Label-free Quantification Research Articles

In Situ and Label-Free Quantification of Membrane Protein–Ligand Interactions Using Optical Imaging Techniques: A Review

Membrane proteins are crucial for various cellular processes and are key targets in pharmacological research. Their interactions with ligands are essential for elucidating cellular mechanisms and advancing drug development. To study these interactions without altering their functional properties in native environments, several advanced optical imaging methods have been developed for in situ and label-free quantification. This review focuses on recent optical imaging techniques such as surface plasmon resonance imaging (SPRi), surface plasmon resonance microscopy (SPRM), edge tracking approaches, and surface light scattering microscopy (SLSM). We explore the operational principles, recent advancements, and the scope of application of these methods. Additionally, we address the current challenges and explore the future potential of these innovative optical imaging strategies in deepening our understanding of biomolecular interactions and facilitating the discovery of new therapeutic agents.

Identification of Plasma Protein Biomarkers for Predicting Lung Cancer.

Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating the development of effective early diagnostic strategies. Despite advancements in imaging and screening technologies, late-stage diagnoses remain common, limiting treatment options and reducing survival rates. Thus, there is a critical need for reliable, minimally invasive biomarkers to improve early detection and patient outcomes. Plasma protein biomarkers offer promising potential for early lung cancer detection and continuous disease monitoring. This study explored the potential of specific plasma protein markers as early indicators of lung cancer. Plasma samples were collected from normal healthy individuals and lung cancer patients, and protein purification and analysis were conducted using LC-MS/MS. A mixed-effect model was applied to select lung cancer-related protein markers based on label-free relative quantification values. We identified 29 proteins with potential for early lung cancer diagnosis, including complement proteins (CFB, C3, C8G, C1QA, C1R, C6), orosomucoid proteins (ORM1, ORM2), ceruloplasmin (CP), alpha-1-B glycoprotein (A1BG), and others. These proteins play diverse roles in immune response, inflammation, and cell signaling, suggesting their relevance in lung cancer pathophysiology. Our findings suggest the potential of plasma proteins as early diagnostic biomarkers for lung cancer. Further validation in larger cohorts is needed to confirm their clinical utility. Integrating these biomarkers into existing diagnostic modalities could enhance early detection accuracy, leading to improved patient outcomes.

Protein aggregation in plant mitochondria lacking Lon1 inhibits translation and induces unfolded protein responses.

Loss of Lon1 led to stunted plant growth and accumulation of nuclear-encoded mitochondrial proteins including Lon1 substrates. However, an in-depth label-free proteomics quantification of mitochondrial proteins in lon1 revealed that the majority of mitochondrial-encoded proteins decreased in abundance. Additionally, we found that lon1 mutants contained protein aggregates in the mitochondrial that were enriched in metabolic enzymes, ribosomal subunits and PPR-containing proteins of the translation apparatus. These mutants exhibited reduced general mitochondrial translation as well as deficiencies in RNA splicing and editing. These findings support the role of Lon1 in maintaining a functional translational apparatus for mitochondrial-encoded gene translation. Transcriptome analysis of lon1 revealed a mitochondrial unfolded protein response reminiscent of the mitochondrial retrograde signalling dependent on the transcription factor ANAC017. Notably, lon1 mutants exhibited transiently elevated ethylene production, and the shortened hypocotyl observed in lon1 mutants during skotomorphogenesis was partially alleviated by ethylene inhibitors. Furthermore, the short root phenotype was partially ameliorated by introducing a mutation in the ethylene receptor ETR1. Interestingly, the upregulation of only a select few target genes was linked to ETR1-mediated ethylene signalling. Together this provides multiple steps in the link between loss of Lon1 and signalling responses to restore mitochondrial protein homoeostasis in plants.

An In vivo Pilot Study to Estimate the Swelling of the Aneurysm Wall Rabbit Model Generated with Pulsed Fluid Against the Aneurysm Wall.

