A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF
نویسندگان
چکیده
Mass spectrometry imaging (MSI) is a powerful tool to visualize the spatial distribution of molecules on a tissue section. The main limitation of MALDI-MSI of proteins is the lack of direct identification. Therefore, this study focuses on a MSI~LC-MS/MS-LF workflow to link the results from MALDI-MSI with potential peak identification and label-free quantitation, using only one tissue section. At first, we studied the impact of matrix deposition and laser ablation on protein extraction from the tissue section. Then, we did a back-correlation of the m/z of the proteins detected by MALDI-MSI to those identified by label-free quantitation. This allowed us to compare the label-free quantitation of proteins obtained in LC-MS/MS with the peak intensities observed in MALDI-MSI. We managed to link identification to nine peaks observed by MALDI-MSI. The results showed that the MSI~LC-MS/MS-LF workflow (i) allowed us to study a representative muscle proteome compared to a classical bottom-up workflow; and (ii) was sparsely impacted by matrix deposition and laser ablation. This workflow, performed as a proof-of-concept, suggests that a single tissue section can be used to perform MALDI-MSI and protein extraction, identification, and relative quantitation.
منابع مشابه
Liquid chromatography-matrix-assisted laser desorption/ionization mass spectrometric imaging with sprayed matrix for improved sensitivity, reproducibility and quantitation.
Matrix-assisted laser desorption/ionization (MALDI) mass spectrometric imaging (MSI) has been employed as a detection method for both capillary electrophoresis (CE)-MALDI and liquid chromatography (LC)-MALDI analyses. Based on our previous studies, here we report a new interface to couple LC with MSI by employing an automated matrix sprayer. The LC trace is directly collected on a ground stainl...
متن کاملSpatial Quantitation of Drugs in tissues using Liquid Extraction Surface Analysis Mass Spectrometry Imaging
Liquid extraction surface analysis mass spectrometry imaging (LESA-MSI) has been shown to be an effective tissue profiling and imaging technique, producing robust and reliable qualitative distribution images of an analyte or analytes in tissue sections. Here, we expand the use of LESA-MSI beyond qualitative analysis to a quantitative analytical technique by employing a mimetic tissue model prev...
متن کاملLiquid chromatography–tandem mass spectrometry (LC-MS) method for the assignment of enalapril and enalaprilat in human plasma
A rapid and sensitive liquid chromatography–tandem mass spectrometry (LC-MS) method was developed for the determination of enalapril and enalaprilat in human plasma. Detection of analytes was achieved by tandem mass spectrometry with electrospray ionization (ESI) interface in positive ion mode which was operated under the multiple-reaction monitoring mode. Sample pretreatment was involved...
متن کاملA quick and Sensitive Liquid Chromatography–tandem Mass Spectrometry (LC-MS) Method for the Determination of Enalapril and Enalaprilat in Human Plasma: Application to a Bioequivalence Study
A rapid and sensitive liquid chromatography–tandem mass spectrometry (LC-MS) method for the estimation of enalapril and enalaprilat in human plasma. Detection of analytes was achieved by tandem mass spectrometry with electrospray ionization (ESI) interface in positive ion mode was operated under the multiple-reaction monitoring mode. Sample pretreatment involved in a one-step protein precipitat...
متن کاملMSSimulator: Simulation of mass spectrometry data.
Mass spectrometry coupled to liquid chromatography (LC-MS and LC-MS/MS) is commonly used to analyze the protein content of biological samples in large scale studies, enabling quantitation and identification of proteins and peptides using a wide range of experimental protocols, algorithms, and statistical models to analyze the data. Currently it is difficult to compare the plethora of algorithms...
متن کامل