A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies

Abstract

IntroductionRaw spectral data from matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) with MS profiling techniques usually contains complex information not readily providing biological insight into disease. The association of identified features within raw data to a known peptide is extremely difficult. Data preprocessing to remove uncertainty characteristics in the data is normally required before performing any further analysis. This study proposes an alternative yet simple solution to preprocess raw MALDI-TOF-MS data for identification of candidate marker ions. Two in-house MALDI-TOF-MS data sets from two different sample sources (melanoma serum and cord blood plasma) are used in our study.MethodRaw MS spectral profiles were preprocessed using the proposed approach to identify peak regions in the spectra. The preprocessed data was then analysed using bespoke machine learning algorithms for data reduction and ion selection. Using the selected ions, an ANN-based predictive model was constructed to examine the predictive power of these ions for classification.ResultsOur model identified 10 candidate marker ions for both data sets. These ion panels achieved over 90% classification accuracy on blind validation data. Receiver operating characteristics analysis was performed and the area under the curve for melanoma and cord blood classifiers was 0.991 and 0.986, respectively.ConclusionThe results suggest that our data preprocessing technique removes unwanted characteristics of the raw data, while preserving the predictive components of the data. Ion identification analysis can be carried out using MALDI-TOF-MS data with the proposed data preprocessing technique coupled with bespoke algorithms for data reduction and ion selection.

MS-labeller: Bioinformatics support for quality assessment on high resolution mass spectrometry sample

“…Data acquired using Bruker Ultraflex III in positive reflectron mode. Routine methodology also seen in our previous studies [12][13][14]. Table 2 summarizes the sample rating from the MS Labeller model and the visual rating results from 3 MS scientists.…”

“…1 presents the workflow for quality assessment on HQC samples. The raw MS data were extracted from the MS instrument and preprocessed using the bespoke signal processing method, described in our previous study [12]. In brief, for each data batch, a reference spectrum based on the entire population was computed and using this reference spectrum, binary operators (+) and (-); indicated the upper (+) and the lower (-) boundary of the peak region.…”

Application of MALDI Mass Spectrometry in Natural Products Analysis

“…According to Ge and Wong [176], a mass spectrum can theoretically be decomposed into three components: the baseline value, the true signal, and the noise. Therefore, baseline and noise can be potentially associated with inter-and intra-sample variability, and data preprocessing is required to reduce the baseline and noise in the raw data before any multivariate analysis can occur [177,178]. Morris and collaborators [175] showed illustrative examples that ignore the basic assumption of experimental planning, namely, blocking and randomization, and how it can completely compromise an experiment.…”

“…Saideman and coworkers [184], using internal calibration, have determined peptide mass accuracies ranging from 0.124 to 5.17 ppm (n = 3). The baseline is associated with a series of matrix materials, impurities, ionization by-products, electronic signal noise, and sample preparation contamination [177,185,186]. Baseline correction methods were reviewed by Hilario and collaborators [187].…”

“…The authors report a final accuracy of 83% on a separate validation set (Breast Cancer Study with MALDI-MS Data) of 78 samples. Tong and coworkers [177] argue that there is a lack of guidelines for data processing, and unexperienced practitioners can add variation when handling complex models. The authors also state that the functionalities of many tools designed for SELDI-MS or LC-ESI-MS are not transferable to MALDI-MS data analysis.…”