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.

CellContrast: Reconstructing spatial relationships in single-cell RNA sequencing data via deep contrastive learning.

A vast amount of single-cell RNA sequencing (SC) data have been accumulated via various studies and consortiums, but the lack of spatial information limits its analysis of complex biological activities. To bridge this gap, we introduce CellContrast, a computational method for reconstructing spatial relationships among SC cells from spatial transcriptomics (ST) reference. By adopting a contrastive learning framework and training with ST data, CellContrast projects gene expressions into a hidden space where proximate cells share similar representation values. We performed extensive benchmarking on diverse platforms, including SeqFISH, Stereo-seq, 10X Visium, and MERSCOPE, on mouse embryo and human breast cells. The results reveal that CellContrast substantially outperforms other related methods, facilitating accurate spatial reconstruction of SC. We further demonstrate CellContrast's utility by applying it to cell-type co-localization and cell-cell communication analysis with real-world SC samples, proving the recovered cell locations empower more discoveries and mitigate potential false positives.

Intercellular extrachromosomal DNA copy-number heterogeneity drives neuroblastoma cell state diversity.

Neuroblastoma exhibits significant inter- and intra-tumor genetic heterogeneity and varying clinical outcomes. Extrachromosomal DNAs (ecDNAs) may drive this heterogeneity by independently segregating during cell division, leading to rapid oncogene amplification. While ecDNA-mediated oncogene amplification is linked to poor prognosis in various cancers, the effects of ecDNA copy-number heterogeneity on intermediate phenotypes are poorly understood. Here, we leverage DNA and RNA sequencing from the same single cells in cell lines and neuroblastoma patients to investigate these effects. By analyzing ecDNA amplicon structures, we reveal extensive intercellular ecDNA copy-number heterogeneity. We also provide direct evidence of how this heterogeneity influences the expression of cargo genes, including MYCN and its downstream targets, and the overall transcriptional state of neuroblastoma cells. Our findings highlight the role of ecDNA copy number in promoting rapid adaptability of cellular states within tumors, underscoring the need for ecDNA-specific treatment strategies to address tumor formation and adaptation.

Single-cell RNA sequencing of estrual mice reveals PM2.5-induced uterine cell heterogeneity and reproductive toxicity.

Fine particulate matter (PM2.5) exposure has been extensively linked to reproductive and developmental dysfunctions, yet the underlying mechanisms remain elusive. This study employed single-cell RNA sequencing (scRNA-seq) to investigate PM2.5-induced changes in uterine cell populations and gene expression profiles in mice during estrus and early pregnancy. Methodologically, we intranasally inoculated mice with 20 µL of 4.0 mg/mL PM2.5 suspension during their estrus and early pregnancy periods. Utilizing scRNA-seq analysis, we revealed significant alterations in cell type composition following PM2.5 exposure. Notably, we observed a marked decrease in the proportion of natural killer (NK) cells in PM2.5-exposed mice (2.00 % vs. 8.97 % in controls). Further functional enrichment analysis identified suppression of the IL-17 signaling pathway in NK cells as a key mechanism of PM2.5-induced toxicity. GSEA analysis showed in-depth details of the downregulated genes in this pathway, including Fosb, S100a8, Tnfaip3, IL-17a, and S100a9. PM2.5 exposure also disrupted intercellular communication within the uterine microenvironment, with the number of cell interactions decreasing from 483 to 315 and interaction strength reducing from 12.43 to 6.78 compared to controls. Histological examination revealed that PM2.5 exposure led to thinning of the endometrium and less prominent main branches in uterine tissues, and immunofluorescence assays corroborated the altered expression of IL-17 pathway components, showing enhanced Hsp90ab1 expression and reduced FOSB, S100A8, and S100A9 expression in PM2.5-exposed uterine tissues. These findings provide novel insights into the cellular mechanisms of PM2.5-induced reproductive toxicity, highlighting the IL-17 signaling pathway in uterine NK cells as a potential target for therapeutic interventions. Our results underscore the need for air quality regulations and open new avenues for developing biomarkers and targeted therapies to mitigate the reproductive risks associated with PM2.5 exposure.

DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates.

