Biology subjects

Gervin, K.

Publications and source records attributed to Gervin, K..

4 recordsLinked to original sources

bioRxiv2022

Multi-omics analysis of paracetamol exposure identifies dysregulated genes involved in neurotoxicity and neuronal differentiation of human embryonic stem cells

Prenatal paracetamol exposure has been associated with neurodevelopmental outcomes in childhood. Pharmacoepigenetic studies show differences in cord blood DNA methylation between paracetamol exposed and unexposed neonates. However, causal implications and impact of long-term prenatal long-term paracetamol exposure on brain development remain unclear. Using a multi-omics approach, we investigated the effects of paracetamol on a model of early human brain development. We exposed human embryonic stem cells undergoing in vitro neuronal differentiation to daily media changes with paracetamol concentrations corresponding to maternal therapeutic doses. Single-cell RNA-seq and ATAC-seq integration identified paracetamol-induced chromatin-opening changes linked to gene expression. Differentially methylated and/or expressed genes were involved in signalling, neurotransmission, and cell fate-determination trajectories. Some genes involved in neuronal injury and development-specific pathways, such as KCNE3, overlapped with differentially methylated genes previously identified in cord blood associated with prenatal paracetamol exposure. Our data suggest that paracetamol may play a causal role in impaired neurodevelopment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/519620v2_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@e1a75forg.highwire.dtl.DTLVardef@b36a1forg.highwire.dtl.DTLVardef@fc25e4org.highwire.dtl.DTLVardef@1bb1c8_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2022

A multi-omics approach to visualize early neuronal differentiation in 4D

Neuronal differentiation of pluripotent stem cells is an established method to study physiology, disease and medication safety. However, the sequence of events in human neuronal differentiation and the ability of in vitro models to recapitulate early brain development are poorly understood. We developed a protocol optimized for the study of early human brain development and neuropharmacological applications. We comprehensively characterized gene expression and epigenetic profiles at four timepoints, as the cells differentiate from embryonic stem cells towards a heterogenous population of progenitors, immature and mature neurons bearing telencephalic signatures. A multi-omics roadmap of neuronal differentiation, combined with searchable interactive gene analysis tools, allows for extensive exploration of early neuronal development and the effect of medications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/478732v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@163539forg.highwire.dtl.DTLVardef@72c09org.highwire.dtl.DTLVardef@dc04eforg.highwire.dtl.DTLVardef@c821d2_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMulti-omics charting a new neuronal differentiation protocol for human ES cells C_LIO_LISingle-cell analyses reveals marker genes during neuronal differentiation C_LIO_LIIdentified transcriptional waves similar to early human brain development C_LIO_LISearchable tools to visualize single-cell gene expression and chromatin state C_LI In BriefWe have developed a novel protocol for human embryonic stem cells to study neural induction and early neuronal differentiation. Multi-omics analyses uncovered cell populations, genes and transcriptional waves defining cell fate commitment. We comprehensively describe epigenetic landscapes and gene expression and provide searchable analysis tools for exploration of the data.

cell biology↗

bioRxiv2019

Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data

BackgroundUmbilical cord blood (UCB) is commonly used in epigenome-wide association studies of prenatal exposures. Accounting for cell type composition is critical in such studies as it reduces confounding due to the cell specificity of DNA methylation (DNAm). In the absence of cell sorting information, statistical methods can be applied to deconvolve heterogeneous cell mixtures. Among these methods, reference-based approaches leverage age appropriate cell-specific DNA-methylation profiles to estimate cellular composition. In UCB, four reference datasets comprising DNAm signatures profiled in purified cell populations have been published using the Illumina 450K and 850K EPIC arrays. These datasets are biologically and technically different, and currently there is no consensus on how to best apply them. Here, we systematically evaluate and compare these datasets and provide recommendations for reference-based UCB deconvolution.\n\nResultsWe first evaluated the four reference datasets to ascertain both the purity of the samples and the potential cell cross-contamination. We filtered samples and combined datasets to obtain a joint UCB reference. We selected deconvolution libraries using two different approaches: automatic selection using the top differentially methylated probes from the function pickCompProbes in minfi and a standardized library selected using the IDOL (Identifying Optimal Libraries) iterative algorithm. We compared the performance of each reference separately and in combination, using the two approaches for reference library selection, and validated the results in an independent cohort (Generation R Study, n=191) with matched FACS measured cell counts. Strict filtering and combination of the references significantly improved the accuracy and efficiency of cell type estimates. Ultimately, the IDOL library outperformed the library from the automatic selection method implemented in pickCompProbes.\n\nConclusionThese results have important implications for epigenetic studies in UCB as implementing this method will optimally reduce confounding due to cellular heterogeneity. This work provides guidelines for future reference-based UCB deconvolution and establishes a framework for combining reference datasets in other tissues.

bioinformatics↗

bioRxiv2018

Differences in DNA methylation of white blood cell types at birth and in adulthood reflect postnatal immune maturation and influence accuracy of cell type prediction

BackgroundDNA methylation profiling of peripheral blood leukocytes has many research applications, and characterizing the changes in DNA methylation of specific white blood cell types between newborn and adult could add insight into the maturation of the immune system. As a consequence of developmental changes, DNA methylation profiles derived from adult white blood cells are poor references for prediction of cord blood cell types from DNA methylation data. We thus examined cell-type specific differences in DNA methylation in leukocyte subsets between cord and adult blood, and assessed the impact of these differences on prediction of cell types in cord blood.\n\nResultsThough all cell types showed differences between cord and adult blood, some specific patterns stood out that reflected how the immune system changes after birth. In cord blood, lymphoid cells showed less variability than in adult, potentially demonstrating their naive status. In fact, cord CD4 and CD8 T cells were so similar that genetic effects on DNA methylation were greater than cell type effects in our analysis, and CD8 T cell frequencies remained difficult to predict, even after optimizing the library used for cord blood composition estimation. Myeloid cells showed fewer changes between cord and adult and also less variability, with monocytes showing the fewest sites of DNA methylation change between cord and adult. Finally, including nucleated red blood cells in the reference library was necessary for accurate cell type predictions in cord blood.\n\nConclusionChanges in DNA methylation with age were highly cell type specific, and those differences paralleled what is known about the maturation of the postnatal immune system.

genetics↗