Differences in microenvironment of lung cancer and pleural effusions by single-cell RNA sequencing.

BACKGROUND: Direct comparison of tumor microenvironment of matched lung cancer biopsies and pleural effusions (PE) from the same patients is critical in understanding tumor biology but has not been performed. This is the first study to compare the lung cancer and PE microenvironment by single-cell RNA sequencing (scRNA-seq). METHODS: Matched lung cancer biopsies and PE were obtained prospectively from ten patients. We isolated CD45+ cells and performed scRNA-seq to compare the biopsies and PE. RESULTS: PE had a higher proportion of CD4+ T cells but lower proportion of CD8+ T cells (False detection rate, FDR = 0.0003) compared to biopsies. There was a higher proportion of naïve CD4+ T cells (FDR = 0.04) and naïve CD8+ T cells (FDR = 0.0008) in PE vs. biopsies. On the other hand, there was a higher proportion of Tregs (FDR = 0.04), effector CD8+ (FDR = 0.006), and exhausted CD8+ T cells (FDR = 0.01) in biopsies. The expression of inflammatory genes in T cells was increased in biopsies vs. PE, including TNF, IFN-É£, IL-1R1, IL-1R2, IL-2, IL-12RB2, IL-18R1, and IL-18RAP (FDR = 0.009, 0.013, 0.029, 0.043, 0.009, 0.013, 0.004, and 0.003, respectively). The gene expression of exhaustion markers in T cells was also increased in tumor biopsies including PDCD1, CTLA4, LAG 3, HAVCR2, TIGIT, and CD160 (FDR = 0.008, 0.003, 0.002, 0.011, 0.006, and 0.049, respectively). CONCLUSIONS: There is a higher proportion of naïve T cells and lower proportion of exhausted T cells and Tregs in PE compared to lung cancer biopsies, which can be leveraged for prognostic and therapeutic applications.

Transcriptional and functional remodeling of lung-resident T cells and macrophages by Simian varicella virus infection.

Introduction: Varicella zoster virus (VZV) causes varicella and can reactivate as herpes zoster, and both diseases present a significant burden worldwide. However, the mechanisms by which VZV establishes latency in the sensory ganglia and disseminates to these sites remain unclear. Methods: We combined a single-cell sequencing approach and a well-established rhesus macaque experimental model using Simian varicella virus (SVV), which recapitulates the VZV infection in humans, to define the acute immune response to SVV in the lung as well as compare the transcriptome of infected and bystander lung-resident T cells and macrophages. Results and discussion: Our analysis showed a decrease in the frequency of alveolar macrophages concomitant with an increase in that of infiltrating macrophages expressing antiviral genes as well as proliferating T cells, effector CD8 T cells, and T cells expressing granzyme A (GZMA) shortly after infection. Moreover, infected T cells harbored higher numbers of viral transcripts compared to infected macrophages. Furthermore, genes associated with cellular metabolism (glycolysis and oxidative phosphorylation) showed differential expression in infected cells, suggesting adaptations to support viral replication. Overall, these data suggest that SVV infection remodels the transcriptome of bystander and infected lung-resident T cells and macrophages.

Monitoring melanoma patients on treatment reveals a distinct macrophage population driving targeted therapy resistance.

Resistance to targeted therapy remains a major clinical challenge in melanoma. To uncover resistance mechanisms, we perform single-cell RNA sequencing on fine-needle aspirates from resistant and responding tumors of patients undergoing BRAFi/MEKi treatment. Among the genes most prominently expressed in resistant tumors is POSTN, predicted to signal to a macrophage population associated with targeted therapy resistance (TTR). Accordingly, tumors from patients with fast disease progression after therapy exhibit high POSTN expression levels and high numbers of TTR macrophages. POSTN polarizes human macrophages toward a TTR phenotype and promotes resistance to targeted therapy in a melanoma mouse model, which is associated with a phenotype change in intratumoral macrophages. Finally, polarized TTR macrophages directly protect human melanoma cells from MEKi-induced killing via CD44 receptor expression on melanoma cells. Thus, interfering with the protective activity of TTR macrophages may offer a strategy to overcome resistance to targeted therapy in melanoma.

