Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry

Diagnosis of human bladder cancer in untreated tissue sections is achieved by using imaging data from desorption electrospray ionization mass spectrometry (DESI-MS) combined with multivariate statistical analysis. We use the distinctive DESI-MS glycerophospholipid (GP) mass spectral profiles to visually characterize and formally classify twenty pairs (40 tissue samples) of human cancerous and adjacent normal bladder tissue samples. The individual ion images derived from the acquired profiles correlate with standard histological hematoxylin and eosin (H&E)-stained serial sections. The profiles allow us to classify the disease status of the tissue samples with high accuracy as judged by reference histological data. To achieve this, the data from the twenty pairs were divided into a training set and a validation set. Spectra from the tumor and normal regions of each of the tissue sections in the training set were used for orthogonal projection to latent structures (O-PLS) treated partial least-square discriminate analysis (PLS-DA). This predictive model was then validated by using the validation set and showed a 5% error rate for classification and a misclassification rate of 12%. It was also used to create synthetic images of the tissue sections showing pixel-by-pixel disease classification of the tissue and these data agreed well with the independent classification that uses histological data by a certified pathologist. This represents the first application of multivariate statistical methods for classification by ambient ionization although these methods have been applied previously to other MS imaging methods. The results are encouraging in terms of the development of a method that could be utilized in a clinical setting through visualization and diagnosis of intact tissue.     

Low C24‐OH and C22‐OH sulfatides in human renal cell carcinoma

Histopathologic diagnosis of renal cell carcinoma (RCC) may sometimes be difficult with small biopsy samples. We applied histology‐directed matrix‐assisted laser desorption/ionization mass spectrometry to RCC samples to evaluate whether and how lipid profiles are different between RCC and normal tissue. We evaluated 59 RCC samples and 24 adjacent normal tissue samples collected from patients who underwent surgery. Five peaks were significantly differently expressed (p < 10^?7) between RCCs and adjacent normal tissue samples. C24‐OH sulfatide (ST‐OH {18:1/24:0}[M‐H]^?; m/z 906.7 in the negative ion mode) and C22‐OH sulfatide (ST‐OH {18:1/22:0}[M‐H]^?; m/z 878.6 in the negative ion mode) were most significantly underexpressed in RCC samples, compared with adjacent normal tissue samples. With 100 random training‐to‐test partitions within these samples, the median prediction accuracy (RCC vs. normal) ranged from 96.3% to 100% at p cutoff values for feature selection ranging from 0.001 to 10^?7. Two oncocytoma samples were predicted as normal tissue by five lipids that were differentially expressed between RCC and normal tissue at p < 10^?7. Clear‐cell, papillary, and chromophobe RCCs were different in lipid profiles. Permutation p‐ values for 0.632+ bootstrap cross‐validated misclassification rates were less than 0.05 for all the classifiers. Thus, lipid profiles differentiate RCC from normal tissue and may possibly classify the histology of RCC. © 2014 The Authors. Journal of Mass Spectrometry published by John Wiley & Sons, Ltd.     

Distinctive Glycerophospholipid Profiles of Human Seminoma and Adjacent Normal Tissues by Desorption Electrospray Ionization Imaging Mass Spectrometry

Desorption electrospray ionization mass spectrometry (DESI-MS) has been successfully used to discriminate between normal and cancerous human tissue from different anatomical sites. On the basis of this, DESI-MS imaging was used to characterize human seminoma and adjacent normal tissue. Seminoma and adjacent normal paired human tissue sections (40 tissues) from 15 patients undergoing radical orchiectomy were flash frozen in liquid nitrogen and sectioned to 15 μm thickness and thaw mounted to glass slides. The entire sample was two-dimensionally analyzed by the charged solvent spray to form a molecular image of the biological tissue. DESI-MS images were compared with formalin-fixed, hematoxylin and eosin (H&E) stained slides of the same material. Increased signal intensity was detected for two seminolipids [seminolipid (16:0/16:0) and seminolipid (30:0)] in the normal tubule testis tissue; these compounds were undetectable in seminoma tissue, as well as from the surrounding fat, muscle, and blood vessels. A glycerophosphoinositol [PI(18:0/20:4)] was also found at increased intensity in the normal testes tubule tissue when compared with seminoma tissue. Ascorbic acid (i.e., vitamin C) was found at increased amounts in seminoma tissue when compared with normal tissue. DESI-MS analysis was successfully used to visualize the location of several types of molecules across human seminoma and normal tissues. Discrimination between seminoma and adjacent normal testes tubules was achieved on the basis of the spatial distributions and varying intensities of particular lipid species as well as ascorbic acid. The increased presence of ascorbic acid within seminoma compared with normal seminiferous tubules was previously unknown.     

