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Single-cell analysis of diverse immune phenotypes in malignant pleural effusion.
The complex interactions among different immune cells have important functions in the development of malignant pleural effusion (MPE). Here we perform single-cell RNA sequencing on 62,382 cells from MPE patients induced by non-small cell lung cancer to describe the composition, lineage, and functional states of infiltrating immune cells in MPE. Immune cells in MPE display a number of transcriptional signatures enriched for regulatory T cells, B cells, macrophages, and dendritic cells compared to corresponding counterparts in blood. Helper T, cytotoxic T, regulatory T, and T follicular helper cells express multiple immune checkpoints or costimulatory molecules. Cell-cell interaction analysis identifies regulatory B cells with more interactions with CD4+ T cells compared to CD8+ T cells. Macrophages are transcriptionally heterogeneous and conform to M2 polarization characteristics. In addition, immune cells in MPE show the general up-regulation of glycolytic pathways associated with the hypoxic microenvironment. These findings show a detailed atlas of immune cells in human MPE and enhance the understanding of potential diagnostic and therapeutic targets in advanced non-small cell lung cancer.
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Cellular heterogeneity of human fallopian tubes in normal and hydrosalpinx disease states identified using scRNA-seq.
Fallopian tube (FT) homeostasis requires dynamic regulation of heterogeneous cell populations and is disrupted in infertility and ovarian cancer. Here, we applied single-cell RNA-seq to profile 59,738 FT cells from four healthy, pre-menopausal subjects. The resulting cell atlas contains 12 major cell types representing epithelial, stromal, and immune compartments. Re-clustering of epithelial cells identified four ciliated and six non-ciliated secretory epithelial subtypes, two of which represent potential progenitor pools: one leading to mature secretory cells and the other contributing to either ciliated cells or one of the stromal cell types. To understand how FT cell numbers and states change in a disease state, we analyzed 17,798 cells from two hydrosalpinx samples and observed shifts in epithelial and stromal populations and cell-type-specific changes in extracellular matrix and TGF-β signaling; this underscores fibrosis pathophysiology. This resource is expected to facilitate future studies aimed at expanding understanding of fallopian tube homeostasis in normal development and disease.
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Molecular dynamics simulation and single-cell and spatial transcriptomics validate immune and prognostic biomarkers in colorectal cancer and construct a clinical prognostic model.
BACKGROUND: Colorectal cancer (CRC) represents a huge global health challenge characterized by significant morbidity and mortality. The urgent need to identify biomarkers through integrative validation research to enhance diagnostic accuracy and prognostic stratification has prompted the exploration of immune and prognostic genes. This study aimed to systematically identify differentially expressed genes (DEGs) associated with both immunity and prognosis in CRC, validate their clinical significance, and construct a reliable prognostic model. METHODS: This research sought to identify DEGs associated with immunity and prognosis in CRC. We examined clinical and RNA sequencing data from 698 CRC patients obtained from The Cancer Genome Atlas (TCGA). Utilizing the Xiantao Academic Platform, we conducted differential expression analysis and identified hub genes associated with immunity and prognosis through Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses, alongside five machine learning algorithms to construct a prognostic model. The hub genes were validated using the Gene Expression Omnibus (GEO) database, molecular docking, molecular dynamics simulation, single-cell and spatial transcription analyses. RESULTS: LASSO and Cox regression analyses, along with five machine learning algorithms, were employed to identify significant genes linked to immunity and prognosis, yielding three hub genes: ULBP2, INHBB, and STC2. Validation of these genes in the GEO dataset GSE21815 demonstrated significant diagnostic performance, with area under the curve (AUC) values of 0.908, 0.742, and 0.934, respectively. A prognostic model integrating clinical factors and hub genes was developed, demonstrating high predictive accuracy for 1-, 3-, and 5-year survival rates. Further analysis revealed significant enrichment in the TGF-β signaling pathway and natural killer cell-mediated cytotoxicity, as evidenced by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The single-sample Gene Set Enrichment Analysis (ssGSEA)-based immune infiltration analysis revealed immune infiltration differences between groups with high and low immune phenotype scores. Molecular docking and dynamics simulations revealed valproic acid, cyclosporine, and genistein as potential therapeutic compounds with strong binding affinities to the hub genes. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics provided insights into hub gene expression patterns and interactions within the tumor microenvironment. CONCLUSIONS: This comprehensive study highlights the potential of ULBP2, INHBB, and STC2 as promising biomarkers for CRC, emphasizing their roles in regulating tumor progression and immune responses. Future studies should focus on targeted therapeutic strategies that utilize these biomarkers to enhance treatment efficacy and patient prognosis.
