Single-cell Transcriptomic Data Research Articles

ScMGATGRN: a multiview graph attention network-based method for inferring gene regulatory networks from single-cell transcriptomic data.

The gene regulatory network (GRN) plays a vital role in understanding the structure and dynamics of cellular systems, revealing complex regulatory relationships, and exploring disease mechanisms. Recently, deep learning (DL)-based methods have been proposed to infer GRNs from single-cell transcriptomic data and achieved impressive performance. However, these methods do not fully utilize graph topological information and high-order neighbor information from multiple receptive fields. To overcome those limitations, we propose a novel model based on multiview graph attention network, namely, scMGATGRN, to infer GRNs. scMGATGRN mainly consists of GAT, multiview, and view-level attention mechanism. GAT can extract essential features of the gene regulatory network. The multiview model can simultaneously utilize local feature information and high-order neighbor feature information of nodes in the gene regulatory network. The view-level attention mechanism dynamically adjusts the relative importance of node embedding representations and efficiently aggregates node embedding representations from two views. To verify the effectiveness of scMGATGRN, we compared its performance with 10 methods (five shallow learning algorithms and five state-of-the-art DL-based methods) on seven benchmark single-cell RNA sequencing (scRNA-seq) datasets from five cell lines (two in human and three in mouse) with four different kinds of ground-truth networks. The experimental results not only show that scMGATGRN outperforms competing methods but also demonstrate the potential of this model in inferring GRNs. The code and data of scMGATGRN are made freely available on GitHub (https://github.com/nathanyl/scMGATGRN).

Leveraging single-cell transcriptomic data to uncover immune suppressive cancer cell subsets in triple-negative canine breast cancers.

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for uncovering the complexities of various physiological and immunopathological conditions in veterinary medicine. However, there is currently limited information on immune-suppressive cancer subsets in canine breast cancers. In this study, we aimed to identify and characterize immune-suppressive subsets of triple-negative canine breast cancer (TNBC) by utilizing integrated scRNA-seq data from published datasets. Published scRNA-seq datasets, including data from six groups of 30 dogs, were subjected to integrated bioinformatic analysis. Immune modulatory TNBC subsets were identified through functional enrichment analysis using immune-suppressive gene sets, including those associated with anti-inflammatory and M2-like macrophages. Key immune-suppressive signaling, such as viral infection, angiogenesis, and leukocyte chemotaxis, was found to play a role in enabling TNBC to evade immune surveillance. In addition, interactome analysis revealed significant interactions between distinct subsets of cancer cells and effector T cells, suggesting potential T-cell suppression. The present study demonstrates a versatile and scalable approach to integrating and analyzing scRNA-seq data, which successfully identified immune-modulatory subsets of canine TNBC. It also revealed potential mechanisms through which TNBC promotes immune evasion in dogs. These findings are crucial for advancing the understanding of the immune pathogenesis of canine TNBC and may aid in the development of new immune-based therapeutic strategies.

DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer.

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

CD8+ T cell associated scoring model helps prognostic diagnosis and immunotherapy selection in patients with colon adenocarcinoma

ObjectiveT cell-mediated immunity plays a crucial role in the immune response against tumors, with CD 8+ T cells playing a leading role in the eradication of cancer cells. Material and methodsA total of 5 datasets were included in this study. Single cell transcriptome data were used to discover CD8+ T cell marker genes, and Bulk transcriptome data from TCGA and GEO were jointly analyzed to screen candidate prognostic genes. lasso regression was performed to construct prognostic models. Immunotherapy cohort (IMvigor 210 and GSE78220) was applied to validate the diagnostic power of markers. ResultSingle-cell transcriptome data identified 65 CD8+ T cell marker genes, highlighting their importance in T cell-mediated immune responses. Among these, 11 genes were identified as CD8+ T-associated differential genes through analysis of bulk data from TCGA and GEO. A prognostic model for 5 genes was identified based on Lasso regression, dividing colon adenocarcinoma (COAD) patients into high- and low-risk groups. This model exhibited higher prognostic accuracy compared to traditional clinicopathological characteristics (age, pathological stage, histological grading). Moreover, the risk score derived from this model successfully differentiated patient responses to immunotherapy, as validated in the IMvigor 210 and GSE78220 cohorts. ConclusionOur research introduces a novel prognostic signature based on CD8+ T cell marker genes, demonstrating significant predictive power for prognosis and immunotherapy response in COAD patients. This model offers a potential tool for improving patient stratification and personalizing treatment strategies.

