Number Of Protein Identifications Research Articles

Secretome processing for proteomics: A methods comparison.

The cancer cell secretome comprises a treasure-trove for biomarkers since it reflects cross-talk between tumor cells and their surrounding environment with high detectability in biofluids. In this study, we evaluated six secretome sample processing workflows coupled to single-shot mass spectrometry: (1) Protein concentration by ultrafiltration with a molecular weight cut-off (MWCO) filter and sample preparation through in-gel digestion (IGD); (2) Acetone protein precipitation coupled to IGD; (3) MWCO filter-based protein concentration followed by to in-solution digestion (ISD); (4) Acetone protein precipitation coupled to ISD; (5) Direct ISD; (6) Secretome lyophilization and ISD. To this end, we assessed workflow triplicates in terms of total number of protein identifications, unique identifications, reproducibility of protein identification and quantification and detectability of small proteins with important functions in cancer biology such as cytokines, chemokines, and growth factors. Our findings revealed that acetone protein precipitation coupled to ISD outperformed the other methods in terms of the number of identified proteins (2246) and method reproducibility (correlation coefficient between replicates (r=0.94, CV=19%). Overall, especially small proteins such as those from the classes mentioned above were better identified using ISD workflows. Concluding, herein we report that secretome protein precipitation coupled to ISD is the method of choice for high-throughput secretome proteomics via single shot nanoLC-MS/MS.

Dimethylthiourea as a Quencher in Hydroxyl Radical Protein Footprinting Experiments.

Hydroxyl radical protein footprinting (HRPF) is a mass-spectrometry-based method for studying protein structures, interactions, conformations, and folding. This method is based on the irreversible labeling of solvent-exposed amino acid side chains by hydroxyl radicals. While catalase is commonly used as a quencher after the labeling of a protein by the hydroxyl radicals to efficiently remove the remaining hydrogen peroxide, it has some disadvantages. Catalase quenching adds a relatively high amount of protein to the sample, limiting the sensitivity of the method due to dynamic range issues and causing significant issues when dealing with more complex samples. We evaluated dimethylthiourea (DMTU) as a replacement for catalase in the quenching HRPF reactions. We observed that DMTU is highly effective at quenching HRPF oxidation. DMTU does not cause the background protein issues that catalase does, resulting in an increased number of protein identifications from complex mixtures. We recommend the replacement of catalase quenching with DMTU for all HRPF experiments.

Spectral library search for improved TMTpro labelled peptide assignment in human plasma proteomics.

Clinical biomarker discovery is often based on the analysis of human plasma samples. However, the high dynamic range and complexity of plasma pose significant challenges to mass spectrometry-based proteomics. Current methods for improving protein identifications require laborious pre-analytical sample preparation. In this study, we developed and evaluated a TMTpro-specific spectral library for improved protein identification in human plasma proteomics. The library was constructed by LC-MS/MS analysis of highly fractionated TMTpro-tagged human plasma, human cell lysates, and relevant arterial tissues. The library was curated using several quality filters to ensure reliable peptide identifications. Our results show that spectral library searching using the TMTpro spectral library improves the identification of proteins in plasma samples compared to conventional sequence database searching. Protein identifications made by the spectral library search engine demonstrated a high degree of complementarity with the sequence database search engine, indicating the feasibility of increasing the number of protein identifications without additional pre-analytical sample preparation. The TMTpro-specific spectral library provides a resource for future plasma proteomics research and optimization of search algorithms for greater accuracy and speed in protein identifications in human plasma proteomics, and is made publicly available to the research community via ProteomeXchange with identifier PXD042546.

Online 2D High-pH and Low-pH Reversed-Phase Nano-LC-MS/MS System for Deep Proteome Analysis.

