Super-resolution segmentation of imaging mass spectrometry data: Solving the issue of low lateral resolution

In the last decade, imaging mass spectrometry has seen incredible technological advances in its applications to biological samples. One computational method of data mining in this field is the spatial segmentation of a sample, which produces a segmentation map highlighting chemically similar regions. An important issue for any imaging mass spectrometry technology is its relatively low spatial or lateral resolution (i.e. a large size of pixel) as compared with microscopy. Thus, the spatial resolution of a segmentation map is also relatively low, that complicates its visual examination and interpretation when compared with microscopy data, as well as reduces the accuracy of any automated comparison. We address this issue by proposing an approach to improve the spatial resolution of a segmentation map. Given a segmentation map, our method magnifies it up to some factor, producing a super-resolution segmentation map. The super-resolution map can be overlaid and compared with a high-res microscopy image. The proposed method is based on recent advances in image processing and smoothes the "pixilated" region boundaries while preserving fine details. Moreover, it neither eliminates nor splits any region. We evaluated the proposed super-resolution segmentation approach on three MALDI-imaging datasets of human tissue sections and demonstrated the superiority of the super-segmentation maps over standard segmentation maps.     

Improving large-scale proteomics by clustering of mass spectrometry data

Tandem mass spectrometry (MS/MS), coupled with liquid chromatography (LC), is a powerful tool for the analysis and comparison of complex protein and peptide mixtures. However, the extremely large amounts of data that result from the process are very complex and difficult to analyze. We show how the clustering of similar spectra from multiple LC-MS/MS runs can help in data management and improve the analysis of complex peptide mixtures. The major effect of spectrum clustering is the reduction of the huge amounts of data to a manageable size. As a result, analysis time is shorter and more data can be stored for further analysis. Furthermore, spectrum quality improvement allows the identification of more peptides with greater confidence, the comparison of complex peptide mixtures is facilitated, and the entire proteomics project is presented in concise form. Pep-Miner is an advanced software tool that implements these clustering-based applications. It proved useful in several comparative proteomics projects involving lung cancer cells and various other cell types. In one of these projects, Pep-Miner reduced 517 000 spectra to 20 900 clusters and identified 2518 peptides derived from 830 proteins. Clustering and identification lasted less than two hours on an IBM Thinkpad T23 computer (laptop). Pep-Miner's unique properties make it a very useful tool for large-scale shotgun proteomics projects.     

Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years, there has been a growing interest in using mass spectrometry for the detection of such biomarkers. The MS signal resulting from MALDI‐TOF measurements is contaminated by different sources of technical variations that can be removed by a prior pre‐processing step. In particular, denoising makes it possible to remove the random noise contained in the signal. Wavelet methodology associated with thresholding is usually used for this purpose. In this study, we adapted two multivariate denoising methods that combine wavelets and PCA to MS data. The objective was to obtain better denoising of the data so as to extract the meaningful proteomic biological information from the raw spectra and reach meaningful clinical conclusions. The proposed methods were evaluated and compared with the classical soft thresholding denoising method using both real and simulated data sets. It was shown that taking into account common structures of the signals by adding a dimension reduction step on approximation coefficients through PCA provided more effective denoising when combined with soft thresholding on detail coefficients.     

Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps

Background  

One of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative metabolites. A major problem of this approach is that in general there is no prior information about an adequate number of clusters.     

Implementation and application of a versatile clustering tool for tandem mass spectrometry data

High-throughput proteomics experiments typically generate large amounts of peptide fragmentation mass spectra during a single experiment. There is often a substantial amount of redundant fragmentation of the same precursors among these spectra, which is usually considered a nuisance. We here discuss the potential of clustering and merging redundant spectra to turn this redundancy into a useful property of the dataset. To this end, we have created the first general-purpose, freely available open-source software application for clustering and merging MS/MS spectra. The application also introduces a novel approach to calculating the similarity of fragmentation mass spectra that takes into account the increased precision of modern mass spectrometers, and we suggest a simple but effective improvement to single-linkage clustering. The application and the novel algorithms are applied to several real-life proteomic datasets and the results are discussed. An analysis of the influence of the different algorithms available and their parameters is given, as well as a number of important applications of the overall approach.     

