Assessing Protein Patterns in Disease Using Imaging Mass

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Assessing Protein Patterns in Disease Using Imaging Mass Spectrometry Pierre Chaurand, Sarah A. Schwartz, and Richard M. Caprioli* Mass Spectrometry Research Center, and Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232-8575 Received December 19, 2003

Direct tissue profiling and imaging mass spectrometry (MS) provides a detailed assessment of the complex protein pattern within a tissue sample. MALDI MS analysis of thin tissue sections results in over of 500 individual protein signals in the mass range of 2 to 70 kDa that directly correlate with protein composition within a specific region of the tissue sample. To date, profiling and imaging MS has been applied to multiple diseased tissues, including human gliomas and nonsmall cell lung cancer. Interrogation of the resulting complex MS data sets has resulted in identification of both disease-state and patient-prognosis specific protein patterns. These results suggest the future usefulness of proteomic information in assessing disease progression, prognosis, and drug efficacy. Keywords: mass spectrometry • MALDI • profiling • imaging • cancer

Introduction Proteomics, the detailed understanding of the role of proteins in health and disease, is a necessary complement to genetic analysis.1-5 Early efforts in proteomics date back several decades to the development of two-dimensional gel electrophoresis and the cataloging of individual gel spots to create nascent protein databases.6,7 The explosion of information in the genomic era has enabled powerful new methods for the analysis of protein targets.3 These include protein identification, recognition of post-translational modifications (such as glycosylation and phosphorylation) and sequence variations,8 assessment of protein function and characterization of proteinprotein interactions. Many of these methods are currently being used for quantification and screening of proteins that differ between health and disease.2,4,9-12 Modern proteomics is made possible by new mass spectrometry (MS) technologies, basically, matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI). These methods can be used to rapidly and accurately measure the molecular weight of peptides and proteins.13,14 In MALDI, a sample of interest and a UV (ultra-violet)-desorbing matrix are co-deposited on a metallic target plate. Upon solvent dehydration, large analyte/matrix cocrystals are formed. Sample analysis is performed by a series of brief UV laser pulses (typically from a nitrogen laser at 337 nm) irradiating the sample over a period of seconds, typically at a frequency of 20 Hz. This leads to desorption and ionization of the peptide/ protein analytes from the sample by forming predominantly singly charged protonated molecular ions of the form [M+H]+. These molecular ions are accelerated by a constant electric field * To whom correspondence should be addressed. Richard Caprioli, 9160 MRB III, Vanderbilt University, Nashville, TN 37232-8575. Tel: (615) 3439207. Fax: (615) 343-8372. E-mail: [email protected]. 10.1021/pr0341282 CCC: $27.50

 2004 American Chemical Society

and separated in time while traveling down a flight tube of fixed dimensions. The precise time-of-flight of these ions is recorded and the mass/charge (m/z) ratio of each molecule is determined through calibration with known standards. Recent improvements in the MALDI time-of-flight technology15-18 in combination with the ongoing expansion of protein and gene databases, have given this analytical tool the resolution, sensitivity, and mass accuracy for the detection, identification, and further characterization of proteins.19-23 In recent years, several investigators have developed MALDI MS based methodologies to investigate single cells24-28 and small tissue fragments.29-32 In these studies, limited numbers of peptides and in some cases low molecular proteins have been detected. Protein profiling and imaging mass spectrometry (IMS) is a new technology that also takes advantage of the methodology and instrumentation of MALDI MS to generate protein profiles and images from blots of tissues transferred on hydrophobic surfaces33,34 or directly from fresh frozen thin tissue sections (5-20 µm in thickness) immobilized on target plates.35-38 The use of either profiling or imaging depends on the overall goal of the experiment. Profiling involves depositing matrix in discrete spots on specific regions of interest on a tissue section or blotted area. This method allows protein profiles to be obtained from defined areas of the tissue and facilitates comparisons between and within tissues (i.e., diseased vs normal). Typically over 400 distinct mass signals highly representative of the local proteome are observed in the m/z range from 2000 to over 100 000. Large numbers of samples may be processed relatively quickly (hours), and the data can be analyzed by computer programs to identify markers that are indicative of ongoing biological processes in the tissue.39 In the higher resolution imaging mode, a tissue section is homogeneously coated with matrix solution so that protein profiles may be acquired over the entire area. The mass Journal of Proteome Research 2004, 3, 245-252