Gene regulatory mechanisms of T cell exhaustion in diffuse large B cell lymphoma based on single-cell transcriptome data

Diffuse large B cell lymphoma (DLBCL) is a heterogeneous and aggressive B cell malignancy that accounts for about 30 % of non-Hodgkin lymphomas. The current standard treatment for DLBCL is rituximab plus chemotherapy, but many patients are refractory or relapse, indicating the need for improved understanding of its molecular pathology. T cell exhaustion is a state of dysfunction or impairment that occurs in chronic infections or cancers, and is associated with poor prognosis in DLBCL. However, the molecular mechanisms of T cell exhaustion in DLBCL are poorly understood. In this study, we performed a comprehensive analysis of T cell exhaustion in DLBCL using public single-cell transcriptome data. We identified different subtypes of T cells and characterized their gene expression features. We found that DLBCL had a significantly higher proportion of exhausted T cells than normal tonsil, and that exhausted T cells had distinct gene expression signatures from non-exhausted T cells. These signatures included genes related to inhibitory receptors, cytokines, transcription factors and metabolic enzymes. We also found that ID3 gene was significantly upregulated in exhausted T cells in DLBCL, which may play a key role in T cell exhaustion. We constructed a protein-protein interaction network, identifying major hub proteins involved in T cell exhaustion or migration. We also performed KEGG and GO enrichment analysis for the differentially expressed genes between exhausted and non-exhausted T cells, and found important signaling pathways related to T cell exhaustion in DLBCL. Our results provide new insights into the molecular mechanisms underlying T cell exhaustion and offer novel therapeutic targets for this complex disease.

Leveraging gene correlations in single cell transcriptomic data

BackgroundMany approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for rare cell types, subtleties of cell states, and details of gene regulatory networks—there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually).ResultsWe approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization—a step that skews distributions, particularly for sparse data—and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene–gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships.ConclusionsNew insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene–gene correlations.

Can the male germline offer insight into mammalian brain size expansion?

Recent advances in single-cell transcriptomic data have greatly expanded our understanding of both spermatogenesis and the molecular mechanisms of male infertility. However, this growing wealth of data could also shed light on a seemingly unrelated biological problem: the genetic basis of mammalian brain size expansion throughout evolution. It is now increasingly recognized that the testis and brain share many cellular and molecular similarities including pivotal roles for the RAS/MAPK and PI3K/AKT/mTOR pathways, mutations in which are known to have a pronounced impact on cell proliferation. Most notably, in the stem cell lineages of both organs, new mutations have been shown to increase cellular output over time. These include 'selfish' mutations in spermatogonial stem cells, which disproportionately increase the proportion of mutant sperm, and-to draw a parallel-human-specific mutations in neural stem cells which, by increasing the number of neurons, have been implicated in neocortical expansion. Here we speculate that the origin for many 'expansion'-associated mutations is the male germline and that as such, a deeper understanding of the mechanisms controlling testicular turnover may yield fresh insight into the biology and evolution of the brain.

CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics.

Recent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication in tissues systematically and with reduced bias. A key challenge is integrating known molecular interactions and measurements into a framework to identify and analyze complex cell-cell communication networks. Previously, we developed a computational tool, named CellChat, that infers and analyzes cell-cell communication networks from single-cell transcriptomic data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass-action-based model, which incorporates the core interaction between ligands and receptors with multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell-cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand-receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell-cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation.

AtML: An Arabidopsis thaliana root cell identity recognition tool for medicinal ingredient accumulation

Arabidopsis thaliana synthesizes various medicinal compounds, and serves as a model plant for medicinal plant research. Single-cell transcriptomics technologies are essential for understanding the developmental trajectory of plant roots, facilitating the analysis of synthesis and accumulation patterns of medicinal compounds in different cell subpopulations. Although methods for interpreting single-cell transcriptomics data are rapidly advancing in Arabidopsis, challenges remain in precisely annotating cell identity due to the lack of marker genes for certain cell types. In this work, we trained a machine learning system, AtML, using sequencing datasets from six cell subpopulations, comprising a total of 6000 cells, to predict Arabidopsis root cell stages and identify biomarkers through complete model interpretability. Performance testing using an external dataset revealed that AtML achieved 96.50% accuracy and 96.51% recall. Through the interpretability provided by AtML, our model identified 160 important marker genes, contributing to the understanding of cell type annotations. In conclusion, we trained AtML to efficiently identify Arabidopsis root cell stages, providing a new tool for elucidating the mechanisms of medicinal compound accumulation in Arabidopsis roots.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals ubiquitin promotes pulmonary fibrosis in chronic pulmonary diseases

