In this review we examine techniques, software, and statistical analyses used in label-free quantitative proteomics studies for area under the curve and spectral counting approaches. Recent advances in the field are discussed in an order that reflects a logical workflow design. Examples of studies that follow this design are presented to highlight the requirement for statistical assessment and further experiments to validate results from label-free quantitation. Limitations of label-free approaches are considered, label-free approaches are compared with labelling techniques, and forward-looking applications for label-free quantitative data are presented. We conclude that label-free quantitative proteomics is a reliable, versatile, and cost-effective alternative to labelled quantitation.

A thermal unfolding study of thaumatin-like protein, chitinase, and invertase isolated from Vitis vinifera Sauvignon blanc and Semillon juice was undertaken. Differential scanning calorimetry demonstrated that chitinase was a major player in heat-induced haze in unfined wines as it had a low melt temperature, and aggregation was observed. The kinetics of chitinase F1 (Sauvignon blanc) unfolding was studied using circular dichroism spectrometry. Chitinase unfolding conforms to Arrhenius behavior having an activation energy of 320 kJ/mol. This enabled a predictive model for protein stability to be generated, predicting a half-life of 9 years at 15 degrees C, 4.7 days at 30 degrees C, and 17 min at 45 degrees C. Circular dichroism studies indicate that chitinase unfolding follows three steps: an initial irreversible step from the native to an unfolded conformation, a reversible step between a collapsed and an unfolded non-native conformation, followed by irreversible aggregation associated with visible haze formation.

Grape chitinase was found to be the primary cause of heat-induced haze formation in white wines. Chitinase was the dominant protein in a haze induced by treating Sauvignon blanc wine at 30 °C for 22 h. In artificial wines and real wines, chitinase concentration was directly correlated to the turbidity of heat-induced haze formation (50 °C for 3 h). Sulfate was confirmed to have a role in haze formation, likely by converting soluble aggregates into larger visible haze particles. Thaumatin-like protein was detected in the insoluble fraction by SDS-PAGE analysis but had no measurable impact on turbidity. Differential scanning calorimetry demonstrated that the complex mixture of molecules in wine plays a role in thermal instability of wine proteins and contributes additional complexity to the wine haze phenomenon.

Rice is susceptible to cold stress and with a future of climatic instability we will be unable to produce enough rice to satisfy increasing demand. A thorough understanding of the molecular responses to thermal stress is imperative for engineering cultivars, which have greater resistance to low temperature stress. In this study we investigated the proteomic response of rice seedlings to 48, 72 and 96 h of cold stress at 12-14°C. The use of both label-free and iTRAQ approaches in the analysis of global protein expression enabled us to assess the complementarity of the two techniques for use in plant proteomics. The approaches yielded a similar biological response to cold stress despite a disparity in proteins identified. The label-free approach identified 236 cold-responsive proteins compared to 85 in iTRAQ results, with only 24 proteins in common. Functional analysis revealed differential expression of proteins involved in transport, photosynthesis, generation of precursor metabolites and energy; and, more specifically, histones and vitamin B biosynthetic proteins were observed to be affected by cold stress.

In this chapter we describe the workflow used in our laboratory for label-free quantitative shotgun proteomics based on spectral counting. The main tools used are a series of R modules known collectively as the Scrappy program. We describe how to go from peptide to spectrum matching in a shotgun proteomics experiment using the XTandem algorithm, to simultaneous quantification of up to thousands of proteins, using normalized spectral abundance factors. The outputs of the software are described in detail, with illustrative examples provided for some of the graphical images generated. While it is not strictly within the scope of this chapter, some consideration is given to how best to extract meaningful biological information from quantitative shotgun proteomics data outputs.