The accumulation of tail fat in sheep is a manifestation of adaptive evolution to the environment. Sheep with different tail types show significant differences in physiological functions and tail fat deposition. Although these differences reflect the developmental mechanism of tail fat under different gene regulation, the situation of sheep tail fat tissue at the single cell level has not been explored, and its molecular mechanism still needs to be further elucidated.

Here, we characterized the genomic features of sheep with different tail types, detected the transcriptomic differences in tail adipose tissue between fat-tailed and thin-tailed sheep, established a single-cell atlas of sheep tail adipose tissue, and screened potential molecular markers (SESN1, RPRD1A and RASGEF1B) that regulate differences in sheep tail fat deposition through multi-omics integrated analysis. We found that the differential mechanism of sheep tail fat deposition not only involves adipocyte differentiation and proliferation, but is also closely related to cell-specific communication networks (When adipocytes act as signal outputters, LAMININ and other signal pathways are strongly expressed in guangling large tailed sheep and hu sheep), including interactions with immune cells and tissue remodeling to drive the typing of tail fat. In addition, we revealed the differentiation trajectory of sheep tail adipocytes through pseudo-time analysis and constructed the cell communication network of sheep tail adipose tissue.

Our results provide insights into the molecular mechanisms of tail fat deposition in sheep with different tail types, and provide a deeper explanation for the development and functional regulation of adipocytes.

Colon adenocarcinoma (COAD) is the most frequently occurring type of colon cancer. Cancer-associated fibroblasts (CAFs) are pivotal in facilitating tumor growth and metastasis; however, their specific role in COAD is not yet fully understood. This research utilizes single-cell RNA sequencing (scRNA-seq) to identify and validate gene markers linked to the malignancy of CAFs.

ScRNA-seq data was downloaded from a database and subjected to quality control, dimensionality reduction, clustering, cell annotation, cell communication analysis, and enrichment analysis, specifically focusing on fibroblasts in tumor tissues compared to normal tissues. Fibroblast subsets were isolated, dimensionally reduced, and clustered, then combined with copy number variation (CNV) inference and pseudotime trajectory analysis to identify genes related to malignancy. A Cox regression model was constructed based on these genes, incorporating LASSO analysis, nomogram construction, and validation.Subsequently, we established two FNDC5-knockdown cell lines and utilized colony formation and transwell assays to investigate the impact of FNDC5 on cellular biological behaviors.

Using scRNA-seq data, we analyzed 8,911 cells from normal and tumor samples, identifying six distinct cell types. Cell communication analysis highlighted interactions between these cell types mediated by ligands and receptors. CNV analysis classified CAFs into three groups based on malignancy levels. Pseudo-time analysis identified 622 pseudotime-related genes and generated a forest plot using univariate Cox regression. Lasso regression identified the independent prognostic gene FNDC5, which was visualized in a nomogram. Kaplan-Meier survival analysis confirmed the prognostic value of FNDC5, showing associations with T stage and distant metastasis. In vitro experiment results demonstrated a strong association between FNDC5 expression levels and the proliferative, migratory, and invasive abilities of colon cancer cells.

We developed a risk model for genes related to the malignancy of CAFs and identified FNDC5 as a potential therapeutic target for COAD.

Hepatocellular carcinoma (HCC) is a deadly malignancy known for its ability to evade immune surveillance. NOP2/Sun RNA methyltransferase family member 2 (NSUN2), an RNA methyltransferase involved in carcinogenesis, has been associated with immune evasion and energy metabolism reprogramming. This study aimed to examine the molecular mechanisms underlying the involvement of NSUN2 in immune evasion and metabolic reprogramming of HCC.

Single-cell transcriptomic sequencing was applied to examine cellular composition changes, particularly immune cell dynamics, in HCC and adjacent normal tissues. Bulk RNA-seq and proteomics identified key genes and proteins. Methylation sequencing and methylated RNA immunoprecipitation (MeRIP) were carried out to characterize the role of NSUN2 in 5-methylcytosine (m5C) modification of sterol O-acyltransferase 2 (SOAT2). Clinical samples from 30 HCC patients were analyzed using reverse transcription-quantitative polymerase chain reaction and Western blotting. Gene expression was manipulated using CRISPR/Cas9 and lentiviral vectors. In vitro co-culture models and metabolomics were used to study HCC cell-T cell interactions, energy metabolism, and immune evasion. Tumor growth in an orthotopic mouse model was monitored by bioluminescence imaging, with subsequent measurements of tumor weight, volume, and immunohistochemical staining.

Single-cell transcriptomic analysis identified a marked increase in malignant cells in HCC tissues. Cell communication analysis indicated that tumor cells might promote cancer progression by evading immune clearance. Multi-omics analyses identified NSUN2 as a key regulator in HCC development. MeRIP confirmed that NSUN2 facilitated the m5C modification of SOAT2. Analysis of human HCC tissue samples demonstrated pronounced upregulation of NSUN2 and SOAT2, along with elevated m5C levels in HCC tissues. In vitro experiments uncovered that NSUN2 augmented the reprogramming of energy metabolism and repressed the activity and cytotoxicity of CD8+ T cells, contributing to immune evasion. In vivo studies further substantiated the role of NSUN2 in fostering immune evasion and tumor formation of HCC by modulating the m5C modification of SOAT2.

The findings highlight the critical role of NSUN2 in driving HCC progression through the regulation of m5C modification on SOAT2. These findings present potential molecular markers for HCC diagnosis and therapeutic targets for its treatment.

Mixed connective tissue disease (MCTD) is a rare autoimmune disease, and little is known about its pathogenesis. Furthermore, MCTD, systemic lupus erythematosus (SLE), and primary Sjögren's syndrome (pSS) share many clinical, laboratory, and immunological manifestations. This overlap complicates early diagnosis and accurate treatment.

