Cell cycle expression heterogeneity predicts degree of differentiation.

Methods that predict fate potential or degree of differentiation from transcriptomic data have identified rare progenitor populations and uncovered developmental regulatory mechanisms. However, some state-of-the-art methods are too computationally burdensome for emerging large-scale data and all methods make inaccurate predictions in certain biological systems. We developed a method in R (stemFinder) that predicts single cell differentiation time based on heterogeneity in cell cycle gene expression. Our method is computationally tractable and is as good as or superior to competitors. As part of our benchmarking, we implemented four different performance metrics to assist potential users in selecting the tool that is most apt for their application. Finally, we explore the relationship between differentiation time and cell fate potential by analyzing a lineage tracing dataset with clonally labelled hematopoietic cells, revealing that metrics of differentiation time are correlated with the number of downstream lineages.

scRNA sequencing technology for PitNET studies.

Pituitary neuroendocrine tumors (PitNETs) are common, most likely benign tumors with complex clinical characteristics related to hormone hypersecretion and/or growing sellar tumor mass. PitNET types are classified according to their expression of specific transcriptional factors (TFs) and hormone secretion levels. Some types show aggressive, invasive, and reoccurrence behavior. Current research is being conducted to understand the molecular mechanisms regulating these high-heterogeneous neoplasms originating from adenohypophysis, and single-cell RNA sequencing (scRNA-seq) technology is now playing an essential role in these studies due to its remarkable resolution at the single-cell level. This review describes recent studies on human PitNETs performed with scRNA-seq technology, highlighting the potential of this approach in revealing these tumor pathologies, behavior, and regulatory mechanisms.

Single-cell sequencing combined with spatial transcriptomics reveals that the IRF7 gene in M1 macrophages inhibits the occurrence of pancreatic cancer by regulating lipid metabolism-related mechanisms.

AIM: The main focus of this study is to explore the molecular mechanism of IRF7 regulation on RPS18 transcription in M1-type macrophages in pancreatic adenocarcinoma (PAAD) tissue, as well as the transfer of RPS18 by IRF7 via exosomes to PAAD cells and the regulation of ILF3 expression. METHODS: By utilising single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data from the Gene Expression Omnibus database, we identified distinct cell types with significant expression differences in PAAD tissue. Among these cell types, we identified those closely associated with lipid metabolism. The differentially expressed genes within these cell types were analysed, and target genes relevant to prognosis were identified. Flow cytometry was employed to assess the expression levels of target genes in M1 and M2 macrophages. Cell lines with target gene knockout were constructed using CRISPR/Cas9 editing technology, and cell lines with target gene knockdown and overexpression were established using lentiviral vectors. Additionally, a co-culture model of exosomes derived from M1 macrophages with PAAD cells was developed. The impact of M1 macrophage-derived exosomes on the lipid metabolism of PAAD cells in the model was evaluated through metabolomics analysis. The effects of M1 macrophage-derived exosomes on the viability, proliferation, division, migration and apoptosis of PAAD cells were assessed using MTT assay, flow cytometry, EdU assay, wound healing assay, Transwell assay and TUNEL staining. Furthermore, a mouse PAAD orthotopic implantation model was established, and bioluminescence imaging was utilised to assess the influence of M1 macrophage-derived exosomes on the intratumoural formation capacity of PAAD cells, as well as measuring tumour weight and volume. The expression of proliferation-associated proteins in tumour tissues was examined using immunohistochemistry. RESULTS: Through combined analysis of scRNA-seq and ST technologies, we discovered a close association between M1 macrophages in PAAD samples and lipid metabolism signals, as well as a negative correlation between M1 macrophages and cancer cells. The construction of a prognostic risk score model identified RPS18 and IRF7 as two prognostically relevant genes in M1 macrophages, exhibiting negative and positive correlations, respectively. Mechanistically, it was found that IRF7 in M1 macrophages can inhibit the transcription of RPS18, reducing the transfer of RPS18 to PAAD cells via exosomes, consequently affecting the expression of ILF3 in PAAD cells. IRF7/RPS18 in M1 macrophages can also suppress lipid metabolism, cell viability, proliferation, migration, invasion and intratumoural formation capacity of PAAD cells, while promoting cell apoptosis. CONCLUSION: Overexpression of IRF7 in M1 macrophages may inhibit RPS18 transcription, reduce the transfer of RPS18 from M1 macrophage-derived exosomes to PAAD cells, thereby suppressing ILF3 expression in PAAD cells, inhibiting the lipid metabolism pathway, and curtailing the viability, proliferation, migration, invasion of PAAD cells, as well as enhancing cell apoptosis, ultimately inhibiting tumour formation in PAAD cells in vivo. Targeting IRF7/RPS18 in M1 macrophages could represent a promising immunotherapeutic approach for PAAD in the future.

