Liam A. McDonnell

Peptide and protein imaging mass spectrometry in cancer research

MALDI mass spectrometry is able to acquire protein profiles directly from tissue that can describe the levels of hundreds of distinct proteins. MALDI imaging MS can simultaneously reveal how each of these proteins varies in heterogeneous tissues. Numerous studies have now demonstrated how MALDI imaging MS can generate different protein profiles from the different cell types in a tumor, which can act as biomarker profiles or enable specific candidate protein biomarkers to be identified. MALDI imaging MS can be directly applied to patient samples where its utility is to accomplish untargeted multiplex analysis of the tissues protein content, enabling the different regions of the tissue to be differentiated on the basis of previously unknown protein profiles/biomarkers. The technique continues to rapidly develop and is now approaching the cusp whereby its potential to provide new diagnostic/prognostic tools for cancer patients can be routinely investigated. Here the latest methodological developments are summarized and its application to a range of tumors is reported in detail. The prospects of MALDI imaging MS are then described from the perspectives of modern pathological practice and MS-based proteomics, to ensure the outlook addresses real clinical needs and reflects the real capabilities of MS-based proteomics of complex tissue samples.

Immunoglobulin G Glycopeptide Profiling by Matrix-Assisted Laser Desorption Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Immunoglobulin G (IgG) fragment crystallizable (Fc) glycosylation is essential for Fc-receptor-mediated activities. Changes in IgG Fc glycosylation have been found to be associated with various diseases. Here we describe a high-throughput IgG glycosylation profiling method. Sample preparation is performed in 96-well plate format: IgGs are purified from 2 microL of human plasma using immobilized protein A. IgGs are cleaved with trypsin, and the resulting glycopeptides are purified by reversed-phase or hydrophilic interaction solid-phase extraction. Glycopeptides are analyzed by intermediate pressure matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FTICR-MS). Notably, both dihydroxybenzoic acid (DHB) and alpha-cyano-4-hydroxycinnamic acid (CHCA) matrixes allowed the registration of sialylated as well as nonsialylated glycopeptides. Data were automatically processed, and IgG isotype-specific Fc glycosylation profiles were obtained. The entire method showed an interday variation below 10% for the six major glycoforms of both IgG1 and IgG2. The method was found suitable for isotype-specific high-throughput IgG glycosylation profiling from human plasma. As an example we successfully applied the method to profile the IgG glycosylation of 62 human samples.

Imaging mass spectrometry statistical analysis

Imaging mass spectrometry is increasingly used to identify new candidate biomarkers. This clinical application of imaging mass spectrometry is highly multidisciplinary: expertise in mass spectrometry is necessary to acquire high quality data, histology is required to accurately label the origin of each pixels mass spectrum, disease biology is necessary to understand the potential meaning of the imaging mass spectrometry results, and statistics to assess the confidence of any findings. Imaging mass spectrometry data analysis is further complicated because of the unique nature of the data (within the mass spectrometry field); several of the assumptions implicit in the analysis of LC-MS/profiling datasets are not applicable to imaging. The very large size of imaging datasets and the reporting of many data analysis routines, combined with inadequate training and accessible reviews, have exacerbated this problem. In this paper we provide an accessible review of the nature of imaging data and the different strategies by which the data may be analyzed. Particular attention is paid to the assumptions of the data analysis routines to ensure that the reader is apprised of their correct usage in imaging mass spectrometry research.

Imaging mass spectrometry of myxoid sarcomas identifies proteins and lipids specific to tumour type and grade, and reveals biochemical intratumour heterogeneity†‡

Myxofibrosarcoma and myxoid liposarcomas are relatively common soft tissue tumours that are characterized by their so‐called myxoid extracellular matrix and have to some extent overlap in histology. The exact composition and potential role of their myxoid extracellular matrix are insufficiently understood. To gain more insight into the biomolecular content of these tumours, we have studied 40 well‐documented myxofibrosarcoma and myxoid liposarcoma cases using imaging mass spectrometry. This technique provides a multiplex biomolecular imaging analysis of the tissue, spanning multiple molecular domains and without a priori knowledge of the tissues biomolecular content. We have developed experimental protocols for analysing the peptide, protein, and lipid content of myxofibrosarcoma and myxoid liposarcomas, and have detected proteins and lipids that are tumour‐type and tumour‐grade specific. In particular, lipid changes observed in myxoid liposarcomas could be related to pathways known to be affected during tumour progression. Unsupervised clustering of the biomolecular signatures was able to classify myxofibrosarcoma and myxoid liposarcomas according to tumour type and tumour grade. Closer examination of histologically similar regions in the tissues revealed intratumour heterogeneity, which was a consistent feature in each of the myxofibrosarcomas studied. In intermediate‐grade myxofibrosarcoma, it was found that single tissue sections could contain regions with biomolecular profiles similar to high‐grade and low‐grade tumours, and that these regions were associated with the tumours nodular structure, thus supporting a concept of tumour progression through clonal selection. Copyright

N-glycosylation of colorectal cancer tissues: a liquid chromatography and mass spectrometry-based investigation

