Abstract
Laser-capture microdissection (LCM) allows the visualization and isolation of morphologically distinct subpopulations of cells from heterogeneous tissue specimens. In combination with formalin-fixed and paraffin-embedded (FFPE) tissue it provides a powerful tool for retrospective and clinically relevant studies of tissue proteins in a healthy and diseased context. We first optimized the protocol for efficient LCM analysis of FFPE tissue specimens. The use of SDS containing extraction buffer in combination with the single-pot solid-phase-enhanced sample preparation (SP3) digest method gave the best results regarding protein yield and protein/peptide identifications. Microdissected FFPE human substantia nigra tissue samples (~3,000 cells) were then analyzed, using tandem mass tag (TMT) labeling and LC-MS/MS, resulting in the quantification of >5,600 protein groups. Nigral proteins were classified and analyzed by abundance, showing an enrichment of extracellular exosome and neuron-specific gene ontology (GO) terms among the higher abundance proteins. Comparison of microdissected samples with intact tissue sections, using a label-free shotgun approach, revealed an enrichment of neuronal cell type markers, such as tyrosine hydroxylase and alpha-synuclein, as well as proteins annotated with neuron-specific GO terms. Overall, this study provides a detailed protocol for laser-capture proteomics using FFPE tissue and demonstrates the efficiency of LCM analysis of distinct cell subpopulations for proteomic analysis using low sample amounts.
Original language | English |
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Pages (from-to) | 839-851 |
Number of pages | 13 |
Journal | Molecular & Cellular Proteomics |
Volume | 19 |
Issue number | 5 |
Early online date | 4 Mar 2020 |
DOIs | |
Publication status | Published - 1 May 2020 |
Keywords
- FFPE
- laser-capture microdissection
- substantia nigra
- TMT
- HPLC
- Parkinson's disease
- Quantification
- Tandem Mass Spectrometry
- Tissues
ASJC Scopus subject areas
- Analytical Chemistry
- Molecular Biology
- Biochemistry