Analysis Of Single-cell RNA-seq Data Research Articles

Disrupting YAP1-mediated glutamine metabolism induces synthetic lethality alongside ODC1 inhibition in osteosarcoma.

Osteosarcoma, a highly malignant primary bone tumor primarily affecting adolescents, frequently develops resistance to initial chemotherapy, leading to metastasis and limited treatment options. Our study aims to uncover novel therapeutic targets for metastatic and recurrent osteosarcoma. In this study, we proved the potential of modulating the YAP1-regulated glutamine metabolic pathway to augment the response of OS to DFMO. We initially employed single-cell transcriptomic data to gauge the activation level of polyamine metabolism in MTAP-deleted OS patients. This was further substantiated by transcriptome sequencing data from recurrent and non-recurrent patient tissues, confirming the activation of polyamine metabolism in progressive OS. Through high-throughput drug screening, we pinpointed CIL56, a YAP1 inhibitor, as a promising candidate for a combined therapeutic strategy with DFMO. In vivo, we utilized PDX and CDX models to validate the therapeutic efficacy of this drug combination. In vitro, we conducted western blot analysis, qPCR analysis, immunofluorescence staining, and PuMA experiments to monitor alterations in molecular expression, distribution, and tumor metastasis capability. We employed CCK-8 and colony formation assays to assess the proliferative capacity of cells in the experimental group. We used flow cytometry and reactive oxygen probes to observe changes in ROS and glutamine metabolism within the cells. Finally, we applied RNA-seq in tandem with metabolomics to identify metabolic alterations in OS cells treated with a DFMO and CIL56 combination. This enabled us to intervene and validate the role of the YAP1-mediated glutamine metabolic pathway in DFMO resistance. Through single-cell RNA-seq data analysis, we pinpointed a subset of late-stage OS cells with significantly upregulated polyamine metabolism. This upregulation was further substantiated by transcriptomic profiling of recurrent and non-recurrent OS tissues. High-throughput drug screening revealed a promising combination strategy involving DFMO and CIL56. DFMO treatment curbs the phosphorylation of YAP1 protein in OS cells, promoting nuclear entry and initiating the YAP1-mediated glutamine metabolic pathway. This reduces intracellular ROS levels, countering DFMO's anticancer effect. The therapeutic efficacy of DFMO can be amplified both in vivo and in vitro by combining it with the YAP1 inhibitor CIL56 or the glutaminase inhibitor CB-839. This underscores the significant potential of targeting the YAP1-mediated glutamine metabolic pathway to enhance efficacy of DFMO. Our findings elucidate YAP1-mediated glutamine metabolism as a crucial bypass mechanism against DFMO, following the inhibition of polyamine metabolism. Our study provides valuable insights into the potential role of DFMO in an "One-two Punch" therapy of metastatic and recurrent osteosarcoma.

COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection

BackgroundSingle-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions.ResultIn this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources.ConclusionsCOMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

A second wave of Notch signaling diversifies the intestinal secretory lineage.

The small intestine is well known for the function of its nutrient-absorbing enterocytes; yet equally critical for the maintenance of homeostasis is a diverse set of secretory cells, all of which are presumed to differentiate from the same intestinal stem cell. Despite major roles in intestinal function and health, understanding how the full spectrum of secretory cell types arises remains a longstanding challenge, largely due to their comparative rarity. Here, we investigate the fate specification of a rare and distinct population of small intestinal epithelial cells found in rats and humans but not mice: C FTR Hi gh E xpressers (CHEs). We use pseudotime trajectory analysis of single-cell RNA-seq data from rat intestinal jejunum to provide evidence that CHEs are specified along the secretory lineage and appear to employ a second wave of Notch-based signal transduction to distinguish these cells from other secretory cell types. We further validate the general order of transcription factors that direct these cells from unspecified progenitors within the crypt and experimentally demonstrate that Notch signaling is necessary to induce CHE fate both in vivo and in vitro . Our results suggest a model in which Notch is reactivated along the secretory lineage to specify the CHE population: a rare secretory cell type with putative functions in localized coordination of luminal pH and direct relevance to cystic fibrosis pathophysiology.

Characterizing efficient feature selection for single-cell expression analysis.

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.

TauFisher predicts circadian time from a single sample of bulk and single-cell pseudobulk transcriptomic data

As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tauFisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher’s performance in adding timestamps to both bulk and single-cell transcriptomic samples collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNAseq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNAseq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.

