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Expression of the foraging gene in adult Drosophila melanogaster.
The foraging gene in Drosophila melanogaster, which encodes a cGMP-dependent protein kinase, is a highly conserved, complex gene with multiple pleiotropic behavioral and physiological functions in both the larval and adult fly. Adult foraging expression is less well characterized than in the larva. We characterized foraging expression in the brain, gastric system, and reproductive systems using a T2A-Gal4 gene-trap allele. In the brain, foraging expression appears to be restricted to multiple sub-types of glia. This glial-specific cellular localization of foraging was supported by single-cell transcriptomic atlases of the adult brain. foraging is extensively expressed in most cell types in the gastric and reproductive systems. We then mapped multiple cis-regulatory elements responsible for parts of the observed expression patterns by a nested cloned promoter-Gal4 analysis. The mapped cis-regulatory elements were consistently modular when comparing the larval and adult expression patterns. These new data using the T2A-Gal4 gene-trap and cloned foraging promoter fusion GAL4's are discussed with respect to previous work using an anti-FOR antibody, which we show here to be non-specific. Future studies of foraging's function will consider roles for glial subtypes and peripheral tissues (gastric and reproductive systems) in foraging's pleiotropic behavioral and physiological effects.
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A high-resolution transcriptomic atlas depicting nitrogen fixation and nodule development in soybean.
Although root nodules are essential for biological nitrogen fixation in legumes, the cell types and molecular regulatory mechanisms contributing to nodule development and nitrogen fixation in determinate nodule legumes, such as soybean (Glycine max), remain incompletely understood. Here, we generated a single-nucleus resolution transcriptomic atlas of soybean roots and nodules at 14 days post inoculation (dpi) and annotated 17 major cell types, including six that are specific to nodules. We identified the specific cell types responsible for each step in the ureides synthesis pathway, which enables spatial compartmentalization of biochemical reactions during soybean nitrogen fixation. By utilizing RNA velocity analysis, we reconstructed the differentiation dynamics of soybean nodules, which differs from those of indeterminate nodules in Medicago truncatula. Moreover, we identified several putative regulators of soybean nodulation and two of these genes, GmbHLH93 and GmSCL1, were as-yet uncharacterized in soybean. Overexpression of each gene in soybean hairy root systems validated their respective roles in nodulation. Notably, enrichment for cytokinin-related genes in soybean nodules led to identification of the cytokinin receptor, GmCRE1, as a prominent component of the nodulation pathway. GmCRE1 knockout in soybean resulted in a striking nodule phenotype with decreased nitrogen fixation zone and depletion of leghemoglobins, accompanied by downregulation of nodule-specific gene expression, as well as almost complete abrogation of biological nitrogen fixation. In summary, this study provides a comprehensive perspective of the cellular landscape during soybean nodulation, shedding light on the underlying metabolic and developmental mechanisms of soybean nodule formation.
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Single-cell RNA sequencing for the study of development, physiology and disease.
An ongoing technological revolution is continually improving our ability to carry out very high-resolution studies of gene expression patterns. Current technology enables the global gene expression profiles of single cells to be defined, facilitating dissection of heterogeneity in cell populations that was previously hidden. In contrast to gene expression studies that use bulk RNA samples and provide only a virtual average of the diverse constituent cells, single-cell studies enable the molecular distinction of all cell types within a complex population mix, such as a tumour or developing organ. For instance, single-cell gene expression profiling has contributed to improved understanding of how histologically identical, adjacent cells make different differentiation decisions during development. Beyond development, single-cell gene expression studies have enabled the characteristics of previously known cell types to be more fully defined and facilitated the identification of novel categories of cells, contributing to improvements in our understanding of both normal and disease-related physiological processes and leading to the identification of new treatment approaches. Although limitations remain to be overcome, technology for the analysis of single-cell gene expression patterns is improving rapidly and beginning to provide a detailed atlas of the gene expression patterns of all cell types in the human body.
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A Single-Cell Transcriptome Atlas of the Mouse Glomerulus.
