Maximizing peptide identification events in proteomic workflows using data-dependent acquisition (DDA)

Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP

Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 m/z), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% n-dodecyl-β-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts.

Phosphoproteomic analysis of APP/PS1 mice of Alzheimer's disease by DIA based mass spectrometry analysis with PRM verification

Traditional Chinese medicine has been utilized in China for approximately thousands of years in clinical settings to prevent Alzheimer's disease (AD) and enhance memory, despite the lack of a systematic exploration of its biological underpinnings. Exciting research has corroborated the beneficial effects of tetrahydroxy stilbene glycoside (TSG), an extract derived from Polygonum multiflorum, in delaying learning and memory impairment in a model that mimics AD. Therefore, the primary objective of this study is to investigate the major function of TSG upon protein regulation in AD. Herein, a novel approach, encompassing data independent acquisition (DIA), DIA phosphorylated proteomics, and parallel reaction monitoring (PRM), was utilized to integrate quantitative proteomic data collected from APP/PS1 mouse model exhibiting toxic intracellular aggregation of Aβ. Initially, we deliberated upon both single and multi-dimensional data pertaining to AD model mice. Furthermore, we authenticated disparities in protein phosphorylation quantity and expression, phosphorylation function, and ultimately phosphorylation kinase analysis. In order to validate the results, we utilized PRM ion monitoring technology to identify potential protein or peptide biomarkers. In the mixed samples, targeted detection of 50 target proteins revealed that 26 to 33 target proteins were stably detected by PRM. In summary, our findings provide new candidates for AD biomarker, which have been identified and validated through protein researches conducted on mouse brains. This offers a wealth of potential resources for extensive biomarker validation in neurodegenerative diseases. SIGNIFICANCE: DIA phosphorylated proteomics technique was used to detect and analyze phosphorylated proteins in brain tissues of mice with AD. Data were analyzed by various bioinformatics tools to explore the phosphorylation events and characterize them related to TSG. The results of DIA were further verified by PRM. Besides, we mapped the major metabolite classes emerging from the analyses to key biological pathways implicated in AD to understand the potential roles of the molecules and the interactions in triggering symptom onset and progression of AD. Meanwhile, we clarified that in the context of AD onset and TSG intervention, the changes in proteins, protein phosphorylation, phosphorylation kinases, and the internal connections.

Evaluation of PAC and FASP Performance: DIA-Based Quantitative Proteomic Analysis

The aim of this study was to compare filter-aided sample preparation (FASP) and protein aggregation capture (PAC) starting from a three-species protein mix (Human, Soybean and Pisum sativum) and two different starting amounts (1 and 10 µg). Peptide mixtures were analyzed by data-independent acquisition (DIA) and raw files were processed by three commonly used software: Spectronaut, MaxDIA and DIA-NN. Overall, the highest number of proteins (mean value of 5491) were identified by PAC (10 µg), while the lowest number (4855) was identified by FASP (1 µg). The latter experiment displayed the worst performance in terms of both specificity (0.73) and precision (0.24). Other tested conditions showed better diagnostic accuracy, with specificity values of 0.95-0.99 and precision values between 0.61 and 0.86. In order to provide guidance on the data analysis pipeline, the accuracy diagnostic of three software was investigated: (i) the highest sensitivity was obtained with Spectronaut (median of 0.67) highlighting the ability of Spectronaut to quantify low-abundance proteins, (ii) the best precision value was obtained by MaxDIA (median of 0.84), but with a reduced number of identifications compared to Spectronaut and DIA-NN data, and (iii) the specificity values were similar (between 0.93 and 0.99). The data are available on ProteomeXchange with the identifier PXD044349.

Spectra without stories: reporting 94% dark and unidentified ancient proteomes

Background

Data-dependent, bottom-up proteomics is widely used for identifying proteins and peptides. However, one key challenge is that 70% of fragment ion spectra consistently fail to be assigned by conventional database searching. This 'dark matter' of bottom-up proteomics seems to affect fields where non-model organisms, low-abundance proteins, non-tryptic peptides, and complex modifications may be present. While palaeoproteomics may appear as a niche field, understanding and reporting unidentified ancient spectra require collaborative innovation in bioinformatics strategies. This may advance the analysis of complex datasets.

Methods

14.97 million high-impact ancient spectra published in Nature and Science portfolios were mined from public repositories. Identification rates, defined as the proportion of assigned fragment ion spectra, were collected as part of deposited database search outputs or parsed using open-source python packages.

Results and Conclusions

We report that typically 94% of the published ancient spectra remain unidentified. This phenomenon may be caused by multiple factors, notably the limitations of database searching and the selection of user-defined reference data with advanced modification patterns. These 'spectra without stories' highlight the need for widespread data sharing to facilitate methodological development and minimise the loss of often irreplaceable ancient materials. Testing and validating alternative search strategies, such as open searching and de novo sequencing, may also improve overall identification rates. Hence, lessons learnt in palaeoproteomics may benefit other fields grappling with challenging data.

Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package. This innovative tool uses the discovery cohort analysis to select precursors, peptides, and proteins that adhere to established targeted assay criteria. TEAQ was applied to Data-Independent Acquisition MS data from plasma samples acquired on an Orbitrap™ Astral™ MS. Identified precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation from 11-point loading curves under three throughputs, to develop a resource for clinical-grade targeted assays. From a clinical cohort of individuals with inflammatory bowel disease (n=492), TEAQ successfully identified 1116 signature peptides for 327 quantifiable proteins from 1180 identified proteins. Embedding stringent selection criteria adaptable to targeted assay development into the analysis of discovery data will streamline the transition to validation and clinical studies.

