Comparing the old and new generation SELDI-TOF MS: implications for serum protein profiling

<p>Abstract</p> <p>Background</p> <p>Although the PBS-IIc SELDI-TOF MS apparatus has been extensively used in the search for better biomarkers, issues have been raised concerning the semi-quantitative nature of the technique and its reproducibility. To overcome these limitations, a new SELDI-TOF MS instrument has been introduced: the PCS 4000 series. Changes in this apparatus compared to the older one are a.o. an increased dynamic range of the detector, an adjusted configuration of the detector sensitivity, a raster scan that ensures more complete desorption coverage and an improved detector attenuation mechanism. In the current study, we evaluated the performance of the old PBS-IIc and new PCS 4000 series generation SELDI-TOF MS apparatus.</p> <p>Methods</p> <p>To this end, two different sample sets were profiled after which the same ProteinChip arrays were analysed successively by both instruments. Generated spectra were analysed by the associated software packages. The performance of both instruments was evaluated by assessment of the number of peaks detected in the two sample sets, the biomarker potential and reproducibility of generated peak clusters, and the number of peaks detected following serum fractionation.</p> <p>Results</p> <p>We could not confirm the claimed improved performance of the new PCS 4000 instrument, as assessed by the number of peaks detected, the biomarker potential and the reproducibility. However, the PCS 4000 instrument did prove to be of superior performance in peak detection following profiling of serum fractions.</p> <p>Conclusion</p> <p>As serum fractionation facilitates detection of low abundant proteins through reduction of the dynamic range of serum proteins, it is now increasingly applied in the search for new potential biomarkers. Hence, although the new PCS 4000 instrument did not differ from the old PBS-IIc apparatus in the analysis of crude serum, its superior performance after serum fractionation does hold promise for improved biomarker detection and identification.</p

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result.</p> <p>Results</p> <p>In this paper, we describe the first systematic comparison of a wide range of normalisation methods, using two objectives that should be met by a good method. These objectives are minimisation of inter-spectra variance and maximisation of signal with respect to class separation. The former is assessed using an estimation of the coefficient of variation, the latter using the classification performance of three types of classifiers on real-world datasets representing two-class diagnostic problems. To obtain a maximally robust evaluation of a normalisation method, both objectives are evaluated over multiple datasets and multiple configurations of baseline correction and peak detection methods. Results are assessed for statistical significance and visualised to reveal the performance of each normalisation method, in particular with respect to using no normalisation. The normalisation methods described have been implemented in the freely available MASDA R-package.</p> <p>Conclusion</p> <p>In the general case, normalisation of mass spectra is beneficial to the quality of data. The majority of methods we compared performed significantly better than the case in which no normalisation was used. We have shown that normalisation methods that scale spectra by a factor based on the dispersion (e.g., standard deviation) of the data clearly outperform those where a factor based on the central location (e.g., mean) is used. Additional improvements in performance are obtained when these factors are estimated locally, using a sliding window within spectra, instead of globally, over full spectra. The underperforming category of methods using a globally estimated factor based on the central location of the data includes the method used by the majority of SELDI users.</p

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data-3

<p><b>Copyright information:</b></p><p>Taken from "Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/88</p><p>BMC Bioinformatics 2008;9():88-88.</p><p>Published online 7 Feb 2008</p><p>PMCID:PMC2258289.</p><p></p>rmalisation method is represented by a different symbol, global variants being annotated with an additional circle. The axes represent p-values of the statistical analysis described earlier, for classification performance and variance reduction on the x-axis and y-axis, respectively. The point (1, 1) indicates the position of unnormalised data. The region in which a method lies represents its performance for each objective with respect to using no normalisation

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data-2

<p><b>Copyright information:</b></p><p>Taken from "Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/88</p><p>BMC Bioinformatics 2008;9():88-88.</p><p>Published online 7 Feb 2008</p><p>PMCID:PMC2258289.</p><p></p>in the figure titles. Black p-values indicate the significance with which "black" methods outperform the "red" methods and red p-values the opposite. Lines between elements indicate a pairing of variables for the statistical tests used. The following groupings are used: . global vs. local estimations of normalisation parameters, . non-zero vs. zero offsets and . scaling using the central location vs. the dispersion of data

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data-0

<p><b>Copyright information:</b></p><p>Taken from "Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/88</p><p>BMC Bioinformatics 2008;9():88-88.</p><p>Published online 7 Feb 2008</p><p>PMCID:PMC2258289.</p><p></p>lemented in the MASDA R-package. Both objectives used for the comparison are estimated for each dataset , normalisation method and configuration of baseline correction and peak detection parameters. For each tuple (, , ), this results in a score (, ) for variance and (, ), (, ), and (, ) for classification performance