Revolutionizing immunology with single-cell RNA sequencing

Single-cell RNA sequencing reveals B cell dynamics and osteoclast activation in Talaromycosis-related bone destruction

OBJECTIVE

To explore the changes in bone destruction caused by Talaromycosis and its potential mechanisms.

METHODS

We assessed bone destruction and its severity using Micro CT, hematoxylin and eosin staining, tartrate-resistant acid phosphatase staining, F-actin staining, and Western blotting. We evaluated the biomechanical properties and pain perception in mice through biomechanical testing and the von Frey test. Single-cell RNA sequencing was used to analyze B cell composition in mouse tibial bone marrow, with findings verified by flow cytometry and qRT-PCR. Inflammatory cytokine levels, reactive oxygen species (ROS), and mitochondrial membrane potential were measured via enzyme-linked immunosorbent assay, immunofluorescence, and flow cytometry. Key proteins in the MAPK signaling pathway were also evaluated using Western blot.

RESULTS

Talaromyces marneffei (TM) infection led to increased osteoclast activity and significant bone destruction, accompanied by a reduction in weight gain, increased pain, and diminished bone biomechanical properties in mice. Post-infection, an increase in the number of B cells, particularly Naïve-B, ProB, and mature B cells, was observed, potentially linked to oxidative phosphorylation processes. TM infection elevated inflammatory cytokines production, ROS production and decreased mitochondrial membrane potential in vivo and in vitro. Furthermore, TM infection enhanced osteoclast differentiation through the activation of MAPK signaling pathways, including p38, ERK, and JNK.

CONCLUSION

TM infection induces B cell maturation and promotes bone destruction in the tibia of mice. This effect may be associated with mitochondrial apoptosis and ROS production during oxidative phosphorylation, potentially through MAPK pathway activation.

The role of single cell transcriptomics for efficacy and toxicity profiling of chimeric antigen receptor (CAR) T cell therapies

CAR T cells are genetically modified T cells that target specific epitopes. CAR T cell therapy has proven effective in difficult-to-treat B cell cancers and is now expanding into hematology and solid tumors. To date, approved CAR therapies target only two specific epitopes on cancer cells. Identifying more suitable targets is challenged by the lack of truly cancer-specific structures and the potential for on-target off-tumor toxicity. We analyzed gene expression of potential targets in single-cell data from cancer and healthy tissues. Because safety and efficacy can ultimately only be defined clinically, we selected approved and investigational targets for which clinical trail data are available. We generated atlases using >300,000 cells from 48 patients with follicular lymphoma, multiple myeloma, and B-cell acute lymphoblastic leukemia, and integrated over 3 million cells from 35 healthy tissues, harmonizing datasets from over 300 donors. To contextualize findings, we compared target expression patterns with outcome data from clinical trials, linking target profiles to efficacy and toxicity, and ranked 15 investigational targets based on their similarity to approved ones. Target expression did not significantly correlate with reported clinical toxicities in patients undergoing therapy. This may be attributed to the intricate interplay of patient-specific variables, the limited amount of metadata, and the complexity underlying toxicity. Nevertheless, our study serves as a resource for retrospective and prospective target evaluation to improve the safety and efficacy of CAR therapies.

Heterogeneity of the tumor immune cell microenvironment revealed by single-cell sequencing in head and neck cancer

Head and neck cancer (HNC) is the sixth most common disease in the world. The recurrence rate of patients is relatively high, and the heterogeneity of tumor immune microenvironment (TIME) cells may be an important reason for this. Single-cell sequencing (SCS) is currently the most promising and mature application in cancer research. It can identify unique genes expressed in cells and study tumor heterogeneity. According to current research, the heterogeneity of immune cells has become an important factor affecting the occurrence and development of HNC. SCSs can provide effective therapeutic targets and prognostic factors for HNC patients through analyses of gene expression levels and cell heterogeneity. Therefore, this study analyzes the basic theory of HNC and the development of SCS technology, elaborating on the application of SCS technology in HNC and its potential value in identifying HNC therapeutic targets and biomarkers.

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.

