Analysis Of Single-cell RNA Sequencing Data Research Articles

Cauchy hyper-graph Laplacian nonnegative matrix factorization for single-cell RNA-sequencing data analysis.

Many important biological facts have been found as single-cell RNA sequencing (scRNA-seq) technology has advanced. With the use of this technology, it is now possible to investigate the connections among individual cells, genes, and illnesses. For the analysis of single-cell data, clustering is frequently used. Nevertheless, biological data usually contain a large amount of noise data, and traditional clustering methods are sensitive to noise. However, acquiring higher-order spatial information from the data alone is insufficient. As a result, getting trustworthy clustering findings is challenging. We propose the Cauchy hyper-graph Laplacian non-negative matrix factorization (CHLNMF) as a unique approach to address these issues. In CHLNMF, we replace the measurement based on Euclidean distance in the conventional non-negative matrix factorization (NMF), which can lessen the influence of noise, with the Cauchy loss function (CLF). The model also incorporates the hyper-graph constraint, which takes into account the high-order link among the samples. The CHLNMF model's best solution is then discovered using a half-quadratic optimization approach. Finally, using seven scRNA-seq datasets, we contrast the CHLNMF technique with the other nine top methods. The validity of our technique was established by analysis of the experimental outcomes.

Prognostic model based on B cell marker genes for NSCLC patients under neoadjuvant immunotherapy by integrated analysis of single-cell and bulk RNA-sequencing data.

Neoadjuvant immunotherapy has evolved as an effective option to treat non-small cell lung cancer (NSCLC). B cells play essential roles in the immune system as well as cancer progression. However, the repertoire of B cells and its association with clinical outcomes remains unclear in NSCLC patients receiving neoadjuvant immunotherapy. Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data for LUAD samples were accessed from the TCGA and GEO databases. LUAD-related B cell marker genes were confirmed based on comprehensive analysis of scRNA-seq data. We then constructed the B cell marker gene signature (BCMGS) and validated it. In addition, we evaluated the association of BCGMS with tumor immune microenvironment (TIME) characteristics. Furthermore, we validated the efficacy of BCGMS in a cohort of NSCLC patients receiving neoadjuvant immunotherapy. A BCMGS was constructed based on the TCGA cohort and further validated in three independent GSE cohorts. In addition, the BCMGS was proven to be significantly associated with TIME characteristics. Moreover, a relatively higher risk score indicated poor clinical outcomes and a worse immune response among NSCLC patients receiving neoadjuvant immunotherapy. We constructed an 18-gene prognostic signature derived from B cell marker genes based on scRNA-seq data, which had the potential to predict the prognosis and immune response of NSCLC patients receiving neoadjuvant immunotherapy.

The Workflow for Computational Analysis of Single-cell RNA-sequencing Data

The Workflow for Computational Analysis of Single-cell RNA-sequencing Data

CTEC: a cross-tabulation ensemble clustering approach for single-cell RNA sequencing data analysis.

Cell-type clustering is a crucial first step for single-cell RNA-seq data analysis. However, existing clustering methods often provide different results on cluster assignments with respect to their own data pre-processing, choice of distance metrics, and strategies of feature extraction, thereby limiting their practical applications. We propose Cross-Tabulation Ensemble Clustering (CTEC) method that formulates two re-clustering strategies (distribution- and outlier-based) via cross-tabulation. Benchmarking experiments on five scRNA-Seq datasets illustrate that the proposed CTEC method offers significant improvements over the individual clustering methods. Moreover, CTEC-DB outperforms the state-of-the-art ensemble methods for single-cell data clustering, with 45.4% and 17.1% improvement over the single-cell aggregated from ensemble clustering method (SAFE) and the single-cell aggregated clustering via Mixture model ensemble method (SAME), respectively, on the two-method ensemble test. The source code of the benchmark in this work is available at the GitHub repository https://github.com/LWCHN/CTEC.git.

ScEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data.

