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.

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.

A Single-Cell RNA Sequencing Guided Multienzymatic Hydrogel Design for Self-Regenerative Repair in Diabetic Mandibular Defects.

Conventional bone tissue engineering materials struggle to reinstate physiological bone remodeling in a diabetic context, primarily due to the compromised repolarization of proinflammatory macrophages to anti-inflammatory macrophages. Here, leveraging single-cell RNA sequencing (scRNA-seq) technology, the pivotal role of nitric oxide (NO) and reactive oxygen species (ROS) is unveiled in impeding macrophage repolarization during physiological bone remodeling amidst diabetes. Guided by scRNA-seq analysis, we engineer a multienzymatic bone tissue engineering hydrogel scaffold (MEBTHS) composed is engineered of methylpropenylated gelatin hydrogel integrated with ruthenium nanozymes, possessing both Ru0 and Ru4+ components. This design facilitates efficient NO elimination via Ru0 while simultaneously exhibiting ROS scavenging properties through Ru4+. Consequently, MEBTHS orchestrates macrophage reprogramming by neutralizing ROS and reversing NO-mediated mitochondrial metabolism, thereby rejuvenating bone marrow-derived mesenchymal stem cells and endothelial cells within diabetic mandibular defects, producing newly formed bone with quality comparable to that of normal bone. The scRNA-seq guided multienzymatic hydrogel design fosters the restoration of self-regenerative repair, marking a significant advancement in bone tissue engineering.

Circulating RAC1 contributed to steroid-sensitive nephrotic syndrome: Mendelian randomization, single-cell RNA-sequencing, proteomic, and experimental evidence.

OBJECTIVES: The small GTPase Rac1 (RAC1) has been linked to podocyte disorders and steroid-sensitive nephrotic syndrome (SSNS). The aim of this study was to explore and validate the potential causal association between circulating RAC1 and SSNS. METHODS: The association between circulating RAC1 and SSNS at both gene expression and proteomic levels was investigated using Mendelian randomization analysis, and further validated by single-cell RNA-sequencing, proteomic analysis, and experimental studies. The genetic instruments comprised cis-expression quantitative trait loci (cis-eQTLs) associated with RAC1 gene expression and protein QTLs correlated with plasma RAC1 protein levels. Causal associations were estimated utilizing the inverse variance weighted and MR-PRESSO methods. Validation of RAC1 expression was conducted through single-cell RNA-sequencing of peripheral blood mononuclear cells from patients with SSNS and healthy controls. Proteomic analysis was performed among patients with minimal change nephrotic syndrome. Experimental validation was conducted using a puromycin aminonucleoside (PAN)-induced nephrosis model. RESULTS: Increased expression of RAC1 was associated with a higher risk of SSNS (gene expression level: odds ratio [OR], 1.53; 95% confidence interval [CI], 1.02-2.28; protein level: OR, 1.82; 95% CI, 1.05-3.17). The results of MR-PRESSO were consistent (gene expression level: OR, 1.49; 95% CI, 1.17-1.92; protein level: OR, 1.81; 95% CI, 1.16-2.85). Single-cell RNA sequencing and proteomic analysis confirmed elevated RAC1 expression in patients with SSNS compared to healthy controls. Experimental data further supported increased RAC1 expression in PAN-induced nephropathy. CONCLUSIONS: Increased expression of RAC1 might be causally associated with SSNS, suggesting that targeting RAC1 might represent a potential therapeutic strategy for SSNS.

Integrative multi-omics analysis reveals the role of tumor-associated endothelial cells and their signature in prognosis of intrahepatic cholangiocarcinoma.

