Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging is a rapidly evolving field in which mass spectrometry techniques are applied directly on tissues to characterize the spatial distribution of various molecules such as lipids, protein/peptides, and recently also N-glycans. Glycans are involved in many biological processes and several glycan changes have been associated with different kinds of cancer, making them an interesting target group to study. An important analytical challenge for the study of glycans by MALDI mass spectrometry is the labile character of sialic acid groups which are prone to in-source/postsource decay, thereby biasing the recorded glycan profile. We therefore developed a linkage-specific sialic acid derivatization by dimethylamidation and subsequent amidation and transferred this onto formalin-fixed paraffin-embedded (FFPE) tissues for MALDI imaging of N-glycans. Our results show (i) the successful stabilization of sialic acids in a linkage specific manner, thereby not only increasing the detection range, but also adding biological meaning, (ii) that no noticeable lateral diffusion is induced during to sample preparation, (iii) the potential of mass spectrometry imaging to spatially characterize the N-glycan expression within heterogeneous tissues.

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MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information.

MALDI imaging and profiling mass spectrometry of proteins typically leads to the detection of a large number of peptides and small proteins but is much less successful for larger proteins: most ion signals correspond to proteins of m/z Ͻ 25,000. This is a severe limitation as many proteins, including cytokines, growth factors, enzymes, and receptors have molecular weights exceeding 25 kDa. The detector technology typically used for protein imaging, a microchannel plate, is not well suited to the detection of high m/z ions and is prone to detector saturation when analyzing complex mixtures. Here we report increased sensitivity for higher mass proteins by using the CovalX high mass HM1 detector (Zurich, Switzerland), which has been specifically designed for the detection of high mass ions and which is much less prone to detector saturation. The results demonstrate that a range of different sample preparation strategies enable higher mass proteins to be analyzed if the detector technology maintains high detection efficiency throughout the mass range. The detector enables proteins up to 70 kDa to be imaged, and proteins up to 110 kDa to be detected, directly from tissue, and indicates new directions by which the mass range amenable to MALDI imaging MS and MALDI profiling MS may be extended. (J Am Soc Mass Spectrom 2010, 21, 1922-1929) © 2010 American Society for Mass Spectrometry S ince its inception ϳ10 y ago, MALDI imaging mass spectrometry (imaging MS) has developed into a powerful and versatile tool for biomedical research [1,2]. It is now routinely used for analyzing peptides and small proteins up to 25 kDa [3-6], administered drugs and their metabolites [7], and recently major improvements have been reported for lipids [8]. Despite this success, proteins exceeding 25 kDa are not routinely detected. Proteins larger than 25 kDa include many proteins with important biological activities, such as most cytokines, growth factors, enzymes, receptors, proproteins, and neuropeptide precursors. Increasing the mass range of proteins amenable to MALDI imaging MS might enable these biologically crucial proteins to be included in current applications, e.g., biomarker discovery.The most common technique currently used to access larger proteins in MALDI imaging MS analyses is based on proteolytic digestion of the tissue's proteins followed by MALDI imaging MS of their tryptic peptides. Note on-tissue digestion has the additional advantage that it can be applied to formalin fixed tissues as proteolytic peptides can be generated that are not bound within the cross-linked protein matrix. For example, Djidja et al. used on-tissue digestion to determine that the 78 kDa protein GRP78 may be a new candidate protein biomarker of pancreatic adenocarcinoma [9]. In principal, this 'bottom-up' strategy could enable proteins of any mass to be detected. In practice the large increase in complexity associated with proteolysing the entire tissue's protein content will cause many tryptic peptides to have identical nominal mass [1], thus under...

Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used to distinguish regions with differential molecular signatures that could not be distinguished using established histologic tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using multiple imaging MS technologies, that reduce data loads and the number of variables by Ͼ100ϫ, and that highlight highly-localized features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable multivariate analysis on large imaging MS datasets spanning multiple tissues. (J