scRNA-seq Analysis Research Articles

Metabolic Transcriptional Activation in Ulcerative Colitis Identified Through scRNA-seq Analysis

Background: Ulcerative colitis is a chronic inflammatory disease affecting the colon. During chronic inflammation of epithelial cells, lipid metabolism via pro-inflammatory eicosanoids is known to modify the immune response. Methods: Starting from the Mammalian Metabolic Database, the expression of metabolic enzymes was investigated in two independent cohorts from transcriptome datasets GSE38713 and GSE11223, which analyzed ulcerative colitis tissue samples from the digestive tract. Results: In the first cohort, 145 differentially expressed enzymes were identified as significantly regulated between ulcerative colitis tissues and normal controls. Overexpressed enzymes were selected to tune an Elastic Net model in the second cohort. Using the best parameters, the model achieved a prediction accuracy for ulcerative colitis with an area under the curve (AUC) of 0.79. Twenty-two metabolic enzymes were found to be commonly overexpressed in both independent cohorts, with decreasing Elastic Net predictive coefficients as follows: LIPG (3.98), PSAT1 (3.69), PGM3 (2.74), CD38 (2.28), BLVRA (1.99), CBR3 (1.94), NT5DC2 (1.76), PHGDH (1.71), GPX7 (1.58), CASP1 (1.56), ASRGL1 (1.4), SOD3 (1.25), CHST2 (0.965), CHST11 (0.95), KYNU (0.94), PLAG2G7 (0.92), SRM (0.87), PTGS2 (0.80), LPIN1 (0.47), ME1 (0.31), PTGDS (0.14), and ADA (0.13). Functional enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database highlighted the main implications of these enzymes in cysteine and methionine metabolism (adjusted p-value = 0.01), arachidonic acid and prostaglandin metabolism (adjusted p-value = 0.01), and carbon metabolism (adjusted p-value = 0.04). A metabolic score based on the transcriptional activation of the validated twenty-two enzymes was found to be significantly greater in Ulcerative colitis samples compared to healthy donor samples (p-value = 1.52 × 10−8). Conclusions: A metabolic expression score was established and reflects the implications of heterogeneous metabolic pathway deregulations in the digestive tract of patients with ulcerative colitis.

Dynamic transcriptome analysis of osteal macrophages identifies distinct subset with senescence features in experimental osteoporosis.

Given the potential fundamental function of osteal macrophages in bone pathophysiology, we study here their precise function in experimental osteoporosis. Gene profiling of osteal macrophages from ovariectomized mice demonstrated the upregulation of genes that were involved in oxidative stress, cell senescence and apoptotic process. A scRNA-seq analysis revealed that osteal macrophages were heterogenously clustered into 6 subsets that expressed proliferative, inflammatory, anti-inflammatory and efferocytosis gene signatures. Importantly, postmenopausal mice exhibited a 20-fold increase in subset-3 that showed a typical gene signature of cell senescence and inflammation. These findings suggest that the decreased production of estrogen due to postmenopause altered the osteal macrophages subsets, resulting in a shift toward cell senescence and inflammatory conditions in the bone microenvironment. Furthermore, adoptive macrophage transfer onto calvarial bone was performed and mice that received oxidative-stressed macrophages exhibited greater osteolytic lesions than control macrophages, suggesting the role of these cells in development of inflammaging in bone microenvironment. Consistently, depletion of senescent cells and oxidative-stressed macrophages subset alleviated the excessive bone loss in postmenopausal mice. Our data provided a new insight into the pathogenesis of osteoporosis and sheds light on a new therapeutic approach for the treatment/prevention of postmenopausal osteoporosis.

A cost-effective protocol for single-cell RNA sequencing of human skin

IntroductionSingle-cell RNA sequencing (scRNAseq) and flow cytometry studies in skin are methodologically complex and costly, limiting their accessibility to researchers worldwide. Ideally, RNA and protein-based analyses should be performed on the same lesion to obtain more comprehensive data. However, current protocols generally focus on either scRNAseq or flow cytometry of healthy skin.MethodsWe present a novel label-free sample multiplexing strategy, building on the souporcell algorithm, which enables scRNAseq analysis of paired blood and skin samples. Additionally, we provide detailed instructions for simultaneous flow cytometry analysis from the same sample, with necessary adaptations for both healthy and inflamed skin specimens.ResultsThis tissue multiplexing strategy mitigates technical batch effects and reduces costs by 2-4 times compared to existing protocols. We also demonstrate the effects of varying enzymatic incubation durations (1, 3, and 16 hours, with and without enzyme P) on flow cytometry outcomes. Comprehensive explanations of bioinformatic demultiplexing steps and a detailed step-by-step protocol of the entire experimental procedure are included.DiscussionThe protocol outlined in this article will make scRNAseq and flow cytometry analysis of skin samples more accessible to researchers, especially those new to these techniques.

