Kidney cancer is a worldwide prevalent urological malignancy and the leading cause of death. Sequencing and omics studies play a crucial role in unraveling its molecular mechanisms and diagnostic, prognostic, and therapeutic relevance. This study aims to offer a comprehensive review of the evolving trends and hotspots of sequencing and omics studies in kidney neoplasms.

We conducted the retrieval of scientific publications on sequencing and omics studies in kidney neoplasms from the Web of Science Core Collection (WoSCC) on July 3, 2023. The R-based bibliometrix package, VOSviewer, and CiteSpace were utilized to conduct the holistic bibliometric analysis to obtain objective and data-driven results. A comprehensive consultation of papers was then proceeded for an in-depth review.

Our investigation yielded a dataset containing 1260 records from 509 sources, with 43,404 references, from 1960 to 2023. Publication and citation frequencies have been consistently growing. In country analysis, China and the USA led the research, displaying substantial collaboration. Notable contributors like TEH BT, SAUTER G, and FUTREAL PA shaped this research landscape. Key journals such as PLoS One, Cancer Research, and New England Journal of Medicine actively participated in and significantly influenced this field. Distinguished publications and references were also revealed, along with their historical citation and co-citation relationships. A panel of keywords, including RCC, biomarker, and multi-omics data were identified and clustered.

We obtained a profound understanding of the developing trends and hotspots of research investigating sequencing and omics studies in kidney neoplasms. Specifically, we have highlighted three hotspots: "explore molecular mechanisms of RCC pathogenesis, progression, and metastasis", "identify molecular biomarkers of RCC for diagnosis, prognosis, and therapeutics", "investigate tumor heterogeneity and tailor personalized therapeutic strategies for RCC". Hopefully, our study will serve as a valuable reference for scientific researchers and clinical practitioners.

Leveraging the capacity to precisely manipulate and analyze individual cells, single-cell technology has rapidly become an indispensable tool in the advancement of cell-based drug delivery systems and innovative cell therapies. This technology offers powerful means to address cellular heterogeneity and significantly enhance therapeutic efficacy. Recent breakthroughs in techniques such as single-cell electroporation, mechanical perforation, and encapsulation, particularly when integrated with microfluidics and bioelectronics, have led to remarkable improvements in drug delivery efficiency, reductions in cytotoxicity, and more precise targeting of therapeutic effects. Moreover, single-cell analyses, including advanced sequencing and high-resolution sensing, offer profound insights into complex disease mechanisms, the development of drug resistance, and the intricate processes of stem cell differentiation. This review summarizes the most significant applications of these single-cell technologies, highlighting their impact on the landscape of modern biomedicine. Furthermore, it provides a forward-looking perspective on future research directions aimed at further optimizing drug delivery strategies and enhancing therapeutic outcomes in the treatment of various diseases.

Colorectal cancer (CRC) is one of the most common malignant tumors in the digestive system worldwide, with its mortality ranking second among all cancers. Studies have indicated that disruptions in circadian rhythm (CR) are associated with the occurrence of various cancers; however, the relationship between CR and CRC requires further evidence, and research on the application of CR in CRC is still limited.

In this study, we employed both bulk and single-cell RNA sequencing to explore the dysregulation of CR in patients with CRC. By constructing a CR subtype classifier, we conducted an in-depth analysis of the prognostic significance, the status of the tumor microenvironment, and response to immune checkpoint blockade (ICB) therapy between different CR clusters. Furthermore, we developed a CR scoring system (CRS) using machine learning to predict overall survival and identified several genes as potential targets affecting CRC prognosis.

Our findings revealed significant alterations in CR genes and status between CRC and normal tissues using bulk and single-cell transcriptome sequencing. Patients with CRC could be categorized into two distinct CR clusters (CR cluster 1 and 2). The prognosis of CR cluster 2, with higher epithelial-mesenchymal transition (EMT) and angiogenesis scores, was significantly worser than that of CR cluster 1. These clusters exhibited distinct levels of tumor-infiltrating lymphocytes. CR cluster 2 with a notably higher proportion of patients with microsatellite-instability-high (MSI-H), potentially benefit from ICB therapy. The proportion of patients belonging to consensus molecular subtype 4 (CMS4) in CR cluster 2 was also notably higher than in CR cluster 1. Additionally, the CRS combined with tumor stage demonstrated superior overall survival prediction efficacy compared to traditional tumor stage. We revealed a potential link between model genes (LSAMP, MS4A2, NAV3, RAB3B, SIX4) and the disruption of CR and patient prognosis.

This study not only provide new insights into the assessment of CR status in CRC patients but also develop a prognosis model based on CR-related genes, offering a new tool for personalized risk assessment in CRC.

Integrating biological data with in silico modeling offers the transformative potential to develop virtual human models, or "digital twins." These models hold immense promise for deepening our understanding of diseases and uncovering new therapeutic strategies. This approach is especially valuable for diseases lacking reliable models. Here we review current modelling efforts in of human lung development, highlighting the role of interdisciplinary collaboration and key advances toward a digital lung twin.

Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer that often displays resistance to conventional cancer therapies, including chemotherapy and radiation therapy. Targeted treatments, including immunotherapies and small molecular inhibitors, have been associated with improved outcomes. However, variations in the patient response and the development of resistance suggest that more models that better recapitulate the pathogenesis and metastatic mechanisms of ccRCC are required to improve our understanding and disease management. Here, we examined the transcriptional landscapes of in vitro cell culture as well as in vivo orthotopic and metastatic NOD/SCID-γ mouse models of ccRCC using a single patient-derived RCC243 cell line to allow unambiguous comparison between models. In our mouse model assays, RCC243 cells formed metastatic tumors, and all tumors retained clear cell morphology irrespective of model type. Notably, gene expression profiles differed markedly between the RCC243 tumor models-cell culture, orthotopic tumors, and metastatic tumors-suggesting an impact of the experimental model system and whether the tumor was orthotopic or metastatic. Furthermore, we found conserved prognostic markers between RCC243 tumor models and human ccRCC patient datasets, and genes upregulated in metastatic RCC243 were associated with worse patient outcomes.