Identification of Novel Regulators of Leaf Senescence Using a Deep Learning Model.

Deep learning has emerged as a powerful tool for investigating intricate biological processes in plants by harnessing the potential of large-scale data. Gene regulation is a complex process that transcription factors (TFs), cooperating with their target genes, participate in through various aspects of biological processes. Despite its significance, the study of gene regulation has primarily focused on a limited number of notable instances, leaving numerous aspects and interactions yet to be explored comprehensively. Here, we developed DEGRN (Deep learning on Expression for Gene Regulatory Network), an innovative deep learning model designed to decipher gene interactions by leveraging high-dimensional expression data obtained from bulk RNA-Seq and scRNA-Seq data in the model plant Arabidopsis. DEGRN exhibited a compared level of predictive power when applied to various datasets. Through the utilization of DEGRN, we successfully identified an extensive set of 3,053,363 high-quality interactions, encompassing 1430 TFs and 13,739 non-TF genes. Notably, DEGRN's predictive capabilities allowed us to uncover novel regulators involved in a range of complex biological processes, including development, metabolism, and stress responses. Using leaf senescence as an example, we revealed a complex network underpinning this process composed of diverse TF families, including bHLH, ERF, and MYB. We also identified a novel TF, named MAF5, whose expression showed a strong linear regression relation during the progression of senescence. The mutant maf5 showed early leaf decay compared to the wild type, indicating a potential role in the regulation of leaf senescence. This hypothesis was further supported by the expression patterns observed across four stages of leaf development, as well as transcriptomics analysis. Overall, the comprehensive coverage provided by DEGRN expands our understanding of gene regulatory networks and paves the way for further investigations into their functional implications.

Spatiotemporal Evolution of Developing Palate in Mice.

The intricate formation of the palate involves a series of complex events, yet its mechanistic basis remains uncertain. To explore major cell populations in the palate and their roles during development, we constructed a spatiotemporal transcription landscape of palatal cells. Palate samples from C57BL/6 J mice at embryonic days 12.5 (E12.5), 14.5 (E14.5), and 16.5 (E16.5) underwent single-cell RNA sequencing (scRNA-seq) to identify distinct cell subsets. In addition, spatial enhanced resolution omics-sequencing (stereo-seq) was used to characterize the spatial distribution of these subsets. Integrating scRNA-seq and stereo-seq with CellTrek annotated mesenchymal and epithelial cellular components of the palate during development. Furthermore, cellular communication networks between these cell subpopulations were analyzed to discover intercellular signaling during palate development. From the analysis of the middle palate, both mesenchymal and epithelial populations were spatially segregated into 3 domains. The middle palate mesenchymal subpopulations were associated with tooth formation, ossification, and tissue remodeling, with initial state cell populations located proximal to the dental lamina. The nasal epithelium of the palatal shelf exhibited richer humoral immune responses than the oral side. Specific enrichment of Tgfß3 and Pthlh signals in the midline epithelial seam at E14.5 suggested a role in epithelial-mesenchymal transition. In summary, this study provides high-resolution transcriptomic information, contributing to a deeper mechanistic understanding of palate biology and pathophysiology.

Letter to editor: single-cell transcriptome analysis upon ECM-remodeling meningioma cells of published cases.

This letter offers a nuanced evaluation of the recent study on single-cell transcriptome analysis of ECM-remodeling meningioma cells. While acknowledging the positive aspects, such as enhanced understanding of tumor heterogeneity and identification of potential therapeutic targets, it also highlights potential limitations, including challenges in data interpretation and validation.The focus on ECM-remodeling may inadvertently overshadow other critical aspects of tumor biology, necessitating a more holistic approach. The abstract concludes by emphasizing the importance of considering the broader context of tumor heterogeneity and microenvironmental influences in future research endeavors to improve clinical outcomes for patients with meningioma and other malignancies.

Integrative Analysis of Machine Learning and Molecule Docking Simulations for Ischemic Stroke Diagnosis and Therapy.