Single-cell transcriptional analysis of murine norovirus infection in a human intestinal cell line.

Noroviruses are a major agent of acute gastroenteritis in humans, but host cell requirements for efficient replication in vitro have not been established. We engineered a human intestinal cell line (designated mCD300lf-hCaco2) expressing the murine norovirus (MNV) receptor, mouse CD300lf to become fully permissive for MNV replication. To explore the replicative machinery and host response of these cells, we performed a single-cell RNA sequencing (scRNA-seq) transcriptomics analysis of an MNV infection over time. Marked similarities were observed between certain global features of MNV infection in human cells compared to those previously reported in mouse cells by whole population transcriptomics such as downregulation of ribosome biogenesis, mitochondrial dysfunction, and cell cycle preference for G1. Our scRNA-seq analysis allowed further resolution of an infected cell population into distinct clusters with varying levels of viral RNA and interferon-stimulated gene ISG15 transcripts. Cells with high viral replication displayed downregulated ribosomal protein small (RPS) and large (RPL) genes and mitochondrial complexes I, III, IV, and V genes during exponential viral propagation. Ferritin subunit genes FTL and FTH1 were also downregulated during active MNV replication, suggesting that inhibition of iron metabolism may increase replication efficiency. Consistent with this, transcriptional activation of these genes with ferric ammonium citrate and overexpression of FTL lowered virus yields. Comparative studies of cells that support varying levels of norovirus replication efficiency, as determined by scRNA-seq may lead to improved human cell-based culture systems and effective viral interventions.IMPORTANCEHuman noroviruses cause acute gastroenteritis in all age groups. Vaccines and antiviral drugs are not yet available, in part, because it is difficult to propagate the viruses causing human disease in standard laboratory cell culture systems. In contrast, a norovirus found in mice [murine norovirus (MNV)] replicates efficiently in murine-based cell culture and has served as a model system. In this study, we established a new human intestinal cell line that was genetically modified to express the murine norovirus receptor so that the human cells became permissive to murine norovirus infection. We then defined the host response to MNV infection in the engineered human cell line at a single-cell resolution and identified cellular genes associated with the highest levels of MNV replication. This study may lead to the improvement of the current human norovirus cell culture systems and help to identify norovirus-host interactions that could be targeted for antiviral drugs.

Integrating machine learning and single-cell transcriptomic analysis to identify potential biomarkers and analyze immune features of ischemic stroke

This study employs machine learning and single-cell transcriptome sequencing (scRNA-seq) analysis to unearth novel biomarkers and delineate the immune characteristics of ischemic stroke (IS), thereby contributing fresh insights into IS treatment strategies.Our research leverages gene expression data sourced from the GEO database. We undertake weighted gene co-expression network analysis (WGCNA) to filter pertinent genes and subsequently employ machine learning algorithms for the identification of feature genes. Concurrently, we rigorously execute quality control measures, dimensionality reduction techniques, and cell annotation on the scRNA-seq data to pinpoint differentially expressed genes (DEGs). The identification of core genes, denoted as Hub genes, among the feature genes and DEGs, is achieved through meticulous overlapping analysis. We illuminate the immune characteristics of these Hub genes using a suite of analytical tools, encompassing CIBERSORT, MCPcounter, and pseudotemporal analysis, all based on immune cell annotations and single-cell transcriptome data.Subsequently, we harness the CMap database to prognosticate potential therapeutic drugs and scrutinize their associations with the identified Hub genes. Our findings unveil robust linkages between three pivotal Hub genes—namely, RNF13, VASP, and CD163—and specific immune cell types such as T cells and neutrophils. These Hub genes predominantly manifest in macrophages and microglial cells within the scRNA-seq immune cell population, exhibiting variances across different stages of cellular differentiation. In conclusion, this study unearths highly pertinent biomarkers for IS diagnosis and elucidates IS-induced immune infiltration characteristics, thus providing a firm foundation for a comprehensive exploration of potential immune mechanisms and the identification of novel therapeutic targets for IS.

Exogenous IL-33 promotes tumor immunity via macroscopic regulation of ILC2s.

