Clinprotools 2

To demonstrate the predictive value of the identified biomarkers the mass spectrometric results were statistically evaluated by ClinProTools 2.2 (Bruker Daltonics, Bremen, Germany) clustering software. Multiple spectra of the analyzed bone samples from different sample cohorts, such as osteosarcoma, non-pathological and pathological (tuberculotic) control samples were distinguished together. Recalibration, spectral alignment, peak normalization, peak detection and peak area calculation of spectra were carried out automatically by ClinProTools. A logistic regression model was performed to identify the significant predictive peaks on the basis of the normalized peak areas. Wilcoxon signed-rank test was used for non-parametric statistical analysis of the different sample cohorts.

The MALDI-TOF results of the samples collected from healthy controls, MDD patients, and schizophrenia patients were further analyzed and compared using PCA, HCA, and ROC curve analysis. PCA and HCA were performed using the ClinProTools 2.2 software (ion peak selection) from Bruker Daltonics, while ROC curve analysis was performed using the SPSS 12.0 software package (SPSS, Chicago, IL, USA). The utilities of this software include ion peak definition (signal-to-noise ratio = 3; relative threshold base peak = 1% in total average spectrum), baseline subtraction (Convex Hull Baseline, 0.8% Baseline Flatness), intensity normalization, peak picking, and statistical testing (Wilcoxon/Kruskal-Wallis and t test/analysis of variance). A p-value less than 0.05 was defined as statistically significant. Receiver operating characteristic curve analysis was used to examine the specificity and sensitivity of the biomarkers to distinguish between samples from MDD patients, healthy controls, and schizophrenia patients. The correlation between true-positive (sensitivity) and false-positive rates (specificity) is presented. All experimental procedure is shown in Figure 9a–h.

All the spectral profiles obtained from the fresh bed bugs’ heads (adults) and cephalothoraxes (immature stage) were visualised using the FlexAnalysis v.3.3 software and ClinProTools 2.2 software (Bruker Daltonics) to evaluate spectral quality (smoothing, baseline subtraction, peak intensities). In order to determine the ability of the MALDITOF-MS tool to distinguish between male and female bed bugs, the MS spectra of 20 randomly selected specimens of both sexes were used for a principal component analysis (PCA) using ClinProTools 2.2^{22 (link),50 (link)}.

MALDI data were imported into ClinProTools™ 2.2 software (Bruker Daltonics) for reprocessing. All spectra underwent baseline correction performed with a TopHat baseline algorithm, along with smoothing according to the Savitzky–Golay algorithm (window size 2.0 m/z in 5 cycles). The total average of the spectra was calculated based on a signal-to-noise threshold of 3 for peak selection, a picking height of 80, and an application of baseline. Peak lists (maximum peak number of 100) of each spectrum were extracted for data processing and statistical analyses. Comparative analyses were carried out between the different experimental conditions depending on the intensity of the selected peaks. The software normalized the spectra before performing statistical principal component analyses (PCA). Using PCA, we analyzed and compared the three characterized molecules specific to immunity in bumble bees, apidaecin (accession C0HKX3, average m/z of 1978), abaecin (accession D2XR04, average m/z of 4397), and defensin 1 (considering cysteine pairing), and a chymotrypsin inhibitor (AMCI, accession numbers K7WRE1, average m/z of 5938 considering cysteine pairing and an N-terminal pyroglutamic acid).

Low-molecular proteins obtained from individual plasma samples after centrifugal ultrafiltration with a 5-kDa filter were analyzed directly by MS without electrophoretic separation. Samples that were not trypsin digested were co-crystallized with sinapinic acid matrix, spotted on a MALDI AnchorChip target and analyzed in the m/z range 2,000-12,000 Da. All samples were analyzed in five technical replications. For data validation, external calibration was performed with a standard mixture of proteins (Bruker Daltonics, Germany). Visualization and analysis of the obtained spectra as well as principal component analysis (PCA) were performed with ClinProTools 2.2 (Bruker Daltonics). Differential components with fold change >2 and p values below 0.01 were considered as statistically significant. Differential LMWPs were identified using TOF/TOF fragmentation. MS/MS data were searched against the Mascot with non-enzyme settings.