Use of matrix-assisted laser desorption/ionisation mass spectrometry in cancer research

Laser desorption/ionization mass spectrometry of L-thyroxine (T4) using combi-matrix of α-cyano-4-hydroxycinnamic acid (CHCA) and graphene

An optimal combi-matrix for MALDI-TOF mass spectrometry was presented for the analysis of L-thyroxine (T4) in human serum. For the selection of the optimal combi-matrix, several kinds of combi-matrices were prepared by mixing the conventional organic matrix of CHCA with nanomaterials, such as graphene, carbon nanotubes, nanoparticles of Pt and TiO2. In order to select the optimal combi-matrix, the absorption at the wavelength of laser radiation (337 nm) for the ionization of sample was estimated using UV–Vis spectrometry. And, the heat absorption properties of these combi-matrices were also analyzed using differential scanning calorimetry (DSC), such as onset temperature and fusion enthalpy. In the case of the combi-matrix of CHCA and graphene, the onset temperature and fusion enthalpy were observed to be lower than those of CHCA, which represented the enhanced transfer of heat to the analyte in comparison with CHCA. From the analysis of optical and thermal properties, the combi-matrix of CHCA and graphene was selected to be an optimal combination for the transfer of laser energy during MALDI-TOF mass spectrometry. The feasibility of the combi-matrix composed of CHCA and graphene was demonstrated for the analysis of T4 molecules using MALDI-TOF mass spectrometry. The combi-matrix of CHCA and graphene was estimated to have an improved limit of detection and a wider detection range in comparison with other kinds of combi-matrices. Finally, the MALDI-TOF MS results of T4 analysis using combi-matrix were statistically compared with those of the conventional immunoassay.

Screening of Urinary Renal Cancer Metabolic Biomarkers with Gold Nanoparticles-assisted Laser Desorption/Ionization Mass Spectrometry

Renal cell carcinoma is a very aggressive and often fatal disease for which there are no specific biomarkers found to date. The purpose of this work was to find features that differentiate urine metabolic profiles of healthy people and cancer patients. Laser desorption/ionization mass spectrometry on gold nanostructures-based techniques were used for the metabolic analysis of urine of 50 patients with kidney cancer. Comparison with data from 50 healthy volunteers led to the discovery of several compounds that may be considered potential renal cell carcinoma (RCC) biomarkers. Statistical analysis of data allowed for the discovery of m/z values that had the greatest impact on group differentiation. A database search enabled the assignment of signals for the most promising 15 features among them: serine, heptanol, 3-methylene-indolenine, 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, phosphodimethylethanolamine, 4-methoxyphenylacetic acid, N-acetylglutamine, 3,5-dihydroxyphenylvaleric acid, hydroxyhexanoylglycine, valyl-leucine, leucyl-histidine, oleamide, 9,12,13-trihydroxyoctadecenoic acid, stearidonyl carnitine and squalene. Differences of metabolite profiles of human urine could be identified by gold nanoparticle-enhanced target (AuNPET) LDI MS method and used for the detection of renal cancer.

Proteomic Biomarkers for the Detection of Endometrial Cancer

Endometrial cancer is the leading gynaecological malignancy in the western world and its incidence is rising in tandem with the global epidemic of obesity. Early diagnosis is key to improving survival, which at 5 years is less than 20% in advanced disease and over 90% in early-stage disease. As yet, there are no validated biological markers for its early detection. Advances in high-throughput technologies and machine learning techniques now offer unique and promising perspectives for biomarker discovery, especially through the integration of genomic, transcriptomic, proteomic, metabolomic and imaging data. Because the proteome closely mirrors the dynamic state of cells, tissues and organisms, proteomics has great potential to deliver clinically relevant biomarkers for cancer diagnosis. In this review, we present the current progress in endometrial cancer diagnostic biomarker discovery using proteomics. We describe the various mass spectrometry-based approaches and highlight the challenges inherent in biomarker discovery studies. We suggest novel strategies for endometrial cancer detection exploiting biologically important protein biomarkers and set the scene for future directions in endometrial cancer biomarker research.

