MALDI mass spectrometry imaging

Proteomics Clin. Appl. 2016, 00, 1–19

1

DOI 10.1002/prca.201500140

REVIEW

MALDI mass spectrometry imaging: A cutting-edge tool for fundamental and clinical histopathology ´ ´ 1,2 , Rita Casadonte1 , Mark Kriegsmann3 , Charles Pottier4 , Gael ¨ Picard de Remi Longuespee 5 4 1,6∗ 2∗ ¨ Kriegsmann and Edwin De Pauw Muller , Philippe Delvenne , Jorg 1

Proteopath GmbH, Trier, Germany ` ` Mass Spectrometry Laboratory, GIGA-Research, Department of Chemistry, University of Liege, Liege, Belgium 3 Institute of Pathology, University of Heidelberg, Heidelberg, Germany 4 ` ` Laboratory of Experimental Pathology, GIGA-Cancer, Department of Pathology, University of Liege, Liege, Belgium 5 ´ Loos, France Imabiotech, MALDI Imaging Service Department, Parc Eurasante, 6 MVZ for Histology, Cytology and Molecular Diagnostics Trier, Trier, Germany 2

Histopathological diagnoses have been done in the last century based on hematoxylin and eosin staining. These methods were complemented by histochemistry, electron microscopy, immunohistochemistry (IHC), and molecular techniques. Mass spectrometry (MS) methods allow the thorough examination of various biocompounds in extracts and tissue sections. Today, mass spectrometry imaging (MSI), and especially matrix-assisted laser desorption ionization (MALDI) imaging links classical histology and molecular analyses. Direct mapping is a major advantage of the combination of molecular profiling and imaging. MSI can be considered as a cutting edge approach for molecular detection of proteins, peptides, carbohydrates, lipids, and small molecules in tissues. This review covers the detection of various biomolecules in histopathological sections by MSI. Proteomic methods will be introduced into clinical histopathology within the next few years.

Received: December 10, 2015 Revised: April 7, 2016 Accepted: May 13, 2016

Keywords: Histology / Imaging / MALDI / Mass spectrometry / Omics / Pathology

Additional supporting information may be found in the online version of this article at the publisher’s web-site

1 ´ ´ Correspondence: Dr. Remi Longuespee, Molekularpathologie Trier (MPT)/Proteopath, Max-Planck-Str. 17 54296 Trier, Germany E-mail: [email protected] Abbreviations: ACBP, acyl-coenzyme A binding protein; bTBI, blast induced mild traumatic brain injury; CAAR, citric acid antigen retrieval; ccRCC, clear cell renal cell carcinoma; COX, cytochrome c oxidase; CPH, Cox-proportional hazard; CRIP1, cysteine rich protein 1; C-ter, C terminal; DESI, desorption electrospray ionization; EOC, epithelial ovarian cancer; FFPE, formalin fixed parrafin embedded; fr/fr, fresh/frozen; FXYD3, FXYD domain-containing ion transport regulator 3; GSTM3, Glutathione S-transferase M3; HCC, hepatocellular carcinoma; HFIP, hexafluoroisopropanol; HNP-1, human neutrophil protein 1; IHC, immunohistochemistry; ISD, in source decay; KLK, kallikrein; LAICP, laser ablation inductively coupled plasma; LC, liquid chromatography; LPCAT, lysophosphatidylcholine acyltranferase; MALDI, matrix assisted laser desorption ionization; MPP+, 1-methyl-4-phenyl pyridinium; MPTP, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridin; MS/MS, tandem mass spectrometry; MS, mass spectrome C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Introduction

Matrix-assisted laser desorption/ionization (MALDI) appeared in the 80’s and allows the analysis of solid samples [1]. In the mid 90’s, the solid nature of the samples was exploited with the direct analysis of tissue sections of diverse try; MSI, mass spectrometry imaging; MYC, myelocytomatosis; NALDI, nanoassisted laser desorption ionization; N-ter, N terminal; PC, phosphatidylcholine; PCA, principal component analysis; PCR, polymerase chain reaction; PD, Parkinson disease; PHO-S, synthetic supplement containing phosphoethanolamine; PI, phosphatidylinositol; PSA, prostate specific antigen; REIMS, rapid evaporating ionization mass spectrometry; SA, sinapinic acid; SAM, significance analysis of microarrays; siRNA, small interfering ribonucleic acid; SMM, spitzoid malignant melanoma; SN, Spitz nevi; ST, sulfatide; SVM, support vector machines; TLE, temporal lobe epilepsy; TMA, tissue micro array; TNM, tumor, node, metastasis ∗ Both authors contributed equally Colour Online: See the article online to view Figs. 1–6 in colour.

www.clinical.proteomics-journal.com

2

´ et al. R. Longuespee

biological models [2–5]. An improvement of the method was proposed by Caprioli et al. and consisted of automation for the acquisition of spectra through areas of interest. Peaks of interest can be assigned to specific tissue areas using special software [6]. MALDI mass spectrometry imaging (MSI) has been introduced as an ideal method to combine classical tissue proteomics [7] and histology. MSI has the potential to complement histopathological evaluation to confirm diagnosis and aid in therapeutic management. Combined with mathematical calculations, MALDI spectral data can be processed in order to give semi quantitative mapping information on tissue sections. Statistical and informatics tools allow evaluating proteomic information and assigning these to different tissue areas. Besides, MSI is a valuable tool to detect new biomarkers. Tissue heterogeneity and complexity can also be evaluated by MSI, whereas classical molecular pathological methods fail. Clinical cancer diagnostics requires not only histological and immunohistochemical investigations but also molecular pathological techniques, such as polymerase chain reaction (PCR), in-situ hybridization and massive-parallel sequencing. The latter method has prerequisites for personalized medicine and patient management. In light of these major improvements, the fact that generally proteins and not genes are the executing molecules is often neglected. It is still largely unclear why certain tumors show spontaneous regression and others grow despite aggressive treatment. Therefore, advances in the characterization of cancer proteomes are highly warranted not only to decipher changes during tumor development, but also to improve tumor classification, and help assessment of prognosis and prediction to therapy. MSI is a promising candidate to tackle the current challenges [8]. Here we review mainly proteomic and lipidomic methods which were introduced into biomedical science and histopathology. This review consists of three parts: (i) the study of the pathogenesis (ii) the establishment of the diagnosis, prognosis and staging, (iii) prediction of response to therapy.

2

MSI approaches for molecular histology

Since its introduction in the 90´s, MSI technique has greatly evolved in terms of sample preparation, instrumentation, and data processing. This evolution accompanied the understanding of fundamentals of complex tissue samples. The initial workflow consisting in matrix application on tissue sections followed by the raster-stepped MALDI analysis of the surface (Fig. 1) have greatly been improved. MSI can be performed on fresh/frozen (fr/fr), as well as on formalin-fixed paraffinembedded (FFPE) tissues [9,10]. Different solvent washes and extraction procedures were developed for the preparation of fr/fr tissues [11–16], and different antigen retrieval methods were tested for FFPE tissues [9, 17–20]. MSI comprises a variety of molecular analyses, e.g. investigation of peptides

C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim


and proteins [21], glycans, [22, 23] small molecules and metabolites [24], lipids [25, 26], or primary antibodies [27]. While peptides, proteins, glycans, small molecules and metabolites can be investigated on fr/fr and FFPE tissues [28], the analysis of all classes of lipids can only be done on fr/fr tissue samples [29]. Today, the qualitative analysis of endogenous compounds in tissues can be complemented by the targeted quantitative evaluation of biomolecules of interest [30, 31]. Concerning instrumentation, a panel of combinations of mass analyzers are now available in order to fulfill specific requirements of a given investigation [32]. Even if this does not constitute the focus of this review, it is also important to mention that a panel of different ion sources exist, that extend the myriad of possible instruments for the analysis of tissues. Atmospheric pressure sources such as rapid evaporation ionization mass spectrometry (REIMS) [33], and desorption electrospray ionization (DESI) [34] present indeed important advantages for application in the clinics, especially because of the low requirements for sample preparation. Alternative techniques are also considered for the parallel identification of compounds that are mapped in tissues. Direct identification of proteins in situ via MALDI MS/MS is possible but difficult, thus liquid chromatography tandem mass spectrometry (LC-MS/MS) can be required to complement MSI in identification analysis [35] (Fig. 1). Other sampling tools, such as laser microdissection, were used to prepare tissues for LC-MS/MS analyses [36]. The introduction of laser microdissection in the MSI workflow would constitutes a link between MALDI imaging and proteomics [7], in order to enlarge the application of MALDI MSI in the field of biomedical sciences. In this context, laser microdissection-based microproteomics methods [37] may be applied to study intratumoral heterogeneity (Fig. 1). The improvement of these methods was accompanied by refinement of bioinformatics methods. The presence of different cell clones in tissues (tumoral heterogeneity) could be responsible for different prognosis or treatment responses [38]. Hierarchical clustering and segmentation methods that allow unsupervised discrimination of different molecular features within tissue sections [18, 39–43], constitute some of the necessary tools to study tumor heterogeneity by MALDI MSI. An example of MSI data processing for the evaluation of tissue heterogeneity is illustrated (Fig. 2). In a breast cancer biopsy section (Fig. 2, inset A), MALDI images revealed the existence of expected and unexpected stroma/cancer ion localizations (Fig. 2, inset B). This molecular-based information was confirmed by the segmentation calculation with intratumoral heterogeneity (Fig. 2, inset C). Alternative approaches to segmentation, such as morphometry are currently being developed, that can tackle the exploration of intratumoral heterogeneity. Morphometric studies allowed us to retrieve ion images that reflect tissue heterogeneity (Fig. 2, inset D). This method was described in detail in the Supporting data. Together with other mathematical methods, such as principal



3

Figure 1. MALDI imaging workflow for applications in histopathology. The insets with the green background stand for the different steps of the possible workflows, and in pink the data obtained using these approaches. The insets with the gray background include all molecular analyses. MALDI imaging basic workflow consists in the homogenous deposition of matrix on tissue section using dedicated devices, and the MALDI analyses of spots covering tissue surface separated by a step of a size defined by the user. Depending on the nature of the tissue and the specific type of analysis, some additional procedures are required. For example, proteolytic digestion is required for bottom-up analysis, especially for FFPE tissues. Solvent washes and/or molecular extraction are also recommended for the analysis of peptides and proteins from fr/fr tissues. The MALDI molecular images show the mapping of peaks of interest through the tissue section, depending on their intensity in each of the analyzed spots. Additional bioinformatics processing such as hierarchical clustering or segmentation can be performed to reveal molecular heterogeneity within tissue sections. The diagnosis can also be done using machine learning algorithms for the objective classification of tissues. MALDI MSI experiments can be combined with other types of analyses for a more informative view of the molecular context of tissues, such as identification analysis through laser microdissection and liquid chromatography (LC) coupled to electro spray ionization (ESI)-MS. Some molecular histology methods such as IHC are already used for diagnosis (green-outlined inset, “present”). In a close future, it would be reasonable to consider MALDI imaging in combination with other analytical methods for molecular histology, as a supportive method for diagnosis and prognosis that are today only established using regular histological methods (blue outlined inset, “future”).

component analysis (PCA) [19], and other classification tools [44–52], MSI investigations are now strengthened by reliable statistics. In a close future, molecular analyses in histopathology will be able to supplement primary diagnosis, classification C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

of diseases and may provide prognostic and predictive information [53]. In this context, MALDI MSI represents one of the best candidates among molecular methods for routine histopathology.


