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The single-cell transcriptional landscape of mammalian organogenesis.
Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external organs. Here we investigate the transcriptional dynamics of mouse organogenesis at single-cell resolution. Using single-cell combinatorial indexing, we profiled the transcriptomes of around 2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation, in a single experiment. The resulting 'mouse organogenesis cell atlas' (MOCA) provides a global view of developmental processes during this critical window. We use Monocle 3 to identify hundreds of cell types and 56 trajectories, many of which are detected only because of the depth of cellular coverage, and collectively define thousands of corresponding marker genes. We explore the dynamics of gene expression within cell types and trajectories over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle.
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Bioinformatic analysis of serpina1 expression in papillary thyroid carcinoma and its potential association with Hashimoto's thyroiditis.
PURPOSE: Previous studies have suggested that SERPINA1 may promote a better prognosis in papillary thyroid carcinoma (PTC) along with Hashimoto's thyroiditis (HT). This study aims to further explore the role of the SERPINA1 gene in PTC and its relationship with HT using multiple databases. METHODS: Transcriptomic data from The Cancer Genome Atlas (TCGA) were utilized to analyze differences in SERPINA1 expression between PTC patients with and without HT. The expression levels of SERPINA1 in tumor tissues and its association with tumor characteristics were assessed using the Wilcoxon test across both patient groups. The impact of SERPINA1 expression on immune cell infiltration in PTC was evaluated using the CIBERSORT tool. Single-cell transcriptomic data from the Gene Expression Omnibus (GEO) were further analyzed to identify SERPINA1-expressing subpopulations based on Thyroid Differentiation Score (TDS) and pseudotime analysis. Gene Set Variation Analysis (GSVA) was employed to characterize pathways associated with SERPINA1, inferring its potential functions. Finally, CellChat was used to investigate key ligand-receptor interactions between SERPINA1-positive subpopulations and other cell types. RESULTS: TCGA data analysis reveals that, compared to normal thyroid tissue, the transcriptional level of SERPINA1 is significantly elevated in PTC tissues. Moreover, the expression of SERPINA1 is closely linked to certain clinical pathological features of PTC and the infiltration of immune cells in the tumor microenvironment. Single-cell transcriptome analysis reveals that SERPINA1 is primarily expressed in thyrocytes and myeloid cells. In thyrocytes, SERPINA1 is associated with complement-related proteins (e.g., C3, CD55). In poorly differentiated thyrocytes, it is linked to protease inhibitors and epithelial-mesenchymal transition (EMT) pathways, while in moderately differentiated thyrocytes, it associates with apolipoproteins APOE and APOC1. In macrophages, SERPINA1 is highly expressed in HT-associated macrophages and unpolarized macrophages, correlating with inflammation and extracellular matrix regulation pathways. Cell-cell interaction analysis indicates that SERPINA1-positive cells interact with other cells in the tumor microenvironment through macrophage migration inhibitory factor (MIF) and fibronectin 1 (FN1). CONCLUSION: Compared to normal thyroid tissue or cells, the expression level of SERPINA1 is elevated in PTC. In cancer cells, SERPINA1 may be associated with the complement system and complement regulator functions. In poorly differentiated thyrocytes, SERPINA1 may primarily function as a protease inhibitor and is closely related to FN1. In moderately differentiated thyrocytes, SERPINA1 is associated with apolipoproteins. In unpolarized macrophages, the function of SERPINA1 may be to act as a serine protease inhibitor, participating in the remodeling of the extracellular matrix. In macrophages within an HT environment, the elevated expression of SERPINA1 may serve as a protective mechanism to limit inflammation. In the tumor microenvironment coexisting with HT, SERPINA1 outside the tumor cells may enter the tumor cells through lipid metabolism pathways. The potential role of SERPINA1 in PTC progression is complex, and the findings of this study require further validation.
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Identification of key ferroptosis-related targets in colorectal cancer: A transcriptomics-driven study via machine learning and AUcell analysis of single-cell RNA-sequencing.