Due to the narrow therapeutic window and high mortality of ischemic stroke, it is of great significance to investigate its diagnosis and therapy. We employed weighted gene coexpression network analysis (WGCNA) to ascertain gene modules related to stroke and used the maSigPro R package to seek the time-dependent genes in the progression of stroke. Three machine learning algorithms were further employed to identify the feature genes of stroke. A nomogram model was built and applied to evaluate the stroke patients. We analyzed single-cell RNA sequencing (scRNA-seq) data to discern microglia subclusters in ischemic stroke. The RNA velocity, pseudo time, and gene set enrichment analysis (GSEA) were performed to investigate the relationship of microglia subclusters. Connectivity map (CMap) analysis and molecule docking were used to screen a therapeutic agent for stroke. A nomogram model based on the feature genes showed a clinical net benefit and enabled an accurate evaluation of stroke patients. The RNA velocity and pseudo time analysis showed that microglia subcluster 0 would develop toward subcluster 2 within 24 h from stroke onset. The GSEA showed that the function of microglia subcluster 0 was opposite to that of subcluster 2. AZ_628, which screened from CMap analysis, was found to have lower binding energy with Mmp12, Lgals3, Fam20c, Capg, Pkm2, Sdc4, and Itga5 in microglia subcluster 2 and maybe a therapeutic agent for the poor development of microglia subcluster 2 after stroke. Our study presents a nomogram model for stroke diagnosis and provides a potential molecule agent for stroke therapy.

Skeletal muscle regeneration failure in ischemic-damaged limbs is associated with pro-inflammatory macrophages and premature differentiation of satellite cells.

BACKGROUND: Chronic limb-threatening ischemia (CLTI), a severe manifestation of peripheral arterial disease (PAD), is associated with a 1-year limb amputation rate of approximately 15-20% and substantial mortality. A key feature of CLTI is the compromised regenerative ability of skeletal muscle; however, the mechanisms responsible for this impairment are not yet fully understood. In this study, we aim to delineate pathological changes at both the cellular and transcriptomic levels, as well as in cell-cell signaling pathways, associated with compromised muscle regeneration in limb ischemia in both human tissue samples and murine models of CLTI. METHODS: We performed single-cell transcriptome analysis of ischemic and non-ischemic muscle from the same CLTI patients and from a murine model of CLTI. In both datasets, we analyzed gene expression changes in macrophage and muscle satellite cell (MuSC) populations as well as differential cell-cell signaling interactions and differentiation trajectories. RESULTS: Single-cell transcriptomic profiling and immunofluorescence analysis of CLTI patient skeletal muscle demonstrated that ischemic-damaged tissue displays a pro-inflammatory macrophage signature. Comparable results were observed in a murine CLTI model. Moreover, integrated analyses of both human and murine datasets revealed premature differentiation of MuSCs to be a key feature of failed muscle regeneration in the ischemic limb. Furthermore, in silico inferences of intercellular communication and in vitro assays highlight the importance of macrophage-MuSC signaling in ischemia induced muscle injuries. CONCLUSIONS: Collectively, our research provides the first single-cell transcriptome atlases of skeletal muscle from CLTI patients and a murine CLTI model, emphasizing the crucial role of macrophages and inflammation in regulating muscle regeneration in CLTI through interactions with MuSCs.

Stratification of enterochromaffin cells by single-cell expression analysis.

Dynamic interactions between gut mucosal cells and the external environment are essential to maintain gut homeostasis. Enterochromaffin (EC) cells transduce both chemical and mechanical signals and produce 5-hydroxytryptamine (5-HT) to mediate disparate physiological responses. However, the molecular and cellular basis for functional diversity of ECs remains to be adequately defined. Here, we integrated single-cell transcriptomics with spatial image analysis to identify fourteen EC clusters that are topographically organized along the gut. Subtypes predicted to be sensitive to the chemical environment and mechanical forces were identified that express distinct transcription factors and hormones. A Piezo2+ population in the distal colon was endowed with a distinctive neuronal signature. Using a combination of genetic, chemogenetic and pharmacological approaches, we demonstrated Piezo2+ ECs are required for normal colon motility. Our study constructs a molecular map for ECs and offers a framework for deconvoluting EC cells with pleiotropic functions.

