Adaptation of Skyline for Targeted Lipidomics

In response to the urgent need for analysis software that is capable of handling data from targeted high-throughput lipidomics experiments, we here present a systematic workflow for the straightforward method design and analysis of selected reaction monitoring data in lipidomics based on lipid building blocks. Skyline is a powerful software primarily designed for proteomics applications where it is widely used. We adapted this tool to a “Plug and Play” system for lipid research. This extension offers the unique capability to assemble targeted mass spectrometry methods for complex lipids easily by making use of building blocks. With simple yet tailored modifications, targeted methods to analyze main lipid classes such as glycerophospholipids, sphingolipids, glycerolipids, cholesteryl-esters, and cholesterol can be quickly introduced into Skyline for easy application by end users without distinct bioinformatics skills. To illustrate the benefits of our novel strategy, we used Skyline to quantify sphingolipids in mesenchymal stem cells. We demonstrate a simple method building procedure for sphingolipids screening, collision energy optimization, and absolute quantification of sphingolipids. In total, 72 sphingolipids were identified and absolutely quantified at the fatty acid scan species level by utilizing Skyline for data interpretation and visualization

Nano-LC/NSI MS Refines Lipidomics by Enhancing Lipid Coverage, Measurement Sensitivity, and Linear Dynamic Range

Nano-liquid chromatography (nLC)–nanoelectrospray (NSI) is one of the cornerstones of mass-spectrometry-based bioanalytics. Nevertheless, the application of nLC is not yet prevalent in lipid analyses. In this study, we established a reproducible nLC separation for global lipidomics and describe the merits of using such a miniaturized system for lipid analyses. In order to enable comprehensive lipid analyses that is not restricted to specific lipid classes, we particularly optimized sample preparation conditions and reversed-phase separation parameters. We further benchmarked the developed nLC system to a commonly used high flow HPLC/ESI MS system in terms of lipidome coverage and sensitivity. The comparison revealed an intensity gain between 2 and 3 orders of magnitude for individual lipid classes and an increase in the linear dynamic range of up to 2 orders of magnitude. Furthermore, the analysis of the yeast lipidome using nLC/NSI resulted in more than a 3-fold gain in lipid identifications. All in all, we identified 447 lipids from the core phospholipid lipid classes (PA, PE, PC, PS, PG, and PI) in Saccharomyces cerevisiae

Nano-LC/NSI MS Refines Lipidomics by Enhancing Lipid Coverage, Measurement Sensitivity, and Linear Dynamic Range

Nano-liquid chromatography (nLC)–nanoelectrospray (NSI) is one of the cornerstones of mass-spectrometry-based bioanalytics. Nevertheless, the application of nLC is not yet prevalent in lipid analyses. In this study, we established a reproducible nLC separation for global lipidomics and describe the merits of using such a miniaturized system for lipid analyses. In order to enable comprehensive lipid analyses that is not restricted to specific lipid classes, we particularly optimized sample preparation conditions and reversed-phase separation parameters. We further benchmarked the developed nLC system to a commonly used high flow HPLC/ESI MS system in terms of lipidome coverage and sensitivity. The comparison revealed an intensity gain between 2 and 3 orders of magnitude for individual lipid classes and an increase in the linear dynamic range of up to 2 orders of magnitude. Furthermore, the analysis of the yeast lipidome using nLC/NSI resulted in more than a 3-fold gain in lipid identifications. All in all, we identified 447 lipids from the core phospholipid lipid classes (PA, PE, PC, PS, PG, and PI) in Saccharomyces cerevisiae

A Comprehensive High-Resolution Targeted Workflow for the Deep Profiling of Sphingolipids

Sphingolipids make up a highly diverse group of biomolecules that not only are membrane components but also are involved in various cellular functions such as signaling and protein sorting. To obtain a quantitative view of the sphingolipidome, sensitive, accurate, and comprehensive methods are needed. Here, we present a targeted reversed-phase liquid chromatography–high-resolution mass spectrometry-based workflow that significantly increases the accuracy of measured sphingolipids by resolving nearly isobaric and isobaric species; this is accomplished by a use of (i) an optimized extraction procedure, (ii) a segmented gradient, and (iii) parallel reaction monitoring of a sphingolipid specific fragmentation pattern. The workflow was benchmarked against an accepted sphingolipid model system, the RAW 264.7 cell line, and 61 sphingolipids were quantified over a dynamic range of 7 orders of magnitude, with detection limits in the low femtomole per milligram of protein level, making this workflow an extremely versatile tool for high-throughput sphingolipidomics

Annotation of fragment ions from stable isotope-labeled lipids.

