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A novel 7-chemokine-genes predictive signature for prognosis and therapeutic response in renal clear cell carcinoma.
Background: Renal clear cell carcinoma (ccRCC) is one of the most prevailing type of malignancies, which is affected by chemokines. Chemokines can form a local network to regulate the movement of immune cells and are essential for tumor proliferation and metastasis as well as for the interaction between tumor cells and mesenchymal cells. Establishing a chemokine genes signature to assess prognosis and therapy responsiveness in ccRCC is the goal of this effort. Methods: mRNA sequencing data and clinicopathological data on 526 individuals with ccRCC were gathered from the The Cancer Genome Atlas database for this investigation (263 training group samples and 263 validation group samples). Utilizing the LASSO algorithm in conjunction with univariate Cox analysis, the gene signature was constructed. The Gene Expression Omnibus (GEO) database provided the single cell RNA sequencing (scRNA-seq) data, and the R package "Seurat" was applied to analyze the scRNA-seq data. In addition, the enrichment scores of 28 immune cells in the tumor microenvironment (TME) were calculated using the "ssGSEA" algorithm. In order to develop possible medications for patients with high-risk ccRCC, the "pRRophetic" package is employed. Results: High-risk patients had lower overall survival in this model for predicting prognosis, which was supported by the validation cohort. In both cohorts, it served as an independent prognostic factor. Annotation of the predicted signature's biological function revealed that it was correlated with immune-related pathways, and the riskscore was positively correlated with immune cell infiltration and several immune checkpoints (ICs), including CD47, PDCD1, TIGIT, and LAG-3, while it was negatively correlated with TNFRSF14. The CXCL2, CXCL12, and CX3CL1 genes of this signature were shown to be significantly expressed in monocytes and cancer cells, according to scRNA-seq analysis. Furthermore, the high expression of CD47 in cancer cells suggested us that this could be a promising immune checkpoint. For patients who had high riskscore, we predicted 12 potential medications. Conclusion: Overall, our findings show that a putative 7-chemokine-gene signature might predict a patient's prognosis for ccRCC and reflect the disease's complicated immunological environment. Additionally, it offers suggestions on how to treat ccRCC using precision treatment and focused risk assessment.
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Single-Cell and Bulk Transcriptomic Integration Reveals an Adverse SPP1 Inflammatory TAM State and INHBA as a Candidate Biomarker in Non-Small Cell Lung Cancer Immunotherapy.
BACKGROUND: Biomarkers that capture the microenvironmental basis of response to neoadjuvant immunotherapy in non-small cell lung cancer (NSCLC) remain limited. We integrated single-cell and bulk transcriptomic data to identify response-associated immune programs with patient-level relevance. METHODS: Single-cell RNA-seq and clinical annotations from GSE207422 were used to construct an NSCLC tumor microenvironment atlas and refine myeloid and T cell states. UCell scoring, sample-level summaries, pseudobulk differential analysis, Monocle2 trajectory inference, and targeted macrophage-T cell interaction analysis were applied. A posttreatment SPP1 inflammatory TAM-derived NR-TAM signature was projected to pretreatment bulk RNA-seq from the same cohort. INHBA was evaluated in pretreatment bulk data, TCGA-LUAD/TCGA-LUSC, and lung cancer cell line assays. RESULTS: SPP1 inflammatory TAM was enriched in nonmajor pathologic response (NMPR) samples and co-occurred with exhausted CD8 T cell abundance, malignant cell hypoxia, and EMT programs. The NR-TAM signature was partially recapitulated in pretreatment bulk RNA-seq, with higher scores in NMPR but substantial group overlap. Exploratory ROC analysis showed modest discrimination for NR-TAM score (AUC = 0.60), INHBA (AUC = 0.64), and the combined model (AUC = 0.64; LOOCV AUC = 0.37). INHBA was higher in NMPR cases, correlated with residual tumor burden and selected TAM-related genes, showed less favorable survival trends particularly in LUSC, and its knockdown reduced proliferation and migration in lung cancer cells. CONCLUSIONS: An SPP1 inflammatory TAM program is associated with unfavorable pathologic response in NSCLC neoadjuvant immunotherapy. INHBA is a prioritized exploratory candidate within this program. Because the evidence is associative and predictive performance is modest, these markers require prospective validation before clinical use.
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Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases.