Single-Cell RNA-Seq Reveals LRRC75A-Expressing Cell Population Involved in VEGF Secretion of Multipotent Mesenchymal Stromal/Stem Cells Under Ischemia.

Human multipotent mesenchymal stromal/stem cells (MSCs) have been utilized in cell therapy for various diseases and their clinical applications are expected to increase in the future. However, the variation in MSC-based product quality due to the MSC heterogeneity has resulted in significant constraints in the clinical utility of MSCs. Therefore, we hypothesized that it might be important to identify and ensure/enrich suitable cell subpopulations for therapies using MSC-based products. In this study, we aimed to identify functional cell subpopulations to predict the efficacy of angiogenic therapy using bone marrow-derived MSCs (BM-MSCs). To assess its angiogenic potency, we observed various levels of vascular endothelial growth factor (VEGF) secretion among 11 donor-derived BM-MSC lines under in vitro ischemic culture conditions. Next, by clarifying the heterogeneity of BM-MSCs using single-cell RNA-sequencing analysis, we identified a functional cell subpopulation that contributed to the overall VEGF production in BM-MSC lines under ischemic conditions. We also found that leucine-rich repeat-containing 75A (LRRC75A) was more highly expressed in this cell subpopulation than in the others. Importantly, knockdown of LRRC75A using small interfering RNA resulted in significant inhibition of VEGF secretion in ischemic BM-MSCs, indicating that LRRC75A regulates VEGF secretion under ischemic conditions. Therefore, LRRC75A may be a useful biomarker to identify cell subpopulations that contribute to the angiogenic effects of BM-MSCs. Our work provides evidence that a strategy based on single-cell transcriptome profiles is effective for identifying functional cell subpopulations in heterogeneous MSC-based products.

Single-cell RNA sequencing reveals the vascular smooth muscle cell phenotypic landscape in aortic aneurysm.

BACKGROUND AND OBJECTIVES: Phenotypic switching in vascular smooth muscle cells (VSMCs) has been linked to aortic aneurysm, but the phenotypic landscape in aortic aneurysm is poorly understood. The present study aimed to analyse the phenotypic landscape, phenotypic differentiation trajectory, and potential functions of various VSMCs phenotypes in aortic aneurysm. METHODS: Single-cell sequencing data of 12 aortic aneurysm samples and 5 normal aorta samples (obtained from GSE166676 and GSE155468) were integrated by the R package Harmony. VSMCs were identified according to the expression levels of ACTA2 and MYH11. VSMCs clustering was determined by the R package 'Seurat'. Cell annotation was determined by the R package 'singleR' and background knowledge of VSMCs phenotypic switching. The secretion of collagen, proteinases, and chemokines by each VSMCs phenotype was assessed. Cell‒cell junctions and cell-matrix junctions were also scored by examining the expression of adhesion genes. Trajectory analysis was performed by the R package 'Monocle2'. qPCR was used to quantify VSMCs markers. RNA fluorescence in situ hybridization (RNA FISH) was performed to determine the spatial localization of vital VSMCs phenotypes in aortic aneurysms. RESULTS: A total of 7150 VSMCs were categorize into 6 phenotypes: contractile VSMCs, fibroblast-like VSMCs, T-cell-like VSMCs, adipocyte-like VSMCs, macrophage-like VSMCs, and mesenchymal-like VSMCs. The proportions of T-cell-like VSMCs, adipocyte-like VSMCs, macrophage-like VSMCs, and mesenchymal-like VSMCs were significantly increased in aortic aneurysm. Fibroblast-like VSMCs secreted abundant amounts of collagens. T-cell-like VSMCs and macrophage-like VSMCs were characterized by high chemokine levels and proinflammatory effects. Adipocyte-like VSMCs and mesenchymal-like VSMCs were associated with high proteinase levels. RNA FISH validated the presence of T-cell-like VSMCs and macrophage-like VSMCs in the tunica media and the presence of mesenchymal-like VSMCs in the tunica media and tunica adventitia. CONCLUSION: A variety of VSMCs phenotypes are involved in the formation of aortic aneurysm. T-cell-like VSMCs, macrophage-like VSMCs, and mesenchymal-like VSMCs play pivotal roles in this process. Video Abstract.

