Microenvironmental changes in familial adenomatous polyposis during colorectal cancer carcinogenesis

Familial adenomatous polyposis (FAP) is a heritable disease that increases the risk of colorectal cancer (CRC) development because of heterozygous mutations in APC. Little is known about the microenvironment of FAP. Here, single-cell RNA sequencing was performed on matched normal tissues, adenomas, and carcinomas from four patients with FAP. We analyzed the transcriptomes of 56,225 unsorted single cells, revealing the heterogeneity of each cell type, and compared gene expression among tissues. Then we compared the gene expression with that of sporadic CRC. Furthermore, we analyzed specimens of 26 FAP patients and 40 sporadic CRC patients by immunohistochemistry. Immunosuppressiveness of myeloid cells, fibroblasts, and regulatory T cells was upregulated even in the early stages of carcinogenesis. CD8+ T cells became exhausted only in carcinoma, although the cytotoxicity of CD8+ T cells was gradually increased according to the carcinogenic step. When compared with those in the sporadic CRC microenvironment, the composition and function of each cell type in the FAP-derived CRC microenvironment had differences. Our findings indicate that an immunosuppressive microenvironment is constructed from a precancerous stage in FAP.

ISMI-VAE: A deep learning model for classifying disease cells using gene expression and SNV data

Various studies have linked several diseases, including cancer and COVID-19, to single nucleotide variations (SNV). Although single-cell RNA sequencing (scRNA-seq) technology can provide SNV and gene expression data, few studies have integrated and analyzed these multimodal data. To address this issue, we introduce Interpretable Single-cell Multimodal Data Integration Based on Variational Autoencoder (ISMI-VAE). ISMI-VAE leverages latent variable models that utilize the characteristics of SNV and gene expression data to overcome high noise levels and uses deep learning techniques to integrate multimodal information, map them to a low-dimensional space, and classify disease cells. Moreover, ISMI-VAE introduces an attention mechanism to reflect feature importance and analyze genetic features that could potentially cause disease. Experimental results on three cancer data sets and one COVID-19 data set demonstrate that ISMI-VAE surpasses the baseline method in terms of both effectiveness and interpretability and can effectively identify disease-causing gene features.

Integrative analysis of single-cell and bulk transcriptome data reveal the significant role of macrophages in lupus nephritis

OBJECTIVE

We attempted to identify abnormal immune cell components and signaling pathways in lupus nephritis (LN) and to identify potential therapeutic targets.

METHODS

Differentially expressed genes (DEGs) between LN and normal kidney tissues were identified from bulk transcriptome data, and functional annotation was performed. The phenotypic changes in macrophages and aberrant intercellular signaling communications within immune cells were imputed from LN scRNA-seq data using trajectory analysis and verified using immunofluorescence staining. Finally, lentivirus-mediated overexpression of LGALS9, the gene encoding Galectin 9, in THP-1 cells was used to study the functional effect of this gene on monocytic cells.

RESULTS

From bulk transcriptome data, a significant activation of interferon (IFN) signaling was observed, and its intensity showed a significantly positive correlation with the abundance of infiltrating macrophages in LN. Analysis of scRNA-seq data revealed 17 immune cell clusters, with macrophages showing the highest enrichment of intercellular signal communication in LN. Trajectory analysis revealed macrophages in LN undergo a phenotypic change from inflammatory patrolling macrophages to phagocytic and then to antigen-presenting macrophages, and secrete various pro-inflammatory factors and complement components. LGALS9 was found significantly upregulated in macrophages in LN, which was confirmed by the immunofluorescence assay. Gene functional study showed that LGALS9 overexpression in THP-1 cells significantly elicited pro-inflammatory activation, releasing multiple immune cell chemoattractants.

CONCLUSION

Our results present an important pathophysiological role for macrophages in LN, and our preliminary results demonstrate significant pro-inflammatory effects of LGALS9 gene in LN macrophages.

The interplay of Cxcl10+/Mmp14+ monocytes and Ccl3+ neutrophils proactively mediates silica-induced pulmonary fibrosis

As a fatal occupational disease with limited therapeutic options, molecular mechanisms underpinning silicosis are still undefined. Herein, single-cell RNA sequencing of the lung tissue of silicosis mice identified two monocyte subsets, which were characterized by Cxcl10 and Mmp14 and enriched in fibrotic mouse lungs. Both Cxcl10+ and Mmp14+ monocyte subsets exhibited activation of inflammatory marker genes and positive regulation of cytokine production. Another fibrosis-unique neutrophil population characterized by Ccl3 appeared to be related to the pro-fibrotic process, specifically the "inflammatory response". Meanwhile, the proportion of monocytes and neutrophils was significantly higher in the serum of silicosis patients and slices of lung tissue from patients with silicosis further validated the over-expression of Cxcl10 and Mmp14 in monocytes, also Ccl3 in neutrophils, respectively. Mechanically, receptor-ligand interaction analysis identified the crosstalk of Cxcl10+/Mmp14+ monocytes with Ccl3+ neutrophils promoting fibrogenesis via coupling of HBEGF-CD44 and CSF1-CSF1R. In vivo, administration of clodronate liposomes, Cxcl10 or Mmp14 siRNA-loaded liposomes, Ccl3 receptor antagonist BX471, CD44 or CSF1R neutralizing antibodies significantly alleviated silica-induced lung fibrosis. Collectively, these results demonstrate that the newly defined Cxcl10+/Mmp14+ monocytes and Ccl3+ neutrophils participate in the silicosis process and highlight anti-receptor-ligand pair treatment as a potentially effective therapeutic strategy in managing silicosis.

Revealing the role of necroptosis microenvironment: FCGBP + tumor-associated macrophages drive primary liver cancer differentiation towards cHCC-CCA or iCCA

Previous research has demonstrated that the conversion of hepatocellular carcinoma (HCC) to intrahepatic cholangiocarcinoma (iCCA) can be stimulated by manipulating the tumor microenvironment linked with necroptosis. However, the specific cells regulating the necroptosis microenvironment have not yet been identified. Additionally, further inquiry into the mechanism of how the tumor microenvironment regulates necroptosis and its impact on primary liver cancer(PLC) progression may be beneficial for precision therapy. We recruited a single-cell RNA sequencing dataset (scRNA-seq) with 34 samples from 4 HCC patients and 3 iCCA patients, and a Spatial Transcriptomic (ST) dataset including one each of HCC, iCCA, and combined hepatocellular-cholangiocarcinoma (cHCC-CCA). Quality control, dimensionality reduction and clustering were based on Seurat software (v4.2.2) process and batch effects were removed by harmony (v0.1.1) software. The pseudotime analysis (also known as cell trajectory) in the single cell dataset was performed by monocle2 software (v2.24.0). Calculation of necroptosis fraction was performed by AUCell (v1.16.0) software. Switch gene analysis was performed by geneSwitches(v0.1.0) software. Dimensionality reduction, clustering, and spatial image in ST dataset were performed by Seurat (v4.0.2). Tumor cell identification, tumor subtype characterization, and cell type deconvolution in spot were performed by SpaCET (v1.0.0) software. Immunofluorescence and immunohistochemistry experiments were used to prove our conclusions. Analysis of intercellular communication was performed using CellChat software (v1.4.0). ScRNA-seq analysis of HCC and iCCA revealed that necroptosis predominantly occurred in the myeloid cell subset, particularly in FCGBP + SPP1 + tumor-associated macrophages (TAMs), which had the highest likelihood of undergoing necroptosis. The existence of macrophages undergoing necroptosis cell death was further confirmed by immunofluorescence. Regions of HCC with poor differentiation, cHCC-CCA with more cholangiocarcinoma features, and the tumor region of iCCA shared spatial colocalization with FCGBP + macrophages, as confirmed by spatial transcriptomics, immunohistochemistry and immunofluorescence. Pseudotime analysis showed that premalignant cells could progress into two directions, one towards HCC and the other towards iCCA and cHCC-CCA. Immunofluorescence and immunohistochemistry experiments demonstrated that the number of macrophages undergoing necroptosis in cHCC-CCA was higher than in iCCA and HCC, the number of macrophages undergoing necroptosis in cHCC-CCA with cholangiocarcinoma features was more than in cHCC-CCA with hepatocellular carcinoma features. Further investigation showed that myeloid cells with the highest necroptosis score were derived from the HCC_4 case, which had a severe inflammatory background on pathological histology and was likely to progress towards iCCA and cHCC-CCA. Switchgene analysis indicated that S100A6 may play a significant role in the progression of premalignant cells towards iCCA and cHCC-CCA. Immunohistochemistry confirmed the expression of S100A6 in PLC, the more severe inflammatory background of the tumor area, the more cholangiocellular carcinoma features of the tumor area, S100A6 expression was higher. The emergence of necroptosis microenvironment was found to be significantly associated with FCGBP + SPP1 + TAMs in PLC. In the presence of necroptosis microenvironment, premalignant cells appeared to transform into iCCA or cHCC-CCA. In contrast, without a necroptosis microenvironment, premalignant cells tended to develop into HCC, exhibiting amplified stemness-related genes (SRGs) and heightened malignancy.

Deciphering the molecular landscape: integrating single-cell transcriptomics to unravel myofibroblast dynamics and therapeutic targets in clear cell renal cell carcinomas

Background

Clear cell renal cell carcinomas (ccRCCs) epitomize the most formidable clinical subtype among renal neoplasms. While the impact of tumor-associated fibroblasts on ccRCC progression is duly acknowledged, a paucity of literature exists elucidating the intricate mechanisms and signaling pathways operative at the individual cellular level.

