Unveiling heterogeneity and prognostic markers in ductal breast cancer through single-cell RNA-seq.

BACKGROUND: Breast cancer (BC) is a heterogeneous disease, with the ductal subtype exhibiting significant cellular diversity that influences prognosis and response to treatment. Single-cell RNA sequencing data from the GEO database were utilized in this study to investigate the underlying mechanisms of cellular heterogeneity and to identify potential prognostic markers and therapeutic targets. METHODS: Bioinformatics analysis was conducted using R packages to analyze the single-cell sequencing data. The presence of highly variable genes and differences in malignant potency within the same BC samples were examined. Differential gene expression and biological function between Type 1 and Type 2 ductal epithelial cells were identified. Lasso regression and Cox proportional hazards regression analyses were employed to identify genes associated with patient prognosis. Experimental validation was performed in vitro and in vivo to confirm the functional relevance of the identified genes. RESULTS: The analysis revealed notable heterogeneity among BC cells, with the presence of highly variable genes and differences in malignant behavior within the same samples. Significant disparities in gene expression and biological function were identified between Type 1 and Type 2 ductal epithelial cells. Through regression analyses, CYP24A1 and TFPI2 were identified as pivotal genes associated with patient prognosis. Kaplan-Meier curves demonstrated their prognostic significance, and experimental validation confirmed their inhibitory effects on malignant behaviors of ductal BC cells. CONCLUSION: This study highlights the cellular heterogeneity in ductal subtype breast cancer and delineates the differential gene expressions and biological functions between Type 1 and Type 2 ductal epithelial cells. The genes CYP24A1 and TFPI2 emerged as promising prognostic markers and therapeutic targets, exhibiting inhibitory effects on BC cell malignancy in vitro and in vivo. These findings offer the potential for improved BC management and the development of targeted treatment strategies.

Elucidating the Transcriptional States of Spermatogenesis-Joint Analysis of Germline and Supporting Cell, Mice and Human, Normal and Perturbed, Bulk and Single-Cell RNA-Seq.

In studying the molecular underpinning of spermatogenesis, we expect to understand the fundamental biological processes better and potentially identify genes that may lead to novel diagnostic and therapeutic strategies toward precision medicine in male infertility. In this review, we emphasized our perspective that the path forward necessitates integrative studies that rely on complementary approaches and types of data. To comprehensively analyze spermatogenesis, this review proposes four axes of integration. First, spanning the analysis of spermatogenesis in the healthy state alongside pathologies. Second, the experimental analysis of model systems (in which we can deploy treatments and perturbations) alongside human data. Third, the phenotype is measured alongside its underlying molecular profiles using known markers augmented with unbiased profiles. Finally, the testicular cells are studied as ecosystems, analyzing the germ cells alongside the states observed in the supporting somatic cells. Recently, the study of spermatogenesis has been advancing using single-cell RNA sequencing, where scientists have uncovered the unique stages of germ cell development in mice, revealing new regulators of spermatogenesis and previously unknown cell subtypes in the testis. An in-depth analysis of meiotic and postmeiotic stages led to the discovery of marker genes for spermatogonia, Sertoli and Leydig cells and further elucidated all the other germline and somatic cells in the testis microenvironment in normal and pathogenic conditions. The outcome of an integrative analysis of spermatogenesis using advanced molecular profiling technologies such as scRNA-seq has already propelled our biological understanding, with additional studies expected to have clinical implications for the study of male fertility. By uncovering new genes and pathways involved in abnormal spermatogenesis, we may gain insights into subfertility or sterility.

Unsupervised Single-Cell Clustering with Asymmetric Within-Sample Transformation and Per-Cluster Supervised Features Selection.

This chapter shows applying the Asymmetric Within-Sample Transformation to single-cell RNA-Seq data matched with a previous dropout imputation. The asymmetric transformation is a special winsorization that flattens low-expressed intensities and preserves highly expressed gene levels. Before a standard hierarchical clustering algorithm, an intermediate step removes noninformative genes according to a threshold applied to a per-gene entropy estimate. Following the clustering, a time-intensive algorithm is shown to uncover the molecular features associated with each cluster. This step implements a resampling algorithm to generate a random baseline to measure up/downregulated significant genes. To this aim, we adopt a GLM model as implemented in DESeq2 package. We render the results in graphical mode. While the tools are standard heat maps, we introduce some data scaling to clarify the results' reliability.