This study addresses the critical issue of evaluating the risk of rupture of unruptured intracranial aneurysms (UIAs) through the assessment of the mechanical properties of the aneurysm wall. To achieve this, an original approach based on the development of an in vivo deformation device prototype (DDP) of the vascular wall is proposed. The DDP operates by pulsing a physiological fluid onto the vascular wall and measuring the resulting deformation using spectral photon counting computed tomography (SPCCT) imaging. In this preliminary study conducted on a rabbit animal model, an aneurysm was induced on the carotid artery, followed by deformation of the aneurysm sac wall using the DDP. The change in luminal volume of the aneurysm sac induced by the deformation of the vascular wall was then quantified. The initial experimental results demonstrated an increase in the luminal volume of the aneurysm sac in relation to the increased flow rate of the fluid pulsed by the DDP onto the arterial wall. Measurement of the pressure generated by the DDP in relation to the different flow rate values imposed by the pulsation system revealed experimental values of the same order of magnitude as dynamic blood pressure. Furthermore, theoretical pressure values on the deformed area, calculated using Euler's theorem, appeared to be correlated with experimental pressure measurements. This equivalence between theory and experiment is a key element in the use of the DDP for estimating the mechanical properties of the vascular wall, particularly for the use of finite element models to characterise the stress state of the deformed vascular wall. This preliminary work thus presents a novel, innovative, and promising approach for the evaluation and management of the risk of rupture of unruptured intracranial aneurysms.

Alterations in the placental proteome in association with the presence of black carbon particles: A discovery study

BackgroundExposure to ambient air pollution is known to cause direct and indirect molecular expression changes in the placenta, on the DNA, mRNA, and protein levels. Ambient black carbon (BC) particles can be found in the human placenta already very early in gestation. However, the effect of in utero BC exposure on the entire placental proteome has never been studied to date. ObjectivesWe explored whether placental proteome differs between mothers exposed to either high or low BC levels throughout the entire pregnancy. MethodsWe used placental tissue samples from the ENVIRONAGE birth cohort, of 20 non-smoking, maternal- and neonate characteristic-matched women exposed to high (n = 10) or low (n = 10) levels of ambient BC throughout pregnancy. We modeled prenatal BC exposure levels based on the mother's home address and measured BC levels in the fetal side of the placenta. The placental proteome was analyzed by nano-liquid chromatography Q-TOF mass spectrometry. PEAKS software was used for protein identification and label-free quantification. Protein-protein interaction and functional pathway enrichment analyses were performed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software. ResultsThe accumulation of BC particles in placenta was 2.19 times higher in the high versus low exposure group (20943.4 vs 9542.7 particles/mm³; p = 0.007). Thirteen proteins showed a ≥2-fold expression difference between the two exposure groups, all overexpressed in the placentas of women prenatally exposed to high BC levels. Three protein-protein interactions were enriched within this group, namely between TIMP3 and COL4A2, SERPINE2 and COL4A2, and SERPINE2 and GP1BB. Functional pathway enrichment analysis put forward pathways involved in extracellular matrix-receptor interaction, fibrin clot formation, and sodium ion transport regulation. DiscussionPrenatal BC exposure affects the placental proteome. Future research should focus on the potential consequences of these alterations on placental functioning, and health and disease during early childhood development.

FLASHQuant: A Fast Algorithm for Proteoform Quantification in Top-Down Proteomics.

Accurate quantification of individual proteoforms is a crucial step in identifying proteome-wide alterations in different biological conditions. Intact proteoforms have been analyzed predominantly by liquid chromatography-mass spectrometry (LC-MS)-based top-down proteomics (TDP) and quantified primarily by the label-free quantification (LFQ) method, as it requires no additional costly labeling. In TDP, due to frequent coelution and complex signal structures, overlapping signals deriving from multiple proteoforms complicate accurate quantification. Here, we introduce FLASHQuant for MS1-level LFQ analysis in TDP, which is capable of automatically resolving and quantifying coeluting proteoforms. In benchmark tests performed with both spike-in proteins and proteome-level mixture data sets, FLASHQuant was shown to perform highly accurate and reproducible quantification in short runtimes of just a few minutes per LC-MS run. In particular, it was demonstrated that resolving overlapping proteoforms boosts the quantification accuracy. FLASHQuant is publicly available as platform-independent open-source software at https://openms.org/flashquant/, accompanied by the simple alignment algorithm ConsensusFeatureGroupDetector for multiple LC-MS runs.