With the emergence of large amount of single-cell RNA sequencing (scRNA-seq) data, the exploration of computational methods has become critical in revealing biological mechanisms. Clustering is a representative for deciphering cellular heterogeneity embedded in scRNA-seq data. However, due to the diversity of datasets, none of the existing single-cell clustering methods shows overwhelming performance on all datasets. Weighted ensemble methods are proposed to integrate multiple results to improve heterogeneity analysis performance. These methods are usually weighted by considering the reliability of the base clustering results, ignoring the performance difference of the same base clustering on different cells. In this paper, we propose a high-order element-wise weighting strategy based self-representative ensemble learning framework: scEWE. By assigning different base clustering weights to individual cells, we construct and optimize the consensus matrix in a careful and exquisite way. In addition, we extracted the high-order information between cells, which enhanced the ability to represent the similarity relationship between cells. scEWE is experimentally shown to significantly outperform the state-of-the-art methods, which strongly demonstrates the effectiveness of the method and supports the potential applications in complex single-cell data analytical problems.

Unsupervised Gene-Cell Collective Representation Learning with Optimal Transport

Cell type identification plays a vital role in single-cell RNA sequencing (scRNA-seq) data analysis. Although many deep embedded methods to cluster scRNA-seq data have been proposed, they still fail in elucidating the intrinsic properties of cells and genes. Here, we present a novel end-to-end deep graph clustering model for single-cell transcriptomics data based on unsupervised Gene-Cell Collective representation learning and Optimal Transport (scGCOT) which integrates both cell and gene correlations. Specifically, scGCOT learns the latent embedding of cells and genes simultaneously and reconstructs the cell graph, the gene graph, and the gene expression count matrix. A zero-inflated negative binomial (ZINB) model is estimated via the reconstructed count matrix to capture the essential properties of scRNA-seq data. By leveraging the optimal transport-based joint representation alignment, scGCOT learns the clustering process and the latent representations through a mutually supervised self optimization strategy. Extensive experiments with 14 competing methods on 15 real scRNA-seq datasets demonstrate the competitive edges of scGCOT.

Abstract 1359: Rewiring CD8+T cell responses to PD-1 immune checkpoint blockade in PDAC by preventing terminal exhaustion via the inhibitory receptor PSGL-1

Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive, poorly immunogenic cancer with increasing incidence and a poor 5-year survival rate of 12%. PDAC tumors are refractory to all currently available treatments, including immune checkpoint blockade (ICB) therapies, and demonstrate limited infiltration of cytotoxic CD8+ T cells that kill tumor cells. There is therefore an urgent, unmet need for novel therapeutic options. Analysis of single-cell RNA-sequencing data of patient PDAC revealed high expression of the novel immune checkpoint, PSGL-1, on infiltrating CD8+ T cells. We hypothesize that PSGL-1-mediated immune suppression is a critical barrier to effective anti-tumor immunity in PDAC. To test this, we utilized a preclinical model of PDAC where orthotopic injection of the KPC.4662 tumor cell line into pancreata recapitulates patient tumor responses with limited T cell infiltrates and uncontrolled tumor growth in C57BL/6 (wildtype) control mice and compared to PSGL-1KO (C57BL/6 background) mice. At endpoint, tumor burden in PSGL-1KO mice was reduced by >50% compared to controls, with 45% of PSGL-1KO animals exhibiting >70% reduction. Total immune infiltration (CD45+) was 2-fold greater in tumors from PSGL-1KO mice, with a significant increase in infiltrating CD3+ T cells. Detailed analyses revealed that infiltrating CD8+ T cells from PSGL-1KO mice were not only more abundant, but also less differentiated towards terminal exhaustion with sustained TCT-1 expression compared to controls. Moreover, PSGL-1KO mice showed protection against metastatic disease. Since our data support a critical role for T cell driven anti-tumor immunity to PDAC, we transiently depleted CD4+ T cells, CD8+ T cells, or both just prior to tumor cell implantation. While depletion of CD8+ T cells did not impede tumor control by PSGL-1KO mice, depletion of all T cells or only CD4+ T cells resulted in the loss of PDAC tumor growth inhibition, demonstrating a key role of CD4+ cells in the response. Given our previous studies demonstrating an intrinsic role for PSGL-1 in development of terminal T cell exhaustion in melanoma, including loss of TCF-1+ CD8+ T cells, we hypothesized that PSGL-1-deficiency would promote responsiveness to PD-1 ICB, which is ineffective as a monotherapy in PDAC patients and mouse models. To test this prediction, PD-1 ICB was therapeutically administered to control and PSGL-1KO mice. While treatment with anti-PD-1 had no effect on the tumor growth in the control animals, PD-1 ICB resulted in complete tumor ablation in 85% of PSGL-1KO animals, demonstrating a profound synergistic effect of PD-1 inhibition in the absence of PSGL-1. From these studies, we conclude that PSGL-1 is a critical inhibitor of T cell anti-tumor responses in PDAC. PSGL-1 therefore represents a novel ICB target with high translational potential for promoting immune responses to PDAC tumors. Citation Format: Jennifer L. Hope, Yijuan Zhang, Hannah A. Hetrick, Gabriele Romano, Sreeja Roy, Michelle Lin, Ashley B. Palete, Swetha Maganti, Dennis C. Otero, Cosimo Commisso, Linda M. Bradley. Rewiring CD8+T cell responses to PD-1 immune checkpoint blockade in PDAC by preventing terminal exhaustion via the inhibitory receptor PSGL-1 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1359.