Spatial transcriptomics (ST) has revolutionized our understanding of tissue architecture, yet constructing comprehensive three-dimensional (3D) cell atlases remains challenging due to technical limitations and high cost. Current approaches typically capture only sparsely sampled two-dimensional sections, leaving substantial gaps that limit our understanding of continuous organ organization. Here, we present SpatialZ, a computational framework that bridges these gaps by generating virtual slices between experimentally measured sections, enabling the construction of dense 3D cell atlases from planar ST data. SpatialZ is designed to operate at single-cell resolution and function independently of gene coverage limitations inherent to specific spatial technologies. Comprehensive validation demonstrates that SpatialZ accurately preserves cell identities, gene expression patterns and spatial relationships. Leveraging the BRAIN Initiative Cell Census Network data, we constructed a 3D hemisphere atlas comprising over 38 million cells. This dense atlas enables new capabilities, including in silico sectioning at arbitrary angles, explorations of gene expression across both 3D volumes and surfaces, 3D mapping of query tissue sections, and discovery of 3D spatial molecular architectures through new synthesized views. To demonstrate its extensibility beyond transcriptomics, we applied SpatialZ to imaging mass cytometry data from human breast cancer, successfully deciphering 3D spatial gradients within the tumor microenvironment. Our approach generates cell atlases that provide previously unattainable 3D resolution of spatial molecular landscapes.
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Single-cell RNA-sequencing of cellular heterogeneity and pathogenic mechanisms in paraquat-induced Parkinson's disease with depression.
Parkinson's disease (PD) is among the most prevalent neurodegenerative diseases, and approximately one third of patients with PD are estimated to have depression. Paraquat (PQ) exposure is an important environmental risk factor for PD. In this study, we established a mouse model of PQ-induced PD with depression to comprehensively investigate cellular heterogeneity and the mechanisms underlying the progression of depression in the context of PD. We utilized single-cell RNA-seq (scRNA-seq) to acquire the transcriptomic atlas of individual cells from model mice and characterize the gene expression profiles in each differentially expressed cell type. We identified a specific glutamatergic neuron cluster responsible for the development of heterogeneous depression-associated changes and established a comprehensive gene expression atlas. Furthermore, functional enrichment and cell trajectory analyses revealed that the mechanisms underlying the progression of PD with depression were associated with specific glutamatergic neurons. Together, our findings provide a valuable resource for deciphering the cellular heterogeneity of PD with depression. The suggested connection between intrinsic transcriptional states of neurons and the progression of depression can provide insight into potential biomarkers and specific targets for anti-depression treatment in patients with PD. SYNOPSIS: Our results obtained using model mice confirm the core effects of PQ exposure on glutamatergic neurons and their potential role in the development of PD with depression.
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Sialyltransferase gene signature as a predictor of prognosis and therapeutic response in kidney renal clear cell carcinoma.
BACKGROUND: Sialyltransferases are enzymes involved in the addition of sialic acid to glycoproteins and glycolipids, influencing various physiological and pathological processes. The expression and function of sialyltransferases in tumors, particularly in kidney renal clear cell carcinoma (KIRC) remained underexplored. This study aimed to develop a prognostic model based on sialyltransferase-related genes (SRGs) to predict the prognosis and treatment response of patients with KIRC. METHODS: We utilized RNA-Seq data of KIRC from The Cancer Genome Atlas (TCGA) database, selecting samples with survival data and clinical outcomes. Somatic mutation and neoantigen data were analyzed using the "maftools" package, and genes involved in the sialylation process were identified through the Molecular Signatures Database. Validation cohorts of KIRC samples were obtained from the International Cancer Genome Consortium (ICGC) database. Single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) platform, and preprocessing, normalization, and dimensionality reduction analyses