Interleukin-33 (IL-33) is a pleiotropic molecule that plays various roles in the body. However, how exogenous IL-33 changes the tumor immune microenvironment remains unclear. Our study revealed that exogenous IL-33 exerts anti-tumor effects and effectively suppresses the progression of subcutaneous melanoma. scRNA-seq analysis revealed that exogenous IL-33 reduced neutrophils accumulation, thereby improving the inhibitory immune environment. Flow cytometry analysis revealed that exogenous IL-33 significantly increased the proportion of eosinophils and group 2 innate lymphoid cells (ILC2s). In addition, we identified genes encoding major histocompatibility complex (MHC) class II molecules in this group of ILC2s, suggesting that ILC2s may play a role in antigen presentation. In Il7rCreArg1flox/flox mice, the decrease of ILC2s led to a reduction in the proportion of eosinophils. Furthermore, we found that exogenous IL-33 effectively promoted the differentiation of ILC2s and their accumulation in tumors, thereby enhancing the anti-tumor immune response. These findings may pave the way for developing new cancer immunotherapies that use IL-33 as an activator to enhance anti-tumor immune responses.

Blockade of foam cell formation by an AP-1 inhibitor attenuates atherosclerosis in diabetes

Abstract Background Atherosclerosis is a major contributor to cardiovascular disease (CVD) related premature deaths in people with diabetes. Residual increased risk of CVD remains even after treatment of standard risk factors including hyperglycaemia. Therefore, there is an unmet clinical need of novel therapies that can directly target the intrinsic pathobiology of atherosclerotic plaque development. Macrophage associated pro-atherosclerotic processes including impaired cholesterol efflux resulting in foam cell formation are important therapeutic targets in atherosclerosis. Expression of genes linked to impaired cholesterol efflux can be regulated by multiple factors including transcription factors (TFs) such as activator protein 1 (AP-1) complex. Although, previous studies have implicated the role of foam cells in atherosclerosis, the role of AP-1 TF complex in foam cell formation in diabetes induced atherosclerosis is not known. Methods Diabetes was induced with streptozotocin (STZ) in atheroprone Apoe-/- mice and 10 weeks later aortae were subjected to single cell RNA sequencing (scRNA-seq) analysis using 10X Genomics platform. In vitro model mimicking diabetes-induced foam cell formation was established in human monocytes THP-1 derived macrophages using high glucose (25mM) and ox-LDL ± a small molecular inhibitor of AP-1, T-5224. AP-1 activity, gene expression and foam cell formation were assessed with activity assay, RT PCR and Oil Red O staining respectively in this setting. Complementary in vitro AP-1 knockdown experiments using shRNA were also performed. Furthermore, preclinical intervention studies were also performed where Apoe-/- mice made diabetic with STZ were treated 5-weeks post diabetes with T-5224 (30 mg/kg body weight daily for 5 weeks). Results scRNA-seq analysis identified a well-defined cluster of foam cells within macrophages with a diabetes specific proatherogenic transcriptomic profile. Differential gene expression analysis showed increased expression of AP-1 members cFos, Atf3, Atf4 and Junb exclusively in foam cell cluster in diabetes (cut off = FDR<0.05, Log2FC -2/2). Comparison with the ENCODE dataset for AP-1 target genes using Harmonizome version 3 showed that indeed multiple differentially expressed genes in diabetes were targets of AP-1. AP-1 members were also upregulated in the THP-1-based in vitro model of high glucose induced foam cell formation. THP-1 derived foam cells showed increased AP-1 activity and lipid uptake. Interestingly, AP-1 inhibition by T-5224 attenuated foam cell formation. AP-1 knockdown experiments validated experimental findings of AP-1 inhibition studies in THP-1 derived macrophages. Importantly, T-5224 treatment blocked foam cell formation resulting in reduced atherosclerosis in diabetic Apoe-/- mice in our intervention study design. Conclusion This study identified the AP-1 inhibition with T-5224 as a potential treatment for the diabetes induced foam cell formation and associated atherosclerosis.

Blockade of endothelial to mesenchymal transition (EndMT) by an inhibitor of histone methyltransferase EZH2 attenuates atherosclerosis in diabetes