The transcriptomic profiling of peripheral blood mononuclear cells (PBMCs) from MCTD patients was performed using both bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) for the first time. Additionally, we applied MCTD scRNA-seq data, along with datasets from SLE (GSE135779) and pSS (GSE157278) from the Gene Expression Omnibus database, to characterize and compare the similarities and heterogeneity among MCTD, SLE, and pSS.

We first resolved transcriptomic changes in peripheral blood immune cells of MCTD, and then revealed the shared and unique features among MCTD, SLE, and pSS. Analyses showed that the percentage of CD8+ effector T cells was increased, while mucosal-associated invariant T cells were decreased in all three diseases. Genes related to the 'interferon (IFN) γ response' and 'IFN α response' were significantly upregulated. SCENIC analysis revealed activation of STAT1 and IRF7 in disease states, targeting IFN-related genes. The IFN-II signaling network was notably elevated in all three diseases. Unique features of MCTD, SLE, and pSS were also identified.

We dissected the immune landscape of MCTD at single-cell resolution, providing new insights into the development of novel biomarkers and immunotherapies for MCTD. Furthermore, we offer insights into the transcriptomic similarities and heterogeneity across different autoimmune diseases, while highlighting prospective therapeutic targets.

As a novel genetic biomarker, the potential role of SH3D21 in hepatocellular carcinoma remains unclear. Here, we decipher the expression and function of SH3D21 in human hepatocellular carcinoma. The expression level and clinical significance of SH3D21 in hepatocellular carcinoma patients, the relationship between SH3D21 and the features of tumor microenvironment (TME) and role of SH3D21 in promoting hepatocellular carcinoma progression were analyzed based on the bulk samples obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Single-cell sequencing samples from Gene Expression Omnibus (GEO) database were employed to verify the prediction mechanism. Additionally, different biological effects of SH3D21 on hepatocellular carcinoma cells were investigated by qRT-PCR, CCK-8 assay, colony forming assay and Western blot analysis. Bioinformatics analysis and in vitro experiments revealed that the expression level of SH3D21 was up-regulated in hepatocellular carcinoma and correlated with the poor prognosis in hepatocellular carcinoma patients. SH3D21 effectively promoted the proliferation, invasion, and migration as well as the formation of immunosuppressive microenvironment of hepatocellular carcinoma. In addition, SH3D21 can activate the PI3K/AKT/mTOR signaling pathway. SH3D21 stimulates the progression of hepatocellular carcinoma by activating the PI3K/AKT/mTOR signaling pathway, and SH3D21 can serve as a prognostic biomarker and therapeutic target for hepatocellular carcinoma.

The combined application of single-cell RNA sequencing (scRNA-seq) and single-cell B-cell receptor sequencing (scBCR-seq) offers a multidimensional perspective for dissecting the immunopathological mechanisms of B cells in autoimmune diseases. This review systematically summarizes the principles of these techniques, the analytical framework, and their key applications in diseases such as systemic lupus erythematosus et. al. It reveals the dynamic correlations between the transcriptome of B-cell subsets and B-cell receptor (BCR) clones. Furthermore, we focus on the potential roles of dual BCR B cells and B/T biphenotypic cells in autoimmunity, emphasizing their exacerbation of disease progression through abnormal clonal expansion and autoantibody secretion. By sorting through cutting-edge advancements and bottleneck issues, this article aims to propel the innovation of multi-omics research and precision treatment paradigms for autoimmune diseases.

Single cell (sc) technologies mark a conceptual and methodological breakthrough in our way to study cells, the base units of life. Thanks to these technological developments, large-scale initiatives are currently ongoing aimed at mapping of all the cell types in the human body, with the ambitious aim to gain a cell-level resolution of physiological development and disease. Since its broad applicability and ease of interpretation scRNA-seq is probably the most common sc-based application. This assay uses high throughput RNA sequencing to capture gene expression profiles at the sc-level. Subsequently, under the assumption that differences in transcriptional programs correspond to distinct cellular identities, ad-hoc computational methods are used to infer cell types from gene expression patterns. A wide array of computational methods were developed for this task. However, depending on the underlying algorithmic approach and associated computational requirements, each method might have a specific range of application, with implications that are not always clear to the end user. Here we will provide a concise overview on state-of-the-art computational methods for cell identity annotation in scRNA-seq, tailored for new users and non-computational scientists. To this end, we classify existing tools in five main categories, and discuss their key strengths, limitations and range of application.

The objective of the present study was to elucidate the mechanism by which glycyrrhizin enhances the antitumor activity of cisplatin in non-small cell lung cancer. Initially, A549 cells were treated with different concentrations of glycyrrhizin (0.25-8 mM) or cisplatin (10-160 µM) for 48 h to investigate the effect of glycyrrhizin combined with cisplatin on A549 cells in vitro. Subsequently, A549 cells were divided into control (untreated), CP (20 µM cisplatin), GL (2 mM glycyrrhizin) and CP + GL (20 µM cisplatin + 2 mM glycyrrhizin) groups to elucidate the underlying mechanism of glycyrrhizin. After 48 h incubation, the viability and colony-forming ability of the cells were assessed using MTT and colony formation assays. Apoptosis levels and cell cycle progression were analyzed using flow cytometry and western blotting was used to evaluate apoptosis- and cell cycle-related proteins. Additionally, comet assays and western blotting were used to evaluate DNA damage and relevant proteins. The results demonstrated both glycyrrhizin and cisplatin individually reduced A549 cell viability in a concentration-dependent manner. Cisplatin demonstrated a lower half-maximal inhibitory concentration (IC50) at higher glycyrrhizin concentrations, with an IC50 value of ~35 µM with 2 mM glycyrrhizin. Furthermore, the combined treatment of glycyrrhizin and cisplatin synergistically reduced cell colony-forming ability, induced apoptosis and arrested the cell cycle at the G2 phase, showing greater efficacy when compared with either treatment individually. In addition, western blotting analysis demonstrated that, in comparison with treatment with cisplatin or glycyrrhizin alone, the combined treatment markedly increased the protein expression levels of B-cell lymphoma 2-associated X protein, cleaved-caspase-3/caspase-3, γH2AX, phosphorylated-checkpoint kinase 1 and phosphorylated-p53/p53, while notably reducing the protein levels of B-cell lymphoma 2, cyclin D1, cyclin-dependent kinase 2 and cyclin-dependent kinase 4. The findings of the present study indicate that glycyrrhizin enhances the antitumor efficacy of cisplatin in non-small cell lung cancer cells by modulating DNA damage and apoptosis.