Methods

Employing single-cell transcriptomic analysis, we meticulously curated UMAP profiles spanning substantial ccRCC populations, delving into the composition and intrinsic signaling pathways of these cohorts. Additionally, Myofibroblasts were fastidiously categorized into discrete subpopulations, with a thorough elucidation of the temporal trajectory relationships between these subpopulations. We further probed the cellular interaction pathways connecting pivotal subpopulations with tumors. Our endeavor also encompassed the identification of prognostic genes associated with these subpopulations through Bulk RNA-seq, subsequently validated through empirical experimentation.

Results

A notable escalation in the nFeature and nCount of Myofibroblasts and EPCs within ccRCCs was observed, notably enriched in oxidation-related pathways. This phenomenon is postulated to be closely associated with the heightened metabolic activities of Myofibroblasts and EPCs. The Myofibroblasts subpopulation, denoted as C3 HMGA1+ Myofibroblasts, emerges as a pivotal subset, displaying low differentiation and positioning itself at the terminal point of the temporal trajectory. Intriguingly, these cells exhibit a high degree of interaction with tumor cells through the MPZ signaling pathway network, suggesting that Myofibroblasts may facilitate tumor progression via this pathway. Prognostic genes associated with C3 were identified, among which TUBB3 is implicated in potential resistance to tumor recurrence. Finally, experimental validation revealed that the knockout of the key gene within the MPZ pathway, MPZL1, can inhibit tumor activity, proliferation, invasion, and migration capabilities.

Conclusion

This investigation delves into the intricate mechanisms and interaction pathways between Myofibroblasts and ccRCCs at the single-cell level. We propose that targeting MPZL1 and the oxidative phosphorylation pathway could serve as potential key targets for treating the progression and recurrence of ccRCC. This discovery paves the way for new directions in the treatment and prognosis diagnosis of ccRCC in the future.

Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation

BACKGROUND

The integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular heterogeneity.

RESULTS

To expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcomes. Furthermore, scDisco has been validated using real datasets, affirming its proficiency in cell clustering quality, retaining batch-specific cell types and identifying condition-specific genes.

CONCLUSION

scDisco is an effective integration method based on variational autoencoders, which improves analytical tasks of reducing batch effects, cell clustering, retaining batch-specific cell types and identifying condition-specific genes.

Decoding the tumor microenvironment and molecular mechanism: unraveling cervical cancer subpopulations and prognostic signatures through scRNA-Seq and bulk RNA-seq analyses

Background

Cervical carcinoma (CC) represents a prevalent gynecological neoplasm, with a discernible rise in prevalence among younger cohorts observed in recent years. Nonetheless, the intrinsic cellular heterogeneity of CC remains inadequately investigated.

Methods

We utilized single-cell RNA sequencing (scRNA-seq) transcriptomic analysis to scrutinize the tumor epithelial cells derived from four specimens of cervical carcinoma (CC) patients. This method enabled the identification of pivotal subpopulations of tumor epithelial cells and elucidation of their contributions to CC progression. Subsequently, we assessed the influence of associated molecules in bulk RNA sequencing (Bulk RNA-seq) cohorts and performed cellular experiments for validation purposes.

Results

Through our analysis, we have discerned C3 PLP2+ Tumor Epithelial Progenitor Cells as a noteworthy subpopulation in cervical carcinoma (CC), exerting a pivotal influence on the differentiation and progression of CC. We have established an independent prognostic indicator-the PLP2+ Tumor EPCs score. By stratifying patients into high and low score groups based on the median score, we have observed that the high-score group exhibits diminished survival rates compared to the low-score group. The correlations observed between these groups and immune infiltration, enriched pathways, single-nucleotide polymorphisms (SNPs), drug sensitivity, among other factors, further underscore their impact on CC prognosis. Cellular experiments have validated the significant impact of ATF6 on the proliferation and migration of CC cell lines.

Conclusion

This study enriches our comprehension of the determinants shaping the progression of CC, elevates cognizance of the tumor microenvironment in CC, and offers valuable insights for prospective CC therapies. These discoveries contribute to the refinement of CC diagnostics and the formulation of optimal therapeutic approaches.

Identification of common stria vascularis cellular alteration in sensorineural hearing loss based on ScRNA-seq

BACKGROUND

The stria vascularis (SV), located in the lateral wall of the cochlea, maintains cochlear fluid homeostasis and mechanoelectrical transduction (MET) activity required for sound wave conduction. The pathogenesis of a number of human inheritable deafness syndromes, age related hearing loss, drug-induced ototoxicity and noise-induced hearing loss results from the morphological changes and functional impairments in the development of the SV. In this study, we investigate the implications of intercellular communication within the SV in the pathogenesis of sensorineural hearing loss (SNHL). We aim to identify commonly regulated signaling pathways using publicly available single-cell transcriptomic sequencing (scRNA-seq) datasets.

METHODS

We analyzed scRNA-seq data, which was derived from studying the cochlear SV in mice with SNHL compared to normal adult mice. After quality control and filtering, we obtained the major cellular components of the mouse cochlear SV and integrated the data. Using Seurat's FindAllMarkers and FindMarkers packages, we searched for novel conservative genes and differential genes. We employed KEGG and GSEA to identify molecular pathways that are commonly altered among different types of SNHL. We utilized pySCENIC to discover new specific regulatory factors in SV subpopulation cells. With the help of CellChat, we identified changes in subpopulation cells showing similar trends across different SNHL types and their alterations in intercellular communication pathways.

RESULTS

Through the analysis of the integrated data, we discovered new conserved genes to SV specific cells and identified common downregulated pathways in three types of SNHL. The enriched genes for these pathways showing similar trends are primarily associated with the Electron Transport Chain, related to mitochondrial energy metabolism. Using the CellChat package, we further found that there are shared pathways in the incoming signaling of specific intermediate cells in SNHL, and these pathways have common upstream regulatory transcription factor of Nfe2l2. Combining the results from pySCENIC and CellChat, we predicted the transcription factor Nfe2l2 as an upstream regulatory factor for multiple shared cellular pathways in IC. Additionally, it serves as an upstream factor for several genes within the Electron Transport Chain.

CONCLUSION

Our bioinformatics analysis has revealed that downregulation of the mitochondrial electron transport chain have been observed in various conditions of SNHL. E2f1, Esrrb, Runx1, Yy1, and Gata2 could serve as novel important common TFs regulating the electron transport chain. Adm has emerged as a potential new marker gene for intermediate cells, while Itgb5 and Tesc show promise as potential new marker genes for marginal cells in the SV. These findings offer a new perspective on SV lesions in SNHL and provide additional theoretical evidence for the same drug treatment and prevention of different pathologies of SNHL.

Unraveling the mechanisms of NK cell dysfunction in aging and Alzheimer's disease: insights from GWAS and single-cell transcriptomics

Background

Aging is an important factor in the development of Alzheimer's disease (AD). The senescent cells can be recognized and removed by NK cells. However, NK cell function is gradually inactivated with age. Therefore, this study used senescence as an entry point to investigate how NK cells affect AD.

Methods

The study validated the correlation between cognition and aging through a prospective cohort of the National Health and Nutrition Examination Survey database. A cellular trajectory analysis of the aging population was performed using single-cell nuclear transcriptome sequencing data from patients with AD and different ages. The genome-wide association study (GWAS) cohort of AD patients was used as the outcome event, and the expression quantitative trait locus was used as an instrumental variable. Causal associations between genes and AD were analyzed by bidirectional Mendelian randomization (MR) and co-localization. Finally, clinical cohorts were constructed to validate the expression of key genes.

Results

A correlation between cognition and aging was demonstrated using 2,171 older adults over 60 years of age. Gene regulation analysis revealed that most of the highly active transcription factors were concentrated in the NK cell subpopulation of AD. NK cell trajectories were constructed for different age populations. MR and co-localization analyses revealed that CHD6 may be one of the factors influencing AD.

Conclusion

We explored different levels of AD and aging from population cohorts, single-cell data, and GWAS cohorts and found that there may be some correlations of NK cells between aging and AD. It also provides some basis for potential causation.

Single-cell transcriptome profiling of primary tumors and paired organoids of pancreatobiliary cancer

Single-cell RNA-seq (scRNA-seq) and cancer organoid model have shown promise in investigating tumor microenvironment heterogeneity and facilitating chemotherapeutic drug testing to inform treatment selection. It is still unknown whether the scRNA-seq results based on organoid can faithfully reflect the heterogeneity of primary pancreatobiliary cancer. To reveal the similarities and differences between primary tumors and their matched organoids at transcriptome level, we conducted scRNA-seq for paired primary tumors and organoids from one cholangiocarcinoma (CCA) and two pancreatic ductal adenocarcinoma (PDAC) patients. We identified inter-patient and intra-tumor heterogeneity and found that the organoids retained copy number variation (CNV) patterns of primary tumors. There was no significant difference in cancer stem cell (CSC) properties between the primary tumors and the organoids, whereas organoid from one PDAC case had increased mesenchymal-score and decreased epithelial-score compared with the primary tumors. All organoids showed a transition tendency from the classical subtype to the basal-like subtype in the transcriptional level. Organoids and primary tumors differed in metabolic and unfolded protein response (UPR) signatures. In addition, we revealed the heterogeneity of cancer associated fibroblasts (CAFs) and T cells, and explored the developmental trajectory of T cells. Our findings facilitate further understanding of organoid model and confirm its application prospects in pancreatobiliary cancer.

Single-cell RNA sequencing reveals the dynamics and heterogeneity of lymph node immune cells during acute and chronic viral infections

Introduction

The host immune response determines the differential outcome of acute or chronic viral infections. The comprehensive comparison of lymphoid tissue immune cells at the single-cell level between acute and chronic viral infections is largely insufficient.

