Unravelling biology and shifting paradigms in cancer with single-cell sequencing

EpiCHAOS: a metric to quantify epigenomic heterogeneity in single-cell data

Epigenetic heterogeneity is a fundamental property of biological systems and is recognized as a potential driver of tumor plasticity and therapy resistance. Single-cell epigenomics technologies have been widely employed to study epigenetic variation between-but not within-cellular clusters. We introduce epiCHAOS: a quantitative metric of cell-to-cell heterogeneity, applicable to any single-cell epigenomics data type. After validation in synthetic datasets, we apply epiCHAOS to investigate global and region-specific patterns of epigenetic heterogeneity across diverse biological systems. EpiCHAOS provides an excellent approximation of stemness and plasticity in development and malignancy, making it a valuable addition to single-cell cancer epigenomics analyses.

New horizons in our understanding of precursor multiple myeloma and early interception

Multiple myeloma is an incurable plasma cell malignancy that evolves over decades through the selection and malignant transformation of monoclonal plasma cells. The evolution from precursor states to symptomatic disease is characterized by an increasing complexity of genomic alterations within the plasma cells and a remodelling of the microenvironment towards an immunosuppressive state. Notably, in patients with advanced disease, similar mechanisms of tumour escape and immune dysfunction mediate resistance to modern T cell-based therapies, such as T cell-engaging bispecific antibodies and chimeric antigen receptor (CAR)-T cells. Thus, an increasing number of clinical trials are assessing the efficiency and safety of these therapies in individuals with newly diagnosed multiple myeloma and high-risk smoldering multiple myeloma. In this Review, we summarize the current knowledge about tumour intrinsic and extrinsic processes underlying progression from precursor states to symptomatic myeloma and discuss the rationale for early interception including the use of T cell-redirecting therapies.

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Integration of single-cell sequencing and bulk transcriptome data develops prognostic markers based on PCLAF+ stem-like tumor cells using artificial neural network in gastric cancer

Background

Gastric cancer stem cells (GCSCs) are important tumour cells involved in tumourigenesis and gastric cancer development. However, their clinical value remains unclear due to the limitations of the available technologies. This study aims to explore the clinical significance of GCSCs, their connection to the tumour microenvironment, and their underlying molecular mechanisms.

Methods

Stem-like tumour cells were identified by mining single-cell transcriptomic data from multiple samples. Integrated analysis of single-cell and bulk transcriptome data was performed to analyse the role of stem-like tumour cells in predicting clinical outcomes by introducing the intermediate variable mRNA stemness degree (SD). Consensus clustering analysis was performed to develop an SD-related molecular classification strategy to assess the clinical characteristics in gastric cancer. A prognostic model was constructed using a customized approach that comprehensively considered SD-related gene signatures based on an artificial neural network.

Results

By analysing single-cell data and validating immunofluorescence results, we identified a PCLAF+ stem-like tumour cell population in GC. By calculating SD, we observed that PCLAF+ stem-like tumour cells were associated with poor prognosis and certain clinical features. The SD was negatively correlated with the abundance of most immune cell types. Furthermore, we proposed an SD-related classification method and prognostic model. In addition, the customised prognostic model can be used to predict whether a patient respond to PD-1/PD-L1 immunotherapy.

Conclusion

We identified a cluster of stem-like cells and elucidated their clinical significance, highlighting the possibility of their use as immunotherapeutic targets.

Single-cell analysis in rheumatic and allergic diseases: insights for clinical practice

Since the advent of single-cell RNA sequencing (scRNA-seq) methodology, single-cell analysis has become a powerful tool for exploration of cellular networks and dysregulated immune responses in disease pathogenesis. Advanced bioinformatics tools have enabled the combined analysis of scRNA-seq data and information on various cell properties, such as cell surface molecular profiles, chromatin accessibility and spatial information, leading to a deeper understanding of pathology. This Review provides an overview of the achievements in single-cell analysis applied to clinical samples of rheumatic and allergic diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, allergic airway diseases and atopic dermatitis, with an expanded scope beyond peripheral blood cells to include local diseased tissues. Despite the valuable insights that single-cell analysis has provided into disease pathogenesis, challenges remain in translating single-cell findings into clinical practice and developing personalized treatment strategies. Beyond understanding the atlas of cellular diversity, we discuss the application of data obtained in each study to clinical practice, with a focus on identifying biomarkers and therapeutic targets.

The development of the occurrence and metastasis of breast cancer by single-cell sequencing

Breast cancer is currently the most frequent malignant tumor and the leading cause of cancer death among women globally. Although the five-year survival rate for early breast cancer has risen to more than 90%, medication resistance persists in advanced breast cancer and some intractable breast cancer, resulting in a poor prognosis, a high recurrence rate, and a low survival rate. Single-cell sequencing (SCS) is the study of a single cell's gene structure and expression level differences in order to discover unusual molecular subgroups, disease development, and a variety of mechanisms. This review briefly discusses single-cell sequencing and its application, and lists the research on single-cell sequencing in the development and metastasis of breast cancer, in order to bring fresh ideas for the comprehensive treatment of breast cancer.

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

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.

Multi-Omics Profiling Unveils the Complexity and Dynamics of Immune Infiltrates in Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is a highly heterogeneous malignancy. The reasons behind the global rise in the incidence of ICC remain unclear, and there exists limited knowledge regarding the immune cells within the tumor microenvironment (TME). In this study, a more comprehensive analysis of multi-omics data was performed using machine learning methods. The study found that the immunoactivity of B cells, macrophages, and T cells in the infiltrating immune cells of ICC exhibits a significantly higher level of immunoactivity in comparison to other immune cells. During the immune sensing and response, the effect of antigen-presenting cells (APCs) such as B cells and macrophages on activating NK cells was weakened, while the effect of activating T cells became stronger. Simultaneously, four distinct subpopulations, namely BLp, MacrophagesLp, BHn, and THn, have been identified from the infiltrating immune cells, and their corresponding immune-related marker genes have been identified. The immune sensing and response model of ICC has been revised and constructed based on our current comprehension. This study not only helps to deepen the understanding the heterogeneity of infiltrating immune cells in ICC, but also may provide valuable insights into the diagnosis, evaluation, treatment, and prognosis of ICC.

Loss of AR-regulated AFF3 contributes to prostate cancer progression and reduces ferroptosis sensitivity by downregulating ACSL4 based on single-cell sequencing analysis

Prostate cancer (PCa) is one of the most common cancers affecting the health of men worldwide. Castration-resistant prostate cancer (CRPC), the advanced and refractory phase of prostate cancer, has multiple mechanisms of resistance to androgen deprivation therapy (ADT) such as AR mutations, aberrant androgen synthase, and abnormal expression of AR-related genes. Based on the research of the AR pathway, new drugs for the treatment of CRPC have been developed in clinical practice, such as Abiraterone and enzalutamide. However, many areas in this pathway are still worth exploring. In this study, single-cell sequencing analysis was utilized to scrutinize significant genes in the androgen receptor (AR) pathway related to CRPC. Our analysis of single-cell sequencing combined with bulk-cell sequencing revealed a substantial downregulation of AR-regulated AFF3 in CRPC. Overexpression of AFF3 restricted the proliferation and migration of prostate cancer cells whilst also increasing their sensitivity towards enzalutamide, while knockdown of AFF3 had the opposite effect. To elucidate the mechanism of tumor inhibition by AFF3, we applied GSVA and GSEA to investigate the metabolic pathways related to AFF3 and revealed that AFF3 had an impact on fatty acids metabolism and ferroptosis through the regulation of ACSL4 protein expression. Based on correlation analysis and flow cytometry, we can speculate that AFF3 can impact the sensitivity of the CRPC cell lines to the ferroptosis inducer (RSL3) by regulating ACSL4. Therefore, our findings may provide new insights into the mechanisms of drug resistance in CRPC, and AFF3 may serve as a novel prognostic biomarker in prostate cancer.