Messenger RNA splicing and degradation are critical for gene expression regulation, the abnormality of which leads to diseases. Previous methods for estimating kinetic rates have limitations, assuming uniform rates across cells. DeepKINET is a deep generative model that estimates splicing and degradation rates at single-cell resolution from scRNA-seq data. DeepKINET outperforms existing methods on simulated and metabolic labeling datasets. Applied to forebrain and breast cancer data, it identifies RNA-binding proteins responsible for kinetic rate diversity. DeepKINET also analyzes the effects of splicing factor mutations on target genes in erythroid lineage cells. DeepKINET effectively reveals cellular heterogeneity in post-transcriptional regulation.

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.

Single-Cell RNA-Sequencing: Opening New Horizons for Breast Cancer Research.

Breast cancer is the most prevalent malignant tumor among women with high heterogeneity. Traditional techniques frequently struggle to comprehensively capture the intricacy and variety of cellular states and interactions within breast cancer. As global precision medicine rapidly advances, single-cell RNA sequencing (scRNA-seq) has become a highly effective technique, revolutionizing breast cancer research by offering unprecedented insights into the cellular heterogeneity and complexity of breast cancer. This cutting-edge technology facilitates the analysis of gene expression profiles at the single-cell level, uncovering diverse cell types and states within the tumor microenvironment. By dissecting the cellular composition and transcriptional signatures of breast cancer cells, scRNA-seq provides new perspectives for understanding the mechanisms behind tumor therapy, drug resistance and metastasis in breast cancer. In this review, we summarized the working principle and workflow of scRNA-seq and emphasized the major applications and discoveries of scRNA-seq in breast cancer research, highlighting its impact on our comprehension of breast cancer biology and its potential for guiding personalized treatment strategies.

Upregulation of CKS2 in immunosuppressive cells is associated with metastasis and poor prognosis in prostate cancer: a single-cell RNA-sequencing analysis.

Background: Metastasis worsens prostate cancer (PCa) prognosis, with the immunosuppressive microenvironment playing a key role in bone metastasis. This study aimed to investigate how an immunosuppressive environment promotes PCa metastasis and worsens prognosis of patients with PCa. Methods: Candidate oncogenes were identified through analysis of the Gene Expression Omnibus (GEO) database. A prognostic model was developed for the purpose of identifying target genes. A single-cell RNA sequencing data from GEO database was used to analyze the localization of target genes in the tumor microenvironment. A pan-cancer analysis was conducted to study the cancer-causing potential of target genes across different types of tumors. Results: Fifty-one genes were found to be differentially expressed in bone metastasis compared to non-metastatic PCa, with CKS2 identified as the most significant gene associated with poor prognosis. CKS2 was shown to be linked to an immunosuppressive microenvironment and osteoclastic bone metastases, as shown by its negative correlation with immune cell infiltration and osteoblast-related gene expression. Moreover, CKS2 was found in immunosuppressive cells and was linked to bone metastasis in PCa. It was also overexpressed in different types of tumors, making it as an oncogenic gene. Conclusions: This research offers a new perspective on the potential utility of CKS2 as a therapeutic target for the prevention of metastatic PCa.

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.

Background: Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction. Methods: Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts. Results: We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions. Conclusions: This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.

Dissection of triple-negative breast cancer microenvironment and identification of potential therapeutic drugs using single-cell RNA sequencing analysis.

Breast cancer remains a leading cause of mortality in women worldwide. Triple-negative breast cancer (TNBC) is a particularly aggressive subtype characterized by rapid progression, poor prognosis, and lack of clear therapeutic targets. In the clinic, delineation of tumor heterogeneity and development of effective drugs continue to pose considerable challenges. Within the scope of our study, high heterogeneity inherent to breast cancer was uncovered based on the landscape constructed from both tumor and healthy breast tissue samples. Notably, TNBC exhibited significant specificity regarding cell proliferation, differentiation, and disease progression. Significant associations between tumor grade, prognosis, and TNBC oncogenes were established via pseudotime trajectory analysis. Consequently, we further performed comprehensive characterization of the TNBC microenvironment. A crucial epithelial subcluster, E8, was identified as highly malignant and strongly associated with tumor cell proliferation in TNBC. Additionally, epithelial-mesenchymal transition (EMT)-associated fibroblast and M2 macrophage subclusters exerted an influence on E8 through cellular interactions, contributing to tumor growth. Characteristic genes in these three cluster cells could therefore serve as potential therapeutic targets for TNBC. The collective findings provided valuable insights that assisted in the screening of a series of therapeutic drugs, such as pelitinib. We further confirmed the anti-cancer effect of pelitinib in an orthotopic 4T1 tumor-bearing mouse model. Overall, our study sheds light on the unique characteristics of TNBC at single-cell resolution and the crucial cell types associated with tumor cell proliferation that may serve as potent tools in the development of effective anti-cancer drugs.

