The octapeptide-repeat surface protein of Entamoeba nuttalli is a novel virulence factor that promotes adherence to host cells.

Entamoeba nuttalli is genetically the closest to Entamoeba histolytica, the causative agent of human amebiasis, and its natural host is Macaca species. A unique E. nuttalli specific surface protein (PTORS) containing 42 repeats of octapeptide was identified by comparative genomic analysis of Entamoeba species. We aimed to elucidate the function of this protein. When trophozoites from various E. nuttalli strains were examined by immunofluorescence microscopy and flow cytometry using a PTORS-specific monoclonal antibody, only a limited proportion of trophozoites were stained, indicating that the protein was not commonly expressed in all E. nuttalli trophozoite. The proportion of trophozoites expressing PTORS increased after passage in hamster livers, suggesting that the protein functions in the virulence of trophozoites in the liver tissue. Single-cell analysis revealed that in the cluster including trophozoites with PTORS gene expression, genes of virulence-related proteins were also upregulated. Trophozoites of E. histolytica transfected with PTORS showed enhanced adherence and subsequent phagocytic activity towards human Jurkat cells, independent of the lectin. E. histolytica trophozoites expressing PTORS formed larger liver abscesses in hamsters. These results demonstrate that PTORS is a novel virulence factor in Entamoeba species.

Single-cell measurement of microbial growth rate with Raman microspectroscopy.

Rates of microbial growth are fundamental to understanding environmental geochemistry and ecology. However, measuring the heterogeneity of microbial activity at the single-cell level, especially within complex populations and environmental matrices, remains a forefront challenge. Stable Isotope Probing (SIP) is a method for assessing microbial growth and involves measuring the incorporation of an isotopic label into microbial biomass. Here, we assess Raman microspectroscopy as a SIP technique, specifically focusing on the measurement of deuterium (2H), a tracer of microbial biomass production. We correlatively measured cells grown in varying concentrations of deuterated water with both Raman spectroscopy and nanoscale secondary ion mass spectrometry (nanoSIMS), generating isotopic calibrations of microbial 2H. Relative to Raman, we find that nanoSIMS measurements of 2H are subject to substantial dilution due to rapid exchange of H during sample washing. We apply our Raman-derived calibration to a numerical model of microbial growth, explicitly parameterizing the factors controlling growth rate quantification and demonstrating that Raman-SIP can sensitively measure the growth of microorganisms with doubling times ranging from hours to years. The measurement of single-cell growth with Raman spectroscopy, a rapid, non-destructive technique, represents an important step towards application of single-cell analysis into complex sample matrices or cellular assemblages.

Aberrant migration features in primary skin fibroblasts of Huntington's disease patients hold potential for unraveling disease progression using an image based machine learning tool.

Huntington's disease (HD) is a complex neurodegenerative disorder with considerable heterogeneity in clinical manifestations. While CAG repeat length is a known predictor of disease severity, this heterogeneity suggests the involvement of additional genetic and environmental factors. Previously we revealed that HD primary fibroblasts exhibit unique features, including distinct nuclear morphology and perturbed actin cap, resembling characteristics seen in Hutchinson-Gilford Progeria Syndrome (HGPS). This study establishes a link between actin cap deficiency and cell motility in HD, which correlates with the HD patient disease severity. Here, we examined single-cell motility imaging features in HD primary fibroblasts to explore in depth the relationship between cell migration patterns and their respective HD patients' clinical severity status (premanifest, mild and severe). The single-cell analysis revealed a decline in overall cell motility in correlation with HD severity, being most prominent in severe HD subgroup and HGPS. Moreover, we identified seven distinct spatial clusters of cell migration in all groups, which their proportion varies within each group becoming a significant HD severity classifier between HD subgroups. Next, we investigated the relationship between Lamin B1 expression, serving as nuclear envelope morphology marker, and cell motility finding that changes in Lamin B1 levels are associated with specific motility patterns within HD subgroups. Based on these data we present an accurate machine learning classifier offering comprehensive exploration of cellular migration patterns and disease severity markers for future accurate drug evaluation opening new opportunities for personalized treatment approaches in this challenging disorder.

Identification of prostate cancer bone metastasis related genes and potential therapy targets by bioinformatics and in vitro experiments.