In bottom-up proteomic profiling, the complexity of proteome composition and wide dynamic range has created challenges on the limited number of protein identification and proteome coverage, especially in sample input-limited nanoflow (nano) LC-MS/MS analysis. Herein, we developed a fully automatic online 2D nano-LC-MS/MS system using both high-pH and low-pH reverse phase (RP) LCs on a single LC instrument toward comprehensive proteomics analysis. Compared to conventional microflow 2D-LC, the high-pH RP trapping column demonstrated a low sample requirement of cellular protein digest at the μg level with good fractionation resolution of >90% peptides in a single fraction. Compared to the offline 2D RP-RP nano-LC-QTOF using C18-HPLC column and C18-Stage Tip, and 1D nano-LC-QTOF system, superior coverage was observed on the higher number of identified protein groups/unique peptides by 1.35-/1.68-, 1.46-/1.75-, and 3.21-/4.35-fold, respectively, using an online 2D RP-RP nano-LC-QTOF mass spectrometer. On the evolution of quantitation performance, the online 2D high-/low-pH RP data-independent acquisition (DIA) showed a higher reproducibility in protein groups intensity (R2 > 0.977) and more quantified proteins than that obtained using the offline 2D high-/low-pH RP DIA approach. Using an advanced Orbitrap Exploris 480 mass spectrometer, ∼1.9-fold higher proteome coverage was also observed in our 2D online RP-RP system (6039 protein groups) compared to the 1D nano-LC system (3133 protein groups). In summary, the online 2D nano-LC-MS/MS platform can be a sensitive and robust approach compatible with conventional nano-LC instruments for deep proteome coverage of trace amounts of samples.

MetaLP: An integrative linear programming method for protein inference in metaproteomics.

Metaproteomics based on high-throughput tandem mass spectrometry (MS/MS) plays a crucial role in characterizing microbiome functions. The acquired MS/MS data is searched against a protein sequence database to identify peptides, which are then used to infer a list of proteins present in a metaproteome sample. While the problem of protein inference has been well-studied for proteomics of single organisms, it remains a major challenge for metaproteomics of complex microbial communities because of the large number of degenerate peptides shared among homologous proteins in different organisms. This challenge calls for improved discrimination of true protein identifications from false protein identifications given a set of unique and degenerate peptides identified in metaproteomics. MetaLP was developed here for protein inference in metaproteomics using an integrative linear programming method. Taxonomic abundance information extracted from metagenomics shotgun sequencing or 16s rRNA gene amplicon sequencing, was incorporated as prior information in MetaLP. Benchmarking with mock, human gut, soil, and marine microbial communities demonstrated significantly higher numbers of protein identifications by MetaLP than ProteinLP, PeptideProphet, DeepPep, PIPQ, and Sipros Ensemble. In conclusion, MetaLP could substantially improve protein inference for complex metaproteomes by incorporating taxonomic abundance information in a linear programming model.

Abstract 2039: Confident analyses of proteins in FFPE tissue: Robust, reliable, reproducible sample preparation enables LC-MS based proteomics assays

Abstract Formalin-fixed paraffin-embedded (FFPE) tissue samples are routinely used in clinical histopathology. While FFPE preserves tissue architecture, removal of paraffin is an essential to achieve high quality LC-MS data. Xylol, despite all its efficacy presents severe health risks and xylol removal compromises assay reproducibility. Superior sample preparation ensures removal of unwanted matrix components and provides reliability, reproducibility and robustness of the assays. This is achieved by an optimal combination of lysis by Adaptive Focused Acoustics (AFA®) with a modified protein aggregation capture (PAC) protocol. This method offers robust, reliable, and reproducible separation of paraffin from the tissue and ensures efficient purification of protein. FFPE scrolls were used in a Covaris plate, incubated post-treatment with Tissue Lysis Buffer, and processed with the Covaris LE220-Plus Focused-ultrasonicator for deparaffinization and DNA shearing. PAC protocol was used to extract from FFPE lysate with additional washing steps to ensure paraffin removal. Sera Mag Speed beads (bead:protein 1:5) were used to induce on-bead protein aggregation followed by washing and resuspension in 100 mM Tris-HCl (pH ~ 8) for digestion with Trypsin and LysC. All samples were measured on a 100 min gradient using a Q Exactive HF-X Orbitrap High Resolution Accurate Mass Spectrometer coupled to an EASY-nLC 1200 UHPLC. FFPE scrolls of human adenoma samples were used and processed in single wells. An optimal combination of heating and AFA ensured reversal of crosslinks and paraffin removal from tissue samples. Protein purification was achieved using the PAC protocol. Finally, a tryptic ‘on-bead’ digest was performed resulting in a clean peptide solution that was desalted prior to LC-MS analysis. A comparison of the xylol-free workflow with that of the common xylol deparaffinization resulted in highly similar peptide, protein peptide identification rates, and protein abundance ranges. On average, 3,900 proteins and 20,842 peptides per single run measurement were quantified. From the perspective of monitoring post-translational modifications, a comparison of this method with the xylol-based protocol showed that crosslink reversal was equally efficient in both protocols. Efficiency of tissue lysis and paraffin removal in relation to the thickness of tissue sections were evaluated in 3 and 10 µm thick adenoma sections. While both section types provided enough material for multiple MS measurements, 10 µm thick sections showed a higher spread of peptide yields. The number of protein identifications, however, was found to be similar under both conditions. To further assess the reproducibility of the AFA and PAC workflow, three tissue sections of a colorectal adenoma were used, resulting in quantification of more than 3,500 proteins in each of the tissue replicates. Citation Format: Tom O'Hare, Eugenio Daviso, Debadeep Bhattacharyya, Sameer Vasantgadkar. Confident analyses of proteins in FFPE tissue: Robust, reliable, reproducible sample preparation enables LC-MS based proteomics assays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2039.

