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Single-Cell RNA Sequencing Reveals Microevolution of the Stickleback Immune System.
The risk and severity of pathogen infections in humans, livestock, or wild organisms depend on host immune function, which can vary between closely related host populations or even among individuals. This immune variation can entail between-population differences in immune gene coding sequences, copy number, or expression. In recent years, many studies have focused on population divergence in immunity using whole-tissue transcriptomics. But, whole-tissue transcriptomics cannot distinguish between evolved differences in gene regulation within cells, versus changes in cell composition within the focal tissue. Here, we leverage single-cell transcriptomic approaches to document signatures of microevolution of immune system structure in a natural system, the three-spined stickleback (Gasterosteus aculeatus). We sampled nine adult fish from three populations with variability in resistance to a cestode parasite, Schistocephalus solidus, to create the first comprehensive immune cell atlas for G. aculeatus. Eight broad immune cell types, corresponding to major vertebrate immune cells, were identified. We were also able to document significant variation in both abundance and expression profiles of the individual immune cell types among the three populations of fish. Furthermore, we demonstrate that identified cell type markers can be used to reinterpret traditional transcriptomic data: we reevaluate previously published whole-tissue transcriptome data from a quantitative genetic experimental infection study to gain better resolution relating infection outcomes to inferred cell type variation. Our combined study demonstrates the power of single-cell sequencing to not only document evolutionary phenomena (i.e., microevolution of immune cells) but also increase the power of traditional transcriptomic data sets.
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Decoding temporal heterogeneity in NSCLC through machine learning and prognostic model construction.
BACKGROUND: Non-small cell lung cancer (NSCLC) is a prevalent and heterogeneous disease with significant genomic variations between the early and advanced stages. The identification of key genes and pathways driving NSCLC tumor progression is critical for improving the diagnosis and treatment outcomes of this disease. METHODS: In this study, we conducted single-cell transcriptome analysis on 93,406 cells from 22 NSCLC patients to characterize malignant NSCLC cancer cells. Utilizing cNMF, we classified these cells into distinct modules, thus identifying the diverse molecular profiles within NSCLC. Through pseudotime analysis, we delineated temporal gene expression changes during NSCLC evolution, thus demonstrating genes associated with disease progression. Using the XGBoost model, we assessed the significance of these genes in the pseudotime trajectory. Our findings were validated by using transcriptome sequencing data from The Cancer Genome Atlas (TCGA), supplemented via LASSO regression to refine the selection of characteristic genes. Subsequently, we established a risk score model based on these genes, thus providing a potential tool for cancer risk assessment and personalized treatment strategies. RESULTS: We used cNMF to classify malignant NSCLC cells into three functional modules, including the metabolic reprogramming module, cell cycle module, and cell stemness module, which can be used for the functional classification of malignant tumor cells in NSCLC. These findings also indicate that metabolism, the cell cycle, and tumor stemness play important driving roles in the malignant evolution of NSCLC. We integrated cNMF and XGBoost to select marker genes that are indicative of both early and advanced NSCLC stages. The expression of genes such as CHCHD2, GAPDH, and CD24 was strongly correlated with the malignant evolution of NSCLC at the single-cell data level. These genes have been validated via histological data. The risk score model that we established (represented by eight genes) was ultimately validated with GEO data. CONCLUSION: In summary, our study contributes to the identification of temporal heterogeneous biomarkers in NSCLC, thus offering insights into disease progression mechanisms and potential therapeutic targets. The developed workflow demonstrates promise for future applications in clinical practice.
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Pan-cancer transcriptional atlas of minimal residual disease links DUSP1 to chemotherapy persistence.
