Single-cell RNA sequencing: Technical advancements and biological applications

Single cell RNA sequencing reveals the role of local renin-angiotensin system in regulating ovarian physiological cycle and promoting PCOS

There is a local renin-angiotensin system (RAS) in the ovary, which is involved in regulating many important physiological processes, but the specific mechanism remains unclear. Polycystic ovarian syndrome (PCOS) is the most frequently reported non-iatrogenic condition with abnormal RAS expression, characterized by overweight or obesity and insulin resistance (IR), both of which are significantly correlated with many long-term complications. These conditions are closely linked to circulatory or local RAS, serving as potential common regulatory nodes. The present study analyzed single-cell RNA sequencing (scRNA-seq) data from mouse ovaries during the reproductive period to obtain the expression levels and location information of RAS components in all cell clusters. It further analyzed the cyclical fluctuations of RAS and the differential gene sets during the estrous cycle. Protein-protein interaction analysis predicted the most closely interacting pathway with RAS, and preliminary evidence of crosstalk between angiotensin II (AngII) and the insulin signaling pathway was identified in the scRNA-seq data. A PCOS mouse model was constructed, replicating clinical reproductive and metabolic complications, and the crosstalk between AngII and IRS1/PI3K/AKT was verified. In conclusion, this study revealed the dynamic changes of the ovarian local RAS at the cellular level during the estrous cycle, and described the role of RAS in regulating ovarian function from a single-cell perspective. It also provided evidence that IR, caused by the crosstalk between AngII and IRS1/PI3K/AKT pathways, may be a potential underlying mechanism of PCOS.

An overview of computational methods in single-cell transcriptomic cell type annotation

The rapid accumulation of single-cell RNA sequencing data has provided unprecedented computational resources for cell type annotation, significantly advancing our understanding of cellular heterogeneity. Leveraging gene expression profiles derived from transcriptomic data, researchers can accurately infer cell types, sparking the development of numerous innovative annotation methods. These methods utilize a range of strategies, including marker genes, correlation-based matching, and supervised learning, to classify cell types. In this review, we systematically examine these annotation approaches based on transcriptomics-specific gene expression profiles and provide a comprehensive comparison and categorization of these methods. Furthermore, we focus on the main challenges in the annotation process, especially the long-tail distribution problem arising from data imbalance in rare cell types. We discuss the potential of deep learning techniques to address these issues and enhance model capability in recognizing novel cell types within an open-world framework.

Single-Cell RNA Sequencing Outperforms Single-Nucleus RNA Sequencing in Analyzing Pancreatic Cell Diversity and Gene Expression in Goats

The objective of this study was to determine whether single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq) was more effective for studying the goat pancreas. Pancreas tissues from three healthy 10-day-old female Xiangdong black goats were processed into single-cell and single-nucleus suspensions. These suspensions were then used to compare cellular composition and gene expression levels following library construction and sequencing. Both scRNA-seq and snRNA-seq were eligible for primary analysis but produced different cell identification profiles in pancreatic tissue. Both methods successfully annotated pancreatic acinar cells, ductal cells, alpha cells, beta cells, and endothelial cells. However, pancreatic stellate cells, immune cells, and delta cells were uniquely annotated by scRNA-seq, while pancreatic stem cells were uniquely identified by snRNA-seq. Furthermore, the genes related to digestive enzymes showed a higher expression in scRNA-seq than in snRNA-seq. In the present study, scRNA-seq detected a great diversity of pancreatic cell types and was more effective in profiling key genes than snRNA-seq, demonstrating that scRNA-seq was better suited for studying the goat pancreas. However, the choice between scRNA-seq and snRNA-seq should consider the sample compatibility, technical differences, and experimental objectives.

Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms

Background

The bile acid metabolism (BAM) and fatty acid metabolism (FAM) have been implicated in Kawasaki disease (KD), but their precise mechanisms remain unclear. Identifying signature cells and genes related to BAM and FAM could offer a deeper understanding of their role in the pathogenesis of KD.

Method

We analyzed the public single-cell RNA sequencing (scRNA-seq) dataset GSE1687323 to characterize the immune cell-type landscape in KD. Gene sets related to BAM and FAM were collected from the Gene Set Enrichment Analysis (GSEA) database and previous literature. We analyzed the cellular heterogeneity of BAM and FAM at the single-cell level using R packages. Through differential expressed genes (DEG) analysis, high-dimensional Weighted Correlation Network Analysis (hdWGCNA) and machine learning algorithms, we identified signature genes associated with both BAM and FAM. The cellular expression patterns of signature genes were further validated using our own scRNA-seq dataset. Finally, quantitative real-time PCR (qRT-PCR) was performed to validate the expression levels of signature genes in KD, and Receiver Operating Characteristic (ROC) curve analysis was conducted to evaluate their diagnostic potential.

Results

Enhanced BAM and FAM were detected in monocytes and natural killer (NK) cells from KD in the public scRNA-seq dataset. Our scRNA-seq data confirmed the signature genes identified by machine learning algorithms: Vimentin (VIM) and chloride intracellular channel 1 (CLIC1) were upregulated in monocytes, while integrin subunit beta 2 (ITGB2) was elevated in NK cells of KD. qRT-PCR results also validated the bioinformatic analysis. Moreover, these genes demonstrated significant diagnostic potential. In the training dataset (GSE68004), the area under the curve (AUC) values and 95% CI were as follows: VIM: 0.914 (0.863-0.966), ITGB2: 0.958 (0.925-0.991), and CLIC1: 0.985 (0.969-1). The validation dataset (GSE73461) yielded similarly robust results, with AUC values and 95% CI: VIM: 0.872 (0.811-0.934), ITGB2: 0.861 (0.795-0.928), and CLIC1: 0.893 (0.837-0.948).

Conclusion

This study successfully identified and validated VIM and CLIC1 in monocytes, as well as ITGB2 in NK cells, as novel metabolism-related genes in KD. These findings suggest that BAM and FAM may play crucial roles in KD pathogenesis. Furthermore, these signature genes hold promising potential as diagnostic biomarkers for KD.

Single-nucleus RNA sequencing reveals ARHGAP28 expression of podocytes as a biomarker in human diabetic nephropathy

Introduction

Diabetic kidney disease (DKD) represents serious diabetes-associated complications, and podocyte loss is an important histologic sign of DKD. The cellular and molecular profiles of podocytes in DKD have yet to be fully elucidated.

Methods

This study analyzed kidney-related single-nucleus RNA-seq datasets (GSE131882, GSE121862, and GSE141115) and human diabetic kidney glomeruli transcriptome profiling (GSE30122). ARHGAP28 expression was validated by western blot and immunohistochemistry.

Results

In human kidney tissues, 154 differentially expressed genes (DEGs) were identified in podocytes, which were enriched in biological processes related to nephron development and extracellular matrix-receptor interactions. Similarly, in the mouse kidney, 344 DEGs were found, clustering in pathways associated with renal development and signaling mechanisms like PI3K/Akt (phosphatidylinositol-3 kinase/protein kinase B) and PPAR (peroxisome proliferator-activated receptor). In diabetic human kidney glomeruli, 438 DEGs were identified, showing significant enrichment in pathways related to diabetic nephropathy. Venn analysis revealed 22 DEGs common across human and mouse podocytes and diabetic glomeruli, with ARHGAP28 being notably overexpressed in podocytes. The diabetic nephropathy model using db/db mice showed that ARHGAP28 expression was significantly upregulated in the kidney cortex and glomeruli. In vitro studies using a high-glucose podocyte model corroborated these findings.