4



Figure 2. MALDI image datasets processing revealing intratumoral heterogeneity. HE staining of a breast cancer tissue (A) reveals the localization of stromal and cancerous tissue regions. The stromal part appears in lighter in the tissue than the tumorous part. The HE section was previously analyzed by MALDI imaging, the brown dots represent the laser shot positions. MALDI images reveal the localization of ions that match the area of stromal (m/z 1515.752) and cancerous (m/z 1198.661) tissues (B). Some other ions have a particular localization in the tumor (m/z 1249.228) and in the stroma (m/z 1546.862), revealing the presence of molecular heterogeneity in tissues. Using the dedicated software SCiLS Lab, a segmentation calculation confirmed the observation made by the MALDI imaging analysis (C). The core of the tumor (light blue and green in the cancer clusters) present different molecular feature to the edge (orange in the cancer clusters) and the stromal tissue surrounding the tumorous region (light blue and orange in the stromal clusters) also presents different characteristics compared to the one which is located on the opposite side (green in the stromal clusters). Inset D shows an example of distribution of ions suggested as heterogeneous by the morphometric analysis. 1: on the basis of a high number of objects, 2: on the basis of a low average X coordinate, 3: on the basis of a high spread in coordinates along the X axis, 4: on the basis of a low surface. The breast cancer tissue was provided ` by the biobank from the University of Liege, with local ethical accreditation.

3

Applications of MSI in the understanding of pathogenesis

The following paragraph is divided into etiopathogenesis of tumors, the second in nontumor diseases.

3.1 Cancer pathogenesis The huge advantage of MSI is its ability to map molecules without their a priori selection as it occurs in IHC. This principle allows for unexpected discoveries and is particularly attractive for the study of the tumor microenvironment.

3.1.1 Breast cancer Seeley et al. proved that some proteins follow a distribution in tissue that do not necessarily match previously established histopathological regions. Tissue heterogeneity has also been explored in breast cancer between stromal and cancerous C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

regions in ductal carcinoma in situ and invasive ductal carcinoma [54], as well as in breast cancer metastasized to lymph nodes [55]. Another study of the breast tumor microenvironment demonstrated proteomic differences between intratumoral and extratumoral stroma and highlighted activation of the tumor stroma [56]. Kang et al. investigated the breast tumor environment and compared the proteomic profiles of the breast cancer interface with the tumor zone and the normal tissue zone by MSI [57]. They identified immunoglobulin heavy constant alpha2 protein as a specific marker for the tissue microenvironment and for early stages of tumoral development. As an alternative to MALDI MSI for targeted investigations, indirect imaging of proteins was also investigated using a breast cancer model [58]. This study consisted in laser ablation inductively coupled plasma (LAICP) MSI of Au/Ag tagged antibodies. There is a growing interest in using MSI to investigate lipids, as failure in their metabolism is related to cancer risk. Lipids, for example phosphatidylinositol (PI) [59] and phosphatidylcholine (PC) 36:1, are implicated in breast cancer and www.clinical.proteomics-journal.com

5


were recently found to be overexpressed [60]. Acylcarnitines, PC, and sphingomyelin were found to be differentially located in tumor and necrotic regions as well as hypoxic tumoral regions [61]. Also, Jiang et al. studied hypoxia in a breast cancer model where a panel of PC was found to be co-localized with hypoxic regions, suggesting a role in the resistance to therapy [62].

3.1.2 Ovarian cancer In epithelial ovarian cancer (EOC), the most comprehensive proteomic studies were conducted by Lemaire et al. where MSI, combined to LC-MS/MS analyses, led to the identification of the C-terminal part of the protein PA28, also known as the immunoproteasome activator 11S or reg-alpha [63]. The presence of this specific marker could be a hint for an immunosuppressive mode associated with ovarian cancer that was already observed by different groups [64–70]. Recently, it was proved that C-ter PA28 showed a prevalence of 77.66 ± 8.77 % in ovarian cancer tissues [16]. In this study, it was shown that a quick assay using hexafluoroisopropanol (HFIP) as a solubilization solvent for sinapinic acid (SA) was helpful to detect C-ter PA28 in early stages of ovarian cancer. This assay revealed C-ter PA28 in tiny cancerous tissue regions in stage 1 EOC biopsies. Using MSI combined with LC-MS/MS, a large panel of biomarkers was also detected in ovarian cancer biopsies. These molecules include apolipoprotein A1, prolargin, hemoplexin, transgelin, and S100 protein [15]. Hoffmann’s team from “The University of Adelaide”, Australia, also did an indepth investigation of an ovarian cancer model by introducing citric acid antigen retrieval (CAAR) method to MSI experiments on FFPE biopsies [17]. They also designed several methods to match nano-LC-MS/MS analyses with MSI experiments, such as the use of internal calibrants sprayed on the tissue sections [71], and the creation of reference datasets of tryptic peptides to facilitate the identification of peptides of interest obtained [9, 36]. As a complementary method to MALDI MSI, tissue profiling has also been proposed to study the origin of epithelial ovarian cancer, especially serous and endometrioid subtypes that are suspected to originate from the fallopian tube and endometrium respectively [19]. This study highlighted the usefulness of the anatomical context for accurate biomarker discovery from tissues. In this study, markers previously described in ovarian serous carcinoma were assigned to serous fallopian tube cancer. Recently, Fata et al. combined the use of MSI, statistical tools, and LC-MS/MS identification to classify ovarian clear cell carcinoma and endometrial clear cell carcinoma. Among the proteins that were differentially expressed, vimentin, annexin 4, and protein 14-3-3 were validated by IHC [72]. Lipids were also the subject of studies in EOC. Liu et al. found high abundances of sulfatides in EOC [73]. Those C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

sulfatides may be considered as important tumor phenotype markers.

3.1.3 Prostate cancer Steurer et al. imaged tissue microarrays (TMAs) from 1044 patients. They could determine peaks that appeared to be associated with transmembrane protease, serine 2-ETS-related gene (TMPRSS2-ERG) fusions (protein/gene) or to the activated pathways after ERG activation [74]. They were able to assign particular signals to clinical characteristics: some m/z values were associated with favorable tumor phenotype such as low Gleason grade or low Ki67 labeling index (LI), whereas a different m/z value was linked to high Ki67 LI and a third one with prolonged time to prostate specific antigen (PSA) recurrence. In addition, several signals were associated with the ERG fusion status of cancers and others with a negative ERG status. These data allowed to characterize molecules that play a role in the TMPRSS2-ERG fusion and simultaneously could detect molecules that become active upon activation of ERG.

3.1.4 Salivary gland cancer Warthin’s tumor is the second most common benign tumor of the salivary glands. Molecular mapping by MSI showed a specific increase of phosphatidylcholine (PC) in the lymphatic follicle, suggesting a different metabolism possibly linked to inflammatory activity [75].

3.1.5 Cancers of the digestive system MSI allowed the detection of large quantities of lysophosphatidylcholine acyltransferase (LPCAT) 4 in colon cancer, which was also confirmed by IHC [76]. In line with these results Uehara et al. found LPCAT 1 overexpression and related lipid alterations in gastric cancer [77]. MSI of colorectal cancer biopsies identified molecular signatures discriminating between healthy and cancerous tissue. These results revealed novel cancer-associated field effects, built on the established concept of field cancerization [78].

3.1.6 Pancreatic cancer TMAs were used to classify pancreatic cancer sections [79]. Additionally, Grp78 protein was found to be related to cell survival [80]. Recently, Grüner et al. applied MSI and LC-MS/MS analyses to discriminate preneoplastic lesions from pancreatic cancer. They identified 26 significant m/z species including albumin and thymosin ␤4 [81]. Shortly after, another team detected suppression of thymosin ␤4 and ubiquitin in highgrade dysplasia [82]. MSI recently allowed in situ proteomic www.clinical.proteomics-journal.com

6



analysis of small neoplastic lesions, adequate to identify the early changes in protein expression products [83].

such as thymosin-␤4 in the perinecrotic areas, S100A6 protein in the perivascular astrocytes [94].

3.1.7 Hepatocellular carcinoma

3.2 Noncancer pathogenesis

Pote et al. identified different forms of histone H4 protein involved in the microvascular invasion of hepatocellular carcinoma. Peaks identified as N-ter acetylated and dimethylated histones H4 were epigenetic markers of this phenomenon [84]. A molecular screening by lipidomics MSI revealed an overexpression of LPCAT1 in cancer, confirmed by RT-PCR. The changes in the composition of phospholipids may promote the development of hepatocellular carcinoma (HCC) [85]. Laouirem et al. have studied the transition between cirrhosis and HCC by MSI followed by top-down proteomics. They identified two markers of cancer risk: kallikrein-related peptidase 6 (KLK6) and Ubi (1-74). The first was induced de novo in cirrhosis and was increased in HCC, while the second was increased in both conditions. Thus, KLK6 catalyzes the production of Ubi (1-74), whose large quantity interferes with the ubiquitination machinery [86]. The examination of phospholipids zonation performed by MSI suggests their association with intrahepatic proinflammatory phenotype, which has broad implications in the etiopathogenesis of nonalcoholic fatty liver disease [87]. Human neutrophil peptide-1 was found to be co-localized with colorectal metastasis in liver [88]. In this study, a top-down method named selected protein monitoring was specifically designed to track markers of interest in histological sections, using in source decay (ISD) technique [18, 89, 90]. The approach allows the direct on-tissue identification of proteins by their fragmentation from N- and C-termini. Lipid signatures were recently depicted by Thomas et al. in human colorectal cancer liver metastases [91]. Abnormal distribution of phospholipids such as sphingomyelin (16:0) was previously reported [92].

3.2.1 Neurology

3.1.8 Renal cancer The identification of peptides of Wilms tumors by MSI could discriminate prognostic differences in various ethnic groups. In addition, some individual peptides were associated with patient and disease characteristics as treatment failure and stage. These data may be the basis for tailored therapies of this tumor entity in the future [93].

