Single-cell RNA sequencing in cancer research

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.

A comprehensive review of the applications of RNA sequencing in celiac disease research

RNA sequencing (RNA-seq) has undergone substantial advancements in recent decades and has emerged as a vital technique for profiling the transcriptome. The transition from bulk sequencing to single-cell and spatial approaches has facilitated the achievement of higher precision at cell resolution. It provides valuable biological knowledge about individual immune cells and aids in the discovery of the molecular mechanisms that contribute to the development of autoimmune diseases. Celiac disease (CeD) is an autoimmune disorder characterized by a strong immune response to gluten consumption. RNA-seq has led to significantly advanced research in multiple fields, particularly in CeD research. It has been instrumental in studies involving comparative transcriptomics, nutritional genomics and wheat research, cancer research in the context of CeD, genetic and noncoding RNA-mediated epigenetic insights, disease monitoring and biomarker discovery, regulation of mitochondrial functions, therapeutic target identification and drug mechanism of action, dietary factors, immune cell profiling and the immune landscape. This review offers a comprehensive examination of recent RNA-seq technology research in the field of CeD, highlighting future challenges and opportunities for its application.

Identification of feature genes and molecular mechanisms involved in cell communication in uveal melanoma through analysis of single‑cell sequencing data

Uveal melanoma (UM) is a highly metastatic cancer with resistance to immunotherapy. The present study aimed to identify novel feature genes and molecular mechanisms in UM through analysis of single-cell sequencing data. For this purpose, data were downloaded from The Cancer Genome Atlas and National Center for Biotechnology Information Gene Expression Omnibus public databases. The statistical analysis function of the CellPhoneDB software package was used to analyze the ligand-receptor relationships of the feature genes. The Metascape database was used to perform the functional annotation of notable gene sets. The randomForestSRC package and random survival forest algorithm were applied to screen feature genes. The CIBERSORT algorithm was used to analyze the RNA-sequencing data and infer the relative proportions of the 22 immune-infiltrating cell types. In vitro, small interfering RNAs were used to knockdown the expression of target genes in C918 cells. The migration capability and viability of these cells were then assessed by gap closure and Cell Counting Kit-8 assays. In total, 13 single-cell sample subtypes were clustered by t-distributed Stochastic Neighbor Embedding and annotated by the R package, SingleR, into 7 cell categories: Tissue stem cells, epithelial cells, fibroblasts, macrophages, natural killer cells, neurons and endothelial cells. The interactions in NK cells|Endothelial cells, Neurons|Endothelial cells, CD74_APP, and SPP1_PTGER4 were more significant than those in the other subsets. T-Box transcription factor 2, tropomyosin 4, plexin D1 (PLXND1), G protein subunit α I2 (GNAI2) and SEC14-like lipid binding 1 were identified as the feature genes in UM. These marker genes were found to be significantly enriched in pathways such as vasculature development, focal adhesion and cell adhesion molecule binding. Significant correlations were observed between key genes and immune cells as well as immune factors. Relationships were also observed between the expression levels of the key genes and multiple disease-related genes. Knockdown of PLXND1 and GNAI2 expression led to significantly lower viability and gap closure rates of C918 cells. Therefore, the results of the present study uncovered cell communication between endothelial cells and other cell types, identified innovative key genes and provided potential targets of gene therapy in UM.

Highly accurate single-color fluorogenic DNA decoding sequencing for mutational genotyping

We proposed a single-color fluorogenic DNA decoding sequencing method designed to improve sequencing accuracy, increase read length and throughput, as well as decrease scanning time. This method involves the incorporation of a mixture of four types of 3'-O-modified nucleotide reversible terminators into each reaction. Among them, two nucleotides are labeled with the same fluorophore, while the remaining two are unlabeled. Only one nucleotide can be extended in each reaction, and an encoding that partially defines base composition can be obtained. Through cyclic interrogation of a template twice with different nucleotide combinations, two sets of encodings are sequentially obtained, enabling the determination of the sequence. We demonstrate the feasibility of this method using established sequencing chemistry, achieving a cycle efficiency of approximately 99.5 %. Notably, this strategy exhibits remarkable efficacy in the detection and correction of sequencing errors, achieving a theoretical error rate of 0.00016 % at a sequencing depth of ×2, which is lower than Sanger sequencing. This method is theoretically compatible with the existing sequencing-by-synthesis (SBS) platforms, and the instrument is simpler, which may facilitate further reductions in sequencing costs, thereby broadening its applications in biology and medicine. Moreover, we demonstrate the capability to detect known mutation sites using information from only a single sequencing run. We validate this approach by accurately identifying a mutation site in the human mitochondrial DNA.

Precision-engineered metal and metal-oxide nanoparticles for biomedical imaging and healthcare applications

The growing field of nanotechnology has witnessed numerous advancements over the past few years, particularly in the development of engineered nanoparticles. Compared with bulk materials, metal nanoparticles possess more favorable properties, such as increased chemical activity and toxicity, owing to their smaller size and larger surface area. Metal nanoparticles exhibit exceptional stability, specificity, sensitivity, and effectiveness, making them highly useful in the biomedical field. Metal nanoparticles are in high demand in biomedical nanotechnology, including Au, Ag, Pt, Cu, Zn, Co, Gd, Eu, and Er. These particles exhibit excellent physicochemical properties, including amenable functionalization, non-corrosiveness, and varying optical and electronic properties based on their size and shape. Metal nanoparticles can be modified with different targeting agents such as antibodies, liposomes, transferrin, folic acid, and carbohydrates. Thus, metal nanoparticles hold great promise for various biomedical applications such as photoacoustic imaging, magnetic resonance imaging, computed tomography (CT), photothermal, and photodynamic therapy (PDT). Despite their potential, safety considerations, and regulatory hurdles must be addressed for safe clinical applications. This review highlights advancements in metal nanoparticle surface engineering and explores their integration with emerging technologies such as bioimaging, cancer therapeutics and nanomedicine. By offering valuable insights, this comprehensive review offers a deep understanding of the potential of metal nanoparticles in biomedical research.

scRNA+ TCR-seq revealed dual TCR T cells antitumor response in the TME of NSCLC

The intricate origins, subsets, and characteristics of TCR (T Cell Receptor) s, along with the mechanisms underpinning the antitumor response of tumor-infiltrating T lymphocytes within the tumor microenvironment (TME) remain enigmatic. Recently, the advent of single-cell RNA+TCR-sequencing (scRNA+TCR seq) has revolutionized TME analysis, providing unprecedented insight into the origins, cell subsets, TCR CDR3 compositions, and the expression patterns of response/depletion factors within individual tumor-infiltrating T lymphocytes. Our analysis of the shared scRNA+TCR seq dataset revealed a substantial presence of dual TCR T cells, characterized by clonal hyperplasia and remarkable migratory prowess across various tissues, including blood, normal, peritumoral, and tumor tissues in non-small cell lung cancer patients. Notably, dual TCR CD8+T cells predominantly fell within the CXCL13+subset, displaying potent antitumor activity and a strong preference for tumor tissue residency. Conversely, dual TCR CD4+T cells were predominantly classified as CD5+ or LMNA+subsets, exhibiting a more even distribution across diverse tissue types. By harnessing scRNA+TCR seq and other cutting-edge technologies, we can delve deeper into the effects and mechanisms that regulate the antitumor response or tolerance of dual TCR T cells. This innovative approach holds immense promise in offering fresh perspectives and avenues for advancing research on TIL (Tumor infiltrating lymphocyte)s within the TME.

