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Rapid Commun Mass Spectrom. 2018 Mar 15;32(5):442-450. doi: 10.1002/rcm.8042.

Protein identification in imaging mass spectrometry through spatially targeted liquid micro-extractions.

Ryan DJ1,2, Nei D2,3, Prentice BM2,3, Rose KL2,3, Caprioli RM1,2,3,4,5, Spraggins JM1,2,3.

Author information

1
Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN, 37235, USA.
2
Mass Spectrometry Research Center, Vanderbilt University, 465 21st Ave S #9160, Nashville, TN, 37235, USA.
3
Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, TN, 37205, USA.
4
Department of Pharmacology, Vanderbilt University, 442 Robinson Research Building, 2220 Pierce Avenue, Nashville, TN, 37232, USA.
5
Department of Medicine, Vanderbilt University, 465 21st Ave S #9160, Nashville, TN, 37235, USA.

Abstract

RATIONALE:

Liquid extraction surface analysis (LESA) can be used to generate spatially directed protein identifications in an imaging mass spectrometry (IMS) workflow. This approach involves the use of robotic micro-extractions coupled to online liquid chromatography (LC). We have characterized the extraction efficiency of this method as well as its ability to identify proteins from a matrix assisted laser/desorption ionization (MALDI) IMS experiment.

METHODS:

Proteins and peptides were extracted from transverse sections of a rat brain and sagittal sections of a mouse pup using liquid surface extractions. Extracts were either analyzed by online LC coupled to a high mass resolution Fourier transform ion cyclotron resonance (FTICR) mass spectrometer or collected offline and analyzed by traditional LC/MS methods. Identifications were made using both top-down and bottom-up methodologies. MALDI images were acquired on a 15T FTICR mass spectrometer at 125 μm spatial resolution.

RESULTS:

Robotic liquid surface extractions are reproducible across various tissue types, providing significantly improved spatial resolution, with respect to extractions, while still allowing for a robust number of protein identifications. A single 2-μL extract can provide identification of over 14,000 peptides with little sample preparation, increasing throughput for spatially targeted workflows. Surface extractions from tissue were coupled directly to LC to gather spatially relevant proteomics data.

CONCLUSIONS:

Robotic liquid surface extractions can be used to interrogate discrete regions of tissue to provide protein identifications with high throughput, accuracy, and robustness. The direct coupling of tissue surface extractions and LC offers a new and effective approach to provide spatial proteomics data in an imaging experiment.

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