In Fig. 1 the complete procedure and data interpretation of biological samples directly analyzed by MALDI-TOF-MS was shown. The typical experiment consist of four stages. Stage 1 is a pre-analytical step where the biological sample are processed and deposited on the MALDI target. This often includes internal standards added to validate the analytical assessment and µSPE extraction (usually Reverse-Phases). In stage 2 the complete screening of the samples derived from pathological and healthy subjects using linear MALDI-TOF-MS technology are presented. In stages 3 and 4 the combined visualization and statistical features allows for an easy and efficient identification of biomarker candidates. Such candidates can later be used in pattern recognition models to analyze independent data to the significance of these tentative biomarkers in an open translational investigation for para-clinical studies. The last step of this process should be the identification of the outlined specific molecular disease marker. This investigation is necessary to pursued the potential functional insight of the outlined marker. Moreover, these data will provide a rational basis on their role in the pathological process, possibly suggesting new pharmacological protocols.
As shown in Fig. 2, the identification of the proteins corresponding to the disease-discriminating signals can be carry out by a RP-HPLC-MS separation to collect purified fractions containing the candidate biomarkers. Samples, generally, need to be preliminary pre-purified (ultrafiltration, SPE, etc.) obtaining a fraction in the interesting mass range. This liquid fraction can be directly injected and separated for fraction collection. Each purified liquid fraction are spotted on the MALDI target and analyzed in order to select only those with the interest signals. Finally the purified liquid fractions selected are digested with database). By avoiding peak identification, the resulting classification may produce an algorithm suitable for prediction but ignoring the molecular basis of this classification. Such strategy could be dangerous since it might lead to a group discrimination based on a stochastic association of events rather then on the clinical groups investigated. Moreover, signals identification and characterization might return useful information on the aetiology and molecular mechanism of the investigated phenomena.
The identification of the candidate biomarkers is giving an important functional insight, possibly shading a light on the implications in the pathological process.
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