Single-cell imaging mass spectrometry

1. Download : Download high-res image (113KB)
2. Download : Download full-size image

     

imzML--a common data format for the flexible exchange and processing of mass spectrometry imaging data

The application of mass spectrometry imaging (MS imaging) is rapidly growing with a constantly increasing number of different instrumental systems and software tools. The data format imzML was developed to allow the flexible and efficient exchange of MS imaging data between different instruments and data analysis software. imzML data is divided in two files which are linked by a universally unique identifier (UUID). Experimental details are stored in an XML file which is based on the HUPO-PSI format mzML. Information is provided in the form of a 'controlled vocabulary' (CV) in order to unequivocally describe the parameters and to avoid redundancy in nomenclature. Mass spectral data are stored in a binary file in order to allow efficient storage. imzML is supported by a growing number of software tools. Users will be no longer limited to proprietary software, but are able to use the processing software best suited for a specific question or application. MS imaging data from different instruments can be converted to imzML and displayed with identical parameters in one software package for easier comparison. All technical details necessary to implement imzML and additional background information is available at www.imzml.org.     

Inclusive sharing of mass spectrometry imaging data requires a converter for all

With continued efforts towards a single MSI data format, data conversion routines must be made universally available. The benefits of a common imaging format, imzML, are slowly becoming more widely appreciated but the format remains to be used by only a small proportion of imaging groups. Increased awareness amongst researchers and continued support from major MS vendors in providing tools for converting proprietary formats into imzML are likely to result in a rapidly increasing uptake of the format. It is important that this does not lead to the exclusion of researchers using older or unsupported instruments. We describe an open source converter, imzMLConverter, to ensure against this. We propose that proprietary formats should first be converted to mzML using one of the widely available converters, such as msconvert and then use imzMLConverter to convert mzML to imzML. This will allow a wider audience to benefit from the imzML format immediately.     

Spatial proteomics of vesicular trafficking: coupling mass spectrometry and imaging approaches in membrane biology

In plants, membrane compartmentalization requires vesicle trafficking for communication among distinct organelles. Membrane proteins involved in vesicle trafficking are highly dynamic and can respond rapidly to changes in the environment and to cellular signals. Capturing their localization and dynamics is thus essential for understanding the mechanisms underlying vesicular trafficking pathways. Quantitative mass spectrometry and imaging approaches allow a system-wide dissection of the vesicular proteome, the characterization of ligand-receptor pairs and the determination of secretory, endocytic, recycling and vacuolar trafficking pathways. In this review, we highlight major proteomics and imaging methods employed to determine the location, distribution and abundance of proteins within given trafficking routes. We focus in particular on methodologies for the elucidation of vesicle protein dynamics and interactions and their connections to downstream signalling outputs. Finally, we assess their biological applications in exploring different cellular and subcellular processes.     

Mass spectrometry imaging with high resolution in mass and space

Mass spectrometry (MS) imaging links molecular information and the spatial distribution of analytes within a sample. In contrast to most histochemical techniques, mass spectrometry imaging can differentiate molecular modifications and does not require labeling of targeted compounds. We have recently introduced the first mass spectrometry imaging method that provides highly specific molecular information (high resolution and accuracy in mass) at cellular dimensions (high resolution in space). This method is based on a matrix-assisted laser desorption/ionization (MALDI) imaging source working at atmospheric pressure which is coupled to an orbital trapping mass spectrometer. Here, we present a number of application examples and demonstrate the benefit of ‘mass spectrometry imaging with high resolution in mass and space.’ Phospholipids, peptides and drug compounds were imaged in a number of tissue samples at a spatial resolution of 5–10 μm. Proteins were analyzed after on-tissue tryptic digestion at 50-μm resolution. Additional applications include the analysis of single cells and of human lung carcinoma tissue as well as the first MALDI imaging measurement of tissue at 3 μm pixel size. MS image analysis for all these experiments showed excellent correlation with histological staining evaluation. The high mass resolution (R = 30,000) and mass accuracy (typically 1 ppm) proved to be essential for specific image generation and reliable identification of analytes in tissue samples. The ability to combine the required high-quality mass analysis with spatial resolution in the range of single cells is a unique feature of our method. With that, it has the potential to supplement classical histochemical protocols and to provide new insights about molecular processes on the cellular level.     