Colorectal cancer is the third most common cancer worldwide with an annual incidence of ∼1 million cases and an annual mortality rate of ∼655,000 individuals. There is an urgent need for identifying novel targets to develop more sensitive, reliable, and specific tests for early stage detection of colon cancer. Post-translational modifications are known to play an important role in cancer progression and immune surveillance of tumors. In the present study, we compared the N-glycan profiles from 13 colorectal cancer tumor tissues and corresponding control colon tissues. The N-glycans were enzymatically released, purified, and labeled with 2-aminobenzoic acid. Aliquots were profiled by hydrophilic interaction liquid chromatography (HILIC-HPLC) with fluorescence detection and by negative mode MALDI-TOF-MS. Using partial least squares discriminant analysis to investigate the N-glycosylation changes in colorectal cancer, an excellent separation and prediction ability were observed for both HILIC-HPLC and MALDI-TOF-MS data. For structure elucidation, information from positive mode ESI-ion trap-MS/MS and negative mode MALDI-TOF/TOF-MS was combined. Among the features with a high separation power, structures containing a bisecting GlcNAc were found to be decreased in the tumor, whereas sulfated glycans, paucimannosidic glycans, and glycans containing a sialylated Lewis type epitope were shown to be increased in tumor tissues. In addition, core-fucosylated high mannose N-glycans were detected in tumor samples. In conclusion, the combination of HILIC and MALDI-TOF-MS profiling of N-glycans with multivariate statistical analysis demonstrated its potential for identifying N-glycosylation changes in colorectal cancer tissues and provided new leads that might be used as candidate biomarkers.

Imaging mass spectrometry at cellular length scales

Imaging mass spectrometry (IMS) allows the direct investigation of both the identity and the spatial distribution of the molecular content directly in tissue sections, single cells and many other biological surfaces. In this protocol, we present the steps required to retrieve the molecular information from tissue sections using matrix-enhanced (ME) and metal-assisted (MetA) secondary ion mass spectrometry (SIMS) as well as matrix-assisted laser desorption/ionization (MALDI) IMS. These techniques require specific sample preparation steps directed at optimal signal intensity with minimal redistribution or modification of the sample analytes. After careful sample preparation, different IMS methods offer a unique discovery tool in, for example, the investigation of (i) drug transport and uptake, (ii) biological processing steps and (iii) biomarker distributions. To extract the relevant information from the huge datasets produced by IMS, new bioinformatics approaches have been developed. The duration of the protocol is highly dependent on sample size and technique used, but on average takes approximately 5 h.

MALDI imaging and profiling MS of higher mass proteins from tissue

MALDI imaging and profiling mass spectrometry of proteins typically leads to the detection of a large number of peptides and small proteins but is much less successful for larger proteins: most ion signals correspond to proteins of m/z < 25,000. This is a severe limitation as many proteins, including cytokines, growth factors, enzymes, and receptors have molecular weights exceeding 25 kDa. The detector technology typically used for protein imaging, a microchannel plate, is not well suited to the detection of high m/z ions and is prone to detector saturation when analyzing complex mixtures. Here we report increased sensitivity for higher mass proteins by using the CovalX high mass HM1 detector (Zurich, Switzerland), which has been specifically designed for the detection of high mass ions and which is much less prone to detector saturation. The results demonstrate that a range of different sample preparation strategies enable higher mass proteins to be analyzed if the detector technology maintains high detection efficiency throughout the mass range. The detector enables proteins up to 70 kDa to be imaged, and proteins up to 110 kDa to be detected, directly from tissue, and indicates new directions by which the mass range amenable to MALDI imaging MS and MALDI profiling MS may be extended.

Multiple Statistical Analysis Techniques Corroborate Intratumor Heterogeneity in Imaging Mass Spectrometry Datasets of Myxofibrosarcoma

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

Imaging Mass Spectrometry Data Reduction: Automated Feature Identification and Extraction

Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used to distinguish regions with differential molecular signatures that could not be distinguished using established histologic tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using multiple imaging MS technologies, that reduce data loads and the number of variables by >100×, and that highlight highly-localized features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable multivariate analysis on large imaging MS datasets spanning multiple tissues.

Mass Spectrometry Image Correlation: Quantifying Colocalization

A typical imaging mass spectrometry data set can contain 100+ images, each describing the distribution of a specific biomolecule. Multivariate and hierarchical clustering techniques have been developed to investigate the correlations within a data set, and have revealed the differential patterns associated with different organs/anatomical features. These methods do not quantify the correlations between the hundreds of molecular distributions produced in an imaging mass spectrometry experiment, and are extremely difficult to apply to multiple tissue section investigations. This latter aspect includes quantifying the correlation between the results of repeat imaging mass spectrometry experiments, a crucial aspect for determining the significance of any measured changes in distribution. To date, the large chemical background and pixel-to-pixel variation in the images has limited the quantification of correlation between imaging mass spectrometry results. Here, we demonstrate how to quantify the correlations between imaging mass spectrometry images, both within a data set and between data sets.