Background Three different cell types constitute the glomerular filter: mesangial cells, endothelial cells, and podocytes. However, to what extent cellular heterogeneity exists within healthy glomerular cell populations remains unknown.Methods We used nanodroplet-based highly parallel transcriptional profiling to characterize the cellular content of purified wild-type mouse glomeruli.Results Unsupervised clustering of nearly 13,000 single-cell transcriptomes identified the three known glomerular cell types. We provide a comprehensive online atlas of gene expression in glomerular cells that can be queried and visualized using an interactive and freely available database. Novel marker genes for all glomerular cell types were identified and supported by immunohistochemistry images obtained from the Human Protein Atlas. Subclustering of endothelial cells revealed a subset of endothelium that expressed marker genes related to endothelial proliferation. By comparison, the podocyte population appeared more homogeneous but contained three smaller, previously unknown subpopulations.Conclusions Our study comprehensively characterized gene expression in individual glomerular cells and sets the stage for the dissection of glomerular function at the single-cell level in health and disease.
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Single-cell RNA sequencing identifies distinct transcriptomic signatures between PMA/ionomycin- and αCD3/αCD28-activated primary human T cells.
Immunologists have activated T cells in vitro using various stimulation methods, including phorbol myristate acetate (PMA)/ionomycin and αCD3/αCD28 agonistic antibodies. PMA stimulates protein kinase C, activating nuclear factor-κB, and ionomycin increases intracellular calcium levels, resulting in activation of nuclear factor of activated T cell. In contrast, αCD3/αCD28 agonistic antibodies activate T cells through ZAP-70, which phosphorylates linker for activation of T cell and SH2-domain-containing leukocyte protein of 76 kD. However, despite the use of these two different in vitro T cell activation methods for decades, the differential effects of chemical-based and antibody-based activation of primary human T cells have not yet been comprehensively described. Using single-cell RNA sequencing (scRNA-seq) technologies to analyze gene expression unbiasedly at the single-cell level, we compared the transcriptomic profiles of the non-physiological and physiological activation methods on human peripheral blood mononuclear cell-derived T cells from four independent donors. Remarkable transcriptomic differences in the expression of cytokines and their respective receptors were identified. We also identified activated CD4 T cell subsets (CD55+) enriched specifically by PMA/ionomycin activation. We believe this activated human T cell transcriptome atlas derived from two different activation methods will enhance our understanding, highlight the optimal use of these two in vitro T cell activation assays, and be applied as a reference standard when analyzing activated specific disease-originated T cells through scRNA-seq.
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Single-cell RNA sequencing via endoscopic ultrasound-guided fine-needle biopsy for pancreatic tumors uncovered an aggressive subclone with a poor prognosis.
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) is a powerful tool which can unveil regulatory connections between genes and cells. However, due to the high demand for tissue quality, most scRNA-seq for pancreatic cancer is carried out by surgical specimen. This study (NCT05767697) aims to gain practical experience in carrying out scRNA-seq using endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB). MATERIALS AND METHODS: With a within-subjects design, two punctures from the same lesion, with a negative pressure of 5 ml (suction group) and without applying suction (non-suction group), were evaluated. Each biopsy sample was separated into three parts: white tissue, red material, and buffer for living cell counting. scRNA-seq was carried out according to the manufacturer's protocol. The pancreatic adenocarcinoma dataset in The Cancer Genome Atlas (TCGA) was used as external validation. RESULTS: A total of 20 patients with 40 specimens were enrolled. The suction group achieved a success rate of 80% (16/20), which was significantly higher than the 10% (2/20) success rate in the non-suction group (P < 0.001). scRNA-seq data were generated for four patients, including two early stage and two late stage. Overall, 15 major cell subtypes, including 4 cancer subclones, were identified across early and late stages. Analysis of differentially expressed genes identified an aggressive subclone highlighted by ubiquitin-conjugating enzyme E2 C (UBE2C) high expression, commonly in late stage. The TCGA dataset also demonstrated that UBE2C high-expression pancreatic cancer had a poor prognosis. CONCLUSIONS: EUS-FNB with a negative pressure of 5 ml is feasible for scRNA-seq in clinical practice. A UBE2C high-expression subclone correlates with a poor prognosis, potentially becoming a new therapeutic target in future studies.
