Interaction between tumor cells and immune cells in the tumor microenvironment is important in cancer development. Immune cells interact with the tumor cells to shape this process. Here, we use ...single-cell RNA sequencing analysis to delineate the immune landscape and tumor heterogeneity in a cohort of patients with HBV-associated human hepatocellular carcinoma (HCC). We found that tumor-associated macrophages suppress tumor T cell infiltration and TIGIT-NECTIN2 interaction regulates the immunosuppressive environment. The cell state transition of immune cells towards a more immunosuppressive and exhaustive status exemplifies the overall cancer-promoting immunocellular landscape. Furthermore, the heterogeneity of global molecular profiles reveals co-existence of intra-tumoral and inter-tumoral heterogeneity, but is more apparent in the latter. This analysis of the immunosuppressive landscape and intercellular interactions provides mechanistic information for the design of efficacious immune-oncology treatments in hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is heterogeneous, rendering its current curative treatments ineffective. The emergence of single-cell genomics represents a powerful strategy in delineating the complex ...molecular landscapes of cancers. In this study, we demonstrated the feasibility and merit of using single-cell RNA sequencing to dissect the intra-tumoral heterogeneity and analyze the single-cell transcriptomic landscape to detect rare cell subpopulations of significance. Exploration of the inter-relationship among liver cancer stem cell markers showed two distinct major cell populations according to EPCAM expression, and the EPCAM+ cells had upregulated expression of multiple oncogenes. We also identified a CD24+/CD44+-enriched cell subpopulation within the EPCAM+ cells which had specific signature genes and might indicate a novel stemness-related cell subclone in HCC. Notably, knockdown of signature gene CTSE for CD24+/CD44+ cells significantly reduced self-renewal ability on HCC cells in vitro and the stemness-related role of CTSE was further confirmed by in vivo tumorigenicity assays in nude mice. In summary, single-cell genomics is a useful tool to delineate HCC intratumoral heterogeneity at better resolution. It can identify rare but important cell subpopulations, and may guide better precision medicine in the long run.
•Single-cell transcriptomics dissected the intra-tumoral heterogeneity of hepatocellular carcinoma (HCC).•HCC single cells showed two distinct major cell populations according to EPCAM expression.•CD24+/CD44+-enriched cell subpopulation was identified within the EPCAM+ cells.•CTSE was the most upregulated signature gene in CD24+/CD44+-enriched cells.•Knockdown of CTSE significantly reduced self-renewal ability in vitro and tumorigenicity in vivo.
Abstract
Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially expressed gene (DEG) analysis. However, ...genes that contribute to cell-to-cell differences are not discoverable with RNA-seq because RNA-seq samples are obtained from a mixture of cells. Single-cell RNA-seq (scRNA-seq) allows the detection of gene expression in each cell. With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages. In this study, we compare seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat. Our results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis. Discrepancies between methods and potential issues in these tools are discussed and recommendations are made.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single ...nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the "true" effect sizes from a set of observed
-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-
-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.
Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has ...provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.
ABSTRACT
Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for ...diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P‐value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.
Abstract Objective Changes in the prevalence, treatment, and management of diabetes in the United States from 1999 to 2006 were studied using data from the National Health and Nutrition Examination ...Survey. Methods Data on 17,306 participants aged 20 years or more were analyzed. Glycemic, blood pressure, and cholesterol targets were glycosylated hemoglobin less than 7.0%, blood pressure less than 130/80 mm Hg, and low-density lipoprotein (LDL) cholesterol less than 100 mg/dL, respectively. Results The prevalence of diagnosed diabetes was 6.5% from 1999 to 2002 and 7.8% from 2003 to 2006 ( P < .05) and increased significantly in women, non-Hispanic whites, and obese people. Although there were no significant changes in the pattern of antidiabetic treatment, the age-adjusted percentage of people with diagnosed diabetes achieving glycemic and LDL targets increased from 43.1% to 57.1% ( P < .05) and from 36.1% to 46.5% ( P < .05), respectively. Glycosylated hemoglobin decreased from 7.62% to 7.15% during this period ( P < .05). The age-adjusted percentage achieving all 3 targets increased insignificantly from 7.0% to 12.2%. Conclusions The prevalence of diagnosed diabetes increased significantly from 1999 to 2006. The proportion of people with diagnosed diabetes achieving glycemic and LDL targets also increased. However, there is a need to achieve glycemic, blood pressure, and LDL targets simultaneously.
Genetic effects on the liability scale are informative for describing the genetic architecture of binary traits, typically diseases. However, most genetic association analyses on binary traits are ...performed by logistic regression, and there is no straightforward method that transforms both effect size estimate and standard error from the logit scale to the liability scale. Here, we derive a simple linear transformation of the log odds ratio and its standard error for a single nucleotide polymorphism (SNP) to an effect size and standard error on the liability scale. We show by analytic calculations and simulations that this approximation is accurate when the disease is common and the SNP effect is small. We also apply this method to estimate the contribution of a SNP near the
RET
gene to the variance of Hirschsprung disease liability, and the age-specific contributions of
APOE4
on the variance of Alzheimer’s disease liability. We discuss the approximate linear inter-relationships between genotype and effect sizes on the observed binary, logit, and liability scales, and the potential applications of the linear approximation to statistical power calculation for binary traits.
Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are ...selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SBMB, UILJ, UKNU, UL, UM, UPUK
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