Background: Colorectal cancer (CRC) has emerged as the third most prevalent malignancy worldwide. The pursuit of dependable molecular signatures stands as a crucial endeavor for tailoring treatment approaches, refining prognostic assessments, and heightening therapeutic efficacy in CRC management. This investigation was conducted to elucidate essential genes and molecular mechanisms associated with ferroptosis in CRC through implementing machine-learning approaches and AUcell analysis. Methods: The GEO repository and FerrDb served as primary sources for extracting information of gene sets on colorectal cancer and iron-dependent cell death mechanisms. To determine potential therapeutic targets with biomarker significance, we implemented LASSO and SVM-RFE methodology. The immune infiltrates were characterized followed by a competing endogenous RNA network analysis. The AUCell R package was utilized to examine the targeted gene activity patterns within individual cell lines using single-cell transcriptome data. The qRT-PCR and Human Protein Atlas (THPA) database were used to validate the expression of target genes. Potential therapeutic were explored through the DGIdb database. Results: Through the application of machine learning methodologies, five genes were identified as pivotal biomarker candidates: AQP8, NOX4, NR5A2, SCD, and TIMP1. The result of AUcell algorithm showed that the distribution of AUC values exhibited a bimodal pattern, with 2733 cells demonstrating elevated AUC values above the threshold of 0.091. The result of qRT-PCR showed that NOX4, SCD, and TIMP1 were significantly upregulated, while the expression of AQP8 and NR5A2 did not exhibit the expected differences. Both mRNA and IHC analyses from HPA database confirmed the abnormal expression of these pivotal candidate biomarkers. Algorithmic assessment via CIBERSORT methodology revealed notable shifts in immune cell composition within the tumor microenvironment of individuals diagnosed with CRC. Furthermore, A competing endogenous RNA network and 51 potential drug candidates were identified. Conclusion: A systematic framework implementing machine-learning approaches and AUcell analysis was established for identifying core ferroptosis genes and validating their functional link to ferroptosis. Meanwhile, a reliable ferroptosis-associated signature was established, which shed new light on the ferroptosis-mediated molecular mechanisms and therapeutic potential underlying CRC.
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diffMIN: Reconstructing Sample-Specific Differential Gene Regulatory Networks Based on Mutual Information.
Gene regulatory mechanisms are pivotal in the study of biological systems. Sample-specific networks (SSNs) have recently proven highly effective in scrutinizing diverse biological processes more precisely. Utilizing SSNs as features for clustering, personalized diagnosis, and treatment has gained increasing significance, surpassing the conventional reliance on differential gene expression. Several computational methods have been developed for the inference of SSNs. However, the existing methods are currently limited to constructing gene regulatory networks for individual samples. We proposed a new method for reconstructing sample-specific differential gene regulatory networks based on mutual information (diffMIN). Analysis of various single-cell datasets demonstrated that diffMIN outperforms existing SSNs algorithms regarding the cell clustering. We utilized diffMIN to analyze transverse aortic constriction (TAC) single-cell data, enabling us to generate differential networks highlighting the changes in pressure-overload, ultimately identifying a monotonic change in regulatory networks during heart failure. Additionally, we applied diffMIN on breast cancer samples from The Cancer Genome Atlas (TCGA). We found that, diffMIN can effectively identify important genes that may not exhibit significant expressional differences but are highly correlated with patient prognosis. This discovery highlights the potential utility of diffMIN in identifying crucial genes in cancer. In conclusion, our method is widely applicable and performs well in cell clustering, identification of key genes, investigation of biological processes, and disease diagnosis by constructing sample-specific differential networks.
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Transcriptomic and single-cell analyses reveal the prognostic value of ETV4 and its role in shaping the immune landscape of colorectal cancer.
