MALDI mass spectrometry imaging: a novel tool for the identification and classification of amyloidosis
Amyloidosis is a group of diseases caused by extracellular accumulation of fibrillar polypeptide aggregates. So far, diagnosis is performed by Congo red staining of tissue sections in combination with polarization microscopy. Subsequent identification of the causative protein by immunohistochemistry...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
10 October 2017
|
| In: |
Proteomics
Year: 2017, Jahrgang: 17, Heft: 22 |
| ISSN: | 1615-9861 |
| DOI: | 10.1002/pmic.201700236 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1002/pmic.201700236 Verlag, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/pmic.201700236 
|
| Verfasserangaben: | Martin Winter, Andreas Tholey, Arnt Kristen, and Christoph Röcken |
| Zusammenfassung: | Amyloidosis is a group of diseases caused by extracellular accumulation of fibrillar polypeptide aggregates. So far, diagnosis is performed by Congo red staining of tissue sections in combination with polarization microscopy. Subsequent identification of the causative protein by immunohistochemistry harbors some difficulties regarding sensitivity and specificity. Mass spectrometry based approaches have been demonstrated to constitute a reliable method to supplement typing of amyloidosis, but still depend on Congo red staining. In the present study, we used matrix-assisted laser desorption/ionization mass spectrometry imaging coupled with ion mobility separation (MALDI-IMS MSI) to investigate amyloid deposits in formalin-fixed and paraffin-embedded tissue samples. Utilizing a novel peptide filter method, we found a universal peptide signature for amyloidoses. Furthermore, differences in the peptide composition of ALλ and ATTR amyloid were revealed and used to build a reliable classification model. Integrating the peptide filter in MALDI-IMS MSI analysis, we developed a bioinformatics workflow facilitating the identification and classification of amyloidosis in a less time and sample-consuming experimental setup. Our findings demonstrate also the feasibility to investigate the amyloid's protein composition, thus paving the way to establish classification models for the diverse types of amyloidoses and to shed further light on the complex process of amyloidogenesis. |
|---|---|
| Beschreibung: | Gesehen am 16.07.2018 |
| Beschreibung: | Online Resource |
| ISSN: | 1615-9861 |
| DOI: | 10.1002/pmic.201700236 |