Over the past decade, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have revolutionized biomedical research, particularly in understanding cellular heterogeneity in kidney diseases. This review summarizes the application and development of scRNA-seq combined with ST in the context of kidney disease. By dissecting cellular heterogeneity at an unprecedented resolution, these advanced techniques have identified novel cell subpopulations and their dynamic interactions within the renal microenvironment. The integration of scRNA-seq with ST has been instrumental in elucidating the cellular and molecular mechanisms underlying kidney development, homeostasis, and disease progression. This approach has not only identified key cellular players in renal pathophysiology but also revealed the spatial organization of cells within the kidney, which is crucial for understanding their functional specialization. This paper highlights the transformative impact of these techniques on renal research that have paved the way for targeted therapeutic interventions and personalized medicine in the management of kidney disease.

Hepatocellular carcinoma (HCC) is the leading cause of tumor-related mortality worldwide. There is an urgent need for predictive biomarkers to guide treatment decisions. This study aimed to identify robust prognostic genes for HCC and to establish a theoretical foundation for clinical interventions.

The HCC datasets were obtained from public databases and then differential expression analysis were used to obtain significant gene expression profiles. Subsequently, univariate Cox regression analysis and PH assumption test were performed, and a risk model was developed using an optimal algorithm from 101 combinations on the TCGA-LIHC dataset to pinpoint prognostic genes. Immune infiltration and drug sensitivity analyses were conducted to assess the impact of these genes and to explore potential chemotherapeutic agents for HCC. Additionally, single-cell analysis was employed to identify key cellular players and their interactions within the tumor microenvironment. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was utilized to validate the roles of these prognostic genes in HCC.

A total of eight prognostic genes were identified (MCM10, CEP55, KIF18A, ORC6, KIF23, CDC45, CDT1, and PLK4). The risk model, constructed based on these genes, was effective in predicting survival outcomes for HCC patients. CEP55 exhibited the strongest positive correlation with activated CD4 T cells. The top 10 drugs showed increased sensitivity in the low-risk group. B cells were identified as key cellular components with the highest interaction numbers and strengths with macrophages in both HCC and control groups. Prognostic genes were more highly expressed in the initial state of B cell differentiation. RT-qPCR confirmed significant upregulation of MCM10, KIF18A, CDC45, and PLK4 in HCC tissues (p< 0.05).

This study successfully identified eight prognostic genes (MCM10, CEP55, KIF18A, ORC6, KIF23, CDC45, CDT1, and PLK4), which provided new directions for exploring the potential pathogenesis and clinical treatment research of HCC.

Preeclampsia, one of the great obstetrical syndromes, manifests through diverse maternal and fetal complications and remains a leading contributor to adverse perinatal outcomes. In this review, we describe our work on single-cell and single-nuclei RNA sequencing to elucidate the molecular mechanisms that underlie early- and late-onset preeclampsia. Analysis of 46 cell types, encompassing approximately 90,000 cells from placental tissues collected after delivery, demonstrated cellular dysregulation in early-onset preeclampsia, whereas late-onset preeclampsia showed comparatively subtle changes. These findings were observed in all cell lines, including all types of trophoblast, lymphoid, myeloid, stromal, and endothelial cells. Key findings in early-onset preeclampsia included disrupted syncytiotrophoblast and extravillous trophoblast angiogenic signaling, characterized by an up-regulation of FLT1 and down-regulation of PGF, consistent with an angiogenic imbalance. The stromal and vascular compartments exhibited stress-induced transcriptomic shifts. Both endothelial cells and pericytes showed evidence of stress, including up-regulation of heat shock proteins and markers of apoptosis. In addition, the inflammation- and stress-responsive states were more abundant in early-onset preeclampsia than in matched controls. Inflammatory pathways were markedly up-regulated in both the maternal and fetal immune cells; for example, we observed a marked increase in pro-inflammatory cytokines, including secreted phosphoprotein 1 and C-X-C motif chemokine ligand 2 and 3. Conversely, late-onset preeclampsia retained adaptive placental features with localized dysregulation of extracellular matrix remodeling and angiogenic markers, underscoring its possible maternal cardiovascular etiology. Single-cell and single-nuclei RNA sequencing investigations of placental tissues support the proposed classification of preeclampsia into a placental dysfunction type, primarily presenting early in pregnancy, and a maternal cardiovascular maladaptation type, primarily presenting later in pregnancy, each with distinct biomarkers, risk factors, and therapeutic targets. The early-onset preeclampsia findings advocate for interventions that target angiogenic pathways, such as RNA-based therapies that target specific cells of the placenta, to modulate soluble fms-like tyrosine kinase-1 levels. In contrast, late-onset preeclampsia management may benefit from maternal cardiovascular optimization, including individualized antihypertensive and metabolic treatments. These results underscore the heterogeneity of preeclampsia, emphasizing the need for individualized diagnostic and therapeutic strategies. This molecular atlas of preeclampsia advances our understanding of the complex interplay among elements of the maternal-placental-fetal array, thereby bridging clinical phenotypes and cellular mechanisms. Future research should focus on integrating these insights into longitudinal studies to develop precision medicine approaches for preeclampsia to enhance outcomes for mothers and neonates.

This study aims to discover sepsis-related biomarkers via in-silico analyses. The single-cell sequencing RNA (sc-RNA) data and metabolism-related genes are obtained from public databases and previous studies, respectively. Cell subpopulations are identified and annotated, followed by performing single-sample geneset enrichment analysis (ssGSEA and identification of differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) is applied to classify specific gene modules, and the key module is subjected to immune infiltration analysis. The communication between the subclusters of monocytes is visualized. Five cell subpopulations (subcluster C1-5) containing a relatively higher percentage of monocytes are identified, with subcluster C4 having the lowest enrichment score of metabolism-related genes. Genes with a higher expression in the subclusters are enriched for antigen processing and presentation of exogenous antigen, lymphocyte differentiation, and leukocyte activation. Subcluster C5 affected other subclusters through galectin 9 (LGALS9)-CD45 and LGALS9-CD44, while other subclusters affected subcluster C5 through MIF-(CD74+C-X-C motif chemokine receptor 4 (CXCR4)) and MIF-(CD74+CD44). Six genes (F-Box Protein 4, FBXO4; Forkhead Box K1, FOXK1; MSH2 with MutS Homolog 2, MSH2; Nop-7-associated 2, NSA2; Transmembrane Protein 128, TMEM128; and SBDS) are determined as the hub genes for sepsis. The 6 hub genes are positively correlated with, among others, monocytes and NK cells, but negatively correlated with neutrophils. This study identifies accurate biomarkers for sepsis, contributing to the diagnosis and treatment of the disease.

