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Pan-cancer analysis identifies migrasome-related genes as a potential immunotherapeutic target: A bulk omics research and single cell sequencing validation.
INTRODUCTION: The migrasome is a newly discovered organelle that resembles extracellular vesicles in structure. However, the function of the migrasome in tumors, particularly in relation to tumor immunity and tumor microenvironment, is unclear. METHODS: Gene expression data, copy number variation raw data, and methylation data of 33 cancer types were downloaded from The Cancer Genome Atlas database. Immunohistochemistry (IHC) based on 114 case of colorectal cancer was used to validate the expression of the migrasome hub-gene. We analyzed the expression, prognosis, genetic variation, and drug sensitivity profiles of migrasome-related genes (MRGs) in pan-cancer datasets. A migrasome score was constructed based on gene set enrichment analysis, and the correlation of migrasomes with the tumor microenvironment was assessed. The CancerSEA was used to perform a single-cell level functional analysis of the migrasome. Additionally, we also analyzed the correlation between migrasomes and tumor mutational burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion scores. Single-cell transcriptome sequencing (scRNA-seq) data was used to assess the activation state of migrasomes in the tumor microenvironment. RESULTS: PIGK expression was significantly up-regulated in 22 of 33 tumors, and high expression of migrasome was estimated to have contributed to poor prognosis. Missense mutations are the most common type of mutation in MRGs. We identified piperlongumine as a potential drug targeting migrasomes. The migrasome score was significantly and positively correlated with the tumor immunity score and the stroma score. In most tumors, the abundance of macrophages in the tumor microenvironment was significantly and positively correlated with the migrasome score. Additionally, the migrasome scores were significantly correlated with the immune checkpoint genes in pan-cancer as well as immune checkpoint therapy-related markers including TMB and MSI. According to scRNA-seq analysis, migrasome differed significantly among cells of the tumor microenvironment. IHC confirmed low expression of ITGA5 and PIGK in colorectal cancer. DISCUSSION: We performed the first pan-cancer analysis of migrasomes and discovered that they play an important role in tumor development and immune escape. Our study provides new insights into the role of migrasomes in tumor prognosis and immunotherapy.
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Kidney Single-Cell Atlas Reveals Myeloid Heterogeneity in Progression and Regression of Kidney Disease.
BACKGROUND: Little is known about the roles of myeloid cell subsets in kidney injury and in the limited ability of the organ to repair itself. Characterizing these cells based only on surface markers using flow cytometry might not provide a full phenotypic picture. Defining these cells at the single-cell, transcriptomic level could reveal myeloid heterogeneity in the progression and regression of kidney disease. METHODS: Integrated droplet- and plate-based single-cell RNA sequencing were used in the murine, reversible, unilateral ureteric obstruction model to dissect the transcriptomic landscape at the single-cell level during renal injury and the resolution of fibrosis. Paired blood exchange tracked the fate of monocytes recruited to the injured kidney. RESULTS: A single-cell atlas of the kidney generated using transcriptomics revealed marked changes in the proportion and gene expression of renal cell types during injury and repair. Conventional flow cytometry markers would not have identified the 12 myeloid cell subsets. Monocytes recruited to the kidney early after injury rapidly adopt a proinflammatory, profibrotic phenotype that expresses Arg1, before transitioning to become Ccr2+ macrophages that accumulate in late injury. Conversely, a novel Mmp12+ macrophage subset acts during repair. CONCLUSIONS: Complementary technologies identified novel myeloid subtypes, based on transcriptomics in single cells, that represent therapeutic targets to inhibit progression or promote regression of kidney disease.
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The transcriptional landscape of developing human trisomy 21 lungs.
