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scRNA-seq versus bulk RNA-seq for profiling the TME. (A and B) Transcriptomic studies of patient biopsies can provide intimate details of important gene signatures involved in tumor progression or response to immunotherapy. (A) In the scRNA-seq workflow, a tumor biopsy sample is first disassociated into a single-cell suspension, and platforms like that of 10X Genomics Chromium are used to generate a uniquely barcoded cDNA library from reverse transcription of the isolated poly-adenylated mRNA within each individual cell. (B) Bulk RNA-seq instead generates cDNA directly without tagging unique transcripts of individual cells. After generation of cDNA via reverse transcription of mRNA, both platforms use PCR amplification, next-generation sequencing, and subsequent downstream informatics to process data. For scRNA-seq, visualization methods such as heat maps show expression of individual genes (rows) for individual cells (columns). Clustering cells with similar expression allows for identification of cell type. Bulk RNA-seq instead returns average gene expression values across the sample cell population, thus preventing cell classification.
Source publication
Immunotherapies such as immune checkpoint blockade and adoptive cell transfer have revolutionized cancer treatment, but further progress is hindered by our limited understanding of tumor resistance mechanisms. Emerging technologies now enable the study of tumors at the single-cell level, providing unprecedented high-resolution insights into the gen...
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Background
In breast cancer, complex interactions between tumor cells and cells within the surrounding stroma, such as macrophages, are critical for tumor growth, progression, and therapeutic response. Recent studies have highlighted the complex nature and heterogeneous populations of macrophages associated with both tumor-promoting and tumor-inhib...
Citations
... The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized the study of tumoral heterogeneity in OC. It has facilitated the identification of critical factors and cellular subpopulations involved in tumor progression [11][12][13]. By enhancing our understanding of tumoral heterogeneity, scRNA-seq offers novel perspectives on cancer biology [14]. ...
Purpose
This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis.
Methods
SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses.
Results
Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets.
Conclusion
GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies.
... Recent advances in single-cell technologies enable whole-transcriptome sequencing at single-cell resolution. The tremendous amount of data generated potentiates our understanding of tumor biology [20][21][22][23]. Meanwhile, subsequent developments in bioinformatics tools facilitate the precise characterization and visualization of the TME, enabling efficient and straightforward interpretation of massive and complex results. ...
Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells “talk” to the tumor through ligand–receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor–immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.
... Advances in high-throughput sequencing and bioinformatics have improved the ability to profile and analyze cfRNA. Emerging single-cell RNA sequencing (scRNA-seq) technologies enabled the study of cfRNA heterogeneity at the single-cell level [26]. ...
As the burden of type 2 diabetes (T2D) continues to escalate globally, there is a growing need for novel, less-invasive biomarkers capable of early diabetes detection and monitoring of disease progression. Liquid biopsy, recognized for its minimally invasive nature, is increasingly being applied beyond oncology, and nevertheless shows its potential when the collection of the tissue biopsy is not possible. This diagnostic approach involves utilizing liquid biopsy markers such as cell-free nucleic acids, extracellular vesicles, and diverse metabolites for the molecular diagnosis of T2D and its related complications. In this context, we thoroughly examine recent developments in T2D liquid biopsy research. Additionally, we discuss the primary challenges and future prospects of employing liquid biopsy in the management of T2D. Prognosis, diagnosis and monitoring of T2D through liquid biopsy could be a game-changing technique for personalized diabetes management.
... Single-cell RNA sequencing (scRNA-Seq), spatial transcriptomics, and single-cell proteomics enable the analysis of heterogeneity across distinct cell populations, uncovering the interplay between tumor cells and the tumor microenvironment (TME), comprising lymphatic or vascular endothelial cells, immune cells, fibroblasts, various signaling molecules, and the extracellular matrix. [7][8][9][10]. Cancer stem cells (CSCs) constitute a minor subset of undifferentiated cells in tumor tissue, with robust self-renewal potential and tumorigenic capabilities, crucially contributing to tumor heterogeneity [11]. ...
Background: The challenge of systemic treatment for hepatocellular carcinoma (HCC) stems from the development of drug resistance, primarily driven by the interplay between cancer stem cells (CSCs) and the tumor microenvironment (TME). However, there is a notable dearth of comprehensive research investigating the crosstalk between CSCs and stromal cells or immune cells within the TME of HCC.
Methods: We procured single-cell RNA sequencing (scRNA-Seq) data from 16 patients diagnosed with HCC. Employing meticulous data quality control and cell annotation procedures, we delineated distinct CSCs subtypes and performed multi-omics analyses encompassing metabolic activity, cell communication, and cell trajectory. These analyses shed light on the potential molecular mechanisms governing the interaction between CSCs and the TME, while also identifying CSCs' developmental genes. By combining these developmental genes, we employed machine learning algorithms and RT-qPCR to construct and validate a prognostic risk model for HCC.
