Knowledge of epigenetic alterations in disease is rapidly increasing owing to the development of genome-wide techniques for their identification. The ever-growing number of genes that show epigenetic ...alterations in disease emphasizes the crucial role of these epigenetic alterations - particularly DNA methylation - for future diagnosis, prognosis and prediction of response to therapies. This Review focuses on epigenetic profiling, which has started to be of clinical value in cancer and may in the future be extended to other diseases, such as neurological and autoimmune disorders.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as ...individual entities and classify them into complex taxonomies.
We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer's disease. Using tools from graph theory, we compute an unbiased quantification of a gene's biological relevance and accurately pinpoint key players in organ function and drivers of diseases.
Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology.
The interpretation of noncoding alterations in cancer genomes presents an unresolved problem in cancer studies. While the impact of somatic variations in protein-coding regions is widely accepted, ...noncoding aberrations are mostly considered as passenger events. However, with the advance of genome-wide profiling strategies, alterations outside the coding context entered the focus, and multiple examples highlight the role of gene deregulation as cancer-driving events. This review describes the implication of noncoding alterations in oncogenesis and provides a theoretical framework for the identification of causal somatic variants using quantitative trait loci (QTL) analysis. Assuming that functional noncoding alterations affect quantifiable regulatory processes, somatic QTL studies constitute a valuable strategy to pinpoint cancer gene deregulation. Eventually, the comprehensive identification and interpretation of coding and noncoding alterations will guide our future understanding of cancer biology.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
DNA methylation is the most studied epigenetic mark and CpG methylation is central to many biological processes and human diseases. Since cancer has highlighted the contribution to disease of ...aberrant DNA methylation patterns, such as the presence of promoter CpG island hypermethylation-associated silencing of tumor suppressor genes and global DNA hypomethylation defects, their importance will surely become apparent in other pathologies. However, advances in obtaining comprehensive DNA methylomes are hampered by the high cost and time-consuming aspects of the single nucleotide methods currently available for whole genome DNA methylation analyses. Following the success of the standard CpG methylation microarrays for 1,505 CpG sites and 27,000 CpG sites, we have validated in vivo the newly developed 450,000 (450K) cytosine microarray (Illumina). The 450K microarray includes CpG and CNG sites, CpG islands/shores/shelves/open sea, non-coding RNA (microRNAs and long non-coding RNAs) and sites surrounding the transcription start sites (-200 bp to -1,500 bp, 5'-UTRs and exons 1) for coding genes, but also for the corresponding gene bodies and 3'-UTRs, in addition to intergenic regions derived from GWAS studies. Herein, we demonstrate that the 450K DNA methylation array can consistently and significantly detect CpG methylation changes in the HCT-116 colorectal cancer cell line in comparison with normal colon mucosa or HCT-116 cells with defective DNA methyltransferases (DKO). The provided validation highlights the potential use of the 450K DNA methylation microarray as a useful tool for ongoing and newly designed epigenome projects.
Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are ...lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
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•The study represents the most comprehensive comparison of scRNA-seq protocols•Power simulations quantify the effect of sensitivity and precision on cost efficiency•The study offers an informed choice among six prominent scRNA-seq methods•The study provides a framework for benchmarking future protocol improvements
Ziegenhain et al. generated data from mouse ESCs to systematically evaluate six prominent scRNA-seq methods. They used power simulations to compare cost efficiencies, allowing for informed choice among existing protocols and providing a framework for future comparisons.
Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in ...principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.
To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.
Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.
Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing the transcriptomes of individual cells in a sample. The latest protocols are scalable to thousands of cells and are ...being used to compile cell atlases of tissues, organs and organisms. However, the protocols differ substantially with respect to their RNA capture efficiency, bias, scale and costs, and their relative advantages for different applications are unclear. In the present study, we generated benchmark datasets to systematically evaluate protocols in terms of their power to comprehensively describe cell types and states. We performed a multicenter study comparing 13 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied to a heterogeneous reference sample resource. Comparative analysis revealed marked differences in protocol performance. The protocols differed in library complexity and their ability to detect cell-type markers, impacting their predictive value and suitability for integration into reference cell atlases. These results provide guidance both for individual researchers and for consortium projects such as the Human Cell Atlas.
Brain metastases are the most common tumor of the brain with a dismal prognosis. A fraction of patients with brain metastasis benefit from treatment with immune checkpoint inhibitors (ICI) and the ...degree and phenotype of the immune cell infiltration has been used to predict response to ICI. However, the anatomical location of brain lesions limits access to tumor material to characterize the immune phenotype. Here, we characterize immune cells present in brain lesions and matched cerebrospinal fluid (CSF) using single-cell RNA sequencing combined with T cell receptor genotyping. Tumor immune infiltration and specifically CD8
T cell infiltration can be discerned through the analysis of the CSF. Consistently, identical T cell receptor clonotypes are detected in brain lesions and CSF, confirming cell exchange between these compartments. The analysis of immune cells of the CSF can provide a non-invasive alternative to predict the response to ICI, as well as identify the T cell receptor clonotypes present in brain metastasis.
DNA methylation patterns are important for establishing cell, tissue, and organism phenotypes, but little is known about their contribution to natural human variation. To determine their contribution ...to variability, we have generated genome-scale DNA methylation profiles of three human populations (Caucasian-American, African-American, and Han Chinese-American) and examined the differentially methylated CpG sites. The distinctly methylated genes identified suggest an influence of DNA methylation on phenotype differences, such as susceptibility to certain diseases and pathogens, and response to drugs and environmental agents. DNA methylation differences can be partially traced back to genetic variation, suggesting that differentially methylated CpG sites serve as evolutionarily established mediators between the genetic code and phenotypic variability. Notably, one-third of the DNA methylation differences were not associated with any genetic variation, suggesting that variation in population-specific sites takes place at the genetic and epigenetic levels, highlighting the contribution of epigenetic modification to natural human variation.
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