were conducted using the "Seurat" package. Differentially expressed sialylation genes were identified using the "limma" package, and their functional enrichment was assessed via Gene Ontology GO and KEGG analyses. Consensus clustering analysis was performed to identify molecular subtypes of KIRC based on sialylation, and drug sensitivity of different subtypes was evaluated using the "pRRophetic" package. A risk signature model comprising 5 SRGs was constructed through univariate and multivariate Cox regression analyses and validated in both the TCGA and ICGC cohorts. The "estimate" package was utilized to calculate immune and stromal scores for each KIRC sample, assessing the tumor immune microenvironment characteristics of different subtypes. RESULTS: Analysis of scRNA-seq data identified 25 cell subtypes, categorized into 9 cell types. CD4 + memory cells exhibited the highest potential interactions with other cell subtypes. We identified 14 differentially expressed sialylation genes and confirmed their enrichment in various biological pathways through GO and KEGG analyses. Consensus clustering analysis based on sialylation identified 2 molecular subtypes: C1 and C2. The C2 subtype demonstrated higher sialylation scores and poorer prognosis. Drug sensitivity analysis indicated that the C1 subtype had better responses to Dasatinib and Lapatinib, whereas the C2 subtype was more sensitive to Epothilone B and Vinorelbine. The risk signature model, constructed with five distinct SRGs, exhibited strong predictive accuracy, as indicated by Area Under the Curve (AUC) values of 0.68, 0.69, and 0.70 for 1-, 3-, and 5-year survival, respectively, across both the TCGA and ICGC validation cohorts. Immune microenvironment analysis revealed that the C1 subtype exhibited higher immune and stromal scores, while the C2 subtype showed significantly enhanced expression of immune checkpoint genes. CONCLUSION: This study successfully developed a prognostic model based on SRGs, effectively predicting the prognosis and drug response of KIRC patients. The model demonstrated significant predictive performance and potential clinical application value. Furthermore, the study highlighted the critical role of sialylation in KIRC, offering new insights into its underlying mechanisms in tumor biology. These findings could guide personalized treatment strategies for KIRC patients, emphasizing the importance of sialylation in cancer prognosis and therapy.
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Integrating multi-modal transcriptomics identifies cellular subtypes with distinct roles in PDAC progression.
PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies, largely due to its highly immunosuppressive and fibrotic tumor microenvironment (TME). However, the spatial and functional organization of its cellular components remains poorly understood. METHODS: We present an integrated transcriptomic atlas of the PDAC TME by combining single-cell RNA sequencing (n = 88; 187,520 cells), Visium spatial transcriptomics (n = 20; 67,933 spots), bulk RNA sequencing (n = 1,383), and high-resolution Xenium spatial transcriptomics (n = 2; 307,679 cells). Key findings were validated using scRNA-seq, bulk datasets, multiplex immunohistochemistry, and spatial imaging. RESULTS: POSTN⁺ fibroblasts and SPP1⁺ macrophages consistently co-infiltrated across 12 independent bulk RNA-seq cohorts, and showed spatial correlation in both Visium and Xenium platforms. Those tumor-promoting cell states were enriched in hypoxic, angiogenesis, and epithelial-mesenchymal transition, and were linked to poor prognosis. In contrast, CCL4⁺ CD8⁺ effector T cells and IGHG1⁺ plasma cells co-occurred within immune-active niches, were enriched for cytotoxic and activation-related pathways, and were associated with improved patient survival. Notably, these protective immune subsets remained detectable despite the immunosuppressive nature of the PDAC TME. Expression specificity of POSTN, SPP1, CCL4, and IGHG1 was validated at the transcriptomic and protein levels. CONCLUSIONS: We delineate two opposing cellular programs in the PDAC TME-tumor-promoting stromal remodeling and anti-tumor immune activation-spatially organized in distinct niches. Those findings suggest that targeting POSTN⁺ fibroblasts and SPP1⁺ macrophages-mediated stromal interactions while promoting CCL4⁺ T cell and IGHG1⁺ plasma cell immunity, may offer new therapeutic strategies for PDAC.
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Hybrid Stem Cell States: Insights Into the Relationship Between Mammary Development and Breast Cancer Using Single-Cell Transcriptomics.
