Potential molecular metabolic mechanisms underlying the effects of cimifugin in gastric cancer through single-cell and bulk RNA sequencing combined with network pharmacology.

Background: Gastric cancer (GC) is a leading cause of cancer-related mortality worldwide, posing a significant clinical challenge due to its complex tumor microenvironment (TME) and metabolic heterogeneity. Despite continuous improvements in treatment strategies including surgery, chemotherapy, and targeted therapies, the metabolic reprogramming in GC continues to impede treatment efficacy, highlighting an urgent need for the development of novel therapeutic strategies. This persistent issue underscores the urgent need for novel therapeutic approaches that can effectively address the diverse and dynamic characteristics of GC. Cimifugin, a traditional Chinese medicine (TCM), has garnered attention for its potential role in alleviating inflammation, neurological disorders, pain, and metabolic disorders. Its multi-targeting properties and minimal side effects suggest a broad potential for cancer management, which is currently being explored. This study aims to delineate the molecular mechanisms that cimifugin may impact within the TME and metabolic pathways of GC, with the expectation of contributing to a deeper understanding of GC and the development of innovative treatment strategies. Methods: We identified the GC-related TME cell types and metabolic profiles and pathways by using relevant data from the single-cell RNA sequencing (scRNA-seq) database GSE134520 and the stomach adenocarcinoma (STAD) data set from The Cancer Genome Atlas (TCGA). We also assessed the effects of cimifugin on MKN28 cell proliferation, invasion, and migration. By using six public platforms, we comprehensively predicted the potential biological targets of cimifugin. Clinical prognosis and immunohistochemistry (IHC), molecular docking, and dynamics simulations were used to confirm the clinical relevance and stability of the aforementioned targets. Results: Cimifugin inhibited MKN28 cell proliferation, migration, and invasion. Cimifugin may potentially act on various metabolic pathways in GC, including folate biosynthesis, xenobiotic metabolism via cytochrome P450 (CYP), glutathione metabolism, steroid hormone biosynthesis, and tryptophan metabolism. Cimifugin was noted to stably bind to three significant core targets associated with metabolic reprogramming in GC: AKR1C2, MAOB, and PDE2A; all three targets were strongly expressed in endocrince cells, pit mucous cells (PMCs), and common myeloid progenitors (CMPs). Conclusions: We verified the pharmacological effects of cimifugin on GC cell proliferation, invasion, and migration. AKR1C2, MAOB, and PDE2A were identified as the key targets of cimifugin in GC-related metabolic reprogramming and pathogenesis. Our research provides preliminary insights into the potential therapeutic effects of cimifugin, which could be considered for future exploration in the context of GC treatment.

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.

Background: Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction. Methods: Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts. Results: We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions. Conclusions: This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.

Exploring the molecular mechanisms of macrophages in islet transplantation using single-cell analysis.

Background: Islet transplantation is a promising treatment for type 1 diabetes that aims to restore insulin production and improve glucose control, but long-term graft survival remains a challenge due to immune rejection. Methods: ScRNA-seq data from syngeneic and allogeneic islet transplantation grafts were obtained from GSE198865. Seurat was used for filtering and clustering, and UMAP was used for dimension reduction. Differentially expressed genes were analyzed between syngeneic and allogeneic islet transplantation grafts. Gene set variation analysis (GSVA) was performed on the HALLMARK gene sets from MSigDB. Monocle 2 was used to reconstruct differentiation trajectories, and cytokine signature enrichment analysis was used to compare cytokine responses between syngeneic and allogeneic grafts. Results: Three distinct macrophage clusters (Mø-C1, Mø-C2, and Mø-C3) were identified, revealing complex interactions and regulatory mechanisms within macrophage populations. The significant activation of macrophages in allogeneic transplants was marked by the upregulation of allograft rejection-related genes and pathways involved in inflammatory and interferon responses. GSVA revealed eight pathways significantly upregulated in the Mø-C2 cluster. Trajectory analysis revealed that Mø-C3 serves as a common progenitor, branching into Mø-C1 and Mø-C2. Cytokine signature enrichment analysis revealed significant differences in cytokine responses, highlighting the distinct immunological environments created by syngeneic and allogeneic grafts. Conclusion: This study significantly advances the understanding of macrophage roles within the context of islet transplantation by revealing the interactions between immune pathways and cellular fate processes. The findings highlight potential therapeutic targets for enhancing graft survival and function, emphasizing the importance of understanding the immunological aspects of transplant acceptance and longevity.

DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates.

Messenger RNA splicing and degradation are critical for gene expression regulation, the abnormality of which leads to diseases. Previous methods for estimating kinetic rates have limitations, assuming uniform rates across cells. DeepKINET is a deep generative model that estimates splicing and degradation rates at single-cell resolution from scRNA-seq data. DeepKINET outperforms existing methods on simulated and metabolic labeling datasets. Applied to forebrain and breast cancer data, it identifies RNA-binding proteins responsible for kinetic rate diversity. DeepKINET also analyzes the effects of splicing factor mutations on target genes in erythroid lineage cells. DeepKINET effectively reveals cellular heterogeneity in post-transcriptional regulation.

Single-cell RNA-seq analysis of rat molars reveals cell identity and driver genes associated with dental mesenchymal cell differentiation.

BACKGROUND: The molecular mechanisms and signaling pathways involved in tooth morphogenesis have been the research focus in the fields of tooth and bone development. However, the cell population in molars at the late bell stage and the mechanisms of hard tissue formation and mineralization remain limited knowledge. RESULTS: Here, we used the rat mandibular first and second molars as models to perform single-cell RNA sequencing (scRNA-seq) analysis to investigate cell identity and driver genes related to dental mesenchymal cell differentiation during the late bell hard tissue formation stage. We identified seven main cell types and investigated the heterogeneity of mesenchymal cells. Subsequently, we identified novel cell marker genes, including Pclo in dental follicle cells, Wnt10a in pre-odontoblasts, Fst and Igfbp2 in periodontal ligament cells, and validated the expression of Igfbp3 in the apical pulp. The dynamic model revealed three differentiation trajectories within mesenchymal cells, originating from two types of dental follicle cells and apical pulp cells. Apical pulp cell differentiation is associated with the genes Ptn and Satb2, while dental follicle cell differentiation is associated with the genes Tnc, Vim, Slc26a7, and Fgfr1. Cluster-specific regulons were analyzed by pySCENIC. In addition, the odontogenic function of driver gene TNC was verified in the odontoblastic differentiation of human dental pulp stem cells. The expression of osteoclast differentiation factors was found to be increased in macrophages of the mandibular first molar. CONCLUSIONS: Our results revealed the cell heterogeneity of molars in the late bell stage and identified driver genes associated with dental mesenchymal cell differentiation. These findings provide potential targets for diagnosing dental hard tissue diseases and tooth regeneration.

AB091. Establishment and application of brainstem glioma organoids.