This study aims to investigate the interplay between tumor-associated endothelial cells (TECs) and immune cells within the tumor microenvironment (TME) and its impact on tumor prognosis. We conducted single-cell RNA sequencing (scRNA-seq) of tumor, normal, and lymph node tissues obtained from intrahepatic cholangiocarcinoma (ICC) patients to reveal the role of TECs in tumor angiogenesis and their significant heterogeneity. Meanwhile, we identified genes highly expressed in TECs and constructed TEC signatures (TEC.Sig). Next, we calculated TEC scores of samples based on TEC.Sig. Patients with higher TEC scores exhibited a higher frequency of KRAS mutations, which was associated with increased infiltration of neutrophils and immature dendritic cells (iDCs), and decreased numbers of natural killer (NK), CD4 + T, and CD8 + T effector memory (Tem) cells, indicating an inflammation-dominated immunosuppressive phenotype. In contrast, BAP1 mutations and CXCL12 overexpression showed a contrasting trend. Spatial transcriptomics analysis and histological experiments further confirmed that TECs interacted with various tumor-killing immune cells through the CXCL12/CXCR4 axis. Multiple tumor immunotherapy datasets confirmed that the TEC.Sig could predict patient responses to immunotherapy. The TEC score is a promising and reliable biomarker for predicting genetic mutations and prognosis in ICC patients. Enhancing the regulation of the CXCL12/CXCR4 signaling pathway may represent a potential novel therapeutic target for ICC treatment.

Discovery of GJC1 as a prognostic biomarker in glioma cells: insights into its cell-cycle relationship and differential expression in non-neuronal cells.

Background: Gliomas, originating from the most common non-neuronal cells in the brain (glial cells), are the most common brain tumors and are associated with high mortality and poor prognosis. Glioma cells exhibit a tendency to disrupt normal cell-cycle regulation, leading to abnormal proliferation and malignant growth. This study investigated the predictive potential of GJC1 in gliomas and explored its relationship with the cell cycle. Methods: Retrospective analysis of RNA-seq and single-cell sequencing data was conducted using the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. The differential expression of GJC1 in gliomas with various pathological features and in different non-neuronal cell groups was analyzed. Functional data were examined using gene set variation analysis (GSVA). Furthermore, CellMiner was used to evaluate the relationship between GJC1 expression and predicted treatment response across these databases. Results: GJC1 expression was enriched in high-grade gliomas and 1p/19q non-codeletion gliomas. GJC1 enrichment was observed in classical and mesenchymal subtypes within the TCGA glioma subtype group. In single-cell subgroup analysis, GJC1 expression was higher in glioma tissues compared to other non-neuronal cells. Additionally, the TCGA classical subtype of glioma cells exhibited more GJC1 expression than the other subgroups. GJC1 emerged as an independent prognostic factor for overall survival in glioma. GSVA unveiled potential mechanisms by which GJC1 may impact cell-cycle regulation in glioma. Finally, a significant correlation was observed between GJC1 expression and the sensitivity of multiple anti-cancer drugs. Conclusion: These findings confirmed GJC1 as a novel biomarker and provided insights into the differential gene expression in non-neuronal cells and the impact of the cell cycle on gliomas. Consequently, GJC1 may be used to predict glioma prognosis and has potential therapeutic value.

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.

Identification and Verification of Key Genes in Colorectal Cancer Liver Metastases Through Analysis of Single-Cell Sequencing Data and TCGA Data.

BACKGROUND: Colorectal cancer (CRC) is highly prevalent worldwide, with more patients experiencing colorectal cancer liver metastases (CRLM). This study aimed to identify key genes in CRLM through single-cell sequencing data reanalysis and experimental validation. METHODS: The study analyzed single-cell RNA-sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for gene functional enrichment analysis. The Cancer Genome Atlas (TCGA) data enabled bulk-RNA expression and survival prognosis analysis. Real-time polymerase chain reaction (qPCR) detected mRNA expression, whereas Western blot determined protein levels. Cell function experiments assessed SPARC's impact on CRC cell behavior. RESULTS: Cluster analysis showed 23 classes among 17 CRLM samples, representing six cell types. A GO and KEGG analysis identified interleukin-1 beta (IL1B), CD2 molecule (CD2), and C-X-C motif chemokine ligand 8 (CXCL8) as significant prognostic factors in CRC. Secreted protein acidic and cysteine rich (SPARC) was one of the top differentially expressed genes (DEGs) in tissue stem cells, confirmed in primary and metastatic lesions. Metastatic lesions showed higher expression of SPARC and CRC stem cell marker leucine-rich repeat containing G protein-coupled receptor 5 (LGR5), which was significantly correlated positively with LGR5 expression. Knockdown of SPARC reduced CRC cell sphere- and colony-formation, invasion, and migration abilities. Overexpression of SPARC significantly increased the malignancy of CRC cells. CONCLUSIONS: Several key genes were identified in the process of CRLM. In CRLM samples and those corresponding to CRC stem cells, SPARC was significantly upregulated. In the therapy of CRLM, SPARC might be a potential target.