The aetiology of bone metastasis in prostate cancer (PCa) remains unclear. This study aims to identify hub genes involved in this process. We utilized machine learning, GO, KEGG, GSEA, Single-cell analysis, ROC methods to identify hub genes for bone metastasis in PCa using the TCGA and GEO databases. Potential drugs targeting these genes were identified. We validated these results using 16 specimens from patients with PCa and analysed the relationship between the hub genes and clinical features. The impact of APOC1 on PCa was assessed through in vitro experiments. Seven hub genes with AUC values of 0.727-0.926 were identified. APOC1, CFH, NUSAP1 and LGALS1 were highly expressed in bone metastasis tissues, while NR4A2, ADRB2 and ZNF331 exhibited an opposite trend. Immunohistochemistry further confirmed these results. The oxidative phosphorylation pathway was significantly enriched by the identified genes. Aflatoxin B1, benzo(a)pyrene, cyclosporine were identified as potential drugs. APOC1 expression was correlated with clinical features of PCa metastasis. Silencing APOC1 significantly inhibited PCa cell proliferation, clonality, and migration in vitro. This study identified 7 hub genes that potentially facilitate bone metastasis in PCa through mitochondrial metabolic reprogramming. APOC1 emerged as a promising therapeutic target and prognostic marker for PCa with bone metastasis.

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.

Role of glycosylation-related gene MGAT1 in pancreatic ductal adenocarcinoma.

Background: pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a very poor prognosis and a complex tumor microenvironment, which plays a key role in tumor progression and treatment resistance. Glycosylation plays an important role in processes such as cell signaling, immune response and protein stability. Materials and methods: single-cell RNA sequencing data and spatial transcriptome data were obtained from GSE197177 and GSE224411, respectively, and RNA-seq data and survival information were obtained from UCSC Xena and TCGA. Multiple transcriptomic data were comprehensively analyzed to explore the role of glycosylation processes in tumor progression, and functional experiments were performed to assess the effects of MGAT1 overexpression on PDAC cell proliferation and migration. Results: In PDAC tumor samples, the glycosylation level of macrophages was significantly higher than that of normal samples. MGAT1 was identified as a key glycosylation-related gene, and its high expression was associated with better patient prognosis. Overexpression of MGAT1 significantly inhibited the proliferation and migration of PDAC cells and affected intercellular interactions in the tumor microenvironment. Conclusion: MGAT1 plays an important role in PDAC by regulating glycosylation levels in macrophages, influencing tumor progression and improving prognosis.MGAT1 is a potential therapeutic target for PDAC and further studies are needed to develop targeted therapeutic strategies against MGAT1 to improve clinical outcomes.

Identification of epithelial-related artificial neural network prognostic models for the prediction of bladder cancer prognosis through comprehensive analysis of single-cell and bulk RNA sequencing.

Background: Bladder cancer (BLCA) presents as a heterogeneous epithelial malignancy. Progress in the early detection and effective treatment of BLCA relies heavily on the identification of novel biomarkers. Therefore, the primary goal of this study is to pinpoint potential biomarkers for BLCA through the fusion of single-cell RNA sequencing and RNA sequencing assessments. Furthermore, the aim is to establish practical clinical prognostic models that can facilitate accurate categorization and individualized therapy for patients. Methods: In this research, training sets were acquired from the TCGA database, whereas validation sets (GSE32894) and single-cell datasets (GSE135337) were extracted from the GEO database. Single-cell analysis was utilized to obtain characteristic subpopulations along with their associated marker genes. Subsequently, a novel BLCA subtype was identified within TCGA-BLCA. Furthermore, an artificial neural network prognostic model was constructed within the TCGA-BLCA cohort and subsequently verified utilizing a validation set. Two machine learning algorithms were employed to screen hub genes. QRT-qPCR was performed to detect the gene expression levels utilized in the construction of prognostic models across various cell lines. Additionally, the cMAP database and molecular docking were utilized for searching small molecule drugs. Results: The results of single-cell analysis revealed the presence of epithelial cells in multiple subpopulations, with 1579 marker genes selected for subsequent investigations. Subsequently, four epithelial cell subtypes were identified within the TCGA-BLCA cohort. Notably, cluster A exhibited a significant survival advantage. Concurrently, an artificial neural network prognostic model comprising 17 feature genes was constructed, accurately stratifying patient risk. Patients categorized in the low-risk group demonstrated a considerable survival advantage. The ROC analysis suggested that the model has strong prognostic ability. Furthermore, the findings of the validation group align consistently with those from the training group. Two types of machine learning algorithms screened NFIC as hub genes. Forskolin, a small molecule drug that binds to NFIC, was identified by employing a cMAP database and molecular docking. Conclusion: The analysis results supplement the research on the role of epithelial cells in BLCA. An artificial neural network prognostic model containing 17 characteristic genes demonstrates the capability to accurately stratify patient risk, thereby potentially improving clinical decision-making and optimizing personalized therapeutic approaches.