Delineation of disease phenotypes associated with esophageal adenocarcinoma by MALDI-IMS-MS analysis of serum N-linked glycans

N-Linked glycans, extracted from patient sera and healthy control individuals, are analyzed by Matrix-assisted laser desorption ionization (MALDI) in combination with ion mobility spectrometry (IMS), mass spectrometry (MS) and pattern recognition methods. MALDI-IMS-MS data were collected in duplicate for 58 serum samples obtained from individuals diagnosed with Barrett's esophagus (BE, 14 patients), high-grade dysplasia (HGD, 7 patients), esophageal adenocarcinoma (EAC, 20 patients) and disease-free control (NC, 17 individuals). A combined mobility distribution of 9 N-linked glycans is established for 90 MALDI-IMS-MS spectra (training set) and analyzed using a genetic algorithm for feature selection and classification. Two models for phenotype delineation are subsequently developed and as a result, the four phenotypes (BE, HGD, EAC and NC) are unequivocally differentiated. Next, the two models are tested against 26 blind measurements. Interestingly, these models allowed for the correct phenotype prediction of as many as 20 blinds. Although applied to a limited number of blind samples, this methodology appears promising as a means of discovering molecules from serum that may have capabilities as markers of disease.

Laser desorption/ionization MS imaging of cancer kidney tissue on silver nanoparticle-enhanced target

Aim: Renal cell carcinoma is a very aggressive and often fatal disease for which there are no specific biomarkers found to date. The purpose of work was to find substances that differentiate the cancerous and healthy tissue by using laser desorption/ionization MS imaging combined with silver nanoparticle-enhanced target. Results: Ion images and comparative analysis of spectra revealed differences in intensities for several metabolites, for which their biochemical properties were discussed. Statistical analysis allowed to distinguish healthy and cancer tissue without the involvement of a pathologist. Conclusion: Laser desorption/ionization MS imaging technology combined with silver nanoparticle-enhanced target enabled rapid visualization of the differences between the clear cell renal cell carcinoma and the healthy part of the kidney tissue.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2011-2012

This review is the seventh update of the original article published in 1999 on the application of MALDI mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2012. General aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, and fragmentation are covered in the first part of the review and applications to various structural types constitute the remainder. The main groups of compound are oligo- and poly-saccharides, glycoproteins, glycolipids, glycosides, and biopharmaceuticals. Much of this material is presented in tabular form. Also discussed are medical and industrial applications of the technique, studies of enzyme reactions, and applications to chemical synthesis. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:255-422, 2017.

Quantification of the PR-39 cathelicidin compound in porcine blood by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

RATIONALE

The PR-39 porcine cathelicidin occurs naturally in animal neutrophils. Its main function is antimicrobial activity, which potentially can be used in antibiotic treatments in veterinary medicine. Investigations concerning such a use require the detection and quantification of PR-39 in a given sample. The aim of this work is to determine the concentration of PR-39 contained in porcine blood.

METHODS

Prior to matrix-assisted laser desorption/ionization (MALDI) analysis, the porcine blood sample was subjected to crude extraction in order to release the active form of PR-39 from the neutrophil granules. Next, gel filtration chromatography was performed to separate PR-39 from other cathelicidins present in porcine blood. Positive ion MALDI time-of-flight (TOF) mass spectra of the resulting portion of lyophilisate with unknown PR-39 content were acquired in linear mode. To quantify PR-39 in the lyophilisate sample, the standard addition method was applied. The PR-39 concentration obtained in the lyophilisate sample was then converted into the peptide concentration in porcine blood.

RESULTS

The linear fit function of the constructed calibration curve indicates an excellent correlation between the PR-39 peak intensity and the added quantity of synthetic PR-39 (R(2) = 0.994) and a low relative standard deviation of the slope = 1.98%. From the x-intercept of the straight line, we estimated the PR-39 concentration in porcine blood to be 20.5 ± 4.6 ng/mL.

CONCLUSIONS

The MALDI method was successfully applied for the quantitative analysis of PR-39 found in porcine blood. Compared with other available methods, it is relatively easy, inexpensive and not time-consuming. Despite the method having lower accuracy than the enzyme-linked immunosorbent assay (ELISA), the results obtained here, by a much simpler method, are in good agreement with the literature data.