Bulk RNA sequencing is one type of RNA sequencing technique, as well as targeted RNA sequencing and whole transcriptome sequencing. It provides valuable insights into gene expression in specific cell populations or regions. However, these methods often miss the diversity of cells within complex tissues. This restriction is overcome by single-cell RNA sequencing, which records gene expression at the single-cell level. It offers a detailed picture of the diversity of cells. It is essential to study glucose homeostasis. It offers thorough explanations of cellular variation. Networks and Governance Dynamics The use of scRNA-seq in islet cells is reviewed in this study, along with sample preparation, sequencing, and computational analysis. It highlights advances in understanding cell types. Gene activity and cell interactions. Along with the challenges and limitations of scRNA-seq, this review highlights the importance of scRNA-seq in understanding complex biological processes and diseases. It is an essential resource for future research and method development in this field, which will help to build personalized treatment.

Tumor-infiltrating B lymphocytes (TILBs) play a pivotal role in shaping the immune microenvironment of tumors (TIME) and in the progression of lung adenocarcinoma (LUAD). However, there remains a scarcity of research that has thoroughly and systematically delineated the characteristics of TILBs in LUAD.

The research employed single-cell RNA sequencing from the GSE117570 dataset to identify markers linked to TILBs. A comprehensive machine learning approach, utilizing ten distinct algorithms, facilitated the creation of a TILB-related index (BRI) across the TCGA, GSE31210, and GSE72094 datasets. We used multiple algorithms to evaluate the relationships between BRI and TIME, as well as immune therapy-related biomarkers. Additionally, we assessed the role of BRI in predicting immune therapy response in two datasets, GSE91061 and GSE126044.

BRI functioned as an independent risk determinant in LUAD, demonstrating a robust and reliable capacity to predict overall survival rates. We observed significant differences in the scores of B cells, M2 macrophages, NK cells, and regulatory T cells between the high and low BRI score groups. Notably, BRI was found to inversely correlate with cytotoxic CD8+ T-cell infiltration (r = -0.43, p < 0.001) and positively correlate with regulatory T cells (r = 0.31, p = 0.008). We also found that patients with lower BRI were more likely to respond to immunotherapy and were associated with reduced IC50 values for standard chemotherapy and targeted therapy drugs, in contrast to higher BRI. Additionally, the BRI-based survival prediction nomogram demonstrated significant promise for clinical application in predicting the 1-, 3-, and 5-year overall survival rates among LUAD patients.

Our study developed a BRI model using ten different algorithms and 101 algorithm combinations. The BRI could be a valuable tool for risk stratification, prognosis, and selection of treatment approaches.

Most eukaryotic genes express more than one mature mRNA, defined as transcript variants. This complex phenomenon arises from various mechanisms, such as using alternative transcription start sites and alternative post-transcriptional processing events. The resulting transcript variants can lead to synthesizing proteins that possess distinct functional domains or may even generate noncoding RNAs, each with unique roles in cellular processes. The generation of these transcript variants is not merely a random occurrence; it is cell-type specific and varies with developmental stages, aging processes, or pathogenesis of diseases. This highlights the biological significance of transcript variants in regulating gene expression and their potential impact on cellular functionality. Despite the biological importance, investigating transcript variants has been hampered by challenges associated with detecting their expression. This review article addresses the advancements in molecular techniques in detecting transcript variants. Traditional methods such as RT-PCR and RT-qPCR can easily detect known transcript variants using primers that target unique exons associated with the variants. Other techniques like RACE-PCR and hybridization-based methods, including Northern blotting, RNase protection assays, and microarrays, have also been utilized to detect transcript variants. Nevertheless, RNA sequencing (RNA-Seq) has emerged as a powerful technique for identifying transcript variants, especially those with previously unknown sequences. The effectiveness of RNA sequencing in transcript variant detection depends on the specific sequencing approach and the precision of data analysis. By understanding the strengths and weaknesses of each laboratory technique, researchers can develop more effective strategies for detecting mRNA transcript variants. This ability will be crucial for our comprehensive understanding of gene regulation and the implications of transcript diversity in various biological contexts.

To elucidate the fundamental principles of single-cell RNA sequencing (scRNA-seq) and summarize its application in asthma research, aiming to enhance understanding of asthma pathophysiology and guide future research directions.

Recent advances and emerging research in scRNA-seq and its role in the pathogenesis of asthma.

This review incorporates studies that analyzed the heterogeneity of asthma cell types and their functional states using scRNA-seq, with particular emphasis on immune cells and airway remodeling. The selection of specific cell types and markers was based on their relevance to asthma pathogenesis, and we discuss the rationale for favoring certain scRNA-seq technologies in these investigations.

ScRNA-seq technology has provided insights into the key mechanisms underlying inflammation and airway remodeling in asthma. It has uncovered the diversity of immune cell subtypes and their specific roles in asthma pathogenesis, revealing critical pathways that contribute to disease progression. These findings offer a theoretical foundation for the development of targeted therapeutic strategies, paving the way for personalized medicine and improved patient outcomes.