Single-cell transcriptomics dissects the transcriptome alterations of hematopoietic stem cells in myelodysplastic neoplasms

BackgroundMyelodysplastic neoplasms (MDS) are myeloid neoplasms characterized by disordered differentiation of hematopoietic stem cells and a predisposition to acute myeloid leukemia (AML). The underline pathogenesis remains unclear.MethodsIn this study, the trajectory of differentiation and mechanisms of leukemic transformation were explored through bioinformatics analysis of single-cell RNA-Seq data from hematopoietic stem and progenitor cells (HSPCs) in MDS patients.ResultsAmong the HSPC clusters, the proportion of common myeloid progenitor (CMP) was the main cell cluster in the patients with excess blasts (EB)/ secondary AML. Cell cycle analysis indicated the CMP of MDS patients were in an active proliferative state. The genes involved in the cell proliferation, such as MAML3 and PLCB1, were up-regulated in MDS CMP. Further validation analysis indicated that the expression levels of MAML3 and PLCB1 in patients with MDS-EB were significantly higher than those without EB. Patients with high expression of PLCB1 had a higher risk of transformation to AML. PLCB1 inhibitor can suppress proliferation, induce cell cycle arrest, and activate apoptosis of leukemic cells in vitro.ConclusionThis study revealed the transcriptomic change of HSPCs in MDS patients along the pseudotime and indicated that PLCB1 plays a key role in the transformation of MDS into leukemia.

CTEC: a cross-tabulation ensemble clustering approach for single-cell RNA sequencing data analysis.

Cell-type clustering is a crucial first step for single-cell RNA-seq data analysis. However, existing clustering methods often provide different results on cluster assignments with respect to their own data pre-processing, choice of distance metrics, and strategies of feature extraction, thereby limiting their practical applications. We propose Cross-Tabulation Ensemble Clustering (CTEC) method that formulates two re-clustering strategies (distribution- and outlier-based) via cross-tabulation. Benchmarking experiments on five scRNA-Seq datasets illustrate that the proposed CTEC method offers significant improvements over the individual clustering methods. Moreover, CTEC-DB outperforms the state-of-the-art ensemble methods for single-cell data clustering, with 45.4% and 17.1% improvement over the single-cell aggregated from ensemble clustering method (SAFE) and the single-cell aggregated clustering via Mixture model ensemble method (SAME), respectively, on the two-method ensemble test. The source code of the benchmark in this work is available at the GitHub repository https://github.com/LWCHN/CTEC.git.

Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs).

The complicated process of neuronal development is initiated early in life, with the genetic mechanisms governing this process yet to be fully elucidated. Single-cell RNA sequencing (scRNA-seq) is a potent instrument for pinpointing biomarkers that exhibit differential expression across various cell types and developmental stages. By employing scRNA-seq on human embryonic stem cells, we aim to identify differentially expressed genes (DEGs) crucial for early-stage neuronal development. Our focus extends beyond simply identifying DEGs. We strive to investigate the functional roles of these genes through enrichment analysis and construct gene regulatory networks to understand their interactions. Ultimately, this comprehensive approach aspires to illuminate the molecular mechanisms and transcriptional dynamics governing early human brain development. By uncovering potential links between these DEGs and intelligence, mental disorders, and neurodevelopmental disorders, we hope to shed light on human neurological health and disease. In this study, we have used scRNA-seq to identify DEGs involved in early-stage neuronal development in hESCs. The scRNA-seq data, collected on days 26 (D26) and 54 (D54), of the in vitro differentiation of hESCs to neurons were analyzed. Our analysis identified 539 DEGs between D26 and D54. Functional enrichment of those DEG biomarkers indicated that the up-regulated DEGs participated in neurogenesis, while the down-regulated DEGs were linked to synapse regulation. The Reactome pathway analysis revealed that down-regulated DEGs were involved in the interactions between proteins located in synapse pathways. We also discovered interactions between DEGs and miRNA, transcriptional factors (TFs) and DEGs, and between TF and miRNA. Our study identified 20 significant transcription factors, shedding light on early brain development genetics. The identified DEGs and gene regulatory networks are valuable resources for future research into human brain development and neurodevelopmental disorders.

Identification of efferocytosis-related subtypes in gliomas and elucidating their characteristics and clinical significance.

