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PubMed2025

Improved Detection of Differentially Abundant Proteins through FDR-Control of Peptide-Identity-Propagation.

The goal of proteomics is to identify and quantify peptides and proteins within a biological sample. Almost all algorithms for the identification of peptides in LC-MS/MS data employ two steps: peptide/spectrum matching and peptide-identity-propagation (PIP), also known as match-between-runs. PIP can routinely account for up to 40% of all results, with that proportion rising as high as 75% in single-cell proteomics. Unlike peptide identities derived through peptide/spectrum matches, for which error estimation has been strictly enforced for decades, peptide identities derived through PIP have not historically been subject to statistical evaluation. As an indispensable component of label-free quantification, PIP needs a statistically rigorous method for estimating its false-discovery rate (FDR). We present a method for FDR control of PIP, called PIP-ECHO, and devise a rigorous protocol for evaluating FDR control of any PIP method. Using three different benchmark data sets, we evaluate PIP-ECHO alongside the PIP procedures implemented by FlashLFQ, IonQuant, and MaxQuant. These analyses show that only PIP-ECHO can accurately control the FDR of PIP at 1% across all data sets. When analyzing a spike-in data set, PIP-ECHO increases both the accuracy and sensitivity of differential expression analysis, yielding substantially more differentially abundant proteins than either MaxQuant or IonQuant.

Proteomics↗

PubMed2026

Urine Proteomics as a Source of Biological Information and Outcome Predictor in Living Kidney Transplantation.

Kidney transplantation (KTx) is the preferred treatment for kidney failure. However, post-transplant management is challenging due to the limited lifespan of transplanted organs. Current methods for monitoring post-transplant complications are invasive and have limitations. Therefore, there is an urgent need for novel non-invasive biomarkers. This study investigates the proteomic composition of urine to understand renal biology during the process of transplantation and to identify potential markers for outcome prediction. Urine samples were collected from donors before transplantation and from recipients 4 weeks and 1 year after transplantation. Proteomic analysis was performed using mass spectrometry and label-free quantification. Statistical analyses included principal component analysis (PCA) and enrichment analysis. The resulting key findings were confirmed in an independent validation cohort. In addition, correlative regression models to evaluate the relationship between protein abundance and clinical outcomes in the further course after transplantation were performed. 106 urine samples in the setting of 70 kidney transplantations were analyzed. PCA revealed distinct clustering of donor and recipient samples, indicating significant proteomic changes after transplantation. Hierarchical clustering and gene ontology analysis identified molecular changes as a response to transplantation and showed an over-representation of relevant pathways related to inflammation, cell immune response and coagulation in both the original and validation cohorts. Multivariate regression analysis, including linear and logistic regression, identified 11 potential protein biomarkers, including ORM2, IL1RAP, APP, and FABP4 as predictors of eGFR 12 months after transplantation and 1 HP as a predictor of infections within the first year after transplantation, respectively. This study underscores the potential of non-invasive urine proteomics for identifying biological processes involved in kidney transplantation and for enhancing post-transplant monitoring and outcome prediction. We identified 12 potential biomarkers with added value to standard clinical parameters linked to transplant outcomes, which will be promising candidates for future outcome monitoring after KTx.

Humans↗

PubMed2026

Quantitative proteomics of molybdenum cofactor biosynthesis and utilization in Caenorhabditis elegans.

The molybdenum cofactor (Moco) is a chemically labile prosthetic group required by a small but essential set of metazoan enzymes, including sulfite oxidase, xanthine dehydrogenase, aldehyde oxidases, and the mitochondrial amidoxime reducing components (MARC). Disruption of Moco biosynthesis in humans causes Molybdenum Cofactor Deficiency (MoCD), a severe neonatal encephalopathy. Caenorhabditis elegans is unique among animals studied so far in that it can meet its Moco requirement through both endogenous biosynthesis and direct uptake of mature Moco from its bacterial diet. However, the organism-wide abundance of the Moco biosynthetic machinery and Moco-dependent enzymes, and their response to altered Moco supply, have remained unknown. Here, using data independent acquisition proteomics with histone anchored absolute quantification, we generated an organism wide quantitative atlas of Moco biosynthesis and utilization in C. elegans under standard and Moco limiting conditions. Components of the biosynthetic pathway showed a strikingly asymmetric abundance. The mitochondrial enzyme MOC-5, which catalyzes the first committed step in Moco biosynthesis, was present at only about 120 copies per genome equivalent, roughly fifty-fold below the downstream cytoplasmic biosynthetic machinery, which ranged from about 5,000 to 8,500 copies per genome equivalent, identifying MOC-5 as a stoichiometric bottleneck. On the utilization side, the MARC paralogs were the dominant Moco consumers, with MARC-1 exceeding 20,000 copies per genome equivalent. Loss of dietary or endogenous Moco selectively depleted the nonsulfurated clients SUOX-1 and MARC-1, whereas biosynthetic proteins remained unchanged, indicating that protein stability, rather than compensatory expression, is the main response to Moco limitation.

