Proteomics Workflow Research Articles

A review of suspension trapping digestion method in bottom-up proteomics.

The standard bottom-up proteomic workflow is comprised of sample preparation, data acquisition, and data analysis. While the latter two parts have made considerable advances in the last decade, sample preparation has remained an important challenge within the workflow due to the multi-step nature of complex biological samples, and still requires much development. Several sample preparation methods have been developed and used in the last two decades, including in-gel, in-solution, on-bead, filter-aided sample preparation, and suspension trapping, to improve reproducibility, efficiency, scalability, and reduce the handling time of this process. One of the most recent methods developed and applied in proteomics studies in recent years is suspension trapping, which combines rapid detergent removal, reactor-type protein digestion, and peptide clean-up in a tip or spin column. Suspension trapping is a simple, rapid, and reproducible digestion method that can effectively handle proteins in low microgram or sub-microgram amounts. This review discusses the benefits of the suspension trapping digestion method in relation to its development and application in bottom-up proteomics studies. We also discuss recent applications of suspension trapping digestion to different sample types and the features of the suspension trapping digestion method compared with other sample preparation methods.

Optimized data-independent acquisition approach for proteomic analysis at single-cell level

BackgroundSingle-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis.MethodsWe report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow.ResultsWe found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number.ConclusionsOur results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

Extinct species identification from late middle Pleistocene and earlier Upper Pleistocene bone fragments and tools not recognizable from their osteomorphological study by an enhanced proteomics protocol

AbstractAncient preserved molecules offer the opportunity of gaining a deeper knowledge on their biological past. However, the development of a proteomic workflow remains a challenge. The analysis of fossils must involve a low quantity of material to avoid damaging the samples. In this study an enhanced proteomic protocol was applied to 5‐milligram samples of about 130,000‐year‐old mammalian bones ranging from the end of the Middle Pleistocene up to the earlier Upper Pleistocene, excavated from Scladina Cave (Sclayn, Belgium). Using sequence homology with modern sequences, a biological classification was successfully achieved and the associated taxonomic ranks to each bone were identified consistently with the information gained from osteomorphological studies and palaeoenvironmental and palaeodietary data. Amino acid substitutions on collagens were identified, thus providing new information on extinct species sequences and helping in taxonomy‐based clustering. Considering samples with no osteomorphological information, such as two fragments of bone retouchers, proteomics successfully identified the families providing paleontologists new information on these objects. Combining osteomorphology studies and amino acid variations identified by proteomics, one of the retouchers was potentially identified as belonging to the Ursus spelaeus species.

Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics.

Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.

Development of Parallel Reaction Monitoring Mass Spectrometry Assay for the Detection of Human Norovirus Major Capsid Protein.

Human Norwalk viruses (HuNoVs), the most common etiological agents of acute gastroenteritis, are genetically diverse RNA viruses that frequently cause mass food poisoning internationally. Although nucleic acid detection methods, such as reverse transcription–quantitative polymerase chain reaction (RT-qPCR), are the gold standard for the diagnosis of norovirus infection, alternative methods are needed for the specific and sensitive viral protein detection for rapid diagnosis and surveillance. In this study, we developed a robust and high-throughput targeted proteomic assay workflow to directly detect the VP1 major capsid protein of HuNoVs. A parallel reaction monitoring (PRM) assay using a high-resolution mass spectrometer was used to detect representative peptides derived from VP1 in six different HuNoV genotypes. An optimized protocol using synthesized heavy isotope-labeled peptides as internal standards was also used to simultaneously genotype and quantify the VP1 protein in human stool specimens. This method is expected to become a new tool for studying the molecular epidemiology of HuNoV and to shed new light on targeted proteomics in clinical practice.

SubCellBarCode: integrated workflow for robust spatial proteomics by mass spectrometry.

