MALDI Imaging Mass Spectrometry

A decade after its inception, MALDI imaging mass spectrometry has become a unique technique in the proteomics arsenal for biomarker hunting in a variety of diseases. At this stage of development, it is important to ask whether we can consider this technique to be sufficiently developed for routine use in a clinical setting or an indispensable technology used in translational research. In this report, we consider the contributions of MALDI imaging mass spectrometry and profiling technologies to clinical studies. In addition, we outline new directions that are required to align these technologies with the objectives of clinical proteomics, including: 1) diagnosis based on profile signatures that complement histopathology, 2) early detection of disease, 3) selection of therapeutic combinations based on the individual patient's entire disease-specific protein network, 4) real time assessment of therapeutic efficacy and toxicity, 5) rational redirection of therapy based on changes in the diseased protein network that are associated with drug resistance, and 6) combinatorial therapy in which the signaling pathway itself is viewed as the target rather than any single “node” in the pathway. A decade after its inception, MALDI imaging mass spectrometry has become a unique technique in the proteomics arsenal for biomarker hunting in a variety of diseases. At this stage of development, it is important to ask whether we can consider this technique to be sufficiently developed for routine use in a clinical setting or an indispensable technology used in translational research. In this report, we consider the contributions of MALDI imaging mass spectrometry and profiling technologies to clinical studies. In addition, we outline new directions that are required to align these technologies with the objectives of clinical proteomics, including: 1) diagnosis based on profile signatures that complement histopathology, 2) early detection of disease, 3) selection of therapeutic combinations based on the individual patient's entire disease-specific protein network, 4) real time assessment of therapeutic efficacy and toxicity, 5) rational redirection of therapy based on changes in the diseased protein network that are associated with drug resistance, and 6) combinatorial therapy in which the signaling pathway itself is viewed as the target rather than any single “node” in the pathway. MS has become a versatile tool that we are familiar with in large part due to important electronic and informatics advancements. The ability to obtain the molecular weight is one of the first steps in the identification of a molecule. With the addition of primary structural information mass spectrometry has become a useful technique to identify molecules within complex mixtures. Biological specimens, such as tissues, urine, or plasma, are complex and highly heterogeneous, which makes them inherently difficult to analyze. Further research and developments are necessary to achieve reliable biological models for understanding and studying pathologies. Therefore, it is of primary importance to identify the constituents of these systems and subsequently understand how they function within the framework of the tissue. With regard to clinical proteomics, there is the added dimension of disease, and therefore, the main goal is to characterize the cellular circuitry with a focus on the impact of the disease and/or therapy on these cellular networks. Mass spectrometry has become a centerpiece technology predominantly in the field of proteomics. Nonetheless a more comprehensive understanding of the constituents of biological systems will be aided by determining the constituent distribution. This anatomical dimension has been added through mass spectrometry imaging (MSI) 1The abbreviations used are:MSImass spectrometry imagingPCAprincipal component analysisXRTx-ray therapyEGFRepidermal growth factor receptorMRImagnetic resonance imaging. especially using MALDI-MSI. mass spectrometry imaging principal component analysis x-ray therapy epidermal growth factor receptor magnetic resonance imaging. MALDI is an ion source that is well compatible with the introduction of raw materials and surfaces. Shortly after its introduction, MALDI was used for direct tissue profiling. The first applications were neurobiological studies on dissected organs from the mollusk Lymnaea stagnalis (1van Veelen P.A. Jiménez C.R. Li K.W. Wildering W.C. Geraerts W.P. Tjaden U.R. van der Greef J. 