3.1.9 Glioblastoma Ait-Belkacem et al. proposed the use of ISD for the proteomic characterization of glioblastoma. Several regions of the tissues were characterized by the presence of specific proteins C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

The pathogenesis of some neurological disorders is still unknown today. Various neurological disorders as schizophrenia and Parkinson’s disease (PD) have been studied by MSI. Since human tissues are not usually available in neurological diseases, animal models are frequently used as valuable source of tissue to study the pathogenesis of these diseases. An unknown mechanism involved in PD was found by the study of region-specific distribution profiles of neuropeptides in a rat brain model [95]. MSI analysis of molecules involved in L-DOPA-induced dyskinesia (a troublesome complication of L-DOPA pharmacotherapy of PD) allowed to consider strategies to reduce symptoms [96]. MSI also revealed the anatomical distribution of 1-methyl-4-phenyl pyridinium (MPP+), the active metabolite of 1-methyl-4-phenyl-1,2,3,6tetrahydropyridin (MPTP), a neurotoxin that is used in the study of the Parkinson’s disease models. For the first time, a toxin was found in all brain regions, before concentrating in the olfactory bulb, the basal ganglia, the ventral mesencephalon, and the locus coeruleus. These regions are differentially affected in PD [97]. In a model of childhood absence epilepsy, MSI was able to discriminate 19 m/z candidates as potential markers using a cross classification design. Seven m/z signals were identified, and among them synapsin-1 was suspected to be involved in epilepsy. These results were confirmed by western blot and IHC. Further evaluation would require functional testing of the role of synapsin-1 in the mechanism of childhood absence epilepsy [98]. The temporal lobe epilepsy (TLE) is one of the most common forms of epilepsy and its pathogenesis remains poorly understood. From microextraction of tissue from patients and controls, a proteomic analysis using MSI identified seven neuropeptides related to the patients’ group. These peptides were localized in the dentate gyrus of the hippocampus [99]. The first analysis of post-mortem human spinal cord samples, obtained from amyotrophic lateral sclerosis patients and controls, revealed particular protein distributions and protein expression modulations, suggesting modifications in protease activity [100]. Lipidomic studies on sphingolipids indicate that MSI could be useful for clinical studies on demyelinating diseases [101]. Distribution analysis of sulfatides (ST) revealed that the composition of nonhydroxylated and hydroxylated STs is reversed at the border of the white and gray matter. MSI for lipidomics was also performed to study abnormal metabolic processes involved in schizophrenia revealing the abnormal distribution of PC molecular species (particularly in the cortical layer of frontal cortex region) [102]. www.clinical.proteomics-journal.com

7


Pathogenesis of blast induced mild traumatic brain injury (bTBI), caused by changes of atmospheric pressure due to explosive detonations, has been extremely controversial. MSI revealed a major increase of the ganglioside GM2 in the hippocampus, thalamus, and hypothalamus after a single blast exposure. This discovery represents an important step in understanding the biological response to bTBI [103]. Sparvero et al. applied MSI to study phospholipids in traumatic brain injury. This work is of particular interest because of the importance of phospholipids in the physiology of the central nervous system as well as polyunsaturated fatty acids sensitivity to free radicals attacks [104]. Additionally, brain ischemic injury was investigated by MSI [105]. The examination of spatial distribution of gangliosides after middle cerebral artery occlusion and reperfusion injury, suggests a role of these in neuronal response to injury [106]. MSI and LC-MS analyses of the cortex, the corpus striatum and hippocampus after transient middle cerebral artery occlusion, have shown that post reperfusion changes, in the metabolism of nucleotides and amino acids, were detected mainly in the cortex and in the corpus striatum but not in hippocampus [107]. A study combining MALDI-TOF-MS and MS/MS techniques was used to characterize changes in lipid composition of dystrophic cells from patient with Duchenne muscular dystrophy [108]. Alzheimer’s disease attracts many investigators to evaluate etiology and pathogenesis. Since metal homeostasis may play an important role in the pathogenesis of the disease, MSI has been used for metals localization [109].

3.2.3 Diabetology The pathogenesis of type-2 diabetes is complex, and understanding the affliction is essential given the number of people around the world affected by the disease and associated comorbidities. Diabetic nephropathy is a common and serious complication of diabetes. High resolution MSI analyses have revealed high amounts of specific lipids (gangliosides, sulfoglycosphingolipids, lysophospholipids, and phosphatidylethanolamines) in diabetic kidneys compared to healthy controls. Furthermore, inhibition of nonenzymatic oxidation showed a reduction of lipids and disease recovery. Taken together, these results suggest that the pathogenesis of diabetic nephropathy comprises intermediate oxidative steps involving specific lipids [114].

3.2.4 Cardiology The localization of Ang II and metabolites in the kidney helped to understand their role in cardiovascular and renal pathologies [115]. The MSI-based examination of atherosclerotic lesions revealed specific molecules in lipid-rich regions in smooth muscle cells [116]. Tanaka et al. studied the distribution of lipid molecules in valve tissue of incompetent great saphenous veins [117]. To study distribution of lipids and metabolites in acute myocardial infarction, Menger et al. analyzed cardiac tissue following a 24 h left anterior descending coronary artery ligation. The results underlined a rising activity of phospholipase A2 [118]. Martin-Lorenzo et al. performed MSI of atherosclerotic ascending aorta of a rabbit model and found differential lipidic patterns in the intima, revealing inflammatory response [119].

3.2.2 Ophthalmology The molecular anatomy of the human optic nerve was recently defined by high spatial resolution MSI analysis of lipids and proteins [110]. In this work, the authors were able to distinguish individual fiber cell bundles, surrounding glial cells and central blood vessels by unique molecular signatures. For example, S100B protein was found distinctly localized in the prelaminar region composed of glial tissue. This protein is known to be abundant in astrocytes and to stimulate glial cell proliferation. Lipid metabolites were detected in a specific layer of the retina of the knockout mouse model of Stargardt disease (a juvenile onset form of a macular degeneration). This discovery may help to find new therapeutic targets of the affliction [111]. MSI also showed a significant increase of endogenous peptides in rats models for Usher’s disease, a rare cause of deaf-blindness. These biomarker candidates were particularly present in the upper colliculi and in the substantia nigra of sick rats [112]. MSI analyses of lenses revealed an increase in sphingomyelin levels, which may have an impact on development of age-related nuclear cataract [113]. C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

3.2.5 Other areas of medicine In dermatology, MSI demonstrated the first unambiguous localization of three allergens of natural latex gloves, and allowed to give explanations about the different allergenic potential of these [120]. Recently, Taverna et al. provided an MSI study on cutaneous pressure ulcers and could show S-100 proteins within pressure ulcer wound beds and adjacent areas [121]. Attia et al. combined data from MSI and magnetic resonance imaging to study inflammation during infection [122]. The visualization and identification of metabolites of microorganisms co-cultured gave new insights to study the particular impact of polymicrobial infections [123]. MSI is able to detect infectious disease, even if the infectious agent is found only in very small quantities [124]. Glaros et al. used MSI for the profiling of molecular markers of Burkholderia mallei infection, a highly contagious zoonotic infectious disease. Host and pathogen proteins were localized in skin and lung abscesses in infected monkeys [125]. www.clinical.proteomics-journal.com

8



Figure 3. MALDI imaging classification of breast and pancreas tumors. Classification study was performed to discriminate pancreas (A) from breast (B) primary tumors, as well as to determine the tumor of origin of pancreas (C) and breast (D) in metastatic liver tissues. Classification results obtained from ClinProTools software (Bruker Daltonik GmbH, Germany) were exported into FlexImaging software (Bruker Daltonik GmbH, Germany), and each classification was visualized in a color-encoded representation. Light blue was used for pancreas and magenta was used for breast carcinoma classification. Non-colored pixels were due to unclassified spectra. All needle cores in both pancreatic and breast TMAs were adenocarcinomas. HE staining was performed after mass spectrometric measurement and removal of the matrix of the very same sections used for classification analysis. Zoomed view of the MSI classification of one pancreas core (A) and one breast core (B) compared to the marking and diagnosis based on histology (black line) show strong correlation with the histopathological diagnosis made by pathologists. Panel C and D show image classification results of individual pancreatic and breast tumor liver metastasis sections. Insets in panel C and D show enlarged views of pancreas and breast adenocarcinoma cells respectively surrounding liver tissue. The model breast cancer tissue was provided by the biobank from the University of Dresden, with the local ethical accreditation.

In situ proteomics by MSI was used to characterize synovial tissues from patients suffering from rheumatoid arthritis and osteoarthritis [126]. Moreover, MSI allowed to detect markers of aging in osteoarthritic cartilage such as fibronectin and collectin-43 [127]. Recently was provided the protein classification and their distribution in osteoarthritic synovial tissues. In this study, heme group, PC, and fibronectin-related peptides were found in the afflicted tissues, on the basis of PCA analyses [128]. Microbial structures may be involved in the pathogenesis of sarcoidosis and may be detected by MSI [129]. Amyloidogenic proteins may be detected by MSI or LCMS. Associated proteins such as vitronectin, serum amyloid A, and serum amyloid P component may be co-localized with Congo red positive areas [130, 131]. Additionally, subtyping of amyloidosis can be achieved by MALDI MSI [131]. MALDI has also been used to provide additional information on the location and nature of glycosphingolipids within Fabry’s disease [132].

4

Applications of MSI in cancer diagnosis, staging, and prognosis

Therapeutic decisions closely rely on tumor classification (tumor, node, metastasis (TNM)-classification) provided by the pathologist. This is supplemented by predictive and prognostic factors which may require also immunohistochemical and C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

molecular investigation. Since these methods have a degree of uncertainty, further objective methods are necessary to provide an adequate personalized therapy. MSI seems to be an adequate tool to improve diagnostics in clinical pathology [53] (Fig. 1).

4.1 Breast cancer In 2014, Casadonte et al. discriminated breast from pancreatic cancer with an overall accuracy of 83.38%, a sensitivity of 85.95%, and a specificity of 76.96% [44]. In this study, mass spectra correlated to the primary tumor regions of both breast and pancreas phenotypes were evaluated by a support vector machine (SVM) algorithm to train a classification model able to discern accurately the tumor types. Additionally, the classification model was successfully validated on an independent test set of primary carcinomas and on metastases of both breast and pancreas tumor types. This article represents an example of the usefulness of TMA for tumor type classification. Figure 3 illustrates a MSI classification result of breast and pancreas primary adenocarcinoma (insets A and B) and metastases in liver tissues (insets C and D). In the future, this kind of analysis may allow the objective diagnosis of pathological tissue types, without bias induced by the subjective interpretation of the pathologist (Fig. 1). Recently, breast cancer was subjected to agreement analysis, which is a combination of different hierarchical www.clinical.proteomics-journal.com

9


Figure 4. Workflow for the study of intratumoral heterogeneity and its correlation with the clinical outcome of the patients [133]. Intratumoral heterogeneity is caused by the differentiation of cancer stem cells due to genetic instability. This can conduct to the appearance of clones that bear highly proliferative effects, leading to the highly invasive development of the tumor, and subsequent poor prognostic of the patient (A). In this study, tissues were analyzed by MALDI MSI and the whole dataset processed by unsupervised segmentation of the mass spectra. Molecular heterogeneity was then highlighted by the determination of specific features in different regions of the tissue. The presence of some molecular features was then correlated to the outcome of the patients (B). MALDI MSI was absolutely necessary here to discriminate tissue regions that could not be recognized using classical histology methods.

clustering methods followed by a comparison of the results for their similarity. This approach was used in order to determine intratumoral molecular heterogeneity that was correlated to different clusters obtained from the overall survival of the patients. It was highlighted that one of the molecular feature was significantly correlated with the metastatic status of the patients and that changes in acetylated histone H4 and histone H2A were associated with poor prognosis (Fig. 4) [133]. The study of tumor heterogeneity may also represent interesting data in pathological reports. The combination of MALDI MSI with laser microdissection-based microproteomics experiments will represent a perfect marriage for the extensive description of molecular actors of bad prognosis in tumors (Fig. 1). Another study from Chung et al. demonstrated that a Cterminal truncated form of S-100 protein (C-ter S100P) could predict disease-free survival in patients with lymph node positive breast cancer. The results obtained by SELDI TOF analysis were confirmed by MSI where the m/z 9261 corresponding to C-ter S100P was expressed 20-fold more in breast cancer tissue, compared to the adjacent unaffected tissue [134]. It is noteworthy that other ionization sources were also exploited for the analysis of tissues extemporaneously. Indeed, DESI, an atmospheric pressure ionization mode, was proposed as a proof-of-concept for the determination of breast cancer margins to help surgeons for rapid intraoperative detection of residual cancer tissue [34].