DeClUt: Decluttering differentially expressed genes through clustering of their expression profiles

BACKGROUND AND OBJECTIVE

differential expression analysis is one of the most popular activities in transcriptomic studies based on next-generation sequencing technologies. In fact, differentially expressed genes (DEGs) between two conditions represent ideal prognostic and diagnostic candidate biomarkers for many pathologies. As a result, several algorithms, such as DESeq2 and edgeR, have been developed to identify DEGs. Despite their widespread use, there is no consensus on which model performs best for different types of data, and many existing methods suffer from high False Discovery Rates (FDR).

METHODS

we present a new algorithm, DeClUt, based on the intuition that the expression profile of differentially expressed genes should form two reasonably compact and well-separated clusters. This, in turn, implies that the bipartition induced by the two conditions being compared should overlap with the clustering. The clustering algorithm underlying DeClUt was designed to be robust to outliers typical of RNA-seq data. In particular, we used the average silhouette function to enforce membership assignment of samples to the most appropriate condition.

RESULTS

DeClUt was tested on real RNA-seq datasets and benchmarked against four of the most widely used methods (edgeR, DESeq2, NOISeq, and SAMseq). Experiments showed a higher self-consistency of results than the competitors as well as a significantly lower False Positive Rate (FPR). Moreover, tested on a real prostate cancer RNA-seq dataset, DeClUt has highlighted 8 DE genes, linked to neoplastic process according to DisGeNET database, that none of the other methods had identified.

CONCLUSIONS

our work presents a novel algorithm that builds upon basic concepts of data clustering and exhibits greater consistency and significantly lower False Positive Rate than state-of-the-art methods. Additionally, DeClUt is able to highlight relevant differentially expressed genes not otherwise identified by other tools contributing to improve efficacy of differential expression analyses in various biological applications.

Obtention of viable cell suspensions from breast cancer tumor biopsies for 3D chromatin conformation and single-cell transcriptome analysis

Molecular and cellular characterization of tumors is essential due to the complex and heterogeneous nature of cancer. In recent decades, many bioinformatic tools and experimental techniques have been developed to achieve personalized characterization of tumors. However, sample handling continues to be a major challenge as limitations such as prior treatments before sample acquisition, the amount of tissue obtained, transportation, or the inability to process fresh samples pose a hurdle for experimental strategies that require viable cell suspensions. Here, we present an optimized protocol that allows the recovery of highly viable cell suspensions from breast cancer primary tumor biopsies. Using these cell suspensions we have successfully characterized genome architecture through Hi-C. Also, we have evaluated single-cell gene expression and the tumor cellular microenvironment through single-cell RNAseq. Both technologies are key in the detailed and personalized molecular characterization of tumor samples. The protocol described here is a cost-effective alternative to obtain viable cell suspensions from biopsies simply and efficiently.

A correctable decoding DNA sequencing with high accuracy and high throughput

Eliminating errors in next-generation sequencing has proven to be challenging. Here we present a novel strategy for DNA sequencing, called correctable two-color fluorogenic DNA decoding sequencing, which can significantly improve sequencing accuracy and throughput by employing a dual-nucleotide addition combined with fluorogenic sequencing-by-synthesis (SBS) chemistry. This sequencing method involves introducing a mixture of natural nucleotide X, labeled unblocked nucleotide X', 3' blocked nucleotide Y*, and labeled 3' blocked nucleotide Y* into each reaction cycle. By cyclically interrogating a template twice with different nucleotide combinations, two sets of base-encoding are sequentially obtained, enabling accurate deduction of base sequence. We demonstrate the remarkable efficacy of this approach in detecting and correcting sequencing errors, achieving a theoretical error rate of 0.0005%, which is twice as accurate as Sanger sequencing. Furthermore, we show the capability to detect known mutation sites using information from only a single sequencing run. The correctable two-color fluorogenic DNA decoding sequencing approach should enable accurate identification of extremely rare genomic variations in diverse applications in biology and medicine.

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

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

Gene regulatory network inference with covariance dynamics

Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data measured after intervention at multiple time points with unknown joint distributions, there is only one known specifically developed method, which does not fully utilize the rich information contained in this data type. We develop an inference method for the GRN in this case, netWork infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to model the dynamics of the covariance matrix, and solve this dynamics as an optimization problem to determine the regulatory relationships. To evaluate its effectiveness, we compare WENDY with other inference methods using synthetic data and experimental data. Our results demonstrate that WENDY performs well across different data sets.

Integrating bulk and single-cell sequencing reveals metastasis-associated CAFs in head and neck squamous cell carcinoma

AIMS

Cancer-associated fibroblasts (CAFs) have been shown to promote the metastasis of head and neck squamous cell carcinoma (HNSCC), but the underlying mechanisms remain unclear. The purpose of this study is to identify gene in CAFs that are associated with metastasis and to preliminarily validate its impact on the metastasis of HNSCC.

MATERIALS AND METHODS

Scissor analysis was utilized to process single-cell and bulk RNA sequencing datasets, identifying genes associated with the metastasis of HNSCC through differential gene expression analysis. A risk model was constructed using LASSO regression analysis. Quantitative real time-PCR and Western blot were employed to measure mRNA and protein expressions, respectively. Multiplex immunohistochemistry (mIHC) was used to assess protein expression in CAFs. siRNA was utilized to achieve gene knockdown. CCK-8 and Transwell assays were employed to evaluate the biological characteristics of HNSCC cells.

KEY FINDINGS

Compare to the nonmetastatic primary CAFs (nmCAFs), tissue inhibitors of metalloproteinase-1 (TIMP1) was founded to be overexpressed in the cells and tissues of metastatic primary CAFs (mCAFs). Knocking down TIMP1 in CAFs can signifucantly inhibit the proliferation, invasion, and migration of HNSCC cells.

SIGNIFICANCE

CAFs facilitate HNSCC cell metastasis by upregulating TIMP1, highlighting TIMP1 as a potential therapeutic target in HNSCC metastasis management.

Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges

Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization.

Insights of immune cell heterogeneity, tumor-initiated subtype transformation, drug resistance, treatment and detecting technologies in glioma microenvironment

BACKGROUND

With the gradual understanding of glioma development and the immune microenvironment, many immune cells have been discovered. Despite the growing comprehension of immune cell functions and the clinical application of immunotherapy, the precise roles and characteristics of immune cell subtypes, how glioma induces subtype transformation of immune cells and its impact on glioma progression have yet to be understood.