Abstract Background Atherosclerosis is a major contributor to cardiovascular disease (CVD) related deaths in individuals with diabetes. Persistent endothelial cell activation caused by factors such as hyperglycaemia induces endothelial to mesenchymal transition (EndMT), which promotes both initiation as well as progression of atherosclerosis. Gene expression changes that occur during EndMT, are regulated by multiple factors including epigenetic modifiers such as the histone methyltransferase EZH2 (Enhancer of zest homolog 2). Recent studies have implicated the role of EndMT in cardiovascular complications of diabetes including atherosclerosis. However, the role of EZH2 in induction of EndMT in diabetes associated atherosclerosis is not known. Methods Aortae of atheroprone diabetic Apoe-/- mice were subjected to single cell RNA sequencing (scRNA-seq) analysis using 10X Genomics platform. In vitro model mimicking diabetes-induced EndMT was established in human aortic endothelial cells (HAECs) using high glucose (25mM) and TNF-α containing media for 72 hrs ± a small molecule inhibitor of EZH2, GSK126. RNA and Chromatin Immunoprecipitation (ChIP) sequencing was performed to identify EZH2-mediated epigenomic and corresponding transcriptomic changes in this setting. Complementary in vitro EZH2 knockdown experiments using shRNA were also performed. Moreover, Apoe-/- mice made diabetic with streptozotocin, were treated with GSK126 (50 mg/kg BW daily for 5 weeks) in an intervention study design. Aortic sections from treated and control mice were subjected to immunofluorescent staining specific for the EndMT pathway. Results scRNA-seq analysis identified an EndMT+ve endothelial cell sub-population with a diabetes specific proatherogenic transcriptomic profile. Comparison with the ENCODE dataset for EZH2 target genes using Harmonizome showed that indeed multiple repressed genes in diabetes were targets of EZH2. Methyltransferase activity of EZH2 was elevated in in vitro settings of EndMT. Interestingly, EZH2 inhibition by GSK126 attenuated EndMT. Gene expression analysis showed that GSK126 treatment rescued 76 of 242 differentially expressed genes in HAECs exposed to EndMT conditions. Several differentially expressed genes including COL1A2, MMP2, NOS3, COL4A1, and TGFB2 (FDR <0.05, Log2 fold change (2x)) were targets of EZH2 validating integrated analysis of our scRNA-seq with the ENCODE dataset. EZH2 knockdown experiments validated experimental findings of EZH2 inhibition studies using GSK126 in HAECs. Importantly, GSK126 treatment blocked EndMT resulting in reduced atherosclerosis in diabetic Apoe-/- mice in intervention studies. Conclusion This study showed that the inhibition of EZH2 with GSK126 is a potential vasculoprotective treatment for the diabetes induced EndMT and associated atherosclerosis.

Smooth muscle cell-specific deletion of S100A4 modifies smooth muscle cell fate and inflammation in murine atherosclerotic lesions

Abstract Background S100A4, a calcium-binding protein, is crucial in smooth muscle cell (SMC) phenotypic switch, yet its role in atherosclerotic plaque development and SMC plasticity remains unclear. Our previous experiments using an S100A4 neutralizing antibody approach in ApoE-/- atherosclerotic-prone mice showed reduced atherosclerotic burden but did not establish a direct link between the various S100A4-expressing cells and atherosclerosis. Methods/Results Herein, we show that aortic Oil RedO staining in ApoE-/-; S100A4-/- mice subjected to a 12-week high-cholesterol-diet (HCD) revealed reduced lesion areas compared to ApoE-/- mice (Figure 1, from 21.29% of the total aorta in ApoE-/-; S100A4+/+ mice to 13.27% in ApoE-/-; S100A4-/- mice), highlighting a pivotal role for S100A4 in atherosclerotic plaque development. We then used a lineage tracing ApoE-/- mouse model, in which we induced an SMC-specific deletion of S100A4 (SMC-S100A4Δ/Δ). After 12 weeks of HCD, SMC-S100A4Δ/Δ and control mice (SMC-S100A4wt/wt) were sacrificed, and aortas were processed for en face Oil Red O staining, immunohistochemistry, and single-cell RNA sequencing (scRNA-seq). SMC-specific deletion of S100A4 reduced the necrotic core area (from 29.75% of total plaque area in SMC-S100A4wt/wt to 18.23% in SMC-S100A4Δ/Δ mice) without affecting total atherosclerotic plaque size suggesting a more potent impact on plaque composition. ScRNA-seq analysis from SMC-S100A4Δ/Δ and SMC-S100A4wt/wt mice showed that the inflammatory macrophage-like SMC phenotype was highly suppressed in the plaque and associated with increased SMC repair phenotypes as well as a global reduction of myeloid inflammatory cells. SMCs also retained specific contractile SMC markers, namely Acta2 and Myh11 (fold change of 2.285 and 2.492, respectively) hinting towards a more stable fibrous cap. Immunofluorescence staining on aortic roots plaque (Figure 2) confirmed those results with an increase of SMCs (from 11.02% to 16.18%) as well as a-smooth muscle actin (a-SMA, Acta2)-positive SMCs (from 3.25% to 5.1%) in SMC-S100A4 Δ/Δ mice. Furthermore, the Gene Set Enrichment Analysis (GSEA) of scRNAseq data revealed that, in SMC-S100A4Δ/Δ mice, genes related to mitochondrial metabolism and oxidative respiration were highly downregulated in all SMC clusters. We investigated SMC mitochondrial fitness in vitro and showed that S100A4-/- SMCs have an increased basal oxygen consumption rate and an increased proton leak (fold change of 1.37 and 1.96 respectively) suggestive of differences in cellular metabolic adaptability. Conclusion Our research emphasizes the significant role of S100A4 in plaque development, demonstrating how SMC-derived S100A4 knockdown affects plaque evolution, reducing inflammation, and promoting stability likely through influencing SMC phenotypic switch through metabolic pathways.