Adenoid cystic carcinoma: insights from molecular characterization and therapeutic advances.

Adenoid cystic carcinoma (ACC) is a malignant tumor primarily originating from the salivary glands, capable of affecting multiple organs. Although ACC typically exhibits slow growth, it is notorious for its propensity for neural invasion, local recurrence, and distant metastasis, making it a particularly challenging cancer to treat. The complexity of ACC's histological and molecular features poses significant challenges to current treatment modalities, which often show limited effectiveness. Recent advancements in single-cell RNA-sequencing (scRNA-seq) have begun to unravel unprecedented insights into the heterogeneity and subpopulation diversity within ACC, revealing distinct cellular phenotypes and origins. This review delves into the intricate pathological and molecular characteristics of ACC, focusing on recent therapeutic advancements. We particularly emphasize the insights gained from scRNA-seq studies that shed light on the cellular landscape of ACC, underscoring its heterogeneity and pathobiology. Moreover, by integrating analyses from public databases, this review proposes novel perspectives for advancing treatment strategies in ACC. This review contributes to the academic understanding of ACC by proposing novel therapeutic approaches informed by cutting-edge molecular insights, paving the way for more effective, personalized therapeutic approaches for this challenging malignancy.

Spatial multi-omics analysis of the microenvironment in traumatic spinal cord injury: a narrative review.

Traumatic spinal cord injury (tSCI) is a severe injury to the central nervous system that is categorized into primary and secondary injuries. Among them, the local microenvironmental imbalance in the spinal cord caused by secondary spinal cord injury includes accumulation of cytokines and chemokines, reduced angiogenesis, dysregulation of cellular energy metabolism, and dysfunction of immune cells at the site of injury, which severely impedes neurological recovery from spinal cord injury (SCI). In recent years, single-cell techniques have revealed the heterogeneity of multiple immune cells at the genomic, transcriptomic, proteomic, and metabolomic levels after tSCI, further deepening our understanding of the mechanisms underlying tSCI. However, spatial information about the tSCI microenvironment, such as cell location and cell-cell interactions, is lost in these approaches. The application of spatial multi-omics technology can solve this problem by combining the data obtained from immunohistochemistry and multiparametric analysis to reveal the changes in the microenvironment at different times of secondary injury after SCI. In this review, we systematically review the progress of spatial multi-omics techniques in the study of the microenvironment after SCI, including changes in the immune microenvironment and discuss potential future therapeutic strategies.

Exploring the molecular mechanisms of macrophages in islet transplantation using single-cell analysis.

Background: Islet transplantation is a promising treatment for type 1 diabetes that aims to restore insulin production and improve glucose control, but long-term graft survival remains a challenge due to immune rejection. Methods: ScRNA-seq data from syngeneic and allogeneic islet transplantation grafts were obtained from GSE198865. Seurat was used for filtering and clustering, and UMAP was used for dimension reduction. Differentially expressed genes were analyzed between syngeneic and allogeneic islet transplantation grafts. Gene set variation analysis (GSVA) was performed on the HALLMARK gene sets from MSigDB. Monocle 2 was used to reconstruct differentiation trajectories, and cytokine signature enrichment analysis was used to compare cytokine responses between syngeneic and allogeneic grafts. Results: Three distinct macrophage clusters (Mø-C1, Mø-C2, and Mø-C3) were identified, revealing complex interactions and regulatory mechanisms within macrophage populations. The significant activation of macrophages in allogeneic transplants was marked by the upregulation of allograft rejection-related genes and pathways involved in inflammatory and interferon responses. GSVA revealed eight pathways significantly upregulated in the Mø-C2 cluster. Trajectory analysis revealed that Mø-C3 serves as a common progenitor, branching into Mø-C1 and Mø-C2. Cytokine signature enrichment analysis revealed significant differences in cytokine responses, highlighting the distinct immunological environments created by syngeneic and allogeneic grafts. Conclusion: This study significantly advances the understanding of macrophage roles within the context of islet transplantation by revealing the interactions between immune pathways and cellular fate processes. The findings highlight potential therapeutic targets for enhancing graft survival and function, emphasizing the importance of understanding the immunological aspects of transplant acceptance and longevity.