Methods

To explore the landscape of immune responses to acute and chronic viral infections, single-cell RNA sequencing(scRNA-seq), scTCR-seq and scBCR-seq were utilized to evaluate the longitudinal dynamics and heterogeneity of lymph node CD45+ immune cells in mouse models of acute (LCMV Armstrong) and chronic (LCMV clone 13) viral infections.

Results

In contrast with acute viral infection, chronic viral infection distinctly induced more robust NK cells and plasma cells at the early stage (Day 4 post-infection) and acute stage (Day 8 post-infection), respectively. Moreover, chronic viral infection exerted decreased but aberrantly activated plasmacytoid dendritic cells (pDCs) at the acute phase. Simultaneously, there were significantly increased IgA+ plasma cells (MALT B cells) but differential usage of B-cell receptors in chronic infection. In terms of T-cell responses, Gzma-high effector-like CD8+ T cells were significantly induced at the early stage in chronic infection, which showed temporally reversed gene expression throughout viral infection and the differential usage of the most dominant TCR clonotype. Chronic infection also induced more robust CD4+ T cell responses, including follicular helper T cells (Tfh) and regulatory T cells (Treg). In addition, chronic infection compromised the TCR diversity in both CD8+ and CD4+ T cells.

Discussion

In conclusion, gene expression and TCR/BCR immune repertoire profiling at the single-cell level in this study provide new insights into the dynamic and differential immune responses to acute and chronic viral infections.

ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy

Background

Neoadjuvant immunotherapy with anti-programmed death-1 (neo-antiPD1) has revolutionized perioperative methods for improvement of overall survival (OS), while approaches for major pathologic response patients' (MPR) recognition along with methods for overcoming non-MPR resistance are still in urgent need.

Methods

We utilized and integrated publicly-available immune checkpoint inhibitors regimens (ICIs) single-cell (sc) data as the discovery datasets, and innovatively developed a cell-communication analysis pipeline, along with a VIPER-based-SCENIC process, to thoroughly dissect MPR-responding subsets. Besides, we further employed our own non-small cell lung cancer (NSCLC) ICIs cohort's sc data for validation in-silico. Afterward, we resorted to ICIs-resistant murine models developed by us with multimodal investigation, including bulk-RNA-sequencing, Chip-sequencing and high-dimensional cytometry by time of flight (CYTOF) to consolidate our findings in-vivo. To comprehensively explore mechanisms, we adopted 3D ex-vivo hydrogel models for analysis. Furthermore, we constructed an ADGRE5-centered Tsurv model from our discovery dataset by machine learning (ML) algorithms for a wide range of tumor types (NSCLC, melanoma, urothelial cancer, etc.) and verified it in peripheral blood mononuclear cells (PBMCs) sc datasets.

Results

Through a meta-analysis of multimodal sequential sc sequencing data from pre-ICIs and post-ICIs, we identified an MPR-expanding T cells meta-cluster (MPR-E) in the tumor microenvironment (TME), characterized by a stem-like CD8+ T cluster (survT) with STAT5-ADGRE5 axis enhancement compared to non-MPR or pre-ICIs TME. Through multi-omics analysis of murine TME, we further confirmed the existence of survT with silenced function and immune checkpoints (ICs) in MPR-E. After verification of the STAT5-ADGRE5 axis of survT in independent ICIs cohorts, an ADGRE5-centered Tsurv model was then developed through ML for identification of MPR patients pre-ICIs and post-ICIs, both in TME and PBMCs, which was further verified in pan-cancer immunotherapy cohorts. Mechanistically, we unveiled ICIs stimulated ADGRE5 upregulation in a STAT5-IL32 dependent manner in a 3D ex-vivo system (3D-HYGTIC) developed by us previously, which marked Tsurv with better survival flexibility, enhanced stemness and potential cytotoxicity within TME.

Conclusion

Our research provides insights into mechanisms underlying MPR in neo-antiPD1 and a well-performed model for the identification of non-MPR.

Integrated Bulk and Single-Cell RNA-Sequencing Reveals the Effects of Circadian Rhythm Disruption on the Metabolic Reprogramming of CD4+ T Cells in Alzheimer's Disease

Although growing evidence suggests close correlations between Alzheimer's disease (AD) and circadian rhythm disruption (CRD), few studies have focused on the influence of circadian rhythm on levels of immune cells in AD. We aimed to delineate the mechanism underlying the effects of circadian related genes on T cell immune function in AD. A total of 112 brain samples were used to construct the CRD-related model by performing weighted gene co-expression network analysis and machine learning algorithms (LASSO, SVM-RFE, and RF). The ssGSEA method was used to calculate the CRDscore in order to quantify CRD status. Using single-cell transcriptome data of CSF cells, we investigated the CD4+ T cell metabolism and cell-cell communication in high- and low-risk CRD groups. Connectivity map (CMap) was applied to explore small molecule drugs targeting CRD, and the expression of the signature gene GPR4 was further validated in AD. The CRDscore algorithm, which is based on 23 circadian-related genes, can effectively classify the CRD status in AD datasets. The single-cell analysis revealed that the CD4+ T cells with high CRDscore were characterized by hypometabolism. Cell communication analysis revealed that CD4+ T cells might be involved in promoting CD8+ T cell adhesion under CRD, which may facilitate T cell infiltration into the brain parenchyma. Overall, this study indicates the potential connotation of circadian rhythm in AD, providing insights into understanding T cell metabolic reprogramming under CRD.

Single-cell RNA sequencing reveals the transcriptional heterogeneity of Tbx18-positive cardiac cells during heart development

The T-box family transcription factor 18 (Tbx18) has been found to play a critical role in regulating the development of the mammalian heart during the primary stages of embryonic development while the cellular heterogeneity and landscape of Tbx18-positive (Tbx18+) cardiac cells remain incompletely characterized. Here, we analyzed prior published single-cell RNA sequencing (scRNA-seq) mouse heart data to explore the heterogeneity of Tbx18+ cardiac cell subpopulations and provide a comprehensive transcriptional landscape of Tbx18+ cardiac cells during their development. Bioinformatic analysis methods were utilized to identify the heterogeneity between cell groups. Based on the gene expression characteristics, Tbx18+ cardiac cells can be classified into a minimum of two distinct cell populations, namely fibroblast-like cells and cardiomyocytes. In terms of temporal heterogeneity, these cells exhibit three developmental stages, namely the MEM stage, ML_P0 stage, and P stage Tbx18+ cardiac cells. Furthermore, Tbx18+ cardiac cells encompass several cell types, including cardiac progenitor-like cells, cardiomyocytes, and epicardial/stromal cells, as determined by specific transcriptional regulatory networks. The scRNA-seq results revealed the involvement of extracellular matrix (ECM) signals and epicardial epithelial-to-mesenchymal transition (EMT) in the development of Tbx18+ cardiac cells. The utilization of a lineage-tracing model served to validate the crucial function of Tbx18 in the differentiation of cardiac cells. Consequently, these findings offer a comprehensive depiction of the cellular heterogeneity within Tbx18+ cardiac cells.

Single-cell RNA sequencing analysis to evaluate antimony exposure effects on cell-lineage communications within the Drosophila testicular niche

The increasing production and prevalence of antimony (Sb)-related products raise concerns regarding its potential hazards to reproductive health. Upon environmental exposure, Sb reportedly induces testicular toxicity during spermatogenesis; moreover, it is known to affect various testicular cell populations, particularly germline stem cell populations. However, the cell-cell communication resulting from Sb exposure within the testicular niche remains poorly understood. To address this gap, herein we analyzed testicular single-cell RNA sequencing data from Sb-exposed Drosophila. Our findings revealed that the epidermal growth factor receptor (EGFR) and WNT signaling pathways were associated with the stem cell niche in Drosophila testes, which may disrupt the homeostasis of the testicular niche in Drosophila. Furthermore, we identified several ligand-receptor pairs, facilitating the elucidation of intercellular crosstalk involved in Sb-mediated reproductive toxicology. We employed scRNA-seq analysis and conducted functional verification to investigate the expression patterns of core downstream factors associated with EGFR and WNT signatures in the testes under the influence of Sb exposure. Altogether, our results shed light on the potential mechanisms of Sb exposure-mediated testicular cell-lineage communications.

Integrating single-cell and bulk transcriptomic analyses to develop a cancer-associated fibroblast-derived biomarker for predicting prognosis and therapeutic response in breast cancer

Background

Cancer-associated fibroblasts (CAFs) contribute to the progression and treatment of breast cancer (BRCA); however, risk signatures and molecular targets based on CAFs are limited. This study aims to identify novel CAF-related biomarkers to develop a risk signature for predicting the prognosis and therapeutic response of patients with BRCA.

Methods

CAF-related genes (CAFRGs) and a risk signature based on these genes were comprehensively analyzed using publicly available bulk and single-cell transcriptomic datasets. Modular genes identified from bulk sequencing data were intersected with CAF marker genes identified from single-cell analysis to obtain reliable CAFRGs. Signature CAFRGs were screened via Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. Multiple patient cohorts were used to validate the prognosis and therapeutic responsiveness of high-risk patients stratified based on the CAFRG-based signature. In addition, the relationship between the CAFRG-based signature and clinicopathological factors, tumor immune landscape, functional pathways, chemotherapy sensitivity and immunotherapy sensitivity was examined. External datasets were used and sample experiments were performed to examine the expression pattern of MFAP4, a key CAFRG, in BRCA.