Proteomics for Microalgae Extracts by High-Resolution Mass Spectrometry

Two protocols of protein extraction from Arthrospira platensis (spirulina) microalgae to study their proteome by mass spectrometry (MS) are here presented. The first is based on an aqueous buffer solution of Tris-HCl and the second on cold acetone. The identification of proteins was carried out by a bottom-up approach, which involves enzymatic digestion of extracted proteins followed by either matrix-assisted laser desorption ionization with time-of-flight (MALDI-TOF) MS or liquid chromatography (LC) coupled with electrospray ionization (ESI) and Fourier-transform tandem MS. While MALDI-TOF MS allowed for a fast peptide mass fingerprinting (PMF) check yet identifying less than 20 proteins in the extracted samples, the data-dependent acquisitions (DDA) mode of reversed-phase (RP) LC-ESI tandem FTMS/MS separations allowed us to recognize more than one hundred proteins by searching into dedicated spectral libraries. The application of MALDI-TOF MS analysis was found, however, of great support for preliminary investigations of cyanobacteria samples before proceeding with the RPLC-ESI-MS/MS DDA investigation, which definitively allows for a qualitative proteome analysis also of minor spirulina proteins in processed foodstuffs. Although the protein content in spirulina can be influenced by cultivation and environmental conditions, e.g., light intensity, climate, and water/air quality, here the qualitative chemical profiles of the examined samples were characterized by similar composition in high-quality proteins as phycocyanins and phycoerythrins.

IQMMA: Efficient MS1 Intensity Extraction Pipeline Using Multiple Feature Detection Algorithms for DDA Proteomics

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called "features", in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years, each relying on its own matching algorithm. Here, we provide an integrated solution, the intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of untargeted peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of Yeast and E. coli digests spiked at different concentrations into the Human K562 digest used as a background, and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2, and OpenMS FeatureFinder. None of them was found optimal when applied individually to all the data sets employed in this work; however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.

Collision energies: Optimization strategies for bottom-up proteomics

Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.

A Current State of Proteomics in Adult and Pediatric Inflammatory Bowel Diseases: A Systematic Search and Review

Inflammatory bowel diseases (IBD) are systemic immune-mediated conditions with predilection for the gastrointestinal tract and include Crohn's disease and ulcerative colitis. Despite the advances in the fields of basic and applied research, the etiopathogenesis remains largely unknown. As a result, only one third of the patients achieve endoscopic remission. A substantial portion of the patients also develop severe clinical complications or neoplasia. The need for novel biomarkers that can enhance diagnostic accuracy, more precisely reflect disease activity, and predict a complicated disease course, thus, remains high. Genomic and transcriptomic studies contributed substantially to our understanding of the immunopathological pathways involved in disease initiation and progression. However, eventual genomic alterations do not necessarily translate into the final clinical picture. Proteomics may represent a missing link between the genome, transcriptome, and phenotypical presentation of the disease. Based on the analysis of a large spectrum of proteins in tissues, it seems to be a promising method for the identification of new biomarkers. This systematic search and review summarize the current state of proteomics in human IBD. It comments on the utility of proteomics in research, describes the basic proteomic techniques, and provides an up-to-date overview of available studies in both adult and pediatric IBD.

Mass spectrometry-based retina proteomics

A subfield of neuroproteomics, retina proteomics has experienced a transformative growth since its inception due to methodological advances in enabling chemical, biochemical, and molecular biology techniques. This review focuses on mass spectrometry's contributions to facilitate mammalian and avian retina proteomics to catalog and quantify retinal protein expressions, determine their posttranslational modifications, as well as its applications to study the proteome of the retina in the context of biology, health and diseases, and therapy developments.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

Clinical application of serum-based proteomics technology in human tumor research

The rapid development of proteomics technology in the past decades has led to further human understanding of tumor research, and in some ways, the technology plays a very important supporting role in the early detection of tumors. Human serum has been shown to contain a variety of proteins closely related to life activities, and the dynamic change in proteins can often reflect the physiological and pathological conditions of the body. Serum has the advantage of easy extraction, so the application of proteomics technology in serum has become a hot spot and frontier area for the study of malignant tumors. However, there are still many difficulties in the standardized use of proteomic technologies, which inevitably limit the clinical application of proteomic technologies due to the heterogeneity of human proteins leading to incomplete whole proteome populations, in addition to most serum protein markers being now not highly specific in aiding the early detection of tumors. Nevertheless, further development of proteomics technologies will greatly increase our understanding of tumor biology and help discover more new tumor biomarkers with specificity that will enable medical technology.

Data-Independent Acquisition (DIA) Is Superior for High Precision Phospho-Peptide Quantification in Magnaporthe oryzae

The dynamic interplay of signaling networks in most major cellular processes is characterized by the orchestration of reversible protein phosphorylation. Consequently, analytic methods such as quantitative phospho-peptidomics have been pushed forward from a highly specialized edge-technique to a powerful and versatile platform for comprehensively analyzing the phosphorylation profile of living organisms. Despite enormous progress in instrumentation and bioinformatics, a high number of missing values caused by the experimental procedure remains a major problem, due to either a random phospho-peptide enrichment selectivity or borderline signal intensities, which both cause the exclusion for fragmentation using the commonly applied data dependent acquisition (DDA) mode. Consequently, an incomplete dataset reduces confidence in the subsequent statistical bioinformatic processing. Here, we successfully applied data independent acquisition (DIA) by using the filamentous fungus Magnaporthe oryzae as a model organism, and could prove that while maintaining data quality (such as phosphosite and peptide sequence confidence), the data completeness increases dramatically. Since the method presented here reduces the LC-MS/MS analysis from 3 h to 1 h and increases the number of phosphosites identified up to 10-fold in contrast to published studies in Magnaporthe oryzae, we provide a refined methodology and a sophisticated resource for investigation of signaling processes in filamentous fungi.