Conclusions

Collectively, this study provides an insight into the function and diagnosis of DKD and indicates that ARHGAP28 in podocytes is a potential biomarker of DKD.

SCITUNA: single-cell data integration tool using network alignment

BACKGROUND

As single-cell genomics experiments increase in complexity and scale, the need to integrate multiple datasets has grown. Such integration enhances cellular feature identification by leveraging larger data volumes. However, batch effects-technical variations arising from differences in labs, times, or protocols-pose a significant challenge. Despite numerous proposed batch correction methods, many still have limitations, such as outputting only dimension-reduced data, relying on computationally intensive models, or resulting in overcorrection for batches with diverse cell type composition.

RESULTS

We introduce a novel method for batch effect correction named SCITUNA, a Single-Cell data Integration Tool Using Network Alignment. We perform evaluations on 39 individual batches from four real datasets and a simulated dataset, which include both scRNA-seq and scATAC-seq datasets, spanning multiple organisms and tissues. A thorough comparison of existing batch correction methods using 13 metrics reveals that SCITUNA outperforms current approaches and is successful at preserving biological signals present in the original data. In particular, SCITUNA shows a better performance than the current methods in all the comparisons except for the multiple batch integration of the lung dataset where the difference is 0.004.

CONCLUSION

SCITUNA effectively removes batch effects while retaining the biological signals present in the data. Our extensive experiments reveal that SCITUNA will be a valuable tool for diverse integration tasks.

Application and Progression of Single-Cell RNA Sequencing in Diabetes Mellitus and Diabetes Complications

Diabetes is a systemic metabolic disorder primarily caused by insulin deficiency and insulin resistance, leading to chronic hyperglycemia. Prolonged diabetes can result in metabolic damage to multiple organs, including the heart, brain, liver, muscles, and adipose tissue, thereby causing various chronic fatal complications such as diabetic retinopathy, diabetic cardiomyopathy, and diabetic nephropathy. Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool for investigating the cell diversity and pathogenesis of diabetes and identifying potential therapeutic targets in diabetes or diabetes complications. This review provides a comprehensive overview of recent applications of scRNA-seq in diabetes-related researches and highlights novel biomarkers and immunotherapy targets with cell-type information for diabetes and its associated complications.

Application of scRNA-seq in Dental Research: Seeking Regenerative Clues From the Structure of Tooth and Periodontium in Physical or Pathological States

This review presents a comprehensive overview of single-cell RNA sequencing (scRNA-seq) analyses used to study tooth and periodontal tissues. The intricate cellular composition of both teeth and periodontium are revealed, leading to the identification of new cell types and tracing lineage profiles for each cell type. Herein, we summarize the progression of dental and periodontal tissue formation, tooth homeostasis, and regenerative mechanisms. scRNA-seq analyses have demonstrated that the cellular constituent ratio of dental and periodontal tissues transforms homeostasis or injury repair. Importantly, single-cell data in the diseased tissue demonstrated a change in both cell types and intercellular communication patterns compared to the normal state. These findings provide valuable insights into the underlying disease mechanisms at the cellular level in the context of single-cell vision, thereby facilitating the investigation of potential therapeutic interventions.

Integrating Hypoxia Signatures from scRNA-seq and Bulk Transcriptomes for Prognosis Prediction and Precision Therapy in Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

Hypoxia, a common feature in many malignancies, is particularly prominent in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Investigating the mechanisms underlying hypoxia is essential for understanding the heterogeneity of CESC and developing personalized therapeutic regimens. Firstly, the CESC-specific hypoxia gene sets shared between single-cell RNA sequencing (scRNA-seq) and bulk data were identified through Weighted Gene Correlation Network Analysis (WGCNA)and FindMarkers analyses. A CESC-specific hypoxia-related score (CSHRS) risk model was constructed using the least absolute shrinkage and selection operator (LASSO)and Cox regression analyses based on these genes. The prognostic differences were analyzed in terms of immune infiltration, mutations, and drug resistance. Finally, a nomogram model was constructed by integrating clinicopathological features to facilitate precision treatment for CESC. This study constructed a CSHRS risk model that divides patients into two groups, and this model can comprehensively evaluate the tumor microenvironment characteristics of CESC, provide accurate prognostic predictions, and offer rational treatment options for patients.

Applications and emerging challenges of single-cell RNA sequencing technology in tumor drug discovery

Current therapeutic drugs are inadequate for curing tumors, highlighting the need for novel tumor drugs. The advancement of single-cell RNA sequencing (scRNA-seq) technology offers new opportunities for tumor drug discovery. This technology allows us to explore tumor heterogeneity and developmental mechanisms at the single-cell level. In this review, we outline the application of scRNA-seq in tumor drug discovery stages, including elucidating tumor mechanisms, identifying targets, screening drugs, and understanding drug action and resistance. We also discuss the challenges and future prospects of using scRNA-seq in drug development, providing a scientific foundation for advancing tumor therapies.

TRAIL induces podocyte PANoptosis via death receptor 5 in diabetic kidney disease

Podocytes can undergo PANoptosis (apoptosis, pyroptosis, and necroptosis). Diabetic kidney disease (DKD) is the leading cause of kidney failure, and podocyte loss is a major event leading to the progression of DKD. Here, we compared single cell RNA sequencing (scRNA-seq) data between three normal and three DKD human kidney samples and found a significant increase of TNFSF10 and TNFRSF10B expression in podocytes of patients with DKD. Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL), coded by TNFSF10, belongs to the TNF superfamily members and TNFRSF10B codes for death receptor 5 (DR5). We confirmed that expression of TRAIL and DR5 increased in podocytes of patients with DKD and correlated with the severity of DKD. In vitro, TNF-α stimulated TRAIL and DR5 expression in cultured human podocytes. Silence of TRAIL or DR5 by small interfering RNA alleviated TNF-α-stimulated podocytes PANoptosis, while overexpression of TRAIL, treatment with recombinant human TRAIL (rh-TRAIL) or the DR5 activator (Bioymifi) enhanced podocytes PANoptosis. In vivo, podocyte-specific deletion of TNFSF10 or TNFRSF10B alleviated podocyte and glomerular injury in high fat diet and streptozotocin-induced obese diabetic mice and was associated with decreased podocyte PANoptosis. Conversely, the induction of TNFSF10 overexpression specifically in podocytes exacerbated albuminuria and kidney injury in diabetic mice with increased podocyte PANoptosis. Additionally, administration of soluble DR5-Fc, an inhibitor of DR5, resulted in a marked reduction in albuminuria and glomerular injury in BTBR ob/ob mice. Our findings suggest a critical autocrine role of TRAIL/DR5 in inducing podocyte injury in DKD via activation of PANoptosis.

