Revolutionizing immunology with single-cell RNA sequencing

Integrating multiomics and Single-Cell communication analysis to uncover Ankylosing spondylitis mechanisms

BACKGROUND

Ankylosing spondylitis (AS) is a chronic inflammatory joint disorder, necessitating early diagnosis and effective treatment. The specific mechanism of action of Cassia twigs in the treatment of AS is not fully understood.

METHODS

Blood samples and clinical data from 28,458 individuals (6,101 with AS, 22,357 without AS) were collected. To construct a predictive model, we utilized logistic regressions and machine learning techniques to create a dynamic nomogram. Immune cell infiltration was evaluated using the GSE73754 dataset. Subsequently, we obtained vertebral bone marrow blood from AS patients for 10X single-cell sequencing. We also extracted and purified total RNA from hip joint ligament tissue samples from six AS patients and six non-AS patients. The genes related to the expression of AS and Cassia twigs were analyzed comprehensively, and the specific drug targets were identified by molecular docking. The interactions between immune cells through cell communication analysis were elucidated.

RESULTS

We developed a dynamic nomogram incorporating the neutrophil count (NEUT) and other variables. Neutrophil immune responses were confirmed through immune infiltration analysis utilizing GSE73754. We observed the early involvement of neutrophils in the pathology of AS. The CAT-expressing Cassia twigs gene could be used as a drug target for the treatment of AS. Moreover, comprehensive RNA analysis revealed notable CAT expression in neutrophils and various other immune cells.

CONCLUSIONS

Neutrophils play dual roles in AS, regulating inflammation and initiating differentiation signals to other cells. The CAT gene, which is expressed in Cassia twigs, has emerged as a potential therapeutic target for AS treatment.

Revolutionizing prognostic predictions in colorectal cancer: Macrophage‑driven transcriptional insights from single‑cell RNA sequencing and gene co‑expression network analysis

Tumor-associated macrophages have become important biomarkers for cancer diagnosis, prognosis and therapy. The dynamic changes in macrophage subpopulations significantly impact the outcomes of cancer immunotherapy. Hence, identifying additional macrophage-related biomarkers is essential for enhancing prognostic predictions in colorectal cancer (CRC) immunotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene Expression Omnibus (GEO) database. The data were processed, normalized and clustered using the 'Seurat' package. Cell types within each cluster were annotated using the 'SingleR' package. Weighted gene co-expression network analysis identified modules corresponding to specific cell types. A non-negative matrix factorization algorithm was employed to segregate different clusters based on the selected module. Differentially expressed genes (DEGs) were identified across various clusters and a prognostic model was constructed using lasso regression and Cox regression analyses. The robustness of the model was validated using The Cancer Genome Atlas (TCGA) database and GEO microarrays. Additionally, the prognosis, immune characteristics and response to immune checkpoint inhibitor (ICI) therapy were individually analyzed. The scRNA-seq data from GSE200997, consisting of 23 samples, were analyzed. Dimensionality reduction and cluster identification allowed the isolation of the primary myeloid cell subpopulations. The macrophage-related brown module was identified, which was further divided into two clusters. Using the DEGs from these clusters, a prognostic model was developed, comprising five macrophage-related genes. The robustness of the model was confirmed using microarray datasets GSE17536, GSE38832 and GSE39582, as well as TCGA cohort. Patients classified as high-risk by the present model exhibited poorer survival rates, lower tumor mutation burden, reduced microsatellite instability, lower tumor purity, more severe tumor immune dysfunction and exclusion, and less benefit from ICIs therapy compared with low-risk patients. The present prognostic model shows promise as a biomarker for risk stratification and predicting therapeutic efficacy in patients with CRC. However, further well-designed prospective studies are necessary to validate the findings.

Dissecting L-glutamine metabolism in acute myeloid leukemia: single-cell insights and therapeutic implications

BACKGROUND

Acute myeloid leukemia (AML) is a rapidly progressing blood cancer. The prognosis of AML can be challenging, emphasizing the need for ongoing research and innovative approaches to improve outcomes in individuals affected by this formidable hematologic malignancy.

METHODS

In this study, we used single-cell RNA sequencing (scRNA-seq) from AML patients to investigate the impact of L-glutamine metabolism-related genes on disease progression.

RESULTS

Our analysis revealed increased glutamine-related activity in CD34 + pre-B cells, suggesting a potential regulatory role in tumorigenesis and AML progression. Furthermore, intercellular communication analysis revealed a significant signaling pathway involving macrophage migration inhibitory factor signaling through CD74 + CD44 within CD34 + pre-B cells, which transmit signals to pre-dendritic cells and monocytes. Ligands for this pathway were predominantly expressed in stromal cells, naïve T cells, and CD34 + pre-B cells. CD74, the pertinent receptor, was predominantly detected in a variety of cellular components, including stromal cells, pre-dendritic cells, plasmacytoid dendritic cells, and hematopoietic progenitors. The study's results provide insights into the possible interplay among these cell types and their collective contribution to the pathogenesis of AML. Moreover, we identified 10 genes associated with AML prognosis, including CCL5, CD52, CFD, FABP5, LGALS1, NUCB2, PSAP, S100A4, SPINK2, and VCAN. Among these, CCL5 and CD52 have been implicated in AML progression and are potential therapeutic targets.

CONCLUSIONS

This thorough examination of AML biology significantly deepens our grasp of the disease and presents pivotal information that could guide the creation of innovative treatment strategies for AML patients.

Investigation of human aging at the single-cell level

Human aging is characterized by a gradual decline in physiological functions and an increased susceptibility to various diseases. The complex mechanisms underlying human aging are still not fully elucidated. Single-cell sequencing (SCS) technologies have revolutionized aging research by providing unprecedented resolution and detailed insights into cellular diversity and dynamics. In this review, we discuss the application of various SCS technologies in human aging research, encompassing single-cell, genomics, transcriptomics, epigenomics, and proteomics. We also discuss the combination of multiple omics layers within single cells and the integration of SCS technologies with advanced methodologies like spatial transcriptomics and mass spectrometry. These approaches have been essential in identifying aging biomarkers, elucidating signaling pathways associated with aging, discovering novel aging cell subpopulations, uncovering tissue-specific aging characteristics, and investigating aging-related diseases. Furthermore, we provide an overview of aging-related databases that offer valuable resources for enhancing our understanding of the human aging process.

Single-cell sequencing: Current applications in various tuberculosis specimen types

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.

Comprehensive single-cell and bulk transcriptomic analyses to develop an NK cell-derived gene signature for prognostic assessment and precision medicine in breast cancer

Background

Natural killer (NK) cells play crucial roles in mediating anti-cancer activity in breast cancer (BRCA). However, the potential of NK cell-related molecules in predicting BRCA outcomes and guiding personalized therapy remains largely unexplored. This study focused on developing a prognostic and therapeutic prediction model for BRCA by incorporating NK cell-related genes.

