Improving Protein Detection Confidence Using SWATH-Mass Spectrometry with Large Peptide Reference Libraries

Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).

PSMD11, PTPRM and PTPRB as novel biomarkers of pancreatic cancer progression

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate of all major cancers. Surgery is the only curative intent therapy, but the majority of patients experience disease relapse. Thus, patients who do not benefit from highly morbid surgical resection needs to be identified and offered palliative chemotherapy instead. In this pilot study, we aimed to identify differentially regulated proteins in plasma and plasma derived microparticles from PDAC patients with poor and good prognosis.

METHODS

Plasma and plasma derived microparticle samples were obtained before surgical resection from PDAC patients. Sequential Windowed Acquisition of all Theoretical fragment ion spectra - Mass Spectrometry (SWATH-MS) proteomic analysis was performed to identify and quantify proteins in these samples. Statistical analysis was performed to identify biomarkers for poor prognosis.

RESULTS

A total of 482 and 1024 proteins were identified from plasma and microparticle samples, respectively, by SWATH-MS analysis. Statistical analysis of the data further identified nine and six differentially (log₂ratio > 1, p < .05) expressed proteins in plasma and microparticles, respectively. Protein tyrosine phosphatases, PTPRM and PTPRB, were decreased in plasma of patients with poor PDAC prognosis, while proteasomal subunit PSMD11 was increased in microparticles of patients with poor prognosis.

CONCLUSION AND GENERAL SIGNIFICANCE

A novel blood-based biomarker signature for PDAC prognosis was identified.

Bioinformatics approaches for improving seminal plasma proteome analysis

Reproduction efficiency of male animals is one of the key factors influencing the sustainability of livestock. Mass spectrometry (MS) based proteomics has become an important tool for studying seminal plasma proteomes. In this review, we summarize bioinformatics analysis strategies for current proteomics approaches, for identifying novel biomarkers of reproductive robustness.

A Swath Label-Free Proteomics insight into the Faah-/- Mouse Liver

Fatty acid amide hydrolase (FAAH) is an important enzyme for lipid metabolism and an interesting pharmacological target, given its role in anandamide breakdown. The FAAH^−/− genotype is the most widely used mouse model to investigate the effects of a complete pharmacological inhibition of this enzyme. In this paper, we explore, by means of label-free SWATH proteomics, the changes in protein expression occurring in the liver of FAAH^−/− knockout (KO) mice. We identified several altered biological processes and pathways, like fatty acid synthesis and glycolysis, which explain the observed phenotype of this mouse. We also observed the alteration of other proteins, like carboxylesterases and S-methyltransferases, apparently not immediately related to FAAH, but known to have important biological roles. Our study, reporting more than 3000 quantified proteins, offers an in-depth analysis of the liver proteome of this model.

A new SWATH ion library for mouse adult hippocampal neural stem cells

Over the last years, the SWATH data-independent acquisition protocol (Sequential Window acquisition of All THeoretical mass spectra) has become a cornerstone for the worldwide proteomics community (Collins et al., 2017) [1]. In this approach, a high-resolution quadrupole-ToF mass spectrometer acquires thousands of MS/MS data by selecting not just a single precursor at a time, but by allowing a broad m/z range to be fragmented. This acquisition window is then sequentially moved from the lowest to the highest mass selection range. This technique enables the acquisition of thousands of high-resolution MS/MS spectra per minute in a standard LC–MS run. In the subsequent data analysis phase, the corresponding dataset is searched in a “triple quadrupole-like” mode, thus not considering the whole MS/MS scan spectrum, but by searching for several precursor to fragment transitions that identify and quantify the corresponding peptide. This search is made possible with the use of an ion library, previously acquired in a classical data dependent, full-spectrum mode (Fabre et al., 2017; Wu et al., 2017) [2], [3]. The SWATH protocol, combining the protein identification power of high-resolution MS/MS spectra with the robustness and accuracy in analyte quantification of triple-quad targeted workflows, has become very popular in proteomics research. The major drawback lies in the ion library itself, which is normally demanding and time-consuming to build. Conversely, through the realignment of chromatographic retention times, an ion library of a given proteome can relatively easily be tailored upon “any” proteomics experiment done on the same proteome. We are thus hereby sharing with the worldwide proteomics community our newly acquired ion library of mouse adult hippocampal neural stem cells. Given the growing effort in neuroscience research involving proteomics experiments (Pons-Espinal et al., 2017; Sarnyai and Guest, 2017; Sethi et al., 2015; Bramini et al., 2016) [4,[5], [6], [7], we believe that this data might be of great help for the neuroscience community. All the here reported data (RAW files, results and ion library) can be freely downloaded from the SWATHATLAS (Deutsch et al., 2008) [8] website (http://www.peptideatlas.org/PASS/PASS01110)