Single-Cell RNA Sequencing Analysis of Gene Regulatory Network Changes in the Development of Lung Adenocarcinoma.

Lung cancer is a highly heterogeneous disease. Cancer cells and other cells within the tumor microenvironment interact to determine disease progression, as well as response to or escape from treatment. Understanding the regulatory relationship between cancer cells and their tumor microenvironment in lung adenocarcinoma is of great significance for exploring the heterogeneity of the tumor microenvironment and its role in the genesis and development of lung adenocarcinoma. This work uses public single-cell transcriptome data (distant normal, nLung; early LUAD, tLung; advanced LUAD, tL/B), to draft a cell map of lung adenocarcinoma from onset to progression, and provide a cell-cell communication view of lung adenocarcinoma in the different disease stages. Based on the analysis of cell populations, it was found that the proportion of macrophages was significantly reduced in the development of lung adenocarcinoma, and patients with lower proportions of macrophages exhibited poor prognosis. We therefore constructed a process to screen an intercellular gene regulatory network that reduces any error generated by single cell communication analysis and increases the credibility of selected cell communication signals. Based on the key regulatory signals in the macrophage-tumor cell regulatory network, we performed a pseudotime analysis of the macrophages and found that signal molecules (TIMP1, VEGFA, SPP1) are highly expressed in immunosuppression-associated macrophages. These molecules were also validated using an independent dataset and were significantly associated with poor prognosis. Our study provides an effective method for screening the key regulatory signals in the tumor microenvironment and the selected signal molecules may serve as a reference to guide the development of diagnostic biomarkers for risk stratification and therapeutic targets for lung adenocarcinoma.

Identification and validation of a novel signature based on cell-cell communication in head and neck squamous cell carcinoma by integrated analysis of single-cell transcriptome and bulk RNA-sequencing.

Background: The heterogeneous crosstalk between tumor cells and other cells in their microenvironment means a notable difference in clinical outcomes of head and neck squamous cell carcinoma (HNSCC). CD8+ T cells and macrophages are effector factors of the immune system, which have direct killing and phagocytosis effects on tumor cells. How the evolution of their role in the tumor microenvironment influences patients clinically remains a mystery. This study aims to investigate the complex communication networks in the HNSCC tumor immune microenvironment, elucidate the interactions between immune cells and tumors, and establish prognostic risk model. Methods: 20 HNSCC samples single-cell rna sequencing (scRNA-seq) data and bulk rna-seq data were derived from public databases. The "cellchat" R package was used to identify cell-to-cell communication networks and prognostic related genes, and then cell-cell communication (ccc) molecular subtypes were constructed by unsupervised clustering. Kaplan-Meier(K-M) survival analysis, clinical characteristics analysis, immune microenvironment analysis, immune cell infiltration analysis and CD8+T cell differentiation correlation analysis were performed. Finally, the ccc gene signature including APP, ALCAM, IL6, IL10 and CD6 was constructed based on univariate Cox analysis and multivariate Cox regression. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) analysis were used to evaluate the model in the train group and the validation group, respectively. Results: With CD8+T cells from naive to exhaustion state, significantly decreased expression of protective factor (CD6 gene) is associated with poorer prognosis in patients with HNSCC. The role of macrophages in the tumor microenvironment has been identified as tumor-associated macrophage (TAM), which can promote tumor proliferation and help tumor cells provide more nutrients and channels to facilitate tumor cell invasion and metastasis. In addition, based on the strength of all ccc in the tumor microenvironment, we identified five prognostic ccc gene signatures (cccgs), which were identified as independent prognostic factors by univariate and multivariate analysis. The predictive power of cccgs was well demonstrated in different clinical groups in train and test cohorts. Conclusion: Our study highlights the propensity for crosstalk between tumors and other cells and developed a novel signature on the basis of a strong association gene for cell communication that has a powerful ability to predict prognosis and immunotherapy response in patients with HNSCC. This may provide some guidance for developing diagnostic biomarkers for risk stratification and therapeutic targets for new therapeutic strategies.