Methods

The data analyzed primarily originated from the TCGA and GEO databases. The prognostic role of NK cells was evaluated, and marker genes of NK cells were identified via single-cell analysis. Module genes closely associated with immunotherapy resistance were identified by bulk transcriptome-based weighted correlation network analysis (WGCNA). Following taking intersection and LASSO regression, NK-related genes (NKRGs) relevant to BRCA prognosis were screened, and the NK-related prognostic signature was subsequently constructed. Analyses were further expanded to clinicopathological relevance, GSEA, tumor microenvironment (TME) analysis, immune function, immunotherapy responsiveness, and chemotherapeutics. Key NKRGs were screened by machine learning and validated by spatial transcriptomics (ST) and immunohistochemistry (IHC).

Results

Tumor-infiltrating NK cells are a favorable prognostic factor in BRCA. By combining scRNA-seq and bulk transcriptomic analyses, we identified 7 NK-related prognostic NKRGs (CCL5, EFHD2, KLRB1, C1S, SOCS3, IRF1, and CCND2) and developed an NK-related risk scoring (NKRS) system. The prognostic reliability of NKRS was verified through survival and clinical relevance analyses across multiple cohorts. NKRS also demonstrated robust predictive power in various aspects, including TME landscape, immune functions, immunotherapy responses, and chemotherapeutic sensitivity. Additionally, KLRB1 and CCND2 emerged as key prognostic NKRGs identified through machine learning and external validation, with their expression correlation with NK cells confirmed in BRCA specimens by ST and IHC.

Conclusions

We developed a novel NK-related gene signature that has proven valuable for evaluating prognosis and treatment response in BRCA, expecting to advance precision medicine of BRCA.

Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective

Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

Single-cell RNA sequencing and spatial transcriptomics of bladder Ewing sarcoma

Bladder Ewing sarcoma/primitive neuroectodermal tumor (bladder ES/PNET) is a rare and highly malignant tumor associated with a poor prognosis, yet its underlying mechanisms remain poorly understood. Here, we employed a combination of single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), and functional analyses to delve into the pathogenesis of bladder ES/PNET. The investigation revealed the presence of specialized types of epithelial cells (referred to as bladder ES-Epi) and mast cells (referred to as bladder ES-Mast) within bladder ES/PNET in comparison to urothelial carcinoma. Notably, TNFRSF12A exhibited significant upregulation in bladder ES/PNET. Furthermore, mast cells possessed the ability to activate epithelial cells through the TNFSF12-TNFRSF12A ligand-receptor signaling pattern. In addition, Enavatuzumab can significantly inhibit the migratory ability of the Ewing sarcoma cell line RD-ES. This groundbreaking study provides unprecedented mechanistic insights into the progression of bladder ES/PNET and introduces a potential therapeutic avenue for treating this challenging malignancy.

Systems immunology approaches to study T cells in health and disease

T cells are dynamically regulated immune cells that are implicated in a variety of diseases ranging from infection, cancer and autoimmunity. Recent advancements in sequencing methods have provided valuable insights in the transcriptional and epigenetic regulation of T cells in various disease settings. In this review, we identify the key sequencing-based methods that have been applied to understand the transcriptomic and epigenomic regulation of T cells in diseases.

Natural killer cell-associated prognosis model characterizes immune landscape and treatment efficacy of diffuse large B cell lymphoma

PURPOSE

NK cells are essential for the detection, identification and prediction of cancer. However, so far, there is no prognostic risk model based on NK cell-related genes to predict the prognosis and treatment outcome of DLBCL patients. This study aimed to explore a risk assessment model that could accurately predict the prognosis and treatment efficacy of DLBCL.

METHODS

Bioinformatics analysis of the expression profiles of DLBCL samples in the GEO database was performed. Cox regression and LASSO regression analysis were used to determine NK cell-related genes associated with patient's prognosis. Based on these genes, a risk assessment model was constructed to predict the prognosis of patients and the effectiveness of treatment. Finally, qRT-PCR was used to verify the expression of gene tags in clinical samples.

RESULTS

We identified seven prognosis-related NK cell-related genes (MAP2K1, PRKCB, TNFRSF10B, IL18, LAMP1, RASGRP1, and SP110), and DLBCL patients were divided into low- and high-risk groups based on these genes. Survival analysis showed that the prognosis of patients with low-risk group was better. Pathway enrichment analysis showed that the differentially expressed genes between the two risk groups were related to immune response pathways. Compared with the high-risk group, the low-risk group had higher infiltration of immune cells in tumor tissues. Besides, compared with high-risk group, low-risk patients by immunotherapy or other commonly used anti-tumor drugs might have better efficacy after treatment. In addition, qRT-PCR showed that the expression of risk genes including TNFRSF10B, IL18 and LAMP1 were significantly increased in most DLBCL samples compared to control samples, while the expression of protective genes including MAP2K1, PRKCB, RASGRP1 and SP110 were significantly decreased.

CONCLUSION

The NK cell-related gene signatures were proved to be a reliable indicator of the success of immunotherapy in patients with DLBCL, thus providing a unique evaluation method.

Heterogeneity of tumor microenvironment cell groups in inflammatory and adenomatous polyposis coli mutant colorectal cancer based on single cell sequencing

Background

The prognosis of colorectal cancer (CRC) is known to vary across different etiologies. Inflammatory bowel disease (IBD) is often identified as a factor contributing to poorer outcomes. However, the mechanisms that link IBD to a worse CRC prognosis remain to be elucidated. We aim to reveal the complex tumor microenvironment of inflammatory CRC and provide a weak theoretical basis for the treatment of different subtypes of CRC.

Methods

We conducted a bioinformatics analysis using single-cell RNA sequencing (scRNA-seq) data from 8,494 individual CRC cells derived from azoxymethane (AOM)/dextran sodium sulfate (DSS) and adenomatous polyposis coli (APC) mutant datasets. The expression of implicated genes in both tumor and adjacent normal tissues was examined via immunohistochemistry and immunofluorescence.

Results

CRC from AOM/DSS treatment contained fewer immune cells relative to APC-mutant CRC. However, a macrophage subcluster enriched for inflammatory factors was more prevalent in AOM/DSS datasets. This subcluster exhibited elevated expression of APOE and BNIP3. Immunofluorescence and immunohistochemistry of patient samples confirmed that the expression of APOE and BNIP3 was higher in adjacent normal tissues compared to tumors.

Conclusions

Our findings shed light on the heterogeneous microenvironments in IBD and APC-mutant CRC. Furthermore, we identify APOE as a potential biomarker for CRC recurrence.

