scholarly journals MetaProClust-MS1: A tool for clustering metaproteomes using rapid MS1 profiling

2021 ◽  
Author(s):  
Caitlin M. A. Simopoulos ◽  
Zhibin Ning ◽  
Leyuan Li ◽  
Mona M Khamis ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Large Scale ◽  
Clinical Settings ◽  
Tandem Mass ◽  
Computational Framework ◽  
Dynamics And Function ◽  
Using Data ◽  
Time Required ◽  
And Function

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.

Data Mining in Protein Identification by Tandem Mass Spectrometry

2011 ◽  
pp. 472-478
Author(s):  
Haipeng Wang
Keyword(s):  
Mass Spectrometry ◽  
Data Mining ◽  
Tandem Mass Spectrometry ◽  
Large Scale ◽  
Protein Identification ◽  
Peptide Fragmentation ◽  
Tandem Mass ◽  
Fragmentation Pattern ◽  
Post Translational Modification ◽  
Challenges And Opportunities

Protein identification (sequencing) by tandem mass spectrometry is a fundamental technique for proteomics which studies structures and functions of proteins in large scale and acts as a complement to genomics. Analysis and interpretation of vast amounts of spectral data generated in proteomics experiments present unprecedented challenges and opportunities for data mining in areas such as data preprocessing, peptide-spectrum matching, results validation, peptide fragmentation pattern discovery and modeling, and post-translational modification (PTM) analysis. This article introduces the basic concepts and terms of protein identification and briefly reviews the state-of-the-art relevant data mining applications. It also outlines challenges and future potential hot spots in this field.

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.

Multiresidue Analysis of Pesticides in Moringa Pods by GC-MS/MS and LC-MS/MS

2020 ◽  
Vol 103 (6) ◽  
pp. 1486-1497
Author(s):  
Anirban Dutta ◽  
Sandip Hingmire ◽  
Kaushik Banerjee
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Ethyl Acetate ◽  
Large Scale ◽  
Tandem Mass ◽  
Multiresidue Analysis ◽  
Method Performance ◽  
Chromatography Tandem Mass Spectrometry ◽  
Ms Analysis ◽  
Accuracy And Precision

Abstract Background Moringa pods are known for their nutritional and health benefits. The cultivation of this crop receives frequent pesticide applications. In the absence of risk assessment data, maximum residue limits of pesticides in this crop are considered at the default level (0.01 mg/kg). However, there exists scarcely any validated method for pesticide residue analysis in this matrix. Objective This study was undertaken to develop and validate a multiresidue method for the simultaneous analysis of multi-class pesticides in moringa pods by gas chromatography-tandem mass spectrometry (GC-MS/MS), and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Method The homogenized sample (10 g) was extracted with acetonitrile (10 mL). The extract was cleaned by dispersive solid-phase extraction using a combination of 50 mg primary secondary amine, 5 mg graphitized carbon black, and 25 mg C18 sorbents, and was directly analyzed by LC-MS/MS. Another portion of the extract was reconstituted in ethyl acetate before GC-MS/MS analysis. The method was validated as per the SANTE/12682/2019 guidelines using GC-MS/MS (180 pesticides) and LC-MS/MS instruments (203 pesticides). Results The method provided a satisfactory analysis of the targeted pesticides with good calibration linearity (r2>0.99), high precision (RSD < 20%), and accuracy (recoveries, 70 to 120%). The reconstitution of the acetonitrile extract in ethyl acetate significantly reduced the matrix effects on GC-MS/MS analysis. The use of matrix-matched standards could correct all recoveries. Conclusions The method offered a large-scale analysis of multi-class pesticides with high accuracy, and precision at 10 ng/g, and higher levels. The method performance complied with the regulatory requirements, and thus, can be implemented in routine testing purposes. Highlights The study reports a validated method for large-scale multiresidue analysis of pesticides in moringa matrix for the first time. The method provided a high throughput analysis of multi-class pesticides with satisfactory selectivity, sensitivity, accuracy, and precision.

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