gCAnno: a graph-based single cell type annotation method.

BACKGROUND: Current single cell analysis methods annotate cell types at cluster-level rather than ideally at single cell level. Multiple exchangeable clustering methods and many tunable parameters have a substantial impact on the clustering outcome, often leading to incorrect cluster-level annotation or multiple runs of subsequent clustering steps. To address these limitations, methods based on well-annotated reference atlas has been proposed. However, these methods are currently not robust enough to handle datasets with different noise levels or from different platforms. RESULTS: Here, we present gCAnno, a graph-based Cell type Annotation method. First, gCAnno constructs cell type-gene bipartite graph and adopts graph embedding to obtain cell type specific genes. Then, naïve Bayes (gCAnno-Bayes) and SVM (gCAnno-SVM) classifiers are built for annotation. We compared the performance of gCAnno to other state-of-art methods on multiple single cell datasets, either with various noise levels or from different platforms. The results showed that gCAnno outperforms other state-of-art methods with higher accuracy and robustness. CONCLUSIONS: gCAnno is a robust and accurate cell type annotation tool for single cell RNA analysis. The source code of gCAnno is publicly available at https://github.com/xjtu-omics/gCAnno .

Identification and immune landscape of sarcopenia-related molecular clusters in inflammatory bowel disease by machine learning and integrated bioinformatics.

Sarcopenia, a prevalent comorbidity of inflammatory bowel disease (IBD), is characterized by diminished skeletal muscle mass and strength. Nevertheless, the underlying interconnected mechanisms remain elusive. This study identified distinct expression patterns of sarcopenia-associated genes (SRGs) across individuals with IBD and in samples of normal tissue. By analyzing SRG expression profiles, we effectively segregated 541 IBD samples into three distinct clusters, each marked by its unique immune landscape. To unravel the transcriptional disruptions underlying these clusters, the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm was employed to spotlight key genes linked to each cluster. A diagnostic model based on four key genes (TIMP1, PLAU, PHLDA1, TGFBI) was established using Random Forest and LASSO (least absolute shrinkage and selection operator) algorithms, and validated with the GSE179285 dataset. Moreover, the GSE112366 dataset facilitated the exploration of gene expression dynamics within the ileum mucosa of UC patients pre- and post-Ustekinumab treatment. Additionally, insights into the intricate relationship between immune cells and these pivotal genes were gleaned from the single-cell RNA dataset GSE162335. In conclusion, our findings collectively underscored the pivotal role of sarcopenia-related genes in the pathogenesis of IBD. Their potential as robust biomarkers for future diagnostic and therapeutic strategies is particularly promising, opening avenues for a deeper understanding and improved management of these interconnected conditions.

Single cell multi-omic analysis identifies key genes differentially expressed in innate lymphoid cells from COVID-19 patients.

Introduction: Innate lymphoid cells (ILCs) are enriched at mucosal surfaces where they respond rapidly to environmental stimuli and contribute to both tissue inflammation and healing. Methods: To gain insight into the role of ILCs in the pathology and recovery from COVID-19 infection, we employed a multi-omics approach consisting of Abseq and targeted mRNA sequencing to respectively probe the surface marker expression, transcriptional profile and heterogeneity of ILCs in peripheral blood of patients with COVID-19 compared with healthy controls. Results: We found that the frequency of ILC1 and ILC2 cells was significantly increased in COVID-19 patients. Moreover, all ILC subsets displayed a significantly higher frequency of CD69-expressing cells, indicating a heightened state of activation. ILC2s from COVID-19 patients had the highest number of significantly differentially expressed (DE) genes. The most notable genes DE in COVID-19 vs healthy participants included a) genes associated with responses to virus infections and b) genes that support ILC self-proliferation, activation and homeostasis. In addition, differential gene regulatory network analysis revealed ILC-specific regulons and their interactions driving the differential gene expression in each ILC. Discussion: Overall, this study provides mechanistic insights into the characteristics of ILC subsets activated during COVID-19 infection.

Analyzing Immune Cell Infiltration and Copper Metabolism in Diabetic Foot Ulcers.