A systematic review of single-cell RNA sequencing applications and innovations

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

New insights on extramedullary granulopoiesis and neutrophil heterogeneity in the spleen and its importance in disease

Neutrophils are traditionally viewed as uncomplicated exterminators that arrive quickly at sites of infection, kill pathogens, and then expire. However, recent studies employing modern transcriptomics coupled with novel imaging modalities have discovered that neutrophils exhibit significant heterogeneity within organs and have complex functional roles ranging from tissue homeostasis to cancer and chronic pathologies. This has revised the view that neutrophils are simplistic butchers, and there has been a resurgent interest in neutrophils. The spleen was described as a granulopoietic organ more than 4 decades ago, and studies indicate that neutrophils are briefly retained in the spleen before returning to circulation after proliferation. Transcriptomic studies have discovered that splenic neutrophils are heterogeneous and distinct compared with those in blood. This suggests that a unique hematopoietic niche exists in the splenic microenvironment, i.e., capable of programming neutrophils in the spleen. During severe systemic inflammation with an increased need of neutrophils, the spleen can adapt by producing neutrophils through emergency granulopoiesis. In this review, we describe the structure and microanatomy of the spleen and examine how cells within the splenic microenvironment help to regulate splenic granulopoiesis. A focus is placed on exploring the increase in splenic granulopoiesis to meet host needs during infection and inflammation. Emerging technologies such as single-cell RNA sequencing, which provide valuable insight into splenic neutrophil development and heterogeneity, are also discussed. Finally, we examine how tumors subvert this natural pathway in the spleen to generate granulocytic suppressor cells to promote tumor growth.

Single-cell RNA sequencing in autoimmune diseases: New insights and challenges

Autoimmune diseases involve a variety of cell types, yet the intricacies of their individual roles within molecular mechanisms and therapeutic strategies remain poorly understood. Single-cell RNA sequencing (scRNA-seq) offers detailed insights into transcriptional diversity at the single-cell level, significantly advancing research in autoimmune diseases. This article explores how scRNA-seq enhances the understanding of cellular heterogeneity and its potential applications in the etiology, diagnosis, treatment, and prognosis of autoimmune diseases. By revealing a comprehensive cellular landscape, scRNA-seq illuminates the functional regulation of different cell subtypes during disease progression. It aids in identifying diagnostic and prognostic markers, and analyzing cell communication networks to uncover potential therapeutic targets. Despite its valuable contributions, addressing the limitations of scRNA-seq is essential for making further advancements.

Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

Single-Cell RNA Sequencing Reveals Peripheral Immune Cell Senescence and Inflammatory Phenotypes in Patients with Premature Ovarian Failure

Background

Premature Ovarian Failure (POF) is a heterogeneous syndrome characterized by ovarian dysfunction, frequently associated with autoimmune factors. The interaction between peripheral and ovarian immune signals remains unclear. Recent advancements in single-cell technology provide a unique opportunity to examine the complex peripheral immune response in POF patients at the microstructural level. This study investigates the immune microenvironment's complexity through the interaction between peripheral and ovarian local immune responses.

Methods

Peripheral blood mononuclear cells (PBMCs) were isolated from three healthy individuals and four POF patients. Single-cell RNA sequencing (scRNA-seq) was used to delineate cell clusters and identify differentially expressed genes (DEGs). Enrichment, SCENIC, and pseudo-time analyses were utilized to explore cellular phenotype diversity, regulatory patterns, and evolutionary trajectories. A POF mouse model was used for validation.

Results

Seven clusters were identified and classified into two groups. POF patients exhibited increased proportions in T cells, NK cells, and B cells as well as upregulated IGLC2, GNLY, GZMB, FCGR3A, and CCL5 expressions compared to healthy controls. Monocytes, particularly non-classical monocytes, exhibited inflammatory phenotypes. CD8+ Effector T cells demonstrated increased cytotoxicity and TCR clonal expansion. The trajectory of CD8+ Effector T cells in POF patients involved the synchronous upregulation of cytotoxic-related genes and immune checkpoint molecules. Notably, CCL5, primarily produced by non-classical monocytes, emerged as a critical factor. Elevated levels of CCL5 in plasma and local ovaries, along with increased CD8+ T cell infiltration, suggested its potential role in chemotaxis and ovarian damage in POF. Validation in the POF mouse model further supported these findings.

Conclusion

In summary, this study provides in-depth insights into the immune landscape of POF, revealing distinct cell populations, pathways, and signaling networks linked to the disease. These findings enhance our understanding of POF's immunological mechanisms, contributing to the development of potential diagnostic and therapeutic strategies.