Single-Cell RNA-Sequencing Analysis Provides Insights into IgA Nephropathy

IgA nephropathy (IgAN) is currently the most common primary glomerular disease worldwide, and early diagnosis and intervention contribute significantly to improving outcomes and reducing the incidence of renal failure. The pathogenesis of IgAN remains incompletely understood. In recent years, the rapid development of single-cell RNA-sequencing (scRNA-seq) technology has provided the high-resolution and rich data necessary to elucidate disease characteristics and enabled the analysis of complex interactions between individual cells and cell types. The application of scRNA-seq in IgAN successfully revealed the landscape of immunological features, including peripheral blood B-cell and Th-cell activation, cytotoxic T-cell depletion, and renal infiltrating cell subtypes, as well as the crucial role of mesangial cells and endothelial cells in the early stage of kidney injury, and also revealed the extensive intercellular interactions between renal cells. Here, we discuss the research progress of scRNA-seq technology in IgAN. These scRNA-seq profiles help us to understand the complex molecular mechanisms of IgAN and develop biomarkers and specific therapeutic strategies.

Single-Cell Analysis Dissects the Effects of Vitamin D on Genetic Senescence Signatures Across Murine Tissues

Background/Objectives: Vitamin D (VD) plays a crucial role in age-related diseases, and its influence on cellular senescence (CS) could help clarify its function in aging. Considering VD's pleiotropic effects and the heterogeneity of CS. Methods: we utilized single-cell RNA sequencing (scRNA-seq) to explore these dynamics across multiple tissues. We analyzed three murine tissue datasets (bone, prostate, and skin) obtained from public repositories, enriching for senescence gene signatures. We then inferred gene regulatory networks (GRNs) at the tissue and cell-type levels and performed two cell communication analyses: one for senescent cells and another for interactions between senescent and non-senescent cells. Results: VD supplementation significantly decreased senescence scores in the skin (p = 3.96×10-134) and prostate (p=1.56×10-34). GRN analysis of the prostate revealed an altered macrophage-fibroblast regulatory relationship. In bone, distinct aging-related modules emerged for different bone lineages. In skin, contrary differentiation patterns between suprabasal and basal cells were observed. The main VD-modulated pathways were involved in inflammation, extracellular matrix remodeling, protein metabolism, and translation. VD reduced fibroblast-macrophage interactions in the prostate and skin but increased overall cellular crosstalk in bone. Conclusions: Our findings demonstrate that VD alleviates CS burden across tissues by modulating inflammation and metabolic processes and promoting differentiation. Key aging-related genes modulated by VD were linked to anabolism and cellular differentiation, suggesting VD's potential for therapeutic interventions targeting age-related diseases.

Mapping Human Uterine Disorders Through Single-Cell Transcriptomics

Disruptions in uterine tissue function contribute to disorders such as endometriosis, adenomyosis, endometrial cancer, and fibroids, which all significantly impact health and fertility. Advances in transcriptomics, particularly single-cell RNA sequencing, have revolutionized uterine biological research by revealing the cellular heterogeneity and molecular mechanisms underlying disease states. Single-cell RNA sequencing and spatial transcriptomics have mapped endometrial and myometrial cellular landscapes, which helped to identify critical cell types, signaling pathways, and phase-specific dynamics. Said transcriptomic technologies also identified stromal and immune cell dysfunctions, such as fibroblast-to-myofibroblast transitions and impaired macrophage activity, which drive fibrosis, chronic inflammation, and lesion persistence in endometriosis. For endometrial cancer, scRNA-seq uncovered tumor microenvironmental complexities, identifying cancer-associated fibroblast subtypes and immune cell profiles contributing to progression and therapeutic resistance. Similarly, studies on adenomyosis highlighted disrupted signaling pathways, including Wnt and VEGF, and novel progenitor cell populations linked to tissue invasion and neuroinflammation, while single-cell approaches characterized smooth muscle and fibroblast subpopulations in uterine fibroids, elucidating their roles in extracellular matrix remodeling and signaling pathways like ERK and mTOR. Despite challenges such as scalability and reproducibility, single-cell transcriptomic approaches may have potential applications in biomarker discovery, therapeutic target identification, and personalized medicine in gynecological disorders.

Molecular dynamics of chemotactic signalling orchestrates dental pulp stem cell fibrosis during aging

Aging often triggers dental pulp fibrosis, resulting in clinical repercussions such as increased susceptibility to dental infections, compromised tooth vitality, and reduced responsiveness to dental interventions. Despite its prevalence, the precise molecular mechanisms underlying this condition remains unclear. Leveraging single-cell transcriptome analysis from both our own and publicly available datasets, we identified Ccrl2+ macrophages as particularly vulnerable during the early stages of aging. Notably, dental pulp progenitors with high expression of RARRES2, a unique ligand for CCRL2, facilitate the selective recruitment of a specific macrophage population to the stem cell niches. This process culminates in the formation of the ligand-receptor complex that engages CMKLR1, a receptor broadly expressed across macrophage populations. This interaction drives macrophage activation and expansion through the RARRES2/CCRL2/CMKLR1 axis. Through rigorous experimental validation, we demonstrated that macrophage activation and expansion within stem cell niches lead to increased secretion of proinflammatory factors, promoting dental pulp fibrosis during aging. Our findings uncover the intricate molecular dynamics of dental pulp aging, emphasizing immune microenvironment interactions. This study provides a novel perspective on potential therapeutic strategies for age-related pulp diseases by targeting macrophages and modulating the immune microenvironment.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

GraCEImpute: A novel graph clustering autoencoder approach for imputation of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) technology establishes a unique view for elucidating cellular heterogeneity in various biological systems. Yet the scRNA-seq data is compromised by a high dropout rate due to the technological limitation, and the substantial data loss poses computational challenges on subsequent analyses. This study introduces a novel graph clustering autoencoder (GCAE)-based imputation approach (GraCEImpute) to address the challenge of missing data in scRNA-seq data. Our comprehensive evaluation demonstrates that the GraCEImpute model outperforms existing approaches in accurately imputing dropout zeros within scRNA-seq data. The proposed GraCEImpute model also demonstrates the significantly enhanced quality of downstream scRNA-seq data analyses, including clustering, differential gene expression (DEG) analysis, and cell trajectory inference. These improvements underscore the GraCEImpute model's potential to facilitate a deeper understanding of cellular processes and heterogeneity through the scRNA-seq data analyses. The source code is released at https://www.healthinformaticslab.org/supp/.

A dynamic peripheral immune landscape during human pregnancy

Extensive immune adaptations occur during pregnancy to ensure successful delivery. However, these changes can increase the risk of disease in the mother. Here, we conducted single-cell RNA sequencing on peripheral blood mononuclear cells from pregnant women at different stages of pregnancy to elucidate the dynamic transcriptional changes in the maternal immune system. Gradual reduced cytotoxicity phenotype in highly variable cytotoxic T and natural killer cell types were observed during pregnancy. Reduced T- and B-cell response-related MHC-II and CD40 signaling as well as enhanced protolerance inducible costimulator and activin signaling may underlie the pregnancy-related weakening of adaptive immunity. Conversely, pro-inflammatory genes and pathways were upregulated in monocytes, possibly to compensate for the reduced T-cell response. Moreover, the transition from adaptive immune reduction to activation in late pregnancy in dendritic cells and CD4+ T cells was also detected. Notably, we proposed a novel view of the pro-aging effect of pregnancy from the perspective of immunity, and this effect may be restored postpartum. This work expands our knowledge of pregnancy immunity and may provide insights into the altered disease risks during pregnancy.

