Single-cell RNA sequencing: Technical advancements and biological applications

Sanguisorba officinalis ethyl acetate extract attenuates ulcerative colitis through inhibiting PI3K-AKT/NF-κB/ STAT3 pathway uncovered by single-cell RNA sequencing

BACKGROUND

Ulcerative colitis (UC) accounts for the untreatable illness nowadays. Bloody stools are the primary symptom of UC, and the first-line drugs used to treat UC are associated with several drawbacks and negative side effects. S. officinalis has long been used as a medicine to treat intestinal infections and bloody stools. However, what the precise molecular mechanism, the exact etiology, and the material basis of the disease remain unclear.

PURPOSE

This work aimed to comprehensively explore pharmacological effects as well as molecular mechanisms underlying the active fraction of S. officinalis, and to produce a comprehensive and brand-new guideline map of its chemical base and mechanism of action.

METHODS

First, different polarity S. officinalis extracts were orally administered to the DSS-induced UC model mice for the sake of investigating its active constituents. Using the UPLC-orbitrap high-resolution mass spectrometry (UPLC-Q-Orbitrap-HRMS) technique, the most active S. officinalis (S. officinalis ethyl acetate fraction, SOEA) extract was characterized. Subsequently, the effectiveness of its active fraction on UC was evaluated through phenotypic observation (such as weight loss, colon length, and stool characteristics), and histological examination of pathological injuries, mRNA and protein expression. Cell profile, cell-cell interactions and molecular mechanisms of SOEA in different cell types of the colon tissue from UC mice were described using single-cell RNA sequencing (scRNA-seq). As a final step, the molecular mechanisms were validated by appropriate molecular biological methods.

RESULTS

For the first time, this study revealed the significant efficacy of SOEA in the treatment of UC. SOEA reduced DAI and body weight loss, recovered the colon length, and mitigated colonic pathological injuries along with mucosal barrier by promoting goblet cell proliferation. Following treatment with SOEA, inflammatory factors showed decreased mRNA and protein expression. SOEA restored the dynamic equilibrium of cell profile and cell-cell interactions in colon tissue. All of these results were attributed to the ability of SOEA to inhibit the PI3K-AKT/NF-κB/STATAT pathway.

CONCLUSIONS

By integrating the chemical information of SOEA derived from UPLC-Q-Orbitrap-HRMS with single-cell transcriptomic data extracted from scRNA-seq, this study demonstrates that SOEA exerts the therapeutic effect through suppressing PI3K-AKT/NF-B/STAT3 pathway to improve clinical symptoms, inflammatory response, mucosal barrier, and intercellular interactions in UC, and effectively eliminates the interference of cellular heterogeneity.

Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing

Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis.

Single-cell landscape of immune cells in human livers affected by HBV-related cirrhosis

Background & Aims

HBV infection is one of the leading causes of liver cirrhosis. However, the immune microenvironment in patients with HBV cirrhosis remains elusive.

Methods

Single-cell RNA sequencing was used to analyse the transcriptomes of 76,210 immune cells in the livers of six healthy individuals and in five patients with HBV cirrhosis.

Results

Patients with HBV cirrhosis have a unique immune ecosystem characterised by an accumulation of macrophage-CD9/IL18, macrophage-C1QA, NK Cell-JUNB, CD4+ T cell-IL7R, and a loss of B cell-IGLC1 clusters. Furthermore, our analysis predicted enhanced cell communication between myeloid cells and all immune cells in patients with HBV-related cirrhosis. Pseudo-time analysis of myeloid cells, natural killer (NK) cells, and B cells demonstrated a significant accumulation of mature cells and a depletion of naive cells in HBV cirrhosis. In addition, we observed an increase in antigen processing and presentation capacities in myeloid cells in patients with HBV cirrhosis, whereas NK cell-mediated cytotoxicity was substantially reduced.

Conclusions

Our results provide valuable insight into the immune landscape of HBV cirrhosis, suggesting that HBV cirrhosis is associated with the accumulation of activated myeloid cells and impaired cytotoxicity in NK cells.

Impact and implications

The absence of single-cell transcriptome profiling of immune cells in HBV cirrhosis hinders our understanding of the underlying mechanisms driving disease progression. To address this knowledge gap, our study unveils a distinctive immune ecosystem in HBV cirrhosis and represents a crucial advancement in elucidating the impact of the immune milieu on the development of this condition. These findings constitute significant strides towards the identification of more effective therapeutic approaches for HBV cirrhosis and are relevant for healthcare professionals, researchers, and pharmaceutical developers dedicated to combating this disease.

Application of single-cell sequencing to the research of tumor microenvironment

Single-cell sequencing is a technique for detecting and analyzing genomes, transcriptomes, and epigenomes at the single-cell level, which can detect cellular heterogeneity lost in conventional sequencing hybrid samples, and it has revolutionized our understanding of the genetic heterogeneity and complexity of tumor progression. Moreover, the tumor microenvironment (TME) plays a crucial role in the formation, development and response to treatment of tumors. The application of single-cell sequencing has ushered in a new age for the TME analysis, revealing not only the blueprint of the pan-cancer immune microenvironment, but also the heterogeneity and differentiation routes of immune cells, as well as predicting tumor prognosis. Thus, the combination of single-cell sequencing and the TME analysis provides a unique opportunity to unravel the molecular mechanisms underlying tumor development and progression. In this review, we summarize the recent advances in single-cell sequencing and the TME analysis, highlighting their potential applications in cancer research and clinical translation.

Single-Cell RNA-Sequencing in Astrocyte Development, Heterogeneity, and Disease

Astrocytes are the most plentiful cell type in the central nervous system (CNS) and perform complicated functions in health and disease. It is obvious that different astrocyte subpopulations, or activation states, are relevant with specific genomic programs and functions. In recent years, the emergence of new technologies such as single-cell RNA sequencing (scRNA-seq) has made substantial advance in the characterization of astrocyte heterogeneity, astrocyte developmental trajectory, and its role in CNS diseases which has had a significant impact on neuroscience. In this review, we present an overview of astrocyte development, heterogeneity, and its essential role in the physiological and pathological environments of the CNS. We focused on the critical role of single-cell sequencing in revealing astrocyte development, heterogeneity, and its role in different CNS diseases.

Integrative single-cell transcriptomic investigation unveils long non-coding RNAs associated with localized cellular inflammation in psoriasis

Psoriasis is a complex, chronic autoimmune disorder predominantly affecting the skin. Accumulating evidence underscores the critical role of localized cellular inflammation in the development and persistence of psoriatic skin lesions, involving cell types such as keratinocytes, mesenchymal cells, and Schwann cells. However, the underlying mechanisms remain largely unexplored. Long non-coding RNAs (lncRNAs), known to regulate gene expression across various cellular processes, have been particularly implicated in immune regulation. We utilized our neural-network learning pipeline to integrate 106,675 cells from healthy human skin and 79,887 cells from psoriatic human skin. This formed the most extensive cell transcriptomic atlas of human psoriatic skin to date. The robustness of our reclassified cell-types, representing full-layer zonation in human skin, was affirmed through neural-network learning-based cross-validation. We then developed a publicly available website to present this integrated dataset. We carried out analysis for differentially expressed lncRNAs, co-regulated gene patterns, and GO-bioprocess enrichment, enabling us to pinpoint lncRNAs that modulate localized cellular inflammation in psoriasis at the single-cell level. Subsequent experimental validation with skin cell lines and primary cells from psoriatic skin confirmed these lncRNAs' functional role in localized cellular inflammation. Our study provides a comprehensive cell transcriptomic atlas of full-layer human skin in both healthy and psoriatic conditions, unveiling a new regulatory mechanism that governs localized cellular inflammation in psoriasis and highlights the therapeutic potential of lncRNAs in this disease's management.

scRNA-seq Reveals the Mechanism of Fatty Acid Desaturase 2 Mutation to Repress Leaf Growth in Peanut (Arachis hypogaea L.)