BACKGROUND: Colorectal cancer (CRC) remains a major health threat with poor prognosis in advanced stages. The transcription factor ETV4 (ETS translocation variant 4) has been implicated in various cancers, but its specific role, prognostic value, and mechanisms in CRC, particularly concerning the tumor immune microenvironment, are not fully understood. METHODS: We performed a comprehensive analysis using transcriptomic data from The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing data from the GEO database (GSE231559). Bioinformatic approaches included survival analysis, immune cell infiltration estimation via CIBERSORT, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and single-cell clustering. Experimental validation was conducted on clinical CRC tissue samples and HCT-116 cells, employing flow cytometry, immunohistochemistry, quantitative real-time PCR (Q-PCR), and Western blot. RESULTS: In the TCGA cohort, ETV4 was significantly upregulated in 650 CRC tissues vs 51 adjacent normal tissues, with high expression linked to poorer overall survival in CRC patients.Immune infiltration analysis revealed correlations between ETV4 expression and specific immune cell subsets, notably macrophages. Flow cytometry and immunohistochemistry confirmed that high ETV4 expression was linked to increased polarization of immunosuppressive M2-type macrophages in the tumor microenvironment. Furthermore, bioinformatic GSVA and subsequent wet-lab experiments demonstrated that ETV4 is a downstream target gene of the WNT/β-catenin signaling pathway. Activation of this pathway upregulated ETV4 expression, while inhibition downregulated it, establishing a functional link. CONCLUSION: This integrative study reveals that ETV4 is a prognostic biomarker in CRC. It promotes tumor progression by reshaping the immunosuppressive microenvironment, particularly through inducing M2 macrophage polarization, and is regulated by the oncogenic WNT/β-catenin pathway. These findings suggest that ETV4 is expected to become a potential diagnostic biomarker and candidate therapeutic target for colorectal cancer, providing a novel direction for subsequent research on the diagnosis and treatment of CRC.
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Decoding adipocyte heterogeneity through single-nucleus transcriptomics unveils subtype-specific adipocytes orchestrate immunosuppressive niches in breast cancer.
BACKGROUND: While adipose tissue constitutes a substantial proportion of breast composition, the functional characteristics and pathological relevance of the adipocyte microenvironment in breast carcinogenesis remain undercharacterized. This study employs single-nucleus RNA sequencing (snRNA-seq) to establish a comprehensive cellular atlas of adipocyte heterogeneity across molecular subtypes of breast cancer, aiming to elucidate subtype-specific adipocyte contributions to tumor microenvironment modulation. METHODS: snRNA-seq was performed on breast adipose tissues isolated from individuals without cancer and treatment-naïve breast cancer. Various adipocyte and pre-adipocyte subclusters were identified. Comparative analyses of cellular distribution and transcriptional profiles were performed across disease states and molecular subtypes. Pseudotime, cell communication, and immunofluorescence analyses were further implemented to investigate cellular dynamics and microenvironment interactions. RESULTS: snRNA-seq data of 86,529 nuclei were obtained. Three adipocyte and seven pre-adipocyte subclusters were identified, of which Adi_LDLR, Pre_Adi_LDLR, and Pre_Adi_LGR4_TGFBR1 were restricted to cancer-associated adipose (CAAs). Adi_LDLR and Pre_Adi_LDLR were enriched in estrogen receptor (ER)-positive CAAs and related to cell senescence and immunosuppression. Pre_Adi_LGR4_TGFBR1 was predominantly present in triple-negative breast cancer, functionally pro-proliferative, immunosuppressive, and lacked normal adipose function. The immunofluorescence intensity of LDLR (p=0.031) and TGFBR1 (p=0.038) was positively associated with disease recurrence, suggesting the formation of immunosuppressive niches by these cancer-specific adipose subsets in both subtypes. Cell communication analyses revealed a specific (pre-) adipocyte-macrophage interaction via ligand-receptor pairs involved in stromal remodeling and tumor migration for ER-positive tumors, whereas tumor proliferation and metastasis for triple-negative ones likely contribute to tumor progression. CONCLUSIONS: This study delineated a distinct adipocyte landscape in breast cancer and subtype-specific immunosuppressive niches fostered by CAAs and (pre-) adipocyte-macrophage interactions. These findings provide novel therapeutic targets for microenvironment-directed interventions in breast oncology.
