Label-free Mass Spectrometry (MS) makes use of novel methods and approaches that aim to recognize and, if possible, to determine the relative amount of proteins in two or more biological samples. Unlike other proteomics methods, label-free methods do not label the proteins.
In our published articles (1-8), we have coupled MS with label-free technologies such as Nucleic Acid Protein Programmable Arrays (NAPPA) and SNAP tag. New England Biolabs has indeed produced a series of alkylguanine-DNA alkyltransferases that react specifically and rapidly with benzylguanine (BG) derivatives, leading to irreversible covalent labeling of the SNAP tag. SNAP tag has a number of features that make it ideal for a variety of applications in protein tagging; in particular, its substrates are chemically inert towards other proteins, thus avoiding nonspecific tagging in cellular applications, as well as for other biomolecular tasks.
Moreover, also the chemistry and the printing of the NAPPA have been improved. The last goal of our research is to develop a standardized analysis procedure, able to analyze the protein-protein interactions occurred on NAPPA array (or other similar arrays) in a label-free manner.
To this aim, we employed a Matrix-assisted laser desorption ionization (MALDI) Time of flight (TOF) MS for NAPPA analysis. MALDI technique, in fact, allows to analyze protein samples co-crystallized with the matrix on a conductive surface; for this reason, NAPPA was performed on a standard microscope glass covered with a thin layer of gold. After the NAPPA expression, the proteins immobilized on the array surface were trypsin digested and immediately after analyzed by MALDI-TOF MS, without needing to be removed from the array surface.
For MS analysis, the array printing was realized in a special geometry getting protein samples with higher density, in order to obtain an amount of protein appropriate for MS analysis. The spots of 300 microns were printed in 12 boxes of 10×10 (spaced of 350 microns, center to center). The spots in a box were of the same gene, and in particular one box a piece was reserved to the sample genes, two boxes were printed with master mix (MM) as negative control and reference samples, and six boxes were printed with the sample genes in an order blinded to the MS user. This was done to check if our approach was able to recognize the unknown expressed genes.
Due to the background noise introduced in the signal by master mix and E. Coli lysate, spectral subtraction is needed for the analysis of the signals acquired by MS. To this goal, an ad-hoc software package was developed (1). In this latter we implemented K-means clustering beyond spectra preprocessing functions. This approach proved to be useful to overcome the troubles related to peak deletion, due to preprocessing or subtraction of spectra.