DeepSCP: utilizing deep learning to boost single-cell proteome coverage.

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target-decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.

A Comparison of Blood Plasma Small Extracellular Vesicle Enrichment Strategies for Proteomic Analysis.

Proteomic analysis of small extracellular vesicles (sEVs) poses a significant challenge. A ‘gold-standard’ method for plasma sEV enrichment for downstream proteomic analysis is yet to be established. Methods were evaluated for their capacity to successfully isolate and enrich sEVs from plasma, minimise the presence of highly abundant plasma proteins, and result in the optimum representation of sEV proteins by liquid chromatography tandem mass spectrometry. Plasma from four cattle (Bos taurus) of similar physical attributes and genetics were used. Three methods of sEV enrichment were utilised: ultracentrifugation (UC), size-exclusion chromatography (SEC), and ultrafiltration (UF). These methods were combined to create four groups for methodological evaluation: UC + SEC, UC + SEC + UF, SEC + UC and SEC + UF. The UC + SEC method yielded the highest number of protein identifications (IDs). The SEC + UC method reduced plasma protein IDs compared to the other methods, but also resulted in the lowest number of protein IDs overall. The UC + SEC + UF method decreased sEV protein ID, particle number, mean and mode particle size, particle yield, and did not improve purity compared to the UC + SEC method. In this study, the UC + SEC method was the best method for sEV protein ID, purity, and overall particle yield. Our data suggest that the method and sequence of sEV enrichment strategy impacts protein ID, which may influence the outcome of biomarker discovery studies.

Tandem Mass Tagging (TMT) Reveals Tissue-Specific Proteome of L4 Larvae of Anisakis simplex s. s.: Enzymes of Energy and/or Carbohydrate Metabolism as Potential Drug Targets in Anisakiasis.

Anisakis simplex s. s. is a parasitic nematode of marine mammals and causative agent of anisakiasis in humans. The cuticle and intestine of the larvae are the tissues most responsible for direct and indirect contact, respectively, of the parasite with the host. At the L4 larval stage, tissues, such as the cuticle and intestine, are fully developed and functional, in contrast to the L3 stage. As such, this work provides for the first time the tissue-specific proteome of A. simplex s. s. larvae in the L4 stage. Statistical analysis (FC ≥ 2; p-value ≤ 0.01) showed that 107 proteins were differentially regulated (DRPs) between the cuticle and the rest of the larval body. In the comparison between the intestine and the rest of the larval body at the L4 stage, 123 proteins were identified as DRPs. Comparison of the individual tissues examined revealed a total of 272 DRPs, with 133 proteins more abundant in the cuticle and 139 proteins more abundant in the intestine. Detailed functional analysis of the identified proteins was performed using bioinformatics tools. Glycolysis and the tricarboxylic acid cycle were the most enriched metabolic pathways by cuticular and intestinal proteins, respectively, in the L4 stage of A. simplex s. s. The presence of two proteins, folliculin (FLCN) and oxoglutarate dehydrogenase (OGDH), was confirmed by Western blot, and their tertiary structure was predicted and compared with other species. In addition, host–pathogen interactions were identified, and potential new allergens were predicted. The result of this manuscript shows the largest number of protein identifications to our knowledge using proteomics tools for different tissues of L4 larvae of A. simplex s. s. The identified tissue-specific proteins could serve as targets for new drugs against anisakiasis.

Simple, efficient and thorough shotgun proteomic analysis with PatternLab V.

Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE. It is freely available at http://www.patternlabforproteomics.org .

High-Throughput, Comprehensive Single-Cell Proteomic Analysis of Xenopus laevis Embryos at the 50-Cell Stage Using a Microplate-Based MICROFASP System.