Revealing the role of SPP1+ macrophages in glioma prognosis and therapeutic targeting by investigating tumor-associated macrophage landscape in grade 2 and 3 gliomas

BackgroundGlioma is a highly heterogeneous brain tumor categorized into World Health Organization (WHO) grades 1–4 based on its malignancy. The suppressive immune microenvironment of glioma contributes significantly to unfavourable patient outcomes. However, the cellular composition and their complex interplays within the glioma environment remain poorly understood, and reliable prognostic markers remain elusive. Therefore, in-depth exploration of the tumor microenvironment (TME) and identification of predictive markers are crucial for improving the clinical management of glioma patients.ResultsOur analysis of single-cell RNA-sequencing data from glioma samples unveiled the immunosuppressive role of tumor-associated macrophages (TAMs), mediated through intricate interactions with tumor cells and lymphocytes. We also discovered the heterogeneity within TAMs, among which a group of suppressive TAMs named TAM-SPP1 demonstrated a significant association with Epidermal Growth Factor Receptor (EGFR) amplification, impaired T cell response and unfavourable patient survival outcomes. Furthermore, by leveraging genomic and transcriptomic data from The Cancer Genome Atlas (TCGA) dataset, two distinct molecular subtypes with a different constitution of TAMs, EGFR status and clinical outcomes were identified. Exploiting the molecular differences between these two subtypes, we developed a four-gene-based prognostic model. This model displayed strong associations with an elevated level of suppressive TAMs and could be used to predict anti-tumor immune response and prognosis in glioma patients.ConclusionOur findings illuminated the molecular and cellular mechanisms that shape the immunosuppressive microenvironment in gliomas, providing novel insights into potential therapeutic targets. Furthermore, the developed prognostic model holds promise for predicting immunotherapy response and assisting in more precise risk stratification for glioma patients.Graphical abstract

SOCS3 regulates pathological retinal angiogenesis through modulating SPP1 expression in microglia and macrophages

Pathological ocular angiogenesis has long been associated with myeloid cell activation. However, the precise cellular and molecular mechanisms governing the intricate crosstalk between the immune system and vascular changes during ocular neovascularization formation remain elusive. In this study, we demonstrated that the absence of the suppressor of cytokine signaling 3 (SOCS3) in myeloid cells led to a substantial accumulation of microglia and macrophage subsets during the neovascularization process. Our single-cell RNA sequencing data analysis revealed a remarkable increase in the expression of the secreted phosphoprotein 1 (Spp1) gene within these microglia and macrophages, identifying subsets of Spp1-expressing microglia and macrophages during neovascularization formation in angiogenesis mouse models. Notably, the number of Spp1-expressing microglia and macrophages exhibited further elevation during neovascularization in mice lacking myeloid SOCS3. Moreover, our investigation unveiled the Spp1 gene as a direct transcriptional target gene of signal transducer and activator of transcription 3. Importantly, pharmaceutical activation of SOCS3 or blocking of SPP1 resulted in a significant reduction in pathological neovascularization. In conclusion, our study highlights the pivotal role of the SOCS3/STAT3/SPP1 axis in the regulation of pathological retinal angiogenesis.

Unraveling the connection between Hashimoto's Thyroiditis and non-alcoholic fatty liver disease: exploring the role of CD4+central memory T cells through integrated genetic approaches.