It is estimated that there are 544.9 million people suffering from chronic respiratory diseases in the world, which is the third largest chronic disease. Although there are various clinical treatment methods, there is no specific drug for chronic pulmonary diseases, including chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and idiopathic pulmonary fibrosis (IPF). Therefore, it is urgent to clarify the pathological mechanism and medication development. Single-cell transcriptome data of human and mouse from GEO database were integrated by “Harmony” algorithm. The data was standardized and normalized by using “Seurat” package, and “SingleR” algorithm was used for cell grouping annotation. The “Findmarker” function is used to find differentially expressed genes (DEGs), which were enriched and analyzed by using "clusterProfiler", and a protein interaction network was constructed for DEGs, and four algorithms are used to find the hub genes. The expression of hub genes were analyzed in independent human and mouse single-cell transcriptome data. Bulk RNA data were used to integrate by the "SVA" function, verify the expression levels of hub genes and build a diagnostic model. The L1000FWD platform was used to screen potential drugs. Through exploring the similarities and differences by integrated single-cell atlas, we found that the lung parenchymal cells showed abnormal oxidative stress, cell matrix adhesion and ubiquitination in COPD, corona virus disease 2019 (COVID-19), ILD and IPF. Meanwhile, the lung resident immune cells showed abnormal Toll-like receptor signals, interferon signals and ubiquitination. However, unlike acute pneumonia (COVID-19), chronic pulmonary disease shows enhanced ubiquitination. This phenomenon was confirmed in independent external human single-cell atlas, but unfortunately, it was not confirmed in mouse single-cell atlas of bleomycin-induced pulmonary fibrosis model and influenza virus-infected mouse model, which means that the model needs to be optimized. In addition, the bulk RNA-Seq data of COVID-19, ILD and IPF was integrated, and we found that the immune infiltration of lung tissue was enhanced, consistent with the single-cell level, UBA52, UBB and UBC were low expressed in COVID-19 and high expressed in ILD, and had a strong correlation with the expression of cell matrix adhesion genes. UBA52 and UBB have good diagnostic efficacy, and salermide and SSR-69071 can be used as their candidate drugs. Our study found that the disorder of protein ubiquitination in chronic pulmonary diseases is an important cause of pathological phenotype of pulmonary fibrosis by integrating scRNA-Seq and bulk RNA-Seq, which provides a new horizons for clinicopathology, diagnosis and treatment.

NR3C2 affects the proliferation and invasiveness of colon cancer cells through the Wnt/β-Catenin signaling pathway

PurposeThe aim of this study was to explore the potential correlation between the nuclear receptor subfamily 3 group C member 2 (NR3C2) and outcomes of colon cancer, along with the mechanisms underlying this association.MethodmRNA (messenger RNA) data and clinical records pertaining to colon cancer were retrieved from The Cancer Genome Atlas (TCGA) database. The analysis of NR3C2 expression discrepancies between normal colon and tumor tissues was conducted using R software. In addition, we also studied the relationship between NR3C2 expression and prognosis, pathological parameters. The relative role of NR3C2 were further predicted through bioinformatics methods and receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of NR3C2 in colon cancer. Single-cell data from colon cancer samples in the GEO (Gene Expression Omnibus) database further investigated the mechanism of the lower survival associated with NR3C2 dysregulation. NR3C2 expression in three fresh colon cancer samples and their respective paracancer samples was determined. Furthermore, colon cancer cell models overexpressing NR3C2 and with knockdown NR3C2 were constructed by lentiviral vector transfection. Cell Counting Kit-8 assay, transplantation of tumors in nude mice and transwell assays were used to examine the proliferation, migration and invasion of colon cancer cells. The effect on the Wnt/β-catenin pathway, activities of cellular autophagy and cell apoptosis were examined by assessing the expression levels of several key proteins, including Bcl-2, Bax, and LC3.ResultsWe found that NR3C2 was found a significantly lower level in colon cancer tissues than in adjacent tissues, which was associated with distant and lymphatic metastases, clinical stage, and poor clinical outcome, and it was an independent prognostic factor and potential marker of colon cancer. Single-cell transcriptome data identified the subset of circulating T and B cells with high expression of NR3C2, which is involved in TNF signaling pathway. Functional experiments show that downregulation of NR3C2 resultsed in the activation of the Wnt/β-catenin signaling pathway, and promotesd the proliferation and invasion of colon cancer cells while suppressing cell autophagy and apoptosis.ConclusionNR3C2 may regulate Wnt/β-catenin to affect the proliferation, invasion apoptosis and autophagy of colon cancer, and this axis is a potential target for the treatment of colon cancer.