Escherichia coli-derived outer-membrane vesicles induce immune activation and progression of cirrhosis in mice and humans.

BACKGROUND AND AIMS: Decompensated cirrhosis with fibrosis progression causes portal hypertension followed by an oedematous intestinal tract. These conditions weaken the barrier function against bacteria in the intestinal tract, a condition called leaky gut, resulting in invasion by bacteria and bacterial components. Here, we investigated the role of outer-membrane vesicles (OMVs) of Escherichia coli, which is the representative pathogenic gut-derived bacteria in patients with cirrhosis in the pathogenesis of cirrhosis. METHODS: We investigated the involvement of OMVs in humans using human serum and ascites samples and also investigated the involvement of OMVs from E. coli in mice using mouse liver-derived cells and a mouse cirrhosis model. RESULTS: In vitro, OMVs induced inflammatory responses to macrophages and neutrophils, including the upregulation of C-type lectin domain family 4 member E (Clec4e), and induced the suppression of albumin production in hepatocytes but had a relatively little direct effect on hepatic stellate cells. In a mouse cirrhosis model, administration of OMVs led to increased liver inflammation, especially affecting the activation of macrophages, worsening fibrosis and decreasing albumin production. Albumin administration weakened these inflammatory changes. In addition, multiple antibodies against bacterial components were increased with a progressing Child-Pugh grade, and OMVs were detected in ascites of patients with decompensated cirrhosis. CONCLUSIONS: In conclusion, OMVs induce inflammation, fibrosis and suppression of albumin production, affecting the pathogenesis of cirrhosis. We believe that our study paves the way for the future prevention and treatment of cirrhosis.

Comparative single-cell transcriptomic analysis reveals differences in signaling pathways in gonadal primordial germ cells between chicken (Gallus gallus) and zebra finch (Taeniopygia guttata).

Primordial germ cells (PGCs) have been used in avian genetic resource conservation and transgenic animal production. Despite their potential applications to numerous avian taxa facing extinction due to habitat loss and degradation, research has largely focused on poultry, such as chickens, in part owing to the difficulty in obtaining intact PGCs from other species. Recently, phenotypic differences between PGCs of chicken and zebra finch, a wild bird with vocal learning, in early embryonic development have been reported. In this study, we used advanced single-cell RNA sequencing (scRNA-seq) technology to evaluate zebra finch and chicken PGCs and surrounding cells, and to identify species-specific characteristics. We constructed single-cell transcriptome landscapes of chicken gonadal PGCs for a comparison with previously reported scRNA-seq data for zebra finch. We identified interspecific differences in several signaling pathways in gonadal PGCs and somatic cells. In particular, NODAL and insulin signaling pathway activity levels were higher in zebra finch than in chickens, whereas activity levels of the downstream FGF signaling pathway, involved in the proliferation of chicken PGCs, were higher in chickens. This study is the first cross-species single-cell transcriptomic analysis targeting birds, revealing differences in germ cell development between phylogenetically distant Galliformes and Passeriformes. Our results provide a basis for understanding the reproductive physiology of avian germ cells and for utilizing PGCs in the restoration of endangered birds and the production of transgenic birds.

Identification of Four Biomarkers of Human Skin Aging by Comprehensive Single Cell Transcriptome, Transcriptome, and Proteomics.

