Public proteomics repositories have expanded rapidly, but large-scale reuse still depends on whether deposited studies are computationally reusable. We provide an evidence-based snapshot of this gap by evaluating 500 non-redundant human ProteomeXchange datasets released in 2025. Experimental design annotation was classified as metadata-based when a deposited table (e.g., SDRF, spreadsheet, or text table) explicitly linked samples, files, channels, or quantitative columns to biological groups; sample-based when groups could only be inferred from sample, column or channel names; and no information when reliable group assignment was not possible. Sample-based datasets represented the largest category, comprising 195 deposits (39.0%), followed by datasets lacking reliable design information (170; 34.0%) and metadata-based datasets (135; 27.0%). Among metadata-based deposits, 81 (60.0%) included additional contextual covariates. Processed quantitative outputs were fully available in 245 datasets (49.0%), partially available in 168 (33.6%), and absent in 87 (17.4%). Combining processed quantitative outputs with available biological design information, 189 datasets (37.8%) supported direct matrix-level reuse. Under a stricter scenario requiring metadata-based design, contextual covariates and available processed quantitative outputs, only 64 datasets (12.8%) fulfilled all criteria. These findings suggest that scalable reuse requires more consistent machine-actionable sample-to-file mapping, interpretable design annotation, processing information and processed outputs, while preserving raw data for harmonized reanalysis.
Cancer cachexia is a devastating systemic syndrome characterized by progressive body weight loss and multi-organ dysfunction, yet the proteome-level mechanisms driving synchronized organ remodeling remain incompletely defined. Here, we applied large-scale data-independent acquisition (DIA) proteomics to a reproducible xenograft model using cachexia-inducing human neuroendocrine carcinoma cells (AkuNEC). Compared with non-implanted controls, AkuNEC-bearing mice developed severe wasting of the heart, liver, kidney, and skeletal muscle. Quantitative profiling revealed extensive multi-organ proteome remodeling, with xenobiotic metabolism emerging as a recurrently altered program across all tissues. This shared "chemical stress" signature was overlaid with distinct organ-specific alterations. The liver, heart, and kidney exhibited convergent suppression of mTORC1 signaling, with the liver displaying additional complex reprogramming involving interferon responses and fatty acid metabolism. In contrast, skeletal muscle showed unique stress features, with coagulation emerging as the most prominent signature alongside xenobiotic metabolism. These findings establish a comprehensive multi-organ proteomic framework for cachexia, identifying systemic remodeling of xenobiotic and endobiotic stress pathways as a unifying pathophysiological feature. This pan-organ alteration implies a fundamental compromise in the host's capacity to detoxify endobiotics and therapeutics, providing a molecular rationale for the unpredictable pharmacokinetics and heightened drug toxicity frequently complicating cachexia management.
Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death worldwide, with atherosclerosis serving as the core pathological mechanism driving ischemic heart disease, ischemic stroke, and peripheral artery disease. Macrophages play a central role in atherosclerosis by internalizing modified lipids and transforming them into foam cells, thereby driving plaque formation and progression. Mitochondrial metabolism is critically involved in regulating macrophage function, and targeting mitochondrial dysfunction may provide strategies for limiting plaque inflammation and improving plaque stability. Advances in high-throughput omics technologies and bioinformatic analysis methods have provided powerful tools for in-depth investigation of mitochondrial function. This review first summarizes macrophage heterogeneity and foam cell formation in atherosclerotic plaques. It then compares mitochondrial omics approaches, including proteomics, interactomics, metabolomics, lipidomics, isotope tracing, and emerging single-cell and spatial omics strategies. Finally, it discusses how mitochondrial dysfunction and metabolic reprogramming contribute to macrophage foam cell formation, plaque inflammation, and atherosclerosis progression.
Serum small extracellular vesicles (sEVs) carry glycoproteins that reflect the pathophysiological states of their cells of origin, yet their glycomic profiles in neuropsychiatric disorders remain largely unexplored. Here, we isolated sEVs from healthy controls and patients with depressive disorder (DD), schizophrenia (SCZ), or brain glioma (BG), and characterized their glycopatterns using a high-throughput lectin microarray. Concentrations and CD81 expression of sEVs were significantly elevated across all disease groups relative to healthy controls. Lectin microarray analysis revealed both shared and disease-specific glycan alterations: PSA-recognized glycans (high-mannose and core-fucosylated structures) were consistently downregulated in all three conditions, whereas STA-binding glycans (GlcNAc oligomers and core GlcNAc of N-glycans) were upregulated in SCZ and BG but slightly decreased in DD. Disease-specific lectin signatures-including UEA-I, PHA-L, and DBA in DD, and ACA in SCZ-enabled clear discrimination among the four groups. The STA/PSA fluorescence ratio, validated in individual samples via sEVs microarray, effectively distinguished SCZ and BG from DD and controls. These findings establish that serum sEVs harbor disease-specific glycopatterns capable of differentiating depression, schizophrenia, and glioblastoma, offering a minimally invasive diagnostic approach and providing insights into glycan-related mechanisms underlying these disorders.