Dealing with missing values in proteomics data

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics

Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.

Omics and Multi-Omics Analysis for the Early Identification and Improved Outcome of Patients with Psoriatic Arthritis

The definitive diagnosis and early treatment of many immune-mediated inflammatory diseases (IMIDs) is hindered by variable and overlapping clinical manifestations. Psoriatic arthritis (PsA), which develops in ~30% of people with psoriasis, is a key example. This mixed-pattern IMID is apparent in entheseal and synovial musculoskeletal structures, but a definitive diagnosis often can only be made by clinical experts or when an extensive progressive disease state is apparent. As with other IMIDs, the detection of multimodal molecular biomarkers offers some hope for the early diagnosis of PsA and the initiation of effective management and treatment strategies. However, specific biomarkers are not yet available for PsA. The assessment of new markers by genomic and epigenomic profiling, or the analysis of blood and synovial fluid/tissue samples using proteomics, metabolomics and lipidomics, provides hope that complex molecular biomarker profiles could be developed to diagnose PsA. Importantly, the integration of these markers with high-throughput histology, imaging and standardized clinical assessment data provides an important opportunity to develop molecular profiles that could improve the diagnosis of PsA, predict its occurrence in cohorts of individuals with psoriasis, differentiate PsA from other IMIDs, and improve therapeutic responses. In this review, we consider the technologies that are currently deployed in the EU IMI2 project HIPPOCRATES to define biomarker profiles specific for PsA and discuss the advantages of combining multi-omics data to improve the outcome of PsA patients.

DirectMS1Quant: Ultrafast Quantitative Proteomics with MS/MS-Free Mass Spectrometry

Recently, we presented the DirectMS1 method of ultrafast proteome-wide analysis based on minute-long LC gradients and MS1-only mass spectra acquisition. Currently, the method provides the depth of human cell proteome coverage of 2500 proteins at a 1% false discovery rate (FDR) when using 5 min LC gradients and 7.3 min runtime in total. While the standard MS/MS approaches provide 4000-5000 protein identifications within a couple of hours of instrumentation time, we advocate here that the higher number of identified proteins does not always translate into better quantitation quality of the proteome analysis. To further elaborate on this issue, we performed a one-on-one comparison of quantitation results obtained using DirectMS1 with three popular MS/MS-based quantitation methods: label-free (LFQ) and tandem mass tag quantitation (TMT), both based on data-dependent acquisition (DDA) and data-independent acquisition (DIA). For comparison, we performed a series of proteome-wide analyses of well-characterized (ground truth) and biologically relevant samples, including a mix of UPS1 proteins spiked at different concentrations into an Echerichia coli digest used as a background and a set of glioblastoma cell lines. MS1-only data was analyzed using a novel quantitation workflow called DirectMS1Quant developed in this work. The results obtained in this study demonstrated comparable quantitation efficiency of 5 min DirectMS1 with both TMT and DIA methods, yet the latter two utilized a 10-20-fold longer instrumentation time.

Localization of Multiple O-Linked Glycans Exhibited in Isomeric Glycopeptides by Hot Electron Capture Dissociation

We describe a method to obtain a comprehensive profile of multiple glycosylations in glycopeptide isoforms. We detected a wide range of abundances of various O-glycoforms in isomeric glycopeptides using hot electron capture dissociation (hot ECD) in liquid chromatography-tandem mass spectrometry. To capture low abundant glycosylated species, a prototype of a ZenoTOF 7600 system incorporating an efficient electron-activated dissociation device to perform hot ECD was operated in targeted or scheduled high-resolution multiple reaction monitoring workflows. In addition, Zeno trap pulsing was activated to enhance the sensitivity of the time-of-flight mass spectrometer. Sixty-nine O-glycopeptides of the long O-glycopeptides in tryptic bovine fetuin digest were obtained with a relative abundance range from 100 to 0.2%, which included sialylated glycans with Neu5Ac and Neu5Gc.

Clostridium novyi’s Alpha-Toxin Changes Proteome and Phosphoproteome of HEp-2 Cells

C. novyi type A produces the alpha-toxin (TcnA) that belongs to the large clostridial glucosylating toxins (LCGTs) and is able to modify small GTPases by N-acetylglucosamination on conserved threonine residues. In contrast, other LCGTs including Clostridioides difficile toxin A and toxin B (TcdA; TcdB) modify small GTPases by mono-o-glucosylation. Both modifications inactivate the GTPases and cause strong effects on GTPase-dependent signal transduction pathways and the consequent reorganization of the actin cytoskeleton leading to cell rounding and finally cell death. However, the effect of TcnA on target cells is largely unexplored. Therefore, we performed a comprehensive screening approach of TcnA treated HEp-2 cells and analyzed their proteome and their phosphoproteome using LC-MS-based methods. With this data-dependent acquisition (DDA) approach, 5086 proteins and 9427 phosphosites could be identified and quantified. Of these, 35 proteins were found to be significantly altered after toxin treatment, and 1832 phosphosites were responsive to TcnA treatment. By analyzing the TcnA-induced proteomic effects of HEp-2 cells, 23 common signaling pathways were identified to be altered, including Actin Cytoskeleton Signaling, Epithelial Adherens Junction Signaling, and Signaling by Rho Family GTPases. All these pathways are also regulated after application of TcdA or TcdB of C. difficile. After TcnA treatment the regulation on phosphorylation level was much stronger compared to the proteome level, in terms of both strength of regulation and the number of regulated phosphosites. Interestingly, various signaling pathways such as Signaling by Rho Family GTPases or Integrin Signaling were activated on proteome level while being inhibited on phosphorylation level or vice versa as observed for the Role of BRCA1 in DNA Damage Response. ZIP kinase, as well as Calmodulin-dependent protein kinases IV & II, were observed as activated while Aurora-A kinase and CDK kinases tended to be inhibited in cells treated with TcnA based on their substrate regulation pattern.

