Biology subjects

Sanguinetti, G.

Publications and source records attributed to Sanguinetti, G..

6 recordsLinked to original sources

bioRxiv2018

Melissa: Bayesian clustering and imputation of single cell methylomes

Measurements of DNA methylation at the single cell level are promising to revolutionise our understanding of epigenetic control of gene expression. Yet, intrinsic limitations of the technology result in very sparse coverage of CpG sites (around 5% to 20% coverage), effectively limiting the analysis repertoire to a semi-quantitative level. Here we introduce Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to quantify spatially-varying methylation profiles across genomic regions from single-cell bisulfite sequencing data (scBS-seq). Melissa clusters individual cells based on local methylation patterns, enabling the discovery of epigenetic differences and similarities among individual cells. The clustering also acts as an effective regularisation method for imputation of methylation on unassayed CpG sites, enabling transfer of information between individual cells. We show both on simulated and real data sets that Melissa provides accurate and biologically meaningful clusterings, and state-of-the-art imputation performance. An R implementation of Melissa is publicly available at https://github.com/andreaskapou/Melissa.

bioinformatics↗

bioRxiv2017

HuD is a neural enhancer of global translation acting on mTORC1-responsive genes and sponged by the Y3 small non-coding RNA

The RNA-binding protein HuD promotes neurogenesis and favors recovery from peripheral axon injury. HuD interacts with many mRNAs, altering both stability and translation efficiency. UV-crosslinking and analysis of cDNA (CRAC) generated a nucleotide resolution map of the HuD RNA interactome in motor neuron-like cells. HuD target sites were identified in 1304 mRNAs, predominantly in the 3UTR, with enrichment for genes involved in protein synthesis and axonogenesis. HuD bound many mRNAs encoding mTORC1-responsive ribosomal proteins and translation factors. Altered HuD expression correlated with the translational efficiency of these mRNAs and overall protein synthesis, in a mTORC1-independent fashion. The predominant HuD target was the abundant, small non-coding RNA Y3, which represented 70% of HuD interaction signal. Y3 functions as a molecular sponge for HuD, dynamically limiting its activity. These findings uncover an alternative route to the mTORC1 pathway for translational control in motor neurons that is tunable by a small non-coding RNA.\n\nGraphical abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/205658_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (69K):\norg.highwire.dtl.DTLVardef@17b2d21org.highwire.dtl.DTLVardef@1cfdc8borg.highwire.dtl.DTLVardef@1984f88org.highwire.dtl.DTLVardef@8da1ee_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2017

Detecting repeated cancer evolution in human tumours from multi-region sequencing data

Carcinogenesis is an evolutionary process driven by the accumulation of genomic aberrations. Recurrent sequences of genomic changes, both between and within patients, reflect repeated evolution that is valuable for anticipating cancer progression. Multi-region sequencing and phylogenetic analysis allow inference of the partial temporal order of genomic changes within a patients tumour. However, the inherent stochasticity of the evolutionary process makes phylogenetic trees from different patients appear very distinct, preventing the robust identification of recurrent evolutionary trajectories. Here we present a novel quantitative method based on a machine learning approach called Transfer Learning (TL) that allows overcoming the stochastic effects of cancer evolution and highlighting hidden recurrences in cancer patient cohorts. When applied to multi-region sequencing datasets from lung, breast and renal cancer (708 samples from 160 patients), our method detected repeated evolutionary trajectories that determine novel patient subgroups, which reproduce in large singlesample cohorts (n=2,641) and have prognostic value. Our method provides a novel patient classification measure that is grounded in the cancer evolution paradigm, and which reveals repeated evolution during tumorigenesis, with implications for our ability to anticipate malignant evolution.

bioinformatics↗

bioRxiv2017

Joint Profiling Of Chromatin Accessibility, DNA Methylation And Transcription In Single Cells

Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a novel single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, methylation and transcription sequencing) uses a GpC methyltransferase to label open chromatin followed by bisulfite and RNA sequencing. We validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.

genomics↗

bioRxiv2017

Transcriptome-wide splicing quantification in single cells

Single cell RNA-seq (scRNA-seq) has revolutionised our understanding of transcriptome variability, with profound implications both fundamental and translational. While scRNA-seq provides a comprehensive measurement of stochasticity in transcription, the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. Here we present BRIE (Bayesian Regression for Isoform Estimation), a Bayesian hierarchical model which resolves these problems by learning an informative prior distribution from sequence features. We show that BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an effective tool for differential isoform quantification between scRNA-seq data sets. BRIE therefore expands the scope of scRNA-seq experiments to probe the stochasticity of RNA-processing.

bioinformatics↗

bioRxiv2016

Domains of methylated CAC and CG target MeCP2 to tune transcription in the brain

Mutations in the gene encoding the methyl-CG binding protein MeCP2 cause neurological disorders including Rett syndrome. The di-nucleotide methyl-CG (mCG) is the canonical MeCP2 DNA recognition sequence, but additional targets including non-methylated sequences have been reported. Here we use brain-specific depletion of DNA methyltransferase to show that DNA methylation is the primary determinant of MeCP2 binding in mouse brain. In vitro and in vivo analyses reveal that MeCP2 binding to non-CG methylated sites in brain is largely confined to the tri-nucleotide sequence mCAC. Structural modeling suggests that mCG and mCAC may be interchangeable as minimal structural perturbation of MeCP2 accompanies binding. MeCP2 binding to chromosomal DNA in mouse brain is proportional to mCG + mCAC density and defines domains within which transcription is sensitive to MeCP2 occupancy. The results suggest that MeCP2 interprets patterns of mCAC and mCG in the brain to negatively modulate transcription of genes critical for neuronal function.

molecular biology↗