Dissecting the Single-Cell Diversity and Heterogeneity Underlying Cervical Precancerous Lesions and Cancer Tissues

The underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.

KaryoTap Enables Aneuploidy Detection in Thousands of Single Human Cells

Investigating chromosomal instability and aneuploidy within tumors is essential for understanding tumorigenesis and developing diagnostic and therapeutic strategies. Single-cell DNA sequencing technologies have enabled such analyses, revealing aneuploidies specific to individual cells within the same tumor. However, it has been difficult to scale the throughput of these methods to detect rare aneuploidies while maintaining high sensitivity. To overcome this deficit, we developed KaryoTap, a method combining custom targeted DNA sequencing panels for the Tapestri platform with a computational framework to enable detection of chromosome- and chromosome arm-scale aneuploidy (gains or losses) and copy number neutral loss of heterozygosity in all human chromosomes across thousands of single cells simultaneously. KaryoTap allows detecting gains and losses with an average accuracy of 83% for arm events and 91% for chromosome events. Importantly, together with chromosomal copy number, our system allows us to detect barcodes and gRNAs integrated into the cells' genome, thus enabling pooled CRISPR- or ORF-based functional screens in single cells. As a proof of principle, we performed a small screen to expand the chromosomes that can be targeted by our recently described CRISPR-based KaryoCreate system for engineering aneuploidy in human cells. KaryoTap will prove a powerful and flexible approach for the study of aneuploidy and chromosomal instability in both tumors and normal tissues.

Pre-metastatic niche: formation, characteristics and therapeutic implication

Distant metastasis is a primary cause of mortality and contributes to poor surgical outcomes in cancer patients. Before the development of organ-specific metastasis, the formation of a pre-metastatic niche is pivotal in promoting the spread of cancer cells. This review delves into the intricate landscape of the pre-metastatic niche, focusing on the roles of tumor-derived secreted factors, extracellular vesicles, and circulating tumor cells in shaping the metastatic niche. The discussion encompasses cellular elements such as macrophages, neutrophils, bone marrow-derived suppressive cells, and T/B cells, in addition to molecular factors like secreted substances from tumors and extracellular vesicles, within the framework of pre-metastatic niche formation. Insights into the temporal mechanisms of pre-metastatic niche formation such as epithelial-mesenchymal transition, immunosuppression, extracellular matrix remodeling, metabolic reprogramming, vascular permeability and angiogenesis are provided. Furthermore, the landscape of pre-metastatic niche in different metastatic organs like lymph nodes, lungs, liver, brain, and bones is elucidated. Therapeutic approaches targeting the cellular and molecular components of pre-metastatic niche, as well as interventions targeting signaling pathways such as the TGF-β, VEGF, and MET pathways, are highlighted. This review aims to enhance our understanding of pre-metastatic niche dynamics and provide insights for developing effective therapeutic strategies to combat tumor metastasis.

Development and validation of a risk prognostic model based on the H. pylori infection phenotype for stomach adenocarcinoma

Background

Stomach adenocarcinoma (STAD) is one of the most common malignancies. Infection of helicobacter pylori (H. pylori) is a major risk factor that leads to the development of STAD. This study constructed a risk model based on the H. pylori-related macrophages for predicting STAD prognosis.

Methods

The single-cell RNA sequencing (scRNA-seq) dataset and the clinic information and RNA-seq datasets of STAD patients were collected for establishing a prognostic model and for validation. The "Seurat" and "harmony" packages were used to process the scRNA-seq data. Key gene modules were sectioned using the "limma" package and the "WGCNA" package. Kaplan-Meier (KM) and Receiver Operating Characteristic Curve (ROC) analyses were performed with "survminer" package. The "GSVA" package was employed for single sample gene set enrichment analysis (ssGSEA). Cell migration and invasion were measured by carrying out wound healing and trans-well assays.

Results

A total of 17397 were screened and classified into 8 cell type clusters, among which the macrophage cluster was closely associated with the H. pylori infection. Macrophages were further categorized into four subtypes (including C1, C2, C3, and C4), and highly variable genes of macrophage subtype C4 could serve as an indicator of the prognosis of STAD. Subsequently, we developed a RiskScore model based on six H. pylori -associated genes (TNFRSF1B, CTLA4, ABCA1, IKBIP, AKAP5, and NPC2) and observed that the high-risk patients exhibited poor prognosis, higher suppressive immune infiltration, and were closely associated with cancer activation-related pathways. Furthermore, a nomogram combining the RiskScore was developed to accurately predict the survival of STAD patients. AB CA 1 in the RiskScore model significantly affected the migration and invasion of tumor cells.

Conclusion

The gene expression profile served as an indicator of the survival for patients with STAD and addressed the clinical significance of using H. pylori-associated genes to treat STAD. The current findings provided novel understandings for the clinical evaluation and management of STAD.

Single-cell RNA sequencing to map tumor heterogeneity in gastric carcinogenesis paving roads to individualized therapy

Gastric cancer (GC) is a highly heterogeneous disease with a complex tumor microenvironment (TME) that encompasses multiple cell types including cancer cells, immune cells, stromal cells, and so on. Cancer-associated cells could remodel the TME and influence the progression of GC and therapeutic response. Single-cell RNA sequencing (scRNA-seq), as an emerging technology, has provided unprecedented insights into the complicated biological composition and characteristics of TME at the molecular, cellular, and immunological resolutions, offering a new idea for GC studies. In this review, we discuss the novel findings from scRNA-seq datasets revealing the origin and evolution of GC, and scRNA-seq is a powerful tool for investigating transcriptional dynamics and intratumor heterogeneity (ITH) in GC. Meanwhile, we demonstrate that the vital immune cells within TME, including T cells, B cells, macrophages, and stromal cells, play an important role in the disease progression. Additionally, we also overview that how scRNA-seq facilitates our understanding about the effects on individualized therapy of GC patients. Spatial transcriptomes (ST) have been designed to determine spatial distribution and capture local intercellular communication networks, enabling a further understanding of the relationship between the spatial background of a particular cell and its functions. In summary, scRNA-seq and other single-cell technologies provide a valuable perspective for molecular and pathological disease characteristics and hold promise for advancing basic research and clinical practice in GC.

Cellular origin and clonal evolution of human dedifferentiated liposarcoma

Dedifferentiated liposarcoma (DDLPS) is the most frequent high-grade soft tissue sarcoma subtype. It is characterized by a component of undifferentiated tumor cells coexisting with a component of well-differentiated adipocytic tumor cells. Both dedifferentiated (DD) and well-differentiated (WD) components exhibit MDM2 amplification, however their cellular origin remains elusive. Using single-cell RNA sequencing, DNA sequencing, in situ multiplex immunofluorescence and functional assays in paired WD and DD components from primary DDLPS tumors, we characterize the cellular heterogeneity of DDLPS tumor and micro-environment. We identify a population of tumor adipocyte stem cells (ASC) showing striking similarities with adipocyte stromal progenitors found in white adipose tissue. We show that tumor ASC harbor the ancestral genomic alterations of WD and DD components, suggesting that both derive from these progenitors following clonal evolution. Last, we show that DD tumor cells keep important biological properties of ASC including pluripotency and that their adipogenic properties are inhibited by a TGF-β-high immunosuppressive tumor micro-environment.

Application of single cell sequencing technology in ovarian cancer research (review)

Ovarian cancer is a malignant tumor of ovary. It has the characteristics of difficult early diagnosis, poor late curative effect and high recurrence rate. It is the biggest disease that seriously threatens women's health. Single cell sequencing technology refers to sequencing the genetic information carried by it at the single cell level to obtain the gene sequence, transcript, protein and epigenetic expression profile information of a certain cell type and conduct integrated analysis. It has unique advantages in the study of tumor occurrence and evolution, and can provide new methods for the study of ovarian cancer. This paper reviews the single cell sequencing technology and its application in ovarian cancer.