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Wisztorski, C. Meriaux, M. Salzet, and I. Fournier, unpublished results).View Large Image Figure ViewerDownload Hi-res image Download (PPT) These developments led to clinical studies using MALDI-MSI technology. Clinical proteomics has many objectives including 1) diagnosis based on signatures as a complement to histopathology, 2) early disease detection, 3) individualized selection of therapeutic combinations that best target the patient's entire disease-specific protein network, 4) real time assessment of therapeutic efficacy and toxicity, 5) rational redirection of therapy based on changes in the diseased protein network that are associated with drug resistance, and 6) combinatorial therapy in which the signaling pathway itself is viewed as the target rather than any single “node” in the pathway. Based on these key objectives, can we consider MALDI-MSI a mature technology for use in clinical studies? What is the potential impact of this technology in anatomy/pathology and disease? By reviewing each objective, do we have sufficient evidence that MALDI-MSI satisfies the criteria imposed by clinical proteomics? We will now specifically address each of these key points. In some cases, diagnosis or tissue classification cannot be easily achieved through standard histological staining. Further refinements based on molecular signatures and statistical data, which are currently missing, are crucial for improved diagnostics. The development of rapid and reliable screening of human tissues for diagnostics (e.g. biopsies or smears) has been improved with modern proteomics. By using MALDI-MSI, a molecular diagnosis could be done on tissue directly in the environment of the tumors. MALDI-MSI could help to detect the tumor boundary or infiltration of adjacent normal tissue that presents a normal histology. It could also help to detect the early stage of pathology that presents no histological modifications and to prevent tumor recurrence at the site of surgical resection. One of the major advances of MSI is the correlation of the MALDI images with histological information. MALDI-MSI software (for a review, see Ref. 40Jardin-Mathé O. Bonnel D. Franck J. Wisztorski M. Macagno E. Fournier I. Salzet M. MITICS (MALDI Imaging Team Imaging Computing System): a new open source mass spectrometry imaging software.J. Proteomics. 2008; 71: 332-345Crossref PubMed Scopus (35) Google Scholar) superimposes the MALDI images over a macroscopic or microscopic optical image of the sample taken before MALDI measurement. Although the primary macroscopic optical image is sufficient to recognize the outline of the tissue and define the measurement area, it is not usually possible to observe histological features in the image (in contrast to microscopic images). For a histological interpretation, it is necessary to use stained tissue sections. Two approaches have been used to correlate histology with MALDI-MSI results: performing MALDI-MSI and histological staining on consecutive sections (41Chaurand P. Schwartz S.A. Billheimer D. Xu B.J. Crecelius A. Caprioli R.M. Integrating histology and imaging mass spectrometry.Anal. Chem. 2004; 76: 1145-1155Crossref PubMed Scopus (288) Google Scholar, 42Lemaire R. Desmons A. Tabet J.C. Day R. Salzet M. Fournier I. Direct analysis and MALDI imaging of formalin-fixed, paraffin-embedded tissue sections.J. Proteome Res. 2007; 6: 1295-1305Crossref PubMed Scopus (257) Google Scholar) or staining the sample after MALDI measurement (43Schwamborn K. Krieg R.C. Reska M. Jakse G. Knuechel R. Wellmann A. Identifying prostate carcinoma by MALDI-Imaging.Int. J. Mol. Med. 2007; 20: 155-159PubMed Google Scholar). The latter technique has been successfully used by pathologists (Fig. 3) (44Walch A. Rauser S. Deininger S.O. Höfler H. MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology.Histochem. Cell Biol. 2008; 130: 421-434Crossref PubMed Scopus (255) Google Scholar), which suggests that combining MALDI-MSI and classic histological staining provides pathologists with more information to make better diagnoses. The next step is not only to perform a diagnosis based on m/z signatures but also on molecular data generated from identification of specific biomarkers that have been characterized as pathological signatures. However, another challenge for pathologists is tissue classification, which is required to catalogue tumors or benign tissues. The major technological improvement that MALDI-MSI provides is the direct identification of novel markers within an in situ context from fixed sections/biopsy embedded in paraffin (e.g. archived material) (42Lemaire R. Desmons A. Tabet J.C. Day R. Salzet M. Fournier I. Direct analysis and MALDI imaging of formalin-fixed, paraffin-embedded tissue sections.J. Proteome Res. 2007; 6: 1295-1305Crossref PubMed Scopus (257) Google Scholar). Several studies on cancer and neurodegenerative diseases have demonstrated that MALDI-MSI is a key technology for identifying biomarkers, assessing their localization, and cross-validation (29Dreisewerd K. Lemaire R. Pohlentz G. Salzet M. Wisztorski M. Berkenkamp S. Fournier I. Molecular profiling of native and matrix-coated tissue slices from rat brain by infrared and ultraviolet laser desorption/ionization orthogonal time-of-flight mass spectrometry.Anal. Chem. 2007; 79: 2463-2471Crossref PubMed Scopus (27) Google Scholar, 45Brown L.M. Helmke S.M. Hunsucker S.W. Netea-Maier R.T. Chiang S.A. Heinz D.E. Shroyer K.R. Duncan M.W. Haugen B.R. Quantitative and qualitative differences in protein expression between papillary thyroid carcinoma and normal thyroid tissue.Mol. 