Introduction: Gliomas, the most prevalent tumors of the central nervous system, are known for their aggressive nature and poor prognosis. The heterogeneity among gliomas leads to varying responses to the same treatments, even among similar glioma types. In our study, we efferocytosis-related subtypes and explored their characteristics in terms of immune landscape, intercellular communication, and metabolic processes, ultimately elucidating their potential clinical implications. Methods and Results: We first identified efferocytosis-related subtypes in Bulk RNA-seq using the NMF algorithm. We then preliminarily demonstrated the correlation of these subtypes with efferocytosis by examining enrichment scores of cell death pathways, macrophage infiltration, and the expression of immune ligands. Our analysis of single-cell RNA-seq data further supported the association of these subtypes with efferocytosis. Through enrichment analysis, we found that efferocytosis-related subtypes differ from other types of gliomas in terms of immune landscape, intercellular communication, and substance metabolism. Moreover, we found that the efferocytosis-related classification is a prognostic factor with robust predictive performance by calculating the AUC values. We also found that efferocytosis-related subtypes, when compared with other gliomas in drug sensitivity, survival, and TIDE scores, show a clear link to the effectiveness of chemotherapy, radiotherapy, and immunotherapy in glioma patients. Discussion: We identified efferocytosis-related subtypes in gliomas by analyzing the expression of 137 efferocytosis-associated genes, exploring their characteristics in immune landscape, intercellular communication, metabolic processes, and genomic variations. Moreover, we discovered that the classification of efferocytosis-related subtypes has a strong prognostic predictive power and holds potential significance in guiding clinical treatment.

Scaling up single-cell RNA-seq data analysis with CellBridge workflow.

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression at the individual cell level, unraveling unprecedented insights into cellular heterogeneity. However, the analysis of scRNA-seq data remains a challenging and time-consuming task, often demanding advanced computational expertise, rendering it impractical for high-volume environments and applications. We present CellBridge, an automated workflow designed to simplify the standard procedures entailed in scRNA-seq data analysis, eliminating the need for specialized computational expertise. CellBridge utilizes state-of-the-art computational methods, integrating a range of advanced functionalities, covering the entire process from raw unaligned sequencing reads to cell type annotation. Hence, CellBridge accelerates the pace of discovery by seamlessly enabling insights into vast volumes of scRNA-seq data, without compromising workflow control and reproducibility. The source code, detailed documentation, and materials required to reproduce the results are available on GitHub and archived in Zenodo. For the CellBridge pre-processing step (v1.0.0), access the GitHub repository at https://github.com/Sanofi-Public/PMCB-ToBridge and the Zenodo archive at https://zenodo.org/records/10246161. For the CellBridge processing step (v1.0.0), visit the GitHub repository at https://github.com/Sanofi-Public/PMCB-CellBridge and the Zenodo archive at https://zenodo.org/records/10246046.

Practical Compass of Single-Cell RNA-Seq Analysis.

This review paper provides step-by-step instructions on the fundamental process, from handling fastq datasets to illustrating plots and drawing trajectories. The number of studies using single-cell RNA-seq (scRNA-seq) is increasing. scRNA-seq revealed the heterogeneity or diversity of the cellular populations. scRNA-seq also provides insight into the interactions between different cell types. User-friendly scRNA-seq packages for ligand-receptor interactions and trajectory analyses are available. In skeletal biology, osteoclast differentiation, fracture healing, ectopic ossification, human bone development, and the bone marrow niche have been examined using scRNA-seq. scRNA-seq data analysis tools are still being developed, even at the fundamental step of dataset integration. However, updating the latest information is difficult for many researchers. Investigators and reviewers must share their knowledge of in silico scRNA-seq for better biological interpretation. This review article aims to provide a useful guide for complex analytical processes in single-cell RNA-seq data analysis.

Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census.

Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin. We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications. Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.

Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network.

The advancement of single-cell sequencing technology has smoothed the ability to do biological studies at the cellular level. Nevertheless, single-cell RNA sequencing (scRNA-seq) data presents several obstacles due to the considerable heterogeneity, sparsity and complexity. Although many machine-learning models have been devised to tackle these difficulties, there is still a need to enhance their efficiency and accuracy. Current deep learning methods often fail to fully exploit the intrinsic interconnections within cells, resulting in unsatisfactory results. Given these obstacles, we propose a unique approach for analyzing scRNA-seq data called scMPN. This methodology integrates multi-layer perceptron and graph neural network, including attention network, to execute gene imputation and cell clustering tasks. In order to evaluate the gene imputation performance of scMPN, several metrics like cosine similarity, median L1 distance and root mean square error are used. These metrics are utilized to compare the efficacy of scMPN with other existing approaches. This research utilizes criteria such as adjusted mutual information, normalized mutual information and integrity score to assess the efficacy of cell clustering across different approaches. The superiority of scMPN over current single-cell data processing techniques in cell clustering and gene imputation investigations is shown by the experimental findings obtained from four datasets with gold-standard cell labels. This observation demonstrates the efficacy of our suggested methodology in using deep learning methodologies to enhance the interpretation of scRNA-seq data.

