Exploring tumor endothelial cells heterogeneity in hepatocellular carcinoma: insights from single-cell sequencing and pseudotime analysis.

Objective: This study aimed to explore the heterogeneity of tumor endothelial cells (TECs) in hepatocellular carcinoma (HCC) and their role in tumor progression, with the goal of identifying new therapeutic targets and strategies to improve patient prognosis. Methods: Single-cell RNA sequencing data from nine primary liver cancer samples were analyzed, obtained from the Gene Expression Omnibus (GEO) database. Data preprocessing, normalization, dimensionality reduction, and batch effect correction were performed based on the Seurat package. HCC cell types were identified using uniform manifold approximation and projection (UMAP) and cluster analysis, and the different cell types were annotated using the CellMarker database. Pseudotime trajectory analysis was conducted with Monocle to explore the differentiation trajectory of TECs. MAPK signaling pathway activity and copy number variations (CNV) in TECs were analyzed in conjunction with data from The Cancer Genome Atlas (TCGA), the trans-well and wound healing assay was used for cell invasion and migration activity assessment. Results: Two subgroups of TECs (TECs 1 and TECs 2) were identified, exhibiting distinct functional activities and signaling pathways. Specifically, TECs 1 may be involved in tumor cell proliferation and inflammatory responses, whereas TECs 2 is not only involved in cell proliferation pathways, but also enriched in pathways such as metabolic synthesis. Pseudotime analysis revealed dynamic changes in TECs subgroups during HCC progression, correlating specific gene expressions (such as PDGFRB, PGF, JUN, and NR4A1). Subsequently, the JUN gene was predicted by performing binding sites and was shown to act as a transcription factor that may regulate the expression of the PGF gene. CNV analysis highlighted key genes and pathways in TECs that might influence HCC progression, and the PGF as key regulatory factor mediated cell proliferation and migration. Conclusion: The study revealed the heterogeneity of TECs in HCC and their potential roles in tumor progression, offering new perspectives and potential therapeutic targets for HCC molecular mechanisms. The findings emphasize the importance of further exploring TECs heterogeneity for understanding HCC pathogenesis and developing personalized treatment strategies.

MAPPING ALZHEIMER'S DISEASE PSEUDO-PROGRESSION WITH MULTIMODAL BIOMARKER TRAJECTORY EMBEDDINGS.

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by progressive cognitive degeneration and motor impairment, affecting millions worldwide. Mapping the progression of AD is crucial for early detection of loss of brain function, timely intervention, and development of effective treatments. However, accurate measurements of disease progression are still challenging at present. This study presents a novel approach to understanding the heterogeneous pathways of AD through longitudinal biomarker data from medical imaging and other modalities. We propose an analytical pipeline adopting two popular machine learning methods from the single-cell transcriptomics domain, PHATE and Slingshot, to project multimodal biomarker trajectories to a low-dimensional space. These embeddings serve as our pseudotime estimates. We applied this pipeline to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to align longitudinal data across individuals at various disease stages. Our approach mirrors the technique used to cluster single-cell data into cell types based on developmental timelines. Our pseudotime estimates revealed distinct patterns of disease evolution and biomarker changes over time, providing a deeper understanding of the temporal dynamics of AD. The results show the potential of the approach in the clinical domain of neurodegenerative diseases, enabling more precise disease modeling and early diagnosis.

Integrating machine learning and single-cell transcriptomic analysis to identify potential biomarkers and analyze immune features of ischemic stroke.