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Integrated Analysis of Single-cell and Bulk RNA-Sequencing Identifies a Signature Based on Cancer-related Fibroblast Marker Genes to Predict Prognosis and Therapy Response in Lung Adenocarcinoma.
Cancer-related fibroblasts (CAFs), crucial in the tumor microenvironment, significantly influence tumorigenesis and extracellular matrix shaping. This study aimed to analyze the expression of CAF marker genes in lung adenocarcinoma (LUAD) and create a prognostic signature. We included 716 LUAD patients from different cohorts, conducting a comprehensive analysis of single-cell RNA sequencing data from the Gene Expression Omnibus (GEO) database, identifying 227 CAF marker genes. Using the Cancer Genome Atlas (TCGA) LUAD cohort, we developed a 3-gene prognostic signature, categorizing patients into high-risk and low-risk groups. The signature's predictive capability was validated across clinical subgroups and GEO cohorts. It was determined as an independent prognostic factor via univariate and multivariate analyses, leading to the construction of a nomogram for clinical prognosis prediction. Immune profile analysis indicated that high-risk patients exhibited immunosuppression and immune cell infiltration, while the tumor immune dysfunction and exclusion score suggested higher immunotherapy sensitivity in the low-risk group. In addition, high-risk patients showed greater sensitivity to several first-line chemotherapeutic drugs. The expression of hub genes was validated using quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) and the Human Protein Atlas (HPA). In conclusion, this study presented a novel prognostic signature for LUAD patients based on CAF marker genes, demonstrating strong predictive power for prognosis and treatment response.
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Single-cell transcriptomic atlas of different endometriosis indicating that an interaction between endometriosis-associated mesothelial cells (EAMCs) and ectopic stromal cells may influence progesterone resistance.
BACKGROUND: Endometriosis is a hormone-dependent disease, which can usually be divided into peritoneal endometriosis (PEM), deep-infiltrating endometriosis (DIE) and ovarian endometriosis (OEM). Although the three pathologic types are essentially the same disease, they differ in pathological manifestations, molecular features, pain symptoms and hormonal responsiveness. However, there is limited literature focusing on the differences among these types. In this study, we employed single-cell RNA sequencing (scRNA-seq) to profile the transcriptome of each type using surgical biopsy samples obtained from six patients. We aimed to explore and elucidate the variations among these different types of endometriosis. RESULTS: We identified five major cell types and 44 subpopulations, including the presence of mesothelial cells in all pathological types, including OEM. Furthermore, we characterised the variations in cell types across different pathological types by employing enrichment analysis to assess functions and pathways. Notably, our findings reveal distinct levels of epithelial-mesenchymal transition (EMT) processes experienced by mesothelial cells within the microenvironment of endometriotic lesions. Through ligand-receptor analysis and referencing relevant literature, we propose that mesothelial cells exert an influence on progesterone resistance in stromal cells through intercellular communication mediated by the FN1-AKT pathway. CONCLUSIONS: Our study comprehensively characterises the heterogeneity of the different pathologic types of endometriosis and offers valuable insights into the underlying mechanisms contributing to variations in progesterone resistance across the three subtypes. KEY POINTS: Single-cell RNA (ScRNA) atlas across types of endometriosis is established. Mesothelial cells are founded in ovarian endometriosis. Endometriosis-associated mesothelial cells (EAMCs) experience various level of epithelial-mesenchymal transition (EMT) process in different subtypes. EAMCs may exert an influence on progesterone resistance in stromal cells through intercellular communication mediated by the FN1-AKT pathway.
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Comprehensive Analysis of the Expression and Prognostic Significance of NPAS1 in Patients with Colorectal Adenocarcinoma Based on Bioinformatics.