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Re-analysis of single-cell transcriptomics reveals a critical role of macrophage-like smooth muscle cells in advanced atherosclerotic plaque.
Aims: Smooth muscle cell (SMC) remodeling poses a critical feature in the development and progression of atherosclerosis. Although fate mapping and in silicon approaches have expanded SMC phenotypes in atherosclerosis, it still remains elusive about the contributions of individual SMC phenotypes and molecular dynamics to advanced atherosclerotic plaque. Methods: Using single-cell transcriptome, we investigated cellular compositions of human carotid plaque laden with atherosclerotic core, followed by in vivo experiments utilizing SMC-lineage tracing technology, bulk RNA sequencing (RNA-seq) and both in vivo and in vitro validation of the underlying molecular mechanism. Results: 5 functionally distinct SMC subtypes were uncovered based on transcriptional features (described as contractile, fibroblast-like, osteogenic, synthetic and macrophage-like) within the niche. A proinflammatory, macrophage-like SMC subtype displaying an intermediary phenotype between SMC and macrophage, exhibits prominent potential in destabilizing plaque. At the molecular level, we explored cluster-specific master regulons by algorithm, and identified interferon regulatory factor-8 (IRF8) as a potential stimulator of SMC-to-macrophage transdifferentiation via activating nuclear factor-κB (NF-κB) signaling. Conclusions: Our study illustrates a comprehensive cell atlas and molecular landscape of advanced atherosclerotic lesion, which might renovate current understanding of SMC biology in atherosclerosis.
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Integrating single-cell sequencing and machine learning to uncover the role of mitophagy in subtyping and prognosis of esophageal cancer.
Globally, esophageal cancer stands as a prominent contributor to cancer-related fatalities, distinguished by its poor prognosis. Mitophagy has a significant impact on the process of cancer progression. This study investigated the prognostic significance of mitophagy-related genes (MRGs) in esophageal carcinoma (ESCA) to elucidate molecular subtypes. By analyzing RNA-seq data from The Cancer Genome Atlas (TCGA), 6451 differentially expressed genes (DEGs) were identified. Cox regression analysis narrowed this list to 14 MRGs with potential prognostic implications. ESCA patients were classified into two distinct subtypes (C1 and C2) based on these genes. Furthermore, leveraging the differentially expressed genes between Cluster 1 and Cluster 2, ESCA patients were classified into two novel subtypes (CA and CB). Importantly, patients in C2 and CA subtypes exhibited inferior prognosis compared to those in C1 and CB (p < 0.05). Functional enrichments and immune microenvironments varied significantly among these subtypes, with C1 and CB demonstrating higher immune checkpoint expression levels. Employing machine learning algorithms like LASSO regression, Random Forest and XGBoost, alongside multivariate COX regression analysis, two core genes: HSPD1 and MAP1LC3B were identified. A prognostic model based on these genes was developed and validated in two external cohorts. Additionally, single-cell sequencing analysis provided novel insights into esophageal cancer microenvironment heterogeneity. Through Coremine database screening, Icaritin emerged as a potential therapeutic candidate to potentially improve esophageal cancer prognosis. Molecular docking results indicated favorable binding efficacies of Icaritin with HSPD1 and MAP1LC3B, contributing to the understanding of the underlying molecular mechanisms of esophageal cancer and offering therapeutic avenues.
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Mosquito cellular immunity at single-cell resolution.
