Biology subjects

Sinitcyn, P.

Publications and source records attributed to Sinitcyn, P..

9 recordsLinked to original sources

bioRxiv2026

Disentangling maternal pregnancy and fetal-specific plasma signatures through integrated proteomics and site-specific N-glycoproteomics

During pregnancy, mother and fetus co-exist albeit as two physiologically distinct individuals, connected through the placenta and the umbilical cord. This connection allows for the exchange of vital substances, while individual blood homeostasis remains intact. We hypothesized that comparing maternal and cord plasma (N-glyco)proteomes would provide insights into the biological communication and unique physiological state of each individual. As comparing only matched maternal and umbilical cord blood plasma misses a crucial identifier in determining whether observed changes are related to infancy, pregnancy or both, we designed a triad study by adding a non-pregnant women control group. This allowed us to distinguish pregnancy-specific proteins and their potentially altered N-glycosylation from those in infancy. We found that the fetus and mother have distinct proteomes, N-glycoproteoforms and alternative complement regulation. Moreover, we observed that pregnancy in women led to specific N-glycosylation changes on a select set of primarily HDL-associated and acute-phase proteins. Lastly, our data provide evidence for an early fetal development of the humoral immune system as especially fetal IgM displayed distinctive N-glycosylation signatures.

developmental biology↗

bioRxiv2026

Rapid Peptide Mapping of Monoclonal Antibodies with Direct Infusion Mass Spectrometry

Peptide mapping is a critical tool for characterizing biotherapeutic proteins and is essential for the development of monoclonal antibody drugs. Here we describe a new direct infusion technology that streamlines peptide mapping data collection and analysis, accelerating the method by up to 100-fold. This method, which we term RaPiD-mAb-MS, combines high-throughput plate-based sample preparation with direct infusion mass spectrometry analysis. RaPiD-mAb-MS allows analysis of 96 samples within [~] 1.5 to 2 hours, routinely achieves >95% sequence coverage, and has been successfully applied to 28 unique antibodies and over 2,000 samples. Here we demonstrate that RaPiD-mAb-MS detects and quantifies oxidation, deamidation, isomerization, glycosylation, and sequence variants with results comparable to conventional LC-MS based methods in a fraction of the time. Further, by eliminating chromatography, data analysis is greatly streamlined and simplified. By allowing for the collection of [~] 1,000 peptide maps per day, RaPiD-mAb-MS is positioned to accelerate all phases of antibody-based drug discovery & development and sets the stage for collection of massive datasets that would allow artificial intelligent prediction of optimal antibody variants and formulations.

biochemistry↗

bioRxiv2026

Protein Language Model Decoys for Target Decoy Competition in Proteomics: Quality Assessment and Benchmarks

Large-scale proteomics relies heavily on target-decoy competition for false discovery rate estimation in peptide identification, and the performance of this strategy depends strongly on the design of the decoy database. Classical generators such as reversal and shuffling remain widely used. Here, we introduce the first protein language model-based (PLM) decoy generation for peptide identification and benchmark it against classical strategies. We evaluate these approaches using three complementary quality-control layers: sequence-based separability, search-engine-agnostic spectral-space diagnostics, and end-to-end mass spectrometry benchmarks, including pipelines with rescoring. Across these analyses, PLM-based decoys are harder for sequence-only neural networks to distinguish than most classical generators, suggesting fewer obvious sequence-level artifacts. However, this signal is only weakly informative for search performance. Spectral diagnostics further show that short peptides occupy a particularly crowded target-decoy space and are therefore especially prone to local collisions across all generators. In full search pipelines, reverse decoys remain a strong baseline, and current PLM-based generators do not yet provide a clear overall advantage. We therefore view PLM-based decoys not as universal replacements for reverse decoys, but as tunable tools for benchmarking, diagnostics, stress testing, and future adaptive decoy optimization, with increasing value as search models become more expressive.

bioinformatics↗

bioRxiv2025

A Technical Evaluation of Plasma Proteomics Technologies

Plasma proteomic technologies are rapidly evolving and of critical importance to the field of biomedical research. Here we report a technical evaluation of six notable plasma proteomic technologies - unenriched (Neat), Acid depletion, PreOmics ENRICHplus, Mag-Net, Seer Proteograph XT, Olink Explore HT. The methods were compared on proteomic depth, reproducibility, linearity, tolerance to lipid interference, and limit of detection/quantification. In total we performed 618 LC-MS/MS experiments and 93 Olink Explore HT assays. The Seer method achieved the greatest proteomic depth ([~]4,500), while Olink detected [~]2,600 proteins. Other MS-based methods ranged from [~]500-2,200. Neat, Mag-Net, Seer, and Olink had strong reproducibility, while PreOmics and Acid showed higher variability. All MS methods showed good linearity with spiked-in C-Reactive Protein (CRP); CRP was surprisingly not in the Olink assay. None of the methods were affected by lipid interference. Seer had more than double the number of quantifiable proteins (4,800) for both LOD and LOQ than the next best method. Olink was comparable to Neat and Mag-Net for LOD, but worse for LOQ. Finally, we tested the applicability of these methods for detecting differences between healthy and cancer groups in a non-small cell lung cancer (NSCLC) cohort.