Unveil Intrahepatic Cholangiocarcinoma Heterogeneity through the Lens of Omics and Multi-Omics Approaches

Intrahepatic cholangiocarcinoma (iCCA) is recognized worldwide as the second leading cause of morbidity and mortality among primary liver cancers, showing a continuously increasing incidence rate in recent years. iCCA aggressiveness is revealed through its rapid and silent intrahepatic expansion and spread through the lymphatic system leading to late diagnosis and poor prognoses. Multi-omics studies have aggregated information derived from single-omics data, providing a more comprehensive understanding of the phenomena being studied. These approaches are gradually becoming powerful tools for investigating the intricate pathobiology of iCCA, facilitating the correlation between molecular signature and phenotypic manifestation. Consequently, preliminary stratifications of iCCA patients have been proposed according to their "omics" features opening the possibility of identifying potential biomarkers for early diagnosis and developing new therapies based on personalized medicine (PM). The focus of this review is to provide new and advanced insight into the molecular pathobiology of the iCCA, starting from single- to the latest multi-omics approaches, paving the way for translating new basic research into therapeutic practices.

Advancements in 3D In Vitro Models for Colorectal Cancer

The process of drug discovery and pre-clinical testing is currently inefficient, expensive, and time-consuming. Most importantly, the success rate is unsatisfactory, as only a small percentage of tested drugs are made available to oncological patients. This is largely due to the lack of reliable models that accurately predict drug efficacy and safety. Even animal models often fail to replicate human-specific pathologies and human body's complexity. These factors, along with ethical concerns regarding animal use, urge the development of suitable human-relevant, translational in vitro models.

ScRNA-seq revealed the tumor microenvironment heterogeneity related to the occurrence and metastasis in upper urinary tract urothelial carcinoma

Metastasis is the greatest clinical challenge for UTUCs, which may have distinct molecular and cellular characteristics from earlier cancers. Herein, we provide single-cell transcriptome profiles of UTUC para cancer normal tissue, primary tumor lesions, and lymphatic metastases to explore possible mechanisms associated with UTUC occurrence and metastasis. From 28,315 cells obtained from normal and tumor tissues of 3 high-grade UTUC patients, we revealed the origin of UTUC tumor cells and the homology between metastatic and primary tumor cells. Unlike the immunomicroenvironment suppression of other tumors, we found no immunosuppression in the tumor microenvironment of UTUC. Moreover, it is imperative to note that stromal cells are pivotal in the advancement of UTUC. This comprehensive single-cell exploration enhances our comprehension of the molecular and cellular dynamics of metastatic UTUCs and discloses promising diagnostic and therapeutic targets in cancer-microenvironment interactions.

Single-cell RNA sequencing reveals that MYBL2 in malignant epithelial cells is involved in the development and progression of ovarian cancer

Background

Ovarian carcinoma (OC) is a prevalent gynecological malignancy associated with high recurrence rates and mortality, often diagnosed at advanced stages. Despite advances in immunotherapy, immune exhaustion remains a significant challenge in achieving optimal tumor control. However, the exploration of intratumoral heterogeneity of malignant epithelial cells and the ovarian cancer tumor microenvironment is still limited, hindering our comprehensive understanding of the disease.

Materials and methods

Utilizing single-cell RNA sequencing (scRNA-seq), we comprehensively investigated the cellular composition across six ovarian cancer patients with omental metastasis. Our focus centered on analysis of the malignant epithelial cells. Employing CytoTRACE and slingshot pseudotime analyses, we identified critical subpopulations and explored associated transcription factors (TFs) influencing ovarian cancer progression. Furthermore, by integrating clinical factors from a large cohort of bulk RNA sequencing data, we have established a novel prognostic model to investigate the impact of the tumor immune microenvironment on ovarian cancer patients. Furthermore, we have investigated the condition of immunological exhaustion.

Results

Our study identified a distinct and highly proliferative subgroup of malignant epithelial cells, known as C2 TOP2A+ TCs. This subgroup primarily consisted of patients who hadn't received neoadjuvant chemotherapy. Ovarian cancer patients with elevated TOP2A expression exhibited heightened sensitivity to neoadjuvant chemotherapy (NACT). Moreover, the transcription factor MYBL2 in this subgroup played a critical role in ovarian cancer development. Additionally, we developed an independent prognostic indicator, the TOP2A TCs Risk Score (TTRS), which revealed a correlation between the High TTRS Group and unfavorable outcomes. Furthermore, immune infiltration and drug sensitivity analyses demonstrated increased responsiveness to Paclitaxel, Cisplatin, and Gemcitabine in the Low TTRS Group.

Conclusion

This research deepens our understanding of malignant epithelial cells in ovarian cancer and enhances our knowledge of the ovarian cancer immune microenvironment and immune exhaustion. We have revealed the heightened susceptibility of the C2 TOP2A+ TCs subgroup to neoadjuvant chemotherapy and emphasized the role of MYBL2 within the C2 subgroup in promoting the occurrence and progression of ovarian cancer. These insights provide valuable guidance for the management of ovarian cancer treatment.

Single-cell mass cytometry in immunological skin diseases

Immune-related skin diseases represent a collective of dermatological disorders intricately linked to dysfunctional immune system processes. These conditions are primarily characterized by an immoderate activation of the immune system or deviant immune responses, involving diverse immune components including immune cells, antibodies, and inflammatory mediators. However, the precise molecular dysregulation underlying numerous individual cases of these diseases and unique subsets respond under disease conditions remains elusive. Comprehending the mechanisms and determinants governing the homeostasis and functionality of diseases could offer potential therapeutic opportunities for intervention. Mass cytometry enables precise and high-throughput quantitative measurement of proteins within individual cells by utilizing antibodies labeled with rare heavy metal isotopes. Imaging mass cytometry employs mass spectrometry to obtain spatial information on cell-to-cell interactions within tissue sections, simultaneously utilizing more than 40 markers. The application of single-cell mass cytometry presents a unique opportunity to conduct highly multiplexed analysis at the single-cell level, thereby revolutionizing our understanding of cell population heterogeneity and hierarchy, cellular states, multiplexed signaling pathways, proteolysis products, and mRNA transcripts specifically in the context of many autoimmune diseases. This information holds the potential to offer novel approaches for the diagnosis, prognostic assessment, and monitoring responses to treatment, thereby enriching our strategies in managing the respective conditions. This review summarizes the present-day utilization of single-cell mass cytometry in studying immune-related skin diseases, highlighting its advantages and limitations. This technique will become increasingly prevalent in conducting extensive investigations into these disorders, ultimately yielding significant contributions to their accurate diagnosis and efficacious therapeutic interventions.

Combining single-cell and bulk RNA sequencing, NK cell marker genes reveal a prognostic and immune status in pancreatic ductal adenocarcinoma

The NK cell is an important component of the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC), also plays a significant role in PDAC development. This study aimed to explore the relationship between NK cell marker genes and prognosis, immune response of PDAC patients. By scRNA-seq data, we found the proportion of NK cells were significantly downregulated in PDAC and 373 NK cell marker genes were screened out. By TCGA database, we enrolled 7 NK cell marker genes to construct the signature for predicting prognosis in PDAC patients. Cox analysis identified the signature as an independent factor for pancreatic cancer. Subsequently, the predictive power of signature was validated by 6 GEO datasets and had an excellent evaluation. Our analysis of relationship between the signature and patients' immune status revealed that the signature has a strong correlation with immunocyte infiltration, inflammatory reaction, immune checkpoint inhibitors (ICIs) response. The NK cell marker genes are closely related to the prognosis and immune capacity of PDAC patients, and they have potential value as a therapeutic target.

Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research

Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.

Progress of single-cell RNA sequencing combined with spatial transcriptomics in tumour microenvironment and treatment of pancreatic cancer

In recent years, single-cell analyses have revealed the heterogeneity of the tumour microenvironment (TME) at the genomic, transcriptomic, and proteomic levels, further improving our understanding of the mechanisms of tumour development. Single-cell RNA sequencing (scRNA-seq) technology allow analysis of the transcriptome at the single-cell level and have unprecedented potential for exploration of the characteristics involved in tumour development and progression. These techniques allow analysis of transcript sequences at higher resolution, thereby increasing our understanding of the diversity of cells found in the tumour microenvironment and how these cells interact in complex tumour tissue. Although scRNA-seq has emerged as an important tool for studying the tumour microenvironment in recent years, it cannot be used to analyse spatial information for cells. In this regard, spatial transcriptomics (ST) approaches allow researchers to understand the functions of individual cells in complex multicellular organisms by understanding their physical location in tissue sections. In particular, in related research on tumour heterogeneity, ST is an excellent complementary approach to scRNA-seq, constituting a new method for further exploration of tumour heterogeneity, and this approach can also provide unprecedented insight into the development of treatments for pancreatic cancer (PC). In this review, based on the methods of scRNA-seq and ST analyses, research progress on the tumour microenvironment and treatment of pancreatic cancer is further explained.

Integration of single-cell sequencing and bulk RNA-seq to identify and develop a prognostic signature related to colorectal cancer stem cells

The prognosis for patients with colorectal cancer (CRC) remains worse than expected due to metastasis, recurrence, and resistance to chemotherapy. Colorectal cancer stem cells (CRCSCs) play a vital role in tumor metastasis, recurrence, and chemotherapy resistance. However, there are currently no prognostic markers based on CRCSCs-related genes available for clinical use. In this study, single-cell transcriptome sequencing was employed to distinguish cancer stem cells (CSCs) in the CRC microenvironment and analyze their properties at the single-cell level. Subsequently, data from TCGA and GEO databases were utilized to develop a prognostic risk model for CRCSCs-related genes and validate its diagnostic performance. Additionally, functional enrichment, immune response, and chemotherapeutic drug sensitivity of the relevant genes in the risk model were investigated. Lastly, the key gene RPS17 in the risk model was identified as a potential prognostic marker and therapeutic target for further comprehensive studies. Our findings provide new insights into the prognostic treatment of CRC and offer novel perspectives for a systematic and comprehensive understanding of CRC development.

Raman cell sorting for single-cell research

Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research. Raman cell sorting technology has garnered attention owing to its non-labeling, non-destructive detection features and the capability to analyze samples containing water. In addition, this technology can provide live cells for subsequent genomics analysis and gene sequencing. This paper emphasizes the importance of single-cell research, describes the single-cell research methods that currently exist, including single-cell manipulation and single-cell identification techniques, and highlights the advantages of Raman spectroscopy in the field of single-cell analysis by comparing it with the fluorescence-activated cell sorting (FACS) technique. It describes various existing Raman cell sorting techniques and introduces their respective advantages and disadvantages. The above techniques were compared and analyzed, considering a variety of factors. The current bottlenecks include weak single-cell spontaneous Raman signals and the requirement for a prolonged total cell exposure time, significantly constraining Raman cell sorting technology's detection speed, efficiency, and throughput. This paper provides an overview of current methods for enhancing weak spontaneous Raman signals and their associated advantages and disadvantages. Finally, the paper outlines the detailed information related to the Raman cell sorting technology mentioned in this paper and discusses the development trends and direction of Raman cell sorting.

Spatially resolved analysis of microenvironmental gradient impact on cancer cell phenotypes

Despite the physiological and pathophysiological significance of microenvironmental gradients, e.g., for diseases such as cancer, tools for generating such gradients and analyzing their impact are lacking. Here, we present an integrated microfluidic-based workflow that mimics extracellular pH gradients characteristic of solid tumors while enabling high-resolution live imaging of, e.g., cell motility and chemotaxis, and preserving the capacity to capture the spatial transcriptome. Our microfluidic device generates a pH gradient that can be rapidly controlled to mimic spatiotemporal microenvironmental changes over cancer cells embedded in a 3D matrix. The device can be reopened allowing immunofluorescence analysis of selected phenotypes, as well as the transfer of cells and matrix to a Visium slide for spatially resolved analysis of transcriptional changes across the pH gradient. This workflow is easily adaptable to other gradients and multiple cell types and can therefore prove invaluable for integrated analysis of roles of microenvironmental gradients in biology.

Integrating single-cell and spatial transcriptomics to elucidate the crosstalk between cancer-associated fibroblasts and cancer cells in hepatocellular carcinoma with spleen-deficiency syndrome

Background and aim

Most patients with hepatocellular carcinoma (HCC) in China have been diagnosed with spleen deficiency syndrome (SDS), which accelerates the progression of HCC by disrupting the tumor microenvironment homeostasis. This study aimed to investigate the intercellular crosstalk in HCC with SDS.

Experimental procedure

An HCC-SDS mouse model was established using orthotopic HCC transplantation based on reserpine-induced SDS. Single-cell data analysis and cancer cell prediction were conducted using Seurat and CopyKAT package, respectively. Intercellular interactions were explored using CellPhoneDB and CellChat and subsequently validated using co-culture assays, ELISA and histological staining. We performed pathway activity analysis using gene set variation analysis and the Seurat package. The extracellular matrix (ECM) remodeling was assessed using a gel contraction assay, atomic force microscopy, and Sirius red staining. The deconvolution of the spatial transcriptomics data using the "CARD" package based on single-cell data.

Results and conclusion

We successfully established the HCC-SDS mouse model. Twenty-nine clusters were identified. The interactions between cancer cells and cancer-associated fibroblasts (CAFs) were significantly enhanced via platelet-derived growth factor (PDGF) signaling in HCC-SDS. CAFs recruited in HCC-SDS lead to ECM remodeling and the activation of TGF-β signaling pathway. Deconvolution of the spatial transcriptome data revealed that CAFs physically surround cancer cells in HCC-SDS. This study reveals that the crosstalk of CAFs-cancer cells is crucial for the tumor-promoting effect of SDS. CAFs recruited by HCC via PDGFA may lead to ECM remodeling through activation of the TGF-β pathway, thereby forming a physical barrier to block immune cell infiltration under SDS.

Integrating single-cell and spatial analysis reveals MUC1-mediated cellular crosstalk in mucinous colorectal adenocarcinoma

BACKGROUND

Mucinous colorectal adenocarcinoma (MCA) is a distinct subtype of colorectal cancer (CRC) with the most aggressive pattern, but effective treatment of MCA remains a challenge due to its vague pathological characteristics. An in-depth understanding of transcriptional dynamics at the cellular level is critical for developing specialised MCA treatment strategies.

METHODS

We integrated single-cell RNA sequencing and spatial transcriptomics data to systematically profile the MCA tumor microenvironment (TME), particularly the interactome of stromal and immune cells. In addition, a three-dimensional bioprinting technique, canonical ex vivo co-culture system, and immunofluorescence staining were further applied to validate the cellular communication networks within the TME.

RESULTS

This study identified the crucial intercellular interactions that engaged in MCA pathogenesis. We found the increased infiltration of FGF7+/THBS1+ myofibroblasts in MCA tissues with decreased expression of genes associated with leukocyte-mediated immunity and T cell activation, suggesting a crucial role of these cells in regulating the immunosuppressive TME. In addition, MS4A4A+ macrophages that exhibit M2-phenotype were enriched in the tumoral niche and high expression of MS4A4A+ was associated with poor prognosis in the cohort data. The ligand-receptor-based intercellular communication analysis revealed the tight interaction of MUC1+ malignant cells and ZEB1+ endothelial cells, providing mechanistic information for MCA angiogenesis and molecular targets for subsequent translational applications.

