High-throughput Protein Identification Research Articles

High-throughput identification of calcium-regulated proteins across diverse proteomes

Calcium ions play important roles in nearly every biological process, yet whole-proteome analysis of calcium effectors has been hindered by a lack of high-throughput, unbiased, and quantitative methods to identify protein-calcium engagement. To address this, we adapted protein thermostability assays in budding yeast, human cells, and mouse mitochondria. Based on calcium-dependent thermostability, we identified 2,884 putative calcium-regulated proteins across human, mouse, and yeast proteomes. These data revealed calcium engagement of signaling hubs and cellular processes, including metabolic enzymes and the spliceosome. Cross-species comparison of calcium-protein engagement and mutagenesis experiments identified residue-specific cation engagement, even within well-known EF-hand domains. Additionally, we found that the dienoyl-coenzyme A (CoA) reductase DECR1 binds calcium at physiologically relevant concentrations with substrate-specific affinity, suggesting direct calcium regulation of mitochondrial fatty acid oxidation. These discovery-based proteomic analyses of calcium effectors establish a key resource to dissect cation engagement and its mechanistic effects across multiple species and diverse biological processes.

Key Proteomics Tools for Fundamental and Applied Microalgal Research.

Microscopic, photosynthetic prokaryotes and eukaryotes, collectively referred to as microalgae, are widely studied to improve our understanding of key metabolic pathways (e.g., photosynthesis) and for the development of biotechnological applications. Omics technologies, which are now common tools in biological research, have been shown to be critical in microalgal research. In the past decade, significant technological advancements have allowed omics technologies to become more affordable and efficient, with huge datasets being generated. In particular, where studies focused on a single or few proteins decades ago, it is now possible to study the whole proteome of a microalgae. The development of mass spectrometry-based methods has provided this leap forward with the high-throughput identification and quantification of proteins. This review specifically provides an overview of the use of proteomics in fundamental (e.g., photosynthesis) and applied (e.g., lipid production for biofuel) microalgal research, and presents future research directions in this field.

High-throughput identification of Toxoplasma gondii effector proteins that target host cell transcription.

Intracellular pathogens and other endosymbionts reprogram host cell transcription to suppress immune responses and recalibrate biosynthetic pathways. This reprogramming is critical in determining the outcome of infection or colonization. We combine pooled CRISPR knockout screening with dual host-microbe single-cell RNA sequencing, a method we term dual perturb-seq, to identify the molecular mediators of these transcriptional interactions. Applying dual perturb-seq to the intracellular pathogen Toxoplasma gondii, we are able to identify previously uncharacterized effector proteins and directly infer their function from the transcriptomic data. We show that TgGRA59 contributes to the export of other effector proteins from the parasite into the host cell and identify an effector, TgSOS1, that is necessary for sustained host STAT6 signaling and thereby contributes to parasite immune evasion and persistence. Together, this work demonstrates a tool that can be broadly adapted to interrogate host-microbe transcriptional interactions and reveal mechanisms of infection and immune evasion.

Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome.

Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions. IMPORTANCE Metaproteomic analysis can provide valuable insights into the functions of microbial and cellular communities by identifying a broad, untargeted set of proteins. The databases used in the analysis of metaproteomic data influence results by defining what proteins can be identified. Moreover, the size of the database impacts the number of identifications after accounting for false discovery rates (FDRs). Few studies have tested the performance of different strategies for building a protein database to identify proteins from metaproteomic data and those that have largely focused on highly diverse microbial communities. We tested a range of databases on CVL samples and found that a hybrid sample-matched approach, using publicly available proteins from organisms present in the samples, as well as proteins translated from metagenomic sequencing of the samples, had the best performance. However, our results also suggest that public sequence databases will continue to improve as more bacterial genomes are published.

MsImpute: Estimation of Missing Peptide Intensity Data in Label-Free Quantitative Mass Spectrometry

Mass spectrometry (MS) enables high-throughput identification and quantification of proteins in complex biological samples and can provide insights into the global function of biological systems. Label-free quantification is cost-effective and suitable for the analysis of human samples. Despite rapid developments in label-free data acquisition workflows, the number of proteins quantified across samples can be limited by technical and biological variability. This variation can result in missing values which can in turn challenge downstream data analysis tasks. General purpose or gene expression-specific imputation algorithms are widely used to improve data completeness. Here, we propose an imputation algorithm designated for label-free MS data that is aware of the type of missingness affecting data. On published datasets acquired by data-dependent and data-independent acquisition workflows with variable degrees of biological complexity, we demonstrate that the proposed missing value estimation procedure by barycenter computation competes closely with the state-of-the-art imputation algorithms in differential abundance tasks while outperforming them in the accuracy of variance estimates of the peptide abundance measurements, and better controls the false discovery rate in label-free MS experiments. The barycenter estimation procedure is implemented in the msImpute software package and is available from the Bioconductor repository.