Caenorhabditis elegans↗

PubMed2025

Unraveling Plant Nuclear Envelope Composition Using Proximity Labeling Proteomics.

The nuclear envelope (NE) defines the eukaryotic cell and functions in a myriad of fundamental cellular processes including but not limited to signal transduction, lipid metabolism, chromatin organization, and nucleocytoplasmic transportation. Although the general structure of the NE is well-conserved across eukaryotic kingdoms, its composition and functions vary substantially between species and remain largely unknown in plants. In this chapter, we describe a proximity-labeling-based proteomic approach to profile novel NE components in the model organism Arabidopsis. This method is generally suitable for the identification of protein components in subcellular compartments or protein complexes that are poorly accessible to traditional mass spectrometry approaches and can be easily applied to other plant species. In addition to giving a step-by-step detailed description of the proximity labeling proteomics procedure in plant samples, we also provide guidelines on the appropriate use of controls and statistical analysis to achieve a highly specific selection of probed candidates.

Proteomics↗

PubMed2024

Combining Data Independent Acquisition With Spike-In SILAC (DIA-SiS) Improves Proteome Coverage and Quantification.

Data-independent acquisition (DIA) is increasingly preferred over data-dependent acquisition due to its higher throughput and fewer missing values. Whereas data-dependent acquisition often uses stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mass differential tags for relative and absolute quantification and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC (SiS) methods use an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA-SiS, leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate, and comprehensive proteome profiling.

Isotope Labeling↗

PubMed2024

Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provides robust protein quantifications in identifying differentially abundant proteins and spatially covariable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial coexpression analysis.

Proteome↗

PubMed2024

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

Journal Article↗

PubMed2026

Proteomic characterization of the acquired enamel pellicle under acidic challenges at early and mature formation stages.

OBJECTIVES: This study aimed to characterize acquired enamel pellicle (AEP) proteomic changes after exposure to citric acid (CA) and hydrochloric acid (HCl) under different pellicle formation times (3 and 120 min) in the same volunteers. DESIGN: Nine healthy volunteers participated in this randomized crossover in vivo study. The AEP was allowed to form for 3 or 120 min and subsequently exposed for 10 s to deionized water (control), 1% CA (pH 2.5), or 0.01 M HCl (pH 2.0). Pellicle samples were collected, followed by protein extraction, tryptic digestion, and analysis by nanoliquid chromatography (nanoLC) coupled to mass spectrometry (MS) with MSE (data-independent acquisition; nanoLC-MSᴱ). Label-free quantitative proteomics were performed for relative quantification using t-test (p < 0.05). RESULTS: At 120 min, CA exposure markedly reduced several typical AEP proteins, especially acidic proline-rich proteins (PRPs). Conversely, basic PRPs were upregulated, suggesting acid-resistance protein signature. At 3 min, basal-layer proteins (PRPs, cystatins, histatins and mucins) were more abundant. Hemoglobins increased 6-8-fold (up to 150-fold in 3 min control), suggesting association with early pellicle formation and an acid-resistant protein signature. CA exposures for 120 min also upregulated typical AEP proteins (PRPs, mucins, cystatins, immunoglobulins), while HCl exposure depleted albumins and lactotransferrin. CONCLUSION: Intrinsic and extrinsic acids induce distinct proteomic signatures in the AEP. Hemoglobin and PRPs appear consistently enriched in the early pellicle layer, reflecting an initial acid-resistant protein signature. These findings provide new insights into the molecular remodeling of the AEP following intrinsic and extrinsic acid exposure, highlighting proteins potentially involved in early-stage pellicle formation.