CONCLUSIONS

Our study provides novel insights into communications among tumour cells with stromal and immune cells that are significantly enriched in the TME during MCA progression, presenting potential prognostic biomarkers and therapeutic strategies for MCA.

KEY POINTS

Tumour microenvironment profiling of MCA is developed. MUC1+ tumour cells interplay with FGF7+/THBS1+ myofibroblasts to promote MCA development. MS4A4A+ macrophages exhibit M2 phenotype in MCA. ZEB1+ endotheliocytes engage in EndMT process in MCA.

Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing

BACKGROUND

Hepatocellular carcinoma represents a significant global burden in terms of cancer-related mortality, posing a substantial risk to human health. Despite the availability of various treatment modalities, the overall survival rates for patients with hepatocellular carcinoma remain suboptimal. The objective of this study was to explore the potential of novel biomarkers and to establish a novel predictive signature utilizing multiple transcriptome profiles.

METHODS

The GSE115469 and CNP0000650 cohorts were utilized for single cell analysis and gene identification. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were utilized in the development and evaluation of a predictive signature. The expressions of hepatocyte-specific genes were further validated using the GSE135631 cohort. Furthermore, immune infiltration results, immunotherapy response prediction, somatic mutation frequency, tumor mutation burden, and anticancer drug sensitivity were analyzed based on various risk scores. Subsequently, functional enrichment analysis was performed on the differential genes identified in the risk model. Moreover, we investigated the expression of particular genes in chronic liver diseases utilizing datasets GSE135251 and GSE142530.

RESULTS

Our findings revealed hepatocyte-specific genes (ADH4, LCAT) with notable alterations during cell maturation and differentiation, leading to the development of a novel predictive signature. The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group. Additionally, a nomogram was devised to forecast the survival rates of patients at 1, 3, and 5 years. Our study demonstrated that the predictive model may play a role in modulating the immune microenvironment and impacting the anti-tumor immune response in hepatocellular carcinoma. The high-risk group exhibited a higher frequency of mutations and was more likely to benefit from immunotherapy as a treatment option. Additionally, we confirmed that the downregulation of hepatocyte-specific genes may indicate the progression of hepatocellular carcinoma and aid in the early diagnosis of the disease.

CONCLUSION

Our research findings indicate that ADH4 and LCAT are genes that undergo significant changes during the differentiation of hepatocytes into cancer cells. Additionally, we have created a unique predictive signature based on genes specific to hepatocytes.

Methylated lncRNAs suppress apoptosis of gastric cancer stem cells via the lncRNA-miRNA/protein axis

BACKGROUND

Long noncoding RNAs (lncRNAs) play essential roles in the tumorigenesis of gastric cancer. However, the influence of lncRNA methylation on gastric cancer stem cells (GCSCs) remains unclear.

METHODS

The N6-methyladenosine (m6A) levels of lncRNAs in gastric cancer stem cells were detected by methylated RNA immunoprecipitation sequencing (MeRIP-seq), and the results were validated by MeRIP-quantitative polymerase chain reaction (qPCR). Specific sites of m6A modification on lncRNAs were detected by single-base elongation- and ligation-based qPCR amplification (SELECT). By constructing and transfecting the plasmid expressing methyltransferase-like 3 (METTL3) fused with catalytically inactivated Cas13 (dCas13b) and guide RNA targeting specific methylation sites of lncRNAs, we obtained gastric cancer stem cells with site-specific methylation of lncRNAs. Reverse transcription (RT)-qPCR and Western blot were used for detecting the stemness of treated gastric cancer stem cells.

RESULTS

The site-specific methylation of PSMA3-AS1 and MIR22HG suppressed apoptosis and promoted stemness of GCSCs. LncRNA methylation enhanced the stability of PSMA3-AS1 and MIR22HG to suppress apoptosis of GCSCs via the PSMA3-AS1-miR-411-3p- or MIR22HG-miR-24-3p-SERTAD1 axis. Simultaneously, the methylated lncRNAs promoted the interaction between PSMA3-AS1 and the EEF1A1 protein or MIR22HG and the LRPPRC protein, stabilizing the proteins and leading to the suppression of apoptosis. The in vivo data revealed that the methylated PSMA3-AS1 and MIR22HG triggered tumorigenesis of GCSCs.

CONCLUSIONS

Our study revealed the requirement for site-specific methylation of lncRNAs in the tumorigenesis of GCSCs, contributing novel insights into cancer development.

Advances in Engineered Macrophages: A New Frontier in Cancer Immunotherapy

Macrophages, as pivotal cells within the tumour microenvironment, significantly influence the impact of and reactions to treatments for solid tumours. The rapid evolution of bioengineering technology has revealed the vast potential of engineered macrophages in immunotherapy, disease diagnosis, and tissue engineering. Given this landscape, the goal of harnessing and innovating macrophages as a novel strategy for solid tumour immunotherapy cannot be overstated. The diverse strategies for engineered macrophages in the realm of cancer immunotherapy, encompassing macrophage drug delivery systems, chimeric antigen receptor macrophage therapy, and synergistic treatment approaches involving bacterial outer membrane vesicles and macrophages, are meticulously examined in this review. These methodologies are designed to enhance the therapeutic efficacy of macrophages against solid tumours, particularly those that are drug-resistant and metastatic. Collectively, these immunotherapies are poised to supplement and refine current solid tumour treatment paradigms, thus heralding a new frontier in the fight against malignant tumours.

Identification of Treg-related prognostic molecular subtypes and individualized characteristics in clear cell renal cell carcinoma through single-cell transcriptomes and bulk RNA sequencing

BACKGROUND

In clear cell renal cell carcinoma (ccRCC), the role of Regulatory T cells (Treg cells) as prognostic and immunotherapy response predictors is not fully explored.

METHODS

Analyzing renal clear cell carcinoma datasets from TISCH, TCGA, and GEO, we focused on 8 prognostic Treg genes to study patient subtypes in ccRCC. We assessed Treg subtypes in relation to patient prognosis, tumor microenvironment, metabolism. Using Cox regression and principal component analysis, we devised Treg scores for individual patient characterization and explored the molecular role of C1QL1, a critical gene in the Treg model, through in vivo and in vitro studies.

RESULTS

Eight Treg-associated prognostic genes were identified, classifying ccRCC patients into cluster A and B. Cluster A patients showed poorer prognosis with distinct clinical and molecular profiles, potentially benefiting more from immunotherapy. Low Treg scores correlated with worse outcomes and clinical progression. Low scores also suggested that patients might respond better to immunotherapy and targeted therapies. In ccRCC, C1QL1 knockdown reduced tumor proliferation and invasion via NF-kb-EMT pathways and decreased Treg cell infiltration, enhancing immune efficacy.

CONCLUSIONS

The molecular subtype and Treg score in ccRCC, based on Treg cell marker genes, are crucial in personalizing ccRCC treatment and underscore C1QL1's potential as a tumor biomarker and target for immunotherapy.

Integrative analysis of bulk and single-cell RNA-seq reveals the molecular characterization of the immune microenvironment and oxidative stress signature in melanoma

Background

The immune microenvironment and oxidative stress of melanoma show significant heterogeneity, which affects tumor growth, invasion and treatment response. Single-cell and bulk RNA-seq data were used to explore the heterogeneity of the immune microenvironment and oxidative stress of melanoma.