Exploring the Role of Ccn3 in Type III Cell of Mice Taste Buds

Different taste cells express unique cell-type markers, enabling researchers to distinguish them and study their functional differentiation. Using single-cell RNA-Seq of taste cells in mouse fungiform papillae, we found that Cellular Communication Network Factor 3 (Ccn3) was highly expressed in Type III taste cells but not in Type II taste cells. Ccn3 is a protein-coding gene involved in various biological processes, such as cell proliferation, angiogenesis, tumorigenesis, and wound healing. Therefore, in this study, we aimed to explore the expression and function of Ccn3 in mouse taste bud cells. Using reverse transcription polymerase chain reaction (RT-PCR), in situ hybridization, and immunohistochemistry (IHC), we confirmed that Ccn3 was predominantly expressed in Type III taste cells. Through IHC, quantitative real-time RT-PCR, gustatory nerve recordings, and short-term lick tests, we observed that Ccn3 knockout (Ccn3-KO) mice did not exhibit any significant differences in the expression of taste cell markers and taste responses compared to wild-type controls. To explore the function of Ccn3 in taste cells, bioinformatics analyses were conducted and predicted possible roles of Ccn3 in tissue regeneration, perception of pain, protein secretion, and immune response. Among them, an immune function is the most plausible based on our experimental results. In summary, our study indicates that although Ccn3 is strongly expressed in Type III taste cells, its knockout did not influence the basic taste response, but bioinformatics provided valuable insights into the possible role of Ccn3 in taste buds and shed light on future research directions.

Advancements in the Application of scRNA-Seq in Breast Research: A Review

Single-cell sequencing technology provides apparent advantages in cell population heterogeneity, allowing individuals to better comprehend tissues and organs. Sequencing technology is currently moving beyond the standard transcriptome to the single-cell level, which is likely to bring new insights into the function of breast cells. In this study, we examine the primary cell types involved in breast development, as well as achievements in the study of scRNA-seq in the microenvironment, stressing the finding of novel cell subsets using single-cell approaches and analyzing the problems and solutions to scRNA-seq. Furthermore, we are excited about the field's promising future.

Advances in integrating single-cell sequencing data to unravel the mechanism of ferroptosis in cancer

Ferroptosis, a commonly observed type of programmed cell death caused by abnormal metabolic and biochemical mechanisms, is frequently triggered by cellular stress. The occurrence of ferroptosis is predominantly linked to pathophysiological conditions due to the substantial impact of various metabolic pathways, including fatty acid metabolism and iron regulation, on cellular reactions to lipid peroxidation and ferroptosis. This mode of cell death serves as a fundamental factor in the development of numerous diseases, thereby presenting a range of therapeutic targets. Single-cell sequencing technology provides insights into the cellular and molecular characteristics of individual cells, as opposed to bulk sequencing, which provides data in a more generalized manner. Single-cell sequencing has found extensive application in the field of cancer research. This paper reviews the progress made in ferroptosis-associated cancer research using single-cell sequencing, including ferroptosis-associated pathways, immune checkpoints, biomarkers, and the identification of cell clusters associated with ferroptosis in tumors. In general, the utilization of single-cell sequencing technology has the potential to contribute significantly to the investigation of the mechanistic regulatory pathways linked to ferroptosis. Moreover, it can shed light on the intricate connection between ferroptosis and cancer. This technology holds great promise in advancing tumor-wide diagnosis, targeted therapy, and prognosis prediction.

Innovative Insights into Single-Cell Technologies and Multi-Omics Integration in Livestock and Poultry

In recent years, single-cell RNA sequencing (scRNA-seq) has marked significant strides in livestock and poultry research, especially when integrated with multi-omics approaches. These advancements provide a nuanced view into complex regulatory networks and cellular dynamics. This review outlines the application of scRNA-seq in key species, including poultry, swine, and ruminants, with a focus on outcomes related to cellular heterogeneity, developmental biology, and reproductive mechanisms. We emphasize the synergistic power of combining scRNA-seq with epigenomic, proteomic, and spatial transcriptomic data, enhancing molecular breeding precision, optimizing health management strategies, and refining production traits in livestock and poultry. The integration of these technologies offers a multidimensional approach that not only broadens the scope of data analysis but also provides actionable insights for improving animal health and productivity.

Single-cell RNA sequencing reveals heterogeneity and differential expression of the maternal-fetal interface during ICP and normal pregnancy

OBJECTIVE

Intrahepatic cholestasis of pregnancy (ICP) can cause adverse perinatal outcomes. Previous studies have demonstrated that the placenta of an ICP pregnancy differs in morphology and gene expression from the placenta of a normal pregnancy. To date, however, the genetic mechanism by which ICP affects the placenta is poorly understood. Therefore, the aim of this study was to investigate the differences in main cell types, gene signatures, cell ratio, and functional changes in the placenta between ICP and normal pregnancy.

METHODS

Single-cell RNA sequencing (scRNA-seq) technology was used to detect the gene expression of all cells at the placental maternal-fetal interface. Two individuals were analyzed - one with ICP and one without ICP. The classification of cell types was determined by a graph-based clustering algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the R software phyper () function and DAVID website. The differentially expressed genes (DEGs) encoding transcription factors (TFs) were identified using getorf and DIAMOND software.

RESULTS

We identified 14 cell types and 22 distinct cell subtypes that showed unique functional properties. Additionally, we found differences in the proportions of fibroblasts 1, helper T (Th) cells, extravillous trophoblasts, and villous cytotrophoblasts, and we observed heterogeneity of gene expression between ICP and control placentas. Furthermore, we identified 263 DEGs that belonged to TF families, including zf-C2H2, HMGI/HMGY, and Homeobox. In addition, 28 imprinted genes were preferentially expressed in specific cell types, such as PEG3 and PEG10 in trophoblasts as well as DLK1 and DIO3 in fibroblasts.

CONCLUSIONS

Our results revealed the differences in cell-type ratios, gene expression, and functional changes between ICP and normal placentas, and heterogeneity was found among cell subgroups. Hence, the imbalance of various cell types affects placental activity to varying degrees, indicating the complexity of the cell networks that form the placental tissue system, and this alteration of placental function is associated with adverse events in the perinatal period.

Single-cell atlas of healthy vocal folds and cellular function in the endothelial-to-mesenchymal transition

The vocal fold is an architecturally complex organ comprising a heterogeneous mixture of various layers of individual epithelial and mesenchymal cell lineages. Here we performed single-cell RNA sequencing profiling of 5836 cells from the vocal folds of adult Sprague-Dawley rats. Combined with immunostaining, we generated a spatial and transcriptional map of the vocal fold cells and characterized the subpopulations of epithelial cells, mesenchymal cells, endothelial cells, and immune cells. We also identified a novel epithelial-to-mesenchymal transition-associated epithelial cell subset that was mainly found in the basal epithelial layers. We further confirmed that this subset acts as intermediate cells with similar genetic features to epithelial-to-mesenchymal transition in head and neck squamous cell carcinoma. Finally, we present the complex intracellular communication network involved homeostasis using CellChat analysis. These studies define the cellular and molecular framework of the biology and pathology of the VF mucosa and reveal the functional importance of developmental pathways in pathological states in cancer.

