair process remain unclear. Here, we report high-resolution scRNA-seq analyses of individually sorted mouse DN3 and DN4 thymocytes, which reveals a G2M cell cycle checkpoint, in addition to the known G1 checkpoint, during Tcrb and Tcra recombination. We also show that inactivation of GSK3β by phosphorylation on Ser389 is essential for DN3/DN4 thymocytes to survive while being stalled at the G1 and G2/M checkpoints. GSK3β promotes death by necroptosis, but not by apoptosis, of DN3/DN4 thymocytes during V(D)J recombination. Failure to inactivate GSK3β in DN3 thymocytes alters the Tcrb gene repertoire primarily through Trbv segment utilization. In addition, preferential recombination of proximal V segments in Tcra depends on GSK3β inactivation. Our study identifies a unique thymocyte survival pathway, enabling them to undergo cell cycle checkpoints for DNA repair during V(D)J recombination of Tcrb and Tcra genes. Thymocyte survival during cell cycle checkpoints for V(D)J recombination DNA repair determines TCRα/β repertoire.

Galaxy as a gateway to bioinformatics: Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for scRNA-seq.

Bioinformatics is fundamental to biomedical sciences, but its mastery presents a steep learning curve for bench biologists and clinicians. Learning to code while analyzing data is difficult. The curve may be flattened by separating these two aspects and providing intermediate steps for budding bioinformaticians. Single-cell analysis is in great demand from biologists and biomedical scientists, as evidenced by the proliferation of training events, materials, and collaborative global efforts like the Human Cell Atlas. However, iterative analyses lacking reinstantiation, coupled with unstandardized pipelines, have made effective single-cell training a moving target. To address these challenges, we present a Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for single-cell RNA sequencing (scRNA-seq) analysis, which offers parallel analytical methods using a graphical interface (buttons) or code. With clear, interoperable materials, MIGHTS facilitates smooth transitions between environments. Bridging the biologist-programmer gap, MIGHTS emphasizes interdisciplinary communication for effective learning at all levels. Real-world data analysis in MIGHTS promotes critical thinking and best practices, while FAIR data principles ensure validation of results. MIGHTS is freely available, hosted on the Galaxy Training Network, and leverages Galaxy interfaces for analyses in both settings. Given the ongoing popularity of Python-based (Scanpy) and R-based (Seurat & Monocle) scRNA-seq analyses, MIGHTS enables analyses using both. MIGHTS consists of 11 tutorials, including recordings, slide decks, and interactive visualizations, and a demonstrated track record of sustainability via regular updates and community collaborations. Parallel pathways in MIGHTS enable concurrent training of scientists at any programming level, addressing the heterogeneous needs of novice bioinformaticians.

GraCEImpute: A novel graph clustering autoencoder approach for imputation of single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) technology establishes a unique view for elucidating cellular heterogeneity in various biological systems. Yet the scRNA-seq data is compromised by a high dropout rate due to the technological limitation, and the substantial data loss poses computational challenges on subsequent analyses. This study introduces a novel graph clustering autoencoder (GCAE)-based imputation approach (GraCEImpute) to address the challenge of missing data in scRNA-seq data. Our comprehensive evaluation demonstrates that the GraCEImpute model outperforms existing approaches in accurately imputing dropout zeros within scRNA-seq data. The proposed GraCEImpute model also demonstrates the significantly enhanced quality of downstream scRNA-seq data analyses, including clustering, differential gene expression (DEG) analysis, and cell trajectory inference. These improvements underscore the GraCEImpute model's potential to facilitate a deeper understanding of cellular processes and heterogeneity through the scRNA-seq data analyses. The source code is released at https://www.healthinformaticslab.org/supp/.

A Risk Model Based on Ferroptosis-Related Genes OSMR, G0S2, IGFBP6, IGHG2, and FMOD Predicts Prognosis in Glioblastoma Multiforme.