The Human Genome Project (HGP) was a large international research effort that timelined between 1990 and 2003, marking the successful mapping of the entire human genome. Despite the promising performance of deep learning models, especially LLMs like Bio-BERT and ProtBERT on well-annotated datasets. Bio-BERT is a pre-trained language model designed for biomedical corpora, it is not able to independently predict gene functionality. A pre-trained model called ProtBERT uses protein sequences to learn features. It is unable to predict gene functionality directly, without more training data. In order to overcome these challenges, a Gene Bio-BERT based framework is proposed for automated gene function prediction utilizing deep learning methods in biomedical data analysis. This Gene Bio-BERT Framework is divided into 3 modules such as Data collection and Preprocessing, Gene Bio-BERT model training, Feature aggregation layer and prediction of functionality. The initial module focuses on data collection and preprocessing. Data is collected using entrez API from NCBI (National Center for Biotechnology Information) to retrieve human gene data. Preprocessing techniques like Tokenization and feature extraction are then used to handle the data. In the second module, a Gene Bio-BERT transformer encoder with an attention-based feature fusion layer and optimized hyperparameters is used to train the model and learn contextual embeddings. The third module generates results by using aggregated transformer representations to produce functional predictions for unknown genes. In predicting gene function, the proposed Gene Bio-BERT model attains an exceptional accuracy of 94.5% and F1 score of 0.87. Additionally, the model's predictive accuracy remains similar when tested on an unannotated gene which gives similarity score 0.84.
Formalin-fixed paraffin-embedded (FFPE) tissues represent a vast archive for translational research that offers access to specimens with clinical outcomes. However, formalin-induced crosslinking hinders efficient protein extraction and limits proteomic applications. We systematically compared three protein extraction workflows, adaptive focused acoustics (AFA) with Covaris buffer (AFA/Covaris-b), AFA with SDS buffer (AFA/SDS-b), and SDS-based lysis followed by SP3 clean-up and digestion (SP3/SDS-b), using hepatocellular carcinoma (HCC) tissues archived for over 30 years. Benchmarking was performed on two HCC cases using three serial tumor sections each, followed by protein quantification using data-independent acquisition mass spectrometry on a ZenoTOF 7600+ instrument. All workflows identified 5206 proteins, with similar mean identifications (AFA/Covaris-b: 4068; AFA/SDS-b: 4134; SP3/SDS-b: 4048), but distinct reproducibility (CVs: 11.9%, 12.2%, and 20.8%, respectively), leading to the exclusion of SP3. Method selection across eight HCC cases, including paired tumor and adjacent non-tumorous tissues, revealed a total of 6018 proteins, with mean identifications of 3876 (AFA/Covaris-b) and 3852 (AFA/SDS-b). Both AFA workflows showed high reproducibility, with slightly lower variability for AFA/Covaris-b (CVs: HCC: 13.5%, non-tumorous liver tissue (NTL): 11.0%) compared with AFA/SDS-b (CVs: HCC: 14.8%, NTL: 11.3%). While upregulated pathways were not consistently detected across workflows, downregulated pathways showed greater concordance between methods. These findings should be interpreted cautiously given the lack of appropriate temporal or fresh-frozen controls. Collectively, AFA-based workflows enable reproducible proteomic profiling of long-archived FFPE tissues and provide a practical approach for methodological evaluation in retrospective proteomics studies. SIGNIFICANCE OF THE STUDY: FFPE tissues are one of the most valuable clinically annotated biospecimen resources worldwide. However, their routine use in proteomics is hindered by extensive protein cross-linking, particularly in long-term archived blocks. Our study addresses this challenge by systematically evaluating and validating protein extraction strategies for FFPE hepatocellular carcinoma tissues stored for more than 30 years. By directly benchmarking AFA-based and SP3 workflows, we show that AFA protocols, particularly those with the Covaris buffer, provide superior reproducibility while maintaining broad proteome coverage. Importantly, we showed that archival samples yielded more than 6,000 quantifiable proteins, with reproducible pathway-level patterns observed across methods, although interpretation of biological signals remains limited by the absence of appropriate controls and potential extraction-related biases. These findings suggest that long-preserved FFPE specimens can serve as useful resources for modern data-independent acquisition mass spectrometry. The ability to extract reproducible proteomic information from decades-old FFPE blocks has important implications; it enables retrospective biomarker discovery, integration with genomic data, and validation of therapeutic targets in richly annotated patient cohorts. By providing a reproducible workflow, this study supports the expanded use of archived FFPE tissues and facilitates future efforts for large-scale, longitudinal cancer proteomics.