FLASHIda enables intelligent data acquisition for top-down proteomics to boost proteoform identification counts

The detailed analysis and structural characterization of proteoforms by top-down proteomics (TDP) has gained a lot of interest in biomedical research. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complexity of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. Here, we present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In an analysis of E. coli lysate, FLASHIda increases the number of unique proteoform level identifications from 800 to 1500 or generates a near-identical number of identifications in one third of the instrument time when compared to standard DDA mode. Furthermore, FLASHIda enables sensitive mapping of post-translational modifications and detection of chemical adducts. As a software extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.

A novel, essential trans-splicing protein connects the nematode SL1 snRNP to the CBC-ARS2 complex

Spliced leader trans-splicing is essential for gene expression in many eukaryotes. To elucidate the molecular mechanism of this process, we characterise the molecules associated with the Caenorhabditis elegans major spliced leader snRNP (SL1 snRNP), which donates the spliced leader that replaces the 5' untranslated region of most pre-mRNAs. Using a GFP-tagged version of the SL1 snRNP protein SNA-1 created by CRISPR-mediated genome engineering, we immunoprecipitate and identify RNAs and protein components by RIP-Seq and mass spectrometry. This reveals the composition of the SL1 snRNP and identifies associations with spliceosome components PRP-8 and PRP-19. Significantly, we identify a novel, nematode-specific protein required for SL1 trans-splicing, which we designate SNA-3. SNA-3 is an essential, nuclear protein with three NADAR domains whose function is unknown. Mutation of key residues in NADAR domains inactivates the protein, indicating that domain function is required for activity. SNA-3 interacts with the CBC-ARS2 complex and other factors involved in RNA metabolism, including SUT-1 protein, through RNA or protein-mediated contacts revealed by yeast two-hybrid assays, localisation studies and immunoprecipitations. Our data are compatible with a role for SNA-3 in coordinating trans-splicing with target pre-mRNA transcription or in the processing of the Y-branch product of the trans-splicing reaction.

Degradation of amoxicillin by newly isolated Bosea sp. Ads-6

Amoxicillin (AMX), one of the micro-amount hazardous pollutants, was frequently detected in environments, and of great risks to environments and human health. Microbial degradation is a promising method to eliminate pollutants. In this study, an efficient AMX-degrading strain, Ads-6, was isolated and characterized. Strain Ads-6, belonging to the genus Bosea, was also able to grow on AMX as the sole carbon and nitrogen source, with a removal of ~60% TOC. Ads-6 exhibited strong AMX-degrading ability at initial concentrations of 0.5-2 mM and pH 6-8. Addition of yeast extract could significantly enhance its degrading ability. Many degradation intermediates were identified by HPLC-MS, including new ones such as two phosphorylated products which were firstly defined in AMX degradation. A new AMX degradation pathway was proposed accordingly. Moreover, the results of comparative transcriptomes and proteomes revealed that β-lactamase, L, D-transpeptidase or its homologous enzymes were responsible for the initial degradation of AMX. Protocatechuate branch of the beta-ketoadipate pathway was confirmed as the downstream degradation pathway. These results in the study suggested that Ads-6 is great potential in biodegradation of antibiotics as well as in the bioremediation of contaminated environments.

Dual roles of drug or its metabolite-protein conjugate: Cutting-edge strategy of drug discovery using shotgun proteomics

Many drugs can bind directly to proteins or be bioactivated by metabolizing enzymes to form reactive metabolites (RMs) that rapidly bind to proteins to form drug-protein conjugates or metabolite-protein conjugates (DMPCs). The close relationship between DMPCs and idiosyncratic adverse drug reactions (IADRs) has been recognized; drug discovery teams tend to avoid covalent interactions in drug discovery projects. Covalent interactions in DMPCs can provide high potency and long action duration and conquer the intractable targets, inspiring drug design, and development. This forms the dual role feature of DMPCs. Understanding the functional implications of DMPCs in IADR control and therapeutic applications requires precise identification of these conjugates from complex biological samples. While classical biochemical methods have contributed significantly to DMPC detection in the past decades, the low abundance and low coverage of DMPCs have become a bottleneck in this field. An emerging transformation toward shotgun proteomics is on the rise. The evolving shotgun proteomics techniques offer improved reproducibility, throughput, specificity, operability, and standardization. Here, we review recent progress in the systematic discovery of DMPCs using shotgun proteomics. Furthermore, the applications of shotgun proteomics supporting drug development, toxicity mechanism investigation, and drug repurposing processes are also reviewed and prospected.

The Integration of Proteomics and Metabolomics Data Paving the Way for a Better Understanding of the Mechanisms Underlying Microbial Acquired Drug Resistance

Due to an increase in the overuse of antimicrobials and accelerated incidence of drug resistant pathogens, antimicrobial resistance has become a global health threat. In particular, bacterial antimicrobial resistance, in both hospital and community acquired transmission, have been found to be the leading cause of death due to infectious diseases. Understanding the mechanisms of bacterial drug resistance is of clinical significance irrespective of hospital or community acquired since it plays an important role in the treatment strategy and controlling infectious diseases. Here we highlight the advances in mass spectrometry-based proteomics impact in bacterial proteomics and metabolomics analysis- focus on bacterial drug resistance. Advances in omics technologies over the last few decades now allows multi-omics studies in order to obtain a comprehensive understanding of the biochemical alterations of pathogenic bacteria in the context of antibiotic exposure, identify novel biomarkers to develop new drug targets, develop time-effectively screen for drug susceptibility or resistance using proteomics and metabolomics.

