Proteomes

15 pages, 1999 KB

Open AccessArticle

Cell Type-Specific Proteomic Cargo in Human Brain Endothelial, Astrocyte, and Neuronal Extracellular Vesicles

by Hope K. Hutson, Guoting Qin, Chengzhi Cai and Gergana G. Nestorova

Proteomes 2026, 14(2), 24; https://doi.org/10.3390/proteomes14020024 - 1 May 2026

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Background: Extracellular vesicles (EVs) mediate intercellular communication in the central nervous system and are a major source of biomarkers. This study characterizes the EV-derived proteome secreted by human endothelial brain cells (HEBCs), astrocytes, and neurons to identify cell-specific roles in intercellular communication in [...] Read more.

Background: Extracellular vesicles (EVs) mediate intercellular communication in the central nervous system and are a major source of biomarkers. This study characterizes the EV-derived proteome secreted by human endothelial brain cells (HEBCs), astrocytes, and neurons to identify cell-specific roles in intercellular communication in the brain. Methods: Mass spectrometry analyses of EVs and corresponding parent cells were performed to identify differentially enriched proteins. Gene Ontology (GO) analysis of statistically significant, abundantly expressed proteins between EVs and parent cells (log₂ fold-change ≥ 2.0, p < 0.05) was performed to assess cell-specific functions. Results: Proteome analysis identified on average 932 proteins in astrocyte EVs (versus 1725 in parent cells), 1040 in HEBC EVs (versus 5451 in parent cells), and 470 in neuronal EVs (versus 578 in parent cells). The analysis indicated that astrocytes had the highest number of significantly abundant proteins (118), followed by HEBCs (24) and neurons (25). Astrocyte EVs were enriched in lipoproteins, complement factors, and protease inhibitors; HEBCs EVs in tight junction proteins, adhesion molecules, and protease regulators; and neuronal EVs in chromatin-associated histones, tubulin isoforms, and RNA-binding proteins. Conclusions: The proteomic signatures of EVs from different neurovascular unit cells suggest specialized roles in blood–brain barrier homeostasis, immune regulation, and synaptic and epigenetic signaling under healthy conditions. These baseline signatures provide a framework for future studies to investigate how brain cell-derived EVs may contribute to neurodegenerative disorders. Full article

(This article belongs to the Section Extracellular Vesicles)

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20 pages, 4891 KB

Open AccessArticle

Dissection of Genotype-Dependent Responses Reveals Leaf Proteome Signatures Associated with Maize Thermotolerance During Flowering Under Enclosure-Imposed Heat Stress

by Ruixiang Liu, Xiaohang Li, Zixin Zha, Meijing Zhang, Lingjie Kong, Yakun Cui, Wenming Zhao, Qingchang Meng, Youhua Wang and Yanping Chen

Proteomes 2026, 14(2), 23; https://doi.org/10.3390/proteomes14020023 - 29 Apr 2026

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Abstract

Background: During maize anthesis, heat stress severely limits productivity—particularly under humid conditions where high humidity suppresses transpirational cooling, forcing tissues to endure direct thermal load. Methods: Using field enclosures to impose enclosure-imposed humid heat shock (EHS), we screened 135 maize inbred lines for [...] Read more.

Background: During maize anthesis, heat stress severely limits productivity—particularly under humid conditions where high humidity suppresses transpirational cooling, forcing tissues to endure direct thermal load. Methods: Using field enclosures to impose enclosure-imposed humid heat shock (EHS), we screened 135 maize inbred lines for flowering-stage yield resilience, using grain weight per ear at maturity under EHS relative to the corresponding control (CK) condition as the primary selection criterion. Based on this screen, we selected two tolerant (R025, R100) and two sensitive (R133, R135) genotypes for data-independent acquisition mass spectrometry (DIA-MS) profiling of the tassel-subtending leaf. Results: At baseline, the selected tolerant lines exhibited a constitutively distinct proteomic state, including lower abundance of light-harvesting complex components and higher abundance or detection frequency of several regulatory proteins, including SRK2E/OST1 and HSF-B2a. Under sustained EHS, the selected sensitive lines showed extensive proteomic disruption, including reduced abundance of photosynthesis-related proteins and oxidative phosphorylation, together with increased abundance of proteins associated with endoplasmic reticulum stress responses and protein turnover. In contrast, the selected tolerant lines displayed a more constrained acclimation response, characterized by relative maintenance of photosynthesis-related proteins together with selective increases in chaperone systems (HSP90/sHSPs) and benzoxazinoid biosynthesis-related proteins. Several proteins showed switch-like detection patterns between the selected tolerant and sensitive lines, including TMEM97-like and a peptidyl-prolyl isomerase, indicating potentially distinct regulatory states. Conclusions: These findings suggest that tolerant performance under enclosure-imposed heat stress is associated with a pre-conditioned proteomic state and enhanced protein homeostasis (proteostasis) buffering capacity that may help preserve photosynthetic function during flowering-stage stress. The identified proteins should be regarded as candidate markers requiring further functional validation before any application in breeding programs aimed at improving adaptation to increasingly frequent heat-stress events. Full article

