Search

Comprehensive proteomic analysis is essential to elucidate molecular pathways and protein functions. Despite tremendous progress in proteomics, current studies still suffer from limited proteomic coverage and dynamic range. Here, we utilize micropillar array columns (µPACs) together with wide-window acquisition and the AI-based CHIMERYS search engine to achieve excellent proteomic comprehensiveness for bulk proteomics, affinity purification mass spectrometry and single cell proteomics. Our data show that µPACs identify ≤50% more peptides and ≤24% more proteins, while offering improved throughput, which is critical for large (clinical) proteomics studies. Combining wide precursor isolation widths of m/z 4-12 with the CHIMERYS search engine identified +51-74% and +59-150% more proteins and peptides, respectively, for single cell, co-immunoprecipitation, and multi-species samples over a conventional workflow at well-controlled false discovery rates. The workflow further offers excellent precision, with CVs <7% for low input bulk samples, and accuracy, with deviations <10% from expected fold changes for regular abundance two-proteome mixes. Compared to a conventional workflow, our entire optimized platform discovered 92% more potential interactors in a protein-protein interaction study on the chromatin remodeler Smarca5/Snf2h. These include previously described Smarca5 binding partners and undescribed ones including Arid1a, another chromatin remodeler with key roles in neurodevelopmental and malignant disorders., (© 2024. The Author(s).)

This work demonstrates the utility of high-throughput nanoLC-MS and label-free quantification (LFQ) for sample-limited bottom-up proteomics analysis, including single-cell proteomics (SCP). Conditions were optimized on a 50 μm internal diameter (I.D.) column operated at 100 nL/min in the direct injection workflow to balance method sensitivity and sample throughput from 24 to 72 samples/day. Multiple data acquisition strategies were also evaluated for proteome coverage, including data-dependent acquisition (DDA), wide-window acquisition (WWA), and wide-window data-independent acquisition (WW-DIA). Analyzing 250 pg HeLa digest with a 10-min LC gradient (72 samples/day) provided >900, >1,800, and >3,000 protein group identifications for DDA, WWA, and WW-DIA, respectively. Total method cycle time was further reduced from 20 to 14.4 min (100 samples/day) by employing a trap-and-elute workflow, enabling 70% mass spectrometer utilization. The method was applied to library-free DIA analysis of single-cell samples, yielding >1,700 protein groups identified. In conclusion, this study provides a high-sensitivity, high-throughput nanoLC-MS configuration for sample-limited proteomics.

The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field., (© 2023 The Authors. Proteomics published by Wiley-VCH GmbH.)

Objectives: Neurologic outcome prediction in out-of-hospital cardiac arrest survivors is highly limited due to the lack of consistent predictors of clinically relevant brain damage. The present study aimed to identify novel biomarkers of neurologic recovery to improve early prediction of neurologic outcome., Design: Prospective, single-center study, SETTING:: University-affiliated tertiary care center., Patients: We prospectively enrolled 96 out-of-hospital cardiac arrest survivors into our study., Interventions: None., Measurements and Main Results: Neurologic outcome was assessed by the Cerebral Performance Categories score. To identify plasma biomarkers for poor neurologic outcome (Cerebral Performance Categories score ≥ 3), we performed a three-step proteomics strategy of preselection by shotgun analyses, crosschecking in brain tissue samples, and verification by targeted proteomic analyses using a multistep statistical modeling approach. Sixty-three patients (66%) had a poor neurologic outcome. Out of a total of 299 proteins, we identified α-enolase, 14-3-3 protein ζ/δ, cofilin-1, and heat shock cognate 71 kDa protein as novel biomarkers for poor neurologic outcome. The implementation of these biomarkers into a clinical multimarker model, consisting of previously identified covariates associated to outcome, resulted in a significant improvement of neurologic outcome prediction (C-index, 0.70; explained variation, 11.9%; p for added value, 0.019)., Conclusions: This study identified four novel biomarkers for the prediction of poor neurologic outcome in out-of-hospital cardiac arrest survivors. The implementation of α-enolase, 14-3-3 protein ζ/δ, cofilin-1, and heat shock cognate 71 kDa protein into a multimarker predictive model along with previously identified risk factors significantly improved neurologic outcome prediction. Each of the proteomically identified biomarkers did not only outperform current risk stratification models but may also reflect important pathophysiologic pathways undergoing during cerebral ischemia.