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reviews spectrometric data is acquired with a predetermined number of laser shots per spot over the area of the section using a discrete Cartesian pattern with a fixed center to center distance between spots, typically ranging from 25 to 200 µm depending on the chosen resolution of the image. Signal intensities at specific m/z values or ranges are then recorded and twodimensional ion density maps, or images, are reconstructed. From a single acquisition, several hundred images, each at a specific molecular weight, can be made. Data acquisition and processing (image reconstruction) is done with specialized software.40-42 The imaging experiment typically takes much longer than profiling, depending on tissue area and the resolution required. Acquisition of high-resolution images may require several tens of thousands of individual spectra and can generate large data files (1-2 gigabytes). An imaging experiment becomes necessary when high-resolution protein distribution information within a tissue section is of importance. For example, when analyzing tumor biopsies, the precise alignment between the various cell types present in the section and the differentially expressed MS signals may lead to a better understanding of molecular processes within the tumor. Profiling and Imaging mass spectrometry (IMS) has been successfully used in many applications,33,43-48 several involving human or mouse tumor tissue. Stoeckli et al. have mapped and identified several proteins found to have sharp localization patterns in a human glioma xenograph.35 Chaurand et al. have obtained profiles from cancerous mouse colon and identified several tumor markers.49 Masumory and colleagues have studied prostate adenocarcinomas and neuroendocrine carcinomas and identified identical protein makers indicative of metastasis between the primary tumor and lesions in extraprostatic organs.50 Yanagisawa and collaborators, have obtained protein patterns in tumor subsets of nonsmall-cell lung cancer that accurately classified and predicted histological groups and patient survival.39 In a more recent study, Schwartz et al. have obtained protein patterns in brain tumor subgroups that also accurately classify and predict histological groups.51 Laser capture microdissection (LCM) has also become an important tool in biological research, permitting the isolation of specific cell populations from frozen tissue samples containing a mixture of cell types.52-54 From these cells both genomic and proteomic material can be extracted. LCM captured cells can also be analyzed by MALDI MS. Comparison of the spectra obtained from cell populations of interest may permit the identification of unique disease or function-related protein markers. Palmer-Toy et al. were first to report the analysis of LCM cells by MALDI MS and have studied both normal and cancerous human breast cells.55 More recently, Xu and colleagues have published detailed LCM/MALDI MS protocols applicable to the study of different cell types in healthy and cancerous human breast tissue.56 Using a similar approach, Bhattacharya and co-workers have investigated various cancer cells microdissected from archived frozen human lung tissue.57 In this article, we review the basics of the Profiling and Imaging MS technology and evaluate its potential in cancer research. The potential of protein profiles obtained from cancer tissue in accurately classifying and predicting histological groups as well as patient survival is shown.

Direct Analysis of Proteins from Thin Tissue Sections Immediately after resection or dissection, tissue samples are snap frozen in liquid nitrogen to limit proteolysis.58 Once frozen, the tissue may be kept at -80 °C until processed. Thin 246