This study employs machine learning and single-cell transcriptome sequencing (scRNA-seq) analysis to unearth novel biomarkers and delineate the immune characteristics of ischemic stroke (IS), thereby contributing fresh insights into IS treatment strategies.Our research leverages gene expression data sourced from the GEO database. We undertake weighted gene co-expression network analysis (WGCNA) to filter pertinent genes and subsequently employ machine learning algorithms for the identification of feature genes. Concurrently, we rigorously execute quality control measures, dimensionality reduction techniques, and cell annotation on the scRNA-seq data to pinpoint differentially expressed genes (DEGs). The identification of core genes, denoted as Hub genes, among the feature genes and DEGs, is achieved through meticulous overlapping analysis. We illuminate the immune characteristics of these Hub genes using a suite of analytical tools, encompassing CIBERSORT, MCPcounter, and pseudotemporal analysis, all based on immune cell annotations and single-cell transcriptome data.Subsequently, we harness the CMap database to prognosticate potential therapeutic drugs and scrutinize their associations with the identified Hub genes. Our findings unveil robust linkages between three pivotal Hub genes-namely, RNF13, VASP, and CD163-and specific immune cell types such as T cells and neutrophils. These Hub genes predominantly manifest in macrophages and microglial cells within the scRNA-seq immune cell population, exhibiting variances across different stages of cellular differentiation. In conclusion, this study unearths highly pertinent biomarkers for IS diagnosis and elucidates IS-induced immune infiltration characteristics, thus providing a firm foundation for a comprehensive exploration of potential immune mechanisms and the identification of novel therapeutic targets for IS.

Landscape of epithelial cell subpopulations in the human esophageal squamous cell carcinoma microenvironment.

Aims: We sought to reveal the landscape of epithelial cell subpopulations in the human esophageal squamous cell carcinoma microenvironment and investigate their parts on esophageal squamous carcinoma (ESCC) development. Background: Epithelial cells play an important role in the occurrence and development of ESCC through multiple mechanisms. While the landscape of epithelial cell subpopulations in ESCC, remains unclear. Objective: Exploring the role of epithelial cell subpopulations in ESCC progression. Methods: Seurat R package was used for single-cell RNA sequencing (scRNA-seq) data filtering, dimensionality reduction, clustering and differentially expressed genes analysis. Cellmarker database was adopted for cell cluster annotation. Functional enrichment analysis was carried out by Gene Ontology (GO) analysis. InferCNV package was conducted for copy number variation (CNV) of epithelial cell subpopulations in all chromosomal regions. Pseudotime trajectory analysis was implemented for exploring differentiation trajectory of epithelial cells subgroups during the cancer progression. CellChat analysis was used for probing the interactions between epithelial cells and NK/T cells. cellular experiments were performed using Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR), Wound-Healing Assay and transwell. Results: 11 major cell subpopulations were identified in ESCC and adjunct tissues. Further reclassification of epithelial cells uncovered 4 subpopulations. Enrichment analysis revealed that highly expressed genes in 4 epithelial cell subpopulations were related to cell proliferation, immune response and angiogenesis. CNV analysis found that UBD + epithelial cells and GAS2L3+ epithelial cells had a higher proportion of CNV. Cell differentiation trajectories disclosed that KRT6C+ and GSTA1+ epithelial cells were in an intermediate state of differentiation, while UBD+ and GAS2L3+ epithelial cells are in an end state of differentiation during ESCC progression. Finally, we found that four epithelial cell subpopulations all inhibited NK/T cells through NECTIN2-TIGIT and CLEC2B-KLRB1. Low ATF3 and DDIT3 mRNA expression inhibited ESCC cell migration and invasion. Conclusion: Here, we obtained a through epithelial cell atlas of ESCC at single-cell resolution, explored the role of epithelial cell in ESCC progression, and unveiled immunosuppressive signals to NK/T cells in promoting ESCC. Our findings expand the comprehension of epithelial cells and offer a theoretical guidance for future anti-epithelial cell treatment of ESCC.

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior.

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures.

Unveiling the cellular landscape: insights from single-cell RNA sequencing in multiple myeloma.