Glioblastoma multiforme (GBM) is a common and highly aggressive brain tumor with a poor prognosis. However, the prognostic value of ferroptosis-related genes (FRGs) and their classification remains insufficiently studied. This study aims to explore the significance of ferroptosis classification and its risk model in GBM using multi-omics approaches and to evaluate its potential in prognostic assessment. Ferroptosis-related genes (FRGs) were retrieved from databases such as FerrDB. The TCGA-GBM and CGGA-GBM datasets were used as training and testing cohorts, respectively. Univariate Cox regression and LASSO regression analyses were performed to establish a risk model comprising five genes (OSMR, G0S2, IGFBP6, IGHG2, FMOD). A Meta-analysis of integrated TCGA and GTEx data was conducted to examine the differential expression of these genes between GBM and normal tissues. Key gene protein expression differences were analyzed using CPTAC and HPA databases. Single-cell RNA sequencing (scRNA-seq) analysis was employed to explore the cell type-specific distribution of these genes. The five-gene risk model demonstrated significant prognostic value in GBM. Meta-analysis revealed distinct expression patterns of the identified genes between GBM and normal tissues. Protein expression analysis confirmed these differences. scRNA-seq analysis highlighted the diverse distribution of these genes across different cell types, offering insights into their biological roles. The ferroptosis-based risk model provides valuable prognostic insights into GBM and highlights potential therapeutic targets, emphasizing the biological significance of ferroptosis-related genes in tumor progression.

Identification of core immune-related genes CTSK, C3, and IFITM1 for diagnosing Helicobacter pylori infection-associated gastric cancer through transcriptomic analysis.

To identify diagnostic genes and mechanisms linking Helicobacter pylori (H. pylori) infection to gastric cancer. Gene expression profiles from GEO were analyzed using differential expression gene (DEG) analysis, weighted gene co-expression network analysis (WGCNA), and functional enrichment. A random forest (RF) model assessed immune-related diagnostic genes, examining their expression, diagnostic performance, prognostic value, and immune cell relationships. Expression patterns of core genes were evaluated with single-cell RNA sequencing (scRNA-seq), and a regulatory network involving miRNA, mRNA, and transcription factors was built. We identified 75 genes and developed an RF model including 15 immune-related genes, notably CTSK, NR4A3, C3, and IFITM1. Except for NR4A3, these genes showed higher expression in datasets, confirmed by in vitro tests. Their diagnostic performance had an AUC>0.7, enhancing to >0.85 in a multi-gene model. Survival analysis linked gene upregulation to poorer prognosis, and scRNA-seq and immune cell infiltration analysis underscored their roles in immune dysregulation and pathogenicity in H. pylori-related gastric cancer. CTSK, C3, and IFITM1 are crucial in H. pylori-related gastric cancer, forming a robust diagnostic model and guiding future diagnostic and therapeutic research.

Increased lamina propria B cells play roles in fructose-induced hypertension of Dahl salt-sensitive rats.

Although the immune system participates in the development of hypertension, the proportional contributions of distinct immune cells remain poorly understood. With the development of transcriptomics, we can profile the transcriptomes of individual immune cells and assess the relative contribution of each immune cell to the development of hypertension. So, we tested the hypothesis that increased lamina propria B cells play roles in fructose-induced hypertension of Dahl salt-sensitive (SS) rats. Eight-week-old Dahl SS and Dahl salt-resistant (SR) male rats were divided into four groups; each group received either tap water (TW) or a 20% fructose solution (HFS) for 4weeks. Systolic blood pressure was measured using the tail-cuff method. Single-cell RNA sequencing (scRNA-seq) analysis was performed on lamina propria (LP) cells and peripheral blood mononuclear cells (PBMCs) obtained from the SS and SR rats subjected to either TW or HFS. Results revealed that high-fructose intake induced hypertension in the SS rats but not in the SR rats. It also increased B cells in LPs but not in PBMCs of the SS rats; their subsets showed increased follicular and naïve B cells. Increased lamina propria B cells play roles in fructose-induced hypertension of SS rats. This finding suggest that targeting B cells could be a potential strategy to mitigate high blood pressure in fructose-induced hypertension.

Identification and validation of serum MUC17 as a non-invasive early warning biomarker for screening of gastric intraepithelial neoplasia.