Dual Fractions Proteomic Analysis of Silica Nanoparticle Interactions with Protein Extracts

Dual-fraction proteomics reveals a novel class of proteins impacted by nanoparticle exposure. Background: Nanoparticles (NPs) interact with cellular proteomes, altering biological processes. Understanding these interactions requires comprehensive analyses beyond solely characterizing the NP corona. Methods: We utilized a dual-fraction mass spectrometry (MS) approach to analyze both NP-bound and unbound proteins in Saccharomyces cerevisiae sp. protein extracts exposed to silica nanoparticles (SiNPs). We identified unique protein signatures for each fraction and quantified protein abundance changes using spectral counts. Results: Strong correlations were observed between protein profiles in each fraction and non-exposed controls, while minimal correlation existed between the fractions themselves. Linear models demonstrated equal contributions from both fractions in predicting control sample abundance. Combining both fractions revealed a larger proteomic response to SiNP exposure compared to single-fraction analysis. We identified 302/56 proteins bound/unbound to SiNPs and an additional 196 “impacted” proteins demonstrably affected by SiNPs. Conclusion: This dual-fraction MS approach provides a more comprehensive understanding of nanoparticle interactions with cellular proteomes. It reveals a novel class of “impacted” proteins, potentially undergoing conformational changes or aggregation due to NP exposure. Further research is needed to elucidate their biological functions and the mechanisms underlying their impact.

Explore the mechanism of yishenjiangya formula in the treatment of senile hypertension based on multi-omics technology

Ethnopharmacological relevanceThe Yishenjiangya formula (YSJ) is a traditional Chinese medicine (TCM) primarily composed of qi-tonifying components. This classic formula is commonly utilized to treat kidney qi deficiency in elderly patients with hypertension. According to TCM, maintaining a balance between qi and blood is crucial for stable blood pressure. Kidney qi deficiency can disrupt this balance, altering fluid shear force and, ultimately, leading to hypertension, particularly in elderly populations. Despite YSJ's efficacy in treating hypertension, its specific anti-hypertensive mechanisms remain unclear. Aim of the studyYSJ is commonly prescribed for elderly patients with hypertension. Earlier metabolomics studies demonstrated that YSJ exerts antihypertensive effects by influencing four key pathways: linoleic acid metabolism, glycerol phospholipid metabolism, arginine and proline metabolism, and steroid hormone biosynthesis. This study aims to combine metabolomic and proteomic analyses to thoroughly understand the molecular biological mechanisms responsible for YSJ's anti-hypertensive properties. MethodsUltra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) metabolomics, combined with Label-Free Quantitation (LFQ) proteomics, was employed to analyze serum samples from elderly individuals with and without hypertension pre- and post-YSJ intervention. Serum levels of candidate proteins were assessed using enzyme-linked immunosorbent assay, and receiver operating characteristic curves were used to evaluate the diagnostic performance of the target proteins. ResultsEight differentially expressed metabolites and three differentially expressed proteins were identified as potential therapeutic targets of YSJ. These substances are primarily involved in unsaturated fatty acid metabolism, fluid shear stress and atherosclerosis pathway, primary bile acid biosynthesis, proline metabolism, apoptosis, and endoplasmic reticulum stress. YSJ exerts its therapeutic effects on hypertension in the elderly by modulating these pathways. ConclusionsYSJ effectively treats senile hypertension. By analyzing the correlation between therapeutic targets and pathways, YSJ's anti-hypertensive effect was achieved by inhibiting lipid peroxidation and matrix degeneration. Combining metabolomics and proteomics provides an effective method for uncovering YSJ's anti-hypertensive mechanisms.