(This article belongs to the Special Issue Plant Genomics and Proteomics)

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3 pages, 682 KB

Open AccessEditorial

Proteomes Annual Report Card 2025

by Jens R. Coorssen and Matthew P. Padula

Proteomes 2026, 14(2), 22; https://doi.org/10.3390/proteomes14020022 - 24 Apr 2026

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Abstract

We begin by expressing our sincere thanks to all Editorial Board Members, Guest Editors, Reviewers, Authors, and the staff in the Editorial Office for their dedicated service in support of Proteomes [...] Full article

29 pages, 3062 KB

Open AccessArticle

Prospective ICH Q2(R2)-Aligned Total-Error Validation of Label-Free Untargeted Proteomics for Host Cell Protein Quantification in Biotherapeutics

by Somar Khalil, Jean-François Dierick, Pascal Bourguignon and Michel Plisnier

Proteomes 2026, 14(2), 21; https://doi.org/10.3390/proteomes14020021 - 23 Apr 2026

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Abstract

Background: Untargeted proteomics enables quantitative host cell protein (HCP) determination in biotherapeutics, yet no workflow has been validated under ICH Q2(R2) for regulated quality control. Methods: A prospective total-error (TE) validation of label-free ddaPASEF proteomics was performed. A stable isotope-labeled whole-proteome [...] Read more.

Background: Untargeted proteomics enables quantitative host cell protein (HCP) determination in biotherapeutics, yet no workflow has been validated under ICH Q2(R2) for regulated quality control. Methods: A prospective total-error (TE) validation of label-free ddaPASEF proteomics was performed. A stable isotope-labeled whole-proteome standard was spiked into NISTmAb at seven levels (20–80 ng) and analyzed in four independent assays (198 injections), supporting one-way random-effects ANOVA with Welch–Satterthwaite adjustment. Peptide-level identification error was evaluated by dual entrapment. Results: Empirical false-discovery proportions were below 1% at q = 0.01. Weighted least-squares regression (R² = 0.993) confirmed stable proportional compression with 81–85% recovery. Repeatability dominated the variance structure (median CV 2.7%); intermediate precision SD ranged from 0.69% to 3.81%. Both 95% β-expectation and 95/95 content tolerance intervals were contained within ±30% at all levels, defining a validated range of 20–80 ng. Abundance-stratified TE profiling revealed concentration-dependent calibration heterogeneity, with stratum-specific intervals within ±35% defining an abundance-aware LLOQ of 3.6 ppm (P95 = 3.87 ppm). Robustness under independent search software (FragPipe v24.0, CCC = 0.998) and cross-platform acquisition (Astral, CCC = 0.980) remained within ±30% limits. Conclusions: This constitutes the first prospective ICH Q2(R2)-aligned validation of untargeted proteomics for HCP quantification, with a transferable statistical framework for high-dimensional analytical methods. Full article

(This article belongs to the Section Proteomics Technology and Methodology Development)

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15 pages, 1002 KB

Open AccessReview

Enabling Next-Generation Mass Spectrometry-Based Proteomics: Standards, Proteoform Resolution, and FAIR, Reproducible, and Quantitative Analysis

by Rui Vitorino

Proteomes 2026, 14(2), 20; https://doi.org/10.3390/proteomes14020020 - 21 Apr 2026

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Abstract

Recent advances in mass spectrometry, data-independent acquisition, proteoform-resolving workflows, and multi-omics integration have significantly expanded the scale and scope of proteomics. However, the reuse and translational application of these datasets are limited by inconsistent standards, insufficient metadata, and inadequate computational interoperability. Proteoform-centric approaches [...] Read more.

