Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver condition globally. Simultaneously, the annual disease burden, particularly liver cirrhosis caused by NAFLD, has increased significantly. Neutrophil Extracellular Traps (NETs) play a crucial role in the progression of this disease and are key to the pathogenesis of NAFLD. However, research into the specific roles of NETs-related genes in NAFLD is still a field requiring thorough investigation. Utilizing techniques like AddModuleScore, ssGSEA, and WGCNA, our team conducted gene screening to identify the genes linked to NETs in both single-cell and bulk transcriptomics. Using algorithms including Random Forest, Support Vector Machine, Least Absolute Shrinkage, and Selection Operator, we identified ZFP36L2 and PHLDA1 as key hub genes. The pivotal role of these genes in NAFLD diagnosis was confirmed using the training dataset GSE164760. This study identified 116 genes linked to NETs across single-cell and bulk transcriptomic analyses. These genes demonstrated enrichment in immune and metabolic pathways. Additionally, two NETs-related hub genes, PHLDA1 and ZFP36L2, were selected through machine learning for integration into a prognostic model. These hub genes play roles in inflammatory and metabolic processes. scRNA-seq results showed variations in cellular communication among cells with different expression patterns of these key genes. In conclusion, this study explored the molecular characteristics of NETs-associated genes in NAFLD. It identified two potential biomarkers and analyzed their roles in the hepatic microenvironment. These discoveries could aid in NAFLD diagnosis and management, with the ultimate goal of enhancing patient outcomes.

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Background: Studies on immunogenic death (ICD) in lung adenocarcinoma are limited, and this study aimed to determine the function of ICD in LUAD and to construct a novel ICD-based prognostic model to improve immune efficacy in lung adenocarcinoma patients. Methods: The data for lung adenocarcinoma were obtained from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). The single-cell data were obtained from Bischoff P et al. To identify subpopulations, we performed descending clustering using TSNE. We collected sets of genes related to immunogenic death from the literature and identified ICD-related genes through gene set analysis of variance (GSVA) and weighted gene correlation network analysis (WGCNA). Lung adenocarcinoma patients were classified into two types using consistency clustering. The difference between the two types was analyzed to obtain differential genes. An immunogenic death model (ICDRS) was established using LASSO-Cox analysis and compared with lung adenocarcinoma models of other individuals. External validation was performed in the GSE31210 and GSE50081 cohorts. The efficacy of immunotherapy was assessed using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and immune microenvironment between different risk groups were investigated. Subsequently, ROC diagnostic curves and KM survival curves were used to screen ICDRS key regulatory genes. Finally, RT-qPCR was used to verify the differential expression of these genes. Results: Eight ICD genes were found to be highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. Additionally, ICDRS demonstrated better predictive ability compared to 11 previously published models. Furthermore, significant differences in biological function and immune cell infiltration were observed in the tumor microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both groups. These findings suggest that the model has good predictive efficacy. Conclusions: The ICD model demonstrated good predictive performance, revealing the tumor microenvironment and providing a new method for evaluating the efficacy of pre-immunization. This offers a new strategy for future treatment of lung adenocarcinoma.

We focus on characterizing cell lines from young and aged-healthy and -AML (acute myeloid leukemia) cell lines, and our goal is to identify the key markers associated with the progression of AML. To characterize the age-related phenotypes in AML cell lines, we consider eigenCell analysis that effectively encapsulates the primary expression level patterns across the cell lines. However, earlier investigations utilizing eigenGenes and eigenCells analysis were based on linear combination of all features, leading to the disturbance from noise features. Moreover, the analysis based on a fully dense loading matrix makes it challenging to interpret the results of eigenCells analysis. In order to address these challenges, we develop a novel computational approach termed network-constrained eigenCells profile estimation, which employs a sparse learning strategy. The proposed method estimates eigenCell based on not only the lasso but also network constrained penalization. The use of the network-constrained penalization enables us to simultaneously select neighborhood genes. Furthermore, the hub genes and their regulator/target genes are easily selected as crucial markers for eigenCells estimation. That is, our method can incorporate insights from network biology into the process of sparse loading estimation. Through our methodology, we estimate sparse eigenCells profiles, where only critical markers exhibit expression levels. This allows us to identify the key markers associated with a specific phenotype. Monte Carlo simulations demonstrate the efficacy of our method in reconstructing the sparse structure of eigenCells profiles. We employed our approach to unveil the regulatory system of immunogenes in both young/aged-healthy and -AML cell lines. The markers we have identified for the age-related phenotype in both healthy and AML cell lines have garnered strong support from previous studies. Specifically, our findings, in conjunction with the existing literature, indicate that the activities within this subnetwork of CD79A could be pivotal in elucidating the mechanism driving AML progression, particularly noting the significant role played by the diminished activities in the CD79A subnetwork. We expect that the proposed method will be a useful tool for characterizing disease-related subsets of cell lines, encompassing phenotypes and clones.