ScRNA-seq reveals the spatiotemporal distribution of camptothecin pathway and transposon activity in Camptotheca acuminata shoot apexes and leaves

Camptotheca acuminata Decne., a significant natural source of the anticancer drug camptothecin (CPT), synthesizes CPT through the monoterpene indole alkaloid (MIA) pathway. In this study, we used single-cell RNA sequencing (scRNA-seq) to generate datasets encompassing over 60,000 cells from C. acuminata shoot apexes and leaves. After cell clustering and annotation, we identified five major cell types in shoot apexes and four in leaves. Analysis of MIA pathway gene expression revealed that most of them exhibited heightened expression in proliferating cells (PCs) and vascular cells (VCs). In contrast to MIA biosynthesis in Catharanthus roseus, CPT biosynthesis in C. acuminata did not exhibit multicellular compartmentalization. Some putative genes encoding enzymes and transcription factors (TFs) related to the biosynthesis of CPT and its derivatives were identified through co-expression analysis. These include 19 cytochrome P450 genes, 8 O-methyltransferase (OMT) genes, and 62 TFs. Additionally, these pathway genes exhibited dynamic expression patterns during VC and EC development. Furthermore, by integrating gene and transposable element (TE) expression data, we constructed novel single-cell transcriptome atlases for C. acuminata. This approach significantly facilitated the identification of rare cell types, including peripheral zone cells (PZs). Some TE families displayed cell type specific, tissue specific, or developmental stage-specific expression patterns, suggesting crucial roles for these TEs in cell differentiation and development. Overall, this study not only provides novel insights into CPT biosynthesis and spatial-temporal TE expression characteristics in C. acuminata, but also serves as a valuable resource for further comprehensive investigations into the development and physiology of this species.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Prognostic model based on B cell marker genes for NSCLC patients under neoadjuvant immunotherapy by integrated analysis of single-cell and bulk RNA-sequencing data

BACKGROUND

Neoadjuvant immunotherapy has evolved as an effective option to treat non-small cell lung cancer (NSCLC). B cells play essential roles in the immune system as well as cancer progression. However, the repertoire of B cells and its association with clinical outcomes remains unclear in NSCLC patients receiving neoadjuvant immunotherapy.

METHODS

Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data for LUAD samples were accessed from the TCGA and GEO databases. LUAD-related B cell marker genes were confirmed based on comprehensive analysis of scRNA-seq data. We then constructed the B cell marker gene signature (BCMGS) and validated it. In addition, we evaluated the association of BCGMS with tumor immune microenvironment (TIME) characteristics. Furthermore, we validated the efficacy of BCGMS in a cohort of NSCLC patients receiving neoadjuvant immunotherapy.

RESULTS

A BCMGS was constructed based on the TCGA cohort and further validated in three independent GSE cohorts. In addition, the BCMGS was proven to be significantly associated with TIME characteristics. Moreover, a relatively higher risk score indicated poor clinical outcomes and a worse immune response among NSCLC patients receiving neoadjuvant immunotherapy.

CONCLUSIONS

We constructed an 18-gene prognostic signature derived from B cell marker genes based on scRNA-seq data, which had the potential to predict the prognosis and immune response of NSCLC patients receiving neoadjuvant immunotherapy.

Identification of a Macrophage marker gene signature to evaluate immune infiltration and therapeutic response in hepatocellular carcinoma

Background

Only a minority of hepatocellular carcinoma (HCC) patients can benefit from systemic regimens. Macrophages, which abundantly infiltrate in HCC, could mediate tumour microenvironment remodelling and immune escape, proving to be powerful weapons in combating HCC. Thus, a deeper understanding of macrophages is necessary for improving existing antitumour treatments.

Methods

With a series of bioinformatic approaches, we comprehensively explored the role of macrophage-related genes in human HCCs from multiple single-cell and bulk RNA sequencing datasets. Unsupervised clustering was performed to cluster the macrophage marker genes (MMGs). GSVA and functional enrichment analysis were used to elucidate the functional differences among the MMG-associated clusters. Subsequently, a component analysis algorithm was used to construct a Macrosig score, and the prognosis, biological characteristics, mutation profile, TME cell infiltration status and drug response of patients with different Macrosig scores were further analysed.

Results

We identified 13 MMGs in 574 HCC samples, based on which three MMG-associated clusters were defined. Overall survival time, clinicopathological features and immune infiltration scores differed among the different clusters. On this basis, 12 hub genes were identified among these clusters; subsequently, a scoring system was constructed to determine the Macrosig score. Importantly, patients with low-Macrosig scores, characterized by increased immune infiltration, increased mutation frequency and increased immune checkpoint expression, including CTLA-4, LAG3, PDCD1 and TIGIT, exhibited enhanced efficacy of immunotherapy when validated in an external database. Moreover, a low-Macrosig score indicates increased sensitivity to AZD.2281, A.443654, ABT.263, ABT.888, AG.014699 and ATRA, while a high Macrosig score indicates increased sensitivity to AZD6482, AKT inhibitor VIII, AS601245, AZ628, AZD.0530 and AZD6244.

Conclusions

A novel scoring system was constructed to guide more effective prognostic evaluation and tailoring therapeutic regimens for HCC patients.

Single-Cell RNA Sequencing Shows T-Cell Exhaustion Landscape in the Peripheral Blood of Patients with Hepatitis B Virus-Associated Acute-on-Chronic Liver Failure

Background/Aims

The occurrence and development of hepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF) is closely related to the immune pathway. We explored the heterogeneity of peripheral blood T cell subsets and the characteristics of exhausted T lymphocytes, in an attempt to identify potential therapeutic target molecules for immune dysfunction in ACLF patients.

Methods

A total of 83,577 T cells from HBV-ACLF patients and healthy controls were screened for heterogeneity by single-cell RNA sequencing. In addition, exhausted T-lymphocyte subsets were screened to analyze their gene expression profiles, and their developmental trajectories were investigated. Subsequently, the expression of exhausted T cells and their capacity in secreting cytokines (interleukin 2, interferon γ, and tumor necrosis factor α) were validated by flow cytometry.

Results

A total of eight stable clusters were identified, among which CD4+ TIGIT+ subset and CD8+ LAG-3+ subset, with high expression of exhaust genes, were significantly higher in the HBV-ACLF patients than in normal controls. As shown by pseudotime analysis, T cells experienced a transition from naïve T cells to effector T cells and then exhausted T cells. Flow cytometry confirmed that the CD4+TIGIT+ subset and CD8+LAG-3+ subset in the peripheral blood of the ACLF patients were significantly higher than those in the healthy controls. Moreover, in vitro cultured CD8+LAG-3+ T cells were significantly fewer capable of secreting cytokines than CD8+LAG-3- subset.

Conclusions

Peripheral blood T cells are heterogeneous in HBV-ACLF. The exhausted T cells markedly increase during the pathogenesis of ACLF, suggesting that T-cell exhaustion is involved in the immune dysfunction of HBV-ACLF patients.

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

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

Identification and validation of a signature based on myofibroblastic cancer-associated fibroblast marker genes for predicting prognosis, immune infiltration, and therapeutic response in bladder cancer

PURPOSE

Myofibroblastic cancer-associated fibroblasts (myCAFs) are important components of the tumor microenvironment closely associated with tumor stromal remodeling and immunosuppression. This study aimed to explore myCAFs marker gene biomarkers for clinical diagnosis and therapy for patients with bladder cancer (BC).

MATERIALS AND METHODS

BC single-cell RNA sequencing (scRNA-seq) data were obtained from the National Center for Biotechnology Information Sequence Read Archive. Transcriptome and clinical data were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Subsequently, univariate Cox and LASSO (Least Absolute Shrinkage and Selection Operator regression) regression analyses were performed to construct a prognostic signature. Immune cell activity was estimated using single-sample gene set enrichment analysis whilst the TIDE (tumor immune dysfunction and exclusion) method was employed to assess patient response to immunotherapy. The chemotherapy response of patients with BC was evaluated using genomics of drug sensitivity in cancer. Furthermore, Immunohistochemistry was used to verify the correlation between MAP1B expression and immunotherapy efficacy. The scRNA-seq data were analyzed to identify myCAFs marker genes.