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sections are cut using a cryostat at below freezing temperatures. The exact cutting temperature (around -15 °C) depends on the type of tissue and the desired thickness of the sections. Tissue sections are usually cut with a thickness of 10-12 µm since they can be easily manipulated without folding or tearing. However, successful MALDI MS tissue analyses have been performed on sections ranging from 5 to 20 µm in thickness. Once the section is cut, it is placed out on a cold flat target plate (maintained in the cryostat chamber at -15 °C) and thawmounted at room temperature. The sections may then be stored or allowed to dehydrate in a vacuum desiccator for 1-2 h before matrix application. To date, sinapinic acid (prepared at 20 mg/mL in acetonitrile/H2O containing 0.1% trifluoroacetic acid - 1/1 volume ratio)58 has proven to be the best MALDI matrix for protein analysis of thin tissue sections. With this solvent system, primarily soluble (hydrophilic) proteins are accessible for MALDI MS analysis. Results have also been obtained using R-cyano 4-hydroxy cinnamic acid as matrix.47 For protein profiling, small drops of matrix are directly deposited on the tissue section. For volumes ranging from 100 nL to 1 µL, drops are usually deposited using an automatic pipet. The protein profiles and images presented here have been acquired on an Applied Biosystems Voyager DE-STR MALDI TOF mass spectrometer operating in the linear mode under optimized delayed extraction conditions.15,16 To obtain significant statistics, MALDI MS spectral acquisition is performed by averaging signals from 100 to 1000 laser shots. Typically 300 to 500 (or more) distinct mass signals are detected in the m/z range from 2000 to 70 000 although in some cases, signals exceeding m/z 200 000 have been observed.38 However, because of the inefficiency of MALDI time-of-flight mass spectrometers to resolve59 and detect higher molecular weight compounds,60,61 a large majority (∼90%) of the signals observed are below m/z 30 000. Figure 1 presents the protein profile obtained after MALDI MS analysis of a 12-µm thick human grade IV glioma tissue section. Over 400 signals were observed in the molecular weight range up to 70 000. Signal intensity variations ranged over 4 orders of magnitudes. Imaging Mass Spectrometry (IMS) can be performed in theory on any MALDI time-of-flight instrument. To automate data acquisition and processing (image reconstruction), custom software was developed.40 This software controls all of the instrumental parameters, in particular x/y movement of the sample stage at a predefined resolution over a specified area on the target plate, and is able to calculate images based on signal intensity for any selected m/z value. Similar efforts have also been made by several other groups.41,42,45 To image tissue sections at high resolution, matrix has to be applied on the sections in a homogeneous manner that does not induce lateral protein migration. Ultimately, for higher resolution imaging, small matrix crystals with dimensions smaller or equal to the diameter of the ionizing laser beam need to form on the section surface. Current coating methods involve spraying matrix over the tissue section,37,58 either with a commercially available glass spray nebulizer (similar to those used to coat thin-layer chromatography plates) connected to a nitrogen tank (nebulizing gas), or by electrospray deposition.46 Alternative coating protocols involve depositing large volumes of matrix directly on the section35 after a protein fixation step.45 When coating using a glass spray nebulizer, multiple spray cycles of 50-100 µL are performed on the section from a distance of about 20 to 30 cm. The sample is allowed to dry at room temperature between each cycle. It is important that the combination of

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Assessing Protein Patterns in Disease

Figure 1. MALDI MS protein profile obtained from a 12 µm thick human grade IV glioma tissue section.

the spray pressure and spray distance be optimized in order to avoid excessive wetting of the section, which can lead to protein delocalization, but allow some protein solubilization and the formation of protein and matrix cocrystals. When sprayed in this manner, sinapinic acid in the solvent system indicated above typically yields crystals ranging from 5 to 50 µm in diameter. The spray cycle is repeated up to 10 times, allowing some air-drying between cycles (30 s to 1 min) to generate a dense and homogeneous crystal layer. The density of the crystal layer may be monitored between cycles by visual observation of the section under a microscope. Figure 2 presents several ion density maps acquired by IMS from a mouse brain in which a tumor was grown.62 GL261 brain cancer cells63,64 were injected in vivo in one hemisphere of the brain. Two weeks after injection, the mouse was sacrificed and the brain sectioned. Figure 2a presents a photomicrograph of a 12µm section mounted on a target plate prior to matrix application using the glass spray nebulizer. The perimeter of the tumor has been outlined. Imaging was performed with a resolution of 110 µm, averaging 20 laser shots per spectrum. The only processing of the data was the removal of background noise. Many molecular images can be produced from these data sets. Figure 2b-l presents 11 different protein expression maps across the tissue section. Some of the individual proteins have been identified, for example, those showing histones (H4, H2B1, and H3) are consistent with the presence of fast developing tumor cells50 in a given area. Identification of the molecular weight markers of interest is performed by well-established methods that consist of extraction of the proteins from the tissue followed by HPLC separation.44,49 After screening by MALDI MS, the HPLC fractions containing the targeted molecular weight markers are digested by trypsin and the resulting

peptides sequenced by tandem mass spectrometry. The proteins are identified by interrogating gene or protein databases with the experimentally recovered sequences.20,21,65,66