RATIONALE: Trisomy 21 (T21), resulting in Down syndrome (DS), is the most prevalent chromosomal abnormality worldwide. While pulmonary disease is a major cause of morbidity and mortality in DS, the ontogeny of pulmonary complications remains poorly understood. We recently demonstrated that T21 lung anomalies, including airway branching and vascular lymphatic -abnormalities, are initiated in utero. Here, we aimed to describe molecular changes at the single-cell level in prenatal T21 lungs. METHODS: Single-cell RNA sequencing was used to generate transcriptomic profiles of individual human lung cells in tissue obtained from T21 (n = 5) and non-T21 (n = 4) prenatal lungs. Clustering of cells, marker identification, uniform manifold approximation and projection representation, and differential expression analysis were performed in Seurat. Cell type annotation and pathway analysis were annotated using ToppFun and a human fetal lung cell atlas. Spatial differences in cellular phenotypes were validated using immunofluorescence staining and fluorescent in situ hybridization. RESULTS: Our results detail changes in gene expression at the time of initiation of histopathological abnormalities in T21 prenatal lungs. Notably, we identify precocious differentiation of epithelial cells, widespread induction of key extracellular matrix molecules in mesenchymal cells, and hyperactivation of IFN signaling in endothelial cells. CONCLUSIONS: This single-cell dataset of T21 lungs greatly expands our understanding of antecedents to pulmonary complications and should facilitate efforts to mitigate respiratory disease in DS.
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Development and validation of a leukemia prognostic model through single-cell RNA sequencing and machine learning approaches.
BACKGROUND: Leukemia prognosis varies significantly among patients, highlighting the need for accurate prediction tools. Emerging evidence suggests that the immune microenvironment plays a crucial role in leukemia progression and treatment response. METHODS: We analyzed RNA expression profiles and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, supplemented by single-cell RNA sequencing datasets. Differential gene expression analysis was performed using stringent criteria (logFC > 1, FDR < 0.05) to identify leukemia-associated genes. Ten distinct machine learning algorithms, including Lasso, CoxBoost, and ensemble methods, were implemented for prognostic model development with cross-platform validation. Single-cell analysis employed Seurat for quality control and cell type annotation, while CellChat algorithm mapped intercellular communication networks. Experimental validation was conducted using quantitative RT-PCR analysis of key immune markers (TLR2, TLR4, CCR7, IL18) in U937 and K562 leukemia cell lines compared to normal peripheral blood mononuclear cells. RESULTS: The machine learning-derived prognostic model demonstrated exceptional predictive performance with area under the curve values of 0.874, 0.891, and 0.925 for 1-, 2-, and 3-year survival endpoints, respectively. Six critical immune regulatory genes (TLR2, TLR4, CCR7, IL18, TIRAP, FOXP3) were identified as both differentially expressed and prognostically significant, with IL18 showing the highest discriminative capacity (AUC = 0.983). RT-PCR validation confirmed significant upregulation of all tested genes in leukemia cell lines: TLR2 (3.8-fold in U937, 2.2-fold in K562), TLR4 (3.4-fold in U937, 1.8-fold in K562), CCR7 (4.1-fold in U937, 2.7-fold in K562), and IL18 (5.2-fold in U937, 3.6-fold in K562) compared to normal controls (all p < 0.05). Single-cell analysis revealed substantial cellular heterogeneity with cell type-specific expression patterns and complex intercellular communication networks involving B cells, T cells, natural killer cells, and dendritic cells. CONCLUSION: This study provides a reliable prognostic tool for leukemia and offers insights into the critical role of the immune microenvironment in leukemia pathogenesis. Our findings may guide the development of personalized immunotherapy strategies for leukemia patients.
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Cleavage Stimulation Factor Subunit 2: Function Across Cancers and Potential Target for Chemotherapeutic Drugs.
