New label free methods for protein relative quantification applied to the investigation of animal model of Huntington Disease.

Spectral Counts approaches (SpCs) are largely employed for the comparison of protein expression profiles in label free differential proteomics applications. Similarly, to other comparative methods, also SpCs based approaches require a normalization procedure before Fold Changes (FC) calculation. Here, we propose new Complexity Based Normalization (CBN) methods that introduced a variable adjustment factor (f), related to the complexity of the sample, both in terms of total number of identified proteins (CBN(P)) and as total number of spectral counts (CBN(S)). Both these new methods were compared with the Normalized Spectral Abundance Factor (NSAF) and the Spectral Counts log Ratio (Rsc), by using standard protein mixtures. Finally, to test the robustness and the effectiveness of the CBNs methods, they were employed for the comparative analysis ofcortical protein extract fromzQ175 mouse brains, model of Huntington Disease (HD), and control animals. On standard mixtures, both CBN methods showed an excellent behavior in terms of reproducibility and coefficients of variation (CVs) in comparison to the other SpCs approaches. Moreover, the CBN(S) was demonstrated to be more sensitive in detecting small difference in proteins amount when applied on biological samples in comparison to CBN(P).