AIM OF THE REVIEW

In this review, we comprehensively center on the four major immune cells within the glioma microenvironment, particularly neutrophils, macrophages, lymphocytes, myeloid-derived suppressor cells (MDSCs), and other significant immune cells. We discuss (1) immune cell subtype markers, (2) glioma-induced immune cell subtype transformation, (3) the mechanisms of each subtype influencing chemotherapy resistance, (4) therapies targeting immune cells, and (5) immune cell-associated single-cell sequencing. Eventually, we identified the characteristics of immune cell subtypes in glioma, comprehensively summarized the exact mechanism of glioma-induced immune cell subtype transformation, and concluded the progress of single-cell sequencing in exploring immune cell subtypes in glioma.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In conclusion, we have analyzed the mechanism of chemotherapy resistance detailly, and have discovered prospective immunotherapy targets, excavating the potential of novel immunotherapies approach that synergistically combines radiotherapy, chemotherapy, and surgery, thereby paving the way for improved immunotherapeutic strategies against glioma and enhanced patient outcomes.

Intratumoral Cell Heterogeneity in Patient-Derived Glioblastoma Cell Lines Revealed by Single-Cell RNA-Sequencing

Glioblastoma cell lines derived from different patients are widely used in tumor biology research and drug screening. A key feature of glioblastoma is the high level of inter- and intratumor heterogeneity that accounts for treatment resistance. Our aim was to investigate whether intratumor heterogeneity is maintained in cell models. Single-cell RNA sequencing was used to investigate the cellular composition of a tumor sample and six patient-derived glioblastoma cell lines. Three cell lines preserved the mutational profile of the original tumor, whereas three others differed from their precursors. Copy-number variation analysis showed significantly rearranged genomes in all the cell lines and in the tumor sample. The tumor had the most complex cell composition, including cancer cells and microenvironmental cells. Cell lines with a conserved genome had less diverse cellularity, and during cultivation, a relative increase in the stem-cell-derived progenitors was noticed. Cell lines with genomes different from those of the primary tumors mainly contained neural progenitor cells and microenvironmental cells. The establishment of cell lines without the driver mutations that are intrinsic to the original tumors may be related to the selection of clones or cell populations during cultivation. Thus, patient-derived glioblastoma cell lines differ substantially in their cellular profile, which should be taken into account in translational studies.

Single-cell RNA sequencing technology in human spermatogenesis: Progresses and perspectives

Spermatogenesis, a key part of the spermiation process, is regulated by a combination of key cells, such as primordial germ cells, spermatogonial stem cells, and somatic cells, such as Sertoli cells. Abnormal spermatogenesis can lead to azoospermia, testicular tumors, and other diseases related to male infertility. The application of single-cell RNA sequencing (scRNA-seq) technology in male reproduction is gradually increasing with its unique insight into deep mining and analysis. The data cover different periods of neonatal, prepubertal, pubertal, and adult stages. Different types of male infertility diseases including obstructive and non-obstructive azoospermia (NOA), Klinefelter Syndrome (KS), Sertoli Cell Only Syndrome (SCOS), and testicular tumors are also covered. We briefly review the principles and application of scRNA-seq and summarize the research results and application directions in spermatogenesis in different periods and pathological states. Moreover, we discuss the challenges of applying this technology in male reproduction and the prospects of combining it with other technologies.

Advancements in Understanding the Hide-and-Seek Strategy of Hibernating Breast Cancer Cells and Their Implications in Oncology from a Broader Perspective: A Comprehensive Overview

Despite recent advancements in technology, breast cancer still poses a significant threat, often resulting in fatal consequences. While early detection and treatments have shown some promise, many breast cancer patients continue to struggle with the persistent fear of the disease returning. This fear is valid, as breast cancer cells can lay dormant for years before remerging, evading traditional treatments like a game of hide and seek. The biology of these dormant breast cancer cells presents a crucial yet poorly understood challenge in clinical settings. In this review, we aim to explore the mysterious world of dormant breast cancer cells and their significant impact on patient outcomes and prognosis. We shed light on the elusive role of the G9a enzyme and many other epigenetic factors in breast cancer recurrence, highlighting its potential as a target for eliminating dormant cancer cells and preventing disease relapse. Through this comprehensive review, we not only emphasise the urgency of unravelling the dynamics of dormant breast cancer cells to improve patient outcomes and advance personalised oncology but also provide a guide for fellow researchers. By clearly outlining the clinical and research gaps surrounding dormant breast cancer cells from a molecular perspective, we aim to inspire further exploration of this critical area, ultimately leading to improved patient care and treatment strategies.

Advances in Single-Cell Techniques for Linking Phenotypes to Genotypes

Single-cell analysis has become an essential tool in modern biological research, providing unprecedented insights into cellular behavior and heterogeneity. By examining individual cells, this approach surpasses conventional population-based methods, revealing critical variations in cellular states, responses to environmental cues, and molecular signatures. In the context of cancer, with its diverse cell populations, single-cell analysis is critical for investigating tumor evolution, metastasis, and therapy resistance. Understanding the phenotype-genotype relationship at the single-cell level is crucial for deciphering the molecular mechanisms driving tumor development and progression. This review highlights innovative strategies for selective cell isolation based on desired phenotypes, including robotic aspiration, laser detachment, microraft arrays, optical traps, and droplet-based microfluidic systems. These advanced tools facilitate high-throughput single-cell phenotypic analysis and sorting, enabling the identification and characterization of specific cell subsets, thereby advancing therapeutic innovations in cancer and other diseases.

Refining the optimal CAF cluster marker for predicting TME-dependent survival expectancy and treatment benefits in NSCLC patients

The tumor microenvironment (TME) plays a pivotal role in the onset, progression, and treatment response of cancer. Among the various components of the TME, cancer-associated fibroblasts (CAFs) are key regulators of both immune and non-immune cellular functions. Leveraging single-cell RNA sequencing (scRNA) data, we have uncovered previously hidden and promising roles within this specific CAF subgroup, paving the way for its clinical application. However, several critical questions persist, primarily stemming from the heterogeneous nature of CAFs and the use of different fibroblast markers in various sample analyses, causing confusion and hindrance in their clinical implementation. In this groundbreaking study, we have systematically screened multiple databases to identify the most robust marker for distinguishing CAFs in lung cancer, with a particular focus on their potential use in early diagnosis, staging, and treatment response evaluation. Our investigation revealed that COL1A1, COL1A2, FAP, and PDGFRA are effective markers for characterizing CAF subgroups in most lung adenocarcinoma datasets. Through comprehensive analysis of treatment responses, we determined that COL1A1 stands out as the most effective indicator among all CAF markers. COL1A1 not only deciphers the TME signatures related to CAFs but also demonstrates a highly sensitive and specific correlation with treatment responses and multiple survival outcomes. For the first time, we have unveiled the distinct roles played by clusters of CAF markers in differentiating various TME groups. Our findings confirm the sensitive and unique contributions of CAFs to the responses of multiple lung cancer therapies. These insights significantly enhance our understanding of TME functions and drive the translational application of extensive scRNA sequence results. COL1A1 emerges as the most sensitive and specific marker for defining CAF subgroups in scRNA analysis. The CAF ratios represented by COL1A1 can potentially serve as a reliable predictor of treatment responses in clinical practice, thus providing valuable insights into the influential roles of TME components. This research marks a crucial step forward in revolutionizing our approach to cancer diagnosis and treatment.