4.2 Prostate cancer Prostate cancer has also been investigated by MSI using a SVM algorithm [50]. As presented by Bonnel et al., it is also C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

possible to determine the molecular signature of a given tissue region of interest, based on known molecular signatures using hierarchical clustering [18]. Gotto et al. have determined that PI was more highly expressed in cancerous prostate tissue compared to healthy epithelium [135]. Recently, the use of MSI allowed determining specific peaks in cancerous prostatic epithelia. Combination of these analyses with LC-MS analyses identified biliverdin reductase B as a biomarker for prostate cancer [51].

4.3 Esophageal cancer Large-scale TMAs were used to discriminate 72 m/z signals associated to tumor cells and 48 were specifically linked to squamous cell carcinoma and 12 to adenocarcinoma [136]. In this study, 300 adenocarcinomas and 177 squamous cell carcinomas with clinical follow up were examined, and it was therefore possible to associate the detected peptides to tumor grade and stage, the presence or absence of nodal metastases, and the overall survival of the patients. In another study was investigated Barrett’s adenocarcinoma and it was possible to correlate cytochrome c oxidase (COX)7A2 and S100-A proteins with survival data [137].

4.4 Cancers of the digestive tract Hierarchical clustering analysis has been employed in gastric cancer by Deininger et al. [39] in order to differentiate normal mucosa from cancerous areas. Balluff et al. described a panel of seven prognostic proteins for intestinal-type gastric cancers. Among those, three proteins, cysteine-rich protein www.clinical.proteomics-journal.com

10



1 (CRIP1), human neutrophil peptide 1 (HNP-1), and S100A6 were validated by IHC. HNP-1, and CRIP1, previously known gastric cancer biomarkers, were discriminant between early and late stage cancer patients, based on univariate Kaplan-Meier analysis and multivariate Cox regression. CRIP1 was a newly discovered marker for this disease [46]. The same author applied agreement analyses to gastric cancer for the correlation of molecular features to disease prognosis [133]. Meding et al. explored the use of MSI and label free proteomics to find proteins associated to lymph-node metastasis in colonic cancer. FXYD domain-containing ion transport regulator 3 (FXYD3), S100A11, and glutathione S-transferase M3 (GSTM3) were identified as new prognostic markers [138].

4.5 Renal carcinoma Significance analysis of microarrays (SAM) was used to explore the clear cell renal cell carcinomas (ccRCC) and permutation t-tests were used to compare spectra from different analyzed spots along the tissue section and determine the molecular features [139]. On this basis, the authors have been able to affirm that some features were significantly different between margin normal versus normal tissues, and margin cancer versus cancer tissue regions. Comparison of these patterns also allowed to state that the “molecular margin” was actually not at the same location as the margin predicted by classical histology (Fig. 5). In another study, ccRCC TMAs were also used for the validation of markers with 96.9–100% accuracy [140]. Tissue microarrays were also used to analyze and classify Wilms tumors. 131 peptide peaks allowed discriminating favorable and unfavorable histological types. Two hundred and three peaks could differentiate patient groups with treatment failure from the ones with success. Seventy one peaks could discriminate high risk from standard, low and very low risk populations (according to Children’s Oncology Group) [141]. Fr/fr tissues were used by Jones et al. to look for diseasespecific protein and lipid markers, and discriminant patterns of recurrence. Using MaTisse database and MS/MS data, they found fatty acid binding protein 7 (FABP7) in nonrecurrent tumors. Thirty nine lipids were discriminatory between tumor and nontumor or recurrent and nonrecurrent tumors. These markers were mainly identified as PC species. Twenty six proteins and 39 lipids were identified for the discrimination of tumor from nontumor tissues and recurrent from nonrecurrent disease. The individual peak levels of analysis also revealed intratumoral heterogeneity [142]. Another S100 protein, S100A11, and ferritin light chain were assigned to papillary RCC using combined PCA analysis from MSI dataset and LC-MS/MS identification [143]. C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Figure 5. Example of the use of MALDI MSI to decipher tumor margins [139]. Tumor recurrence after surgery occurs frequently, thus suggesting that a specific molecular context could exist in the region surrounding the tumor margins. In this study, MALDI MSI was used to analyze a region of the tissue section covering the tumor, the tumor margin, the normal margin, and the normal tissue (panel A). Spectra from normal versus tumor tissue, and margin normal versus tumor margin were compared using significance analysis of microarrays (SAM) and permutation t-test statistics. The results revealed the presence of molecular features that began changing near the histological margin (panel B, lines A and C) while other features changed after the histological margin (lines B and D).

4.6 Hepatology Le Faouder et al. revealed fingerprints for the discrimination between hepatocellular carcinoma (HCC) and cirrhosis [144]. Laouirem et al. recently reported the use of MSI to compare cirrhosis with or without HCC, and top-down proteomics approaches to characterize differential biomarkers [86]. Another study on biomarkers of HCC was conducted by Han et al. who described specific molecular signatures through the tissue section, from the normal regions to the center of the tumor [145]. Marquardt et al. used MSI on fr/fr tissues to confirm the diagnosis of HCC in biopsies. Based on classification methods, four m/z ratios could discriminate HCC from fibrotic liver tissue [146]. www.clinical.proteomics-journal.com

11


4.7 Melanoma Hardesty et al. determined proteomic signatures of survival and recurrence in metastatic melanoma. A univariate Coxproportional hazard (CPH) model was used to evaluate proteomic features associated with patient survival and timeto-recurrence. Calcyclin, thymosin ␤4, thymosin ␤10, and ubiquitin were identified as unfavorable markers for survival, whereas histones H3, H4, and H2B were found associated to survival. Increased expression of cytochrome C and calmodulin correlated with longer time to recurrence [147]. Lazova et al. used an MSI-based proteomic approach to differentiate Spitz nevi (SN) from spitzoid malignant melanomas (SMM) [148]. Fifty-six SN and fifty-eight SMM specimens were used to create and validate an algorithm classification model that could discriminate SN from SMM with 97% sensitivity and 90% specificity. Melanoma was also studied using nano-assisted laser desorption ionization (NALDI) MSI [149]. In this study, the authors searched for the changes in the lipidomic content of a murine model of melanoma treated by synthetic supplement containing phosphoethanolamine (PHO-S). They found a substantial reduction in the abundance of phospholipid biomarkers.

4.8 Lung cancer Lung cancer was used for classification and prediction in histological groups, based on 1600 peaks [150]. Only 15 protein peaks were then applied for the classification of primary tumors and metastatic tissues with poor or good prognosis. Groseclose et al. studied FFPE tissues of lung cancer. A subset of 73 tryptic peptides was associated with adenocarcinoma (more than 98% accuracy) [45]. Combined with other methods, MSI allowed the identification of acyl-coenzyme A binding protein (ACBP) as a protein predicting the risk of non-small cell lung cancer and controlling tumor progression by regulating the B-oxidation [151]. ACBP protein was overexpressed in pre-invasive and invasive lung cancer. Further studies have shown that this overexpression was linked with a worse survival and that its role in cancer progression was due to interference with regulating ␤-oxidation, in accordance with previous findings [151].

4.9 Brain tumors Stoekli et al. studied mouse brain tumors, this representing one of the first applications of MSI. S100 proteins were found to be expressed in the tumor core whereas thymosin␤4 was detected in proliferative areas near the tumor core [152]. Multiple further studies were dedicated to the distinction between tumor types and grades [8, 153, 154]. Also, this model has been used in combination with magnetic resonance imaging [54, 155], proving that the method was able to discriminate tumors from normal tissues. Recently, Calli C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

garis et al. used MSI for potential near-real-time delineation of pituitary adenomas. ISD fragments as well as entire hormones were detected for the discrimination of pituitary tumors and pituitary glands. Subtyping of pituitary adenomas secreting different hormones was also possible using PCA and machine learning analyses [156]. To note, the alternative atmospheric ionization source DESI has proven its efficiency in improving decision-making during surgical interventions [157, 158].

4.10 Myxoid sarcomas Willems et al. discriminated myxoid sarcoma types, grades and showed intratumoral heterogeneity on the basis of lipid analysis [40].

4.11 Lymphoma A recent lipidomic analysis allowed to realize the link between specific lipid profiles and lymphomas derived from the overexpression of v-myc avian viral oncogene homolog myelocytomatosis (MYC) [159].

4.12 Bladder cancer ¨ In 2011, Ozdemir et al. used MSI to classify papillary noninvasive bladder cancer, according to the new classification that was introduced in 2004, differentiating low grade (LG) and high grade (HG) [160]. The analysis of fr/fr tissues, allowed them to classify the tissue using SVM on the basis of 23 peaks in the mass range from 900 to 16000 Da. MSI was able to reach a rate of true classification of 87.5% for G2 HG tumors and of 78.3% for G2 LG tumors. G1, that are all LG, and G3, that are all HG tumors, were separated with an overall cross validation of 97.2%.

4.13 Penile cancer This rare cancer was explored by MSI for the first time by Flatley et al. This study aimed to compare the MS profile of normal epithelial tissue to the one of squamous cell carcinoma. S100A4 protein was found to be associated to squamous cell carcinoma and was validated by IHC [161].

4.14 Thyroid cancer MSI was used for the analysis of papillary thyroid carcinoma. In parallel, proteins were extracted from tissues and analyzed by LC-MS/MS. Among the localized proteins, ribosomal protein P2 was found specific for cancerous tissues [162]. Other preliminary studies by MSI were performed for thyroid fine needle aspiration smears [163]. www.clinical.proteomics-journal.com

12



Figure 6. Example of the use of MALDI as a starting experiment for the understanding of clinical response to chemotherapy [164]. MSI analyses were performed for protein exploration on tissues from patient who responded or did not respond to neoadjuvant chemotherapy in the oesophageal adenocarcinoma treatment. Eight proteins accurately discriminated responders from nonresponders that were mapped in a hierarchical clustering, (A). Among those proteins, COX7A2, a mitochondrial respiratory complex protein, presented the best profile in discriminating responders from nonresponders (B), and its overexpression was validated by immunohistochemistry in nonresponders tissues (C). Electron microscopy revealed mitochondrial defects in patients where low COX7A2 expression was found (D). Further, biological assays were performed for the validation of the functional implication of COX7A2 in chemoresistance (E, F). siRNA targeting the COX7A2 were used on OE19 cells (a cell line expressing normal level of COX7A2) and showed that the combined blocking of COX7A2 production and the treatment with cisplatin/5-FU tended to provoke cell death. This study represents a clear example that MALDI MSI is highly valuable in workflows for life science research.

