Automated MALDI Matrix Coating System for Multiple Tissue Samples for Imaging Mass Spectrometry

2012 ◽  
Vol 23 (3) ◽  
pp. 563-569 ◽  
Author(s):  
William P. Mounfield ◽  
Timothy J. Garrett
2016 ◽  
Vol 51 (12) ◽  
pp. 1168-1179 ◽  
Author(s):  
Faizan Zubair ◽  
Paul E. Laibinis ◽  
William G. Swisher ◽  
Junhai Yang ◽  
Jeffrey M. Spraggins ◽  
...  

2020 ◽  
Author(s):  
Boone Prentice ◽  
Daniel Ryan ◽  
Kerri Grove ◽  
D. Shannon Cornett ◽  
Richard Caprioli ◽  
...  

In the analysis of biological tissue by imaging mass spectrometry (IMS), the limit of detection and dynamic range are of paramount importance in obtaining experimental results that provide insight into underlying biological processes. Many important biomolecules are present in the tissue milieu in low concentrations and in complex mixtures with other compounds of widely ranging abundances, challenging the limits of analytical technologies. In many IMS experiments, the ion signal can be dominated by a few highly abundant ion species. On trap-based instrument platforms that accumulate ions prior to mass analysis, these high abundance ions can diminish the detection and dynamic range of lower abundance ions. Herein, we characterize two strategies for combating these challenges during IMS experiments on a hybrid QqFT-ICR MS. In one iteration, the mass resolving capabilities of a quadrupole mass filter are used to selectively enrich for ions of interest via a technique previously termed continuous accumulation of selected ions (CASI). Secondly, we have introduced a supplemental dipolar AC waveform to the quadrupole mass filter of a commercial QqFT-ICR mass spectrometer to perform selected ion ejection prior to the ion accumulation region. This setup allows the selective ejection of the most abundant ion species prior to ion accumulation, thereby greatly improving the molecular depth with which IMS can probe tissue samples.<br>


The Analyst ◽  
2018 ◽  
Vol 143 (1) ◽  
pp. 133-140 ◽  
Author(s):  
William T. Andrews ◽  
Susan B. Skube ◽  
Amanda B. Hummon

MALDI-TOF imaging mass spectrometry (IMS) is a common technique used for analyzing tissue samples, as it allows the user to detect multiple different analytes simultaneously.


2019 ◽  
Author(s):  
Katja Ovchinnikova ◽  
Vitaly Kovalev ◽  
Lachlan Stuart ◽  
Theodore Alexandrov

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]


2016 ◽  
Vol 52 (63) ◽  
pp. 9801-9804 ◽  
Author(s):  
M. Giampà ◽  
M. B. Lissel ◽  
T. Patschkowski ◽  
J. Fuchser ◽  
V. H. Hans ◽  
...  

A novel MALDI matrix MAPS, able to visualize deviating metabolism in glioma using a routine MALDI-ToF-MSI procedure, is presented.


The Analyst ◽  
2012 ◽  
Vol 137 (24) ◽  
pp. 5757 ◽  
Author(s):  
Selina Rahman Shanta ◽  
Tae Young Kim ◽  
Ji Hye Hong ◽  
Jeong Hwa Lee ◽  
Chan Young Shin ◽  
...  

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