Recent advances in mass spectrometry, data-independent acquisition, proteoform-resolving workflows, and multi-omics integration have significantly expanded the scale and scope of proteomics. However, the reuse and translational application of these datasets are limited by inconsistent standards, insufficient metadata, and inadequate computational interoperability. Proteoform-centric approaches provide higher molecular resolution by capturing intact protein variants and patterns of post-translational modification. Computational methods, including selected applications of machine learning and large language models (LLMs), are increasingly used for tasks such as spectral prediction and pattern discovery in clinical proteomics datasets. Despite these advancements, FAIR (Findable, Accessible, Interoperable, and Reusable) data practices, proteoform biology, and AI analytics are often pursued independently. This work presents an integrated framework for next-generation proteomics in which standardization and FAIR (Findable, Accessible, Interoperable, and Reusable) principles establish machine-actionable foundations for proteoform-resolved analysis and computational inference. It examines community efforts to promote data sharing and interoperability, as well as strategies for characterizing proteoforms using bottom-up, middle-down, and top-down approaches. It also highlights emerging AI and ML applications within the proteomics workflow. The framework emphasizes the importance of treating proteoforms as primary computational entities and adopting FAIR practices during data collection to enable reproducible and interpretable modeling. Finally, it introduces an architectural model that integrates FAIR infrastructures and proteoform resolution. In addition, practical recommendations for making AI-ready proteomics, including a minimal community checklist to support reproducibility, benchmarking, and translational scalability, are provided. Full article

(This article belongs to the Section Proteomics Technology and Methodology Development)

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20 pages, 872 KB

Open AccessReview

Proteostasis, Assisted Reproductive Technologies, and Neurodevelopmental Differences: An Integrative Perspective

by Alberto Fucarino, Yousef Mohamadi, Francesco Cappello, Federica Scalia, Giulia Russo, Giuseppe Gullo and Leila Noori

Proteomes 2026, 14(2), 19; https://doi.org/10.3390/proteomes14020019 - 21 Apr 2026

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Abstract

Proteostasis, defined as the coordinated regulation of protein synthesis, folding, trafficking, and degradation, is essential for maintaining cellular integrity and supporting normal development. During reproduction and early life stages, efficient proteostasis is crucial for gamete quality, successful fertilization, embryonic development, and neurodevelopmental outcomes. [...] Read more.

Proteostasis, defined as the coordinated regulation of protein synthesis, folding, trafficking, and degradation, is essential for maintaining cellular integrity and supporting normal development. During reproduction and early life stages, efficient proteostasis is crucial for gamete quality, successful fertilization, embryonic development, and neurodevelopmental outcomes. Increasing evidence suggests that impaired proteostasis contributes to infertility and may be intertwined with biological vulnerabilities associated with assisted reproductive technologies [ARTs]. This review provides an integrative perspective on the role of disrupted proteostasis in infertility, ART procedures, and neurodevelopmental differences [NDD]. We review epidemiological and molecular findings indicating proteostasis failure in both male and female infertility, with particular emphasis on molecular chaperones. Among these, heat shock protein 60 [Hsp60] is discussed as a central mediator linking mitochondrial function, protein quality control, and reproductive competence. We further highlight that ART procedures coincide with sensitive periods of epigenetic reprogramming and proteostasis regulation during early embryogenesis, indicating that disturbances in proteostasis may affect epigenetic stability and subsequent neurodevelopmental outcomes. In addition, this review emphasizes the importance of proteoforms and proteome complexity as critical determinants of reproductive success and neurodevelopmental robustness in the context of ART. Finally, we discuss the potential of proteomic and chaperone-based biomarkers as emerging tools to optimize ART strategies, improve gamete and embryo selection, and enhance risk assessment and clinical outcomes. The current review underscores proteostasis as a fundamental yet underrecognized mechanism linking reproductive biology, ART outcomes, and long-term neurodevelopment while highlighting future directions for translational investigations. Full article

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3 pages, 379 KB

Open AccessCorrection

Correction: Banu et al. The Proteome of Dictyostelium discoideum Across Its Entire Life Cycle Reveals Sharp Transitions Between Developmental Stages. Proteomes 2026, 14, 3

by Sarena Banu, P. V. Anusha, Pedro Beltran-Alvarez, Mohammed M. Idris, Katharina C. Wollenberg Valero and Francisco Rivero

Proteomes 2026, 14(2), 18; https://doi.org/10.3390/proteomes14020018 - 21 Apr 2026