Quantitative proteomic analysis shows involvement of the p38 MAPK pathway in bovine parainfluenza virus type 3 replication

BackgroundBovine parainfluenza virus type 3 (BPIV3) infection often causes respiratory tissue damage and immunosuppression and further results in bovine respiratory disease complex (BRDC), one of the major diseases in dairy cattle, caused huge economical losses every year. However, the pathogenetic and immunoregulatory mechanisms involved in the process of BPIV3 infection remain unknown. However, the pathogenetic and immunoregulatory mechanisms involved in the process of BPIV3 infection remain unknown. Proteomics is a powerful tool for high-throughput identification of proteins, which has been widely used to understand how viruses interact with host cells.MethodsIn the present study, we report a proteomic analysis to investigate the whole cellular protein alterations of MDBK cells infected with BPIV3. To investigate the infection process of BPIV3 and the immune response mechanism of MDBK cells, isobaric tags for relative and absolute quantitation analysis (iTRAQ) and Q-Exactive mass spectrometry-based proteomics were performed. The differentially expressed proteins (DEPs) involved in the BPIV3 invasion process in MDBK cells were identified, annotated, and quantitated.ResultsA total of 116 proteins, which included 74 upregulated proteins and 42 downregulated proteins, were identified as DEPs between the BPIV3-infected and the mock-infected groups. These DEPs included corresponding proteins related to inflammatory response, immune response, and lipid metabolism. These results might provide some insights for understanding the pathogenesis of BPIV3. Fluorescent quantitative PCR and western blotting analysis showed results consistent with those of iTRAQ identification. Interestingly, the upregulated protein MKK3 was associated with the p38 MAPK signaling pathway.ConclusionsThe results of proteomics analysis indicated BPIV3 infection could activate the p38 MAPK pathway to promote virus replication.

Microdroplet mass spectrometry: Accelerating reaction and application.

Electrospray ionization (ESI) and desorption electrospray ionization (DESI) are common soft ionization method of mass spectrometry (MS). However, recent studies revealed that some chemical reactions can be induced or greatly accelerated in the sprayed microdroplets compared to the same reaction in the bulk. These open a new area in using microdroplet MS to explore new chemistry and develop new applications. This minireview will introduce microdroplet chemistries and explore various microdroplet techniques most of which are ESI- or DESI-based extensions by incorporating transfer tube, supersonic nebulizing gas, droplet fusion, spray extraction, laser irradiation, or laser ablation for online/offline MS analysis. Potential applications associated with new techniques, including real-time reaction monitoring, high-throughput reaction screening, protein identification, and protein characterization, are also described. Future outlook, such as coupling microdroplet MS with separation techniques, is proposed and discussed.

PIONEER: Pipeline for Generating High-Quality Spectral Libraries for DIA-MS Data.

Data-independent-acquisition mass spectrometry (DIA-MS) is a state-of-the-art proteomic technique for high-throughput identification and quantification of peptides and proteins. Interpretation of DIA-MS data relies on the use of a spectral library, which is optimally created from data acquired from the same samples in data-dependent acquisition (DDA) mode. As DIA-MS quantification relies on the spectral libraries, having a high-quality, non-redundant, and comprehensive spectral library is essential. This article describes the major steps for creating a high-quality spectral library using a combination of multiple complementary search engines. We discuss appropriate strategies to control the false discovery rate for the final spectral library as a result of merging multiple searches. © 2021 The Authors Current Protocols © 2021 Wiley Periodicals LLC. Basic Protocol 1: Searching DDA-MS files with multiple search engines Basic Protocol 2: Merging results from multiple search engines Basic Protocol 3: Creating spectral libraries from merged results Alternate Protocol: Using CLI for automating tasks Support Protocol: Creating concatenated FASTA files.