Integrative Pan-Cancer Analysis Reveals the Oncogenic Role of MND1 and Validation of MND1's Role in Breast Cancer

Purpose

Meiotic nuclear division 1 (MND1) is a meiosis-specific protein that promotes lung adenocarcinoma progression. However, its expression and biological function across cancers remain largely unexplored.

Patients and Methods

The expression, prognostic significance, mutation status, and methylation profile of MND1 in various cancers were comprehensively analyzed using the TIMER, GTEX, Kaplan-Meier plotter, cBioPortal, and GSCA databases. Additionally, we constructed a PPI network, enrichment analysis and single-cell transcriptomic sequencing to elucidate the underlying mechanism of MND1. Furthermore, we investigated the association between MND1 expression and drug sensitivity using CellMiner. Moreover, we also explored the correlation between MND1 expression and immune infiltration. Finally, we validated the functional role of MND1 in breast cancer through IHC staining, CCK8, EdU, colony formation, and flow cytometry assays.

Results

MND1 has been reported to be highly expressed in Pan-cancer, High MND1 expression was significantly associated with poor prognosis in cancers. Additionally, MND1 mutation frequency is high in most cancers, and its expression correlates with methylation. Furthermore, MND1 expression significantly correlates with immune checkpoint blockade (ICB) markers, including PD-L1, PD-1, and CTLA-4. The PPI network reveals interactions between MND1 and PSMC3IP, BRCA1, and BRCA2. Enrichment analysis and single-cell sequencing indicate that MND1 positively correlates with cell cycle. ROC curve reveals favorable diagnostic efficacy of MND1 in breast cancer. In vitro, MND1 overexpression promotes breast cancer cell proliferation and increases the expression of key cell cycle regulators (CDK4, CDK6, and cyclin D3), accelerating the G1/S phase transition and leading to abnormal breast cancer cell proliferation. The immunohistochemical analysis revealed a robust expression of MND1 in breast cancer tissues, exhibiting a significant positive correlation with PD-L1 and FOXP3.

Conclusion

MND1 is an oncogene and may serve as a biomarker for cancer prognosis and immunotherapy. Targeting MND1 may be a potential tumor treatment strategy.

Transcriptomic subtyping of gastrointestinal malignancies

Gastrointestinal (GI) cancers are highly heterogeneous at multiple levels. Tumor heterogeneity can be captured by molecular profiling, such as genetic, epigenetic, proteomic, and transcriptomic classification. Transcriptomic subtyping has the advantage of combining genetic and epigenetic information, cancer cell-intrinsic properties, and the tumor microenvironment (TME). Unsupervised transcriptomic subtyping systems of different GI malignancies have gained interest because they reveal shared biological features across cancers and bear prognostic and predictive value. Importantly, transcriptomic subtypes accurately reflect complex phenotypic states varying not only per tumor region, but also throughout disease progression, with consequences for clinical management. Here, we discuss methodologies of transcriptomic subtyping, proposed taxonomies for GI malignancies, and the challenges posed to clinical implementation, highlighting opportunities for future transcriptomic profiling efforts to optimize clinical impact.

Single-cell transcriptomic analysis reveals the antiangiogenic role of Mgarp in diabetic retinopathy

INTRODUCTION

Diabetic retinopathy (DR) is a common vascular complication of diabetes mellitus and a leading cause of vision loss worldwide. Endothelial cell (EC) heterogeneity has been observed in the pathogenesis of DR. Elucidating the underlying mechanisms governing EC heterogeneity may provide novel insights into EC-specific therapies for DR.

RESEARCH DESIGN AND METHODS

We used the single-cell data from the Gene Expression Omnibus database to explore EC heterogeneity between diabetic retinas and non-diabetic retinas and identify the potential genes involved in DR. CCK-8 assays, EdU assays, transwell assays, and tube formation assays were conducted to determine the role of the identified gene in angiogenic effects.

RESULTS

Our analysis identified three distinct EC subpopulations in retinas and revealed that Mitochondria-localized glutamic acid-rich protein (Mgarp) gene is potentially involved in the pathogenesis of DR. Silencing of Mgarp significantly suppressed the proliferation, migration, and tube formation capacities in retinal endothelial cells.

CONCLUSIONS

This study not only offers new insights into transcriptomic heterogeneity and pathological alteration of retinal ECs but also holds the promise to pave the way for antiangiogenic therapy by targeting EC-specific gene.

OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing

Single-cell sequencing is frequently affected by "omission" due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly "omitted" cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of "omitted" cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.

Single-cell RNA sequencing in ovarian cancer: revealing new perspectives in the tumor microenvironment

Single-cell sequencing technology has emerged as a pivotal tool for unraveling the complexities of the ovarian tumor microenvironment (TME), which is characterized by its cellular heterogeneity and intricate cell-to-cell interactions. Ovarian cancer (OC), known for its high lethality among gynecologic malignancies, presents significant challenges in treatment and diagnosis, partly due to the complexity of its TME. The application of single-cell sequencing in ovarian cancer research has enabled the detailed characterization of gene expression profiles at the single-cell level, shedding light on the diverse cell populations within the TME, including cancer cells, stromal cells, and immune cells. This high-resolution mapping has been instrumental in understanding the roles of these cells in tumor progression, invasion, metastasis, and drug resistance. By providing insight into the signaling pathways and cell-to-cell communication mechanisms, single-cell sequencing facilitates the identification of novel therapeutic targets and the development of personalized medicine approaches. This review summarizes the advancement and application of single-cell sequencing in studying the stromal components and the broader TME in OC, highlighting its implications for improving diagnosis, treatment strategies, and understanding of the disease's underlying biology.

Application of spatial-omics to the classification of kidney biopsy samples in transplantation

Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.

NOX4-mediated astrocyte ferroptosis in Alzheimer's disease

This study investigates NADPH oxidase 4 (NOX4) involvement in iron-mediated astrocyte cell death in Alzheimer's Disease (AD) using single-cell sequencing data and transcriptomes. We analyzed AD single-cell RNA sequencing data, identified astrocyte marker genes, and explored biological processes in astrocytes. We integrated AD-related chip data with ferroptosis-related genes, highlighting NOX4. We validated NOX4's role in ferroptosis and AD in vitro and in vivo. Astrocyte marker genes were enriched in AD, emphasizing their role. NOX4 emerged as a crucial player in astrocytic ferroptosis in AD. Silencing NOX4 mitigated ferroptosis, improved cognition, reduced Aβ and p-Tau levels, and alleviated mitochondrial abnormalities. NOX4 promotes astrocytic ferroptosis, underscoring its significance in AD progression.

Leveraging single-cell sequencing to classify and characterize tumor subgroups in bulk RNA-sequencing data

PURPOSE

Accurate classification of cancer subgroups is essential for precision medicine, tailoring treatments to individual patients based on their cancer subtypes. In recent years, advances in high-throughput sequencing technologies have enabled the generation of large-scale transcriptomic data from cancer samples. These data have provided opportunities for developing computational methods that can improve cancer subtyping and enable better personalized treatment strategies.