Fatty Acid Desaturase 2 (FAD2) controls the conversion of oleic acids into linoleic acids. Mutations in FAD2 not only increase the high-oleic content, but also repress the leaf growth. However, the mechanism by which FAD2 regulates the growth pathway has not been elucidated in peanut leaves with single-cell resolution. In this study, we isolated fad2 mutant leaf protoplast cells to perform single-cell RNA sequencing. Approximately 24,988 individual cells with 10,249 expressed genes were classified into five major cell types. A comparative analysis of 3495 differentially expressed genes (DEGs) in distinct cell types demonstrated that fad2 inhibited the expression of the cytokinin synthesis gene LOG in vascular cells, thereby repressing leaf growth. Further, pseudo-time trajectory analysis indicated that fad2 repressed leaf cell differentiation, and cell-cycle evidence displayed that fad2 perturbed the normal cell cycle to induce the majority of cells to drop into the S phase. Additionally, important transcription factors were filtered from the DEG profiles that connected the network involved in high-oleic acid accumulation (WRKY6), activated the hormone pathway (WRKY23, ERF109), and potentially regulated leaf growth (ERF6, MYB102, WRKY30). Collectively, our study describes different gene atlases in high-oleic and normal peanut seedling leaves, providing novel biological insights to elucidate the molecular mechanism of the high-oleic peanut-associated agronomic trait at the single-cell level.

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

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

ScRNA-seq revealed disruption in CD8+ NKG2A+ natural killer T cells in patients after liver transplantation and immunosuppressive therapy

BACKGROUND

Liver transplantation (LT) offers a good survival chance for both the patient in short or long term, but still faces many challenges in the treatment of LT, such as the side effects associated with long-term immunosuppression, which is one of the side effects that occurs in most patients. However, the dynamics of the cellular immune system composition over time during immune tolerance to LT after immunosuppressive therapy are not known.

METHODS

Using single-cell transcriptome sequencing, we analyzed five peripheral blood samples (one normal individual and four patients who underwent LT and received immunosuppressive therapy for 2 months, 1 year, 3 years, and 7 years, respectively) for immune cell composition and gene expression.

RESULTS

A total of 17,462 peripheral blood mononuclear cells were acquired from a normal individual without LT and patients who underwent LT and received immunosuppressive therapy for 2 months, 1 year, 3 years, and 7 years, respectively. A total of 24 cell clusters were obtained and categorized into four different cell types based on gene expression characteristics as follows: eight clusters of T cells, two clusters of B cells, two clusters of neutrophils, two clusters of monocytes, natural killer cells, and natural killer T (NKT) cells (n = 4), and six other cell clusters. Cell subset analysis, pseudotime analysis, and intercellular communication analysis revealed that the CD8+ NKT cells specifically expressed NKG2A (KLRC1, CD159A), which may be an important cell group for CD8+ NKG2A+ NKT cells in LT, thereby highlighting the heterogeneity and functional diversity in patients who undergo LT.

CONCLUSIONS

We comprehensively analyzed single-cell RNA sequencing data from a normal individual and patients who underwent LT and elucidated the mechanism underlying the development of immune tolerance in LT. CD8+ NKT cells specifically expressing KLRC1 play a crucial role in LT, and dynamic monitoring of these cells may provide novel avenues for the diagnosis and treatment of LT-related immune rejection.

A novel f -divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f -divergence based generative adversarial imputation method, called sc- f GAIN, for the scRNA-seq data imputation. Our studies identify four f -divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc- f GAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc- f GAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation bias. The flexibility offered by the f -divergence allows the sc- f GAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

Advances in single-cell RNA sequencing and its applications in cancer research

Cancers are a group of heterogeneous diseases characterized by the acquisition of functional capabilities during the transition from a normal to a neoplastic state. Powerful experimental and computational tools can be applied to elucidate the mechanisms of occurrence, progression, metastasis, and drug resistance; however, challenges remain. Bulk RNA sequencing techniques only reflect the average gene expression in a sample, making it difficult to understand tumor heterogeneity and the tumor microenvironment. The emergence and development of single-cell RNA sequencing (scRNA-seq) technologies have provided opportunities to understand subtle changes in tumor biology by identifying distinct cell subpopulations, dissecting the tumor microenvironment, and characterizing cellular genomic mutations. Recently, scRNA-seq technology has been increasingly used in cancer studies to explore tumor heterogeneity and the tumor microenvironment, which has increased the understanding of tumorigenesis and evolution. This review summarizes the basic processes and development of scRNA-seq technologies and their increasing applications in cancer research and clinical practice.

Identification of Functional Modules and Key Pathways Associated with Innervation in Graft Bone-CGRP Regulates the Differentiation of Bone Marrow Mesenchymal Stem Cells via p38 MAPK and Wnt6/β-Catenin

Bone resorption occurs after bone grafting, however, contemporaneous reconstruction of the innervation of the bone graft is a potential treatment to maintain the bone mass of the graft. The innervation of bone is an emerging research topic. To understand the potential molecular mechanisms of bone innervation after bone grafting, we collected normal iliac bone tissue as well as bone grafts with or without innervation from nine patients 1 year after surgery and performed RNA sequencing. We identified differentially expressed genes) from these samples and used the gene ontology and Kyoto Encyclopedia of Genes and Genomes databases for functional enrichment and signaling pathway analysis. In parallel, we established protein-protein interaction networks to screen functional modules. Based on bioinformatic results, we validated in vitro the osteogenic differentiation potential of rat bone marrow mesenchymal stem cells (BMMSCs) after calcitonin gene-related peptide (CGRP) stimulation and the expression of p38 MAPK and Wnt6/β-catenin pathways during osteogenesis. Our transcriptome analysis of bone grafts reveals functional modules and signaling pathways of innervation which play a vital role in the structural and functional integration of the bone graft. Simultaneously, we demonstrate that CGRP regulates the differentiation of BMMSCs through p38 MAPK and Wnt6/β-catenin.

Dynamic thresholding and tissue dissociation optimization for CITE-seq identifies differential surface protein abundance in metastatic melanoma

Multi-omics profiling by CITE-seq bridges the RNA-protein gap in single-cell analysis but has been largely applied to liquid biopsies. Applying CITE-seq to clinically relevant solid biopsies to characterize healthy tissue and the tumor microenvironment is an essential next step in single-cell translational studies. In this study, gating of cell populations based on their transcriptome signatures for use in cell type-specific ridge plots allowed identification of positive antibody signals and setting of manual thresholds. Next, we compare five skin dissociation protocols by taking into account dissociation efficiency, captured cell type heterogeneity and recovered surface proteome. To assess the effect of enzymatic digestion on transcriptome and epitope expression in immune cell populations, we analyze peripheral blood mononuclear cells (PBMCs) with and without dissociation. To further assess the RNA-protein gap, RNA-protein we perform codetection and correlation analyses on thresholded protein values. Finally, in a proof-of-concept study, using protein abundance analysis on selected surface markers in a cohort of healthy skin, primary, and metastatic melanoma we identify CD56 surface marker expression on metastatic melanoma cells, which was further confirmed by multiplex immunohistochemistry. This work provides practical guidelines for processing and analysis of clinically relevant solid tissue biopsies for biomarker discovery.

Single-cell RNA sequencing reveals the transcriptomic characteristics of peripheral blood mononuclear cells in hepatitis B vaccine non-responders

The emergence of a vaccine against hepatitis B has proven to be an important milestone in the prevention of this disease; however, 5%-10% of vaccinated individuals do not generate an immune response to the vaccine, and its molecular mechanism has not been clarified. In this study, single-cell RNA sequencing was performed on peripheral blood mononuclear cells (PBMCs) from three volunteers with a high immune response (HR) and three with no immune response (NR) to the hepatitis B vaccine. We found that the antigen-presenting activity scores of various antigen-presenting cells, the mitogen-activated protein kinase (MAPK) pathway activity scores of naive B cells, and the cell activity scores of three types of effector T cells were significantly decreased, whereas the cytotoxicity scores of CD3highCD16lowKLRG1high natural killer T (NKT) cells were significantly increased in the NR group compared with those in the HR group. Additionally, the expression levels of some classical molecules associated with distinct signaling pathways-including HLA-B, HLA-DRB5, BLNK, BLK, IL4R, SCIMP, JUN, CEBPB, NDFIP1, and TXNIP-were significantly reduced in corresponding subsets of PBMCs from the NR group relative to those of the HR group. Furthermore, the expression of several cytotoxicity-related effector molecules, such as GNLY, NKG7, GZMB, GZMM, KLRC1, KLRD1, PRF1, CST7, and CTSW, was significantly higher in CD3highCD16lowKLRG1high NKT cells derived from non-responders. Our study provides a molecular basis for the lack of response to the hepatitis B vaccine, including defective antigen presentation, decreased T cell activity, and reduced IL-4 secretion, as well as novel insight into the role of NKT cells in the immune response to the hepatitis B vaccine.

