Name: Shotgun Lipidomics of Rodent Tissues (Video) | JoVE Journal
Uploaded: 2022-11-18T13:00:00.000+00:00
Duration: 11 min 46 s
Description: Learn shotgun lipidomics for quantifying over 400 molecular lipids across 14 lipid classes in rodent tissues. This rapid 10-minute method enables toxicity biomarker identification and lipid mechanism research.

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5 minutes

The authors would like to thank the study team and especially acknowledge the technical assistance and support of the bioresearch and aerosol teams at PMI R&#38;D, Philip Morris International Research Laboratories Pte. Ltd., Singapore, and PMI R&#38;D, Philip Morris Products S.A., Neuch&#226;tel, Switzerland. The authors thank Sam Ansari for managing the biobanking and acknowledge the support of Sindhoora Bhargavi Gopala&#160;Reddy for editing a draft of the manuscript.

All authors are employees of Philip Morris International. Philip Morris International is the sole source of funding and sponsor of this project.

Although advances in MS have yielded a variety of methods to accurately monitor many lipid species, the previous lipid profiling of various mammalian tissues did not show consistent results and, consequently, the particular tissue-specific functions of lipids remain unclear. In comparison with protein functional analysis, for which more robust approaches to knock out the expression of particular compounds are available, the majority of lipids cannot be selectively turned off or overexpressed in tissues, making the functional evaluation of lipids difficult. Advanced profiling of tissue lipidome concentrations may provide an alternative approach to identify associations between circulating lipids and human diseases.
In evaluating comprehensive lipidomics methods capable of qualitatively and quantitatively covering the endogenous lipid profile of any mouse tissue, we gave preference to the shotgun lipidomics method. In general, two opposite types of sample analysis are possible: either a completely untargeted screening of lipids using liquid chromatography (LC)-based separation with further mass spectrometric detection to reveal the total lipid complexity of the sample, or targeted approaches, which mostly allow for very precise quantification of specific lipids of interest. In contrast, the strong feature of the presented shotgun lipidomics workflow is the rapid comprehensive coverage of hundreds of endogenous lipids from predefined lipid classes, which can still be performed in a robust semi-quantitative manner.
The ionization efficiencies of different lipid classes are structure-dependent and can vary drastically depending on the different experimental ionization conditions. In contrast to LC-based separation methods, shotgun analysis minimizes these differences due to the direct simultaneous infusion of the whole lipid extract under the same ionization conditions into the MS instrument. The use of isotopically labeled lipid analogs or structurally similar non-endogenous standards allows semi-quantification of all lipid classes. Shotgun lipidomics provides low inter- and intra-run variability during MS analysis; as a result, this method produces lower coefficients of variation21 than untargeted liquid chromatography-based methods, which require multiple standards for adequate quantification. Importantly, while no external calibration curve is used in the current method, the method is still considered fully quantitative32.
One level of labeled internal standard (or unlabeled standard that is not endogenously expressed) per lipid class is sufficient for the quantification of most lipids. Only a few publications have reported a partial method validation for a shotgun lipidomics method. For example, in Gryzbek et al.17 and Surma et al.21, reversed calibration curves were prepared using internal standards and a fixed amount of sample matrix. Linearity was assessed by the linear regression of log-transformed lipid amounts and their intensities and reported as R2 and slope, respectively. The limit of detection (LOD) and limit of quantitation (LOQ) were determined by weighted linear regression based on a signal-to-noise ratio of 3 for LOD and 10 for LOQ. For most lipid classes, LOQ was defined between 2-9.8 pmol for adipose tissue and 0.05-5&#181;M in plasma. In both cases, non-endogenous single internal standards per class were used to derive estimates for all lipids in the class. However, in this work, we do not provide LOD/LOQ due to several concerns: the endogenous matrix is not compound-free, and the surrogate matrix for tissues is unavailable-with this, the spike of small known amounts of standards is not possible. We do not perform a classical targeted quantification approach with the use of a calibration curve series of a particular compound normalized by its identical isotopically labeled standard due to the nonexistence of pure standards and the very limited availability of isotopically labeled lipids. Orbitrap detectors automatically perform the conversion of the transient signal by applying a Fourier transformation, and some signal is already substituted-as a result, the lower concentration range will be only linear down to some minimum signal, below which the molecule will not be detectable anymore. Xcalibur software signal-to-noise values depend on the m/z ratio of the molecule; as a result, the compounds of each lipid class, containing different combinations of fatty acids, will have different noise values. Moreover, LOQ/LOQ values are strictly linked to the type of matrix, and when quantification of the lipidome is done in different rodent tissues, it should be reflected by the assessment of LOQs for each tissue type individually.