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations and impaired apoptosis, all of which lead to excessive proliferation of malignant blood cells in the bone marrow. It is these mutations that cause tumor heterogeneity, which is linked to a higher risk of relapse and death and makes anti-AML treatments like HSCT, chemotherapy, and immunotherapy (ICI, CAR T-cell-based therapies, and cancer vaccines) less effective. Single-cell RNA sequencing (scRNA-seq) also makes it possible to find cellular subclones and profile tumors, which opens up new diagnostic and therapeutic targets for better AML management. The HSCT process works better when genetic and transcriptional information about the patient and donor stem cells is collected. This saves time and lowers the risk of harmful side effects happening in the body.

Ovarian cancer (OV) remains the deadliest gynecological malignancy, with non-coding RNA-mediated transcriptomic deregulation significantly influencing its prognosis and heterogeneous progression. In this study, we prioritized miRNA-mediated gene expression profiles by identifying key negative correlations between miRNA-mRNA pairs. We developed a machine learning-based non-coding index (NCI), incorporating a four-gene signature (GAS1, GFPT2, ZFHX4, and KCNA1) to predict patient prognosis and therapeutic response. Validation across multiple datasets revealed that OV patients with higher NCI scores had significantly poorer survival outcomes and resistance to immunotherapy. Additionally, we established a four-class subtyping taxonomy through unsupervised clustering, validated in four independent datasets. The S1 and S3 subtypes were characterized by high NCI scores, abundant stromal and immune infiltration, with the S3 subtype exhibiting the worst survival. Conversely, the S2 subtype showed downregulation of immune response genes, while the S4 subtype displayed epithelial differentiation and favourable prognosis. Integrative analyses of bulk and single-cell transcriptomic data revealed that the S3 subtype had a significantly higher fibroblast proportion compared to other subtypes, whereas the S1 subtype was marked by high T cell content. Through ridge regression-based drug sensitivity analyses, we prioritized candidate therapeutics for each subtype. Notably, the S3 subtype demonstrated sensitivity to dasatinib but resistance to methotrexate. Finally, we developed a user-friendly Shiny-based website to facilitate the application of our prognostic and subtype classification models (https://jli-bioinfo.shinyapps.io/NCI_online/). This study establishes a critical prognostic marker and proposes a novel molecular classification framework grounded in miRNA-regulated gene expression profiles, advancing our understanding of the non-coding mechanisms driving OV heterogeneity.

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.

Spatially resolved transcriptomics (SRT) simultaneously measure spatial location, histology images, and transcriptional profiles of cells or regions in undissociated tissues. Integrative analysis of multi-modal SRT data holds immense potential for understanding biological mechanisms. Here, we present a flexible multi-modal contrastive learning for the integration of SRT data (MuCST), which joins denoising, heterogeneity elimination, and compatible feature learning. MuCST accurately identifies spatial domains and is applicable to diverse datasets platforms. Overall, MuCST provides an alternative for integrative analysis of multi-modal SRT data ( https://github.com/xkmaxidian/MuCST ).

Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology for elucidating cellular heterogeneity at unprecedented resolution. However, technical limitations such as limited sequencing depth and mRNA capture efficiency often result in zero counts, commonly referred to as "dropout zeros" in scRNA-seq data. These zeros pose significant challenges to downstream analysis, as they can distort the interpretation of cellular transcriptomes. While numerous computational methods have been developed to address this challenge, existing approaches frequently suffer from either insufficient imputation of zeros (under-imputation) or excessive modification of zeros (over-imputation). Here, we propose a precisely balanced imputation (PbImpute) method designed to achieve optimal equilibrium between dropout recovery and biological zero preservation in scRNA-seq data. PbImpute employs a multistage approach: (1) Initial discrimination between technical dropouts and biological zeros through parameter optimization of a new zero-inflated negative binomial (ZINB) distribution model, followed by initial imputation; (2) Application of a uniquely designed static repair algorithm to enhance data fidelity; (3) Secondary dropout identification based on gene expression frequency and partition-specific coefficient of variation; (4) Graph-embedding neural network-based imputation; and (5) Implementation of a uniquely designed dynamic repair mechanism to mitigate over-imputation effects. PbImpute distinguishes itself by uniquely integrating ZINB modeling with static and dynamic repair. This advantageous combined approach achieves a balance between over- and under-imputation, while simultaneously preserving true biological zeros and reducing signal distortion. Comprehensive evaluation using both simulated and real scRNA-seq data sets demonstrated that PbImpute achieves superior performance (F1 Score = 0.88 at 83% dropout rate, ARI = 0.78 on PBMC) in discriminating between technical dropouts and biological zeros compared to state-of-the-art methods. The method significantly improves gene-gene and cell-cell correlation structures, enhances differential expression analysis sensitivity, optimizes clustering resolution and dimensional reduction visualization, and facilitates more accurate trajectory inference. Ablation studies confirmed the essential contribution of both the imputation and repair modules to the method's performance. The code is available at https://github.com/WyBioTeam/PbImpute. By enhancing the accuracy of scRNA-seq data imputation, PbImpute can improve the identification of cell subpopulations and the detection of differentially expressed genes, thereby facilitating more precise analyses of cellular heterogeneity and advancing disease research.

Lineage tracing is a valuable technique that has greatly facilitated the exploration of cell origins and behavior. With the continuous development of single-cell sequencing technology, lineage tracing technology based on the single-cell level has become an important method to study biological development. Single-cell Lineage tracing technology plays an important role in the hematological system. It can help to answer many important questions, such as the heterogeneity of hematopoietic stem cell function and structure, and the heterogeneity of malignant tumor cells in the hematological system. Many studies have been conducted to explore the field of hematology by applying this technology. This review focuses on the superiority of the emerging single-cell lineage tracing technologies of Integration barcodes, CRISPR barcoding, and base editors, and summarizes their applications in the hematology system. These studies have suggested the vast potential in unraveling complex cellular behaviors and lineage dynamics in both normal and pathological contexts.

