Novel molecular tumour classification using MALDI–mass spectrometry imaging of tissue micro-array

Abstract

The development of tissue micro-array (TMA) technologies provides insights into high-throughput analysis of proteomics patterns from a large number of archived tumour samples. In the work reported here, matrix-assisted laser desorption/ionisation-ion mobility separation-mass spectrometry (MALDI-IMS-MS) profiling and imaging methodology has been used to visualise the distribution of several peptides and identify them directly from TMA sections after on-tissue tryptic digestion. A novel approach that combines MALDI-IMS-MSI and principal component analysis-discriminant analysis (PCA-DA) is described, which has the aim of generating tumour classification models based on protein profile patterns. The molecular classification models obtained by PCA-DA have been validated by applying the same statistical analysis to other tissue cores and patient samples. The ability to correlate proteomic information obtained from samples with known and/or unknown clinical outcome by statistical analysis is of great importance, since it may lead to a better understanding of tumour progression and aggressiveness and hence improve diagnosis, prognosis as well as therapeutic treatments. The selectivity, robustness and current limitations of the methodology are discussed.

The C-terminal fragment of the immunoproteasome PA28S (Reg alpha) as an early diagnosis and tumor-relapse biomarker: evidence from mass spectrometry profiling

“…A decade of developments in instrumentation and chemistry has been required to achieve optimal extraction, detection, and spatial resolution when mapping compounds at the tissue level (Franck et al 2009a, b;Lemaire et al 2006a, b;2007a;van Remoortere et al 2010). These developments, combined with using the appropriate data-processing tools, gave rise to the discovery of biomarkers for diverse pathologies (Bonnel et al 2011;Djidja et al 2010;McCombie et al 2005;Stauber et al 2008), of which cancer is the most studied. MALDI mass spectrometry imaging (MALDI-MSI) technology makes it possible to study the molecular profiles of the benign and malignant portions of a solid tumor.…”

Interactive hyperspectral approach for exploring and interpreting DESI-MS images of cancerous and normal tissue sections

“…30,31 The strategy of selecting some individual spectra of representative pixels in the MALDI image was also used to perform principal component analysis-discriminant analysis (PCA-DA), namely to obtain classification rules based on the PCs as new variables. 32 Application of PCA has also been described for DESI-MS images of tumor and normal tissue sections in different types of human cancers. 4,8 In these cases, PCA was applied to the hyperspectral data using the score values, coded with a color scale, to represent multivariate images of the samples investigated.…”

Matrix‐assisted laser desorption/ionization imaging protocol for in situ characterization of tryptic peptide identity and distribution in formalin‐fixed tissue

“…[35] 2.2 Protocol-specific solutions 2.2.1 10 mM citric acid monohydrate pH 6 [29,36] ▪ 1.05 g citric acid monohydrate (Sigma-Aldrich, Japan) ▪ Dissolve in 480 mL H 2 O ▪ pH to 6.0 using 1 M NaOH (~13 mL) (Merck, Darmstadt, Germany) ▪ Make up volume to 500 mL with H 2 O 2.2.2 100 mM ammonium bicarbonate (NH 4 [32] ▪ 0.4 pmol/mL angiotensin I [34][35][36][37][38][39][40][41][42][43] ( Figure 1. Protocol for mapping the distribution and determining the identity of tryptic peptides generated in situ.…”

“…NB: Although multivariate statistics are best used to select peaks of interest, [12,41,42] to keep the workflow as straightforward as possible, peak selection here is manual only and based on operator visualized ion intensity maps for peptides of interest. NB: Steps 6.1 to 6.5 use flexImaging NB: Steps 6.6 onwards use in-house data processing 6.1 When the MALDI-IMS data set loads completely in flexImaging (step 5.10), a sum spectrum will be generated in the Spectrum Display window.…”