In fact, the method offers a high linear dynamic quantification range of up to four orders of magnitude33&#160;and very good sensitivity to cover the most important endogenous structural lipids, which can be further increased by technical improvements in MS acquisition32. The coefficients of variation of the mean lipid concentrations were mostly below 15%, meaning that shotgun lipidomics complies with Food and Drug Administration (FDA) requirements as a method to be considered for good laboratory practice (GLP) and good clinical practice (GCLP) studies34.
However, it must be noted that, due to their different polarity, some lipid classes become much more impacted by the contribution of specific conjugated FAs. This leads to the distortion in the intensity response in equimolar mixtures containing a broad range of conjugated FAs, resulting in quantification bias, as highlighted by Koivusalo et al.35 for phospholipid classes. Of note, these authors presented data for a broad range of FAs, from 24-48 chain length, which likely does not reflect the situation in a real biological sample. The response for polyunsaturated species was 40% higher than for fully saturated species, but this effect was only observed for higher concentrations. When the mixture was progressively diluted, the effect of unsaturation gradually diminished and virtually disappeared at 0.1 pmol/&#181;L per species. Additionally, all measurements were done on ion trap and triples quadrupoles instruments and not on Q-Exactive equipment.
Another advantage of the presented workflow is its technical flexibility, which allows adaptation to specific project requirements. For example, any lipid extraction protocol - such as the modified Bligh and Dyer36, methyl tert-butyl ether37, or butanol-methanol38 methods - can be used for preparing a total lipid extract before MS analysis. The main limitation of chloroform-methanol extraction is that the lower phase contains the lipid fraction, which complicates routine work and especially automation; in addition, the toxicity of chloroform must be considered. Tert-butyl ether extraction is widely used for lipid analysis of plasma samples37, and an automated version has been proposed21. In this case, we chose the BUME method because it provides even better recoveries for the spiked PG, PI, PA, and PS lipid classes38, lower solvent consumption, and the possibility of automation39, while the overall concentrations quantified for tissue samples extracted by all three methods were comparable. Additionally, while the sample extraction was performed manually in the current work, it is also possible to obtain reproducible and precise results from large sample sets by automated sample preparation and lipid extraction in a 96-well format40,41. This allows one to implement lipidomics analysis in large-scale clinical and toxicological studies.
In the current work, we performed the MS acquisition of positive and negative modes separately without polarity switching as described by Schuhmann et al.42. The stability of the nanomate signal is better in negative mode for a slightly less concentrated solution than in positive mode. Additionally, we developed a fully traceable procedure with converter software from .raw files to mzML, which provides the values to be specified in the LipidXplorer software - with this, manual resolution slope calculations are not required. We also applied different noise setting substitutions because, in positive mode, the noise levels of the spectra are higher than in negative mode. All the steps were optimized for traceable, high-throughput routine analyses.