Single-cell dissection reveals promotive role of ENO1 in leukemia stem cell self-renewal and chemoresistance in acute myeloid leukemia.

BACKGROUND: Quiescent self-renewal of leukemia stem cells (LSCs) and resistance to conventional chemotherapy are the main factors leading to relapse of acute myeloid leukemia (AML). Alpha-enolase (ENO1), a key glycolytic enzyme, has been shown to regulate embryonic stem cell differentiation and promote self-renewal and malignant phenotypes in various cancer stem cells. Here, we sought to test whether and how ENO1 influences LSCs renewal and chemoresistance within the context of AML. METHODS: We analyzed single-cell RNA sequencing data from bone marrow samples of 8 relapsed/refractory AML patients and 4 healthy controls using bioinformatics and machine learning algorithms. In addition, we compared ENO1 expression levels in the AML cohort with those in 37 control subjects and conducted survival analyses to correlate ENO1 expression with clinical outcomes. Furthermore, we performed functional studies involving ENO1 knockdown and inhibition in AML cell line. RESULTS: We used machine learning to model and infer malignant cells in AML, finding more primitive malignant cells in the non-response (NR) group. The differentiation capacity of LSCs and progenitor malignant cells exhibited an inverse correlation with glycolysis levels. Trajectory analysis indicated delayed myeloid cell differentiation in NR group, with high ENO1-expressing LSCs at the initial stages of differentiation being preserved post-treatment. Simultaneously, ENO1 and stemness-related genes were upregulated and co-expressed in malignant cells during early differentiation. ENO1 level in our AML cohort was significantly higher than the controls, with higher levels in NR compared to those in complete remission. Knockdown of ENO1 in AML cell line resulted in the activation of LSCs, promoting cell differentiation and apoptosis, and inhibited proliferation. ENO1 inhibitor can impede the proliferation of AML cells. Furthermore, survival analyses associated higher ENO1 expression with poorer outcome in AML patients. CONCLUSIONS: Our findings underscore the critical role of ENO1 as a plausible driver of LSC self-renewal, a potential target for AML target therapy and a biomarker for AML prognosis.

Integrating transcriptomics, eQTL, and Mendelian randomization to dissect monocyte roles in severe COVID-19 and gout flare.

Introduction: There are considerable similarities between the pathophysiology of gout flare and the dysregulated inflammatory response in severe COVID-19 infection. Monocytes are the key immune cells involved in the pathogenesis of both diseases. Therefore, it is critical to elucidate the molecular basis of the function of monocytes in gout and COVID-19 in order to develop more effective therapeutic approaches. Methods: The single-cell RNA sequencing (scRNA-seq), large-scale genome-wide association studies (GWAS), and expression quantitative trait loci (eQTL) data of gout and severe COVID-19 were comprehensively analyzed. Cellular heterogeneity and intercellular communication were identified using the scRNA-seq datasets, and the monocyte-specific differentially expressed genes (DEGs) between COVID-19, gout and normal subjects were screened. In addition, the correlation of the DEGs with severe COVID-19 and gout flare was analyzed through GWAS statistics and eQTL data. Results: The scRNA-seq analysis exhibited that the proportion of classical monocytes was increased in both severe COVID-19 and gout patient groups compared to healthy controls. Differential expression analysis and MR analysis showed that NLRP3 was positively associated with the risk of severe COVID-19 and involved 11 SNPs, of which rs4925547 was not significantly co-localized. In contrast, IER3 was positively associated with the risk of gout and involved 9 SNPs, of which rs1264372 was significantly co-localized. Discussion: Monocytes have a complex role in gout flare and severe COVID-19, which underscores the potential mechanisms and clinical significance of the interaction between the two diseases.