Results: We successfully identified CSCs subtypes residing within malignant cells. Through meticulous enrichment analysis and assessment of metabolic activity, we discovered anomalous metabolic patterns within the CSCs microenvironment, including hypoxia and glucose deprivation. Moreover, CSCs exhibited aberrant activity in signaling pathways associated with lipid metabolism. Furthermore, our investigations into cell communication unveiled that CSCs possess the capacity to modulate stromal cells and immune cells through the secretion of MIF or MDK, consequently exerting regulatory control over the TME. Finally, through cell trajectory analysis, we found developmental genes of CSCs. Leveraging these genes, we successfully developed and validated a prognostic risk model (APCS, ADH4, FTH1, and HSPB1) with machine learning and RT-qPCR.
Conclusions: By means of single-cell multi-omics analysis, this study offers valuable insights into the potential molecular mechanisms governing the interaction between CSCs and the TME, elucidating the pivotal role CSCs play within the TME. Additionally, we have successfully established a comprehensive clinical prognostic model through bulk RNA-Seq data.
... Table 2 lists some of these studies [for BCP-ALL, also see (75)]. However, with the increased resolution on a cellular level, there is again potentially a loss of ability to detect glycosyltransferase transcripts due to their low expression combined with technical limitations of scRNAseq to detect all transcripts present in a cell (146). Such data, in combination with other data described below, could be used to generate a prediction of glycotraits expressed on cells that constitute only a minute fraction of the total bone marrow compartment including Minimal Residual Disease (MRD), the [frequently low percentage of] leukemia cells that can persist after chemotherapy. ...
Leukemic B-cells are lodged in the bone marrow [BM], a complex organ composed of many cell types and extracellular matrix. Determining how the reciprocal interactions between these components are regulated is critical to our understanding of the factors that allow leukemia cells to survive, multiply and withstand treatment. All cells in the bone marrow are surrounded by a glycocalyx, a glycan-rich layer of high complexity, which regulates such cell-cell and cell-matrix interactions. However, the structure and function of the glycan components of the biomolecules that constitute this layer have not been explored in much detail. Gaps are difficult to fill due to technical limitations as well as the fact that the composition of the BM in health, disease and aging is not static. This also applies to B-lineage malignancies that develop or persist in BM such as B-cell precursor acute lymphoblastic leukemia and Multiple Myeloma, and the effects of their treatment. In contrast, the proteomes and transcriptomes of different human bone marrow cells have been studied more extensively. A combination of technologies now increasingly allows correlations to be made between the expression of glycosyltransferases and glycan structures in cell lines, which could be extrapolated to RNAseq data from primary cells. Glycopeptide analysis will also be invaluable in providing details of specific glycan occupancy on glycoproteins, even if only as a snapshot in time. Functional studies on CD19, CD138/SDC1 and BCMA/TNFRSF17 have already demonstrated the importance of their glycosylation. Additional studies using such approaches are likely to find many more other instances in which malignant B-cell homeostasis is regulated by glycosylation, and lead to the identification of new targets to treat B-cell malignancies.
... These strategies, enriched by emerging technologies, are carving fresh and promising avenues in the treatment of cancer. Particularly notable are efforts to reprogram TAMs for cancer immunotherapy (14,114) and the application of single-cell technologies for precision interventions (115)(116)(117)(118). ...
Tumor-associated macrophages (TAMs) are integral to the tumor microenvironment (TME), influencing cancer progression significantly. Attracted by cancer cell signals, TAMs exhibit unparalleled adaptability, aligning with the dynamic tumor milieu. Their roles span from promoting tumor growth and angiogenesis to modulating metastasis. While substantial research has explored the fundamentals of TAMs, comprehending their adaptive behavior, and leveraging it for novel treatments remains challenging. This review delves into TAM polarization, metabolic shifts, and the complex orchestration of cytokines and chemokines determining their functions. We highlight the complexities of TAM-targeted research focusing on their adaptability and potential variability in therapeutic outcomes. Moreover, we discuss the synergy of integrating TAM-focused strategies with established cancer treatments, such as chemotherapy, and immunotherapy. Emphasis is laid on pioneering methods like TAM reprogramming for cancer immunotherapy and the adoption of single-cell technologies for precision intervention. This synthesis seeks to shed light on TAMs’ multifaceted roles in cancer, pinpointing prospective pathways for transformative research and enhancing therapeutic modalities in oncology.
... Single-cell NGS technologies studying GEMMs, human tissues, and PDXs allow us to perform longitudinal lineage tracing across the different cell states that arise during tumor development, therapy resistance, migration, and metastasis [361][362][363]. As presented in this review, NGS assays have been used to provide high-resolution information relevant to intratumoral heterogeneity and the tumor microenvironment at genetic, transcriptional, and epigenetic levels and to identify crucial factors and cell states that promote tumor progression, therapy resistance, and migration [364][365][366][367][368]. ...