Reconstruction of single-cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition.

Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single-cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along the partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of the EED and EZH2 genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointing ITGB4, LAMA3, and LAMB3 as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found that CENPF, CKS1B, and MKI67 showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the identification of the latter group of genes sheds light on potential cell cycle targets for modulating EMT trajectories.

Establishment and transcriptome analysis of single blastomere-derived cell lines from zebrafish.

Maintaining chromosome euploidy in zebrafish embryonic cells is challenging because of the degradation of genomic integrity during cell passaging. In this study, we report the derivation of zebrafish cell lines from single blastomeres. These cell lines have a stable chromosome status attributed to BMP4 and exhibit continuous proliferation in vitro. Twenty zebrafish cell lines are successfully established from single blastomeres. Single-cell transcriptome sequencing analysis confirms the fidelity of gene expression profiles throughout long-term culturing of at least 45 passages. The long-term cultured cells are specialized into epithelial cells, exhibiting similar expression patterns validated by integrative transcriptomic analysis. Overall, this work provides a protocol for establishing zebrafish cell lines from single blastomeres, which can serve as valuable tools for in vitro investigations of epithelial cell dynamics in terms of life-death balance and cell fate determination during normal homeostasis.

Gene Regulatory Network Analysis of Decidual Stromal Cells and Natural Killer Cells.

Human reproductive success relies on the proper differentiation of the uterine endometrium to facilitate implantation, formation of the placenta, and pregnancy. This process involves two critical types of decidual uterine cells: endometrial/decidual stromal cells (dS) and uterine/decidual natural killer (dNK) cells. To better understand the transcription factors governing the in vivo functions of these cells, we analyzed single-cell transcriptomics data from first-trimester terminations of pregnancy, and for the first time conducted gene regulatory network analysis of dS and dNK cell subpopulations. Our analysis revealed stromal cell populations that corresponded to previously described in vitro decidualized cells and senescent decidual cells. We discovered new decidualization driving transcription factors of stromal cells for early pregnancy, including DDIT3 and BRF2, which regulate oxidative stress protection. For dNK cells, we identified transcription factors involved in the immunotolerant (dNK1) subpopulation, including IRX3 and RELB, which repress the NFKB pathway. In contrast, for the less immunotolerant (dNK3) population we predicted TBX21 (T-bet) and IRF2-mediated upregulation of the interferon pathway. To determine the clinical relevance of our findings, we tested the overrepresentation of the predicted transcription factors target genes among cell type-specific regulated genes from pregnancy disorders, such as recurrent pregnancy loss and preeclampsia. We observed that the predicted decidualized stromal and dNK1-specific transcription factor target genes were enriched with the genes downregulated in pregnancy disorders, whereas the predicted dNK3-specific targets were enriched with genes upregulated in pregnancy disorders. Our findings emphasize the importance of stress tolerance pathways in stromal cell decidualization and immunotolerance promoting regulators in dNK differentiation.

Single-cell transcriptomic sequencing analysis of mechanistic insights into the IFN-γ signaling pathway in different tumor cells.