Methods

The R package Seurat facilitated the analysis of the single-cell dataset, while Harmony, another R package, was employed for batch effect correction. Cell types were classified using Uniform Manifold Approximation and Projection (UMAP). The Secreted Signaling algorithm from CellChatDB.human was applied to elucidate cell-to-cell communication patterns within the single-cell data. Consensus clustering analysis for the skin cutaneous melanoma (SKCM) samples was executed with the R package ConsensusClusterPlus. To quantify immune infiltrating cells, we utilized CIBERSORT, ESTIMATE, and TIMERxCell algorithms provided by the R package Immuno-Oncology Biological Research (IOBR). Single nucleotide variant (SNV) analysis was conducted using Maftools, an R package specifically designed for this purpose. Subsequently, the expression levels of PXDN and PAPSS2 genes were assessed in melanoma tissues compared to adjacent normal tissues. Furthermore, in vitro experiments were conducted to evaluate the proliferation and reactive oxygen species expression in melanoma cells following transfection with siRNA targeting PXDN and PAPSS2.

Results

Malignant tumor cell populations were reclassified based on a comprehensive single-cell dataset analysis, which yielded six distinct tumor subsets. The specific marker genes identified for these subgroups were then used to interrogate the Cancer Genome Atlas Skin Cutaneous Melanoma (TCGA-SKCM) cohort, derived from bulk RNA sequencing data, resulting in the delineation of two immune molecular subtypes. Notably, patients within the cluster2 (C2) subtype exhibited a significantly more favorable prognosis compared to those in the cluster1 (C1) subtype. An alignment of immune characteristics was observed between the C2 subtype and unique immune functional tumor cell subsets. Genes differentially expressed across these subtypes were subsequently leveraged to construct a predictive risk model. In vitro investigations further revealed elevated expression levels of PXDN and PAPSS2 in melanoma tissue samples. Functional assays indicated that modulation of PXDN and PAPSS2 expression could influence the production of reactive oxygen species (ROS) and the proliferative capacity of melanoma cells.

Conclusion

The constructed six-gene signature can be used as an immune response and an oxidative stress marker to guide the clinical diagnosis and treatment of melanoma.

Tumor Microenvironment Landscapes Supporting EGFR-mutant NSCLC Are Modulated at the Single-cell Interaction Level by Unesbulin Treatment

Lung cancer is the leading cause of cancer deaths. Lethal pulmonary adenocarcinomas (ADC) present with frequent mutations in the EGFR. Genetically engineered murine models of lung cancer expedited comprehension of the molecular mechanisms driving tumorigenesis and drug response. Here, we systematically analyzed the evolution of tumor heterogeneity in the context of dynamic interactions occurring with the intermingled tumor microenvironment (TME) by high-resolution transcriptomics. Our effort identified vulnerable tumor-specific epithelial cells, as well as their cross-talk with niche components (endothelial cells, fibroblasts, and tumor-infiltrating immune cells), whose symbiotic interface shapes tumor aggressiveness and is almost completely abolished by treatment with Unesbulin, a tubulin binding agent that reduces B cell-specific Moloney murine leukemia virus integration site 1 (BMI-1) activity. Simultaneous magnetic resonance imaging (MRI) analysis demonstrated decreased tumor growth, setting the stage for future investigations into the potential of novel therapeutic strategies for EGFR-mutant ADCs.

SIGNIFICANCE

Targeting the TME is an attractive strategy for treatment of solid tumors. Here we revealed how EGFR-mutant landscapes are affected at the single-cell resolution level during Unesbulin treatment. This novel drug, by targeting cancer cells and their interactions with crucial TME components, could be envisioned for future therapeutic advancements.

Interpreting single-cell messages in normal and aberrant hematopoiesis with the Cell Marker Accordion

Single-cell technologies offer a unique opportunity to explore cellular heterogeneity in hematopoiesis, reveal malignant hematopoietic cells with clinically significant features and measure gene signatures linked to pathological pathways. However, reliable identification of cell types is a crucial bottleneck in single-cell analysis. Available databases contain dissimilar nomenclature and non-concurrent marker sets, leading to inconsistent annotations and poor interpretability. Furthermore, current tools focus mostly on physiological cell types, lacking extensive applicability in disease. We developed the Cell Marker Accordion, a user-friendly platform for the automatic annotation and biological interpretation of single-cell populations based on consistency weighted markers. We validated our approach on peripheral blood and bone marrow single-cell datasets, using surface markers and expert-based annotation as the ground truth. In all cases, we significantly improved the accuracy in identifying cell types with respect to any single source database. Moreover, the Cell Marker Accordion can identify disease-critical cells and pathological processes, extracting potential biomarkers in a wide variety of contexts in human and murine single-cell datasets. It characterizes leukemia stem cell subtypes, including therapy-resistant cells in acute myeloid leukemia patients; it identifies malignant plasma cells in multiple myeloma samples; it dissects cell type alterations in splicing factor-mutant cells from myelodysplastic syndrome patients; it discovers activation of innate immunity pathways in bone marrow from mice treated with METTL3 inhibitors. The breadth of these applications elevates the Cell Marker Accordion as a flexible, faithful and standardized tool to annotate and interpret hematopoietic populations in single-cell datasets focused on the study of hematopoietic development and disease.

Single-cell transcriptome sequencing provides insight into multiple chemotherapy resistance in a patient with refractory DLBCL: a case report

Relapsed and refractory diffuse large B-cell lymphoma (DLBCL) is associated with poor prognosis. As such, a comprehensive analysis of intratumoral components, intratumoral heterogeneity, and the immune microenvironment is essential to elucidate the mechanisms driving the progression of DLBCL and to develop new therapeutics. Here, we used single-cell transcriptome sequencing and conventional bulk next-generation sequencing (NGS) to understand the composite tumor landscape of a single patient who had experienced multiple tumor recurrences following several chemotherapy treatments. NGS revealed several key somatic mutations that are known to contribute to drug resistance. Based on gene expression profiles at the single-cell level, we identified four clusters of malignant B cells with distinct transcriptional signatures, showing high intra-tumoral heterogeneity. Among them, heterogeneity was reflected in activating several key pathways, human leukocyte antigen (HLA)-related molecules' expression, and key oncogenes, which may lead to multi-drug resistance. In addition, FOXP3+ regulatory CD4+ T cells and exhausted cytotoxic CD8+ T cells were identified, accounted for a significant proportion, and showed highly immunosuppressive properties. Finally, cell communication analysis indicated complex interactions between malignant B cells and T cells. In conclusion, this case report demonstrates the value of single-cell RNA sequencing for visualizing the tumor microenvironment and identifying potential therapeutic targets in a patient with treatment-refractory DLBCL. The combination of NGS and single-cell RNA sequencing may facilitate clinical decision-making and drug selection in challenging DLBCL cases.

Patterns of metastatic spread and tumor burden in unselected cancer patients using PET imaging: Implications for the oligometastatic spectrum theory

Introduction and background

Metastatic disease has been proposed as a continuum, with no clear cut-off between oligometastatic and polymetastatic disease. This study aims to quantify tumor burden and patterns of spread in unselected metastatic cancer patients referred for PET-based staging, response assessment of restaging.

Materials and methods

All oncological fluorodeoxyglucose (FDG-) and prostate-specific membrane antigen (PSMA-) positron emission tomography (PET) scans conducted at a single academic center in 2020 were analyzed. Imaging reports of all patients with metastatic disease were reviewed and assessed.

Results

For this study, 7,000 PET scans were screened. One third of PET scans (n = 1,754; 33 %) from 1,155 unique patients showed presence of metastatic disease from solid malignancies, of which 601 (52 %) and 554 (48 %) were classified as oligometastatic (maximum 5 metastases) and polymetastatic (>5 metastases), respectively. Lung and pleural cancer, skin cancer, and breast cancer were the most common primary tumor histologies with 132 (23.8 %), 88 (15.9 %), and 72 (13.0 %) cases, respectively. Analysis of the number of distant metastases showed a strong bimodal distribution of the metastatic burden with 26 % of patients having one solitary metastasis and 43 % of patients harboring >10 metastases. Yet, despite 43 % of polymetastatic patients having >10 distant metastases, their pattern of distribution was restricted to one or two organs in about two thirds of patients, and there was no association between the number of distant metastases and the number of involved organs.