Similarities between stem cells and cancer cells have implicated mammary stem cells in breast carcinogenesis. Recent evidence suggests that normal breast stem cells exist in multiple phenotypic states: epithelial, mesenchymal, and hybrid epithelial/mesenchymal (E/M). Hybrid E/M cells in particular have been implicated in breast cancer metastasis and poor prognosis. Mounting evidence also suggests that stem cell phenotypes change throughout the life course, for example, through embryonic development and pregnancy. The goal of this study was to use single cell RNA-sequencing to quantify cell state distributions of the normal mammary (NM) gland throughout developmental stages and when perturbed into a stem-like state in vitro using conditional reprogramming (CR). Using machine learning based dataset alignment, we integrate multiple mammary gland single cell RNA-seq datasets from human and mouse, along with bulk RNA-seq data from breast tumors in the Cancer Genome Atlas (TCGA), to interrogate hybrid stem cell states in the normal mammary gland and cancer. CR of human mammary cells induces an expanded stem cell state, characterized by increased expression of embryonic stem cell associated genes. Alignment to a mouse single-cell transcriptome atlas spanning mammary gland development from in utero to adulthood revealed that NM cells align to adult mouse cells and CR cells align across the pseudotime trajectory with a stem-like population aligning to the embryonic mouse cells. Three hybrid populations emerge after CR that are rare in NM: KRT18+/KRT14+ (hybrid luminal/basal), EPCAM+/VIM+ (hybrid E/M), and a quadruple positive population, expressing all four markers. Pseudotime analysis and alignment to the mouse developmental trajectory revealed that E/M hybrids are the most developmentally immature. Analyses of single cell mouse mammary RNA-seq throughout pregnancy show that during gestation, there is an enrichment of hybrid E/M cells, suggesting that these cells play an important role in mammary morphogenesis during lactation. Finally, pseudotime analysis and alignment of TCGA breast cancer expression data revealed that breast cancer subtypes express distinct developmental signatures, with basal tumors representing the most "developmentally immature" phenotype. These results highlight phenotypic plasticity of normal mammary stem cells and provide insight into the relationship between hybrid cell populations, stemness, and cancer.
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Multiomic analysis defines the first microRNA atlas across all small intestinal epithelial lineages and reveals novel markers of almost all major cell types.
MicroRNA-mediated regulation is critical for the proper development and function of the small intestinal (SI) epithelium. However, it is not known which microRNAs are expressed in each of the cell types of the SI epithelium. To bridge this important knowledge gap, we performed comprehensive microRNA profiling in all major cell types of the mouse SI epithelium. We used flow cytometry and fluorescence-activated cell sorting with multiple reporter mouse models to isolate intestinal stem cells, enterocytes, goblet cells, Paneth cells, enteroendocrine cells, tuft cells, and secretory progenitors. We then subjected these cell populations to small RNA-sequencing. The resulting atlas revealed highly enriched microRNA markers for almost every major cell type (https://sethupathy-lab.shinyapps.io/SI_miRNA/). Several of these lineage-enriched microRNAs (LEMs) were observed to be embedded in annotated host genes. We used chromatin-run-on sequencing to determine which of these LEMs are likely cotranscribed with their host genes. We then performed single-cell RNA-sequencing to define the cell type specificity of the host genes and embedded LEMs. We observed that the two most enriched microRNAs in secretory progenitors are miR-1224 and miR-672, the latter of which we found is deleted in hominin species. Finally, using several in vivo models, we established that miR-152 is a Paneth cell-specific microRNA.NEW & NOTEWORTHY In this study, first, microRNA atlas (and searchable web server) across all major small intestinal epithelial cell types is presented. We have demonstrated microRNAs that uniquely mark several lineages, including enteroendocrine and tuft. Identification of a key marker of mouse secretory progenitor cells, miR-672, which we show is deleted in humans. We have used several in vivo models to establish miR-152 as a specific marker of Paneth cells, which are highly understudied in terms of microRNAs.
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Evaluation of the impact of glycolysis-related gene signatures on prognosis and therapeutic targeting in lung adenocarcinoma.
