scholarly journals Towards comprehensive plasma proteomics by orthogonal protease digestion

2021 ◽  
Author(s):  
Andrea Fossati ◽  
Alicia L. Richards ◽  
Kuei-Ho Chen ◽  
Devan Jaganath ◽  
Adithya Cattamanchi ◽  
...  
Keyword(s):  
Protein Identification ◽  
Clinical Proteomics ◽  
Plasma Proteome ◽  
Important Goal ◽  
Comprehensive Overview ◽  
Quantitative Accuracy ◽  
Protease Digestion ◽  
Intracellular Proteins ◽  
Technology Benefits ◽  
Spectral Libraries

AbstractRapid and consistent protein identification across large clinical cohorts is an important goal for clinical proteomics. With the development of data-independent technologies (DIA/SWATH-MS), it is now possible to analyze hundreds of samples with great reproducibility and quantitative accuracy. However, this technology benefits from empirically derived spectral libraries that define the detectable set of peptides and proteins. Here we apply a simple and accessible tip-based workflow for the generation of spectral libraries to provide a comprehensive overview on the plasma proteome in individuals with and without active tuberculosis (TB). To boost protein coverage, we utilized non-conventional proteases such as GluC and AspN together with the gold standard trypsin, identifying more than 30,000 peptides mapping to 3,309 proteins. Application of this library to quantify plasma proteome differences in TB infection recovered more than 400 proteins in 50 minutes of MS-acquisition, including diagnostic Mycobacterium tuberculosis (Mtb) proteins that have previously been detectable primarily by antibody-based assays and intracellular proteins not previously described to be in plasma.

Exploring human plasma proteome strategies: High efficiency in-solution digestion protocol for multi-dimensional protein identification technology

2006 ◽  
Vol 1111 (2) ◽  
pp. 175-191 ◽  
Author(s):  
Qinhua Cindy Ru ◽  
Luwang Andy Zhu ◽  
Richard A. Katenhusen ◽  
Jordan Silberman ◽  
Henry Brzeski ◽  
...  
Keyword(s):  
Human Plasma ◽  
Protein Identification ◽  
High Efficiency ◽  
Plasma Proteome ◽  
Human Plasma Proteome

scholarly journals Data-independent acquisition method for ubiquitinome analysis reveals regulation of circadian biology

2020 ◽  
Author(s):  
Fynn M. Hansen ◽  
Maria C. Tanzer ◽  
Franziska Brüning ◽  
Isabell Bludau ◽  
Brenda A. Schulman ◽  
...  
Keyword(s):  
Large Scale ◽  
Tnf Alpha ◽  
Circadian Cycle ◽  
Cellular Processes ◽  
Data Independent Acquisition ◽  
Protein Ubiquitination ◽  
Quantitative Accuracy ◽  
Protein Receptors ◽  
Spectral Libraries ◽  
Acquisition Method

SUMMARYProtein ubiquitination is involved in virtually all cellular processes. Enrichment strategies employing antibodies targeting ubiquitin-derived diGly remnants combined with mass spectrometry (MS) have enabled investigations of ubiquitin signaling at a large scale. However, so far the power of data independent acquisition (DIA) with regards to sensitivity in single run analysis and data completeness have not yet been explored. We developed a sensitive workflow combining diGly antibody-based enrichment and optimized Orbitrap-based DIA with comprehensive spectral libraries together containing more than 90,000 diGly peptides. This approach identified 35,000 diGly peptides in single measurements of proteasome inhibitor-treated cells – double the number and quantitative accuracy of data dependent acquisition. Applied to TNF-alpha signaling, the workflow comprehensively captured known sites while adding many novel ones. A first systems-wide investigation of ubiquitination of the circadian cycle uncovered hundreds of cycling ubiquitination sites and dozens of cycling ubiquitin clusters within individual membrane protein receptors and transporters, highlighting novel connections between metabolism and circadian regulation.