Background: Aging is characterized by the gradual loss of physiological integrity, resulting in impaired function and easier death. This deterioration is a major risk factor for major human pathological diseases, including cancer, diabetes, cardiovascular disease and neurodegenerative diseases. It is very important to find biomarkers that can prevent aging. Methods: Q-Exactive-MS was used for proteomic detection of young and senescence fibroblast. The key senescence-related molecules (SRMs) were identified by integrating transcriptome and proteomics from aging tissue/cells, and the correlation between these differentially expressed genes and well-known aging-related pathways. Next, we validated the expression of these molecules using qPCR, and explored the correlation between them and immune infiltrating cells. Finally, the enriched pathways of the genes significantly related to the four differential genes were identified using the single cell transcriptome. Results: we first combined proteomics and transcriptome to identified four SRMs. Data sets including GSE63577, GSE64553, GSE18876, GSE85358, and qPCR confirmed that ETF1, PLBD2, ASAH1, and MOXD1 were identified as SRMs. Then the correlation between SRMs and aging-related pathways was excavated and verified. Next, we verified the expression of SRMs at the tissue level and qPCR, and explored the correlation between them and immune infiltrating cells. Finally, at the single-cell transcriptome level, we verified their expression and explored the possible pathway by which they lead to aging. Briefly, ETF1 may affect the changes of inflammatory factors such as IL-17, IL-6, and NFKB1 by indirectly regulating the enrichment and differentiation of immune cells. MOXD1 may regulate senescence by affecting the WNT pathway and changing the cell cycle. ASAH1 may affect development and regulate the phenotype of aging by affecting cell cycle-related genes. Conclusion: In general, based on the analysis of proteomics and transcriptome, we identified four SRMs that may affect aging and speculated their possible mechanisms, which provides a new target for preventing aging, especially skin aging.

Colorectal Cancer Cell Differentiation Trajectory Predicts Patient Immunotherapy Response and Prognosis.

OBJECTIVES: This study aimed to investigate the differentiation state and clinical significance of colorectal cancer cells, as well as to predict the immune response and prognosis of patients based on differentiation-related genes of colorectal cancer. INTRODUCTION: Colorectal cancer cells exhibit different differentiation states under the influence of the tumor microenvironment, which determines the cell fates. METHODS: We combined single-cell sequencing (scRNA-seq) data from The Cancer Genome Atlas source with extensive transcriptome data from the Gene Expression Omnibus database. We obtained colorectal cancer differentiation-related genes using cell trajectory analysis and developed a colorectal cancer differentiation-related gene based molecular typing and prognostic model to predict the immune response and prognosis of patients with colorectal cancer. RESULTS: We identified 5 distinct cell differentiation subsets and 620 colorectal cancer differentiation-related genes. Colorectal cancer differentiation-related genes were significantly associated with metabolism, angiogenesis, and immunity. We separated patients into 3 subtypes based on colorectal cancer differentiation-related gene expression in the tumor and found differences among the different subtypes in immune infiltration status, immune checkpoint gene expression, clinicopathological features, and overall survival. Immunotherapeutic interventions involving a highly expressed immune checkpoint blockade may be selectively effective in the corresponding cancer subtypes. We built a risk score prediction model (5-year AUC: .729) consisting of the 4 most important predictors of survival (TIMP1, MMP1, LGALS4, and ITLN1). Finally, we generated and validated a nomogram consisting of the risk score and clinicopathological variables. CONCLUSION: This study highlights the significance of genes involved in cell differentiation for clinical prognosis and immunotherapy in patients and provides prospective therapeutic targets for colorectal cancer.

A Novel Artificial Neural Network Prognostic Model Based on a Cancer-Associated Fibroblast Activation Score System in Hepatocellular Carcinoma.

