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Cross-species and cross-platform analysis reveals the application value of guinea pig retina in myopia research at single-cell resolution.
This study delineates the single-cell transcriptomic landscape of pigmented guinea pig retinas-an emerging model for myopia research-through integrated single-cell and bulk RNA sequencing, cross-species comparative analysis, and functional validation using form-deprivation myopia models with intravitreal brimonidine intervention. Our results identified six major retinal cell types (rod cells, cone cells, bipolar cells, Müller glial cells, astrocytes, and retinal pigment epithelial cells) subdivided into 16 distinct subclusters, revealing multilineage pseudo time trajectories and specialized intercellular signaling networks: rod cells via NEGR/CADM, cone cells via MPZ, bipolar cells via SEMA6/PTPRM, and astrocytes via ESAM. Cross-species alignment demonstrated closer phylogenetic alignment with human retinal architecture than murine counterparts in pseudo time trajectories and cellular crosstalk. In therapeutic models, brimonidine treatment decelerated axial elongation while inducing selective reductions in cone cells, bipolar cells, and pigment epithelium, paralleled by concordant gene expression shifts. By establishing the first single cell atlas of guinea pig retina and validating its similarities to human ocular biology, this work not only consolidates guinea pigs as a valuable model for myopia research but also infers cellular and molecular dynamics underlying antimyopic interventions, offering a cross-species framework for retinal disease mechanism exploration and therapeutic development.
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Spatial transcriptomics: new dimension of understanding biological complexity.
Cells and tissues are exquisitely organized in a complex but ordered manner to form organs and bodies so that individuals can function properly. The spatial organization and tissue architecture represent a keynote property underneath all living organisms. Molecular architecture and cellular composition within intact tissues play a vital role in a variety of biological processes, such as forming the complicated tissue functionality, precise regulation of cell transition in all living activities, consolidation of central nervous system, cellular responses to immunological and pathological cues. To explore these biological events at a large scale and fine resolution, a genome-wide understanding of spatial cellular changes is essential. However, previous bulk RNA sequencing and single-cell RNA sequencing technologies could not obtain the important spatial information of tissues and cells, despite their ability to detect high content transcriptional changes. These limitations have prompted the development of numerous spatially resolved technologies which provide a new dimension to interrogate the regional gene expression, cellular microenvironment, anatomical heterogeneity and cell-cell interactions. Since the advent of spatial transcriptomics, related works that use these technologies have increased rapidly, and new methods with higher throughput and resolution have grown quickly, all of which hold great promise to accelerate new discoveries in understanding the biological complexity. In this review, we briefly discussed the historical evolution of spatially resolved transcriptome. We broadly surveyed the representative methods. Furthermore, we summarized the general computational analysis pipeline for the spatial gene expression data. Finally, we proposed perspectives for technological development of spatial multi-omics.
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Functional Roles and Targets of COVID-19 in Blood Cells Determined Using Bioinformatics Analyses.
BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global epidemic with a high mortality rate. In this study, our goal was to identify the function and associated targets of SARS-CoV-2 from circulating monocytes in the blood and peripheral blood mononuclear cell (PBMC) dataset of patients with COVID-19. METHODS: The Gene Expression Omnibus database (GSE164805 and GSE180594) was used to identify differentially expressed genes (DEGs). Gene ontology function analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed using the DAVID database. RESULTS: Gene ontology analysis of DEG revealed that GSE164805 and GSE180594 were involved in the regulation of cell migration, upregulation of cell proliferation, and in the activation of the mitogen-activated protein kinase signaling pathway. Kyoto Encyclopedia of Genes and Genomes analysis of GSE164805 revealed that the DEGs were enriched in peroxisome, melanogenesis, and actin regulation. Peroxisome genes were highly expressed in patients with mild and severe COVID-19. Bioinformatics analysis to compare GSE180594 and public data for the single-cell atlas of the peripheral immune response in patients with COVID-19 showed that interferon-associated genes were highly increased in acute COVID-19 PBMC and in CD14+ and CD16+ monocytes from patients with COVID-19. CONCLUSIONS: We comprehensively analyzed the blood cell gene expression profile data of patients with COVID-19 using bioinformatics methods to preliminary understand the functions and associated targets of DEGs in the blood cells of these patients. Thus, our data provide targets for potential therapies against COVID-19.
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Single-cell transcriptomic analysis revealing changes in retinal cell subpopulation levels and the pathways involved in diabetic retinopathy.
BACKGROUND: Diabetic retinopathy (DR) is a common microvascular complication of diabetes and one of the most common causes of visual impairment and blindness. However, it is not yet known how abnormal retinal cell subpopulations contribute to disease progression. METHODS: In this study, we used the Gene Expression Omnibus database to construct a single-cell atlas of DR and healthy samples to explore changes in the abundance of different cell subpopulations, and identify the molecular pathways potentially involved in DR. RESULTS: Our results showed that DR was associated with significantly reduced numbers of bipolar cells, Müller glia, retinal pigment epithelial cells, and cone photoreceptors, but was also associated with significantly greater numbers of pericytes, rod photoreceptors, anaplastic cells, and microglia. Our results suggest that subpopulations of Müller glia, microglia, endothelial cells, and bipolar cells in DR tissues may be involved in various oxidative stress- and inflammation-related pathways. CONCLUSIONS: In summary, we showed that Müller glia, endothelial cells, microglia, and bipolar cells in DR tissues are involved in oxidative stress- and inflammation-related pathways, which may contribute to the progression of the disease and ultimately lead to visual impairment and blindness. This study will provide a theoretical basis for further exploring the specific mechanism of DR.