Considerations for powering a clinical proteomics study: Normal variability in the human plasma proteome

2009 ◽  
Vol 3 (3) ◽  
pp. 394-407 ◽  
Author(s):  
David Jackson ◽  
Athula Herath ◽  
Jonathan Swinton ◽  
David Bramwell ◽  
Rajesh Chopra ◽  
...  
Keyword(s):  
Human Plasma ◽  
Clinical Proteomics ◽  
Plasma Proteome ◽  
Proteomics Study ◽  
Normal Variability ◽  
Human Plasma Proteome

scholarly journals Removing the hidden data dependency of DIA with predicted spectral libraries

2019 ◽  
Author(s):  
B. Van Puyvelde ◽  
S. Willems ◽  
R. Gabriels ◽  
S. Daled ◽  
L. De Clerck ◽  
...  
Keyword(s):  
Data Analysis ◽  
Clinical Proteomics ◽  
Spectrometric Data ◽  
Stochastic Sampling ◽  
Data Independent Acquisition ◽  
Use Of Data ◽  
Analysis Strategies ◽  
Stochastic Data ◽  
Hidden Data ◽  
Spectral Libraries

Data-Independent Acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data-dependent acquisition (DDA) libraries for deep peptide-centric detection. We here show that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter-laboratory comparison.Significance of the StudyData-independent acquisition (DIA) is quickly developing into the most comprehensive strategy to analyse a sample on a mass spectrometer. Correspondingly, a wave of data analysis strategies has followed suit, improving the yield from DIA experiments with each iteration. As a result, a worldwide wave of investments in DIA is already taking place in anticipation of clinical applications. Yet, there is considerable confusion about the most useful and efficient way to handle DIA data, given the plethora of possible approaches with little regard for compatibility and complementarity. In our manuscript, we outline the currently available peptide-centric DIA data analysis strategies in a unified graphic called the DIAmond DIAgram. This leads us to an innovative and easily adoptable approach based on predicted spectral information. Most importantly, our contribution removes what is arguably the biggest bottleneck in the field: the current need for Data Dependent Acquisition (DDA) prior to DIA analysis. Fractionation, stochastic data acquisition, processing and identification all introduce bias in the library. By generating libraries through data independent, i.e. deterministic acquisition, stochastic sampling in the DIA workflow is now fully omitted. This is a crucial step towards increased standardization. Additionally, our results demonstrate that a proteome-wide predicted spectral library can surrogate an exhaustive DDA Pan-Human library that was built based on 331 prior DDA runs.

scholarly journals Influence of the Au Cluster Enhancer on Vibrational Spectra of Nucleotides in MD Simulation of a SERS Sensor

Proceedings ◽  
2020 ◽  
Vol 60 (1) ◽  
pp. 25
Author(s):  
Tatiana Zolotoukhina ◽  
Momoko Yamada ◽  
Shingo Iwakura
Keyword(s):  
Vibrational Spectra ◽  
Protein Identification ◽  
Reaction Coordinates ◽  
Surface Enhanced ◽  
Conformational Variations ◽  
Surface Enhanced Raman ◽  
Enhanced Raman Scattering ◽  
Spectral Libraries ◽  
Eam Potential

Surface-enhanced Raman scattering (SERS) nanoprobes have shown tremendous potential in in vivo imaging. The development of single oligomer resolution in the SERS promotes experiments on DNA and protein identification using SERS as a nanobiosensor. As Raman scanners rely on a multiple spectrum acquisition, the faster imaging in real-time is required. SERS weak signal requires averaging of the acquired spectra that erases information on conformation and interaction. To build spectral libraries, the simulation of measurement conditions and conformational variations for the nucleotides relative to enhancer nanostructures would be desirable. In the molecular dynamic (MD) model of a sensing system, we simulate vibrational spectra of the cytosine nucleotide in FF2/FF3 potential in the dynamic interaction with the Au20 nanoparticles (NP) (EAM potential). Fourier transfer of the density of states (DOS) was performed to obtain the spectra of bonds in reaction coordinates for nucleotides at a resolution 20 to 40 cm−1. The Au20 was optimized by ab initio DFT GGA and relaxed by MD. The optimal localization of nucleotide vs. NP was defined and spectral modes of both components vs. interaction studied. Bond-dependent spectral maps of nucleotide and NP have shown response to interaction. The marker frequencies of the Au20—nucleotide interaction have been evaluated.

scholarly journals A novel computational approach applicable to human microbiome studies – urinary tract microbiome exaple

2015 ◽  
Author(s):  
Antonio Starcevic ◽  
Janko Diminic ◽  
Mario Cindric ◽  
Kristina Perica ◽  
Marina Ceprnja ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Protein Identification ◽  
Rapid Development ◽  
Peptide Mass Fingerprinting ◽  
Mass Spectrometry Data ◽  
Clinical Proteomics ◽  
Tandem Mass ◽  
Peptide Mass ◽  
Speed Accuracy