Photoaffinity labelling strategies for mapping the small molecule-protein interactome.

Nearly all FDA approved drugs and bioactive small molecules exert their effects by binding to and modulating proteins. Consequently, understanding how small molecules interact with proteins at an molecular level is a central challenge of modern chemical biology and drug development. Complementary to structure-guided approaches, chemoproteomics has emerged as a method capable of high-throughput identification of proteins covalently bound by small molecules. To profile noncovalent interactions, established chemoproteomic workflows typically incorporate photoreactive moieties into small molecule probes, which enable trapping of small molecule-protein interactions (SMPIs). This strategy, termed photoaffinity labelling (PAL), has been utilized to profile an array of small molecule interactions, including for drugs, lipids, metabolites, and cofactors. Herein we describe the discovery of photocrosslinking chemistries, including a comparison of the strengths and limitations of implementation of each chemotype in chemoproteomic workflows. In addition, we highlight key examples where photoaffinity labelling has enabled target deconvolution and interaction site mapping.

In vivo mRNA display enables large-scale proteomics by next generation sequencing

Large-scale proteomic methods are essential for the functional characterization of proteins in their native cellular context. However, proteomics has lagged far behind genomic approaches in scalability, standardization, and cost. Here, we introduce in vivo mRNA display, a technology that converts a variety of proteomics applications into a DNA sequencing problem. In vivo-expressed proteins are coupled with their encoding messenger RNAs (mRNAs) via a high-affinity stem-loop RNA binding domain interaction, enabling high-throughput identification of proteins with high sensitivity and specificity by next generation DNA sequencing. We have generated a high-coverage in vivo mRNA display library of the Saccharomyces cerevisiae proteome and demonstrated its potential for characterizing subcellular localization and interactions of proteins expressed in their native cellular context. In vivo mRNA display libraries promise to circumvent the limitations of mass spectrometry-based proteomics and leverage the exponentially improving cost and throughput of DNA sequencing to systematically characterize native functional proteomes.

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion.

Mass spectrometry-based proteomics technologies are prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm, MealTime-MS, that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS' efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

Comprehensive Proteomic Profiling of Pressure Ulcers in Patients with Spinal Cord Injury Identifies a Specific Protein Pattern of Pathology.

Objective: Severe pressure ulcers (PUs) do not respond to conservative wound therapy and need surgical repair. To better understand the pathogenesis and to advance on new therapeutic options, we focused on the proteomic analysis of PU, which offers substantial opportunities to identify significant changes in protein abundance during the course of PU formation in an unbiased manner.Approach: To better define the protein pattern of this pathology, we performed a proteomic approach in which we compare severe PU tissue from spinal cord injury (SCI) patients with control tissue from the same patients.Results: We found 76 proteins with difference in abundance. Of these, 10 proteins were verified as proteins that define the pathology: antithrombin-III, alpha-1-antitrypsin, kininogen-1, alpha-2-macroglobulin, fibronectin, apolipoprotein A-I, collagen alpha-1 (XII) chain, haptoglobin, apolipoprotein B-100, and complement factor B.Innovation: This is the first study to analyze differential abundance protein of PU tissue from SCI patients using high-throughput protein identification and quantification by tandem mass tags followed by liquid chromatography tandem mass spectrometry.Conclusion: Differential abundance proteins are mainly involved in tissue regeneration. These proteins might be considered as future therapeutic options to enhance the physiological response and permit cellular repair of damaged tissue.

Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics.

Shotgun proteomics is the method of choice for high-throughput protein identification; however, robust statistical methods are essential to automatize this task while minimizing the number of false identifications. The standard method for estimating the false discovery rate (FDR) of individual identifications and keeping it below a threshold (typically 1%) is the target-decoy approach. However, numerous works have shown that FDR at the protein level may become much larger than FDR at the peptide level. The development of an appropriate scoring model to identify proteins from their peptides using high-throughput shotgun proteomics is highly needed. In this study, we present a novel protein-level scoring algorithm that uses the scores of the identified peptides and maintains all of the properties expected for a true protein probability. We also present a refinement of the picked method to calculate FDR at the protein level. These algorithms can be used together as a robust identification workflow suitable for large-scale proteomics, and we show that the identification performance of this workflow is superior to that of other widely used methods in several samples and using different search engines. Our protein probability model offers the scientific community an algorithm that is easy to integrate into protein identification workflows for the automated analysis of shotgun proteomics data.