BACKGROUND: Traditional preclinical experiments on brainstem gliomas mainly rely on patient-derived primary cell lines, but there are problems such as low success rate in establishment and inability to preserve tumor heterogeneity, which limit the clinical transformation. As a new type of in vitro tumor model, organoids have similar structure and function to the original tumor, requiring less tissue for cultivation, with short cycle and high success rate, which is particularly suitable for brainstem glioma biopsy. There is currently no precedent for the successful construction of brainstem glioma organoid models. This new established organoid provides us a more robust preclinical tool for comprehending the pathogenesis and conducting drug screening for this kind of disease. METHODS: Cultivate patient-derived brainstem glioma organoids in vitro, verify the genetic fidelity and consistency of the organoids through morphological experiments as well as sequencing technology. Then explore the evolutionary direction of multiple types of brainstem gliomas through pseudo-time series analysis. Complete drug screening, natural killer (NK) cell co-culture, oncolytic virus therapy, and other treatments based on organoids in vitro, and evaluate the efficacy. Complete co-culture of organoids and Institute of Cancer Research (ICR) mouse brain slices in vitro. Establish patient-derived organoid xenograft (PDOX) mouse models derived from organoids in vivo. RESULTS: The establishment of organoids of all types of brainstem gliomas was completed for the first time in the world, with a total of 41/48 organoid models derived from patients, with a success rate of 85.4%, covering all segments and pathological types. The results of morphological experiments and sequencing showed that the genetic characteristics of organoids were highly consistent with those of tumor tissues. Drug screening tests for temozolomide and panobinostat were completed in vitro, and NK cell co-culture and oncolytic virus therapy testing were achieved. Co-culture of brainstem glioma organoids and mouse brain slices was achieved in vitro. Furthermore, a PDOX model of brainstem glioma was established. CONCLUSIONS: Brainstem glioma organoids can be established maturely, stably, and reliably, and can be used for preclinical drug testing for patients. Animal models derived from brainstem glioma organoids have broad preclinical experimental value.

Single-cell RNA-Seq analysis of molecular changes during radiation-induced skin injury: the involvement of Nur77.

Introduction: Ionizing radiation has been widely used in industry, medicine, military and agriculture. Radiation-induced skin injury is a significant concern in the context of radiotherapy and accidental exposure to radiation. The molecular changes at the single-cell level and intercellular communications during radiation-induced skin injury are not well understood. Methods: This study aims to illustrate this information in a murine model and human skin samples from a radiation accident using single-cell RNA sequencing (scRNA-Seq). We further characterize the functional significance of key molecule, which may provide a potential therapeutic target. ScRNA-Seq was performed on skin samples from a nuclear accident patient and rats exposed to ionizing radiation. Bioinformatic tools were used to analyze the cellular heterogeneity and preferential mRNAs. Comparative analysis was performed to identify dysregulated pathways, regulators, and ligand-receptor interactions in fibroblasts. The function of key molecule was validated in skin cells and in three mouse models of radiation-induced skin injury. Results: 11 clusters in human skin and 13 clusters of cells in rat skin were depicted respectively. Exposure to ionizing radiation caused changes in the cellular population (upregulation of fibroblasts and endothelial cells, downregulation of keratinocytes). Fibroblasts and keratinocytes possessed the most interaction pairs with other cell lineages. Among the five DEGs common to human and rat skins, Nur77 was highly expressed in fibroblasts, which mediated radiosensitivity by cell apoptosis and modulated crosstalk between macrophages, keratinocytes and endothelial cells in radiation-induced skin injury. In animal models, Nur77 knock-out mice (Nur77 -/-) showed more severe injury after radiation exposure than wild-type counterparts in three models of radiation-induced skin injury with complex mechanisms. Conclusion: The study reveals a single-cell transcriptional framework during radiation-induced skin injury, which provides a useful resource to uncover key events in its progression. Nur77 is a novel target in radiation-induced skin injury, which provides a potential therapeutic strategy against this disease.

A Comparison of Tools That Identify Tumor Cells by Inferring Copy Number Variations from Single-Cell Experiments in Pancreatic Ductal Adenocarcinoma.

Single-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must be performed, including identification of the tumor cells by markers and algorithms that infer copy number variations (CNVs). We compared the performance of sciCNV, InferCNV, CopyKAT and SCEVAN tools that identify tumor cells by inferring CNVs from scRNA-seq data. Sequencing data from Pancreatic Ductal Adenocarcinoma (PDAC) patients, adjacent and healthy tissues were analyzed, and the predicted tumor cells were compared to those identified by well-assessed PDAC markers. Results from InferCNV, CopyKAT and SCEVAN overlapped by less than 30% with InferCNV showing the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). We show that the predictions are highly dependent on the sample and the software used, and that they return so many false positives hence are of little use in verifying or filtering predictions made via tumor biomarkers. We highlight how critical this processing can be, warn against the blind use of these software and point out the great need for more reliable algorithms.

