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MALDI-MSI is a powerful technology for localizing drug and metabolite distributions in biological tissues. To enhance our understanding of tuberculosis (TB) drug efficacy and how efficiently certain drugs reach their site of action, MALDI-MSI was applied to image the distribution of the second-line TB drug moxifloxacin at a range of time points after dosing. The ability to perform multiple monitoring of selected ion transitions in the same experiment enabled extremely sensitive imaging of moxifloxacin within tuberculosis-infected rabbit lung biopsies in less than 15 min per tissue section. Homogeneous application of a reference standard during the matrix spraying process enabled the ion-suppressing effects of the inhomogeneous lung tissue to be normalized. The drug was observed to accumulate in granulomatous lesions at levels higher than that in the surrounding lung tissue from 1.5 h postdose until the final time point. MALDI-MSI moxifloxacin distribution data were validated by quantitative LC/MS/MS analysis of lung and granuloma extracts from adjacent biopsies taken from the same animals. Drug distribution within the granulomas was observed to be inhomogeneous, and very low levels were observed in the caseum in comparison to the cellular granuloma regions. In this experiment the MALDI-MRM-MSI method was shown to be a rapid and sensitive method for analyzing the distribution of anti-TB compounds and will be applied to distribution studies of additional drugs in the future.

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Matrix-assisted laser desorption/ionization mass spectrometry imaging is a technique for direct analysis of tissue sections without the use of molecular tags or contrast agents. The combination of spatial and mass resolution results in large and complex data sets that require powerful and efficient analysis and interpretation tools. Conventional images, derived from a specific analyte mass, do not identify the spatially localized correlations between analytes that are latent in the data. A new approach to find and visualize these correlations is presented. Clustering methods are used to classify pixels by spectral similarity, facilitating definition of distinct spatial regions. Principal component and discriminant analyses are combined to comprehensively identify changes in the mass spectra between regions. Images are generated by projecting the spectra of each pixel on the discriminant spectra; contrast is then a function of multiple correlated peaks.