The early diagnosis and treatment of Gastric Intraepithelial Neoplasia (GIN) are pivotal for improving the survival rates of patients with gastric cancer (GC). Regrettably, reliable noninvasive biomarkers for GIN screening are currently lacking. mRNA data from the GEO database, pan-cancer data from the TCGA database, and a gene list of exocrine proteins were subjected to integrated analysis to identify a noninvasive biomarker for GIN. The scRNA-seq data analysis, IHC and Elisa were employed to validate the expression of the biomarker in the serum and tissues of clinical patients across different pathological stages. MUC17 has been identified as a non-invasive diagnostic marker for GIN. It is upregulated in GIN prior to the onset of gastric carcinogenesis and downregulated in other tumors, with high GC specificity. The area under the curve values of serum MUC17 for differentiating chronic gastritis (CG) from low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and early gastric cancer (EGC) were 0.8788, 0.8544, and 0.9513, respectively. Additionally, low plasma MUC17 levels were found to be significantly lower in gastric ulcer (GU), gastric neuroendocrine tumor (GNET), and gastrointestinal stromal tumor (GIST) compared to GIN. The AUC for differentiating between GIN and GU, GNET, or GIST was 0.7803, 0.9244 and 0.9796, respectively. These findings suggest that plasma MUC17 levels hold substantial promise as a screening biomarker for individuals with GIN and EGC, effectively identifying high-risk groups that necessitate further gastroscopy.

Single-Cell RNA-Sequencing of Zebrafish Olfactory Epithelium Identifies Odor-Responsive Candidate Olfactory Receptors.

Single-cell RNA-sequencing (scRNA-seq) is a powerful method to comprehensively overlook gene expression profiles of individual cells in various tissues, providing fundamental datasets for classification of cell types and further functional analyses. Here we adopted scRNA-seq analysis for the zebrafish olfactory sensory neurons which respond to water-borne odorants and pheromones to elicit various behaviors crucial for survival and species preservation. Firstly, a single-cell dissociation procedure of the zebrafish olfactory rosettes was optimized by using cold-active protease, minimizing artifactual neuronal activation. Secondly, various cell types were classified into distinct clusters, based on the expressions of well-defined marker genes. Notably, we validated non-overlapping expressions of different families of olfactory receptors among the clusters of olfactory sensory neurons. Lastly, we succeeded in estimating candidate olfactory receptors responding to a particular odor stimulus by carefully scrutinizing correlated expressions of immediate early genes. Thus, scRNA-seq is a useful measure for the analysis of olfactory sensory neurons not only in classifying functional cell types but also in identifying olfactory receptor genes for given odorants and pheromones.

Elucidating the Role of Estrogen Effects in Leukemia: Insights from Single-Cell RNA Sequencing and Mendelian Randomization.

Background: Epidemiological studies have confirmed the potential role of estrogen effects in influencing the development and outcome of leukemia. Estrogen effects are increasingly attracting research interest for their potential antitumor effects beyond gynecological tumors. However, their causal relationship remains unclear. Methods: In a novel approach, this study integrates single-cell RNA sequencing (scRNA-seq) with Mendelian randomization (MR) to explore the relationship between estrogen (and its receptor) and leukemia (and its related proteins). This integration showcases the uniqueness of our methodology and provides a new perspective for understanding the molecular relationship between them. Secondary analyses using genetic risk scores (GRS) were performed to further verify the robustness of the results. Results: Our scRNA-seq analysis identified 14 BMMC mononuclear cell subsets, and the result showed that the estrogen receptor was implicated in leukemia. The MR results showed that there was a relationship between estradiol and leukemia inhibitory factor (β = 0.0621; P = 0.0229), and leukemia inhibitory factor receptor (β = 0.0665; P = 0.0218). The result of GRS analysis verified the MR analysis. Conclusions: While both scRNA-seq and MR have yielded intriguing results, inconsistencies between these methodologies hint at a more elaborate underlying mechanism. The observed discrepancies underscore the complexity of the estrogen effects-leukemia relationship, suggesting that elucidating these interactions demands larger cohorts and enhanced sequencing depth in future studies. This research paves the way for a more nuanced understanding of the role of estrogen effects in leukemia and sets the stage for targeted therapeutic interventions.

Integrative Multi-Omics Analysis of Crohn's Disease and Metabolic Syndrome: Unveiling the Underlying Molecular Mechanisms of Comorbidity