Lung Single-Cell Transcriptomics Offers Insights into the Pulmonary Interstitial Toxicity Caused by Silica Nanoparticles

The adverse respiratory outcomes motivated by silica nanoparticles (SiNPs) exposure have received increasing attention. Herein, we aim to elucidate the interplay of diverse cell populations in the lungs and key contributors in triggering lung injuries caused by SiNPs. We conducted a subchronic respiratory exposure model of SiNPs via intratracheal instillation in Wistar rats, where rats were administered with 1.5, 3.0, or 6.0 mg/kg body weight SiNPs once a week for 12 times in total. We revealed that SiNPs caused pulmonary interstitial injury in rats by histopathological examination and pulmonary hydroxyproline determination. Further, a single-cell RNA-Seq via screening 10 457 cells in the rat lungs disclosed cell-specific responses to SiNPs and cell-to-cell interactions within the alveolar macrophages, epithelial cells, and fibroblasts from rat lungs. These disturbed responses were principally related to the dysregulation of protein homeostasis (proteostasis), accompanied by an inflammatory response in macrophages, cell death in epithelial, proliferation, and extracellular matrix deposition in fibroblast. These cell-specific responses may serve a synergistic role in the pathogenesis of pulmonary interstitial disease triggered by SiNPs. In particular, the analyses of gene interaction networks and gene-disease associations filtered out heat shock proteins (Hsps) family genes crucial to the observed pulmonary lesions caused by SiNPs. Of note, both GEO database analysis and our experiments' validation indicated that Hsps, especially Hspd1, may be a key contributor to pulmonary interstitial injury, possibly through triggering oxidative stress, immune response, and disrupting protein homeostasis. Taken together, our study provides insights into pulmonary toxic effects and underlying molecular mechanisms of SiNPs from a single-cell perspective.

Understanding the heterogeneity of natural killer cells at the maternal-fetal interface: implications for pregnancy health and disease

Natural killer (NK) cells are the most abundant leukocytes located at the maternal-fetal interface; they respond to pregnancy-related hormones and play a pivotal role in maintaining the homeostatic micro-environment during pregnancy. However, due to the high heterogeneity of NK cell subsets, their categorization has been controversial. Here, we review previous studies on uterine NK cell subsets, including the classic categorization based on surface markers, functional molecules, and developmental stages, as well as single-cell RNA sequencing-based clustering approaches. In addition, we summarize the potential pathways by which endometrial NK cells differentiate into decidual NK (dNK) cells, as well as the differentiation pathways of various dNK subsets. Finally, we compared the alterations in the NK cell subsets in various pregnancy-associated diseases, emphasizing the possible contribution of specific subsets to the development of the disease.

Single-nucleus multiomics unravels the genetic mechanisms underlying musk secretion in Chinese forest musk deer (Moschus berezovskii)

Musk secreted by the musk glands in male forest musk deer (FMD; Moschus berezovskii) is highly valued for its pharmaceutical and perfumery applications. However, the regulatory mechanisms underlying musk secretion are not well understood. This study aimed to investigate the genes and transcription factors involved in musk secretion across different periods and ages. We analyzed the musk glands of adult male FMD during the non-secretory and secretory periods, as well as juvenile and adult male FMD during the secretory period, using single-cell multiome ATAC+gene expression technique. Our analysis identified 13 cell types, including acinar cells of Types 1 and 2. Chromatin accessibility analysis and gene expression data confirmed that the genes Map3k2, Hsd17b12, and Jun are critical for musk secretion. Additionally, EHF, NR4A2, and FOXO1 proteins play crucial regulatory roles. Weighted gene co-expression network analysis (WGCNA) highlighted the importance of GnRH signaling pathway in musk secretion. Gene set enrichment analysis (GSEA) showed that the steroid hormone biosynthesis pathway is notably enriched in acinar cells. Furthermore, intercellular communication appears to influence both the initiation and maintenance of musk secretion. These findings provide valuable insights into the molecular pathways of musk secretion in FMD, offering potential avenues for increasing musk production and developing treatment for inflammation and tumors.

Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data

BACKGROUND

Gene interaction networks are graphs in which nodes represent genes and edges represent functional interactions between them. These interactions can be at multiple levels, for instance, gene regulation, protein-protein interaction, or metabolic pathways. To analyse gene interaction networks at a large scale, gene co-expression network analysis is often applied on high-throughput gene expression data such as RNA sequencing data. With the advance in sequencing technology, expression of genes can be measured in individual cells. Single-cell RNA sequencing (scRNAseq) provides insights of cellular development, differentiation and characteristics at the transcriptomic level. High sparsity and high-dimensional data structures pose challenges in scRNAseq data analysis.

RESULTS

In this study, a sparse inverse covariance matrix estimation framework for scRNAseq data is developed to capture direct functional interactions between genes. Comparative analyses highlight high performance and fast computation of Stein-type shrinkage in high-dimensional data using simulated scRNAseq data. Data transformation approaches also show improvement in performance of shrinkage methods in non-Gaussian distributed data. Zero-inflated modelling of scRNAseq data based on a negative binomial distribution enhances shrinkage performance in zero-inflated data without interference on non zero-inflated count data.

CONCLUSION

The proposed framework broadens application of graphical model in scRNAseq analysis with flexibility in sparsity of count data resulting from dropout events, high performance, and fast computational time. Implementation of the framework is in a reproducible Snakemake workflow https://github.com/calathea24/ZINBGraphicalModel and R package ZINBStein https://github.com/calathea24/ZINBStein .

Single-cell sequencing technology in skin wound healing

Skin wound healing is a complicated biological process that mainly occurs in response to injury, burns, or diabetic ulcers. It can also be triggered by other conditions such as dermatitis and melanoma-induced skin cancer. Delayed healing or non-healing after skin injury presents an important clinical issue; therefore, further explorations into the occurrence and development of wound healing at the cellular and molecular levels are necessary. Single-cell sequencing (SCS) is used to sequence and analyze the genetic messages of a single cell. Furthermore, SCS can accurately detect cell expression and gene sequences. The use of SCS technology has resulted in the emergence of new concepts pertaining to wound healing, making it an important tool for studying the relevant mechanisms and developing treatment strategies. This article discusses the application value of SCS technology, the effects of the latest research on skin wound healing, and the value of SCS technology in clinical applications. Using SCS to determine potential biomarkers for wound repair will serve to accelerate wound healing, reduce scar formation, optimize drug delivery, and facilitate personalized treatments.