Zebrafish Thrombocyte Transcriptome Analysis and Functional Genomics.

Our laboratory is interested in investigating the maturation process of zebrafish thrombocytes, which are functional equivalents to human platelets. We have adopted the zebrafish model to gain insights into mammalian platelet production, or thrombopoiesis. Notably, zebrafish exhibit two distinct populations of thrombocytes in their circulating blood: young and mature thrombocytes. This observation is intriguing because maturation appears to occur in circulation, yet the precise mechanisms governing this maturation remain elusive. Our goal is to understand the mechanisms underlying thrombocyte maturation by conducting single-cell RNA sequencing (scRNA-Seq) on young and mature thrombocytes, analyzing these transcriptomes to identify genes specific to each thrombocyte population, and elucidating the role of these genes in the maturation process, by quantifying thrombocyte numbers after the piggyback knockdown of each of these genes. In this chapter, we present a comprehensive, step-by-step protocol detailing the multifaceted methodology involved in understanding thrombocyte maturation, which encompasses the collection of zebrafish blood, the separation of young and mature thrombocytes using flow cytometry, scRNA-Seq analysis of these distinct thrombocyte populations, identification of genes specific to young and mature thrombocytes, and subsequent validation through gene knockdown techniques.

Inferring Novel Cells in Single-Cell RNA-Sequencing Data.

Single-cell RNA-sequencing (scRNA-seq) is a powerful technology that allows researchers to study gene expression heterogeneity within a tissue or cell population. One of the major advantages of scRNA-seq is that it allows researchers to identify and characterize novel cell types or subpopulations within a tissue that may be missed by traditional bulk RNA-sequencing methods. Although many existing methods have been developed to recognize known cell types, inferring novel cells may still be challenging in routine scRNA-seq analysis. Here we describe three lines of methods for inferring novel cells: unsupervised and outlier-detection-based methods, supervised and semi-supervised methods, and copy number variation (CNV)-based methods, as well as the corresponding situations that each method applies. We also provide implementation code and example usages to illustrate the available methods.

Inferring Tree-Shaped Single-Cell Trajectories with Totem.

Single-cell transcriptomics allows unbiased characterization of cell heterogeneity in a sample by profiling gene expression at single-cell level. These profiles capture snapshots of transient or steady states in dynamic processes, such as cell cycle, activation, or differentiation, which can be computationally ordered into a "flip-book" of cell development using trajectory inference methods. However, prediction of more complex topology structures, such as multifurcations or trees, remains challenging. In this chapter, we present two user-friendly protocols for inferring tree-shaped single-cell trajectories and pseudotime from single-cell transcriptomics data with Totem. Totem is a trajectory inference method that offers flexibility in inferring both nonlinear and linear trajectories and usability by avoiding the cumbersome fine-tuning of parameters. The QuickStart protocol provides a simple and practical example, whereas the GuidedStart protocol details the analysis step-by-step. Both protocols are demonstrated using a case study of human bone marrow CD34+ cells, allowing the study of the branching of three lineages: erythroid, lymphoid, and myeloid. All the analyses can be fully reproduced in Linux, macOS, and Windows operating systems (amd64 architecture) with >8 Gb of RAM using the provided docker image distributed with notebooks, scripts, and data in Docker Hub (elolab/repro-totem-ti). These materials are shared online under open-source license at https://elolab.github.io/Totem-protocol .

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.

Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.

mTORC1 Signaling in Brain Endothelial Progenitors Contributes to CCM Pathogenesis.