Machine learning-based integration of CD8 T cell-related gene signatures for comprehensive prognostic assessment in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) stands as the most prevalent histological subtype of lung cancer, exhibiting heterogeneity in outcomes and diverse responses to therapy. CD8 T cells are consistently present throughout all stages of tumor development and play a pivotal role within the tumor microenvironment (TME). Our objective was to investigate the expression profiles of CD8 T cell marker genes, establish a prognostic risk model based on these genes in LUAD, and explore its relationship with immunotherapy response. Methods: By leveraging the expression data and clinical records from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, we identified 23 consensus prognostic genes. Employing ten machine-learning algorithms, we generated 101 combinations, ultimately selecting the optimal algorithm to construct an artificial intelligence-derived prognostic signature named riskScore. This selection was based on the average concordance index (C-index) across three testing cohorts. Results: RiskScore emerged as an independent risk factor for overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) in LUAD. Notably, riskScore exhibited notably superior predictive accuracy compared to traditional clinical variables. Furthermore, we observed a positive correlation between the high-risk riskScore group and tumor-promoting biological functions, lower tumor mutational burden (TMB), lower neoantigen (NEO) load, and lower microsatellite instability (MSI) scores, as well as reduced immune cell infiltration and an increased probability of immune evasion within the TME. Of significance, the immunophenoscore (IPS) score displayed significant differences among risk subgroups, and riskScore effectively stratified patients in the IMvigor210 and GSE135222 immunotherapy cohort based on their survival outcomes. Additionally, we identified potential drugs that could target specific risk subgroups. Conclusions: In summary, riskScore demonstrates its potential as a robust and promising tool for guiding clinical management and tailoring individualized treatments for LUAD patients.

Exploring the Influence of T Cell Marker Gene Expression on the Pathobiology and Clinical Prognostic Outcomes in Intestinal-Type Gastric Carcinoma.

BACKGROUND: Gastric cancer (GC) poses a significant global health challenge. This study is aimed at elucidating the role of the immune system, particularly T cells and their subtypes, in the pathogenesis and progression of intestinal-type gastric carcinoma (GC), and at evaluating the predictive utility of a T cell marker gene-based risk score for overall survival. METHODS: We performed an extensive analysis using single-cell RNA sequencing data to map the diversity of immune cells and identify specific T cell marker genes within GC. Pseudotime trajectory analysis was employed to observe the expression patterns of tumor-related pathways and transcription factors (TFs) at various disease stages. We developed a risk score using data from The Cancer Genome Atlas (TCGA) as a training set and validated it with the GSE15459 dataset. RESULTS: Our analysis revealed distinct patterns of T cell marker gene expression associated with different stages of GC. The risk score, based on these markers, successfully stratified patients into high-risk and low-risk groups with significantly different overall survival prospects. High-risk patients exhibited poorer survival outcomes compared to low-risk patients (p < 0.05). Additionally, the risk score was capable of identifying patients across a spectrum from chronic atrophic gastritis to early GC. CONCLUSION: The findings enhance the understanding of the tumor immune microenvironment in GC and propose new immunotherapeutic targets. The T cell marker gene-based risk score offers a potential tool for gastroenterologists to tailor treatment plans more precisely according to the cancer's severity.

