Comprehensive analysis of scRNA-Seq and bulk RNA-Seq data reveals dynamic changes in tumor-associated neutrophils in the tumor microenvironment of hepatocellular carcinoma and leads to the establishment of a neutrophil-related prognostic model.

BACKGROUND: Analysis of hepatocellular carcinoma (HCC) single-cell sequencing data was conducted to explore the role of tumor-associated neutrophils in the tumor microenvironment. METHODS: Analysis of single-cell sequencing data from 12 HCC tumor cores and five HCC paracancerous tissues identified cellular subpopulations and cellular marker genes. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to establish and validate prognostic models. xCELL, TIMER, QUANTISEQ, CIBERSORT, and CIBERSORT-abs analyses were performed to explore immune cell infiltration. Finally, the pattern of tumor-associated neutrophil roles in tumor microenvironmental components was explored. RESULTS: A total of 271 marker genes for tumor-associated neutrophils were identified based on single-cell sequencing data. Prognostic models incorporating eight genes were established based on TCGA data. Immune cell infiltration differed between the high- and low-risk groups. The low-risk group benefited more from immunotherapy. Single-cell analysis indicated that tumor-associated neutrophils were able to influence macrophage, NK cell, and T-cell functions through the IL16, IFN-II, and SPP1 signaling pathways. CONCLUSION: Tumor-associated neutrophils regulate immune functions by influencing macrophages and NK cells. Models incorporating tumor-associated neutrophil-related genes can be used to predict patient prognosis and immunotherapy responses.

Multi-omics characterization of a scoring system to quantify hypoxia patterns in patients with head and neck squamous cell carcinoma.

BACKGROUND: The 5-year survival rate of patients with head and neck squamous cell carcinoma (HNSCC) remains < 50%. Hypoxia patterns are a hallmark of HNSCC that are associated with its occurrence and progression. However, the precise role of hypoxia during HNSCC, such as the relationship between hypoxia, tumor immune landscape and cell communication orchestration remains largely unknown. The current study integrated data from bulk and single-cell RNA sequencing analyses to define the relationship between hypoxia and HNSCC. METHODS: A scoring system named the hypoxia score (HS) was constructed based on hypoxia-related genes (HRGs) expression. The predictive value of HS response for patient outcomes and different treatments was evaluated. Single-cell datasets and cell communication were utilized to rule out cell populations which hypoxia targeted on. RESULTS: The survival outcomes, immune/Estimate scores, responses to targeted inhibitors, and chemotherapeutic, and immunotherapy responses were distinct between a high HS group and a low HS group (all P < 0.05). Single-cell datasets showed different distributions of HS in immune cell populations (P < 0.05). Furthermore, HLA-DPA1/CD4 axis was identified as a unique interaction between CD4 + T Conv and pDC cells. CONCLUSIONS: Altogether, the quantification for hypoxia patterns is a potential biomarker for prognosis, individualized chemotherapeutic and immunotherapy strategies. The portrait of cell communication characteristics over the HNSCC ecosystem enhances the understanding of hypoxia patterns in HNSCC.

Single-cell and bulk RNA-seq unveils the immune infiltration landscape associated with cuproptosis in cerebral cavernous malformations.