RESULTS

Combined with bulk RNA-sequencing data, we constructed a two-gene (COL6A1 and MAP1B) risk signature. In patients with BC, the signature demonstrated outstanding prognostic value, immune infiltration, and immunotherapy response. This signature served as a crucial guide for the selection of anti-tumor chemotherapy medications. Additionally, immunohistochemistry confirmed that MAP1B expression was significantly correlated with immunotherapy efficacy.

CONCLUSIONS

Our findings revealed a typical prognostic signature based on myCAF marker genes, which offers patients with BC a novel treatment target alongside theoretical justification.

Comprehensive analysis of macrophage-related genes in prostate cancer by integrated analysis of single-cell and bulk RNA sequencing

Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the "mgeneSim" function.

Dietary factors and their influence on immunotherapy strategies in oncology: a comprehensive review

Immunotherapy is emerging as a promising avenue in oncology, gaining increasing importance and offering substantial advantages when compared to chemotherapy or radiotherapy. However, in the context of immunotherapy, there is the potential for the immune system to either support or hinder the administered treatment. This review encompasses recent and pivotal studies that assess the influence of dietary elements, including vitamins, fatty acids, nutrients, small dietary molecules, dietary patterns, and caloric restriction, on the ability to modulate immune responses. Furthermore, the article underscores how these dietary factors have the potential to modify and enhance the effectiveness of anticancer immunotherapy. It emphasizes the necessity for additional research to comprehend the underlying mechanisms for optimizing the efficacy of anticancer therapy and defining dietary strategies that may reduce cancer-related morbidity and mortality. Persistent investigation in this field holds significant promise for improving cancer treatment outcomes and maximizing the benefits of immunotherapy.

Intestinal cell diversity and treatment responses in a parasitic nematode at single cell resolution

BACKGROUND

Parasitic nematodes, significant pathogens for humans, animals, and plants, depend on diverse organ systems for intra-host survival. Understanding the cellular diversity and molecular variations underlying these functions holds promise for developing novel therapeutics, with specific emphasis on the neuromuscular system's functional diversity. The nematode intestine, crucial for anthelmintic therapies, exhibits diverse cellular phenotypes, and unraveling this diversity at the single-cell level is essential for advancing knowledge in anthelmintic research across various organ systems.

RESULTS

Here, using novel single-cell transcriptomics datasets, we delineate cellular diversity within the intestine of adult female Ascaris suum, a parasitic nematode species that infects animals and people. Gene transcripts expressed in individual nuclei of untreated intestinal cells resolved three phenotypic clusters, while lower stringency resolved additional subclusters and more potential diversity. Clusters 1 and 3 phenotypes displayed variable congruence with scRNA phenotypes of C. elegans intestinal cells, whereas the A. suum cluster 2 phenotype was markedly unique. Distinct functional pathway enrichment characterized each A. suum intestinal cell cluster. Cluster 2 was distinctly enriched for Clade III-associated genes, suggesting it evolved within clade III nematodes. Clusters also demonstrated differential transcriptional responsiveness to nematode intestinal toxic treatments, with Cluster 2 displaying the least responses to short-term intra-pseudocoelomic nematode intestinal toxin treatments.

CONCLUSIONS

This investigation presents advances in knowledge related to biological differences among major cell populations of adult A. suum intestinal cells. For the first time, diverse nematode intestinal cell populations were characterized, and associated biological markers of these cells were identified to support tracking of constituent cells under experimental conditions. These advances will promote better understanding of this and other parasitic nematodes of global importance, and will help to guide future anthelmintic treatments.

An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors

Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease.

Anti-stromal nanotherapeutics for hepatocellular carcinoma

Hepatocellular carcinoma (HCC), the most commonly diagnosed primary liver cancer, has become a leading cause of cancer-related death worldwide. Accumulating evidence confirms that the stromal constituents within the tumor microenvironment (TME) exacerbate HCC malignancy and set the barriers to current anti-HCC treatments. Recent developments of nano drug delivery system (NDDS) have facilitated the application of stroma-targeting therapeutics, disrupting the stromal TME in HCC. This review discusses the stromal activities in HCC development and therapy resistance. In addition, it addresses the delivery challenges of NDDS for stroma-targeting therapeutics (termed anti-stromal nanotherapeutics in this review), and provides recent advances in anti-stromal nanotherapeutics for safe, effective, and specific HCC therapy.

Comparative transcriptomic analysis of bovine mesenchymal stromal cells reveals tissue-source and species-specific differences

Mesenchymal stromal cells (MSCs) have the potential to be used as therapeutics, but their efficacy varies due to cellular heterogeneity, which is not fully understood. After characterizing donor-matched bovine MSC from adipose tissue (AT), bone marrow (BM), and peripheral blood (PB), we performed single-cell RNA sequencing (scRNA-seq) to evaluate overarching similarities and differences across these three tissue-derived MSCs. Next, the transcriptomic profiles of the bovine MSCs were compared to those of equine MSCs, derived from the same tissue sources and previously published by our group, and revealed species-specific differences. Finally, the transcriptomic profile from bovine BM-MSCs was compared to mouse and human BM-MSCs and demonstrated that bovine BM-MSCs share more common functionally relevant gene expression profiles with human BM-MSCs than compared to murine BM-MSCs. Collectively, this study presents the cow as a potential non-traditional animal model for translational MSC studies based on transcriptomic profiles similar to human MSCs.

Single-cell transcriptomics analysis of cellular heterogeneity and immune mechanisms in neurodegenerative diseases

Single-cell transcriptomics analysis is an advanced technology that can describe the intracellular transcriptome in complex tissues. It profiles and analyses datasets by single-cell RNA sequencing. Neurodegenerative diseases are identified by the abnormal apoptosis of neurons in the brain with few or no effective therapy strategies at present, which has been a growing healthcare concern and brought a great burden to society. The transcriptome of individual cells provides deep insights into previously unforeseen cellular heterogeneity and gene expression differences in neurodegenerative disorders. It detects multiple cell subsets and functional changes during pathological progression, which deepens the understanding of the molecular underpinnings and cellular basis of neurodegenerative diseases. Furthermore, the transcriptome analysis of immune cells shows the regulation of immune response. Different subtypes of immune cells and their interaction are found to contribute to disease progression. This finding enables the discovery of novel targets and biomarkers for early diagnosis. In this review, we emphasize the principles of the technology, and its recent progress in the study of cellular heterogeneity and immune mechanisms in neurodegenerative diseases. The application of single-cell transcriptomics analysis in neurodegenerative disorders would help explore the pathogenesis of these diseases and develop novel therapeutic methods.

Single-cell RNA sequencing reveals the dynamics and heterogeneity of lymph node immune cells during acute and chronic viral infections

Introduction

The host immune response determines the differential outcome of acute or chronic viral infections. The comprehensive comparison of lymphoid tissue immune cells at the single-cell level between acute and chronic viral infections is largely insufficient.

