<?xml version="1.0" encoding="UTF-8"?>
<STUDY_SET xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <STUDY accession="ERP136603" alias="c83be597-56fe-43cb-9bd8-2bedb5fe28ef" center_name="METU">
    <IDENTIFIERS>
      <PRIMARY_ID>ERP136603</PRIMARY_ID>
      <EXTERNAL_ID namespace="BioProject">PRJEB51940</EXTERNAL_ID>
      <SUBMITTER_ID namespace="METU">c83be597-56fe-43cb-9bd8-2bedb5fe28ef</SUBMITTER_ID>
    </IDENTIFIERS>
    <DESCRIPTOR>
      <STUDY_TITLE>Somatic copy number variant load in neurons of healthy controls and Alzheimerâ€™s disease patients</STUDY_TITLE>
      <STUDY_TYPE existing_study_type="Other"/>
      <STUDY_ABSTRACT>The possible role of somatic copy number variations (CNVs) in Alzheimer's disease (AD) aetiology has been controversial. Although cytogenetic studies suggested increased CNV loads in AD brains, a recent single-cell whole-genome sequencing (scWGS) experiment, studying frontal cortex brain samples, found no such evidence. Here we readdressed this issue using low-coverage scWGS on pyramidal neurons dissected using laser capture microdissection (LCM) across five brain regions: entorhinal cortex, temporal cortex, hippocampal CA 1, hippocampal CA 3, and the cerebellum. Studying data from 1301 cells obtained from 20 brains, we found a low CNV load across all tested brain regions. Among reliably detected CNVs, deletions were more frequent compared to duplications. We did not find any difference between brains from AD (13 individuals, n=688 cells) and control samples (7 individuals, n=613 cells) in terms of CNV frequency. Interestingly, LCM-isolated cells show higher within-cell read depth variation compared to cells isolated with fluorescence activated cell sorting (FACS), which we argue may have both biological and technical causes. Indeed, we found that LCM-isolated neurons in AD patients harbour slightly more read depth variability than neurons of healthy controls, which might be related to the reported hyperploid profiles of some AD-affected neurons. We also propose a principal component analysis-based denoising approach that significantly reduces within-cell read depth variation in scWGS data.</STUDY_ABSTRACT>
      <CENTER_PROJECT_NAME>undefined</CENTER_PROJECT_NAME>
      <STUDY_DESCRIPTION>The possible role of somatic copy number variations (CNVs) in Alzheimer's disease (AD) aetiology has been controversial. Although cytogenetic studies suggested increased CNV loads in AD brains, a recent single-cell whole-genome sequencing (scWGS) experiment, studying frontal cortex brain samples, found no such evidence. Here we readdressed this issue using low-coverage scWGS on pyramidal neurons dissected using laser capture microdissection (LCM) across five brain regions: entorhinal cortex, temporal cortex, hippocampal CA 1, hippocampal CA 3, and the cerebellum. Studying data from 1301 cells obtained from 20 brains, we found a low CNV load across all tested brain regions. Among reliably detected CNVs, deletions were more frequent compared to duplications. We did not find any difference between brains from AD (13 individuals, n=688 cells) and control samples (7 individuals, n=613 cells) in terms of CNV frequency. Interestingly, LCM-isolated cells show higher within-cell read depth variation compared to cells isolated with fluorescence activated cell sorting (FACS), which we argue may have both biological and technical causes. Indeed, we found that LCM-isolated neurons in AD patients harbour slightly more read depth variability than neurons of healthy controls, which might be related to the reported hyperploid profiles of some AD-affected neurons. We also propose a principal component analysis-based denoising approach that significantly reduces within-cell read depth variation in scWGS data.</STUDY_DESCRIPTION>
    </DESCRIPTOR>
    <STUDY_ATTRIBUTES>
      <STUDY_ATTRIBUTE>
        <TAG>ENA-FIRST-PUBLIC</TAG>
        <VALUE>2023-04-01</VALUE>
      </STUDY_ATTRIBUTE>
      <STUDY_ATTRIBUTE>
        <TAG>ENA-LAST-UPDATE</TAG>
        <VALUE>2023-04-01</VALUE>
      </STUDY_ATTRIBUTE>
    </STUDY_ATTRIBUTES>
  </STUDY>
</STUDY_SET>