This discovery study investigated the impact of MODY-associated mutations in hepatocyte nuclear factors HNF1A and HNF1B on the cellular proteome, aiming to identify affected pathways and advance understanding of diabetes pathogenesis. Human induced pluripotent stem cells (hiPSCs) carrying the HNF1A frameshift mutation (p.Pro291fsinsC) were differentiated into pancreatic progenitors, and renal proximal tubule epithelial cells (RPTECs) were engineered to overexpress HNF1B with the S148L point mutation. Label-free quantitative proteomics was performed using data-dependent and data-independent acquisition on Orbitrap and timsTOF mass spectrometers. Pathway enrichment was analyzed using Qiagen IPA, Hallmark gene sets, and STRING networks. Comprehensive proteome coverage (over 7000 proteins) revealed consistent downregulation of oxidative phosphorylation, mitochondrial function, and interferon signaling pathways. Both models exhibited suppression of innate immune responses, with overlapping downregulated proteins, including members of the OAS, IFIT, and MX1 families. Using label-free proteomics, we show that MODY-associated mutations in HNF1A and HNF1B suppress mitochondrial function and interferon signaling and are additionally associated with reduced abundance of predicted targets such as A1CF as well as diabetes-related proteins including SCGN, supporting their role in diabetes pathogenesis.
ABSTRACT The following article for this Special Issue was published in an earlier Issue . K. Y. Kartowikromo, J. S. Pizzo, I. Jerin, A. M. Hamid, “Advancements in Ion Mobility‐Based Diagnostics for Infectious Diseases,” Proteomics 25 (2025): e13976. https://doi.org/10.1002/pmic.13976 . https://onlinelibrary.wiley.com/doi/10.1002/pmic.13976
Aging accompanies metabolic dysregulation, wherein the liver exhibits high sensitivity to age-related changes. AMP-activated protein kinase α2 (AMPKα2), a key energy metabolism regulator, lacks investigation regarding germline knockout effects on aged liver phosphoproteins. This study investigated germline AMPKα2 knockout effects on aged mouse liver through morphological analysis, Western blot (WB), and phosphoproteomics. AMPKα2 knockout significantly exacerbated glucose-lipid metabolism dysfunction, inflammatory responses, and age-related morphological changes, with enhanced senescent phenotypes validated by WB. Data-independent acquisition (DIA) phosphoproteomic analysis identified 4,448 specific phosphopeptides, among which 316 significantly differentially modified peptides. AMPKα2 knockout enhanced phosphorylation of glucose-lipid metabolism proteins, such as acetyl-CoA carboxylase 1 (Acaca) at S118, S80, S79, S157, and S117 sites, and genomic instability proteins, such as HSP90β (Hsp90ab1) at S255. Conversely, stress response protein HSP27 (Hspb1) phosphorylation at S86 was significantly reduced, validated by WB. This study revealed novel molecular signatures of AMPKα2 knockout in exacerbating hepatic aging and metabolic dysfunction, suggesting HSP27 as a potential AMPKα2 downstream effector through site-specific phosphorylation. This work first delineated the phosphoproteomic landscape of aged liver in AMPKα2 knockout mice, establishing foundations for targeting specific protein phosphorylation sites as therapeutic targets for age-related liver diseases.