PURPOSE: This study aimed to investigate the relationship between the interferon-gamma (IFN-γ) pathway in different tumor microenvironments (TME) and patients' prognosis, as well as the regulatory mechanisms of this pathway in tumor cells. METHODS: Using RNA-seq data from the TCGA database, we analyzed the predictive value of the IFN-γ pathway across various tumors. We employed a univariate Cox regression model to assess the prognostic significance of IFN-γ signaling in different tumor types. Additionally, we analyzed single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database to examine the distribution characteristics of the IFN-γ pathway and explore its regulatory mechanisms, highlighting how IFN-γ influenced cellular interactions within the TME. RESULTS: Our analysis revealed a significant association between the IFN-γ pathway and adverse prognosis in pan-cancer tissues (P < 0.001). Interestingly, this correlation varied regarding positive and negative regulation across different tumor types. Through a detailed examination of scRNA-seq data, we found that the IFN-γ pathway exerted substantial regulatory effects on stromal and immune cells. In contrast, its expression and regulatory patterns in tumor cells exhibited diversity and heterogeneity. Further analysis indicated that the IFN-γ pathway not only enhanced the immunogenicity of tumor cells but also inhibited their proliferation. Cell-cell interaction analysis confirmed the pivotal role of the IFN-γ pathway within the overall regulatory network. Moreover, we identified HMGB2 (high mobility group box 2) in T cells as a potential key regulator of tumor cell proliferation. CONCLUSIONS: The IFN-γ pathway exhibited a dual function by both suppressing tumor cell proliferation and enhancing their immunogenicity, positioning it as a pivotal target for refined cancer diagnosis and cancer strategies.

Microglial heterogeneity in the ischemic stroke mouse brain of both sexes.

BACKGROUND: Ischemic stroke elicits a complex and sustained immune response in the brain. Immunomodulatory treatments have long held promise for improving stroke outcomes, yet none have succeeded in the clinical setting. This lack of success is largely due to our incomplete understanding of how immune cells respond to stroke. The objective of the current study was to dissect the effect of permanent stroke on microglia, the resident immune cells within the brain parenchyma. METHODS: A permanent middle cerebral artery occlusion (pMCAO) model was used to induce ischemic stroke in young male and female mice. Microglia were sorted from fluorescence reporter mice after pMCAO or sham surgery and then subjected to single-cell RNA sequencing analysis. Various methods, including flow cytometry, RNA in situ hybridization, immunohistochemistry, whole-brain imaging, and bone marrow transplantation, were also employed to dissect the microglial response to stroke. Stroke outcomes were evaluated by infarct size and behavioral tests. RESULTS: First, we showed the morphologic and spatial changes in microglia after stroke. We then performed single-cell RNA sequencing analysis on microglia isolated from sham and stroke mice of both sexes. The data indicate no major sexual dimorphism in the microglial response to permanent stroke. Notably, we identified seven potential stroke-associated microglial clusters, including four major clusters characterized by a disease-associated microglia-like signature, a highly proliferative state, a macrophage-like profile, and an interferon (IFN) response signature, respectively. Importantly, we provided evidence that the macrophage-like cluster may represent the long-sought stroke-induced microglia subpopulation with increased CD45 expression. Lastly, given that the IFN-responsive subset constitutes the most prominent microglial population in the stroke brain, we used fludarabine to pharmacologically target STAT1 signaling and found that fludarabine treatment improved long-term stroke outcome. CONCLUSIONS: Our findings shed new light on microglia heterogeneity in stroke pathology and underscore the potential of targeting specific microglial populations for effective stroke therapies.

Integrating Multi-omics to Identify Age-Related Macular Degeneration Subtypes and Biomarkers.