The molecular functions of a protein are defined by its inherent properties in relation to its environment and interaction network. Within a cell, this environment and network are defined by the subcellular location of the protein. Consequently, it is crucial to know the localization of a protein to fully understand its functions. Recently, we have developed a mass spectrometry- (MS) and bioinformatics-based pipeline to generate a proteome-wide resource for protein subcellular localization across multiple human cancer cell lines ( www.subcellbarcode.org ). Here, we present a detailed wet-lab protocol spanning from subcellular fractionation to MS-sample preparation and analysis. A key feature of this protocol is that it includes all generated cell fractions without discarding any material during the fractionation process. We also describe the subsequent quantitative MS-data analysis, machine learning-based classification, differential localization analysis and visualization of the output. For broad applicability, we evaluated the pipeline by using MS data generated by two different peptide pre-fractionation approaches, namely high-resolution isoelectric focusing and high-pH reverse-phase fractionation, as well as direct analysis without pre-fractionation by using long-gradient liquid chromatography-MS. Moreover, an R package covering the dry-lab part of the method was developed and made available through Bioconductor. The method is straightforward and robust, and the entire protocol, from cell harvest to classification output, can be performed within 1-2 weeks. The protocol enables accurate classification of proteins to 15 compartments and 4 neighborhoods, visualization of the output data and differential localization analysis including treatment-induced protein relocalization, condition-dependent localization or cell type-specific localization. The SubCellBarCode package is freely available at https://bioconductor.org/packages/devel/bioc/html/SubCellBarCode.html .

Quantitative proteomics analysis reveals core and variable tick salivary proteins at the tick-vertebrate host interface.

Few studies have examined tick proteomes, how they adapt to their environment, and their roles in the parasite-host interactions that drive tick infestation and pathogen transmission. Here we used a proteomics approach to screen for biologically and immunologically relevant proteins acting at the tick-host interface during tick feeding and, as proof of principle, measured host antibody responses to some of the discovered candidates. We used a label-free quantitative proteomic workflow to study salivary proteomes of (i) wild Ixodes ricinus ticks fed on different hosts, (ii) wild or laboratory ticks fed on the same host, and (iii) adult ticks cofed with nymphs. Our results reveal high and stable expression of several protease inhibitors and other tick-specific proteins under different feeding conditions. Most pathways functionally enriched in sialoproteomes were related to proteolysis, endopeptidase, and amine-binding activities. The generated catalogue of tick salivary proteins enabled the selection of six candidate secreted immunogenic peptides for rabbit immunizations, three of which induced strong and durable antigen-specific antibody responses in rabbits. Furthermore, rabbits exposed to ticks mounted immune responses against the candidate peptides/proteins, confirming their expression at the tick-vertebrate interface. Our approach provides insights into tick adaptation strategies to different feeding conditions and promising candidates for developing antitick vaccines or markers of exposure of vertebrate hosts to tick bites.

Abstract 6348: A cloud-scalable software suite for large-cohort proteogenomics data analysis and visualization