Protein-protein interactions are central to the dynamic regulation of signaling pathways and provide critical insight into the cellular mechanisms underlying human disease. Our previous study demonstrated biochemical and genetic interactions between FUZ and GPR161 in sonic hedgehog signaling during spinal neural tube development. In this study, we sought to identify novel interacting proteins of FUZ and GPR161 to further characterize their biochemical and functional relationships. Using affinity-based liquid chromatography-tandem mass spectrometry of immunoprecipitated complexes from cells overexpressing FUZ and GPR161, we identified 159 shared co-interacting proteins, along with 289 proteins exclusive to FUZ and 617 proteins exclusive to GPR161. Gene Ontology (GO) analysis of the co-interactome revealed significant enrichment in proteasomal catabolic processes and intracellular trafficking pathways. GO analysis of the FUZ-specific interactome showed enrichment in cell cycle progression, mitochondrial membrane, and RNA metabolism, whereas the GPR161-specific interactome was enriched in receptor complex and endoplasmic reticulum-Golgi transport pathways. These findings were supported by STRING network analysis. Among the prioritized candidates, FKBP8 was validated as a binding partner of both FUZ and GPR161. Collectively, our proteomic study defines the protein interaction networks of FUZ and GPR161, highlighting their distinct and cooperative roles in multiple cellular processes and providing a foundation for future functional studies.
Pseudomonas aeruginosa is highly adaptable to environmental cues, including light. While specific light-responsive mechanisms have been studied in P. aeruginosa, the effects of light on biofilm proteomes remain unclear. Here, we employed quantitative proteomics to examine responses to white and red light in P. aeruginosa PA14 wild-type and bacteriophytochrome BphP null (ΔbphP) biofilms. Biofilms were cultivated under dark, white, and red light conditions. Peptides were analyzed using an Orbitrap Exploris 240 mass spectrometer. Across five experimental groups (n = 3), 3,875 proteins were identified and quantified with >99.9% data completeness and CV <15%. Of these, 862 proteins were differentially expressed (>1.5-fold, FDR 5%) in response to light. Red light induced the upregulation of proteins involved in osmotic stress response and carbohydrate metabolism in wild-type biofilms. Catalase KatE was upregulated >20-fold, while catalase KatA was suppressed, indicating a differential oxidative stress response. These patterns were reversed in a ΔbphP mutant, suggesting BphP-dependent regulation. Light exposure also reduced expression of the virulence factors elastase (LasB) and protease LasA (LasA). Several targets of the AlgB/RpoN network, including the anti-sigma factor MucA, showed BphP-linked changes. These findings confirm the role of BphP as a key regulator of light-mediated proteomic adaptation in P. aeruginosa biofilms.
Archival formalin-fixed, paraffin-embedded (FFPE) tissues are invaluable for retrospective clinical research, yet the impact of multi-decade storage on quantitative proteomic fidelity remains poorly understood. We conducted a systematic, decade-resolved proteomic assessment using data-independent acquisition (DIA) LC-MS/MS to evaluate FFPE specimens spanning 50 years. This study included 33 gastric cancer cases collected at approximately 10-year intervals from 1972 to 2022. From each case, tumor tissue, matched non-tumorous mucosa, and muscle layer were macrodissected and processed simultaneously using a standardized workflow to minimize technical bias. Longitudinal analysis across five decades demonstrated that a substantial portion of the detectable proteome remains quantitatively stable. While storage- and archival-era-associated factors contributed to measurable variance, tissue-specific biological signatures were the dominant drivers of proteomic profiles. The muscle layer exhibited the highest stability, serving as a structural reference for evaluating archival ageing. Differential expression analysis between tumor and nontumorous mucosa identified robust pathological signatures that persisted even in 50-year-old specimens. Although we observed era-specific variations in protein detectability, a core set of differentially expressed proteins and their associated functional pathways-including cell cycle and metabolic remodeling-were consistently preserved across the evaluable year groups. Notably, the treatment-naïve 1972 cohort provided high-depth biological insights comparable to contemporary samples. Our findings establish that long-term archival storage does not preclude biologically interpretable proteomic profiles, although storage- and archival-era-associated shifts should be explicitly accounted for in analysis and interpretation.
Metal ions are crucial for viral processes like replication, structural integrity and immune modulation, despite that the metalloproteome of Monkeypox virus (MPXV) remains largely unexplored. Monkeypox virus is a re-emerging zoonotic Orthopoxvirus with a 197 kb genome encoding over 183 proteins. Here, in this report we aimed to identify and explore the metal-associated proteome of the MPXV. Derived from employing a structure driven pipeline, followed by the functional annotation, the subcellular localization, evolutionary aspect, and structural validation with the established experimental evidences. Yielded a set of approximately 21 % high-confidence putative metal-associated proteins with a potential as metal-binding proteins. Functional annotation, Gene Ontology (GO) enrichment, and KEGG Orthology (KO) term assignment also revealed significant enrichment of pathways primarily related to functions such as, viral replication, genome maintenance, transcription, virion assembly, and host immune modulation. These metal-associated proteins are hypothesized to perform critical biological roles throughout the viral life cycle and pathogenesis. This includes nucleotide metabolism, transcriptional regulation, redox balance and immune evasion, and virion morphogenesis. These findings were further strengthened by subcellular localization analysis, which predicts the presence of metalloproteins within MPXV and its viral factories. Further, suggesting the spatial distribution of various metals and its utilization in the host organism. Facilitating the activities from viral attachment, entry, replication, transcription, viral assembly, and release as either mature virion (MV) or intracellular mature virion (IMV). Comparison and mapping these identified metal-associated MPXV proteins to Vaccinia virus followed by the virus-host network analysis highlighted the proposed role of metal-associated proteins within conserved Orthopoxvirus interaction pathways.