Chemotherapy is a commonly effective treatment for most types of cancer. However, many patients experience a relapse due to minimal residual disease (MRD) after chemotherapy. Previous studies have analyzed the changes induced by chemotherapy for specific types of cancer, but our study is the first to comprehensively analyze MRD across various types of cancer. We included both bulk and single-cell RNA sequencing datasets. We compared the expression of the entire genome and calculated scores for canonical pathway signatures and immune infiltrates before and after chemotherapy across different types of cancer. Our findings revealed that DUSP1 was the most significantly and widely enriched gene in pan-cancer MRD. DUSP1 was found to be essential for MRD formation and played a role in T cell-fibroblast communications and the cytotoxic function of CD4 + T cells. Overall, our analysis provides a comprehensive understanding of the changes caused by chemotherapy and identifies potential targets for preventing and eliminating MRD, which could lead to long-term survival benefits for patients.
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Exploring the interaction between immune cells in the prostate cancer microenvironment combining weighted correlation gene network analysis and single-cell sequencing: An integrated bioinformatics analysis.
BACKGROUND: The rise of treatment resistance and variability across malignant profiles has made precision oncology an imperative in today's medical landscape. Prostate cancer is a prevalent form of cancer in males, characterized by significant diversity in both genomic and clinical characteristics. The tumor microenvironment consists of stroma, tumor cells, and various immune cells. The stromal components and tumor cells engage in mutual communication and facilitate the development of a low-oxygen and pro-cancer milieu by producing cytokines and activating pro-inflammatory signaling pathways. METHODS: In order to discover new genes associated with tumor cells that interact and facilitate a hypoxic environment in prostate cancer, we conducted a cutting-edge bioinformatics investigation. This included analyzing high-throughput genomic datasets obtained from the cancer genome atlas (TCGA). RESULTS: A combination of weighted gene co-expression network analysis and single-cell sequencing has identified nine dysregulated immune hub genes (AMACR, KCNN3, MME, EGFR, FLT1, GDF15, KDR, IGF1, and KRT7) that are believed to have significant involvement in the biological pathways involved with the advancement of prostate cancer enviriment. In the prostate cancer environment, we observed the overexpression of GDF15 and KRT7 genes, as well as the downregulation of other genes. Additionally, the cBioPortal platform was used to investigate the frequency of alterations in the genes and their effects on the survival of the patients. The Kaplan-Meier survival analysis indicated that the changes in the candidate genes were associated with a reduction in the overall survival of the patients. CONCLUSIONS: In summary, the findings indicate that studying the genes and their genomic changes may be used to develop precise treatments for prostate cancer. This approach involves early detection and targeted therapy.
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KLRD1 (CD94): A Prognostic Biomarker and Therapeutic Candidate in Head and Neck Squamous Cell Carcinoma.
Background: Killer Cell Lectin Like Receptor D1 (KLRD1) plays a crucial role in antitumor immunity. However, its expression patterns across various cancers, its relationship with patient prognosis, and its potential as an immunotherapy target remain inadequately understood. Methods: We analyzed KLRD1 expression across various cancer types using multi-omics data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases, correlating it with patient prognosis. Single-cell RNA sequencing data were employed to further explore KLRD1 expression in natural killer (NK) cells and exhausted CD8+ T cells (CD8Tex). Functional enrichment analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) identified the biological processes and pathways associated with KLRD1. Immune infiltration analysis, conducted via CIBERSORT, assessed the relationship between KLRD1 expression and immune cell infiltration within the tumor microenvironment. Furthermore, the Tracking Tumor Immunophenotype (TIP) meta-server and Easier tool were employed to assess the role of KLRD1 in the cancer immunity cycle and to predict immunotherapy responses. Drug sensitivity was predicted using tools like CellMiner and the Genomics of Drug Sensitivity in Cancer (GDSC) database to explore the link between KLRD1 expression and responsiveness to various anticancer drugs. Results: KLRD1 exhibits significant differential expression and strong prognostic value across cancers, particularly as an independent prognostic factor in head and neck squamous cell carcinoma (HNSC). Single-cell analysis revealed high expression of KLRD1 in NK and CD8Tex cells, indicating its critical role in antitumor immune responses. Functional enrichment analyses showed that KLRD1 is involved in several immune-related signaling pathways, including NK cell-mediated cytotoxicity and T cell receptor pathways. Immune infiltration analysis further confirmed a positive correlation between KLRD1 expression and the infiltration of various immune cells. Moreover, higher KLRD1 expression in HNSC is associated with enhanced immune pathway activity, increased sensitivity to cell division inhibitors, and the identification of arachidonyltrifluoromethane as a potential compound to counteract its oncogenic effects. Conclusion: In HNSC, KLRD1 is a key prognostic marker and potential target for personalized immunotherapy.