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The application of MALDI MS imaging to archived materials could lead to the creation of an international disease marker database that would facilitate the development of early diagnostics for various pathologies as well as for follow-up examination of disease progression. Therefore, the addition of statistical analysis will be very important for the comparison of the different tissue components (e.g. tumor versus benign or healthy). Each tissue type depends upon the nature of its composition of cells. Thus, biocomputational methods are absolutely necessary to identify individualized molecular patterns to aid in diagnosis and prognosis. The advantage of MALDI-MSI is the ability to obtain a large collection of mass spectra spread out over a tissue section while retaining the absolute spatial location of these measurements for subsequent analysis and imaging. One of the statistical techniques to reduce the complexity of the information in multidimensional data sets in MALDI-MSI is principal component analysis (PCA) (55Van de Plas R. Ojeda F. Dewil M. Van Den Bosch L. De Moor B. Waelkens E. Prospective exploration of biochemical tissue composition via imaging mass spectrometry guided by principal component analysis.Pac. Symp. Biocomput. 2007; : 458-469PubMed Google Scholar). PCA is a multivariate preanalysis tool that allows for the correlation and identification of the major spatial and mass-related trends in the data that guide further downstream analysis (56Djidja M.C. Carolan V. Loadman P.M. Clench M.R. Method development for protein profiling in biological tissues by matrix-assisted laser desorption/ionisation mass spectrometry imaging.Rapid Commun. Mass Spectrom. 2008; 22: 1615-1618Crossref PubMed Scopus (10) Google Scholar). PCA reduces the dimensionality of the data set but does not classify the spectra. This is a transformation of the original coordinate system defined by peak intensities to a coordinate system that better explains the variance within the data set. This has been recently used in a prostate cancer study (43Schwamborn K. Krieg R.C. Reska M. Jakse G. Knuechel R. Wellmann A. Identifying prostate carcinoma by MALDI-Imaging.Int. J. Mol. Med. 2007; 20: 155-159PubMed Google Scholar). The next required step is the hierarchical clustering of the tissue based on PCA statistical analyses that reflect the most important variance of ions within the tissue (57McCombie G. Staab D. Stoeckli M. Knochenmuss R. Spatial and spectral correlations in MALDI mass spectrometry images by clustering and multivariate analysis.Anal. Chem. 2005; 77: 6118-6124Crossref PubMed Scopus (146) Google Scholar). Dendrograms can be constructed, and each branch represents ions present in the same group of cells (e.g. epithelial cancer cells versus benign cells). Thus, this representation provides access to huge numbers of individual spectra and reduces the complexity of the data set. It can also be correlated with histology as previously used for mouse kidney (Fig. 4) (44Walch A. Rauser S. Deininger S.O. Höfler H. MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology.Histochem. Cell Biol. 2008; 130: 421-434Crossref PubMed Scopus (255) Google Scholar), gastric (58Deininger S.O. Ebert M.P. Fütterer A. Gerhard M. Röcken C. MALDI imaging combined with hierarchical clustering as a new tool for the interpretation of complex human cancers.J. Proteome Res. 2008; 7: 5230-5236Crossref PubMed Scopus (196) Google Scholar), and ovarian cancer (Fig. 5).Fig. 5Hierarchical clustering using the ClinProt tool (Bruker Daltonics) after PCA of a stage 4 mucous ovarian carcinoma section covered with ionic matrix using the Shimadzu CHIP 1000 microspotter. a, optical image of the ovarian carcinoma section. b–f, reconstructed selected dendrograms and corresponding images. The two main branches reflect the carcinoma (red; a) and the healthy (green; f) parts in the section. b and c are two carcinoma subclasses, and d is a subclass of the healthy part. e represents a merge of the two branches.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Based on MALDI MS profiling (Fig. 6a) and imaging strategies (Fig. 6, b and c), several biomarkers have been identified in various cancer studies. In stage III and IV ovarian cancer, a highly prevalent (80%) biomarker has been identified using MALDI MS and nano-LC-nano-ESI MS using MS and MS/MS after separation by reverse phase HPLC and trypsin enzymatic digestion. This marker with an m/z of 9744 corresponds to an 84-amino acid fragment from the 11 S proteasome activator complex (PA28 α or REG-α) (33Lemaire R. Menguellet S.A. Stauber J. Marchaudon V. Lucot J.P. Collinet P. Farine M.O. Vinatier D. Day R. Ducoroy P. Salzet M. Fournier I. Specific MALDI imaging and profiling for biomarker hunting and validation: fragment of the 11S proteasome activator complex, Reg alpha fragment, is a new potential ovary cancer biomarker.J. Proteome Res. 2007; 6: 4127-4134Crossref PubMed Scopus (165) Google Scholar). This biomarker was validated using MALDI MSI (Fig. 6, b and c), classic immunohistochemistry with an antibody raised against the