scholarly journals DirectMS1: MS/MS-free identification of 1000 proteins of cellular proteomes in 5 minutes

2019 ◽  
Author(s):  
Mark V. Ivanov ◽  
Julia A. Bubis ◽  
Vladimir Gorshkov ◽  
Irina A. Tarasova ◽  
Lev I. Levitsky ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Biomarker Discovery ◽  
Tandem Mass ◽  
Label Free ◽  
Hela Cell Line ◽  
Sequence Coverage ◽  
Proteome Coverage ◽  
Successful Attempt ◽  
Higher Sensitivity ◽  
Lengthy Analysis

AbstractProteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty-cycle. It was always tempting to implement approaches which do not require MS/MS, yet, they were constantly failing in achieving meaningful depth of quantitative proteome coverage within short experimental times, which is particular important for clinical or biomarker discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free and label-free method for bottom-up proteomics. We demonstrate identification of 1000 protein groups for a standard HeLa cell line digest using 5-minute LC gradients. The amount of loaded sample was varied in a range from 1 ng to 500 ng, and the method demonstrated 10-fold higher sensitivity compared with the standard MS/MS-based approach. Due to significantly higher sequence coverage obtained by the developed method, it outperforms all popular MS/MS-based label-free quantitation approaches.

scholarly journals Fast Proteome Identification and Quantification from Data-Dependent Acquisition–Tandem Mass Spectrometry (DDA MS/MS) Using Free Software Tools

2019 ◽  
Vol 2 (1) ◽  
pp. 8 ◽  
Author(s):  
Jesse Meyer
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Gene Knockout ◽  
Tandem Mass ◽  
Label Free ◽  
Software Packages ◽  
Complex Process ◽  
Data Files ◽  
Using Data ◽  
Free Quantification

The identification of nearly all proteins in a biological system using data-dependent acquisition (DDA) tandem mass spectrometry has become routine for organisms with relatively small genomes such as bacteria and yeast. Still, the quantification of the identified proteins may be a complex process and often requires multiple different software packages. In this protocol, I describe a flexible strategy for the identification and label-free quantification of proteins from bottom-up proteomics experiments. This method can be used to quantify all the detectable proteins in any DDA dataset collected with high-resolution precursor scans and may be used to quantify proteome remodeling in response to drug treatment or a gene knockout. Notably, the method is statistically rigorous, uses the latest and fastest freely-available software, and the entire protocol can be completed in a few hours with a small number of data files from the analysis of yeast.

scholarly journals Affinity Capture Enrichment versus Affinity Depletion: A Comparison of Strategies for Increasing Coverage of Low-Abundant Human Plasma Proteins

2020 ◽  
Vol 21 (16) ◽  
pp. 5903
Author(s):  
Nicolai Bjødstrup Palstrøm ◽  
Lars Melholt Rasmussen ◽  
Hans Christian Beck
Keyword(s):  
Mass Spectrometry ◽  
Small Molecule ◽  
Plasma Proteins ◽  
Biomarker Discovery ◽  
Plasma Samples ◽  
Label Free ◽  
Affinity Capture ◽  
Liquid Chromatography Tandem Mass ◽  
Removal System ◽  
Equalization Method

In the present study, we evaluated four small molecule affinity-based probes based on agarose-immobilized benzamidine (ABA), O-Phospho-L-Tyrosine (pTYR), 8-Amino-hexyl-cAMP (cAMP), or 8-Amino-hexyl-ATP (ATP) for their ability to remove high-abundant proteins such as serum albumin from plasma samples thereby enabling the detection of medium-to-low abundant proteins in plasma samples by mass spectrometry-based proteomics. We compared their performance with the most commonly used immunodepletion method, the Multi Affinity Removal System Human 14 (MARS14) targeting the top 14 most abundant plasma proteins and also the ProteoMiner protein equalization method by label-free quantitative liquid chromatography tandem mass spectrometry (LC-MSMS) analysis. The affinity-based probes demonstrated a high reproducibility for low-abundant plasma proteins, down to picomol per mL levels, compared to the Multi Affinity Removal System (MARS) 14 and the Proteominer methods, and also demonstrated superior removal of the majority of the high-abundant plasma proteins. The ABA-based affinity probe and the Proteominer protein equalization method performed better compared to all other methods in terms of the number of analyzed proteins. All the tested methods were highly reproducible for both high-abundant plasma proteins and low-abundant proteins as measured by correlation analyses of six replicate experiments. In conclusion, our results demonstrated that small-molecule based affinity-based probes are excellent alternatives to the commonly used immune-depletion methods for proteomic biomarker discovery studies in plasma. Data are available via ProteomeXchange with identifier PXD020727.

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