Single-cell and bulk transcriptomic datasets enable the development of prognostic models based on dynamic changes in the tumor immune microenvironment in patients with hepatocellular carcinoma and portal vein tumor thrombus

BackgroundHepatocellular carcinoma (HCC) patients exhibiting portal vein tumor thrombosis (PVTT) face a high risk of rapid malignant progression and poor outcomes, with this issue being compounded by a lack of effective treatment options. The integration of bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) datasets focused on samples from HCC patients with PVTT has the potential to yield unprecedented insight into the dynamic changes in the tumor microenvironment (TME) and associated immunological characteristics in these patients, providing an invaluable tool for the reliable prediction of disease progression and treatment responses.MethodsscRNA-seq data from both primary tumor (PT) and PVTT cells were downloaded from the Gene Expression Omnibus (GEO) database, while the International Cancer Genome Consortium (ICGC) and Cancer Genome Atlas (TCGA) databases were used to access bulk RNA-seq datasets. scRNA-seq, clustering, GSVA enrichment, mutational profiling, and predictive immunotherapeutic treatment analyses were conducted using these data with the goal of systematically assessing the heterogeneity of PT and PVTT cells and establishing a model capable of predicting immunotherapeutic and prognostic outcomes in patients with HCC.ResultsThese analyses revealed that PVTT cells exhibited patterns of tumor proliferation, stromal activation, and low levels of immune cell infiltration, presenting with immune desert and immune rejection-like phenotypes. PT cells, in contrast, were found to exhibit a pattern of immunoinflammatory activity. Core PVTT-associated genes were clustered into three patterns consistent with the tumor immune rejection and immune desert phenotypes. An established clustering model was capable of predicting tumor inflammatory stage, subtype, TME stromal activity, and patient outcomes. PVTT signature genes were further used to establish a risk model, with the risk scores derived from this model providing a tool to evaluate patient clinicopathological features including clinical stage, tumor differentiation, histological subtype, microsatellite instability status, and tumor mutational burden. These risk scores were also able to serve as an independent predictor of patient survival outcomes, responses to adjuvant chemotherapy, and responses to immunotherapy. In vitro experiments were used to partially validate the biological prediction results.ConclusionThese results offer new insight into the biological and immunological landscape of PVTT in HCC patients, By utilizing individual patient risk scores, providing an opportunity to guide more effective immunotherapeutic interventional efforts.

Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data

BackgroundGene interaction networks are graphs in which nodes represent genes and edges represent functional interactions between them. These interactions can be at multiple levels, for instance, gene regulation, protein-protein interaction, or metabolic pathways. To analyse gene interaction networks at a large scale, gene co-expression network analysis is often applied on high-throughput gene expression data such as RNA sequencing data. With the advance in sequencing technology, expression of genes can be measured in individual cells. Single-cell RNA sequencing (scRNAseq) provides insights of cellular development, differentiation and characteristics at the transcriptomic level. High sparsity and high-dimensional data structures pose challenges in scRNAseq data analysis.ResultsIn this study, a sparse inverse covariance matrix estimation framework for scRNAseq data is developed to capture direct functional interactions between genes. Comparative analyses highlight high performance and fast computation of Stein-type shrinkage in high-dimensional data using simulated scRNAseq data. Data transformation approaches also show improvement in performance of shrinkage methods in non-Gaussian distributed data. Zero-inflated modelling of scRNAseq data based on a negative binomial distribution enhances shrinkage performance in zero-inflated data without interference on non zero-inflated count data.ConclusionThe proposed framework broadens application of graphical model in scRNAseq analysis with flexibility in sparsity of count data resulting from dropout events, high performance, and fast computational time. Implementation of the framework is in a reproducible Snakemake workflow https://github.com/calathea24/ZINBGraphicalModel and R package ZINBStein https://github.com/calathea24/ZINBStein.

CancerSCEM 2.0: an updated data resource of single-cell expression map across various human cancers.