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Patterns of Transposable Element Expression and Insertion in Cancer.
Human transposable element (TE) activity in somatic tissues causes mutations that can contribute to tumorigenesis. Indeed, TE insertion mutations have been implicated in the etiology of a number of different cancer types. Nevertheless, the full extent of somatic TE activity, along with its relationship to tumorigenesis, have yet to be fully explored. Recent developments in bioinformatics software make it possible to analyze TE expression levels and TE insertional activity directly from transcriptome (RNA-seq) and whole genome (DNA-seq) next-generation sequence data. We applied these new sequence analysis techniques to matched normal and primary tumor patient samples from the Cancer Genome Atlas (TCGA) in order to analyze the patterns of TE expression and insertion for three cancer types: breast invasive carcinoma, head and neck squamous cell carcinoma, and lung adenocarcinoma. Our analysis focused on the three most abundant families of active human TEs: Alu, SVA, and L1. We found evidence for high levels of somatic TE activity for these three families in normal and cancer samples across diverse tissue types. Abundant transcripts for all three TE families were detected in both normal and cancer tissues along with an average of ~80 unique TE insertions per individual patient/tissue. We observed an increase in L1 transcript expression and L1 insertional activity in primary tumor samples for all three cancer types. Tumor-specific TE insertions are enriched for private mutations, consistent with a potentially causal role in tumorigenesis. We used genome feature analysis to investigate two specific cases of putative cancer-causing TE mutations in further detail. An Alu insertion in an upstream enhancer of the CBL tumor suppressor gene is associated with down-regulation of the gene in a single breast cancer patient, and an L1 insertion in the first exon of the BAALC gene also disrupts its expression in head and neck squamous cell carcinoma. Our results are consistent with widespread somatic activity of human TEs leading to numerous insertion mutations that can contribute to tumorigenesis in a variety of tissues.
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GLN2 as a key biomarker and therapeutic target: evidence from a comprehensive pan-cancer study using molecular, functional, and bioinformatic analyses.
BACKGROUND: GNL2, a nuclear protein, is involved in ribosome production and cell cycle regulation. However, its expression and function in different types of tumors are not well understood. Comprehensive studies across multiple cancer types are needed to assess the potential of GNL2 as a diagnostic, prognostic, and immunological marker. METHODS: mRNA expression data, copy number alteration threshold data, masked copy number segmentation data, and DNA methylation 450 K data from The Cancer Genome Atlas (TCGA) pan-cancer cohort were obtained from the Firehose database. Additional data, including miRNA, The Cancer Proteome Atlas (TCPA), mutation data, and clinical information, were sourced from the University of California Santa Cruz (UCSC) Xena database. The cBioPortal database facilitates the examination of GNL2 mutation frequency, location, and 3D structure in the TCGA database. Gene Expression Omnibus (GEO) data verified the transcriptome level expression in the TCGA cohort. Protein expression levels were analyzed via the Human Protein Atlas (HPA) database and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Gene set enrichment analysis (GSEA) was employed to investigate the biological role of GNL2 across cancers. Multiple immune infiltration algorithms from the TIMER2.0 database were utilized to examine the correlation between GNL2 expression and the tumor immune microenvironment. The transcriptome-wide immune infiltration results were validated using 72 single-cell datasets from the Tumor Immune Single-cell Hub (TISCH) database. Pan-cancer survival maps were constructed, and GNL2 expression in different molecular subtypes across cancers was examined. The relationship between GNL2 and drug resistance was investigated using data from CellMiner, GDSC, and CTRP. The Comparative Toxicogenomics Database (CTD) was used to identify chemicals affecting GNL2 expression. RESULTS: GNL2 is located primarily in the nucleus, and its expression is regulated mainly through somatic copy number alteration (SCNA) and aberrant DNA methylation, according to TCGA data. Database analysis and immunohistochemical results from clinical samples revealed high GNL2 expression in most tumors, which was correlated with diagnostic significance. High GNL2 expression often indicates a poor prognosis with pan-cancer prognostic value. Gene set enrichment analysis (GSEA) suggested that GNL2 is involved in tumor development through cell proliferation-related pathways. GNL2 expression is correlated with the expression of immune-related genes and the infiltration levels of multiple immune cells. The relationships between GNL2 and various drugs and chemicals were examined, revealing its influence on drug sensitivity and identifying five chemicals countering GNL2-mediated pro-cancer effects. CONCLUSION: Comprehensive bioinformatics analysis of GNL2 in pan-cancer tissues, combined with experimental validation, elucidated the pan-cancer expression pattern of GNL2, determined its diagnostic and prognostic value, and explored the biological functions of GNL2. GNL2 may be involved in the regulation of cell cycle progression and remodeling of the tumor microenvironment and is associated with poor prognosis as a risk factor in most tumors. The potential of GNL2-based cancer therapies is emphasized, assisting in predicting the response to chemotherapy.