Objective: The aim of this research was to gain a thorough understanding of the processes involved in cell communication and discover potential indicators for treating multiple myeloma (MM) through the use of single-cell RNA sequencing (scRNA-seq). And explored the expression of multiple myeloma-related subgroups on metal ion-related pathways to explore the relationship between MM and metal ions. Methods: We performed a fair examination using single-cell RNA sequencing on 32 bone marrow specimens collected from 22 individuals at different points of MM advancement and 9 individuals without any health issues. To analyze the scRNA-seq data, we employed advanced computational algorithms, including Slingshot, Monocle2, and other methodologies. Specifically, Slingshot and Monocle2 enabled us to simulate the biological functionalities of different cell populations and map trajectories of cell developmental pathways. Additionally, we utilized the UMAP algorithm, a powerful dimension reduction technique, to cluster cells and identify genes that were differentially expressed across clusters. Results: Our study revealed distinct gene expression patterns and molecular pathways within each patient, which exhibited associations with disease progression. The analysis provided insights into the tumor microenvironment (TME), intra- and inter-patient heterogeneity, and cell-cell interactions mediated by ligand-receptor signaling. And found that multiple myeloma-related subgroups were expressed higher levels in MMP and TIMP pathways, there were some associations. Conclusion: Our study presents a fresh perspective for future research endeavors and clinical interventions in the field of MM. The identified gene expression patterns and molecular pathways hold immense potential as therapeutic targets for the treatment of multiple myeloma. The utilization of scRNA-seq technology has significantly contributed to a more precise understanding of the complex cellular processes and interactions within MM. Through these advancements, we are now better equipped to unravel the underlying mechanisms driving the development and progression of this complex disease.

Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. METHODS: Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI). RESULTS: Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI. DISCUSSION: Our risk score holds great potential to improve the precision of early risk assessment. HIGHLIGHTS: Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification.

Analysis of the Mechanism Underlying Radiotherapy Resistance Caused by Oligodendroglia Cells in Glioblastoma by Applying the Single-cell RNA Sequencing Technology.

BACKGROUND: Glioblastoma (GBM) is an aggressive malignancy. The inherent resistance of GBM to radiotherapy poses great challenges for clinical treatment. OBJECTIVES: The primary objective of this study is to explore the molecular mechanisms of radiotherapy resistance in GBM and identify the key influencing factors that contribute to this phenomenon. METHODS: The single-cell RNA sequencing (scRNA-seq) data of GBM were downloaded from the Gene Expression Omnibus (GEO) database. Cells were clustered using the Seurat R package, and the clusters were annotated using the CellMarker database. Pseudotime analysis was conducted using Monocle2. Marker scores were calculated based on the RNA-seq data of GBM from the UCSC database, and the enrichment of Hallmark gene sets was measured with the AUCell package. Furthermore, the most frequently mutated genes were identified using the simple nucleotide variation data from The Cancer Genome Atlas (TCGA) applying the maftools package. RESULTS: This study identified two oligodendrocyte subsets (ODC3 and ODC4) as radiotherapy-resistant groups in GBM. Enrichment and Pseudotime analysis revealed that the inflammatory response and immune activation pathways were enriched in ODC3, while the cell division and interferon response pathways were enriched in ODC4. The enrichment scores of hallmark gene sets further confirmed that ODC3 and ODC4 subpopulations developed radiotherapy resistance via distinct molecular mechanisms. Analysis of gene mutation frequencies showed that TP53 exhibited the most significant change in mutation frequency, indicating that it was an important risk factor involved in radiotherapy resistance in GBM. CONCLUSION: We identified two ODC subpopulations that exhibited resistance to radiotherapy, providing a new perspective and potential targets for personalized treatment strategies for GBM.

Exploring the Influence of T Cell Marker Gene Expression on the Pathobiology and Clinical Prognostic Outcomes in Intestinal-Type Gastric Carcinoma.