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Single-cell transcriptome landscape of zebrafish liver reveals hepatocytes and immune cell interactions in understanding nonalcoholic fatty liver disease.
Nonalcoholic fatty liver disease (NAFLD) is becoming the most common chronic liver disease in the world. Immunity is the major contributing factor in NAFLD; however, the interaction of immune cells and hepatocytes in disease progression has not been fully elucidated. As a popular species for studying NAFLD, zebrafish, whose liver is a complex immune system mediated by immune cells and non-immune cells in maintaining immune tolerance and homeostasis. Understanding the cellular composition and immune environment of zebrafish liver is of great significance for its application in NAFLD. Here, we established a liver atlas that consists of 10 cell types using single-cell RNA sequencing (scRNA-seq). By examining the heterogeneity of hepatocytes and analyzing the expression of NAFLD-associated genes in the specific cluster, we provide a potential target cell model to study NAFLD. Additionally, our analysis identified two subtypes of distinct resident macrophages with inflammatory and non-inflammatory functions and characterized the successive stepwise development of T cell subclusters in the liver. Importantly, we uncovered the possible regulation of macrophages and T cells on target cells of fatty liver by analyzing the cellular interaction between hepatocytes and immune cells. Our data provide valuable information for an in-depth study of immune cells targeting hepatocytes to regulate the immune balance in NAFLD.
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Schwann Cell-Mediated M2-Like Macrophage Polarization in Rhabdomyosarcoma.
OBJECTIVE: To investigate the cellular components and immunological characteristics of the head and neck rhabdomyosarcoma (RMS) microenvironment. METHODS: We conducted single-cell transcriptomics to analyze the cellular components of the RMS microenvironment. CellChat was utilized for analyzing intercellular interactions. The cancer genome atlas database was used for validation. CIBERSORT was applied for immune infiltration profiling. Functional enrichment analyses were performed using the Kyoto Encyclopedia of Genes and Genomes. Gene set scores were calculated using single-sample gene set enrichment analysis. Subcutaneous allograft models and bulk RNA sequencing were used for validation. Flow cytometry and immunohistochemistry were used to identify M2-like macrophages. RESULTS: Our findings revealed an extremely low presence of neutrophils in RMS samples compared with normal sample. RMS sample with high Schwann cell infiltration exhibited an increase in M2-like macrophage infiltration. Receptor-ligand pairs, specifically MIF-CD74 and PTN-SDC3, were identified between Schwann cells and M2-like macrophages. In the RMS sample characterized by significant Schwann cell infiltration, M2-like macrophages demonstrate robust expression of axon guidance factors and are enriched in the axon guidance pathway. CONCLUSIONS: Our study provides valuable insights into the microenvironment and immunological characteristics of RMS, offering crucial information for further research and potential therapeutic strategies.
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Single-cell and spatial transcriptome analysis reveals the cellular heterogeneity of liver metastatic colorectal cancer.
In this study, we comprehensively charted the cellular landscape of colorectal cancer (CRC) and well-matched liver metastatic CRC using single-cell and spatial transcriptome RNA sequencing. We generated 41,892 CD45- nonimmune cells and 196,473 CD45+ immune cells from 27 samples of six CRC patients, and found that CD8_CXCL13 and CD4_CXCL13 subsets increased significantly in liver metastatic samples that exhibited high proliferation ability and tumor-activating characterization, contributing to better prognosis of patients. Distinct fibroblast profiles were observed in primary and liver metastatic tumors. F3+ fibroblasts enriched in primary tumors contributed to worse overall survival by expressing protumor factors. However, MCAM+ fibroblasts enriched in liver metastatic tumors might promote generation of CD8_CXCL13 cells through Notch signaling. In summary, we extensively analyzed the transcriptional differences of cell atlas between primary and liver metastatic tumors of CRC by single-cell and spatial transcriptome RNA sequencing, providing different dimensions of the development of liver metastasis in CRC.
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Integrated single-cell and experimental validation links effector chondrocyte-associated oxidative stress to EIF6-related p47phox-NOX2 signaling in osteoarthritis.