Results

Integrated analyses of single-cell and bulk transcriptomic data as well as prognostic screening revealed a total of 43 prognostic CAFRGs; of which, 14 genes (TLN2, SGCE, SDC1, SAV1, RUNX1, PDLIM4, OSMR, NT5E, MFAP4, IGFBP6, CTSO, COL12A1, CCDC8 and C1S) were identified as signature CAFRGs. The CAFRG-based risk signature exhibited favorable efficiency and accuracy in predicting survival outcomes and clinicopathological progression in multiple BRCA cohorts. Functional enrichment analysis suggested the involvement of the immune system, and the immune infiltration landscape significantly differed between the risk groups. Patients with high CAF-related risk scores (CAFRSs) exhibited tumor immunosuppression, enhanced cancer hallmarks and hyposensitivity to chemotherapy and immunotherapy. Five compounds were identified as promising therapeutic agents for high-CAFRS BRCA. External datasets and sample experiments validated the downregulation of MFAP4 and its strong correlation with CAFs in BRCA.

Conclusions

A novel CAF-derived gene signature with favorable predictive performance was developed in this study. This signature may be used to assess prognosis and guide individualized treatment for patients with BRCA.

Heterogeneity and synaptic plasticity analysis of hippocampus based on db-/- mice induced diabetic encephalopathy

Chronic hyperglycemia can cause changes in synaptic plasticity of hippocampal cells, which has accelerated the pathological process of cognitive dysfunction. However, the heterogeneity of the hippocampal cell populations under long term high glucose statement remains largely unknown. To mimic chronic hyperglycemia induced cognitive function deficit in vivo, db-/- diabetic mice was selected and Novel Object Recognition(NOR) behavior tests were performed. Based on diabetic induced cognitive impairment(CI) animal model, single-cell RNA sequencing was performed in the hippocampus of CI group (21,379 cells) or control group (20,045 cells), and single cell RNA sequencing was applied, and then the single cell atlas of gene expression was profiled. The comprehensive analysis explicated 18 nerve cell clusters, including 9 distinct sub-clusters, More in-depth analysis of oligodendrocyte precursor cells(OPCs) showed five distinct OPCs sub-clusters including expressing marker gene Lingo2-OPCs, Kcnc1-OPCs, Sst-OPCs, Slc6a1-OPCs and Lhfpl3-OPCs, which seems to be able to proliferate, migrate, and finally differentiate into mature oligodendrocytes and produce myelin. To be noted, differentially expressed genes(DEGs) of the Sst-OPCs sub-cluster indicated that the genes participating in neuroactive ligand-receptor interaction, nervous system development and inflammatory process were up-regulated in diabetic induced cognitive impairment(DCI) groups compared to normal control groups. Integrating the data of neuroplasticity regulation, the 20th top-enriched biological process was associated with neuroplasticity regulation in CI groups compared to control groups. Among these neuroplasticity-related genes, the intersectional gene Sstr2 may play an important role in neuroplasticity regulation. Focused on neuroplasticity regulation and its related specific genes may provide potential new clues for the treatment of diabetes mellitus complicated with cognitive impairment. In summary, we showed the comprehensively transcriptional landscape of hippocampal cells in the db-/- diabetic mice with cognitive dysfunction, distinctive cell sub-clusters and the gene expression characteristics were identified, and also their special functions were proposed, which may give new clues and potential targets for diagnosis and treatment of diabetic encephalopathy.

A guide to single-cell RNA sequencing analysis using web-based tools for non-bioinformatician

Single-cell RNA sequencing (scRNA-seq) is a technique that has proven to be a powerful tool for a wide range of fields and research studies. However, scRNA-seq data analysis has been dominated by scientists highly trained in bioinformatics or those with extensive computational experience and understanding. Recently, this trend has begun to shift as more user-friendly web-based scRNA-seq analysis tools have been developed that require little computational experience to use. However, barriers persist for nonbioinformaticians in using this technique. Complex, unfamiliar language and scarce comprehensive literature guidance to provide a framework for understanding scRNA-seq analysis outputs are among the obstacles. This work introduces many popular web-based tools for scRNA-seq and provides a general overview of their user interfaces and features. Then, a comprehensive start-to-finish introductory scRNA-seq analysis pipeline is described in detail, which aims to enable researchers to carry out scRNA-seq analysis, regardless of computational experience. Companion video tutorials can be found at "EasyScRNAseqTutorials" on YouTube (https://www.youtube.com/@scrnaseqtutorials). However, as scRNA-seq continues to penetrate new fields and expand in importance, there remains a need for more literature to help overcome barriers to its use by explaining further the highly complex and advanced analyses that are introduced within this paper.

An integrative analysis of single-cell and bulk transcriptome and bidirectional mendelian randomization analysis identified C1Q as a novel stimulated risk gene for Atherosclerosis

Background

The role of complement component 1q (C1Q) related genes on human atherosclerotic plaques (HAP) is less known. Our aim is to establish C1Q associated hub genes using single-cell RNA sequencing (scRNA-seq) and bulk RNA analysis to diagnose and predict HAP patients more effectively and investigate the association between C1Q and HAP (ischemic stroke) using bidirectional Mendelian randomization (MR) analysis.

Methods

HAP scRNA-seq and bulk-RNA data were download from the Gene Expression Omnibus (GEO) database. The C1Q-related hub genes was screened using the GBM, LASSO and XGBoost algorithms. We built machine learning models to diagnose and distinguish between types of atherosclerosis using generalized linear models and receiver operating characteristics (ROC) analyses. Further, we scored the HALLMARK_COMPLEMENT signaling pathway using ssGSEA and confirmed hub gene expression through qRT-PCR in RAW264.7 macrophages and apoE-/- mice. Furthermore, the risk association between C1Q and HAP was assessed through bidirectional MR analysis, with C1Q as exposure and ischemic stroke (IS, large artery atherosclerosis) as outcomes. Inverse variance weighting (IVW) was used as the main method.

Results

We utilized scRNA-seq dataset (GSE159677) to identify 24 cell clusters and 12 cell types, and revealed seven C1Q associated DEGs in both the scRNA-seq and GEO datasets. We then used GBM, LASSO and XGBoost to select C1QA and C1QC from the seven DEGs. Our findings indicated that both training and validation cohorts had satisfactory diagnostic accuracy for identifying patients with HPAs. Additionally, we confirmed SPI1 as a potential TF responsible for regulating the two hub genes in HAP. Our analysis further revealed that the HALLMARK_COMPLEMENT signaling pathway was correlated and activated with C1QA and C1QC. We confirmed high expression levels of C1QA, C1QC and SPI1 in ox-LDL-treated RAW264.7 macrophages and apoE-/- mice using qPCR. The results of MR indicated that there was a positive association between the genetic risk of C1Q and IS, as evidenced by an odds ratio (OR) of 1.118 (95%CI: 1.013-1.234, P = 0.027).

Conclusion

The authors have effectively developed and validated a novel diagnostic signature comprising two genes for HAP, while MR analysis has provided evidence supporting a favorable association of C1Q on IS.

Exploring distinct properties of endometrial stem cells through advanced single-cell analysis platforms

The endometrium is a dynamic tissue that undergoes cyclic changes in response to ovarian hormones during the menstrual cycle. These changes are crucial for pregnancy establishment and maintenance. Endometrial stem cells play a pivotal role in endometrial regeneration and repair by differentiating into various cell types within the endometrium. However, their involvement in endometrial disorders such as endometriosis, infertility, and endometrial cancer is still not fully understood yet. Traditional bulk sequencing methods have limitations in capturing heterogeneity and complexity of endometrial stem cell populations. To overcome these limitations, recent single-cell analysis techniques, including single-cell RNA sequencing (scRNA-Seq), single-cell ATAC sequencing (scATAC-Seq), and spatial transcriptomics, have emerged as valuable tools for studying endometrial stem cells. In this review, although there are still many technical limitations that require improvement, we will summarize the current state-of-the-art single-cell analysis techniques for endometrial stem cells and explore their relevance to related diseases. We will discuss studies utilizing various single-cell analysis platforms to identify and characterize distinct endometrial stem cell populations and investigate their dynamic changes in gene expression and epigenetic patterns during menstrual cycle and differentiation processes. These techniques enable the identification of rare cell populations, capture heterogeneity of cell populations within the endometrium, and provide potential targets for more effective therapies.

The utilization of single-cell sequencing technology in investigating the immune microenvironment of ccRCC

The growth and advancement of ccRCC are strongly associated with the presence of immune infiltration and the tumor microenvironment, comprising tumor cells, immune cells, stromal cells, vascular cells, myeloid-derived cells, and extracellular matrix (ECM). Nevertheless, as a result of the diverse and constantly evolving characteristics of the tumor microenvironment, prior advanced sequencing methods have frequently disregarded specific less prevalent cellular traits at varying intervals, thereby concealing their significance. The advancement and widespread use of single-cell sequencing technology enable us to comprehend the source of individual tumor cells and the characteristics of a greater number of individual cells. This, in turn, minimizes the impact of intercellular heterogeneity and temporal heterogeneity of the same cell on experimental outcomes. This review examines the attributes of the tumor microenvironment in ccRCC and provides an overview of the progress made in single-cell sequencing technology and its particular uses in the current focus of immune infiltration in ccRCC.

ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering

MOTIVATION

Categorizing cells into distinct types can shed light on biological tissue functions and interactions, and uncover specific mechanisms under pathological conditions. Since gene expression throughout a population of cells is averaged out by conventional sequencing techniques, it is challenging to distinguish between different cell types. The accumulation of single-cell RNA sequencing (scRNA-seq) data provides the foundation for a more precise classification of cell types. It is crucial building a high-accuracy clustering approach to categorize cell types since the imbalance of cell types and differences in the distribution of scRNA-seq data affect single-cell clustering and visualization outcomes.