ScRNA-seq reveals the complex heterogeneity and functional roles of immune cells in asthma, offering key insights into disease mechanisms and the potential for targeted therapies. However, challenges remain, such as the need for further refinement of data integration methods and addressing the limited clinical applicability of current findings. Future research should focus on overcoming these limitations, improving cell type annotation, and expanding studies to include longitudinal and clinical data to better understand disease dynamics and therapy responses.

Pancreatic adenocarcinoma (PAAD) is a fatal malignant tumor of the digestive system, and immunotherapy has currently emerged as a key therapeutic approach for treating PAAD, with its efficacy closely linked to T-cell subsets and the tumor immune microenvironment. However, reliable predictive markers to guide clinical immunotherapy for PAAD are not available. We analyzed the single-cell RNA sequencing (scRNA-seq) data focused on PAAD from the GeneExpressionOmnibus (GEO) database. Then, the information from the Cancer Genome Atlas (TCGA) database was integrated to develop and validate a prognostic risk model derived from T-cell marker genes. Subsequently, the correlation between these risk models and the effectiveness of immunotherapy was explored. Analysis of scRNA-seq data uncovered six T-cell subtypes and 1837 T-cell differentially expressed genes (DEGs). Combining these data with the TCGA dataset, we constructed a T-cell prognostic risk model containing 16 DEGs, which can effectively predict patient survival and immunotherapy outcomes. We have found that patients in the low-risk group had better prognostic outcomes, increased immune cell infiltration, and signs of immune activation compared to those in the high-risk group. Additionally, analysis of tumor mutation burden showed higher mutation rates in patients with PAAD in the high-risk group. Risk scores with immune checkpoint gene expression and drug sensitivity analysis provide patients with multiple therapeutic targets and drug options. Our study constructed a prognostic risk model for PAAD patients based on T-cell marker genes, providing valuable insights into predicting patient prognosis and the effectiveness of immunotherapy.

Spatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments. Seq-Scope repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, overcoming these limitations. It offers submicrometer resolution, high capture efficiency, rapid turnaround time and precise annotation of histopathology at a much lower cost than commercial alternatives. This protocol details the implementation of Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell, allowing the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. We describe the preparation of a fresh-frozen tissue section for both histological imaging and sequencing library preparation and provide a streamlined computational pipeline with comprehensive instructions to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single-cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Aside from array production and sequencing, which can be done in batches, tissue processing, library preparation and running the computational pipeline can be completed within 3 days by researchers with experience in molecular biology, histology and basic Unix skills. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

We employed single-cell transcriptome sequencing to reveal the dynamic gene expression changes during the differentiation of adipose-derived stromal cells (ADSCs) into astrocytes. Single-cell RNA sequencing was conducted on cells from the ADSCs group and the induced groups at 2, 7, 14, and 21 days using the 10 × Chromium platform. Data underwent quality control and dimensionality reduction. Cell differentiation trajectories were constructed using Monocle2, and differentially expressed genes (DEGs) in each cell cluster were identified using differential selection algorithms. DEGs at each time point were annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and regulatory intensities of transcription factors were analyzed using SCENIC. Integrating all groups, a total of five samples were divided into 13 cell clusters (0-12 clusters). DEGs between clusters and those compared with ADSCs at various induced time points showed distinct specificities. Monocle2 constructed cell differentiation trajectories; ADSCs can differentiate into mature astrocytes not only through the direct pathway from the 1 branch to the 3 branch but also through an indirect pathway, involving the 1 branch to the 2 branch before progressing to the 3 branch. SCENIC analysis highlighted the critical regulatory roles of STAT1, MYEF2, and SOX6 transcription factors during the differentiation of ADSCs into astrocytes. ADSCs can differentiate into mature astrocytes through two distinct pathways: direct and indirect. By the 14th day of induction, mature astrocytes have formed, characterized by a cell cycle arrest in mitosis. Further induction leads to degenerative senescence changes in differentiated cells.

Neuroinflammation and abnormal energy metabolism have been shown to significantly contribute to the progression of Alzheimer's disease (AD). Adenylate kinase 5 (AK5), an enzyme predominantly expressed in the brain regulates ATP metabolism, has an unclear role in energy metabolism and neuroinflammation in AD.

The AD datasets were derived from the GEO public database to analyze the expression levels of AK5 in AD and normal samples and to assess the relationship between AK5 expression and the clinical characteristics of AD patients. Functional enrichment analysis was employed to investigate the effects of changes in AK5 expression on energy metabolism and immunoinflammation in AD, as well as the underlying mechanisms. Moreover, the influence of AK5 expression variations on oligodendrocyte development was assessed, and the predicted outcomes were validated through cellular experiments.

Bioinformatic analysis revealed that AK5 was lowly expressed in AD olfactory lobe tissues, accompanied by increased immunoinflammation and apoptosis. Increased expression of AK5 was associated with the activation of AMPK signaling, enhanced oxidative phosphorylation, and overall stimulation of energy metabolism. In oligodendrocytes treated with Aβ1-42, overexpression of AK5 resulted in elevated levels of P-AMPK, SIRT1, and BCL-2 proteins, while reducing the levels of BAX, CASPASE-3, and NF-κB proteins. This modulation activated AMPK signaling, thereby inhibiting neuroinflammation and apoptosis. In contrast, low levels of AK5 expression during early differentiation triggered inflammatory responses and increased apoptosis in oligodendrocytes.

AK5 inhibits AD oligodendrocyte apoptosis by activating the AMPK signaling pathway.

Advances in sequencing technology have led to a dramatic increase in the number of single-cell transcriptomic datasets. In the field of parasitology, these datasets typically describe the gene expression patterns of a given parasite species at the single-cell level under experimental conditions, in specific hosts or tissues, or at different life cycle stages. However, while this wealth of available data represents a significant resource, analysing these datasets often requires expert computational skills, preventing a considerable proportion of the parasitology community from meaningfully integrating existing single-cell data into their work. Here, we present paraCell, a novel software tool that allows the user to visualize and analyse pre-loaded single-cell data without requiring any programming ability. The source code is free to allow remote installation. On our web server, we demonstrated how to visualize and re-analyse published Plasmodium and Trypanosoma datasets. We have also generated Toxoplasma-mouse and Theileria-cow scRNA-seq datasets to highlight the functionality of paraCell for pathogen-host interaction. The analysis of the data highlights the impact of the host interferon-γ response and gene expression profiles associated with disease susceptibility by these intracellular parasites, respectively.