For identification, shotgun lipidomics analysis can exploit the unique behavior of different lipid classes, which form unique adducts in different polarity modes. In this method, PC and PE species with the same molecular mass that overlap in the positive ionization mode can be fully separated in the negative ionization mode, as PC forms acetate or formate adducts, and PE is ionized in a deprotonated form. Moreover, (partial) structural deconvolution is possible for the method utilizing not only the molecular formula but also the bulk fatty acid structure. For example, FA identification on the level of total carbon and total unsaturation count works for all phospholipids, DAGs, TAGs, and lyso-phospholipids. The bottom-up quantification of each isomeric form can be partially performed for some of the phospholipid classes43 but is much more complex for DAGs and TAGs due to the unequal signal response of different FAs in the MS2 spectra.
It is also important to emphasize the need to implement appropriate quality control procedures, fully aligned with recent initiatives in this field44. As we wish to ensure proper data traceability and reproducibility between and within the laboratory, a number of steps have been taken such as proper randomization of the samples for all steps of the analysis, working with supplier-certified standard mixtures, the inclusion of quality control samples, procedures to verify batch acceptance or rejection, and the creation of an internal database to track QC performance over the long term. Also consistent with these initiatives is the need for a standardized method to address sample stability. In general, the majority of structural lipids are not impacted by lipid oxidation, which is more relevant for oxilipins, oxidized lipids, and polyunsaturated fatty acids that are, therefore, much more sensitive to storage and handling conditions. However, the correct assessment of sample stability is still a technically challenging task.
However, this protocol has a limitation when it comes to determining submolecular levels of compounds. Considering that there is no separation of the total extract, all isoforms of lipids with the same molecular mass but different fatty acid compositions are merged in the MS analysis. For most classes, it is possible to achieve partial deconvolution of the structure by using the fragmentation ratios of residual fatty acid fragments in MS2. However, the independent quantification of each isoform remains a particularly challenging task owing to the big differences in the fragmentation behaviors of different isoforms and the fact that pure chemical standards are not available in large enough variety to define the compensation values. Another limitation is that the ESI process inevitably generates artifacts, resulting in the artificial generation of peaks for some lipids such as DAGs, Pas, and FAs, which can lead to erroneous quantification.
Next, we summarize the most critical parts of the protocol based on our experience. The first one is related to the fact that each mouse tissue type has a unique lipid profile in terms of both the lipid amount and class ratios. With this, the starting amounts of the tissue based on the total protein content before extraction must be carefully determined in order not to saturate the MS signal and not to leave the dynamic quantification range due to lipid aggregation at high concentrations33 or - at the opposite extreme - to provide enough MS signal to cover the major lipid compounds for each lipid class.
The second critical aspect is to ensure a proper alignment of the position of the direct infusion nano-source chip outlet with the transfer capillary of the mass spectrometer. Considering that full calibration of the mass spectrometer in both modes is performed weekly, swapping between the calibration source and the nano-source chip setup can be the reason for dramatic variability in signal intensity due to misalignment during installation.
Another critical part of the protocol is the careful handling of the internal standard mix. As this mixture contains a significant amount of dichloromethane, once opened, it should be consumed quickly to avoid long storage and multiple uses leading to evaporation and artificial concentration change. Moreover, consistent handling of the standard mixture after removal from &#8722;20 &#176;C storage is important, since temperature differences can lead to volume inconsistencies during the pipetting with air-cushion pipettes. An option would be to replace dichloromethane in the standard resuspension buffer with pure methanol, which could improve the handling convenience but might negatively impact the solubility of some lipid classes and, thus, decrease quantification accuracy for these lipid classes.
The final critical part is data processing. The data processing workflow is combining the Peak by Peak software conversion from .raw to .mzML format, applying MS2 scan averaging and MS1 and MS2 noise filtration, as well as peak picking and data compression. As an alternative, the Proteowizard software can be also used for data conversion, but, in this case, several settings in LipidXplorer have to be defined manually. All the complexity of shotgun lipidomics is particularly concentrated on the step of deconvolution of direct injection MS1 and MS2 spectra. The open-source LipidXplorer software imports the converted spectra from mzML file format based on mass accuracy, mass resolution, and the slope of its change with increasing m/z. The software merges multiple individual MS and MS/MS spectra acquired within an analysis run. Afterward, it aligns individual peaks within different sample runs, and, in each cluster of aligned peaks, it replaces their masses with their single intensity-weighted average mass, while their abundances in each data file remain untouched. Representative masses of aligned peak clusters and individual peak intensities are stored in a master scan database.The master scan database contains all MS1 and MS2 spectra generated for all samples in the batch and can be deconvoluted to lipid identifications by queries written in the molecular fragmentation query language (MFQL).