BACKGROUND: Cerebral vascular malformations (CCMs) are primarily found within the brain, where they result in increased risk for stroke, seizures, and focal neurological deficits. The unique feature of the brain vasculature is the blood-brain barrier formed by the brain neurovascular unit. Recent studies suggest that loss of CCM genes causes disruptions of blood-brain barrier integrity as the inciting events for CCM development. CCM lesions are proposed to be initially derived from a single clonal expansion of a subset of angiogenic venous capillary endothelial cells (ECs) and respective resident endothelial progenitor cells (EPCs). However, the critical signaling events in the subclass of brain ECs/EPCs for CCM lesion initiation and progression are unclear. METHODS: Brain EC-specific CCM3-deficient (Pdcd10BECKO) mice were generated by crossing Pdcd10fl/fl mice with Mfsd2a-CreERT2 mice. Single-cell RNA-sequencing analyses were performed by the chromium single-cell platform (10× genomics). Cell clusters were annotated into EC subtypes based on visual inspection and GO analyses. Cerebral vessels were visualized by 2-photon in vivo imaging and tissue immunofluorescence analyses. Regulation of mTOR (mechanistic target of rapamycin) signaling by CCM3 and Cav1 (caveolin-1) was performed by cell biology and biochemical approaches. RESULTS: Single-cell RNA-sequencing analyses from P10 Pdcd10BECKO mice harboring visible CCM lesions identified upregulated CCM lesion signature and mitotic EC clusters but decreased blood-brain barrier-associated EC clusters. However, a unique EPC cluster with high expression levels of stem cell markers enriched with mTOR signaling was identified from early stages of the P6 Pdcd10BECKO brain. Indeed, mTOR signaling was upregulated in both mouse and human CCM lesions. Genetic deficiency of Raptor (regulatory-associated protein of mTOR), but not of Rictor (rapamycin-insensitive companion of mTOR), prevented CCM lesion formation in the Pdcd10BECKO model. Importantly, the mTORC1 (mTOR complex 1) pharmacological inhibitor rapamycin suppressed EPC proliferation and ameliorated CCM pathogenesis in Pdcd10BECKO mice. Mechanistic studies suggested that Cav1/caveolae increased in CCM3-depleted EPC-mediated intracellular trafficking and complex formation of the mTORC1 signaling proteins. CONCLUSIONS: CCM3 is critical for maintaining blood-brain barrier integrity and CCM3 loss-induced mTORC1 signaling in brain EPCs initiates and facilitates CCM pathogenesis.

Neuropilin-1 is a novel host factor modulating the entry of hepatitis B virus.

BACKGROUND & AIMS: Sodium taurocholate cotransporting polypeptide (NTCP) has been identified as the cellular receptor for hepatitis B virus (HBV). However, hepatocytes expressing NTCP exhibit varying susceptibilities to HBV infection. This study aimed to investigate whether other host factors modulate the process of HBV infection. METHODS: Liver biopsy samples obtained from children with hepatitis B were used for single-cell sequencing and susceptibility analysis. Primary human hepatocytes, HepG2-NTCP cells, and human liver chimeric mice were used to analyze the effect of candidate host factors on HBV infection. RESULTS: Single-cell sequencing and susceptibility analysis revealed a positive correlation between neuropilin-1 (NRP1) expression and HBV infection. In the HBV-infected cell model, NRP1 overexpression before HBV inoculation significantly enhanced viral attachment and internalization, and promoted viral infection in the presence of NTCP. Mechanistic studies indicated that NRP1 formed a complex with LHBs and NTCP. The NRP1 b domain mediated its interaction with conserved arginine residues at positions 88 and 92 in the preS1 domain of the HBV envelope protein LHBs. This NRP1-preS1 interaction subsequently promoted the binding of preS1 to NTCP, facilitating viral infection. Moreover, disruption of the NRP1-preS1 interaction by the NRP1 antagonist EG00229 significantly attenuated the binding affinity between NTCP and preS1, thereby inhibiting HBV infection both in vitro and in vivo. CONCLUSIONS: Our findings indicate that NRP1 is a novel host factor for HBV infection, which interacts with preS1 and NTCP to modulate HBV entry into hepatocytes. IMPACT AND IMPLICATIONS: HBV infection is a global public health problem, but the understanding of the early infection process of HBV remains limited. Through single-cell sequencing, we identified a novel host factor, NRP1, which modulates HBV entry by interacting with HBV preS1 and NTCP. Moreover, antagonists targeting NRP1 can inhibit HBV infection both in vitro and in vivo. This study could further advance our comprehension of the early infection process of HBV.

Coding and Non-Coding Transcriptomic Landscape of Aortic Complications in Marfan Syndrome.