Peptide mass fingerprinting is a term which describes technique which utilizes ESI or MALDI MS followed by tandem mass spectrometry sequencing. This technique has become a cornerstone for protein identification. Today, applications using peptide mass fingerprinting in biomedical analyses are a major driving force behind its rapid development. However, efficient and accurate analyses of generally big protein tandem mass spectrometry data sets require robust software. In terms of final goal, which is data interpretation, the role of software and underlying algortihms is at least equally important as the technique itself, a fact which is often neglected. High-throughput mass spectrometry instruments can readily generate hundreds of thousands of spectra. This fact combined with the ever growing size of genomic databases imposes tremendous demands for potential successful softvare solutions. In fact, it is the process of comparing large-scale mass spectrometry data with large databases that remains the toughest bottleneck in proteomics. Here we present a completely novel approach based on natural language processing which is not just another improvement of existing approaches, but represents a paradigm shift. It doesn't rely on peak intesity for database peptide matching and it uses newly developed concept of microbial proteome fingerptints for strain/species identification. Since this new algorithm doesn't rely on sequence alignment but instead utilizes a concept of singular proteome fingerprints rather than sets of unrelated peptides, it proposes an elegant solution for this most troubling step in proteome analyses. Abandoning BLAST and other alignment based methods. results in far superior processing speed, accuracy and sensitivity. The above mentioned algortithm can be used to analyse not only proteomes but also metaproteomes coming from mixed microbe communities as in the case presented – human urine samples taken from a hospital. The method itself is completely generic, not developed with any specific platform in mind, which makes it highly versatile, able to turn any existing device into highly efficient metaproteome analyzer without siginificant costs related to purchase of new equipment. This work was funded by HRZZ (Croatian Science Foundation) research project “Clinical proteomics of microorganisms”.

scholarly journals Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Claire Tonry ◽  
Stephen Finn ◽  
John Armstrong ◽  
Stephen R. Pennington
Keyword(s):  
Prostate Cancer ◽  
Disease Management ◽  
Early Stage ◽  
Treatment Options ◽  
Specific Antigen ◽  
Disease Recurrence ◽  
Clinical Proteomics ◽  
Comprehensive Overview ◽  
Clinical Biomarkers ◽  
Effective Manner

AbstractFollowing the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990′s, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3–5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.

scholarly journals Vibrational Spectra of Nucleotides in the Presence of the Au Cluster Enhancer in MD Simulation of a SERS Sensor

Biosensors ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 37
Author(s):  
Tatiana Zolotoukhina ◽  
Momoko Yamada ◽  
Shingo Iwakura
Keyword(s):  
Vibrational Spectra ◽  
Density Functional ◽  
Protein Identification ◽  
Generalized Gradient ◽  
Reaction Coordinates ◽  
Conformational Variations ◽  
Surface Enhanced Raman ◽  
Spectral Libraries ◽  
Eam Potential

Surface-enhanced Raman scattering (SERS) nanoprobes have shown tremendous potential in in vivo imaging. The development of single oligomer resolution in the SERS promotes experiments on DNA and protein identification using SERS as a nanobiosensor. As Raman scanners rely on a multiple spectrum acquisition, faster imaging in real-time is required. SERS weak signal requires averaging of the acquired spectra that erases information on conformation and interaction. To build spectral libraries, the simulation of measurement conditions and conformational variations for the nucleotides relative to enhancer nanostructures would be desirable. In the molecular dynamic (MD) model of a sensing system, we simulate vibrational spectra of the cytosine nucleotide in FF2/FF3 potential in the dynamic interaction with the Au20 nanoparticles (NP) (EAM potential). Fourier transfer of the density of states (DOS) was performed to obtain the spectra of bonds in reaction coordinates for nucleotides at a resolution of 20 to 40 cm−1. The Au20 was optimized by ab initio density functional theory with generalized gradient approximation (DFT GGA) and relaxed by MD. The optimal localization of nucleotide vs. NP was defined and the spectral modes of both components vs. interaction studied. Bond-dependent spectral maps of nucleotide and NP have shown response to interaction. The marker frequencies of the Au20—nucleotide interaction have been evaluated.

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