An End-to-End Deep Hybrid Autoencoder Based Method for Single-Cell RNA-Seq Data Analysis.

Single-cell RNA sequencing technology provides powerful support for researchers to understand the complex mechanisms of cells at the single-cell level. Due to the high sparsity, technical noise, and computational complexity of single-cell transcriptome data, the existing data analysis methods are unable to effectively extract the fine-grained characteristics of scRNA-seq data, resulting in inaccurately analyze the heterogeneity of the individual cell from a great quantity of cell mixtures. To address these shortcomings, we proposed an end-to-end analysis method called dhaSCA, which integrates the Graph convolutional neural network (GCN) feature learning and downstream tasks such as classification and imputation into a unified deep learning manner. dhaSCA uses hybrid GCN-MLP deep autoencoder and to capture structural information between cells, and learn the low dimensional cell representation. It also introduces downstream tasks as constraints to guide the model to learn more accurate cell features. We conducted various experiments to evaluate the performance of dhaSCA based on eight real RNA-Seq datasets, including classification, imputation, clustering, and visualization. The results show that dhaSCA outperforms other state-of-the-art methods in these downstream tasks. Therefore, dhaSCA is able to obtain a richer representation of cells, and provides strong support for efficient analysis of single-cell data.

Combination of CRISPR-Cas9-RNP and Single-Cell RNAseq to Identify Cell State-Specific FOXJ1 Functions in the Human Airway Epithelium.

The study of the airway epithelium in vitro is routinely performed using air-liquid culture (ALI) models from nasal or bronchial basal cells. These 3D experimental models allow to follow the regeneration steps of fully differentiated mucociliary epithelium and to study gene function by performing gene invalidation. Recent progress made with CRISPR-based techniques has overcome the experimental difficulty of this approach, by a direct transfection of ribonucleoprotein complexes combining a mix of synthetic small guide RNAs (sgRNAs) and recombinant Cas9. The approach shows more than 95% efficiency and does not require any selection step. A limitation of this approach is that it generates cell populations that contain heterogeneous deletions, which makes the evaluation of invalidation efficiency difficult. We have successfully used Flongle sequencing (Nanopore) to quantify the number of distinct deletions. We describe the use of CRISPR-Cas9 RNP in combination with single-cell RNA sequencing to functionally characterize the impact of gene invalidation in ALI cultures. The complex ecosystem of the airway epithelium, composed of many cell types, makes single-cell approaches particularly relevant to study cell type, or cell state-specific events. This protocol describes the invalidation of FOXJ1 in ALI cultures through the following steps: (1) Establishment of basal cell cultures from nasal turbinates, (2) CRISPR-Cas9 RNP invalidation of FOXJ1, (3) Quantification of FOXJ1 invalidation efficiency by Nanopore sequencing, (4) Dissociation of ALI cultures and single-cell RNAseq, (5) Analysis of single-cell RNAseq data from FOXJ1-invalidated cells.We confirm here that FOXJ1 invalidation impairs the final differentiation step of multiciliated cells and provides a framework to explore other gene functions.

SGAE: single-cell gene association entropy for revealing critical states of cell transitions during embryonic development.

The critical point or pivotal threshold of cell transition occurs in early embryonic development when cell differentiation culminates in its transition to specific cell fates, at which the cell population undergoes an abrupt and qualitative shift. Revealing such critical points of cell transitions can track cellular heterogeneity and shed light on the molecular mechanisms of cell differentiation. However, precise detection of critical state transitions proves challenging when relying on single-cell RNA sequencing data due to their inherent sparsity, noise, and heterogeneity. In this study, diverging from conventional methods like differential gene analysis or static techniques that emphasize classification of cell types, an innovative computational approach, single-cell gene association entropy (SGAE), is designed for the analysis of single-cell RNA-seq data and utilizes gene association information to reveal critical states of cell transitions. More specifically, through the translation of gene expression data into local SGAE scores, the proposed SGAE can serve as an index to quantitatively assess the resilience and critical properties of genetic regulatory networks, consequently detecting the signal of cell transitions. Analyses of five single-cell datasets for embryonic development demonstrate that the SGAE method achieves better performance in facilitating the characterization of a critical phase transition compared with other existing methods. Moreover, the SGAE value can effectively discriminate cellular heterogeneity over time and performs well in the temporal clustering of cells. Besides, biological functional analysis also indicates the effectiveness of the proposed approach.