Background: Diabetes impairs wound healing, notably in diabetic foot ulcers (DFU). Stress, marked by the accumulation of lipoylated mitochondrial enzymes and the depletion of Fe-S cluster proteins, triggers cuproptosis-a distinct form of cell death. The involvement of copper in the pathophysiology of DFU has been recognized, and currently, a copper-based therapeutic strategy is emerging as a viable option for enhancing ulcer healing. This study investigates genes linked to copper metabolism in DFU, aiming to uncover potential targets for therapeutic intervention. Methods: Two diabetic wound Gene Expression Omnibus (GEO) datasets were analyzed to study immune cell dysregulation in diabetic wounds. Differentially expressed genes related to copper metabolism were identified and analyzed using machine learning methods. Gene ontology, pathway enrichment, and immune infiltration analyses were performed using DFU samples. The expression of identified genes was validated using qRT-PCR and single-cell RNA sequencing. Results: Ten genes associated with copper metabolism were identified. Among these, SLC31A1 and ADNP were found to be significantly differentially expressed in DFU. Notably, SLC31A1 exhibited higher expression in macrophages, whereas ADNP was found to be highly expressed in fibroblasts and chondrocytes. Conclusion: The study indicates a close link between copper metabolism, the infiltration of immune cells, and DFU. It proposes that copper metabolism could influence the progression of DFU through the activation of immune responses. These observations offer fresh perspectives on the underlying mechanisms of DFU and identify potential targets for therapeutic intervention.

Comprehensive analysis of mitophagy-related genes in diagnosis and heterogeneous endothelial cells in chronic rhinosinusitis: based on bulk and single-cell RNA sequencing data.

Background: Chronic rhinosinusitis (CRS) is a complex inflammatory disorder affecting the nasal and paranasal sinuses. Mitophagy, the process of selective mitochondrial degradation via autophagy, is crucial for maintaining cellular balance. However, the role of mitophagy in CRS is not well-studied. This research aims to examine the role of mitophagy-related genes (MRGs) in CRS, with a particular focus on the heterogeneity of endothelial cells (ECs). Methods: We employed both bulk and single-cell RNA sequencing data to investigate the role of MRGs in CRS. We compiled a combined database of 92 CRS samples and 35 healthy control samples from the Gene Expression Omnibus (GEO) database and we explored the differential expression of MRGs between them. A logistic regression model was built based on seven key genes identified through Random Forests and Support Vector Machines - Recursive Feature Elimination (SVM-RFE). Consensus cluster analysis was used to categorize CRS patients based on MRG expression patterns and weighted gene co-expression network analysis (WGCNA) was performed to find modules of highly correlated genes of the different clusters. Single-cell RNA sequencing data was utilized to analyze MRGs and EC heterogeneity in CRS. Results: Seven hub genes-SQSTM1, SRC, UBA52, MFN2, UBC, RPS27A, and ATG12-showed differential expression between two groups. A diagnostic model based on hub genes showed excellent prognostic accuracy. A strong positive correlation was found between the seven hub MRGs and resting dendritic cells, while a significant negative correlation was observed with mast cells and CD8+ T cells. CRS could be divided into two subclusters based on MRG expression patterns. WGCNA analysis identified modules of highly correlated genes of these two different subclusters. At the single-cell level, two types of venous ECs with different MRG scores were identified, suggesting their varying roles in CRS pathogenesis, especially in the non-eosinophilic CRS subtype. Conclusion: Our comprehensive study of CRS reveals the significant role of MRGs and underscores the heterogeneity of ECs. We highlighted the importance of Migration Inhibitory Factor (MIF) and TGFb pathways in mediating the effects of mitophagy, particularly the MIF. Overall, our findings enhance the understanding of mitophagy in CRS, providing a foundation for future research and potential therapeutic developments.

Characterisation of macrophage infiltration and polarisation based on integrated transcriptomic and histological analyses in Primary Sjögren's syndrome.

Background: Primary Sjögren's syndrome (pSS) is a progressive inflammatory autoimmune disease. Immune cell infiltration into glandular lobules and ducts and glandular destruction are the pathophysiological hallmarks of pSS. Macrophages are one of the most important cells involved in the induction and regulation of an inflammatory microenvironment. Although studies have reported that an abnormal tissue microenvironment alters the metabolic reprogramming and polarisation status of macrophages, the mechanisms driving macrophage infiltration and polarisation in pSS remain unclear. Methods: Immune cell subsets were characterised using the single-cell RNA sequencing (scRNA-seq) data of peripheral blood mononuclear cells (PBMCs) from patients with pSS (n = 5) and healthy individuals (n = 5) in a public dataset. To evaluate macrophage infiltration and polarisation in target tissues, labial salivary gland biopsy tissues were subjected to histological staining and bulk RNA-seq (pSS samples, n = 24; non-pSS samples, n = 12). RNA-seq data were analysed for the construction of macrophage co-expression modules, enrichment of biological processes and deconvolution-based screening of immune cell types. Results: Detailed mapping of PBMCs using scRNA-seq revealed five major immune cell subsets in pSS, namely, T cells, B cells, natural killer (NK) cells, dendritic cells (DCs) and monocyte-macrophages. The monocyte-macrophage subset was large and had strong inflammatory gene signatures. This subset was found to play an important role in the generation of reactive oxygen species and communicate with other innate and adaptive immune cells. Histological staining revealed that the number of tissue-resident macrophages was high in damaged glandular tissues, with the cells persistently surrounding the tissues. Analysis of RNA-seq data using multiple algorithms demonstrated that the high abundance of pro-inflammatory M1 macrophages was accompanied by the high abundance of other infiltrating immune cells, senescence-associated secretory phenotype and evident metabolic reprogramming. Conclusion: Macrophages are among the most abundant innate immune cells in PBMCs and glandular tissues in patients with pSS. A bidirectional relationship exists between macrophage polarisation and the inflammatory microenvironment, which may serve as a therapeutic target for pSS.