Identifying polyamine related biomarkers in diagnosis and treatment of ulcerative colitis by integrating bulk and single-cell sequencing data.

Ulcerative colitis (UC) is a chronic inflammatory disorder of the colon, and its pathogenesis remains unclear. Polyamine metabolic enzymes play a crucial role in UC. In this study, we aimed to identify pivotal polyamine-related genes (PRGs) and explore the underlying mechanism between PRGs and the disease status and therapeutic response of UC. We analyzed mRNA-sequencing data and clinical information of UC patients from the GEO database and identified NNMT, PTGS2, TRIM22, TGM2, and PPARG as key PRGs associated with active UC using differential expression analysis and weighted gene co-expression network analysis (WCGNA). Receiver operator characteristic curve (ROC) analysis confirmed the accuracy of these key genes in UC and colitis-associated colon cancer (CAC) diagnosis, and we validated their relationship with therapeutic response in external verification sets. Additionally, single-cell analysis revealed that the key PRGs were specific to certain immune cell types, emphasizing the vital role of intestinal tissue stem cells in active UC. The results were validated in vitro and in vivo experiments, including the colitis mice model and CAC mice model. In conclusion, these key PRGs effectively predict the progression of UC patients and could serve as new pharmacological biomarkers for the therapeutic response of UC.

Toxicity Equivalency Factors for Tetrodotoxin Analogues Determined with Automated Patch Clamp on Voltage-Gated Sodium Channels in Neuro-2a Cells.

Tetrodotoxin (TTX) is a potent marine neurotoxin, responsible for numerous poisoning incidents and some human fatalities. To date, more than 30 TTX analogues have been identified, but their individual toxicities and roles in poisoning remain largely unknown. In this work, the toxicity equivalency factors (TEFs) of five TTX analogues were determined by assessing the blockade of voltage-gated sodium channels in Neuro-2a cells using automated patch clamp (APC). All TTX analogues were less toxic than TTX. The derived TEFs were applied to the individual TTX analogues concentrations measured in pufferfish samples, using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). A comparison of these results with those obtained from APC analysis demonstrated that TEFs can be effectively used to translate LC-MS/MS analytical data into meaningful toxicological information. This is the first study to utilize APC device for the toxicological assessment of TTX analogues, highlighting its potential as a bioanalytical tool for seafood safety management and human health protection.

Single-cell transcriptomic analysis reveals that the APP-CD74 axis promotes immunosuppression and progression of testicular tumors.

Testicular tumors represent the most common malignancy among young men. Nevertheless, the pathogenesis and molecular underpinning of testicular tumors remain largely elusive. We aimed to delineate the intricate intra-tumoral heterogeneity and the network of intercellular communication within the tumor microenvironment. A total of 40,760 single-cell transcriptomes were analyzed, encompassing samples from six individuals with seminomas, two patients with mixed germ cell tumors, one patient with a Leydig cell tumor, and three healthy donors. Five distinct malignant subclusters were identified in the constructed landscape. Among them, malignant 1 and 3 subclusters were associated with a more immunosuppressive state and displayed worse disease-free survival. Further analysis identified that APP-CD74 interactions were significantly strengthened between malignant 1 and 3 subclusters and 14 types of immune subpopulations. In addition, we established an aberrant spermatogenesis trajectory and delineated the global gene alterations of somatic cells in seminoma testes. Sertoli cells were identified as the somatic cell type that differed the most from healthy donors to seminoma testes. Cellular communication between spermatogonial stem cells and Sertoli cells is disturbed in seminoma testes. Our study delineates the intra-tumoral heterogeneity and the tumor immune microenvironment in testicular tumors, offering novel insights for targeted therapy. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Identification of programmed cell death-related genes and diagnostic biomarkers in endometriosis using a machine learning and Mendelian randomization approach.