Abstract Comprehensive assessment of the flow of genetic information through multi-omic data integration can reveal the molecular consequences of genetic variation underlying human disease. Next generation sequencing (NGS) is used to identify genetic variants and characterize gene function (e.g. transcriptome and epigenome), while mass spectrometry is used to assess the proteome through characterization of protein abundances, modifications, and interactions. A new plasma profiling platform, the Proteograph࣪ Product Suite, leverages multiple nanoparticles with distinct physiochemical properties to enable deep plasma proteome analyses at scale. Here, we present a cloud-based, data analysis software platform called Proteograph Analysis Suite (PAS) for proteogenomic data analyses through the integration of proteomics data derived from the Proteograph with genomic variant information derived from NGS experiments. PAS features include an experiment data management system, analysis protocols, an analysis setup wizard, and tools for reviewing and visualizing results. PAS can support both Data Independent Analysis (DIA) and Data Dependent Analysis (DDA) proteomics workflows and is compatible with variant call format (vcf) files from NGS workflows to enable personalized database searches. To assess quality of the resulting data PAS includes various metrics like peptide/protein group intensity, protein sequence coverage, relative protein abundance distribution, peptide and protein group counts. Visualizations including principal component analysis, hierarchical clustering, and heatmaps allow intuitive identification of experimental trends. To enable biological insights, differential expression analyses results are reported with interactive visualizations such as volcano plots, protein interaction maps, and protein-set enrichment. From data to insight, PAS provides an easy-to-use and efficient suite of functionality to enable proteogenomic data analysis. Integration of proteomics and genomics data require a variety of tools, many of which require command-line interfaces and operating system-specific requirements that can act as a barrier for researchers to adapt new data analysis tools. Here, we demonstrate the utility of PAS by analyzing samples from the Proteograph NSCLC plasma dataset1. PAS can analyze VCF files generated from NGS pipelines in combination with mass spec data to identify peptide variants using personalized libraries. Using the cloud-based architecture computational tasks are distributed for rapid analysis. The integrated proteogenomics viewers allow variant IDs to be interpreted in the context of genomic coordinates, protein sequence, functional domains and features. Together, these results show the utility of PAS for seamless and fast proteomic data analysis. Reference: 1 Blume, J. E. et al. Nature Communications, 2020 Citation Format: Aaron S. Gajadhar, Margaret K. Donovan, Harsharn Auluck, Yan Berk, Yuandan Lou, Theo Platt, Serafim Batzoglou. A cloud-scalable software suite for large-cohort proteogenomics data analysis and visualization [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 6348.

Proteome profiling of cerebrospinal fluid reveals biomarker candidates for Parkinson’s disease

SummaryParkinson’s disease (PD) is a growing burden worldwide, and there is no reliable biomarker used in clinical routines to date. Cerebrospinal fluid (CSF) is routinely collected in patients with neurological symptoms and should closely reflect alterations in PD patients’ brains. Here, we describe a scalable and sensitive mass spectrometry (MS)-based proteomics workflow for CSF proteome profiling. From two independent cohorts with over 200 individuals, our workflow reproducibly quantifies over 1,700 proteins from minimal CSF amounts. Machine learning determines OMD, CD44, VGF, PRL, and MAN2B1 to be altered in PD patients or to significantly correlate with clinical scores. We also uncover signatures of enhanced neuroinflammation in LRRK2 G2019S carriers, as indicated by increased levels of CTSS, PLD4, and HLA proteins. A comparison with our previously acquired urinary proteomes reveals a large overlap in PD-associated changes, including lysosomal proteins, opening up new avenues to improve our understanding of PD pathogenesis.

Noninvasive proteomic biomarkers for alcohol-related liver disease

Alcohol-related liver disease (ALD) is a major cause of liver-related death worldwide, yet understanding of the three key pathological features of the disease—fibrosis, inflammation and steatosis—remains incomplete. Here, we present a paired liver–plasma proteomics approach to infer molecular pathophysiology and to explore the diagnostic and prognostic capability of plasma proteomics in 596 individuals (137 controls and 459 individuals with ALD), 360 of whom had biopsy-based histological assessment. We analyzed all plasma samples and 79 liver biopsies using a mass spectrometry (MS)-based proteomics workflow with short gradient times and an enhanced, data-independent acquisition scheme in only 3 weeks of measurement time. In plasma and liver biopsy tissues, metabolic functions were downregulated whereas fibrosis-associated signaling and immune responses were upregulated. Machine learning models identified proteomics biomarker panels that detected significant fibrosis (receiver operating characteristic–area under the curve (ROC–AUC), 0.92, accuracy, 0.82) and mild inflammation (ROC–AUC, 0.87, accuracy, 0.79) more accurately than existing clinical assays (DeLong’s test, P < 0.05). These biomarker panels were found to be accurate in prediction of future liver-related events and all-cause mortality, with a Harrell’s C-index of 0.90 and 0.79, respectively. An independent validation cohort reproduced the diagnostic model performance, laying the foundation for routine MS-based liver disease testing.

Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.

Zwitter-ionic monolith-based spintip column coupled with Evosep One liquid chromatography for high-throughput proteomic analysis

A high-throughput proteomic workflow with good sensitivity and reproducibility is highly demanding for proteomic studies of large clinical cohorts. We present a workflow that seamlessly integrates the zwitter-ionic monolith-based spintip (ZIM-Tip) with the Evosep One liquid chromatography system to address this challenge. Disposable ZIM-Tips were prepared with satisfying permeability based on photo-initiated free radical polymerization. Sample preparation steps, including ion-exchange-based protein concentration, reduction, alkylation, and enzymatic digestion, were processed on the ZIM-Tips in 2 h with about 10% sample loss. The peptides recovered from ZIM-Tips were directly loaded on Evotips for desalting and proteomic data acquisition. In one-hour high performance liquid chromatography-MS/MS run, more than 4000 proteins were consistently identified from 1 µg of cell lysate using timsTOF Pro-mass spectrometer in data-dependent acquisition mode (DDA). At least 20 samples with protein amount of 1 µg could be processed each day. Good intra- and inter-day precision in quantification were demonstrated with median coefficient of variation (CV) values of less than 20% and 30%, respectively. The average Pearson correlation coefficients of each two sets of samples are 0.934 and 0.901, respectively. Collectively, the ZIM-Tip technology offers an useful solution for clinical cohort studies with demand for large sample amounts and low sample input while maintaining in-depth proteome coverage.

Infrared Laser Ablation Microsampling for Small Volume Proteomics.

Infrared (IR) laser ablation was used to remove localized tissue regions from which proteins were extracted and processed with a low volume sample preparation workflow for bottom-up proteomics by liquid chromatography tandem mass spectrometry (LC-MS/MS). A polytetrafluoroethylene (PTFE) coated glass slide with 2 mm diameter microwells was used to capture ablated rat brain tissue for in situ protein digestion with submicroliter solution volumes. The resulting peptides were analyzed with LC-MS/MS for protein identification and label-free quantification. The method was used to identify an average of 600, 1350, and 1900 proteins from ablation areas of 0.01, 0.04, and 0.1 mm2, respectively, from a 50 μm thick rat brain tissue section. Differential proteomics of 0.01 mm2 regions captured from cerebral cortex and corpus callosum was accomplished to demonstrate the capabilities of the approach.

Protocol for Increasing the Sensitivity of MS-Based Protein Detection in Human Chorionic Villi.

An important step in the proteomic analysis of missing proteins is the use of a wide range of tissues, optimal extraction, and the processing of protein material in order to ensure the highest sensitivity in downstream protein detection. This work describes a purification protocol for identifying low-abundance proteins in human chorionic villi using the proposed “1DE-gel concentration” method. This involves the removal of SDS in a short electrophoresis run in a stacking gel without protein separation. Following the in-gel digestion of the obtained holistic single protein band, we used the peptide mixture for further LC–MS/MS analysis. Statistically significant results were derived from six datasets, containing three treatments, each from two tissue sources (elective or missed abortions). The 1DE-gel concentration increased the coverage of the chorionic villus proteome. Our approach allowed the identification of 15 low-abundance proteins, of which some had not been previously detected via the mass spectrometry of trophoblasts. In the post hoc data analysis, we found a dubious or uncertain protein (PSG7) encoded on human chromosome 19 according to neXtProt. A proteomic sample preparation workflow with the 1DE-gel concentration can be used as a prospective tool for uncovering the low-abundance part of the human proteome.

SPIN enables high throughput species identification of archaeological bone by proteomics.