Abnormal glycolysis is one of the hallmarks of cancer and plays a significant role in its progression. This study investigates the association between glycolysis genes and the progression of lung adenocarcinoma (LUAD). Utilizing various bioinformatics techniques, the research explores the heterogeneity of glycolysis genes in different LUAD cell types, identifies glycolysis-related prognostic signatures (GRPS). We obtained one training set for model construction from the Cancer Genome Atlas (TCGA) database, and also obtained four LUAD gene expression datasets as validation sets from the Gene Expression Omnibus (GEO) database. The single-cell RNA sequencing (scRNA seq) data also comes from the GEO database. Firstly, the "limma" R package was used to identify differentially expressed glycolysis related genes, and a machine learning computational framework composed of multiple combinations was used to preliminarily screen for glycolysis related prognostic markers (GRPS) in LUAD. Based on these GRPS, prognostic features were developed and validated through survival analysis, column chart development, and ROC curve analysis. The ssGSEA algorithm, ESTIMATE algorithm, and seven integrated computational algorithms from the TIMER 2.0 database were used to analyze the immune cell infiltration patterns of different risk groups. Analyze scRNA seq data to evaluate the distribution of GRPS and intercellular communication among various cell types, and further determine the core GRPS through the "hdWGCNA" and "ConstructNetwork" packages. In addition, we also evaluated the responsiveness of high and low-risk groups to 198 drugs using the "OncoPredict" software package. Result: We found that the glycolytic activity score of tumor tissue was significantly higher than that of normal tissue, and a total of 49 upregulated genes and 15 downregulated genes were selected from the total. Based on a machine learning computational framework, a total of 8 GRPS were screened, which constitute the prognostic features of LUAD patients. This feature demonstrates strong prognostic value, as confirmed by univariate and multivariate Cox regression analysis. Significant differences in tumor microenvironment (TME) immune infiltration were observed between high and low-risk groups. ScRNA seq revealed the distribution and expression of cell type specific GRPS, particularly in T cells, epithelial cells, and fibroblasts, while also revealing the strong cell-cell communication ability of the high GRPS group. The hdWGCNA analysis ultimately identified five core GRPS, namely DDIT4, FKBP4, CHPF, EFNA3, and B3GNT3. In addition, there are significant differences in sensitivity to most drugs between high-risk and low-risk cohorts, with WIKI4 and Lapatinib negatively correlated with risk scores, while Doramapimod and Niraparib positively correlated with risk scores. This study established a GRPS based risk feature for LUAD, demonstrating strong predictive power for prognosis assessment. The drug sensitivity results also provide drug guidance for the clinical application of this feature, all of which provide important clinical utility for the prognosis of LUAD. At the same time, the intercellular communication network was plotted based on the GRPS score, providing insights into the pathogenesis of LUAD and offering new ideas for developing targeted therapies and precision medicine methods.
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Peripheral lncRNA-IL1RAP Dysregulation in Schizophrenia: A Multi-Omics Bridge Between Immunity and Diagnosis.
BACKGROUND: Schizophrenia (SCZ) is frequently accompanied by peripheral immune dysregulation, yet robust and reproducible blood-based molecular markers remain limited. OBJECTIVE: We sought a compact long non-coding RNA (lncRNA) signal in peripheral blood leukocytes (PBL) and examined how it aligns with immune-linked transcriptional programs and cellular compartments, using bulk RNA sequencing (RNA-seq), targeted validation, and a healthy-donor peripheral blood mononuclear cell (PBMC) single-cell reference for localization. METHODS: PBL from 50 first-episode or unmedicated SCZ patients and 50 matched controls underwent RNA-seq. We performed reference-guided transcript annotation and generated lncRNA and mRNA count matrices. Differential expression was assessed with edgeR (TMM normalization). Candidate lncRNAs were prioritized using sample-level lncRNA-mRNA co-variation, followed by qRT-PCR validation in an independent cohort and evaluation with a two-feature logistic regression model using repeated 10-fold cross-validation. Pathway-scale analyses and weighted gene co-expression network analysis (WGCNA) summarized coordinated programs. Single-cell data from healthy donors were used for expression localization only, not case-control testing. RESULTS: We observed a small set of dysregulated lncRNAs alongside broader mRNA changes. Two candidates at the CCR3 and IL1RAP loci (TCONS_00134168/lncRNA-CCR3 and TCONS_00138311/lncRNA-IL1RAP) showed consistent case-control directionality and were supported by qRT-PCR. The two-lncRNA model showed strong internal discrimination (AUC = 0.933) but weaker, uncertain performance in a small external qRT-PCR set (AUC = 0.656). Enrichment analyses highlighted synapse-related annotations, RNA processing/translation, and immune signaling, with recurrent involvement of IL1RAP-linked IL-1 branches. WGCNA placed lncRNA-IL1RAP and IL1RAP within diagnosis-inversely associated co-expression programs, whereas lncRNA-CCR3 showed a more transcript-specific pattern. In the healthy-reference single-cell atlas, IL1B/IL1RAP/HSF1 signals were most prominent in the monocyte/macrophage compartment. CONCLUSION: Together, these findings support an exploratory two-lncRNA candidate marker concept, while underscoring the need for larger multi-center validation and targeted mechanistic follow-up without implying causality.