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Temporal and spatial atlas of eosinophil specialization across tissues.
Eosinophils are multifunctional granulocytes involved in immune regulation, metabolism and tissue repair; however, the extent of their heterogeneity across tissues and the principles governing their specialization are not well defined. Here, we present a single-cell transcriptomic and proteomic atlas of mouse eosinophils spanning immune, barrier and metabolic sites. By integrating transcriptional profiling, high-dimensional surface proteomics and in vivo fate mapping, we show that eosinophil identity is shaped by both tissue-derived cues and the duration of local residency. Long-lived eosinophils in the small intestine diversify into transcriptionally and phenotypically distinct subsets, whereas short-lived and intermediate-lived populations in the lungs and colon, respectively, remain comparatively uniform. We identify trajectory-associated surface markers that stratify eosinophil maturation from bone-marrow progenitors to long-term tissue-resident subsets. This atlas establishes a unified framework in which tissue-specific residency time drives eosinophil maturation and diversification, providing a molecular toolkit for resolving eosinophil states in health and disease.
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Simultaneous recognition and segmentation of cells: application in C.elegans.
MOTIVATION: Automatic recognition of cell identities is critical for quantitative measurement, targeting and manipulation of cells of model animals at single-cell resolution. It has been shown to be a powerful tool for studying gene expression and regulation, cell lineages and cell fates. Existing methods first segment cells, before applying a recognition algorithm in the second step. As a result, the segmentation errors in the first step directly affect and complicate the subsequent cell recognition step. Moreover, in new experimental settings, some of the image features that have been previously relied upon to recognize cells may not be easy to reproduce, due to limitations on the number of color channels available for fluorescent imaging or to the cost of building transgenic animals. An approach that is more accurate and relies on only a single signal channel is clearly desirable. RESULTS: We have developed a new method, called simultaneous recognition and segmentation (SRS) of cells, and applied it to 3D image stacks of the model organism Caenorhabditis elegans. Given a 3D image stack of the animal and a 3D atlas of target cells, SRS is effectively an atlas-guided voxel classification process: cell recognition is realized by smoothly deforming the atlas to best fit the image, where the segmentation is obtained naturally via classification of all image voxels. The method achieved a 97.7% overall recognition accuracy in recognizing a key class of marker cells, the body wall muscle (BWM) cells, on a dataset of 175 C.elegans image stacks containing 14 118 manually curated BWM cells providing the 'ground-truth' for accuracy. This result was achieved without any additional fiducial image features. SRS also automatically identified 14 of the image stacks as involving ±90° rotations. With these stacks excluded from the dataset, the recognition accuracy rose to 99.1%. We also show SRS is generally applicable to other cell types, e.g. intestinal cells. AVAILABILITY: The supplementary movies can be downloaded from our web site http://penglab.janelia.org/proj/celegans_seganno. The method has been implemented as a plug-in program within the V3D system (http://penglab.janelia.org/proj/v3d), and will be released in the V3D plugin source code repository. CONTACT: pengh@janelia.hhmi.org.