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Cell-Type-Specific Gene Regulatory Networks of Pro-Inflammatory and Pro-Resolving Lipid Mediator Biosynthesis in the Immune System.
Lipid mediators are important regulators in inflammatory responses, and their biosynthetic pathways are targeted by commonly used anti-inflammatory drugs. Switching from pro-inflammatory lipid mediators (PIMs) to specialized pro-resolving (SPMs) is a critical step toward acute inflammation resolution and preventing chronic inflammation. Although the biosynthetic pathways and enzymes for PIMs and SPMs have now been largely identified, the actual transcriptional profiles underlying the immune cell type-specific transcriptional profiles of these mediators are still unknown. Using the Atlas of Inflammation Resolution, we created a large network of gene regulatory interactions linked to the biosynthesis of SPMs and PIMs. By mapping single-cell sequencing data, we identified cell type-specific gene regulatory networks of the lipid mediator biosynthesis. Using machine learning approaches combined with network features, we identified cell clusters of similar transcriptional regulation and demonstrated how specific immune cell activation affects PIM and SPM profiles. We found substantial differences in regulatory networks in related cells, accounting for network-based preprocessing in functional single-cell analyses. Our results not only provide further insight into the gene regulation of lipid mediators in the immune response but also shed light on the contribution of selected cell types in their biosynthesis.
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Mapping the Single-cell Differentiation Landscape of Osteosarcoma.
The genetic and intratumoral heterogeneity observed in human osteosarcomas (OS) poses challenges for drug development and the study of cell fate, plasticity, and differentiation, processes linked to tumor grade, cell metastasis, and survival. To pinpoint errors in OS differentiation, we transcriptionally profiled 31,527 cells from a tissue-engineered model that directs MSCs toward adipogenic and osteoblastic fates. Incorporating pre-existing chondrocyte data, we applied trajectory analysis and non-negative matrix factorization (NMF) to generate the first human mesenchymal differentiation atlas. This 'roadmap' served as a reference to delineate the cellular composition of morphologically complex OS tumors and quantify each cell's lineage commitment. Projecting these signatures onto a bulk RNA-seq OS dataset unveiled a correlation between a stem-like transcriptomic phenotype and poorer survival outcomes. Our study takes the critical first step in accurately quantifying OS differentiation and lineage, a prerequisite to better understanding global differentiation bottlenecks that might someday be targeted therapeutically.
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Single-cell transcriptomic profiling of rat iridocorneal angle at perinatal stages: Revisiting the development of periocular mesenchyme.
The periocular mesenchyme (POM) gives rise to key structures in the ocular anterior segment, and its malformation leads to anterior segment dysgenesis (ASD) with iridocorneal angle (ICA) abnormalities. However, the transcriptional profile of the POM and the regulatory mechanisms governing cell-fate decision during anterior eye and ICA development remain poorly understood. In this study, we performed a comprehensive time-series analysis by sequencing rat anterior ocular samples collected at five consecutive perinatal stages: embryonic days 16.5 and 18.5, the day of birth, and postnatal days 4 and 8, at the single-cell level and validated a portion of in silico findings with immunostaining. High-quality transcriptomes were obtained from 59,416 cells with diverse embryonic origins. A prominent transcriptional shift was observed in POM cells, coinciding with anatomical alterations around the ICA shortly after birth. We illustrated the molecular signatures of five POM subclusters while tracing their developmental trajectories. Additionally, we identified key driver genes, as well as cell type-specific and stage-wise gene modules underlying lineage specification. Furthermore, the switch of regulon network and cellular crosstalk associated with POM maturation were unveiled. Lastly, we mapped ASD-relevant genes to this single-cell atlas, revealing distinct expression patterns. Collectively, this study provides a transcriptomic blueprint for understanding normal POM and ICA development, as well as a valuable reference for future research into ASD pathogenesis.
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Integrating multi-omics analysis identifies DNA damage-related gene CLSPN as a biomarker in gastric cancer.