Macrophages in Cardiovascular Fibrosis: Novel Subpopulations, Molecular Mechanisms, and Therapeutic Targets

Cardiovascular fibrosis is a common pathological process that contributes to the development and progression of various cardiovascular diseases. Despite being widely believed to be an irreversible and relentless process, preclinical models and clinical trials have shown that cardiovascular fibrosis is an extremely dynamic process. Additionally, as part of the innate immune system, macrophages are heterogeneous cells that are pivotal in tissue regeneration and fibrosis. They participate in fibroblast activation, extracellular matrix remodelling, and the regression of fibrosis. Although we have made some advances in understanding macrophages in cardiovascular fibrosis, a gap still remains between their identification and conversion into effective treatments. Moreover, the traditional M1-M2 paradigm faces many challenges because it does not sufficiently clarify macrophage diversity and their functions. Exploring novel macrophage-based therapies is urgent for cardiovascular fibrosis treatment. Single-cell techniques have shed light on identifying novel subpopulations that differ in function and molecular signature under steady-state and pathological conditions. In this review, we outline the developmental origins of macrophages, which underlie their functions; and recent technology development in the single-cell era. In addition, we describe the markers and mediators of the newly defined macrophage subpopulations and the molecular mechanisms involved to elucidate potential approaches for targeting macrophages in cardiovascular fibrosis.

Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations, all leading to excessive proliferation of malignant blood cells in the bone marrow. Tumor heterogeneity due to the acquisition of new somatic alterations leads to a high rate of resistance to current therapies or reduces the efficacy of hematopoietic stem cell transplantation (HSCT), thus increasing the risk of relapse and mortality. Single-cell RNA sequencing (scRNA-seq) will enable the classification of AML and guide treatment approaches by profiling patients with different facets of the same disease, stratifying risk, and identifying new potential therapeutic targets at the time of diagnosis or after treatment. ScRNA-seq allows the identification of quiescent stem-like cells, and leukemia stem cells responsible for resistance to therapeutic approaches and relapse after treatment. This method also introduces the factors and mechanisms that enhance the efficacy of the HSCT process. Generated data of the transcriptional profile of the AML could even allow the development of cancer vaccines and CAR T-cell therapies while saving valuable time and alleviating dangerous side effects of chemotherapy and HSCT in vivo. However, scRNA-seq applications face various challenges such as a large amount of data for high-dimensional analysis, technical noise, batch effects, and finding small biological patterns, which could be improved in combination with artificial intelligence models.

Dynamic Immune Response Landscapes of Avian Peripheral Blood Post-Vaccination Against Infectious Bronchitis Virus Infection

Background/Objectives: Despite decades of extensive vaccinations against avian infectious bronchitis virus (IBV) infection, outbreaks caused by constantly emerging variants due to genome recombination between different viral strains, including vaccine strains, occur annually worldwide. The development of novel vaccines with favorable safety and effectiveness is required but is hindered by a limited understanding of vaccination against IBV. Methods: Here, we performed a comprehensive analysis of the in vivo dynamics of peripheral blood mononuclear cells (PBMCs) in specific pathogen-free chickens inoculated with the widely used live attenuated IBV vaccine strain H120 at single-cell level, using high-throughput single-cell transcriptome sequencing (scRNA-seq). Results: High-quality sequencing dataset for four scRNA-seq data containing the transcriptomes of 29,846 individual chicken PBMCs were obtained, defining 22 populations and 7 cell types based on distinct molecular signatures and known markers. Further integrative analysis constructed the time series dynamic cell transition and immune response landscapes within the two weeks post-prime vaccination against IBV. Enhanced crosstalk between antigen-presenting cells and T lymphocytes was revealed as early as four days post-vaccination. The specific immune cell populations and their comprehensive cellular and molecular networks involved in the initiation phase of antiviral adaptive immune responses were elucidated in details. Conclusions: Our study provides a comprehensive view of the dynamic initiation of immune responses in chickens against IBV infection at the cellular and molecular levels, which provides theoretical support and potential solutions for the future rational design of safe and effective vaccines, the augmentation of the efficacy of current vaccines, and the optimization of immune programs.

The role of CD101 and Tim3 in the immune microenvironment of gastric cancer and their potential as prognostic biomarkers

BACKGROUND

Gastric cancer (GC) is a prevalent malignancy. Current treatment modalities, including surgery, chemotherapy, radiotherapy, and targeted therapy, have limitations in early detection and personalized treatment, necessitating the discovery of novel biomarkers and therapeutic strategies. This study aims to elucidate the molecular mechanisms underlying GC, focusing on the differentially expressed genes (DEGs) of CD101- Tim3+ CD8+ T cells (CCT precursors) and CD101+ Tim3+ CD8+ T cells (CCT).

