Processing and classification of protein mass spectra

Hilario, Mélanie; Kalousis, Alexandros; Pellegrini, Christian; Müller, Markus

doi:10.1002/mas.20072

Cited by 158 publications

(146 citation statements)

References 179 publications

(255 reference statements)

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“…Previous work on SIMS data has demonstrated that binning is "the most effective technique to improve PCA performance" [17], in agreement with our preliminary experimental work (results not shown). Many variants of PCA or multivariate analyses have been reported for SIMS imaging mass spectrometry [11, 13, 18 -20], even applied to 3D distributions [21], and many other classification systems have been used in protein mass spectrometry [22]. The auto-alignment procedure benefits from PCA in noise reduction and the availability of more than onecomponent images for a more robust alignment and not classification.…”

Section: Discussionmentioning

confidence: 99%

Automated, feature-based image alignment for high-resolution imaging mass spectrometry of large biological samples

Broersen

Liere

Altelaar

et al. 2008

J. Am. Soc. Mass Spectrom.

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High-resolution imaging mass spectrometry of large biological samples is the goal of several research groups. In mosaic imaging, the most common method, the large sample is divided into a mosaic of small areas that are then analyzed with high resolution. Here we present an automated alignment routine that uses principal component analysis to reduce the uncorrelated noise in the imaging datasets, which previously obstructed automated image alignment. An additional signal quality metric ensures that only those regions with sufficient signal quality are considered. We demonstrate that this algorithm provides superior alignment performance than manual stitching and can be used to automatically align large imaging mass spectrometry datasets comprising many individual mosaic tiles. (J Am Soc Mass Spectrom 2008, 19, 823-832) © 2008 American Society for Mass Spectrometry I maging mass spectrometry is a rapidly developing analytical tool because it provides the ability to map the profiles of specific biomolecules, in which the intrinsic mass of the molecule differentiates between any modified forms; to record the distributions of multiple analytes in parallel; and to perform these analyses without a label and with clinical samples [1]. This combination of specificity, parallel detection, and non-targeted analysis has led to great excitement for its potential as a discovery tool.It is the goal of many research groups to be able to perform high-resolution analysis of large samples, thus combining the ability to examine distributions between organs/tumors and their surroundings as well as to investigate the subcellular/intracellular locations of the biomolecules. The central premise of this approach is that the subcellular locations will provide some of the information required to explain differences in the more global patterns.High spatial resolution measurements are a wellestablished ability of secondary ion mass spectrometry (SIMS). Recent advances in both ionization efficiency (polyatomic primary ions) and sample preparation have significantly improved the sensitivity for detecting intact, medium-sized molecular ions (Ͻ1000 Da) from tissues and cells [1][2][3][4][5][6]. High-resolution images of small peptides, lipids, cholesterol, vitamins, and pharmaceuticals have all been reported, and through the use of large polyatomic primary ions three-dimensional (3D) molecular imaging results are beginning to appear [7][8][9].The images are normally created by moving the ionization beam in a set pattern across the sample and performing mass analysis at each point of the raster (spatially correlated mass spectrometry). The raster pattern typically uses 8-or 10-bit encoding; as a result the maximum analysis field contains 256 ϫ 256 (or 1024 ϫ 1024) pixels. In a typical high spatial resolution SIMS measurement the pixel size is about 200 nm, meaning maximum analysis areas of approximately 50 or 200 m, respectively. This is much smaller than many of the biological samples of interest; for example, a tissue section of an adult rat ...

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Section: Discussionmentioning

confidence: 99%

Automated, feature-based image alignment for high-resolution imaging mass spectrometry of large biological samples

Broersen

Liere

Altelaar

et al. 2008

J. Am. Soc. Mass Spectrom.

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“…by Hilario et al [8] Under this assumption, we can first process each mass spectrum independently over the mass dimension, for which peptide peaks are selected from the mass spectrum. In the next step, the selected peptides are assembled over the retention time (fractions) in order to study the elution profile in the LC dimension.…”

Section: Load New Fractionmentioning

confidence: 99%

“…To quantify the sensitivity of the COFRADIC methodology, i.e., the ability to detect low-abundant as well as high-abundant peptide peaks, we used the dynamic range expressed in decibels (dB), which was calculated as: 10 log 10 P max P min (8) with P max and P min denoting the maximum and minimum peptide abundance measured in an experiment, obtained via Eqn (5). A dynamic range of 37 dB was found for all COFRADIC replicates.…”

Section: Human Blood Samplementioning

confidence: 99%

A strategy for the prior processing of high‐resolution mass spectral data obtained from high‐dimensional combined fractional diagonal chromatography