Advancements in Single-Cell RNA Sequencing and Spatial Transcriptomics for Central Nervous System Disease

The incidence of central nervous system (CNS) disease has persistently increased over the last several years. There is an urgent need for effective methods to improve the cure rates of CNS disease. However, the precise molecular basis underlying the development and progression of major CNS diseases remains elusive. A complete molecular map will contribute to research on CNS disease treatment strategies. Emerging technologies such as single-cell RNA sequencing (scRNA-seq) and Spatial Transcriptomics (ST) are potent tools for exploring the molecular complexity, cell heterogeneity, and functional specificity of CNS disease. scRNA-seq and ST can provide insights into the disease at cellular and spatial transcription levels. This review presents a survey of scRNA-seq and ST studies on CNS diseases, such as chronic neurodegenerative diseases, acute CNS injuries, and others. These studies offer novel perspectives in treating and diagnosing CNS diseases by discovering new cell types or subtypes associated with the disease, proposing new pathophysiological mechanisms, uncovering novel therapeutic targets, and identifying putative biomarkers.

Single-Cell RNA Sequencing Deconstructs the Distribution of Immune Cells Within Abdominal Aortic Aneurysms in Mice

BACKGROUND

Inflammation is a key component in the development of abdominal aortic aneurysm (AAA), yet insights into the roles of immune cells and their interactions in this process are limited.

METHODS

Using single-cell RNA transcriptomic analysis, we deconstructed the CD45+ cell population in elastase-induced murine AAA at the single-cell level. We isolated each group of immune cells from murine AAA tissue at different time points and divided them into several subtypes, listed the remarkable differentially expressed genes, explored the developmental trajectories of immune cells, and demonstrated the interactions among them.

RESULTS

Our findings reveal significant differences in several immune cell subsets, including macrophages, dendritic cells, and T cells, within the AAA microenvironment compared with the normal aorta. Especially, conventional dendritic cell type 1 exclusively existed in the AAA tissue rather than the normal aortas. Via CellChat analysis, we identified several intercellular communication pathways like visfatin, which targets monocyte differentiation and neutrophil extracellular trap-mediated interaction between neutrophils and dendritic cells, which might contribute to AAA development. Some of these pathways were validated in human AAA.

CONCLUSIONS

Despite the absence of external pathogenic stimuli, AAA tissues develop a complex inflammatory microenvironment involving numerous immune cells. In-depth studies of the inflammatory network shall provide new strategies for patients with AAA.

Single-cell sequencing reveals glial cell involvement in development of neuropathic pain via myelin sheath lesion formation in the spinal cord

BACKGROUND

Neuropathic pain (NP), which results from injury or lesion of the somatosensory nervous system, is intimately associated with glial cells. The roles of microglia and astrocytes in NP have been broadly described, while studies on oligodendrocytes have largely focused on axonal myelination. The mechanisms of oligodendrocytes and their interactions with other glial cells in NP development remain uncertain.

METHODS

To explore the function of the interaction of the three glial cells and their interactions on myelin development in NP, we evaluated changes in NP and myelin morphology after a chronic constriction injury (CCI) model in mice, and used single-cell sequencing to reveal the subpopulations characteristics of oligodendrocytes, microglia, and astrocytes in the spinal cord tissues, as well as their relationship with myelin lesions; the proliferation and differentiation trajectories of oligodendrocyte subpopulations were also revealed using pseudotime cell trajectory and RNA velocity analysis. In addition, we identified chemokine ligand-receptor pairs between glial cells by cellular communication and verified them using immunofluorescence.

RESULTS

Our study showed that NP peaked on day 7 after CCI in mice, a time at which myelin lesions were present in both the spinal cord and sciatic nerve. Oligodendrocytes, microglia, and astrocytes subpopulations in spinal cord tissue were heterogeneous after CCI and all were involved in suppressing the process of immune defense and myelin production. In addition, the differentiation trajectory of oligodendrocytes involved a unidirectional lattice process of OPC-1-Oligo-9, which was arrested at the Oligo-2 stage under the influence of microglia and astrocytes. And the CADM1-CADM1, NRP1-VEGFA interactions between glial cells are enhanced after CCI and they had a key role in myelin lesions and demyelination.

CONCLUSIONS

Our study reveals the close relationship between the differentiation block of oligodendrocytes after CCI and their interaction with microglia and astrocytes-mediated myelin lesions and NP. CADM1/CADM1 and NRP-1/VEGFA may serve as potential therapeutic targets for use in the treatment of NP.

Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage

BACKGROUND

Increased oxidative stress (OS) activity following intracerebral hemorrhage (ICH) had significantly impacting patient prognosis. Identifying optimal genes associated with OS could enhance the understanding of OS after ICH.

METHODS

We employed single-cell RNA sequencing (scRNA-seq) to investigate the heterogeneity of OS across various cellular tiers following ICH, aiming to acquire biological insights into ICH. We utilized AUCell, Ucell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, to identify hub genes influencing high OS post-ICH. Furthermore, we employed four machine learning algorithms, eXtreme Gradient Boosting, Boruta, Random Forest, and Least Absolute Shrinkage and Selection Operator, to identify the optimal feature genes. To validate the accuracy of our analysis, we conducted validation in ICH animal experiments.

RESULTS

After analyzing the scRNA-seq dataset using various algorithms, we found that OS activity exhibited heterogeneity across different cellular layers following ICH, with particularly heightened activity observed in monocytes. Further integration of bulk data and machine learning algorithms revealed that ANXA2 and COTL1 were closely associated with high OS after ICH. Our animal experiments demonstrated an increase in OS expression post-ICH. Additionally, the protein expression of ANXA2 and COTL1 was significantly elevated and co-localized with microglia. Pearson correlation coefficient analysis revealed a significant correlation between ANXA2 and OS, indicating strong consistency (r = 0.84, p < 0.05). Similar results were observed for COTL1 and OS (r = 0.69, p < 0.05).

CONCLUSIONS

Following ICH, ANXA2 and COTL1 might penetrate the brain via monocytes, localize within microglia, and enhance OS activity. This might help us better understand OS after ICH.

Single-Cell RNA Sequencing Reveals a Unique Fibroblastic Subset and Immune Disorder in Lichen Sclerosus Urethral Stricture

Purpose

Lichen sclerosus urethral stricture disease (LS USD) is a refractory and progressive disease primarily affecting the anterior urethra in males. Various potential etiological factors, such as genetics, autoimmunity, infection, and exposure to infectious urine, have been suggested. However, the accurate etiology of LS in the male urethra remains unclear.

Patients and Methods

In this study, we conducted single-cell RNA sequencing to identify the transcriptional profiles of three patients with LS USD and three patients with non-LS USD. Immunofluorescence was used to confirm the single-cell sequence results.

Results

Our study revealed distinct subsets of vein endothelial cells (ECs), smooth muscle cells (SMCs), and fibroblasts (FBs) with high proportions in LS USD, contributing to the tissue microenvironment primarily involved in proinflammatory and immune responses. In particular, FBs displayed a unique subset, Fib7, which is exclusively present in LS USD, and exhibited high expression levels of SAA1 and SAA2. The accumulation of macrophages, along with the dysregulated ratios of M1/M2-like phenotype macrophages, may be engaged in the pathogenesis of LS USD. Through cell-cell communication analysis, we identified significant interactions involving CXCL8/ACKR1 and CCR7/CCL19 in LS USD. Remarkably, Fib7 exhibited exclusive communication with IL-1B macrophages through the SAA1/FPR2 receptor-ligand pair.