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Single-cell analysis of microglial activation after traumatic brain injury reveals immune signaling pathways linked to mitochondrial dysfunction and brain aging.
OBJECTIVE: Microglia are the primary immune cells in the central nervous system (CNS); however, their temporal and spatial responses to traumatic brain injury (TBI) at the single-cell level remain poorly defined. This study aimed to map the dynamic microglial responses to TBI using single-cell transcriptomics and validate key signaling pathways in vitro. METHODS: A single-cell transcriptomic atlas was reconstructed from publicly available datasets comprising cortical, hippocampal, and blood samples from 35 mice (11 blood, 12 cortex, and 12 hippocampus) subjected to TBI or sham treatment at 24 h and after 7 days. Comparative analyses were conducted to investigate the heterogeneity of myeloid cells, including monocytes, macrophages, and microglia, with a particular focus on activated microglia. The key findings were further validated using quantitative PCR (qPCR) in an in vitro TBI-mimicking model, employing lipopolysaccharide (LPS)-stimulated microglial cell lines to assess changes in gene expression. RESULTS: TBI induced rapid immune remodeling, including an increase in activated microglia in the cortex, enriched in leukocyte differentiation pathways, and elevated macrophage populations in the cortex and hippocampus, enriched in chemotaxis functions at 24 h. Ligand-receptor (LR) analysis revealed three major signaling axes-Ccl2/Ccl7-Ccr2, Tnf-Tnfrsf1b, and Grn-Flna-associated with monocyte recruitment, M1 polarization, and macrophage differentiation. Validation using qPCR confirmed significant upregulation of Ccl2, Tnf, and Grn in LPS-stimulated microglia, which is consistent with single-cell findings. CONCLUSION: This study provides the first integrative single-cell transcriptomic map of microglial-myeloid interactions after TBI across multiple tissues and time points, linking microglial signaling to mitochondrial dysfunction and neuroinflammation. These findings lay the foundation for therapeutic strategies targeting myeloid-driven immune regulation in TBI.
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A single-cell transcriptome atlas of human euploid and aneuploid blastocysts.
Aneuploidy is frequently detected in early human embryos as a major cause of early pregnancy failure. However, how aneuploidy affects cellular function remains elusive. Here, we profiled the transcriptomes of 14,908 single cells from 203 human euploid and aneuploid blastocysts involving autosomal and sex chromosomes. Nearly all of the blastocysts contained four lineages. In aneuploid chromosomes, 19.5% ± 1.2% of the expressed genes showed a dosage effect, and 90 dosage-sensitive domains were identified. Aneuploidy leads to prevalent genome-wide transcriptome alterations. Common effects, including apoptosis, were identified, especially in monosomies, partially explaining the lower cell numbers in autosomal monosomies. We further identified lineage-specific effects causing unstable epiblast development in aneuploidies, which was accompanied by the downregulation of TGF-β and FGF signaling, which resulted in insufficient trophectoderm maturation. Our work provides crucial insights into the molecular basis of human aneuploid blastocysts and may shed light on the cellular interaction during blastocyst development.
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Integrative cross-species transcriptome analysis reveals earlier occurrence of myelopoiesis in pre-circulation primates compared to mice.