Methods

To explore the landscape of immune responses to acute and chronic viral infections, single-cell RNA sequencing(scRNA-seq), scTCR-seq and scBCR-seq were utilized to evaluate the longitudinal dynamics and heterogeneity of lymph node CD45+ immune cells in mouse models of acute (LCMV Armstrong) and chronic (LCMV clone 13) viral infections.

Results

In contrast with acute viral infection, chronic viral infection distinctly induced more robust NK cells and plasma cells at the early stage (Day 4 post-infection) and acute stage (Day 8 post-infection), respectively. Moreover, chronic viral infection exerted decreased but aberrantly activated plasmacytoid dendritic cells (pDCs) at the acute phase. Simultaneously, there were significantly increased IgA+ plasma cells (MALT B cells) but differential usage of B-cell receptors in chronic infection. In terms of T-cell responses, Gzma-high effector-like CD8+ T cells were significantly induced at the early stage in chronic infection, which showed temporally reversed gene expression throughout viral infection and the differential usage of the most dominant TCR clonotype. Chronic infection also induced more robust CD4+ T cell responses, including follicular helper T cells (Tfh) and regulatory T cells (Treg). In addition, chronic infection compromised the TCR diversity in both CD8+ and CD4+ T cells.

Discussion

In conclusion, gene expression and TCR/BCR immune repertoire profiling at the single-cell level in this study provide new insights into the dynamic and differential immune responses to acute and chronic viral infections.

A neutrophil extracellular trap-related risk score predicts prognosis and characterizes the tumor microenvironment in multiple myeloma

Multiple myeloma (MM) is a distinguished hematologic malignancy, with existing studies elucidating its interaction with neutrophil extracellular traps (NETs), which may potentially facilitate tumor growth. However, systematic investigations into the role of NETs in MM remain limited. Utilizing the single-cell dataset GSE223060, we discerned active NET cell subgroups, namely neutrophils, monocytes, and macrophages. A transcriptional trajectory was subsequently constructed to comprehend the progression of MM. Following this, an analysis of cellular communication in MM was conducted with a particular emphasis on neutrophils, revealing an augmentation in interactions albeit with diminished strength, alongside abnormal communication links between neutrophils and NK cells within MM samples. Through the intersection of differentially expressed genes (DEGs) between NET active/inactive cells and MM versus healthy samples, a total of 316 genes were identified. This led to the development of a 13-gene risk model for prognostic prediction based on overall survival, utilizing transcriptomics dataset GSE136337. The high-risk group manifested altered immune infiltration and heightened sensitivity to chemotherapy. A constructed nomogram for predicting survival probabilities demonstrated encouraging AUCs for 1, 3, and 5-year survival predictions. Collectively, our findings unveil a novel NET-related prognostic signature for MM, thereby providing a potential avenue for therapeutic exploration.

Unraveling T cell exhaustion in the immune microenvironment of osteosarcoma via single-cell RNA transcriptome

Osteosarcoma (OS) represents a profoundly invasive malignancy of the skeletal system. T cell exhaustion (Tex) is known to facilitate immunosuppression and tumor progression, but its role in OS remains unclear. In this study, single-cell RNA sequencing data was employed to identify exhausted T cells within the tumor immune microenvironment (TIME) of OS. We found that exhausted T cells exhibited substantial infiltration in OS samples. Pseudotime trajectory analysis revealed a progressive increase in the expression of various Tex marker genes, including PDCD1, CTLA4, LAG3, ENTPD1, and HAVCR2 in OS. GSVA showed that apoptosis, fatty acid metabolism, xenobiotic metabolism, and the interferon pathway were significantly activated in exhausted T cells in OS. Subsequently, a prognostic model was constructed using two Tex-specific genes, MYC and FCGR2B, which exhibited exceptional prognostic accuracy in two independent cohorts. Drug sensitivity analysis revealed that OS patients with a low Tex risk were responsive to Dasatinib and Pazopanib. Finally, immunohistochemistry verified that MYC and FCGR2B were significantly upregulated in OS tissues compared with adjacent tissues. This study investigates the role of Tex within the TIME of OS, and offers novel insights into the mechanisms underlying disease progression as well as the potential treatment strategies for OS.

scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data

Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by single feature extraction approaches, lack of adaptability to immune cell types with similar molecular profiles but distinct functions and a failure to account for the impact of cell label noise on model accuracy, all of which compromise the precision of annotation. To address these challenges, we developed a supervised approach called scMMT. We proposed a novel feature extraction technique to uncover more valuable information. Additionally, we constructed a multi-task learning framework based on the GradNorm method to enhance the recognition of challenging immune cells and reduce the impact of label noise by facilitating mutual reinforcement between cell type annotation and protein prediction tasks. Furthermore, we introduced logarithmic weighting and label smoothing mechanisms to enhance the recognition ability of rare cell types and prevent model overconfidence. Through comprehensive evaluations on multiple public datasets, scMMT has demonstrated state-of-the-art performance in various aspects including cell type annotation, rare cell identification, dropout and label noise resistance, protein expression prediction and low-dimensional embedding representation.

Current advancements in B-cell receptor sequencing fast-track the development of synthetic antibodies

Synthetic antibodies (Abs) are a class of engineered proteins designed to mimic the functions of natural Abs. These are produced entirely in vitro, eliminating the need for an immune response. As such, synthetic Abs have transformed the traditional methods of raising Abs. Likewise, deep sequencing technologies have revolutionized genomics and molecular biology. These enable the rapid and cost-effective sequencing of DNA and RNA molecules. They have allowed for accurate and inexpensive analysis of entire genomes and transcriptomes. Notably, via deep sequencing it is now possible to sequence a person's entire B-cell receptor immune repertoire, termed BCR sequencing. This procedure allows for big data explorations of natural Abs associated with an immune response. Importantly, the identified sequences have the ability to improve the design and engineering of synthetic Abs by offering an initial sequence framework for downstream optimizations. Additionally, machine learning algorithms can be introduced to leverage the vast amount of BCR sequencing datasets to rapidly identify patterns hidden in big data to effectively make in silico predictions of antigen selective synthetic Abs. Thus, the convergence of BCR sequencing, machine learning, and synthetic Ab development has effectively promoted a new era in Ab therapeutics. The combination of these technologies is driving rapid advances in precision medicine, diagnostics, and personalized treatments.

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer

BACKGROUND

There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC.

METHODS

Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors.

RESULTS

The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1.

CONCLUSION

This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

Technologies to Study Genetics and Molecular Pathways

Over the last few decades, the study of congenital heart disease (CHD) has benefited from various model systems and the development of molecular biological techniques enabling the analysis of single gene as well as global effects. In this chapter, we first describe different models including CHD patients and their families, animal models ranging from invertebrates to mammals, and various cell culture systems. Moreover, techniques to experimentally manipulate these models are discussed. Second, we introduce cardiac phenotyping technologies comprising the analysis of mouse and cell culture models, live imaging of cardiogenesis, and histological methods for fixed hearts. Finally, the most important and latest molecular biotechniques are described. These include genotyping technologies, different applications of next-generation sequencing, and the analysis of transcriptome, epigenome, proteome, and metabolome. In summary, the models and technologies presented in this chapter are essential to study the function and development of the heart and to understand the molecular pathways underlying CHD.