METHODS

Here in this study, we evaluated different feature selection schemes in the context of meningioma classification. To integrate interpretable features from the bulk (n = 77 samples) and single-cell profiling (∼ 10 K cells), we developed an algorithm named CLIPPR which combines the top-performing single-cell models, RNA-inferred copy number variation (CNV) signals, and the initial bulk model to create a meta-model.

RESULTS

While the scheme relying solely on bulk transcriptomic data showed good classification accuracy, it exhibited confusion between malignant and benign molecular classes in approximately ∼ 8% of meningioma samples. In contrast, models trained on features learned from meningioma single-cell data accurately resolved the sub-groups confused by bulk-transcriptomic data but showed limited overall accuracy. CLIPPR showed superior overall accuracy and resolved benign-malignant confusion as validated on n = 789 bulk meningioma samples gathered from multiple institutions. Finally, we showed the generalizability of our algorithm using our in-house single-cell (∼ 200 K cells) and bulk TCGA glioma data (n = 711 samples).

CONCLUSION

Overall, our algorithm CLIPPR synergizes the resolution of single-cell data with the depth of bulk sequencing and enables improved cancer sub-group diagnoses and insights into their biology.

Single-cell RNA sequencing elucidated the landscape of breast cancer brain metastases and identified ILF2 as a potential therapeutic target

Distant metastasis remains the primary cause of morbidity in patients with breast cancer. Hence, the development of more efficacious strategies and the exploration of potential targets for patients with metastatic breast cancer are urgently needed. The data of six patients with breast cancer brain metastases (BCBrM) from two centres were collected, and a comprehensive landscape of the entire tumour ecosystem was generated through the utilisation of single-cell RNA sequencing. We utilised the Monocle2 and CellChat algorithms to investigate the interrelationships among each subcluster. In addition, multiple signatures were collected to evaluate key components of the subclusters through multi-omics methodologies. Finally, we elucidated common expression programs of malignant cells, and experiments were conducted in vitro and in vivo to determine the functions of interleukin enhancer-binding factor 2 (ILF2), which is a key gene in the metastasis module, in BCBrM progression. We found that subclusters in each major cell type exhibited diverse characteristics. Besides, our study indicated that ILF2 was specifically associated with BCBrM, and experimental validations further demonstrated that ILF2 deficiency hindered BCBrM progression. Our study offers novel perspectives on the heterogeneity of BCBrM and suggests that ILF2 could serve as a promising biomarker or therapeutic target for BCBrM.

Establishment and validation of a novel CD8+ T cell-associated prognostic signature for predicting clinical outcomes and immunotherapy response in hepatocellular carcinoma via integrating single-cell RNA-seq and bulk RNA-seq

CD8+ T lymphocytes are critical in the immune response against neoplasms, yet the prognostic relevance of CD8+ T cell-associated genes in hepatocellular carcinoma (HCC) is not fully understood. We sourced single-cell RNA-sequencing (scRNA-seq) and bulk RNA-seq data for HCC from the GSE98638 dataset and The Cancer Genome Atlas (TCGA) repository. We utilized Weighted Gene Correlation Network Analysis (WGCNA) to identify CD8+ T cell-related genes. A clinical prognostic model for risk stratification was then constructed via Cox-Lasso regression analysis. The Immunophenotypic Score (IPS) was utilized to evaluate the potential of immunotherapeutic interventions in the categorized cohorts. Validation of the expression of CD8+ T cell-associated risk genes was performed using quantitative reverse transcription PCR (qRT-PCR). Integrating scRNA-seq with RNA-seq data, we identified five CD8+ T cell-related signature genes: IKBKE, ATP1B3, MSC, ADA, and BATF. Notably, HCC patients in the high-risk group had markedly decreased overall survival. Elevated infiltration levels of CD8+ T cells, B cells, and macrophages were observed in the high-risk group. Moreover, there was a positive correlation between the risk score and immune checkpoints (ICPs), including PDCD1, CD274, and CTLA4. Patients within the high-risk group subject to PD1 and CTLA4 blockade exhibited higher IPS levels. Additionally, the expression of the five risk genes was upregulated in HCC cell lines and tissues compared to normal cells and tissues. Our findings establish a prognostic signature based on CD8+ T cells, offering a potent predictive model for clinical outcomes and responsiveness to immunotherapy in HCC patients.

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.

Advances in single-cell long-read sequencing technologies

With an increase in accuracy and throughput of long-read sequencing technologies, they are rapidly being assimilated into the single-cell sequencing pipelines. For transcriptome sequencing, these techniques provide RNA isoform-level information in addition to the gene expression profiles. Long-read sequencing technologies not only help in uncovering complex patterns of cell-type specific splicing, but also offer unprecedented insights into the origin of cellular complexity and thus potentially new avenues for drug development. Additionally, single-cell long-read DNA sequencing enables high-quality assemblies, structural variant detection, haplotype phasing, resolving high-complexity regions, and characterization of epigenetic modifications. Given that significant progress has primarily occurred in single-cell RNA isoform sequencing (scRiso-seq), this review will delve into these advancements in depth and highlight the practical considerations and operational challenges, particularly pertaining to downstream analysis. We also aim to offer a concise introduction to complementary technologies for single-cell sequencing of the genome, epigenome and epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.

Macrophage migration inhibitory factor mediates skin aging via CD74: Insights from single-cell and bulk RNA sequencing data

Cell-cell communication is crucial for regulating signaling and cellular function. However, the precise cellular and molecular changes remain poorly understood in skin aging. Based on single-cell and bulk RNA data, we explored the role of cell-cell ligand-receptor interaction in skin aging. We found that the macrophage migration inhibitory factor (MIF)/CD74 ligand-receptor complex was significantly upregulatedin aged skin, showing the predominant paracrine effect of keratinocytes on fibroblasts. Enrichment analysis and in vitro experiment revealed a close association of the activation of the MIF/CD74 with inflammatory pathways and immune response. Mechanistically, MIF/CD74 could significantly inhibit PPARγ protein, which thus significantly increased the degree of fibroblast senescence, and significantly up-regulated the expression of senescence-associated secretory phenotype (SASP) factors and FOS gene. Therefore, our study reveals that MIF/CD74 inhibits the activation of the PPAR signaling pathway, subsequently inducing the production of SASP factors and the upregulation of FOS expression, ultimately accelerating fibroblast senescence.

scRNA+TCR+BCR-seq revealed the proportions and gene expression patterns of dual receptor T and B lymphocytes in NPC and NLH

While the relationship between single receptor lymphocytes and cancer has been deeply researched, the origin and biological roles of dual receptor lymphocytes in tumor microenvironment (TME) remain largely unknown. And since nasopharyngeal carcinoma (NPC) is a type of cancer closely associated with immune infiltration, studying the TME of NPC holds particular significance. Utilizing single-cell RNA sequencing combined with T cell receptor (TCR) and B cell receptor (BCR) sequencing (scRNA + TCR + BCR-seq), we analyzed data from 7 patients with NPC and 3 patients with nasopharyngeal lymphatic hyperplasia (NLH). In our research, it was firstly found that the presence of dual receptor lymphocytes in both the TME of NPC and the inflammatory environment of NLH. We also confirmed their clonal expansion, suggesting their potential involvement in the immune response. Subsequently, we further discovered the lineage and the pairing characteristics. It was found that the dual receptor lymphocytes in NPC and NLH mainly originate from memory cells, and the predominant pairing type for dual TCR was β+α1+α2 and dual BCR was heavy+κ+λ. By further analyzing their gene expression, we compared the function of dual receptor cells with single receptor cells in the context of both NPC and NLH. This groundbreaking research has enhanced our comprehension of the features of dual-receptor cells and has contributed to a better understanding of the TME in NPC. By comparing with NLH, it illuminates part of the alterations in the process of malignant transformation in NPC. These findings present the potential to acquire improved diagnostic markers and treatment modalities.

