scholarly journals Current applications of proteomics: a key and novel approach

2019 ◽  
Vol 6 (6) ◽  
pp. 1953
Author(s):  
Vipin Kumar Sharma ◽  
Ravi Kumar
Keyword(s):  
Selected Reaction Monitoring ◽  
Reaction Monitoring ◽  
Preclinical Research ◽  
Data Independent Acquisition ◽  
Novel Approach ◽  
Parallel Reaction Monitoring ◽  
Recent Developments ◽  
Detection Of Biomarkers ◽  
Microarray Chips ◽  
Urine And Serum

Proteomics represented vital applications of technologies in the identification and quantification of high to moderate proteins (cellular signalling networks) found in biological matrix such as tissues, cells and fluids. Proteomics based technical knowledge is applied and verified in several preclinical research settings such as invention of diagnostic markers for specific disease and have shown to be increased in clinical applications. Extensive studies on proteomics resulted in detection of biomarkers that have been highly advanced in using diseases for cancer, lungs, cardiovascular, renal and neuro-regenerative and Parkinson's disease by introducing human origins for biocompatibility such as urine and serum. Advancement in the proteomic methods is conferring candidate right direction for clinical usage. In this review, recent developments and widely used proteomics approaches such as Mass Spectrometry (MS), Microarray chips are elaborately addressed and also focused merits and demerits of commonly used advanced approaches such as Selected Reaction Monitoring (SRM), Parallel Reaction Monitoring (PRM) and Data Independent Acquisition (DIA) and other used proteomics and that roles, in order to aid clinicians, were also discussed in the light of biomedical applications.

Application of selected reaction monitoring and parallel reaction monitoring for investigation of HL-60 cell line differentiation

2017 ◽  
Vol 23 (4) ◽  
pp. 202-208 ◽  
Author(s):  
Svetlana E Novikova ◽  
Olga V Tikhonova ◽  
Leonid K Kurbatov ◽  
Tatiana E Farafonova ◽  
Igor V Vakhrushev ◽  
...  
Keyword(s):  
Cell Line ◽  
Selected Reaction Monitoring ◽  
Reaction Monitoring ◽  
Parallel Reaction ◽  
Parallel Reaction Monitoring

Combinatory Data-Independent Acquisition and Parallel Reaction Monitoring Method for Deep Profiling of Gangliosides

2020 ◽  
Vol 92 (15) ◽  
pp. 10830-10838
Author(s):  
Hua Li ◽  
Ruilian Xu ◽  
Lijun Yang ◽  
Hemi Luan ◽  
Shili Chen ◽  
...  
Keyword(s):  
Reaction Monitoring ◽  
Parallel Reaction ◽  
Monitoring Method ◽  
Data Independent Acquisition ◽  
Parallel Reaction Monitoring

scholarly journals Detection of Antimicrobial Peptides in Stratum Corneum by Mass Spectrometry

2021 ◽  
Vol 22 (8) ◽  
pp. 4233
Author(s):  
Adrienn Jenei ◽  
Gergő Kalló ◽  
Zsolt Dajnoki ◽  
Krisztián Gáspár ◽  
Andrea Szegedi ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Stratum Corneum ◽  
Skin Diseases ◽  
Skin Barrier ◽  
Tape Stripping ◽  
Reaction Monitoring ◽  
Sample Collection ◽  
Novel Approach ◽  
Parallel Reaction Monitoring ◽  
Immunomodulatory Peptides

Antimicrobial and immunomodulatory peptides (AMPs) are considered as the key players in the maintenance of skin barrier functions. Here, we developed a novel approach for the examination of AMPs in the outermost layer of the epidermis, namely stratum corneum (SC). The SC sample collection by tape stripping was coupled with detection by highly specific and sensitive parallel reaction monitoring (PRM)-based mass spectrometry. We found that hexane-free processing of SC samples produced higher protein yield compared to hexane-based extraction. Of the 18 investigated peptides, 9 could be detected either in healthy or in inflamed skin specimens. Regarding the amount of S100A8, LCN2, LACRT and LYZ significant topographical differences were described among gland poor (GP), sebaceous gland rich (SGR) and apocrine gland rich (AGR) healthy skin regions. We applied a minimally invasive, reproducible approach for sampling, which can be assessed for research and diagnostic purposes and for monitoring the effectiveness of therapies in skin diseases.