RESULT

To achieve single-cell type detection, we propose a meta-learning-based single-cell clustering model called ScLSTM. Specifically, ScLSTM transforms the single-cell type detection problem into a hierarchical classification problem based on feature extraction by the siamese long-short term memory (LSTM) network. The similarity matrix derived from the improved sigmoid kernel is mapped to the siamese LSTM feature space to analyze the differences between cells. ScLSTM demonstrated superior classification performance on 8 scRNA-seq data sets of different platforms, species, and tissues. Further quantitative analysis and visualization of the human breast cancer data set validated the superiority and capability of ScLSTM in recognizing cell types.

Single-cell RNA-seq reveals characteristics in tumor microenvironment of PDAC with MSI-H following neoadjuvant chemotherapy with anti-PD-1 therapy

Accumulating evidence suggests the minority of patients with advanced pancreatic ductal adenocarcinoma (PDAC) that have microsatellite instability high (MSI-H) can benefit from immune checkpoint inhibitors (ICIs). However, the effects of ICIs on the tumor microenvironment (TME) of PDAC remain elusive. We conducted single-cell RNA-seq (scRNA-seq) analysis on a residual lesion from a MSI-H PDAC patient who received a radical operation after eight cycles of neoadjuvant treatment (nab-paclitaxel/gemcitabine plus pembrolizumab). Multiple tumor subclusters were identified in residual lesion after neoadjuvant treatment, one of which was mainly composed of cells in the S and G2M phases. This subcluster also had enriched expression of MKI67 and PCNA and cell cycle-related signatures and was thus defined as a proliferating tumor subcluster. This subcluster had higher S_score, Fatty acid_score, UPR_score, and Glycolysis_score than others. We also identified characteristics of the TME after neoadjuvant treatment by comparing the excised primary tumors form nontreated PDAC and the residual lesion. The residual lesion was characterized with activated pancreatic stellate cells (PSCs) and exhausted T cells (Tex). We compared the receptor-ligand interactions between the two groups, and found that no checkpoint receptor-ligand pairs between T cells and tumor cells were identified in the residual lesion, while there were many checkpoint receptor-ligand pairs in the nontreated primary PDAC. In conclusion, our findings revealed the characteristics of residual lesion of advanced PDAC with MSI-H upon combination treatment of chemotherapy and immunotherapy, which might provide some valuable clues for solving the puzzle of ICI in PDAC.

Consistent analysis of differentially expressed genes across 7 cell types in papillary thyroid carcinoma

Single-cell transcriptome sequencing (scRNA-seq) provides a higher resolution of cellular differences than bulk RNA-seq, enabling the dissection of cell-type-specific responses to perturbations in papillary thyroid carcinoma (PTC). However, cellular genomic features are highly heterogeneous and have a large number of genes without any expression signals, which hinders the statistical power to identify differentially expressed genes and may generate many false-positive results. To overcome this challenge, we conducted an integrative analysis on two PTC scRNA-seq datasets and cross-validated consistent differential expression. By combining results from 32 common cell types in the two studies, we identified 31 consistently differentially expressed genes (DEGs) across seven cell types, including B cells, endothelial cells, epithelial cells, monocytes, NK cells, smooth muscle cells, and T cells. Functional enrichment analysis revealed that these genes are important for the adaptive immune response and autoimmune thyroid diseases. The additional disease-free survival analysis also confirmed that these 31 genes significantly affected patient survival time in large scale thyroid cancer cohort. Furthermore, we experimentally validated one of the top consistent DEGs as a potential biomarker gene of PTC epithelial cells, KRT7, which may be a upstream gene for the NF-κB signaling pathway. The result shows that KRT7 may promote thyroid cancer metastasis through the epithelial-mesenchymal transition and NF-κB signaling pathway. In summary, our single-cell transcriptome integration-based approach may provide insights into the important role of NF-κB in the underlying biology of the PTC.

Construction and validation of a novel senescence-related risk score can help predict the prognosis and tumor microenvironment of gastric cancer patients and determine that STK40 can affect the ROS accumulation and proliferation ability of gastric cancer cells

Background

In recent years, significant molecules have been found in gastric cancer research. However, their precise roles in the disease's development and progression remain unclear. Given gastric cancer's heterogeneity, prognosis prediction is challenging. This study aims to assess patient prognosis and immune therapy efficacy using multiple key molecules.

Method

The WGCNA algorithm was employed to identify modules of genes closely related to immunity. A prognostic model was established using the Lasso-Cox method to predict patients' prognosis. Single-sample gene set enrichment analysis (ssGSEA) was conducted to quantify the relative abundance of 16 immune cell types and 13 immune functions. The relationship between risk score and TMB, MSI, immune checkpoints, and DNA repair genes was examined to predict the effectiveness of immune therapy. GO and KEGG analyses were performed to explore potential pathways and mechanisms associated with the genes of interest. Single-cell RNA sequencing was utilized to investigate the expression patterns of key genes in different cell types.

Results

Through the WGCNA algorithm and Lasso-Cox algorithm selected KL, SERPINE1, and STK40 as key genes for constructing the prognostic model. The SSGSEA algorithm was employed to evaluate the infiltration of immune cells and immune functions in different patients, and their association with the risk score was investigated. The high-risk group exhibited lower TMB and MSI compared to the low-risk group. MMR and immune checkpoint analysis revealed a significant correlation between the risk score and multiple molecules. Finally, we also believe that STK40 is the most critical senescence-related gene affecting the progression of gastric cancer. In vitro experiments showed that ROS accumulation and cell proliferation ability of gastric cancer cells were impaired when STK40 was knocked down.

Conclusion

In summary, we've constructed a prognostic model utilizing key genes for gastric cancer prognosis, while also showcasing its efficacy in predicting patient response to immunotherapy.

Integration of scRNA-Seq and bulk RNA-Seq uncover perturbed immune cell types and pathways of Kawasaki disease

Introduction

Kawasaki disease (KD) is an acute febrile illness primarily affecting children and characterized by systemic inflammation and vasculitis that can lead to coronary artery complications. The aim of this study was to gain a comprehensive understanding of immune dysregulation in KD.

Methods

To this end, we employed integration of single-cell RNA sequencing (scRNA-Seq) and bulk RNA sequencing (bulk RNA-Seq) data. Furthermore, we conducted flow cytometry analysis for a cohort of 82 KD patients.

Results

Our analysis revealed significant heterogeneity within immune cell populations in KD patients, with distinct clusters of T cells, B cells, and natural killer (NK) cells. Importantly, CD4+ naïve T cells in KD patients were found to predominantly differentiate into Treg cells and Th2 cells, potentially playing a role in the excessive inflammation and vascular damage characteristic of the disease. Dysregulated signaling pathways were also identified, including the mTOR signaling pathway, cardiomyopathy pathway, COVID-19 signaling pathway, and pathways involved in bacterial or viral infection.

Discussion

These findings provide insights into the immunopathogenesis of KD, emphasizing the importance of immune cell dysregulation and dysregulated signaling pathways. Integration of scRNA-Seq and bulk RNA-Seq data offers a comprehensive view of the molecular and cellular alterations in KD and highlights potential therapeutic targets for further investigation. Validation and functional studies are warranted to elucidate the roles of the identified immune cell types and pathways in KD pathogenesis and to develop targeted interventions to improve patient outcomes.

Hypomethylation-induced regulatory programs in T cells unveiled by transcriptomic analyses

Regulatory T cells (Tregs) are essential mediators of tolerance mitigating aberrant immune responses. While naturally occurring Treg (nTreg) development and function are directed by epigenetic events, induced Treg (iTreg) identity and mechanisms of action remain elusive. Mirroring the epigenetic circuits of nTregs, we and others have used hypomethylation agents (HAs) to ex vivo convert T cells into iTregs (HA-iTregs) and further showed that the suppressive properties of the HA-iTregs are predominantly confined in an emergent population, which de novo expresses the immunomodulatory molecule HLA-G, consequently providing a surface marker for isolation of the suppressive HA-iTreg compartment (G+ cells). We isolated the HA-induced G+ cells and their G- counterparts and employed high-throughput RNA-sequencing (RNA-seq) analyses to uncover the G+-specific transcriptomic changes guiding T cells toward a regulatory trajectory upon their exposure to HA. We found a distinct transcriptional upregulation of G+ cells accompanied by enrichment of immune-response-related pathways. Although single-cell RNA-seq profiling revealed regulatory G+ cells to have molecular features akin to nTregs, when assessed in conjunction with the comparative transcriptomic analysis and profiling of secreted cytokines against the non-suppressive G- cells, FOXP3 and other T-helper signatures appear to play a minor role in their suppressive phenotype. We found an ectopic expression of IDO-1 and CCL17/22 in G+ cells, denoting that in vitro exposure of T cells to HA may well unlock myeloid suppressor genes. This report provides transcriptional data shaping the molecular identity of a highly purified and potent HA-iTreg population and hints toward ectopic myeloid-specific molecular mechanisms mediating HA-iTreg function.

Single-cell sequencing of tumor-associated macrophages in a Drosophila model

Introduction

Tumor-associated macrophages may act to either limit or promote tumor growth, yet the molecular basis for either path is poorly characterized.

Methods

We use a larval Drosophila model that expresses a dominant-active version of the Ras-oncogene (RasV12) to study dysplastic growth during early tumor progression. We performed single-cell RNA-sequencing of macrophage-like hemocytes to characterize these cells in tumor- compared to wild-type larvae. Hemocytes included manually extracted tumor-associated- and circulating cells.

