Oxidation-reduction process of Arabidopsis thaliana roots induced by bisphenol compounds based on RNA-seq analysis.

Bisphenol compounds (BPs) have various industrial uses and can enter the environment through various sources. To evaluate the ecotoxicity of BPs and identify potential gene candidates involved in the plant toxicity, Arabidopsis thaliana was exposed to bisphenol A (BPA), BPB, BPE, BPF, and BPS at 1, 3, 10 mg/L for a duration of 14 days, and their growth status were monitored. At day 14, roots and leaves were collected for internal BPs exposure concentration detection, RNA-seq (only roots), and morphological observations. As shown in the results, exposure to BPs significantly disturbed root elongation, exhibiting a trend of stimulation at low concentration and inhibition at high concentration. Additionally, BPs exhibited pronounced generation of reactive oxygen species, while none of the pollutants caused significant changes in root morphology. Internal exposure concentration analysis indicated that BPs tended to accumulate in the roots, with BPS exhibiting the highest level of accumulation. The results of RNA-seq indicated that the shared 211 differently expressed genes (DEGs) of these 5 exposure groups were enriched in defense response, generation of precursor metabolites, response to organic substance, response to oxygen-containing, response to hormone, oxidation-reduction process and so on. Regarding unique DEGs in each group, BPS was mainly associated with the redox pathway, BPB primarily influenced seed germination, and BPA, BPE and BPF were primarily involved in metabolic signaling pathways. Our results provide new insights for BPs induced adverse effects on Arabidopsis thaliana and suggest that the ecological risks associated with BPA alternatives cannot be ignored.

Experimental Framework for Assessing Mouse Retinal Regeneration Through Single-Cell RNA-Sequencing.

Retinal degenerative diseases including age-related macular degeneration and glaucoma are estimated to currently affect more than 14 million people in the United States, with an increased prevalence of retinal degenerations in aged individuals. An expanding aged population who are living longer forecasts an increased prevalence and economic burden of visual impairments. Improvements to visual health and treatment paradigms for progressive retinal degenerations slow vision loss. However, current treatments fail to remedy the root cause of visual impairments caused by retinal degenerations-loss of retinal neurons. Stimulation of retinal regeneration from endogenous cellular sources presents an exciting treatment avenue for replacement of lost retinal cells. In multiple species including zebrafish and Xenopus, Müller glial cells maintain a highly efficient regenerative ability to reconstitute lost cells throughout the organism's lifespan, highlighting potential therapeutic avenues for stimulation of retinal regeneration in humans. Here, we describe how the application of single-cell RNA-sequencing (scRNA-seq) has enhanced our understanding of Müller glial cell-derived retinal regeneration, including the characterization of gene regulatory networks that facilitate/inhibit regenerative responses. Additionally, we provide a validated experimental framework for cellular preparation of mouse retinal cells as input into scRNA-seq experiments, including insights into experimental design and analyses of resulting data.

Integrative analyses of bulk and single-cell RNA-seq reveals the correlation between SPP1+ macrophages and resistance to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.

Neoadjuvant chemoimmunotherapy (NACI) has significant implications for the treatment of esophageal cancer. However, its clinical efficacy varies considerably among patients, necessitating further investigation into the underlying mechanisms. The rapid advancement of single-cell RNA sequencing (scRNA-seq) technology facilitates the analysis of patient heterogeneity at the cellular level, particularly regarding treatment outcomes. In this study, we first analyzed scRNA-seq data of esophageal squamous cell carcinoma (ESCC) following NACI, obtained from the Gene Expression Omnibus (GEO) database. After performing dimensionality reduction, clustering, and annotation on the scRNA-seq data, we employed CellChat to investigate differences in cell-cell communication among samples from distinct efficacy groups. The results indicated that macrophages in the non-responder exhibited stronger cell communication intensity compared to those in responders, with SPP1 and GALECTIN signals showing the most significant differences between the two groups. This finding underscores the crucial role of macrophages in the efficacy of NACI. Subsequently, reclustering of macrophages revealed that Mac-SPP1 may be primarily responsible for treatment resistance, while Mac-C1QC appears to promote T cell activation. Finally, we conducted transcriptome sequencing on ESCC tissues obtained from 32 patients who underwent surgery following NACI. Utilizing CIBERSORT, CIBERSORTx, and WGCNA, we analyzed the heterogeneity of tumor microenvironment among different efficacy groups and validated the correlation between SPP1+ macrophages and resistance to NACI in ESCC using publicly available transcriptome sequencing datasets. These findings suggest that SPP1+ macrophages may represent a key factor contributing to resistance against NACI in ESCC.

