This protocol offers step-by-step instructions for preparation of raw blood plasma for liquid chromatography – tandem mass spectrometry (LC-MS/MS) analysis in clinical proteomics studies. The entire transformation from plasma proteins to desalted tryptic peptides takes only 3–4 h. The protocol can be adopted for large-scale studies and automation.


We describe a protocol for Lipidomic analysis of tissue culture cells, tissues, and purified organelles. Lipids are a class of molecules that have roles in energy storage, plasma membrane integrity, and signaling events. This protocol provides direction on how to extract lipids from plasma, cells, tissue, and purified organelles for analysis by liquid chromatography (LC)-MS.


We describe a protocol for multiplexed proteomic analysis using neutron-encoded (NeuCode) stable isotope labeling of amino acids in cells (SILAC) or mice (SILAM). This method currently enables simultaneous comparison of up to nine treatment and control proteomes. Isotopologs of lysine are introduced into cells or mammals, via the culture medium or diet, respectively, to metabolically label the proteome. Labeling time is ∼2 weeks for cultured cells and 3-4 weeks for mammals. he resultant peptides are chromatographically separated and then mass analyzed. During mass spectrometry (MS) data acquisition, high-resolution MS1 spectra (≥240,000 resolving power at m/z = 400) reveal the embedded isotopic signatures, enabling relative quantification, while tandem mass spectra, collected at lower resolutions, provide peptide identities. Both types of spectra are processed using NeuCode-enabled MaxQuant software. In total, the approximate completion time for the protocol is 3-5 weeks.

Collaborating Investigator/s

David Pagliarini (The Morgridge Institute for Research)

Summary

This project necessitated discovery lipidomics analyses for profiling of global lipid changes.

The biosynthesis of coenzyme Q presents a paradigm for how cells surmount hydrophobic barriers in lipid biology. Here, we reveal that this process relies on custom lipid-binding properties of COQ9. Overall, our results provide a mechanism for how a lipid-binding protein might access, select, and deliver specific cargo from a membrane to promote biosynthesis.

The collected discovery lipidomics data of this project served to advance LipiDex, the Center’s discovery lipidomics software tool, and enhanced its utility for analysis of a different organismal lipidome that could be repeated in the future projects.


Isotopic labeling is commonly applied for investigating intracellular metabolism. The general workflow is to first introduce isotopically-labeled metabolites into the culture medium, then at defined time points wash and harvest cells, process samples for metabolomics analysis, and analyze the samples for isotopic enrichment in specified metabolite pools. Here we apply this technique to primary hepatocytes from mice. We introduce either 13C5 glutamine or 13C6 glucose at the typical media concentrations, 1:1 replacing the 12C version with 13C version. Cells are harvested at 0 and 30 min after isotope introduction, metabolites are extracted and then analyzed by GC-MS and LC-MS. The resulting data are used to compare relative 13C isotopic labeling in metabolites between different genetic mutants. This strategy is not suitable for directly quantifying metabolic flux (i.e., Metabolic flux analysis), but is useful for describing relative metabolic flux between two models. The expected time to complete is ~3-5 days.

Collaborating Investigator/s

David Pagliarini (The Morgridge Institute for Research)

Summary

This project necessitated discovery lipidomics analyses for profiling of global lipid changes.

The biosynthesis of coenzyme Q presents a paradigm for how cells surmount hydrophobic barriers in lipid biology. Here, we reveal that this process relies on custom lipid-binding properties of COQ9. Overall, our results provide a mechanism for how a lipid-binding protein might access, select, and deliver specific cargo from a membrane to promote biosynthesis.

The collected discovery lipidomics data of this project served to advance LipiDex, the Center’s discovery lipidomics software tool, and enhanced its utility for analysis of a different organismal lipidome that could be repeated in the future projects.

Collaborating Investigator/s

Johan Auwerx (Ecole Polytechnique Fédérale de Lausanne)

Summary

To enable QTL mapping, an outstandingly large mouse cohort had to be characterized via quantitative lipidomics. The project’s focus on mitochondrial function required through characterization of low-abundance mitochondria-specific lipid species, such as cardiolipins, which was carried out in plasma to generate the most holistic view of the organism.

This project was a driver in the development of a high throughput quantitative lipidomics platform that was later built upon for the use in other studies.

Collaborating Investigator/s

Marv Wickens (University of Wisconsin-Madison)

Summary

This discovery-driven DBP required through and unbiased exploration of the relatively large knockout collection via multi-omic methodology.

The challenges of exploring this large multi-omic dataset propelled development of our web-based multi-omic data exploration and visualization approach that later culminated in the release of MS-Portal software, now customarily used for all multi-omic projects.

Collaborating Investigator/s

Alan Attie (University of Wisconsin-Madison)

Summary

The project extended the use of the Center’s high throughput label-free proteomics technology to novel and challenging tissue type, pancreatic islets, which required dramatically modifying sample preparation and handling procedures.

The developed sample preparation and handling procedures became a part of our streamlined and versatile methodology for high throughput label-free proteomics, now compatible with most sample types.

Collaborating Investigator/s

Johan Auwerx (Ecole Polytechnique Fédérale de Lausanne)

Summary

To enable QTL mapping, an outstandingly large mouse cohort had to be characterized via quantitative lipidomics. The project’s focus on mitochondrial function required through characterization of low-abundance mitochondria-specific lipid species, such as cardiolipins, in the most metabolically active tissue – liver.

This project drove development of the high throughput quantitative lipidomics platform that was later built upon for the use in other studies.