Stromal circuits involving tumor-associated macrophages and cancer-associated fibroblasts.

The tumor associated macrophages (TAM) represent one of most abundant subpopulations across several solid cancers and their number/frequency is associated with a poor clinical outcome. It has been clearly demonstrated that stromal cells, such as the cancer associated fibroblasts (CAFs), may orchestrate TAM recruitment, survival and reprogramming. Today, single cell-RNA sequencing (sc-RNA seq) technologies allowed a more granular knowledge about TAMs and CAFs phenotypical and functional programs. In this mini-review we discuss the recent discoveries in the sc-RNA seq field focusing on TAM and CAF identity and their crosstalk in the tumor microenvironment (TME) of solid cancers.

Integrating bulk and single-cell RNA sequencing data reveals epithelial-mesenchymal transition molecular subtype and signature to predict prognosis, immunotherapy efficacy, and drug candidates in low-grade gliomas.

Objective: Epithelial-mesenchymal transition (EMT) is a tightly regulated and dynamic process occurring in both embryonic development and tumor progression. Our study aimed to comprehensively explore the molecular subtypes, immune landscape, and prognostic signature based on EMT-related genes in low-grade gliomas (LGG) in order to facilitate treatment decision-making and drug discovery. Methods: We curated EMT-related genes and performed molecular subtyping with consensus clustering algorithm to determine EMT expression patterns in LGG. The infiltration level of diverse immune cell subsets was evaluated by implementing the single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithms. The distinctions in clinical characteristics, mutation landscape, and immune tumor microenvironment (TME) among the subtypes were subjected to further investigation. Gene Set Variation Analysis (GSVA) was performed to explore the biological pathways that were involved in subtypes. The chemo drug sensitivity and immunotherapy of subtypes were estimated through GDSC database and NTP algorithm. To detect EMT subtype-related prognostic gene modules, the analysis of weighted gene co-expression network (WGCNA) was performed. The LASSO algorithm was utilized to construct a prognostic risk model, and its efficacy was verified through an independent CGGA dataset. Finally, the expression of the hub genes from the prognostic model was evaluated through the single-cell dataset and in-vitro experiment. Results: The TCGA-LGG dataset revealed the creation of two molecular subtypes that presented different prognoses, clinical implications, TME, mutation landscapes, chemotherapy, and immunotherapy. A three-gene signature (SLC39A1, CTSA and CLIC1) based on EMT expression pattern were established through WGCNA analysis. Low-risk patients showed a positive outlook, increased immune cell presence, and higher expression of immune checkpoint proteins. In addition, several promising drugs, including birinapant, fluvastatin, clofarabine, dasatinib, tanespimycin, TAK-733, GDC-0152, AZD8330, trametinib and ingenol-mebutate had great potential to the treatment of high risk patients. Finally, CTSA and CLIC1 were highly expressed in monocyte cell through single-cell RNA sequencing analysis. Conclusion: Our research revealed non-negligible role of EMT in the TME diversity and complexity of LGG. A prognostic signature may contribute to the personalized treatment and prognostic determination.

Analysis of cuproptosis in hepatocellular carcinoma using multi-omics reveals a comprehensive HCC landscape and the immune patterns of cuproptosis.

