ProPickML: Advancing Clinical Diagnostics with Automated Peak Picking in Label-Free Targeted Proteomics.

In targeted proteomics utilizing Selected Reaction Monitoring (SRM), the precise detection of specific peptides within complex mixtures remains a significant challenge, particularly due to noise and interference in chromatograms. Existing methodologies, such as isotopic labeling and scoring algorithms, offer partial solutions but are constrained by high run times and elevated false discovery rates. To address these limitations, we have developed ProPickML a machine learning-based tool designed to accurately identify peptide peaks across diverse data sets, independent of the assumed presence of the peptide.

LC-SRM Combined With Machine Learning Enables Fast Identification and Quantification of Bacterial Pathogens in Urinary Tract Infections.

Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate antibiotherapy to patients and to avoid the emergence of drug-resistant pathogens. While the gold standard requires 24 h to 48 h of bacteria culture prior to MALDI-TOF species identification, we propose a culture-free workflow, enabling bacterial identification and quantification in less than 4 h using 1 ml of urine.

Vitamin C modulates the levels of several proteins of the mitochondrial complex III and its activity in the mouse liver.

Ascorbate is a crucial antioxidant and essential cofactor of biosynthetic and regulatory enzymes. Unlike humans, mice can synthesize ascorbate thanks to the key enzyme gulonolactone oxidase (Gulo). In the present study, we used the Gulo mouse model, which cannot synthesize their own ascorbate to determine the impact of this vitamin on the liver proteome of specific subcellular organelles.

DIA Proteomics and Machine Learning for the Fast Identification of Bacterial Species in Biological Samples.

Identification of bacterial species in biological samples is essential in many applications. However, the standard methods usually use a time-consuming bacterial culture (24-48 h) and sometimes lack in specificity. To overcome these limitations, we developed a new protocol, combining LC-MS/MS analysis in Data Independent Acquisition mode and machine learning algorithms, enabling the accurate identification of the bacterial species contaminating a sample in a few hours without bacterial culture.

Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers.

Background: Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers.

The production of preconditioned freeze-dried Oenococcus oeni primes its metabolism to withstand environmental stresses encountered upon inoculation into wine.

Oenococcus oeni is the most resistant lactic acid bacteria species to the environmental stresses encountered in wine, particularly the acidity, presence of ethanol and phenolic compounds. Indigenous strains develop spontaneously following the yeast-driven alcoholic fermentation and may perform the malolactic fermentation whereby improving taste, aroma, and the microbial stability of wine. However, spontaneous fermentation is sometimes delayed, prolonged or incomplete.

DIA proteomics data from a UPS1-spiked protein mixture processed with six software tools.

In this article, we provide a proteomic reference dataset that has been initially generated for a benchmarking of software tools for Data-Independent Acquisition (DIA) analysis. This large dataset includes 96 DIA files acquired from a complex proteomic standard composed of an protein background spiked-in with 8 different concentrations of 48 human proteins (UPS1 Sigma). These 8 samples were analyzed in triplicates on an Orbitrap mass spectrometer with 4 different DIA window schemes.

T cell apoptosis characterizes severe Covid-19 disease.

Severe SARS-CoV-2 infections are characterized by lymphopenia, but the mechanisms involved are still elusive. Based on our knowledge of HIV pathophysiology, we hypothesized that SARS-CoV-2 infection-mediated lymphopenia could also be related to T cell apoptosis. By comparing intensive care unit (ICU) and non-ICU COVID-19 patients with age-matched healthy donors, we found a strong positive correlation between plasma levels of soluble FasL (sFasL) and T cell surface expression of Fas/CD95 with the propensity of T cells to die and CD4 T cell counts.

Proteomic Analysis of Maternal Urine for the Early Detection of Preeclampsia and Fetal Growth Restriction.

Background: To explore the use of maternal urine proteome for the identification of preeclampsia biomarkers.

Methods: Maternal urine samples from women with and without preeclampsia were used for protein discovery followed by a validation study. The targeted proteins of interest were then measured in urine samples collected at 20-24 and 30-34 weeks among nine women who developed preeclampsia, one woman with fetal growth restriction, and 20 women with uncomplicated pregnancies from a longitudinal study.

Vitamin C Differentially Impacts the Serum Proteome Profile in Female and Male Mice.

A suboptimal blood vitamin C (ascorbate) level increases the risk of several chronic diseases. However, the detection of hypovitaminosis C is not a simple task, as ascorbate is unstable in blood samples. In this study, we examined the serum proteome of mice lacking the gulonolactone oxidase (Gulo) required for the ascorbate biosynthesis.

Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard.

Over the past decade, the data-independent acquisition mode has gained popularity for broad coverage of complex proteomes by LC-MS/MS and quantification of low-abundance proteins. However, there is no consensus in the literature on the best data acquisition parameters and processing tools to use for this specific application. Here, we present the most comprehensive comparison of DIA workflows on Orbitrap instruments published so far in the field of proteomics.

Lipoprotein Proteomics and Aortic Valve Transcriptomics Identify Biological Pathways Linking Lipoprotein(a) Levels to Aortic Stenosis.

Lipoprotein(a) (Lp(a)) is one of the most important risk factors for the development of calcific aortic valve stenosis (CAVS). However, the mechanisms through which Lp(a) causes CAVS are currently unknown. Our objectives were to characterize the Lp(a) proteome and to identify proteins that may be differentially associated with Lp(a) in patients with versus without CAVS.

A Comparative Analysis of the Lipoprotein(a) and Low-Density Lipoprotein Proteomic Profiles Combining Mass Spectrometry and Mendelian Randomization.

Background: Lipoprotein(a) (Lp[a]), which consists of a low-density lipoprotein (LDL) bound to apolipoprotein(a), is one of the strongest genetic risk factors for atherosclerotic cardiovascular diseases. Few studies have performed hypothesis-free direct comparisons of the Lp(a) and the LDL proteomes. Our objectives were to compare the Lp(a) and the LDL proteomic profiles and to evaluate the effect of lifelong exposure to elevated Lp(a) or LDL cholesterol levels on the plasma proteomic profile.

Fast and Accurate Bacterial Species Identification in Urine Specimens Using LC-MS/MS Mass Spectrometry and Machine Learning.

Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDI-TOF-MS technology has become a tool of choice for microbial identification but has several drawbacks: it requires a long step of bacterial culture before analysis (≥24 h), has a low specificity and is not quantitative. We developed a new strategy for identifying bacterial species in urine using specific LC-MS/MS peptidic signatures.

Identification of protein markers for extracellular vesicle (EV) subsets in cow's milk.

Extracellular vesicles (EVs), like exosomes, are small membrane vesicles involved in cell-to-cell communications that modulate numerous biological processes. We previously discovered a new EV subset in milk (sedimenting at 35,000 g; 35 K) that protected its cargo (RNAs and proteins) during simulated digestion and was more enriched in microRNAs than exosomes (sedimenting at 100 K). Here, we used LC-MS/MS to push further the comparison between these two pellets.