The field of single-cell RNA sequencing (scRNA-seq) has advanced rapidly in the past decade, generating significant amounts of valuable data for researchers to study complex tumor profiles. This data is crucial for gaining innovative insights into cancer biology. CancerSCEM (https://ngdc.cncb.ac.cn/cancerscem) is a public resource that integrates, analyzesand visualizes scRNA-seq data related to cancer, and it provides invaluable support to numerous cancer-related studies. With CancerSCEM 2.0, scRNA-seq data have increased from 208 to 1466 datasets, covering tumor, matching-normaland peripheral blood samples across 127 research projects and 74 cancer types. The new version of this resource enhances transcriptome analysis by adding copy number variation evaluation, transcription factor enrichment, pseudotime trajectory construction, and diverse biological feature scoring. It also introduces a new cancer metabolic map at the single-cell level, providing an intuitive understanding of metabolic regulation across different cancer types. CancerSCEM 2.0 has a more interactive analysis platform, including four modules and 14 analytical functions, allowing researchers to perform cancer scRNA-seq data analyses in various dimensions. These enhancements are expected to expand the usability of CancerSCEM 2.0 to a broader range of cancer research and clinical applications, potentially revolutionizing our understanding of cancer mechanisms and treatments.

Identification of molecular subtypes based on bile acid metabolism in cholangiocarcinoma

BackgroundCholangiocarcinoma is a highly heterogeneous tumor with bile acid metabolism involving in its development. The aim of this study was to characterize bile acid metabolism and identify specific subtypes to better stratify cholangiocarcinoma patients for individualized treatment and prognostic assessment.MethodsA total of 30 bile acids were quantified using the ultra-performance liquid chromatography tandem mass spectrometry. Using Consensus clustering, the molecular subtypes related to bile acid metabolism were identified. The prognosis, clinicopathologic characteristics, immune landscape, and therapeutic response were compared between these subtypes. The single-cell RNA sequencing (scRNA-seq) analysis and preliminary cell experiment were also conducted to verify our findings.ResultsThe altered bile acid profile and genetic variation of bile acid metabolism-related genes in cholangiocarcinoma were demonstrated. The cholangiocarcinoma was categorized into bile acid metabolism-active and -inactive subtypes with different prognoses, clinicopathologic characteristics, tumor microenvironments (TME) and therapeutic responses. This categorization was reproducible and predictable. Specifically, the bile acid metabolism-active subtype showed a poor prognosis with an immunosuppressive microenvironment and an inactive response to immunotherapy, while the bile acid metabolism-inactive subtype showed the opposite characteristics. Moreover, the scRNA-seq revealed that immunotherapy altered bile acid metabolism in TME of cholangiocarcinoma. Finally, a prognostic signature related to bile acid metabolism was developed, which exhibited strong power for prognostic assessment of cholangiocarcinoma. Consistently, these results were verified by immunohistochemistry, cell proliferation, migration, and apoptosis assays.ConclusionIn conclusion, a novel cholangiocarcinoma classification based on bile acid metabolism was established. This classification was significant for the estimation of TME and prognosis.

Systematic analysis of human colorectal cancer scRNA-seq revealed limited pro-tumoral IL-17 production potential in gamma delta T cells

Gamma delta T cells play a crucial role in anti-tumor immunity due to their cytotoxic properties. However, the role and extent of γδ T cells in production of pro-tumorigenic interleukin-17 (IL-17) within the tumor microenvironment of colorectal cancer (CRC) remains controversial. In this study, we re-analyzed nine published human CRC whole-tissue single-cell RNA sequencing datasets, identifying 18,483 γδ T cells out of 951,785 total cells, in the neoplastic or adjacent normal tissue of 165 human CRC patients. Our results confirm that tumor-infiltrating γδ T cells exhibit high cytotoxicity-related transcription in both tumor and adjacent normal tissues, but critically, none of the γδ T cell clusters showed IL-17 production potential. We also identified various γδ T cell subsets, including poised effector-like T cells, tissue-resident memory T cells, progenitor exhausted-like T cells, and exhausted T cells, and noted an increased expression of cytotoxic molecules in tumor-infiltrating γδ T cells compared to their normal area counterparts. We proposed anti-tumor γδ T effector cells may arise from tissue-resident progenitor cells based on the trajectory analysis. Our work demonstrates that γδ T cells in CRC primarily function as cytotoxic effector cells rather than IL-17 producers, mitigating the concerns about their potential pro-tumorigenic roles in CRC, highlighting the importance of accurately characterizing these cells for cancer immunotherapy research and the unneglectable cross-species discrepancy between the mouse and human immune system in the study of cancer immunology.

Construction of mitochondrial quality regulation genes-related prognostic model based on bulk-RNA-seq analysis in multiple myeloma.