Age-related macular degeneration (AMD) is one of the most common causes of irreversible vision loss in the elderly. Its pathogenesis is likely multifactorial, involving a complex interaction of metabolic and environmental factors, and remains poorly understood. Previous studies have shown that mitochondrial dysfunction and oxidative stress play a crucial role in the development of AMD. Oxidative damage to the retinal pigment epithelium (RPE) has been identified as one of the major mediators in the pathogenesis of age-related macular degeneration (AMD). Therefore, this article combines transcriptome sequencing (RNA-seq) and single-cell sequencing (scRNA-seq) data to explore the role of mitochondria-related genes (MRGs) in AMD. Firstly, differential expression analysis was performed on the raw RNA-seq data. The intersection of differentially expressed genes (DEGs) and MRGs was performed. This paper proposes a deep subspace nonnegative matrix factorization (DS-NMF) algorithm to perform a multi-layer nonlinear transformation on the intersection of gene expression profiles corresponding to AMD samples. The age of AMD patients is used as prior information at the network's top level to change the data distribution. The classification is based on reconstructed data with altered distribution. The types obtained significantly differ in scores of multiple immune-related pathways and immune cell infiltration abundance. Secondly, an optimal AMD diagnosis model was constructed using multiple machine learning algorithms for external and qRT-PCR verification. Finally, ten potential therapeutic drugs for AMD were identified based on cMAP analysis. The AMD subtypes identified in this article and the diagnostic model constructed can provide a reference for treating AMD and discovering new drug targets.

A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset.

Cell-type annotation is a critical step in single-cell data analysis. With the development of numerous cell annotation methods, it is necessary to evaluate these methods to help researchers use them effectively. Reference datasets are essential for evaluation, but currently, the cell labels of reference datasets mainly come from computational methods, which may have computational biases and may not reflect the actual cell-type outcomes. This study first constructed an experimentally labeled immune cell-subtype single-cell dataset of the same batch and systematically evaluated 18 cell annotation methods. We assessed those methods under five scenarios, including intra-dataset validation, immune cell-subtype validation, unsupervised clustering, inter-dataset annotation, and unknown cell-type prediction. Accuracy and ARI were evaluation metrics. The results showed that SVM, scBERT, and scDeepSort were the best-performing supervised methods. Seurat was the best-performing unsupervised clustering method, but it couldn't fully fit the actual cell-type distribution. Our results indicated that experimentally labeled immune cell-subtype datasets revealed the deficiencies of unsupervised clustering methods and provided new dataset support for supervised methods.

Single-cell RNA sequencing in diabetic kidney disease: a literature review.

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD), and its pathogenesis has not been clarified. Current research suggests that DKD involves multiple cell types and extra-renal factors, and it is particularly important to clarify the pathogenesis and identify new therapeutic targets. Single-cell RNA sequencing (scRNA-seq) technology is high-throughput sequencing of the transcriptomes of individual cells at the single-cell level, which is an effective technology for exploring the development of diseases by comparing genetic information, reflecting the differences in genetic information between cells, and identifying different cell subpopulations. Accumulating evidence supports the role of scRNA-seq in revealing the pathogenesis of diabetes and strengthening our understanding of the molecular mechanisms of DKD. We reviewed the scRNA-seq data this time. Then, we analyzed and discussed the applications of scRNA-seq technology in DKD research, including annotation of cell types, identification of novel cell types (or subtypes), identification of intercellular communication, analysis of cell differentiation trajectories, gene expression detection, and analysis of gene regulatory networks, and lastly, we explored the future perspectives of scRNA-seq technology in DKD research.

Identification of CGNL1 as a diagnostic marker in fibroblasts of diabetic foot ulcers: Insights from single cell RNA sequencing and bulk sequencing data.

OBJECTIVES: This study aimed to explore the unique transcriptional feature of fibroblasts subtypes and the role of ferroptosis in diabetic foot ulcers (DFUs). METHODS: The GEO (Gene Expression Omnibus) was searched to obtain the DFUs single-cell and transcriptional datasets. After identifying cell types by classic marker genes, the integrated single-cell dataset was used to run trajectory inference, RNA velocity, and ligand-receptor interaction analysis. Next, bulk RNA-seq datasets of DFUs were analyzed to the key ferroptosis genes. RESULTS: Here, we profile 83529 single transcriptomes from the foot samples utilizing single-cell sequencing (scRNA-seq) data of DFU from GEO database and identified 12 cell types, with fibroblasts exhibiting elevated levels of ferroptosis activity and substantial cellular heterogeneity. Our results defined six main fibroblast subsets that showed mesenchymal, secretory-reticular, secretory-papillary, pro-inflammatory, myogenesis, and healing-enriched functional annotations. Trajectory inference and cell-cell communication analysis revealed two major cell fates with subpopulations of fibroblasts and altered ligand-receptor interactions. Bulk RNA sequencing data identified CGNL1 as a distinctive diagnostic signature in fibroblasts. Notably, CGNL1 positively correlated with pro-inflammatory fibroblasts. CONCLUSIONS: Overall, our analysis delineated the heterogeneity present in cell populations of DFUs, showing distinct fibroblast subtypes characterized by their own unique transcriptional features and enrichment functions. Our study will help us better understand DFUs pathogenesis and identifies CGNL1 as a potential target for DFUs therapies.

