Effects of Mecp2 loss of function in embryonic cortical neurons: A bioinformatics strategy to sort out non-neuronal cells variability from transcriptome profiling

Marcella Vacca, Kumar Parijat Tripathi, Luisa Speranza, Riccardo Aiese Cigliano, Francesco Scalabrì, Federico Marracino, Michele Madonna, Walter Sanseverino, Carla Perrone-Capano, Mario Rosario Guarracino, Maurizio D'Esposito

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Background: Mecp2 null mice model Rett syndrome (RTT) a human neurological disorder affecting females after apparent normal pre- and peri-natal developmental periods. Neuroanatomical studies in cerebral cortex of RTT mouse models revealed delayed maturation of neuronal morphology and autonomous as well as non-cell autonomous reduction in dendritic complexity of postnatal cortical neurons. However, both morphometric parameters and high-resolution expression profile of cortical neurons at embryonic developmental stage have not yet been studied. Here we address these topics by using embryonic neuronal primary cultures from Mecp2 loss of function mouse model. Results: We show that embryonic primary cortical neurons of Mecp2 null mice display reduced neurite complexity possibly reflecting transcriptional changes. We used RNA-sequencing coupled with a bioinformatics comparative approach to identify and remove the contribution of variable and hard to quantify non-neuronal brain cells present in our in vitro cell cultures. Conclusions: Our results support the need to investigate both Mecp2 morphological as well as molecular effect in neurons since prenatal developmental stage, long time before onset of Rett symptoms.

Original languageEnglish (US)
Article numberS14
JournalBMC bioinformatics
Volume17
Issue number2
DOIs
StatePublished - Jan 20 2016
Externally publishedYes

Keywords

  • Embryonic cortical neurons
  • MeCP2
  • Neural cells
  • Primary branching
  • RNA-sequencing
  • Rett syndrome

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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