Simulation of single-protein nanopore sensing shows feasibility for whole-proteome identification

Single-molecule techniques for protein sequencing are making headway towards single-cell proteomics and are projected to propel our understanding of cellular biology and disease. Yet, single cell proteomics presents a substantial unmet challenge due to the unavailability of protein amplification techniques, and the vast dynamic-range of protein expression in cells. Here, we describe and computationally investigate the feasibility of a novel approach for single-protein identification using tri-color fluorescence and plasmonic-nanopore devices. Comprehensive computer simulations of denatured protein translocation processes through the nanopores show that the tri-color fluorescence time-traces retain sufficient information to permit pattern-recognition algorithms to correctly identify the vast majority of proteins in the human proteome. Importantly, even when taking into account realistic experimental conditions, which restrict the spatial and temporal resolutions as well as the labeling efficiency, and add substantial noise, a deep-learning protein classifier achieves 97% whole-proteome accuracies. Applying our approach for protein datasets of clinical relevancy, such as the plasma proteome or cytokine panels, we obtain ~98% correct protein identification. This study suggests the feasibility of a method for accurate and high-throughput protein identification, which is highly versatile and applicable.

Proteomics Standards Initiative Extended FASTA Format.

Mass-spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs) in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI extended FASTA format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backward compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff .

High-throughput identification of heavy metal binding proteins from the byssus of chinese green mussel (Perna viridis) by combination of transcriptome and proteome sequencing.

The Byssus, which is derived from the foot gland of mussels, has been proved to bind heavy metals effectively, but few studies have focused on the molecular mechanisms behind the accumulation of heavy metals by the byssus. In this study, we integrated high-throughput transcriptome and proteome sequencing to construct a comprehensive protein database for the byssus of Chinese green mussel (Perna viridis), aiming at providing novel insights into the molecular mechanisms by which the byssus binds to heavy metals. Illumina transcriptome sequencing generated a total of 55,670,668 reads. After filtration, we obtained 53,047,718 clean reads and subjected them to de novo assembly using Trinity software. Finally, we annotated 73,264 unigenes and predicted a total of 34,298 protein coding sequences. Moreover, byssal samples were analyzed by proteome sequencing, with the translated protein database from the foot transcriptome as the reference for further prediction of byssal proteins. We eventually determined 187 protein sequences in the byssus, of which 181 proteins are reported for the first time. Interestingly, we observed that many of these byssal proteins are rich in histidine or cysteine residues, which may contribute to the byssal accumulation of heavy metals. Finally, we picked one representative protein, Pvfp-5-1, for recombinant protein synthesis and experimental verification of its efficient binding to cadmium (Cd2+) ions.

Identification of prothymosin alpha (PTMA) as a biomarker for esophageal squamous cell carcinoma (ESCC) by label-free quantitative proteomics and Quantitative Dot Blot (QDB)

BackgroundEsophageal cancer (EC) is one of the malignant tumors with a poor prognosis. The early stage of EC is asymptomatic, so identification of cancer biomarkers is important for early detection and clinical practice.MethodsIn this study, we compared the protein expression profiles in esophageal squamous cell carcinoma (ESCC) tissues and adjacent normal esophageal tissues from five patients through high-resolution label-free mass spectrometry. Through bioinformatics analysis, we found the differentially expressed proteins of ESCC. To perform the rapid identification of biomarkers, we adopted a high-throughput protein identification technique of Quantitative Dot Blot (QDB). Meanwhile, the QDB results were verified by classical immunohistochemistry.ResultsIn total 2297 proteins were identified, out of which 308 proteins were differentially expressed between ESCC tissues and normal tissues. By bioinformatics analysis, the four up-regulated proteins (PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins (Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin) were selected and validated in ESCC by Western Blot. Furthermore, we performed the QDB and IHC analysis in 64 patients and 117 patients, respectively. The PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. Therefore, we suggest that PTMA might be a potential candidate biomarker for ESCC.ConclusionIn this study, label-free quantitative proteomics combined with QDB revealed that PTMA expression was up-regulated in ESCC tissues, and PTMA might be a potential candidate for ESCC. Since Western Blot cannot achieve rapid and high-throughput screening of mass spectrometry results, the emergence of QDB meets this demand and provides an effective method for the identification of biomarkers.