Mitochondrial quality regulation plays an important role in affecting the treatment sensitivity of multiple myeloma (MM). We aimed to develop a mitochondrial quality regulation genes (MQRGs)-related prognostic model for MM patients. The Genomic Data Commons-MM of bulk RNA-seq, mutation, and single-cell RNA-seq (scRNA-seq) dataset were downloaded, and the MQRGs gene set was collected previous study. "maftools" and CIBERSORT were used for mutation and immune-infiltration analysis. Subsequently, the "ConsensusClusterPlus" was used to perform the unsupervised clustering analysis, "survminer" and "ssGSEA" R package was used for the Kaplan-Meier survival and enrichment analysis, "limma" R, univariate and Least Absolute Shrinkage and Selection Operator Cox were used for RiskScore model. The "timeROC" R package was used for Receiver Operating Characteristic Curve analysis. Finally, the "Seurat" R package was used for scRNA-seq analysis. These MQRGs are mainly located on chromosome-1,2,3,7, and 22 and had significant expression differences among age, gender, and stage groups, in which PPARGC1A and PPARG are the high mutation genes. Most MQRGs expression are closely associated with the plasma cells infiltration and can divide the patients into 2 different prognostic clusters (C1, C2). Then, 8 risk models were screened from 60 DEGs for RiskScore, which is an independent prognostic factor and effectively divided the patients into high and low risk groups with significant difference of immune checkpoint expression. Nomogram containing RiskScore can accurately predict patient prognosis, and a series of specific transcription factor PRDM1 and IRF1 were identified. We described the based molecular features and developed a high effective MQRGs-related prognostic model in MM.

Comprehensive single-cell and bulk transcriptomic analyses to develop an NK cell-derived gene signature for prognostic assessment and precision medicine in breast cancer.

Natural killer (NK) cells play crucial roles in mediating anti-cancer activity in breast cancer (BRCA). However, the potential of NK cell-related molecules in predicting BRCA outcomes and guiding personalized therapy remains largely unexplored. This study focused on developing a prognostic and therapeutic prediction model for BRCA by incorporating NK cell-related genes. The data analyzed primarily originated from the TCGA and GEO databases. The prognostic role of NK cells was evaluated, and marker genes of NK cells were identified via single-cell analysis. Module genes closely associated with immunotherapy resistance were identified by bulk transcriptome-based weighted correlation network analysis (WGCNA). Following taking intersection and LASSO regression, NK-related genes (NKRGs) relevant to BRCA prognosis were screened, and the NK-related prognostic signature was subsequently constructed. Analyses were further expanded to clinicopathological relevance, GSEA, tumor microenvironment (TME) analysis, immune function, immunotherapy responsiveness, and chemotherapeutics. Key NKRGs were screened by machine learning and validated by spatial transcriptomics (ST) and immunohistochemistry (IHC). Tumor-infiltrating NK cells are a favorable prognostic factor in BRCA. By combining scRNA-seq and bulk transcriptomic analyses, we identified 7 NK-related prognostic NKRGs (CCL5, EFHD2, KLRB1, C1S, SOCS3, IRF1, and CCND2) and developed an NK-related risk scoring (NKRS) system. The prognostic reliability of NKRS was verified through survival and clinical relevance analyses across multiple cohorts. NKRS also demonstrated robust predictive power in various aspects, including TME landscape, immune functions, immunotherapy responses, and chemotherapeutic sensitivity. Additionally, KLRB1 and CCND2 emerged as key prognostic NKRGs identified through machine learning and external validation, with their expression correlation with NK cells confirmed in BRCA specimens by ST and IHC. We developed a novel NK-related gene signature that has proven valuable for evaluating prognosis and treatment response in BRCA, expecting to advance precision medicine of BRCA.

A Single-Cell RNA Sequencing Guided Multienzymatic Hydrogel Design for Self-Regenerative Repair in Diabetic Mandibular Defects.

Conventional bone tissue engineering materials struggle to reinstate physiological bone remodeling in a diabetic context, primarily due to the compromised repolarization of proinflammatory macrophages to anti-inflammatory macrophages. Here, leveraging single-cell RNA sequencing (scRNA-seq) technology, the pivotal role of nitric oxide (NO) and reactive oxygen species (ROS) is unveiled in impeding macrophage repolarization during physiological bone remodeling amidst diabetes. Guided by scRNA-seq analysis, we engineer a multienzymatic bone tissue engineering hydrogel scaffold (MEBTHS) composed is engineered of methylpropenylated gelatin hydrogel integrated with ruthenium nanozymes, possessing both Ru0 and Ru4+ components. This design facilitates efficient NO elimination via Ru0 while simultaneously exhibiting ROS scavenging properties through Ru4+. Consequently, MEBTHS orchestrates macrophage reprogramming by neutralizing ROS and reversing NO-mediated mitochondrial metabolism, thereby rejuvenating bone marrow-derived mesenchymal stem cells and endothelial cells within diabetic mandibular defects, producing newly formed bone with quality comparable to that of normal bone. The scRNA-seq guided multienzymatic hydrogel design fosters the restoration of self-regenerative repair, marking a significant advancement in bone tissue engineering.

Single-cell RNA-seq analysis revealed the stemness of a specific cluster of B cells in acute lymphoblastic leukemia progression.

