24 research outputs found

    Probing sporadic and familial Alzheimer's disease using induced pluripotent stem cells.

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    Our understanding of Alzheimer's disease pathogenesis is currently limited by difficulties in obtaining live neurons from patients and the inability to model the sporadic form of the disease. It may be possible to overcome these challenges by reprogramming primary cells from patients into induced pluripotent stem cells (iPSCs). Here we reprogrammed primary fibroblasts from two patients with familial Alzheimer's disease, both caused by a duplication of the amyloid-β precursor protein gene (APP; termed APP(Dp)), two with sporadic Alzheimer's disease (termed sAD1, sAD2) and two non-demented control individuals into iPSC lines. Neurons from differentiated cultures were purified with fluorescence-activated cell sorting and characterized. Purified cultures contained more than 90% neurons, clustered with fetal brain messenger RNA samples by microarray criteria, and could form functional synaptic contacts. Virtually all cells exhibited normal electrophysiological activity. Relative to controls, iPSC-derived, purified neurons from the two APP(Dp) patients and patient sAD2 exhibited significantly higher levels of the pathological markers amyloid-β(1-40), phospho-tau(Thr 231) and active glycogen synthase kinase-3β (aGSK-3β). Neurons from APP(Dp) and sAD2 patients also accumulated large RAB5-positive early endosomes compared to controls. Treatment of purified neurons with β-secretase inhibitors, but not γ-secretase inhibitors, caused significant reductions in phospho-Tau(Thr 231) and aGSK-3β levels. These results suggest a direct relationship between APP proteolytic processing, but not amyloid-β, in GSK-3β activation and tau phosphorylation in human neurons. Additionally, we observed that neurons with the genome of one sAD patient exhibited the phenotypes seen in familial Alzheimer's disease samples. More generally, we demonstrate that iPSC technology can be used to observe phenotypes relevant to Alzheimer's disease, even though it can take decades for overt disease to manifest in patients

    Application of a low cost array-based technique — TAB-Array — for quantifying and mapping both 5mC and 5hmC at single base resolution in human pluripotent stem cells

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    Abstract5-hydroxymethylcytosine (5hmC), an oxidized derivative of 5-methylcytosine (5mC), has been implicated as an important epigenetic regulator of mammalian development. Current procedures use DNA sequencing methods to discriminate 5hmC from 5mC, limiting their accessibility to the scientific community. Here we report a method that combines TET-assisted bisulfite conversion with Illumina 450K DNA methylation arrays for a low-cost high-throughput approach that distinguishes 5hmC and 5mC signals at base resolution. Implementing this approach, termed “TAB-array”, we assessed DNA methylation dynamics in the differentiation of human pluripotent stem cells into cardiovascular progenitors and neural precursor cells. With the ability to discriminate 5mC and 5hmC, we identified a large number of novel dynamically methylated genomic regions that are implicated in the development of these lineages. The increased resolution and accuracy afforded by this approach provides a powerful means to investigate the distinct contributions of 5mC and 5hmC in human development and disease

    DNA Methylation

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    <p><b>A</b>. X Chromosome DNA Methylation and XIST Expression. Methylation levels of genes in the X-chromosome (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118307#pone.0118307.s009" target="_blank">S6A Table</a>) are shown on the heatmap. Hierarchical clustering was performed on the samples, as indicated by the dendrogram. The genes are ordered according to their location (from the beginning to the end of the chromosome). Samples that show loss of DNA methylation for the “Enz” cluster are highlighted in blue, those that show DNA methylation for the “Ecm” cluster are highlighted in pink, and for both clusters in mauve. Genes located in the regions of loss of DNA methylation are listed to the right of the heatmap. XIST expression is shown on the line graph, with the detection limit for the microarray indicated by the red line. <b>B</b>. DNA methylation at imprinted loci. Methylation levels for imprinted probes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118307#pone.0118307.s009" target="_blank">S6B Table</a>) are shown on the heatmap. Hierarchical clustering was performed on the samples, as indicated by the dendrogram. The genes are ordered according to chromosome location; genes are listed to the left. The inset at the right shows a detail of the NESP/GNAS complex locus, indicating the positions of the CpG sites that were hypermethylated (red triangle) vs. hypomethylated (green triangle) in the late passage samples relative to the NESP/GNAS and NESPAS exons. <b>C, D, E</b>. Heatmaps showing differential DNA methylation genes for early vs. late passage <b>(C)</b>, mechanical vs. enzymatic passage <b>(D)</b>, and Mef vs. Ecm substrate <b>(E)</b>. In heatmap <b>(C)</b>, the black boxes indicate genes for which the DNA methylation levels in the late passage MefMech (P103) samples was more similar to those in the early passage samples. Probes were selected by multivariate regression. Functional enrichments identified by GREAT analysis are shown to the right of the heatmaps, visualized using REVIGO [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118307#pone.0118307.ref013" target="_blank">13</a>]. Samples were arranged according to passage and culture method, and hierarchical clustering was performed on the genes only. In the functional enrichment results, the size of the node indicated the number of contributing GO terms, and color of the nodes indicates the FDR (darker color for lower FDR), and the edge length indicates the similarity between GO terms (shorter edge for more similar terms).</p

    Equally potent?

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    Joint probabilistic modeling of single-cell multi-omic data with totalVI.

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    The paired measurement of RNA and surface proteins in single cells with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, combining these paired views into a unified representation of cell state is made challenging by the unique technical characteristics of each measurement. Here we present Total Variational Inference (totalVI; https://scvi-tools.org ), a framework for end-to-end joint analysis of CITE-seq data that probabilistically represents the data as a composite of biological and technical factors, including protein background and batch effects. To evaluate totalVI's performance, we profiled immune cells from murine spleen and lymph nodes with CITE-seq, measuring over 100 surface proteins. We demonstrate that totalVI provides a cohesive solution for common analysis tasks such as dimensionality reduction, the integration of datasets with different measured proteins, estimation of correlations between molecules and differential expression testing
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