BACKGROUND: Gastric cancer (GC) poses a significant global health challenge. This study is aimed at elucidating the role of the immune system, particularly T cells and their subtypes, in the pathogenesis and progression of intestinal-type gastric carcinoma (GC), and at evaluating the predictive utility of a T cell marker gene-based risk score for overall survival. METHODS: We performed an extensive analysis using single-cell RNA sequencing data to map the diversity of immune cells and identify specific T cell marker genes within GC. Pseudotime trajectory analysis was employed to observe the expression patterns of tumor-related pathways and transcription factors (TFs) at various disease stages. We developed a risk score using data from The Cancer Genome Atlas (TCGA) as a training set and validated it with the GSE15459 dataset. RESULTS: Our analysis revealed distinct patterns of T cell marker gene expression associated with different stages of GC. The risk score, based on these markers, successfully stratified patients into high-risk and low-risk groups with significantly different overall survival prospects. High-risk patients exhibited poorer survival outcomes compared to low-risk patients (p < 0.05). Additionally, the risk score was capable of identifying patients across a spectrum from chronic atrophic gastritis to early GC. CONCLUSION: The findings enhance the understanding of the tumor immune microenvironment in GC and propose new immunotherapeutic targets. The T cell marker gene-based risk score offers a potential tool for gastroenterologists to tailor treatment plans more precisely according to the cancer's severity.

Single-cell RNA-sequencing Analysis Reveals the Promoting Role of the Hedgehog Pathway in Epithelial Cells during Cervical Cancer Progression.

AIM: This study explored the role of the Hedgehog pathway in epithelial cells during cervical cancer [CC] progression, providing new insights for improving current CC treatment. BACKGROUND: Abnormal activation of the Hedgehog signaling pathway is associated with the malignant transformation of CC epithelial cells. Single-cell atlas of CC and the role of Hedgehog pathway in epithelial cells during CC progression remain to be explored. OBJECTIVE: To comprehensively analyze the mechanism of Hedgehog pathway activation in CC epithelial cells and its impact on tumor progression by applying single-cell RNA sequencing [scRNA-seq] analysis. METHODS: The scRNA-seq data were acquired from the Gene Expression Omnibus [GEO] database and then processed with the Seurat package. FindNeighbors and Find- Clusters functions were applied to cluster the cells. The CellMarker database was used for subgroup annotation. Differentially expressed genes [DEGs] in each cell subgroup were filtered by FindAllMarkers function. Biological function analysis for the gene set of interest was performed using Clusterprofiler package. AUCell function was employed to calculate the score of the Hedgehog pathway. The differentiation trajectory in epithelial cell subtypes was generated by performing Pseudotime analysis. Finally, protein-protein network [PPI] was used to investigate the interactions between the Hedgehog pathway and other pathways enriched in the gene set of interest. RESULTS: A total of 9 major cell subpopulations were classified and the proportion of epithelial cells was the highest in CC samples. Further analysis revealed that the Hedgehog pathway was abnormally activated in STYK1+ and TP73+ epithelial cell subtypes. Pseudo-time trajectory analysis showed that the differentiation trajectory of STYK1+ epithelial cells gradually transformed into defense-to-virus cells or into proliferation cells, while TP73+ epithelial cells eventually differentiated into two branches of response to estrogen and virus-induced proliferation. PPI analysis showed that the Hedgehog pathway was involved in the proliferation and viral process of epithelial cells in CC. CONCLUSION: The current study comprehensively analyzed the features of CC samples and differentiation trajectories of epithelial cell subtypes, as well as the role of the Hedgehog pathway in epithelial cells during CC progression. More importantly, effective target genes were discovered for the molecular diagnosis and precise treatment of CC.

scMaSigPro: Differential Expression Analysis along Single-Cell Trajectories.

