Developing Multidimensional Skyline Spectral Libraries for Rapid Lipid Analysis
Published on 04-25-202212:58 PM by
qpham289| Updated on 04-25-202212:59 PM
Multidimensional measurements integrating liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS) provide valuable polarity, structural and mass information simultaneously for lipidomic analyses and show tremendous power for attaining more confident lipid identifications. For all of their advantages, LC-IMS-MS measurements are highly complex and result in huge datasets which are difficult to process in a timely fashion. Thus, developing a data analysis workflow that is capable of accurate and rapid molecular analyses is essential.
The freely available, open-source software Skyline offers targeted processing of lipid data which ultimately allows for confident identification of diverse lipid species. We have developed sample-specific lipid spectral libraries which include over 700 target lipids from multiple lipid categories. Each target lipid is populated with m/z values, normalized retention times, ion mobility collision cross section (CCS) values, and known fragmentation patterns. These values were manually extracted from LC-IMS-MS experimental data and verified using existing literature.
Recently developed aspects of the Skyline small molecule interface are utilized in this workflow including IMS spectrum filtering and retention time prediction (iRT) using a set of ~20 endogenous lipids for gradient correction and LC alignment. Application of lipid CCS value filtering further increased lipid annotation confidence and greatly improved the signal to noise ratio for the target species. These lipid spectral libraries have undergone additional validation studies and have recently been made publicly available through Skyline’s online repository Panorama.
In comparison to previous studies of NIST SRM 1950, this workflow when coupled with an LCIMS-CID-MS platform gave hundreds of confident annotations using a single sample extraction and analysis platform.
Key Learning Objectives:
Learn to overcome challenges with large, highly complex datasets generated from liquid chromatography, ion mobility spectrometry and mass spectrometry lipidomic analyses.
Developing sample-specific multidimensional lipid libraries using Skyline.
Build a workflow leveraging Skyline automation features such as small-molecule spectral libraries, drift time filtering, iRT retention time prediction, analysis of multiple adducts, and neutral loss fragments.
Who Should Attend:
Drug development scientists
Brought to you by:
Kaylie Kirkwood, Ph.D. North Carolina State university
Kelly McSweeney Contributing Editor C&EN Media Group