Exploring the diversity of cancer-associated fibroblasts: insights into mechanisms of drug resistance

Introduction: Among the various stromal cell types within the tumor microenvironment, cancer-associated fibroblasts (CAFs) emerge as the predominant constituent, exhibiting a diverse array of oncogenic functions not intrinsic to normal fibroblasts. Their involvement spans across all stages of tumorigenesis, encompassing initiation, progression, and metastasis. Current understanding posits the coexistence of distinct subpopulations of CAFs within the tumor microenvironment across a spectrum of solid tumors, showcasing both pro- and antitumor activities. Recent advancements in single-cell transcriptomics have revolutionized our ability to meticulously dissect the heterogeneity inherent to CAF populations. Furthermore, accumulating evidence underscores the pivotal role of CAFs in conferring therapeutic resistance to tumors against various drug modalities. Consequently, efforts are underway to develop pharmacological agents specifically targeting CAFs. Methods: This review embarks on a comprehensive analysis, consolidating data from 36 independent single-cell RNA sequencing investigations spanning 17 distinct human malignant tumor types. Results: Our exploration centers on elucidating CAF population markers, discerning their prognostic relevance, delineating their functional contributions, and elucidating the underlying mechanisms orchestrating chemoresistance. Discussion: Finally, we deliberate on the therapeutic potential of harnessing CAFs as promising targets for intervention strategies in clinical oncology.

Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications

Background

Prostate cancer (PCa) is one of the most threatening health problems for the elderly males. However, our understanding of the disease has been limited by the research technology for a long time. Recently, the maturity of sequencing technology and omics studies has been accelerating the studies of PCa, establishing themselves as an essential impetus in this field.

Methods

We assessed Web of Science (WoS) database for publications of sequencing and omics studies in PCa on July 3rd, 2023. Bibliometrix was used to conduct ulterior bibliometric analysis of countries/affiliations, authors, sources, publications, and keywords. Subsequently, purposeful large amounts of literature reading were proceeded to analyze research hotspots in this field.

Results

3325 publications were included in the study. Research associated with sequencing and omics studies in PCa had shown an obvious increase recently. The USA and China were the most productive countries, and harbored close collaboration. CHINNAIYAN AM was identified as the most influential author, and CANCER RESEARCH exhibited huge impact in this field. Highly cited publications and their co-citation relationships were used to filtrate literatures for subsequent literature reading. Based on keyword analysis and large amounts of literature reading, 'the molecular pathogenesis of PCa' and 'the clinical application of sequencing and omics studies in PCa' were summarized as two research hotspots in the field.

Conclusion

Sequencing technology had a deep impact on the studies of PCa. Sequencing and omics studies in PCa helped researchers reveal the molecular pathogenesis, and provided new possibilities for the clinical practice of PCa.

A review of single-cell transcriptomics and epigenomics studies in maternal and child health

Single-cell sequencing technologies enhance our understanding of cellular dynamics throughout pregnancy. We outlined the workflow of single-cell sequencing techniques and reviewed single-cell studies in maternal and child health. We conducted a literature review of single cell studies on maternal and child health using PubMed. We summarized the findings from 16 single-cell atlases of the human and mammalian placenta across gestational stages and 31 single-cell studies on maternal exposures and complications including infection, obesity, diet, gestational diabetes, pre-eclampsia, environmental exposure and preterm birth. Single-cell studies provides insights on novel cell types in placenta and cell type-specific marks associated with maternal exposures and complications.

Combining a glycolysis‑related prognostic model based on scRNA‑Seq with experimental verification identifies ZFP41 as a potential prognostic biomarker for HCC

Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis, and its heterogeneity affects the response to clinical treatments. Glycolysis is highly associated with HCC therapy and prognosis. The present study aimed to identify a novel biomarker for HCC by exploring the heterogeneity of glycolysis in HCC. The intersection of both marker genes of glycolysis‑related cell clusters from single‑cell RNA sequencing analysis and mRNA data of liver HCC from The Cancer Genome Atlas were used to construct a prognostic model through Cox proportional hazard regression and the least absolute shrinkage and selection operator Cox regression. Data from the International Cancer Genome Consortium were used to validate the results of the analysis. Immune status analysis was then conducted. A significant gene in the prognostic model was identified as a potential biomarker and was verified through in vitro experiments. The results revealed that the glycolysis‑related prognostic model divided patients with HCC into high‑ and low‑risk groups. A nomogram combining the model and clinical features exhibited accurate predictive ability, with an area under the curve of 0.763 at 3 years. The high‑risk group exhibited a higher expression of checkpoint genes and lower tumor immune dysfunction and exclusion scores, suggesting that this group may be more likely to benefit from immunotherapy. The tumor tissues had a higher zinc finger protein (ZFP)41 mRNA and protein expression compared with the adjacent tissues. In vitro analyses revealed that ZFP41 played a crucial role in cell viability, proliferation, migration, invasion and glycolysis. On the whole, the present study demonstrates that the glycolysis‑related prognostic gene, ZFP41, is a potential prognostic biomarker and therapeutic target, and may play a crucial role in glycolysis and malignancy in HCC.

Single-Cell Sequencing Reveals MYOF-Enriched Monocyte/Macrophage Subcluster as a Favorable Prognostic Factor in Sepsis

This research utilized single-cell RNA sequencing to map the immune cell landscape in sepsis, revealing 28 distinct cell clusters and categorizing them into nine major types. Delving into the monocyte/macrophage subclusters, 12 unique subclusters are identified and pathway enrichment analyses are conducted using KEGG and GO, discovering enriched pathways such as oxidative phosphorylation and antigen processing. Further GSVA and AUCell assessments show varied activation of interferon pathways, especially in subclusters 4 and 11. The clinical correlation analysis reveals genes significantly linked to survival outcomes. Additionally, cellular differentiation in these subclusters is explored. Building on these insights, the differential gene expression within these subclusters is specifically scrutinized, which reveal MYOF as a key gene with elevated expression levels in the survivor group. This finding is further supported by in-depth pathway enrichment analysis and the examination of cellular differentiation trajectories, where MYOF's role became evident in the context of immune response regulation and sepsis progression. Validating the role of the MYOF gene in sepsis, a dose-dependent response to LPS in THP-1 cells and C57 mice is observed. Finally, inter-cellular communications are analyzed, particularly focusing on the MYOF+Mono/Macro subcluster, which indicates a pivotal role in immune regulation and potential therapeutic targeting.