Hemocytes limit the capacity of mosquitoes to transmit human pathogens. Here we profile the transcriptomes of 8506 hemocytes of Anopheles gambiae and Aedes aegypti mosquito vectors. Our data reveal the functional diversity of hemocytes, with different subtypes of granulocytes expressing distinct and evolutionarily conserved subsets of effector genes. A previously unidentified cell type in An. gambiae, which we term "megacyte," is defined by a specific transmembrane protein marker (TM7318) and high expression of lipopolysaccharide-induced tumor necrosis factor-α transcription factor 3 (LL3). Knockdown experiments indicate that LL3 mediates hemocyte differentiation during immune priming. We identify and validate two main hemocyte lineages and find evidence of proliferating granulocyte populations. This atlas of medically relevant invertebrate immune cells at single-cell resolution identifies cellular events that underpin mosquito immunity to malaria infection.
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CBA: Cluster-Guided Batch Alignment for Single Cell RNA-seq.
The power of single-cell RNA sequencing (scRNA-seq) in detecting cell heterogeneity or developmental process is becoming more and more evident every day. The granularity of this knowledge is further propelled when combining two batches of scRNA-seq into a single large dataset. This strategy is however hampered by technical differences between these batches. Typically, these batch effects are resolved by matching similar cells across the different batches. Current approaches, however, do not take into account that we can constrain this matching further as cells can also be matched on their cell type identity. We use an auto-encoder to embed two batches in the same space such that cells are matched. To accomplish this, we use a loss function that preserves: (1) cell-cell distances within each of the two batches, as well as (2) cell-cell distances between two batches when the cells are of the same cell-type. The cell-type guidance is unsupervised, i.e., a cell-type is defined as a cluster in the original batch. We evaluated the performance of our cluster-guided batch alignment (CBA) using pancreas and mouse cell atlas datasets, against six state-of-the-art single cell alignment methods: Seurat v3, BBKNN, Scanorama, Harmony, LIGER, and BERMUDA. Compared to other approaches, CBA preserves the cluster separation in the original datasets while still being able to align the two datasets. We confirm that this separation is biologically meaningful by identifying relevant differential expression of genes for these preserved clusters.
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Integrated analysis of single‑cell and bulk RNA sequencing data to construct a risk assessment model based on plasma cell immune‑related genes for predicting patient prognosis and therapeutic response in lung adenocarcinoma.
Plasma cells serve a crucial role in the human immune system and are important in tumor progression. However, the specific role of plasma cell immune-related genes (PCIGs) in tumor progression remains unclear. Therefore, the present study aimed to establish a risk assessment model for patients with lung adenocarcinoma (LUAD) based on PCIGs. The data used in the present study were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. After identifying nine PCIGs, a risk assessment model was constructed and a nomogram was developed for predicting patient prognosis. To explore the molecular mechanism and clinical significance, gene set enrichment analysis (GSEA), tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis and drug sensitivity prediction were performed. Furthermore, the accuracy of the model was validated using reverse transcription-quantitative PCR (RT-qPCR). The present study constructed a risk assessment model consisting of nine PCIGs. Kaplan-Meier survival curves indicated a worse prognosis in the high-risk subgroup (risk score ≥0.982) compared with that in the low-risk subgroup. The nomogram exhibited predictive value for survival prediction (area under the curve=0.727). GSEA enrichment analysis revealed enrichment of the focal adhesion and extracellular matrix-receptor interaction pathways in the high-risk group. Moreover, the high-risk group exhibited a higher TMB, as demonstrated by the TME analysis showing lower ESTIMATE scores. Drug sensitivity prediction facilitated potential drug selection. Subsequently, differential gene expression was validated in multiple LUAD cell lines using RT-qPCR. In conclusion, the risk assessment model based on nine PCIGs may be used to predict the prognosis and drug selection in patients with LUAD.
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A single-cell atlas of immunocytes in the spleen of a mouse model of Wiskott-Aldrich syndrome.