Integration of single-cell sequencing and bulk transcriptome data develops prognostic markers based on PCLAF+ stem-like tumor cells using artificial neural network in gastric cancer

BackgroundGastric cancer stem cells (GCSCs) are important tumour cells involved in tumourigenesis and gastric cancer development. However, their clinical value remains unclear due to the limitations of the available technologies. This study aims to explore the clinical significance of GCSCs, their connection to the tumour microenvironment, and their underlying molecular mechanisms. MethodsStem-like tumour cells were identified by mining single-cell transcriptomic data from multiple samples. Integrated analysis of single-cell and bulk transcriptome data was performed to analyse the role of stem-like tumour cells in predicting clinical outcomes by introducing the intermediate variable mRNA stemness degree (SD). Consensus clustering analysis was performed to develop an SD-related molecular classification strategy to assess the clinical characteristics in gastric cancer. A prognostic model was constructed using a customized approach that comprehensively considered SD-related gene signatures based on an artificial neural network. ResultsBy analysing single-cell data and validating immunofluorescence results, we identified a PCLAF+ stem-like tumour cell population in GC. By calculating SD, we observed that PCLAF+ stem-like tumour cells were associated with poor prognosis and certain clinical features. The SD was negatively correlated with the abundance of most immune cell types. Furthermore, we proposed an SD-related classification method and prognostic model. In addition, the customised prognostic model can be used to predict whether a patient respond to PD-1/PD-L1 immunotherapy. ConclusionWe identified a cluster of stem-like cells and elucidated their clinical significance, highlighting the possibility of their use as immunotherapeutic targets.

Abstract P355: Multiomic Analyses Reveal Sex-specific Mechanisms Governing Cardiac Fibrosis.

Cardiac fibrosis—the deposition of excess extracellular matrix in the heart—occurs during pathological cardiac remodelling and precedes cardiac dysfunction and heart failure. Experimental and clinical data show that both cardiac fibrosis and heart failure exhibit sex-specific differences, particularly in the absence of ischemic injury. However, no study to date has systematically examined this, or the hormonal mechanisms driving these differences, using high-resolution single-cell and spatial omics. To address this gap, we applied single-cell and spatial omics to map the quality and distribution of non-ischemic cardiac fibrosis in human and mouse hearts. Further, to query the impact of sex hormones on cardiac fibrosis development, we analysed hearts of castrated (CAST) and ovariectomized (OVX) mice, which were ectopically infused with angiotensin-II (AngII). Using single cell transcriptomic data of hypertensive mouse hearts, we identified a fibroblast population driving cardiac fibrosis with a unique gene signature. We confirmed the presence of these cells in multiple mouse models and aged donor human hearts. Spatial mapping of fibrosis showed a sex-specific distribution of fibrosis, specifically in the development of perivascular fibrosis in the heart. Hypertensive male mice exhibited extensive perivascular fibrosis, and coronary artery adventitial hypertrophy, which was absent in females. However, OVX resulted in the development of perivascular fibrosis in hypertensive mice suggesting a cardioprotective role of estrogen. Confirming these observations, spatial transcriptomic analyses showed distinct fibrotic signatures driving perivascular and interstitial fibrosis, and transcriptomic differences between females and males. Our findings provide fundamental new insights towards the importance of biological sex in driving cardiac fibrosis, pointing to new mechanisms that may be manipulated to ameliorate development of heart failure.

Deciphering the Dysregulating IGF-1–SP1–CD248 Pathway in Fibroblast Functionality during Diabetic Wound Healing