Cuproptosis represents a novel copper-dependent regulated cell death, distinct from other known cell death processes. In this report, a comprehensive analysis of cuproptosis in hepatocellular carcinoma (HCC) was conducted using multi-omics including genomics, bulk RNA-seq, single cell RNA-seq and proteomics. ATP7A, PDHA1 and DLST comprised the top 3 mutation genes in The Cancer Genome Atlas (TCGA)-LIHC; 9 cuproptosis-related genes showed significant, independent prognostic values. Cuproptosis-related hepatocytes were identified and their function were evaluated in single cell assays. Based on cuproptosis-related gene expressions, two immune patterns were found, with the cuproptosis-C1 subtype identified as a cytotoxic immune pattern, while the cuproptosis-C2 subtype was identified as a regulatory immune pattern. Cuproptosis-C2 was associated with a number of pathways involving tumorigenesis. A prognosis model based on differentially expressed genes (DEGs) of cuproptosis patterns was constructed and validated. We established a cuproptosis index (CPI) and further performed an analysis of its clinical relevance. High CPI values were associated with increased levels of alpha-fetoprotein (AFP) and advanced tumor stages. Taken together, this comprehensive analysis provides important, new insights into cuproptosis mechanisms associated with human HCC.

Integration of multimodal data in the developing tooth reveals candidate regulatory loci driving human odontogenic phenotypes.

Human odontogenic aberrations such as abnormal tooth number and delayed tooth eruption can occur as a symptom of rare syndromes or, more commonly, as nonsyndromic phenotypes. These phenotypes can require extensive and expensive dental treatment, posing a significant burden. While many dental phenotypes are heritable, most nonsyndromic cases have not been linked to causal genes. We demonstrate the novel finding that common sequence variants associated with human odontogenic phenotypes are enriched in developmental craniofacial enhancers conserved between human and mouse. However, the bulk nature of these samples obscures if this finding is due to the tooth itself or the surrounding tissues. We therefore sought to identify enhancers specifically active in the tooth anlagen and quantify their contribution to the observed genetic enrichments. We systematically identified 22,001 conserved enhancers active in E13.5 mouse incisors using ChIP-seq and machine learning pipelines and demonstrated biologically relevant enrichments in putative target genes, transcription factor binding motifs, and in vivo activity. Multi-tissue comparisons of human and mouse enhancers revealed that these putative tooth enhancers had the strongest enrichment of odontogenic phenotype-associated variants, suggesting a role for dysregulation of tooth developmental enhancers in human dental phenotypes. The large number of these regions genome-wide necessitated prioritization of enhancer loci for future investigations. As enhancers modulate gene expression, we prioritized regions based on enhancers' putative target genes. We predicted these target genes and prioritized loci by integrating chromatin state, bulk gene expression and coexpression, GWAS variants, and cell type resolved gene expression to generate a prioritized list of putative odontogenic phenotype-driving loci active in the developing tooth. These genomic regions are of particular interest for downstream experiments determining the role of specific dental enhancer:gene pairs in odontogenesis.

Glomerular Endothelial Cells Are the Coordinator in the Development of Diabetic Nephropathy.

The prevalence of diabetes is consistently rising worldwide. Diabetic nephropathy is a leading cause of chronic renal failure. The present study aimed to explore the crosstalk among the different cell types inside diabetic glomeruli, including glomerular endothelial cells, mesangial cells, podocytes, and immune cells, by analyzing an online single-cell RNA profile (GSE131882) of patients with diabetic nephropathy. Differentially expressed genes in the glomeruli were processed by gene enrichment and protein-protein interactions analysis. Glomerular endothelial cells, as well as podocytes, play a critical role in diabetic nephropathy. A subgroup of glomerular endothelial cells possesses characteristic angiogenesis genes, indicating that angiogenesis takes place in the progress of diabetic nephropathy. Immune cells such as macrophages, T lymphocytes, B lymphocytes, and plasma cells also contribute to the disease progression. By using iTALK, the present study reports complicated cellular crosstalk inside glomeruli. Dysfunction of glomerular endothelial cells and immature angiogenesis result from the activation of both paracrine and autocrine signals. The present study reinforces the importance of glomerular endothelial cells in the development of diabetic nephropathy. The exploration of the signaling pathways involved in aberrant angiogenesis reported in the present study shed light on potential therapeutic target(s) for diabetic nephropathy.

Unique microglia recovery population revealed by single-cell RNAseq following neurodegeneration.

Microglia are brain immune cells that constantly survey their environment to maintain homeostasis. Enhanced microglial reactivity and proliferation are typical hallmarks of neurodegenerative diseases. Whether specific disease-linked microglial subsets exist during the entire course of neurodegeneration, including the recovery phase, is currently unclear. Taking a single-cell RNA-sequencing approach in a susceptibility gene-free model of nerve injury, we identified a microglial subpopulation that upon acute neurodegeneration shares a conserved gene regulatory profile compared to previously reported chronic and destructive neurodegeneration transgenic mouse models. Our data also revealed rapid shifts in gene regulation that defined microglial subsets at peak and resolution of neurodegeneration. Finally, our discovery of a unique transient microglial subpopulation at the onset of recovery may provide novel targets for modulating microglia-mediated restoration of brain health.