Chemical proteomics approaches often yield low peptide recovery because they aim to enrich low-abundance proteins. Poor sample handling further reduces recovery through peptide adsorption to plastic surfaces and losses due to vacuum evaporation. We systematically mapped these losses across buffers, volumes, plastics and pH, then developed an Evotip-compatible handling protocol that minimizes adsorption and removes the need for vacuum evaporation. Losses to plastic adsorption and vacuum evaporation were most apparent at low inputs (< 200 ng) and with larger volumes (> 20 µL) but were also dependent on acidification as well as the buffer and plastic used. The optimized workflow: direct acidification of peptides, frozen storage if needed, and direct loading onto Evotips, resulted in up to ∼90-fold gains versus workflows incorporating vacuum concentration steps, a common practice in proteomics sample preparation, when peptide input was limited to 10 ng. This optimized sample handling method enables high-throughput chemical proteomics by reducing manual handling steps and enables the characterization of low abundance proteins previously lost during sample preparation.
Crassostrea gigas survives in the intertidal zone by relying on tissue-specific mucus secreted by different organs. However, the macroscopic differentiation of mucus physical properties and their molecular mechanisms remain poorly understood. Here, we integrated proteomics and rheological analysis to investigate the molecular basis potentially underlying the viscosity hierarchy of mucus and its functional match across three key tissues (mantle, labial palps, and gill) in the C. gigas. Proteomic analysis suggested that varying levels of extracellular matrix (ECM) components and glycosaminoglycan (GAG) synthesis are closely associated with this physical hierarchy. Specifically, active GAG synthesis and high ECM enrichment in the labial palps are consistent with a dense hydrated gel network. In contrast, progressively lower ECM accumulation and downregulated carbohydrate metabolism in the mantle and gill correlate with weaker hydrogen bond networks. Rheological characterization suggested a clear viscosity hierarchy that is consistent with each tissue's physiological role: labial palps mucus displayed the highest complex viscosity (0.046 Pa·s), reflecting properties that may contribute to feeding; mantle mucus exhibits elastic-dominant suitable for surface protection; and gill mucus showed the lowest viscosity (0.028 Pa·s), which is consistent with facilitating gas exchange. Ultimately, this study indicates that tissue-specific molecular and metabolic profiles are associated with mucus viscoelasticity to meet physiological requirements. These findings not only provide insight into the tissue-specific molecular basis of mucus viscosity in C. gigas, but also provide inspiration for designing marine bionic intelligent hydrogels and the development of anti-corrosion coatings, addressing longstanding challenges in offshore engineering.
Cancer cachexia is a devastating systemic syndrome, yet tumor-intrinsic programs that enable cachexia induction remain poorly defined. Here we dissect cachexia-inducing tumor evolution using a uniquely paired model derived from a rare human duodenal neuroendocrine carcinoma, in which repeated in vivo passaging converts a non-cachexia-inducing cell line (TCC-NECT-2) into a cachexia-inducing derivative (AkuNEC). AkuNEC xenografts reproducibly induced progressive body weight loss, whereas TCC-NECT-2 xenografts did not. Using data-independent acquisition (DIA) proteomics, we quantified the proteomes of the paired cell lines and their matched xenograft tumors and performed complementary comparisons (cell line-to-cell line, cell-to-tumor within each lineage, and tumor-to-tumor). Proteomic profiling revealed robust divergence between TCC-NECT-2 and AkuNEC cells in vitro, and extensive remodeling upon xenograft formation in both lineages. Importantly, cachexia-inducing tumors displayed lineage-specific in vivo remodeling and an in vivo tumor proteome enriched for epithelial-mesenchymal transition, hypoxia, and cholesterol homeostasis, whereas no significant pathway enrichment was detected among proteins decreased in AkuNEC tumors under the applied false discovery rate (FDR) threshold. These results establish cachexia as an acquired tumor phenotype shaped by in vivo selection and provide a proteome-wide resource for prioritizing tumor-intrinsic candidates for future functional studies.