Single-cell RNA sequencing (scRNA-seq) has enabled the dissection of complex tumor ecosystems. Recognition of malignant cells as an essential step has a profound impact on downstream interpretation. However, most existing computational strategies are based on prior knowledge of canonical cell-type markers. We have developed a marker-free approach, the Seed-Cluster based Approach for NEoplastic cells Recognition (SCANER), to identify malignant cells based on significant gene expression variations caused by genomic instability. Upon analyzing different cancer types, SCANER achieved superior accuracy and robustness in identifying malignant cells, effectively addressing dropout events and tumor purity variations. Besides, SCANER can significantly detect copy number variations (CNVs) in malignant cells compared to nonmalignant cells, which is further confirmed through the paired whole exome sequencing data. In conclusion, SCANER has the potential to facilitate the biological exploration of the tumor ecosystem by accurately identifying malignant cells and it is applicable across various solid cancer types regardless of prior knowledge. SCANER is available at https://github.com/woolingxiang/SCANER.

Single-cell omics technologies have transformed our investigation of genomic, transcriptomic, and proteomic landscapes at the individual cell level. In particular, the application of single-cell RNA sequencing has unveiled the complex transcriptional variations inherent in cardiac cells, offering valuable perspectives into their dynamics. This review focuses on the integration of single-cell omics with induced pluripotent stem cells (iPSCs) in the context of cardiovascular research, offering a unique avenue to deepen our understanding of cardiac biology. By synthesizing insights from various single-cell technologies, we aim to elucidate the molecular intricacies of heart health and diseases. Beyond current methodologies, we explore the potential of emerging paradigms such as single-cell/spatial omics, delving into their capacity to reveal the spatial organization of cellular components within cardiac tissues. Furthermore, we anticipate their transformative role in shaping the future of cardiovascular research. This review aims to contribute to the advancement of knowledge in the field, offering a comprehensive perspective on the synergistic potential of transcriptomic analyses, iPSC applications, and the evolving frontier of spatial omics.

Understanding the status and function of tumor-infiltrating immune cells is essential for improving immunotherapeutic effects and predicting the clinical response in human patients with carcinoma. However, little is known about tumor-infiltrating immune cells, and the corresponding research results in hepatocellular carcinoma (HCC) are limited.

To investigate potential biomarker genes that are important for the development of HCC and to understand how immune cell subsets react throughout this process.

Using single-cell RNA sequencing and T-cell receptor sequencing, the heterogeneity and potential functions of immune cell subpopulations from HCC tissue and normal tissue adjacent to carcinoma, as well as their possible interactions, were analyzed.

Eight T-cell clusters from patients were analyzed and identified using bioinformatics, including six typical major T-cell clusters and two newly identified T-cell clusters, among which Fc epsilon receptor 1G+ T cells were characterized by the upregulation of Fc epsilon receptor 1G, tyrosine kinase binding protein, and T cell receptor delta constant, whereas metallothionein 1E+ T cells proliferated significantly in tumors. Differentially expressed genes, such as regulator of cell cycle, cysteine and serine rich nuclear protein 1, SMAD7 and metallothionein 1E, were identified as significantly upregulated in tumors and have potential as biomarkers. In association with T-cell receptor analysis, we inferred the clonal expansion characteristics of each T-cell cluster in HCC patients.

We identified lymphocyte subpopulations and potential biomarker genes critical for HCC development and revealed the clonal amplification of infiltrating T cells. These data provide valuable resources for understanding the response of immune cell subsets in HCC.

The molecular pathogenesis of ocular adnexal mucosa-associated lymphoid tissue (MALT) lymphoma and IgG4-related ophthalmic disease (IgG4-ROD) remains incompletely understood. Differentiating between the two diseases is vital given that the diagnostic evaluation and treatment approaches can vary significantly; this difficulty in distinction is exacerbated by the absence of specific biomarkers. This study aimed to investigate the differences between these two diseases based on their cellular composition, transcriptional heterogeneity, and the immune microenvironment using single-cell RNA transcriptional sequencing (scRNA-seq) technology.

We collected orbital lacrimal gland region tissue samples from three patients with MALT lymphoma and another three with IgG4-ROD and performed single-cell sequencing experiments. Subsequently, we conducted bioinformatics analyses, including cell subpopulation segmentation and inter-group comparison, tumor cell identification, functional enrichment analysis, and pseudotime trajectory analysis. Furthermore, we analyzed the cellular communication between tumor B-cell and T-cell subsets within the immune microenvironment of MALT lymphoma tissues. We performed immunofluorescence assays to verify the co-expression of receptor-ligand pairs.

A total of six major cell subpopulations were identified, with B-cells and T-cells being the predominant cell types. All B-cell subpopulations in MALT lymphomas are malignant, exhibiting significant intratumoral and intertumoral heterogeneity. Reclustering of the T-cell subpopulation identified five major T-cell subpopulations. Pseudotime analysis revealed that CD4+ naive T-cells in MALT lymphoma patients were highly likely to differentiate into follicular helper T-cells, whereas, in IgG4-ROD patients, CD4+ naive T-cells were highly likely to differentiate into regulatory T-cells. Intercellular communication analysis revealed that the CD27-CD70 immune checkpoint receptor-ligand pair and CXCL13-CXCR5 chemokine receptor-ligand pair were significantly upregulated between malignant B-cells and T-cells subpopulations.

This study is the first to conduct a comparative single-cell transcriptome sequencing analysis of ocular adnexal MALT lymphoma and IgG4-ROD. Our results reveal the cellular composition, key pathways, and critical immune microenvironment implicated in the development of these two diseases. These findings provide important insights into the pathogenesis of these two diseases and highlight the differences between them.

Endometriosis is a common chronic neuroinflammatory disease with a poorly understood pathogenesis. Molecular changes and specific immune cell infiltration in the eutopic endometrium are critical to disease progression. This study aims to explore immune mechanisms and molecular differences in the proliferative eutopic endometrium of endometriosis by integrating bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data, and to develop diagnostic and predictive models for the disease.

Gene expression profiles from the proliferative endometrium of endometriosis patients and healthy controls were obtained from the Gene Expression Omnibus. Single-cell RNA-seq data were processed using R packages, and cell clusters' contributions to endometriosis were calculated. Differentially expressed genes (DEGs) from bulk RNA-seq were intersected with significant mesenchymal cell genes from scRNA-seq, and a predictive model was constructed using LASSO analysis. Key gene mechanisms were explored through Gene Set Enrichment and Variation Analyses. miRNA networks and transcriptional regulation analyses were conducted, and potential drugs were predicted using the Connectivity Map database. RT-qPCR validated key gene expression.