Humans↗

PubMed2025

Phosphoproteomics analysis provides novel insight into the mechanisms of extreme desiccation tolerance of the desert moss Syntrichia caninervis.

Syntrichia caninervis is a model species for research on desiccation tolerance (DT) because it is capable of rapidly responding to drastic changes in water conditions. Phosphorylation, a key post-translational modification process that is rapid and reversible, enables the rapid regulation of protein functions, aiding plants to quickly adapt to changing environments. Modifications to phosphorylation may play a crucial role in the DT of S. caninervis, although no studies have been published. Here, we report a 4D label-free high-resolution dynamic proteomic and phosphoproteomic analysis of S. caninervis during dehydration and rehydration, allowing for the quantification of 2854 proteins and 1177 phosphoproteins, including 1447 differentially expressed proteins (DEPs) and 699 differentially phosphorylated proteins (DPPs). Among the phosphoproteins, 36.5% displayed changes in protein abundance. The proteomic and phosphoproteomic changes involved proteins (DEPs and DPPs) that were mainly involved in photosynthesis, glutathione metabolism, the citrate cycle, and the biosynthesis of secondary metabolism pathways during dehydration. During rehydration, DEPs and DPPs were mainly associated with processes related to ribosome and energy metabolism. In summary, during dehydration, phosphorylation mainly regulates signal transduction and metabolic processes, allowing plants to adapt to a loss of water. During rehydration, phosphorylation controls repair and recovery mechanisms, restoring metabolic activity and reestablishing cellular functions. ScDHAR1, a protein involved in glutathione metabolism, was differentially phosphorylated at two serine sites (S29 and S218) in response to desiccation. Further analysis revealed that phosphorylation of S29/S218 in ScDHAR1 significantly increased its enzymatic activity, thereby enhancing the DT of S. caninervis in situ. This work establishes a phosphoprotein database for a DT moss. These findings not only broaden our understanding of S. caninervis DT but also fill knowledge gaps in the field of phosphoproteomics in DT mosses, while providing valuable data resources for future related research.

Phosphoproteins↗

PubMed2026

Label-Free Urinary Proteomics Uncovers Immune-Related Non-Invasive Biomarkers for Primary Biliary Cholangitis.

Diagnosis of primary biliary cholangitis (PBC) currently depends on invasive liver biopsy or serum markers with inadequate diagnostic performance. This study aimed to identify non-invasive urinary protein biomarkers for PBC detection. Urine specimens from biopsy-verified PBC patients and healthy controls were processed through ultracentrifugation-based protein extraction, enzymatic digestion, and HPLC-ESI-IT/MS proteomic profiling; protein quantification was completed using Spectronaut v14.8. We identified 194 differentially expressed urinary proteins (109 upregulated, 85 downregulated) and screened 10 immune-related candidates through GO and KEGG enrichment. Pearson correlation further filtered three core proteins, osteopontin (SPP1/OPN), RAMP3 and S100A8, that correlated significantly with key PBC biochemical indices (ALP, GGT, AST, ALT, IgM, p < 0.05). Elevated urinary concentrations of OPN, RAMP3 and S100A8 were validated by ELISA in an independent cohort containing 30 PBC patients and 20 healthy volunteers. In summary, urinary OPN, RAMP3 and S100A8 are markedly increased in PBC patients and hold promise as non-invasive diagnostic biomarkers for PBC; however, their diagnostic specificity against other cholestatic and autoimmune liver diseases remains to be evaluated, and further confirmation in larger multicenter cohorts with disease control groups is warranted.

Humans↗

PubMed2025

Label-Free Quantitative Phosphoproteomics in the Fission Yeast Schizosaccharomyces pombe.

Protein phosphorylation is a dynamic, reversible posttranslational modification that plays an important role in the regulation of cell signaling. Recently, label-free quantitative (LFQ) phosphoproteomics has become a powerful tool to analyze the phosphorylation of proteins within complex samples. In this chapter, we describe how to apply LFQ phosphoproteomics that is based on Fe-IMAC phosphopeptide enrichment followed by strong anion exchange (SAX) and porous graphitic carbon (PGC) fractionation strategies for identification and quantification of changes in the phosphoproteome in the fission yeast Schizosaccharomyces pombe.

Schizosaccharomyces↗