Middle-down approach: a choice to sequence and characterize proteins/proteomes by mass spectrometry.

Owing to rapid growth in the elucidation of genome sequences of various organisms, deducing proteome sequences has become imperative, in order to have an improved understanding of biological processes. Since the traditional Edman method was unsuitable for high-throughput sequencing and also for N-terminus modified proteins, mass spectrometry (MS) based methods, mainly based on soft ionization modes: electrospray ionization and matrix-assisted laser desorption/ionization, began to gain significance. MS based methods were adaptable for high-throughput studies and applicable for sequencing N-terminus blocked proteins/peptides too. Consequently, over the last decade a new discipline called ‘proteomics’ has emerged, which encompasses the attributes necessary for high-throughput identification of proteins. ‘Proteomics’ may also be regarded as an offshoot of the classic field, ‘biochemistry’. Many protein sequencing and proteomic investigations were successfully accomplished through MS dependent sequence elucidation of ‘short proteolytic peptides (typically: 7–20 amino acid residues), which is called the ‘shotgun’ or ‘bottom-up (BU)’ approach. While the BU approach continues as a workhorse for proteomics/protein sequencing, attempts to sequence intact proteins without proteolysis, called the ‘top-down (TD)’ approach started, due to ambiguities in the BU approach, e.g., protein inference problem, identification of proteoforms and the discovery of posttranslational modifications (PTMs). The high-throughput TD approach (TD proteomics) is yet in its infancy. Nevertheless, TD characterization of purified intact proteins has been useful for detecting PTMs. With the hope to overcome the pitfalls of BU and TD strategies, another concept called the ‘middle-down (MD)’ approach was put forward. Similar to BU, the MD approach also involves proteolysis, but in a restricted manner, to produce ‘longer’ proteolytic peptides than the ones usually obtained in BU studies, thereby providing better sequence coverage. In this regard, special proteases (OmpT, Sap9, IdeS) have been used, which can cleave proteins to produce longer proteolytic peptides. By reviewing ample evidences currently existing in the literature that is predominantly on PTM characterization of histones and antibodies, herein we highlight salient features of the MD approach. Consequently, we are inclined to claim that the MD concept might have widespread applications in future for various research areas, such as clinical, biopharmaceuticals (including PTM analysis) and even for general/routine characterization of proteins including therapeutic proteins, but not just limited to analysis of histones or antibodies.

A Multichannel Gel Electrophoresis and Continuous Fraction Collection Apparatus for High-Throughput Protein Separation and Characterization.

We developed a multichannel gel electrophoresis system that continuously collects fractions as protein bands migrate to the bottom of gel columns. The device uses several short linear gel columns, each of a different percent acrylamide, to achieve a separation power similar to that of a long gradient gel. A "counter-free-flow" elution technique allows continuous and simultaneous fraction collection from multiple channels at low cost. Using the system with SDS-PAGE, 300μg samples of protein can be separated and eluted into 48-96 fractions over a mass range of 10-150kDa in 2.5h. Each eluted protein can be recovered at 50% efficiency or higher in ~500μL. The system can also be used for native gel electrophoresis, but protein aggregation limits the loading capacity to about 50μg per channel and reduces resolution. This system has the potential to be coupled with mass spectrometry to achieve high-throughput protein identification.

Obesity Proteomics: An Update on the Strategies and Tools Employed in the Study of Human Obesity.

Proteomics has become one of the most important disciplines for characterizing cellular protein composition, building functional linkages between protein molecules, and providing insight into the mechanisms of biological processes in a high-throughput manner. Mass spectrometry-based proteomic advances have made it possible to study human diseases, including obesity, through the identification and biochemical characterization of alterations in proteins that are associated with it and its comorbidities. A sizeable number of proteomic studies have used the combination of large-scale separation techniques, such as high-resolution two-dimensional gel electrophoresis or liquid chromatography in combination with mass spectrometry, for high-throughput protein identification. These studies have applied proteomics to comprehensive biochemical profiling and comparison studies while using different tissues and biological fluids from patients to demonstrate the physiological or pathological adaptations within their proteomes. Further investigations into these proteome-wide alterations will enable us to not only understand the disease pathophysiology, but also to determine signature proteins that can serve as biomarkers for obesity and related diseases. This review examines the different proteomic techniques used to study human obesity and discusses its successful applications along with its technical limitations.