Characterizing age-related changes in intact mitochondrial proteoforms in murine hearts using quantitative top-down proteomics

BackgroundCardiovascular diseases (CVDs) are the leading cause of death worldwide, and the prevalence of CVDs increases markedly with age. Due to the high energetic demand, the heart is highly sensitive to mitochondrial dysfunction. The complexity of the cardiac mitochondrial proteome hinders the development of effective strategies that target mitochondrial dysfunction in CVDs. Mammalian mitochondria are composed of over 1000 proteins, most of which can undergo post-translational modifications (PTMs). Top-down proteomics is a powerful technique for characterizing and quantifying proteoform sequence variations and PTMs. However, there are still knowledge gaps in the study of age-related mitochondrial proteoform changes using this technique. In this study, we used top-down proteomics to identify intact mitochondrial proteoforms in young and old hearts and determined changes in protein abundance and PTMs in cardiac aging.MethodsIntact mitochondria were isolated from the hearts of young (4-month-old) and old (24-25-month-old) mice. The mitochondria were lysed, and mitochondrial lysates were subjected to denaturation, reduction, and alkylation. For quantitative top-down analysis, there were 12 runs in total arising from 3 biological replicates in two conditions, with technical duplicates for each sample. The collected top-down datasets were deconvoluted and quantified, and then the proteoforms were identified.ResultsFrom a total of 12 LC-MS/MS runs, we identified 134 unique mitochondrial proteins in the different sub-mitochondrial compartments (OMM, IMS, IMM, matrix). 823 unique proteoforms in different mass ranges were identified. Compared to cardiac mitochondria of young mice, 7 proteoforms exhibited increased abundance and 13 proteoforms exhibited decreased abundance in cardiac mitochondria of old mice. Our analysis also detected PTMs of mitochondrial proteoforms, including N-terminal acetylation, lysine succinylation, lysine acetylation, oxidation, and phosphorylation. Data are available via ProteomeXchange with the identifier PXD051505.ConclusionBy combining mitochondrial protein enrichment using mitochondrial fractionation with quantitative top-down analysis using ultrahigh-pressure liquid chromatography (UPLC)-MS and label-free quantitation, we successfully identified and quantified intact proteoforms in the complex mitochondrial proteome. Using this approach, we detected age-related changes in abundance and PTMs of mitochondrial proteoforms in the heart.

Decoding the impact of neighboring amino acids on ESI-MS intensity output through deep learning

Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the connection between sequence and intensity output remains poorly understood. In this work, we explore combinations of amino acid pairs (i.e., dimer motifs) to determine a potential relationship between the local amino acid environment and MS1 intensity. For this purpose, a deep learning (DL) model, consisting of an encoder-decoder with an attention mechanism, was built. The attention mechanism allowed to identify the most relevant motifs. Specific patterns were consistently observed where a bulky/aromatic and hydrophobic AA followed by a cationic AA as well as consecutive bulky/aromatic and hydrophobic AAs were found important for the prediction of the MS1 intensity. Correlating attention weights to mean MS1 intensities revealed that some important motifs, particularly containing Trp, His, and Cys, were linked with low responding peptides whereas motifs containing Lys and most bulky hydrophobic AAs were often associated with high responding peptides. Moreover, Asn-Gly was associated with low response. The model predicts MS1 response with a mean average percentage error of ∼11 % and a Pearson correlation coefficient of ∼0.64. While dimer representation of peptide sequences did not improve predictive capacity compared to single AA representation in earlier work, this work adds valuable insight for a better understanding of peptide response in MS analysis. SignificanceMass spectrometry is not inherently quantitative, and the response of a compound relies not only on its concentration but also on the molecular composition. For mass spectrometry-based analysis of peptides, such as in bottom-up proteomics, this directly implies that the response cannot be used directly to quantify individual peptides. Moreover, the dependency of the response on the amino acid sequence of individual peptides remains poorly understood. Using a deep learning model based on a recurrent neural network with an attention mechanism, we here investigate how the presence of dimer motifs within a peptide affects the MS1 response through the analysis of intended equimolar peptide pools comprising almost 200,000 unique peptides in total. Not only do we identify certain dimer classes and specific dimers that substantially affect the MS1 response, but the model is also able to predict peptide intensity with low error rates within the independent test subset. The findings not only improve our understanding of the link between sequence and response for peptides but also highlight the potential of utilizing deep learning for developing methods allowing for absolute, label-free peptide quantification.

Data-Independent Acquisition Shortens the Analytical Window of Single-Cell Proteomics to Fifteen Minutes in Capillary Electrophoresis Mass Spectrometry.