PURPOSE:This study aimed to investigate the effects of nutritional intervention on the milk fat globule membrane (MFGM) proteome of colostrum from lactating yaks to provide a theoretical basis for understanding potential links to yak colostrum quality and calf health. MATERIALS:Twenty healthy pregnant yaks were randomly assigned to a control group (grazing) and a nutritional intervention group (grazing + supplemental feeding with 1.0 kg concentrate and 3.0 kg oat hay). The trial lasted for 21 days prepartum and 7 days postpartum. Colostrum samples collected from days 1-7 postpartum were analyzed. Data-independent acquisition (DIA) proteomics technology was used to analyze differences in MFGM protein expression, and bioinformatics analysis (GO, KEGG) was also performed to interpret the functions of differentially expressed proteins (DEPs). RESULTS:A total of 1972 MFGM proteins were identified, and 661 DEPs were screened. Among them, 85 DEPs were significantly upregulated in the TR group (α-S1-casein, α-S2-casein, κ-casein, and glutathione peroxidase (GPX3)), and 576 were downregulated (xanthine oxidase (XDH) and perilipin (PLIN2)) GO enrichment analysis revealed that DEPs were associated with immune effector processes, complement activation, and antioxidant responses. KEGG pathway analysis indicated that DEPs were enriched in pathways such as tight junction, endocytosis, complement, and coagulation cascades. CONCLUSIONS:The nutritional intervention was associated with increased abundance of caseins and certain immune-related proteins and with reduced abundance of lipid metabolism-related proteins. These changes suggest an altered composition of MFGM proteins, with patterns that may be relevant to immunomodulatory (complement activation) and antioxidant (GPX3 upregulation) functions in colostrum. However, direct evidence for effects on calf health outcomes was not obtained, and these interpretations require further validation.
Saffron (Crocus sativus L.) is one of the world's most valuable spices, renowned for its distinctive aroma, flavor, and pharmacological properties derived from apocarotenoids such as crocin, picrocrocin, and safranal, which accumulate in the stigmas during flower development. Despite their economic and medicinal importance, the molecular regulation of apocarotenoid biosynthesis across floral developmental stages remains poorly understood, particularly at the proteomic level. To address this gap, we applied an LC-MS/MS-based proteomic approach combined with bioinformatic and structural analyses to characterize stage-specific protein expression across five developmental stages: corm with floral shoot buds (A1), flower inside the sheath (S1), just outside the sheath (S2), flower at unopened state (S3), and flower at opened state (S4). Differential abundance analysis, gene ontology enrichment, STRING-based protein-protein interaction networks, KEGG pathway mapping, and structural modeling identified 57 developmentally regulated proteins linked to stigma differentiation and apocarotenoid metabolism. Stage-specific protein sets comprising 128 (A1), 44 (S1), 38 (S2), 29 (S3), and 29 (S4) proteins were selected using stringent statistical thresholds and validated through Limma-based differential expression analysis. Key enzymes, including PSY2, CCD2, ALDH2B4, and UGT707B1, emerged as central regulators of apocarotenoid biosynthesis during floral maturation. Overall, this study provides a comprehensive proteomic framework underlying stigma development and apocarotenoid accumulation in saffron, offering valuable molecular targets for improving metabolite yield and quality. The data supporting this study have been deposited in the ProteomeXchange repository under the identifier PXD076029.
The protein sample preparation methods for shotgun proteomics are nowadays well-established unlike the ones for whole protein analysis. The goal of my work has been to create a simple methodology which provides a single uncomplicated sample preparation tool for these two fields. Nowadays the bulk of proteomics work is done using detergents for protein solubilization. The presented concept, which is based on unspecific adsorption of protein molecules on wide pore materials, allows for protein capture and clean-up from solutions of the most commonly used sodium dodecyl sulfate detergent. It could also be applied to proteins in detergent-free solutions. After the capture and clean-up, proteins could be either cleaved for the downstream peptide analysis or eluted for the whole protein analysis. If required, the eluted whole proteins could be recaptured and cleaved into peptides. Depending on the experimental goals, the sample preparation device could be fitted with embedded proteolytic enzymes to simplify routine sample processing and/or reversed phase media for the downstream peptide or protein separation.