Heterogeneity of the tumor immune microenvironment and clinical interventions

The tumor immune microenvironment (TIME) is broadly composed of various immune cells, and its heterogeneity is characterized by both immune cells and stromal cells. During the course of tumor formation and progression and anti-tumor treatment, the composition of the TIME becomes heterogeneous. Such immunological heterogeneity is not only present between populations but also exists on temporal and spatial scales. Owing to the existence of TIME, clinical outcomes can differ when a similar treatment strategy is provided to patients. Therefore, a comprehensive assessment of TIME heterogeneity is essential for developing precise and effective therapies. Facilitated by advanced technologies, it is possible to understand the complexity and diversity of the TIME and its influence on therapy responses. In this review, we discuss the potential reasons for TIME heterogeneity and the current approaches used to explore it. We also summarize clinical intervention strategies based on associated mechanisms or targets to control immunological heterogeneity.

Optimization of method for achieving a single-cell suspension from mouse corneas

The single-cell RNA-sequencing (scRNA-seq) technique is used to explore the biological characteristics of tissues under pathological and physiological conditions that include certain chronic eye diseases. Harvesting of single-cell suspensions is one challenge inherent to scRNA-seq procedures. This study aimed to use an optimized method to digest a whole mouse cornea to harvest single-cell suspensions. We utilized five different mouse cornea digestion methods to obtain single-cell suspensions: (1) 5 dissected mouse corneas were cut into pieces (∼0.5 mm) and digested in trypsin for 10 min, and this digestion was repeated for 10 cycles; (2) 5 dissected mouse corneas were cut into pieces and incubated with 5 mg/ml collagenase A at 37 °C for 1h and then further digested in trypsin at 37 °C for 10 min; (3) used the same approach as that used in method 2, but the second digestion step was performed in TrypLE for 20 min; (4) used the same approach as that used in method 2, but the concentration of collagenase A was 2 mg/ml and the incubation time was 2h; (5) used the same approach as that used in method 3, but the corneas were incubated in 2 mg/ml collagenase A at 37 °C for 2h. Trypan blue staining was used to calculate the cell viability and agglomeration rate. The cell types and percentages were determined using immunofluorescence staining. RNA integrity number (RIN) was measured by Agilent 2100. Method 1 showed the lowest cell yield (0.375 × 10⁵), epithelial cell percentage, and less than 70% cell viability, thus not a proper protocol. Method 2 showed the highest cell viability (over 90%), percentage of single-cell (89.53%), and high cell quantity (1.05 × 10⁵). Method 3 had a significantly lower cell viability (55.30%). Cell agglomeration rates of method 4 and 5 reached up to 20% and 13%, and with lower cell viability (72.51%, 59.87%, respectively) and decreased epithelial cell rate compared to method 2 and 3. The results suggest that method 2 (5 mg/ml collagenase A and trypsin) is a preferred protocol for digesting mouse cornea to obtain single-cell suspension which achieves the criterion of single-cell RNA sequencing.

Polysaccharides and Structural Proteins as Components in Three-Dimensional Scaffolds for Breast Cancer Tissue Models: A Review

Breast cancer is the most common cancer among women, and even though treatments are available, efficiency varies with the patients. In vitro 2D models are commonly used to develop new treatments. However, 2D models overestimate drug efficiency, which increases the failure rate in later phase III clinical trials. New model systems that allow extensive and efficient drug screening are thus required. Three-dimensional printed hydrogels containing active components for cancer cell growth are interesting candidates for the preparation of next generation cancer cell models. Macromolecules, obtained from marine- and land-based resources, can form biopolymers (polysaccharides such as alginate, chitosan, hyaluronic acid, and cellulose) and bioactive components (structural proteins such as collagen, gelatin, and silk fibroin) in hydrogels with adequate physical properties in terms of porosity, rheology, and mechanical strength. Hence, in this study attention is given to biofabrication methods and to the modification with biological macromolecules to become bioactive and, thus, optimize 3D printed structures that better mimic the cancer cell microenvironment. Ink formulations combining polysaccharides for tuning the mechanical properties and bioactive polymers for controlling cell adhesion is key to optimizing the growth of the cancer cells.

A fibroblast-associated signature predicts prognosis and immunotherapy in esophageal squamous cell cancer

Background

Current paradigms of anti-tumor therapies are not qualified to evacuate the malignancy ascribing to cancer stroma's functions in accelerating tumor relapse and therapeutic resistance. Cancer-associated fibroblasts (CAFs) has been identified significantly correlated with tumor progression and therapy resistance. Thus, we aimed to probe into the CAFs characteristics in esophageal squamous cancer (ESCC) and construct a risk signature based on CAFs to predict the prognosis of ESCC patients.

Methods

The GEO database provided the single-cell RNA sequencing (scRNA-seq) data. The GEO and TCGA databases were used to obtain bulk RNA-seq data and microarray data of ESCC, respectively. CAF clusters were identified from the scRNA-seq data using the Seurat R package. CAF-related prognostic genes were subsequently identified using univariate Cox regression analysis. A risk signature based on CAF-related prognostic genes was constructed using Lasso regression. Then, a nomogram model based on clinicopathological characteristics and the risk signature was developed. Consensus clustering was conducted to explore the heterogeneity of ESCC. Finally, PCR was utilized to validate the functions that hub genes play on ESCC.

Results

Six CAF clusters were identified in ESCC based on scRNA-seq data, three of which had prognostic associations. A total of 642 genes were found to be significantly correlated with CAF clusters from a pool of 17080 DEGs, and 9 genes were selected to generate a risk signature, which were mainly involved in 10 pathways such as NRF1, MYC, and TGF-Beta. The risk signature was significantly correlated with stromal and immune scores, as well as some immune cells. Multivariate analysis demonstrated that the risk signature was an independent prognostic factor for ESCC, and its potential in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the CAF-based risk signature and clinical stage was developed, which exhibited favorable predictability and reliability for ESCC prognosis prediction. The consensus clustering analysis further confirmed the heterogeneity of ESCC.

Conclusion

The prognosis of ESCC can be effectively predicted by CAF-based risk signatures, and a comprehensive characterization of the CAF signature of ESCC may aid in interpreting the response of ESCC to immunotherapy and offer new strategies for cancer treatment.

The combination therapy of isomucronulatol 7-O-beta-glucoside (IMG) and CEP-9722 targeting ferroptosis-related biomarkers in non-small cell lung cancer (NSCLC)

BACKGROUND

NSCLC is a malignant tumor with a high incidence. Ferroptosis presents an essential function in regulating carcinogenesis and tumor progression. However, the ferroptosis-associated prognostic model based on single-cell sequencing of NSCLC remains unexplored. Our study aims to establish a potential predictive model for NSCLC patients and provide available targeted drugs for clinical treatment.

METHODS

The data on NSCLC patients were collected from TCGA and GEO databases to analyze their gene expression profiles. ConsensusCluster was adopted to divide the patients into different groups based on ferroptosis-related genes. Then, the univariable Cox and LASSO analyses were applied to data analysis and model establishment. Single-cell analysis was used to explore the risk score genes in different cell populations and states. The protein levels of these genes were also investigated through the HPA database. Drug sensitivity was evaluated in CellMiner database. CCK8 and colony formation assays were performed to validate potential drugs' effects on lung cancer cell lines.

RESULTS

A ferroptosis-related prognostic model involving 14 genes in NSCLC patients was established. The risk score model was developed in training set GSE31210 and validated in the test set TCGA. The low-risk score group showed a better prognosis than the high-risk score group. The single-cell analysis revealed that the risk score genes were mainly derived from lung tumor cells. Most risk score genes were more highly expressed in tumor tissue than in normal tissue, according to the HPA database. Besides, these genes were associated with 106 drugs in CellMiner database. Finally, the drug effects on NSCLC cell growth were evaluated by cck8 and colony formation.