An Assessment of the Utility of Tissue Smears in Rapid Cancer Profiling with Desorption Electrospray Ionization Mass Spectrometry (DESI-MS)

Mass spectrometry imaging with desorption electrospray ionization mass spectrometry (DESI-MS) is used to characterize cancer from ex vivo slices of tissues. The process is time-consuming. The use of tissue smears for DESI-MS analysis has been proposed as it eliminates the time required to snap-freeze and section the tissue. To assess the utility of tissue smears for rapid cancer characterization, principal component analysis (PCA) was performed to evaluate the concordance between DESI-MS profiles of breast cancer from tissue slices and smears prepared on various surfaces. PCA suggested no statistical discrimination between DESI-MS profiles of tissue sections and tissue smears prepared on glass, polytetrafluoroethylene (PTFE), and porous PTFE. However, the abundances of cancer biomarker ions varied between sections and smears, with DESI-MS analysis of tissue sections yielding higher ion abundances of cancer biomarkers compared with smears. Coefficient of variance (CV) analysis suggests DESI-MS profiles from tissue smears are as reproducible as the ones from tissue sections. The limit of detection with smear samples from single pixel analysis is comparable to tissue sections that average the signal from a tissue area of 0.01 mm². The smears prepared on the PTFE surface possessed a higher degree of homogeneity compared with the smears prepared on the glass surface. This allowed single MS scans (~1 s) from random positions across the surface of the smear to be used in rapid cancer typing with good reproducibility, providing pathologic information for cancer typing at speeds suitable for clinical utility.

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Graphical Abstract ?

     

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.     

Data Processing for 3D Mass Spectrometry Imaging

Data processing for three dimensional mass spectrometry (3D-MS) imaging was investigated, starting with a consideration of the challenges in its practical implementation using a series of sections of a tissue volume. The technical issues related to data reduction, 2D imaging data alignment, 3D visualization, and statistical data analysis were identified. Software solutions for these tasks were developed using functions in MATLAB. Peak detection and peak alignment were applied to reduce the data size, while retaining the mass accuracy. The main morphologic features of tissue sections were extracted using a classification method for data alignment. Data insertion was performed to construct a 3D data set with spectral information that can be used for generating 3D views and for data analysis. The imaging data previously obtained for a mouse brain using desorption electrospray ionization mass spectrometry (DESI-MS) imaging have been used to test and demonstrate the new methodology.     

Data quality in tissue analysis using desorption electrospray ionization

There has been a recent surge in applications of mass spectrometry (MS) to tissue analysis, particularly lipid-based tissue imaging using ambient ionization techniques. This recent growth highlights the need to examine the effects of sample handling, storage conditions, and experimental protocols on the quality of the data obtained. Variables such as time before freezing after organ removal, storage time at −80 °C, time stored at room temperature, heating, and freeze/thaw cycles were investigated for their effect on the data quality obtained in desorption electrospray ionization (DESI)-MS using mouse brain. In addition, analytical variables such as tissue thickness, drying times, and instrumental conditions were also examined for their impact on DESI-MS data. While no immediate changes were noted in the DESI-MS lipid profiles of the mouse brain tissue after spending 1 h at room temperature when compared to being frozen immediately following removal, minor changes were noted between the tissue samples after 7 months of storage at −80 °C. In tissue sections stored at room temperature, degradation was noted in 24 h by the appearance of fatty acid dimers, which are indicative of high fatty acid concentrations, while in contrast, those sections stored at −80 °C for 7 months showed no significant degradation. Tissue sections were also subjected to up to six freeze/thaw cycles and showed increasing degradation following each cycle. In addition, tissue pieces were subjected to 50 °C temperatures and analyzed at specific time points. In as little as 2 h, degradation was observed in the form of increased fatty acid dimer formation, indicating that enzymatic processes forming free fatty acids were still active in the tissue. We have associated these dimers with high concentrations of free fatty acids present in the tissue during DESI-MS experiments. Analytical variables such as tissue thickness and time left to dry under nitrogen were also investigated, with no change in the resulting profiles at thickness from 10 to 25 μm and with optimal signal obtained after just 20 min in the dessicator. Experimental conditions such as source parameters, spray solvents, and sample surfaces are all shown to impact the quality of the data. Inter-section (relative standard deviation (%RSD), 0.44–7.2%) and intra-sample (%RSD, 4.0–8.0%) reproducibility data show the high quality information DESI-MS provides. Overall, the many variables investigated here showed DESI-MS to be a robust technique, with sample storage conditions having the most effect on the data obtained, and with unacceptable sample degradation occurring during room temperature storage.     

