Patch-Seq Protocol to Analyze the Electrophysiology, Morphology and Transcriptome of Whole Single Neurons Derived From Human Pluripotent Stem Cells

Mark van den Hurk, Jennifer Erwin, Gene Yeo, Fred Gage, Cedric Bardy

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)
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Abstract

The human brain is composed of a complex assembly of about 171 billion heterogeneous cellular units (86 billion neurons and 85 billion non-neuronal glia cells). A comprehensive description of brain cells is necessary to understand the nervous system in health and disease. Recently, advances in genomics have permitted the accurate analysis of the full transcriptome of single cells (scRNA-seq). We have built upon such technical progress to combine scRNA-seq with patch-clamping electrophysiological recording and morphological analysis of single human neurons in vitro. This new powerful method, referred to as Patch-seq, enables a thorough, multimodal profiling of neurons and permits us to expose the links between functional properties, morphology, and gene expression. Here, we present a detailed Patch-seq protocol for isolating single neurons from in vitro neuronal cultures. We have validated the Patch-seq whole-transcriptome profiling method with human neurons generated from embryonic and induced pluripotent stem cells (ESCs/iPSCs) derived from healthy subjects, but the procedure may be applied to any kind of cell type in vitro. Patch-seq may be used on neurons in vitro to profile cell types and states in depth to unravel the human molecular basis of neuronal diversity and investigate the cellular mechanisms underlying brain disorders.

Original languageEnglish
Article number261
Number of pages19
JournalFrontiers in Molecular Neuroscience
Volume11
Early online date2018
DOIs
Publication statusPublished - 10 Aug 2018

Keywords

  • Cellular phenotyping
  • Electrophysiology
  • Human neuron transcriptome
  • Induced pluripotent stem cell (iPSC)
  • Neuronal diversity
  • Patch clamping
  • Patch-seq
  • Single-cell RNA-seq

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