CONCLUSIONS

We identified an effective ferroptosis-related prognostic model based on single-cell sequencing. The potential prediction model is devoted to exploring clinical therapeutic targets for NSCLC.

Ketogenic Diet Alleviates Renal Interstitial Fibrosis in UUO Mice by Regulating Macrophage Proliferation

The ketogenic diet (KD), a high-fat and extremely low-carbohydrate dietary regimen, has long been acknowledged as a highly beneficial dietary therapy for the treatment of intractable epilepsy throughout the last decade. Because of its significant therapeutic potential for a variety of ailments, KD is increasingly attracting study interest. In renal fibrosis, KD has received little attention. This study aimed to determine whether KD protects against renal fibrosis in unilateral ureteral obstruction (UUO) models and the possible mechanisms. The ketogenic diet, according to our findings, reduces UUO-induced kidney injury and fibrosis in mice. KD dramatically decreased the number of F4/80⁺macrophages in kidneys. Next, immunofluorescence results revealed a reduction in the number of F4/80⁺Ki67⁺macrophages in the KD group. Furthermore, our study evaluated the impact of β-hydroxybutyric acid (β-OHB) in RAW246.7 macrophages in vitro. We found that β-OHB inhibits macrophage proliferation. Mechanistically, β-OHB inhibits macrophage proliferation may be via the FFAR3-AKT pathway. Collectively, our study indicated that KD ameliorates UUO-induced renal fibrosis by regulating macrophage proliferation. KD may be an effective therapy method for renal fibrosis due to its protective impact against the disorder.

Cellular complexity of the peripheral nervous system: Insights from single-cell resolution

Single-cell RNA sequencing allows the division of cell populations, offers precise transcriptional profiling of individual cells, and fundamentally advances the comprehension of cellular diversity. In the peripheral nervous system (PNS), the application of single-cell RNA sequencing identifies multiple types of cells, including neurons, glial cells, ependymal cells, immune cells, and vascular cells. Sub-types of neurons and glial cells have further been recognized in nerve tissues, especially tissues in different physiological and pathological states. In the current article, we compile the heterogeneities of cells that have been reported in the PNS and describe cellular variability during development and regeneration. The discovery of the architecture of peripheral nerves benefits the understanding of the cellular complexity of the PNS and provides a considerable cellular basis for future genetic manipulation.

Placental cell type deconvolution reveals that cell proportions drive preeclampsia gene expression differences

The placenta mediates adverse pregnancy outcomes, including preeclampsia, which is characterized by gestational hypertension and proteinuria. Placental cell type heterogeneity in preeclampsia is not well-understood and limits mechanistic interpretation of bulk gene expression measures. We generated single-cell RNA-sequencing samples for integration with existing data to create the largest deconvolution reference of 19 fetal and 8 maternal cell types from placental villous tissue (n = 9 biological replicates) at term (n = 40,494 cells). We deconvoluted eight published microarray case-control studies of preeclampsia (n = 173 controls, 157 cases). Preeclampsia was associated with excess extravillous trophoblasts and fewer mesenchymal and Hofbauer cells. Adjustment for cellular composition reduced preeclampsia-associated differentially expressed genes (log2 fold-change cutoff = 0.1, FDR < 0.05) from 1154 to 0, whereas downregulation of mitochondrial biogenesis, aerobic respiration, and ribosome biogenesis were robust to cell type adjustment, suggesting direct changes to these pathways. Cellular composition mediated a substantial proportion of the association between preeclampsia and FLT1 (37.8%, 95% CI [27.5%, 48.8%]), LEP (34.5%, 95% CI [26.0%, 44.9%]), and ENG (34.5%, 95% CI [25.0%, 45.3%]) overexpression. Our findings indicate substantial placental cellular heterogeneity in preeclampsia contributes to previously observed bulk gene expression differences. This deconvolution reference lays the groundwork for cellular heterogeneity-aware investigation into placental dysfunction and adverse birth outcomes.

Single-cell discovery of the scene and potential immunotherapeutic target in hypopharyngeal tumor environment

Hypopharyngeal carcinoma is a cancer with the worst prognosis. We constructed the first single-cell transcriptome map for hypopharyngeal carcinoma and explored its underlying mechanisms. We systematically studied single-cell transcriptome data of 17,599 cells from hypopharyngeal carcinoma and paracancerous tissues. We identified categories of cells by dimensionality reduction and performed further subgroup analysis. Focusing on the potential mechanism in the cellular communication of hypopharyngeal carcinoma, we predicted ligand-receptor interactions and verified them via immunohistochemical and cellular experiments. In total, seven cell types were identified, including epithelial and myeloid cells. Subsequently, subgroup analysis showed significant tumor heterogeneity. Based on the pathological type of squamous cell carcinoma, we focused on intercellular communication between epithelial cells and various cells. We predicted the crosstalk and inferred the regulatory effect of cellular active ligands on the surface receptor of epithelial cells. From the top potential pairs, we focused on the BMPR2 receptor for further research, as it showed significantly higher expression in epithelial cancer tissue than in adjacent tissue. Further bioinformatics analysis, immunohistochemical staining, and cell experiments also confirmed its cancer-promoting effects. Overall, the single-cell perspective revealed complex crosstalk in hypopharyngeal cancer, in which BMPR2 promotes its proliferation and migration, providing a rationale for further study and treatment of this carcinoma.

Single-cell RNA-Seq analysis of diabetic wound macrophages in STZ-induced mice

The crucial role of macrophages in the healing of chronic diabetic wounds is widely known, but previous in vitro classification and marker genes of macrophages may not be fully applicable to cells in the microenvironment of chronic wounds. The heterogeneity of macrophages was studied and classified at the single-cell level in a chronic wound model. We performed single-cell sequencing of CD45 + immune cells within the wound edge and obtained 17 clusters of cells, including 4 clusters of macrophages. One of these clusters is a previously undescribed population of macrophages possessing osteoclast gene expression, for which analysis of differential genes revealed possible functions. We also analysed the differences in gene expression between groups of macrophages in the control and diabetic wound groups at different sampling times. We described the differentiation profile of mononuclear macrophages, which has provided an important reference for the study of immune-related mechanisms in diabetic chronic wounds.

Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment

Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.

Single-cell RNA sequencing in orthopedic research

Although previous RNA sequencing methods have been widely used in orthopedic research and have provided ideas for therapeutic strategies, the specific mechanisms of some orthopedic disorders, including osteoarthritis, lumbar disc herniation, rheumatoid arthritis, fractures, tendon injuries, spinal cord injury, heterotopic ossification, and osteosarcoma, require further elucidation. The emergence of the single-cell RNA sequencing (scRNA-seq) technique has introduced a new era of research on these topics, as this method provides information regarding cellular heterogeneity, new cell subtypes, functions of novel subclusters, potential molecular mechanisms, cell-fate transitions, and cell‒cell interactions that are involved in the development of orthopedic diseases. Here, we summarize the cell subpopulations, genes, and underlying mechanisms involved in the development of orthopedic diseases identified by scRNA-seq, improving our understanding of the pathology of these diseases and providing new insights into therapeutic approaches.

Important Cells and Factors from Tumor Microenvironment Participated in Perineural Invasion

Perineural invasion (PNI) as the fourth way for solid tumors metastasis and invasion has attracted a lot of attention, recent research reported a new point that PNI starts to include axon growth and possible nerve "invasion" to tumors as the component. More and more tumor-nerve crosstalk has been explored to explain the internal mechanism for tumor microenvironment (TME) of some types of tumors tends to observe nerve infiltration. As is well known, the interaction of tumor cells, peripheral blood vessels, extracellular matrix, other non-malignant cells, and signal molecules in TME plays a key role in the occurrence, development, and metastasis of cancer, as to the occurrence and development of PNI. We aim to summarize the current theories on the molecular mediators and pathogenesis of PNI, add the latest scientific research progress, and explore the use of single-cell spatial transcriptomics in this invasion way. A better understanding of PNI may help to understand tumor metastasis and recurrence and will be beneficial for improving staging strategies, new treatment methods, and even paradigm shifts in our treatment of patients.