Hematopoiesis originates in the yolk sac, which forms prior to the establishment of blood circulation and exhibits distinct developmental processes between primates and mice. Despite increasing appreciation of yolk sac hematopoiesis for its lifelong contribution to the adult hematopoietic system and its regulatory roles in organogenesis, cross-species differences, particularly before the onset of blood circulation, remain incompletely understood. In this study, we constructed an integrative cross-species transcriptome atlas of pre-circulation hematopoiesis in humans, monkeys ( Macaca fascicularis), and mice. This analysis identified conserved populations between primates and mice, while also revealing more differentiated myeloid, erythroid, and megakaryocytic lineages in pre-circulation primates compared to mice. Specifically, SPP1-expressing macrophages were detected in primates before the onset of blood circulation but were absent in mice. Cell-cell communication analysis identified CSF1 + extraembryonic mesoderm cells as a potential supportive niche for macrophage generation, with ligand-receptor interactions between macrophages and other cell populations in the human yolk sac. Interestingly, pre-circulation SPP1 + macrophages exhibited hallmark signatures reminiscent of a macrophage subset that positively regulates hematopoietic stem cell generation. Our findings provide a valuable cross-species resource, advancing our understanding of human pre-circulation yolk sac hematopoiesis and offering a theoretical basis for the regeneration of functional blood cells.
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Application of single-cell RNA sequencing analysis of novel breast cancer phenotypes based on the activation of ferroptosis-related genes.
Ferroptosis is distinct from classic apoptotic cell death characterized by the accumulation of reactive oxygen species (ROS) and lipid peroxides on the cell membrane. Increasing findings have demonstrated that ferroptosis plays an important role in cancer development, but the exploration of ferroptosis in breast cancer is limited. In our study, we aimed to establish a ferroptosis activation-related model based on the differentially expressed genes between a group exhibiting high ferroptosis activation and a group exhibiting low ferroptosis activation. By using machine learning to establish the model, we verified the accuracy and efficiency of our model in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) set and gene expression omnibus (GEO) dataset. Additionally, our research innovatively utilized single-cell RNA sequencing data to systematically reveal the microenvironment in the high and low FeAS groups, which demonstrated differences between the two groups from comprehensive aspects, including the activation condition of transcription factors, cell pseudotime features, cell communication, immune infiltration, chemotherapy efficiency, and potential drug resistance. In conclusion, different ferroptosis activation levels play a vital role in influencing the outcome of breast cancer patients and altering the tumor microenvironment in different molecular aspects. By analyzing differences in ferroptosis activation levels, our risk model is characterized by a good prognostic capacity in assessing the outcome of breast cancer patients, and the risk score can be used to prompt clinical treatment to prevent potential drug resistance. By identifying the different tumor microenvironment landscapes between the high- and low-risk groups, our risk model provides molecular insight into ferroptosis in breast cancer patients.
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Identification and Functional Analysis of Transcriptome Profiles, Long Non-Coding RNAs, Single-Nucleotide Polymorphisms, and Alternative Splicing from the Oocyte to the Preimplantation Stage of Sheep by Single-Cell RNA Sequencing.
Numerous dynamic and complicated processes characterize development from the oocyte to the embryo. However, given the importance of functional transcriptome profiles, long non-coding RNAs, single-nucleotide polymorphisms, and alternative splicing during embryonic development, the effect that these features have on the blastomeres of 2-, 4-, 8-, 16-cell, and morula stages of development has not been studied. Here, we carried out experiments to identify and functionally analyze the transcriptome profiles, long non-coding RNAs, single-nucleotide polymorphisms (SNPs), and alternative splicing (AS) of cells from sheep from the oocyte to the blastocyst developmental stages. We found between the oocyte and zygote groups significantly down-regulated genes and the second-largest change in gene expression occurred between the 8- and 16-cell stages. We used various methods to construct a profile to characterize cellular and molecular features and systematically analyze the related GO and KEGG profile of cells of all stages from the oocyte to the blastocyst. This large-scale, single-cell atlas provides key cellular information and will likely assist clinical studies in improving preimplantation genetic diagnosis.