Effect of fentanyl on HIV expression in peripheral blood mononuclear cells.

Introduction: Illicit drug use, particularly the synthetic opioid fentanyl, presents a significant global health challenge. Previous studies have shown that fentanyl enhances viral replication; yet, the mechanisms by which it affects HIV pathogenesis remain unclear. This study investigated the impact of fentanyl on HIV replication in CD4+ T lymphocytes. Methods: CD4+ T lymphocytes from HIV-negative donors were activated, infected with HIVNL4-3, and treated with fentanyl. HIV proviral DNA and p24 antigen expression were quantified using real-time PCR and ELISA, respectively. Single-cell RNA libraries were analyzed to identify differentially expressed genes. Results: Results indicated that fentanyl treatment increased HIV p24 expression and proviral DNA levels, and naltrexone mitigated these effects. Single-cell RNAseq analysis identified significantly altered gene expression in CD4+ T lymphocytes. Discussion: The results of our findings suggest that fentanyl promotes HIV replication ex vivo, emphasizing the need for a deeper understanding of opioid-virus interactions to develop better treatment strategies for individuals with HIV and opioid use disorder.

Immunometabolic changes and potential biomarkers in CFS peripheral immune cells revealed by single-cell RNA sequencing.

The pathogenesis of Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) remains unclear, though increasing evidence suggests inflammatory processes play key roles. In this study, single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) was used to decipher the immunometabolic profile in 4 ME/CFS patients and 4 heathy controls. We analyzed changes in the composition of major PBMC subpopulations and observed an increased frequency of total T cells and a significant reduction in NKs, monocytes, cDCs and pDCs. Further investigation revealed even more complex changes in the proportions of cell subpopulations within each subpopulation. Gene expression patterns revealed upregulated transcription factors related to immune regulation, as well as genes associated with viral infections and neurodegenerative diseases.CD4+ and CD8+ T cells in ME/CFS patients show different differentiation states and altered trajectories, indicating a possible suppression of differentiation. Memory B cells in ME/CFS patients are found early in the pseudotime, indicating a unique subtype specific to ME/CFS, with increased differentiation to plasma cells suggesting B cell overactivity. NK cells in ME/CFS patients exhibit reduced cytotoxicity and impaired responses, with reduced expression of perforin and CD107a upon stimulation. Pseudotime analysis showed abnormal development of adaptive immune cells and an enhanced cell-cell communication network converging on monocytes in particular. Our analysis also identified the estrogen-related receptor alpha (ESRRA)-APP-CD74 signaling pathway as a potential biomarker for ME/CFS in peripheral blood. In addition, data from the GSE214284 database confirmed higher ESRRA expression in the monocyte cell types of male ME/CFS patients. These results suggest a link between immune and neurological symptoms. The results support a disease model of immune dysfunction ranging from autoimmunity to immunodeficiency and point to amyloidotic neurodegenerative signaling pathways in the pathogenesis of ME/CFS. While the study provides important insights, limitations include the modest sample size and the evaluation of peripheral blood only. These findings highlight potential targets for diagnostic biomarkers and therapeutic interventions. Further research is needed to validate these biomarkers and explore their clinical applications in managing ME/CFS.

Single-cell RNA sequencing profiling of mouse cardiac cells in response to retinoic acid.