Sequencing technology as a major impetus in the advancement of studies into rheumatism: A bibliometric study

Background

Rheumatism covers a wide range of diseases with complex clinical manifestations and places a tremendous burden on humans. For many years, our understanding of rheumatism was seriously hindered by technology constraints. However, the increasing application and rapid advancement of sequencing technology in the past decades have enabled us to study rheumatism with greater accuracy and in more depth. Sequencing technology has made huge contributions to the field and is now an indispensable component and powerful tool in the study of rheumatism.

Methods

Articles on sequencing and rheumatism, published from 1 January 2000 to 25 April 2022, were retrieved from the Web of Science™ (Clarivate™, Philadelphia, PA, USA) database. Bibliometrix, the open-source tool, was used for the analysis of publication years, countries, authors, sources, citations, keywords, and co-words.

Results

The 1,374 articles retrieved came from 62 countries and 350 institutions, with a general increase in article numbers during the last 22 years. The leading countries in terms of publication numbers and active cooperation with other countries were the USA and China. The most prolific authors and most popular documents were identified to establish the historiography of the field. Popular and emerging research topics were assessed by keywords and co-occurrence analysis. Immunological and pathological process in rheumatism, classification, risks and susceptibility, and biomarkers for diagnosis were among the hottest themes for research.

Conclusions

Sequencing technology has been widely applied in the study of rheumatism and propells research in the area of discovering novel biomarkers, related gene patterns and physiopathology. We suggest that further efforts be made to advance the study of genetic patterns related to rheumatic susceptibility, pathogenesis, classification and disease activity, and novel biomarkers.

Single-cell transcriptome analysis identifies Versican(+) myofibroblast as a hallmark for thoracic aortic aneurysm marked by activation of PI3K-AKT signaling pathway

BACKGROUND

Thoracic aortic aneurysm (TAA) is a silent but dangerous cardiovascular disease. Understanding molecular mechanisms of TAA on single-cell level might provide new strategies for preventing and treating TAA.

METHODS

Single-cell RNA sequencing was performed on control and aneurysmal thoracic aorta to find out specific cell clusters and cell types. Western blot and histological staining were used to verify the findings of single-cell transcriptome analysis. Characteristics of Versican (VCAN) overexpressed myofibroblast was evaluated through bioinformatic methods and experimental validation.

RESULTS

A total of 3 control and 8 TAA specimens were used for single-cell transcriptome analysis including 48,128 thoracic aortic cells. Among these cells, we found out a specific cell cluster containing both hallmarks of smooth muscle cell (SMC) and fibroblast. Thus, we defined these cells as myofibroblast. Further single-cell transcriptome analysis identified VCAN as a cellular marker of myofibroblast. Western blot and histological staining revealed that VCAN(+) myofibroblast was significantly increased in TAA specimens compared with control individuals. Differential analysis, functional, pathway enrichment analysis and cell-cell communication analysis demonstrated that VCAN(+) myofibroblast was closely associated with previous reported TAA associated pathological process including SMC proliferation, SMC migration and extracellular matrix (ECM) disruption. Pathway analysis found out significant activation of PI3K-AKT signaling pathway within VCAN(+) myofibroblast, which was further confirmed by experimental validation.

CONCLUSIONS

Single-cell RNA sequencing identified VCAN(+) myofibroblast as a typical cellular hallmark of TAA. These cells might participate in the pathogenesis of TAA through activation of PI3K-AKT signaling pathway to link SMC proliferation, SMC migration and ECM disruption.

Oligodendrocyte lineage cells: Advances in development, disease, and heterogeneity

Oligodendrocyte progenitor cells (OPCs) originate in the ventricular zone (VZ) of the brain and spinal cord, and their primary function is to differentiate into oligodendrocytes (OLs). Studies have shown that OPCs and OLs are pathologically and physiologically heterogeneous. Previous transcriptome analyses used Bulk RNA-seq, which compares average gene expression in cells and does not allow for heterogeneity. In recent years, the development of single-cell sequencing (scRNA-seq) and single-cell nuclear sequencing (snRNA-seq) has allowed us to study an individual cell. In this review, sc/snRNA-seq was used to study the different subpopulations of OL lineage cells, their developmental trajectories, and their applications in related diseases. These techniques can distinguish different subpopulations of cells, and identify differentially expressed genes in particular cell types under certain conditions, such as treatment or disease. It is of great significance to the study of the occurrence, prevention, and treatment of various diseases.

Combining single-cell transcriptomics and CellTagging to identify differentiation trajectories of human adipose-derived mesenchymal stem cells

BACKGROUND

Mesenchymal stromal cells (MSCs) have attracted great attention in the application of cell-based therapy because of their pluripotent differentiation and immunomodulatory ability. Due to the limited number of MSCs isolated from donor tissues, a large number of MSCs need to be expanded in a traditional two-dimensional cell culture device to obtain a sufficient therapeutic amount. However, long-term cultivation of MSCs in vitro has been proven to reduce their differentiation potential and change their immunomodulatory characteristics. We aimed to explore the cellular heterogeneity and differentiation potential of different MSCs expanded in vitro and reconstruct the complex cloning track of cells in the process of differentiation.

METHODS

Single cell transcriptome sequencing was combined with 'CellTagging', which is a composite barcode indexing method that can capture the cloning history and cell identity in parallel to track the differentiation process of the same cell over time.

RESULTS

Through the single-cell transcriptome and CellTagging, we found that the heterogeneity of human adipose tissue derived stem cells (hADSCs) in the early stage of culture was very limited. With the passage, the cells spontaneously differentiated during the process of division and proliferation, and the heterogeneity of the cells increased. By tracing the differentiation track of cells, we found most cells have the potential for multidirectional differentiation, while a few cells have the potential for unidirectional differentiation. One subpopulation of hADSCs with the specific osteoblast differentiation potential was traced from the early stage to the late stage, which indicates that the differentiation trajectories of the cells are determined in the early stages of lineage transformation. Further, considering that all genes related to osteogenic differentiation have not yet been determined, we identified that there are some genes that are highly expressed specifically in the hADSC subsets that can successfully differentiate into osteoblasts, such as Serpin Family E Member 2 (SERPINE2), Secreted Frizzled Related Protein 1 (SFRP1), Keratin 7 (KRT7), Peptidase Inhibitor 16 (PI16), and Carboxypeptidase E (CPE), which may be key regulatory genes for osteogenic induction, and finally proved that the SERPINE2 gene can promote the osteogenic process.

CONCLUSION

The results of this study contribute toward the exploration of the heterogeneity of hADSCs and improving our understanding of the influence of heterogeneity on the differentiation potential of cells. Through this study, we found that the SERPINE2 gene plays a decisive role in the osteogenic differentiation of hADSCs, which lays a foundation for establishing a more novel and complete induction system.

Overview of gene expression techniques with an emphasis on vitamin D related studies

Each cell controls when and how its genes must be expressed for proper function. Every function in a cell is driven by signaling molecules through various regulatory cascades. Different cells in a multicellular organism may express very different sets of genes, even though they contain the same DNA. The set of genes expressed in a cell determines the set of proteins and functional RNAs it contains, giving it its unique properties. Malfunction in gene expression harms the cell and can lead to the development of various disease conditions. The use of rapid high-throughput gene expression profiling unravels the complexity of human disease at various levels. Peripheral blood mononuclear cells (PBMC) have been used frequently to understand gene expression homeostasis in various disease conditions. However, more studies are required to validate whether PBMC gene expression patterns accurately reflect the expression of other cells or tissues. Vitamin D, which is responsible for a multitude of health consequences, is also an immune modulatory hormone with major biological activities in the innate and adaptive immune systems. Vitamin D exerts its diverse biological effects in target tissues by regulating gene expression and its deficiency, is recognized as a public health problem worldwide. Understanding the genetic factors that affect vitamin D has the potential benefit that it will make it easier to identify individuals who require supplementation. Different technological advances in gene expression can be used to identify and assess the severity of disease and aid in the development of novel therapeutic interventions. This review focuses on different gene expression approaches and various clinical studies of vitamin D to investigate the role of gene expression in identifying the molecular signature of the disease.