RNA-seq and bulk RNA-seq data analysis of cancer-related fibroblasts (CAF) in LUAD to construct a CAF-based risk signature.

Angiogenesis, metastasis, and resistance to therapy are all facilitated by cancer-associated fibroblasts (CAFs). A CAF-based risk signature can be used to predict patients' prognoses for Lung adenocarcinoma (LUAD) based on CAF characteristics. The Gene Expression Omnibus (GEO) database was used to gather signal-cell RNA sequencing (scRNA-seq) data for this investigation. The GEO and TCGA databases were used to gather bulk RNA-seq and microarray data for LUAD. The scRNA-seq data were analyzed using the Seurat R program based on the CAF markers. Our goal was to use differential expression analysis to discover differentially expressed genes (DEGs) across normal and tumor samples in the TCGA dataset. Pearson correlation analysis was utilized to discover prognostic genes related with CAF, followed by univariate Cox regression analysis. Using Lasso regression, a risk signature based on CAF-related prognostic genes was created. A nomogram model was created based on the clinical and pathological aspects. 5 CAF clusters were identified in LUAD, 4 of which were associated with prognosis. From 2811 DEGs, 1002 genes were found to be significantly correlated with CAF clusters, which led to the creation of a risk signature with 8 genes. These 8 genes were primarily connected with 41 pathways, such as antigen paocessing and presentation, apoptosis, and cell cycle. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for LUAD, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of LUAD. CAF-based risk signatures can be effective in predicting the prognosis of LUAD, and they may provide new strategies for cancer treatments by interpreting the response of LUAD to immunotherapy.

A combined analysis of bulk RNA-seq and scRNA-seq was performed to investigate the molecular mechanisms associated with the occurrence of myocardial infarction.

BACKGROUND: Myocardial infarction (MI) induces complex transcriptional changes across diverse cardiac cell types. Single-cell RNA sequencing (scRNA-seq) provides an unparalleled ability to discern cellular diversity during infarction, yet the veracity of these discoveries necessitates confirmation. This investigation sought to elucidate MI mechanisms by integrating scRNA-seq and bulk RNA-seq data. METHODS: Publicly available scRNA-seq (GSE136088) and bulk RNA-seq (GSE153485) data from mice MI models were analyzed. Cell types were annotated, and differential expression analysis conducted. Bulk RNA-seq underwent quality control, principal component analysis, and differential expression analysis. RESULTS: In scRNA-seq data, the comparison between MI and sham groups unveiled a reduction in endothelial cell populations, but macrophages and monocytes increased. Within fibroblast subgroups, three distinct categories were discerned, with two exhibiting upregulation in MI. Notably, endothelial cells exhibited an elevated expression of genes associated with apoptosis and ferroptosis. In bulk RNA-seq analysis, distinct patterns emerged when comparing MI and sham groups. Specifically, six genes linked to endothelial ferroptosis exhibited heightened expression in MI group, thereby corroborating the scRNA-seq findings. Moreover, the examination of isolated cardiac macrophages from mice MI model revealed increased expression of Spp1, Col1a2, Col3a1, Ctsd, and Lgals3 compared to sham group, thus substantiating the dysregulation of macrophage apoptosis-related proteins following MI. CONCLUSION: MI altered the transcriptomic landscapes of cardiac cells with increased expression of apoptotic genes. Moreover, the upregulation of macrophage apoptosis marker was confirmed within MI models. The presence of endothelial cell depletion and ferroptosis in MI has been demonstrated.

scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.

Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. However, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. Herein, we introduce a novel deep learning-based algorithm for single-cell clustering, designated scDFN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scDFN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scDFN, as determined by better the Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI) metrics. Additionally, scDFN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scDFN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics.

A single-cell RNA-seq dataset describing macrophages in NSCLC tumor and peritumor tissues.

Examining tumor-associated macrophages in the immune microenvironment of non-small cell lung cancer (NSCLC) is essential for gaining an understanding of the genesis and development of NSCLC as well as for identifying key clinical therapeutic targets. Although previous studies have reported the diverse phenotypes and functions of macrophages in tumor tissues, thereby highlighting their significant role in the tumor microenvironment, the characteristic differences and correlations between tumor and peritumor tissue-derived macrophages that are necessary for an understanding of NSCLC progression remain unclear. Based on single-cell RNA sequencing, we generated a comprehensive dataset of transcriptomes from NSCLC tumor and peritumor tissues, thereby facilitating comprehensive analysis and providing significant insights. In summary, our dataset will serve as a valuable transcriptomic resource for further studies investigating NSCLC development.

scBubbletree: computational approach for visualization of single cell RNA-seq data.

BACKGROUND: Visualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space. RESULTS: We present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as "bubbles" at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. We demonstrate this with large scRNA-seq data sets, including one with over 1.2 million cells. CONCLUSIONS: To facilitate coherent quantification and visualization of scRNA-seq data we developed the R-package scBubbletree, which is freely available as part of the Bioconductor repository at: https://bioconductor.org/packages/scBubbletree/.

A novel approach to the analysis of Overall Survival (OS) as response with Progression-Free Interval (PFI) as condition based on the RNA-seq expression data in The Cancer Genome Atlas (TCGA).

BACKGROUND: Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. RESULTS: Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. CONCLUSIONS: Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach.

RNA-seq analyses on gametogenic tissues of alfalfa (Medicago sativa) revealed plant reproduction- and ploidy-related genes.

BACKGROUND: In alfalfa (Medicago sativa), the coexistence of interfertile subspecies (i.e. sativa, falcata and coerulea) characterized by different ploidy levels (diploidy and tetraploidy) and the occurrence of meiotic mutants capable of producing unreduced (2n) gametes, have been efficiently combined for the establishment of new polyploids. The wealth of agronomic data concerning forage quality and yield provides a thorough insight into the practical benefits of polyploidization. However, many of the underlying molecular mechanisms regarding gene expression and regulation remained completely unexplored. In this study, we aimed to address this gap by examining the transcriptome profiles of leaves and reproductive tissues, corresponding to anthers and pistils, sampled at different time points from diploid and tetraploid Medicago sativa individuals belonging to progenies produced by bilateral sexual polyploidization (dBSP and tBSP, respectively) and tetraploid individuals stemmed from unilateral sexual polyploidization (tUSP). RESULTS: Considering the crucial role played by anthers and pistils in the reduced and unreduced gametes formation, we firstly analyzed the transcriptional profiles of the reproductive tissues at different stages, regardless of the ploidy level and the origin of the samples. By using and combining three different analytical methodologies, namely weighted-gene co-expression network analysis (WGCNA), tau (τ) analysis, and differentially expressed genes (DEGs) analysis, we identified a robust set of genes and transcription factors potentially involved in both male sporogenesis and gametogenesis processes, particularly in crossing-over, callose synthesis, and exine formation. Subsequently, we assessed at the same floral stage, the differences attributable to the ploidy level (tBSP vs. dBSP) or the origin (tBSP vs. tUSP) of the samples, leading to the identification of ploidy and parent-specific genes. In this way, we identified, for example, genes that are specifically upregulated and downregulated in flower buds in the comparison between tBSP and dBSP, which could explain the reduced fertility of the former compared to the latter materials. CONCLUSIONS: While this study primarily functions as an extensive investigation at the transcriptomic level, the data provided could represent not only a valuable original asset for the scientific community but also a fully exploitable genomic resource for functional analyses in alfalfa.

Cross-disorder and disease-specific pathways in dementia revealed by single-cell genomics.