Marfan syndrome (MFS) is a rare congenital disorder of the connective tissue, leading to thoracic aortic aneurysms (TAA) and dissection, among other complications. Currently, the most efficient strategy to prevent life-threatening dissection is preventive surgery. Periodic imaging applying complex techniques is required to monitor TAA progression and to guide the timing of surgical intervention. Thus, there is an acute demand for non-invasive biomarkers for diagnosis and prognosis, as well as for innovative therapeutic targets of MFS. Unraveling the intricate pathomolecular mechanisms underlying the syndrome is vital to address these needs. High-throughput platforms are particularly well-suited for this purpose, as they enable the integration of different datasets, such as transcriptomic and epigenetic profiles. In this narrative review, we summarize relevant studies investigating changes in both the coding and non-coding transcriptome and epigenome in MFS-induced TAA. The collective findings highlight the implicated pathways, such as TGF-ß signaling, extracellular matrix structure, inflammation, and mitochondrial dysfunction. Potential candidates as biomarkers, such as miR-200c, as well as therapeutic targets emerged, like Tfam, associated with mitochondrial respiration, or miR-632, stimulating endothelial-to-mesenchymal transition. While these discoveries are promising, rigorous and extensive validation in large patient cohorts is indispensable to confirm their clinical relevance and therapeutic potential.

Single-cell RNA sequencing reveals special basal cells and fibroblasts in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is the most predominant type of idiopathic interstitial pneumonia and has an increasing incidence, poor prognosis, and unclear pathogenesis. In order to investigate the molecular mechanisms underlying IPF further, we performed single-cell RNA sequencing analysis on three healthy controls and five IPF lung tissue samples. The results revealed a significant shift in epithelial cells (ECs) phenotypes in IPF, which may be attributed to the differentiation of alveolar type 2 cells to basal cells. In addition, several previously unrecognized basal cell subtypes were preliminarily identified, including extracellular matrix basal cells, which were increased in the IPF group. We identified a special population of fibroblasts that highly expressed extracellular matrix-related genes, POSTN, CTHRC1, COL3A1, COL5A2, and COL12A1. We propose that the close interaction between ECs and fibroblasts through ligand-receptor pairs may have a critical function in IPF development. Collectively, these outcomes provide innovative perspectives on the complexity and diversity of basal cells and fibroblasts in IPF and contribute to the understanding of possible mechanisms in pathological lung fibrosis.

Unraveling plant-microbe symbioses using single-cell and spatial transcriptomics.

Plant-microbe symbioses require intense interaction and genetic coordination to successfully establish in specific cell types of the host and symbiont. Traditional RNA-seq methodologies lack the cellular resolution to fully capture these complexities, but single-cell and spatial transcriptomics (ST) are now allowing scientists to probe symbiotic interactions at an unprecedented level of detail. Here, we discuss the advantages that novel spatial and single-cell transcriptomic technologies provide in studying plant-microbe endosymbioses and highlight key recent studies. Finally, we consider the remaining limitations of applying these approaches to symbiosis research, which are mainly related to the simultaneous capture of both plant and microbial transcripts within the same cells.

Altered ontogeny and transcriptomic signatures of tissue-resident pulmonary interstitial macrophages ameliorate allergic airway hyperresponsiveness.

Introduction: Environmental exposures and experimental manipulations can alter the ontogenetic composition of tissue-resident macrophages. However, the impact of these alterations on subsequent immune responses, particularly in allergic airway diseases, remains poorly understood. This study aims to elucidate the significance of modified macrophage ontogeny resulting from environmental exposures on allergic airway responses to house dust mite (HDM) allergen. Methods: We utilized embryonic lineage labeling to delineate the ontogenetic profile of tissue-resident macrophages at baseline and following the resolution of repeated lipopolysaccharide (LPS)-induced lung injury. We investigated differences in house dust mite (HDM)-induced allergy to assess the influence of macrophage ontogeny on allergic airway responses. Additionally, we employed single-cell RNA sequencing (scRNAseq) and immunofluorescent staining to characterize the pulmonary macrophage composition, associated pathways, and tissue localization. Results: Our findings demonstrate that the ontogeny of homeostatic alveolar and interstitial macrophages is altered after the resolution from repeated LPS-induced lung injury, leading to the replacement of embryonic-derived by bone marrow-derived macrophages. This shift in macrophage ontogeny is associated with reduced HDM-induced allergic airway responses. Through scRNAseq and immunofluorescent staining, we identified a distinct subset of resident-derived interstitial macrophages expressing genes associated with allergic airway diseases, localized adjacent to terminal bronchi, and diminished by prior LPS exposure. Discussion: These results suggest a pivotal role for pulmonary macrophage ontogeny in modulating allergic airway responses. Moreover, our findings highlight the implications of prior environmental exposures in shaping future immune responses and influencing the development of allergies. By elucidating the mechanisms underlying these phenomena, this study provides valuable insights into potential therapeutic targets for allergic airway diseases and avenues for further research into immune modulation and allergic disease prevention.