DNA damage exhibits a strong correlation with gastric cancer (GC). However, there is still a paucity of comprehensive, in-depth investigations into this relationship. We aimed to explore the association between DNA damage-related genes and GC to provide insights into its molecular mechanisms and potential biomarkers. For this study, Bulk RNA sequencing data of GC were obtained from The Cancer Genome Atlas (TCGA), single-cell RNA sequencing datasets were retrieved from the Gene Expression Omnibus (GEO), and a DNA damage-associated gene set was sourced from the GeneCards database. Through the application of survival analysis, differential expression gene analysis, and weighted gene co-expression network analysis, we identified DNA damage-related genes potentially linked to GC. Subsequently, multiple machine learning approaches were employed to screen out hub genes with considerable diagnostic potential. Analysis of bulk RNA sequencing data verified gene expression patterns in GC. Single-cell analysis further demonstrated cell-type-specific gene expression, and immunohistochemical experiments were conducted to validate the potential biomarker utility of key genes. Our findings revealed that thirteen DNA damage-related genes that may be linked to GC were identified. Subsequently, CLSPN and SALL4 were screened out as hub genes possessing considerable diagnostic potential. Analysis of bulk RNA sequencing data verified the upregulated expression of these two genes in GC, thereby underscoring their predictive significance. Across multiple machine learning methods, CLSPN was consistently ranked as the gene with the highest importance. Single-cell analysis further demonstrated that CLSPN is predominantly highly expressed in tumor cells, which emphasizes its cell-type-specific function in GC. To validate CLSPN's potential as a biomarker, we conducted immunohistochemical experiments; these experiments showed high CLSPN expression in GC tissues, and the expression levels were significantly correlated with age, tumor size, pT stage and lymph node metastasis. This study reinforces the link between DNA damage and GC and offers fresh perspectives on its underlying molecular mechanisms. Nonetheless, further validation in clinical evaluation is essential to confirm its practical value for GC management strategies.
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Single-cell RNA sequencing reveals a peripheral landscape of immune cells in Schistosomiasis japonica.
BACKGROUND: Schistosomiasis, also known as bilharzia, is a devastating parasitic disease. This progressive and debilitating helminth disease is often associated with poverty and can lead to chronic poor health. Despite ongoing research, there is currently no effective vaccine for schistosomiasis, and praziquantel remains the only available treatment option. According to the progression of schistosomiasis, infections caused by schistosomes are classified into three distinct clinical phases: acute, chronic and advanced schistosomiasis. However, the underlying immune mechanism involved in the progression of schistosomiasis remains poorly understood. METHODS: We employed single-cell RNA sequencing (scRNA-seq) to profile the immune landscape of Schistosomiasis japonica infection based on peripheral blood mononuclear cells (PBMCs) from a healthy control group (n = 4), chronic schistosomiasis group (n = 4) and advanced schistosomiasis group (n = 2). RESULTS: Of 89,896 cells, 24 major cell clusters were ultimately included in our analysis. Neutrophils and NK/T cells accounted for the major proportion in the chronic group and the healthy group, and monocytes dominated in the advanced group. A preliminary study showed that NKT cells were increased in patients with schistosomiasis and that CXCR2 + NKT cells were proinflammatory cells. Plasma cells also accounted for a large proportion of B cells in the advanced group. MHC molecules in monocytes were notably lower in the advanced group than in the chronic group or the healthy control group. However, monocytes in the advanced group exhibited high expression of FOLR3 and CCR2. CONCLUSIONS: Overall, this study enhances our understanding of the immune mechanisms involved in schistosomiasis. It provides a transcriptional atlas of peripheral immune cells that may contribute to elimination of the disease. This preliminary study suggests that the increased presence of CCR2 + monocyte and CXCR2 + NKT cells might participate in the progression of schistosomiasis.
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Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis.
Molecular profiling of single cells has advanced our knowledge of the molecular basis of development. However, current approaches mostly rely on dissociating cells from tissues, thereby losing the crucial spatial context of regulatory processes. Here, we apply an image-based single-cell transcriptomics method, sequential fluorescence in situ hybridization (seqFISH), to detect mRNAs for 387 target genes in tissue sections of mouse embryos at the 8-12 somite stage. By integrating spatial context and multiplexed transcriptional measurements with two single-cell transcriptome atlases, we characterize cell types across the embryo and demonstrate that spatially resolved expression of genes not profiled by seqFISH can be imputed. We use this high-resolution spatial map to characterize fundamental steps in the patterning of the midbrain-hindbrain boundary (MHB) and the developing gut tube. We uncover axes of cell differentiation that are not apparent from single-cell RNA-sequencing (scRNA-seq) data, such as early dorsal-ventral separation of esophageal and tracheal progenitor populations in the gut tube. Our method provides an approach for studying cell fate decisions in complex tissues and development.