Reduced fibroblast activity is a critical factor in the progression of diabetic ulcers. CD248, a transmembrane glycoprotein prominently expressed in activated fibroblasts, plays a pivotal role in wound healing. However, the role of CD248 in diabetic wound healing and the CD248 regulatory pathway remains largely unexplored. Our study shows that CD248 expression is significantly reduced in skin wounds from both diabetic patients and mice. Single-cell transcriptome data analyses reveal a marked reduction of CD248-enriched secretory-reticular fibroblasts in diabetic wounds. We identify insulin-like growth factor-1 (IGF-1) as a key regulator of CD248 expression through the Akt/mTOR signaling pathway and the Sp1 transcription factor. Overexpression of CD248 enhances fibroblast motility, elucidating the underrepresentation of CD248-enriched fibroblasts in diabetic wounds. Immunohistochemical staining of diabetic wound samples further confirm low SP1 expression and fewer CD248-positive secretory-reticular fibroblasts. Further investigation reveals that elevated tumor necrosis factor α (TNFα) levels in diabetic environment promotes IGF-1 resistance, and inhibiting IGF-1-induced CD248 expression. In summary, our findings underscore the critical role of the IGF1-SP1-CD248 axis in activating reticular fibroblasts during wound-healing processes. Targeting this axis in fibroblasts could help develop a therapeutic regimen for diabetic ulcers.

Re-analysis of single cell and spatial transcriptomics data reveals B cell landscape in gastric cancer microenvironment and its potential crosstalk with tumor cells for clinical prognosis

BackgroundAt present, immunotherapy has become a powerful treatment for advanced gastric cancer (AGC), but not all patients can benefit from it. According to the latest research, the impact of B cell subpopulations on the immune microenvironment of gastric cancer (GC) is unknown. Exploring whether the interaction between B cells and tumor cells in GC affects the effectiveness of immunotherapy has attracted our interest.MethodsThis study involved the re-analysis of single-cell RNA (scRNA) and spatial transcriptomics (ST) data from publicly available datasets. The focus was on investigating the subpopulations and differentiation trajectories of B cells in the gastric cancer (GC) tumor immune microenvironment (TIME). Spatial transcriptomics (ST) and multiple immunofluorescence (mIF) revealed a clear co-localization pattern between B cells and tumor cells. Multiple immunotherapy datasets were collected to identify unique immunotherapy biomarkers. The unique immunotherapeutic potential of targeting CCL28 was validated through a mouse gastric cancer model. In addition, flow cytometry revealed changes in the tumor immune microenvironment targeting CCL28.ResultsThe re-analysis of ST data from multiple cancer types revealed a co-localization pattern between B cells and tumor cells. A significant number of IgA plasma cells were identified in the GC TIME. Five different tumor-infiltrating B cell subpopulations and two unique B cell differentiation trajectories were characterized, along with seven GC-related states. By analyzing the communication between GC cells and B cells, it was further discovered that tumor cells can influence and recruit plasma cells through CCL28-CCR10 signaling. Additionally, there was a crosstalk between GC cells and B cells. Finally, we identified the LAMA/CD44 signaling axis as a potential prognostic marker for immunotherapy through a large amount of immunotherapy data. We also validated through various animal tumor models that targeting CCL28 can significantly promote CD8+T cell infiltration and function in the TME by regulating B cell and plasma cell functions, and has the ability to synergize immunotherapy.ConclusionThe co-localization and crosstalk between GC cells and B cells significantly affect the efficacy of immunotherapy, and inhibiting the CCL28-CCR10 signal axis is a potential immunotherapy target for GC. Meanwhile, LAMA/CD44 pair may be a potential adverse indicator for immunotherapy and tumor prognosis.

A Quantitative Computational Framework for Allopolyploid Single-Cell Data Integration and Core Gene Ranking in Development.

Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome-divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. In this study, we develop a quantitative computational framework, called "pseudo-genome divergence quantification" (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from single-cell RNA sequencing data allows for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagnostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies have demonstrated that applying pgDQ to single-cell and bulk tissue transcriptomic data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution.