Fourier transform infrared imaging analysis in discrimination studies of squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) of the oral cavity and oropharynx represents more than 95% of all malignant neoplasms in the oral cavity. Histomorphological evaluation of this cancer type is invasive and remains a time consuming and subjective technique. Therefore, novel approaches for histological recognition are necessary to identify malignancy at an early stage. Fourier transform infrared (FTIR) imaging has become an essential tool for the detection and characterization of the molecular components of biological processes, such as those responsible for the dynamic properties of tumor progression. FTIR imaging is a modern analytical technique enabling molecular imaging of a complex biological sample and is based on the absorption of IR radiation by vibrational transitions in covalent bonds. One major advantage of this technique is the acquisition of local molecular expression profiles, while maintaining the topographic integrity of the tissue and avoiding time-consuming extraction, purification, and separation steps. With this imaging technique, it is possible to obtain unique images of the spatial distribution of proteins, lipids, carbohydrates, cholesterols, nucleic acids, phospholipids, and small molecules with high spatial resolution. Analysis and visualization of FTIR imaging datasets are challenging and the use of chemometric tools is crucial in order to take advantage of the full measurement. Therefore, methodologies for this task based on the novel developed algorithm for multivariate image analysis (MIA) are often necessary. In the present study, FTIR imaging and data analysis methods were combined to optimize the tissue measurement mode after deparaffinization and subsequent data evaluation (univariate analysis and MIAs). We demonstrate that it is possible to collect excellent IR spectra from formalin-fixed paraffin-embedded (FFPE) tissue microarrays (TMAs) of OSCC tissue sections employing an optimised analytical protocol. The correlation of FTIR imaging to the morphological tissue features obtained by histological staining of the sections demonstrated that many histomorphological tissue patterns can be visualized in the colour images. The different algorithms used for MIAs of FTIR imaging data dramatically increased the information content of the IR images from squamous cell tissue sections. These findings indicate that intra-operative and surgical specimens of squamous cell carcinoma tissue can be characterized by FTIR imaging.     

Desorption electrospray ionization mass spectrometry (DESI-MS) applied to the speciation of arsenic compounds from fern leaves

The different chemical forms of arsenic compounds, including inorganic and organic species, present distinct environmental impacts and toxicities. Desorption electrospray ionization mass spectrometry (DESI-MS) is an excellent technique for in situ analysis, as it operates under atmospheric pressure and room temperature and is conducted with no/minimal sample pretreatment. Aimed at expanding its scope, DESI-MS is applied herein for the quick and reliable detection of inorganic (arsenate—As(V): AsO₄ ^3? and arsenite—As(III): AsO₂ ^?) and organic (dimethylarsinic acid—DMA: (CH₃)₂AsO(OH) and disodium methyl arsonate hexahydrate: CH₃AsO₃·2Na·6H₂O) arsenic compounds in fern leaves. Operational conditions of DESI-MS were optimized with DMA standard deposited on paper surfaces to improve ionization efficiency and detection limits. Mass spectra data for all arsenic species were acquired in both the positive and negative ion modes. The positive ion mode was shown to be useful in detecting both the organic and inorganic arsenic compounds. The negative ion mode was shown only to be useful in detecting As(V) species. Moreover, MS/MS spectra were recorded to confirm the identity of each arsenic compound by the characteristic fragmentation profiles. Optimized conditions of DESI-MS were applied to the analysis of fern leaves. LC-ICP-MS was employed to confirm the results obtained by DESI-MS and to quantify the arsenic species in fern leaves. The results confirmed the applicability of DESI-MS in detecting arsenic compounds in complex matrices.     