BACKGROUND: Oxidative stress is closely involved in osteoarthritis (OA), but the cell-type-specific oxidative stress landscape and related regulatory molecules in human cartilage remain incompletely defined. This study aimed to identify effector chondrocyte (EC)-associated oxidative stress features in OA and to prioritize candidate molecules linked to p47phox-NOX2-related ROS responses. METHODS: Public human cartilage single-cell RNA sequencing datasets were integrated to construct an OA cartilage atlas. Oxidative stress activity was evaluated using multiple scoring methods, and high oxidative stress (HOS) cells were defined according to the top 10% of integrated oxidative stress scores. Cell-state and communication features were examined using CytoTRACE and CellChat. High-dimensional weighted gene co-expression network analysis, machine learning, protein-protein interaction network analysis, summary-data-based Mendelian randomization, and molecular docking were combined to prioritize candidate genes. EIF6 and YWHAB were examined in an inflammatory EC-like model, and EIF6-associated p47phox/TGF-β responses were further assessed in DMM-induced OA mouse tissues. RESULTS: Single-cell analysis identified an EC population enriched in OA cartilage. ECs showed relatively high oxidative stress activity and represented the largest component of HOS cells. The proportion of ECs among HOS cells increased in OA samples, and HOS cells were concentrated near the EC-enriched region in the UMAP space. HOS/EC-enriched populations showed broad communication with other chondrocyte populations and were associated with inflammation, stress-related signaling, and extracellular matrix remodeling pathways. Integrated computational analyses prioritized EIF6 and YWHAB as candidate molecules associated with EC-related oxidative stress. In inflammatory EC-like cells, IL-1β and TNF-α increased EIF6 and YWHAB expression. EIF6 or YWHAB knockdown attenuated IL-1β-induced p47phox and NOX2 expression and reduced ROS-related fluorescence, with EIF6 knockdown showing a stronger effect. Co-immunoprecipitation suggested altered YWHAB-p47phox interaction under inflammatory stimulation. In DMM-induced OA tissues, EIF6 knockdown reduced p47phox upregulation, whereas EIF6 overexpression increased TGF-β staining, which was partly attenuated by p47phox knockdown. CONCLUSION: This study identifies EC-associated oxidative stress features in OA cartilage and prioritizes EIF6/YWHAB-associated p47phox-NOX2 signaling as a candidate axis linked to inflammatory ROS-related responses and tissue-level OA responses.
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eRNAs Identify Immune Microenvironment Patterns and Provide a Novel Prognostic Tool in Acute Myeloid Leukemia.
Background: Enhancer RNAs (eRNAs) play an essential role in tumorigenesis as non-coding RNAs transcribed from enhancer regions. However, the landscape of eRNAs in acute myeloid leukemia (AML) and the potential roles of eRNAs in the tumor microenvironment (TME) remain unclear. Method: Gene expression data collected from The Cancer Genome Atlas (TCGA) project were combined with Histone ChIP-seq so as to reveal the comprehensive landscape of eRNAs. Single-sample gene set enrichment analysis algorithm (ssGSEA) and ESTIMATE were employed to enumerate immune cell infiltration and tumor purity. Results: Most prognostic eRNAs were enriched in immune-related pathways. Two distinct immune microenvironment patterns, the immune-active subtype and the immune-resistant subtype, were identified in AML. We further developed an eRNA-derived score (E-score) that could quantify immune microenvironment patterns and predict the response to immune checkpoint inhibitor (ICI) treatment. Finally, we established a prognostic nomogram combining E-score and other clinical features, which showed great discriminative power in both the training set [Harrell's concordance index (C index): 0.714 (0.651-0.777), p < 0.0001] and validation set [C index: 0.684 (0.614-0.755), p < 0.0001]. Calibration of the nomogram was also validated independently. Conclusion: In this study, we systematically understood the roles of eRNAs in regulating TME diversity and complexity. Moreover, our E-score model provided the first predictive model for ICI treatment in AML.
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Senescence gene expression in clear cell renal cell carcinoma: Role of tumor immune microenvironment and senescence-associated survival prediction.
Clear cell renal cell carcinoma (ccRCC) constitutes the most prevalent histopathologic subtype of renal cell carcinoma. The interplay between aging and cancer is complicated, and we provide a relatively new set of senescence genes that has not yet been used in the study of clear cell renal cell carcinoma. Our objective is to investigate the involvement of senescence in the development and diagnosis of ccRCC. RNA-seq and clinical data for ccRCC was obtained from the cancer genome atlas and gene expression omnibus databases. Consensus clustering analysis was performed to identify novel molecular subgroups. Tumor immune status was assessed using estimating stromal and immune cells in malignancy using expression data, microenvironment cell populations, and single-sample gene set enrichment analysis analyses. Functional analysis, including gene ontology, gene set variation analysis, and gene set enrichment analysis, was conducted to explore potential mechanisms. A prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis. Decision trees and nomograms were developed for survival prediction. SenMayo classified ccRCC patients into 2 molecular subtypes with significantly different survival rates, and significant differences in their immune status, characterized by poor prognosis with relatively high immune status. Besides, the differentially expressed genes between the 2 subgroups were mainly enriched in immune-related pathways. The burden of aging tissues and cells may lead to immune dysregulation and drug resistance, which could contribute to poor prognosis in ccRCC patients. Risk models, decision trees, and nomogram for ccRCC survival prediction have great potential applications. In conclusion, our study establishes a clear association between aging in ccRCC and the immune microenvironment, demonstrating the predictive potential of senescence genes for ccRCC prognosis.