Background: Endometriosis (EM) is a prevalent gynecological disorder frequently associated with irregular menstruation and infertility. Programmed cell death (PCD) is pivotal in the pathophysiological mechanisms underlying EM. Despite this, the precise pathogenesis of EM remains poorly understood, leading to diagnostic delays. Consequently, identifying biomarkers associated with PCD is critical for advancing the diagnosis and treatment of EM. Methods: This study used datasets from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) following preprocessing. By cross-referencing these DEGs with genes associated with PCD, differentially expressed PCD-related genes (DPGs) were identified. Enrichment analyses for KEGG and GO pathways were conducted on these DPGs. Additionally, Mendelian randomization and machine learning techniques were applied to identify biomarkers strongly associated with EM. Results: The study identified three pivotal biomarkers: TNFSF12, AP3M1, and PDK2, and established a diagnostic model for EM based on these genes. The results revealed a marked upregulation of TNFSF12 and PDK2 in EM samples, coupled with a significant downregulation of AP3M1. Single-cell analysis further underscored the potential of TNFSF12, AP3M1, and PDK2 as biomarkers for EM. Additionally, molecular docking studies demonstrated that these genes exhibit significant binding affinities with drugs currently utilized in clinical practice. Conclusion: This study systematically elucidated the molecular characteristics of PCD in EM and identified TNFSF12, AP3M1, and PDK2 as key biomarkers. These findings provide new directions for the early diagnosis and personalized treatment of EM.

Optimizing single-tube nested PCR for enhanced genotyping of single cells and in vitro produced bovine embryos.

Single-tube nested PCR (STnPCR) is a technique that improves nested PCR, reducing potential contamination and false-positive results, enhancing the amplification sensitivity. Despite being commonly used for the detection of microorganisms, STnPCR can be a valuable tool for bovine genotyping, encompassing essential targets as ROSA26 and TSPY, pivotal in the fields of animal reproduction, genetic improvement, and transgenic research. The objective of this study was to improve and innovate STnPCR for gene detection in cattle. We aimed to detect the ROSA26 and TSPY genes using low-concentration DNA samples, including single cells, small cell groups (one to five cells), in vitro-produced embryos, and bovine tissue samples. Moreover, we refined STnPCR for gene detection in up to single cells by conducting sensitivity testing with different concentration ratios of internal and external primers. Successful amplification of the ROSA26 and TSPY genes was achieved across all tested primer concentrations, even in single cells, with more consistent results observed at lower primer concentrations. Additionally, simultaneous gene amplification was achieved through STnPCR multiplexing, representing the first study of multiplex STnPCR in cattle. These outcomes not only confirm its effectiveness in detecting genetic markers for animal genetic improvement and transgenic elements but also pave the way for its widespread adoption in reproductive studies in bovines.

Single-Cell Cortical Transcriptomics Reveals Common and Distinct Changes in Cell-Cell Communication in Alzheimer's and Parkinson's Disease.

Alzheimer's disease (AD) and Parkinson's disease (PD) cause significant neuronal loss and severely impair daily living. Despite different clinical manifestations, these disorders share common pathological molecular hallmarks, including mitochondrial dysfunction and synaptic degeneration. A detailed comparison of molecular changes at single-cell resolution in the cortex, as one of the main brain regions affected in both disorders, may reveal common susceptibility factors and disease mechanisms. We performed single-cell transcriptomic analyses of post-mortem cortical tissue from AD and PD subjects and controls to identify common and distinct disease-associated changes in individual genes, cellular pathways, molecular networks, and cell-cell communication events, and to investigate common mechanisms. The results revealed significant disease-specific, shared, and opposing gene expression changes, including cell type-specific signatures for both diseases. Hypoxia signaling and lipid metabolism emerged as significantly modulated cellular processes in both AD and PD, with contrasting expression alterations between the two diseases. Furthermore, both pathway and cell-cell communication analyses highlighted shared significant alterations involving the JAK-STAT signaling pathway, which has been implicated in the inflammatory response in several neurodegenerative disorders. Overall, the analyses revealed common and distinct alterations in gene signatures, pathway activities, and gene regulatory subnetworks in AD and PD. The results provide insights into coordinated changes in pathway activity and cell-cell communication that may guide future diagnostics and therapeutics.

Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2.

Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.

Multi-Omics analysis elucidates tumor microenvironment and intratumor microbes of angiogenesis subtypes in colon cancer.