5

Application of MALDI MSI to predict response to therapy

Detection of therapeutic targets is essential in modern oncology and it is certain that the importance of targeted therapies will increase in the future. A clinical application of MSI is the determination of HER2 status based on its proteomic profile [49]. By MSI, the HER2 C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

status could be predicted with 83% sensitivity and 92% specificity. Furthermore, the same researchers could also determine HER2 status in gastric cancer [47]. Recently, Aichler et al. have combined MSI experiments with LC-MS identification of the detected peptides with the use of biological assays to validate the relevance of the identified markers. This work revealed that the clinical response to chemotherapy in esophageal carcinoma was linked to


13


mitochondria defects and loss of specific subunits cytochrome c oxidase (COX) [164]. This study, illustrated in Fig. 6, clearly demonstrates the importance of the combination of different analytical tools and biological assays for the validation of candidates. After the priming discovery phase, small interfering ribonucleic acids (siRNA) were used for the functional validation of COX implication in cancer cell proliferation and colony formation. The study opens new perspectives for the appropriate use of MSI and proteomics methods in biomarkers discovery and validation [165].

6

Conclusion and prospects

Up-to-date, histopathological diagnosis is based on histology, immunohistochemistry and molecular analyses. These methods can be supported by proteomic techniques such as MSI or LC-MS/MS. Since MSI combines the detection of peptide profiles with morphological information, this technique seems to be most adequate to improve histopathological diagnosis. The increasing speed of spectral data acquisition, mathematical, statistical and bioinformatic processing, and instrumental improvements in MSI are the requirements for the introduction of this technique into histopathological diagnosis. An example of clinical application of MS is the mapping of cancer metabolome by MS in frozen sections obtained during operation of tumors to support surgeon´s decision. MS-based analyses in frozen sections is done at atmospheric pressure and does not require vacuum or tissue preprocessing with sources such as DESI [166, 167] and REIMS [33, 168]. The future of clinical diagnostics will be strongly influenced by “omics” approaches. Furthermore, this is the basis for biomarker detection. Contrary to antibody techniques, MSI offers the unique possibility to detect biomarkers or other molecules in the tissue without prior knowledge of the biological structure of these molecules. However, identification of these molecules by using MALDI MS/MS may be difficult. Consequently, LC-MS/MS is a perfect complementary method to MSI analyses. Combining MSI and LC-MS/MS analyses may provide further insight into distribution of molecules within various tissue compartments. Attempts are being proposed to combine MSI and solvent-based tissue surface sampling to access more informative proteomics data from microanatomies. Currently, those approaches are limited to the detection of drugs [169], lipids [170], and proteins from fr/fr tissues [171]. Future applications may include bottom-up proteomics in FFPE tissues, e.g. by analyzing proteolytic peptides after trypsin digestion. Today, laser microdissection is one of the most precise methods for tissue sampling, since groups of pure cells can be selected, processed, and analyzed. Recently, our team developed a laser microdissection-based microproteomic method of FFPE tissues. Applying this technique, identification of 1000 to more than 1400 proteins from a sample containing C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

about 2700 cells is possible [37]. Laser microdissection-based tissue proteomics may soon be the cornerstone for biomarker discovery studies of FFPE tissue samples. In a close future, coupling MSI with shotgun microproteomics would permit the extensive proteomic profiling of heterogeneous tissues regions. One application could be the evaluation of peptides correlated to a good or bad prognosis of tumors. In parallel, MALDI MSI or shotgun proteomic, could improve classification of various histopathological states and is a prerequisite for objective histopathological diagnosis of tumors. These techniques may revolutionize the working process in institutes of pathology (Fig. 1). R.L. acknowledges his funders: the University of Liège and Proteopath. The authors also thank the members of the biobank from the University of Liège, Kamilia El Kandoussi, and Stéphanie Gofflot for providing sections of the breast cancer model tissue. The authors have declared no conflict of interest.

7

References [1] Hillenkamp, F., Karas, M., Mass spectrometry of peptides and proteins by matrix-assisted ultraviolet laser desorption/ionization. Methods Enzymol. 1990, 193, 280–295. [2] Jimenez, C. R., van Veelen, P. A., Li, K. W., Wildering, W. C. et al., Neuropeptide expression and processing as revealed by direct matrix-assisted laser desorption ionization mass spectrometry of single neurons. J Neurochem 1994, 62, 404–407. [3] van Strien, F. J., Jespersen, S., van der Greef, J., Jenks, B. G., Roubos, E. W., Identification of POMC processing products in single melanotrope cells by matrix-assisted laser desorption/ionization mass spectrometry. FEBS Lett 1996, 379, 165–170. [4] Garden, R. W., Moroz, L. L., Moroz, T. P., Shippy, S. A., Sweedler, J. V., Excess salt removal with matrix rinsing: direct peptide profiling of neurons from marine invertebrates using matrix-assisted laser desorption/ionization time-offlight mass spectrometry. J. Mass Spectromet. 1996, 31, 1126–1130. [5] Stahl, B., Linos, A., Karas, M., Hillenkamp, F., Steup, M., Analysis of fructans from higher plants by matrixassisted laser desorption/ionization mass spectrometry. Anal. Biochem. 1997, 246, 195–204. [6] Caprioli, R. M., Farmer, T. B., Gile, J., Molecular imaging of biological samples: localization of peptides and proteins using MALDI-TOF MS. Analyt. Chem. 1997, 69, 4751–4760. [7] Longuespee, R., Fleron, M., Pottier, C., Quesada-Calvo, F. et al., Tissue proteomics for the next decade? Towards a molecular dimension in histology. Omics 2014, 18, 539– 552. [8] Chaurand, P., Sanders, M. E., Jensen, R. A., Caprioli, R. M., Proteomics in diagnostic pathology: profiling and imaging proteins directly in tissue sections. Am J Pathol 2004, 165, 1057–1068. www.clinical.proteomics-journal.com

14


[9] Gustafsson, O. J., Eddes, J. S., Meding, S., McColl, S. R. et al., Matrix-assisted laser desorption/ionization imaging protocol for in situ characterization of tryptic peptide identity and distribution in formalin-fixed tissue. Rapid Commun Mass Spectrom 2013, 27, 655–670. [10] Wisztorski, M., Franck, J., Salzet, M., Fournier, I., MALDI direct analysis and imaging of frozen versus FFPE tissues: what strategy for which sample? Methods Mol Biol 2010, 656, 303–322. [11] Yang, J., Caprioli, R. M., Matrix sublimation/ recrystallization for imaging proteins by mass spectrometry at high spatial resolution. Analyt. Chem. 2011, 83, 5728–5734. [12] Reiersen, H., Rees, A. R., Trifluoroethanol may form a solvent matrix for assisted hydrophobic interactions between peptide side chains. Protein Engineer. 2000, 13, 739–743. [13] Franck, J., Longuespee, R., Wisztorski, M., Van Remoortere, A. et al., MALDI mass spectrometry imaging of proteins exceeding 30,000 daltons. Med Sci Monit 2010, 16, BR293– BR299. [14] van Remoortere, A., van Zeijl, R. J., van den Oever, N., Franck, J. et al., MALDI imaging and profiling MS of higher mass proteins from tissue. J Am Soc Mass Spectrom 2010, 21, 1922–1929. [15] El Ayed, M., Bonnel, D., Longuespee, R., Castelier, C. et al., MALDI imaging mass spectrometry in ovarian cancer for tracking, identifying, and validating biomarkers. Med Sci Monit 2010, 16, BR233–BR245.


embedded tissue sections using MALDI mass spectrometry. ACS Chem. Biol. 2014, 9, 2149–2156. [23] Powers, T. W., Neely, B. A., Shao, Y., Tang, H. et al., MALDI imaging mass spectrometry profiling of N-glycans in formalin-fixed paraffin embedded clinical tissue blocks and tissue microarrays. PLoS One 2014, 9, e106255. [24] Buck, A., Ly, A., Balluff, B., Sun, N. et al., High-resolution MALDI-FT-ICR MS imaging for the analysis of metabolites from formalin-fixed, paraffin-embedded clinical tissue samples. J. Pathol. 2015, 237, 123–132. [25] Yalcin, E. B., de la Monte, S. M., Review of matrix-assisted laser desorption ionization-imaging mass spectrometry for lipid biochemical histopathology. J. Histochem. Cytochem. 2015, 63, 762–771. [26] Arafah, K., Longuespee, R., Desmons, A., Kerdraon, O. et al., Lipidomics for clinical diagnosis: dye-assisted laser desorption/ionization (DALDI) method for lipids detection in MALDI mass spectrometry imaging. Omics 2014, 18, 487– 498. [27] Longuespee, R., Boyon, C., Desmons, A., Kerdraon, O. et al., Spectroimmunohistochemistry: a novel form of MALDI mass spectrometry imaging coupled to immunohistochemistry for tracking antibodies. Omics 2014, 18, 132–141. [28] Gorzolka, K., Walch, A., MALDI mass spectrometry imaging of formalin-fixed paraffin-embedded tissues in clinical research. Histol. Histopathol. 2014, 29, 1365–1376. [29] Gode, D., Volmer, D. A., Lipid imaging by mass spectrometry - a review. Analyst 2013, 138, 1289–1315.

[16] Longuespee, R., Boyon, C., Castellier, C., Jacquet, A. et al., The C-terminal fragment of the immunoproteasome PA28S (Reg alpha) as an early diagnosis and tumor-relapse biomarker: evidence from mass spectrometry profiling. Histochem Cell Biol 2012, 138, 141–154.

[30] Jadoul, L., Longuespee, R., Noel, A., De Pauw, E., A spiked tissue-based approach for quantification of phosphatidylcholines in brain section by MALDI mass spectrometry imaging. Analyt. Bioanalyt. Chem. 2015, 407, 2095– 2106.

[17] Gustafsson, J. O., Oehler, M. K., McColl, S. R., Hoffmann, P., Citric acid antigen retrieval (CAAR) for tryptic peptide imaging directly on archived formalin-fixed paraffin-embedded tissue. J Proteome Res 2010, 9, 4315–4328.

[31] Jadoul, L., Smargiasso, N., Pamelard, F., Alberts, D. et al., An improved molecular histology method for ion suppression monitoring and quantification of phosphatidyl cholines during MALDI MSI lipidomics analyses. Omics 2016, 20, 110– 121.