A practical handbook on single-cell RNA sequencing data quality control and downstream analysis.

Advancements in single-cell analysis have facilitated high-resolution observation of the transcriptome in individual cells. However, standards for obtaining high-quality cells and data analysis pipeline remain variable. Here, we provide the groundwork for improving the quality of single-cell analysis by delineating guidelines for selecting high-quality cells and considerations throughout the analysis. This review will streamline researchers' access to single-cell analysis and serve as a valuable guide for analysis.

The influence of CLEC5A on early macrophage-mediated inflammation in COPD progression.

Chronic obstructive pulmonary disease (COPD) is a complex syndrome with poorly understood mechanisms driving its early progression (GOLD stages 1-2). Elucidating the genetic factors that influence early-stage COPD, particularly those related to airway inflammation and remodeling, is crucial. This study analyzed lung tissue sequencing data from patients with early-stage COPD (GSE47460) and smoke-exposed mice. We employed Weighted Gene Co-Expression Network Analysis (WGCNA) and machine learning to identify potentially pathogenic genes. Further analyses included single-cell sequencing from both mice and COPD patients to pinpoint gene expression in specific cell types. Cell-cell communication and pseudotemporal analyses were conducted, with findings validated in smoke-exposed mice. Additionally, Mendelian randomization (MR) was used to confirm the association between candidate genes and lung function/COPD. Finally, functional validation was performed in vitro using cell cultures. Machine learning analysis of 30 differentially expressed genes identified 8 key genes, with CLEC5A emerging as a potential pathogenic factor in early-stage COPD. Bioinformatics analyses suggested a role for CLEC5A in macrophage-mediated inflammation during COPD. Two-sample Mendelian randomization linked CLEC5A single nucleotide polymorphisms (SNPs) with Forced Expiratory Volume in One Second (FEV1), FEV1/Forced Vital Capacity (FVC) and early/later on COPD. In vitro, the knockdown of CLEC5A led to a reduction in inflammatory markers within macrophages. Our study identifies CLEC5A as a critical gene in early-stage COPD, contributing to its pathogenesis through pro-inflammatory mechanisms. This discovery offers valuable insights for developing early diagnosis and treatment strategies for COPD and highlights CLEC5A as a promising target for further investigation.

Revealing the characteristics of SETD2-mutated clear cell renal cell carcinoma through tumor heterogeneity analysis.