Vascular calcification (VC) poses significant challenges in cardiovascular health. This study employs single-cell transcriptome sequencing to dissect cellular dynamics in this process. We identify distinct cell subgroups, notably in vascular smooth muscle cells (VSMCs), and observe differences between calcified atherosclerotic cores and adjacent regions. Further exploration reveals ID3 as a key gene regulating VSMC function. In vitro experiments demonstrate ID3's interaction with USP1 and E12, modulating cell proliferation and osteogenic differentiation. Animal models confirm the critical role of the USP1/ID3/E12/P21 axis in VC. This study sheds light on a novel regulatory mechanism, offering potential therapeutic targets.

Acute Myeloid Leukemia (AML) is a complex hematological malignancy distinguished by its heterogeneity in genetic aberrations, cellular composition, and clinical outcomes. This diversity complicates the development of effective, universally applicable therapeutic strategies and highlights the necessity for personalized approaches to treatment. In our study, we utilized high-resolution single-cell RNA sequencing from publicly available datasets to dissect the complex cellular landscape of AML. This approach uncovered a diverse array of cellular subpopulations within the bone marrow samples of AML patients. Through meticulous analysis, we identified 156 differentially expressed cytokine-related genes that underscore the nuanced interplay between AML cells and their microenvironment. Leveraging this comprehensive dataset, we constructed a prognostic risk score model based on seven pivotal cytokine-related genes: CCL23, IL2RA, IL3RA, IL6R, INHBA, TNFSF15, and TNFSF18. The mRNA levels of 7 genes in the risk score model have significant different. This model was rigorously validated across several independent AML patient cohorts, showcasing its robust prognostic capability to stratify patients into distinct risk categories. Patients classified under the high-risk category exhibited significantly poorer survival outcomes compared to their low-risk counterparts, underscoring the model's clinical relevance. Additionally, our in-depth investigation into the immune landscape revealed marked differences in immune cell infiltration and cytokine signaling between the identified risk groups, shedding light on potential immune-mediated mechanisms driving disease progression and treatment resistance. This comprehensive analysis not only advances our understanding of the cellular and molecular underpinnings of AML but also introduces a novel, clinically applicable risk score model. This tool holds significant promise for enhancing the precision of prognostic assessments in AML, thereby paving the way for more tailored and effective therapeutic interventions. Our findings represent a pivotal step toward the realization of personalized medicine in the management of AML, offering new avenues for research and treatment optimization in this challenging disease landscape.

Ovarian cancers (OC) and cervical cancers (CC) have poor survival rates. Tumor-infiltrating lymphocytes (TILs) play a pivotal role in prognosis, but shared immune mechanisms remain elusive.

We integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to explore immune regulation in OC and CC, focusing on the PI3K/AKT pathway and FLT3 as key modulators. Seurat and Harmony were employed for batch correction and dimensionality reduction. FLT3 expression was mapped with spatial data from 10 × Genomics.

FLT3, identified as a regulator through the PI3K/AKT pathway, showed positive correlations with T cells, NK cells, and B cells. FLT3-high regions exhibited increased immune infiltration, particularly in CC, enhancing survival outcomes.

This study provides the first spatially resolved evidence of FLT3's immune-modulatory role in OC and CC, positioning it as a promising immunotherapeutic target. FLT3-targeted strategies may offer new options for patients resistant to conventional therapies.

Myostatin is a paracrine myokine that regulates muscle mass in a variety of species, including humans. In this work, we report a functional role for myostatin as an endocrine hormone that directly promotes pituitary follicle-stimulating hormone (FSH) synthesis and thereby ovarian function in mice. Previously, this FSH-stimulating role was attributed to other members of the transforming growth factor-β family, the activins. Our results both challenge activin's eponymous role in FSH synthesis and establish an unexpected endocrine axis between skeletal muscle and the pituitary gland. Our data also suggest that efforts to antagonize myostatin to increase muscle mass may have unintended consequences on fertility.

Diabetic retinopathy is a major ocular complication of diabetes, characterized by progressive retinal microvascular damage and significant visual impairment in working-age adults. Traditional bulk RNA sequencing offers overall gene expression profiles but does not account for cellular heterogeneity. Single-cell RNA sequencing overcomes this limitation by providing transcriptomic data at the individual cell level and distinguishing novel cell subtypes, developmental trajectories, and intercellular communications. Researchers can use single-cell sequencing to draw retinal cell atlases and identify the transcriptomic features of retinal cells, enhancing our understanding of the pathogenesis and pathological changes in diabetic retinopathy. Additionally, single-cell sequencing is widely employed to analyze retinal organoids and single extracellular vesicles. Single-cell multi-omics sequencing integrates omics information, whereas stereo-sequencing analyzes gene expression and spatiotemporal data simultaneously. This review discusses the protocols of single-cell sequencing for obtaining single cells from retina and accurate sequencing data. It highlights the applications and advancements of single-cell sequencing in the study of normal retinas and the pathological changes associated with diabetic retinopathy. This underscores the potential of these technologies to deepen our understanding of the pathogenesis of diabetic retinopathy that may lead to the introduction of new therapeutic strategies.