[18] Bonnel, D., Longuespee, R., Franck, J., Roudbaraki, M. et al., Multivariate analyses for biomarkers hunting and validation through on-tissue bottom-up or in-source decay in MALDI-MSI: application to prostate cancer. Analyt. Bioanalyt. Chem. 2011, 401, 149–165. [19] Longuespee, R., Gagnon, H., Boyon, C., Strupat, K. et al., Proteomic analyses of serous and endometrioid epithelial ovarian cancers - cases studies - molecular insights of a possible histological etiology of serous ovarian cancer. Proteomics. Clinical Applications 2013, 7, 337–354. [20] Casadonte, R., Caprioli, R. M., Proteomic analysis of formalin-fixed paraffin-embedded tissue by MALDI imaging mass spectrometry. Nature Protocols 2011, 6, 1695– 1709. [21] McDonnell, L. A., Corthals, G. L., Willems, S. M., van Remoortere, A. et al., Peptide and protein imaging mass spectrometry in cancer research. J. Proteomics 2010, 73, 1921– 1944. [22] Toghi Eshghi, S., Yang, S., Wang, X., Shah, P. et al., Imaging of N-linked glycans from formalin-fixed paraffin-


[32] Bodzon-Kulakowska, A., Suder, P., Imaging mass spectrometry: instrumentation, applications, and combination with other visualization techniques. Mass Spectrometr. Rev. 2016, 35, 147–169. [33] Golf, O., Strittmatter, N., Karancsi, T., Pringle, S. D. et al., Rapid evaporative ionization mass spectrometry imaging platform for direct mapping from bulk tissue and bacterial growth media. Analyt. Chem. 2015, 87, 2527–2534. [34] Calligaris, D., Caragacianu, D., Liu, X., Norton, I. et al., Application of desorption electrospray ionization mass spectrometry imaging in breast cancer margin analysis. Proc. Natl Acad. Sci. USA 2014, 111, 15184–15189. [35] Maier, S. K., Hahne, H., Moghaddas Gholami, A., Balluff, B. et al., Comprehensive identification of proteins from MALDI imaging. Mol Cell Proteomics 2013, 12, 2901–2910. [36] Meding, S., Martin, K., Gustafsson, O. J., Eddes, J. S. et al., Tryptic peptide reference data sets for MALDI imaging mass spectrometry on formalin-fixed ovarian cancer tissues. J Proteome Res 2013, 12, 308–315. www.clinical.proteomics-journal.com


[37] Longuespee, R., Alberts, D., Pottier, C., Smargiasso, N. et al., A laser microdissection-based workflow for FFPE tissue microproteomics: Important considerations for small sample processing. Methods 2016, http://dx.doi.org/ 10.1016/j.ymeth.2015.12.008, in press.

15 [51] Pallua, J. D., Schaefer, G., Seifarth, C., Becker, M. et al., MALDI-MS tissue imaging identification of biliverdin reductase B overexpression in prostate cancer. J Proteomics 2013, 91, 500–514.

[38] Jamal-Hanjani, M., Quezada, S. A., Larkin, J., Swanton, C., Translational implications of tumor heterogeneity. Clinical Cancer Res. 2015, 21, 1258–1266.

[52] Meding, S., Nitsche, U., Balluff, B., Elsner, M. et al., Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging. J Proteome Res 2012, 11, 1996–2003.

[39] Deininger, S. O., Ebert, M. P., Futterer, A., Gerhard, M., Rocken, C., MALDI imaging combined with hierarchical clustering as a new tool for the interpretation of complex human cancers. J Proteome Res 2008, 7, 5230–5236.

[53] Kriegsmann, J., Kriegsmann, M., Casadonte, R., MALDI TOF imaging mass spectrometry in clinical pathology: a valuable tool for cancer diagnostics (review). International J. Oncol. 2015, 46, 893–906.

[40] Willems, S. M., van Remoortere, A., van Zeijl, R., Deelder, A. M. et al., Imaging mass spectrometry of myxoid sarcomas identifies proteins and lipids specific to tumour type and grade, and reveals biochemical intratumour heterogeneity. J Pathol 2010, 222, 400–409.

[54] Seeley, E. H., Caprioli, R. M., Molecular imaging of proteins in tissues by mass spectrometry. Proc. Natl Acad. Sci. USA 2008, 105, 18126–18131.

[41] Alexandrov, T., Becker, M., Deininger, S. O., Ernst, G. et al., Spatial segmentation of imaging mass spectrometry data with edge-preserving image denoising and clustering. J Proteome Res 2010, 9, 6535–6546. [42] Alexandrov, T., Becker, M., Guntinas-Lichius, O., Ernst, G., von Eggeling, F., MALDI-imaging segmentation is a powerful tool for spatial functional proteomic analysis of human larynx carcinoma. J. Cancer Res. Clinical Oncol. 2013, 139, 85–95. [43] McCombie, 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, 6118–6124. [44] Casadonte, R., Kriegsmann, M., Zweynert, F., Friedrich, K. et al., Imaging mass spectrometry to discriminate breast from pancreatic cancer metastasis in formalinfixed paraffin-embedded tissues. Proteomics 2014, 14, 956– 964. [45] Groseclose, M. R., Massion, P. P., Chaurand, P., Caprioli, R. M., High-throughput proteomic analysis of formalin-fixed paraffin-embedded tissue microarrays using MALDI imaging mass spectrometry. Proteomics 2008, 8, 3715–3724. [46] Balluff, B., Rauser, S., Meding, S., Elsner, M. et al., MALDI imaging identifies prognostic seven-protein signature of novel tissue markers in intestinal-type gastric cancer. Am J Pathol 2011, 179, 2720–2729. [47] Balluff, B., Elsner, M., Kowarsch, A., Rauser, S. et al., Classification of HER2/neu status in gastric cancer using a breast-cancer derived proteome classifier. J Proteome Res 2010, 9, 6317–6322. [48] Hanselmann, M., Kothe, U., Kirchner, M., Renard, B. Y. et al., Toward digital staining using imaging mass spectrometry and random forests. J Proteome Res 2009, 8, 3558–3567.

[55] Seeley, E. H., Caprioli, R. M., Imaging mass spectrometry: Towards clinical diagnostics. Proteomics Clin Appl 2008, 2, 1435–1443. [56] Dekker, T. J., Balluff, B. D., Jones, E. A., Schone, C. D. et al., Multicenter matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) identifies proteomic differences in breast-cancer-associated stroma. J Proteome Res 2014, 13, 4730–4738. [57] Kang, S., Maeng, H., Kim, B. G., Qing, G. M. et al., In situ identification and localization of IGHA2 in the breast tumor microenvironment by mass spectrometry. J Proteome Res 2012, 11, 4567–4574. [58] Seuma, J., Bunch, J., Cox, A., McLeod, C. et al., Combination of immunohistochemistry and laser ablation ICP mass spectrometry for imaging of cancer biomarkers. Proteomics 2008, 8, 3775–3784. [59] Kawashima, M., Iwamoto, N., Kawaguchi-Sakita, N., Sugimoto, M. et al., High-resolution imaging mass spectrometry reveals detailed spatial distribution of phosphatidylinositols in human breast cancer. Cancer science 2013, 104, 1372–1379. [60] Ide, Y., Waki, M., Hayasaka, T., Nishio, T. et al., Human breast cancer tissues contain abundant phosphatidylcholine(36ratio1) with high stearoyl-CoA desaturase-1 expression. PLoS One 2013, 8, e61204. [61] Chughtai, K., Jiang, L., Greenwood, T. R., Glunde, K., Heeren, R. M., Mass spectrometry images acylcarnitines, phosphatidylcholines, and sphingomyelin in MDA-MB-231 breast tumor models. J. Lipid Res. 2013, 54, 333–344. [62] Jiang, L., Chughtai, K., Purvine, S. O., Bhujwalla, Z. M. et al., MALDI-mass spectrometric imaging revealing hypoxiadriven lipids and proteins in a breast tumor model. Analyt. Chem. 2015, 87, 5947–5956.

[49] Rauser, S., Marquardt, C., Balluff, B., Deininger, S. O. et al., Classification of HER2 receptor status in breast cancer tissues by MALDI imaging mass spectrometry. J Proteome Res 2010, 9, 1854–1863.

[63] Lemaire, R., Menguellet, S. A., Stauber, J., Marchaudon, V. et al., 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– 4134.

[50] Schwamborn, K., Krieg, R. C., Reska, M., Jakse, G. et al., Identifying prostate carcinoma by MALDI-Imaging. Int J Mol Med 2007, 20, 155–159.

[64] Nonaka, H., Saga, Y., Fujiwara, H., Akimoto, H. et al., Indoleamine 2,3-dioxygenase promotes peritoneal dissemination of ovarian cancer through inhibition of natural



16



killercell function and angiogenesis promotion. Int J Oncol 2011, 38, 113–120.

spectrometry imaging of tissue micro-array. Anal Bioanal Chem 2010, 397, 587–601.

[65] Clarke, B., Tinker, A. V., Lee, C. H., Subramanian, S. et al., Intraepithelial T cells and prognosis in ovarian carcinoma: novel associations with stage, tumor type, and BRCA1 loss. Mod Pathol 2009, 22, 393–402.

[80] Djidja, M. C., Claude, E., Snel, M. F., Scriven, P. et al., MALDI-ion mobility separation-mass spectrometry imaging of glucose-regulated protein 78 kDa (Grp78) in human formalin-fixed, paraffin-embedded pancreatic adenocarcinoma tissue sections. J Proteome Res 2009, 8, 4876–4884.

[66] Curiel, T. J., Regulatory T cells and treatment of cancer. Curr Opin Immunol 2008, 20, 241–246. [67] Preston, C. C., Goode, E. L., Hartmann, L. C., Kalli, K. R., Knutson, K. L., Immunity and immune suppression in human ovarian cancer. Immunotherapy 2011, 3, 539–556. [68] Yigit, R., Massuger, L. F., Figdor, C. G., Torensma, R., Ovarian cancer creates a suppressive microenvironment to escape immune elimination. Gynecol Oncol 2010, 117, 366–372. [69] Giuntoli, R. L., 2nd, Webb, T. J., Zoso, A., Rogers, O. et al., Ovarian cancer-associated ascites demonstrates altered immune environment: implications for antitumor immunity. Anticancer Res. 2009, 29, 2875–2884. [70] Longuespee, R., Boyon, C., Desmons, A., Vinatier, D. et al., Ovarian cancer molecular pathology. Cancer Metastasis Rev. 2012, 31, 713–732. [71] Gustafsson, J. O., Eddes, J. S., Meding, S., Koudelka, T. et al., Internal calibrants allow high accuracy peptide matching between MALDI imaging MS and LC-MS/MS. J Proteomics 2012, 75, 5093–5105. [72] Fata, C. R., Seeley, E. H., Desouki, M. M., Du, L. et al., Are clear cell carcinomas of the ovary and endometrium phenotypically identical? A proteomic analysis. Hum. Pathol. 2015, 46, 1427–1436. [73] Liu, Y., Chen, Y., Momin, A., Shaner, R. et al., Elevation of sulfatides in ovarian cancer: an integrated transcriptomic and lipidomic analysis including tissue-imaging mass spectrometry. Mol. Cancer 2010, 9, 186. [74] Steurer, S., Borkowski, C., Odinga, S., Buchholz, M. et al., MALDI mass spectrometric imaging based identification of clinically relevant signals in prostate cancer using largescale tissue microarrays. Int J Cancer 2013, 133, 920–928. [75] He, Q., Takizawa, Y., Hayasaka, T., Masaki, N. et al., Increased phosphatidylcholine (16:0/16:0) in the folliculus lymphaticus of Warthin tumor. Anal Bioanal Chem 2014, 406, 5815– 5825. [76] Kurabe, N., Hayasaka, T., Ogawa, M., Masaki, N. et al., Accumulated phosphatidylcholine (16:0/16:1) in human colorectal cancer; possible involvement of LPCAT4. Cancer Sci, 104, 1295–1302. [77] Uehara, T., Kikuchi, H., Miyazaki, S., Iino, I. et al., Overexpression of Lysophosphatidylcholine Acyltransferase 1 and Concomitant Lipid Alterations in Gastric Cancer. Ann. Surg. Oncol. 2015, 23, 206–213. [78] Mirnezami, R., Spagou, K., Vorkas, P. A., Lewis, M. R. et al., Chemical mapping of the colorectal cancer microenvironment via MALDI imaging mass spectrometry (MALDI-MSI) reveals novel cancer-associated field effects. Mol Oncol, 8, 39–49. [79] Djidja, M. C., Claude, E., Snel, M. F., Francese, S. et al., Novel molecular tumour classification using MALDI-mass