MOTIVATION: Understanding the dynamics of gene expression across different cellular states is crucial for discerning the mechanisms underneath cellular differentiation. Genes that exhibit variation in mean expression as a function of Pseudotime and between branching trajectories are expected to govern cell fate decisions. We introduce scMaSigPro, a method for the identification of differential gene expression patterns along Pseudotime and branching paths simultaneously. RESULTS: We assessed the performance of scMaSigPro using synthetic and public datasets. Our evaluation shows that scMaSigPro outperforms existing methods in controlling the False Positive Rate and is computationally efficient. AVAILABILITY AND IMPLEMENTATION: scMaSigPro is available as a free R package (version 4.0 or higher) under the GPL(≥2) license on GitHub at 'github.com/BioBam/scMaSigPro' and archived with version 0.03 on Zenodo at 'zenodo.org/records/12568922'.

Causal relationship between immune cells and hepatocellular carcinoma: a Mendelian randomisation study.

Background: Hepatocellular carcinoma (HCC), the predominant malignancy of the digestive tract, ranks as the third most common cause of cancer-related mortality globally, significantly impeding human health and lifespan. Emerging immunotherapeutic approaches have ignited fresh optimism for patient outcomes. This investigation probes the link between 731 immune cell phenotypes and HCC through Mendelian Randomization and single-cell sequencing, aiming to unearth viable drug targets and dissect HCC's etiology. Methods: We conducted an exhaustive two-sample Mendelian Randomization analysis to ascertain the causal links between immune cell features and HCC, utilizing publicly accessible genetic datasets to explore the causal connections of 731 immune cell traits with HCC susceptibility. The integrity, diversity, and potential horizontal pleiotropy of these findings were rigorously assessed through extensive sensitivity analyses. Furthermore, single-cell sequencing was employed to penetrate the pathogenic underpinnings of HCC. Results: Establishing a significance threshold of pval_Inverse.variance.weighted at 0.05, our study pinpointed five immune characteristics potentially elevating HCC risk: B cell % CD3- lymphocyte (TBNK panel), CD25 on IgD+ (B cell panel), HVEM on TD CD4+ (Maturation stages of T cell panel), CD14 on CD14+ CD16- monocyte (Monocyte panel), CD4 on CD39+ activated Treg ( Treg panel). Conversely, various cellular phenotypes tied to BAFF-R expression emerged as protective elements. Single-cell sequencing unveiled profound immune cell phenotype interactions, highlighting marked disparities in cell communication and metabolic activities. Conclusion: Leveraging MR and scRNA-seq techniques, our study elucidates potential associations between 731 immune cell phenotypes and HCC, offering a window into the molecular interplays among cellular phenotypes, and addressing the limitations of mono-antibody therapeutic targets.

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 .

A spatial portrait of the human sebaceous gland transcriptional program.

Sebaceous glands (SG) and their oily secretion (sebum) are indispensable for maintaining skin structure and function, and their deregulation causes skin disorders including but not limited to acne. Recent studies also indicate that sebum may have important immunomodulatory activities and may influence whole-body energy metabolism. However, the progressive transcriptional changes of sebocytes that lead to sebum production have never been characterized in detail. Here, we exploited the high cellular resolution provided by sebaceous hyperplasia and integrated spatial transcriptomics, pseudo time analysis, RNA velocity, and functional enrichment to map the landscape of sebaceous differentiation. Our results were validated by comparison with published SG transcriptome data and further corroborated by assessing the protein expression pattern of a subset of the transcripts in the public repository Human Protein Atlas. Departing from four sebocyte differentiation stages generated by unsupervised clustering, we demonstrate consecutive modulation of cellular functions associable with specific gene sets, from cell proliferation and oxidative phosphorylation via lipid synthesis to cell death. Both validation methods confirmed the biological significance of our results. Our report is complemented by a freely available and browsable online tool. Our data provide the first high-resolution spatial portrait of the SG transcriptional landscape and deliver starting points for experimentally assessing novel candidate molecules for regulating SG homeostasis in health and disease.

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome.

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560-680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

Investigating the basis of lineage decisions and developmental trajectories in the dorsal spinal cord through pseudotime analyses.