Congenital heart disease (CHD) is the leading cause of birth defect-related mortality. CHD is a multifactorial, complex disease involving environmental factors playing important roles. To elucidate the cardiac cellular and molecular mechanisms of cardiac malformation, we administered pregnant mice with a single dose of all-trans retinoic acid (RA) at E8.5, as the CHD model. We performed single-cell RNA sequencing on cardiac cells from developing mouse hearts spanning from E8.5 to E17.5 after RA administration. A total of 69,447 cells were obtained from seven developmental stages ranging from E8.5 to E17.5. RA significantly impacted various CM subpopulations, particularly the outflow tract CMs at E9.0 by reduction of Tdgf1 expression. RA also influences the transition of endocardial-to-mesenchymal cells by decreasing the Stmn2 levels, which may contribute to abnormal valve development. In addition, RA altered the metabolic pattern of epicardial cells at E11.5 and promoted its differentiation potential. Taken together, these results are valuable for the development of preventive and therapeutic strategies for CHDs.

Single-cell RNA sequencing offers opportunities to explore the depth of physiology, adaptation, and biochemistry in non-model organisms exposed to pollution.

Single-cell Sequencing technology (scSeq) has revolutionized our understanding of individual cells, uncovering unprecedented heterogeneity within tissues and cell populations, principality through single-cell RNA Sequencing (scRNA-Seq). This short review highlights the pivotal role of scRNA-Seq in elucidating genotype-phenotype relationships, particularly in biological systems. Based on published articles, our analysis involved manual curation and automated Scopus tools to illustrate recent advances in the application of scRNA-Seq. The results reveal that scRNA-Seq has been extensively utilized in various biological areas, including biochemistry, genetics, molecular biology, immunology, and microbiology, followed by health sciences covering studies related to the nervous system, immune system, human health, development, and diseases, with a particular focus on cancer research. However, the potential of scRNA-Seq extends beyond disease research, offering insights into non-model organisms' responses to environmental contaminants. By enabling the study of cellular reactions at a molecular level, scRNA-Seq provides a comprehensive understanding of intracellular heterogeneity that enhances our comprehension of physiological, biochemical, and pathological environmental impacts on non-model organisms exposed to pollution. This understanding has many practical benefits, as it can aid in regulation and conservation efforts that benefit the environment and the use of economically essential and ecologically relevant organisms.

Validation of non-destructive morphology-based selection of cerebral cortical organoids by paired morphological and single-cell RNA-seq analyses.

Organoids, self-organized cell aggregates, contribute significantly to developing disease models and cell-based therapies. Organoid-to-organoid variations, however, are inevitable despite the use of the latest differentiation protocols. Here, we focused on the morphology of organoids formed in a cerebral organoid differentiation culture and assessed their cellular compositions by single-cell RNA sequencing analysis. The data revealed that organoids primarily composed of non-neuronal cells, such as those from the neural crest and choroid plexus, showed unique morphological features. Moreover, we demonstrate that non-destructive morphological analysis can accurately distinguish organoids composed of cerebral cortical tissues from other cerebral tissues, thus enhancing experimental accuracy and reliability to ensure the safety of cell-based therapies.

Single-cell RNA profiling of ocular adnexal sebaceous carcinoma reveals a complex tumor microenvironment and identifies new biomarkers.