[81] Gruner, B. M., Hahne, H., Mazur, P. K., Trajkovic-Arsic, M. et al., MALDI imaging mass spectrometry for in situ proteomic analysis of preneoplastic lesions in pancreatic cancer. PLoS One 2012, 7, e39424. [82] Rebours, V., Le Faouder, J., Laouirem, S., Mebarki, M. et al., In situ proteomic analysis by MALDI imaging identifies ubiquitin and thymosin-beta4 as markers of malignant intraductal pancreatic mucinous neoplasms. Pancreatology, 14, 117–124. [83] Gruner, B. M., Hahne, H., Mazur, P. K., Trajkovic-Arsic, M. et al., MALDI imaging mass spectrometry for in situ proteomic analysis of preneoplastic lesions in pancreatic cancer. PLoS One, 7, e39424. [84] Pote, N., Alexandrov, T., Le Faouder, J., Laouirem, S. et al., Imaging mass spectrometry reveals modified forms of histone H4 as new biomarkers of microvascular invasion in hepatocellular carcinomas. Hepatology 2013, 58, 983– 994. [85] Morita, Y., Sakaguchi, T., Ikegami, K., Goto-Inoue, N. et al., Lysophosphatidylcholine acyltransferase 1 altered phospholipid composition and regulated hepatoma progression. J Hepatol, 59, 292–299. [86] Laouirem, S., Le Faouder, J., Alexandrov, T., Mestivier, D. et al., Progression from cirrhosis to cancer is associated with early ubiquitin post-translational modifications: identification of new biomarkers of cirrhosis at risk of malignancy. J Pathol 2014, 234, 452–463. [87] Wattacheril, J., Seeley, E. H., Angel, P., Chen, H. et al., Differential intrahepatic phospholipid zonation in simple steatosis and nonalcoholic steatohepatitis. PLoS One 2013, 8, e57165. [88] Calligaris, D., Longuespee, R., Debois, D., Asakawa, D. et al., Selected protein monitoring in histological sections by targeted MALDI-FTICR in-source decay imaging. Anal Chem 2013, 85, 2117–2126. [89] Debois, D., Bertrand, V., Quinton, L., De Pauw-Gillet, M. C., De Pauw, E., MALDI-in source decay applied to mass spectrometry imaging: a new tool for protein identification. Anal Chem 2010, 82, 4036–4045. [90] Debois, D., Smargiasso, N., Demeure, K., Asakawa, D. et al., MALDI in-source decay, from sequencing to imaging. Top Curr Chem 2013, 331, 117–141. [91] Thomas, A., Patterson, N. H., Marcinkiewicz, M. M., Lazaris, A. et al., Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies. Anal Chem 2013, 85, 2860–2866. [92] Shimma, S., Sugiura, Y., Hayasaka, T., Hoshikawa, Y. et al., MALDI-based imaging mass spectrometry revealed abnor-



mal distribution of phospholipids in colon cancer liver metastasis. J. Chromatogr. 2007, 855, 98–103. [93] Libes, J. M., Seeley, E. H., Li, M., Axt, J. R. et al., Race disparities in peptide profiles of North American and Kenyan Wilms tumor specimens. J Am Coll Surg, 218, 707–720. [94] Ait-Belkacem, R., Berenguer, C., Villard, C., Ouafik, L. et al., MALDI imaging and in-source decay for top-down characterization of glioblastoma. Proteomics 2014, 14, 1290–1301. [95] Hanrieder, J., Ljungdahl, A., Andersson, M., MALDI imaging mass spectrometry of neuropeptides in Parkinson’s disease. J Vis Exp 2012, 60, 3445. [96] Ljungdahl, A., Hanrieder, J., Falth, M., Bergquist, J., Andersson, M., Imaging mass spectrometry reveals elevated nigral levels of dynorphin neuropeptides in L-DOPA-induced dyskinesia in rat model of Parkinson’s disease. PLoS One 2011, 6, e25653. [97] Kadar, H., Le Douaron, G., Amar, M., Ferrie, L. et al., MALDI mass spectrometry imaging of 1-methyl-4phenylpyridinium (MPP+) in mouse brain. Neurotox Res, 25, 135–145.

17 [107] Irie, M., Fujimura, Y., Yamato, M., Miura, D., Wariishi, H., Integrated MALDI-MS imaging and LC-MS techniques for visualizing spatiotemporal metabolomic dynamics in a rat stroke model. Metabolomics, 10, 473–483. [108] Touboul, D., Piednoel, H., Voisin, V., De La Porte, S. et al., Changes of phospholipid composition within the dystrophic muscle by matrix-assisted laser desorption/ionization mass spectrometry and mass spectrometry imaging. Eur J Mass Spectrom (Chichester, Eng) 2004, 10, 657–664. [109] Braidy, N., Poljak, A., Marjo, C., Rutlidge, H. et al., Metal and complementary molecular bioimaging in Alzheimer’s disease. Frontiers Aging Neurosci. 2014, 6, 138. [110] Anderson, D. M., Spraggins, J. M., Rose, K. L., Schey, K. L., High spatial resolution imaging mass spectrometry of human optic nerve lipids and proteins. J. Am. Soc. Mass Spectrom. 2015, 26, 940–947. [111] Anderson, D. M., Ablonczy, Z., Koutalos, Y., Spraggins, J. et al., High resolution MALDI imaging mass spectrometry of retinal tissue lipids. J. Am. Soc. Mass Spectrom. 2014, 25, 1394–1403.

[98] Lagarrigue, M., Alexandrov, T., Dieuset, G., Perrin, A. et al., New analysis workflow for MALDI imaging mass spectrometry: application to the discovery and identification of potential markers of childhood absence epilepsy. J Proteome Res, 11, 5453–5463.

[112] Chatterji, B., Dickhut, C., Mielke, S., Kruger, J. et al., MALDI imaging mass spectrometry to investigate endogenous peptides in an animal model of Usher’s disease. Proteomics 2014, 14, 1674–1687.

[99] Meriaux, C., Franck, J., Park, D. B., Quanico, J. et al., Human temporal lobe epilepsy analyses by tissue proteomics. Hippocampus, 24, 628–642.

[113] Deeley, J. M., Hankin, J. A., Friedrich, M. G., Murphy, R. C. et al., Sphingolipid distribution changes with age in the human lens. J. Lipid Res. 2010, 51, 2753–2760.

[100] Hanrieder, J., Ekegren, T., Andersson, M., Bergquist, J., MALDI imaging of post-mortem human spinal cord in amyotrophic lateral sclerosis. J Neurochem 2013, 124, 695–707.

[114] Grove, K. J., Voziyan, P. A., Spraggins, J. M., Wang, S. et al., Diabetic nephropathy induces alterations in the glomerular and tubule lipid profiles. J Lipid Res, 55, 1375–1385.

[101] Yuki, D., Sugiura, Y., Zaima, N., Akatsu, H. et al., Hydroxylated and non-hydroxylated sulfatide are distinctly distributed in the human cerebral cortex. Neuroscience 2011, 193, 44–53.

[115] Grobe, N., Elased, K. M., Cool, D. R., Morris, M., Mass spectrometry for the molecular imaging of angiotensin metabolism in kidney. Am J Physiol Endocrinol Metab 2012, 302, E1016–E1024.

[102] Matsumoto, J., Sugiura, Y., Yuki, D., Hayasaka, T. et al., Abnormal phospholipids distribution in the prefrontal cortex from a patient with schizophrenia revealed by matrixassisted laser desorption/ionization imaging mass spectrometry. Anal Bioanal Chem 2011, 400, 1933–1943.

[116] Zaima, N., Sasaki, T., Tanaka, H., Cheng, X. W. et al., Imaging mass spectrometry-based histopathologic examination of atherosclerotic lesions. Atherosclerosis 2011, 217, 427– 432.

[103] Woods, A. S., Colsch, B., Jackson, S. N., Post, J. et al., Gangliosides and ceramides change in a mouse model of blast induced traumatic brain injury. ACS Chem Neurosci, 4, 594– 600. [104] Sparvero, L. J., Amoscato, A. A., Kochanek, P. M., Pitt, B. R. et al., Mass-spectrometry based oxidative lipidomics and lipid imaging: applications in traumatic brain injury. J Neurochem 2010, 115, 1322–1336. [105] Koizumi, S., Yamamoto, S., Hayasaka, T., Konishi, Y. et al., Imaging mass spectrometry revealed the production of lyso-phosphatidylcholine in the injured ischemic rat brain. Neuroscience 2010, 168, 219–225. [106] Whitehead, S. N., Chan, K. H., Gangaraju, S., Slinn, J. et al., Imaging mass spectrometry detection of gangliosides species in the mouse brain following transient focal cerebral ischemia and long-term recovery. PLoS One 2011, 6, e20808. C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

[117] Tanaka, H., Zaima, N., Sasaki, T., Yamamoto, N. et al., Loss of lymphatic vessels and regional lipid accumulation is associated with great saphenous vein incompetence. J Vasc Surg 2012, 55, 1440–1448. [118] Menger, R. F., Stutts, W. L., Anbukumar, D. S., Bowden, J. A. et al., MALDI mass spectrometric imaging of cardiac tissue following myocardial infarction in a rat coronary artery ligation model. Anal Chem 2012, 84, 1117–1125. [119] Martin-Lorenzo, M., Balluff, B., Maroto, A. S., Carreira, R. J. et al., Molecular anatomy of ascending aorta in atherosclerosis by MS Imaging: Specific lipid and protein patterns reflect pathology. J. Proteomics 2015, 126, 245–251. [120] Marchetti-Deschmann, M., Allmaier, G., Allergenic compounds on the inner and outer surfaces of natural latex gloves: MALDI mass spectrometry and imaging of proteinous allergens. J Mass Spectrom 2009, 44, 61–70. [121] Taverna, D., Pollins, A. C., Sindona, G., Caprioli, R. M., Nanney, L. B., Imaging mass spectrometry for assessing www.clinical.proteomics-journal.com

