Journal
PROTEOMICS
Volume 8, Issue 18, Pages 3809-3815Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/pmic.200800331
Keywords
automated data acquisition; image reconstruction; imaging mass spectrometry; mass spectrometry imaging; matrix-assisted laser desorption-ionization
Funding
- National Institutes of Health [DE018866]
- National Institute on Drug Abuse [DA017940, DA018310]
- Proctor and Gamble
- NATIONAL INSTITUTE OF DENTAL &CRANIOFACIAL RESEARCH [R01DE018866] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON DRUG ABUSE [R01DA017940, P30DA018310] Funding Source: NIH RePORTER
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The characterization and localization of peptides and proteins in tissues provides information that aids in understanding their function and in characterizing disease states. Over the past decades, the use of MS for the profiling and imaging of biological compounds from tissues has evolved into a powerful modality to accomplish these studies. One recently described sampling approach, the stretched sample method (Monroe, E. B. et al., Anal. Chem. 2006, 78, 6826-6832), places a tissue section onto an array of glass beads embedded on a Parafilm M membrane. When the membrane is stretched, it separates the tissue section into thousands of cell-sized pieces for tissue profiling by MALDI-MS. The physical separation between beads eliminates analyte redistribution during matrix application and allows long analyte extraction periods without loss of spatial resolution. Here, we enhance this sampling approach by introducing algorithms that enable the reconstruction of ion images from these stretched samples. As the first step, a sample-tailored data acquisition method is devised to obtain mass spectra exclusively from the beads, thereby reducing the time, instrument resources, and data handling required for such MS imaging (MSI) experiments. Next, an image reconstruction algorithm matches data acquired from the stretched sample to the initial bead locations. The efficacy of this method is demonstrated using peptide-coated beads with known peptide distributions and appears well-suited to the MSI of heterogeneous tissue samples.
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