PURPOSE: Ocular adnexal sebaceous carcinoma (OaSC) is an aggressive malignancy that often necessitates orbital exenteration. Its tumor composition and transcriptional profile remain largely unknown, which poses a significant barrier to medical advances. Here, we report the first in-depth transcriptomic analysis of OaSC at the single-cell resolution and discern mechanisms underlying cancer progression for the discovery of potential globe-sparing immunotherapies, targeted therapies, and biomarkers to guide clinical management. DESIGN: Laboratory investigation with a retrospective observational case series. METHODS: Single-cell RNA sequencing was performed on six patient specimens: three primary tumors, two tumors with pagetoid spread, and a normal tarsus sample. Cellular components were identified via gene signatures. Molecular pathways underlying tumorigenesis and pagetoid spread were discerned via gene ontology analysis of the differentially expressed genes between specimens. CALML5 immunohistochemistry was performed on an archival cohort of OaSC, squamous cell carcinoma (SCC), ocular surface squamous neoplasia (OSSN), and basal cell carcinoma (BCC) cases. RESULTS: Analysis of 29,219 cells from OaSC specimens revealed tumor, immune, and stromal cells. Tumor-infiltrating immune cells include a diversity of cell types, including exhausted T-cell populations. In primary OaSC tumors, mitotic nuclear division and oxidative phosphorylation pathways are upregulated, while lipid biosynthesis and metabolism pathways are downregulated. Epithelial tissue migration pathways are upregulated in tumor cells undergoing pagetoid spread. scRNA-seq analyses also revealed that CALML5 is upregulated in OaSC tumor cells. Diffuse nuclear and cytoplasmic CALML5 staining was present in 28 of 28 (100%) OaSC cases. Diffuse nuclear and membranous CALML5 staining was present in 5 of 25 (20%) SCC and OSSN cases, while diffuse nuclear staining was present in 1 of 12 (8%) BCC cases. CONCLUSIONS: This study reveals a complex OaSC tumor microenvironment and confirms that the CALML5 immunohistochemical stain is a sensitive diagnostic marker.

UCHL1 Overexpression Is Related to the Aggressive Phenotype of Non-small Cell Lung Cancer.

BACKGROUND: Ubiquitin C-terminal hydrolase L1 (UCHL1), which encodes thiol protease that hydrolyzes a peptide bond at the C-terminal glycine residue of ubiquitin, regulates cell differentiation, proliferation, transcriptional regulation, and numerous other biological processes and may be involved in lung cancer progression. UCHL1 is mainly expressed in the brain and plays a tumor-promoting role in a few cancer types; however, there are limited reports regarding its role in lung cancer. METHODS: Single-cell RNA (scRNA) sequencing using 10X chromium v3 was performed on a paired normal-appearing and tumor tissue from surgical specimens of a patient who showed unusually rapid progression. To validate clinical implication of the identified biomarkers, immunohistochemical (IHC) analysis was performed on 48 non-small cell lung cancer (NSCLC) tissue specimens, and the correlation with clinical parameters was evaluated. RESULTS: We identified 500 genes overexpressed in tumor tissue compared to those in normal tissue. Among them, UCHL1, brain expressed X-linked 3 (BEX3), and midkine (MDK), which are associated with tumor growth and progression, exhibited a 1.5-fold increase in expression compared to that in normal tissue. IHC analysis of NSCLC tissues showed that only UCHL1 was specifically overexpressed. Additionally, in 48 NSCLC specimens, UCHL1 was specifically upregulated in the cytoplasm and nuclear membrane of tumor cells. Multivariable logistic analysis identified several factors, including smoking, tumor size, and high-grade dysplasia, to be typically associated with UCHL1 overexpression. Survival analyses using The Cancer Genome Atlas (TCGA) datasets revealed that UCHL1 overexpression is substantially associated with poor survival outcomes. Furthermore, a strong association was observed between UCHL1 expression and the clinicopathological features of patients with NSCLC. CONCLUSION: UCHL1 overexpression was associated with smoking, tumor size, and high-grade dysplasia, which are typically associated with a poor prognosis and survival outcome. These findings suggest that UCHL1 may serve as an effective biomarker of NSCLC.

Single-cell transcriptome and chromatin accessibility mapping of upper lip and primary palate fusion.