OBJECTIVE: Olfactory receptor 2 (Olfr2) has been identified in a minimum of 30% of vascular macrophages, and its depletion was shown to reduce atherosclerosis progression. Mononuclear phagocytes, including monocytes and macrophages within the vessel wall, are major players in atherosclerosis. Single-cell RNA sequencing studies revealed that atherosclerotic artery walls encompass several monocytes and vascular macrophages, defining at least nine distinct subsets potentially serving diverse functions in disease progression. This study investigates the functional phenotype and ontogeny of Olfr2-expressing vascular macrophages in atherosclerosis.
RESULTS: Olfr2+ macrophages rapidly increase in Apoe-/- mice\'s aorta when fed a Western diet (WD). Mass cytometry showed that Olfr2+ cells are clustered within the CD64 high population and enriched for CD11c and Ccr2 markers. Olfr2+ macrophages express many pro-inflammatory cytokines, including Il1b, Il6, Il12, and Il23, and chemokines, including Ccl5, Cx3cl1, Cxcl9, and Ccl22. By extracting differentially expressed genes from bulk RNA sequencing (RNA-seq) of Olfr2+ vs. Olfr2- macrophages, we defined a signature that significantly mapped to single-cell data of plaque myeloid cells, including monocytes, subendothelial MacAir, and Trem2Gpnmb foamy macrophages. By adoptive transfer experiments, we identified that Olfr2 competent monocytes from CD45.1Apoe-/-Olfr2+/+ mice transferred into CD45.2Apoe-/-Olfr2-/- recipient mice fed WD for 12 weeks, accumulate in the atherosclerotic aorta wall already at 72 h, and differentiate in macrophages. Olfr2+ macrophages showed significantly increased BrdU incorporation compared to Olfr2- macrophages. Flow cytometry confirmed that at least 50% of aortic Olfr2+ macrophages are positive for BODIPY staining and have increased expression of both tumour necrosis factor and interleukin 6 compared to Olfr2- macrophages. Gene set enrichment analysis of the Olfr2+ macrophage signature revealed a similar enrichment pattern in human atherosclerotic plaques, particularly within foamy/TREM2hi-Mφ and monocytes.
CONCLUSIONS: In summary, we conclude that Olfr2+ macrophages in the aorta originate from monocytes and can accumulate at the early stages of disease progression. These cells can undergo differentiation into MacAir and Trem2Gpnmb foamy macrophages, exhibiting proliferative and pro-inflammatory potentials. This dynamic behaviour positions them as key influencers in shaping the myeloid landscape within the atherosclerotic plaque.

Bone marrow-derived mesenchymal stem cells (BMSCs) exhibit multi-lineage differentiation potential and robust proliferative capacity. The late stage of differentiation signifies the functional maturation and characterization of specific cell lineages, which is crucial for studying lineage-specific differentiation mechanisms. However, the molecular processes governing late-stage BMSC differentiation remain poorly understood. This study aimed to elucidate the key biological processes involved in late-stage BMSC differentiation. Publicly available transcriptomic data from human BMSCs were analyzed after approximately 14 days of osteogenic, adipogenic, and chondrogenic differentiation. Thirty-one differentially expressed genes (DEGs) associated with differentiation were identified. Pathway enrichment analysis indicated that the DEGs were involved in extracellular matrix (ECM)-receptor interactions, focal adhesion, and glycolipid biosynthesis, a ganglion series process. Subsequently, the target genes were validated using publicly available single-cell RNA-seq data from mouse BMSCs. Lamc1 exhibited predominant distribution in adipocytes and osteoblasts, primarily during the G2/M phase. Tln2 and Hexb were expressed in chondroblasts, osteoblasts, and adipocytes, while St3gal5 was abundantly distributed in stem cells. Cell communication analysis identified two receptors that interact with LAMCI. q-PCR results confirmed the upregulation of Lamc1, Tln2, Hexb, and St3gal5 during osteogenic differentiation and their downregulation during adipogenic differentiation. Knockdown of Lamc1 inhibited adipogenic and osteogenic differentiation. In conclusion, this study identified four genes, Lamc1, Tln2, Hexb, and St3gal5, that may play important roles in the late-stage differentiation of BMSCs. It elucidated their interactions and the pathways they influence, providing a foundation for further research on BMSC differentiation.