Overall, the method covers the identification of DAG, TAG, and SE lipids based on positive mode and PC, PE, PS, PI, PA, PG, SM, LPC, and LPE lipids based on negative mode acquisition. During lipid identification, isotopic correction is performed for MS1 and MS2, and adjusted intensities are reported in the .xlsx output file. Alternatively, several other software are available to process shotgun data, such as ALEX45 and LipidHunter46.
Fatty acids - major nuclear and cellular membrane components - are stored in the form of phospholipids for further conversion into bioactive molecules. They can be converted by lysophospholipid acyltransferase enzymes into lysophospholipids through the Lands pathway47. The LPCAT3 enzyme, for example, is known to show high specificity to incorporate AA into lysophosphatidylcholine and lysophosphatidylserine intermediates. The relatively high expression of these enzymes was reported within inflammatory cells, such as alveolar macrophages and bronchial epithelial cells47, leading to the release of larger relative proportions of AA-containing phospholipids in those cells. However, following their liberation from membrane phospholipids by phospholipase A2, the conversion of PUFAs into various types of lipid mediators is catalyzed by many enzymes48. Further, these PUFAs can become the substrate for the production of pro-inflammatory and anti-inflammatory prostaglandins via the action of cyclooxygenase (COX)-1 and COX-2 enzymes47. Phospholipids are one of the sources of lipid mediators released at particular locations where these mediators are required to demonstrate their biological effects.
The lung is a complex organ comprising multiple cell types, each playing overlapping and niche roles in facilitating normal lung development and function. Only a few studies have been done to isolate and profile different cell types in the mouse or human lung (e.g., alveolar type 2 cells)49,50; other major lung cell types have not been characterized. Another interesting study51 was carried out to isolate and perform lipid analysis of endothelial, epithelial, mesenchymal, and mixed immune cells in the mouse lung. It was observed that the concentration of PUFAs incorporated in PCO and PG was enriched in the immune cells. Considering the fact that CS induces an increase in immune cells within the lung (4-5 fold), as assessed by bronchoalveolar lavage (BAL)52, the total lipid changes observed in this study could be explained by a cumulative increase in immune cell recruitment in the mouse lung.
In conclusion, the observed distortion of monounsaturated fatty acids and PUFAs incorporated in phospholipids can reflect the excess of certain FAs within the cell and/or constitute an intracellular resource of oxilipin precursors that are overproduced under oxidative stress and inflammatory conditions. However, additional data on free EPA, DHA, and other oxilipins are essential to clarify this point and are outside the scope of the current method application.

Cigarette smoke (CS) is recognized as a main risk factor associated with the development of chronic inflammatory diseases of the lung, including pulmonary carcinoma, bronchitis, and chronic obstructive pulmonary disease (COPD)1. Beyond the lung impact, CS exposure plays a significant role in the development of other diseases such as atherosclerotic coronary artery disease and peripheral vascular disease1. Cardiovascular disease, together with COPD, are the first and third leading causes of death worldwide, respectively. Toxicological risk assessment approaches have historically relied on the use of animal models such as rodents. In vivo nose-only or full-body rat and mouse models are commonly used for studying the long-term effects of exposure to CS.
