Nanostructure Initiator Mass Spectrometry for tissue imaging in metabolomics: Future prospects and perspectives

Calavia, R.; Annanouch, Fatima Ezahra; Correig, X.; Yanes, Óscar

doi:10.1016/j.jprot.2012.05.002

Cited by 35 publications

(22 citation statements)

References 36 publications

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“…To increase the ionization efficiency of molecules in positive mode NIMS, researchers are investigating the use of photoacid initiators and different nanostructured surfaces. 26 The development of matrix-free methods for neuropeptide MSI will mitigate spatial resolution limits and potential analyte delocalization associated with analyte diffusion in MALDI MSI experiments. It is possible that these advances will someday facilitate matrix-free analysis of neuropeptides in tissue, but MALDI currently remains the preferred method for neuropeptide localization and in situ measurement experiments in C. borealis brain tissue.…”

Section: Discussionmentioning

confidence: 99%

Comparison of NIMS and MALDI platforms for neuropeptide and lipid mass spectrometric imaging in C. borealis brain tissue

et al. 2013

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Nanostructure-initiator mass spectrometry (NIMS) is a recently developed matrix-free laser desorption/ionization technique that has shown promise for peptide analyses. It is also useful in mass spectrometric imaging (MSI) studies of small molecule drugs, metabolites, and lipids, minimizing analyte diffusion caused by matrix application. In this study, NIMS and matrix-assisted laser desorption/ionization (MALDI) MSI of a crustacean model organism Cancer borealis brain were compared. MALDI was found to perform better than NIMS in these neuropeptide imaging experiments. Twelve neuropeptides were identified in MALDI MSI experiments whereas none were identified in NIMS MSI experiments. In addition, lipid profiles were compared using each ionization method. Both techniques provided similar lipid profiles in the m/z range 700 – 900.

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Section: Discussionmentioning

confidence: 99%

Comparison of NIMS and MALDI platforms for neuropeptide and lipid mass spectrometric imaging in C. borealis brain tissue

et al. 2013

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“…Following sample preparation, an ultraviolet (UV) or infrared (IR) laser is used in MA(LDI)-MS to desorb and ionize the molecules. The mechanisms involved in desorption/ionization are still not fully understood and depend upon the LDI approach (McDonnell & Heeren, 2007;Rainer, Qureshi, & Bonn, 2011;Calavia et al, 2012;Silina & Volmer, 2013;Hillenkamp & Peter-Katalinic, 2014). The spatial resolution of the MS image is determined by the matrix-crystal size, the possible lateral compound diffusion occurring along the matrix-deposition process, and the laserbeam diameter of a specific instrument [normally between 10 and 250 mm (Norris & Caprioli, 2013).…”

Section: Msi Workflowmentioning

confidence: 99%

Signal preprocessing, multivariate analysis and software tools for MA(LDI)‐TOF mass spectrometry imaging for biological applications

Ràfols

Vilalta

Brezmes

et al. 2016

Mass Spectrometry Reviews

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Mass spectrometry imaging (MSI) is a label-free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach. MSI data processing is challenging due to several factors. The large volume of mass spectra involved in a MSI experiment makes choosing the correct computational strategies critical. Furthermore, pixel to pixel variation inherent in the technique makes choosing the correct preprocessing steps critical. The primary aim of this review was to provide an overview of the data-processing steps and tools that can be applied to an MSI experiment, from preprocessing the raw data to the more advanced strategies for image visualization and segmentation. This review is particularly aimed at researchers performing MSI experiments and who are interested in incorporating new data-processing features, improving their computational strategy, and/or desire access to data-processing tools currently available. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:281-306, 2018.

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“…21, 27 The best known and most versatile examples are the organic laser-absorbing compounds for MALDI, but a similar effect can be achieved with metal coatings, 18 derivatized nanoparticles, 85 and nanostructured substrates. 86, 87 Selecting the most effective matrix for a given specimen is certainly a challenge, but in MSI it is equally important to optimize extraction (high sensitivity) without significant chemical delocalization (loss of spatial information). Unfortunately these two goals compete; pneumatic spray of organic matrices 72 offers superior sensitivity while drier applications such as sublimation preserve small spatial features by generating a uniform coat of μm-sized crystals.…”

Section: Challenges and Opportunities In Mass Spectrometric And Rmentioning

confidence: 99%

Correlated imaging – a grand challenge in chemical analysis

Masyuko

Lanni

Sweedler

et al. 2013

Analyst

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Correlated chemical imaging is an emerging strategy for acquisition of images by combining information from multiplexed measurement platforms to track, visualize, and interpret in situ changes in the structure, organization, and activities of interesting chemical systems, frequently spanning multiple decades in space and time. Acquiring and correlating information from complementary imaging experiments has the potential to expose complex chemical behavior in ways that are simply not available from single methods applied in isolation, thereby greatly amplifying the information gathering power of imaging experiments. However, in order to correlate image information across platforms, a number of issues must be addressed. First, signals are obtained from disparate experiments with fundamentally different figures of merit, including pixel size, spatial resolution, dynamic range, and acquisition rates. In addition, images are often acquired on different instruments in different locations, so the sample must be registered spatially so that the same area of the sample landscape is addressed. The signals acquired must be correlated in both spatial and temporal domains, and the resulting information has to be presented in a way that is readily understood. These requirements pose special challenges for image cross-correlation that go well beyond those posed in single technique imaging approaches. The special opportunities and challenges that attend correlated imaging are explored by specific reference to correlated mass spectrometric and Raman imaging, a topic of substantial and growing interest.

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Nanostructure Initiator Mass Spectrometry for tissue imaging in metabolomics: Future prospects and perspectives

Cited by 35 publications

References 36 publications

Comparison of NIMS and MALDI platforms for neuropeptide and lipid mass spectrometric imaging in C. borealis brain tissue

Comparison of NIMS and MALDI platforms for neuropeptide and lipid mass spectrometric imaging in C. borealis brain tissue

Signal preprocessing, multivariate analysis and software tools for MA(LDI)‐TOF mass spectrometry imaging for biological applications

Correlated imaging – a grand challenge in chemical analysis

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