Conclusion

Our study provides a profound understanding of the tissue microenvironment in LS USD, which may be valuable for understanding the pathogenesis of LS USD.

Single-cell RNA sequencing technology in human spermatogenesis: Progresses and perspectives

Spermatogenesis, a key part of the spermiation process, is regulated by a combination of key cells, such as primordial germ cells, spermatogonial stem cells, and somatic cells, such as Sertoli cells. Abnormal spermatogenesis can lead to azoospermia, testicular tumors, and other diseases related to male infertility. The application of single-cell RNA sequencing (scRNA-seq) technology in male reproduction is gradually increasing with its unique insight into deep mining and analysis. The data cover different periods of neonatal, prepubertal, pubertal, and adult stages. Different types of male infertility diseases including obstructive and non-obstructive azoospermia (NOA), Klinefelter Syndrome (KS), Sertoli Cell Only Syndrome (SCOS), and testicular tumors are also covered. We briefly review the principles and application of scRNA-seq and summarize the research results and application directions in spermatogenesis in different periods and pathological states. Moreover, we discuss the challenges of applying this technology in male reproduction and the prospects of combining it with other technologies.

Prognostic model of fibroblasts in idiopathic pulmonary fibrosis by combined bulk and single-cell RNA-sequencing

Background

Fibroblasts play an important role in the development of idiopathic pulmonary fibrosis (IPF).

Methods

We employed single-cell RNA-sequencing data obtained from the Gene Expression Omnibus database to perform cell clustering and annotation analyses. We then performed secondary clustering of fibroblasts and conducted functional enrichment and cell trajectory analyses of the two newly defined fibroblast subtypes. Bulk RNA-sequencing data were used to perform consensus clustering and weighted gene co-expression network analysis. We constructed a fibroblast-related prognostic model using least absolute shrinkage, selection operator regression, and Cox regression analysis. The prognostic model was validated using a validation dataset. Immune infiltration and functional enrichment analyses were conducted for patients in the high- and low-risk IPF groups.

Results

We characterized two fibroblast subtypes that are active in IPF (F3+ and ROBO2+). Using fibroblast-related genes, we identified five genes (CXCL14, TM4SF1, CYTL1, SOD3, and MMP10) for the prognostic model. The area under the curve values of our prognostic model were 0.852, 0.859, and 0.844 at one, two, and three years in the training set, and 0.837, 0.758, and 0.821 at one, two, and three years in the validation set, respectively.

Conclusion

This study annotates and characterizes different subtypes of fibroblasts in IPF.

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 technologies in psoriasis

Psoriasis is a chronic and recurrent inflammatory skin disorder. The primary manifestation of psoriasis arises from disturbances in the cutaneous immune microenvironment, but the specific functions of the cellular components within this microenvironment remain unknown. Recent advancements in single-cell technologies have enabled the detection of multi-omics at the level of individual cells, including single-cell transcriptome, proteome, and metabolome, which have been successfully applied in studying autoimmune diseases, and other pathologies. These techniques allow the identification of heterogeneous cell clusters and their varying contributions to disease development. Considering the immunological traits of psoriasis, an in-depth exploration of immune cells and their interactions with cutaneous parenchymal cells can markedly advance our comprehension of the mechanisms underlying the onset and recurrence of psoriasis. In this comprehensive review, we present an overview of recent applications of single-cell technologies in psoriasis, aiming to improve our understanding of the underlying mechanisms of this disorder.

HyperCell: Advancing Cell Type Classification with Hyperdimensional Computing

Single-cell RNA sequencing (scRNA-seq) has revolutionized genomics, enabling the exploration of cellular heterogeneity at an unprecedented resolution. However, scRNA-seq data poses challenges, including high dimensionality, inherent noise, and sparse gene expression. In this paper, we propose a novel approach, utilizing hyperdimensional computing, to enhance cell type classification accuracy in scRNA-seq datasets. We use the QuantHD method for high-dimensional hypervector encoding and iterative training. Experiments on diverse datasets subjected to both split by batch and random split settings demonstrate the superiority of our proposed model in handling noise and outperforming established classification methods such as XGBoost, Seurat reference mapping, and scANVI. Our findings highlight the potential of hyperdimensional computing to advance single-cell data analysis, yielding deep insights into cellular dynamics, tissue functions, and disease mechanisms. This work paves the way for more accurate cell type annotation and brings new opportunities for biomedical research and personalized medicine.

Advance and Application of Single-cell Transcriptomics in Auditory Research

Hearing loss and deafness, as a worldwide disability disease, have been troubling human beings. However, the auditory organ of the inner ear is highly heterogeneous and has a very limited number of cells, which are largely uncharacterized in depth. Recently, with the development and utilization of single-cell RNA sequencing (scRNA-seq), researchers have been able to unveil the complex and sophisticated biological mechanisms of various types of cells in the auditory organ at the single-cell level and address the challenges of cellular heterogeneity that are not resolved through by conventional bulk RNA sequencing (bulk RNA-seq). Herein, we reviewed the application of scRNA-seq technology in auditory research, with the aim of providing a reference for the development of auditory organs, the pathogenesis of hearing loss, and regenerative therapy. Prospects about spatial transcriptomic scRNA-seq, single-cell based genome, and Live-seq technology will also be discussed.

Persistent transcriptional changes in cardiac adaptive immune cells following myocardial infarction: New evidence from the re-analysis of publicly available single cell and nuclei RNA-sequencing data sets

INTRODUCTION

Chronic immunopathology contributes to the development of heart failure after a myocardial infarction. Both T and B cells of the adaptive immune system are present in the myocardium and have been suggested to be involved in post-MI immunopathology.

METHODS

We analyzed the B and T cell populations isolated from previously published single cell RNA-sequencing data sets (PMID: 32130914, PMID: 35948637, PMID: 32971526 and PMID: 35926050), of the mouse and human heart, using differential expression analysis, functional enrichment analysis, gene regulatory inferences, and integration with autoimmune and cardiovascular GWAS.

RESULTS

Already at baseline, mature effector B and T cells are present in the human and mouse heart, having increased activity in transcription factors maintaining tolerance (e.g. DEAF1, JDP2, SPI-B). Following MI, T cells upregulate pro-inflammatory transcript levels (e.g. Cd11, Gzmk, Prf1), while B cells upregulate activation markers (e.g. Il6, Il1rn, Ccl6) and collagen (e.g. Col5a2, Col4a1, Col1a2). Importantly, pro-inflammatory and fibrotic transcription factors (e.g. NFKB1, CREM, REL) remain active in T cells, while B cells maintain elevated activity in transcription factors related to immunoglobulin production (e.g. ERG, REL) in both mouse and human post-MI hearts. Notably, genes differentially expressed in post-MI T and B cells are associated with cardiovascular and autoimmune disease.

CONCLUSION

These findings highlight the varied and time-dependent dynamic roles of post-MI T and B cells. They appear ready-to-go and are activated immediately after MI, thus participate in the acute wound healing response. However, they subsequently remain in a state of pro-inflammatory activation contributing to persistent immunopathology.