Valkenborg

Thomas²,

Krols³

et al. 2008

J. Mass Spectrom.

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Combined fractional diagonal chromatography (COFRADIC) is a novel suite of gel-free technologies for the identification of biomarkers in complex peptide mixtures. For this purpose, reversed-phase high performance liquid chromatography (HPLC) technology and, in this case, matrix assisted laser desorption /ionization-time of flight (MALDI-TOF) mass spectrometers are extensively used. The particular characteristic of COFRADIC mass spectrometry data is the high number of chromatographic fractions, over which a peptide can be scattered. This can obstruct the quantification of the peptide abundance in the biological sample, which is required for statistical analysis. On the other hand, because of the superior peptide sorting properties of the methodology, the mass spectra become less crowded. Consequently, each peptide appears in a mass spectrum as a series of peaks with peak heights proportional to the probability of occurrence of the isotopic variants of the peptide. In this manuscript, we propose an analysis strategy concerned with the preprocessing of COFRADIC mass spectra prior to a downstream statistical analysis. The preprocessing algorithm produces for each mass spectrum a peptide list by exploiting the characteristic features that should be associated with peaks corresponding to an isotopically resolved cluster of peptide peaks. This reduction step is necessary to facilitate the clustering used in a next step to assemble the validated monoisotopic peptide peaks found over several fractions into a single peptide abundance. To assess the performance of the algorithm, two technical experiments were conducted. The proposed strategy is memory and computationally efficient.

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“…As a result, pattern-based methods can achieve higher sample throughput than identity-based approaches, thereby enabling the analysis of larger numbers of patient samples for a given study. Furthermore pattern-based biomarker discovery usually utilizes powerful multivariate pattern recognition methods (23,24). However, identifying the peptides and proteins that constitute the pattern remains essential but is often difficult or impossible using these methods.…”

mentioning

confidence: 99%

PEPPeR, a Platform for Experimental Proteomic Pattern Recognition

Jaffe

Mani

Leptos

et al. 2006

Molecular & Cellular Proteomics

134

147

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Quantitative proteomics holds considerable promise for elucidation of basic biology and for clinical biomarker discovery. However, it has been difficult to fulfill this promise due to over-reliance on identification-based quantitative methods and problems associated with chromatographic separation reproducibility. Here we describe new algorithms termed "Landmark Matching" and "Peak Matching" that greatly reduce these problems. Landmark Matching performs time base-independent propagation of peptide identities onto accurate mass LC-MS features in a way that leverages historical data derived from disparate data acquisition strategies. Peak Matching builds upon Landmark Matching by recognizing identical molecular species across multiple LC-MS experiments in an identity-independent fashion by clustering. We have bundled these algorithms together with other algorithms, data acquisition strategies, and experimental designs to create a Platform for Experimental Proteomic Pattern Recognition (PEPPeR). These developments enable use of established statistical tools previously limited to microarray analysis for treatment of proteomics data. We demonstrate that the proposed platform can be calibrated across 2.5 orders of magnitude and can perform robust quantification of ratios in both simple and complex mixtures with good precision and error characteristics across multiple sample preparations. We also demonstrate de novo marker discovery based on statistical significance of unidentified accurate mass components that changed between two mixtures. These markers were subsequently identified by accurate mass-driven MS/MS acquisition and demonstrated to be contaminant proteins associated with known proteins whose concentrations were designed to change between the two mixtures. These results have provided a real world validation of the platform for marker discovery. There is tremendous interest in the use of mass spectrometry as a quantitative technology to measure peptide and protein abundances for comprehensive, system-wide biological research (1, 2). Quantitative proteomics may be used to systematically identify and quantify proteins and their modifications as a function of cell cycle, differentiation, or chemical treatment to obtain novel insights into basic cellular biology. Proteomics also holds promise for discovery of proteins in readily accessible biofluids that are diagnostic or prognostic of a disease condition. Such proteins are termed "biomarkers."The need for robust methods to obtain relative quantification is particularly acute in proteomics-based biomarker discovery where comparative data across multiple patient samples should be obtained (3). Biomarker discovery usually uses biofluids that greatly increase the magnitude of the challenge for quantitative proteomics due to the very high dynamic range of protein abundance (Ϸ10 12 for blood) and the enormous diversity of proteins present in such samples (4).Currently available MS platforms for quantitative proteomics fall roughly into three categories: 1) identity-ba...

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Processing and classification of protein mass spectra

Cited by 158 publications

References 179 publications

Automated, feature-based image alignment for high-resolution imaging mass spectrometry of large biological samples

Automated, feature-based image alignment for high-resolution imaging mass spectrometry of large biological samples

A strategy for the prior processing of high‐resolution mass spectral data obtained from high‐dimensional combined fractional diagonal chromatography

PEPPeR, a Platform for Experimental Proteomic Pattern Recognition

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