BACKGROUND: Neuronal PAS domain protein 1 (NPAS1) is a protein-coding gene expressed mainly in the central nervous system and plays a key role in nervous system development. The expression and prognostic value of NPAS1 in colorectal adenocarcinoma (COAD) are unknown. METHODS: The expression and clinicopathological data of COAD from Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed. NPAS1 gene expression in colon cancer tissues was validated by Western blotting and immunohistochemical staining. Function and immune infiltration were established using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and single sample Gene Set Enrichment Analysis (ssGSEA). Methylation levels were analyzed utilizing the UALCAN database. NPAS1-related mutations were analyzed using cBioPortal. Single-cell and tissue-specific expression of NPAS1 was examined using the Human Protein Atlas. Protein-protein interactions and transcription factor networks of NPAS1-associated genes were analyzed with STRING and Network Analyst. RESULTS: Our study found that the expression level of NPAS1 was higher in normal tissues compared to COAD tissues, higher NPAS1 expression was associated with better survival outcomes. The bioinformatics analysis confirmed that NPAS1 co-expressed genes were linked to diverse signalling pathways and cellular functions. NPAS1 is correlated with CD56bright, cytotoxic and NK cells, and negatively correlated with helper T cells and Tcm cells. The DNA methylation level of NPAS1 in tumor tissues was elevated compared to normal tissues. We analyzed the mutation characteristics of NPAS1, single-cell expression profiling of NPAS1, and protein-protein interactions involving genes associated with NPAS1. These discoveries offered perspectives on the etiology, identification, and management of COAD. CONCLUSION: This study revealed a significant decrease of NPAS1 expression in COAD tissues, exhibiting associations with the clinical stage, prognosis, immune infiltration, and DNA methylation of COAD.
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Prognostic genes in hepatocellular carcinoma and their function: an analysis integrating high-throughput-based spatial transcriptomics and single-cell RNA-sequencing.
BACKGROUND: Identifying novel prognostic indicators for patients with hepatocellular carcinoma (HCC) is critical to improving patient outcomes. Mapping the spatial composition of HCC tumors remains challenging. Therefore, this study aimed to identify prognostic genes and assess their role in HCC by integrating spatial transcriptome sequencing (ST-seq) and single-cell RNA sequencing (scRNA-seq). METHODS: HCC-related datasets, including The Cancer Genome Atlas (TCGA)-HCC, International Cancer Genome Consortium (ICGC)-HCC, GSE149614, and GSE203612, were examined in this study. Single-cell and spatial transcriptome analyses were first conducted and followed by cell communication analysis. Target cell types with differential proportions and higher enrichment were identified. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was implemented to select prognostic genes and construct a risk model. Subsequently, a nomogram incorporating independent prognostic factors was developed, and immune microenvironment and immunotherapy response were analyzed. Finally, pseudotime analysis was performed to determine the developmental trajectory of the target cell types. RESULTS: Among hepatocytes, T/natural killer (NK) cells, B cells, myeloid cells, fibroblasts, and endothelial cells, hepatocytes and T/NK cells were identified as target cell types. ST analysis delineated seven cell subclusters (C0-C6). Cell communication analysis indicated that hepatocytes interact more strongly with other cell types compared to T/NK cells. The number of interactions between C4 and other cell subclusters was higher than the interaction intensity. Of the seven prognostic genes, ADH4, IGFBP3, and LCAT were downregulated in HCC, while AKR1B10, GAGE2A, MAGEA6, and UCHL1 were upregulated. The patients deemed to have a higher risk according to the model had shorter survival. The nomogram that was created by combining the seven prognostic genes had a good ability to forecast the patients' probability of survival. In the immunoinfiltration analysis, UCHL1 expression had the highest positive correlation with T/NK cell abundance. Moreover, in silico analysis suggested a potential association between high-risk status and reduced sensitivity to immunotherapy. Pseudotime analysis indicated that the expression of UCHL1, GAGE2A, MAGEA6, ADH4, and LCAT displayed an inverted U shape during the differentiation of hepatocytes, with the expression of UCHL1 was elevated at the end of T/NK cell differentiation. CONCLUSIONS: This study identified hepatocytes, T/NK cells, and seven prognostic genes in HCC, constructed a risk model and nomogram capable of predicting patient prognosis, and revealed through computational prediction that patients in the high-risk group might exhibit a poor response to immunotherapy, thereby providing new potential tools for the clinical treatment of HCC.