We report both the design of a high-throughput MICROFASP (a miniaturized filter aided sample preparation) system and its use for the comprehensive proteomic analysis of single blastomeres isolated from 50-cell stage Xenopus laevis embryos (∼200 ng of yolk-free protein/blastomere). A single run of the MICROFASP system was used to process 146 of these blastomeres in parallel. Three samples failed to generate signals presumably due to membrane clogging. Two cells were lost due to operator error. Of the surviving samples, 32 were analyzed using a Q Exactive HF mass spectrometer in survey experiments (data not included). The 109 remaining blastomeres were analyzed using a capillary LC-ESI-MS/MS system coupled to an Orbitrap Fusion Lumos mass spectrometer, which identified a total of 4189 protein groups and 40,998 unique peptides. On average, 3468 ± 229 protein groups and 14,525 ± 2437 unique peptides were identified from each blastomere, which is the highest throughput and deepest proteome coverage to date of single blastomeres at this stage of development. We also compared two dissociation buffers, Newport and calcium-magnesium-free (CMFM) buffers; the two buffers generated similar numbers of protein identifications (3615 total protein IDs from use of the Newport dissociation buffer and 3671 total protein IDs from use of the CMFM buffer).

Optimization of proteomics sample preparation for identification of host and bacterial proteins in mouse feces.

Bottom-up proteomics is a powerful method for the functional characterization of mouse gut microbiota. To date, most of the bottom-up proteomics studies of the mouse gut rely on limited amounts of fecal samples. With mass-limited samples, the performance of such analyses is highly dependent on the protein extraction protocols and contaminant removal strategies. Here, protein extraction protocols (using different lysis buffers) and contaminant removal strategies (using different types of filters and beads) were systematically evaluated to maximize quantitative reproducibility and the number of identified proteins. Overall, our results recommend a protein extraction method using a combination of sodium dodecyl sulfate (SDS) and urea in Tris-HCl to yield the greatest number of protein identifications. These conditions led to an increase in the number of proteins identified from gram-positive bacteria, such as Firmicutes and Actinobacteria, which is a challenging task. Our analysis further confirmed these conditions led to the extraction of non-abundant bacterial phyla such as Proteobacteria. In addition, we found that, when coupled to our optimized extraction method, suspension trap (S-Trap) outperforms other contaminant removal methods by providing the most reproducible method while producing the greatest number of protein identifications. Overall, our optimized sample preparation workflow is straightforward and fast, and requires minimal sample handling. Furthermore, our approach does not require high amounts of fecal samples, a vital consideration in proteomics studies where mice produce smaller amounts of feces due to a particular physiological condition. Our final method provides efficient digestion of mouse fecal material, is reproducible, and leads to high proteomic coverage for both host and microbiome proteins.

Proteomic characterization of pilot scale hot-water extracts from the industrial carrageenan red seaweed Eucheuma denticulatum

Seaweeds have a long history as a resource for polysaccharides/hydrocolloids extraction for use in the food industry due to their functionality as stabilizing agents. In addition to the carbohydrate content, seaweeds also contains a significant amount of protein, which may find application in food and feed. Here, we present a novel combination of transcriptomics, proteomics, and bioinformatics to determine the protein composition in two pilot-scale extracts from Eucheuma denticilatum (Spinosum) obtained via hot-water extraction. Although the quality of extracted protein appeared quite poor based on SDS-PAGE analysis, extracts were characterized by qualitative and quantitative proteomics using LC-MS/MS and a de-novo transcriptome assembly for construction of a suitable protein database. A novel concept of length-normalization for relative quantification of sub-optimal protein extracts with partial, non-specific digestion is introduced and validated against conventional methods for relative quantification of proteins. Despite a limited number of protein identifications due to poor protein quality, our data suggest that the majority of quantified protein in the extracts (>75%) is constituted by merely three previously uncharacterized proteins. Putative subcellular localization for the quantified proteins was determined by bioinformatic prediction using several predictors, and by correlating with the expected copy number from the transcriptome analysis, we find that the extracts appear highly enriched in extracellular proteins. This implies that the extraction method used predominantly extracts extracellular proteins, and thus appear ineffective for cellular disruption and subsequent release of intracellular proteins. Nevertheless, the highly abundant proteins may be potential substrates for targeted hydrolysis and release of bioactive peptides. Ultimately, this study highlight the potential of quantitative proteomics for characterization of alternative protein sources intended for use in foods and evaluating protein extraction process efficiency through novel combinations with bioinformatic analysis.