Discrimination of Osteonecrosis and Normal Tissues by Near-Infrared Spectroscopy and Successive Projections Algorithm-Linear Discriminant Analysis

Osteonecrosis of femoral head (ONFH) is a disease characterized by an impaired blood flow in the bone. The pathogenesis is still unknown, which makes an exact diagnosis troublesome and heavily dependent on experience. Exploring the information of molecular level by modern spectroscopy may help to discover the underlying pathogenesis and find its diagnostic application in clinical medicine. The study focuses on the combination of near-infrared (NIR) spectroscopy and classification models for discriminating ONFH and normal tissues. A total of 128 surgical specimens was prepared and NIR spectra were recorded by an integrating sphere. The experiment data set was divided into three subsets, i.e., the training set, validation set, and test set. Successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to compress variables and build the diagnostic model. Partial least square-discriminant analysis (PLS-DA) was used as the reference. Principal component analysis (PCA) was used for exploratory analysis. The results showed that compared to PLS-DA, SPA-LDA provided a more parsimonious model using only seven variables and achieved better performance, i.e., sensitivity of 90.5 and 85%, and specificity of 100 and 95.5% for the validation and test sets, respectively. It indicated that NIR spectroscopy combined with SPA-LDA algorithm was a feasible aid tool for discriminating ONFH from normal tissue.     

Proteomic analysis of laser capture microdissected human prostate cancer and in vitro prostate cell lines

Specific populations of normal and malignant epithelium from three radical prostatectomy tissue specimens were procured by laser capture microdissection (LCM) and analyzed by two-dimensional polyacrylamide gel electrophoresis (2-D PAGE). Six proteins that were only seen in malignant cells and two proteins that were only seen in benign epithelium were reproducibly observed in two of two cases examined. Furthermore, these proteins were not observed in the 2-D PAGE profiles from the patient-matched microdissected stromal cell populations, but were seen in the protein profiles from the undissected whole cryostat sections. One of these proteins was determined to be prostate-specific antigen (PSA) by Western blot analysis, and intriguingly the remaining protein candidates were found to be at least as abundant as the PSA protein. Comparison of 2-D PAGE profiles of microdissected cell with matched in vitro cell lines from the same patient, and metastatic prostate cancer cell lines (LnCaP and PC3) showed striking differences between prostate cells in vivo and in vitro with less than 20% shared proteins. The data demonstrate that 2-D PAGE analysis of LCM-derived cells can reliably detect alterations in protein expression associated with prostate cancer, and that these differentially expressed proteins are produced in high enough levels which could allow for their clinical utility as new targets for therapeutic intervention, serum markers, and/or imaging markers.     

Raman endoscopy for in vivo differentiation between benign and malignant ulcers in the stomach

The aim of this study was to evaluate the clinical utility of an image-guided Raman endoscopy technique for in vivo differential diagnosis of benign and malignant ulcerous lesions in the stomach. A rapid-acquisition image-guided Raman endoscopy system with 785 nm laser excitation has been developed to acquire in vivo gastric tissue Raman spectra within 0.5 s during clinical gastroscopic examinations. A total of 1102 in vivo Raman spectra were acquired from 71 gastric patients, in which 924 Raman spectra were from normal tissue, 111 Raman spectra were from benign ulcers whereas 67 Raman spectra were from ulcerated adenocarcinoma. There were distinctive spectral differences in Raman spectra among normal mucosa, benign ulcers and malignant ulcers, particularly in the spectral ranges of 800-900, 1000-1100, 1245-1335, 1440-1450 and 1500-1800 cm(-1), which primarily contain signals related to proteins, DNA, lipids and blood. The malignant ulcerous lesions showed Raman signals to be mainly associated with abnormal nuclear activity and decrease in lipids as compared to benign ulcers. Partial least squares-discriminant analysis (PLS-DA) was employed to generate multi-class diagnostic algorithms for classification of Raman spectra of different gastric tissue types. The PLS-DA algorithms together with leave-one tissue site-out, cross validation technique yielded diagnostic sensitivities of 90.8%, 84.7%, 82.1%, and specificities of 93.8%, 94.5%, 95.3%, respectively, for classification of normal mucosa, benign and malignant ulcerous lesions in the stomach. This work demonstrates that image-guided Raman endoscopy technique associated with PLS-DA diagnostic algorithms has for the first time promising clinical potential for rapid, in vivo diagnosis and detection of malignant ulcerous gastric lesions at the molecular level.     