Single-cell and bulk RNA sequencing analysis of B cell marker genes in TNBC TME landscape and immunotherapy

Objective

This study amied to investigate the prognostic characteristics of triple negative breast cancer (TNBC) patients by analyzing B cell marker genes based on single-cell and bulk RNA sequencing.

Methods

Utilizing single-cell sequencing data from TNBC patients, we examined tumor-associated B cell marker genes. Transcriptomic data from The Cancer Genome Atlas (TCGA) database were used as the foundation for predictive modeling. Independent validation set was conducted using the GSE58812 dataset. Immune cell infiltration into the tumor was assessed through various, including XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA. The TIDE score was utilized to predict immunotherapy outcomes. Additional investigations were conducted on the immune checkpoint blockade gene, tumor mutational load, and the GSEA enrichment analysis.

Results

Our analysis encompassed 22,106 cells and 20,556 genes in cancerous tissue samples from four TNBC patients, resulting in the identification of 116 B cell marker genes. A B cell marker gene score (BCMG score) involving nine B cell marker genes (ZBP1, SEL1L3, CCND2, TNFRSF13C, HSPA6, PLPP5, CXCR4, GZMB, and CCDC50) was developed using TCGA transcriptomic data, revealing statistically significant differences in survival analysis (P<0.05). Functional analysis demonstrated that marker genes were predominantly associated with immune-related pathways. Notably, substantial differences between the higher and lower- BCMG score groups were observed in terms of immune cell infiltration, immune cell activity, tumor mutational burden, TIDE score, and the expression of immune checkpoint blockade genes.

Conclusion

This study has established a robust model based on B-cell marker genes in TNBC, which holds significant potential for predicting prognosis and response to immunotherapy in TNBC patients.

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma

BACKGROUND

Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adenocarcinoma (LUAD) are largely unknown.

METHODS

In this study, 1658 LUAD patients from different cohorts were included. In addition, 724 cancer patients who received immunotherapy were also included. To identify DC marker genes in LUAD, we used single-cell RNAsequencing data for analysis and determined 83 genes as DC marker genes. Following that, integrative machine learning procedure was developed to construct a signature for DC marker genes.

RESULTS

Using TCGA bulk-RNA sequencing data as the training set, we developed a signature consisting of seven genes and classified patients by their risk status. Another six independent cohorts demonstrated the signature' s prognostic power, and multivariate analysis demonstrated it was an independent prognostic factor. LUAD patients in the high-risk group displayed more advanced features, discriminatory immune-cell infiltrations and immunosuppressive states. Cell-cell communication analysis indicates that tumor cells with lower risk scores communicate more actively with the tumor microenvironment. Eight independent immunotherapy cohorts revealed that patients with low-risk had better immunotherapy responses. Drug sensitivity analysis indicated that targeted therapy agents exhibited greater sensitivity to low-risk patients, while chemotherapy agents displayed greater sensitivity to high-risk patients. In vitro experiments confirmed that CTSH is a novel protective factor for LUAD.

CONCLUSIONS

An unique signature based on DC marker genes that is highly predictive of LUAD patients' prognosis and response to immunotherapy. CTSH is a new biomarker for LUAD.

Single-cell sequencing: New insights for intervertebral disc degeneration

Over the past decade, single-cell RNA sequencing (scRNA-seq) has revolutionized research on biological mechanisms of diseases. Moreover, this technique has been utilized to identify and characterize unique cell types and subpopulations, thereby illuminating cellular heterogeneity. The true value of scRNA-seq lies in its ability to detect transcriptional alterations or perturbed pathways within specific cell types under pathological conditions. In the context of intervertebral disc degeneration (IVDD), the pathophysiological foundation is largely rooted in inflammation. The primary target cells of IVDD are nucleus pulposus cells, annulus fibrosus cells, cartilage endplate cells, and macrophages. The advancements in scRNA-seq technology have triggered remarkable progress in IVDD treatment, leading to breakthroughs in the identification of cell subsets, functional analysis, novel therapeutic targets, and the differentiation and development of various cell types. This review is the first of its kind to introduce the application of scRNA-seq techniques in IVDD, with a focus on the most recent scRNA-seq studies that have defined the populations of various cell types and specific cell-cell interactions in IVDD. Furthermore, we highlight several promising future research directions for scRNA-seq in IVDD.

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

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

A novel eight-gene Immune Cell-Associated Predictive Gene model to predict recurrence in triple-negative breast cancer

Background

Tumour tissue contains not only tumour cells but also some stromal cells and immune cells. This is one composition of the immune microenvironment of the tumour and causes a significant effect on the prognostic factors and recurrence of malignant tumor.

Methods

In this research, single-cell RNA data from triple-negative breast cancers (TNBCs) were comprehensively analyzed and 1,527 marker genes expressed in immune cells were identified. Subsequently, RNA sequencing and clinical data from 360 patients in the Triple Negative Breast Cancer database at the Fudan University Shanghai Cancer Center (FUSCC) were divided into two groups in a 1:1 ratio, the training group and the validation group. An eight-gene Immune Cell-Associated Predictive Gene (ICAPG) model for predicting breast cancer (BC) recurrence was developed using mRNA data from the training group combined with immune cell marker genes. Based on this model, subjects were divided into two different risk level groups. The predictive power of the model was fully validated using the validation group and The Cancer Genome Atlas (TCGA) database. The localization and expression of these eight genes were then confirmed in a single-cell database. ssGSEA and CIBERSORT algorithms were used to characterize the differences in immune cell infiltration between the two different risk groups.

Results

The eight-gene ICAPG model was proven to be effective in the validation group. The low-risk group patients presented higher criterion of infiltration of CD8+ T cells and higher levels of tumour-infiltrating lymphocytes (TILs). In addition, the relationship between predictive models and homologous recombination deficiency (HRD) was explored and it was revealed that subjects from the high-risk group tended to have higher HRD values.

Conclusions

This research established a new predictive model on the basis of immune cell marker genes that might effectively predict relapse in TNBC patients.

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.

A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC

Background

Most patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for cancer development, and its transcriptomic profile may be associated with treatment response and differential outcomes. The aim of this study was to develop a new predictive signature for chemotherapy in patients with HGSOC.

Methods

Two HGSOC single-cell RNA sequencing datasets from patients receiving chemotherapy were reinvestigated. The subtypes of endoplasmic reticulum stress-related XBP1+ B cells, invasive metastasis-related ACTB+ Tregs, and proinflammatory-related macrophage subtypes with good predictive power and associated with chemotherapy response were identified. These results were verified in an independent HGSOC bulk RNA-seq dataset for chemotherapy. Further validation in clinical cohorts used quantitative real-time PCR (qRT-PCR).