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SARS-CoV-2 Entry Genes Are Most Highly Expressed in Nasal Goblet and Ciliated Cells within Human Airways.
The SARS-CoV-2 coronavirus, the etiologic agent responsible for COVID-19 coronavirus disease, is a global threat. To better understand viral tropism, we assessed the RNA expression of the coronavirus receptor, ACE2, as well as the viral S protein priming protease TMPRSS2 thought to govern viral entry in single-cell RNA-sequencing (scRNA-seq) datasets from healthy individuals generated by the Human Cell Atlas consortium. We found that ACE2, as well as the protease TMPRSS2, are differentially expressed in respiratory and gut epithelial cells. In-depth analysis of epithelial cells in the respiratory tree reveals that nasal epithelial cells, specifically goblet/secretory cells and ciliated cells, display the highest ACE2 expression of all the epithelial cells analyzed. The skewed expression of viral receptors/entry-associated proteins towards the upper airway may be correlated with enhanced transmissivity. Finally, we showed that many of the top genes associated with ACE2 airway epithelial expression are innate immune-associated, antiviral genes, highly enriched in the nasal epithelial cells. This association with immune pathways might have clinical implications for the course of infection and viral pathology, and highlights the specific significance of nasal epithelia in viral infection. Our findings underscore the importance of the availability of the Human Cell Atlas as a reference dataset. In this instance, analysis of the compendium of data points to a particularly relevant role for nasal goblet and ciliated cells as early viral targets and potential reservoirs of SARS-CoV-2 infection. This, in turn, serves as a biological framework for dissecting viral transmission and developing clinical strategies for prevention and therapy.
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Decoding ALS from the tail end of RNA.
In this issue of Cell Genomics, McKeever et al.1 generate a single-nucleus transcriptomic atlas of ALS/FTLD brain and reveal widespread alternative polyadenylation changes. Their findings highlight 3' end RNA processing as a central integrator of stress responses, cell-type specificity, and disease susceptibility, offering new mechanistic insight and potential therapeutic directions.
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Decoding heart failure subtypes with neural networks via differential explanation analysis.
Single-cell transcriptomics offers critical insights into the molecular mechanisms of heart failure (HF) with reduced or preserved ejection fraction. However, understanding these mechanisms is hindered by the growing complexity of single-cell data and the difficulty in unmasking meaningful differential gene signatures among HF types. Machine learning, particularly deep neural networks (NNs), address these challenges by learning transcriptional patterns, reconstructing expression profiles and effectively classifying cells but often lacks interpretability. Recent advances in explainable AI (XAI) offer tools to clarify model decisions. Yet pinpointing differentially regulated genes with these tools remains challenging. We introduce a novel method to identify differentially explained genes (DXGs) based on importance scores derived from custom-built NNs. We highlight the superiority of DXGs in identifying HF subtypes-specific pathways that provide new insights into different types of HF. Offering a robust foundation for future research and therapeutic exploration in expanding transcriptome atlases.
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UCN-Centric Prognostic Model for Predicting Overall Survival and Immune Response in Colorectal Cancer.
Colorectal cancer (CRC), a prevalent malignancy, ranks third in global incidence and second in mortality rates. Despite advances in screening methods such as colonoscopy, the accurate diagnosis of CRC remains challenging due to the absence of reliable biomarkers. This study aimed to develop a robust prognostic model for precise CRC outcome prediction. Employing weighted co-expression network analysis (WGCNA) and Cox regression analysis on data from The Cancer Genome Atlas (TCGA), we identified a panel of 12 genes strongly associated with patient survival. This gene panel facilitated accurate CRC outcome predictions, which is also validated via the external validation cohort GSE17536. We conducted further investigations into the key gene, urocortin (UCN), using single-cell transcriptomic data and immune infiltration analysis in CRC patients. Our results revealed a significant correlation between high UCN expression and the reduced prevalence of key immune cells, including B cells, CD4+ cytotoxic T cells, CD8+ T cells, and NKT cells. Functional experiments showed that UCN gene interference in the CRC cell lines significantly decreased cancer cell proliferation, underscoring UCN's role in intestinal immunity modulation. The UCN-centric prognostic model developed enhances prognosis prediction accuracy and offers critical insights for CRC diagnosis and therapeutic interventions.