Medullary thyroid cancer: single-cell transcriptome and tumor evolution

BackgroundMedullary thyroid cancer (MTC) is a rare neuroendocrine tumor that originates from the parafollicular C cells of thyroid gland. Understanding the fundamental pathophysiology of MTC is essential for clinical management. Single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for identifying distinct cell types, offering a new biological foundation for comprehending the MTC ecosystem and developing precise treatment.MethodsFormalin fixed and paraffin-embedded (FFPE) samples of primary and adjacent non-cancerous tissues of three MTC cases were collected, and single-cell transcriptome data of MTC were obtained by using scRNA-seq technology. Annotated cell subpopulations were categorized and functionally enriched by principal component analysis, differential gene expression, and cell clustering analysis, to explore the biological process of tumor evolution that may be involved in each cell subpopulation. The copy number variation (CNV) profile was used to distinguish the malignancy of parafollicular thyroid cells, and the evolutionary trajectories of normal cells and tumor cells were revealed by the proposed time series analysis. The highly expressed genes in each cell subpopulation were analyzed by the FindAllMarker function of Seurat software, and verified by immunohistochemistry and fluorescence in situ hybridization. The prognostic value of specific cell subtypes was validated using large-scale public datasets.ResultsA total of 32,544 cells were obtained from the MTC tissue samples and 11,751 cells from the adjacent non-cancerous samples, which were classified into 7 heterogenous subpopulations by using R package of Seurat module. Copy number variations (CNVs) were significantly higher in tumor tissues than in adjacent non-tumor samples, predominantly enriched in subtypes C2 and C4. In addition, the pseudo-time for trajectory analysis suggested that the evolution of MTC tumor cells might begin with the C2 subtype, then transition to the early cancer subgroup C3, and further differentiate into four major malignant cell subpopulations C0, C1, C5 and C6. Survival analysis of a thyroid cancer cohort using the TCGA dataset revealed that high expression of genes linked to the C0 subcluster was correlated with poorer overall survival compared to low expression. Immunohistochemical staining showed that MAP3K4 was highly expressed in MTC tissues compared to adjacent non-cancerous tissues. Fluorescence in situ hybridization also confirmed the amplification of these two genes in MTC samples.ConclusionsBy conducting scRNA-seq on FFPE samples, we mapped the single-cell transcriptome of MTC, uncovering the tumor heterogeneity and unique biological features of each cellular subpopulation. The biological roles of identified tumor cell subpopulations such as C0 and C3 subtypes of parafollicular cells suggested the potential to discover new therapeutic targets and biomarkers for MTC, providing valuable insights for future translational and clinical research.

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.

Single-cell transcriptome landscape of the kidney reveals potential innate immune regulation mechanisms in hybrid yellow catfish after Aeromonas hydrophila infection

Aeromonas hydrophila, the pathogen that is the causative agent of motile Aeromonas septicemia (MAS) disease, commonly attacks freshwater fishes, including yellow catfish (Pelteobagrus fulvidraco). Although the kidney is one of the most important organs involved in immunity in fish, its role in disease progression has not been fully elucidated. Understanding the cellular composition and innate immune regulation mechanisms of the kidney of yellow catfish is important for the treatment of MAS. In this study, single-cell RNA sequencing (scRNA-seq) was performed on the kidney of hybrid yellow catfish (Pelteobagrus fulvidraco ♀ × Pelteobagrus vachelli ♂) after A. hydrophila infection. Nine types of kidney cells were identified using marker genes, and a transcription module of marker genes in the main immune cells of hybrid yellow catfish kidney tissue was constructed using in-situ hybridization. In addition, the single-cell transcriptome data showed that the differentially expressed genes of macrophages were primarily enriched in the Toll-like receptor and Nod-like receptor signaling pathways. The expression levels of genes involved in these pathways were upregulated in macrophages following A. hydrophila infection. Transmission electron microscopy and TUNEL analysis revealed the cellular characteristics of macrophages before and after A. hydrophila infection. These data provide empirical support for in-depth research on the role of the kidney in the innate immune response of hybrid yellow catfish.

Placental Origins of Preeclampsia: Insights from Multi-Omic Studies.

Preeclampsia (PE) is a major cause of maternal and neonatal morbidity and mortality worldwide, with the placenta playing a central role in disease pathophysiology. This review synthesizes recent advancements in understanding the molecular mechanisms underlying PE, focusing on placental genes, proteins, and genetic variants identified through multi-omic approaches. Transcriptomic studies in bulk placental tissue have identified many dysregulated genes in the PE placenta, including the PE signature gene, Fms-like tyrosine kinase 1 (FLT1). Emerging single-cell level transcriptomic data have revealed key cell types and molecular signatures implicated in placental dysfunction and PE. However, the considerable variability among studies underscores the need for standardized methodologies and larger sample sizes to enhance the reproducibility of results. Proteomic profiling of PE placentas has identified numerous PE-associated proteins, offering insights into potential biomarkers and pathways implicated in PE pathogenesis. Despite significant progress, challenges such as inconsistencies in study findings and lack of validation persist. Recent fetal genome-wide association studies have identified multiple genetic loci associated with PE, with ongoing efforts to elucidate their impact on placental gene expression and function. Future directions include the integration of multi-omic data, validation of findings in diverse PE populations and clinical subtypes, and the development of analytical approaches and experimental models to study the complex interplay of placental and maternal factors in PE etiology. These insights hold promise for improving risk prediction, diagnosis, and management of PE, ultimately reducing its burden on maternal and neonatal health.