New HCC Subtypes Based on CD8 Tex-Related lncRNA Signature Could Predict Prognosis, Immunological and Drug Sensitivity Characteristics of Hepatocellular Carcinoma.

Purpose: Hepatocellular carcinoma has become one of the severe diseases threatening human health. T cell exhaustion is deemed as a reason for immunotherapy resistance. However, little is known about the roles of CD8 Tex-related lncRNAs in HCC. Materials and Methods: We processed single-cell RNA sequencing to identify CD8 Tex-related genes. CD8 Tex-related lncRNAs were identified based on their correlations with mRNAs. Unsupervised clustering approach was used to identify molecular clusters of CD8 Tex-related lncRNAs. Differences in prognosis and immune infiltration between the clusters were explored. Machine learning algorithms were used to construct a prognostic signature. Samples were classified as low- and high-risk groups based on their risk scores. We identified prognosis-related lncRNAs and constructed a ceRNA network. In vitro experiments were conducted to investigate the impacts of CD8 Tex-related lncRNAs on proliferation and apoptosis of HCC cells. Results: We clarified cell types within two HCC single-cell datasets. We identified specific markers of CD8 Tex cells and analyzed their potential functions. Twenty-eight lncRNAs were identified as CD8 Tex-related. Based on CD8 Tex-related lncRNAs, samples were categorized into two distinct clusters, which exhibited significant differences in survival rates and immune infiltration. Ninety-six algorithm combinations were employed to establish a prognostic signature. RSF emerged as the one with the highest C-index. Patients in high- and low-risk groups exhibited marked differences in prognosis, enriched pathways, mutations and drug sensitivities. MCM3AP-AS1, MAPKAPK5-AS1 and PART1 were regarded as prognosis-related lncRNAs. A ceRNA network was constructed based on CD8 Tex-related lncRNAs and mRNAs. Experiments on cell lines and organoids indicated that downregulation of MCM3AP-AS1, MAPKAPK5-AS1 and PART1 suppressed cell proliferation and induced apoptosis. Conclusion: CD8 Tex-related lncRNAs played crucial roles in HCC progression. Our findings provided new insights into the regulatory mechanisms of CD8 Tex-related lncRNAs in HCC.

Single Cell RNAseq Analysis of Cytokine-Treated Human Islets: Association of Cellular Stress with Impaired Cytokine Responsiveness.

Pancreatic ß-cells are essential for survival, being the only cell type capable of insulin secretion. While they are believed to be vulnerable to damage by inflammatory cytokines such as interleukin-1 beta (IL-1ß) and interferon-gamma, we have recently identified physiological roles for cytokine signaling in rodent ß-cells that include the stimulation of antiviral and antimicrobial gene expression and the inhibition of viral replication. In this study, we examine cytokine-stimulated changes in gene expression in human islets using single-cell RNA sequencing. Surprisingly, the global responses of human islets to cytokine exposure were remarkably blunted compared to our previous observations in the mouse. The small population of human islet cells that were cytokine responsive exhibited increased expression of IL-1ß-stimulated antiviral guanylate-binding proteins, just like in the mouse. Most human islet cells were not responsive to cytokines, and this lack of responsiveness was associated with high expression of genes encoding ribosomal proteins. We further correlated the expression levels of RPL5 with stress response genes, and when expressed at high levels, RPL5 is predictive of failure to respond to cytokines in all endocrine cells. We postulate that donor causes of death and isolation methodologies may contribute to stress of the islet preparation. Our findings indicate that activation of stress responses in human islets limits cytokine-stimulated gene expression, and we urge caution in the evaluation of studies that have examined cytokine-stimulated gene expression in human islets without evaluation of stress-related gene expression.

SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer.

Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.

Summary data-based Mendelian randomization and single-cell RNA sequencing analyses identify immune associations with low-level LGALS9 in sepsis.

Sepsis is one of the major challenges in intensive care units, characterized by the complexity of the host immune status. To gain a deeper understanding of the pathogenesis of sepsis, it is crucial to study the phenotypic changes in immune cells and their underlying molecular mechanisms. We conducted Summary data-based Mendelian randomization analysis by integrating genome-wide association studies data for sepsis with expression quantitative trait locus data, revealing a significant decrease in the expression levels of 17 biomarkers in sepsis patients. Furthermore, based on single-cell RNA sequencing data, we elucidated potential molecular mechanisms at single-cell resolution and identified that LGALS9 inhibition in sepsis patients leads to the activation and differentiation of monocyte and T-cell subtypes. These findings are expected to assist researchers in gaining a more in-depth understanding of the immune dysregulation in sepsis.