Delving deeper into the mechanisms fundamental to HIV-associated immunopathology using single-cell sequencing techniques: A scoping review of current literature.

Human immunodeficiency virus (HIV) infection has evolved into an established global pandemic over the past four decades; however, despite massive research investment globally, the precise underlying mechanisms which are fundamental to HIV-related pathogenesis remain unclear. Single cell ribonucleic acid (RNA) sequencing methods are increasingly being used for the identification of specific cell-type transcriptional changes in HIV infection. In this scoping review, we have considered information extracted from fourteen published HIV-associated single-cell RNA sequencing-related studies, hoping to throw light on the underlying mechanisms of HIV infection and pathogenesis, and to explore potential candidate biomarkers for HIV disease progression and antiviral treatment. Generally, HIV positive individuals tend to manifest disturbances of frequency of multiple cellular types, and specifically exhibit diminished levels of CD4+ T-cells and enriched numbers of CD8+ T-cells. Cell-specific transcriptional changes tend to be linked to cell permissiveness, hyperacute or acute HIV infection, viremia, and cell productivity. The transcriptomes of CD4+ T-cell and CD8+ T-cell subpopulations are also observed to change in HIV-positive diabetic individuals, spontaneous HIV controllers, individuals with high levels of HIV viremia, and those in an acute phase of HIV infection. The transcriptional changes seen in B cells, natural killer (NK) cells, and myeloid dendritic cells (mDCs) of HIV-infected individuals demonstrate that the humoral immune response, antiviral response, and immune response regulation, respectively, are all altered following HIV infection. Antiretroviral therapy (ART) plays a crucial role in achieving immune reconstitution, in improving immunological disruption, and in mitigating immune system imbalances in HIV-infected individuals, while not fully restoring inherent cellular transcription to levels seen in HIV-negative individuals. The preceding observations not only illustrate compelling advances in the understanding of HIV-associated immunopathogenesis, but also identify specific cell-type transcriptional changes that may serve as potential biomarkers for HIV disease monitoring and therapeutic targeting.

Elucidating the role of tumor-associated ALOX5+ mast cells with transformative function in cervical cancer progression via single-cell RNA sequencing.

Background: Cervical cancer (CC) is the fourth most common malignancy among women globally and serves as the main cause of cancer-related deaths among women in developing countries. The early symptoms of CC are often not apparent, with diagnoses typically made at advanced stages, which lead to poor clinical prognoses. In recent years, numerous studies have shown that there is a close relationship between mast cells (MCs) and tumor development. However, research on the role MCs played in CC is still very limited at that time. Thus, the study conducted a single-cell multi-omics analysis on human CC cells, aiming to explore the mechanisms by which MCs interact with the tumor microenvironment in CC. The goal was to provide a scientific basis for the prevention, diagnosis, and treatment of CC, with the hope of improving patients' prognoses and quality of life. Method: The present study acquired single-cell RNA sequencing data from ten CC tumor samples in the ArrayExpress database. Slingshot and AUCcell were utilized to infer and assess the differentiation trajectory and cell plasticity of MCs subpopulations. Differential expression analysis of MCs subpopulations in CC was performed, employing Gene Ontology, gene set enrichment analysis, and gene set variation analysis. CellChat software package was applied to predict cell communication between MCs subpopulations and CC cells. Cellular functional experiments validated the functionality of TNFRSF12A in HeLa and Caski cell lines. Additionally, a risk scoring model was constructed to evaluate the differences in clinical features, prognosis, immune infiltration, immune checkpoint, and functional enrichment across various risk scores. Copy number variation levels were computed using inference of copy number variations. Result: The obtained 93,524 high-quality cells were classified into ten cell types, including T_NK cells, endothelial cells, fibroblasts, smooth muscle cells, epithelial cells, B cells, plasma cells, MCs, neutrophils, and myeloid cells. Furthermore, a total of 1,392 MCs were subdivided into seven subpopulations: C0 CTSG+ MCs, C1 CALR+ MCs, C2 ALOX5+ MCs, C3 ANXA2+ MCs, C4 MGP+ MCs, C5 IL32+ MCs, and C6 ADGRL4+ MCs. Notably, the C2 subpopulation showed close associations with tumor-related MCs, with Slingshot results indicating that C2 subpopulation resided at the intermediate-to-late stage of differentiation, potentially representing a crucial transition point in the benign-to-malignant transformation of CC. CNVscore and bulk analysis results further confirmed the transforming state of the C2 subpopulation. CellChat analysis revealed TNFRSF12A as a key receptor involved in the actions of C2 ALOX5+ MCs. Moreover, in vitro experiments indicated that downregulating the TNFRSF12A gene may partially inhibit the development of CC. Additionally, a prognosis model and immune infiltration analysis based on the marker genes of the C2 subpopulation provided valuable guidance for patient prognosis and clinical intervention strategies. Conclusions: We first identified the transformative tumor-associated MCs subpopulation C2 ALOX5+ MCs within CC, which was at a critical stage of tumor differentiation and impacted the progression of CC. In vitro experiments confirmed the inhibitory effect of knocking down the TNFRSF12A gene on the development of CC. The prognostic model constructed based on the C2 ALOX5+MCs subset demonstrated excellent predictive value. These findings offer a fresh perspective for clinical decision-making in CC.

