scholarly journals Trypsin and MALDI matrix pre-coated targets simplify sample preparation for mapping proteomic distributions within biological tissues by imaging mass spectrometry

2016 ◽  
Vol 51 (12) ◽  
pp. 1168-1179 ◽  
Author(s):  
Faizan Zubair ◽  
Paul E. Laibinis ◽  
William G. Swisher ◽  
Junhai Yang ◽  
Jeffrey M. Spraggins ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Imaging Mass Spectrometry ◽  
Biological Tissues ◽  
Maldi Matrix

scholarly journals Modern Methods of Sample Preparation for the Analysis of Oxylipins in Biological Samples

Molecules ◽  
2019 ◽  
Vol 24 (8) ◽  
pp. 1639 ◽  
Author(s):  
Liakh ◽  
Pakiet ◽  
Sledzinski ◽  
Mika
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Biological Samples ◽  
Solid Phase ◽  
Biological Tissues ◽  
Gas Chromatography Mass Spectrometry ◽  
Liquid Liquid Extraction ◽  
Wide Range ◽  
Popular Subject ◽  
Technological Platforms

Oxylipins are potent lipid mediators derived from polyunsaturated fatty acids, which play important roles in various biological processes. Being important regulators and/or markers of a wide range of normal and pathological processes, oxylipins are becoming a popular subject of research; however, the low stability and often very low concentration of oxylipins in samples are a significant challenge for authors and continuous improvement is required in both the extraction and analysis techniques. In recent years, the study of oxylipins has been directly related to the development of new technological platforms based on mass spectrometry (LC–MS/MS and gas chromatography–mass spectrometry (GC–MS)/MS), as well as the improvement in methods for the extraction of oxylipins from biological samples. In this review, we systematize and compare information on sample preparation procedures, including solid-phase extraction, liquid–liquid extraction from different biological tissues.

scholarly journals Optimization of a minimal sample preparation protocol for imaging mass spectrometry of unsectioned juvenile invertebrates

2019 ◽  
Vol 55 (4) ◽  
pp. e4458 ◽  
Author(s):  
Katherine E. Zink ◽  
Denise A. Tarnowski ◽  
Mark J. Mandel ◽  
Laura M. Sanchez
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Imaging Mass Spectrometry ◽  
Minimal Sample

scholarly journals Sample Preparation and Analysis for Imaging Mass Spectrometry

2017 ◽  
pp. 43-50 ◽  
Author(s):  
Genea Edwards ◽  
Annia Mesa ◽  
Robert I. Vazquez-Padron ◽  
Jane-Marie Kowalski ◽  
Sanjoy K. Bhattacharya
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Imaging Mass Spectrometry

scholarly journals Recognizing off-sample mass spectrometry images with machine and deep learning

2019 ◽  
Author(s):  
Katja Ovchinnikova ◽  
Vitaly Kovalev ◽  
Lachlan Stuart ◽  
Theodore Alexandrov
Keyword(s):  
Mass Spectrometry ◽  
Deep Learning ◽  
Imaging Mass Spectrometry ◽  
Metabolite Identification ◽  
Maldi Matrix ◽  
Public Data ◽  
Combinatorial Model ◽  
Downstream Analysis ◽  
Public Datasets ◽  
Imaging Ms

AbstractMotivationImaging mass spectrometry (imaging MS) is a powerful technology for revealing localizations of hundreds of molecules in tissue sections. However, imaging MS data is polluted with off-sample ions caused by caused by sample preparation, particularly by the MALDI matrix application. The presence of the off-sample ion images confounds and hinders metabolite identification and downstream analysis.ResultsWe created a high-quality gold standard of 23238 manually tagged ion images from 87 public datasets from the METASPACE knowledge base. We developed several machine and deep learning methods for recognizing off-sample ion images. Deep residual learning performed the best with the F1 score of 0.97. Spatio-molecular biclustering method achieved the F1 scores of 0.96 and 0.93 in semi- and fully-automated scenarios, respectively. Molecular co-localization method achieved the F1 score of 0.90. We investigated the clusters of the DHB matrix, the most common MALDI matrix, and characterized parameters of a clusters combinatorial model. This work addresses an important issue in imaging MS and illustrates how public data, modern web technologies, and machine and deep learning open novel avenues in imaging MS.Availability and ImplementationData and source code are available at: https://github.com/metaspace2020/[email protected]

Sample Preparation of Biological Tissues and Cells for the Time-of-Flight Secondary Ion Mass Spectrometry

2020 ◽  
Vol 75 (6) ◽  
pp. 701-710
Author(s):  
A. A. Gulin ◽  
V. A. Nadtochenko ◽  
V. N. Pogorelova ◽  
M. Ya. Melnikov ◽  
A. G. Pogorelov
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Secondary Ion Mass Spectrometry ◽  
Time Of Flight ◽  
Biological Tissues ◽  
Ion Mass Spectrometry ◽  
Secondary Ion
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