The development of successful therapeutics for dementias requires an understanding of their shared and distinct molecular features in the human brain. We performed single-nuclear RNA-seq and ATAC-seq in Alzheimer's disease (AD), frontotemporal dementia (FTD), and progressive supranuclear palsy (PSP), analyzing 41 participants and ∼1 million cells (RNA + ATAC) from three brain regions varying in vulnerability and pathological burden. We identify 32 shared, disease-associated cell types and 14 that are disease specific. Disease-specific cell states represent glial-immune mechanisms and selective neuronal vulnerability impacting layer 5 intratelencephalic neurons in AD, layer 2/3 intratelencephalic neurons in FTD, and layer 5/6 near-projection neurons in PSP. We identify disease-associated gene regulatory networks and cells impacted by causal genetic risk, which differ by disorder. These data illustrate the heterogeneous spectrum of glial and neuronal compositional and gene expression alterations in different dementias and identify therapeutic targets by revealing shared and disease-specific cell states.

Clinical cell-surface targets in metastatic and primary solid cancers.

Therapies against cell-surface targets (CSTs) represent an emerging treatment class in solid malignancies. However, high-throughput investigations of CST expression across cancer types have been reliant on data sets of mostly primary tumors, despite therapeutic use most commonly in metastatic disease. We identified a total of 818 clinical trials of CST therapies with 78 CSTs. We assembled a data set spanning RNA-seq and microarrays in 7,927 benign samples, 16,866 primary tumor samples, and 6,124 metastatic tumor samples. We also utilized single-cell RNA-seq data from 36 benign tissues and 558 primary and metastatic tumor samples, and matched RNA versus protein expression in 29 benign tissue samples, 1,075 tumor samples, and 942 cell lines. High RNA expression accurately predicted high protein expression across CST therapies in benign tissues, tumor samples, and cell lines. We compared metastatic versus primary tumor expression, identified potential opportunities for repositioning, and matched cell lines to tumor types based on CST and global RNA expression. We evaluated single-cell heterogeneity across tumors, and identified rare normal cell subpopulations that may contribute to toxicity. Finally, we identified combinations of CST therapies for which bispecific approaches could improve tumor specificity. This study helps better define the landscape of CST expression in metastatic and primary cancers.

Revealing a cancer-associated fibroblast-based risk signature for pancreatic adenocarcinoma through single-cell and bulk RNA-seq analysis.

PURPOSE: Proliferation of stromal connective tissue is a hallmark of pancreatic adenocarcinoma (PAAD). The engagement of activated cancer-associated fibroblasts (CAFs) contributes to the progression of PAAD through their involvement in tumor fibrogenesis. However, the prognostic significance of CAF-based risk signature in PAAD has not been explored. METHODS: The single-cell RNA sequencing (scRNA-seq) data sourced from GSE155698 within the Gene Expression Omnibus (GEO) database was supplemented by bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and microarray data retrieved from the GEO database. The scRNA-seq data underwent processing via the Seurat package to identify distinct CAF clusters utilizing specific CAF markers. Differential gene expression analysis between normal and tumor samples was conducted within the TCGA-PAAD cohort. Univariate Cox regression analysis pinpointed genes associated with CAF clusters, identifying prognostic CAF-related genes. These genes were utilized in LASSO regression to craft a predictive risk signature. Subsequently, integrating clinicopathological traits and the risk signature, a nomogram model was constructed. RESULTS: Our scRNA-seq analysis unveiled four distinct CAF clusters in PAAD, with two linked to PAAD prognosis. Among 207 identified DEGs, 148 exhibited significant correlation with these CAF clusters, forming the basis of a seven-gene risk signature. This signature emerged as an independent predictor in multivariate analysis for PAAD and demonstrated predictive efficacy in immunotherapeutic outcomes. Additionally, a novel nomogram, integrating age and the CAF-based risk signature, exhibited robust predictability and reliability in prognosticating PAAD. Moreover, the risk signature displayed substantial correlations with stromal and immune scores, as well as specific immune cell types. CONCLUSIONS: The prognosis of PAAD can be accurately predicted using the CAF-based risk signature, and a thorough analysis of the PAAD CAF signature may aid in deciphering the patient's immunotherapy response and presenting fresh cancer treatment options.

Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations.

An integration of 3D chromatin structure and gene expression at single-cell resolution has yet been demonstrated. Here, we develop a computational method, a multiomic data integration (MUDI) algorithm, which integrates scHi-C and scRNA-seq data to precisely define the 3D-regulated and biological-context dependent cell subpopulations or topologically integrated subpopulations (TISPs). We demonstrate its algorithmic utility on the publicly available and newly generated scHi-C and scRNA-seq data. We then test and apply MUDI in a breast cancer cell model system to demonstrate its biological-context dependent utility. We find the newly defined topologically conserved associating domain (CAD) is the characteristic single-cell 3D chromatin structure and better characterizes chromatin domains in single-cell resolution. We further identify 20 TISPs uniquely characterizing 3D-regulated breast cancer cellular states. We reveal two of TISPs are remarkably resemble to high cycling breast cancer persister cells and chromatin modifying enzymes might be functional regulators to drive the alteration of the 3D chromatin structures. Our comprehensive integration of scHi-C and scRNA-seq data in cancer cells at single-cell resolution provides mechanistic insights into 3D-regulated heterogeneity of developing drug-tolerant cancer cells.

Effects of Leydig cell elimination on testicular interstitial cell populations: characterization by scRNA-seq and immunocytochemical techniques.

Background: The mammalian testicular interstitial cells are not well-defined. The present study characterized the interstitial cell types and their turnover dynamics in adult rats. Additionally, the heterogeneity of the mesenchymal population and the effects of Leydig cell elimination on interstitial homeostasis were further analyzed by scRNA-seq datasets and immunocytochemical techniques. Methods: Interstitial cells were defined at the transcriptomic level by scRNA-seq and then confirmed and quantified with protein markers. The dividing activity of the major cell types was determined by continuous EdU labeling of the animals for one week. Some of the rats were also treated with a dose of ethylenedimethylsulfonate (EDS) to examine how the loss of Leydig cells (LCs) could affect interstitial homeostasis for three weeks. Results: Seven interstitial cell types were identified, including cell types (percentage of the whole interstitial population) as follows: Leydig (44.6%), macrophage and dendritic (19.1%), lymphoid (6.2%), vascular endothelial (7.9%), smooth muscle (10.7%), and mesenchymal (11.5%) cells. The EdU experiment indicated that most cell types were dividing at relatively low levels (<9%) except for the mesenchymal cells (MCs, 17.1%). Further analysis of the transcriptome of MCs revealed 4 subgroups with distinct functions, including 1) glutathione metabolism and xenobiotic detoxification, 2) ROS response and AP-1 signaling, 3) extracellular matrix synthesis and binding, and 4) immune response and regulation. Stem LCs (SLCs) are primarily associated with subgroup 3, expressing ARG1 and GAP43. EDS treatment not only eliminated LCs but also increased subgroup 3 and decreased subgroups 1 and 2 of the mesenchymal population. Moreover, EDS treatment increased the division of immune cells by more than tenfold in one week. Conclusion: Seven interstitial cell types were identified and quantified for rat testis. Many may play more diversified roles than previously realized. The elimination of LCs led to significant changes in MCs and immune cells, indicating the importance of LCs in maintaining testicular interstitial homeostasis.

scRNA-Explorer: An End-user Online Tool for Single Cell RNA-seq Data Analysis Featuring Gene Correlation and Data Filtering.