Mesenchymal cells in the proliferative eutopic endometrium were identified as major contributors to endometriosis pathogenesis. LASSO regression identified eight key genes: SYNE2, TXN, NUPR1, CTSK, GSN, MGP, IER2, and CXCL12. The predictive model based on these genes achieved AUC values of 1.00 and 0.8125 in training and validation cohorts. Immune infiltration analysis showed increased CD8+ T cells and monocytes in the eutopic endometrium of endometriosis patients. Drug target prediction indicated that drugs like Retinol, Orantinib, Piperacillin, and NECA were negatively correlated with the expression profiles of endometriosis. RT-qPCR validated gene expression in patients aligned with bioinformatics analysis.

Significant transcriptomic changes and altered immune cell infiltration in the proliferative eutopic endometrium potentially contribute to endometriosis pathogenesis. Our predictive model based on the key genes demonstrates high diagnostic accuracy, offering insights for diagnosis and potential treatment strategies.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for understanding cellular heterogeneity, providing unprecedented resolution in molecular regulation analysis. Existing supervised learning approaches for cell type annotation primarily utilize gene expression profiles from scRNA-seq data. Although some methods incorporated gene interaction network information, they fail to use cell-specific gene association networks. This limitation overlooks the unique gene interaction patterns within individual cells, potentially compromising the accuracy of cell type classification. We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). These networks are constructed based on highly variable genes and inherently capture both gene expression patterns and gene association network structure features. Extensive experimental validation demonstrates that WCSGNet consistently achieves superior cell type classification performance, ranking among the top-performing methods while maintaining robust stability across diverse datasets. Notably, WCSGNet exhibits a distinct advantage in handling imbalanced datasets, outperforming existing methods in these challenging scenarios. All datasets and codes for reproducing this work were deposited in a GitHub repository (https://github.com/Yi-ellen/WCSGNet).

In tuberculosis (TB) infection, monocytes play a crucial role in regulating the balance between immune tolerance and immune response through various mechanisms. A deeper understanding of the roles of monocyte subsets in TB immune responses may facilitate the development of novel immunotherapeutic strategies and improve TB prevention and treatment.

We retrieved and processed raw single-cell RNA-seq data from SRP247583. Single-cell RNA-seq combined with bioinformatics analysis was employed to investigate the roles of monocytes in TB progression.

Our findings revealed that classical monocytes expressing inflammatory mediators increased as the disease progressed, whereas non-classical monocytes expressing molecules associated with anti-pathogen infection were progressively depleted. Pseudotime analysis delineated the differentiation trajectory of monocytes from classical to intermediate to non-classical subsets. An abnormal differentiation trajectory to non-classical monocytes may represent a key mechanism underlying TB pathogenesis, with CEBPB and CORO1A identified as genes potentially related to TB development. Analysis of key transcription factors in non-classical monocytes indicated that IRF9 was the only downregulated transcription factor with high AUC activity in this subset. The expression of IRF9 exhibited a decreasing trend in both latent TB infection (LTBI) and active TB groups. Furthermore, dysregulation of transcription factor regulatory networks appeared to impair ferroptosis, with ferroptosis-associated genes MEF2C, MICU1, and PRR5 identified as potential targets of IRF9. Through cell communication analysis, we found that interactions between non-classical monocytes and other subpopulations may mediate TB progression, with MIF and LGALS9 highlighted as potential signaling pathways.

This study employs bioinformatics analysis in conjunction with single-cell sequencing technology to uncover the crucial role of monocyte subsets in tuberculosis infection.

Recent advancements in methodologies and technologies have enabled the simultaneous measurement of multiple omics data, which provides a comprehensive understanding of cellular heterogeneity. However, existing methods have limitations in accurately identifying cell types while maintaining model interpretability, especially in the presence of noise.

We propose a novel method called scMFG, which leverages feature grouping and group integration techniques for the integration of single-cell multi-omics data. By organizing features with similar characteristics within each omics layer through feature grouping. Furthermore, scMFG ensures a consistent feature grouping approach across different omics layers, promoting comparability of diverse data types. Additionally, scMFG incorporates a matrix factorization-based approach to enable the integrated results remain interpretable.

We comprehensively evaluated scMFG's performance on four complex real-world datasets generated using diverse sequencing technologies, highlighting its robustness in accurately identifying cell types. Notably, scMFG exhibited superior performance in deciphering cellular heterogeneity at a finer resolution compared to existing methods when applied to simulated datasets. Furthermore, our method proved highly effective in identifying rare cell types, showcasing its robust performance and suitability for detecting low-abundance cellular populations. The interpretability of scMFG was successfully validated through its specific association of outputs with specific cell types or states observed in the neonatal mouse cerebral cortices dataset. Moreover, we demonstrated that scMFG is capable of identifying cell developmental trajectories even in datasets with batch effects.

Our work presents a robust framework for the analysis of single-cell multi-omics data, advancing our understanding of cellular heterogeneity in a comprehensive and interpretable manner.

Diabetic nephropathy (DN) is one of the main causes of end-stage renal disease (ESRD), but its mechanism has not been clearly studied. We utilized integrative transcriptome analysis to explore the pathogenesis of DN.

We conducted an analysis by combining bulk dataset and single-cell transcriptome dataset. Through this approach, we identified that Serpine2 may regulate the 'collagen-containing extracellular matrix' pathway involved in DN. Subsequently, we established DN animal and cell models using db/db mice and mesangial cells (MCs) to validate the role of Serpine2 in DN. In the animal model, we detected the expression level of Serpine2 in DN using western blotting (WB) and immunofluorescence (IF) assays. To further clarify the molecular mechanism of Serpine2 in DN, we knocked down Serpine2 and observed its effects on MCs proliferation and extracellular matrix (ECM) accumulation.