Exploring the connection between Hashimoto's thyroiditis (HT) and non-alcoholic fatty liver disease (NAFLD) through integrated genetic approaches. We utilized integrated genetic approaches, such as single-cell RNA sequencing (scRNA-seq) data analysis, Mendelian Randomization (MR), colocalization analysis, cell communication, and metabolic analyses, to investigate potential correlations between HT and NAFLD. Through the integrated analysis of scRNA-seq data from individuals with HT, NAFLD, and healthy controls, we observed an upregulation in the proportion of CD4+central memory (CD4+CM) T cells among T cells in both diseases. A total of 63 differentially expressed genes (DEGs) were identified in the CD4+CM cells after the differential analysis. By using MR, 8 DEGs (MAGI3, CSGALNACT1, CAMK4, GRIP1, TRAT1, IL7R, ERN1, and MB21D2) were identified to have a causal relationship with HT, and 4 DEGs (MAGI3, RCAN3, DOCK10, and SAMD12) had a causal relationship with NAFLD. MAGI3 was found to be causally linked to both HT and NAFLD. Therefore, MAGI3 was designated as the marker gene. Reverse MR and Steiger filtering showed no evidence of reverse causality. Colocalization analyses further indicated close links between MAGI3 and HT as well as NAFLD. Finally, based on the expression levels of MAGI3, we stratified CD4+CM cells into two subsets: MAGI3+CD4+CM cells and MAGI3-CD4+CM cells. Functional analyses revealed significant differences between the two subsets, potentially related to the progression of the two diseases. This study delves into the potential connections between HT and NAFLD through integrated genetic methods. Our research reveals an elevated proportion of CD4+CM cells within T cells in both HT and NAFLD. Through MR and colocalization analysis, we identify specific genes causally linked to HT and NAFLD, such as MAGI3. Ultimately, based on MAGI3 expression levels, we categorize CD4+CM cells into MAGI3+CD4+CM cells and MAGI3-CD4+CM cells, uncovering significant differences between them through functional analyses.

Single cell-transcriptomic analysis informs the lncRNA landscape in metastatic castration resistant prostate cancer

Metastatic castration-resistant prostate cancer (mCRPC) is a lethal form of prostate cancer. Although long-noncoding RNAs (lncRNAs) have been implicated in mCRPC, past studies have relied on bulk sequencing methods with low depth and lack of single-cell resolution. Hence, we performed a lncRNA-focused analysis of single-cell RNA-sequencing data (n = 14) from mCRPC biopsies followed by integration with bulk multi-omic datasets. This yielded 389 cell-enriched lncRNAs in prostate cancer cells and the tumor microenvironment (TME). These lncRNAs demonstrated enrichment with regulatory elements and exhibited alterations during prostate cancer progression. Prostate-lncRNAs were correlated with AR mutational status and response to treatment with enzalutamide, while TME-lncRNAs were associated with RB1 deletions and poor prognosis. Finally, lncRNAs identified between prostate adenocarcinomas and neuroendocrine tumors exhibited distinct expression and methylation profiles. Our findings demonstrate the ability of single-cell analysis to refine our understanding of lncRNAs in mCRPC and serve as a resource for future mechanistic studies.

High CD133 expression in proximal tubular cells in diabetic kidney disease: good or bad?