LineageVAE: Reconstructing Historical Cell States and Transcriptomes toward Unobserved Progenitors.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the cell state. However, its destructive nature prohibits measuring gene expression changes during dynamic processes such as embryogenesis. Although recent studies integrating scRNA-seq with lineage tracing have provided clonal insights between progenitor and mature cells, challenges remain. Because of their experimental nature, observations are sparse, and cells observed in the early state are not the exact progenitors of cells observed at later time points. To overcome these limitations, we developed LineageVAE, a novel computational methodology that utilizes deep learning based on the property that cells sharing barcodes have identical progenitors. RESULTS: LineageVAE is a deep generative model that transforms scRNA-seq observations with identical lineage barcodes into sequential trajectories toward a common progenitor in a latent cell state space. This method enables the reconstruction of unobservable cell state transitions, historical transcriptomes, and regulatory dynamics at a single-cell resolution. Applied to hematopoiesis and reprogrammed fibroblast datasets, LineageVAE demonstrated its ability to restore backward cell state transitions and infer progenitor heterogeneity and transcription factor activity along differentiation trajectories. AVAILABILITY AND IMPLEMENTATION: The LineageVAE model was implemented in Python using the PyTorch deep learning library. The code is available on GitHub at https://github.com/LzrRacer/LineageVAE/. SUPPLEMENTARY INFORMATION: Available at Bioinformatics online.

Single-cell transcriptomics reveals tumor landscape in ovarian carcinosarcoma. / 单细胞转录组学揭示卵巢癌肉瘤的肿瘤特征.

OBJECTIVES: The present study used single-cell RNA sequencing (scRNA-seq) to characterize the cellular composition of ovarian carcinosarcoma (OCS) and identify its molecular characteristics. METHODS: scRNA-seq was performed in resected primary OCS for an in-depth analysis of tumor cells and the tumor microenvironment. Immunohistochemistry staining was used for validation. The scRNA-seq data of OCS were compared with those of high-grade serous ovarian carcinoma (HGSOC) tumors and other OCS tumors. RESULTS: Both malignant epithelial and malignant mesenchymal cells were observed in the OCS patient of this study. We identified four epithelial cell subclusters with different biological roles. Among them, epithelial subcluster 4 presented high levels of breast cancer type 1 susceptibility protein homolog (BRCA1) and DNA topoisomerase 2-α (TOP2A) expression and was related to drug resistance and cell cycle. We analyzed the interaction between epithelial and mesenchymal cells and found that fibroblast growth factor (FGF) and pleiotrophin (PTN) signalings were the main pathways contributing to communication between these cells. Moreover, we compared the malignant epithelial and mesenchymal cells of this OCS tumor with our previous published HGSOC scRNA-seq data and OCS data. All the epithelial subclusters in the OCS tumor could be found in the HGSOC samples. Notably, the mesenchymal subcluster C14 exhibited specific expression patterns in the OCS tumor, characterized by elevated expression of cytochrome P450 family 24 subfamily A member 1 (CYP24A1), collagen type XXIII α1 chain (COL23A1), cholecystokinin (CCK), bone morphogenetic protein 7 (BMP7), PTN, Wnt inhibitory factor 1 (WIF1), and insulin-like growth factor 2 (IGF2). Moreover, this subcluster showed distinct characteristics when compared with both another previously published OCS tumor and normal ovarian tissue. CONCLUSIONS: This study provides the single-cell transcriptomics signature of human OCS, which constitutes a new resource for elucidating OCS diversity.

TNBC response to paclitaxel phenocopies interferon response which reveals cell cycle-associated resistance mechanisms.