systems biology↗

bioRxiv2024

Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases

The circulating antibody repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is the most widely used proteomics technique for profiling the sequence diversity of circulating antibodies (Ab-seq), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal antibodies with known protein sequences in 70 different combinations of concentration and oligoclonality, both with and without polyclonal serum IgG background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 LC-MS/MS runs. We quantified the dependence of MS-based Ab-seq identification on antibody sequence, concentration, protease, background signal diversity, and bioinformatics setups. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced antibody identification. De novo peptide sequencing showed similar performance to database-dependent methods for higher antibody concentrations, but de novo antibody reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based antibody profiling.

immunology↗

bioRxiv2024

Deep Learning Predicts Non-Normal Peptide FAIMS Mobility Distributions Directly from Sequence

Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in database search. There are methods to accurately predict peptide ion mobility through drift tube devices, but methods to predict mobility through high-field asymmetric waveform ion mobility (FAIMS) are underexplored. Here, we successfully model peptide ions FAIMS mobility using a multi-label multi-output classification scheme to account for non-normal transmission distributions. We trained two models from over 100,000 human peptide precursors: a random forest and a long-term short-term memory (LSTM) neural network. Both models had different strengths, and the ensemble average of model predictions produced higher F2 score than either model alone. Finally, we explore cases where the models make mistakes and demonstrate predictive performance of F2=0.66 (AUROC=0.928) on a new test dataset of nearly 40,000 different E. coli peptide ions. The deep learning model is easily accessible via https://faims.xods.org.

bioinformatics↗

bioRxiv2023

One-Hour Phosphoproteome Analysis with the Orbitrap Astral Mass Spectrometer

Owing to its roles in cellular signal transduction, protein phosphorylation plays critical roles in myriad cell processes. That said, detecting and quantifying protein phosphorylation has remained a challenge. We describe the use of a novel mass spectrometer (Orbitrap Astral) coupled with data-independent acquisition (DIA) to achieve rapid and deep analysis of human and mouse phosphoproteomes. With this method we map approximately 30,000 unique human phosphorylation sites within a half-hour of data collection. The technology was benchmarked to other state-of-the-art MS platforms using both synthetic peptide standards and with EGF-stimulated HeLa cells. We applied this approach to generate a phosphoproteome multi-tissue atlas of the mouse. Altogether, we detected 81,120 unique phosphorylation sites within 12 hours of measurement. With this unique dataset, we examine the sequence, structural, and kinase specificity context of protein phosphorylation. Finally, we highlight the discovery potential of this resource with multiple examples of novel phosphorylation events relevant to mitochondrial and brain biology.

systems biology↗

bioRxiv2021

Data independent acquisition enables deep and fast label-free dynamic organellar mapping

The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we have drastically enhanced the performance of DOMs through data-independent acquisition (DIA) mass spectrometry (MS). DIA-DOMs achieve twice the depth of our previous workflow in the same MS runtime, and substantially improve profiling precision and reproducibility. We leveraged this gain to establish flexible map formats scaling from rapid analyses to ultra-deep coverage. Our fastest format takes only [~]2.5h/map and enables high-throughput experimental designs. Furthermore, we introduce DOM-QC, an open-source software tool for in-depth standardized analysis of DOMs and other profiling data. We then applied DIA-DOMs to capture subcellular localization changes in response to starvation and disruption of lysosomal pH in HeLa cells, which revealed a subset of Golgi proteins that cycle through endosomes. DIA-DOMs offer a superior workflow for label-free spatial proteomics as a systematic phenotype discovery tool.

biochemistry↗

bioRxiv2021

Widespread multi-targeted therapy resistance via drug-induced secretome fucosylation

Cancer secretome is a reservoir for aberrant glycosylation. How therapies alter this post-translational cancer hallmark and the consequences thereof remain elusive. Here we show that an elevated secretome fucosylation is a pan-cancer signature of both response and resistance to multiple targeted therapies. Large-scale pharmacogenomics revealed that fucosylation genes display widespread association with resistance to these therapies. In both cancer cell cultures and patients, targeted kinase inhibitors distinctively induced core fucosylation of secreted proteins less than 60 kDa. Label-free proteomics of N-glycoproteomes revealed that fucosylation of the antioxidant PON1 is a critical component of the therapy-induced secretome. Core fucosylation in the Golgi impacts PON1 stability and folding prior to secretion, promoting a more degradation-resistant PON1. Non-specific and PON1-specific secretome de-N-glycosylation both limited the expansion of resistant clones in a tumor regression model. Our findings demonstrate that core fucosylation is a common modification indirectly induced by targeted therapies that paradoxically promotes resistance.

cancer biology↗