BackgroundProximal tubular cells (PTCs) play a critical role in the progression of diabetic kidney disease (DKD). As one of important progenitor markers, CD133 was reported to indicate the regeneration of dedifferentiated PTCs in acute kidney disease. However, its role in chronic DKD is unclear. Therefore, we aimed to investigate the expression patterns and elucidate its functional significance of CD133 in DKD.MethodsData mining was employed to illustrate the expression and molecular function of CD133 in PTCs in human DKD. Subsequently, rat models representing various stages of DKD progression were established. The expression of CD133 was confirmed in DKD rats, as well as in human PTCs (HK-2 cells) and rat PTCs (NRK-52E cells) exposed to high glucose. The immunofluorescence and flow cytometry techniques were utilized to determine the expression patterns of CD133, utilizing proliferative and injury indicators. After overexpression or knockdown of CD133 in HK-2 cells, the cell proliferation and apoptosis were detected by EdU assay, real-time cell analysis and flow analysis. Additionally, the evaluation of epithelial, progenitor cell, and apoptotic indices was performed through western blot and quantitative RT-PCR analyses.ResultsThe expression of CD133 was notably elevated in both human and rat PTCs in DKD, and this expression increased as DKD progressed. CD133 was found to be co-expressed with CD24, KIM-1, SOX9, and PCNA, suggesting that CD133+ cells were damaged and associated with proliferation. In terms of functionality, the knockdown of CD133 resulted in a significant reduction in proliferation and an increase in apoptosis in HK-2 cells compared to the high glucose stimulus group. Conversely, the overexpression of CD133 significantly mitigated high glucose-induced cell apoptosis, but had no impact on cellular proliferation. Furthermore, the Nephroseq database provided additional evidence to support the correlation between CD133 expression and the progression of DKD. Analysis of single-cell RNA-sequencing data revealed that CD133+ PTCs potentially play a role in the advancement of DKD through multiple mechanisms, including heat damage, cell microtubule stabilization, cell growth inhibition and tumor necrosis factor-mediated signaling pathway.ConclusionOur study demonstrates that the upregulation of CD133 is linked to cellular proliferation and protects PTC from apoptosis in DKD and high glucose induced PTC injury. We propose that heightened CD133 expression may facilitate cellular self-protective responses during the initial stages of high glucose exposure. However, its sustained increase is associated with the pathological progression of DKD. In conclusion, CD133 exhibits dual roles in the advancement of DKD, necessitating further investigation.

UPP1 promotes lung adenocarcinoma progression through the induction of an immunosuppressive microenvironment

The complexity of the tumor microenvironment (TME) is a crucial factor in lung adenocarcinoma (LUAD) progression. To gain deeper insights into molecular mechanisms of LUAD, we perform an integrative single-cell RNA sequencing (scRNA-seq) data analysis of 377,574 cells from 117 LUAD patient samples. By linking scRNA-seq data with bulk gene expression data, we identify a cluster of prognostic-related UPP1high tumor cells. These cells, primarily situated at the invasive front of tumors, display a stronger association with the immunosuppressive components in the TME. Our cytokine array analysis reveals that the upregulation of UPP1 in tumor cells leads to the increased release of various immunosuppressive cytokines, with TGF-β1 being particularly prominent. Furthermore, this UPP1 upregulation also elevates the expression of PD-L1 through the PI3K/AKT/mTOR pathway, which contributes to the suppression of CD8 + T cells. Cytometry by time-of-flight (CyTOF) analysis provides additional evidence of the role of UPP1 in shaping the immunosuppressive nature of the TME. Using patient-derived organoids (PDOs), we discover that UPP1high tumors exhibit relatively increased sensitivity to Bosutinib and Dasatinib. Collectively, our study highlights the immunosuppressive role of UPP1 in LUAD, and these findings may provide insights into the molecular features of LUAD and facilitate the development of personalized treatment strategies.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed disulfidptosis genes-based molecular subtypes and a prognostic signature in lung adenocarcinoma.

Disulfidoptosis is an unconventional form of programmed cell death that distinguishes itself from well-established cell death pathways like ferroptosis, pyroptosis, and necroptosis. Initially, we conducted a single-cell analysis of the GSE131907 dataset from the GEO database to identify disulfidoptosis-related genes (DRGs). We utilized differentially expressed DRGs to classify TCGA samples with an unsupervised clustering algorithm. Prognostic models were built using Cox regression and LASSO regression. Two DRG-related clusters (C1 and C2) were identified based on the DEGs from single-cell sequencing data analysis. In comparison to C1, C2 exhibited significantly worse overall prognosis, along with lower expression levels of immune checkpoint genes (ICGs) and chemoradiotherapy sensitivity-related genes (CRSGs). Furthermore, C2 displayed a notable enrichment in metabolic pathways and cell cycle-associated mechanisms. C2 was also linked to the development and spread of tumors. We created a prognostic risk model known as the DRG score, which relies on the expression levels of five DRGs. Patients were categorized into high-risk and low-risk groups depending on their DRG score, with the former group being linked to a poorer prognosis and higher TMB score. Moreover, the DRG score displayed significant correlations with CRSGs, ICGs, the tumor immune dysfunction and exclusion (TIDE) score, and chemotherapeutic sensitivity. Subsequently, we identified a significant correlation between the DRG score and monocyte macrophages. Additionally, crucial DRGs were additionally validated using qRT-PCR. Our new DRG score can predict the immune landscape and prognosis of LUAD, serving as a reference for immunotherapy and chemotherapy.

CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.

Effective identification of cell types is of critical importance in single-cell RNA-sequencing (scRNA-seq) data analysis. To date, many supervised machine learning-based predictors have been implemented to identify cell types from scRNA-seq datasets. Despite the technical advances of these state-of-the-art tools, most existing predictors were single classifiers, of which the performances can still be significantly improved. It is therefore highly desirable to employ the ensemble learning strategy to develop more accurate computational models for robust and comprehensive identification of cell types on scRNA-seq datasets. We propose a two-layer stacking model, termed CTISL (Cell Type Identification by Stacking ensemble Learning), which integrates multiple classifiers to identify cell types. In the first layer, given a reference scRNA-seq dataset with known cell types, CTISL dynamically combines multiple cell-type-specific classifiers (i.e. support-vector machine and logistic regression) as the base learners to deliver the outcomes for the input of a meta-classifier in the second layer. We conducted a total of 24 benchmarking experiments on 17 human and mouse scRNA-seq datasets to evaluate and compare the prediction performance of CTISL and other state-of-the-art predictors. The experiment results demonstrate that CTISL achieves superior or competitive performance compared to these state-of-the-art approaches. We anticipate that CTISL can serve as a useful and reliable tool for cost-effective identification of cell types from scRNA-seq datasets. The webserver and source code are freely available at http://bigdata.biocie.cn/CTISLweb/home and https://zenodo.org/records/10568906, respectively.

Age-related dysregulation of intestinal epithelium fucosylation is linked to an increased risk of colon cancer.

Colon cancer affects people of all ages. However, its frequency, as well as the related morbidity and mortality, are high among older adults. The complex physiological changes in the aging gut substantially limit the development of cancer therapies. Here, we identify a potentially unique intestinal microenvironment that is linked with an increased risk of colon cancer in older adults. Our findings show that aging markedly influenced persistent fucosylation of the apical surfaces of intestinal epithelial cells, which resulted in a favorable environment for tumor growth. Furthermore, our findings shed light on the importance of the host-commensal interaction, which facilitates the dysregulation of fucosylation and promotes tumor growth as people get older. We analyzed colonic microbial populations at the species level to find changes associated with aging that could contribute to the development of colon cancer. Analysis of single-cell RNA-sequencing data from previous publications identified distinct epithelial cell subtypes involved in dysregulated fucosylation in older adults. Overall, our study provides compelling evidence that excessive fucosylation is associated with the development of colon cancer, that age-related changes increase vulnerability to colon cancer, and that a dysbiosis in microbial diversity and metabolic changes in the homeostasis of older mice dysregulate fucosylation levels with age.

Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning.

Cell type identification is an important task for single-cell RNA-sequencing (scRNA-seq) data analysis. Many prediction methods have recently been proposed, but the predictive accuracy of difficult cell type identification tasks is still low. In this work, we proposed a novel Gaussian noise augmentation-based scRNA-seq contrastive learning method (GsRCL) to learn a type of discriminative feature representations for cell type identification tasks. A large-scale computational evaluation suggests that GsRCL successfully outperformed other state-of-the-art predictive methods on difficult cell type identification tasks, while the conventional random genes masking augmentation-based contrastive learning method also improved the accuracy of easy cell type identification tasks in general.

Cytoplasmatic Localization of Six1 in Male Testis and Spermatogonial Stem Cells.