BACKGROUND: Cerebral cavernous malformations (CCMs) are vascular abnormalities associated with deregulated angiogenesis. Their pathogenesis and optimal treatment remain unclear. This study aims to investigate the molecular signatures of cuproptosis, a newly identified type of cell death, associated with CCMs development. METHODS: Bulk RNA sequencing (RNA-seq) from 15 CCM and 6 control samples were performed with consensus clustering and clustered to two subtypes based on expression levels of cuproptosis-related genes (CRGs). Differentially expressed genes and immune infiltration between subtypes were then identified. Machine learning algorithms including the least absolute shrinkage and selection operator and random forest were employed to screen for hub genes for CCMs associated with cuproptosis. Furthermore, Pathway enrichment and correlation analysis were used to explore the functions of hub genes and their association with immune phenotypes in CCMs. An external dataset was then employed for validation. Finally, employing the Cellchat algorithm on a single-cell RNA-seq dataset, we explored potential mechanisms underlying the participation of these hub genes in cell-cell communication in CCMs. RESULTS: Our study revealed two distinct CCM subtypes with differential pattern of CRG expression and immune infiltration. Three hub genes (BTBD10, PFDN4, and CEMIP) were identified and validated, which may significantly associate with CCM pathogenesis. These genes were found to be significantly upregulated in CCM endothelial cells (ECs) and were validated through immunofluorescence and western blot analysis. Single-cell RNA-seq analysis revealed the cellular co-expression patterns of these hub genes, particularly highlighting the high expression of BTBD10 and PFDN4 in ECs. Additionally, a significant co-localization was also observed between BTBD10 and the pivotal cuproptosis gene FDX1 in Mki67+ tip cells, indicating the crucial role of cuproptosis for angiogenesis in CCMs. The study also explored the cell-cell communication between subcluster of ECs expressing these hub genes and immune cells, particularly M2 macrophages, suggesting a role for these interactions in CCM pathogenesis. CONCLUSION: This study identifies molecular signatures linking cuproptosis to CCMs pathogenesis. Three hub genes-PFDN4, CEMIP, and BTBD10-may influence disease progression by modulating immunity. Further research is needed to understand their precise disease mechanisms and evaluate their potential as biomarkers or therapeutic targets for CCMs.

Identification of the key genes and mechanisms associated with transcatheter arterial chemoembolisation refractoriness in hepatocellular carcinoma.

BACKGROUND: Transcatheter arterial embolisation (TACE) is the primary treatment for intermediate-stage hepatocellular carcinoma (HCC) patients while some HCC cases have shown resistance to TACE. AIM: To investigate the key genes and potential mechanisms correlated with TACE refractoriness in HCC. METHODS: The microarray datasets of TACE-treated HCC tissues, HCC and non-HCC tissues were collected by searching multiple public databases. The respective differentially expressed genes (DEGs) were attained via limma R package. Weighted gene co-expression network analysis was employed for identifying the significant modules related to TACE non-response. TACE refractoriness-related genes were obtained by intersecting up-regulated TACE-associated and HCC-associated DEGs together with the genes in significant modules related to TACE non-response. The key genes expression in the above two pairs of samples was compared respectively via Wilcoxon tests and standard mean differences model. The prognostic value of the key genes was evaluated by Kaplan-Meier curve. Multivariate analysis was utilised to investigate the independent prognostic factor in key genes. Single-cell RNA (scRNA) sequencing analysis was conducted to explore the cell types in HCC. TACE refractoriness-related genes activity was calculated via AUCell packages. The CellChat R package was used for the investigation of the cell-cell communication between the identified cell types. RESULTS: HCC tissues of TACE non-responders (n = 66) and TACE responders (n = 81), HCC (n = 3941) and non-HCC (n = 3443) tissues were obtained. The five key genes, DLG associated protein 5 (DLGAP5), Kinesin family member 20A (KIF20A), Assembly factor for spindle microtubules (ASPM), Kinesin family member 11 (KIF11) and TPX2 microtubule nucleation factor (TPX2) in TACE refractoriness-related genes, were identified. The five key genes were all up-regulated in the TACE non-responders group and the HCC group. High expression of the five key genes predicted poor prognosis in HCC. Among the key genes, TPX2 was an independent prognostic factor. Four cell types, hepatocytes, embryonic stem cells, T cells and B cells, were identified in the HCC tissues. The TACE refractoriness-related genes expressed primarily in hepatocytes and embryonic stem cells. Hepatocytes, as the providers of ligands, had the strongest interaction with embryonic stem cells that provided receptors. CONCLUSION: Five key genes (DLGAP5, KIF20A, ASPM, KIF11 and TPX2) were identified as promoting refractory TACE. Hepatocytes and embryonic stem cells were likely to boost TACE refractoriness.

snRNA-seq of human cutaneous neurofibromas before and after selumetinib treatment implicates role of altered Schwann cell states, inter-cellular signaling, and extracellular matrix in treatment response.