Single-cell analysis via manifold fitting: A framework for RNA clustering and beyond.

Single-cell RNA sequencing (scRNA-seq) data, susceptible to noise arising from biological variability and technical errors, can distort gene expression analysis and impact cell similarity assessments, particularly in heterogeneous populations. Current methods, including deep learning approaches, often struggle to accurately characterize cell relationships due to this inherent noise. To address these challenges, we introduce scAMF (Single-cell Analysis via Manifold Fitting), a framework designed to enhance clustering accuracy and data visualization in scRNA-seq studies. At the heart of scAMF lies the manifold fitting module, which effectively denoises scRNA-seq data by unfolding their distribution in the ambient space. This unfolding aligns the gene expression vector of each cell more closely with its underlying structure, bringing it spatially closer to other cells of the same cell type. To comprehensively assess the impact of scAMF, we compile a collection of 25 publicly available scRNA-seq datasets spanning various sequencing platforms, species, and organ types, forming an extensive RNA data bank. In our comparative studies, benchmarking scAMF against existing scRNA-seq analysis algorithms in this data bank, we consistently observe that scAMF outperforms in terms of clustering efficiency and data visualization clarity. Further experimental analysis reveals that this enhanced performance stems from scAMF's ability to improve the spatial distribution of the data and capture class-consistent neighborhoods. These findings underscore the promising application potential of manifold fitting as a tool in scRNA-seq analysis, signaling a significant enhancement in the precision and reliability of data interpretation in this critical field of study.

Integrative analysis of bulk and single-cell RNA sequencing reveals sphingolipid metabolism and immune landscape in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is characterized by its aggressive behavior and complex molecular heterogeneity, posing significant challenges for treatment and prognostication. This study offers a comprehensive analysis of ccRCC by leveraging both bulk and single-cell RNA sequencing data, with a specific aim to unravel the complexities of sphingolipid metabolism and the intricate dynamics within the tumor microenvironment (TME). By examining ccRCC samples sourced from public databases, our investigation delves deep into the genetic and transcriptomic landscape of this cancer type. Employing advanced analytical techniques, we have identified pivotal patterns in gene expression and cellular heterogeneity, with a special focus on the roles and interactions of various immune cells within the TME. Significantly, our research has unearthed insights into the dynamics of sphingolipid metabolism in ccRCC, shedding light on its potential implications for tumor progression and strategies for immune evasion. A novel aspect of this study is the development of a risk score model designed to enhance prognostic predictions for ccRCC patients, which is currently pending external validation to ascertain its clinical utility. Despite its contributions, the study is mindful of its limitations, including a reliance on observational data from public sources and a primary focus on RNA sequencing data, which may constrain the depth and generalizability of the findings. The study does not encompass critical aspects, such as protein expression, posttranslational modifications, and comprehensive metabolic profiles. Moreover, its retrospective design underscores the necessity for future prospective studies to solidify these preliminary conclusions. Our findings illuminate the intricate interplay between genetic alterations, sphingolipid metabolism, and immune responses in ccRCC. This research not only enhances our understanding of the molecular foundations of ccRCC but also paves the way for the development of targeted therapies and personalized treatment modalities. The study underlines the importance of cautious interpretation of results and champions ongoing research using diverse methodologies to thoroughly comprehend and effectively combat this formidable cancer type.

Protective effects of Ganoderma lucidum spores on estradiol benzoate-induced TEC apoptosis and compromised double-positive thymocyte development.