Our single-cell analysis of DN models highlighted a pivotal role for MCs in the disease's initiation. Next, through Cytoscape analysis of differentially expressed genes (DEGs) in MCs, we identified the following 10 hub genes: Acta2, Angpt2, Ccn1, Col4a1, Col4a2, Col8a1, Kdr, Thbs1, Tpm4 and Serpine2. Subsequently, we identified that Serpine2 and Kdr were also significantly DEGs in the bulk analysis of glomeruli. Additionally, our integrated gene set enrichment analysis of bulk dataset and single-cell RNA dataset revealed that the 'collagen-containing extracellular matrix' was a key pathway in DN progression. Serpine2 was one of the crucial genes involved in regulating this pathway. Therefore, we speculated that the regulation of the 'collagen-containing extracellular matrix' pathway by Serpine2 was an important mechanism. Importantly, WB and IF staining confirmed that Serpine2 expression was upregulated in the MCs of diabetic mice. Knockdown of Serpine2 in cultured MCs alleviated high-glucose-induced excessive MCs proliferation and ECM accumulation. Finally, we found that ERK agonist Ro 67-7476 eliminated the effect of Serpine2 siRNA.

In summary, Serpine2 regulates MCs proliferation and ECM synthesis through activation of the ERK1/2 pathway, which is an important pathogenesis mechanism of DN. These findings offer fresh perspectives on the mechanisms of glomerulosclerosis in DN pathogenesis and may provide new targets for treating DN.

Human stem cells derived from the apical papilla (SCAPs) are recognized for their multilineage differentiation potential and their capacity for functional tooth root regeneration. However, the molecular mechanisms underlying odonto/osteogenic differentiation remain largely unexplored. In this study, we utilized single-cell RNA sequencing (scRNA-seq) to conduct an in-depth analysis of the transcriptional changes associated with chemically induced osteogenesis in SCAPs.

scRNA-seq identified SCAPs as distinct subpopulations. Differentially expressed genes (DEGs) and Gene Ontology (GO) analyses were conducted to evaluate the potential function of each cluster. Pseudotime trajectory analysis was employed to elucidate the potential differentiation processes of the identified SCAP populations. To investigate the osteo/odontogenic potential of Deiodinase Iodothyronine Type 2 (DIO2) on SCAPs, we performed alkaline phosphatase staining, western blot analysis, Alizarin Red S staining and immunofluorescence staining. Additionally, SCAP components were transplanted into mouse calvarial defects to evaluate osteogenesis in vivo.

The analysis of cell clusters derived from our scRNA-seq data revealed a significant shift in cellular composition when cells were cultured in a mineralization induction medium compared to those cultured in a complete medium. Both groups exhibited heterogeneity, with some cells intrinsically predisposed to osteogenesis and others appearing to be primed for proliferative functions. Notably, we identified a subpopulation characterized by high expression of DIO2, which exhibited pronounced osteogenic activity during differentiation.

Our study is the first to reveal a shift in the cellular composition of SCAPs when cultured in a mineralization induction medium compared to a complete medium. Following both in vitro and in vivo validation, the DIO2+ subpopulation exhibited the highest transcriptional similarity to osteogenic function, suggesting its potential utility in tissue regeneration applications.

Background: Head and neck squamous cell carcinoma (HNSC) is a globally prevalent malignancy with high mortality rates. RNA-binding proteins (RBPs) are crucial regulators of gene expression and play significant roles in cancer development. However, a comprehensive understanding of RBPs at the single-cell level in HNSC remains limited.ObjectiveThis study aims to investigate the role of RBPs in the stepwise progression of HNSC at the single-cell level, focusing on their expression patterns, prognostic potential, and involvement in key signaling pathways.MethodsWe analyzed single-cell RNA-sequencing data from HNSC samples across four stages, from normal tissue to precancerous leukoplakia, then to primary cancer and finally to metastatic tumors, examining the expression of 2141 previously reported RBPs. We identified RBP-based cell clusters and explored their associations with disease stages, cell types, and cancer progression. A prognostic risk model was developed based on RBPs with significant relevance to patient outcomes.ResultsRBPs displayed distinct cell type-specific expression patterns across different stages of HNSC. We found a significant correlation between RBP-based cell clusters and cancer progression. Notably, a prognostic model was constructed using RBPs such as CELF2, which showed downregulation from early leukoplakia to advanced cancer stages. Fibroblast RBPs were dynamically regulated, particularly in extracellular matrix remodeling, with key proteins like CFL1 and PFN1 linked to improved prognosis. Furthermore, we identified heterogeneity in RBP regulation of the Macrophage Migration Inhibitory Factor (MIF) signaling pathway across cell types during the precancerous stage.ConclusionsOur findings highlight the crucial roles of RBPs in HNSC progression and suggest their potential as therapeutic targets and prognostic markers, offering insights into personalized treatment strategies.

Southern corn rust (SCR), caused by Puccinia polysora Underw (P. polysora), is a catastrophic disease affecting maize, leading to significant global yield losses. The disease manifests primarily as pustules on the upper surface of corn leaves, obscuring our understanding of its cellular heterogeneity, the maize's response to its infection and the underlying gene expression regulatory mechanisms. In this study, we dissected the heterogeneity of maize's response to P. polysora infection using single-cell RNA sequencing. We delineated cell-type-specific gene expression alterations in six leaf cell types, creating the inaugural single-cell atlas of a maize leaf under fungal assault. Crucially, by reconstructing cellular trajectories in susceptible line N110 and resistant line R99 during infection, we identified diverse regulatory programs that fortify R99's resistance across different leaf cell types. This research uncovers an immune-like state in R99 leaves, characterized by the expression of various fungi-induced genes in the absence of fungal infection, particularly in guard and epidermal cells. Our findings also highlight the role of the fungi-induced glycoside hydrolase family 18 chitinase 7 protein (ZmChit7) in conferring resistance to P. polysora. Collectively, our results shed light on the mechanisms of maize resistance to fungal pathogens through comparative single-cell transcriptomics, offering a valuable resource for pinpointing novel genes that bolster resistance to P. polysora.

Gossypium spp. pigment glands are a good model for studying plant secretory cavity structures. GoPGF (GOSSYPIUM PIGMENT GLAND FORMATION) is a well-characterized master transcription factor that controls gland formation in cotton; however, little is known about its transcriptional regulation. This study integrates yeast one-hybrid sequencing data and the previously reported single-cell RNA sequencing data to identify upstream GoPGF binding proteins. Several transcription factors preferentially expressed in pigment gland cells (PGCs) are identified, including the cotton AINTEGUMENTA ortholog GhANT. Silencing of GhANT produces a defective leaf-specific PGC phenotype. Knockdown of GhANT reduces mesophyll gland number and gossypol production, while CRISPR-mediated GhANT knockout suppresses mesophyll development. Overexpression of GhANT increases organ size but not cell size. GhANT binds to two CCG boxes in the GoPGF promoter to trigger GoPGF-GhJUB1-regulated gland formation. Our study dissects the subtle regulation of tissue-specific gland morphogenesis in cotton and provides molecular mechanisms to study secretory cavity structures widespread among vascular plants.

