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Lipid-associated macrophage remodeling is present in the tumor microenvironment after high-grade glioma recurrence.
BACKGROUND: The inevitable recurrence of most high-grade gliomas (HGG) despite aggressive treatment poses a significant challenge to clinicians. Recently it has been found that exploring the characteristics of cell populations and their biological role in the tumour microenvironment is essential to understand the mechanisms behind tumour recurrence. Focusing on a combination of extensive RNA-seq and single-cell RNA sequencing (scRNA-seq) data, this study explores the mechanisms underlying the process of tumour microenvironment remodelling after HGG recurrence, providing new insights into the recurrence and treatment of HGG. METHODS: Clinical information and bulk RNA-Seq data provided by the Chinese Glioma Genome Atlas (CGGA) data and single-cell sequencing data were analysed to compare the differences in the tumour microenvironments in primary and recurrent gliomas using R Seurat and ssGSEA scoring methods. The ssGSEA scores and HGG key clinical features was used to construct a prognostic model by univariate Cox analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, with different expressions of lipid-associated macrophages (LAMs) were also investigated. Finally, GO and KEGG were performed on LAMs signature gene annotations to look for possible biological functions and pathways. RESULTS: We identified a specific macrophage subpopulation defined as lipid-associated macrophages and LAMs cells were significantly increased in patients with HGG recurrence and predicted a poor prognosis. Functionally, LAMs may act as mediators of the immune response, and COX regression analysis revealed that LAMs were found to be an independent prognostic factor for glioma. CONCLUSION: Our comprehensive analysis suggests that there is a remodelling of LAMs in the tumour microenvironment after HGG recurrence. LAMs could serve as a potential prognostic predictor for patients with glioma, and LAMs could play a role by participating in the immune response and glioma-associated immune checkpoints in order to help maintain the malignant phenotype of the neuroglial tumor cells.
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scTOP: physics-inspired order parameters for cellular identification and visualization.
Advances in single-cell RNA-sequencing (scRNA-seq) provide an unprecedented window into cellular identity. The increasing abundance of data requires new theoretical and computational frameworks for understanding cell fate determination, accurately classifying cell fates from expression data, and integrating knowledge from cell atlases. Here, we present single-cell Type Order Parameters (scTOP): a statistical-physics-inspired approach for constructing "order parameters" for cell fate given a reference basis of cell types. scTOP can quickly and accurately classify cells at a single-cell resolution, generate interpretable visualizations of developmental trajectories, and assess the fidelity of engineered cells. Importantly, scTOP does this without using feature selection, statistical fitting, or dimensional reduction (e.g., UMAP, PCA, etc.). We illustrate the power of scTOP utilizing a wide variety of human and mouse datasets (both in vivo and in vitro ). By reanalyzing mouse lung alveolar development data, we characterize a transient perinatal hybrid alveolar type 1/alveolar type 2 (AT1/AT2) cell population that disappears by 15 days post-birth and show that it is transcriptionally distinct from previously identified adult AT2-to-AT1 transitional cell types. Visualizations of lineage tracing data on hematopoiesis using scTOP confirm that a single clone can give rise to as many as three distinct differentiated cell types. We also show how scTOP can quantitatively assess the transcriptional similarity between endogenous and transplanted cells in the context of murine pulmonary cell transplantation. Finally, we provide an easy-to-use Python implementation of scTOP. Our results suggest that physics-inspired order parameters can be an important tool for understanding development and characterizing engineered cells.
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Single-cell RNA sequencing reveals transdifferentiation of parathyroid chief cells into oxyphil cells in patients with uremic secondary hyperparathyroidism.
The parathyroid gland is one of the main organs that regulate calcium and phosphorus metabolism. It is mainly composed of chief cells and oxyphil cells. Oxyphil cell counts are low in the parathyroid glands of healthy adults but are dramatically increased in patients with uremia and secondary hyperparathyroidism (SHPT). Increased oxyphil cell counts are related to drug treatment resistance, but the origin of oxyphil cells and the mechanism of proliferation remain unknown. Herein, three types of parathyroid nodules (chief cell nodules, oxyphil cell nodules and mixed nodules, respectively) excised from parathyroid glands of uremic SHPT patients were used for single-cell RNA sequencing (scRNA-seq), other molecular biology studies, and transplantation into nude mice. Through scRNA-seq of parathyroid mixed nodules from three patients with uremic SHPT, we established the first transcriptomic map of the human parathyroid and found a chief-to-oxyphil cell transdifferentiation characterized by gradual mitochondrial enrichment associated with the uremic milieu. Notably, the mitochondrial enrichment and cellular proliferation of chief cell and oxyphil cell nodules decreased significantly after leaving the uremic milieu via transplantation into nude mice. Remarkably, the phenotype of oxyphil cell nodules improved significantly in the nude mice as characterized by decreased mitochondrial content and the proportion of oxyphil cells to chief cells. Thus, our study provides a comprehensive single-cell transcriptome atlas of the human parathyroid and elucidates the origin of parathyroid oxyphil cells and their underlying transdifferentiating mechanism. These findings enhance our understanding of parathyroid disease and may open new treatment perspectives for patients with chronic kidney disease.