The role of the stria vascularis in neglected otologic disease

The stria vascularis (SV) has been shown to play a critical role in the pathogenesis of many diseases associated with sensorineural hearing loss (SNHL), including age-related hearing loss (ARHL), noise-induced hearing loss (NIHL), hereditary hearing loss (HHL), and drug-induced hearing loss (DIHL), among others. There are a number of other disorders of hearing loss that may be relatively neglected due to being underrecognized, poorly understood, lacking robust diagnostic criteria or effective treatments. A few examples of these diseases include autoimmune inner ear disease (AIED) and/or autoinflammatory inner ear disease (AID), Meniere's disease (MD), sudden sensorineural hearing loss (SSNHL), and cytomegalovirus (CMV)-related hearing loss (CRHL). Although these diseases may often differ in etiology, there have been recent studies that support the involvement of the SV in the pathogenesis of many of these disorders. We strive to highlight a few prominent examples of these frequently neglected otologic diseases and illustrate the relevance of understanding SV composition, structure and function with regards to these disease processes. In this study, we review the physiology of the SV, lay out the importance of these neglected otologic diseases, highlight the current literature regarding the role of the SV in these disorders, and discuss the current strategies, both approved and investigational, for management of these disorders.

Integration of single-cell and bulk transcriptomics reveals immune-related signatures in keloid

BACKGROUND

Keloid is a pathological dermatological condition that manifests as an overgrowth scar secondary to skin trauma. This study endeavored to excavate immune-related signatures of keloid based on single-cell RNA (scRNA) sequencing data and bulk RNA sequencing data.

METHOD

The keloid-relevant scRNA sequencing dataset GSE163973 and bulk RNA sequencing dataset GSE113619 were mined from the GEO database. The "Seurat" R package was utilized for data quality control, cell clustering, and investigation of marker genes of each cell cluster. The "SingleR" package helped match the marker genes of the corresponding cluster to specific cell types. Moreover, the R package "Monocle" was deployed for pseudotemporal ordering analysis, and the "clusterProfiler" was applied for functional and pathway enrichment analysis. The immune-related signatures were then identified, and potential targeted drugs were predicted via the DGIdb database. Verification of the immune-related signatures in clinical validation samples was implemented by RT-qPCR.

RESULTS

Totally 23 cell clusters were screened and classified into 10 cell types based on the scRNA sequencing data. The keloid group had a significantly higher endothelial cell proportion than the control group. As enrichment analysis was applied in both differentially expressed genes (DEGs) of scRNA and bulk RNA sequencing data, we found they were enriched in multiple common immune-related pathways and biological processes. Meanwhile, we acquired three immune-related signatures (VCAM1, CALCRL, and HLA-DPB1) by intersecting the above DEGs with immune-related genes (IRGs). Then, we predicted 16 drugs potentially targeting the biomarkers through the DGIdb database. Finally, the outcome of RT-qPCR of clinical validation samples further verified the results.

CONCLUSION

In conclusion, we analyzed the cell types and functional differences in the keloid through scRNA and bulk RNA sequencing data. We identified three immune-related signatures (VCAM1, CALCRL, and HLA-DPB1) in keloid, providing a basis for further in-depth investigation of the molecular mechanisms of keloid and exploration of therapeutic targets.

Associative analysis of multi-omics data indicates that acetylation modification is widely involved in cigarette smoke-induced chronic obstructive pulmonary disease

We aimed to study the molecular mechanisms of chronic obstructive pulmonary disease (COPD) caused by cigarette smoke more comprehensively and systematically through different perspectives and aspects and to explore the role of protein acetylation modification in COPD. We established the COPD model by exposing C57BL/6J mice to cigarette smoke for 24 weeks, then analyzed the transcriptomics, proteomics, and acetylomics data of mouse lung tissue by RNA sequencing (RNA-seq) and liquid chromatography-tandem mass spectrometry (LC-MS/MS), and associated these omics data through unique algorithms. This study demonstrated that the differentially expressed proteins and acetylation modification in the lung tissue of COPD mice were co-enriched in pathways such as oxidative phosphorylation (OXPHOS) and fatty acid degradation. A total of 19 genes, namely, ENO3, PFKM, ALDOA, ACTN2, FGG, MYH1, MYH3, MYH8, MYL1, MYLPF, TTN, ACTA1, ATP2A1, CKM, CORO1A, EEF1A2, AKR1B8, MB, and STAT1, were significantly and differentially expressed at all the three levels of transcription, protein, and acetylation modification simultaneously. Then, we assessed the distribution and expression in different cell subpopulations of these 19 genes in the lung tissues of patients with COPD by analyzing data from single-cell RNA sequencing (scRNA-seq). Finally, we carried out the in vivo experimental verification using mouse lung tissue through quantitative real-time PCR (qRT-PCR), Western blotting (WB), immunofluorescence (IF), and immunoprecipitation (IP). The results showed that the differential acetylation modifications of mouse lung tissue are widely involved in cigarette smoke-induced COPD. ALDOA is significantly downregulated and hyperacetylated in the lung tissues of humans and mice with COPD, which might be a potential biomarker for the diagnosis and/or treatment of COPD.

ProgClust: A progressive clustering method to identify cell populations

Identifying different types of cells in scRNA-seq data is a critical task in single-cell data analysis. In this paper, we propose a method called ProgClust for the decomposition of cell populations and detection of rare cells. ProgClust represents the single-cell data with clustering trees where a progressive searching method is designed to select cell population-specific genes and cluster cells. The obtained trees reveal the structure of both abundant cell populations and rare cell populations. Additionally, it can automatically determine the number of clusters. Experimental results show that ProgClust outperforms the baseline method and is capable of accurately identifying both common and rare cells. Moreover, when applied to real unlabeled data, it reveals potential cell subpopulations which provides clues for further exploration. In summary, ProgClust shows potential in identifying subpopulations of complex single-cell data.

Bulk and single-cell transcriptome analyses of islet tissue unravel gene signatures associated with pyroptosis and immune infiltration in type 2 diabetes

INTRODUCTION

Type 2 diabetes (T2D) is a common chronic heterogeneous metabolic disorder. However, the roles of pyroptosis and infiltrating immune cells in islet dysfunction of patients with T2D have yet to be explored. In this study, we aimed to explore potential crucial genes and pathways associated with pyroptosis and immune infiltration in T2D.

METHODS

To achieve this, we performed a conjoint analysis of three bulk RNA-seq datasets of islets to identify T2D-related differentially expressed genes (DEGs). After grouping the islet samples according to their ESTIMATE immune scores, we identified immune- and T2D-related DEGs. A clinical prediction model based on pyroptosis-related genes for T2D was constructed. Weighted gene co-expression network analysis was performed to identify genes positively correlated with pyroptosis-related pathways. A protein-protein interaction network was established to identify pyroptosis-related hub genes. We constructed miRNA and transcriptional networks based on the pyroptosis-related hub genes and performed functional analyses. Single-cell RNA-seq (scRNA-seq) was conducted using the GSE153885 dataset. Dimensionality was reduced using principal component analysis and t-distributed statistical neighbor embedding, and cells were clustered using Seurat. Different cell types were subjected to differential gene expression analysis and gene set enrichment analysis (GSEA). Cell-cell communication and pseudotime trajectory analyses were conducted using the samples from patients with T2D.

RESULTS

We identified 17 pyroptosis-related hub genes. We determined the abundance of 13 immune cell types in the merged matrix and found that these cell types were correlated with the 17 pyroptosis-related hub genes. Analysis of the scRNA-seq dataset of 1892 islet samples from patients with T2D and controls revealed 11 clusters. INS and IAPP were determined to be pyroptosis-related and candidate hub genes among the 11 clusters. GSEA of the 11 clusters demonstrated that the myc, G2M checkpoint, and E2F pathways were significantly upregulated in clusters with several differentially enriched pathways.

DISCUSSION

This study elucidates the gene signatures associated with pyroptosis and immune infiltration in T2D and provides a critical resource for understanding of islet dysfunction and T2D pathogenesis.

Integrating single-cell analysis and machine learning to create glycosylation-based gene signature for prognostic prediction of uveal melanoma

BACKGROUND

Increasing evidence suggests a correlation between glycosylation and the onset of cancer. However, the clinical relevance of glycosylation-related genes (GRGs) in uveal melanoma (UM) is yet to be fully understood. This study aimed to shed light on the impact of GRGs on UM prognosis.

