Optimisation of laboratory methods for whole transcriptomic RNA analyses in human left ventricular biopsies and blood samples of clinical relevance

PLoS One. 2019 Mar 14;14(3):e0213685. doi: 10.1371/journal.pone.0213685. eCollection 2019.

Abstract

This study aimed to optimise techniques for whole transcriptome and small RNA analyses on clinical tissue samples from patients with cardiovascular disease. Clinical samples often represent a particular challenge to extracting RNA of sufficient quality for robust RNA sequencing analysis, and due to availability, it is rarely possible to optimise techniques on the samples themselves. Therefore, we have used equivalent samples from pigs undergoing cardiopulmonary bypass surgery to test different protocols for optimal RNA extraction, and then validated the protocols in human samples. Here we present an assessment of the quality and quantity of RNA obtained using a variety of commercially-available RNA extraction kits on both left ventricular biopsies and blood plasma. RNA extraction from these samples presents different difficulties; left ventricular biopsies are small and fibrous, while blood plasma has a low RNA content. We have validated our optimised extraction techniques on human clinical samples collected as part of the ARCADIA (Association of non-coding RNAs with Coronary Artery Disease and type 2 Diabetes) cohort study, resulting in successful whole transcriptome and small RNA sequencing of human left ventricular tissue.

Publication types

  • Multicenter Study
  • Observational Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Animals
  • Biopsy / methods*
  • Cardiopulmonary Bypass
  • Coronary Artery Disease / diagnosis
  • Coronary Artery Disease / metabolism
  • Diabetes Mellitus, Type 2 / diagnosis
  • Diabetes Mellitus, Type 2 / metabolism
  • Disease Models, Animal
  • Electrophoresis, Capillary
  • Female
  • Gene Expression Profiling / methods*
  • Heart Ventricles / metabolism
  • Heart Ventricles / pathology*
  • Humans
  • Male
  • MicroRNAs / metabolism
  • Middle Aged
  • Prospective Studies
  • Quality Control
  • RNA / analysis*
  • Sequence Analysis, RNA
  • Swine
  • Transcriptome*

Substances

  • MicroRNAs
  • RNA