For example, generally, 6 months of smoke exposure induces histological and functional abnormalities in murine lungs that mimic those of human disease, including emphysema, airway remodeling, and pulmonary hypertension, although the changes are relatively mild compared with those observed in long-term human smokers2. In both animal and human tissues, studies have observed a wide range of molecular changes in response to CS exposure, including oxidative stress responses, inflammation, and structural tissue changes3,4. Not surprisingly, CS exposure has also been reported to have a far-reaching impact on the lung lipidome, including effects on surfactant lipids, lipid signaling mediators, and structural lipids4,5,6.
To characterize the bulk lipid changes induced by the long-term CS exposure of mouse lungs, we performed a fast and quantitative shotgun direct infusion mass spectrometry analysis. After the introduction of the shotgun lipidomics method in 20057, this method has been effectively used to extend our knowledge on lipid cellular metabolism8,9,10 in model systems such as yeast11, Caenorhabditis elegans12, and Drosophila melanogaster13, as well as in a wide range of mammalian sample types such as cell lines14,15,16 various human17,18 and rodent tissues19,20 and body fluids21,22.
Over the last decades, studies have revealed the complexity of cellular responses to environmental changes, involving thousands of interconnected proteins, lipids, and metabolites. This has made it clear that using state-of-the-art analytical techniques is essential for gaining an in-depth view of the molecular machineries and for uncovering the full magnitude of exogenous physiological impacts. In this context, the comprehensive, quantitative lipid fingerprints produced by shotgun lipidomics approaches can effectively add to our knowledge on lipid cellular metabolism8,9,10.
Regarding CS exposure as a risk factor for several diseases, toxicological risk assessment approaches have historically relied on the use of animal models such as rodents. Shotgun lipidomics MS provides a fast, sensitive, and quantitative analytical tool for assessing lipidome perturbation in numerous sample types. The unique feature of shotgun lipidomics is the automated direct-injection analysis of a total lipid extract-spiked with labeled internal standards-without chromatographic separation into a high-resolution MS instrument by using a conductive nanochip generating an electrospray ionization (ESI) nanospray23.
The mass-to-charge ratio information that is simultaneously acquired in the MS1 mode provides the total lipid footprint of all intact endogenous lipids. Optionally, the MS2/MS3 mode, in which the parent lipid molecules are fragmented and analyzed, provides additional structural information. Data analysis requires specialized software and includes the deconvolution of spectra and peak assignments in pooled spectra that lead to lipid identification and putative chemical structure elucidation. Additionally, absolute quantification can be performed by spiking a labeled internal standards mixture containing at least one lipid standard per lipid class of interest. Overall, with the present technique, MS analysis taking a few minutes per sample can cover the identification and quantification of up to 800 lipids from 14 lipid classes24 in rodent tissues.