Results and discussion

We identified five distinct hemocyte clusters. In addition to RasV12 larvae, we included a tumor model where the activation of effector caspases was inhibited, mimicking an apoptosis-resistant setting. Circulating hemocytes from both tumor models differ qualitatively from control wild-type cells-they display an enrichment for genes involved in cell division, which was confirmed using proliferation assays. Split analysis of the tumor models further reveals that proliferation is strongest in the caspase-deficient setting. Similarly, depending on the tumor model, hemocytes that attach to tumors activate different sets of immune effectors-antimicrobial peptides dominate the response against the tumor alone, while caspase inhibition induces a shift toward members of proteolytic cascades. Finally, we provide evidence for transcript transfer between hemocytes and possibly other tissues. Taken together, our data support the usefulness of Drosophila to study the response against tumors at the organismic level.

ScRNA-seq reveals the correlation between M2 phenotype of tumor-associated macrophages and lymph node metastasis of breast cancer

The process of lymphatic metastasis was proved to be associated with podoplanin-expressing macrophages in breast cancer (BC). This study aimed to investigate the role of the M2 phenotype of tumor-associated macrophages and mine the key M2 macrophages-related genes for lymph node metastasis in BC. We downloaded the GSE158399 dataset from the Gene Expression Omnibus (GEO) database, which includes transcriptomic profiles of individual cells from primary tumors, negative lymph nodes (NLNs), and positive lymph nodes (PLNs) of breast cancer patients. The cell subsets were identified by clustering analysis after quality control of the scRNA-seq using Seurat. The activation and migration capability of M2 macrophages were evaluated with R package "GSVA". The key M2 macrophages-related genes were screened from the differential expressed genes (DEGs) and M2 macrophages activation and migration gene sets collected from MSigDB database. Our analysis identified three main cell types in primary tumors, NLNs, and PLNs: basal cells, luminal cells, and immune cell subsets. The further cell type classification of immune cell subsets indicated M2 macrophages accumulation in NLs and PLs. The GSVA enrichment scores for activation and migration capability were increased significantly in M2 macrophages from primary tumors than NLNs and PLNs (p-value < 0.001). Seven M2 macrophages activation-related and 15 M2 macrophages migration-related genes were significantly up-regulated in primary tumors than NLNs and PLNs. The proportion and GSVA enrichment scores for activation and migration of M2 macrophages may be potential markers for lymph node metastasis in breast cancer. Our study demonstrated that twenty-two up-regulated mRNA may be possible therapeutic targets for lymph node metastasis in breast cancer.

Exploring the role of macrophages in the progression from atypical hyperplasia to endometrial carcinoma through single-cell transcriptomics and bulk transcriptomics analysis

Introduction

In this study, we aimed to identify key genes in endometrial cancer by conducting single-cell analysis of macrophages.

Methods

We sourced clinical data from the TCGA database as well as supplementary datasets GSE201926 and GSE173682. Using bulk-seq data of atypical endometrial hyperplasia and endometrial cancer, we pinpointed key differentially expressed genes. Single-cell RNA sequencing was utilized for further gene expression analysis. Cluster analysis was conducted on TCGA tumor data, identifying two distinct subtypes. Statistical methods employed included LASSO regression for diagnostic modeling and various clustering algorithms for subtype identification.

Results

We found that subtype B was closely related to cellular metabolism. A diagnostic model was established using LASSO regression and was based on the genes CDH18, H19, PAGE2B, PXDN, and THRB. This model effectively differentiated the prognosis of cervical cancer. We also constructed a prognosis model and a column chart based on these key genes.

Discussion

Through CIBERSORT analysis, CDH18 and PAGE2B were found to be strongly associated with macrophage M0. We propose that these genes influence the transformation from atypical endometrial hyperplasia to endometrial cancer by affecting macrophage M0. In conclusion, these key genes may serve as therapeutic targets for endometrial cancer. A new endometrial cancer risk prognosis model and column chart have been constructed based on these genes, offering a reliable direction for future cervical cancer treatment.

Unveiling novel insights in prostate cancer through single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technology that provides insights at the individual cell level. In contrast to traditional bulk RNA-seq, which captures gene expression at an average level and may overlook important details, scRNA-seq examines each individual cell as a fundamental unit and is particularly well-suited for identifying rare cell populations. Analogous to a microscope that distinguishes various cell types within a tissue sample, scRNA-seq unravels the heterogeneity and diversity within a single cell species, offering great potential as a leading sequencing method in the future. In the context of prostate cancer (PCa), a disease characterized by significant heterogeneity and multiple stages of progression, scRNA-seq emerges as a powerful tool for uncovering its intricate secrets.

Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics

Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.

scASGC: An adaptive simplified graph convolution model for clustering single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is now a successful technique for identifying cellular heterogeneity, revealing novel cell subpopulations, and forecasting developmental trajectories. A crucial component of the processing of scRNA-seq data is the precise identification of cell subpopulations. Although many unsupervised clustering methods have been developed to cluster cell subpopulations, the performance of these methods is vulnerable to dropouts and high dimensionality. In addition, most existing methods are time-consuming and fail to adequately account for potential associations between cells. In the manuscript, we present an unsupervised clustering method based on an adaptive simplified graph convolution model called scASGC. The proposed method builds plausible cell graphs, aggregates neighbor information using a simplified graph convolution model, and adaptively determines the most optimal number of convolution layers for various graphs. Experiments on 12 public datasets show that scASGC outperforms both classical and state-of-the-art clustering methods. In addition, in a study of mouse intestinal muscle containing 15,983 cells, we identified distinct marker genes based on the clustering results of scASGC. The source code of scASGC is available at https://github.com/ZzzOctopus/scASGC.

Development and validation a prognostic model based on natural killer T cells marker genes for predicting prognosis and characterizing immune status in glioblastoma through integrated analysis of single-cell and bulk RNA sequencing

BACKGROUND

Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM.

METHODS

We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes.

RESULTS

We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors.

CONCLUSIONS

By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM.

Exploring the optimization of autoencoder design for imputing single-cell RNA sequencing data

Autoencoders are the backbones of many imputation methods that aim to relieve the sparsity issue in single-cell RNA sequencing (scRNA-seq) data. The imputation performance of an autoencoder relies on both the neural network architecture and the hyperparameter choice. So far, literature in the single-cell field lacks a formal discussion on how to design the neural network and choose the hyperparameters. Here, we conducted an empirical study to answer this question. Our study used many real and simulated scRNA-seq datasets to examine the impacts of the neural network architecture, the activation function, and the regularization strategy on imputation accuracy and downstream analyses. Our results show that (i) deeper and narrower autoencoders generally lead to better imputation performance; (ii) the sigmoid and tanh activation functions consistently outperform other commonly used functions including ReLU; (iii) regularization improves the accuracy of imputation and downstream cell clustering and DE gene analyses. Notably, our results differ from common practices in the computer vision field regarding the activation function and the regularization strategy. Overall, our study offers practical guidance on how to optimize the autoencoder design for scRNA-seq data imputation.

Single cell RNA-seq analysis with a systems biology approach to recognize important differentially expressed genes in pancreatic ductal adenocarcinoma compared to adjacent non-cancerous samples by targeting pancreatic endothelial cells

Pancreatic ductal adenocarcinoma (PDAC) is a cancer that is usually diagnosed at late stages. This highly aggressive tumor is resistant to most therapeutic approaches, necessitating identification of differentially expressed genes to design new therapies. Herein, we have analyzed single cell RNA-seq data with a systems biology approach to identify important differentially expressed genes in PDAC samples compared to adjacent non-cancerous samples. Our approach revealed 1462 DEmRNAs, including 1389 downregulated DEmRNAs (like PRSS1 and CLPS) and 73 upregulated DEmRNAs (like HSPA1A and SOCS3), 27 DElncRNAs, including 26 downregulated DElncRNAs (like LINC00472 and SNHG7) and 1 upregulated DElncRNA (SNHG5). We also listed a number of dysregulated signaling pathways, abnormally expressed genes and aberrant cellular functions in PDAC which can be used as possible biomarkers and therapeutic targets in this type of cancer.

Hyperhomocysteinemia potentiates megakaryocyte differentiation and thrombopoiesis via GH-PI3K-Akt axis

Hyperhomocysteinemia (HHcy) is closely associated with thrombotic diseases such as myocardial infarction and stroke. Enhanced platelet activation was observed in animals and humans with HHcy. However, the influence of HHcy on thrombopoiesis remains largely unknown. Here, we reported increased platelet count (PLT) in mice and zebrafish with HHcy. In hypertensive patients (n = 11,189), higher serum level of total Hcy was observed in participants with PLT ≥ 291 × 10⁹/L (full adjusted β, 0.59; 95% CI 0.14, 1.04). We used single-cell RNA sequencing (scRNA-seq) to characterize the impact of Hcy on transcriptome, cellular heterogeneity, and developmental trajectories of megakaryopoiesis from human umbilical cord blood (hUCB) CD34⁺ cells. Together with in vitro and in vivo analysis, we demonstrated that Hcy promoted megakaryocytes (MKs) differentiation via growth hormone (GH)-PI3K-Akt axis. Moreover, the effect of Hcy on thrombopoiesis is independent of thrombopoietin (TPO) because administration of Hcy also led to a significant increase of PLT in homozygous TPO receptor (Mpl) mutant mice and zebrafish. Administration of melatonin effectively reversed Hcy-induced thrombopoiesis in mice. ScRNA-seq showed that melatonin abolished Hcy-facilitated MK differentiation and maturation, inhibited the activation of GH-PI3K-Akt signaling. Our work reveals a previously unrecognized role of HHcy in thrombopoiesis and provides new insight into the mechanisms by which HHcy confers an increased thrombotic risk.Trial Registration clinicaltrials.gov Identifier: NCT00794885.