The cleavage stimulation factor subunit complex is involved in the cleavage and polyadenylation of 3'-end pre-mRNAs that regulate mRNA formation and processing. However, cleavage stimulation factor subunit 2 (CSTF2) was found to play a more critical regulatory role across cancers. General cancer data sets from The Cancer Genome Atlas and Genotype-Tissue Expression project were thus downloaded for differential analysis, and the possible functions and mechanisms of CSTF2 in general cancer were analyzed using the Compartments database, cBioPortal database, Tumor Immune Single-cell Hub database, and Comparative Toxigenomics database using gene set enrichment analysis and R software. The results showed that CSTF2 could affect DNA repair and methylation in tumor cells. In addition, CSTF2 was associated with multiple tumor immune infiltrates in a wide range of cancers, and its high expression was associated with multiple immune checkpoints; therefore, it could serve as a potential target for many drug molecules. We also proved that CSTF2 promotes oral cell proliferation and migration. The high diagnostic efficacy of CSTF2 suggested that this gene may act as a new biomarker and personalized therapeutic target for a variety of tumors.
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An integrated transcriptomic cell atlas of human endoderm-derived organoids.
Human pluripotent stem cells and tissue-resident fetal and adult stem cells can generate epithelial tissues of endodermal origin in vitro that recapitulate aspects of developing and adult human physiology. Here, we integrate single-cell transcriptomes from 218 samples covering organoids and other models of diverse endoderm-derived tissues to establish an initial version of a human endoderm-derived organoid cell atlas. The integration includes nearly one million cells across diverse conditions, data sources and protocols. We compare cell types and states between organoid models and harmonize cell annotations through mapping to primary tissue counterparts. Focusing on the intestine and lung, we provide examples of mapping data from new protocols and show how the atlas can be used as a diverse cohort to assess perturbations and disease models. The human endoderm-derived organoid cell atlas makes diverse datasets centrally available and will be valuable to assess fidelity, characterize perturbed and diseased states, and streamline protocol development.
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A complete Sigmodon hispidus genome and dynamic single-cell transcriptomics reveal evolutionarily conserved responses to RSV infection.
The cotton rat (Sigmodon hispidus) has emerged as a preclinical model for human respiratory syncytial virus (RSV) research because of its ability to support effective viral replication and recapitulate disease; however, the mechanisms supporting this evolutionarily conserved phenotype remain unclear. Here, we report both the chromosomal genome and respiratory single-cell transcriptomic atlas of the cotton rat throughout the course of RSV infection. Phylogenetic analysis showed that cotton rats are in different evolutionary branches from common mouse and rat models and exhibit substantial conservation of tissue profiles with humans. The respiratory cell atlas, alveolar cell trajectory, and distribution of potential viral receptors are also similar to those of humans, explaining the superiority of cotton rats as an RSV infection model. The target cells of RSV, including club cells, secretory2 cells, mesothelial cells, and nerve-associated astrocytes, were expanded after infection. We also identified differential transcriptional regulators, specific antiviral proteins, and cytokines that could be translated into potential clinical markers in humans. Candidate host factors were also identified, which will enable the exploration of host-RSV interactions and offer a source of therapeutic targets.
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Identification and validation of RNA-binding protein-related gene signature revealed potential associations with immunosuppression and drug sensitivity in glioma.
BACKGROUND: Glioma is the most common central nervous system tumor in adults, and a considerable part of them are high-degree ones with high malignancy and poor prognosis. At present, the classification and treatment of glioma are mainly based on its histological characteristics, so studies at the molecular level are needed. METHODS: RNA-seq data from The Cancer Genome Atlas (TCGA) datasets (n = 703) and Chinese Glioma Genome Atlas (CGGA) were utilized to find out the differentially expressed RNA-binding proteins (RBPs) between normal cerebral tissue and glioma. A prediction system for the prognosis of glioma patients based on 11 RBPs was established and validated using uni- and multi-variate Cox regression analyses. STITCH and CMap databases were exploited to identify putative drugs and their targets. Single sample gene set enrichment analysis (ssGSEA) was used to calculate scores of specific immune-related gene sets. IC50 of over 20,000 compounds in 60 cancer cell lines was collected from the CellMiner database to test the drug sensitivity prediction value of the RBP-based signature. RESULTS: We established a reliable prediction system for the prognosis of glioma patients based on 11 RBPs including THOC3, LSM11, SARNP, PABPC1L2B, SMN1, BRCA1, ZC3H8, DZIP1L, HEXIM2, LARP4B, and ZC3H12B. These RBPs were primarily associated with ribosome and post-transcriptional regulation. RBP-based risk scores were closely related to immune cells and immune function. We also confirmed the potential of the signature to predict the drug sensitivity of currently approved or evaluated drugs. CONCLUSIONS: Differentially expressed RBPs in glioma can be used as a basis for prognosis prediction, new drugs screening and drug sensitivity prediction. As RBP-based glioma risk scores were associated with immunity, immunotherapy may become an important treatment for glioma in the future.