Paclitaxel is a standard of care neoadjuvant therapy for patients with triple negative breast cancer (TNBC); however, it shows limited benefit for locally advanced or metastatic disease. Here we used a coordinated experimental-computational approach to explore the influence of paclitaxel on the cellular and molecular responses of TNBC cells. We found that escalating doses of paclitaxel resulted in multinucleation, promotion of senescence, and initiation of DNA damage induced apoptosis. Single-cell RNA sequencing (scRNA-seq) of TNBC cells after paclitaxel treatment revealed upregulation of innate immune programs canonically associated with interferon response and downregulation of cell cycle progression programs. Systematic exploration of transcriptional responses to paclitaxel and cancer-associated microenvironmental factors revealed common gene programs induced by paclitaxel, IFNB, and IFNG. Transcription factor (TF) enrichment analysis identified 13 TFs that were both enriched based on activity of downstream targets and also significantly upregulated after paclitaxel treatment. Functional assessment with siRNA knockdown confirmed that the TFs FOSL1, NFE2L2 and ELF3 mediate cellular proliferation and also regulate nuclear structure. We further explored the influence of these TFs on paclitaxel-induced cell cycle behavior via live cell imaging, which revealed altered progression rates through G1, S/G2 and M phases. We found that ELF3 knockdown synergized with paclitaxel treatment to lock cells in a G1 state and prevent cell cycle progression. Analysis of publicly available breast cancer patient data showed that high ELF3 expression was associated with poor prognosis and enrichment programs associated with cell cycle progression. Together these analyses disentangle the diverse aspects of paclitaxel response and identify ELF3 upregulation as a putative biomarker of paclitaxel resistance in TNBC.

Single cell RNA-sequencing of feline peripheral immune cells with V(D)J repertoire and cross species analysis of T lymphocytes.

Introduction: The domestic cat (Felis catus) is a valued companion animal and a model for virally induced cancers and immunodeficiencies. However, species-specific limitations such as a scarcity of immune cell markers constrain our ability to resolve immune cell subsets at sufficient detail. The goal of this study was to characterize circulating feline T cells and other leukocytes based on their transcriptomic landscape and T-cell receptor repertoire using single cell RNA-sequencing. Methods: Peripheral blood from 4 healthy cats was enriched for T cells by flow cytometry cell sorting using a mouse anti-feline CD5 monoclonal antibody. Libraries for whole transcriptome, alpha/beta T cell receptor transcripts and gamma/delta T cell receptor transcripts were constructed using the 10x Genomics Chromium Next GEM Single Cell 5' reagent kit and the Chromium Single Cell V(D)J Enrichment Kit with custom reverse primers for the feline orthologs. Results: Unsupervised clustering of whole transcriptome data revealed 7 major cell populations - T cells, neutrophils, monocytic cells, B cells, plasmacytoid dendritic cells, mast cells and platelets. Sub cluster analysis of T cells resolved naive (CD4+ and CD8+), CD4+ effector T cells, CD8+ cytotoxic T cells and gamma/delta T cells. Cross species analysis revealed a high conservation of T cell subsets along an effector gradient with equitable representation of veterinary species (horse, dog, pig) and humans with the cat. Our V(D)J repertoire analysis demonstrated a skewed T-cell receptor alpha gene usage and a restricted T-cell receptor gamma junctional length in CD8+ cytotoxic T cells compared to other alpha/beta T cell subsets. Among myeloid cells, we resolved three clusters of classical monocytes with polarization into pro- and anti-inflammatory phenotypes in addition to a cluster of conventional dendritic cells. Lastly, our neutrophil sub clustering revealed a larger mature neutrophil cluster and a smaller exhausted/activated cluster. Discussion: Our study is the first to characterize subsets of circulating T cells utilizing an integrative approach of single cell RNA-sequencing, V(D)J repertoire analysis and cross species analysis. In addition, we characterize the transcriptome of several myeloid cell subsets and demonstrate immune cell relatedness across different species.

Single-cell analysis defines LGALS1 + fibroblasts that promote proliferation and migration of intrahepatic cholangiocarcinoma.