Protein Profiling of Tumors The following examples demonstrate the use of MALDI MS in obtaining protein expression profiles from frozen sections of resected human brain51 and lung39 tumors and assessing their predictive value for disease classification and survival. The data sets were acquired at the Mass Spectrometry Research Center at Vanderbilt University. The experimental scheme used in these studies, outlined in Figure 3, follows the tissue profiling technique described above. For optical evaluation and comparison, serial sections were stained with hematoxylin and eosin (H&E) and examined by trained pathologists. MALDI MS data were collected using standardized instrumental acquisition parameters and processing parameters including calibration, baseline correction, and smoothing. For the lung tumor data set, common protein signals across multiple samples were aligned by the use of a generic algorithm. In this approach, m/z windows are created that maximize the number of peaks in a bin from different samples and minimize the number of peaks in a bin from the same sample. The optimal bins were used to define individual protein peaks within a large data set for statistical analysis. The statistical analysis for both experiments has been detailed elsewhere (brain cancer,51 lung cancer39). Briefly, unsupervised and supervised (lung tumor data only) hierarchical multi-variant cluster analyses were applied to investigate the patterns with respect to the statistically significant discriminator proteins as well as the biological status and, when applicable, patient information. The statistical Journal of Proteome Research • Vol. 3, No. 2, 2004 247

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Figure 2. IMS analysis of a 12-µm thick coronal mouse brain section containing a tumor.62 (a) Photomicrograph of the section before matrix application. The area containing the tumor has been outlined. (b-l) Ion density maps obtained at different m/z values with an imaging resolution of 110 µm. The ion density maps are depicted as pseudo-color images with white representing the highest protein concentration and black the lowest.

Figure 3. General scheme used for data acquisition and processing of MALDI MS data obtained from normal and cancerous tissues.

significance cutoffs are determined by the type of analysis performed (see references). Analysis of Brain Tumors. In this study, the analysis of different brain tumor grades was primarily performed to assess the potential of MALDI MS profiling for tumor classification.51 The sample set used consisted of a total of 20 biopsies: 5 biopsies from nontumor lateral temporal neocortex (obtained from patients undergoing anterior temporal lobectomy and amygdalohippocampectomy for histologically confirmed mesial temporal sclerosis), 14 biopsies from grade II, III, and IV primary brain tumors, and 1 tumor of neural crest origin. 248

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Although the reference “nontumor” tissues are not per say healthy, these samples do not contain cancer cells. The biopsies were processed and the MALDI MS data was acquired as detailed above. The mass spectra collected from the 20 patient biopsies were analyzed and the resulting peak lists from these data were generated. Each spectrum consisted of between 200 and 400 individual protein signals. In combining these lists, a total of 647 protein signals detected across all patient samples were used in the clustering analysis. Figure 4a presents the MALDI MS analysis of a heterogeneous biopsy, which maintained two distinct morphological regions, white matter and cortex (with infiltrating tumor cells). The results demonstrate the protein profile similarity within the two white matter regions as well as the pattern distinctions between the white matter and cortex regions. This illustrates well the potential of MALDI MS as an effective method for the analysis of cells with different morphologies. Further studies demonstrate (Figure 4b) that MALDI MS can be used to discriminate between different histological glioma grades. Grade II glial neoplasms (astrocytoma and oligodendroglioma) and anaplastic astrocytoma (grade III) can be accurately and reproducibly distinguished from glioblastoma multiforme (grade IV) by the presence or absence of peaks across a broad spectrum of analyzable protein signals. Initial bioinformatic investigation, using an unsupervised, hierarchical, cluster analysis, shows that MALDI MS can be used to distinguish subgroups of tumor tissue from each other as well as from nontumor tissue. Cluster analysis is a simple approach that selects a variable and then divides the sample set into those objects that do or do not possess that variable (in this case, a selected protein signal, whose identity need not be known in advance). This type of analysis results in a dendrogram, presented in Figure 4c, that defines the similarity in signal patterns between individual spectra. Analysis results demonstrate that samples from different histological subgroups cluster in their own region. Although there are discernible differences in the spectra of grade II and grade III gliomas, initial bioinformatic clustering differentiates most strongly between normal, grade II and III tumors, and glioblastoma multiforme. Tumor samples 10-1 and 10-2 came from different regions of a single glioblastoma, about 5 cm apart. Although they were analyzed separately, in a blinded fashion, they not only segregate with the other glioblastomas, but also, more importantly, the two samples are most similar to one another. This provides additional confirmation of the robust nature of this analysis. In addition, tumor sample 18, which is the tumor of neural crest origin, shared essentially no identity with any tumor or with normal brain tissues. This further suggests that MALDI MS is a robust method to identify tumors of unknown origin or to distinguish between two tumors that share histological features but have different origins. Analysis of Nonsmall Cell Lung Cancer (NSCLC). MALDI mass spectra were obtained from 80 lung tumors and 14 normal lung tissues.39 A class prediction model was built using the protein profiles from a training data set of 34 lung tumors and 8 normal lung samples to assess their statistical significance. This model was then applied to a blinded test data set consisting of 32 lung tumors and 6 normal lung samples to estimate the rate of misclassification. Representative examples of MALDI MS spectra from three different tissue samples (two primary NSCLC and one normal lung tissue) are presented in Figure 5a. Examples of MS peaks which were identified by the statistical analysis as optimum discriminatory patterns between normal and tumor are indicated by asterisks. Overall, more than

