Quantitative Proteomics

DDA – Data dependent acquisition

In contrast to label-dependent quantitative proteomics such as SILAC- (Ong et al, MCP 2002), ICPL- (Schmidt et al, Proteomics 2005) or iTRAQ-methods (Ross et al, MCP 2004), label-free LC-MS/MS-based comparative proteomics using peptide peak intensity comparisons allows accurate quantification of tissue samples without additional error-prone in vitro labelling reactions (Hauck et al, MCP 2010; Stoop et al, MCP 2010). To this end complex tissue samples are either directly, or after application of subfractionation steps, digested with trypsin and resulting peptides are loaded and separated on a HPLC system directly coupled to a LTQ OrbitrapXL or Q Exactive™ HF hybrid quadrupole-Orbitrap mass spectrometers (Thermo Fisher Scientific).

In a typical data-dependent acquisition, the mass spectrometer generates a full-scan mass spectra to determine the molecular weights of the peptide species present and then acquires MS/MS spectra on the N most intense peptides (see figure below). Hundreds of MS/MS spectra can be generated in a single run and downstream data analysis tools are applied for peptide identification and quantification.

In our facility, we have profound experience using Progenesis QI software (Nonlinear Dynamics - Waters). Profile data of the MS scans are transformed to peak lists with respective peak m/z values, intensities, abundances (areas under the peaks) and m/z width. MS/MS spectra are treated similarly. After selecting the most complex sample as a reference, the retention times of the other samples are aligned by manual and automatic alignment to a maximal overlay of the 2D features. Raw abundances of all features are normalized to allow correction for factors resulting from experimental variation. After normalization, statistical analysis is performed using the normalized abundances for one-way analysis of variance (ANOVA) calculations of all detected features. MS/MS spectra are exported as Mascot generic file (mgf) and used for peptide identification with Mascot (Matrix Science) in suitable protein databases. Assigned peptides are re-imported into the Progenesis software. For quantification, the total cumulative abundance of each protein is calculated by summing the abundances of all peptides allocated to this respective protein. Calculations of the protein p-values are then performed on the sum of the normalized abundances across all runs. Using this method, several hundreds of proteins can be identified and quantified within one experiment, providing mass spectrometric information on the differences in the compositions of compared tissue samples.

© Figure adapted from Ludwig et al., Mol Sys Biol 2018

DIA – Data independent acquisition

One of the biggest challenges in discovery proteomics is the identification and precise quantification of protein abundances in highly complex samples that can consist of thousands of proteins. The number of peptides released by these proteins during the sample preparation is even higher (> 100 000) and can exceed the maximal number of spectra that can be acquired in the traditional data-dependent acquisition (DDA) workflows (also referred to as ‘shotgun’ proteomics). This known problem of undersampling can be circumvented by using an alternative acquisition method.

In data-independent acquisition (DIA) mode, all ions of the entire mass range are sequentially isolated in broader m/z windows and fragmented together. The resulting highly complex fragment spectra derive from multiple precursor ions what makes the data-analysis highly complex. One strategy to identify peptides at high confidence is based on peptide spectral libraries. Recent improvements in software tools now enable the identification of >7000 protein groups from a single DIA run. Directly compared to the established DDA workflows this is an improvement of more than 30%. These improvements in protein identifications don’t come at the expense of a less precise quantification. The label-free quantification of all identified proteins which is based on several fragment ions is even slightly more precise (median CVs below 10%). In addition to these improvements in the peptide identification and quantification, the resulting raw data can be re-interrogated over and over using updated peptide spectral libraries. We established DIA workflows for the analysis of a broad variety of human, mouse, and pig samples.

© Figure adapted from Ludwig et al., Mol Sys Biol 2018