The incidence rate of intrahepatic cholangiocarcinoma (ICC), which has a poor prognosis, is rapidly increasing. To investigate the intratumor heterogeneity of ICC, we analyzed single-cell RNA sequencing data from the primary tumor and adjacent normal tissues of 14 treatment-naïve patients. We identified ten major cell types, along with 45 subclusters of cells. Notably, we identified a fibroblast cluster, Fibroblast_LUM+, which was preferably enriched in tumor tissues and actively interacted with cholangiocytes. LGALS1 was verified as a marker gene of Fibroblast_LUM+, contributing to the malignant phenotype of ICC. The higher amount of LGALS1 + fibroblasts were associated with poorer overall survival in ICC patients. LGALS1 + fibroblasts activated the proliferation and migration of tumor cells by upregulating the expression levels of CCR2, ADAM15, and ß-integrin. Silencing LGALS1 in cancer-associated fibroblasts (CAFs) suppressed CAF-augmented tumor cell migration and invasion in vitro as well as tumor formation in vivo, suggesting that blockade of LGALS1 serves as a potential therapeutic approach for ICC. Taken together, our single-cell analysis provides insight into the interaction between malignant cells and specific subtypes of fibroblasts. Our work will further the understanding of the intratumor heterogeneity of ICC and provide novel strategies for the treatment of ICC by targeting fibroblasts in the tumor microenvironment.

Unveiling novel insights in acute myeloid leukemia through single-cell RNA sequencing.

Acute myeloid leukemia (AML) is a complex and heterogeneous group of aggressive hematopoietic stem cell disease. The presence of diverse and functionally distinct populations of leukemia cells within the same patient's bone marrow or blood poses a significant challenge in diagnosing and treating AML. A substantial proportion of AML patients demonstrate resistance to induction chemotherapy and a grim prognosis upon relapse. The rapid advance in next generation sequencing technologies, such as single-cell RNA-sequencing (scRNA-seq), has revolutionized our understanding of AML pathogenesis by enabling high-resolution interrogation of the cellular heterogeneity in the AML ecosystem, and their transcriptional signatures at a single-cell level. New studies have successfully characterized the inextricably intertwined interactions among AML cells, immune cells and bone marrow microenvironment and their contributions to the AML development, therapeutic resistance and relapse. These findings have deepened and broadened our understanding the complexity and heterogeneity of AML, which are difficult to detect with bulk RNA-seq. This review encapsulates the burgeoning body of knowledge generated through scRNA-seq, providing the novel insights and discoveries it has unveiled in AML biology. Furthermore, we discuss the potential implications of scRNA-seq in therapeutic opportunities, focusing on immunotherapy. Finally, we highlight the current limitations and future direction of scRNA-seq in the field.

Characterization of the malignant cells and microenvironment of infantile fibrosarcoma via single-cell RNA sequencing.

Background: Infantile fibrosarcoma (IFS) is the most prevalent soft tissue sarcoma in children under 1 year old and is known for its rapid growth. The tumor lacks specific immunohistochemical tumor marker and a general view of tumor microenvironment (TME). Its primary therapeutic intervention places patients at a risk of disability or mutilation. This study aimed to elucidate the universal transcriptional characteristics of IFS and explore novel targets for diagnosis and therapy using single-cell RNA sequencing (scRNA-seq). Methods: Fresh tissue samples of IFS for scRNA-seq were collected from four patients before other treatments were administered. We conducted cell clustering, inferring copy number variation from scRNA-seq (InferCNV) analysis, gene differential expression analysis, cell function evaluation, Pearson correlation analysis, and cell-cell and ligand-receptor interaction analysis to investigate the distinct ecosystem of IFS. Results: According to the single-cell resolution data, we depicted the cell atlas of IFS, which comprised 14 cell populations. Through comparison with normal cells, the malignant cells were distinguished, and potential novel markers (POSTN, IGFBP2 and CTHRC1) were identified. We also found four various functional malignant cell subtypes, three of which exhibited cancer stem cells (CSCs) phenotypes, and investigated the interplay between these subtypes and nonmalignant cells in the TME of IFS. Endothelial cells and macrophages were found to dominate the cell-cell communication landscape within the microenvironment, promoting tumorigenesis via multiple receptor-ligand interactions. Conclusions: This study provides a comprehensive characterization of the tumor transcriptome and TME of IFS at the cellular level, offering valuable insights for clinically significant advancements in the immunohistochemical diagnosis and treatment of IFS.