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Abstract

In the original publication [...] Full article

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20 pages, 4205 KB

Open AccessArticle

Computational Phosphosite-Specific Network Analysis of YES1 Y426 Reveals Cancer-Associated Phosphorylation Patterns

by Afreen Khanum, Leona Dcunha, Suhail Subair, Athira Perunelly Gopalakrishnan, Akhina Palollathil and Rajesh Raju

Proteomes 2026, 14(2), 17; https://doi.org/10.3390/proteomes14020017 - 16 Apr 2026

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Abstract

Background: YES1 is an Src family non-receptor tyrosine-protein kinase that regulates cell growth, migration, survival, and oncogenic signaling. Although YES1 activation mechanisms and substrates have been extensively studied, its phosphosite-specific regulation across diverse biological contexts remains poorly understood. Methods: We performed a large-scale [...] Read more.

Background: YES1 is an Src family non-receptor tyrosine-protein kinase that regulates cell growth, migration, survival, and oncogenic signaling. Although YES1 activation mechanisms and substrates have been extensively studied, its phosphosite-specific regulation across diverse biological contexts remains poorly understood. Methods: We performed a large-scale integrative analysis of 3825 publicly available human mass spectrometry-based phosphoproteomic datasets to map YES1 phosphorylation events. Co-modulation, co-occurrence, evolutionary conservation, and disease-association analyses were conducted to characterize the functional and clinical relevance of site-specific YES1 phosphorylation. Results: Y426 emerged as the predominant YES1 phosphosite across diverse biological conditions, localized within the activation loop of the kinase domain and conserved across Src family kinases. Co-modulation analysis identified 421 positively and 102 negatively associated phosphosites enriched in biological processes related to cell cycle regulation, transcription, cytoskeletal remodeling, apoptosis, and carcinogenesis. Among these high-confidence protein phosphosites, we identified 24 binary interactors, 5 upstream regulators, and 8 candidate downstream substrates. Comparison with DisGeNet cancer biomarkers showed overlap between YES1-associated phosphoproteomic signatures and site-specific oncogenic markers across multiple cancers, such as breast cancer, colorectal cancer, leukemia, and lung adenocarcinoma. Conclusions: This study provides a systems-level, phosphosite-focused view of YES1 signaling and supports a central regulatory role for Y426 within global phosphoregulatory and cancer-associated networks. Full article

(This article belongs to the Section Multi-Omics Studies that Include Proteomics)

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29 pages, 768 KB

Open AccessReview

Beyond Reanalysis: Critical Issues in Data Reuse for Solid Tumor Proteomics

by Federica Franzetti, Nicole Giugni, Manuel Airoldi, Heather Bondi, Tiziana Alberio and Mauro Fasano

Proteomes 2026, 14(2), 16; https://doi.org/10.3390/proteomes14020016 - 7 Apr 2026

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Abstract

Proteomics represents a fundamental layer for understanding the molecular complexity of solid tumors by quantifying protein abundance and capturing proteoforms and post-translational modifications undetected in genomics or transcriptomics analyses. As mass spectrometry-based technologies and public proteomics repositories have expanded, opportunities for large-scale data [...] Read more.

Proteomics represents a fundamental layer for understanding the molecular complexity of solid tumors by quantifying protein abundance and capturing proteoforms and post-translational modifications undetected in genomics or transcriptomics analyses. As mass spectrometry-based technologies and public proteomics repositories have expanded, opportunities for large-scale data reuse have grown accordingly. Nevertheless, data availability has not been translated into straightforward reuse: differences in experimental design, acquisition strategies, quantification workflows and metadata quality still limit the reproducibility and cross-study comparability. In this review, proteomics data reuse is defined as the systematic reanalysis and integration of publicly available datasets to support precision oncology applications such as biomarker assessment and antibody–drug conjugate target prioritization. We discuss reuse as an end-to-end analytical process, focusing on data analysis workflows, harmonization strategies, and the impact of heterogeneous experimental and analytical choices on interoperability. The increased application of artificial intelligence in proteomics data integration and reuse is also addressed, highlighting its analytical potential while underscoring the risks of overinterpretation when biological context and data structure are not adequately considered. Using colorectal and prostate cancer as representative examples, we illustrate how proteomics data reuse can support biological discovery and translational research, while critically examining the factors that limit robustness and clinical relevance. Full article

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Proteomes, Volume 14, Issue 2 (June 2026) – 9 articles

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