Neurofibromatosis Type 1 (NF1) is caused by loss of function variants in the NF1 gene. Most patients with NF1 develop skin lesions called cutaneous neurofibromas (cNFs). Currently the only approved therapeutic for NF1 is selumetinib, a mitogen -activated protein kinase (MEK) inhibitor. The purpose of this study was to analyze the transcriptome of cNF tumors before and on selumetinib treatment to understand both tumor composition and response. We obtained biopsy sets of tumors both pre- and on- selumetinib treatment from the same individuals and were able to collect sets from four separate individuals. We sequenced mRNA from 5844 nuclei and identified 30,442 genes in the untreated group and sequenced 5701 nuclei and identified 30,127 genes in the selumetinib treated group. We identified and quantified distinct populations of cells (Schwann cells, fibroblasts, pericytes, myeloid cells, melanocytes, keratinocytes, and two populations of endothelial cells). While we anticipated that cell proportions might change with treatment, we did not identify any one cell population that changed significantly, likely due to an inherent level of variability between tumors. We also evaluated differential gene expression based on drug treatment in each cell type. Ingenuity pathway analysis (IPA) was also used to identify pathways that differ on treatment. As anticipated, we identified a significant decrease in ERK/MAPK signaling in cells including Schwann cells but most specifically in myeloid cells. Interestingly, there is a significant decrease in opioid signaling in myeloid and endothelial cells; this downward trend is also observed in Schwann cells and fibroblasts. Cell communication was assessed by RNA velocity, Scriabin, and CellChat analyses which indicated that Schwann cells and fibroblasts have dramatically altered cell states defined by specific gene expression signatures following treatment (RNA velocity). There are dramatic changes in receptor-ligand pairs following treatment (Scriabin), and robust intercellular signaling between virtually all cell types associated with extracellular matrix (ECM) pathways (Collagen, Laminin, Fibronectin, and Nectin) is downregulated after treatment. These response specific gene signatures and interaction pathways could provide clues for understanding treatment outcomes or inform future therapies.

hdWGCNA and Cellular Communication Identify Active NK Cell Subtypes in Alzheimer's Disease and Screen for Diagnostic Markers through Machine Learning.

BACKGROUND: Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of ß-amyloid plaques and the formation of neurofibrillary tangles. Given this context, it becomes imperative to develop an early and accurate biomarker model for AD diagnosis, employing machine learning and bioinformatics analysis. METHODS: In this study, single-cell data analysis was employed to identify cellular subtypes that exhibited significant differences between the diseased and control groups. Following the identification of NK cells, hdWGCNA analysis and cellular communication analysis were conducted to pinpoint NK cell subset with the most robust communication effects. Subsequently, three machine learning algorithms-LASSO, Random Forest, and SVM-RFE-were employed to jointly screen for NK cell subset modular genes highly associated with AD. A logistic regression diagnostic model was then designed based on these characterized genes. Additionally, a protein-protein interaction (PPI) networks of model genes was established. Furthermore, unsupervised cluster analysis was conducted to classify AD subtypes based on the model genes, followed by the analysis of immune infiltration in the different subtypes. Finally, Spearman correlation coefficient analysis was utilized to explore the correlation between model genes and immune cells, as well as inflammatory factors. RESULTS: We have successfully identified three genes (RPLP2, RPSA, and RPL18A) that exhibit a high association with AD. The nomogram based on these genes provides practical assistance in diagnosing and predicting patients' outcomes. The interconnected genes screened through PPI are intricately linked to ribosome metabolism and the COVID-19 pathway. Utilizing the expression of modular genes, unsupervised cluster analysis unveiled three distinct AD subtypes. Particularly noteworthy is subtype C3, characterized by high expression, which correlates with immune cell infiltration and elevated levels of inflammatory factors. Hence, it can be inferred that the establishment of an immune environment in AD patients is closely intertwined with the heightened expression of model genes. CONCLUSION: This study has not only established a valuable diagnostic model for AD patients but has also delved deeply into the pivotal role of model genes in shaping the immune environment of individuals with AD. These findings offer crucial insights into early AD diagnosis and patient management strategies.