METHODS

Utilizing a multi-omics approach, including transcriptomic sequencing, single-cell RNA sequencing, cell communication analysis, and enrichment analysis.

RESULTS

We identified 140 genes significantly associated with overall survival in GC patients, including LYAR, ASCL2, and EMP2. A risk score model based on 14 prognostic genes was constructed, demonstrating a significant inverse correlation with survival time (p < 0.05). Immune response analysis indicated decreased infiltration of Activated B cells, CD56bright natural killer cells, and Monocytes in the high-risk group, while CD56dim natural killer cells and Gamma delta T cells were significantly increased, suggesting alterations in the immune microenvironment that influence patient prognosis. Furthermore, drug sensitivity analysis revealed potential responsiveness of high-risk patients to BI-2536, supporting personalized treatment approaches. Cell communication analysis indicated reduced intercellular interactions in PD-1 immunotherapy groups, highlighting the impact of immunotherapy on the tumor microenvironment. GSEA (Gene Set Enrichment Analysis) and GSVA (Gene Set Variation Analysis) revealed enrichment in DNA replication and proteasome pathways in high-risk groups, providing insights into the molecular mechanisms of GC.

CONCLUSIONS

This study established a foundation for future exploration of targeted therapies and personalized treatment strategies in GC.

Integrated analysis of single-cell and bulk-RNA sequencing for the cellular senescence in prognosis of lung adenocarcinoma

Non-small cell lung cancer (NSCLC), half of which are lung adenocarcinoma (LUAD), is one of the most widely spread cancers in the world. Telomerase, which maintains telomere length and chromosomal integrity, enables cancer cells to avoid replicative senescence. When telomerase is inhibited, cancer cells' senescence began, preventing them from growing indefinitely. Cellular senescence and telomeres are intrinsically linked. As of yet, still laking a systematic study of the involvement of telomere-senescence related genes in lung adenocarcinoma development. In this study, myeloid cells were identified as the cell type which are most correlated with cellular senescence based on its highest telomere-related gene activity. GO, KEGG, GSEA and GSVA analyses were used to explore the biological function of telomere-senescence related genes in LUAD. The combined analysis of single-cell RNA-sequencing and bulk-RNA sequencing identified a gene signature composed of 14 genes which can accurately predict the prognosis of patients with LUAD. In one training and four validation sets, patients with higher Telomere Related Gene Signature (TRGS) had a worse prognosis than those with lower TRGS. Different TRGS patient groups showed varying degrees of immune cell infiltration, frequency of gene missense mutation, sensitivity to different drugs, and tumor mutation burden (TMB). Collectively, we developed a brand new signature composed of telomere-senescence related genes that can accurately predicts patients' prognosis in LUAD, which provides new insights for future research into the role of cellular senescence in LUAD.

ImageDoubler: image-based doublet identification in single-cell sequencing

Single-cell sequencing provides detailed insights into individual cell behaviors within complex systems based on the assumption that each cell is uniquely isolated. However, doublets-where two or more cells are sequenced together-disrupt this assumption and can lead to potential data misinterpretations. Traditional doublet detection methods primarily rely on simulated genomic data, which may be less effective in homogeneous cell populations and can introduce biases from experimental processes. Therefore, we introduce ImageDoubler in this study, an innovative image-based model that identifies doublets and missing samples leveraging the Fluidigm single-cell sequencing image data. Our approach showcases a notable doublet detection efficacy, achieving a rate up to 93.87% and registering a minimum improvement of 33.1% in F1 scores compared to existing genomic-based methods. This advancement highlights the potential of using imaging to glean insight into developing doublet detection algorithms and exposes the limitations inherent in current genomic-based techniques.

Single-cell RNA sequencing and spatial transcriptome analysis in bladder cancer: Current status and future perspectives

Background

Bladder cancer is one of the most prevalent malignancies, and the mechanisms underlying its progression and the role of the tumor microenvironment (TME) are unclear. Recent advancements in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) enable detailed analysis of the cellular heterogeneity, gene expression, and cell-cell interactions in bladder diseases.

Methodology

We conducted a comprehensive search for recent articles that have investigated bladder diseases using scRNA-seq and ST.

Results

scRNA-seq and ST have led to significant discoveries in bladder disease research. These technologies have enabled the identification of multiple molecular subtypes within individual tumors and of the mechanisms of treatment resistance. Additionally, molecular differences based on gender have been explored, explaining the heterogeneity of the incidence and progression of bladder cancer. These findings deepen our understanding of the pathology of bladder diseases and highlight the transformative potential of scRNA-seq and ST in identifying novel biomarkers and therapeutic targets.