Species determination based on genetic evidence is an indispensable tool in archaeology, forensics, ecology, and food authentication. Most available analytical approaches involve compromises with regard to the number of detectable species, high cost due to low throughput, or a labor-intensive manual process. Here, we introduce “Species by Proteome INvestigation” (SPIN), a shotgun proteomics workflow for analyzing archaeological bone capable of querying over 150 mammalian species by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Rapid peptide chromatography and data-independent acquisition (DIA) with throughput of 200 samples per day reduce expensive MS time, whereas streamlined sample preparation and automated data interpretation save labor costs. We confirm the successful classification of known reference bones, including domestic species and great apes, beyond the taxonomic resolution of the conventional peptide mass fingerprinting (PMF)-based Zooarchaeology by Mass Spectrometry (ZooMS) method. In a blinded study of degraded Iron-Age material from Scandinavia, SPIN produces reproducible results between replicates, which are consistent with morphological analysis. Finally, we demonstrate the high throughput capabilities of the method in a high-degradation context by analyzing more than two hundred Middle and Upper Palaeolithic bones from Southern European sites with late Neanderthal occupation. While this initial study is focused on modern and archaeological mammalian bone, SPIN will be open and expandable to other biological tissues and taxa.

A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues.

Mass spectrometry can provide deep coverage of post-translational modifications (PTMs), although enrichment of these modifications from complex biological matrices is often necessary due to their low stoichiometry in comparison to non-modified analytes. Most enrichment workflows of PTMs on peptides in bottom-up proteomics workflows, where proteins are enzymatically digested before the resulting peptides are analyzed, only enrich one type of modification. It is the entire complement of PTMs, however, that leads to biological functions, and enrichment of a single type of PTM may miss such crosstalk of PTMs. PTM crosstalk has been observed between protein glycosylation and phosphorylation, the two most common PTMs in human proteins and also the two most studied PTMs using mass spectrometry workflows. Using the simultaneous enrichment strategy described herein, both PTMs are enriched from post-mortem human pancreatic tissue, a complex biological matrix. Dual-functional Ti(IV)-immobilized metal affinity chromatography is used to separate various forms of glycosylation and phosphorylation simultaneously in multiple fractions in a convenient spin tip-based method, allowing downstream analyses of potential PTM crosstalk interactions. This enrichment workflow for glyco- and phosphopeptides can be applied to various sample types to achieve deep profiling of multiple PTMs and identify potential target molecules for future studies.

Multi‐omics Sample Preparation Workflow for Proteins and DNA Using the Reversible Protein Tag ProMTag

Sample preparation is a crucial first step for both genomics and proteomics workflows. Removal of contaminants such as salts, detergents, and other biologics while maintaining high yields of the desired product is key to reproducible and informative results from these analyses. More and more frequently, these ‐omics technologies are being used in tandem to gain deeper insights into biological processes. However, multi‐omics sample preparation remains tedious and usually requires many steps in multiple different sample preparation workflows.In this study, we present a new multi‐omics workflow for the simultaneous preparation of DNA and protein samples from a single starting cell lysate. We accomplished this using the ProMTag reversible click chemistry technology that allows for reversible modification of the surface of proteins. Using ProMTag we were able to tag proteins in a cell lysate, bind them to ProMTag capture resin, and then precipitate nucleic acids so they also stay with the resin. With the nucleic acids and proteins bound to the resin, we were then able to wash away detergents, salts, and other contaminants. We then eluted the nucleic acids by resolubilizing in a nucleic acid elution buffer. We then were able to reverse the ProMTag by adding the protein elution buffer and eluted the sample in a mass spectrometry (MS) compatible buffer ready for proteomic analysis. Using this workflow we got yields &gt;75% for protein and &gt;90% for DNA. Gel electrophoresis showed a genomic DNA band free of degradation and the 260/280 absorption ratio indicated a pure DNA sample. Whole genome sequencing and MS proteomics analysis were performed and compared to traditional separate sample preparation workflows for DNA and proteins. This work establishes a new, high yield, reproducible workflow for the simultaneous preparation of DNA and proteins for genomics and proteomic analysis from a single starting sample.

Synthetic Soil Aggregates: Bioprinted Habitats for High-Throughput Microbial Metaphenomics.