New perspective of diabetes response to an antioxidant treatment through metabolic fingerprinting of urine by capillary electrophoresis

Benefits of capillary electrophoresis to provide a comprehensive snapshot of multiple metabolites in biological samples have been exploited. Afterwards, multivariate statistical methods can be employed in order to mine additional information from the data. Urine fingerprints of control and diabetic rats have shown the clear effects of an antioxidant treatment on diabetic animals, which were not seen in controls, in a rapid, simple and cost-effective way without identifying a single marker. The procedure involves the measurement of samples with a relatively inexpensive tool such as CE-UV, without any previous treatment other than filtration and the application of chemometric tools [PCA (principal components analysis) and PLS-DA (partial least squares discriminant analysis)]. Data pre-treatment of electrophoretic profiles (alignment, normalization and baseline correction) has shown to be key for further chemometric treatment. Once developed, the methodology can easily be applied for a rapid in vivo screening of extracts with potential in vitro activity. Classification was supported by that produced after PCA and PLS-DA of target variables obtained with selectively designed, time and reagent consuming methods.     

Detection of collagens in brain tumors based on FTIR imaging and chemometrics

Fourier transform infrared (FTIR) imaging has been used as a molecular histopathology tool on brain tissue sections after intracranial implantation and development of glioma tumors. Healthy brain tissue (contralateral lobe) as well as solid and diffuse tumor tissues were compared for their collagen contents. IR spectra were extracted from IR images for determining the secondary structure of protein contents and compared to pure product spectra of collagens (types I, III, IV, V, and VI). Multivariate statistical analyses of variance and correspondence factorial analysis were performed to differentiate healthy and tumor brain tissues as well as their classification according to their secondary structure profiles. Secondary structure profiles revealed that no collagen was present in healthy tissues; they are also significantly different from solid and diffuse tumors (p < 0.05). Solid and diffuse tumors could be discriminated with respect to the secondary structure profile of fibrillar and non-fibrillar collagens, respectively. We can thus propose to develop FTIR imaging for histopathology examination of tumors on the basis of collagen contents.     

A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding

The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.     

In vivo fluorescence spectroscopy of nonmelanoma skin cancer

In vivo and ex vivo tissue autofluorescence (endogenous fluorescence) have been employed to investigate the presence of markers that could be used to detect tissue abnormalities and/or malignancies. We present a study of the autofluorescence of normal skin and tumor in vivo, conducted on 18 patients diagnosed with nonmelanoma skin cancers (NMSC). We observed that both in basal cell carcinomas (BCC) and squamous cell carcinomas (SCC) the endogenous fluorescence due to tryptophan residues was more intense in tumor than in normal tissue, probably due to epidermal thickening and/or hyperproliferation. Conversely, the fluorescence intensity associated with dermal collagen crosslinks was generally lower in tumors than in the surrounding normal tissue, probably because of degradation or erosion of the connective tissue due to enzymes released by the tumor. The decrease of collagen fluorescence in the connective tissue adjacent to the tumor loci was validated by fluorescence imaging on fresh-frozen tissue sections obtained from 33 NMSC excised specimens. Our results suggest that endogenous fluorescence of NMSC, excited in the UV region of the spectrum, has characteristic features that are different from normal tissue and may be exploited for noninvasive diagnostics and for the detection of tumor margins.     

Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma

BackgroundIt is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC.MethodsTwo expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes.Results1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups.ConclusionsIn summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.     