Childhood acute lymphoblastic leukemia (ALL) is a common pediatric cancer. The heterogeneous characterization of B cells in ALL progression poses new challenges to researchers. We used single-cell sequencing to explore the critical role of B cells in regulating the ALL immune microenvironment. We collected the single cell (sc) RNA-seq data of ALL and health sample from the gene expression omnibus (GEO) database, the "Seurat" and "harmony" R package was used for quality control and scRNA-seq analysis, in which the CellMarker2.0 database was used for cell type annotation. Subsequently, the FindAllMarkers function was used to identify the differentially expressed genes (DEGs) among various cell types and the DAVID database was applied for the biological process of DEGs. Then, the "inferCNV" package was used for copy number variation, regulons and cell communication were performed by SCENIC tool and CellChat package. The role of the target gene in regulating ALL progression was assessed using RT-qPCR, Transwell and scratch healing assays. We identified nine mainly cell clusters after scRNA-seq analysis, in which the B cells had higher infiltration proportion in the ALL samples and were sub-clustered into five cell sub-groups. The B cells 1 is closely associated with cell proliferation and stemness (TNFAIP3 and KDM5B), and the significant CNV of amplification occurred on chr6 and chr21 that supported stemness of B cells1. RXRB is a key transcription factor mediated the proliferation of B cells 1, which in turn suppressed hematopoietic stem cells (HSCs) proliferation and promoted cytotoxic NK/T cells activation through diverse cell communication ways. One of the key regulators of B cells is MYC, which promotes the migration and invasive ability of cell line leukemia cell lines. This study reveals the stemness characteristics of B cells and their critical role in ALL progression, a finding that provides new potential directions for the development of targeted therapies against ALL.

Multiple Machine Learning Models, Molecular Subtyping and Singlecell Analysis Identify PANoptosis-related Core Genes and their Association with Subtypes in Crohn's Disease.

PANoptosis plays an important role in many inflammatory diseases. However, there are no reports on the association between PANoptosis and CD. This study used five machine learning algorithms - least absolute shrinkage and selection operator, support vector machine, random forest, decision tree and Gaussian mixture models - to construct CD's PANoptosis signature. Unsupervised hierarchical clustering analysis was used to identify PANoptosis-associated subgroups of CD. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted to compare the PANoptosis-associated subgroups of CD among the potential biological mechanisms. Single sample GSEA was used to assess immune microenvironmental differences among the subgroups. The potential role of PANoptosis in CD was further explored using single-cell RNA-Seq (scRNA-Seq) for PANoptosis scoring, differential analysis, pseudotime analysis, cellular communication analysis and weighted gene co-expression network analysis (WGCNA) analysis. CD's PANoptosis signature consisted of seven genes: CEACAM6, CHP2, PIK3R1, CASP10, PSMB1, PSMB8 and UBC. The PANoptosis signature in multiple cohorts had a strong ability to recognise CD. The levels of immune cell infiltration and the vigour of the immune responses significantly varied between the two subpopulations of CD associated with PANoptosis. Multiple lines of evidence from the GO, KEGG, GSEA, GSVA, scRNA-Seq and WGCNA analyses suggested that I-kappaB kinase/NF- kappaB signalling, mitogen-activated protein kinase (MAPK), leukocyte activation and leukocyte migration were mechanisms closely associated with PANoptosis in CD. This study is the first to construct a PANoptosis signature with excellent efficacy in recognising CD. PANoptosis may mediate the process of CD through inflammatory and immune mechanisms, such as NF- kappaB, MAPK and leukocyte migration.

Diethylhexyl phthalate induces immune dysregulation and is an environmental immune disruptor

Diethylhexyl phthalate (DEHP) is the most abundant phthalate compound in the environment, and has been linked with multiple human diseases. The immune system is closely associated with the occurrence and progression of various diseases. However, minimal research has addressed the impact of DEHP on the immune system. In this study, single-cell RNA sequencing was performed using spleen tissue of mice to comprehensively determine alterations of the immune system in response to DEHP. The results showed that DEHP exposure reduced the absolute number of peripheral white blood cells (WBCs), including lymphocytes, monocytes, eosinophils, basophils, and neutrophils in mice. In addition, scRNA-seq analyses showed that inflammatory signaling and the expression of heat shock proteins (HSPs) were reduced in all peripheral immune cell populations. Furthermore, we established a mice cecal ligation and puncture (CLP) model, and showed that DEHP exacerbated sepsis-induced immunosuppression and organ damage. These results suggest that DEHP is an environmental immune disruptor that undermines the immune system, exacerbating acute infections and organ damage. Our findings offer a novel perspective on the hazards of DEHP to human health.