Separation in single-cell mass spectrometry (MS) improves molecular coverage and quantification; however, it also elongates measurements, thus limiting analytical throughput to study large populations of cells. Here, we advance the speed of bottom-up proteomics by capillary electrophoresis (CE) high-resolution mass spectrometry (MS) for single-cell proteomics. We adjust the applied electrophoresis potential to readily control the duration of electrophoresis. On the HeLa proteome standard, shorter separation times curbed proteome detection using data-dependent acquisition (DDA) but not data-independent acquisition (DIA) on an Orbitrap analyzer. This DIA method identified 1161 proteins vs 401 proteins by the reference DDA within a 15 min effective separation from single HeLa-cell-equivalent (∼200 pg) proteome digests. Label-free quantification found these exclusively DIA-identified proteins in the lower domain of the concentration range, revealing sensitivity improvement. The approach also significantly advanced the reproducibility of quantification, where ∼76% of the DIA-quantified proteins had <20% coefficient of variation vs ∼43% by DDA. As a proof of principle, the method allowed us to quantify 1242 proteins in subcellular niches in a single, neural-tissue fated cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. DIA integration enhanced throughput by ∼2-4 fold and sensitivity by a factor of ∼3 in single-cell (subcellular) CE-MS proteomics.

A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data

Background Expression proteomics involves the global evaluation of protein abundances within a system. In turn, differential expression analysis can be used to investigate changes in protein abundance upon perturbation to such a system. Methods Here, we provide a workflow for the processing, analysis and interpretation of quantitative mass spectrometry-based expression proteomics data. This workflow utilises open-source R software packages from the Bioconductor project and guides users end-to-end and step-by-step through every stage of the analyses. As a use-case we generated expression proteomics data from HEK293 cells with and without a treatment. Of note, the experiment included cellular proteins labelled using tandem mass tag (TMT) technology and secreted proteins quantified using label-free quantitation (LFQ). Results The workflow explains the software infrastructure before focusing on data import, pre-processing and quality control. This is done individually for TMT and LFQ datasets. The application of statistical differential expression analysis is demonstrated, followed by interpretation via gene ontology enrichment analysis. Conclusions A comprehensive workflow for the processing, analysis and interpretation of expression proteomics is presented. The workflow is a valuable resource for the proteomics community and specifically beginners who are at least familiar with R who wish to understand and make data-driven decisions with regards to their analyses.

NQuant Enables Precise Quantitative N-Glycomics.

N-glycosylation is a highly heterogeneous post-translational modification that modulates protein function. Defects in N-glycosylation are directly linked to various human diseases. Despite the importance of quantifying N-glycans with high precision, existing glycoinformatics tools are limited. Here, we developed nQuant, a glycoinformatics tool that enables label-free and isotopic labeling quantification of N-glycomics data obtained via LC-MS/MS, ensuring a low false quantitation rate. Using the label-free quantification module, we profiled the N-glycans released from purified glycoproteins and HEK293 cells as well as the dynamic changes of N-glycosylation during mouse corpus callosum development. Through the isotopic labeling quantification module, we revealed the dynamic changes of N-glycans in acute promyelocytic leukemia cells after all-trans retinoic acid treatment. Taken together, we demonstrate that nQuant enables fast and precise quantitative N-glycomics.

Electrophoresis-Correlative Data-Independent Acquisition (Eco-DIA) Improves the Sensitivity of Mass Spectrometry for Limited Proteome Amounts.

Capillary zone electrophoresis (CE) combines high separation power, scalability, and speed to limited proteome analyses by mass spectrometry (MS). However, compressed separation in CE challenges the duty cycle of tandem MS, even during data-independent acquisition (DIA). To help remedy this limitation, we introduce the concept of electrophoresis-correlative (Eco) data acquisition for CE-MS. We recognize CE electrospray ionization (ESI) to sort peptide ions into reproducible mass-to-charge (m/z) vs migration time (MT) trends in the solution phase, before subsequent ionization and m/z analysis. We proposed that such a correlation can be leveraged to improve the economy of data acquisition. We test this hypothesis using DIA frames that are tailored to the observed m/z-MT trends. The resulting Eco-DIA method substantially improves the bandwidth utilization of tandem MS during CE-MS. In proof-of-principle studies, Eco-DIA identified and quantified ∼38% more proteins from 1 ng of the HeLa proteome digest compared to the classical DIA, without the assistance of a project-specific tandem MS spectral library. Eco-DIA was able to quantify ∼51% more proteins with <10% coefficient of variation vs the control DIA approach. Based on label-free quantification, the proteins that were exclusively measured by Eco-MS occupied the lower dynamic range of the detected proteome concentration, revealing sensitivity enhancement. In addition to marking the inception of Eco-MS, this work lays the foundation for the development of next-generation data acquisition strategies that leverage electrophoretic ion sorting for high-sensitivity proteomics.