METHODS

To identify the most influential genes in UM, we employed the AUCell and WGCNA algorithms. The GRGs signature was established by integrating bulk RNA-seq and scRNA-seq data. UM patients were separated into two groups based on their risk scores, the GCNS_low and GCNS_high groups, and the differences in clinicopathological correlation, functional enrichment, immune response, mutational burden, and immunotherapy between the two groups were examined. The role of the critical gene AUP1 in UM was validated through in vitro and in vivo experiments.

RESULTS

The GRGs signature was comprised of AUP1, HNMT, PARP8, ARC, ALG5, AKAP13, and ISG20. The GCNS was a significant prognostic factor for UM, and high GCNS correlated with poorer outcomes. Patients with high GCNS displayed heightened immune-related characteristics, such as immune cell infiltration and immune scores. In vitro experiments showed that the knockdown of AUP1 led to a drastic reduction in the viability, proliferation, and invasion capability of UM cells.

CONCLUSION

Our gene signature provides an independent predictor of UM patient survival and represents a starting point for further investigation of GRGs in UM. It offers a novel perspective on the clinical diagnosis and treatment of UM.

Effective Preprocessing of Single-Cell RNA-Seq for Unravelling Alzheimer's Disease Signatures

The development in the field of biomedical technology has brought significant progress in the diagnosis and prediction of many complex diseases. Part of this development is the single-cell RNA sequencing analysis, which allows the study of a complex disease in great depth at the cellular level. Such analyses can decipher the mechanisms that cause complex diseases, such as Alzheimer's disease (AD). However, the increasing depth in the collection of single-cell RNA sequencing data implies, in addition to greater challenges, the production of a large amount of information, which needs careful analysis. Toward this direction, we examine the approach to single-cell RNA sequencing data through the development of an exploratory data analysis methodology. For this purpose, a combination of various tools is presented for their effective and efficient processing. At the same time, reference is made to the relevant biological concepts, the goals and challenges of the studies, and the workflows of sequencing, preprocessing, and analysis of the data. Our framework is applied to Alzheimer's disease data providing evidence that such data are quite complex while the appropriate preprocess step can boost the machine learning processes for identifying AD signatures.

Renoprotective anti-CD45RB antibody induces B cell production in systemic lupus erythematosus based on single-cell RNA-seq analysis

OBJECTIVE

Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease that commonly affects the kidney. Single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for characterizing individual cells and elucidating biological mechanisms at the cellular level. The purpose of this study was to identify the mechanism underlying kidney injury in SLE using scRNA-seq technology.

METHODS

scRNA-seq data of peripheral blood mononuclear cells (PBMCs) in SLE were retrieved from the GEO database, followed by batch effect elimination, dimensionality reduction, cluster analysis, cell annotation and enrichment analysis. A model of SLE was developed in NZB/WF1 mice. Effects of anti-CD45RB antibody on the SLE-induced kidney injury were evaluated, and we measured the distribution of regulatory T cells and B cells in mouse spleen and kidney tissues, levels of kidney function-related indexes, deposition of IgG and C3 in the glomeruli, and the levels of inflammatory cytokines.

RESULTS

CD45RB was a specific marker gene of B cell clusters and had influence on the B cells. anti-CD45RB antibody treatment induced regulatory B cells and consequently arrested the kidney injury caused by SLE. In addition, depletion of regulatory T cells was found to partially undermine the alleviatory effect of anti-CD45RB antibody on SLE-induced kidney injury.

CONCLUSION

Collectively, our data suggest that anti-CD45RB antibody can prevent the SLE-induced kidney injury, pointing to anti-CD45RB antibody as a potential therapeutic strategy in kidney injury-related disease.

Single-cell RNA sequencing in the context of neuropathic pain: progress, challenges, and prospects

Neuropathic pain, characterized by persistent or intermittent spontaneous pain as well as some unpleasant abnormal sensations, is one of the most prevalent health problems in the world. Ectopic nerve activity, central and peripheral nociceptive sensitization and many other potential mechanisms may participate in neuropathic pain. The complexity and ambiguity of neuropathic pain mechanisms result in difficulties in pain management, and existing treatment plans provide less-than-satisfactory relief. In recent years, single-cell RNA sequencing (scRNA-seq) has been increasingly applied and has become a powerful means for biological researchers to explore the complexity of neurobiology. This technique can be used to perform unbiased, high-throughput and high-resolution transcriptional analyses of neuropathic pain-associated cells, improving the understanding of neuropathic pain mechanisms and enabling individualized pain management. To date, scRNA-seq has been preliminarily used in neuropathic pain research for applications such as compiling a dorsal root ganglion atlas, identifying new cell types and discovering gene regulatory networks associated with neuropathic pain. Although scRNA-seq is a relatively new technique in the neuropathic pain field, there have been several studies based on animal models. However, because of the various differences between animals and humans, more attention should be given to translational medicine research. With the aid of scRNA-seq, researchers can further explore the mechanism of neuropathic pain to improve the clinical understanding of the diagnosis, treatment and management of neuropathic pain.

Application of Single-Cell RNA Sequencing in Ovarian Development

The ovary is a female reproductive organ that plays a key role in fertility and the maintenance of endocrine homeostasis, which is of great importance to women's health. It is characterized by a high heterogeneity, with different cellular subpopulations primarily containing oocytes, granulosa cells, stromal cells, endothelial cells, vascular smooth muscle cells, and diverse immune cell types. Each has unique and important functions. From the fetal period to old age, the ovary experiences continuous structural and functional changes, with the gene expression of each cell type undergoing dramatic changes. In addition, ovarian development strongly relies on the communication between germ and somatic cells. Compared to traditional bulk RNA sequencing techniques, the single-cell RNA sequencing (scRNA-seq) approach has substantial advantages in analyzing individual cells within an ever-changing and complicated tissue, classifying them into cell types, characterizing single cells, delineating the cellular developmental trajectory, and studying cell-to-cell interactions. In this review, we present single-cell transcriptome mapping of the ovary, summarize the characteristics of the important constituent cells of the ovary and the critical cellular developmental processes, and describe key signaling pathways for cell-to-cell communication in the ovary, as revealed by scRNA-seq. This review will undoubtedly improve our understanding of the characteristics of ovarian cells and development, thus enabling the identification of novel therapeutic targets for ovarian-related diseases.

Integrating Rehabilomics into the Multi-Omics Approach in the Management of Multiple Sclerosis: The Way for Precision Medicine?

Over recent years, significant improvements have been made in the understanding of (epi)genetics and neuropathophysiological mechanisms driving the different forms of multiple sclerosis (MS). For example, the role and importance of the bidirectional communications between the brain and the gut-also referred to as the gut-brain axis-in the pathogenesis of MS is receiving increasing interest in recent years and is probably one of the most promising areas of research for the management of people with MS. However, despite these important advances, it must be noted that these data are not-yet-used in rehabilitation. Neurorehabilitation is a cornerstone of MS patient management, and there are many techniques available to clinicians and patients, including technology-supported rehabilitation. In this paper, we will discuss how new findings on the gut microbiome could help us to better understand how rehabilitation can improve motor and cognitive functions. We will also see how the data gathered during the rehabilitation can help to get a better diagnosis of the patients. Finally, we will discuss how these new techniques can better guide rehabilitation to lead to precision rehabilitation and ultimately increase the quality of patient care.

Recent advances in high-throughput single-cell transcriptomics and spatial transcriptomics

Single-cell RNA sequencing (scRNA-seq) has been developed for characterizing the transcriptome of cells that are rare but of biological significance. With cell barcoding and microchip technologies, a suite of high-throughput scRNA-seq protocols enable transcriptome profiling in thousands of individual cells at single-cell resolution for classifying cell types, discovering novel cell populations, investigating cellular heterogeneity and elucidating lineage trajectories. Microchip technologies including microfluidics- and microwell-based platforms play a major role in high-throughput scRNA-seq. As the emerging technology, spatial transcriptomics integrates cellular transcriptomics with their spatial coordinates within tissues for spatially deciphering cellular composition, heterogeneity and cell-cell communications. Spatial transcriptomics has been increasingly recognized as one of the most powerful tools for discovering new biology and advancing precision medicine. Microfluidics as an enabling technology plays an increasingly important role in spatial transcriptomics. We review the technological spectrum and advances in high-throughput scRNA-seq and spatial transcriptomics, discuss their advantages and limitations, and pitch into new biology learned from these new tools.