Disorders associated with the immune system burden multiple organs, although the shared biology exists across the diseases. Preceding family-based studies reveal that immune diseases are heritable to varying degrees, providing the basis for immunogenomics. The recent cost reduction in genetic analysis intensively promotes biobank-scale studies and the development of frameworks for statistical genetics. The accumulating multi-layer omics data, including genome-wide association studies (GWAS) and RNA-sequencing at single-cell resolution, enable us to dissect the genetic backgrounds of immune-related disorders. Although autoimmune and allergic diseases are generally categorized into different disease categories, epidemiological studies reveal the high incidence of autoimmune and allergic disease complications, suggesting the shared genetics and biology between the disease categories. Biobank resources and consortia cover multiple immune-related disorders to accumulate phenome-wide associations of genetic variants and enhance researchers to analyze the shared and heterogeneous genetic backgrounds. The emerging post-GWAS and integrative multi-omics analyses provide genetic and biological insights into the multicategorical disease associations.

OBJECTIVE: Ovarian metastasis of gastric cancer (GC), commonly referred to as Krukenberg tumors, leads to a poor prognosis. However, the cause of metastasis remains unknown. Here, we present an integrated single-cell RNA sequencing (scRNA-Seq) analysis of the immunological microenvironment of two paired clinical specimens with ovarian metastasis of GC.
METHODS: scRNA-Seq was performed to determine the immunological microenvironment in ovarian metastasis of gastric cancer. CellChat was employed to analyze cell-cell communications across different cell types. Functional enrichment analysis was done by enrichKEGG in clusterProfiler. GEPIA2 was used to assess the influence of certain genes and gene signatures on prognosis.
RESULTS: The ovarian metastasis tissues exhibit a heterogenous immunological microenvironment compared to the primary tumors. Exhaustion of T and B cells is observed in the ovarian metastasis tissues. Compared to the paired adjacent non-tumoral and primary tumors, the ratio of endothelial cells and fibroblasts is high in the ovarian metastasis tissues. Compared to primary ovarian cancers, we identify a specific group of tumor-associated fibroblasts with MFAP4 and CAPNS1 expression in the ovarian metastatic tissues of GC. We further define metastasis-related-endothelial and metastasis-related-fibroblast signatures and indicate that patients with these high signature scores have a poor prognosis. In addition, the ovarian metastasis tissue has a lower level of intercellular communications compared to the primary tumor.
CONCLUSIONS: Our findings reveal the immunological microenvironment of ovarian metastasis of gastric cancer and will promote the discovery of new therapeutic strategies for ovarian metastasis in gastric cancer.

Metastatic clear cell renal cell carcinoma has heterogenous tumor microenvironment (TME). Among the metastatic lesions, pancreas metastasis is rare and controversy in treatment approaches. Here, extensive primary and metastatic lesion samples were included by single-cell RNA-seq to decipher the distinct metastasis TME. The hypoxic and inflammatory TME of pancreas metastasis was decoded in this study, and the activation of PAX8-myc signaling, and metabolic reprogramming were observed. The active components including endothelial cells, fibroblasts and T cells were profiled. Meanwhile, we also evaluated the effect of anti-angiogenesis treatment in the pancreas metastasis patient. The potential mechanisms of pancreatic tropism, instability of genome, and the response of immunotherapy were also discussed in this work. Taken together, our findings suggest a clue to the heterogeneity in metastasis TME and provide evidence for the treatment of pancreas metastasis in renal cell carcinoma patients.

UNASSIGNED: Metabolic liver diseases, including alcohol- and non-alcoholic fatty liver diseases (ALD/NAFLD), are characterized by inflammation and decreased ability to prevent infections. Patients with severe alcohol-associated hepatitis (sAH) are particularly susceptible to infections while undergoing treatment with steroids. Understanding the immunological mechanisms for these responses is critical to managing the treatment of patients with metabolic liver diseases. Cytotoxic NK cells and CD8 T cells, using cytolytic granules, serve an important immunological role by killing infected cells, including monocytes. However, patients with sAH have dysfunctional NK cells, which cannot kill target cells, though the mechanism is unknown.
UNASSIGNED: We performed an exploratory study using single-cell RNA-seq (scRNA-seq) (n = 4) and multi-panel intracellular flow cytometry (n = 7-8 for all patient groups) on PBMCs isolated from patients with sAH and healthy controls (HC).
UNASSIGNED: ScRNA-seq revealed receptors in NK cells and CD8 T cells required for cytotoxic cell recognition of activated monocytes were downregulated in patients with sAH compared to healthy controls. Granulysin was the most downregulated gene in both NK cells and effector CD8 T cells. In NK cells from HC, expression of granulysin, perforin, and granzymes A and B was highly correlated; however, in sAH, these genes lost coordinate expression, indicative of dysfunctional cytolytic granule formation. Finally, the expression of cytolytic granule proteins in NK cells was decreased from sAH, indicating reduced cytolytic granules.
UNASSIGNED: Together, these results suggest a loss of cytotoxic cell function in PBMCs from sAH that may contribute to a decreased ability to communicate with other immune cells, such as monocytes, and prevent the killing of infected cells, thus increasing the risk of infection.