The dynamics of microbial processes are difficult to study in natural soil, owing to the small spatial scales on which microorganisms operate and to the opacity and chemical complexity of the soil habitat. To circumvent these challenges, we have created a 3D-bioprinted habitat that mimics aspects of natural soil aggregates while providing a chemically defined and translucent alternative culturing method for soil microorganisms. Our Synthetic Soil Aggregates (SSAs) retain the porosity, permeability, and patchy resource distribution of natural soil aggregates—parameters that are expected to influence emergent microbial community interactions. We demonstrate the printability and viability of several different microorganisms within SSAs and show how the SSAs can be integrated into a multi-omics workflow for single SSA resolution genomics, metabolomics, proteomics, lipidomics, and biogeochemical assays. We study the impact of the structured habitat on the distribution of a model co-culture microbial community and find that it is significantly different from the spatial organization of the same community in liquid culture, indicating a potential for SSAs to reproduce naturally occurring emergent community phenotypes. The SSAs have the potential as a tool to help researchers quantify microbial scale processes in situ and achieve high-resolution data from the interplay between environmental properties and microbial ecology.

Endotyping Chronic Obstructive Pulmonary Disease, Bronchiectasis, and the "Chronic Obstructive Pulmonary Disease-Bronchiectasis Association".

Rationale: Bronchiectasis and chronic obstructive pulmonary disease (COPD) are two disease entities with overlapped clinical features, and codiagnosis frequently occurs (termed the "COPD-bronchiectasis association"). Objectives: To investigate the sputum microbiome and proteome in patients with bronchiectasis, COPD, and the COPD-bronchiectasis association with the aim of identifying endotypes that may inform treatment. Methods: Sputum microbiome and protein profiling were carried out using 16S rRNA amplicon sequencing and a label-free proteomics workflow, respectively, in a cohort comprising patients with COPD (n = 43), bronchiectasis (n = 30), and the COPD-bronchiectasis association (n = 48). Results were validated in an independent cohort of 91 patients (n = 28-31 each group) using targeted measurements of inflammatory markers, mucins, and bacterial culture. Measurements and Main Results: Principal component analysis of sputum microbiome and protein profiles showed a partial separation between the COPD and the "COPD-bronchiectasis association" group. Further analyses revealed that patients with the "COPD-bronchiectasis association" had a higher abundance of proteobacteria, higher expression of mucin-5AC and proteins from the "neutrophil degranulation" pathway compared to those with COPD. In contrast, patients with COPD had an elevated expression of mucin-5B and several peptidase inhibitors, higher abundance of common commensal taxa, and a greater microbiome diversity. The profiles of "COPD-bronchiectasis association" and bronchiectasis groups were largely overlapping. Five endotypes were proposed with differential inflammatory, mucin, and microbiological features. The key features related to the "COPD-bronchiectasis association" were validated in an independent cohort. Conclusions: Neutrophilic inflammation, differential mucin expression, and Gram-negative infection are dominant traits in patients with the "COPD-bronchiectasis association."

Human peripheral blood mononuclear cells: A review of recent proteomic applications.

Human peripheral blood mononuclear cells (PBMCs) represent a sentinel blood sample which reacts to different pathophysiological stimuli in the form of immunological responses/immunophenotypic changes. The study of molecular content of PBMCs can provide better understanding of immune processes giving the possibility of monitoring the health conditions of the host organism. Proteomic analysis of PBMCs can achieve mentioned goal as important immune-related biomarkers are easily accessible for analysis. PBMCs have been gaining attention in different research areas including preclinical or clinical investigations. In this review, recent applications of proteomic analysis of PBMCs are described and discussed. Approaches are divided based on different proteomic workflows such as in-gel, in-solution and on-filter modes. The effect of various diseases such as autoimmune, cancer, neurodegenerative, viral, metabolic, and various immune stimulations such as radiation, vaccine, corticosteroids over PBMCs proteome, are described with emphasis on promising protein biomarker candidates.