Citalopram exposure of hESCs during neuronal differentiation identifies dysregulated genes involved in neurodevelopment and depression.

Selective serotonin reuptake inhibitors (SSRIs), including citalopram, are widely used antidepressants during pregnancy. However, the effects of prenatal exposure to citalopram on neurodevelopment remain poorly understood. We aimed to investigate the impact of citalopram exposure on early neuronal differentiation of human embryonic stem cells using a multi-omics approach. Citalopram induced time- and dose-dependent effects on gene expression and DNA methylation of genes involved in neurodevelopmental processes or linked to depression, such as BDNF, GDF11, CCL2, STC1, DDIT4 and GAD2. Single-cell RNA-sequencing analysis revealed distinct clusters of stem cells, neuronal progenitors and neuroblasts, where exposure to citalopram subtly influenced progenitor subtypes. Pseudotemporal analysis showed enhanced neuronal differentiation. Our findings suggest that citalopram exposure during early neuronal differentiation influences gene expression patterns associated with neurodevelopment and depression, providing insights into its potential neurodevelopmental impact and highlighting the importance of further research to understand the long-term consequences of prenatal SSRI exposure.

Single-cell RNA sequencing data analysis utilizing multi-type graph neural networks.

Single-cell RNA sequencing (scRNA-seq) is the sequencing technology of a single cell whose expression reflects the overall characteristics of the individual cell, facilitating the research of problems at the cellular level. However, the problems of scRNA-seq such as dimensionality reduction processing of massive data, technical noise in data, and visualization of single-cell type clustering cause great difficulties for analyzing and processing scRNA-seq data. In this paper, we propose a new single-cell data analysis model using denoising autoencoder and multi-type graph neural networks (scDMG), which learns cell-cell topology information and latent representation of scRNA-seq data. scDMG introduces the zero-inflated negative binomial (ZINB) model into a denoising autoencoder (DAE) to perform dimensionality reduction and denoising on the raw data. scDMG integrates multiple-type graph neural networks as the encoder to further train the preprocessed data, which better deals with various types of scRNA-seq datasets, resolves dropout events in scRNA-seq data, and enables preliminary classification of scRNA-seq data. By employing TSNE and PCA algorithms for the trained data and invoking Louvain algorithm, scDMG has better dimensionality reduction and clustering optimization. Compared with other mainstream scRNA-seq clustering algorithms, scDMG outperforms other state-of-the-art methods in various clustering performance metrics and shows better scalability, shorter runtime, and great clustering results.

Senescent endothelial cells promote liver metastasis of uveal melanoma in single-cell resolution.

BACKGROUND: Uveal melanoma (UM), the most common adult intraocular tumor, is characterized by high malignancy and poor prognosis in advanced stages. Angiogenesis is critical for UM development, however, not only the role of vascular endothelial dysfunction in UM remains unknown, but also their analysis at the single-cell level has been lacking. A comprehensive analysis is essential to clarify the role of the endothelium in the development of UM. METHODS: By using single-cell RNA transcriptomics data of 11 cases of primary and liver metastasis UM, we analyzed the endothelial cell status. In addition, we analyzed and validated ECs in the in vitro model and collected clinical specimens. Subsequently, we explored the impact of endothelial dysfunction on UM cell migration and explored the mechanisms responsible for the endothelial cell abnormalities and the reasons for their peripheral effects. RESULTS: UM metastasis has a significantly higher percentage of vascular endothelial cells compared to in situ tumors, and endothelial cells in metastasis show significant senescence. Senescent endothelial cells in metastatic tumors showed significant Krüppel-like factor 4 (KLF4) upregulation, overexpression of KLF4 in normal endothelial cells induced senescence, and knockdown of KLF4 in senescent endothelium inhibited senescence, suggesting that KLF4 is a driver gene for endothelial senescence. KLF4-induced endothelial senescence drove tumor cell migration through a senescence-associated secretory phenotype (SASP), of which the most important component of the effector was CXCL12 (C-X-C motif chemokine ligand 12), and participated in the composition of the immunosuppressive microenvironment. CONCLUSION: This study provides an undesirable insight of senescent endothelial cells in promoting UM metastasis.