Single-cell landscape of undifferentiated pleomorphic sarcoma

Undifferentiated pleomorphic sarcoma (UPS) is a highly aggressive malignant soft tissue tumor with a poor prognosis; however, the identity and heterogeneity of tumor populations remain elusive. Here, eight major cell clusters were identified through the RNA sequencing of 79,569 individual cells of UPS. UPS originates from mesenchymal stem cells (MSCs) and features undifferentiated subclusters. UPS subclusters were predicted to exist in two bulk RNA datasets, and had a prognostic value in The Cancer Genome Atlas (TCGA) dataset. The functional heterogeneity of malignant UPS cells and the immune microenvironment were characterized. Additionally, the fused cells were innovatively detected by expressing both monocyte/macrophage markers and other subcluster-associated genes. Based on the ligand-receptor interaction analysis, cellular interactions with epidermal growth factor receptor (EGFR) and vascular endothelial growth factor receptor (VEGFR) were abundant. Furthermore, 73% of patients with UPS (48/66) showed positive EGFR expression, which was associated with a poor prognosis. EGFR blockade with cetuximab inhibited tumor growth in a patient-derived xenograft model. Our transcriptomic studies delineate the landscape of UPS intratumor heterogeneity and serve as a foundational resource for further discovery and therapeutic exploration.

Integrating bulk and single-cell sequencing data to construct a Scissor+ dendritic cells prognostic model for predicting prognosis and immune responses in ESCC

Esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity with various immune cell infiltration patterns, which have been associated with therapeutic sensitivity and resistance. In particular, dendritic cells (DCs) are recently discovered to be associated with prognosis and survival in cancer. However, how DCs differ among ESCC patients has not been fully comprehended. Recently, the advance of single-cell RNA sequencing (scRNA-seq) enables us to profile the cell types, states, and lineages in the heterogeneous ESCC tissues. Here, we dissect the ESCC tumor microenvironment at high resolution by integrating 192,078 single cells from 60 patients, including 4379 DCs. We then used Scissor, a method that identifies cell subpopulations from single-cell data that are associated bulk samples with genomic and clinical information, to stratify DCs into Scissorhi and Scissorlow subtypes. We applied the Scissorhi gene signature to stratify ESCC scRNAseq patient, and we found that PD-L1, TIGIT, PVR and IL6 ligand-receptor-mediated cell interactions existed mainly in Scissorhi patients. Finally, based on the Scissor results, we successfully developed a validated prognostic risk model for ESCC and further validated the reliability of the risk prediction model by recruiting 40 ESCC clinical patients. This information highlights the importance of these genes in assessing patient prognosis and may help in the development of targeted or personalized therapies for ESCC.

Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature

The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promising therapeutic modality, demonstrating remarkable potential for substantially prolonging the overall survival of individuals afflicted with LUAD. However, there is currently a lack of reliable signatures for identifying patients who would benefit from immunotherapy. We conducted a comparative analysis of two immunotherapy cohorts (OAK and POPLAR) and utilized single-factor COX regression to identify genes that significantly impact the prognosis of LUAD. Based on the TCGA-LUAD dataset, we employed a combination of 101 machine learning algorithms to construct a model and selected the optimal model. The model was validated on five GEO datasets and compared with 144 previously published signatures to assess its performance. Subsequently, we explored the underlying biological mechanisms through tumor mutation burden analysis, enrichment analysis, and immune infiltration analysis. An immunotherapy prognostic prediction signature (IPPS) was constructed based on 13 genes, showing robust performance in the TCGA-LUAD dataset. IPPS exhibited consistent predictive accuracy in the validation cohorts. Compared to 144 previously published signatures, IPPS consistently ranked among the top in terms of C-index values. Further exploration revealed differences between high and low-IPPS groups in terms of tumor mutation burden, pathway enrichment, and immune infiltration. IPPS demonstrates strong predictive capabilities for the prognosis of LUAD patients, offering the potential to identify suitable candidates for immunotherapy and contribute to precision treatment strategies for LUAD.

A workflow for the enrichment, the identification, and the isolation of non-apoptotic single circulating tumor cells for RNA sequencing analysis

Circulating tumor cells (CTCs) are constantly shed by tumor tissue and can serve as a valuable analyte for a gene expression analysis from a liquid biopsy. However, a high proportion of CTCs can be apoptotic leading to rapid mRNA decay and challenging the analysis of their transcriptome. We established a workflow to enrich, to identify, and to isolate single CTCs including the discrimination of apoptotic and non-apoptotic CTCs for further single CTC transcriptome analysis. Viable tumor cells-we first used cells from breast cancer cell lines followed by CTCs from metastatic breast cancer patients-were enriched with the CellSearch system from diagnostic leukapheresis products, identified by immunofluorescence analysis for neoplastic markers, and isolated by micromanipulation. Then, their cDNA was generated, amplified, and sequenced. In order to exclude early apoptotic tumor cells, staining with Annexin V coupled to a fluorescent dye was used. Annexin V staining intensity was associated with decreased RNA integrity as well as lower numbers of total reads, exon reads, and detected genes in cell line cells and CTCs. A comparative RNA analysis of single cells from MDA-MB-231 and MCF7 cell lines revealed the expected differential transcriptome profiles. Enrichment and staining procedures of cell line cells that were spiked into blood had only little effect on the obtained RNA sequencing data compared to processing of naïve cells. Further, the detection of transcripts of housekeeping genes such as GAPDH was associated with a significantly higher quality of expression data from CTCs. This workflow enables the enrichment, detection, and isolation of single CTCs for individual transcriptome analyses. The discrimination of apoptotic and non-apoptotic cells allows to focus on CTCs with a high RNA integrity to ensure a successful transcriptome analysis.

Comprehensive Analysis of Lung Adenocarcinoma and Brain Metastasis through Integrated Single-Cell Transcriptomics

Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer, comprising approximately half of all cases. It is often diagnosed at advanced stages with brain metastasis (BM), resulting in high mortality rates. Current BM management involves complex interventions and conventional therapies that offer limited survival benefits with neurotoxic side effects. The tumor microenvironment (TME) is a complex system where cancer cells interact with various elements, significantly influencing tumor behavior. Immunotherapies, particularly immune checkpoint inhibitors, target the TME for cancer treatment. Despite their effectiveness, it is crucial to understand metastatic lung cancer and the specific characteristics of the TME, including cell-cell communication mechanisms, to refine treatments. Herein, we investigated the tumor microenvironment of brain metastasis from lung adenocarcinoma (LUAD-BM) and primary tumors across various stages (I, II, III, and IV) using single-cell RNA sequencing (scRNA-seq) from publicly available datasets. Our analysis included exploring the immune and non-immune cell composition and the expression profiles and functions of cell type-specific genes, and investigating the interactions between different cells within the TME. Our results showed that T cells constitute the majority of immune cells present in primary tumors, whereas microglia represent the most dominant immune cell type in BM. Interestingly, microglia exhibit a significant increase in the COX pathway. Moreover, we have shown that microglia primarily interact with oligodendrocytes and endothelial cells. One significant interaction was identified between DLL4 and NOTCH4, which demonstrated a relevant association between endothelial cells and microglia and between microglia and oligodendrocytes. Finally, we observed that several genes within the HLA complex are suppressed in BM tissue. Our study reveals the complex molecular and cellular dynamics of BM-LUAD, providing a path for improved patient outcomes with personalized treatments and immunotherapies.