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Single-cell transcriptomics of the developing lateral geniculate nucleus reveals insights into circuit assembly and refinement.
Coordinated changes in gene expression underlie the early patterning and cell-type specification of the central nervous system. However, much less is known about how such changes contribute to later stages of circuit assembly and refinement. In this study, we employ single-cell RNA sequencing to develop a detailed, whole-transcriptome resource of gene expression across four time points in the developing dorsal lateral geniculate nucleus (LGN), a visual structure in the brain that undergoes a well-characterized program of postnatal circuit development. This approach identifies markers defining the major LGN cell types, including excitatory relay neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells. Most cell types exhibit significant transcriptional changes across development, dynamically expressing genes involved in distinct processes including retinotopic mapping, synaptogenesis, myelination, and synaptic refinement. Our data suggest that genes associated with synapse and circuit development are expressed in a larger proportion of nonneuronal cell types than previously appreciated. Furthermore, we used this single-cell expression atlas to identify the Prkcd-Cre mouse line as a tool for selective manipulation of relay neurons during a late stage of sensory-driven synaptic refinement. This transcriptomic resource provides a cellular map of gene expression across several cell types of the LGN, and offers insight into the molecular mechanisms of circuit development in the postnatal brain.
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Single-cell transcriptomics reveals that human milk feeding shapes neonatal immune cell interleukin signaling pathways in a nonrandomized clinical trial.
BACKGROUND: Several studies have indicated the benefits of human milk feeding to infants however, mechanisms behind positive health outcomes have not been investigated. OBJECTIVES: The study aimed to characterize circulating immune cell subpopulation gene expression in human milk-fed (HMF) compared with cow milk formula-fed (FF) infants using single-cell transcriptomics. METHODS: Peripheral blood mononuclear cells (PBMCs) were isolated from healthy HMF (n = 6), and FF (n = 3) infants who were 3-3.5 mo old and enrolled in a nonrandomized clinical trial. Single-cell RNA sequencing was used to generate a PBMC atlas and evaluate gene expression in immune cell subsets. Differential expression analysis was performed on each cell type independently after clustering the cells by similar marker gene expression using the scGEAToolbox. Differentially expressed genes were subjected to pathway analyses using an online functional enrichment analysis program. RESULTS: The relative abundance (%) of T and B lymphocytes, natural killer (NK) cells, and plasmacytoid dendritic cells were similar, whereas monocytes were higher in FF infants than in HMF infants (22.6 ± 10.7 compared with 8.3 ± 5.6; P = 0.0314). In addition, innate and adaptive immune cells from FF infants exhibited a higher activation state compared with HMF infants. We identified 16 distinct cell subsets from the major immune cell types: 3 monocyte subsets, 4 NK subsets, 2 B cell subsets, and 7 T cell subsets. Transcriptional profiles of each peripheral innate and adaptive immune cell subtype varied between HMF and FF infants. Pathway enrichment analysis of cell-specific transcriptional changes within subsets of major cell types revealed that the interleukin (IL)-4/IL-13 signaling pathways were upregulated in FF infants relative to HMF infants. CONCLUSIONS: These findings suggest that human milk downregulates peripheral immune cell cytokine transcriptional signatures linked to allergic inflammation and infection relative to formula feeding.