Improved imaging resolution in desorption electrospray ionization mass spectrometry

The imaging resolution of desorption electrospray ionization mass spectrometry (DESI-MS) was investigated using printed patterns on paper and thin-layer chromatography (TLC) plate surfaces. Resolution approaching 40 microm was achieved with a typical DESI-MS setup, which is approximately 5 times better than the best resolution reported previously. This improvement was accomplished with careful control of operational parameters (particularly spray tip-to-surface distance, solvent flow rate, and spacing of lane scans). In addition, an appropriately strong analyte/surface interaction and uniform surface texture on the size scale no larger than the desired imaging resolution were required to achieve this resolution. Overall, conditions providing the smallest possible effective desorption/ionization area in the DESI impact plume region and minimizing the analyte redistribution on the surface during analysis led to improved DESI-MS imaging resolution.     

Analysis of colorectal adenocarcinoma tissue by desorption electrospray ionization mass spectrometric imaging

Negative ion desorption electrospray ionization (DESI) was used for the analysis of an ex vivo tissue sample set comprising primary colorectal adenocarcinoma samples and colorectal adenocarcinoma liver metastasis samples. Frozen sections (12 μm thick) were analyzed by means of DESI imaging mass spectrometry (IMS) with spatial resolution of 100 μm using a computer-controlled DESI imaging stage mounted on a high resolution Orbitrap mass spectrometer. DESI-IMS data were found to predominantly feature complex lipids, including phosphatidyl-inositols, phophatidyl-ethanolamines, phosphatidyl-serines, phosphatidyl-ethanolamine plasmalogens, phosphatidic acids, phosphatidyl-glycerols, ceramides, sphingolipids, and sulfatides among others. Molecular constituents were identified based on their exact mass and MS/MS fragmentation spectra. An identified set of molecules was found to be in good agreement with previously reported DESI imaging data. Different histological tissue types were found to yield characteristic mass spectrometric data in each individual section. Histological features were identified by comparison to hematoxylin-eosin stained neighboring sections. Ions specific to certain histological tissue types (connective tissue, smooth muscle, healthy mucosa, healthy liver parenchyma, and adenocarcinoma) were identified by semi-automated screening of data. While each section featured a number of tissue-specific species, no potential global biomarker was found in the full sample set for any of the tissue types. As an alternative approach, data were analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) which resulted in efficient separation of data points based on their histological types. A pixel-by-pixel tissue identification method was developed, featuring the PCA/LDA analysis of authentic data set, and localization of unknowns in the resulting 60D, histologically assigned LDA space. Novel approach was found to yield results which are in 95% agreement with the results of classical histology. KRAS mutation status was determined for each sample by standard molecular biology methods and a similar PCA/LDA approach was developed to assess the feasibility of the determination of this important parameter using solely DESI imaging data. Results showed that the mutant and wild-type samples fully separated. DESI-MS and molecular biology results were in agreement in 90% of the cases.     

Multivariate curve resolution modeling of liquid chromatography-mass spectrometry data in a comparative study of the different endogenous metabolites behavior in two tomato cultivars treated with carbofuran pesticide

A metabonomic study based on the application of multivariate curve resolution and alternating least squares (MCR-ALS) to three-way data sets obtained by liquid chromatography coupled to mass spectrometry detection (LC-MS) was carried out for Rambo and Raf tomato cultivars treated with carbofuran pesticide. Samples were picked up during a 21 days period after treatment and analyzed by LC-MS in scan mode, along with the corresponding blank samples. Then, MCR-ALS was applied to the three-way data sets using column wise augmented matrices, and the evolutionary profiles as a function of the time after treatment were estimated for the metabolites present in both cultivars, as well as their corresponding pure spectra estimations. A comparative study using those estimations showed that some of these metabolites followed different behavior for the different cultivars after treatment. Since all treated and untreated Rambo and Raf samples were picked up according to the same sampling protocol and in a similar state of maturation, any difference in the behavior between profiles can be interpreted as an effect due to the presence of pesticide and to the kind of cultivar. Based on this hypothesis, several PLS-DA approaches were tested to check if it would be possible to classify samples by using the metabolites MCR estimations. Results showed that PLS-DA models for classification of treated or non-treated (blank) samples were the best ones obtained (98.44% of correct classifications for the validation set), which supports the stress effects related to carbofuran treatment. In addition, excellent discrimination among the four groups could be attained (89.06% of correct classifications for the validation set).