ObjectivesThe focus of this study is on identifying a potential association between Crohn's disease (CD), a chronic inflammatory bowel condition, and metabolic syndrome (Mets), characterized by a cluster of metabolic abnormalities, including high blood pressure, abnormal lipid levels, and overweight. While the link between CD and MetS has been suggested in the medical community, the underlying molecular mechanisms remain largely unexplored. MethodsUsing microarray data from the Gene Expression Omnibus (GEO) database, we conducted a differential gene expression analysis and applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify genes shared between CD and MetS. To further elucidate the functions of these shared genes, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and constructed protein-protein interaction (PPI) networks. For key gene screening, we used Random Forest and Least Absolute Shrinkage and Selection Operator (LASSO) regression and constructed a diagnostic prediction model with the Extreme Gradient Boosting (XGBoost) algorithm. Additionally, CIBERSORT and Gene Set Variation Analysis (GSVA) were employed to examine the relationships between these genes and immune cell infiltration, as well as metabolic pathways. Mendelian randomization and colocalization analyses were also conducted to explore causal links between genes and disease. Lastly, single-cell RNA sequencing (scRNA-seq) was used to validate the functionality of these key genes. ResultsThrough the use of the limma R package and WGCNA, we identified 1,767 co-expressed genes common to both CD and MetS, which are notably enriched in pathways related to immune responses and metabolic regulation. After thorough analysis, 34 key genes were highlighted, demonstrating their potential utility in prognostic models. These genes were closely linked to tissue immune responses and metabolic functions. Subsequent scRNA-seq analysis confirmed the strong diagnostic potential of PIM2 and PBX2, with especially prominent expression in T and B cells. ConclusionThis study identifies shared regulatory genes between CD and MetS, advancing the development of precise diagnostic tools. In particular, PIM2 and PBX2 were found to be positively associated with hypoxia and hemoglobin metabolism pathways, suggesting their involvement in the modulation of cellular processes. These findings improve our understanding of the molecular mechanisms underlying the comorbidity of CD and MetS, offering novel targets for integrated therapeutic interventions.

C3G improves lipid droplet accumulation in the proximal tubules of high-fat diet-induced ORG mice.

Obesity-related glomerulopathy (ORG) represents an escalating public health with no effective treatments currently available. Abnormal lipid metabolism and lipid droplet deposition in the kidneys are key contributors to ORG. Cyanidin-3-glucoside (C3G) has shown potential in regulating lipid metabolism and may offer reno-protective effects; however, its therapeutic efficacy and underlying mechanisms in ORG remain unclear. An ORG mouse model was established, followed by an 8-week C3G intervention. The mice were divided into three groups: normal control (CT) group, ORG group, and C3G treatment group. Fecal 16S rRNA sequencing, metabolomics of feces-serum-kidney, and kidney single-cell RNA sequencing (scRNA-seq) were performed to investigate the effects and mechanisms of C3G. Compared to CT mice, ORG mice exhibited elevated serum CHO, TG, Cys-C, UACR, urinary Kim-1, and NAG levels, along with glomerular hypertrophy and tubular injury. These biochemical and pathological indicators improved following C3G treatment. Fecal 16S analysis revealed reduced gut microbiota diversity in ORG mice compared to CT mice, while C3G intervention increased gut microbiota diversity. Metabolic profiling of feces, serum, and kidney indicated reprogramming of glycerophospholipid metabolism in ORG mice, ameliorated by C3G treatment. Further analysis demonstrated that abnormal glycerophospholipid metabolites correlated with blood lipids, urinary protein, urinary tubular injury markers, and gut microbiota, specifically Lachnospiraceae and Blautia. Additionally, scRNA-seq analysis identified activation of the PPARγ/CD36 pathway in proximal tubule cells (PTCs) of ORG mice. C3G improved abnormal glycerophospholipid metabolism and alleviated injury in PTCs by inhibiting the PPARγ/CD36 pathway. C3G reduces lipid droplet accumulation in the PTCs of ORG mice by modulating the gut microbiota and inhibiting the PPARγ/CD36 pathway. These findings offer new insights and therapeutic targets for ORG.

A Distinguished Roadmap of Fibroblast Senescence in Predicting Immunotherapy Response and Prognosis Across Human Cancers.

The resistance of tumors to immune checkpoint inhibitors (ICI) may be intricately linked to cellular senescence, although definitive clinical validation remains elusive. In this study, comprehensive pan-cancer scRNA-seq analyses identify fibroblasts as exhibiting the most pronounced levels of cellular senescence among tumor-associated cell populations. To elucidate this phenomenon, a fibroblast senescence-associated transcriptomic signature (FSS), which correlated strongly with protumorigenic signaling pathways and immune dysregulation that fosters tumor progression, is developed. Leveraging the FSS, the machine learning (ML) framework demonstrates exceptional accuracy in predicting ICI response and survival outcomes, achieving superior area under curve (AUC) values across validation, testing, and in-house cohorts. Strikingly, FSS consistently outperforms established signatures in predictive robustness across diverse cancer subtypes. From an integrative analysis of 17 CRISPR/Cas9 libraries, CDC6 emerges as a pivotal biomarker for pan-cancer ICI response and prognostic stratification. Mechanistically, experimental evidence reveals that CDC6 in tumor cells orchestrates fibroblast senescence via TGF-β1 secretion and oxidative stress, subsequently reprogramming the tumor microenvironment and modulating ICI response. These findings underscore the translational potential of targeting fibroblast senescence as a novel therapeutic strategy to mitigate immune resistance and enhance antitumor efficacy.

Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells

Double negative T (DNT) cells are a unique subset of CD3 + TCRαβ + T lymphocytes that lack CD4, CD8, or NK1.1 expression and constitute 3–5% of the total T cell population in C57BL/6 mice. They have increasingly gained recognition for their novel roles in the immune system, especially under autoimmune conditions. Conventional machine learning approaches such as principal component analysis have been employed in single-cell RNA sequencing (scRNA-seq) analysis to characterize DNT cells. However, advanced deep learning models such as Single Cell Variational Inference (scVI) have the capability to capture nonlinear gene expression patterns in the sequencing data. In this study, employing the deep learning methodology, we have revealed novel markers for splenic DNT cells in C57BL/6 mice which were validated with flow cytometry analysis. We classified DNT cells into two subgroups, naïve DNT (nDNT) cells differentiated by the expression of Ly6C and activated DNT (aDNT) cells differentiated by the expression of MHC-II. A prior study had predicted elevated expression of CD137/4-1BB encoded by Tnfrsf9 in nDNT cells; however, our analysis predicted and validated that CD137 was a marker for aDNT cells instead of nDNT cells. Innovatively, our data also identified CD30 encoded by Tnfrsf8 and CD153/CD30L encoded by Tnfsf8 as additional markers for aDNT cells. In addition, we classified three subgroups in nDNT cells and two subgroups in aDNT cells. Our scVI analysis suggested, and flow cytometry analysis confirmed, that Ly49G2 encoded by Slamf7 was a marker for the nDNT0 subgroup. Importantly, we validated that MHC-II was indeed expressed by a subset of human DNT cells suggesting the presence of a human aDNT population. Furthermore, we found increased expression of CD30, CD153, and CD137 on aDNT cells in MRL/lpr mice compared to those in C57BL/6 mice suggesting potential pathogenic roles of these molecules in autoimmunity. Together, our comprehensive analysis has uncovered and validated novel markers for different subpopulations of DNT cells that can be used in the phenotypic and/or functional characterization of these relatively rare cells in health and disease.

Unveiling chemotherapy-induced immune landscape remodeling and metabolic reprogramming in lung adenocarcinoma by scRNA-sequencing

Chemotherapy is widely used to treat lung adenocarcinoma (LUAD) patients comprehensively. Considering the limitations of chemotherapy due to drug resistance and other issues, it is crucial to explore the impact of chemotherapy and immunotherapy on these aspects. In this study, tumor samples from nine LUAD patients, of which four only received surgery and five received neoadjuvant chemotherapy, were subjected to scRNA-seq analysis. In vitro and in vivo assays, including flow cytometry, immunofluorescence, Seahorse assay, and tumor xenograft models, were carried out to validate our findings. A total of 83,622 cells were enrolled for subsequent analyses. The composition of cell types exhibited high heterogeneity across different groups. Functional enrichment analysis revealed that chemotherapy drove significant metabolic reprogramming in tumor cells and macrophages. We identified two subtypes of macrophages: Anti-mac cells (CD45+CD11b+CD86+) and Pro-mac cells (CD45+CD11b+ARG +) and sorted them by flow cytometry. The proportion of Pro-mac cells in LUAD tissues increased significantly after neoadjuvant chemotherapy. Pro-mac cells promote tumor growth and angiogenesis and also suppress tumor immunity. Moreover, by analyzing the remodeling of T and B cells induced by neoadjuvant therapy, we noted that chemotherapy ignited a relatively more robust immune cytotoxic response toward tumor cells. Our study demonstrates that chemotherapy induces metabolic reprogramming within the tumor microenvironment of LUAD, particularly affecting the function and composition of immune cells such as macrophages and T cells. We believe our findings will offer insight into the mechanisms of drug resistance and provide novel therapeutic targets for LUAD in the future.