BACKGROUND: Angiogenesis plays an important role in colon cancer (CC) progression. AIM: To investigate the tumor microenvironment (TME) and intratumor microbes of angiogenesis subtypes (AGSs) and explore potential targets for antiangiogenic therapy in CC. METHODS: The data were obtained from The Cancer Genome Atlas database and Gene Expression Omnibus database. K-means clustering was used to construct the AGSs. The prognostic model was constructed based on the differential genes between two subtypes. Single-cell analysis was used to analyze the expression level of SLC2A3 on different cells in CC, which was validated by immunofluorescence. Its biological functions were further explored in HUVECs. RESULTS: CC samples were grouped into two AGSs (AGS-A and AGS-B) groups and patients in the AGS-B group had poor prognosis. Further analysis revealed that the AGS-B group had high infiltration of TME immune cells, but also exhibited high immune escape. The intratumor microbes were also different between the two subtypes. A convenient 6-gene angiogenesis-related signature (ARS), was established to identify AGSs and predict the prognosis in CC patients. SLC2A3 was selected as the representative gene of ARS, which was higher expressed in endothelial cells and promoted the migration of HUVECs. CONCLUSION: Our study identified two AGSs with distinct prognoses, TME, and intratumor microbial compositions, which could provide potential explanations for the impact on the prognosis of CC. The reliable ARS model was further constructed, which could guide the personalized treatment. The SLC2A3 might be a potential target for antiangiogenic therapy.

Mass cytometric single cell immune profiles of peripheral blood from acute myeloid leukemia patients in complete remission with measurable residual disease.

Measurable residual disease (MRD) is detected in approximately a quarter of AML chemotherapy responders, serving as a predictor for relapse and shorter survival. Immunological control of residual disease is suggested to prevent relapse, but the mechanisms involved are not fully understood. We present a peripheral blood single cell immune profiling by mass cytometry using a 42-antibody panel with particular emphasis on markers of cellular immune response. Six healthy donors were compared with four AML patients with MRD (MRD+) in first complete remission (CR1MRD+). Three of four patients demonstrated a favorable genetic risk profile, while the fourth patient had an unfavorable risk profile (complex karyotype, TP53-mutation) and a high level of MRD. Unsupervised clustering using self-organizing maps and dimensional reduction analysis was performed for visualization and analysis of immune cell subsets. CD57+ natural killer (NK)-cell subsets were found to be less abundant in patients than in healthy donors. Both T and NK cells demonstrated elevated expression of activity and maturation markers (CD44, granzyme B, and phosho-STAT5 Y694) in patients. Although mass cytometry remains an expensive method with limited scalability, our data suggest the utility for employing a 42-plex profiling for cellular immune surveillance in whole blood, and possibly as a biomarker platform in future clinical trials. The findings encourage further investigations of single cell immune profiling in CR1MRD+ AML-patients.

Weighted gene co-expression network analysis and single-cell sequence analysis uncover immune landscape and reveal hub genes of necroptosis in macrophages in myocardial ischaemia-reperfusion injury.

Myocardial ischaemia-reperfusion injury (MIRI) caused by the treatment of acute myocardial infarction (AMI) is the primary cause of severe ventricular remodelling, heart failure (HF), and high mortality. In recent studies, research on the role of necroptosis in MIRI has focused on cardiomyocytes, but new biomarkers and immunocyte mechanisms of necroptosis are rarely studied. In the present study, weighted gene co-expression network analysis (WGCNA) algorithms were used to establish a weighted gene co-expression network, and Casp1, Hpse, Myd88, Ripk1, and Tpm3 were identified as biological markers of necroptosis using least absolute shrinkage, selection operator (LASSO) regression and support vector machine (SVM) feature selection algorithms. The role and discriminatory power of these five genes in MIRI had never been studied. Single-cell and cell-talk analyses showed that hub genes of necroptosis were focused on macrophages, which mediate the functions of monocytes, fibroblasts, haematopoietic stem cells, and cardiomyocytes, primarily through the TNF/TNFRSF1A interaction. The polarisation and functional activation of macrophages were affected by the MIF signalling network (MIF CD74/CXCR4 and MIF CD74/CD44) of other cells. The results of the immune infiltration assay showed that the five genes involved in necroptosis were significantly related to the infiltration and functional activity of M2 macrophages. TWS-119 is predicted to be a molecular drug that targets key MIRI genes. A mouse model was established to confirm the expression of five hub genes, and ventricular remodelling increased with time after ischaemia-reperfusion injury (IRI). Therefore, Casp1, Hpse, Myd88, Ripk1, and Tpm3 may be key genes regulating necroptosis and polarisation in macrophages, and causing ventricular remodelling.

Identification of a novel survival and immune microenvironment related ceRNA regulatory network for hepatocellular carcinoma based on circHECTD1.