Sine oculis homeobox 1 (Six1) is an important factor for embryonic development and carcinoma malignancy. However, the localization of Six1 varies due to protein size and cell types in different organs. In this study, we focus on the expression and localization of Six1 in male reproductive organ via bioinformatics analysis and immunofluorescent detection. The potential interacted proteins with Six1 were also predicted by protein-protein interactions (PPIs) and Enrichr analysis. Bioinformatic data from The Cancer Genome Atlas and Genotype-Tissue Expression project databases showed that SIX1 was highly expressed in normal human testis, but low expressed in the testicular germ cell tumor sample. Human Protein Atlas examination verified that SIX1 level was higher in normal than that in cancer samples. The sub-localization of SIX1 in different reproductive tissues varies but specifically in the cytoplasm and membrane in testicular cells. In mouse cells, single cell RNA-sequencing data analysis indicated that Six1 expression level was higher in mouse spermatogonial stem cells (mSSCs) and differentiating spermatogonial than in other somatic cells. Immunofluorescence staining showed the cytoplasmic localization of Six1 in mouse testis and mSSCs. Further PPIs and Enrichr examination showed the potential interaction of Six1 with bone morphogenetic protein 4 (Bmp4) and catenin Beta-1 (CtnnB1) and stem cell signal pathways. Cytoplasmic localization of Six1 in male testis and mSSCs was probably associated with stem cell related proteins Bmp4 and CtnnB1 for stem cell development.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed T cell marker genes based molecular sub-types and a prognostic signature in lung adenocarcinoma

Immunotherapy has emerged as a promising modality for addressing advanced or conventionally drug-resistant malignancies. When it comes to lung adenocarcinoma (LUAD), T cells have demonstrated significant influence on both antitumor activity and the tumor microenvironment. However, their specific contributions remain largely unexplored. This investigation aimed to delineate molecular subtypes and prognostic indicators founded on T cell marker genes, thereby shedding light on the significance of T cells in LUAD prognosis and precision treatment. The cellular phenotypes were identified by scrutinizing the single-cell data obtained from the GEO repository. Subsequently, T cell marker genes derived from single-cell sequencing analyses were integrated with differentially expressed genes from the TCGA repository to pinpoint T cell-associated genes. Utilizing Cox analysis, molecular subtypes and prognostic signatures were established and subsequently verified using the GEO dataset. The ensuing molecular and immunological distinctions, along with therapy sensitivity between the two sub-cohorts, were examined via the ESTIMATE, CIBERSORT, and ssGSEA methodologies. Compartmentalization, somatic mutation, nomogram development, chemotherapy sensitivity prediction, and potential drug prediction analyses were also conducted according to the risk signature. Additionally, real-time qPCR and the HPA database corroborated the mRNA and protein expression patterns of signature genes in LUAD tissues. In summary, this research yielded an innovative T cell marker gene-based signature with remarkable potential to prognosis and anticipate immunotherapeutic outcomes in LUAD patients.

Predicting mitophagy-related genes and unveiling liver endothelial cell heterogeneity in hepatic ischemia-reperfusion injury.

Hepatic Ischemia-Reperfusion Injury (HIRI) is a major complication in liver transplants and surgeries, significantly affecting postoperative outcomes. The role of mitophagy, essential for removing dysfunctional mitochondria and maintaining cellular balance, remains unclear in HIRI. To unravel the role of mitophagy-related genes (MRGs) in HIRI, we assembled a comprehensive dataset comprising 44 HIRI samples alongside 44 normal control samples from the Gene Expression Omnibus (GEO) database for this analysis. Using Random Forests and Support Vector Machines - Recursive Feature Elimination (SVM-RFE), we pinpointed eight pivotal genes and developed a logistic regression model based on these findings. Further, we employed consensus cluster analysis for classifying HIRI patients according to their MRG expression profiles and conducted weighted gene co-expression network analysis (WGCNA) to identify clusters of genes that exhibit high correlation within different modules. Additionally, we conducted single-cell RNA sequencing data analysis to explore insights into the behavior of MRGs within the HIRI. We identified eight key genes (FUNDC1, VDAC1, MFN2, PINK1, CSNK2A2, ULK1, UBC, MAP1LC3B) with distinct expressions between HIRI and controls, confirmed by PCR validation. Our diagnostic model, based on these genes, accurately predicted HIRI outcomes. Analysis revealed a strong positive correlation of these genes with monocytic lineage and a negative correlation with B and T cells. HIRI patients were divided into three subclusters based on MRG profiles, with WGCNA uncovering highly correlated gene modules. Single-cell analysis identified two types of endothelial cells with different MRG scores, indicating their varied roles in HIRI. Our study highlights the critical role of MRGs in HIRI and the heterogeneity of endothelial cells. We identified the macrophage migration inhibitory factor (MIF) and cGAS-STING (GAS) pathways as regulators of mitophagy's impact on HIRI. These findings advance our understanding of mitophagy in HIRI and set the stage for future research and therapeutic developments.