Multiscale processing of mass spectrometry data

This work addresses the problem of extracting signal content from protein mass spectrometry data. A multiscale decomposition of these spectra is used to focus on local scale-based structure by defining scale-specific features. Quantification of features is accompanied by an efficient method for calculating the location of features which avoids estimation of signal-to-noise ratios or bandwidths. Scale-based histograms serve as spectral-density-like functions indicating the regions of high density of features in the data. These regions provide bins within which features are quantified and compared across samples. As a preliminary step, the locations of prominent features within coarse-scale bins may be used for a crude registration of spectra. The multiscale decomposition, the scale-based feature definition, the calculation of feature locations, and subsequent quantification of features are carried out by way of a translation-invariant wavelet analysis.     

Spatial organization of lipids in the human retina and optic nerve by MALDI imaging mass spectrometry

MALDI imaging mass spectrometry (IMS) was used to characterize lipid species within sections of human eyes. Common phospholipids that are abundant in most tissues were not highly localized and observed throughout the accessory tissue, optic nerve, and retina. Triacylglycerols were highly localized in accessory tissue, whereas sulfatide and plasmalogen glycerophosphoethanolamine (PE) lipids with a monounsaturated fatty acid were found enriched in the optic nerve. Additionally, several lipids were associated solely with the inner retina, photoreceptors, or retinal pigment epithelium (RPE); a plasmalogen PE lipid containing DHA (22:6), PE(P-18:0/22:6), was present exclusively in the inner retina, and DHA-containing glycerophosphatidylcholine (PC) and PE lipids were found solely in photoreceptors. PC lipids containing very long chain (VLC)-PUFAs were detected in photoreceptors despite their low abundance in the retina. Ceramide lipids and the bis-retinoid, N-retinylidene-N-retinylethanolamine, was tentatively identified and found only in the RPE. This MALDI IMS study readily revealed the location of many lipids that have been associated with degenerative retinal diseases. Complex lipid localization within retinal tissue provides a global view of lipid organization and initial evidence for specific functions in localized regions, offering opportunities to assess their significance in retinal diseases, such as macular degeneration, where lipids have been implicated in the disease process.     

Segmentation of multi-isotope imaging mass spectrometry data for semi-automatic detection of regions of interest

Multi-isotope imaging mass spectrometry (MIMS) associates secondary ion mass spectrometry (SIMS) with detection of several atomic masses, the use of stable isotopes as labels, and affiliated quantitative image-analysis software. By associating image and measure, MIMS allows one to obtain quantitative information about biological processes in sub-cellular domains. MIMS can be applied to a wide range of biomedical problems, in particular metabolism and cell fate [1], [2], [3]. In order to obtain morphologically pertinent data from MIMS images, we have to define regions of interest (ROIs). ROIs are drawn by hand, a tedious and time-consuming process. We have developed and successfully applied a support vector machine (SVM) for segmentation of MIMS images that allows fast, semi-automatic boundary detection of regions of interests. Using the SVM, high-quality ROIs (as compared to an expert's manual delineation) were obtained for 2 types of images derived from unrelated data sets. This automation simplifies, accelerates and improves the post-processing analysis of MIMS images. This approach has been integrated into "Open MIMS," an ImageJ-plugin for comprehensive analysis of MIMS images that is available online at http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php.     