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Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming.
Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.
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The Inflammatory Characteristics of Symptomatic Glioma Associated With Poor Prognosis and Chemoresistance via Tumor Necrosis Factor Signaling Pathway.
BACKGROUND: Among gliomas, the most common primary malignant brain tumor, incidental gliomas account for 2.5%-5% of cases. The controversy over whether to pursue immediate treatment or adopt a wait-and-see approach remains, and more molecular and immunological evidence is needed for definitive treatment decisions. METHODS: Total RNA sequencing (RNA-seq) data and single cell RNA sequencing (scRNA-seq) data were retrospectively analyzed to compare the molecular and immunological tumor microenvironment differences between incidental glioma and symptomatic glioma samples. These were classified using symptom data from The Cancer Genome Atlas (TCGA) and public dataset. RESULTS: RNA-seq analysis of the GBMLGG dataset identified 343 genes upregulated in symptomatic glioma and 118 in incidental glioma, with 104 common genes upregulated in symptomatic glioma across both the TCGA and Chinese Glioma Genome Atlas (CGGA) datasets. Enrichment analysis revealed that these 104 genes in symptomatic glioma were significantly associated with immunological pathways. scRNA-seq analysis of glioma revealed 11 cell types, including T cells, myeloid cells, and oligodendrocytes, with the tumor necrosis factor (TNF) signaling pathway strongly influencing other cell types, particularly myeloid cells. Enrichment and survival analyses showed that TNF signaling is associated with temozolomide resistance and poorer prognosis in glioma patients. CONCLUSION: The findings suggest that symptomatic glioma enhances inflammatory responses linked to poor prognosis and chemoresistance. This supports the hypothesis that immediate treatment of incidental glioma may improve patient outcomes over a wait-and-see approach.
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Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina.
Most genetic risk variants linked to ocular diseases are non-protein coding and presumably contribute to disease through dysregulation of gene expression, however, deeper understanding of their mechanisms of action has been impeded by an incomplete annotation of the transcriptional regulatory elements across different retinal cell types. To address this knowledge gap, we carried out single-cell multiomics assays to investigate gene expression, chromatin accessibility, DNA methylome and 3D chromatin architecture in human retina, macula, and retinal pigment epithelium (RPE)/choroid. We identified 420,824 unique candidate regulatory elements and characterized their chromatin states in 23 sub-classes of retinal cells. Comparative analysis of chromatin landscapes between human and mouse retina cells further revealed both evolutionarily conserved and divergent retinal gene-regulatory programs. Leveraging the rapid advancements in deep-learning techniques, we developed sequence-based predictors to interpret non-coding risk variants of retina diseases. Our study establishes retina-wide, single-cell transcriptome, epigenome, and 3D genome atlases, and provides a resource for studying the gene regulatory programs of the human retina and relevant diseases.
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Single-Cell RNA Sequencing Reveals Atlas of Yak Testis Cells.
Spermatogenesis is a complex process that involves proliferation and differentiation of diploid male germ cells into haploid flagellated sperm and requires intricate interactions between testicular somatic cells and germ cells. The cellular heterogeneity of this process presents a challenge in analyzing the different cell types at various developmental stages. Single-cell RNA sequencing (scRNA-seq) provides a useful tool for exploring cellular heterogeneity. In this study, we performed a comprehensive and unbiased single-cell transcriptomic study of spermatogenesis in sexually mature 4-year-old yak using 10× Genomics scRNA-seq. Our scRNA-seq analysis identified six somatic cell types and various germ cells, including spermatogonial stem cells, spermatogonia, early-spermatocytes, late-spermatocytes, and spermatids in yak testis. Pseudo-timing analysis showed that Leydig and myoid cells originated from common progenitor cells in yaks. Moreover, functional enrichment analysis demonstrated that the top expressed genes in yak testicular somatic cells were significantly enriched in the cAMP signaling pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, and ECM receptor interactions. Throughout the spermatogenesis process, genes related to spermatogenesis, cell differentiation, DNA binding, and ATP binding were expressed. Using immunohistochemical techniques, we identified candidate marker genes for spermatogonial stem cells and Sertoli cells. Our research provides new insights into yak spermatogenesis and the development of various types of cells in the testis, and presents more reliable marker proteins for in vitro culture and identification of yak spermatogonial stem cells in the later stage.