Quantitative proteomics in medication-related osteonecrosis of the jaw: a proof-of-concept study

OBJECTIVE

Medication-related osteonecrosis of the jaw (MRONJ) is a paradoxical effect associated with bone modifying agents (BMAs) and other drugs. Currently no valuable diagnostic or prognosis biomarkers exist. This goal of this research was to study MRONJ related salivary proteome.

MATERIALS AND METHODS

This case-control aimed to study salivary proteome in MRONJ versus control groups i) formed from BMAs consumers and ii) healthy individuals to unravel biomarkers. 38 samples of unstimulated whole saliva (18 MRONJ patients, 10 BMA consumers, and 10 healthy controls) were collected. Proteomic analysis by SWATH-MS coupled to bioinformatics analysis was executed.

RESULTS

586 proteins were identified, 175 proteins showed significant differences among MRONJ versus controls. SWATH-MS revealed differentially expressed proteins among three groups, which have never isolated. These proteins had distinct roles including cell envelope organization, positive regulation of vesicle fusion, positive regulation of receptor binding, or regulation of low-density lipoprotein particle clearance. Integrative analysis prioritised 3 proteins (MMP9, AACT and HBD). Under receiver operating characteristic analysis, this panel discriminated MRONJ with a sensitivity of 90% and a specificity of 78.9%.

CONCLUSION

These findings may inform of a novel biomarker panel for MRONJ prediction or diagnosis. Nonetheless, further research is needed to validate this panel.

Proteome and microbiota analyses characterizing dynamic coral-algae-microbe tripartite interactions under simulated rapid ocean acidification

Ocean acidification (OA) is a pressing issue currently and in the future for coral reefs. The importance of maintenance interactions among partners of the holobiont association in the stress response is well appreciated; however, the candidate molecular and microbial mechanisms that underlie holobiont stress resilience or susceptibility remain unclear. Here, to assess the effects of rapid pH change on coral holobionts at both the protein and microbe levels, combined proteomics and microbiota analyses of the scleractinian coral Galaxea fascicularis exposed to three relevant OA scenarios, including current (pH_T = 8.15), preindustrial (pH_T = 8.45) and future IPCC-2100 scenarios (pH_T = 7.85), were conducted. The results demonstrated that pH changes had no significant effect on the physiological calcification rate of G. fascicularis in a 10-day experiment; however, significant differences were recorded in the proteome and 16S profiling. Proteome variance analysis identified some of the core biological pathways in coral holobionts, including coral host infection and immune defence, and maintaining metabolic compatibility involved in energy homeostasis, nutrient cycling, antibiotic activity and carbon budgets of coral-Symbiodiniaceae interactions were key mechanisms in the early OA stress response. Furthermore, microbiota changes indicate substantial microbial community and functional disturbances in response to OA stress, potentially compromising holobiont health and fitness. Our results may help to elucidate many complex mechanisms to describe scleractinian coral holobiont responses to OA and raise interesting questions for future studies.

Data-Independent Acquisition Enables Robust Quantification of 400 Proteins in Non-Depleted Canine Plasma

Mass spectrometry-based plasma proteomics offers a major advance for biomarker discovery in the veterinary field, which has traditionally been limited to quantification of a small number of proteins using biochemical assays. The development of foundational data and tools related to sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry has allowed for quantitative profiling of a significant number of plasma proteins in humans and several animal species. Enabling SWATH in dogs enhances human biomedical research as a model species, and significantly improves diagnostic and disease monitoring capability. In this study, a comprehensive peptide spectral library specific to canine plasma proteome was developed and evaluated using SWATH for protein quantification in non-depleted dog plasma. Specifically, plasma samples were subjected to various orthogonal fractionation and digestion techniques, and peptide fragmentation data corresponding to over 420 proteins was collected. Subsequently, a SWATH-based assay was introduced that leveraged the developed resource and that enabled reproducible quantification of 400 proteins in non-depleted plasma samples corresponding to various disease conditions. The ability to profile the abundance of such a significant number of plasma proteins using a single method in dogs has the potential to accelerate biomarker discovery studies in this species.

Implementation of Clinical Phosphoproteomics and Proteomics for Personalized Medicine

The identification of biomarkers for companion diagnostics is revolutionizing the development of treatments tailored to individual patients in different disease areas including cancer. Precision medicine is most frequently based on the detection of genomic markers that correlate with the efficacy of selected targeted therapies. However, since nongenetic mechanisms also contribute to disease biology, there is a considerable interest of using proteomic techniques as additional source of biomarkers to personalize therapies. In this chapter, we describe label-free mass spectrometry methods for proteomic and phosphoproteomic analysis compatible with routine analysis of clinical samples. We also outline bioinformatic pipelines based on statistical learning that use these proteomics datasets as input to quantify kinase activities and predict drug responses in cancer cells.

Looking deeper into ocular surface health: an introduction to clinical tear proteomics analysis

Ocular surface diseases are becoming more prevalent worldwide. Reasons for this include the ongoing population ageing and increasing use of digital displays, although ophthalmologists have a wide selection of tools, which can be implemented in the evaluation of the ocular surface health, methods, which enable the in-depth study of biological functions are gaining more interest. These new approaches are needed, since the individual responses to ocular surface diseases and treatments can vary from person to person, and the correlations between clinical signs and symptoms are often low. Modern mass spectrometry (MS) methods can produce information on hundreds of tear proteins, which in turn can provide valuable information on the biological effects occurring on the ocular surface. In this review article, we will provide an overview of the different aspects, which are part of a successful tear proteomics study design and equip readers with a better understanding of the methods most suited for their MS-based tear proteomics study in the field of ophthalmology and ocular surface.