Influence of Software Settings on the Identification Rate, Quantification Results, and Reproducibility in Profiling Post-Translational Modifications by Microflow Liquid Chromatography-Ion Mobility-Quadrupole Time-Of-Flight Analysis Using PEAKS Software.

The influence of data evaluation parameters on qualitative and quantitative results of untargeted shotgun profiling of enzymatic and nonenzymatic post-translational modifications (PTMs) was investigated in a model of bovine whey protein α-lactalbumin heated with lactose. Based on the same raw data, individual adjustments to the protein database and enzyme settings of PEAKS studio software increased the identification rate from 27 unmodified peptides to 48 and from 322 peptides in total to 535. The qualitative and quantitative reproducibility was also assessed based on 18 measurements of one sample across three batches. A total of 570 peptides were detected. While 89 peptides were identified in all measurements, the majority of peptides (161) were detected only once and mostly based on nonindicative spectra. The reproducibility of label-free quantification (LFQ) in six measurements of the same sample was similar after processing the data by either the PTM algorithm or the LFQ algorithm. In both cases, about one-third of the peptides showed a coefficient of variation of above 20%. However, the LFQ algorithm increased the number of quantified peptides from 75 to 179. Data are available at the PRIDE Archive with the data set identifier PXD050363.

Platinum-perovskite nanocomposite-based Exosensor for specific detection of prostate cancer in clinical settings.

Exosomes, extracellular vesicles (EVs) with an average size of 50-150nm, transfer various biomolecules and exchange signaling molecules between cells in a paracrine manner. Molecular investigations have revealed that EVs can reflect real-time metabolic changes in normal- and cancer-origin cells and thus harbor valid diagnostic biomarkers. Despite these advantages, the detection of low concentrations of cancer cell EVs in biological fluids is still a great challenge. Here, a new electrochemical Exosensor based on platinum-perovskite is developed for the direct detection of EVs using a biotinylated monoclonal CD63 antibody as a capture element. The label-free method exhibited higher sensitivity with a lower limit of quantification of 2000 EVs/µL with a dynamic linear range (LDR) of 2000 to 14,000 EVs/μL compared with other available methods. To enhance the selectivity of detection, EVs were simultaneously sandwiched between secondary antibodies of PSA (prostate-specific antigen), as an FDA-approved prostate cancer biomarker. Data indicated that this Exosensor can distinguish normal and cancer EVs in samples from healthy individuals and prostate cancer patients. Taken together, this technology offers a unique approach to label-free quantification of EVs and cancer detection in the early stages.

Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst.

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows, including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Predicting Peptide Ionization Efficiencies for Electrospray Ionization Mass Spectrometry Using Machine Learning.

Mass spectrometry (MS) is inherently an information-rich technique. In this era of big data, label-free MS quantification for nontargeted studies has gained increasing popularity, especially for complex systems. One of the cornerstones of successful label-free quantification is the predictive modeling of ionization efficiency (IE) based on solutes' physicochemical properties. While many have studied IE modeling for small molecules, there are limited reports on peptide IEs. In this study, we leverage the stoichiometric relationship in trypsin digests of well-characterized monoclonal antibodies (mAbs) to compile a data set of relative ionization efficiencies (RIEs) for 241 peptides. From each peptide's sequence, we computed a set of physiochemical descriptors, which were then used to train machine learning regression models to predict RIEs. Peptides shorter than 20 amino acids had RIEs that were highly correlated to their molecular weight. A random forest (RF) model was able to best predict the RIEs of a test data set with a mean relative error of 23.9%. For larger peptides, a multilayer perceptron (MLP) model improved RIE prediction compared to current best practices, reducing mean relative error from 60.5% to 32.0%. Finally, we also show the application of the RF model in label-free relative protein quantification and improving the quantification of peptide post-translational modifications (PTMs). This approach to predicting peptide IEs from their sequences enables the development of accurate label-free quantification workflows for peptide and protein analysis.