Results

By combining cluster-specific genes for the aforementioned cell subtypes, we constructed a chemotherapy response prediction model containing 43 signature genes that achieved an area under the receiver operator curve (AUC) of 0.97 (p = 2.1e-07) for the GSE156699 cohort (88 samples). A huge improvement was achieved compared to existing prediction models with a maximum AUC of 0.74. In addition, its predictive capability was validated in multiple independent bulk RNA-seq datasets. The qRT-PCR results demonstrate that the expression of the six genes has the highest diagnostic value, consistent with the trend observed in the analysis of public data.

Conclusions

The developed chemotherapy response prediction model can be used as a valuable clinical decision tool to guide chemotherapy in HGSOC patients.

Application of single-cell RNA sequencing methods to develop B cell targeted treatments for autoimmunity

The COVID-19 pandemic coincided with several transformative advances in single-cell analysis. These new methods along with decades of research and trials with antibody therapeutics and RNA based technologies allowed for highly effective vaccines and treatments to be produced at astonishing speeds. While these tools were initially focused on models of infection, they also show promise in an autoimmune setting. Self-reactive B cells play important roles as antigen-presenting cells and cytokine and autoantibody producers for many autoimmune diseases. Yet, current therapies to target autoreactive B cells deplete all B cells irrespective of their pathogenicity. Development of self-reactive B cell targeting therapies that would spare non-pathogenic B cells are needed to treat disease while allowing effective immune responses to other ailments. Single-cell RNA sequencing (scRNA-seq) approaches will aid in identification of the pathogenic self-reactive B cells operative in autoimmunity and help with development of more favorable precision targeted therapies.

Natural killer cell-related prognostic risk model predicts prognosis and treatment outcomes in triple-negative breast cancer

Background

Natural killer (NK) cells are crucial to the emergence, identification, and prognosis of cancers. The roles of NK cell-related genes in the tumor immune microenvironment (TIME) and immunotherapy treatment are unclear. Triple-negative breast cancer (TNBC) is a highly aggressive malignant tumor. Hence, this study was conducted to develop a reliable risk model related to NK cells and provide a novel system for predicting the prognosis of TNBC.

Methods

NK cell-related genes were collected from previous studies. Based on TCGA and GEO database, univariate and LASSO cox regression analysis were used to establish the NK cell-related gene signature. The patients with TNBC were separated to high-risk and low-risk groups. After that, survival analysis was conducted and the responses to immunotherapies were evaluated on the basis of the signature. Moreover, the drug sensitivity of some traditional chemotherapeutic drugs was assessed by using the "oncoPredict" R package. In addition, the expression levels of the genes involved in the signature were validated by using qRT-PCR in TNBC cell lines.

Results

The patients with TNBC were divided into high- and low-risk groups according to the median risk score of the 5-NK cell-related gene signature. The low-risk group was associated with a better clinical outcome. Besides, the differentially expressed genes between the different risk groups were enriched in the biological activities associated with immunity. The tumor immune cells were found to be highly infiltrated in the low-risk groups. In accordance with the TIDE score and immune checkpoint-related gene expression analysis, TNBC patients in the low-risk groups were suggested to have better responses to immunotherapies. Eventually, some classical anti-tumor drugs were shown to be less effective in high-risk groups than in low-risk groups.

Conclusion

The 5-NK cell-related gene signature exhibit outstanding predictive performance and provide fresh viewpoints for evaluating the success of immunotherapy. It will provide new insights to achieve precision and integrated treatment for TNBC in the future.

Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing Data

Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease in children. The heterogeneity of the disease can be investigated via single-cell RNA sequencing (scRNA-seq) for its gap in the literature. Firstly, five types of immune cells (plasma cells, naive CD4 T cells, memory-activated CD4 T cells, eosinophils, and neutrophils) were significantly different between normal control (NC) and JIA samples. WGCNA was performed to identify genes that exhibited the highest correlation to differential immune cells. Then, 168 differentially expressed immune cell-related genes (DE-ICRGs) were identified by overlapping 13,706 genes identified by WGCNA and 286 differentially expressed genes (DEGs) between JIA and NC specimens. Next, four key genes, namely SOCS3, JUN, CLEC4C, and NFKBIA, were identified by a protein-protein interaction (PPI) network and three machine learning algorithms. The results of functional enrichment revealed that SOCS3, JUN, and NFKBIA were all associated with hallmark TNF-α signaling via NF-κB. In addition, cells in JIA samples were clustered into four groups (B cell, monocyte, NK cell, and T cell groups) by single-cell data analysis. CLEC4C and JUN exhibited the highest level of expression in B cells; NFKBIA and SOCS3 exhibited the highest level of expression in monocytes. Finally, real-time quantitative PCR (RT-qPCR) revealed that the expression of three key genes was consistent with that determined by differential analysis. Our study revealed four key genes with prognostic value for JIA. Our findings could have potential implications for JIA treatment and investigation.

The cerebrospinal fluid immune cell landscape in animal models of multiple sclerosis

The fluid compartment surrounding the central nervous system (CNS) is a unique source of immune cells capable of reflecting the pathophysiology of neurologic diseases. While human clinical and experimental studies often employ cerebrospinal fluid (CSF) analysis, assessment of CSF in animal models of disease are wholly uncommon, particularly in examining the cellular component. Barriers to routine assessment of CSF in animal models of multiple sclerosis (MS) include limited sample volume, blood contamination, and lack of feasible longitudinal approaches. The few studies characterizing CSF immune cells in animal models of MS are largely outdated, but recent work employing transcriptomics have been used to explore new concepts in CNS inflammation and MS. Absence of extensive CSF data from rodent and other systems has curbed the overall impact of experimental models of MS. Future approaches, including examination of CSF myeloid subsets, single cell transcriptomics incorporating antigen receptor sequencing, and use of diverse animal models, may serve to overcome current limitations and provide critical insights into the pathogenesis of, and therapeutic developments for, MS.

Multi-view clustering by CPS-merge analysis with application to multimodal single-cell data

Multi-view data can be generated from diverse sources, by different technologies, and in multiple modalities. In various fields, integrating information from multi-view data has pushed the frontier of discovery. In this paper, we develop a new approach for multi-view clustering, which overcomes the limitations of existing methods such as the need of pooling data across views, restrictions on the clustering algorithms allowed within each view, and the disregard for complementary information between views. Our new method, called CPS-merge analysis, merges clusters formed by the Cartesian product of single-view cluster labels, guided by the principle of maximizing clustering stability as evaluated by CPS analysis. In addition, we introduce measures to quantify the contribution of each view to the formation of any cluster. CPS-merge analysis can be easily incorporated into an existing clustering pipeline because it only requires single-view cluster labels instead of the original data. We can thus readily apply advanced single-view clustering algorithms. Importantly, our approach accounts for both consensus and complementary effects between different views, whereas existing ensemble methods focus on finding a consensus for multiple clustering results, implying that results from different views are variations of one clustering structure. Through experiments on single-cell datasets, we demonstrate that our approach frequently outperforms other state-of-the-art methods.