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Figure 4. Analysis of proteomic patterns in brain cancer (adapted from reference 51). (a) MALDI MS analysis of brain with infiltrative tumor cells. A definite shift from a homogeneous, normal white matter (WM) pattern (A), to brain with infiltrative tumor cells (ITC) from a glioblastoma (B), back to normal white matter (C) can be seen. To the left of the spectra is the tissue section that was analyzed. The cellular morphology of the sampled spots was determined by alignment with a serial H&E stained section. Similar findings are seen elsewhere within the m/z 2000-21 000 range used in this study. (b) MALDI-MS spectra from gliomas of different histological grades. A, normal temporal lobe. B, low grade (grade II) astrocytoma. C, Grade III astrocytoma. D, Glioblastoma multiforme. (c) Cluster analysis and tumor discrimination by spectral analysis. Peak lists from MALDI MS analysis of 20 human brain biopsy samples were compared by hierarchical cluster analysis. Samples are grouped according to peak similarity (%); this grouping is displayed by a tree diagram (dendrogram) that joins biopsies that share features in common at discrete nodes (9). The node placement along the x-axis demonstrates the degree of similarity between the samples joined by the node. Table 1. Classification of Samples in Training and Test Cohort According the Protein Expression Profiles Measured by MALDI MS Directly from Normal and NSCLC Tissue Sections (adapted from ref 39)

1600 individual protein signals were detected across all patient samples and used in the clustering analysis. Supervised classification analysis identified 91 differentially expressed mass signals that best segregated samples into tumor/nontumor

groups as assessed by pathology. Using these top differentially expressed markers, a cluster analysis was performed to visualize the molecular classification sheme. Figure 5b presents the classification obtained for the training data set consisting of the protein expression profiles of NSCLC and normal lung tissue. Tissue samples were correctly segregated into two groups, normal and NSCLC. Similarly, supervised classification identified 27 mass peaks that discriminated between the different histological groups of NSCLC. Figure 5c presents the classification obtained for the training data set consisting of the protein expression profiles of NSCLC. The NSCLC were perfectly separated into known major histological groups: adenocarcinoma (n ) 14), squamous-cell carcinoma (n ) 15), and large-cell carcinoma (n ) 5). These models were then applied to the blinded validation samples in the test cohort to define the misclassification rate. The results from this analysis are presented in Table 1. All of the samples were correctly classified apart from one large-cell carcinoma in the test data set that was misclassified as an adenocarcinoma. It is possible, however, that this tumor was actually an adenocarcinoma that was too poorly differentiated to be identified as such by light microscopy. The variation within protein expression profiles was statistically compared with patient survival rates. SignifiJournal of Proteome Research • Vol. 3, No. 2, 2004 249