Conclusions

Integrating scRNA-seq and ST has considerably enhanced our understanding of tumor heterogeneity and the tumor microenvironment within tissues. These insights may lead to the development of personalized therapies and the improvement of patient outcomes. Several challenges, such as technical limitations and access difficulties, need to be addressed for the future clinical application of these technologies.

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.

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.

Single-cell RNA sequencing reveals multiple immune cell subpopulations promote the formation of abnormal bone microenvironment in osteoporosis

With the aging of the population, the incidence of osteoporosis (OP) is on the rise, but the ecology of immune cell subpopulations in OP is poorly understood. Therefore, identifying cell subpopulations involved in promoting the development of OP may facilitate the development of new treatments. Based on bioinformatics analysis, we constructed a single-cell landscape of the OP microenvironment and identified immune cell subpopulations in OP to further explore the role of different subpopulations in the abnormal bone microenvironment. Among macrophages (Mac), the Mac_OLR1 subpopulation has an M1-like phenotype and significantly activates cytokine and osteoclast differentiation pathways, interacting with osteoclasts via the HBEGF-CD9 axis. In neutrophils (Neut), the Neut_RSAD2 subpopulation significantly activated cytokine and osteoclast differentiation pathways and had a high neutrophil extracellular trap (NET) score, and H1FX was identified as its potential regulator. In effector memory T (Tem) cells, the Tem_CCL4 subpopulation significantly activated osteoclast differentiation and immune inflammation-related pathways and highly expressed proinflammatory molecules such as CCL4, CCL4L2, CCL5 and IFNG. In B cells, the abundance of the B_ACSM3 subpopulation was significantly increased in the OP group and the osteoclast differentiation pathway was significantly activated, and MYB was identified as its potential regulator. In summary, we identified several immune cell subpopulations that may be involved in promoting the formation of OP, further identified the transcription factors that regulate these subpopulations, and speculated that the development of OP may be accompanied by immune inflammatory responses mediated by these subpopulations. These findings provide candidate molecules and cells for future OP research and may help facilitate the development of new therapies.

Time-resolved single-cell transcriptomic sequencing

Cells experience continuous transformation under both physiological and pathological circumstances. Single-cell RNA sequencing (scRNA-seq) is competent in disclosing the disparities of cells; nevertheless, it poses challenges in linking the individual cell state at distinct time points. Although computational approaches based on scRNA-seq data have been put forward for trajectory analysis, the result is based on assumptions and fails to reflect the actual states. Consequently, it is necessary to incorporate a "time anchor" into the scRNA-seq library for the temporal documentation of the dynamic expression pattern. This review comprehensively overviews the time-resolved single-cell transcriptomic sequencing methodologies and applications. As scRNA-seq functions as the basis for profiling single-cell expression patterns, the review initially introduces various scRNA-seq approaches. Subsequently, the review focuses on the different experimental strategies for introducing a "time anchor" to scRNA-seq, highlighting their principles, strengths, weaknesses, and comparing their adaptation in various scenarios. Next, it provides a brief summary of applications in immunity response, cancer progression, and embryo development. Finally, the review concludes with a forward-looking perspective on future advancements in time-resolved single-cell transcriptomic sequencing.

scGraphformer: unveiling cellular heterogeneity and interactions in scRNA-seq data using a scalable graph transformer network

The precise classification of cell types from single-cell RNA sequencing (scRNA-seq) data is pivotal for dissecting cellular heterogeneity in biological research. Traditional graph neural network (GNN) models are constrained by reliance on predefined graphs, limiting the exploration of complex cell-to-cell relationships. We introduce scGraphformer, a transformer-based GNN that transcends these limitations by learning an all-encompassing cell-cell relational network directly from scRNA-seq data. Through an iterative refinement process, scGraphformer constructs a dense graph structure that captures the full spectrum of cellular interactions. This comprehensive approach enables the identification of subtle and previously obscured cellular patterns and relationships. Evaluated on multiple datasets, scGraphformer demonstrates superior performance in cell type identification compared to existing methods and showcases its scalability with large-scale datasets. Our method not only provides enhanced cell type classification ability but also reveals the underlying cell interactions, offering deeper insights into functional cellular relationships. The scGraphformer thus holds the potential to significantly advance the field of single-cell analysis and contribute to a more nuanced understanding of cellular behavior.

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.

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.

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.