The viral etiology of EBV-associated gastric cancers contributes to their unique pathology, clinical outcomes, treatment responses and immune landscape

Epstein-Barr virus (EBV) is a pathogen known to cause a number of malignancies, often taking years for them to develop after primary infection. EBV-associated gastric cancer (EBVaGC) is one such malignancy, and is an immunologically, molecularly and pathologically distinct entity from EBV-negative gastric cancer (EBVnGC). In comparison with EBVnGCs, EBVaGCs overexpress a number of immune regulatory genes to help form an immunosuppressive tumor microenvironment (TME), have improved prognosis, and overall have an "immune-hot" phenotype. This review provides an overview of the histopathology, clinical features and clinical outcomes of EBVaGCs. We also summarize the differences between the TMEs of EBVaGCs and EBVnGCs, which includes significant differences in cell composition and immune infiltration. A list of available EBVaGC and EBVnGC gene expression datasets and computational tools are also provided within this review. Finally, an overview is provided of the various chemo- and immuno-therapeutics available in treating gastric cancers (GCs), with a focus on EBVaGCs.

Toolkit for mapping the clonal landscape of tumor-infiltrating B cells

Our current understanding of whether B cell involvement in the tumor microenvironment benefits the patient or the tumor - in distinct cancers, subcohorts and individual patients - is quite limited. Both statements are probably true in most cases: certain clonal B cell populations contribute to the antitumor response, while others steer the immune response away from the desired mechanics. To step up to a new level of understanding and managing B cell behaviors in the tumor microenvironment, we need to rationally discern these roles, which are cumulatively defined by B cell clonal functional programs, specificities of their B cell receptors, specificities and isotypes of the antibodies they produce, and their spatial interactions within the tumor environment. Comprehensive analysis of these characteristics of clonal B cell populations is now becoming feasible with the development of a whole arsenal of advanced technical approaches, which include (1) methods of single-cell and spatial transcriptomics, genomics, and proteomics; (2) methods of massive identification of B cell specificities; (3) methods of deep error-free profiling of B cell receptor repertoires. Here we overview existing techniques, summarize their current application for B cells studies and propose promising future directions in advancing B cells exploration.

The Link Between Dysregulated Immunometabolism and Vascular Damage: Implications for the Development of Atherosclerosis in Systemic Lupus Erythematosus and Other Rheumatic Diseases

A bimodal pattern of mortality in systemic lupus erythematosus (SLE) exists. Early-stage deaths are predominantly caused by infection, whereas later-stage deaths are mainly caused by atherosclerotic disease. Further, although SLE-related mortality has reduced considerably in recent years, cardiovascular (CV) events remain one of the leading causes of death in people with SLE. Accelerated atherosclerosis in SLE is attributed to both an increase in traditional CV risk factors and the inflammatory effects of SLE itself. Many of these changes occur within the microenvironment of the vascular-immune interface, the site of atherosclerotic plaque development. Here, an intimate interaction between endothelial cells, vascular smooth muscle cells, and immune cells dictates physiological vs pathological responses to a chronic type 1 interferon environment. Low-density neutrophils (LDNs) have also been implicated in eliciting vasculature-damaging effects at such lesion sites. These changes are thought to be governed by dysfunctional metabolism of immune cells in this niche due at least in part to the chronic induction of type 1 interferons. Understanding these novel pathophysiological mechanisms and metabolic pathways may unveil potential innovative pharmacological targets and therapeutic opportunities for atherosclerosis, as well as shed light on the development of premature atherosclerosis in patients with SLE who develop CV events.

Molecular Classifications in Gastric Cancer: A Call for Interdisciplinary Collaboration

Gastric cancer (GC) is a heterogeneous disease, often diagnosed at advanced stages, with a 5-year survival rate of approximately 20%. Despite notable technological advancements in cancer research over the past decades, their impact on GC management and outcomes has been limited. Numerous molecular alterations have been identified in GC, leading to various molecular classifications, such as those developed by The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG). Other authors have proposed alternative perspectives, including immune, proteomic, or epigenetic-based classifications. However, molecular stratification has not yet transitioned into clinical practice for GC, and little attention has been paid to alternative molecular classifications. In this review, we explore diverse molecular classifications in GC from a practical point of view, emphasizing their relationships with clinicopathological factors, prognosis, and therapeutic approaches. We have focused on classifications beyond those of TCGA and the ACRG, which have been less extensively reviewed previously. Additionally, we discuss the challenges that must be overcome to ensure their impact on patient treatment and prognosis. This review aims to serve as a practical framework to understand the molecular landscape of GC, facilitate the development of consensus molecular categories, and guide the design of innovative molecular studies in the field.

Management of Uterine Fibroids and Sarcomas: The Palermo Position Paper

BACKGROUND

Uterine fibroids are benign monoclonal tumors originating from the smooth muscle cells of the myometrium, constituting the most prevalent pathology within the female genital tract. Uterine sarcomas, although rare, still represent a diagnostic challenge and should be managed in centers with adequate expertise in gynecological oncology.

OBJECTIVES

This article is aimed to summarize and discuss cutting-edge elements about the diagnosis and management of uterine fibroids and sarcomas.

METHODS

This paper is a report of the lectures presented in an expert meeting about uterine fibroids and sarcomas held in Palermo in February 2023.

OUTCOME

Overall, the combination of novel molecular pathways may help combine biomarkers and expert ultrasound for the differential diagnosis of uterine fibroids and sarcomas. On the one hand, molecular and cellular maps of uterine fibroids and matched myometrium may enhance our understanding of tumor development compared to histologic analysis and whole tissue transcriptomics, and support the development of minimally invasive treatment strategies; on the other hand, ultrasound imaging allows in most of the cases a proper mapping the fibroids and to differentiate between benign and malignant lesions, which need appropriate management.

CONCLUSIONS AND OUTLOOK

The choice of uterine fibroid management, including pharmacological approaches, surgical treatment, or other strategies, such as high-intensity focused ultrasound (HIFU), should be carefully considered, taking into account the characteristics of the patient and reproductive prognosis.

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.

Single-cell RNA sequencing to understand host-virus interactions

Single-cell RNA sequencing (scRNA-seq) has allowed for the profiling of host and virus transcripts and host-virus interactions at single-cell resolution. This review summarizes the existing scRNA-seq technologies together with their strengths and weaknesses. The applications of scRNA-seq in various virological studies are discussed in depth, which broaden the understanding of the immune atlas, host-virus interactions, and immune repertoire. scRNA-seq can be widely used for virology in the near future to better understand the pathogenic mechanisms and discover more effective therapeutic strategies.