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

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.

2019 ◽  
Vol 55 (53) ◽  
pp. 7595-7598 ◽  
Author(s):  
Yue Yu ◽  
Si-Hao Zhu ◽  
Fang Yuan ◽  
Xiao-Hui Zhang ◽  
Yan-Ye Lu ◽  
...  

A label-free ultrasensitive method was established for the simultaneous determination of RNA modified nucleotides based on a sheathless capillary electrophoresis–tandem mass spectrometry system and successfully applied to investigate the effects of exposure to nickel ions on RNA epigenetics.


PROTEOMICS ◽  
2011 ◽  
Vol 11 (3) ◽  
pp. 495-500 ◽  
Author(s):  
Divya Krishnamurthy ◽  
Yishai Levin ◽  
Laura W. Harris ◽  
Yagnesh Umrania ◽  
Sabine Bahn ◽  
...  

2021 ◽  
Author(s):  
Caitlin M. A. Simopoulos ◽  
Zhibin Ning ◽  
Leyuan Li ◽  
Mona M Khamis ◽  
Xu Zhang ◽  
...  

Metaproteomics is used to explore the composition, dynamics and function of microbial communities. However, acquiring data by tandem mass spectrometry is time consuming and resource intensive. To mediate this challenge, we present MetaProClust-MS1, a computational framework for microbiome screening developed to reduce the time required for data acquisition by mass spectrometry. In this proof-of-concept study, we tested MetaProClust-MS1 on data acquired using short 15 minute MS1-only mass spectrometry gradients and compared the results to those produced using data acquired by a traditional tandem mass spectrometry approach. MetaProClust-MS1 identified robust microbiome shifts caused by xenobiotics in both datasets. Cluster topologies were also significantly correlated. We demonstrate that MetaProClust-MS1 is able to rapidly screen microbiomes using only short MS1 profiles. This approach can be used to prioritize samples for deep metaproteomic analysis and will be especially useful in large-scale metaproteomic screens or in clinical settings where rapid results are required.


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