Osteoarthritis (OA), a degenerative joint disease characterized by progressive cartilage degeneration, is one of the leading causes of disability worldwide owing to the limited regenerative capacity of adult articular cartilage. Currently, there are no disease-modifying pharmacological or surgical therapies for OA. Fetal mammals, in contrast to adults, are capable of regenerating injured cartilage in the first two trimesters of gestation. A deeper understanding of the properties intrinsic to the response of fetal tissue to injury would allow us to modulate the way in which adult tissue responds to injury. In this study, we employed secretome proteomics to compare fetal and adult protein regulation in response to cartilage injury using an ovine cartilage defect model. The most relevant events comprised proteins associated with the immune response and inflammation, proteins specific for cartilage tissue and cartilage development, and proteins involved in cell growth and proliferation. Alarmins S100A8, S100A9 and S100A12 and coiled-coil domain containing 88A (CCDC88A), which are associated with inflammatory processes, were found to be significantly upregulated following injury in adult, but not in fetal animals. By contrast, cartilage-specific proteins like proteoglycan 4 were upregulated in response to injury only in fetal sheep postinjury. Our results demonstrate the power and relevance of the ovine fetal cartilage regeneration model presented here for the first time. The identification of previously unrecognized modulatory proteins that plausibly affect the healing process holds great promise for potential therapeutic interventions., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2018. Published by The Company of Biologists Ltd.)

Peripheral blood mononuclear cells are important players in immune regulation relying on a complex network of signaling pathways. In this study, we evaluated the power of label-free quantitative shotgun proteomics regarding the comprehensive characterization of signaling pathways in such primary cells by studying regulation of protein abundance, post-translational modifications and nuclear translocation events. The effects of inflammatory stimulation and the treatment of stimulated cells with dexamethasone were investigated. Therefore, a previously published dataset accessible via ProteomeXchange consisting of 6901 identified protein groups was re-evaluated. These data enabled us to comprehensively map the c-JUN, ERK5 and NF-κB signaling cascade in a semi-quantitative fashion. Without the application of any enrichment, 3775 highly confident phosphopeptides derived from 1249 proteins including 66 kinases were identified. Efficient subcellular fractionation and subsequent comparative analysis identified previously unrecognized inflammation-associated nuclear translocation events of proteins such as histone-modifying proteins, zinc finger proteins as well as transcription factors. Profound effects of inflammatory stimulation and dexamethasone treatment on histone H3 and ZFP161 localization represent novel findings and were verified by immunofluorescence. In conclusion, we demonstrate that multiple regulatory events resulting from the activity of signaling pathways can be determined out of untargeted shotgun proteomics data., Significance: Relevant functional events such as phosphorylation and nuclear translocation of proteins were extracted from high-resolution mass spectrometry data and provided additional biological information contained in shotgun proteomics data., (Copyright © 2016. Published by Elsevier B.V.)

To shed light on the multistep process of squamous cell carcinoma development and the underlying pathologic mechanisms, we performed comparative proteome analysis of keratinocytes, keratinocytes stimulated with Il-1beta, and A431 epidermoid carcinoma cells. Fractionation of the cells into supernatant, nucleus, and cytoplasm was followed by protein separation, proteolytic digest, and nano-LC separation, and fragmentation using an ion trap mass spectrometer. Specific bioinformatics tools were used to generate a list of keratinocyte-specific proteins. Ninety percent of these proteins were found to be upregulated in keratinocytes versus the A431 cells. Classification of the identified proteins by biologic function and gene set enrichment analysis revealed that keratinocytes produced more proteins involved in cell differentiation, cell adhesion, cell junction, calcium ion, calmodulin binding, cytoskeleton organization, and cytokinesis, whereas A431 produced more proteins involved in cell cycle checkpoint, cell cycle process, RNA processing and transport, DNA damage and repair, RNA and DNA binding, and chromatin remodeling. The protein signatures of A431 and normal keratinocytes treated with IL-1beta showed marked similarity, confirming that inflammation is an important step in malignant transformation in nonmelanoma skin cancer. Thus, proteome profiling and bioinformatic processing may support the understanding of the underlying mechanisms, with the potential to facilitate development of early biomarkers and patient-tailored therapy., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)