Dorsal interneurons (dIs) in the spinal cord encode the perception of touch, pain, heat, itchiness and proprioception. Previous studies using genetic strategies in animal models have revealed important insights into dI development, but the molecular details of how dIs arise as distinct populations of neurons remain incomplete. We have developed a resource to investigate dI fate specification by combining a single-cell RNA-Seq atlas of mouse embryonic stem cell-derived dIs with pseudotime analyses. To validate this in silico resource as a useful tool, we used it to first identify genes that are candidates for directing the transition states that lead to distinct dI lineage trajectories, and then validated them using in situ hybridization analyses in the developing mouse spinal cord in vivo. We have also identified an endpoint of the dI5 lineage trajectory and found that dIs become more transcriptionally homogeneous during terminal differentiation. This study introduces a valuable tool for further discovery about the timing of gene expression during dI differentiation and demonstrates its utility in clarifying dI lineage relationships.

Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.

Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations in expression profiles reflecting underlying biological processes. Benchmarking studies have compared many of the computational methods used to reconstruct cellular dynamics; however, researchers still encounter challenges in their analysis due to uncertainty with respect to selecting the most appropriate methods and parameters. Even among universal data processing steps used by trajectory inference methods such as feature selection and dimension reduction, trajectory methods' performances are highly dataset-specific. To address these challenges, we developed Escort, a novel framework for evaluating a dataset's suitability for trajectory inference and quantifying trajectory properties influenced by analysis decisions. Escort evaluates the suitability of trajectory analysis and the combined effects of processing choices using trajectory-specific metrics. Escort navigates single-cell trajectory analysis through these data-driven assessments, reducing uncertainty and much of the decision burden inherent to trajectory inference analyses. Escort is implemented in an accessible R package and R/Shiny application, providing researchers with the necessary tools to make informed decisions during trajectory analysis and enabling new insights into dynamic biological processes at single-cell resolution.

[Dynamic trajectory and cell communication of different cell clusters in malignant progression of glioblastoma].

OBJECTIVE: To delve deeply into the dynamic trajectories of cell subpopulations and the communication network among immune cell subgroups during the malignant progression of glioblastoma (GBM), and to endeavor to unearth key risk biomarkers in the GBM malignancy progression, so as to provide a more profound understanding for the treatment and prognosis of this disease by integrating transcriptomic data and clinical information of the GBM patients. METHODS: Utilizing single-cell sequencing data analysis, we constructed a cell subgroup atlas during the malignant progression of GBM. The Monocle2 tool was employed to build dynamic progression trajectories of the tumor cell subgroups in GBM. Through gene enrichment analysis, we explored the biological processes enriched in genes that significantly changed with the malignancy progression of GBM tumor cell subpopulations. CellChat was used to identify the communication network between the different immune cell subgroups. Survival analysis helped in identifying risk molecular markers that impacted the patient prognosis during the malignant progression of GBM. This method ological approach offered a comprehensive and detailed examination of the cellular and molecular dynamics within GBM, providing a robust framework for understanding the disease' s progression and potential therapeutic targets. RESULTS: The analysis of single-cell sequencing data identified 6 different cell types, including lymphocytes, pericytes, oligodendrocytes, macrophages, glioma cells, and microglia. The 27 151 cells in the single-cell dataset included 3 881 cells from the patients with low-grade glioma (LGG), 10 166 cells from the patients with newly diagnosed GBM, and 13 104 cells from the patients with recurrent glioma (rGBM). The pseudo-time analysis of the glioma cell subgroups indicated significant cellular heterogeneity during malignant progression. The cell interaction analysis of immune cell subgroups revealed the communication network among the different immune subgroups in GBM malignancy, identifying 22 biologically significant ligand-receptor pairs across 12 key biological pathways. Survival analysis had identified 8 genes related to the prognosis of the GBM patients, among which SERPINE1, COL6A1, SPP1, LTF, C1S, AEBP1, and SAA1L were high-risk genes in the GBM patients, and ABCC8 was low-risk genes in the GBM patients. These findings not only provided new theoretical bases for the treatment of GBM, but also offered fresh insights for the prognosis assessment and treatment decision-making for the GBM patients. CONCLUSION: This research comprehensively and profoundly reveals the dynamic changes in glioma cell subpopulations and the communication patterns among the immune cell subgroups during the malignant progression of GBM. These findings are of significant importance for understanding the complex biological processes of GBM, providing crucial new insights for precision medicine and treatment decisions in GBM. Through these studies, we hope to provide more effective treatment options and more accurate prognostic assessments for the patients with GBM.