The Application of Single-Cell RNA Sequencing in the Inflammatory Tumor Microenvironment

The initiation and progression of tumors are complex. The cancer evolution-development hypothesis holds that the dysregulation of immune balance is caused by the synergistic effect of immune genetic factors and environmental factors that stimulate and maintain non-resolving inflammation. Throughout the cancer development process, this inflammation creates a microenvironment for the evolution and development of cancer. Research on the inflammatory tumor microenvironment (TME) explains the initiation and progression of cancer and guides anti-cancer immunotherapy. Single-cell RNA sequencing (scRNA-seq) can detect the transcription levels of cells at the single-cell resolution level, reveal the heterogeneity and evolutionary trajectory of infiltrated immune cells and cancer cells, and provide insight into the composition and function of each cell group in the inflammatory TME. This paper summarizes the application of scRNA-seq in inflammatory TME.

Single-cell transcriptome sequencing of macrophages in common cardiovascular diseases

Macrophages are strategically located throughout the body at key sites in the immune system. A key feature in atherosclerosis is the uptake and accumulation of lipoproteins by arterial macrophages, leading to the formation of foam cells. After myocardial infarction, macrophages derived from monocytes infiltrate the infarcted heart. Macrophages are also closely related to adverse remodeling after heart failure. An in-depth understanding of the functions and characteristics of macrophages is required to study heart health and pathophysiological processes; however, the heterogeneity and plasticity explained by the classic M1/M2 macrophage paradigm are too limited. Single-cell sequencing is a high-throughput sequencing technique that enables the sequencing of the genome or transcriptome of a single cell. It effectively complements the heterogeneity of gene expression in a single cell that is ignored by conventional sequencing and can give valuable insights into the development of complex diseases. In the present review, we summarize the available research on the application of single-cell transcriptome sequencing to study the changes in macrophages during common cardiovascular diseases, such as atherosclerosis, myocardial infarction, and heart failure. This article also discusses the contribution of this knowledge to understanding the pathogenesis, development, diagnosis, and treatment of heart diseases.

Inferring direction of associations between histone modifications using a neural processes-based framework

Current technologies do not allow predicting interactions between histone post-translational modifications (HPTMs) at a system-level. We describe a computational framework, imputation-followed-by-inference, to predict directed association between two HPTMs using EpiTOF, a mass cytometry-based platform that allows profiling multiple HPTMs at a single-cell resolution. Using EpiTOF profiles of >55,000,000 peripheral mononuclear blood cells from 158 healthy human subjects, we show that neural processes (NP) have significantly higher accuracy than linear regression and k-nearest neighbors models to impute the abundance of an HPTM. Next, we infer the direction of association to show we recapitulate known HPTM associations and identify several previously unidentified ones in healthy individuals. Using this framework in an influenza vaccine cohort, we identify changes in associations between 6 pairs of HPTMs 30 days following vaccination, of which several have been shown to be involved in innate memory. These results demonstrate the utility of our framework in identifying directed interactions between HPTMs.

Mass cytometry and single-cell RNA sequencing reveal immune cell characteristics of active and inactive phases of Crohn's disease

Objectives

For Crohn's disease (CD), the alternation of the active phase and inactive phase may be related to humoral immunity and cellular immunity. This study aims to understand the characteristics of immune cells in patients with active CD (CDa) and inactive CD (CDin).

Methods

Mass cytometry (CyTOF) and single-cell RNA sequencing (scRNA-seq) data about CDa, CDin, and healthy control (HC) were included. CyTOF analysis was performed to capture gated subsets, including T cells, T regulatory (Treg) cells, B cells, innate immune cells, and natural killer (NK) cells. Differential analysis was used to identify different immune cell subsets among CDa, CDin, and HC. ScRNA-seq analysis was used to verify the results of CyTOF. CD-related signaling pathways were obtained using KEGG pathway enrichment analysis. CellChat analysis was used to infer the cell communication network among immune cell subsets.

Results

Compared to patients with CDin, patients with CDa had higher abundances of CD16⁺CD38⁺CD4⁺CXCR3⁺CCR6⁺ naive T cells, HLA-DR⁺CD38⁺IFNγ⁺TNF⁺ effector memory (EM) T cells, HLA-DR⁺IFNγ⁺ naive B cells, and CD14⁺⁺CD11C⁺IFNγ⁺IL1B⁺ monocytes. KEGG analysis showed the similarity of pathway enrichment for the earlier four subsets, such as thermogenesis, oxidative phosphorylation, and metabolic pathways. The patients with CDin were characterized by an increased number of CD16⁺CD56^dimCD44⁺HLA-DR⁺IL22⁺ NK cells. Compared to HC, patients with CDa demonstrated a low abundance of HLA-DR⁺CCR6⁺ NK cells and a high abundance of FOXP3⁺CD44⁺ EM Tregs. CellChat analysis revealed the interaction network of cell subsets amplifying in CDa compared with CDin.

Conclusion

Some immune subsets cells were identified for CDa and CDin. These cells may be related to the occurrence and development of CD and may provide assistance in disease diagnosis and treatment.

Multiplexed Immunoassay Using Quantum Dots to Monitor Proteins Secreted from Single Cells at Near-Single Molecule Resolution

Single-cell secretion studies find important applications in molecular diagnostics, therapeutic target identification, and basic biology research. One increasingly important area of research is non-genetic cellular heterogeneity, a phenomenon that can be studied by assessing secretion of soluble effector proteins from single cells. This is particularly impactful for immune cells, as secreted proteins such as cytokines, chemokines, and growth factors are the gold standard for identifying phenotype. Current methods that rely upon immunofluorescence suffer from low detection sensitivity, requiring thousands of molecules to be secreted per cell. We have developed a quantum dot (QD)-based single-cell secretion analysis platform that can be used in different sandwich immunoassay formats to dramatically lower detection threshold, such that only one to a few molecules need be secreted per cell. We have also expanded this work to include multiplexing capabilities for different cytokines and employed this platform to study macrophage polarization under different stimuli at the single-cell level.

Revelations in Thymic Epithelial Cell Biology and Heterogeneity from Single-Cell RNA Sequencing and Lineage Tracing Methodologies

Thymic epithelial cells (TECs) make up the thymic microenvironments that support the generation of a functionally competent and self-tolerant T-cell repertoire. Cortical (c)TECs, present in the cortex, are essential for early thymocyte development including selection of thymocytes expressing functional TCRs (positive selection). Medullary (m)TECs, located in the medulla, play a key role in late thymocyte development, including depletion of self-reactive T cells (negative selection) and selection of regulatory T cells. In recent years, transcriptomic analysis by single-cell (sc)RNA sequencing (Seq) has revealed TEC heterogeneity previously masked by population-level RNA-Seq or phenotypic studies. We summarize the discoveries made possible by scRNA-Seq, including the identification of novel mTEC subsets, advances in understanding mTEC promiscuous gene expression, and TEC alterations from embryonic to adult stages. Whereas pseudotime analyses of scRNA-Seq data can suggest relationships between TEC subsets, experimental methods such as lineage tracing and reaggregate thymic organ culture (RTOC) are required to test these hypotheses. Lineage tracing - namely, of β5t or Aire expressing cells - has exposed progenitor and parent-daughter cellular relationships within TEC.