Emerging technologies in mass spectrometry imaging

Mass spectrometry imaging (MSI) as an analytical tool for bio-molecular and bio-medical research targets accurate compound localization and identification. In terms of dedicated instrumentation, this translates into the demand for more detail in the image dimension (spatial resolution) and in the spectral dimension (mass resolution and accuracy), preferably combined in one instrument. At the same time, large area biological tissue samples require fast acquisition schemes, instrument automation and a robust data infrastructure. This review discusses the analytical capabilities of an "ideal" MSI instrument for bio-molecular and bio-medical molecular imaging. The analytical attributes of such an ideal system are contrasted with technological and methodological challenges in MSI. In particular, innovative instrumentation for high spatial resolution imaging in combination with high sample throughput is discussed. Detector technology that targets various shortcomings of conventional imaging detector systems is highlighted. The benefits of accurate mass analysis, high mass resolving power, additional separation strategies and multimodal three-dimensional data reconstruction algorithms are discussed to provide the reader with an insight in the current technological advances and the potential of MSI for bio-medical research.     

Automatic image segmentation with linear clustering for quantification of neointimal formation after surgical vein grafting

OBJECTIVE: To identify extracellular matrix deposition on combined Masson elastin stains from cross-sectional, fixed vein grafts. STUDY DESIGN: Source vectors from RGB components of color images are transformed into new vectors with most of the energy concentrated in fewer coefficients based on the eigenvalues and eigenvectors of their co-variance matrix so their dimension can be reduced for efficient computation and analysis. The vectors are distributed in a triangular shape in which most vectors are located in a long, narrow strip that can be approximated by a straight line while a separate group of vectors from collagen areas form a loose cluster away from the line. An iterative procedure has been developed for the representative vectors in the 2 centroids for linear and circular clusters. The linear centroid consists of all vectors in a straight line, and the centroid of the circular cluster is a single vector. Vector classification is based on the measure of its distance to each of the 2 centroids. RESULTS: The automatic segmentation of the collagen content pixels in green-blue matches the image background color. CONCLUSION: The procedure automatically quantifies and characterizes the neointimal deposition after surgical vein grafting in mice.     

Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform

Mass spectrometry is being used to find disease-related patterns in mixtures of proteins derived from biological fluids. Questions have been raised about the reproducibility and reliability of peak quantifications using this technology. We collected nipple aspirate fluid from breast cancer patients and healthy women, pooled them into a quality control sample, and produced 24 replicate SELDI spectra. We developed a novel algorithm to process the spectra, denoising with the undecimated discrete wavelet transform (UDWT), and evaluated it for consistency and reproducibility. UDWT efficiently decomposes spectra into noise and signal. The noise is consistent and uncorrelated. Baseline correction produces isolated peak clusters separated by flat regions. Our method reproducibly detects more peaks than the method implemented in Ciphergen software. After normalization and log transformation, the mean coefficient of variation of peak heights is 10.6%. Our method to process spectra provides improvements over existing methods. Denoising using the UDWT appears to be an important step toward obtaining results that are more accurate. It improves the reproducibility of quantifications and supplies tools for investigation of the variations in the technology more carefully. Further study will be required, because we do not have a gold standard providing an objective assessment of which peaks are present in the samples.     

Spatial metabolomics using imaging mass spectrometry to identify the localization of asparaptine A in Asparagus officinalis

Spatial metabolomics uses imaging mass spectrometry (IMS) to localize metabolites within tissue section. Here, we performed matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resonance-IMS (MALDI-FTICR-IMS) to identify the localization of asparaptine A, a naturally occurring inhibitor of angiotensin-converting enzyme, in green spears of asparagus (Asparagus officinalis). Spatial metabolome data were acquired in an untargeted manner. Segmentation analysis using the data characterized tissue-type-dependent and independent distribution patterns in cross-sections of asparagus spears. Moreover, asparaptine A accumulated at high levels in developing lateral shoot tissues. Quantification of asparaptine A in lateral shoots using liquid chromatography-tandem mass spectrometry (LC-MS/MS) validated the IMS analysis. These results provide valuable information for understanding the function of asparaptine A in asparagus, and identify the lateral shoot as a potential region of interest for multiomics studies to examine gene-to-metabolite associations in the asparaptine A biosynthesis.