Histology-Guided High-Resolution Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging

• Published in: Analytical chemistry (Washington) 2015-12, Vol.87 (24), p.11978-11983
• Main Authors: Heijs, Bram, Abdelmoula, Walid M, Lou, Sha, Briaire-de Bruijn, Inge H, Dijkstra, Jouke, Bovée, Judith V. M. G, McDonnell, Liam A
• Format: Article
• Language: English
• Subjects: Analytical chemistry; Cellular; Desorption; Histological Techniques - methods; Histological Techniques - standards; Histology; Humans; Imaging; Ionization; Ions; Lasers; Liposarcoma, Myxoid - diagnosis; Liposarcoma, Myxoid - pathology; Mass spectrometry; Matrix; Paraffin Embedding; Patients; Reproducibility of Results; Signatures; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.5b03610

Mass spectrometry imaging (MSI) is widely used for clinical research because when combined with histopathological analysis the molecular signatures of specific cells/regions can be extracted from the often-complex histologies of pathological tissues. The ability of MSI to stratify patients according to disease, prognosis, and response is directly attributable to this cellular specificity. MSI developments are increasingly focused on further improving specificity, through higher spatial resolution to better localize the signals or higher mass resolution to better resolve molecular ions. Higher spatial/mass resolution leads to increased data size and longer data acquisition times. For clinical applications, which analyze large series of patient tissues, this poses a challenge to keep data load and acquisition time manageable. Here we report a new tool to perform histology guided MSI; instead of analyzing large parts of each tissue section the histology from adjacent tissue sections is used to focus the analysis on the areas of interest, e.g., comparable cell types in different patient tissues, thereby minimizing data acquisition time and data load. The histology tissue section is annotated and then automatically registered to the MSI-prepared tissue section; the registration transformation is then applied to the annotations, enabling them to be used to define the MSI measurement regions. Using a series of formalin-fixed, paraffin-embedded human myxoid liposarcoma tissues, we demonstrate an 80% reduction of data load and acquisition time, thereby enabling high resolution (mass or spatial) to be more readily applied to clinical research. The software is freely available for download.