A Comparison of Cell-Cell Interaction Prediction Tools Based on scRNA-seq Data.

Computational prediction of cell-cell interactions (CCIs) is becoming increasingly important for understanding disease development and progression. We present a benchmark study of available CCI prediction tools based on single-cell RNA sequencing (scRNA-seq) data. By comparing prediction outputs with a manually curated gold standard for idiopathic pulmonary fibrosis (IPF), we evaluated prediction performance and processing time of several CCI prediction tools, including CCInx, CellChat, CellPhoneDB, iTALK, NATMI, scMLnet, SingleCellSignalR, and an ensemble of tools. According to our results, CellPhoneDB and NATMI are the best performer CCI prediction tools, among the ones analyzed, when we define a CCI as a source-target-ligand-receptor tetrad. In addition, we recommend specific tools according to different types of research projects and discuss the possible future paths in the field.

Single-Cell and CellChat Resolution Identifies Collecting Duct Cell Subsets and Their Communications with Adjacent Cells in PKD Kidneys.

ADPKD is a genetic disorder with a molecular complexity that remains poorly understood. In this study, we sampled renal cells to construct a comprehensive and spatiotemporally resolved gene expression atlas in whole Pkd1 mutant polycystic mouse kidneys at single-cell resolution. We characterized cell diversity and identified novel collecting duct (CD) cell subtypes in cystic kidneys. We further found that CD cells appear to take different cell fate trajectories, and the first and the most important step might take place around day 14 in Pkd1 homozygous kidneys. After that day, increased numbers of CD cells showed highly proliferative and fibrotic characteristics, as detected in later-stage Pkd1 homozygous kidneys, both of which should contribute to cyst growth and renal fibrosis. With a newly developed modeling algorithm, called CellChat Explorer, we identify cell-to-cell communication networks mediated by the ligand receptor, such as MIF-CD44/CD74, in cystic kidneys, and confirm them via the expression patterns of ligands and receptors in four major cell types, which addresses the key question as to whether and how Pkd1 mutant renal epithelial cells affect their neighboring cells. The allele-specific gene expression profiles show that the secretion of cytokines by Pkd1 mutant epithelial cells may affect the gene expression profiles in recipient cells via epigenetic mechanisms, and vice versa. This study can be used to drive precision therapeutic targeting of ADPKD.

Cell-Cell Communication Alterations via Intercellular Signaling Pathways in Substantia Nigra of Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative movement disorder characterized with dopaminergic neuron (DaN) loss within the substantia nigra (SN). Despite bulk studies focusing on intracellular mechanisms of PD inside DaNs, few studies have explored the pathogeneses outside DaNs, or between DaNs and other cells. Here, we set out to probe the implication of intercellular communication involving DaNs in the pathogeneses of PD at a systemic level with bioinformatics methods. We harvested three online published single-cell/single-nucleus transcriptomic sequencing (sc/snRNA-seq) datasets of human SN (GSE126838, GSE140231, and GSE157783) from the Gene Expression Omnibus (GEO) database, and integrated them with one of the latest integration algorithms called Harmony. We then applied CellChat, the latest cell-cell communication analytic algorithm, to our integrated dataset. We first found that the overall communication quantity was decreased while the overall communication strength was enhanced in PD sample compared with control sample. We then focused on the intercellular communication where DaNs are involved, and found that the communications between DaNs and other cell types via certain signaling pathways were selectively altered in PD, including some growth factors, neurotrophic factors, chemokines, etc. pathways. Our bioinformatics analysis showed that the alteration in intercellular communications involving DaNs might be a previously underestimated aspect of PD pathogeneses with novel translational potential.