CircRNA/miRNA/mRNA axis participates in the progression of partial bladder outlet obstruction

BACKGROUND

More and more evidence showed that circRNA/miRNA/mRNA axis played a vital role in the pathogenesis of some diseases. However, the role of circRNA/miRNA/mRNA axis in partial bladder outlet obstruction (pBOO) remains unknown. Our study aimed to explore the complex regulatory mechanism of circRNA/miRNA/mRNA axis in pBOO.

METHODS

The pBOO rat model was established, and the bladder tissues were collected for mRNA sequencing. The differentially expressed mRNAs were analyzed by high-throughput sequencing, and the GO and KEGG analysis of the differentially expressed mRNAs were performed. Competing endogenous RNAs (ceRNAs) analysis identified the potential regulation function of circRNA/miRNA/mRNA axis in pBOO. qRT-PCR detected the expression of circRNA/miRNA/mRNA. miRanda software was performed to predict the relationship between circRNA and miRNA, miRNA and mRNA.

RESULTS

Compared with the sham group, a total of 571 mRNAs were differentially expressed in the pBOO group, of which 286 were up-regulated and 285 were down-regulated. GO analysis showed that the mRNAs were mainly involved in cellular process, single-organism process, and cell, etc. KEGG analysis showed that the enriched signaling pathways were metabolic pathways, cell adhesion molecules (CAMs), and HTLV-I infection, etc. Based on the previous transcriptome data and differentially expressed circRNAs, we drew the ceRNA network regulation diagram. qRT-PCR results confirmed that chr3:113195876|113197193/rno-miR-30c-1-3p/Gata4, chr1:126188351|126195625/rno-miR-153-5p/Diaph3, and chr9:81258380|81275269/rno-miR-135b-5p/Pigr axis may have ceRNA function. miRanda confirmed there have the binding sites of circRNA/miRNA/mRNA axis.

CONCLUSIONS

CircRNA/miRNA/mRNA axis was involved in the progression of pBOO. Our research on the circRNA/miRNA/mRNA axis revealed new pathogenesis and treatment strategies for pBOO.

Significance of monocyte infiltration in patients with gastric cancer: A combined study based on single cell sequencing and TCGA

BACKGROUND

Gastric cancer is still one of the most lethal tumor diseases in the world. Despite some improvements, the prognosis of patients with gastric cancer is still not accurately predicted.

METHODS

Based on single cell sequencing data, we conducted a detailed analysis of gastric cancer patients and normal tissues to determine the role of monocytes in the progression of gastric cancer. WCGA facilitated our search for Grade-related genes in TCGA. Then, according to the marker genes and cell differentiation genes of monocytes, we determined the cancer-promoting genes of monocytes. Based on LASSO regression, we established a prognostic model using TCGA database. The accuracy of the model was verified by PCA, ROC curve, survival analysis and prognostic analysis. Finally, we evaluated the significance of the model in clinical diagnosis and treatment by observing drug sensitivity, immune microenvironment and immune checkpoint expression in patients with different risk groups.

RESULTS

Monocytes were poorly differentiated in tumor microenvironment. It mainly played a role in promoting cancer in two ways. One was to promote tumor progression indirectly by interacting with other tumor stromal cells. The other was to directly connect with tumor cells through the MIF and TNF pathway to play a tumor-promoting role. The former was more important in these two ways. A total of 292 monocyte tumor-promoting genes were obtained, and 12 genes were finally included in the construction of the prognosis model. A variety of validation methods showed that our model had an accurate prediction ability. Drug sensitivity analysis could provide guidance for clinical medication of patients. The results of immune microenvironment and immune checkpoint also indicated the reasons for poor prognosis of high-risk patients.

CONCLUSION

In conclusion, we provided a 12-gene risk score formula and nomogram for gastric cancer patients to assist clinical drug therapy and prognosis prediction. This model had good accuracy and clinical significance.

Integrative cross-species analysis of GABAergic neuron cell types and their functions in Alzheimer's disease

Understanding the phenotypic and functional diversity of cerebral cortical GABAergic neurons requires a comprehensive analysis of key transcriptional signatures and neuronal subtype identity. However, the diversity and conservation of GABAergic neurons across multiple mammals remain unclear. Here, we collected the single-nucleus RNA sequencing (snRNA-seq) datasets of cerebral cortex from human, macaque, mouse, and pig to identify the conserved neuronal cell types across species. After systematic analysis of the heterogeneity of GABAergic neurons, we defined four major conserved GABAergic neuron subclasses (Inc SST, Inc LAMP5, Inc PVALB, and Inc VIP) across species. We characterized the species-enriched subclasses of GABAergic neurons from four mammals, such as Inc Meis2 in mouse. Then, we depicted the genetic regulatory network (GRNs) of GABAergic neuron subclasses, which showed the conserved and species-specific GRNs for GABAergic neuron cell types. Finally, we investigated the GABAergic neuron subclass-specific expression modules of Alzheimer's disease (AD)-related genes in GABAergic neuron cell types. Overall, our study reveals the conserved and divergent GABAergic neuron subclasses and GRNs across multiple species and unravels the gene expression modules of AD-risk genes in GABAergic neuron subclasses, facilitating the GABAergic neurons research and clinical treatment.

CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data

CellMarker 2.0 (http://bio-bigdata.hrbmu.edu.cn/CellMarker or http://117.50.127.228/CellMarker/) is an updated database that provides a manually curated collection of experimentally supported markers of various cell types in different tissues of human and mouse. In addition, web tools for analyzing single cell sequencing data are described. We have updated CellMarker 2.0 with more data and several new features, including (i) Appending 36 300 tissue-cell type-maker entries, 474 tissues, 1901 cell types and 4566 markers over the previous version. The current release recruits 26 915 cell markers, 2578 cell types and 656 tissues, resulting in a total of 83 361 tissue-cell type-maker entries. (ii) There is new marker information from 48 sequencing technology sources, including 10X Chromium, Smart-Seq2 and Drop-seq, etc. (iii) Adding 29 types of cell markers, including protein-coding gene lncRNA and processed pseudogene, etc. Additionally, six flexible web tools, including cell annotation, cell clustering, cell malignancy, cell differentiation, cell feature and cell communication, were developed to analysis and visualization of single cell sequencing data. CellMarker 2.0 is a valuable resource for exploring markers of various cell types in different tissues of human and mouse.

Exploration the global single-cell ecological landscape of adenomyosis-related cell clusters by single-cell RNA sequencing

Background: Adenomyosis (AM) is a common benign uterine disease that threatens the normal life of patients. Cells associated with microenvironmental immune ecology are crucial in AM, although they are not as well understood at the cellular level.

Methods: Single-cell sequencing (scRNA-seq) data were used to construct an AM global single-cell map, to further identify relevant cell clusters and infer chromosomal copy number variation (CNV) in AM samples. The biological functions of cell clusters were explored and cellular evolutionary processes were inferred by enrichment analysis and pseudotime analysis. In addition, a gene regulatory network (GRN) analysis was constructed to explore the regulatory role of transcription factors in AM progression.

Results: We obtained the expression profiles of 42260 cells and identified 10 cell clusters. By comparing the differences in cell components between AM patients and controls, we found that significant abundance of endometrial cells (EC), epithelial cells (Ep), endothelial cells (En), and smooth muscle cells (SMC) in AM patients. Cell clusters with high CNV levels possessing tumour-like features existed in the ectopic endometrium samples. Moreover, the Ep clusters were significantly involved in leukocyte transendothelial cell migration and apoptosis, suggesting an association with cell apoptosis and migration. En clusters were mainly involved in pathways in cancer and apoptosis, indicating that En has certain malignant features.

Conclusion: This study identified cell clusters with immune-related features, investigated the changes in the immune ecology of the microenvironment of these cells during AM, and provided a new strategy for the treatment of AM.