Background: Renal cell carcinoma (RCC) is the most prevalent type of malignant kidney tumor in adults, with clear cell renal cell carcinoma (ccRCC) comprising about 75% of all cases. The SETD2 gene, which is involved in the modification of histone proteins, is often found to have alterations in ccRCC. Yet, our understanding of how these SETD2 mutations affect ccRCC characteristics and behavior within the tumor microenvironment is still not fully understood. Methods: We conducted a detailed analysis of single-cell RNA sequencing (scRNA-seq) data from ccRCC. First, the data was preprocessed using the Python package, "scanpy." High variability genes were pinpointed through Pearson's correlation coefficient. Dimensionality reduction and clustering identification were performed using Principal Component Analysis (PCA) and the Leiden algorithm. Malignant cell identification was conducted with the "InferCNV" R package, while cell trajectories and intercellular communication were depicted using the Python packages "VIA" and "cellphoneDB." We then employed the R package "Deseq2" to determine differentially expressed genes (DEGs) between groups. Using high-dimensional weighted gene correlation network analysis (hdWGCNA), co-expression modules were identified. We intersected these modules with DEGs to establish prognostic models through univariate Cox and the least absolute shrinkage and selection operator (LASSO) method. Results: We identified 69 and 53 distinctive cell clusters, respectively. These were classified further into 12 unique cell types. This analysis highlighted the presence of an abnormal tumor sub-cluster (MT + group), identified by high mitochondrial-encoded protein gene expression and an indication of unfavorable prognosis. Investigation of cellular interactions spotlighted significant interactions between the MT + group and endothelial cells, macrophaes. In addition, we developed a prognostic model based on six characteristic genes. Notably, risk scores derived from these genes correlated significantly with various clinical features. Finally, a nomogram model was established to facilitate more accurate outcome prediction, incorporating four independent risk factors. Conclusion: Our findings provide insight into the crucial transcriptomic characteristics of ccRCC associated with SETD2 mutation. We discovered that this mutation-induced subcluster could stimulate M2 polarization in macrophages, suggesting a heightened propensity for metastasis. Moreover, our prognostic model demonstrated effectiveness in forecasting overall survival for ccRCC patients, thus presenting a valuable clinical tool.

Characterizing Rare Dormant and Cycling Lineages Using the Watermelon System.

Barcode-based lineage tracing approaches enable molecular characterization of clonal cell families. Barcodes that are expressed as mRNA can be used to deconvolve lineage identity from single-cell RNA sequencing transcriptional data. Here we describe the Watermelon system, which facilitates the simultaneous tracing of lineage, transcriptional, and proliferative state at a single cell level.

Expression patterns of keratin family members during tooth development and the role of keratin 17 in cytodifferentiation of stratum intermedium and stellate reticulum.

Keratins are typical intermediate filament proteins of the epithelium that exhibit highly specific expression patterns related to the epithelial type and stage of cellular differentiation. They are important for cytoplasmic stability and epithelial integrity and are involved in various intracellular signaling pathways. Several keratins are associated with enamel formation. However, information on their expression patterns during tooth development remains lacking. In this study, we analyzed the spatiotemporal expression of keratin family members during tooth development using single-cell RNA-sequencing (scRNA-seq) and microarray analysis. scRNA-seq datasets from postnatal Day 1 mouse molars revealed that several keratins are highly expressed in the dental epithelium, indicating the involvement of keratin family members in cellular functions. Among various keratins, keratin 5 (Krt5), keratin 14 (Krt14), and keratin 17 (Krt17) are highly expressed in the tooth germ; KRT17 is specifically expressed in the stratum intermedium (SI) and stellate reticulum (SR). Depletion of Krt17 did not affect cell proliferation in the dental epithelial cell line SF2 but suppressed their differentiation ability. These results suggest that Krt17 is essential for SI cell differentiation. Furthermore, scRNA-seq results indicated that Krt5, Krt14, and Krt17 exhibited distinct expression patterns in ameloblast, SI, and SR cells. Our findings contribute to the elucidation of novel mechanisms underlying tooth development.

Identification of novel immune-related signatures for keloid diagnosis and treatment: insights from integrated bulk RNA-seq and scRNA-seq analysis.

