<?xml version="1.0" encoding="UTF-8"?>
<STUDY_SET xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <STUDY center_name="GEO" alias="GSE106678" accession="SRP124611">
    <IDENTIFIERS>
      <PRIMARY_ID>SRP124611</PRIMARY_ID>
      <EXTERNAL_ID namespace="BioProject" label="primary">PRJNA417665</EXTERNAL_ID>
      <EXTERNAL_ID namespace="GEO">GSE106678</EXTERNAL_ID>
    </IDENTIFIERS>
    <DESCRIPTOR>
      <STUDY_TITLE>Dissecting cell-type composition and activity-dependent transcriptional state in mammalian brains by massively parallel single-nucleus RNA-Seq</STUDY_TITLE>
      <STUDY_TYPE existing_study_type="Transcriptome Analysis"/>
      <STUDY_ABSTRACT>Massively parallel single-cell RNA sequencing can precisely resolve cellular diversity in a high-throughput manner at low cost, but unbiased isolation of intact single cells from complex tissues, such as adult mammalian brains, is challenging. Here, we integrate sucrose-gradient assisted nuclei purification with droplet microfluidics to develop a highly scalable single-nucleus RNA-Seq approach (sNucDrop-Seq), which is free of enzymatic dissociation and nuclei sorting. By profiling ~18,000 nuclei isolated from cortical tissues of adult mice, we demonstrate that sNucDrop-Seq not only accurately reveals neuronal and non-neuronal subtype composition with high sensitivity, but also enables in-depth analysis of transient transcriptional states driven by neuronal activity, at single-cell resolution, in vivo. Overall design: Single-nucleus RNA sequencing analysis of adult mouse cerebral cortex</STUDY_ABSTRACT>
      <CENTER_PROJECT_NAME>GSE106678</CENTER_PROJECT_NAME>
    </DESCRIPTOR>
    <STUDY_LINKS>
      <STUDY_LINK>
        <XREF_LINK>
          <DB>pubmed</DB>
          <ID>29220646</ID>
        </XREF_LINK>
      </STUDY_LINK>
    </STUDY_LINKS>
  </STUDY>
</STUDY_SET>