Single-cell lineage tracing based on CRISPR-Cas9 gene editing enables the simultaneous linkage of cell states and lineage history at a high resolution. Despite its immense potential in resolving the cell fate determination and genealogy within an organism, existing implementations of this technology suffer from limitations in recording capabilities and considerable barcode dropout. Here, we introduce DuTracer, a versatile tool that utilizes two orthogonal gene editing systems to record cell lineage history at single-cell resolution in an inducible manner. DuTracer shows the ability to enhance lineage recording with minimized target dropouts and potentially deeper tree depths. Applying DuTracer in mouse embryoid bodies and neuromesodermal organoids illustrates the lineage relationship of different cell types and proposes potential lineage-biased molecular drivers, showcased by identifying transcription factor Foxb1 as a modulator in the cell fate determination of neuromesodermal progenitors. Collectively, DuTracer facilitates the precise and regulatory interrogation of cellular lineages of complex biological processes.

Background: Lung cancer has the highest mortality rate among all cancers, for which immunotherapy can frequently lead to drug resistance. To understand the molecular mechanisms behind immune escape in patients with lung cancer and develop predictive and therapeutic targets, we carried out analytical experiments using single-cell sequencing. Methods: We collected eight tumor tissue samples from eight patients with lung adenocarcinoma and categorized them based on the positive reactions for programmed cell death ligand 1 (PD-L1) expression levels. Single-cell sequencing analysis was employed to create a comprehensive cellular landscape. Uniform Manifold Approximation and Projection was used to show the proportion of immune and endothelial cells, along with a map depicting the distribution of different cell types. Cells were subdivided according to molecular markers; the subpopulations were grouped based on PD-L1 levels and tumor marker-positive reactions. The correlation between the occurrence of the PD-L1 reaction and the response time of immune cells was explored; differential gene expression between the groups was elucidated. Finally, quantitative polymerase chain reaction (qPCR) was used to examine the relationship between key differentially expressed genes and PD-L1 immune escape checkpoint response. Results: A total of 58,810 single cells were analyzed, identifying seven distinct cell types. In the PD-L1-positive sample group, B cells, astrocytes, endothelial cells, outer skin cells, and tissue stem cells were present in higher proportions, whereas T and dendritic cells were the main cells in the PD-L1-negative sample group. According to the molecular markers, the seven cell types were divided into 17 cell clusters, with one cluster classified as tumor cells, showing PD-L1 positivity. Eleven molecular markers with different expression levels were simultaneously screened (NAPSA, MUC1, WFDC2, MYO6, LYZ, IGHG4, IGLL5, IGHM, IGKC, AQP3, and IGFBP7), and their association with the PD-L1/PD-1 immune escape axis response was confirmed by qPCR. Conclusion: Our study suggests that PD-L1-mediated immune escape may occur at a later stage of tumor progression, involving both PD-L1-positive and negative immune cells. Additionally, we identified 11 differentially expressed genes that could provide insights into the potential mechanisms of immune escape in patients with lung cancer. These findings offer promising molecular targets for the detection and treatment of immune escape in clinical settings.

Recent advancements in single-cell RNA sequencing have greatly expanded our knowledge of the heterogeneous nature of tissues. However, robust and accurate cell type annotation continues to be a major challenge, hindered by issues such as marker specificity, batch effects, and a lack of comprehensive spatial and interaction data. Traditional annotation methods often fail to adequately address the complexity of cellular interactions and gene regulatory networks.

We proposed scMCGraph, a comprehensive computational framework that integrates gene expression with pathway activity to accurately annotate cell types within diverse scRNA-seq datasets. Initially, our model constructs multiple pathway-specific views using various pathway databases, which reflect both gene expression and pathway activities. These pathway-specific views are then integrated into a consensus graph. The consensus graph is subsequently utilized to reconstruct the multiple pathway views. Our model demonstrated exceptional robustness and accuracy across various analyses, including cross-platform, cross-time, cross-sample, and clinical dataset evaluations.

scMCGraph represents a significant advance in cell type annotation. The experiments have demonstrated that introducing pathway information significantly improves the learning of cell-cell graphs, with their resulting consensus graph enhancing the predictive performance of cell type prediction. Different pathway databases provide complementary data, and an increase in the number of pathways can also boost model performance. Extensive testing shows that in various cross-dataset application scenarios, scMCGraph consistently exhibits both accuracy and robustness.

The activity of miRNA varies across different cell populations and systems, as part of the mechanisms that distinguish cell types and roles in living organisms and in human health and disease. Typically, miRNA regulation drives changes in the composition and levels of protein-coding RNA and of lncRNA, with targets being down-regulated when miRNAs are active. The term "miRNA activity" is used to refer to this transcriptional effect of miRNAs. This study introduces miTEA-HiRes, a method designed to facilitate the evaluation of miRNA activity at high resolution. The method applies to single-cell transcriptomics, type-specific single-cell populations, and spatial transcriptomics data. By comparing different conditions, differential miRNA activity is inferred. For instance, miTEA-HiRes analysis of peripheral blood mononuclear cells comparing Multiple Sclerosis patients to control groups revealed differential activity of miR-20a-5p and others, consistent with the literature on miRNA underexpression in Multiple Sclerosis. We also show miR-519a-3p differential activity in specific cell populations.

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.

Southern corn rust (SCR) caused by Puccinia polysora Underw. has recently emerged as a focal point of study because of its extensive distribution, significant damage, and high prevalence in maize growing areas such as the United States, Canada, and China. P. polysora is an obligate biotrophic fungal pathogen that cannot be cultured in vitro or genetically modified, thus complicating the study of the molecular bases of its pathogenicity. High temperatures and humid environmental conditions favor SCR development. In severe cases, SCR may inhibit photosynthesis and cause early desiccation of maize, a decrease in kernel weight, and yield loss. Consequently, an expedited and accurate detection approach for SCR is essential for plant protection and disease management. Significant progress has been made in elucidating the pathogenic mechanisms of P. polysora, identifying resistance genes and developing SCR-resistant cultivars. A detailed understanding of the molecular interactions between maize and P. polysora will facilitate the development of novel and effective approaches for controlling SCR. This review gives a concise overview of the biological characteristics and symptoms of SCR, its life cycle, the molecular basis of interactions between maize and P. polysora, the genetic resistance of maize to SCR, the network of maize resistance to P. polysora infection, SCR management, and future perspectives.

Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technique in molecular biology and genomics, revealing the cellular heterogeneity. However, scRNA-seq data often suffer from dropout events, meaning that certain genes exhibit very low or even zero expression levels due to technical limitations. Existing imputation methods for dropout events lack comprehensive evaluations in downstream analyses and do not demonstrate robustness across various scenarios. In response to this challenge, we propose a consensus clustering-based imputation (CCI) method. CCI performs clustering on each subset of data sampling across genes and summarizes clustering outcomes to define cellular similarities. CCI leverages the information from similar cells and employs the similarities to impute gene expression levels. Our comprehensive evaluations demonstrate that CCI not only reconstructs the original data pattern, but also improves the performance of downstream analyses. CCI outperforms existing methods for data imputation under different scenarios, exhibiting accuracy, robustness, and generalization.

With the development of high-throughput sequencing technology, the analysis of single-cell RNA sequencing data has become the focus of current research. Matrix analysis and processing of downstream gene expression after preprocessing is a hot topic for researchers. This paper proposed an iterative block matrix completion algorithm, called SeqBMC, based on matrix factorisation. The algorithm is used to complete the missing value of the gene expression matrix caused by the defect of sequencing technology. The gene frequency of the matrix is used to block the matrix, and then the matrix factorisation algorithm is used to complete the small matrix after the block, and then the biological zeros that may exist in the block matrix are retained. Experimental results show that the matrix completion algorithm can significantly improve the classification performance of the gene expression matrix after completion with 86.81% F1 score, which is conducive to the recognition of cell types in sequencing data. Moreover, this completion method can be completed only by the machine learning method without too much prior knowledge related to biology and has good effects. Compared with ALRA, SeqBMC increased 5.47% accuracy and 5.03% F1 score. It indicates that SeqBMC has significant advantages in the matrix completion of single-cell RNA sequencing data.

Kawasaki disease [KD] is a systemic disorder characterized by acute febrile illness due to widespread medium-vessel vasculitis, mainly affecting children. Despite the ongoing advanced research into the disease pathophysiology and molecular mechanisms, the exact etiopathogenesis of KD is still an enigma. Recently, single-cell RNA sequencing [scRNA-seq], has been utilized to elucidate the pathophysiology of KD at a resolution higher than that of previous methods.

In the present article, we re-emphasize the pivotal role of this high-resolution technique, scRNA-seq, in the characterization of immune cell transcriptomic profile and signaling/response pathways in KD and explore the diagnostic, prognostic, and therapeutic potential of this new technique in KD. Using combinations of the search phrases 'KD, scRNA-seq, CAA, childhood vasculitis' a literature search was carried out on Scopus, Google Scholar, and PubMed until the beginning of 2024.

scRNA-seq presents a transformative tool for dissecting KD at the cellular level. By revealing rare cell populations, gene expression alterations, and disease-specific pathways, scRNA-seq aids in understanding the intricacies of KD pathogenesis. This review will provide new insights into pathogenesis of KD and the field of applications of scRNA-seq in personalized therapeutics for KD in the future.

Pathology, a cornerstone of medical diagnostics and research, is undergoing a revolutionary transformation fueled by digital technology, molecular biology advancements, and big data analytics. Digital pathology converts conventional glass slides into high-resolution digital images, enhancing collaboration and efficiency among pathologists worldwide. Integrating artificial intelligence (AI) and machine learning (ML) algorithms with digital pathology improves diagnostic accuracy, particularly in complex diseases like cancer. Molecular pathology, facilitated by next-generation sequencing (NGS), provides comprehensive genomic, transcriptomic, and proteomic insights into disease mechanisms, guiding personalized therapies. Immunohistochemistry (IHC) plays a pivotal role in biomarker discovery, refining disease classification and prognostication. Precision medicine integrates pathology's molecular findings with individual genetic, environmental, and lifestyle factors to customize treatment strategies, optimizing patient outcomes. Telepathology extends diagnostic services to underserved areas through remote digital pathology. Pathomics leverages big data analytics to extract meaningful insights from pathology images, advancing our understanding of disease pathology and therapeutic targets. Virtual autopsies employ non-invasive imaging technologies to revolutionize forensic pathology. These innovations promise earlier diagnoses, tailored treatments, and enhanced patient care. Collaboration across disciplines is essential to fully realize the transformative potential of these advancements in medical practice and research.

Bone marrow inflammaging is a low-grade chronic inflammation that induces bone marrow aging. Multiple age-related and inflammatory diseases involve bone marrow inflammaging. Whether common pathological pathways exist in bone marrow inflammaging remains unclear.

We collected bone marrow from telomerase-deficient mice (telomerase RNA component, TERCko/ko), 5 × FAD mice and Dmp1 Cre -DTA ki/wt mice and High-fat diet-fed mice (HFD), and lumbar 5 nerve compression mice. We performed scRNA-Seq analysis on bone marrow obtained from these mouse models to investigate the potential shared pathway of bone marrow inflammation.

We identified the monocyte/macrophage lineage was activated via the App-Cd74 axis in multiple aging and inflammatory mouse models. Increased expression of CD38 and Ly6a, and decreased expression of Col1a and Lif in macrophages serve as shared changes in different mouse models. The activated macrophages, interacting with other cells, control the expansion of B cells via the CD52-Siglec-G axis. The Ccl6-Ccr2 and Ccl9-Ccr1 ligand-receptor pairs, along with Fn1 and C3-related pathways in macrophages, were associated with immune cell activation and the recruitment of lymphocytes. Interactions with mesenchymal cells were enriched for integrins (Itga4), Fn1, and adhesion molecules (Vcam1).

Our study demonstrates that monocyte/macrophage lineage stimulation is a key event in bone marrow inflammaging. We identified common differentially expressed genes and activated pathways in this lineage, suggesting potential targets for future interventions.

Our study revealed shared genes and ligand-receptor pairs in the activated monocyte/macrophage lineage within inflammaging bone marrow. These findings offer potential therapeutic targets for cell-specific anti-inflammatory treatments.