Cleft lip and/or primary palate (CL/P) represent a prevalent congenital malformation, the aetiology of which is highly intricate. Although it is generally accepted that the condition arises from failed fusion between the upper lip and primary palate, the precise mechanism underlying this fusion process remains enigmatic. In this study, we utilized transposase-accessible chromatin sequencing (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) to interrogate lambdoidal junction tissue derived from C57BL/6J mouse embryos at critical stages of embryogenesis (10.5, 11.5 and 12.5 embryonic days). We successfully identified distinct subgroups of mesenchymal and ectodermal cells involved in the fusion process and characterized their unique transcriptional profiles. Furthermore, we conducted cell differentiation trajectory analysis, revealing a dynamic repertoire of genes that are sequentially activated or repressed during pseudotime, facilitating the transition of relevant cell types. Additionally, we employed scATAC data to identify key genes associated with the fusion process and demonstrated differential chromatin accessibility across major cell types. Finally, we constructed a dynamic intercellular communication network and predicted upstream transcriptional regulators of critical genes involved in important signalling pathways. Our findings provide a valuable resource for future studies on upper lip and primary palate development, as well as congenital defects.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq revealed key genes for bone metastasis and chemoresistance in prostate cancer.

BACKGROUND: Prostate cancer (PCa) is a serious malignancy. The main causes of PCa aggravation and death are unexplained resistance to chemotherapy and bone metastases. OBJECTIVE: This study aimed to investigate the molecular mechanisms associated with the dynamic processes of progression, bone metastasis, and chemoresistance in PCa. METHODS: Through comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data, Gene Expression Omnibus (GEO) tumor progression and metastasis-related genes were identified. These genes were subjected to lasso regression modeling using the Cancer Genome Atlas (TCGA) database. Tartrate-resistant acid phosphatase (TRAP) staining and real-time quantitative PCR (RT-qPCR) were used to evaluate osteoclast differentiation. CellMiner was used to confirm the effect of LDHA on chemoresistance. Finally, the relationship between LDHA and chemoresistance was verified using doxorubicin-resistant PCa cell lines. RESULTS: 7928 genes were identified as genes related to tumor progression and metastasis. Of these, 7 genes were found to be associated with PCa prognosis. The scRNA-seq and TCGA data showed that the expression of LDHA was higher in tumors and associated with poor prognosis of PCa. In addition, upregulation of LDHA in PCa cells induces osteoclast differentiation. Additionally, high LDHA expression was associated with resistance to Epirubicin, Elliptinium acetate, and doxorubicin. Cellular experiments demonstrated that LDHA knockdown inhibited doxorubicin resistance in PCa cells. CONCLUSIONS: LDHA may play a potential contributory role in PCa initiation and development, bone metastasis, and chemoresistance. LDHA is a key target for the treatment of PCa.

Revealing the association between East Asian oral microbiome and colorectal cancer through Mendelian randomization and multi-omics analysis.

Background: Colorectal cancer (CRC) poses a global health threat, with the oral microbiome increasingly implicated in its pathogenesis. This study leverages Mendelian Randomization (MR) to explore causal links between oral microbiota and CRC using data from the China National GeneBank and Biobank Japan. By integrating multi-omics approaches, we aim to uncover mechanisms by which the microbiome influences cellular metabolism and cancer development. Methods: We analyzed microbiome profiles from 2017 tongue and 1915 saliva samples, and GWAS data for 6692 CRC cases and 27178 controls. Significant bacterial taxa were identified via MR analysis. Single-cell RNA sequencing and enrichment analyses elucidated underlying pathways, and drug predictions identified potential therapeutics. Results: MR identified 19 bacterial taxa significantly associated with CRC. Protective effects were observed in taxa like RUG343 and Streptococcus_umgs_2425, while HOT-345_umgs_976 and W5053_sp000467935_mgs_712 increased CRC risk. Single-cell RNA sequencing revealed key pathways, including JAK-STAT signaling and tyrosine metabolism. Drug prediction highlighted potential therapeutics like Menadione Sodium Bisulfite and Raloxifene. Conclusion: This study establishes the critical role of the oral microbiome in colorectal cancer development, identifying specific microbial taxa linked to CRC risk. Single-cell RNA sequencing and drug prediction analyses further elucidate key pathways and potential therapeutics, providing novel insights and personalized treatment strategies for CRC.