BACKGROUND: Keloid is a disease characterized by proliferation of fibrous tissue after the healing of skin tissue, which seriously affects the daily life of patients. However, the clinical treatment of keloids still has limitations, that is, it is not effective in controlling keloids, resulting in a high recurrence rate. Thus, it is urgent to identify new signatures to improve the diagnosis and treatment of keloids. METHOD: Bulk RNA seq and scRNA seq data were downloaded from the GEO database. First, we used WGCNA and MEGENA to co-identify keloid/immune-related DEGs. Subsequently, we used three machine learning algorithms (Randomforest, SVM-RFE, and LASSO) to identify hub immune-related genes of keloid (KHIGs) and investigated the heterogeneous expression of KHIGs during fibroblast subpopulation differentiation using scRNA-seq. Finally, we used HE and Masson staining, quantitative reverse transcription-PCR, western blotting, immunohistochemical, and Immunofluorescent assay to investigate the dysregulated expression and the mechanism of retinoic acid in keloids. RESULTS: In the present study, we identified PTGFR, RBP5, and LIF as KHIGs and validated their diagnostic performance. Subsequently, we constructed a novel artificial neural network molecular diagnostic model based on the transcriptome pattern of KHIGs, which is expected to break through the current dilemma faced by molecular diagnosis of keloids in the clinic. Meanwhile, the constructed IG score can also effectively predict keloid risk, which provides a new strategy for keloid prevention. Additionally, we observed that KHIGs were also heterogeneously expressed in the constructed differentiation trajectories of fibroblast subtypes, which may affect the differentiation of fibroblast subtypes and thus lead to dysregulation of the immune microenvironment in keloids. Finally, we found that retinoic acid may treat or alleviate keloids by inhibiting RBP5 to differentiate pro-inflammatory fibroblasts (PIF) to mesenchymal fibroblasts (MF), which further reduces collagen secretion. CONCLUSION: In summary, the present study provides novel immune signatures (PTGFR, RBP5, and LIF) for keloid diagnosis and treatment, and identifies retinoic acid as potential anti-keloid drugs. More importantly, we provide a new perspective for understanding the interactions between different fibroblast subtypes in keloids and the remodeling of their immune microenvironment.

T-distributed Stochastic Neighbor Network for unsupervised representation learning.

Unsupervised representation learning (URL) is still lack of a reasonable operator (e.g. convolution kernel) for exploring meaningful structural information from generic data including vector, image and tabular data. In this paper, we propose a simple end-to-end T-distributed Stochastic Neighbor Network (TsNet) for URL with clustering downstream task. Concretely, our TsNet model has three major components: (1) an adaptive connectivity distribution learning module is presented to construct a pairwise graph for preserving the local structure of generic data; (2) a T-distributed stochastic neighbor embedding based loss function is designed to learn a transformation between embeddings and original data, which improves the discrimination of representations; (3) a nonlinear parametric mapping is learned via our TsNet on an unsupervised generalized manner, which can address the "out-of-sample" issue. By combining these components, our method is able to considerably outperform previous related unsupervised learning approaches on visualization and clustering of generic data. A simple deep neural network equipped on our model respectively achieves 74.90%, 76.56% ACC and NMI, which is 8% relative improvement over previous state-of-the-art on real single-cell RNA-sequencing (scRNA-seq) datasets clustering.

NDC80/HEC1 promotes macrophage polarization and predicts glioma prognosis via single-cell RNA-seq and in vitro experiment.

INTRODUCTION: Glioma is the most frequent and lethal form of primary brain tumor. The molecular mechanism of oncogenesis and progression of glioma still remains unclear, rendering the therapeutic effect of conventional radiotherapy, chemotherapy, and surgical resection insufficient. In this study, we sought to explore the function of HEC1 (highly expressed in cancer 1) in glioma; a component of the NDC80 complex in glioma is crucial in the regulation of kinetochore. METHODS: Bulk RNA and scRNA-seq analyses were used to infer HEC1 function, and in vitro experiments validated its function. RESULTS: HEC1 overexpression was observed in glioma and was indicative of poor prognosis and malignant clinical features, which was confirmed in human glioma tissues. High HEC1 expression was correlated with more active cell cycle, DNA-associated activities, and the formation of immunosuppressive tumor microenvironment, including interaction with immune cells, and correlated strongly with infiltrating immune cells and enhanced expression of immune checkpoints. In vitro experiments and RNA-seq further confirmed the role of HEC1 in promoting cell proliferation, and the expression of DNA replication and repair pathways in glioma. Coculture assay confirmed that HEC1 promotes microglial migration and the transformation of M1 phenotype macrophage to M2 phenotype. CONCLUSION: Altogether, these findings demonstrate that HEC1 may be a potential prognostic marker and an immunotherapeutic target in glioma.