Systematic Characterization and Identification of Saikosaponins in Extracts From Bupleurum marginatum var. stenophyllum Using UPLC-PDA-Q/TOF-MS

Saikosaponins comprise a large group of chemical components present in the Bupleurum species that have attracted attention in the field of medicine because of their significant biological activities. Due to the high polarity, structural similarity, and the presence of several isomers of this class of components, their structural identification is extremely challenging. In this study, the mass spectrometric fragmentation pathways, UV spectral features, and chromatographic behavior of different types of saikosaponins were investigated using 24 standard substances. Saikosaponins containing carbonyl groups (C=O) in the aglycone produced fragment ions by loss of 30 Da, and in addition, type IV saikosaponins could produce [aglycone−CH₂OH−OH−H]⁻ and [aglycone−H₂O−H]⁻ fragment ions through neutral losses at positions C₁₆ and C₁₇. The above characteristic ions can be used to identify saikosaponins. More notably, the identification process of saikosaponins was systematically summarized, and using this method, 109 saikosaponins were identified or tentatively characterized from the saikosaponins extract of Bupleurum marginatum var. stenophyllum (BMS) using UPLC-PDA-Q/TOF-MS with both data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes, of which 25 were new compounds and 60 were first discovered from BMS. Further studies revealed that the saikosaponins profiles of BMS, Bupleurum chinense DC (BC), and Bupleurum marginatum Wall. ex DC (BMW) were very similar. This work is of great significance for the basic research of the Bupleurum species and provides strong technical support to solve the resource problems associated with Radix Bupleuri.

Biochemical and Proteomic Characterization, and Pharmacological Insights of Indian Red Scorpion Venom Toxins

The Indian red scorpion (Mesobuthus tamulus) is one of the world's deadliest scorpions, with stings representing a life-threatening medical emergency. This species is distributed throughout the Indian sub-continent, including eastern Pakistan, eastern Nepal, and Sri Lanka. In India, Indian red scorpions are broadly distributed in western Maharashtra, Saurashtra, Kerala, Andhra Pradesh, Tamil Nadu, and Karnataka; however, fatal envenomations have been recorded primarily in the Konkan region of Maharashtra. The Indian red scorpion venom proteome comprises 110 proteins belonging to 13 venom protein families. The significant pharmacological activity is predominantly caused by the low molecular mass non-enzymatic Na+ and K+ ion channel toxins. Other minor toxins comprise 15.6% of the total venom proteome. Indian red scorpion stings induce the release of catecholamine, which leads to pathophysiological abnormalities in the victim. A strong correlation has been observed between venom proteome composition and local (swelling, redness, heat, and regional lymph node involvement) and systemic (tachycardia, mydriasis, hyperglycemia, hypertension, toxic myocarditis, cardiac failure, and pulmonary edema) manifestations. Immediate administration of antivenom is the preferred treatment for Indian red scorpion stings. However, scorpion-specific antivenoms have exhibited poor immunorecognition and neutralization of the low molecular mass toxins. The proteomic analysis also suggests that Indian red scorpion venom is a rich source of pharmacologically active molecules that may be envisaged as drug prototypes. The following review summarizes the progress made towards understanding the venom proteome of the Indian red scorpion and addresses the current understanding of the pathophysiology associated with its sting.

Application of Mass Spectrometry-Based Proteomics to Barley Research

Barley (Hordeum vulgare) is the fourth most cultivated crop in the world in terms of production volume, and it is also the most important raw material of the malting and brewing industries. Barley belongs to the grass (Poaceae) family and plays an important role in food security and food safety for both humans and livestock. With the global population set to reach 9.7 billion by 2050, but with less available and/or suitable land for agriculture, the use of biotechnology tools in breeding programs are of considerable importance in the quest to meet the growing food gap. Proteomics as a member of the "omics" technologies has become popular for the investigation of proteins in cereal crops and particularly barley and its related products such as malt and beer. This technology has been applied to study how proteins in barley respond to adverse environmental conditions including abiotic and/or biotic stresses, how they are impacted during food processing including malting and brewing, and the presence of proteins implicated in celiac disease. Moreover, proteomics can be used in the future to inform breeding programs that aim to enhance the nutritional value and broaden the application of this crop in new food and beverage products. Mass spectrometry analysis is a valuable tool that, along with genomics and transcriptomics, can inform plant breeding strategies that aim to produce superior barley varieties. In this review, recent studies employing both qualitative and quantitative mass spectrometry approaches are explored with a focus on their application in cultivation, manufacturing, processing, quality, and the safety of barley and its related products.

Fragmentation and Mobility Separation of Peptide and Protein Ions in a Trapped-Ion Mobility Device

Ion mobility separations (IMS) have increasingly been coupled with mass spectrometry to increase peak capacity and deconvolute complex mass spectra in proteomics workflows. IMS separations can be integrated prior to or following the collisional activation step. Post-activation IMS separations have demonstrated many advantages, yet few instrument platforms are capable of this feat. Here, we present the fragmentation of peptide ions within a commercially available trapped-ion mobility spectrometry device. Fragmentation is initiated prior to mobility analysis enabling the separation of generated product ions. The added separation step deconvolutes product ion spectra and permits improved annotation of product ions. Furthermore, we demonstrate the isolation and fragmentation of mobility separated product ions with the downstream quadrupole and collisional cell. When applied to melittin and ubiquitin, this ion mobility assisted pseudo-MS³ fragmentation approach generates sequence coverage ∼50% greater than that of typical MS² analyses. We envision this ion-mobility-assisted fragmentation technique as the foundation of a powerful new pseudo-MS³ workflow for application toward middle- or top-down proteomics.

