Author(s)

  • Maike Langini (Presenting Author) | Medicines Discovery Catapult | Alderley Park, SK10 4ZF, MACCLESFIELD , United Kingdom
  • Irma O'Meara | Medicines Discovery Catapult | Alderley Park, SK10 4ZF, MACCLESFIELD , United Kingdom
  • Ping K. Yip | Centre for Neuroscience, Surgery & Trauma, Blizard Institute, Barts and The London School of Medicine and Dentistry | Queen Mary University of London, E1 2AT, London, United Kingdom
  • Christopher E.G. Uff | Centre for Neuroscience, Surgery & Trauma, Blizard Institute, Barts and The London School of Medicine and Dentistry | Queen Mary University of London, E1 2AT, London, United Kingdom
  • Christopher E.G. Uff | Department of Neurosurgery | Royal London Hospital, Whitechapel, E1 1FR, London, United Kingdom
  • Bruno Bellina | Medicines Discovery Catapult | Alderley Park, SK10 4ZF, MACCLESFIELD , United Kingdom

Abstract

For proteomic analyses in tissue the first choice is fresh frozen, but chemically fixed tissues are more commonly available. In recent years, proteomics in formalin-fixed paraffin-embedded, FFPE, tissue has progressed to where the sample preparation can be described as well documented. Other sample types such as formalin-fixed sucrose embedded, FFSE, tissues an alternative fixation technique employed in animal facilities and hospitals are still challenging for untargeted approaches.
Preparing FFSE tissues following the published FFPE tissue protocols, yields a very limited number of proteins identifications via mass spectrometry, MS, based proteomics. Based on this, we adapted a method based on published protocols to enable MS based analysis on FFSE tissues. The work describes the test of bead-, gel-, and column-based sample preparation methods in addition to various precipitation and de-crosslinking techniques to analyse the overall proteins. To focus on specific tissue regions, a MS imaging, MSI, method was developed to analyse the 2D distribution of proteins in tissue sections. The final protocol is reaching 2000+ protein identifications per sample with 37.7 % of MSI features matching to the MS data.
To increase the range of accessible sample tissues for MS based proteomics we have developed a method to analyse proteins via MS and MSI in FFSE brain tissue. This method has been successfully implemented in a study of FFSE brain tissue from traumatic brain injury patients.