Defining developmental trajectories of prosensory cells in human inner ear organoids at single-cell resolution

The inner ear sensory epithelia contain mechanosensitive hair cells and supporting cells. Both cell types arise from SOX2-expressing prosensory cells, but the mechanisms underlying the diversification of these cell lineages remain unclear. To determine the transcriptional trajectory of prosensory cells, we established a SOX2-2A-ntdTomato human embryonic stem cell line using CRISPR/Cas9, and performed single-cell RNA-sequencing analyses with SOX2-positive cells isolated from inner ear organoids at various time points between differentiation days 20 and 60. Our pseudotime analysis suggests that vestibular type II hair cells arise primarily from supporting cells, rather than bi-fated prosensory cells in organoids. Moreover, ion channel- and ion-transporter-related gene sets were enriched in supporting cells vs. prosensory cells, whereas Wnt signaling-related gene sets were enriched in hair cells vs. supporting cells. These findings provide valuable insights into how prosensory cells give rise to hair cells and supporting cells during human inner ear development, and may provide a clue to promote hair cell regeneration from resident supporting cells in individuals with hearing loss or balance disorders.

Exploring PANoptosis in breast cancer based on scRNA-seq and bulk-seq

Background

PANoptosis, a cell death pathway involving pyroptosis, apoptosis, and necroptosis, is pivotal in the development of malignancy. However, in the field of breast cancer, the interaction between PANoptosis and tumor cells has not been thoroughly explored.

Methods

We downloaded breast cancer data and GSE176078 single-cell sequencing dataset from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to obtain PANoptosis-associated genes. To construct prognostic models, COX and LASSO regression was used to identify PANoptosis-associated genes with prognostic value. Finally, immune infiltration analysis and differential analysis of biological functions were performed.

Results

Risk grouping was performed according to the prognostic model constructed by COX regression and LASSO regression. The low-risk group showed a better prognosis (P < 0.05) and possessed higher levels of immune infiltration and expression of immune checkpoint-related genes. In addition, the lower the risk score, the higher the degree of microsatellite instability (MSI). Meanwhile, radixin (RDX), the gene with the highest hazard ratio (HR) value among PANoptosis prognosis-related genes, was explicitly expressed in artery Iendothelial cells (ECs) and was widely involved in signaling pathways such as immune response and cell proliferation, possessing rich biological functions.

Conclusion

We demonstrated the potential of PANoptosis-based molecular clustering and prognostic features in predicting the survival of breast cancer patients. Furthermore, this study has led to a deeper understanding of the role of PANoptosis in breast cancer and has the potential to provide new directions for immunotherapy of breast cancer.

Single-cell profiling reveals distinct immune response landscapes in tuberculous pleural effusion and non-TPE

Background

Tuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb) and remains a major health threat worldwide. However, a detailed understanding of the immune cells and inflammatory mediators in Mtb-infected tissues is still lacking. Tuberculous pleural effusion (TPE), which is characterized by an influx of immune cells to the pleural space, is thus a suitable platform for dissecting complex tissue responses to Mtb infection.

Methods

We employed singe-cell RNA sequencing to 10 pleural fluid (PF) samples from 6 patients with TPE and 4 non-TPEs including 2 samples from patients with TSPE (transudative pleural effusion) and 2 samples with MPE (malignant pleural effusion).

Result

Compared to TSPE and MPE, TPE displayed obvious difference in the abundance of major cell types (e.g., NK, CD4+T, Macrophages), which showed notable associations with disease type. Further analyses revealed that the CD4 lymphocyte population in TPE favored a Th1 and Th17 response. Tumor necrosis factors (TNF)-, and XIAP related factor 1 (XAF1)-pathways induced T cell apoptosis in patients with TPE. Immune exhaustion in NK cells was an important feature in TPE. Myeloid cells in TPE displayed stronger functional capacity for phagocytosis, antigen presentation and IFN-γ response, than TSPE and MPE. Systemic elevation of inflammatory response genes and pro-inflammatory cytokines were mainly driven by macrophages in patients with TPE.

Conclusion

We provide a tissue immune landscape of PF immune cells, and revealed a distinct local immune response in TPE and non-TPE (TSPE and MPE). These findings will improve our understanding of local TB immunopathogenesis and provide potential targets for TB therapy.

Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD), the most common histotype of lung cancer, may have variable prognosis due to molecular variations. The research strived to establish a prognostic model based on malignancy-related risk score (MRRS) in LUAD.

Methods

We applied the single-cell RNA sequencing (scRNA-seq) data from Tumor Immune Single Cell Hub database to recognize malignancy-related geneset. Meanwhile, we extracted RNA-seq data from The Cancer Genome Atlas database. The GSE68465 and GSE72094 datasets from the Gene Expression Omnibus database were downloaded to validate the prognostic signature. Random survival forest analysis screened MRRS with prognostic significance. Multivariate Cox analysis was leveraged to establish the MRRS. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the malignancy-related signature. In addition, we used qRT-PCR to explore the expression profile of MRRS-constructed genes in LUAD cells.

Results

The scRNA-seq analysis revealed the markers genes of malignant celltype. The MRRS composed of 7 malignancy-related genes was constructed for each patient, which was shown to be an independent prognostic factor. The results of the GSE68465 and GSE72094 datasets validated MRRS's prognostic value. Further analysis demonstrated that MRRS was involved in oncogenic pathways, genetic mutations, and immune functions. Moreover, the results of qRT-PCR were consistent with bioinformatics analysis.

Conclusion

Our research recognized a novel malignancy-related signature for predicting the prognosis of LUAD patients and highlighted a promising prognostic and treatment marker for LUAD patients.

Integration analysis of single-cell and spatial transcriptomics reveal the cellular heterogeneity landscape in glioblastoma and establish a polygenic risk model

Background

Glioblastoma (GBM) is adults' most common and fatally malignant brain tumor. The heterogeneity is the leading cause of treatment failure. However, the relationship between cellular heterogeneity, tumor microenvironment, and GBM progression is still elusive.

Methods

Integrated analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing (stRNA-seq) of GBM were conducted to analyze the spatial tumor microenvironment. We investigated the subpopulation heterogeneity of malignant cells through gene set enrichment analyses, cell communications analyses, and pseudotime analyses. Significantly changed genes of the pseudotime analysis were screened to create a tumor progress-related gene risk score (TPRGRS) using Cox regression algorithms in the bulkRNA-sequencing(bulkRNA-seq) dataset. We combined the TPRGRS and clinical characteristics to predict the prognosis of patients with GBM. Furthermore, functional analysis was applied to uncover the underlying mechanisms of the TPRGRS.

Results

GBM cells were accurately charted to their spatial locations and uncovered their spatial colocalization. The malignant cells were divided into five clusters with transcriptional and functional heterogeneity, including unclassified malignant cells and astrocyte-like, mesenchymal-like, oligodendrocytes-progenitor-like, and neural-progenitor-like malignant cells. Cell-cell communications analysis in scRNA-seq and stRNA-seq identified ligand-receptor pairs of the CXCL, EGF, FGF, and MIF signaling pathways as bridges implying that tumor microenvironment may cause malignant cells' transcriptomic adaptability and disease progression. Pseudotime analysis showed the differentiation trajectory of GBM cells from proneural to mesenchymal transition and identified genes or pathways that affect cell differentiation. TPRGRS could successfully divide patients with GBM in three datasets into high- and low-risk groups, which was proved to be a prognostic factor independent of routine clinicopathological characteristics. Functional analysis revealed the TPRGRS associated with growth factor binding, cytokine activity, signaling receptor activator activity functions, and oncogenic pathways. Further analysis revealed the association of the TPRGRS with gene mutations and immunity in GBM. Finally, the external datasets and qRT-PCR verified high expressions of the TPRGRS mRNAs in GBM cells.

Conclusion

Our study provides novel insights into heterogeneity in GBM based on scRNA-seq and stRNA-seq data. Moreover, our study proposed a malignant cell transition-based TPRGRS through integrated analysis of bulkRNA-seq and scRNA-seq data, combined with the routine clinicopathological evaluation of tumors, which may provide more personalized drug regimens for GBM patients.

Integrated analysis of single-cell and bulk RNA-sequencing reveals tumor heterogeneity and a signature based on NK cell marker genes for predicting prognosis in hepatocellular carcinoma

Background: Natural killer (NK) cells are a type of innate immune cell that recognize and eliminate tumor cells and infected cells, without prior sensitization or activation. Herein, we aimed to construct a predictive model based on NK cell-related genes for hepatocellular carcinoma (HCC) patients and assess the feasibility of utilizing this model for prognosis prediction. Methods: Single-cell RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database to identify marker genes of NK cells. Univariate Cox and lasso regression were performed to further establish a signature in the TCGA dataset. Subsequently, qPCR and immunohistochemistry (IHC) staining were employed to validate the expression levels of prognosis signature genes in HCC. The effectiveness of the model was further validated using two external cohorts from the GEO and ICGC datasets. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, and biological function were compared for different genetic subtypes and risk groups. Finally, molecular docking was performed to evaluate the binding affinity between the hub gene and chemotherapeutic drugs. Results: A total of 161 HCC-related NK cell marker genes (NKMGs) were identified, 28 of which were significantly associated with overall survival in HCC patients. Based on differences in gene expression characteristics, HCC patients were classified into three subtypes. Ten prognosis genes (KLRB1, CD7, LDB2, FCER1G, PFN1, FYN, ACTG1, PABPC1, CALM1, and RPS8) were screened to develop a prognosis model. The model not only demonstrated excellent predictive performance on the training dataset, but also were successfully validated on two independent external datasets. The risk scores derived from the model were shown to be an independent prognosis factor for HCC and were correlated with pathological severity. Moreover, qPCR and IHC staining confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis. Finally, molecular docking revealed favorable binding energies between the hub gene ACTG1 and chemotherapeutic drugs. Conclusion: In this study, we developed a model for predicting the prognosis of HCC based on NK cells. The utilization of NKMGs as innovative biomarkers showed promise in the prognosis assessment of HCC.