In the majority of downstream analysis pipelines for single-cell RNA sequencing (scRNA-seq), techniques like dimensionality reduction and feature selection are employed to address the problem of high-dimensional nature of the data. These approaches involve mapping the data onto a lower-dimensional space, eliminating less informative genes, and pinpointing the most pertinent features. This process ultimately leads to a reduction in the number of dimensions used for downstream analysis, which in turn speeds up the computation of large-scale scRNA-seq data. Most approaches are directed to isolate from biological background the genes characterizing different cells and or the condition under study by establishing lists of differentially expressed or coexpressed genes. Herein, we present scRNA-Explorer an open-source online tool for simplified and rapid scRNA-seq analysis designed with the end user in mind. scRNA-Explorer utilizes: (i) Filtering out uninformative cells in an interactive manner via a web interface, (ii) Gene correlation analysis coupled with an extra step of evaluating the biological importance of these correlations, and (iii) Gene enrichment analysis of correlated genes in order to find gene implication in specific functions. We developed a pipeline to address the above problem. The scRNA-Explorer pipeline allows users to interrogate in an interactive manner scRNA-sequencing data sets to explore via gene expression correlations possible function(s) of a gene of interest. scRNA-Explorer can be accessed at https://bioinformatics.med.uoc.gr/shinyapps/app/scrnaexplorer.

Single nucleus/cell RNA-seq of the chicken hypothalamic-pituitary-ovarian axis offers new insights into the molecular regulatory mechanisms of ovarian development.

The hypothalamic-pituitary-ovarian (HPO) axis represents a central neuroendocrine network essential for reproductive function. Despite its critical role, the intrinsic heterogeneity within the HPO axis across vertebrates and the complex intercellular interactions remain poorly defined. This study provides the first comprehensive, unbiased, cell type-specific molecular profiling of all three components of the HPO axis in adult Lohmann layers and Liangshan Yanying chickens. Within the hypothalamus, pituitary, and ovary, seven, 12, and 13 distinct cell types were identified, respectively. Results indicated that the pituitary adenylate cyclase activating polypeptide (PACAP), follicle-stimulating hormone (FSH), and prolactin (PRL) signaling pathways may modulate the synthesis and secretion of gonadotropin-releasing hormone (GnRH), FSH, and luteinizing hormone (LH) within the hypothalamus and pituitary. In the ovary, interactions between granulosa cells and oocytes involved the KIT, CD99, LIFR, FN1, and ANGPTL signaling pathways, which collectively regulate follicular maturation. The SEMA4 signaling pathway emerged as a critical mediator across all three tissues of the HPO axis. Additionally, gene expression analysis revealed that relaxin 3 (RLN3), gastrin-releasing peptide (GRP), and cocaine- and amphetamine regulated transcripts (CART, also known as CARTPT) may function as novel endocrine hormones, influencing the HPO axis through autocrine, paracrine, and endocrine pathways. Comparative analyses between Lohmann layers and Liangshan Yanying chickens demonstrated higher expression levels of GRP, RLN3, CARTPT, LHCGR, FSHR, and GRPR in the ovaries of Lohmann layers, potentially contributing to their superior reproductive performance. In conclusion, this study provides a detailed molecular characterization of the HPO axis, offering novel insights into the regulatory mechanisms underlying reproductive biology.

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

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

starTracer is an accelerated approach for precise marker gene identification in single-cell RNA-Seq analysis.

Revealing the heterogeneity among tissues is the greatest advantage of single-cell-sequencing. Marker genes not only act as the key to correctly identify cell types, but also the bio-markers for cell-status under certain experimental imputations. Current analysis methods such as Seurat and Monocle employ algorithms which compares one cluster to all the rest and select markers according to statistical tests. This pattern brings redundant calculations and thus, results in low calculation efficiency, specificity and accuracy. To address these issues, we introduce starTracer, a novel algorithm designed to enhance the efficiency, specificity and accuracy of marker gene identification in single-cell RNA-seq data analysis. starTracer operates as an independent pipeline, which exhibits great flexibility by accepting multiple input file types. The primary output is a marker matrix, where genes are sorted by the potential to function as markers, with those exhibiting the greatest potential positioned at the top. The speed improvement ranges by 2 ~ 3 orders of magnitude compared to Seurat, as observed across three independent datasets with lower false positive rate as observed in a simulated testing dataset with ground-truth. It's worth noting that starTracer exhibits increasing speed improvement with larger data volumes. It also excels in identifying markers in smaller clusters. These advantages solidify starTracer as an important tool for single-cell RNA-seq data, merging robust accuracy with exceptional speed.