scholarly journals Picky – a simple online method designer for targeted proteomics

2017 ◽  
Author(s):  
Henrik Zauber ◽  
Marieluise Kirchner ◽  
Matthias Selbach
Keyword(s):  
Time Window ◽  
Time Windows ◽  
Rapid Analysis ◽  
Design Tool ◽  
Selected Reaction Monitoring ◽  
Spectrometric Analysis ◽  
Reaction Monitoring ◽  
Parallel Reaction ◽  
Elution Time ◽  
Parallel Reaction Monitoring

Targeted proteomic approaches like selected reaction monitoring (SRM) and parallel reaction monitoring (PRM) are increasingly popular because they enable sensitive and rapid analysis of a preselected set of proteins1-3. However, developing targeted assays is tedious and requires the selection, synthesis and mass spectrometric analysis of candidate peptides before the actual samples can be analyzed. The SRMatlas and ProteomeTools projects recently published fragmentation spectra of synthetic peptides covering the entire human proteome4,5. These datasets provide very valuable resources. However, extracting the relevant data for selected proteins of interest is not straightforward. For example, developing scheduled acquisition methods (i.e. analyzing specific peptides in defined elution time windows) is complicated and requires adjustments to specific chromatographic conditions employed. Moreover, the number of peptide candidates to be targeted in parallel often exceeds the analytical abilities of the mass spectrometer. In this case, the key question is which peptides can be omitted without losing too much information. Ideally, a method design tool would automatically select the most informative peptides in each retention time window. Until now, none of the available tools automatically generates such optimized scheduled SRM and PRM methods (Figure S1).

scholarly journals Data-Independent Acquisition and Parallel Reaction Monitoring Mass Spectrometry Identification of Serum Biomarkers for Ovarian Cancer

2017 ◽  
Vol 12 ◽  
pp. 117727191771094 ◽  
Author(s):  
Navin Rauniyar ◽  
Gang Peng ◽  
TuKiet T Lam ◽  
Hongyu Zhao ◽  
Gil Mor ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Reaction Monitoring ◽  
Parallel Reaction ◽  
Reactive Protein ◽  
Data Independent Acquisition ◽  
Clinical Assay ◽  
Parallel Reaction Monitoring ◽  
Literature Searches ◽  
Mass Spectrometry Identification ◽  
Immunological Assays

A data-independent acquisition (DIA)/parallel reaction monitoring (PRM) workflow was implemented to identify improved ovarian cancer biomarkers. Data-independent acquisition on ovarian cancer versus control sera and literature searches identified 50 biomarkers and indicated that apolipoprotein A-IV (ApoA-IV) is the most significantly differentially regulated protein. Parallel reaction monitoring with Targeted Ovarian Cancer Proteome Assay validated differential ApoA-IV expression and quantified 9 other biomarkers. Random Forest (RF) analyses achieved 92.3% classification accuracy and confirmed ApoA-IV as the leading biomarker. Indeed, all samples were classified correctly with an [ApoA-IV] breakpoint. The next best biomarkers were C-reactive protein, transferrin, and transthyretin. The Targeted Ovarian Cancer Proteome Assay suggests that ApoA-IV is a more reliable biomarker than had been determined by immunological assays and it is a better biomarker than ApoA-I, which is in the OVA1 test for ovarian cancer. This research provides a PRM/RF approach together with 4 promising biomarkers to speed the development of a clinical assay for ovarian cancer.

scholarly journals Technical advances in proteomics: new developments in data-independent acquisition

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 419 ◽  
Author(s):  
Alex Hu ◽  
William S. Noble ◽  
Alejandro Wolf-Yadlin
Keyword(s):  
De Novo ◽  
Multiple Reaction Monitoring ◽  
Reaction Monitoring ◽  
Data Independent Acquisition ◽  
Parallel Reaction Monitoring ◽  
Liquid Chromatography Tandem Mass ◽  
Technical Advances ◽  
Precise Quantification ◽  
Spectral Libraries ◽  
Identification And Quantification

The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low‑abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.

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