Turner syndrome (TS) can be determined by karyotype analysis, marked by the loss of one X chromosome in females. However, the genes involved in autoimmunity in TS patients remain unclear. In this study, we aimed to analyze differences in immune gene expression between a patient with TS, a healthy female, and a female patient with Graves' disease using single-cell RNA sequencing (scRNA-seq) analysis of antigen-specific CD4(+) T cells. We identified 43 differentially expressed genes in the TS patient compared with the healthy female and the female patient with Graves' disease. Many of these genes have previously been suggested to play a role in immune system regulation. This study provides valuable insights into the differences in immune-related gene expression between TS patients, healthy individuals, and those with autoimmune diseases.

Kidney renal clear cell carcinoma (KIRC) is the most prevalent subtype of kidney cancer. Although multiple therapeutic agents have been proven effective in KIRC, their clinical application has been hindered by a lack of reliable biomarkers. This study focused on the prognostic value and function of drug absorption, distribution, metabolism, and excretion- (ADME-) related genes (ARGs) in KIRC to enhance personalized therapy. The critical role of ARGs in KIRC microenvironment was confirmed by single cell RNA-seq analysis and spatial transcriptome sequencing analysis for the first time. Then, an ADME-related prognostic signature (ARPS) was developed by the bulk RNA-seq analysis. The ARPS, created through Cox regression, LASSO, and stepAIC analyses, identified eight ARGs that stratified patients into high-risk and low-risk groups. High-risk patients had significantly poorer overall survival. Multivariate analysis confirmed the independent predictive ability of ARPS, and an ARPS-based nomogram was constructed for clinical application. Gene ontology and KEGG pathway analyses revealed immune-related functions and pathways enriched in these groups, with low-risk patients showing better responses to immunotherapy. Finally, the expression of ARGs was validated by qRT-PCR and Western blotting experiments. These findings underscore the prognostic significance of ARPS in KIRC and its potential application in guiding personalized treatment strategies.

Myocardial metabolism is closely related to functional changes after myocardial infarction (MI).

This study aimed to present an integrative examination of human ischemic cardiomyopathy.

We used both GSE121893 single-cell suspension sequencing and GSE19303 transcription microarray data sets from the GEO database, along with a murine MI model for full-spectrum metabolite detection. Through a systematic investigation that involved differential metabolite identification and functional enrichment analysis, we shed light on the pivotal role of energy metabolism dysregulation in the progression of MI.

Our findings revealed an association between the core regulatory genes CDKN1A, FOS, ITGB4, and MAP2K1 and the underlying pathophysiology of the disease. These genes are identified as critical elements in the complex landscape of myocardial ischemic disorder, highlighting novel insights into therapeutic targets and the intricate biological mechanisms involved.

This analysis provides a framework for future research on the metabolic alterations associated with MI.

Clinical application of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is a promising approach for the treatment of heart diseases. However, the tumorigenicity of hiPSC-CMs remains a concern for their clinical applications and the composition of the hiPSC-CM subtypes need to be clearly identified.

In the present study, hiPSC-CMs were induced from hiPSCs via modulation of Wnt signaling followed by glucose deprivation purification. The structure, function, subpopulation composition, and tumorigenic risk of hiPSC-CMs were evaluated by single-cell RNA sequencing (scRNAseq), whole exome sequencing (WES), and integrated molecular biology, cell biology, electrophysiology, and/or animal experiments.

The high purity of hiPSC-CMs, determined by flow cytometry analysis, was generated. ScRNAseq analysis of differentiation day (D) 25 hiPSC-CMs did not identify the transcripts representative of undifferentiated hiPSCs. WES analysis showed a few newly acquired confidently identified mutations and no mutations in tumor susceptibility genes. Further, no tumor formation was observed after transplanting hiPSC-CMs into NOD-SCID mice for 3 months. Moreover, D25 hiPSC-CMs were composed of subtypes of ventricular-like cells (23.19%) and atrial-like cells (66.45%) in different cell cycle stages or mature levels, based on the scRNAseq analysis. Furthermore, a subpopulation of more mature ventricular cells (3.21%) was identified, which displayed significantly up-regulated signaling pathways related to myocardial contraction and action potentials. Additionally, a subpopulation of cardiomyocytes in an early differentiation stage (3.44%) experiencing nutrient stress-induced injury and heading toward apoptosis was observed.

This study confirmed the biological safety of hiPSC-CMs and described the composition and expression profile of cardiac subtypes in hiPSC-CMs which provide standards for quality control and theoretical supports for the translational applications of hiPSC-CMs.

Renal cell carcinoma (RCC), exhibiting remarkable heterogeneity, can be highly infiltrated by regulatory T cells (Tregs). However, the relationship between Treg and the heterogeneity of RCC remains to be explored.

We acquired single-cell RNA-seq profiles and 537 bulk RNA-seq profiles of TCGA-KIRC cohort. Through clustering, monocle2 pseudotime and prognostic analyses, we identified Treg states-related prognostic genes (TSRPGs), then constructing the RCC Treg states-related prognostic classification (RCC-TSC). We also explored its prognostic significance and multi-omics landmarks. Additionally, we utilized correlation analysis to establish regulatory networks, and predicted candidate inhibitors. More importantly, in Xinhua cohort of 370 patients with kidney neoplasm, we used immunohistochemical (IHC) staining for classification, then employing statistical analyses including Chi-square tests and multivariate Cox proportional hazards regression analysis to explore its clinical relevance.

We defined 44 TSRPGs in four different monocle states, and identified high immune infiltration RCC (HIRC, LAG3+, Mki67+) as the highly exhausted subtype with the worst prognosis in RCC-TSC (p < 0.001). BATF-LAG3-immune cells axis might be its underlying metastasis-related mechanism. Immunotherapy and inhibitors including sunitinib potentially conferred best therapeutic effects for HIRC. Furthermore, we successfully validated HIRC subtype as an independent prognostic factor within the Xinhua cohort (OS, HR = 16.68, 95% CI = 1.88-148.1, p = 0.011; PFS, HR = 4.43, 95% CI = 1.55-12.6, p = 0.005).