Background: CircHECTD1 (circ_0031450) is highly expressed in hepatocellular carcinoma (HCC) tissues and may act as an oncogene. Its specific competitive endogenous RNA (ceRNA) mechanism remains to be further elucidated. Methods: Several databases and online platforms, including pathway activity, immune checkpoint, and overall survival analyses, were used to predict targets, download datasets, and perform online analyses. The R software was used for differential gene expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), clinical relevance, receiver operator characteristic curve, and single-cell analysis. Cytoscape software was used to construct ceRNAs, protein-protein interactions (PPI), and pivotal networks. Results: The ceRNA, PPI, and pivotal networks were successfully constructed. Pathway enrichment analysis was mainly related to apoptosis, cell cycle, and epithelial-mesenchymal transition (EMT) pathways. Six pivotal targets related to survival, immune infiltration, immune checkpoints, clinical stage, and diagnosis of patients with HCC were identified. The recovery function and pathway enrichment results were consistent with previous results. Single-cell analysis suggested that the pivotal targets were highly expressed in T cells. Conclusion: We successfully constructed a prognosis and immune microenvironment-related ceRNA network based on circHECTD1, providing new insights for diagnosing and treating HCC.

A cell-based fluorescent system and statistical framework to detect meiosis-like induction in plants.

Genetic gains made by plant breeders are limited by generational cycling rates and flowering time. Several efforts have been made to reduce the time to switch from vegetative to reproductive stages in plants, but these solutions are usually species-specific and require flowering. The concept of in vitro nurseries is that somatic plant cells can be induced to form haploid cells that have undergone recombination (creating artificial gametes), which can then be used for cell fusion to enable breeding in a Petri dish. The induction of in vitro meiosis, however, is the largest current bottleneck to in vitro nurseries. To help overcome this, we previously described a high-throughput, bi-fluorescent, single cell system in Arabidopsis thaliana, which can be used to test the meiosis-like induction capabilities of candidate factors. In this present work, we validated the system using robust datasets (>4M datapoints) from extensive simulated meiosis induction tests. Additionally, we determined false-detection rates of the fluorescent cells used in this system as well as the ideal tissue source for factor testing.

Single-Cell Analysis Identifies Distinct Populations of Cytotoxic CD4+ T Cells Linked to the Therapeutic Efficacy of Immune Checkpoint Inhibitors in Metastatic Renal Cell Carcinoma.

Background: The involvement of cytotoxic CD4+ T cells (CD4+ CTLs) and their potential role in dictating the response to immune checkpoint inhibitors (ICIs) in patients with metastatic renal cell carcinoma (mRCC) remains an unexplored area of research. Methods: Utilizing single-cell RNA sequencing, we analyzed the immunophenotype and expression patterns of CD4+ T lymphocyte subtypes in mRCC patients, followed by preliminary validation via multi-immunofluorescent staining. In addition, we obtained a comprehensive immunotherapy dataset encompassing single-cell RNA sequencing datasets and bulk RNA-seq cohorts from the European Genome-Phenome Archive and ArrayExpress database. Utilizing the CIBERSORTx deconvolution algorithms, we derived a signature score for CD4+ CTLs from the bulk-RNA-seq datasets of the CheckMate 009/025 clinical trials. Results: Single-cell analysis of CD4+ T lymphocytes in mRCC reveals several cancer-specific states, including diverse phenotypes of regulatory T cells. Remarkably, we observe that CD4+ CTLs cells constitute a substantial proportion of all CD4+ T lymphocyte sub-clusters in mRCC patients, highlighting their potential significance in the disease. Furthermore, within mRCC patients, we identify two distinct cytotoxic states of CD4+ T cells: CD4+GZMK+ T cells, which exhibit a weaker cytotoxic potential, and CD4+GZMB+ T cells, which demonstrate robust cytotoxic activity. Both regulatory T cells and CD4+ CTLs originate from proliferating CD4+ T cells within mRCC tissues. Intriguingly, our trajectory analysis indicates that the weakly cytotoxic CD4+GZMK+ T cells differentiate from their more cytotoxic CD4+GZMB+ counterparts. In comparing patients with lower CD4+ CTLs levels to those with higher CD4+ CTLs abundance in the CheckMate 009 and 25 immunotherapy cohorts, the latter group exhibited significantly improved OS and PFS probability. Conclusion: Our study underscores the pivotal role that intratumoral CD4+ CTLs may play in bolstering anti-tumor immunity, suggesting their potential as a promising biomarker for predicting response to ICIs in patients with mRCC.