Construction of a pathological model of skin lesions in acute herpes zoster virus infection and its molecular mechanism.

Varicella-zoster virus (VZV), a common pathogen with humans as the sole host, causes primary infection and undergoes a latent period in sensory ganglia. The recurrence of VZV is often accompanied by severe neuralgia in skin tissue, which has a serious impact on the life of patients. During the acute infection of VZV, there are few related studies on the pathophysiological mechanism of skin tissue. In this study, transcriptome sequencing data from the acute response period within 2 days of VZV antigen stimulation of the skin were used to explore a model of the trajectory of skin tissue changes during VZV infection. It was found that early VZV antigen stimulation caused activation of mainly natural immune-related signaling pathways, while in the late phase activation of mainly active immune-related signaling pathways. JAK-STAT, NFκB, and TNFα signaling pathways are gradually activated with the progression of infection, while Hypoxia is progressively inhibited. In addition, we found that dendritic cell-mediated immune responses play a dominant role in the lesion damage caused by VZV antigen stimulation of the skin. This study provides a theoretical basis for the study of the molecular mechanisms of skin lesions during acute VZV infection.

Single-Cell Nuclear Sequencing Atlas Revealed the Induction of Parkinson's Disease by RELN+ Neuron 3 and the Gene Regulatory Network of MSRA.

AIMS: To determine the cell types that promoted the progression of Parkinson's disease (PD) using the substantia nigra in the brain tissues derived from patients with PD and normal controls. BACKGROUND: PD is an incurable neurodegenerative disease that threatens the physical activity of the aging population, and the complex molecular mechanisms remain be comprehensively elucidated. OBJECTIVE: To describe potential disease-promoting cell types in PD and to provide a theoretical basis. METHODS: Single-cell nuclear sequencing data of nine PD samples and control samples from Gene Expression Omnibus (GEO) were included, and heterogeneous cell subpopulations in the substantia nigra were identified by annotation analysis. Potential pathogenic cell subpopulations of PD were determined based on the expression data of marker genes. Cell differentiation trajectories and communication networks were generated by Pseudotime trajectory analysis and cell communication analysis. Furthermore, single-- cell regulatory network inference and clustering (SCENIC) analysis was conducted to determine the regulatory network of transcription factor-target genes in PD. RESULTS: Among the nine cell subpopulations classified, RELN+neuron 3 showed reduced abundance and dopamine secretion capacity in PD and was therefore considered as a promoter of PD pathogenesis and progression. The regulatory network of MSRA action was involved in the developmental process of cells in the central nervous system, indicating that MSRA and its targets might serve as potential therapeutic targets for PD. RELN+neuron 3 had two directions of differentiation, specifically, branch 1 exhibited a high apoptotic profile and branch 2 exhibited a high cell death profile. In addition, the intensity of EPHA and EPHB signaling was attenuated between RELN+neuron 3 and other cell subpopulations. CONCLUSION: To conclude, this study identified a subpopulation of RELN+neuron 3 cells with markedly reduced abundance in the brain substantia nigra in PD. The MSRA-involved gene regulatory networks was considered as a novel therapeutic network for PD.