Molecular imaging with mass spectrometry: instrumental and methodological advances for biological applications

Mass spectrometry imaging (MSI);secondary ion mass spectrometry (SIMS);matrix-assisted laser desorption ionization (MALDI);mass spectrometry (MS);Molecular imaging;Chemical imaging;Bacterial biofilm;Pseudomonas aeruginosa;Quinolone;Rhamnolipid

Lanni, Eric

关键词: Mass spectrometry imaging (MSI);    secondary ion mass spectrometry (SIMS);    matrix-assisted laser desorption ionization (MALDI);    mass spectrometry (MS);    Molecular imaging;    Chemical imaging;    Bacterial biofilm;    Pseudomonas aeruginosa;    Quinolone;    Rhamnolipid;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/50714/Eric_Lanni.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Mass spectrometry imaging (MSI) is an analytical technique providing a unique combination of capabilities: label-free, non-targeted, and highly multiplex detection and imaging of chemical species ranging in mass from single protons to large proteins exceeding 100 kDa, identifiable by mass and confirmed (when possible) by structural information obtained through tandem MS experiments. These capabilities make MSI a powerful tool for biological investigations especially in cases where analytes of interest are not known a priori, and where one wishes to comprehensively survey the spatiochemical composition of a specimen in order to generate more refined hypotheses that will guide subsequent targeted work. In the past several decades MSI has been extensively developed for and applied to macroscopic or “tissue-level” biomolecular imaging studies, and parallel effort has focused on improving MSI capability in the microscopic or “cell-scale” regime in a number of ways. The latter work faces many additional challenges, however, such as designing instrumentation with suitable spatial resolution as well as achieving sufficient sampling efficiency (through instrumentation and experimental protocols) to detect the minute amounts of a compound which are present in micro-scale volumes.This thesis presents a body of work with two main goals: 1) improving cell-scale MSI capabilities through development of new instrumentation and compatible methodologies, and 2) developing methods for combining MSI with other imaging techniques in order to enhance the information gleaned from an experiment. A main step towards the first goal here is development of a hybrid mass spectrometer of novel design which combines two complementary MSI probes – a laser and a polyatomic ion beam – on a system with other advantageous features such as high mass resolution and tandem MS capability. The system is characterized, applied to visualize chemical distributions on single cultured neurons, and combined with electron microscopy to correlate chemical localizations with physical features of the cells. Towards the second goal, MSI is combined with confocal Raman microscopy (CRM) – a label-free photospectroscopic chemical imaging technique – to obtain complementary information about the molecular composition of bacterial biofilms. A new method is similarly developed for combining multiple MSI approaches in order to precisely correlate chemical images on multiple size scales, and this is also applied to bacterial biofilm imaging.Finally, the two main efforts of the work are integrated to demonstrate “heterocorrelated imaging” where CRM is combined with MSI performed on the new hybrid instrument in order to visualize secondary metabolite distributions in the biofilms. A novel microspot array approach is developed for precise correlation of images generated by the two different techniques, and the hybrid instrument’s tandem MS capability is employed with the SIMS microprobe to confirm tentative mass assignments by in situ analysis. Correlation of these two techniques here also importantly demonstrates how cross-validation between mass- and vibration-based chemical imaging modalities can address ambiguity in each individual data set.