Intraindividual variability of semen quality, proteome, and sncRNA profiles in a healthy cohort of young adults.

Within-subject variability of semen parameters and molecular components of ejaculates in young men remains poorly understood. To investigate intraindividual variability (IIV) of semen parameters and molecular markers in repeated ejaculates from young men. Semen parameters were assessed in samples collected 6-8 days apart from 164 18-19-year old participants of the Russian Children's Study, a prospective cohort. Subsets of paired samples were used for label-free quantitation and targeted mass-spectrometry of proteins in seminal plasma (SP) and seminal extracellular vesicles (EVs), and for small non-coding RNA (sncRNA) profiling in EVs and spermatozoa using RNA-seq. The mean difference between two ejaculates, within-subject variation, intraclass correlation, and concordance correlation were used to assess IIV for all parameters. Low variability with high reproducibility and high reliability was considered if CVw<15% and ICC>0.90, respectively. Analytical variability was low for all investigated parameters in technical replicates. IIV was assessed for basic semen parameters and proteins in SPs and EVs: 319 and 777 proteins, respectively, using untargeted analysis; 9 and 10 proteins using targeted quantification. We also described the IIV for sncRNA, including microRNA, piwi-interacting RNA, tRNA, and tRNA-derived small RNA (tsRNA) in EVs (409 sncRNA and 78 tsRNA) and in spermatozoa (265 sncRNA and 15 tsRNA). We identified 22 and 27 non-overlapping proteins in SP and EVs, respectively, and 46 and 9 sncRNA, including 5 and 0 tsRNA in seminal EVs and spermatozoa, respectively, with low variability. The fatty acid synthase (FAS) had the lowest IIV in both media in targeted protein quantification. We identified a number of proteins and sncRNA with low variability among 111 proteins, 176 sncRNA, and 12 tsRNA which were previously suggested as biomarkers of male fertility and reproductive outcomes: lactotransferrin, cysteine-rich secretory protein 3, alpha-1-antichymotrypsin, epididymal sperm-binding protein 1, glutathione S-transferase Mu 3, alpha-1-acid glycoprotein 2, serum amyloid P-component, aminopeptidase N, neprilysin, FAS, and miR-10b-3p, miR-122-5p, miR-205-5p, miR-222-3p, miR-34c-5p, miR-509-3-5p, miR-888-5p, miR-892a, miR-363-3p, miR-941, miR-146a-5p, miR-744-5p. These molecules have low IIV and may be promising candidate biomarkers of male fertility and reproductive health.

Modeling and geometrization in PGNAA

Prompt Gamma Neutron Analysis Activation is a widely used technique for analyzing materials. This technique defines graphs (reference spectrum collection, or libraries) of spectral intensity as a function of energy (channels) for the elements inserted in a sample. The Monte Carlo Library Least Squares (MCLLS) is the dominant approach in the PGNAA technique. The main difficulties faced in the MCLLS domain are (1) numerical instabilities in the least-squares stage (Library Least Squares (LLS)); (2) overdetermination of the system of equations; (3) linear dependence in the libraries; (4) gamma radiation scattering; (5) high computational costs. The present work proposes optimizing the LLS module to face the abovementioned problems using the Greedy Randomized Adaptive Search Procedure (GRASP) and Continuous Greedy Randomized Adaptive Search Procedure (CGRASP) algorithms. The search for the spectral count peaks of the libraries leads to a partitioning of the data before applying the GRASP and CGRASP algorithms. The methodological procedures also address estimating the spectral counts of an unknown library possibly integrates the sample. The results show (1) efficient partitioning of the input data (2) evidence of suitable precision of the weight fractions of the libraries that make up the sample (average precision of the order of 3.16% against 8.8% of other methods); (3) success in the approximation and estimation of the unknown library (average precision of 4.25%) present in the sample. Our method proved to be promising in improving the determination of percentage count fractions by the least-squares module and showing the advantages of data partitioning.