Dataset Information

Lipidomics profiling by high-resolution LC-MS and high-energy collisional dissociation fragmentation: focus on characterization of mitochondrial cardiolipins and monolysocardiolipins.

ABSTRACT: A liquid chromatography-mass spectrometry (LC-MS) method was used for separation of lipid classes as well as both qualitative and semiquantitative detection of individual lipids in biological samples. Data were acquired using high-resolution full-scan MS and high-energy collisional dissociation (HCD) all ion fragmentation. The method was evaluated for efficient separation and detection in both positive and negative ionization mode using standards spanning six lipid classes. Platform linearity and robustness, related to the mitochondrial lipid cardiolipin (CL), were assessed using extracted ion chromatograms with mass tolerance windows of 5 ppm or less from full scan exact mass measurements. The platform CL limit of detection was determined to be 5 pmol (0.9 ?M) on the column, with mass accuracy <1.5 ppm, retention time coefficients of variation (CV) < 0.5%, and area CV < 13%. This mass accuracy was critical to the identification of unknown CL species in mitochondria samples, through the elimination of false positives. In addition to detection and relative quantitation of CL species in mitochondria, CL structures were characterized through the use of alternating HCD scans at different energies to produce diagnostic fragmentations on all ions in the analysis. The developed lipid profiling method was applied to mitochondrial samples from an animal study related to the linkages between diet, mitochondrial function, and disease. The analysis identified 28 unique CL species and two monolysocardiolipin species that are often associated with mitochondrial stress and dysfunction.

SUBMITTER: Bird SS

PROVIDER: S-EPMC3031668 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Publications

Lipidomics profiling by high-resolution LC-MS and high-energy collisional dissociation fragmentation: focus on characterization of mitochondrial cardiolipins and monolysocardiolipins.

Bird Susan S SS Marur Vasant R VR Sniatynski Matthew J MJ Greenberg Heather K HK Kristal Bruce S BS

Analytical chemistry 20101230 3

A liquid chromatography-mass spectrometry (LC-MS) method was used for separation of lipid classes as well as both qualitative and semiquantitative detection of individual lipids in biological samples. Data were acquired using high-resolution full-scan MS and high-energy collisional dissociation (HCD) all ion fragmentation. The method was evaluated for efficient separation and detection in both positive and negative ionization mode using standards spanning six lipid classes. Platform linearity an ...[more]

PMID: 21192696

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Project description:There is a growing need both clinically and experimentally to improve the characterization of blood lipids. A liquid chromatography-mass spectrometry (LC-MS) method, developed for the qualitative and semiquantitative detection of lipids in biological samples and previously validated in mitochondrial samples, was now evaluated for the profiling of serum lipids. Data were acquired using high-resolution, full scan MS and high-energy, collisional dissociation (HCD), all ion fragmentation. The method was designed for efficient separation and detection in both positive and negative ionization mode and evaluated using standards spanning seven lipid classes. Platform performance, related to the identification and characterization of serum triglycerides (TGs), was assessed using extracted ion chromatograms with mass tolerance windows of 5 ppm or less from full scan exact mass measurements determined using SIEVE nondifferential LC-MS analysis software. The platform showed retention time coefficients of variation (CV) of <0.3%, mass accuracy values of <2 ppm error, and peak area CV of <13%, with the majority of that error coming from sample preparation and extraction rather than the LC-MS analysis, and linearity was shown to be over 4 orders of magnitude (r(2) = 0.999) for the standard TG (15:0)(3) spiked into serum. Instrument mass accuracy and precision were critical to the identification of unknown TG species, in part because these parameters enabled us to reduce false positives. In addition to detection and relative quantitation of TGs in serum, TG structures were characterized through the use of alternating HCD scans at different energies to produce diagnostic fragmentations on all ions in the analysis. The lipidomics method was applied to serum samples from 192 rats maintained on diets differing in macronutrient composition. The analysis identified 86 TG species with 81 unique masses that varied over 3.5 orders of magnitude and showed diet-dependency, consistent with TGs linking diet and disease risk.

Project description:Highly specialized cells are fundamental for proper functioning of complex organs. Variations in cell-type specific gene expression and protein composition have been linked to a variety of diseases. Although single cell technologies have emerged as valuable tools to address this cellular heterogeneity, a majority of these workflows lack sufficient in situ resolution for functional classification of cells and are associated with extremely long analysis time, especially when it comes to in situ proteomics. In addition, lack of understanding of single cell dynamics within their native environment limits our ability to explore the altered physiology in disease development. This limitation is particularly relevant in the mammalian brain, where different cell types perform unique functions and exhibit varying sensitivities to insults. The hippocampus, a brain region crucial for learning and memory, is of particular interest due to its obvious involvement in various neurological disorders. Here, we present a combination of experimental and data integration approaches for investigation of cellular heterogeneity and functional disposition within the mouse brain hippocampus using MALDI Imaging mass spectrometry (MALDI-IMS) and shotgun proteomics (LC-MS/MS) coupled with laser-capture microdissection (LCM) along with spatial transcriptomics. Within the dentate gyrus granule cells we identified two proteomically distinct cellular subpopulations that are characterized by a substantial number of discriminative proteins. These cellular clusters contribute to the overall functionality of the dentate gyrus by regulating redox homeostasis, mitochondrial organization, RNA processing, and microtubule organization. Importantly, most of the identified proteins matched their transcripts, verifying the in situ protein identification and supporting their functional analyses. By combining high-throughput spatial proteomics with transcriptomics, our approach enables reliable near-single-cell scale identification of proteins and profiling of inter-cellular heterogeneity within similar cell-types in tissues. This methodology has the potential to be applied to different biological conditions and tissues, providing a deeper understanding of cellular subpopulations in situ.

Dataset Information

Lipidomics profiling by high-resolution LC-MS and high-energy collisional dissociation fragmentation: focus on characterization of mitochondrial cardiolipins and monolysocardiolipins.

Publications

Lipidomics profiling by high-resolution LC-MS and high-energy collisional dissociation fragmentation: focus on characterization of mitochondrial cardiolipins and monolysocardiolipins.

Similar Datasets

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