MULTI-SEGMENT DIRECT INJECT NANO-ESI-LTQ-FT-ICR-MS/MS FOR PROTEIN IDENTIFICATION

Multi-Segment Direct Inject nano-ESI-LTQ-FT-ICR-MS/MS For Protein Identification

Multi-Segment Direct Inject nano-ESI-LTQ-FT-ICR-MS/MS For Protein Identification

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Abstract Reversed phase high performance liquid chromatography (HPLC) interfaced to electrospray tandem mass spectrometry (MS/MS) is commonly used for the identification of peptides from proteolytically cleaved proteins embedded in a polyacrylamide gel matrix as well as for metabolomics screening.HPLC separations are time consuming (30-60 min average), costly (columns and mobile phase reagents), and carry CURCUMIN ULTRA the risk of column carry over between samples.The use of a chip-based nano-ESI platform (Advion NanoMate) based on replaceable nano-tips for sample introduction eliminates sample cross-contamination, provides unchanging sample matrix, and enhances spray stability with attendant increases in reproducibility.Recent papers have established direct infusion nano-ESI-MS/MS utilizing the NanoMate for protein identification of gel spots based on full range MS scans with data dependent MS/MS.

In a full range scan, discontinuous ion suppression due to sample matrix Equine - Grooming - Tail Bags can impair identification of putative mass features of interest in both the proteomic and metabolomic workflows.In the current study, an extension of an established direct inject nano-ESI-MS/MS method is described that utilizes the mass filtering capability of an ion-trap for ion packet separation into four narrow mass ranges (50 amu overlap) with segment specific dynamic data dependent peak inclusion for MS/MS fragmentation (total acquisition time of 3 minutes).Comparison of this method with a more traditional nanoLC-MS/MS based protocol utilizing solvent/sample stream splitting to achieve nanoflow demonstrated comparable results for protein identification from polyacrylamide gel matrices.The advantages of this method include full automation, lack of cross-contamination, low cost, and high throughput.

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