Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data

A Comprehensive Proteome of Human Corneal Epithelial Cells Constructed by Cross-platform DIA-Mass Spectrometry

The corneal epithelium serves as the front barrier against environmental stimuli and pathogens on the ocular surface. A comprehensive protein profile of the corneal epithelium would be crucial for understanding the molecular mechanisms that are related to corneal disease. This work demonstrated a library-free data-independent acquisition (DIA) approach across different mass spectrometers and proteomic software to build a comprehensive proteomic dataset for human corneal epithelial cells (HCECs). With the combinational use of different data-independent acquisition technologies of multiple mass spectrometers, including Sciex ZenoTOF 7600 (DIA-SWATH), Bruker TimsTOF Pro2 (DIA-PASEF), and ThermoFisher Orbitrap Fusion Lumos (DIA-HRMS1), protein identification and quantification were performed with superior sensitivity and resolution. By using a library-free DIA approach, this study constructed a more diverse and unbiased proteomic profile of human corneal epithelial cells (HCECs), comprising 11,954 protein groups (1% FDR). This represents the largest corneal proteome reported to date. All raw proteomic data were deposited to ProteomeXchange Consortium via Proteomics Identifications database (PRIDE) with the dataset identifier accession number PXD059451. Our findings hold the potential to enhance future understanding of corneal pathologies and transformative therapeutics.

Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids

INTRODUCTION

Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.

AREAS COVERED

We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.

EXPERT OPINION

Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

Proteomic Plasticity in the Coral Montipora capitata Gamete Bundles after Parent Thermal Bleaching

Coral reefs are vital to marine biodiversity and human livelihoods, but they face significant threats from climate change. Increased ocean temperatures drive massive "bleaching" events, during which corals lose their symbiotic algae and the important metabolic resources those algae provide. Proteomics is a crucial tool for understanding coral function and tolerance to thermal stress, as proteins drive physiological processes and accurately represent cell functional phenotypes. We examined the physiological condition of coral (Montipora capitata) gametes from parents that either experienced thermal bleaching or were nonbleached controls by comparing data dependent (DDA) and data independent (DIA) acquisition methods and peptide quantification (spectral counting and area-under-the-curve, AUC) strategies. For DDA, AUC captured a broader dynamic range than spectral counting. DIA yielded better coverage of low abundance proteins than DDA and a higher number of proteins, making it the more suitable method for detecting subtle, yet biologically significant, shifts in protein abundance in gamete bundles. Gametes from bleached corals showed a broadscale decrease in metabolic proteins involved in carbohydrate metabolism, citric acid cycle, and protein translation. This metabolic plasticity could reveal how organisms and their offspring acclimatize and adapt to future environmental stress, ultimately shaping the resilience and dynamics of coral populations.

An In-Depth Coho Salmon (Oncorhynchus kisutch) Ovarian Follicle Proteome Reveals Coordinated Changes Across Diverse Cellular Processes during the Transition From Primary to Secondary Growth

Teleost fishes are a highly diverse, ecologically essential group of aquatic vertebrates that include coho salmon (Oncorhynchus kisutch). Coho are semelparous and all ovarian follicles develop synchronously. Owing to their ubiquitous distribution, teleosts provide critical sources of food worldwide through subsistence, commercial fisheries, and aquaculture. Enhancement of hatchery practices requires detailed knowledge of teleost reproductive physiology. Despite decades of research on teleost reproductive processes, an in-depth proteome of teleost ovarian development has yet to be generated. We have described a coho salmon ovarian proteome of over 5700 proteins, generated with data independent acquisition, revealing the proteins that change through the transition from primary to secondary ovarian follicle development. This transition is critical during the onset of puberty and for determining egg quality and embryonic development. Primary follicle development was marked by differential abundances of proteins in carbohydrate metabolism, protein turnover, and the complement pathway, suggesting elevated metabolism as the follicles develop through stages of oogenesis. The greatest proteomic shift occurred during the transition from primary to secondary follicle growth, with increased abundance of proteins underlying cortical alveoli formation, extracellular matrix reorganization, iron binding, and cell-cell signaling. This work provides a foundation for identifying biomarkers of salmon oocyte stage and quality.

[A clinical investigation of constructing a diagnostic model for sepsis-induced coagulopathy utilizing data-independent acquisition proteomics]

Objective: This study used data-independent acquisition (DIA) proteomics to analyze plasma protein expression in sepsis-induced coagulopathy (SIC), identify key biomarkers, and develop a diagnostic model. Methods: This prospective study included 46 adult sepsis patients from the intensive care unit. Patients were categorized into a general sepsis group (n=26) and an SIC group (n=20) based on established SIC criteria. Plasma samples underwent proteomic and bioinformatics analyses to identify differentially expressed protein (DEP) using LASSO regression and Random Forest. A diagnostic model was constructed and assessed via receiver operating characteristic (ROC) curve analysis. Results: The baseline data revealed that SIC patients exhibited longer prothrombin times, lower platelet counts, and higher D-dimer, fibrin degradation products, blood lactate, SOFA scores, and APACHE Ⅱ scores compared with general sepsis patients (P<0.05). DIA proteomics identified 2 637 proteins, with 240 DEP meeting the criteria (fold change >1.5, P<0.05), including 81 upregulated and 159 downregulated DEP. Subcellular localization analysis revealed that DEPs were predominantly extracellular and nuclear. Gene ontology (GO) annotation showed that DEP were mainly involved in cellular physiology, biological regulation, and stress response processes in biological processes. Domain annotation revealed a predominance of immunoglobulin V regions in DEP, which are crucial for antigen recognition and binding. KEGG enrichment analysis showed significant enrichment of DEP in pathways related to natural killer cell-mediated cytotoxicity, glycosylphosphatidylinositol anchor biosynthesis, tumor necrosis factor signaling, and NF-κB signaling. LASSO regression identified angiogenin and C-type lectin domain family 10 member A as key DEP. The SIC diagnostic nomogram showed an area under the curve of 0.896, with 0.731 specificity and 0.900 sensitivity. Conclusion: The nomogram incorporating angiogenin and C-type lectin domain family 10 member A provides an accurate tool for SIC diagnosis.