MALDI mass spectrometry imaging analysis of pituitary adenomas for near-real-time tumor delineation

We present a proof of concept study designed to support the clinical development of mass spectrometry imaging (MSI) for the detection of pituitary tumors during surgery. We analyzed by matrix-assisted laser desorption/ionization (MALDI) MSI six nonpathological (NP) human pituitary glands and 45 hormone secreting and nonsecreting (NS) human pituitary adenomas. We show that the distribution of pituitary hormones such as prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) in both normal and tumor tissues can be assessed by using this approach. The presence of most of the pituitary hormones was confirmed by using MS/MS and pseudo-MS/MS methods, and subtyping of pituitary adenomas was performed by using principal component analysis (PCA) and support vector machine (SVM). Our proof of concept study demonstrates that MALDI MSI could be used to directly detect excessive hormonal production from functional pituitary adenomas and generally classify pituitary adenomas by using statistical and machine learning analyses. The tissue characterization can be completed in fewer than 30 min and could therefore be applied for the near-real-time detection and delineation of pituitary tumors for intraoperative surgical decision-making.

MALDI-TOF MS as evolving cancer diagnostic tool: a review

Recent developments in mass spectrometry have introduced clinical proteomics to the forefront of diseases diagnosis, offering reliable, robust and efficient analytical method for biomarker discovery and monitoring. MALDI-TOF is a powerful tool for surveying proteins and peptides comprising the realm for clinical analysis. MALDI-TOF has the potential to revolutionize cancer diagnostics by facilitating biomarker discovery, enabling tissue imaging and quantifying biomarker levels. Healthy (control) and cancerous tissues can be analyzed on the basis of mass spectrometry (MALDI-TOF) imaging to identify cancer-specific changes that may prove to be clinically useful. We review MALDI-TOF profiling techniques as tools for detection of cancer biomarkers in various cancers. We mainly discuss recent advances including period from 2011 to 2013.

Mass Spectrometry Imaging: facts and perspectives from a non-mass spectrometrist point of view

Mass Spectrometry Imaging (MSI, also called Imaging Mass Spectrometry) can be used to map molecules according to their chemical abundance and spatial distribution. This technique is not widely used in mass spectrometry circles and is barely known by other scientists. In this review, a brief overview of the mass spectrometer hardware used in MSI and some of the possible applications of this powerful technique are discussed. I intend to call attention to MSI uses from cell biology to histopathology for biological scientists who have little background in mass spectrometry. MSI facts and perspectives are presented from a non-mass spectrometrist point of view.

Interactive hyperspectral approach for exploring and interpreting DESI-MS images of cancerous and normal tissue sections

Desorption electrospray ionization (DESI) is an ambient mass spectrometry (MS) technique that can be operated in an imaging mode. It is known to provide valuable information on disease state and grade based on lipid profiles in tissue sections. Comprehensive exploration of the spatial and chemical information contained in 2D MS images requires further development of methods for data treatment and interpretation in conjunction with multivariate analysis. In this study, we employ an interactive approach based on principal component analysis (PCA) to interpret the chemical and spatial information obtained from MS imaging of human bladder, kidney, germ cell and prostate cancer and adjacent normal tissues. This multivariate strategy facilitated distinction between tumor and normal tissue by correlating the lipid information with pathological evaluation of the same samples. Some common lipid ions, such as those of m/z 885.5 and m/z 788.5, nominally PI(18 : 0/20 : 4) and PS(18 : 0/18 : 1), as well as ions of free fatty acids and their dimers, appeared to be highly characterizing for different types of human cancers, while other ions, such as those of m/z 465.5 (cholesterol sulfate) for prostate cancer tissue and m/z 795.5 (seminolipid 16 : 0/16 : 0) for germ tissue, appeared to be extremely selective for the type of tissue analyzed. These data confirm that lipid profiles can reflect not only the disease/health state of tissue but also are characteristic of tissue type. The manual interactive strategy presented here is particularly useful to visualize the information contained in hyperspectral MS images by automatically connecting regions of PCA score space to pixels of the 2D physical object. The procedures developed in this study consider all the spectral variables and their inter-correlations, and guide subsequent investigations of the mass spectra and single ion images to allow one to maximize characterization between different regions of any DESI-MS image.