Global Comparative Label-Free Yeast Proteome Analysis by LC-MS/MS After High-pH Reversed-Phase Peptide Fractionation Using Solid-Phase Extraction Cartridges.

Discovery-driven comparative proteomics employing the bottom-up strategy with label-free quantification on high-resolution mass analyzers like an Orbitrap in a hybrid instrument has the capacity to reveal unique biological processes in the context of plant metabolic engineering. However, proteins are very heterogeneous in nature with a wide range of expression levels, and overall coverage may be suboptimal regarding both the number of protein identifications and sequence coverage of the identified proteins using conventional data-dependent acquisitions without sample fractionation before online nanoflow liquid chromatography-mass spectrometry (LC-MS) and tandem mass spectrometry (MS/MS). In this chapter, we detail a simple and robust method employing high-pH reversed-phase (HRP) peptide fractionation using solid-phase extraction cartridges for label-free proteomic analyses. Albeit HRP fractionation separates peptides according to their hydrophobicity like the subsequent nanoflow gradient reversed-phased LC relying on low pH mobile phase, the two methods are orthogonal. Presented here as a protocol with yeast (Saccharomyces cerevisiae) as a frequently used model organism and hydrogen peroxide to exert cellular stress and survey its impact compared to unstressed control as an example, the described workflow can be adapted to a wide range of proteome samples for applications to plant metabolic engineering research.

Comparison of Sample Preparation Methods for Shotgun Proteomic Studies in Aquaculture Species.

Proteomics has been recently introduced in aquaculture research, and more methodological studies are needed to improve the quality of proteomics studies. Therefore, this work aims to compare three sample preparation methods for shotgun LC–MS/MS proteomics using tissues of two aquaculture species: liver of turbot Scophthalmus maximus and hepatopancreas of Mediterranean mussel Mytilus galloprovincialis. We compared the three most common sample preparation workflows for shotgun analysis: filter-aided sample preparation (FASP), suspension-trapping (S-Trap), and solid-phase-enhanced sample preparations (SP3). FASP showed the highest number of protein identifications for turbot samples, and S-Trap outperformed other methods for mussel samples. Subsequent functional analysis revealed a large number of Gene Ontology (GO) terms in turbot liver proteins (nearly 300 GO terms), while fewer GOs were found in mussel proteins (nearly 150 GO terms for FASP and S-Trap and 107 for SP3). This result may reflect the poor annotation of the genomic information in this specific group of animals. FASP was confirmed as the most consistent method for shotgun proteomic studies; however, the use of the other two methods might be important in specific experimental conditions (e.g., when samples have a very low amount of protein).

Narrow Precursor Mass Range for DIA-MS Enhances Protein Identification and Quantification in Arabidopsis.

Data independent acquisition–mass spectrometry (DIA–MS) is becoming widely utilised for robust and accurate quantification of samples in quantitative proteomics. Here, we describe the systematic evaluation of the effects of DIA precursor mass range on total protein identification and quantification. We show that a narrow mass range of precursors (~250 m/z) for DIA–MS enables a higher number of protein identifications. Subsequent application of DIA with narrow precursor range (from 400 to 650 m/z) on an Arabidopsis sample with spike-in known proteins identified 34.7% more proteins than in conventional DIA (cDIA) with a wide precursor range of 400–1200 m/z. When combining several DIA–MS analyses with narrow precursor ranges (i.e., 400–650, 650–900 and 900–1200 m/z), we were able to quantify 10,099 protein groups with a median coefficient of variation of <6%. These findings represent a 54.7% increase in the number of proteins quantified than with cDIA analysis. This is particularly important for low abundance proteins, as exemplified by the six-protein mix spike-in. In cDIA only five out of the six-protein mix were quantified while our approach allowed accurate quantitation of all six proteins.

Benchmarking Non-Targeted Metabolomics Using Yeast-Derived Libraries.