SOX18 meditates the resistance of Bmi1-expressing cells to cetuximab in HNSCC.

Head and neck squamous cell carcinoma (HNSCC) is the most common type of malignancy in the head and neck region worldwide. The therapeutic strategies for HNSCC remain unsatisfying and limited. Here, we found a population of resistant Bmi1-expressing cells in the presence of cetuximab treatment and reported a novel role of SRY-box transcription factor 18 (SOX18), a member of the SOX family, in promoting HNSCC resistance to cetuximab. This study aimed to investigate the regulatory mechanism of Sox18 in Bmi1-positive cells and to search for better therapeutic targets. We successfully obtained Bmi1CreER, RosatdTomato, and RosaDTA mice and identified Bmi1-expressing cells through lineage tracing. SOX18 expression in HNSCC and normal tissues was analyzed by immunohistochemistry, colocalization of Sox18, and Bmi1-expressing cells was analyzed by immunofluorescence, and SOX18 expression in SCC9 cell lines was quantified by western blotting and quantitative real-time PCR. The investigation of the mechanism of SOX18-mediated cetuximab resistance in Bmi1-positive cells was based on the analysis of single-cell RNA-seq data obtained from the Gene Expression Omnibus (GEO) database. Western blotting was performed to verify the results obtained from the single-cell RNA-seq analysis. In our study, we demonstrated that Bmi1-expressing cells were resistant to cetuximab treatment and that depletion of Bmi1-expressing cells improved cetuximab efficacy in HNSCC. We then discovered that Sox18 mediated the stem cell-like properties of Bmi1-expressing cells and promoted cellular cetuximab resistance through an oxidative phosphorylation pathway. There was a significant downregulation of key genes in the oxidative phosphorylation pathway in Sox18 knockout cell lines. Taken together, the findings of our study suggest that Sox18 mediates the resistance of Bmi1-expressing cells to cetuximab in HNSCC via the oxidative phosphorylation pathway.

ScRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing

BackgroundSingle-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights.ResultsHere, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files.ConclusionsWe developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest. We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/. Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.

Integrated single-cell RNA-seq analysis reveals the vital cell types and dynamic development signature of atherosclerosis.

Introduction: In the development of atherosclerosis, the remodeling of blood vessels is a key process involving plaque formation and rupture. So far, most reports mainly believe that macrophages, smooth muscle cells, and endothelial cells located at the intima and media of artery play the key role in this process. Few studies had focused on whether fibroblasts located at adventitia are involved in regulating disease process. Methods and results: In this study, we conducted in-depth analysis of single-cell RNA-seq data of the total of 18 samples from healthy and atherosclerotic arteries. This study combines several analysis methods including transcription regulator network, cell-cell communication network, pseudotime trajectory, gene set enrichment analysis, and differential expression analysis. We found that SERPINF1 is highly expressed in fibroblasts and is involved in the regulation of various signaling pathways. Conclusion: Our research reveals a potential mechanism of atherosclerosis, SERPINF1 regulates the formation and rupture of plaques through the Jak-STAT signaling pathway, which may provide new insights into the pathological study of disease. Moreover, we suggest that SRGN and IGKC as potential biomarkers for unstable arterial plaques.

Single-cell RNA-seq data analysis based on directed graph neural network.

Single-cell RNA sequencing (scRNA-seq) data scale surges with high-throughput sequencing technology development. However, although single-cell data analysis is a powerful tool, various issues have been reported, such as sequencing sparsity and complex differential patterns in gene expression. Statistical or traditional machine learning methods are inefficient, and the accuracy needs to be improved. The methods based on deep learning can not directly process non-Euclidean spatial data, such as cell diagrams. In this study, we have developed graph autoencoders and graph attention network for scRNA-seq analysis based on a directed graph neural network named scDGAE. Directed graph neural networks cannot only retain the connection properties of the directed graph but also expand the receptive field of the convolution operation. Cosine similarity, median L1 distance, and root-mean-squared error are used to measure the gene imputation performance of different methods with scDGAE. Furthermore, adjusted mutual information, normalized mutual information, completeness score, and Silhouette coefficient score are used to measure the cell clustering performance of different methods with scDGAE. Experiment results show that the scDGAE model achieves promising performance in gene imputation and cell clustering prediction on four scRNA-seq data sets with gold-standard cell labels. Furthermore, it is a robust framework that can be applied to general scRNA-Seq analyses.