A crustacean neuropeptide spectral library for data-independent acquisition (DIA) mass spectrometry applications

Neuropeptides have tremendous potential for application in modern medicine, including utility as biomarkers and therapeutics. To overcome the inherent challenges associated with neuropeptide identification and characterization, data-independent acquisition (DIA) is a fitting mass spectrometry (MS) method of choice to achieve sensitive and accurate analysis. It is advantageous for preliminary neuropeptidomic studies to occur in less complex organisms, with crustacean models serving as a popular choice due to their relatively simple nervous system. With spectral libraries serving as a means to interpret DIA-MS output spectra, and Cancer borealis as a model of choice for neuropeptide analysis, we performed the first spectral library mapping of crustacean neuropeptides. Leveraging pre-existing data-dependent acquisition (DDA) spectra, a spectral library was built using PEAKS Online. The library is comprised of 333 unique neuropeptides. The identification results obtained through the use of this spectral library were compared with those achieved through library-free analysis of crustacean brain, pericardial organs (PO), and thoracic ganglia (TG) tissues. A statistically significant increase (Student's t-test, P value < 0.05) in the number of identifications achieved from the TG data was observed in the spectral library results. Furthermore, in each of the tissues, a distinctly different set of identifications was found in the library search compared to the library-free search. This work highlights the necessity for the use of spectral libraries in neuropeptide analysis, illustrating the advantage of spectral libraries for interpreting DIA spectra in a reproducible manner with greater neuropeptidomic depth.

Data-independent acquisition in metaproteomics

INTRODUCTION

Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data.

AREAS COVERED

This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed.

EXPERT OPINION

Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.

Removal of Exogenous Stimuli Reveals a Canalization of Circadian Physiology in a Vertically Migrating Copepod

Diel rhythms are observed across taxa and are important for maintaining synchrony between the environment and organismal physiology. A striking example of this is the diel vertical migration undertaken by zooplankton, some of which, such as the 5 mm-long copepod Pleuromamma xiphias (P. xiphias), migrate hundreds of meters daily between the surface ocean and deeper waters. Some of the molecular pathways that underlie the expressed phenotype at different stages of this migration are entrained by environmental variables (e.g., day length and food availability), while others are regulated by internal clocks. We identified a series of proteomic biomarkers that vary across ocean DVM and applied them to copepods incubated in 24 h of darkness to assess circadian control. The dark-incubated copepods shared some proteomic similarities to the ocean-caught copepods (i.e., increased abundance of carbohydrate metabolism proteins at night). Shipboard-incubated copepods demonstrated a clearer distinction between night and day proteomic profiles, and more proteins were differentially abundant than in the in situ copepods, even in the absence of the photoperiod and other environmental cues. This pattern suggests that there is a canalization of rhythmic diel physiology in P. xiphias that reflects likely circadian clock control over diverse molecular pathways.

Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypertensive benefits due to their direct intestinal absorption. Our previous explorations have confirmed that endogenous goat milk short-chain peptides are also an essential source of ANGICon-EIPs. Nonetheless, there are limited explorations on endogenous ANGICon-EIPs owing to the limitations of the extraction and enrichment of endogenous peptides, currently. This review outlined ameliorated pre-treatment strategies, data acquisition methods, and tools for the prediction of peptide structure and function, aiming to provide creative ideas for discovering novel ANGICon-EIPs. Currently, deep learning-based peptide structure and function prediction algorithms have achieved significant advancements. The convolutional neural network (CNN) and peptide sequence-based multi-label deep learning approach for determining the multi-functionalities of bioactive peptides (MLBP) can predict multiple peptide functions with absolute true value and accuracy of 0.699 and 0.708, respectively. Utilizing peptide sequence input, torsion angles, and inter-residue distance to train neural networks, APPTEST predicted the average backbone root mean square deviation (RMSD) value of peptide (5-40 aa) structures as low as 1.96 Å. Overall, with the exploration of more neural network architectures, deep learning could be considered a critical research tool to reduce the cost and improve the efficiency of identifying novel endogenous ANGICon-EIPs.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Chemical Proteomics Reveals Protein Tyrosination Extends Beyond the Alpha-Tubulins in Human Cells

Tubulin detyrosination-tyrosination cycle regulates the stability of microtubules. With respect to α-tubulins, the tyrosination level is maintained by a single tubulin-tyrosine ligase (TTL). However, the precise dynamics and tubulin isoforms which undergo (de)tyrosination in neurons are unknown. Here, we exploit the substrate promiscuity of the TTL to introduce an O-propargyl-l-tyrosine to neuroblastoma cells and neurons. Mass spectrometry-based chemical proteomics in neuroblastoma cells using the O-propargyl-l-tyrosine probe revealed previously discussed tyrosination of TUBA4A, MAPRE1, and other non-tubulin proteins. This finding was further corroborated in differentiating neurons. Together we present the method for tubulin tyrosination profiling in living cells. Our results show that detyrosination-tyrosination is not restricted to α-tubulins with coded C-terminal tyrosine and is thus involved in fine-tuning of the tubulin and non-tubulin proteins during neuronal differentiation.