Non-targeted analysis by high-resolution mass spectrometry (HRMS) is an essential discovery tool in metabolomics. To date, standardization and validation remain a challenge. Community-wide accepted cost-effective benchmark materials are lacking. In this work, we propose yeast (Pichia pastoris) extracts derived from fully controlled fermentations for this purpose. We established an open-source metabolite library of >200 identified metabolites based on compound identification by accurate mass, matching retention times, and MS/MS, as well as a comprehensive literature search. The library includes metabolites from the classes of (1) organic acids and derivatives (2) nucleosides, nucleotides, and analogs, (3) lipids and lipid-like molecules, (4) organic oxygen compounds, (5) organoheterocyclic compounds, (6) organic nitrogen compounds, and (7) benzoids at expected concentrations ranges of sub-nM to µM. As yeast is a eukaryotic organism, key regulatory elements are highly conserved between yeast and all annotated metabolites were also reported in the human metabolome database (HMDB). Orthogonal state-of-the-art reversed-phase (RP-) and hydrophilic interaction chromatography mass spectrometry (HILIC-MS) non-targeted analysis and authentic standards revealed that 104 out of the 206 confirmed metabolites were reproducibly recovered and stable over the course of three years when stored at −80 °C. Overall, 67 out of these 104 metabolites were identified with comparably stable areas over all three yeast fermentation and are the ideal starting point for benchmarking experiments. The provided yeast benchmark material enabled not only to test for the chemical space and coverage upon method implementation and developments but also allowed in-house routines for instrumental performance tests. Transferring the quality control strategy of proteomics workflows based on the number of protein identification in HeLa extracts, metabolite IDs in the yeast benchmarking material can be used as metabolomics quality control. Finally, the benchmark material opens new avenues for batch-to-batch corrections in large-scale non-targeted metabolomics studies.

Extraction optimization for combined metabolomics, peptidomics, and proteomics analysis of gut microbiota samples.

Multiomic studies are increasingly performed to gain a deeper understanding of molecular processes occurring in a biological system, such as the complex microbial communities (i.e., microbiota) that reside the distal gut. While a combination of metabolomics and proteomics is more commonly used, multiomics studies including peptidomcis characterization are less frequently undertaken. Here, we investigated three different extraction methods, chosen for their previous use in extracting metabolites, peptides, and proteins, and compared their ability to perform metabolomic, peptidomic, and proteomic analysis of mouse cecum content. The methanol/chloroform/water extraction performed the best for metabolomic and peptidomic analysis as it detected the largest number of small molecules and identified the largest number of peptides, but the acidified methanol extraction performed best for proteomics analysis as it had the highest number of protein identifications. The methanol/chloroform/water extraction was further analyzed by identifying metabolites with tandem mass spectrometry (MS/MS) analysis and by gene ontology analysis for the peptide and protein results to provide a multiomics analysis of the gut microbiota.

Proteomics Using Protease Alternatives to Trypsin Benefits from Sequential Digestion with Trypsin

Trypsin is the most used enzyme in proteomics. Nevertheless, proteases with complementary cleavage specificity have been applied in special circumstances. In this work, we analyzed the characteristics of five protease alternatives to trypsin for protein identification and sequence coverage when applied to S. pombe whole cell lysates. The specificity of the protease heavily impacted the number of proteins identified. Proteases with higher specificity led to the identification of more proteins than proteases with lower specificity. However, AspN, GluC, chymotrypsin, and proteinase K largely benefited from being paired with trypsin in sequential digestion, as had been shown by us for elastase before. In the most extreme case, predigesting with trypsin improves the number of identified proteins for proteinase K by 731%. Trypsin predigestion also improved the protein identifications of other proteases, AspN (+62%), GluC (+80%), and chymotrypsin (+21%). Interestingly, the sequential digest with trypsin and AspN yielded even a higher number of protein identifications than digesting with trypsin alone.

Optimization of Synovial Fluid Collection and Processing for NMR Metabolomics and LC-MS/MS Proteomics.

Synovial fluid (SF) is of great interest for the investigation of orthopedic pathologies, as it is in close proximity to various tissues that are primarily altered during these disease processes and can be collected using minimally invasive protocols. Multi-“omic” approaches are commonplace, although little consideration is often given for multiple analysis techniques at sample collection. Nuclear magnetic resonance (NMR) metabolomics and liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics are two complementary techniques particularly suited to the study of SF. However, currently there are no agreed upon standard protocols that are published for SF collection and processing for use with NMR metabolomic analysis. Furthermore, the large protein concentration dynamic range present within SF can mask the detection of lower abundance proteins in proteomics. While combinational ligand libraries (ProteoMiner columns) have been developed to reduce this dynamic range, their reproducibility when used in conjunction with SF, or on-bead protein digestion protocols, has yet to be investigated. Here we employ optimized protocols for the collection, processing, and storage of SF for NMR metabolite analysis and LC-MS/MS proteome analysis, including a Lys-C endopeptidase digestion step prior to tryptic digestion, which increased the number of protein identifications and improved reproducibility for on-bead ProteoMiner digestion.