Introduction: Hepatocellular carcinoma (HCC) ranks fourth as the most common cause of cancer-related death. It is vital to identify the mechanism of progression and predict the prognosis for patients with HCC. Previous studies have found that cancer-associated fibroblasts (CAFs) promote tumor proliferation and immune exclusion. However, the information about CAF-related genes is still elusive. Methods: The data were obtained from The Cancer Genome Atlas, International Cancer Genome Consortium, and Gene Expression Omnibus databases. On the basis of single-cell transcriptome and ligand-receptor interaction analysis, CAF-related genes were selected. By performing Cox regression and random forest, we filtered 12 CAF-related prognostic genes for the construction of the ANN model based on the CAF activation score (CAS). Then, functional, immune, mutational, and clinical analyses were performed. Results: We constructed a novel ANN prognostic model based on 12 CAF-related prognostic genes. Cancer-related pathways were enriched, and higher activated cell crosstalk was identified in high-CAS samples. High immune activity was observed in high-CAS samples. We detected three differentially mutated genes (NBEA, RYR2, and FRAS1) between high- and low-CAS samples. In clinical analyses, we constructed a nomogram to predict the prognosis of patients with HCC. 5-Fluorouracil had higher sensitivity in high-CAS samples than in low-CAS samples. Moreover, some small-molecule drugs and the immune response were predicted. Conclusion: We constructed a novel ANN model based on CAF-related genes. We revealed information about the ANN model through functional, mutational, immune, and clinical analyses.

Single-Cell Transcriptomic Analysis Reveals the Crosstalk Propensity Between the Tumor Intermediate State and the CD8+ T Exhausted State to be Associated with Clinical Benefits in Melanoma.

Heterogeneous crosstalk between tumor cells and CD8+ T cells leads to substantial variation in clinical benefits from immunotherapy in melanoma. Due to spatial distribution and functional state heterogeneity, it is still unknown whether there is a crosstalk propensity between tumor cells and CD8+ T cells in melanoma, and how this crosstalk propensity affects the clinical outcome of patients. Using public single-cell transcriptome data, extensive heterogeneous functional states and ligand-receptor interactions of tumor cells and CD8+ T cells were revealed in melanoma. Furthermore, based on the association between cell-cell communication intensity and cell state activity in a single cell, we identified a crosstalk propensity between the tumor intermediate state and the CD8+ T exhausted state. This crosstalk propensity was further verified by pseudo-spatial proximity, spatial co-location, and the intra/intercellular signal transduction network. At the sample level, the tumor intermediate state and the CD8+ T exhausted state synergistically indicated better prognosis and both reduced in immunotherapy-resistant samples. The risk groups defined based on these two cell states could comprehensively reflect tumor genomic mutations and anti-tumor immunity information. The low-risk group had a higher BRAF mutation fraction as well as stronger antitumor immune response. Our findings highlighted the crosstalk propensity between the tumor intermediate state and the CD8+ T exhausted state, which may serve as a reference to guide the development of diagnostic biomarkers for risk stratification and therapeutic targets for new therapeutic strategies.

HMGB1/Foxp1 regulates hypoxia-induced inflammatory response in macrophages.

Forkhead box protein P1 (Foxp1) is a kind of tumor suppressor gene, and the role of Foxp1 in the macrophages of myocardial infarction (MI) has not been studied yet. Here, we verified the role of the transcription factor high mobility group box 1 (HMGB1) and its target gene Foxp1 in the inflammatory response. In this study, the key genes HMGB1 and Foxp1 in the macrophages of mouse MI model were screened out through single-cell transcriptome analysis of GSE136088 (GEO database). In vitro experiment indicated that hypoxia induced the inflammatory response in RAW264.7 macrophages, promoted the secretion of inflammatory factors (tumor necrosis factor α [TNF-α], interleukin 6 [IL-6], and IL-1ß) and the activation of NLRP3 inflammasome (NLRP3, ASC, and pro-caspase-1). Meanwhile, HMGB1 increased while Foxp1 decreased in hypoxia-treated RAW264.7 macrophages. HMGB1 bound to the upstream promoter region of Foxp1 as demonstrated by the dual-luciferase reporter assay, chromatin immunoprecipitation (ChIP)-quantitative polymerase chain reaction (qPCR) and agarose gel electrophoresis. As a transcription factor, HMGB1 regulated Foxp1 expression. The secretion of inflammatory factors and the expression of NLRP3 inflammasome protein were changed when the expression of HMGB1 and Foxp1 was regulated in the hypoxia-treated RAW264.7 macrophages. This study verified that HMGB1 could aggravate the hypoxia-treated inflammatory response of macrophages through downregulating Foxp1, which not only provides evidence to support the role of HMGB1/Foxp1 in macrophages but also offers another angle for the treatment of MI.