Backgroud: Thymic atrophy marks the onset of immune aging, precipitating developmental anomalies in T cells. Numerous clinical and preclinical investigations have underscored the regulatory role of Ganoderma lucidum spores (GLS) in T cell development. However, the precise mechanisms underlying this regulation remain elusive. Methods: In this study, a mice model of estradiol benzoate (EB)-induced thymic atrophy was constructed, and the improvement effect of GLS on thymic atrophy was evaluated. Then, we employs multi-omics techniques to elucidate how GLS modulates T cell development amidst EB-induced thymic atrophy in mice. Results: GLS effectively mitigates EB-induced thymic damage by attenuating apoptotic thymic epithelial cells (TECs) and enhancing the output of CD4+ T cells into peripheral blood. During thymic T cell development, sporoderm-removed GLS (RGLS) promotes T cell receptor (TCR) α rearrangement by augmenting V-J fragment rearrangement frequency and efficiency. Notably, biased Vα14-Jα18 rearrangement fosters double-positive (DP) to invariant natural killer T (iNKT) cell differentiation, partially contingent on RGLS-mediated restriction of peptide-major histocompatibility complex I (pMHCⅠ)-CD8 interaction and augmented CD1d expression in DP thymocytes, thereby promoting DP to CD4+ iNKT cell development. Furthermore, RGLS amplifies interaction between a DP subpopulation, termed DPsel-7, and plasmacytoid dendritic cells (pDCs), likely facilitating the subsequent development of double-negative iNKT1 cells. Lastly, RGLS suppresses EB-induced upregulation of Abpob and Apoa4, curbing the clearance of CD4+Abpob+ and CD4+Apoa4+ T cells by mTECs, resulting in enhanced CD4+ T cell output. Discussion: These findings indicate that the RGLS effectively mitigates EB-induced TEC apoptosis and compromised double-positive thymocyte development. These insights into RGLS's immunoregulatory role pave the way for its potential as a T-cell regeneration inducer.

Dynamic changes of the immune microenvironment in the development of gastric cancer caused by inflammation.

Precancerous lesions typically precede gastric cancer (GC), but the molecular mechanisms underlying the transition from these lesions to GC remain unclear. Therefore, it is urgent to understand this transition from precancerous lesions to GC, which is crucial for the early diagnosis and treatment of GC. In this study, we merged multiple single-cell RNA sequencing datasets to investigate the molecular changes in distinct cell types associated with the progression of GC. First, we observed an increasing abundance of immune cells and a decrease in non-immune cells from non-atrophic gastritis to GC. Five immune cell types were significantly enriched in GC compared to precancerous lesions. Moreover, we found that the interleukin (IL)-17 signaling pathway and Th17 cell differentiation were significantly up-regulated in immune cell subsets during GC progression. Some genes in these processes were predominantly expressed at the GC stage, highlighting their potential as diagnostic markers. Furthermore, we validated our findings using bulk RNA sequencing data from The Cancer Genome Atlas and confirmed consistent immune cell changes during GC progression. Our study provides insights into the immune infiltration and signaling pathways involved in the development of GC, contributing to the development of early diagnosis and targeted treatment strategies for this malignancy.

Single-cell RNA sequencing in endometrial cancer: exploring the epithelial cells and the microenvironment landscape.

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a powerful tool for dissecting cellular heterogeneity and understanding the intricate biology of diseases, including cancer. Endometrial cancer (EC) stands out as the most prevalent gynecological malignancy in Europe and the second most diagnosed worldwide, yet its cellular complexity remains poorly understood. In this review, we explore the contributions of scRNA-seq studies to shed light on the tumor cells and cellular landscape of EC. We discuss the diverse tumoral and microenvironmental populations identified through scRNA-seq, highlighting the implications for understanding disease progression. Furthermore, we address potential limitations inherent in scRNA-seq studies, such as technical biases and sample size constraints, emphasizing the need for larger-scale research encompassing a broader spectrum of EC histological subtypes. Notably, a significant proportion of scRNA-seq analyses have focused on primary endometrioid carcinoma tumors, underscoring the need to incorporate additional histological and aggressive types to comprehensively capture the heterogeneity of EC. By critically evaluating the current state of scRNA-seq research in EC, this review underscores the importance of advancing towards more comprehensive studies to accelerate our understanding of this complex disease.