Quantitative Mass Spectrometry-Based Proteomics: An Overview

In recent decades, mass spectrometry has moved more than ever before into the front line of protein-centered research. After being established at the qualitative level, the more challenging question of quantification of proteins and peptides using mass spectrometry has become a focus for further development. In this chapter, we discuss and review actual strategies and problems of the methods for the quantitative analysis of peptides, proteins, and finally proteomes by mass spectrometry. The common themes, the differences, and the potential pitfalls of the main approaches are presented in order to provide a survey of the emerging field of quantitative, mass spectrometry-based proteomics.

Bringing New Methods to the Seed Proteomics Platform: Challenges and Perspectives

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins, and carbohydrates in their seeds, distinctly dominate modern agriculture, thus play an essential role in food industry and fuel production. Therefore, seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, seed development and germination as well as age- and stress-related alterations in seed vigor, longevity, nutritional value, and safety can be addressed by a broad panel of analytical, biochemical, and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes accompanying plant development, senescence, and response to biotic or abiotic stress. Among individual post-genomic methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. During the recent decades, multiple methodological advances were introduced in different branches of life science, although only some of them were established in seed proteomics so far. Therefore, here we discuss main methodological approaches already employed in seed proteomics, as well as those still waiting for implementation in this field of plant research, with a special emphasis on sample preparation, data acquisition, processing, and post-processing. Thereby, the overall goal of this review is to bring new methodologies emerging in different areas of proteomics research (clinical, food, ecological, microbial, and plant proteomics) to the broad society of seed biologists.

Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.

Mapping Taste-Relevant Food Peptidomes by Means of Sequential Window Acquisition of All Theoretical Fragment Ion-Mass Spectrometry

During the last few years, key taste-active compounds have been isolated and identified by means of a combination of a time- and lab-consuming successive fractionation and sensory characterization. Because the peptidome of fermented, protein-rich food is very complex, new strategies are necessary to accelerate the identification of taste-active peptides. In this study, two advanced mass spectrometric approaches were developed to comprehensively map the bitter tasting peptidome of fermented foods by data-independent acquisition (DIA) using sequential window acquisition of all theoretical fragment ion-mass spectrometry (SWATH-MS) and an in silico-assisted triple quadrupole (QQQ)-based targeted proteomics approach, separately. Application of both techniques on two fresh cheese samples as well as on crude medium-pressure liquid chromatography fractions exhibiting intense bitter taste, followed by filtering the hydrophobic target peptides (Q value of ≥1200 cal/mol) showing a signal-to-noise ratio of ≥10 and a fold change of ≥3 when comparing the less bitter to the more bitter cheese sample, revealed the candidate bitter peptides, which were then validated by means of synthetic reference peptides and human sensory evaluation. The bitter peptides were then quantitated in the fresh cheese samples as well as in a series of dairy products by means of QQQ-MS and SWATH-MS, separately. Although the QQQ-MS method showed 2-80-fold lower limits of quantitation (LOQ), the SWATH-MS method could be shown for the first time to enable the comprehensive quantitation of all sensorially relevant key bitter peptides with LOQs far below the bitter taste recognition concentration of each peptide.

Mass Spectrometric (MS) Analysis of Proteins and Peptides

The human genome is sequenced and comprised of ~30,000 genes, making humans just a little bit more complicated than worms or flies. However, complexity of humans is given by proteins that these genes code for because one gene can produce many proteins mostly through alternative splicing and tissue-dependent expression of particular proteins. In addition, post-translational modifications (PTMs) in proteins greatly increase the number of gene products or protein isoforms. Furthermore, stable and transient interactions between proteins, protein isoforms/proteoforms and PTM-ed proteins (protein-protein interactions, PPI) add yet another level of complexity in humans and other organisms. In the past, all of these proteins were analyzed one at the time. Currently, they are analyzed by a less tedious method: mass spectrometry (MS) for two reasons: 1) because of the complexity of proteins, protein PTMs and PPIs and 2) because MS is the only method that can keep up with such a complex array of features. Here, we discuss the applications of mass spectrometry in protein analysis.

A Critical Review of Bottom-Up Proteomics: The Good, the Bad, and the Future of this Field

Proteomics is the field of study that includes the analysis of proteins, from either a basic science prospective or a clinical one. Proteins can be investigated for their abundance, variety of proteoforms due to post-translational modifications (PTMs), and their stable or transient protein–protein interactions. This can be especially beneficial in the clinical setting when studying proteins involved in different diseases and conditions. Here, we aim to describe a bottom-up proteomics workflow from sample preparation to data analysis, including all of its benefits and pitfalls. We also describe potential improvements in this type of proteomics workflow for the future.

SMfinder: Small Molecules Finder for Metabolomics and Lipidomics Analysis

Metabolomics and lipidomics studies are becoming increasingly popular but available tools for automated data analysis are still limited. The major issue in untargeted metabolomics is linked to the lack of efficient ranking methods allowing accurate identification of metabolites. Herein, we provide a user-friendly open-source software, named SMfinder, for the robust identification and quantification of small molecules. The software introduces an MS2 false discovery rate approach, which is based on single spectral permutation and increases identification accuracy. SMfinder can be efficiently applied to shotgun and targeted analysis in metabolomics and lipidomics without requiring extensive in-house acquisition of standards as it provides accurate identification by using available MS2 libraries in instrument independent manner. The software, downloadable at www.ifom.eu/SMfinder, is suitable for untargeted, targeted, and flux analysis.