Simple Summary
Prostate cancer remains the most frequent cause of cancer morbidity, the second most frequent cause of cancer mortality in men in the developed world and is an exemplar of a heterogeneous disease. Stemness phenotypes and lineage plasticity have been highlighted as key drivers of heterogeneity observed both across patients and within the same patient. However, markers that indicate the presence or absence of these events remain to be identified. Next-generation sequencing has proven to be a beneficial approach to distinguish predictive and prognostic biomarkers in various diseases, including prostate cancer. This review explores measurable metrics that can reliably reflect lineage plasticity at the genomic, transcriptomic, and epigenomic levels, as well as bioinformatic tools that can be used to identify measures of lineage-plasticity in prostate cancer, in order to inform preclinical and clinical research.
Abstract
Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.
... ScRNA-seq has become an invaluable tool for studying TME, heterogeneity, pathogenesis, metastasis, invasion, therapeutic interventions, and diagnosis of various tumors in the oncology discipline . ScRNA-seq can document the transcriptomics of every individual cell and delivers critical insights when applied to primary samples and cancer cell lines (Guruprasad et al. 2020;Sun et al. 2021). ScRNA-seq has the potential to reveal molecular mechanisms that control tumor heterogeneity and define distinctive elements in cellular phenotypes (Gupta and Kuznicki 2020;Zhang et al. 2021). ...
The variation and heterogeneity within cells are the fundamental features of stem cells. Each tissue has a resident stem cell niche compartmentalized to perform destined physiology and maintenance of tissue homeostasis. The stem cell niche is populated with tissue-specific pluripotent and multipotent cell types amid differentiated and intermediate progenitor cell types, thus forming a heterogeneous milieu. Therefore, identifying the stem cell population with high clonal propagation and potency for human application remains a significant challenge for researchers globally. With recent advancements in the high throughput sequencing platform, single-cell transcriptomic sequencing technology provides in-depth analysis of the expression profile of a genome at a single-cell level. This versatile technology would be a transformative approach to biomedical research as it can efficiently analyze cellular heterogeneity and identify minor subset populations of clinical importance. Single-cell sequencing technology has developed rapidly in recent years with the advent and advancement of cell sorting and nucleic acid extraction methods. Further, applying single-cell sequencing in different types of stem cells, including pluripotent stem cells, tissue-specific resident stem cells, and cancer stem cells, would lead to several exciting discoveries in stem cell research. The current chapter will lucidly narrate the basic and advanced levels of single-cell genomics and its interpretation and applications in the thematic area of stem cell biology. This chapter will also provide a glimpse of the application of single-cell sequencing technology in tissue engineering and organoid culture to a great extent. In summary, we will apprehend the latest progress and future perspectives of single-cell sequencing in stem cell biology in this chapter.
... Эффективность ICI зависит от взаимодействия опухоли и клеток микроокружения [99][100][101][102][103]. Секвенирование РНК на уровне единичных клеток показало гетерогенность опухолевых, иммунных и стромальных Обзоры Reviews клеток при уротелиальной карциноме. ...
The implementation of innovative methods of drug therapy and biotherapy into clinical practice has significantly changed the treatment tactics for metastatic urothelial cancer. Currently, treatment regimens are successfully supplemented with immunotherapy (immune checkpoint inhibitors) or targeted therapy, and the effectiveness of such combinations can be quite high, but the optimal sequence of different types of drug therapy remains to be established. The development of correct algorithms using reliable biomarkers is necessary to select the correct sequence of prescribing drugs. Until now, the expression of programmed cell death-ligand 1 (PD-L1) and changes in fibroblast growth factor receptors 1–4 (FGFR1–4) have been the fundamental markers for choosing alternative treatment regimens for metastatic urothelial cancer. At the same time, the list of useful and sufficiently informative biomarkers is expanding, and therefore we tried to summarize the available data on the known biological markers for selection of treatment tactics for metastatic urothelial cancer.
... In addition to discovering differences in cellular composition and characteristics, certain rare cell populations that are obscured by bulk RNA-seq are identified by using scRNA-seq (10). At the same time, by examining the tumor microenvironment at the level of individual cell, scRNA-seq has helped identify the valuable role of non-tumor cells in tumor development (11). Moreover, metastatic samples have been used to identify intrinsic features associated with metastatic for targeted therapy (12,13). ...
Gastric cancer is one of the most serious malignant tumor and threatens the health of people worldwide. Its heterogeneity leaves many clinical problems unsolved. To treat it effectively, we need to explore its heterogeneity. Single-cell transcriptome sequencing, or single-cell RNA sequencing (scRNA-seq), reveals the complex biological composition and molecular characteristics of gastric cancer at the level of individual cells, which provides a new perspective for understanding the heterogeneity of gastric cancer. In this review, we first introduce the current procedure of scRNA-seq, and discuss the advantages and limitations of scRNA-seq. We then elaborate on the research carried out with scRNA-seq in gastric cancer in recent years, and describe how it reveals cell heterogeneity, the tumor microenvironment, oncogenesis and metastasis, as well as drug response in to gastric cancer, to facilitate early diagnosis, individualized therapy, and prognosis evaluation.