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Figure 5. Analysis of proteomic patterns in nonsmall cell lung cancer (adapted from reference 39). (a) MALDI MS protein profiles obtained from normal and cancerous human lung tissue sections. (b) Hierarchical cluster analysis of 8 normal lung tissue and 34 resected primary NSCLC samples according to the protein expression patterns of 91 protein signals. Each row represents an individual protein signal and each column represents an individual sample. The protein signals allow differentiation with 100% accuracy between normal tissue and NSCLC samples (c) Hierarchical cluster analysis of 37 resected primary NSCLC samples according to the protein expression patterns of 27 protein signals. The protein signals allow to differentiate with 100% accuracy between the different types of NSCLC. (nl) normal, (sq) squamous-cell carcinoma, (ad) adenocarcinoma, (la) large cell carcinoma. (d) Kaplan-Meier survival curves for groups with poor and good prognosis according to proteomic pattern comprised of 15 distinct MS peaks.

cant protein signals were selected using statistical tests based on their correlation with known prognostic features of these patients. A proteomic pattern composed of 15 distinct mass peaks was identified that divided the resected NSCLC patients into a group with poor prognosis (median survival 6 months, n ) 25) and one with good prognosis (median survival 33 months, n ) 41). Figure 5d presents the corresponding KaplanMeier survival curves for the groups with poor and good prognosis according to the protein expression patterns of these 15 mass peaks. Several of the protein biomarkers that showed expression variations between normal tissue and lung cancer have been identified using the strategy outlined above. In particular, the proteins SUMO-2 (small ubiquitin-related modifier-2 protein, MW 10 519), thymosin Beta 4 (MW 4963.5) and ubiquitin (MW 8564) were found to be overexpressed in NSCLC with respect to normal lung.39 SUMO proteins consist of a class of ubiquitinlike proteins that are conjugated by a set of enzymes to cellular regulatory proteins, including oncogenes and tumor suppressor genes. These proteins may have important roles in the control of cell growth, differentiation, apoptosis, cell cycle, DNA repair, stress response, and nuclear transport. SUMO protein conjugation affects the substrates subcellular localization and stability as well as transcriptional activities. Thus, SUMO protein conjugation might be of previously unsuspected importance 250

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in lung tumorigenesis. Abundance of thymosin β-4 in several forms of cancer such has proliferating glioblastoma and neoplastic lesions (compared to benign) has also been previously reported.35,67 Thymosin β-4 is able to sequester cytoplasmic monomeric actin and its expression in tumor cells has been correlated with tumorigenicity and metastatic potential in malignant fibrosarcoma cell lines.68 Interestingly, there is a discrepancy between the expression levels of thymosin β-4 in this study and that of mRNA previously reported and analyzed by microarray,69 emphasizing again the importance of investigating diseases at the proteomic level.

Concluding Remarks Profiling and imaging mass spectrometry is a new technology that provides new insights to molecular process ongoing in living systems. The potential of such a molecular imaging technology is considerable. The fundamental contributions of the technology in rapidly providing molecular weight specific profiles and images, at relatively high resolution and sensitivity provides important information in the investigation of cellular processes in both health and disease. Imaging MS is of extraordinary benefit as a discovery tool because one does not need to know in advance the specific proteins that have changed in a comparative study. Furthermore, the cellular origins and relative concentrations of the markers across the

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section can be assessed. Once a marker of interest is identified, its precise (subcellular) location, concentration, regulation and function may be investigated, to help understand disease progression at the molecular level. Although current imaging MS technology does not allow individual cellular analysis, we anticipate that new developments will allow this application soon. Clinically, Imaging MS can provide a molecular assessment of the progression of tumor and treatment obtained from biopsies, with the potential to identify tumor sub-populations and predict patient survival that is not evident based on the cellular phenotype determined histologically.39 Further, assessment of the efficacy of drug treatment through comparative proteomics is feasible.70 The information obtained by IMS significantly augments, but does not replace, existing molecular diagnostic tools. Together, these tools promise to promote new discoveries in biology and medicine.

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