18


cutaneous wound healing: analysis of pressure ulcers. J. Proteome Res. 2015, 14, 986–996. [122] Attia, A. S., Schroeder, K. A., Seeley, E. H., Wilson, K. J. et al., Monitoring the inflammatory response to infection through the integration of MALDI IMS and MRI. Cell Host Microbe 2012, 11, 664–673. [123] Moree, W. J., Phelan, V. V., Wu, C. H., Bandeira, N. et al., Interkingdom metabolic transformations captured by microbial imaging mass spectrometry. Proc Natl Acad Sci USA 2012, 109, 13811–13816. [124] Moore, J. L., Caprioli, R. M., Skaar, E. P., Advanced mass spectrometry technologies for the study of microbial pathogenesis. Curr Opin Microbiol, 19C, 45–51. [125] Glaros, T. G., Blancett, C. D., Bell, T. M., Natesan, M., Ulrich, R. G., Serum biomarkers of Burkholderia mallei infection elucidated by proteomic imaging of skin and lung abscesses. Clinical Proteomics 2015, 12, 7. [126] Kriegsmann, M., Seeley, E. H., Schwarting, A., Kriegsmann, J. et al., MALDI MS imaging as a powerful tool for investigating synovial tissue. Scand J Rheumatol 2012, 41, 305– 309. [127] Peffers, M. J., Cillero-Pastor, B., Eijkel, G. B., Clegg, P. D., Heeren, R. M., Matrix assisted laser desorption ionization mass spectrometry imaging identifies markers of ageing and osteoarthritic cartilage. Arthritis Res Ther 2014, 16, R110. [128] Cillero-Pastor, B., Eijkel, G. B., Blanco, F. J., Heeren, R. M., Protein classification and distribution in osteoarthritic human synovial tissue by matrix-assisted laser desorption ionization mass spectrometry imaging. Anal Bioanal Chem 2014, 407, 2213–2222. [129] Oswald-Richter, K. A., Beachboard, D. C., Seeley, E. H., Abraham, S. et al., Dual analysis for mycobacteria and propionibacteria in sarcoidosis BAL. J Clin Immunol 2012, 32, 1129–1140. [130] Nakanishi, T., Ito, M., Nirasawa, T., Tsuji, M., Takubo, T., Topologies of amyloidogenic proteins in Congo redpositive sliced sections of formalin-fixed paraffin embedded tissues by MALDI-MS imaging coupled with on-tissue tryptic digestion. Clin Biochem, 46, 1595–1600. [131] Casadonte, R., Kriegsmann, M., Deininger, S. O., Amann, K. et al., Imaging mass spectrometry analysis of renal amyloidosis biopsies reveals protein co-localization with amyloid deposits. Analyt. Bioanalyt. Chem. 2015, 407, 5323– 5331. [132] Roy, S., Touboul, D., Brunelle, A., Germain, D. P. et al., [Imaging mass spectrometry: a new tool for the analysis of skin biopsy. Application in Fabry’s disease]. Annales Pharmaceut. Francaises 2006, 64, 328–334. [133] Balluff, B., Frese, C. K., Maier, S. K., Schone, C. et al., De novo discovery of phenotypic intratumour heterogeneity using imaging mass spectrometry. J Pathol 2014, 235, 3– 13. [134] Chung, L., Phillips, L., Lin, M. Z., Moore, K. et al., A novel truncated form of S100P predicts disease-free survival in patients with lymph node positive breast cancer. Cancer letters 2015, 368, 64–70.



[135] Goto, T., Terada, N., Inoue, T., Nakayama, K. et al., The expression profile of phosphatidylinositol in high spatial resolution imaging mass spectrometry as a potential biomarker for prostate cancer. PLoS One, 9, e90242. [136] Quaas, A., Bahar, A. S., von Loga, K., Seddiqi, A. S. et al., MALDI imaging on large-scale tissue microarrays identifies molecular features associated with tumour phenotype in oesophageal cancer. Histopathology 2013, 63, 455–462. [137] Elsner, M., Rauser, S., Maier, S., Schone, C. et al., MALDI imaging mass spectrometry reveals COX7A2, TAGLN2 and S100-A10 as novel prognostic markers in Barrett’s adenocarcinoma. J Proteomics 2012, 75, 4693–4704. [138] Meding, S., Balluff, B., Elsner, M., Schone, C. et al., Tissuebased proteomics reveals FXYD3, S100A11 and GSTM3 as novel markers for regional lymph node metastasis in colon cancer. J. Pathol. 2012, 228, 459–470. [139] Oppenheimer, S. R., Mi, D., Sanders, M. E., Caprioli, R. M., Molecular analysis of tumor margins by MALDI mass spectrometry in renal carcinoma. J Proteome Res 2010, 9, 2182– 2190. [140] Morgan, T. M., Seeley, E. H., Fadare, O., Caprioli, R. M., Clark, P. E., Imaging the clear cell renal cell carcinoma proteome. J. Urol. 2013, 189, 1097–1103. [141] Murphy, A. J., Pierce, J., Seeley, E. H., Sullivan, L. M. et al., Peptide spectra in Wilms tumor that associate with adverse outcomes. J. Surg. Res. 2015, 196, 332–338. [142] Jones, E. E., Powers, T. W., Neely, B. A., Cazares, L. H. et al., MALDI imaging mass spectrometry profiling of proteins and lipids in clear cell renal cell carcinoma. Proteomics 2014, 14, 924–935. [143] Na, C. H., Hong, J. H., Kim, W. S., Shanta, S. R. et al., Identification of protein markers specific for papillary renal cell carcinoma using imaging mass spectrometry. Molecules Cells 2015, 38, 624–629. [144] Le Faouder, J., Laouirem, S., Chapelle, M., Albuquerque, M. et al., Imaging mass spectrometry provides fingerprints for distinguishing hepatocellular carcinoma from cirrhosis. J Proteome Res 2011, 10, 3755–3765. [145] Han, E. C., Lee, Y. S., Liao, W. S., Liu, Y. C. et al., Direct tissue analysis by MALDI-TOF mass spectrometry in human hepatocellular carcinoma. Clin. Chim. Acta 2011, 412, 230– 239. [146] Marquardt, C., Tolstik, T., Bielecki, C., Kaufmann, R. et al., MALDI imaging-based classification of hepatocellular carcinoma and non-malignant lesions in fibrotic liver tissue. Zeitschrift fur Gastroenterologie 2015, 53, 33–39. [147] Hardesty, W. M., Kelley, M. C., Mi, D., Low, R. L., Caprioli, R. M., Protein signatures for survival and recurrence in metastatic melanoma. J. Proteomics 2011, 74, 1002–1014. [148] Lazova, R., Seeley, E. H., Keenan, M., Gueorguieva, R., Caprioli, R. M., Imaging mass spectrometry–a new and promising method to differentiate Spitz nevi from Spitzoid malignant melanomas. Am J Dermatopathol 2012, 34, 82– 90. [149] Tata, A., Fernandes, A. M., Santos, V. G., Alberici, R. M. et al., Nanoassisted laser desorption-ionization-MS imaging of tumors. Anal Chem 2012, 84, 6341–6345. www.clinical.proteomics-journal.com


[150] Yanagisawa, K., Shyr, Y., Xu, B. J., Massion, P. P. et al., Proteomic patterns of tumour subsets in non-small-cell lung cancer. Lancet 2003, 362, 433–439. [151] Harris, F. T., Rahman, S. M., Hassanein, M., Qian, J. et al., Acyl-coenzyme a-binding protein regulates Beta-oxidation required for growth and survival of non-small cell lung cancer. Cancer Prev Res (Phila), 7, 748–757. [152] Stoeckli, M., Chaurand, P., Hallahan, D. E., Caprioli, R. M., Imaging mass spectrometry: a new technology for the analysis of protein expression in mammalian tissues. Nature Med. 2001, 7, 493–496. [153] Schwartz, S. A., Weil, R. J., Thompson, R. C., Shyr, Y. et al., Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry. Cancer Res. 2005, 65, 7674–7681. [154] Chaurand, P., Schwartz, S. A., Caprioli, R. M., Assessing protein patterns in disease using imaging mass spectrometry. J. Proteome Res. 2004, 3, 245–252. [155] Sinha, T. K., Khatib-Shahidi, S., Yankeelov, T. E., Mapara, K. et al., Integrating spatially resolved three-dimensional MALDI IMS with in vivo magnetic resonance imaging. Nature Meth. 2008, 5, 57–59. [156] Calligaris, D., Feldman, D. R., Norton, I., Olubiyi, O. et al., MALDI mass spectrometry imaging analysis of pituitary adenomas for near-real-time tumor delineation. Proc. Natl Acad. Sci. USA 2015, 112, 9978–9983. [157] Calligaris, D., Norton, I., Feldman, D. R., Ide, J. L. et al., Mass spectrometry imaging as a tool for surgical decisionmaking. J Mass Spectrom 2013, 48, 1178–1187. [158] Santagata, S., Eberlin, L. S., Norton, I., Calligaris, D. et al., Intraoperative mass spectrometry mapping of an oncometabolite to guide brain tumor surgery. Proc Natl Acad Sci USA 2014, 111, 11121–11126. [159] Eberlin, L. S., Gabay, M., Fan, A. C., Gouw, A. M. et al., Alteration of the lipid profile in lymphomas induced by MYC overexpression. Proc Natl Acad Sci U S A 2014, 111, 10450– 10455. [160] Oezdemir, R. F., Gaisa, N. T., Lindemann-Docter, K., Gostek, S. et al., Proteomic tissue profiling for the improvement of grading of noninvasive papillary urothelial neoplasia. Clinical Biochem. 2012, 45, 7–11. [161] Flatley, B., Quaye, C., Johnson, E., Freeman, A. et al., Distribution analysis of the putative cancer marker S100A4


19 across invasive squamous cell carcinoma penile tissue. EuPA Open Proteomics 2015, 7, 1–10. [162] Min, K. W., Bang, J. Y., Kim, K. P., Kim, W. S. et al., Imaging mass spectrometry in papillary thyroid carcinoma for the identification and validation of biomarker proteins. J. Korean Med. Sci. 2014, 29, 934–940. [163] Pagni, F., Mainini, V., Garancini, M., Bono, F. et al., Proteomics for the diagnosis of thyroid lesions: preliminary report. Cytopathology 2014, 26, 318–324. [164] Aichler, M., Elsner, M., Ludyga, N., Feuchtinger, A. et al., Clinical response to chemotherapy in oesophageal adenocarcinoma patients is linked to defects in mitochondria. J Pathol 2013, 230, 410–419. [165] Aichler, M., Luber, B., Lordick, F., Walch, A., Proteomic and metabolic prediction of response to therapy in gastric cancer. World J. Gastroenterol. 2014, 20, 13648–13657. [166] Calligaris, D., Feldman, D. R., Norton, I., Brastianos, P. K. et al., Molecular typing of meningiomas by desorption electrospray ionization mass spectrometry imaging for surgical decision-making. Internat. J. Mass Spectromet. 2015, 377, 690–698. [167] Guenther, S., Muirhead, L. J., Speller, A. V., Golf, O. et al., Spatially resolved metabolic phenotyping of breast cancer by desorption electrospray ionization mass spectrometry. Cancer Res. 2015, 75, 1828–1837. [168] Balog, J., Kumar, S., Alexander, J., Golf, O. et al., In vivo endoscopic tissue identification by rapid evaporative ionization mass spectrometry (REIMS). Angewandte Chem. 2015, 54, 11059–11062. [169] Prideaux, B., ElNaggar, M. S., Zimmerman, M., Wiseman, J. M. et al., Mass spectrometry imaging of levofloxacin distribution in TB-infected pulmonary lesions by MALDI-MSI and continuous liquid microjunction surface sampling. Internat. J. Mass Spectromet. 2015, 377, 699– 708. [170] Rao, W., Pan, N., Yang, Z., High resolution tissue imaging using the single-probe mass spectrometry under ambient conditions. J. Am. Soc. Mass Spectromet. 2015, 26, 986– 993. [171] Wisztorski, M., Desmons, A., Quanico, J., Fatou, B. et al., Spatially-resolved protein surface microsampling from tissue sections using liquid extraction surface analysis. Proteomics 2016 in press.