Exploration of the potential common pathogenic mechanisms in COVID-19 and silicosis by using bioinformatics and system biology

Silicosis is an occupational lung disease that is common worldwide. In recent years, coronavirus disease 2019 (COVID-19) has provided daunting challenges to public healthcare systems globally. Although multiple studies have shown a close link between COVID-19 and other respiratory diseases, the inter-relational mechanisms between COVID-19 and silicosis remain unclear. This study aimed to explore the shared molecular mechanisms and drug targets of COVID-19 and silicosis. Gene expression profiling identified four modules that were most closely associated with both diseases. Furthermore, we performed functional analysis and constructed a protein-protein interaction network. Seven hub genes (budding uninhibited by benzimidazoles 1 [BUB1], protein regulator of cytokinesis 1 [PRC1], kinesin family member C1 [KIFC1], ribonucleotide reductase regulatory subunit M2 [RRM2], cyclin-dependent kinase inhibitor 3 [CDKN3], Cyclin B2 [CCNB2], and minichromosome maintenance complex component 6 [MCM6]) were involved in the interaction between COVID-19 and silicosis. We investigated how diverse microRNAs and transcription factors regulate these seven genes. Subsequently, the correlation between the hub genes and infiltrating immune cells was explored. Further in-depth analyses were performed based on single-cell transcriptomic data from COVID-19, and the expression of hub-shared genes was characterized and located in multiple cell clusters. Finally, molecular docking results reveal small molecular compounds that may improve COVID-19 and silicosis. The current study reveals the common pathogenesis of COVID-19 and silicosis, which may provide a novel reference for further research.

Single-cell multi-omics analysis reveals dysfunctional Wnt signaling of spermatogonia in non-obstructive azoospermia

Background

Non-obstructive azoospermia (NOA) is the most severe type that leads to 1% of male infertility. Wnt signaling governs normal sperm maturation. However, the role of Wnt signaling in spermatogonia in NOA has incompletely been uncovered, and upstream molecules regulating Wnt signaling remain unclear.

Methods

Bulk RNA sequencing (RNA-seq) of NOA was used to identify the hub gene module in NOA utilizing weighted gene co-expression network analyses (WGCNAs). Single-cell RNA sequencing (scRNA-seq) of NOA was employed to explore dysfunctional signaling pathways in the specific cell type with gene sets of signaling pathways. Single-cell regulatory network inference and clustering (pySCENIC) for Python analysis was applied to speculate putative transcription factors in spermatogonia. Moreover, single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) determined the regulated genes of these transcription factors. Finally, spatial transcriptomic data were used to analyze cell type and Wnt signaling spatial distribution.

Results

The Wnt signaling pathway was demonstrated to be enriched in the hub gene module of NOA by bulk RNA-seq. Then, scRNA-seq data revealed the downregulated activity and dysfunction of Wnt signaling of spermatogonia in NOA samples. Conjoint analyses of the pySCENIC algorithm and scATAC-seq data indicated that three transcription factors (CTCF, AR, and ARNTL) were related to the activities of Wnt signaling in NOA. Eventually, spatial expression localization of Wnt signaling was identified to be in accordance with the distribution patterns of spermatogonia, Sertoli cells, and Leydig cells.

Conclusion

In conclusion, we identified that downregulated Wnt signaling of spermatogonia in NOA and three transcription factors (CTCF, AR, and ARNTL) may be involved in this dysfunctional Wnt signaling. These findings provide new mechanisms for NOA and new therapeutic targets for NOA patients.

Single-cell RNA sequencing reveals sexual diversity in the human bladder and its prospective impacts on bladder cancer and urinary tract infection

BACKGROUND

Some bladder-related diseases, such as bladder urinary tract infection (UTI) and bladder cancer (BCa), have significant six differences in incidence and prognosis. However, the molecular mechanisms underlying these sex differences are still not fully understood. Understanding the sex-biased differences in gene expression in normal bladder cells can help resolve these problems.

METHODS

We first collected published single-cell RNA sequencing (scRNA-seq) data of normal human bladders from females and males to map the bladder transcriptomic landscape. Then, Gene Ontology (GO) analysis and gene set enrichment analysis (GSEA) were used to determine the significant pathways that changed in the specific cell populations. The Monocle2 package was performed to reconstruct the differentiation trajectories of fibroblasts. In addition, the scMetabolism package was used to analyze the metabolic activity at the single-cell level, and the SCENIC package was used to analyze the regulatory network.

RESULTS

In total, 27,437 cells passed stringent quality control, and eight main cell types in human bladder were identified according to classical markers. Sex-based differential gene expression profiles were mainly observed in human bladder urothelial cells, fibroblasts, B cells, and T cells. We found that urothelial cells in males demonstrated a higher growth rate. Moreover, female fibroblasts produced more extracellular matrix, including seven collagen genes that may mediate BCa progression. Furthermore, the results showed that B cells in female bladders exhibited more B-cell activated signals and a higher expression of immunoglobulin genes. We also found that T cells in female bladders exhibited more T-cell activated signals. These different biological functions and properties of these cell populations may correlate with sex differences in UTI and BCa, and result in different disease processes and outcomes.

CONCLUSIONS

Our study provides reasonable insights for further studies of sex-based physiological and pathological disparities in the human bladder, which will contribute to the understanding of epidemiological differences in UTI and BCa.

Cross-species scRNA-seq reveals the cellular landscape of retina and early alterations in type 2 diabetes mice

Single-cell RNA sequencing (scRNA-seq) analysis have provided an unprecedented resolution for the studies on diabetic retinopathy (DR). However, the early changes in the retina in diabetes remain unclear. A total of 8 human and mouse scRNA-seq datasets, containing 276,402 cells were analyzed individually to comprehensively delineate the retinal cell atlas. The neural retinas were isolated from the type 2 diabetes (T2D) and control mice, and scRNA-seq analysis was conducted to evaluate the early effects of diabetes on the retina. Bipolar cell (BC) heterogeneity were identified. We found some stable BCs across multiple datasets, and explored their biological functions. A new RBC subtype (Car8_RBC) in the mouse retina was validated using the multi-color immunohistochemistry. AC149090.1 was significantly upregulated in the rod cells, ON cone BCs (CBCs), OFF CBCs, and RBCs in T2D mice. Additionally, the interneurons, especially BCs, were the most vulnerable cells to diabetes by integrating scRNA-seq and genome-wide association studies (GWAS) analyses. In conclusion, this study delineated a cross-species retinal cell atlas and uncovered the early pathological alterations in the retina of T2D mice.

Identification of a novel signature based on macrophage-related marker genes to predict prognosis and immunotherapeutic effects in hepatocellular carcinoma

Background

Tumor-related macrophages (TAMs) have emerged as an essential part of the immune regulatory network in hepatocellular carcinoma (HCC). Constructing a TAM-related signature is significant for evaluating prognosis and immunotherapeutic response of HCC patients.

Methods

Informative single-cell RNA sequencing (scRNA-seq) dataset was obtained from the Gene Expression Omnibus (GEO) database, and diverse cell subpopulations were identified by clustering dimension reduction. Moreover, we determined molecular subtypes with the best clustering efficacy by calculating the cumulative distribution function (CDF). The ESTIMATE method, CIBERSORT (cell-type identification by estimating relative subsets of RNA transcripts) algorithm and publicly available tumor immune dysfunction and exclusion (TIDE) tools were used to characterize the immune landscape and tumor immune escape status. A TAM-related gene risk model was constructed through Cox regression and verified in multiple datasets and dimensions. We also performed functional enrichment analysis to detect potential signaling pathways related to TAM marker genes.

Results

In total, 10 subpopulations and 165 TAM-related marker genes were obtained from the scRNA-seq dataset (GSE149614). After clustering 3 molecular subtypes based on TAM-related marker genes, we found significantly different prognostic survival and immune signatures among the three subtypes. Subsequently, a 9-gene predictive signature (TPP1, FTL, CXCL8, CD68, ATP6V1F, CSTB, YBX1, LGALS3, and APLP2) was identified as an independent prognostic factor for HCC patients. Those patients with high RiskScore had a lower survival rate and benefited less from immunotherapy than those with low RiskScore. Moreover, more samples of the Cluster C subtype were enriched in the high-risk group, with higher tumor immune escape incidence.

Conclusions

We constructed a TAM-related signature with excellent efficacy for predicting prognostic survival and immunotherapeutic responses in HCC patients.

Subject clustering by IF-PCA and several recent methods

Subject clustering (i.e., the use of measured features to cluster subjects, such as patients or cells, into multiple groups) is a problem of significant interest. In recent years, many approaches have been proposed, among which unsupervised deep learning (UDL) has received much attention. Two interesting questions are 1) how to combine the strengths of UDL and other approaches and 2) how these approaches compare to each other. We combine the variational auto-encoder (VAE), a popular UDL approach, with the recent idea of influential feature-principal component analysis (IF-PCA) and propose IF-VAE as a new method for subject clustering. We study IF-VAE and compare it with several other methods (including IF-PCA, VAE, Seurat, and SC3) on 10 gene microarray data sets and eight single-cell RNA-seq data sets. We find that IF-VAE shows significant improvement over VAE, but still underperforms compared to IF-PCA. We also find that IF-PCA is quite competitive, slightly outperforming Seurat and SC3 over the eight single-cell data sets. IF-PCA is conceptually simple and permits delicate analysis. We demonstrate that IF-PCA is capable of achieving phase transition in a rare/weak model. Comparatively, Seurat and SC3 are more complex and theoretically difficult to analyze (for these reasons, their optimality remains unclear).