Data Dependent-Independent Acquisition (DDIA) Proteomics

Data dependent acquisition (DDA) and data independent acquisition (DIA) are traditionally separate experimental paradigms in bottom-up proteomics. In this work, we developed a strategy combining the two experimental methods into a single LC-MS/MS run. We call the novel strategy data dependent-independent acquisition proteomics, or DDIA for short. Peptides identified from DDA scans by a conventional and robust DDA identification workflow provide useful information for interrogation of DIA scans. Deep learning based LC-MS/MS property prediction tools, developed previously, can be used repeatedly to produce spectral libraries facilitating DIA scan extraction. A complete DDIA data processing pipeline, including the modules for iRT vs RT calibration curve generation, DIA extraction classifier training, and false discovery rate control, has been developed. Compared to another spectral library-free method, DIA-Umpire, the DDIA method produced a similar number of peptide identifications, but nearly twice as many protein group identifications. The primary advantage of the DDIA method is that it requires minimal information for processing its data.

Reconstructing kinase network topologies from phosphoproteomics data reveals cancer-associated rewiring

Understanding how oncogenic mutations rewire regulatory-protein networks is important for rationalizing the mechanisms of oncogenesis and for individualizing anticancer treatments. We report a chemical phosphoproteomics method to elucidate the topology of kinase-signaling networks in mammalian cells. We identified >6,000 protein phosphorylation sites that can be used to infer >1,500 kinase-kinase interactions and devised algorithms that can reconstruct kinase network topologies from these phosphoproteomics data. Application of our methods to primary acute myeloid leukemia and breast cancer tumors quantified the relationship between kinase expression and activity, and enabled the identification of hitherto unknown kinase network topologies associated with drug-resistant phenotypes or specific genetic mutations. Using orthogonal methods we validated that PIK3CA wild-type cells adopt MAPK-dependent circuitries in breast cancer cells and that the kinase TTK is important in acute myeloid leukemia. Our phosphoproteomic signatures of network circuitry can identify kinase topologies associated with both phenotypes and genotypes of cancer cells.

Developing Well-Annotated Species-Specific Protein Databases Using Comparative Proteogenomics

Proteomics is a mass spectrometry-based discipline that aims to analyze proteomes and their functions. Many proteomic studies require well-developed protein databases for reference. However, most proteomes are not well-annotated, aside from model organisms. Techniques like six-frame translation, ab initio gene prediction, and EST databases can aid in maximizing the amount of proteins identified in proteomics experiments, however, each of these has its downfalls. Proteogenomics is a term used to describe the union of proteomics, genomics and transcriptomics to assist in the identification of peptides which would help build better annotated proteome databases. Here, current proteomic and proteogenomic methods will be reviewed, and an example of a comparative proteomics method using lake trout liver samples will be described.

Hair Proteome Variation at Different Body Locations on Genetically Variant Peptide Detection for Protein-Based Human Identification

Human hair contains minimal intact nuclear DNA for human identification in forensic and archaeological applications. In contrast, proteins offer a pathway to exploit hair evidence for human identification owing to their persistence, abundance, and derivation from DNA. Individualizing single nucleotide polymorphisms (SNPs) are often conserved as single amino acid polymorphisms in genetically variant peptides (GVPs). Detection of GVP markers in the hair proteome via high-resolution tandem mass spectrometry permits inference of SNPs with known statistical probabilities. To adopt this approach for forensic investigations, hair proteomic variation and its effects on GVP identification must first be characterized. This research aimed to assess variation in single-inch head, arm, and pubic hair, and discover body location-invariant GVP markers to distinguish individuals. Comparison of protein profiles revealed greater body location-specific variation in keratin-associated proteins and intracellular proteins, allowing body location differentiation. However, robust GVP markers derive primarily from keratins that do not exhibit body location-specific differential expression, supporting GVP identification independence from hair proteomic variation at the various body locations. Further, pairwise comparisons of GVP profiles with 8 SNPs demonstrated greatest interindividual variation and high intraindividual consistency, enabling similar differentiative potential of individuals using single hairs irrespective of body location origin.

Molecular insights into cancer drug resistance from a proteomics perspective

INTRODUCTION

Resistance to chemotherapy and development of specific and effective molecular targeted therapies are major obstacles facing current cancer treatment. Comparative proteomic approaches have been employed for the discovery of putative biomarkers associated with cancer drug resistance and have yielded a number of candidate proteins, showing great promise for both novel drug target identification and personalized medicine for the treatment of drug-resistant cancer. Areas covered: Herein, we review the recent advances and challenges in proteomics studies on cancer drug resistance with an emphasis on biomarker discovery, as well as understanding the interconnectivity of proteins in disease-related signaling pathways. In addition, we highlight the critical role that post-translational modifications (PTMs) play in the mechanisms of cancer drug resistance. Expert opinion: Revealing changes in proteome profiles and the role of PTMs in drug-resistant cancer is key to deciphering the mechanisms of treatment resistance. With the development of sensitive and specific mass spectrometry (MS)-based proteomics and related technologies, it is now possible to investigate in depth potential biomarkers and the molecular mechanisms of cancer drug resistance, assisting the development of individualized therapeutic strategies for cancer patients.

apQuant: Accurate Label-Free Quantification by Quality Filtering

Label-free quantification of shotgun proteomics data is a frequently used strategy, offering high dynamic range, sensitivity, and the ability to compare a high number of samples without additional labeling effort. Here, we present a bioinformatics approach that significantly improves label-free quantification results. We employ Percolator to assess the quality of quantified peptides. This allows to extract accurate and reliable quantitative results based on false discovery rate. Benchmarking our approach on previously published public data shows that it considerably outperforms currently available algorithms. apQuant is available free of charge as a node for Proteome Discoverer.