Imaging mass spectrometry (IMS) encompasses a variety of techniques that enable the chemical imaging of analytes, which range in size from atoms and small molecules to intact proteins, directly from biological tissues. IMS is transforming specific areas in biological research with its unique combination of chemical and spatial information. Innovations in instrumentation and imaging protocols will make this approach invaluable at many stages of the drug discovery process, including pharmacological target screening and evaluating the distribution of drug and drug metabolites in cells and tissues. The fundamentals and unique methodology of IMS are discussed, along with exciting new applications to drug discovery science.
"In MSI, basically, mass spectra are acquired by stepping the microprobe (focused laser beams, ions currents . . . ) across the sample, typically in a raster pattern, to read the details of its chemical composition, in our case the complete lipidome. Then, using different software, images of the distribution of a given analyte [characterized by mass-to-charge (m/z) values] can be generated in which each pixel is colored using a scaled false color according to its relative intensity (Rubakhin et al., 2005; Watrous et al., 2011). Importantly, a high correlation between the resulting molecular images and the histology of the tissue sections is maintained (Thomas et al., 2013). "
[Show abstract][Hide abstract] ABSTRACT: These are definitively exciting times for membrane lipid researchers. Once considered just as the cell membrane building blocks, the important role these lipids play is steadily being acknowledged. The improvement occurred in mass spectrometry techniques (MS) allows the establishment of the precise lipid composition of biological extracts. However, to fully understand the biological function of each individual lipid species, we need to know its spatial distribution and dynamics. In the past 10 years, the field has experienced a profound revolution thanks to the development of MS-based techniques allowing lipid imaging (MSI). Images reveal and verify what many lipid researchers had already shown by different means, but none as convincing as an image: each cell type presents a specific lipid composition, which is highly sensitive to its physiological and pathological state. While these techniques will help to place membrane lipids in the position they deserve, they also open the black box containing all the unknown regulatory mechanisms accounting for such tailored lipid composition. Thus, these results urges to different disciplines to redefine their paradigm of study by including the complexity revealed by the MSI techniques.
Frontiers in Physiology 02/2015; 6. DOI:10.3389/fphys.2015.00003 · 3.53 Impact Factor
"e l s e v i e r . c o m / l o c a t e / i j m s including drug development , forensic sciences , clinical research, and biomarker discovery . Designed to utilize the natural water content of cells and tissues as a matrix, LAESI uses mid-IR laser pulses at 2940 nm to excite the OÀ ÀH vibrations in the sample for efficient energy deposition . "
[Show abstract][Hide abstract] ABSTRACT: Mass spectrometry imaging (MSI) by laser ablation electrospray ionization (LAESI) enables the lateral mapping of molecular distributions in untreated biological tissues. However, direct sampling and ionization by LAESI-MSI limits the differentiation of isobaric ions (e.g., structural isomers) in a complex sample. Ion mobility separation (IMS) of LAESI-generated species is sufficiently fast to be integrated with the MSI experiments. Here, we present an imaging technique based on a novel combination of LAESI-MSI with IMS that enables in vivo and in situ imaging with enhanced coverage for small metabolites. Ionized molecules produced at each pixel on the tissue were separated by a traveling wave IMS and analyzed by a high performance quadrupole time-of-flight mass spectrometer. Plant (Pelargonium peltatum leaves) and animal tissues (frozen mouse brain sections) were imaged under atmospheric pressure. In LAESI-IMS-MSI, a multidimensional dataset of m/z, drift time (DT), ion intensity, and spatial coordinates was collected. Molecular images for the P. peltatum leaf illustrated that the distributions of flavonoid glycoside ions are aligned with a vein pattern in the tissue. Differentiation of isobaric ions over DT reduced the chemical interferences and allowed separate imaging of these ions. Molecular images were constructed for selected ions in the sagittal sections of the mouse brain, and isobaric species were distinguished by differences in drift times corresponding to distinct molecular structures or conformations. We demonstrated that IMS enhanced the metabolite coverage of LAESI in biological tissues and provided new perspective on MSI for isobaric species.
International Journal of Mass Spectrometry 02/2015; 377(1):681-689. DOI:10.1016/j.ijms.2014.06.025 · 1.97 Impact Factor
"An intensity of a spectrum at an m/z-value represents the relative abundance of a compound with this m/z-value. Although MALDI is not a quantitative technique, it can to some extent be used for semi-quantitative comparisons based on the relative abundance of molecules within a spectrum or, after normalization of spectra (more on it later), between spectra . "
[Show abstract][Hide abstract] ABSTRACT: Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry, also called MALDI-imaging, is a label-free bioanalytical technique used for spatially-resolved chemical analysis of a sample. Usually, MALDI-imaging is exploited for analysis of a specially prepared tissue section thaw mounted onto glass slide. A tremendous development of the MALDI-imaging technique has been observed during the last decade. Currently, it is one of the most promising innovative measurement techniques in biochemistry and a powerful and versatile tool for spatially-resolved chemical analysis of diverse sample types ranging from biological and plant tissues to bio and polymer thin films. In this paper, we outline computational methods for analyzing MALDI-imaging data with the emphasis on multivariate statistical methods, discuss their pros and cons, and give recommendations on their application. The methods of unsupervised data mining as well as supervised classification methods for biomarker discovery are elucidated. We also present a high-throughput computational pipeline for interpretation of MALDI-imaging data using spatial segmentation. Finally, we discuss current challenges associated with the statistical analysis of MALDI-imaging data.
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