The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy.

In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.

Tumor Cell-Derived Complement Component C1r Acts as a Prognostic Biomarker and Promotes Esophageal Squamous Cell Carcinoma Progression.

BACKGROUND: Mounting evidence indicates that complement components play a crucial role in cancer progression. Recent findings indicate that certain complement components display a significant rise in expression within esophageal squamous cell carcinoma (ESCC). However, the specific tumorigenic functions of these components remain unclear. This study focuses on investigating the expression pattern of C1r, elucidating a role for C1r in ESCC, as well as exploring underlying mechanisms controlled by C1r. METHODS: The expression of C1r in ESCC tissues, malignant epithelial cells, and its relationship with survival were analyzed using the Gene Expression Omnibus (GEO) database and tissue microarrays. Single-cell RNA sequencing (scRNA-seq) was used to study the expression of C1r in malignant epithelial cells. C1r knockdown or C1r overexpression in cultured ESCC cells were used to assess the effects of C1r on proliferation, migration, invasion, cell-matrix adhesion, apoptosis, and growth of xenografted tumors in immunocompromised (nude) mice. Western blotting was used to detect the expression of MMP-1 and MMP-10 in C1r knockdown or C1r overexpressing ESCC cells. RESULTS: C1r was highly expressed in ESCC tissues, malignant epithelial cells, and cultured ESCC cell lines. High C1r expression indicated a poor prognosis. Knockdown of C1r significantly suppressed the proliferation, migration, invasion, cell-matrix adhesion, and promoted apoptosis in cultured ESCC cells. Additionally, knockdown of C1r markedly inhibited tumor growth in nude mice. Overexpression of C1r had the opposite effects. C1r induced the expression of MMP-1 and MMP-10. CONCLUSIONS: C1r is highly expressed in ESCC and promotes the progression of this tumor type. Our findings suggest that C1r may serve as a novel prognostic biomarker and therapeutic target in ESCC.

Single-cell transcriptome reveals a novel mechanism of C-Kit+-liver sinusoidal endothelial cells in NASH.

AIM: To understand how liver sinusoidal endothelial cells (LSECs) respond to nonalcoholic steatohepatitis (NASH). METHODS: We profiled single-LSEC from livers of control and MCD-fed mice. The functions of C-Kit+-LSECs were determined using coculture and bone marrow transplantation (BMT) methods. RESULTS: Three special clusters of single-LSEC were differentiated. C-Kit+-LSECs of cluster 0, Msr1+-LSECs of cluster 1 and Bmp4+Selp+-VECs of cluster 2 were revealed, and these cells with diverse ectopic expressions of genes participated in regulation of endothelial, fibrosis and lipid metabolism in NASH. The number of C-Kit+-primary LSECs isolated from MCD mice was lower than control mice. Immunofluorescence co-staining of CD31 and C-KIT showed C-Kit+-LSECs located in hepatic sinusoid were also reduced in NASH patients and MCD mice, compared to AIH patients and control mice respectively. Interestingly, lipotoxic hepatocytes/HSCs cocultured with C-Kit+-LSECs or the livers of MCD mice receipting of C-Kit+-BMCs (bone marrow cells) showed less steatosis, inflammation and fibrosis, higher expression of prolipolytic FXR and PPAR-α, lower expression of TNF-α and α-SMA. Furthermore, coculturing or BMT of C-Kit+-endothelial derived cells could increase the levels of hepatic mitochondrial LC3B, decrease the degree of mitochondrial damage and ROS production through activating Pink1-mediated mitophagy pathway in NASH. CONCLUSIONS: Hence, a novel transcriptomic view of LSECs was revealed to have heterogeneity and complexity in NASH. Importantly, a cluster of C-Kit+-LSECs was confirmed to recovery Pink1-related mitophagy and NASH progression.