The constant evolution of proteomic technologies promises to have a considerable impact in helping disease diagnosis and prediction of response to treatment by providing high resolution maps of key molecular effectors present in a wide variety of samples such as tissues and biofluids. From an analytical point of view, urine offers several advantages such as ease of collection and major stability of the proteome with respect to others biofluids1. Proteomic analysis of urine is of special interest in biomarker discovery studies on urological cancers, since it allows noninvasive sampling in proximity to the tissues of interest2. In particular, a sample that seems to be promising for studying prostate-related pathologies is the EPS-urine3,4 (i.e., a urine sample collected after a digital rectal exam (DRE)). This latter operation prior to sample collection enriches urine with prostate specific proteins. EPS-urine is a good candidate to investigate disorders related to the prostate gland5 including prostate cancer (PCa), since through DRE, proteins secreted by the tumor can be poured into the urine sample, increasing the chance of detecting cancer tissue-specific proteins.
A crucial role in allowing detection and quantification of potential protein biomarkers is played by mass spectrometry (MS). Over the last two decades, MS-based protocols for proteomic analysis have allowed an ever-increasing number of proteins to be detected in a single LC-MS/MS run thanks to continuous improvements in MS instrumentation and in data analysis software6.
MS-based proteomic sample preparation generally involves enzymatic digestion of the protein mixture, which can be achieved via a wide variety of protocols such as: in-solution digestion, MStern blotting7, suspension trapping (S-trap)8, solid-phase-enhanced sample preparation (SP3)9, in-StageTip digestion10 and filter aided sample preparation (FASP)11. All protocols can be used for urinary proteomics, even though results may vary with respect to the number of identified proteins and peptides and in terms of reproducibility12.
In this work, our attention was focused on the analysis of EPS-urine by the FASP protocol. The FASP protocol was originally designed to analyze proteins extracted from tissues and cell cultures, but its use was then expanded to the analysis of other sample types, such as urine13. With respect to straightforward in-solution digestion FASP is a more flexible proteomic approach14, since by achieving effective removal of detergents and other contaminants such as salts from the protein mixture before enzymatic digestion15, it allows the choice of optimal protein solubilization conditions. Moreover, an additional characteristic of FASP is that it provides a means for sample concentration. This is of particular interest for urinary proteomic analysis, because it allows to start from relatively large sample volumes (hundreds of microliters). In the light of the potential of the FASP protocol, several studies have focused the attention on workflow automation, with the aim of reducing experimental variability and processing an elevated number of samples in parallel16.
In our workflow, FASP is followed by LC-MS/MS acquisition in data-independent analysis (DIA), which provides high proteome coverage, good quantitative precision and low incidence of missing values. The DIA approach is a sensitive method where all ions are selected for MS/MS events, opposite to what happens in data-dependent analysis (DDA) where only ions with the highest intensity are fragmented. The mass spectrometer, operating in DIA mode, performs scan cycles with different isolation width covering the whole m/z precursor range. This approach allows to reproducibly detect a high number of peptides per unit time, providing a proteomics snapshot of the sample17. Moreover, data generated by DIA have another interesting characteristic: the possibility of a posteriori analysis18. DIA data are more complex than those obtained by DDA, because MS/MS spectra in DIA result from the co-isolation of several precursor ions within each m/z window19. Disentangling the composite MS/MS spectra into distinct and specific peptide signals is achieved by using two fundamental elements: a spectral library and a dedicated software for data analysis. The spectral library is generated by a data-dependent experiment, usually involving peptide fractionation to maximize proteome coverage, which provides a list of thousands of experimentally determined precursor ions and MS/MS spectra of peptides detectable in the sample under consideration. The data analysis software, instead, uses the information contained in the spectral library to interpret the DIA data by generating specific extracted ion chromatograms which allow peptide detection and quantification. While library-free DIA data analysis is now feasible, library-based DIA still provides better results in terms of proteome coverage20.
The sample preparation protocol here described (Figure 1) consists of the following steps: a centrifugation step (to remove cell debris), FASP digestion, StageTip purification21, quantification of proteins and DIA analysis. This protocol has been designed for the analysis of EPS-urine in the context of prostate cancer biomarker discovery, but it can be applied to proteomic analysis of any urine sample.