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Gene expression variation in Arabidopsis embryos at single-nucleus resolution.
Soon after fertilization of egg and sperm, plant genomes become transcriptionally activated and drive a series of coordinated cell divisions to form the basic body plan during embryogenesis. Early embryonic cells rapidly diversify from each other, and investigation of the corresponding gene expression dynamics can help elucidate underlying cellular differentiation programs. However, current plant embryonic transcriptome datasets either lack cell-specific information or have RNA contamination from surrounding non-embryonic tissues. We have coupled fluorescence-activated nuclei sorting together with single-nucleus mRNA-sequencing to construct a gene expression atlas of Arabidopsis thaliana early embryos at single-cell resolution. In addition to characterizing cell-specific transcriptomes, we found evidence that distinct epigenetic and transcriptional regulatory mechanisms operate across emerging embryonic cell types. These datasets and analyses, as well as the approach we devised, are expected to facilitate the discovery of molecular mechanisms underlying pattern formation in plant embryos. This article has an associated 'The people behind the papers' interview.
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Comprehensive analysis of mitochondria-associated genes in glioblastoma via single-cell and bulk RNA sequencing.
Glioblastoma (GBM) is a highly aggressive brain tumor with limited treatment options and poor survival. Mitochondrial dysfunction and metabolic reprogramming, particularly the Warburg effect, are increasingly recognized as critical drivers of GBM progression. Here, we integrated single-cell (scRNA-seq) and bulk RNA-sequencing (bulk RNA-seq) data to comprehensively examine mitochondria-associated genes in GBM. We identified differentially expressed mitochondrial genes with prognostic significance and constructed a 9-gene risk signature—ACOT7, THEM5, MTHFD2, ABCB7, PICK1, PDK3, ARMCX6, GSTK1, and SSBP1—using LASSO and Cox regression. This signature robustly stratified patients into high- and low-risk groups in both The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts, remaining an independent prognostic factor in multivariate analyses. Time-dependent ROC AUCs were 0.729, 0.813, and 0.828 at 1, 2, and 3 years in TCGA, and 0.597, 0.650, and 0.546 in CGGA. A nomogram integrating the signature with clinical variables achieved AUCs of 0.649, 0.820, and 0.854 at 1, 3, and 5 years, with good 1/2/3-year calibration. Functional enrichment and clustering analyses revealed distinct metabolic phenotypes and survival differences between subtypes. Single-cell pseudotime analysis showed a transition from oxidative phosphorylation to glycolysis in malignant cells, aligning with the Warburg effect and implicating metabolic reprogramming in immune modulation. Our findings underscore the prognostic value of mitochondria-associated genes and suggest potential therapeutic targets for disrupting GBM metabolism. Overall, these results establish a mitochondria-centric prognostic model supported by single-cell context; findings are hypothesis-generating and warrant prospective and experimental validation.
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The heterogeneous immune landscape between lung adenocarcinoma and squamous carcinoma revealed by single-cell RNA sequencing.
A thorough interrogation of the immune landscape is crucial for immunotherapy strategy selection and prediction of clinical responses in non-small-cell lung cancer (NSCLC) patients. Single-cell RNA sequencing (scRNA-seq) techniques have prompted the opportunity to dissect the distinct immune signatures between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two major subtypes of NSCLC. Here, we performed scRNA-seq on 72,475 immune cells from 40 samples of tumor and matched adjacent normal tissues spanning 19 NSCLC patients, and drew a systematic immune cell transcriptome atlas. Joint analyses of the distinct cellular compositions, differentially expressed genes (DEGs), cell-cell interactions, pseudotime trajectory, transcriptomic factors and prognostic factors based on The Cancer Genome Atlas (TCGA), revealed the central roles of cytotoxic and effector T and NK cells and the distinct functional macrophages (Mφ) subtypes in the immune microenvironment heterogeneity between LUAD and LUSC. The dominant subtype of Mφ was FABP4-Mφ in LUAD and SPP1-Mφ in LUSC. Importantly, we identified a novel lymphocyte-related Mφ cluster, which we named SELENOP-Mφ, and further established its antitumor role in both types, especially in LUAD. Our comprehensive depiction of the immune heterogeneity and definition of Mφ clusters could help design personalized treatment for lung cancer patients in clinical practice.