Through integrated bioinformatics analysis and a large-sample retrospective clinical study, we successfully established RCC-TSC and a diagnostic kit, which could stratify RCC patients with different prognosis and to guide personalized treatment.

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.

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

This study addresses the challenges in the early diagnosis of deep infiltrating endometriosis (DIE) by exploring the potential role of the deubiquitinating enzyme USP14. By analyzing the GSE141549 dataset from the Gene Expression Omnibus (GEO) database, using bioinformatics methods and three machine learning algorithms (LASSO, Random Forest, and Support Vector Machine), the key feature gene USP14 was identified. The results indicated that USP14 is significantly upregulated in DIE and exhibits good predictive value (AUC = 0.786). Further analysis revealed the important role of USP14 in muscle function, cellular growth factor response, and maintenance of chromosome structure, and its close association with various immune cell functions. Immunohistochemical staining confirmed the high expression of USP14 in DIE tissues. This study provides a new molecular target for the early diagnosis of DIE, which holds significant clinical implications and potential application value.

Transforming growth factor (TGF)β1 induces plasma membrane (PM) accumulation of glucose transporter 1 (Glut1) required for glycolysis of hepatic stellate cells (HSCs) and HSC activation. This study aimed to understand how Glut1 is anchored/docked onto the PM of HSCs.

HSC expression of protein kinase M zeta isoform (PKMζ) was detected by reverse transcription polymerase chain reaction (RT-PCR), Western blotting, and immunofluorescence. PKMζ level was manipulated by short hairpin RNA (shRNA) or overexpression; HSC activation was assessed by cell expression of activation markers; PM Glut1, glucose uptake, and glycolysis of HSCs were analyzed by biotinylation, 2-NBDG-based assay, and Seahorse Glycolysis Stress Test. Phospho-mutants of vasodilator-stimulated phosphorylated protein (VASP) were created by site-directed mutagenesis. TGFβ transcriptome was obtained by RNA sequencing. Single-cell RNA sequencing datasets and immunofluorescence were leveraged to analyze PKMζ expression in cancer-associated fibroblasts (CAFs) of colorectal liver metastases. Function of HSC PKMζ was determined by tumor/HSC co-implantation study.

Primary human and murine HSCs express PKMζ, but not full-length PKCζ. PKMζ knockdown suppresses, whereas PKMζ overexpression potentiates PM accumulation of Glut1, glycolysis, and HSC activation induced by TGFβ1. Mechanistically, PKMζ binds to and induces VASP phosphorylation at serines 157 and 239 facilitating anchoring/docking of Glut1 onto the PM of HSCs. PKMζ expression is increased in the CAFs of murine and patient colorectal liver metastases compared with quiescent HSCs. Targeting PKMζ suppresses transcriptome, CAF activation of HSCs, and colorectal tumor growth in mice.

Because HSCs are also a major contributor of liver fibrosis, our data highlight PKMζ and VASP as targets to inhibit metabolic reprogramming, HSC activation, liver fibrosis, and the pro-metastatic microenvironment of the liver.

Microorganisms co-exist and co-evolve in nature, forming intricate ecological communities. The interspecies cross-talk within these communities creates and sustains their great biosynthetic potential, making them an important source of natural medicines and high-value-added chemicals. However, conventional investigations into microbial metabolites are typically carried out in pure cultures, resulting in the absence of specific activating factors and consequently causing a substantial number of biosynthetic gene clusters to remain silent. This, in turn, hampers the in-depth exploration of microbial biosynthetic potential and frequently presents researchers with the challenge of rediscovering compounds. In response to this challenge, the coculture strategy has emerged to explore microbial biosynthetic capabilities and has shed light on the study of cross-talk mechanisms. These elucidated mechanisms will contribute to a better understanding of complex biosynthetic regulations and offer valuable insights to guide the mining of secondary metabolites. This review summarizes the research advances in microbial cross-talk mechanisms, with a particular focus on the mechanisms that activate the biosynthesis of secondary metabolites. Additionally, the instructive value of these mechanisms for developing strategies to activate biosynthetic pathways is discussed. Moreover, challenges and recommendations for conducting in-depth studies on the cross-talk mechanisms are presented.

This study investigates the role of Annexin A1 (ANXA1) in regulating T cell function and its implications in bone marrow adiposity in aplastic anaemia (AA). Utilizing single-cell sequencing analysis, we compared bone marrow tissues from AA patients and healthy individuals, focusing on T cell subgroups and their impact on bone marrow pathology. Our findings reveal a significant activation of CD8+ T cells in AA, driven by reduced ANXA1 expression. This heightened T cell activity promotes adipogenesis in bone marrow-derived mesenchymal stem cells via IFN-γ secretion. Overexpression of ANXA1 was found to suppress this process, suggesting its therapeutic potential in AA treatment. The study highlights ANXA1 as a crucial regulator in the AA-associated immune microenvironment and bone marrow adiposity. KEY POINTS: This study found that ANXA1 is significantly downregulated in AA and provides detailed insights into its critical role in the disease. The study demonstrates the excessive activation of CD8+ T cells in the progression of AA. The research shows that the overexpression of ANXA1 can effectively inhibit the activation of CD8+ T cells. The study confirms that overexpression of ANXA1 reduces the secretion of the cytokine IFN-γ, decreases adipogenesis in bone marrow-derived mesenchymal stem cells and may improve AA symptoms. This research provides new molecular targets for the treatment of AA.