Conclusion

The majority of metastatic cancer patients are characterized by either a solitary metastasis or a high tumor burden with >10 metastases, the latter was often associated with affecting a limited number of organs. These findings support both the spectrum theory of metastasis and the seed and soil hypothesis and can support in designing the next generation of clinical trials in the field of oligometastatic disease.

The role of microglia heterogeneity in synaptic plasticity and brain disorders: Will sequencing shed light on the discovery of new therapeutic targets?

Microglia play a crucial role in interacting with neuronal synapses and modulating synaptic plasticity. This function is particularly significant during postnatal development, as microglia are responsible for removing excessive synapses to prevent neurodevelopmental deficits. Dysregulation of microglial synaptic function has been well-documented in various pathological conditions, notably Alzheimer's disease and multiple sclerosis. The recent application of RNA sequencing has provided a powerful and unbiased means to decipher spatial and temporal microglial heterogeneity. By identifying microglia with varying gene expression profiles, researchers have defined multiple subgroups of microglia associated with specific pathological states, including disease-associated microglia, interferon-responsive microglia, proliferating microglia, and inflamed microglia in multiple sclerosis, among others. However, the functional roles of these distinct subgroups remain inadequately characterized. This review aims to refine our current understanding of the potential roles of heterogeneous microglia in regulating synaptic plasticity and their implications for various brain disorders, drawing from recent sequencing research and functional studies. This knowledge may aid in the identification of pathogenetic biomarkers and potential factors contributing to pathogenesis, shedding new light on the discovery of novel drug targets. The field of sequencing-based data mining is evolving toward a multi-omics approach. With advances in viral tools for precise microglial regulation and the development of brain organoid models, we are poised to elucidate the functional roles of microglial subgroups detected through sequencing analysis, ultimately identifying valuable therapeutic targets.

scCircle-seq unveils the diversity and complexity of extrachromosomal circular DNAs in single cells

Extrachromosomal circular DNAs (eccDNAs) have emerged as important intra-cellular mobile genetic elements that affect gene copy number and exert in trans regulatory roles within the cell nucleus. Here, we describe scCircle-seq, a method for profiling eccDNAs and unraveling their diversity and complexity in single cells. We implement and validate scCircle-seq in normal and cancer cell lines, demonstrating that most eccDNAs vary largely between cells and are stochastically inherited during cell division, although their genomic landscape is cell type-specific and can be used to accurately cluster cells of the same origin. eccDNAs are preferentially produced from chromatin regions enriched in H3K9me3 and H3K27me3 histone marks and are induced during replication stress conditions. Concomitant sequencing of eccDNAs and RNA from the same cell uncovers the absence of correlation between eccDNA copy number and gene expression levels, except for a few oncogenes, including MYC, contained within a large eccDNA in colorectal cancer cells. Lastly, we apply scCircle-seq to one prostate cancer and two breast cancer specimens, revealing cancer-specific eccDNA landscapes and a higher propensity of eccDNAs to form in amplified genomic regions. scCircle-seq is a scalable tool that can be used to dissect the complexity of eccDNAs across different cell and tissue types, and further expands the potential of eccDNAs for cancer diagnostics.

Single-Cell RNA Sequencing Revealed That the Enrichment of TPI1+ Malignant Hepatocytes Was Linked to HCC Metastasis and Immunosuppressive Microenvironment

Background

Tumor metastasis is the leading cause of high mortality in hepatocellular carcinoma (HCC). The metastasis-related HCC microenvironment is characterized by high heterogeneity. Single-cell RNA sequencing (scRNA-seq) may aid in determining specific cell clusters involved in regulating the immune microenvironment of HCC.

Methods

The scRNA-seq data of 10 HCC samples were collected from the Gene Expression Omnibus (GEO) database GSE124395. Correlations between key gene expression and clinicopathological data were determined using public databases. HCC tissues and matched tumor-adjacent and normal tissue samples were obtained by surgical resection at Sichuan Cancer Hospital. Immune cell infiltration analysis was performed and verified by immunohistochemistry and immunofluorescent staining.

Results

Nine malignant hepatocyte clusters with different marker genes and biological functions were identified. C3_Hepatocyte-SERF2 and C6_Hepatocyte-IL13RA2 were mainly involved in the regulation of the immune microenvironment, which was also a significant pathway in regulating HCC metastasis. Key genes in malignant hepatocyte clusters that associated with HCC metastasis were further screened by LASSO regression analysis. TPI1, a key gene in C6_Hepatocyte-IL13RA2 and HCC metastasis, could participate in regulating the HCC immune microenvironment in The Cancer Genome Atlas (TCGA) and Tumor Immune Estimation Resource (TIMER) databases. Moreover, immunohistochemistry analysis demonstrated that TPI1 expression was positively correlated with HCC metastasis and poor prognosis, while negatively correlated with CD8+ T cell infiltration. The negative correlation between TPI1 expression and CD8+ T cell infiltration was further confirmed by immunofluorescence staining.

Conclusion

In summary, a cluster of TPI1+ malignant hepatocytes was associated with the suppression of CD8+ T cell infiltration and HCC metastasis, providing novel insights into potential biomarkers for immunotherapy in HCC.

Single-cell RNA sequencing of anaplastic ependymoma and H3K27M-mutant diffuse midline glioma

BACKGROUND

Anaplastic ependymoma and H3K27M-mutant diffuse midline glioma are two common subtypes of brain tumors with poor long-term prognosis. The present study analyzed and compared the differences in cell types between two tumors by single-cell RNA sequencing (scRNA-seq) technology.

METHODS

ScRNA-seq was performed to profile cells from cancer tissue from anaplastic ependymoma patient and H3K27M-mutant diffuse midline glioma patient. Cell clustering, marker gene identification, cell type annotation, copy number variation analysis and function analysis of differentially expressed genes were then performed.

RESULTS

A total of 11,219 cells were obtained from anaplastic ependymoma and H3K27M mutant diffuse midline glioma, and these cells categorized into 12 distinct clusters. Each cell cluster could be characterized with specific cell markers to indicate cellular heterogeneity. Five cell types were annotated in each sample, including astrocyte, oligodendrocytes, microglial cell, neural progenitor cell and immune cell. The cluster types and proportion of cell types were not consistent between the two brain tumors. Functional analyses suggest that these cell clusters are involved in tumor-associated pathways, with slight differences in the cells of origin between the two tumors. In addition, cell communication analysis showed that the NRG3-ERBB4 pair is a key Ligand-receptor pair for anaplastic ependymoma, while in H3K27M-mutant diffuse midline glioma it is the PTN-PTPRZ1 pair that establishes contact with other cells.

CONCLUSION

There was intratumor heterogeneity in anaplastic ependymoma and H3K27M mutant diffuse midline glioma, and that the subtype differences may be due to differences in the origin of the cells.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data

The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.

Paradigm Shift: A Comprehensive Review of Ovarian Cancer Management in an Era of Advancements

Ovarian cancer (OC) is the female genital malignancy with the highest lethality. Patients present a poor prognosis mainly due to the late clinical presentation allied with the common acquisition of chemoresistance and a high rate of tumour recurrence. Effective screening, accurate diagnosis, and personalised multidisciplinary treatments are crucial for improving patients' survival and quality of life. This comprehensive narrative review aims to describe the current knowledge on the aetiology, prevention, diagnosis, and treatment of OC, highlighting the latest significant advancements and future directions. Traditionally, OC treatment involves the combination of cytoreductive surgery and platinum-based chemotherapy. Although more therapeutical approaches have been developed, the lack of established predictive biomarkers to guide disease management has led to only marginal improvements in progression-free survival (PFS) while patients face an increasing level of toxicity. Fortunately, because of a better overall understanding of ovarian tumourigenesis and advancements in the disease's (epi)genetic and molecular profiling, a paradigm shift has emerged with the identification of new disease biomarkers and the proposal of targeted therapeutic approaches to postpone disease recurrence and decrease side effects, while increasing patients' survival. Despite this progress, several challenges in disease management, including disease heterogeneity and drug resistance, still need to be overcome.

