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The targeted LC-MS/MS method has been widely applied for peptide quantification, offering sensibility, specificity, and reproducibility to the analysis. However, it requires the prior selection of targets, including the construction of a spectral library. Here, we present a dataset comprising peptide mass spectra for targeted LC-MS/MS method setup, applied to a set of human complement system proteins. Additionally, we selected a group of peptides and demonstrated their stability and reproducibility in quantification. This dataset is invaluable for studies aiming at the quantification of the complement system proteins by targeted LC-MS/MS, as it provides data for spectral library construction and a list of selected peptides.

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The selection of a suitable proteotypic peptide remains a challenge for designing a targeted quantitative proteomics assay. Although the criteria are well-established in the literature, the selection of these peptides is often performed in a subjective and time-consuming manner. Here, we have developed a practical and semiautomated workflow implemented in an open-source program named Typic. Typic is designed to run in a command line and a graphical interface to help selecting a list of proteotypic peptides for targeted quantitation. The tool combines the input data and downloads additional data from public repositories to produce a file per protein as output. Each output file includes relevant information to the selection of proteotypic peptides organized in a table, a colored ranking of peptides according to their potential value as targets for quantitation and auxiliary plots to assist users in the task of proteotypic peptides selection. Taken together, Typic leads to a practical and straightforward data extraction from multiple data sets, allowing the identification of most suitable proteotypic peptides based on established criteria, in an unbiased and standardized manner, ultimately leading to a more robust targeted proteomics assay.

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Testing of large populations for virus infection is now a reality worldwide due to the coronavirus (SARS-CoV-2) pandemic. The demand for SARS-CoV-2 testing using alternatives other than PCR led to the development of mass spectrometry (MS)-based assays. However, MS for SARS-CoV-2 large-scale testing have some downsides, including complex sample preparation and slow data analysis. Here, we describe a high-throughput targeted proteomics method to detect SARS-CoV-2 directly from nasopharyngeal and oropharyngeal swabs. This strategy employs fully automated sample preparation mediated by magnetic particles, followed by detection of SARS-CoV-2 nucleoprotein peptides by turbulent flow chromatography coupled with tandem mass spectrometry.

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BACKGROUND: Casbene synthase (CS) is responsible for the first committed step in the biosynthesis of phorbol esters (PE) in the Euphorbiaceae. PE are abundant in the seeds of the biofuel crop Jatropha curcas and its toxicity precludes the use of the protein-rich cake obtained after oil extraction as an animal feed and the toxicity of the fumes derived from burning PE containing biofuel is also a matter of concern. This toxicity is a major hindrance to exploit the potential of this crop as a source of raw material to produce biodiesel. For this reason, the current research on J. curcas is mainly focused on the understanding of the biosynthesis and site of synthesis of PE, as an avenue for the development of genotypes unable to synthesize PE in its seeds. RESULTS: Here, we present targeted proteomics assays (SRM and PRM) to detect and quantify CS in leaves, endosperm, and roots of two J. curcas genotypes with contrasting levels of PE. These assays were based on the use of reference isotopic labeled synthetic peptides (ILSP) predicted from 12 gene models of CS from the J. curcas genome. CONCLUSION: Our targeted proteomics methods were able to detect and quantify, for the first time, CS gene products and demonstrate the distribution of CS isoforms only in roots from J. curcas genotypes with a high and low concentration of PE. These methods can be expanded to monitor CS, at the protein level, in different tissues and genotypes of J. curcas.

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High-density lipoprotein (HDL) is a diverse group of particles with multiple cardioprotective functions. HDL proteome follows HDL particle complexity. Many proteins were described in HDL, but consistent quantification of HDL protein cargo is still a challenge. To address this issue, the aim of this work was to compare data-independent acquisition (DIA) and parallel reaction monitoring (PRM) methodologies in their abilities to differentiate HDL subclasses through their proteomes. To this end, we first evaluated the analytical performances of DIA and PRM using labeled peptides in pooled digested HDL as a biological matrix. Next, we compared the quantification capabilities of the two methodologies for 24 proteins found in HDL2 and HDL3 from 19 apparently healthy subjects. DIA and PRM exhibited comparable linearity, accuracy, and precision. Moreover, both methodologies worked equally well, differentiating HDL subclasses' proteomes with high precision. Our findings may help to understand HDL functional diversity.

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The fact that cancer is a leading cause of death all around the world has naturally sparked major efforts in the pursuit of novel and more efficient biomarkers that could better serve as diagnostic tools, prognostic predictors, or therapeutical targets in the battle against this type of disease. Mass spectrometry-based proteomics has proven itself as a robust and logical alternative to the immuno-based methods that once dominated the field. Nevertheless, intrinsic limitations of classic proteomic approaches such as the natural gap between shotgun discovery-based methods and clinically applicable results have called for the implementation of more direct, hypothesis-based studies such as those made available through targeted approaches, that might be able to streamline biomarker discovery and validation as a means to increase survivability of affected patients. In fact, the paradigm shifting potential of modern targeted proteomics applied to cancer research can be demonstrated by the large number of advancements and increasing examples of new and more useful biomarkers found during the course of this review in different aspects of cancer research. Out of the many studies dedicated to cancer biomarker discovery, we were able to devise some clear trends, such as the fact that breast cancer is the most common type of tumor studied and that most of the research for any given type of cancer is focused on the discovery diagnostic biomarkers, with the exception of those that rely on samples other than plasma and serum, which are generally aimed toward prognostic markers. Interestingly, the most common type of targeted approach is based on stable isotope dilution-selected reaction monitoring protocols for quantification of the target molecules. Overall, this reinforces that notion that targeted proteomics has already started to fulfill its role as a groundbreaking strategy that may enable researchers to catapult the number of viable, effective, and validated biomarkers in cancer clinical practice.

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Patients with psychiatric disorders exhibit dysfunctions in peripheral and central metabolism. This may be a root cause of impaired neuronal function, manifested as changes in mood, behavior, and cognitive capabilities in patients suffering with these conditions. Here we describe a selective reaction monitoring mass spectrometry (SRM-MS)-based targeted proteomic protocol for precise simultaneous quantitation of three glycolytic enzymes in postmortem brain tissue extracts. The SRM-MS approach has several advantages in terms of sensitivity, reproducibility, and reduced sample consumption, compared to traditional MS methods.

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Proteins and proteomes are dynamic and complex. The accurate identification and measurement of their properties such as abundance, location, and turnover are challenging tasks. Even though high-throughput proteomics has significantly evolved, the technique still lacks fully quantitative and reproducible qualities. A mass spectrometry-based targeted proteomic strategy called selective reaction monitoring (SRM) has emerged in recent years as an important multiplex platform to precisely quantify sets of proteins in multiple samples. This has several advantages in terms of sensitivity, reproducibility, and sample consumption compared to classical methods including those based on antibodies. Here, we present a detailed protocol for quantitation of panels of proteins from cell line extracts using the SRM targeted proteomics approach.

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