<p>A) Positive FTMS² spectrum of protonated PC 34:2(+[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.ref002" target="_blank">2</a>]H13). The fragment ions identify the molecular lipid species as PC(+[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.ref002" target="_blank">2</a>]H13) 16:1–18:1. B) Positive FTMS² spectrum of protonated PC 32:0(+[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.ref002" target="_blank">2</a>]H6). The fragment ions identify the molecular lipid species as PC 16:0(+[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.ref002" target="_blank">2</a>]H3)-16:0(+[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.ref002" target="_blank">2</a>]H3). C) Negative FTMS² spectrum of deprotonated PI 34:1(+[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.ref002" target="_blank">2</a>]H6). The fragment ions identify the molecular lipid species PI(+[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.ref002" target="_blank">2</a>]H6) 16:0–18:1. D) Negative FTMS² spectrum of the formate adduct of Cer 44:0;4(+[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.ref013" target="_blank">13</a>]C2[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.ref015" target="_blank">15</a>]N). The annotated fragment ions identify the molecular species as Cer 18:0;3(+[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.ref013" target="_blank">13</a>]C2[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.ref015" target="_blank">15</a>]N)/26:0;1. Note that non-annotated fragment ions derive from co-isolated lipids. Fragmentation diagrams for the lipid molecules and indicated fragment ion <i>m/z</i> values are shown in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.s008" target="_blank">S6 Fig</a></b>.</p

Annotated fragment ion spectra of representative lipid molecules from five different lipid categories.

<p>Fragment ion <i>m/z</i> values are denoted according to the three-step procedure outlined in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.g002" target="_blank">Fig 2</a></b>. The shorthand notation includes nomenclature based on both charged and neutral fragments (separated by “|”) (step 2). Annotation shown in boldface is prioritized based on the guidelines outlined in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.g002" target="_blank">Fig 2</a></b> (step 3). Non-prioritized shorthand notation is occasionally omitted to avoid overly congested mass spectra. The representation of fragment ion <i>m/z</i> values by mass-balanced chemical reactions and fragment structures are shown in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.s005" target="_blank">S3 Fig</a></b> (step 1). A) Negative FTMS² spectrum of deprotonated ACoA 19:0. B) Positive FTMS² spectrum of ammoniated TAG 18:0–18:1–18:2. C) Positive FTMS² spectrum of protonated PE O-18:1p/20:4. D) Negative FTMS² spectrum of deprotonated and doubly charged CL 14:1–14:1–14:-15:1. E) Negative FTMS³ spectrum of FA 18:1 carboxylate anion <i>m/z</i> 281.3 derived from PC 16:0–18:1(9). F) Positive FTMS² spectrum of protonated SM 18:1;2/17:0. G) Negative FTMS² spectrum of deprotonated Cer 18:1;2/17:0;1. H) Positive FTMS² spectrum of ammoniated SE 27:1/19:0 (cholesteryl ester 19:0).</p

CID of lipid molecules produces several types of fragments that can be used for annotating intact lipid molecules at three different levels.

<p>The shorthand notation of the fragment ions is described in the sections: Results and discussion, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.s001" target="_blank">S1 Text</a>. LCFs, lipid class-selective fragments; MLFs, molecular lipid species-specific fragments; DBFs, double bond location-specific fragments.</p

Identification of PS 20:4–22:6 in mouse hippocampus.

<p>A) Negative FTMS spectrum of mouse hippocampus. The precursor ion matching deprotonated PS 42:10 is highlighted in boldface. B) Negative FTMS² spectrum of <i>m/z</i> 854.6 with detection of MLFs and LCFs matching PS 20:4–22:6, annotated in boldface.</p

LDA software supports automated annotation of lipid fragment ions.

<p>A) Negative ion mode extracted ion chromatogram of <i>m/z</i> 718.5379±0.013, corresponding to deprotonated PE 34:0 (i.e., synthetic standard PE 17:0–17:0). B) Negative ion mode FTMS² spectrum of <i>m/z</i> 718.5. Fragment ions are automatically annotated by LDA and collectively used to identify the molecular lipid species PE 17:0–17:0. C) Positive ion mode extracted ion chromatogram of <i>m/z</i> 675.5897±0.013, corresponding to sodiated DAG 38:0 (i.e., synthetic standard DAG 18:0–20:0). D) Positive ion mode FTMS² spectrum of <i>m/z</i> 675.6. Fragment ions are automatically annotated by LDA and collectively used to identify the molecular lipid species DAG 18:0–20:0.</p

Outline of three-step procedure for implementing shorthand notation of lipid fragment <i>m/z</i> values.

<p>Step 1: Detected fragment ion <i>m/z</i> values are first recapitulated using mass-balanced chemical reactions showing putative structures of both charged and neutral fragments. Step 2: These fragments are then annotated using fragment type-specific annotation rules (described in detail in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.s001" target="_blank">S1 Text</a></b>). Step 3: Prioritizing the nomenclature to use for shorthand notation of detected fragment ion <i>m/z</i> values is based on fragment type, charge and mass difference between charged fragments and composites of neutral fragments (also described in detail in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.s001" target="_blank">S1 Text</a></b>). Note that the shorthand notation of fragment ion <i>m/z</i> values can be based on combinations of fragment types (i.e. DBFs, MLFs, LCFs and iMLFs).</p