Single-cell transcriptomic analysis reveals tumor cell heterogeneity and immune microenvironment features of pituitary neuroendocrine tumors

BACKGROUND

Pituitary neuroendocrine tumors (PitNETs) are one of the most common types of intracranial tumors. Currently, the cellular characteristics of normal pituitary and various other types of PitNETs are still not completely understood.

METHODS

We performed single-cell RNA sequencing (scRNA-seq) on 4 normal samples and 24 PitNET samples for comprehensive bioinformatics analysis. Findings regarding the function of PBK in the aggressive tumor cells were validated by siRNA knockdown, overexpression, and transwell experiments.

RESULTS

We first constructed a reference cell atlas of the human pituitary. Subsequent scRNA-seq analysis of PitNET samples, representing major tumor subtypes, shed light on the intrinsic cellular heterogeneities of the tumor cells and tumor microenvironment (TME). We found that the expression of hormone-encoding genes defined the major variations of the PIT1-lineage tumor cell transcriptomic heterogeneities. A sub-population of TPIT-lineage tumor cells highly expressing GZMK suggested a novel subtype of corticotroph tumors. In immune cells, we found two clusters of tumor-associated macrophages, which were both highly enriched in PitNETs but with distinct functional characteristics. In PitNETs, the stress response pathway was significantly activated in T cells. While a majority of these tumors are benign, our study unveils a common existence of aggressive tumor cells in the studied samples, which highly express a set of malignant signature genes. The following functional experiments confirmed the oncogenic role of selected up-regulated genes. The over-expression of PBK could promote both tumor cell proliferation and migration, and it was also significantly associated with poor prognosis in PitNET patients.

CONCLUSIONS

Our data and analysis manifested the basic cell types in the normal pituitary and inherent heterogeneity of PitNETs, identified several features of the tumor immune microenvironments, and found a novel epithelial cell sub-population with aggressive signatures across all the studied cases.

Single-cell sequencing reveals SATB2/NOTCH1 signaling promotes the progression of malignancy of epithelial cells from papillary thyroid cancer

Although most papillary thyroid cancers (PTCs) are deemed to have a favorable clinical course and outcome, some develop an aggressive biological behavior at diagnosis or during treatment. Single-cell RNA sequencing (scRNA-seq), which is based on quantifying the features of individual cells to resolve tumor tissue heterogeneity, was used to uncover gene regulatory relationships and trace the transcriptional trajectories underlying the malignant transformation. In this study, we performed single-cell sequencing on samples from four PTC patients and one benign thyroid tumor patient. These included two papillary thyroid microcarcinoma cancers (PTMC) patients, two age-matched advanced PTC patients with invading surrounding tissues, and one patient undergoing surgical treatment due to a benign thyroid tumor. We constructed a new PTC RNA spectrum using single-cell sequencing. Single-cell sequencing analysis indicated that there was a highly invasive subgroup in the PTC epithelial cells, the expression of SATB2 (special AT-rich binding protein-2) was related to the prognosis and clinical progress of PTC, and SATB2 could promote the proliferation, migration, and invasion of PTC cells. We found that NOTCH1 was the key target gene of SATB2, and the activation of the SATB2/NOTCH1 pathway was one of the reasons for the high carcinogenicity of this subgroup.

scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data

BACKGROUND

The rapid emergence of single-cell RNA-seq (scRNA-seq) data presents remarkable opportunities for broad investigations through integration analyses. However, most integration models are black boxes that lack interpretability or are hard to train.

RESULTS

To address the above issues, we propose scInterpreter, a deep learning-based interpretable model. scInterpreter substantially outperforms other state-of-the-art (SOTA) models in multiple benchmark datasets. In addition, scInterpreter is extensible and can integrate and annotate atlas scRNA-seq data. We evaluated the robustness of scInterpreter in a variety of situations. Through comparison experiments, we found that with a knowledge prior, the training process can be significantly accelerated. Finally, we conducted interpretability analysis for each dimension (pathway) of cell representation in the embedding space.

CONCLUSIONS

The results showed that the cell representations obtained by scInterpreter are full of biological significance. Through weight sorting, we found several new genes related to pathways in PBMC dataset. In general, scInterpreter is an effective and interpretable integration tool. It is expected that scInterpreter will bring great convenience to the study of single-cell transcriptomics.

Unraveling the enigma of B cells in diffuse large B-cell lymphoma: unveiling cancer stem cell-like B cell subpopulation at single-cell resolution

Background

Diffuse large B-cell lymphoma (DLBCL) represents the most prevalent form of aggressive non-Hodgkin lymphoma. Despite receiving standard treatment, a subset of patients undergoes refractory or recurrent cases, wherein the involvement of cancer stem cells (CSCs) could be significant.

Methods

We comprehensively characterized B cell subpopulations using single-cell RNA sequencing data from three DLBCL samples and one normal lymph tissue. The CopyKat R package was employed to assess the malignancy of B cell subpopulations based on chromosomal copy number variations. CIBERSORTx software was utilized to estimate the proportions of B cell subpopulations in 230 DLBCL tissues. Furthermore, we employed the pySCENIC to identify key transcription factors that regulate the functionality of B cell subpopulations. By employing CellphoneDB, we elucidated the interplay among tumor microenvironment components within the B cell subpopulations. Finally, we validated our findings through immunofluorescence experiments.

Results

Our analysis revealed a specific cancer stem cell-like B cell subpopulation exhibiting self-renewal and multilineage differentiation capabilities based on the exploration of B cell subpopulations in DLBCL and normal lymph tissues at the single-cell level. Notably, a high infiltration of cancer stem cell-like B cells correlated with a poor prognosis, potentially due to immune evasion mediated by low expression of major histocompatibility complex molecules. Furthermore, we identified key transcription factor regulatory networks regulated by HMGB3, SAP30, and E2F8, which likely played crucial roles in the functional characterization of the cancer stem cell-like B cell subpopulation. The existence of cancer stem cell-like B cells in DLBCL was validated through immunofluorescent staining. Finally, cell communication between B cells and tumor-infiltrating T cell subgroups provided further insights into the functional characterization of the cancer stem cell-like B cell subpopulation.

Conclusions

Our research provides a systematic description of a specific cancer stem cell-like B cell subpopulation associated with a poor prognosis in DLBCL. This study enhances our understanding of CSCs and identifies potential therapeutic targets for refractory or recurrent DLBCL patients.

Single cell multi-omics reveal intra-cell-line heterogeneity across human cancer cell lines

Human cancer cell lines have long served as tools for cancer research and drug discovery, but the presence and the source of intra-cell-line heterogeneity remain elusive. Here, we perform single-cell RNA-sequencing and ATAC-sequencing on 42 and 39 human cell lines, respectively, to illustrate both transcriptomic and epigenetic heterogeneity within individual cell lines. Our data reveal that transcriptomic heterogeneity is frequently observed in cancer cell lines of different tissue origins, often driven by multiple common transcriptional programs. Copy number variation, as well as epigenetic variation and extrachromosomal DNA distribution all contribute to the detected intra-cell-line heterogeneity. Using hypoxia treatment as an example, we demonstrate that transcriptomic heterogeneity could be reshaped by environmental stress. Overall, our study performs single-cell multi-omics of commonly used human cancer cell lines and offers mechanistic insights into the intra-cell-line heterogeneity and its dynamics, which would serve as an important resource for future cancer cell line-based studies.