This study evaluated the cellular heterogeneity and molecular mechanisms of hepatocellular carcinoma (HCC). Single cell RNA sequencing (scRNA-seq), transcriptomic data, histone lactylation-related genes were collected from public databases. Cell-cell interaction, trajectory, pathway, and spatial transcriptome analyses were executed. Differential expression and survival analyses were conducted. Western blot, Real-time reverse transcription PCR (qRT-PCR), and Cell Counting Kit 8 (CCK8) assay were used to detect the expression of αSMA, AKR1B10 and its target genes, and verify the roles of AKR1B10 in HCC cells. Hepatic stellate cell (HSC) subgroups strongly interacted with tumor cell subgroups, and their spatial distribution was heterogeneous. Two candidate prognostic genes (AKR1B10 and RMRP) were obtained. LONP1, NPIPB3, and ZSWIM6 were determined as AKR1B10 targets. Besides, the expression levels of AKR1B10 and αSMA were significantly increased in LX-2 + HepG2 and LX-2 + HuH7 groups compared to those in LX-2 group, respectively. sh-AKR1B10 significantly inhibited the HCC cell proliferation and change the expression of AKR1B10 target genes, Bcl-2, Bax, Pan Kla, and H3K18la at protein levels. Our findings unveil the pivotal role of HSCs in HCC pathogenesis through regulating histone lactylation.

Tuberculosis (TB) is a chronic disease caused by Mycobacterium tuberculosis (M.tb) and responsible for millions of deaths worldwide each year. It has a complex pathogenesis that primarily affects the lungs but can also impact systemic organs. In recent years, single-cell sequencing technology has been utilized to characterize the composition and proportion of immune cell subpopulations associated with the pathogenesis of TB disease since it has a high resolution that surpasses conventional techniques. This paper reviews the current use of single-cell sequencing technologies in TB research and their application in analysing specimens from various sources of TB, primarily peripheral blood and lung specimens. The focus is on how these technologies can reveal dynamic changes in immune cell subpopulations, genes and proteins during disease progression after M.tb infection. Based on the current findings, single-cell sequencing has significant potential clinical value in the field of TB research. Next, we will focus on the real-world applications of the potential targets identified through single-cell sequencing for diagnostics, therapeutics and the development of effective vaccines.

To understand the cellular and molecular dynamics in the early stages of lung cancer, we explored a mouse model of orthotopic tumor transplant created from the Lewis Lung Carcinoma (LLC) cell line. Employing single-cell RNA sequencing, we analyzed the cellular landscape during tumor engraftment, focusing particularly on LLC cells harboring the Kras G12C mutation. This allowed us to identify LLC tumor cells via the detection of mutant Kras transcripts and observe elevated levels of Myc and mesenchymal gene expression. Moreover, our study revealed significant alterations in the lung microenvironment, including the activation of tissue remodeling genes in fibroblasts and the downregulation of MHC class II genes in myeloid subsets. Additionally, T/NK cell subsets displayed more regulatory phenotypes, coupled with reduced proliferation in CD8+ T cells. Collectively, these findings enhance our understanding of lung cancer progression, particularly in a tumor microenvironment with low immunogenicity. [BMB Reports 2024; 57(11): 484-489].

Cardiovascular diseases (CVDs) impose a significant burden worldwide. Despite the elucidation of the etiology and underlying molecular mechanisms of CVDs by numerous studies and recent discovery of effective drugs, their morbidity, disability, and mortality are still high. Therefore, precise risk stratification and effective targeted therapies for CVDs are warranted. Recent improvements in single-cell RNA sequencing and spatial transcriptomics have improved our understanding of the mechanisms and cells involved in cardiovascular phylogeny and CVDs. Single-cell RNA sequencing can facilitate the study of the human heart at remarkably high resolution and cellular and molecular heterogeneity. However, this technique does not provide spatial information, which is essential for understanding homeostasis and disease. Spatial transcriptomics can elucidate intracellular interactions, transcription factor distribution, cell spatial localization, and molecular profiles of mRNA and identify cell populations causing the disease and their underlying mechanisms, including cell crosstalk. Herein, we introduce the main methods of RNA-seq and spatial transcriptomics analysis and highlight the latest advances in cardiovascular research. We conclude that single-cell RNA sequencing interprets disease progression in multiple dimensions, levels, perspectives, and dynamics by combining spatial and temporal characterization of the clinical phenome with multidisciplinary techniques such as spatial transcriptomics. This aligns with the dynamic evolution of CVDs (e.g., "angina-myocardial infarction-heart failure" in coronary artery disease). The study of pathways for disease onset and mechanisms (e.g., age, sex, comorbidities) in different patient subgroups should improve disease diagnosis and risk stratification. This can facilitate precise individualized treatment of CVDs.

The objective of this study was to profile the transcriptional profiles of peripheral blood mononuclear cells (PBMCs) and their immune repertoires affected by anti-synthetase syndrome (ASS) at the single-cell level.

We performed single-cell RNA sequencing (scRNA-seq) analysis of PBMCs and bulk RNA sequencing for patients with ASS (N=3) and patients with anti-melanoma differentiation-associated gene 5-positive dermatomyositis (MDA5+ DM, N=3) along with healthy controls (HCs, N=4). As ASS and MDA5+ DM have similar organ involvements, MDA5+ DM was used as a disease control. The immune repertoire was constructed by reusing the same scRNA-seq datasets. Importantly, flow cytometry was performed to verify the results from the scRNA-seq analysis.

After meticulous annotation of PBMCs, we noticed a significant decrease in the proportion of mucosal-associated invariant T (MAIT) cells in ASS patients compared to HCs, while there was a notable increase in the proportion of proliferative NKT cells. Compared with MDA5+ DM patients, in their PBMCs ASS patients presented substantial enrichment of interferon pathways, which were primarily mediated by IFN-II, and displayed a weak immune response. Furthermore, ASS patients exhibited more pronounced metabolic abnormalities, which may in turn affect oxidative phosphorylation pathways. Monocytes from ASS patients appear to play a crucial role as receptive signaling cells for the TNF pathway. Immunophenotyping analysis of PBMCs from ASS patients revealed an increasing trend for the clone type CQQSYSTPWTF.

Using single-cell genomic datasets of ASS PBMCs, we revealed a distinctive profile in the immune system of individuals with ASS, compared to that with MDA5+ DM or healthy controls.