A bibliometric study of the intellectual base and global research hotspots for single-cell sequencing [2009-2022] in breast cancer

Background

Breast cancer is the most widespread malignant tumor worldwide. Single-cell sequencing technology offers novel insights and methods to understand the onset, progression, and treatment of tumors. Nevertheless, there is currently an absence of a thorough and unbiased report on the comprehensive research status of single-cell sequencing in breast cancer. This study seeks to summarize and quantify the dynamics and trends of research on breast cancer single-cell sequencing by bibliometric analysis.

Methods

Research articles and reviews related to breast cancer single-cell sequencing were selected from the WoSCC database. Visualization of data regarding countries, institutions, authors, references, and keywords was performed by CiteSpace and VOSviewer software.

Results

583 articles and reviews were analyzed in this study. The quantity of publications related to breast cancer single-cell sequencing has been increasing annually. These studies originate from 302 institutions in 46 countries, with YMAX S WICHA producing the most publications and WANG Y being the most cited author. Nature Communications is the most researched journal, while Nature holds the highest number of citations. These journals predominantly cover topics in the molecular/biological/immunological fields. Moreover, an analysis of reference and keyword bursts revealed that current research trends in this area are primarily centered on "clonal evolution," "tumor microenvironment," and "immunotherapy."

Conclusion

Breast cancer single-cell sequencing is a rapidly growing area of scientific interest. Future research requires more frequent and in-depth collaborations among countries, institutions, and authors. Furthermore, "clonal evolution," "tumor microenvironment," and "immunotherapy" are likely to become major focal points in upcoming research on breast cancer single-cell sequencing.

Optimal sequencing budget allocation for trajectory reconstruction of single cells

BACKGROUND

Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations.

RESULTS

Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.

Macrophage migration inhibitory factor mediates skin aging via CD74: Insights from single-cell and bulk RNA sequencing data

Cell-cell communication is crucial for regulating signaling and cellular function. However, the precise cellular and molecular changes remain poorly understood in skin aging. Based on single-cell and bulk RNA data, we explored the role of cell-cell ligand-receptor interaction in skin aging. We found that the macrophage migration inhibitory factor (MIF)/CD74 ligand-receptor complex was significantly upregulatedin aged skin, showing the predominant paracrine effect of keratinocytes on fibroblasts. Enrichment analysis and in vitro experiment revealed a close association of the activation of the MIF/CD74 with inflammatory pathways and immune response. Mechanistically, MIF/CD74 could significantly inhibit PPARγ protein, which thus significantly increased the degree of fibroblast senescence, and significantly up-regulated the expression of senescence-associated secretory phenotype (SASP) factors and FOS gene. Therefore, our study reveals that MIF/CD74 inhibits the activation of the PPAR signaling pathway, subsequently inducing the production of SASP factors and the upregulation of FOS expression, ultimately accelerating fibroblast senescence.

Integrated Analysis Construct a Tumor-Associated Macrophage Novel Signature with Promising Implications in Predicting the Prognosis and Immunotherapeutic Response of Gastric Cancer Patients

BACKGROUND

Gastric cancer (GC) remains one of the most prevalent malignant tumors worldwide. At present, tumor-associated macrophages (TAMs) are essential in the progression, metastasis, and drug resistance of tumors. Therefore, TAMs can be a crucial target for tumor treatment.

AIMS

We intended to investigate the TAM characteristics in GC and develop a risk signature based on TAM to predict the prognosis of GC patients.

METHODS

The single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data were acquired from a publicly available database. We utilized the Seurat pipeline to process the scRNA-seq data and determine TAM cell types using marker genes. Univariate Cox regression analysis was utilized to examine TAM-related prognostic genes, and then we employed Lasso-Cox regression analysis, and Multivariate Cox regression analysis established a novel risk profile to forecast the clinical value of the model with a new nomogram combining risk profiles and clinicopathological characteristics.

RESULTS

The current study employed scRNA-seq data to identify five TAM clusters in GC, among which four were significantly associated with GC prognosis. Accordingly, we further developed a TAM-related risk signature utilizing nine genes. After evaluation, our model accurately predicted the prognosis of gastric cancer. Generally, GC patients with low TAMS scores exhibited a more favorable prognosis, greater benefits from immunotherapy, and higher levels of immune cell infiltration.

CONCLUSIONS

The prognosis of GC can be effectively predicted by TAM-based risk signatures, and the signature may provide a new perspective for comprehensively guiding clinical diagnosis, prediction, and immunotherapy for gastric cancer.

Advancing skeletal health and disease research with single-cell RNA sequencing

Orthopedic conditions have emerged as global health concerns, impacting approximately 1.7 billion individuals worldwide. However, the limited understanding of the underlying pathological processes at the cellular and molecular level has hindered the development of comprehensive treatment options for these disorders. The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized biomedical research by enabling detailed examination of cellular and molecular diversity. Nevertheless, investigating mechanisms at the single-cell level in highly mineralized skeletal tissue poses technical challenges. In this comprehensive review, we present a streamlined approach to obtaining high-quality single cells from skeletal tissue and provide an overview of existing scRNA-seq technologies employed in skeletal studies along with practical bioinformatic analysis pipelines. By utilizing these methodologies, crucial insights into the developmental dynamics, maintenance of homeostasis, and pathological processes involved in spine, joint, bone, muscle, and tendon disorders have been uncovered. Specifically focusing on the joint diseases of degenerative disc disease, osteoarthritis, and rheumatoid arthritis using scRNA-seq has provided novel insights and a more nuanced comprehension. These findings have paved the way for discovering novel therapeutic targets that offer potential benefits to patients suffering from diverse skeletal disorders.

SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition

Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications

Background

Prostate cancer (PCa) is one of the most threatening health problems for the elderly males. However, our understanding of the disease has been limited by the research technology for a long time. Recently, the maturity of sequencing technology and omics studies has been accelerating the studies of PCa, establishing themselves as an essential impetus in this field.

Methods

We assessed Web of Science (WoS) database for publications of sequencing and omics studies in PCa on July 3rd, 2023. Bibliometrix was used to conduct ulterior bibliometric analysis of countries/affiliations, authors, sources, publications, and keywords. Subsequently, purposeful large amounts of literature reading were proceeded to analyze research hotspots in this field.

Results

3325 publications were included in the study. Research associated with sequencing and omics studies in PCa had shown an obvious increase recently. The USA and China were the most productive countries, and harbored close collaboration. CHINNAIYAN AM was identified as the most influential author, and CANCER RESEARCH exhibited huge impact in this field. Highly cited publications and their co-citation relationships were used to filtrate literatures for subsequent literature reading. Based on keyword analysis and large amounts of literature reading, 'the molecular pathogenesis of PCa' and 'the clinical application of sequencing and omics studies in PCa' were summarized as two research hotspots in the field.

Conclusion

Sequencing technology had a deep impact on the studies of PCa. Sequencing and omics studies in PCa helped researchers reveal the molecular pathogenesis, and provided new possibilities for the clinical practice of PCa.

scTPC: a novel semisupervised deep clustering model for scRNA-seq data

MOTIVATION

Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging.

RESULTS

This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework.

AVAILABILITY AND IMPLEMENTATION

scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780.