Wiskott-Aldrich syndrome (WAS) is a disorder characterized by rare X-linked genetic immune deficiency with mutations in the Was gene, which is specifically expressed in hematopoietic cells. The spleen plays a major role in hematopoiesis and red blood cell clearance. However, to date, comprehensive analyses of the spleen in wild-type (WT) and WASp-deficient (WAS-KO) mice, especially at the transcriptome level, have not been reported. In this study, single-cell RNA sequencing (scRNA-seq) was adopted to identify various types of immune cells and investigate the mechanisms underlying immune deficiency. We identified 30 clusters and 10 major cell subtypes among 11,269 cells; these cell types included B cells, T cells, dendritic cells (DCs), natural killer (NK) cells, monocytes, macrophages, granulocytes, stem cells and erythrocytes. Moreover, we evaluated gene expression differences among cell subtypes, identified differentially expressed genes (DEGs), and performed enrichment analyses to identify the reasons for the dysfunction in these different cell populations in WAS. Furthermore, some key genes were identified based on a comparison of the DEGs in each cell type involved in specific and nonspecific immune responses, and further analysis showed that these key genes were previously undiscovered pathology-related genes in WAS-KO mice. In summary, we present a landscape of immune cells in the spleen of WAS-KO mice based on detailed data obtained at single-cell resolution. These unprecedented data revealed the transcriptional characteristics of specific and nonspecific immune cells, and the key genes were identified, laying a foundation for future studies of WAS, especially studies into novel and underexplored mechanisms that may improve gene therapies for WAS.
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MAZ-mediated tumor progression and immune evasion in hormone receptor-positive breast cancer: Targeting tumor microenvironment and PCLAF+ subtype-specific therapy.
BACKGROUND: Breast cancer had been the most frequently diagnosed cancer among women, making up nearly one-third of all female cancers. Hormone receptor-positive breast cancer (HR+BC) was the most prevalent subtype of breast cancer and exhibited significant heterogeneity. Despite advancements in endocrine therapies, patients with advanced HR+BC often faced poor outcomes due to the development of resistance to treatment. Understanding the molecular mechanisms behind this resistance, including tumor heterogeneity and changes in the tumor microenvironment, was crucial for overcoming resistance, identifying new therapeutic targets, and developing more effective personalized treatments. METHODS: The study utilized single-cell RNA sequencing (scRNA-seq) data sourced from the Gene Expression Omnibus database and The Cancer Genome Atlas to analyze HR+BC and identify key cellular characteristics. Cell type identification was achieved through Seurat's analytical tools, and subtype differentiation trajectories were inferred using Slingshot. Cellular communication dynamics between tumor cell subtypes and other cells were analyzed with the CellChat. The pySCENIC package was utilized to analyze transcription factors regulatory networks in the identified tumor cell subtypes. The results were verified by in vitro experiments. A risk scoring model was developed to assess patient outcomes. RESULTS: This study employed scRNA-seq to conduct a comprehensive analysis of HR+BC tumor subtypes, identifying the C3 PCLAF+ tumor cells subtype, which demonstrated high proliferation and differentiation potential. C3 PCLAF+ tumor cells subtype was found to be closely associated with cancer-associated fibroblasts through the MK signaling pathway, facilitating tumor progression. Additionally, we discovered that MAZ was significantly expressed in C3 PCLAF+ tumor cells subtype, and in vitro experiments confirmed that MAZ knockdown inhibited tumor growth, accentuating its underlying ability as a therapeutic target. Furthermore, we developed a novel prognostic model based on the expression profile of key prognostic genes within the PCLAF+/MAZ regulatory network. This model linked high PCLAF+ tumor risk scores with poor survival outcomes and specific immune microenvironment characteristics. CONCLUSION: This study utilized scRNA-seq to reveal the role of the C3 PCLAF+ tumor cells subtype in HR+BC, emphasizing its association with poor prognosis and resistance to endocrine therapies. MAZ, identified as a key regulator, contributed to tumor progression, while the tumor microenvironment had a pivotal identity in immune evasion. The findings underscored the importance of overcoming drug resistance, recognizing novel treatment targets, and crafting tailored diagnosis regimens.