<table><tbody><tr><td>1, 5, and 2 mL self-lock tubes</td><td>Eppendorf</td><td>30120086, 30120094</td><td></td></tr><tr><td>3 mm stainless still beads</td><td>Qiagen</td><td>69997</td><td></td></tr><tr><td>4.1 &amp;micro;m nozzle chip</td><td>Advion</td><td>HD-D-384</td><td></td></tr><tr><td>Acetic acid</td><td>Sigma Aldrich</td><td>45754-100ML-F</td><td></td></tr><tr><td>Ammonium acetate</td><td>Honeywell</td><td>14267-25G</td><td></td></tr><tr><td>Ammonium bicarbonate</td><td>Sigma Aldrich</td><td>09830-500G</td><td></td></tr><tr><td>Bovine serum albumin standard, 2 mg/mL</td><td>Thermo Scientific</td><td>&amp;nbsp;23209</td><td></td></tr><tr><td>Butanol</td><td>Honeywell</td><td>33065-2.5L</td><td></td></tr><tr><td>Chloroform</td><td>Sigma Aldrich</td><td>650498-1L</td><td></td></tr><tr><td>Dichloromethane</td><td>Honeywell</td><td>34856-1L</td><td></td></tr><tr><td>Ethyl acetate</td><td>Honeywell</td><td>33211</td><td></td></tr><tr><td>Greiner CELLSTAR 96 well plates</td><td>Sigma</td><td>M9686</td><td></td></tr><tr><td>Heptane</td><td>Sigma Aldrich</td><td>34873-2.5L</td><td></td></tr><tr><td>Isopropanol</td><td>Fisher Scientific</td><td>A461</td><td></td></tr><tr><td>Methanol</td><td>Fisher Scientific</td><td>A456</td><td></td></tr><tr><td>Mouse pooled plasma</td><td>BioIvt</td><td></td><td></td></tr><tr><td>Mouse SPLASH standard</td><td>Avanti Polar Lipids</td><td>330710X</td><td>Internal standard</td></tr><tr><td>Nunc 96-flat bottom well transparent plates</td><td>VWR</td><td>62409-068</td><td></td></tr><tr><td>Plastic spatula</td><td>Sigma</td><td>Z560049-300EA</td><td></td></tr><tr><td>Quick Start Bradford 1x Dye reagent</td><td>BioRad</td><td>5000205</td><td></td></tr><tr><td>Serum diluent</td><td>Sigma Aldrich</td><td>D5197</td><td></td></tr><tr><td>&lt;strong&gt;Equipment/software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>CryoPrep CP02 impactor instrument</td><td>Covaris</td><td>&amp;nbsp; &amp;nbsp;</td><td>Magnetic hammer</td></tr><tr><td>Centrifuge 5427R</td><td>Eppendorf</td><td>.&amp;nbsp; &amp;nbsp;</td><td>Centrifuge</td></tr><tr><td>ChipSoft 8.3</td><td>Advion Biosciences</td><td>.&amp;nbsp; &amp;nbsp;</td><td>Software to set up method and acquisition on the Triversa nanomate robot</td></tr><tr><td>LipidXplorer 1.2.8.1</td><td>N/A</td><td>.&amp;nbsp; &amp;nbsp;</td><td>Software to identify lipids</td></tr><tr><td>Peak By Peak</td><td>SpectroSwiss</td><td>.&amp;nbsp; &amp;nbsp;</td><td>Software to convert .raw data from MS to .mzml format</td></tr><tr><td>ProteoWizard</td><td>ProteoWizard</td><td>.&amp;nbsp; &amp;nbsp;</td><td>Alternative (open source) software to convert .raw data from MS to .mzml format</td></tr><tr><td>Q-Exactive MS</td><td>Thermo Fisher</td><td>.&amp;nbsp; &amp;nbsp;</td><td>High resolution orbitrap mass spectrometer</td></tr><tr><td>Qiagen Tissue Lyser II</td><td>Qiagen</td><td>.&amp;nbsp; &amp;nbsp;</td><td>Tissue lyser</td></tr><tr><td>SpeedVac SPD140DDA</td><td>Thermo Fisher</td><td>.&amp;nbsp; &amp;nbsp;</td><td>Vacuum concentrator</td></tr><tr><td>Tecan Infinite M nano plus</td><td>Tecan</td><td>.&amp;nbsp; &amp;nbsp;</td><td>Plate reader</td></tr><tr><td>ThermoMixer C</td><td>Eppendorf</td><td>.&amp;nbsp; &amp;nbsp;</td><td>Thermomixer</td></tr><tr><td>TriVersa Nanomate</td><td>Advion Biosciences</td><td>.&amp;nbsp; &amp;nbsp;</td><td>Direct infusion nano-source</td></tr><tr><td>Xcalibur 4.3</td><td>Thermo Scientific</td><td>.&amp;nbsp; &amp;nbsp;</td><td>Software to set up method and acquisition on the Q-Exactive MS</td></tr></tbody></table>

shotgun lipidomics of rodent tissues

Shotgun Lipidomics of Rodent Tissues

Lipids play a vital role as essential components of all prokaryotic and eukaryotic cells. Constant technological improvements in mass spectrometry have made lipidomics a powerful analytical tool for monitoring tissue lipidome compositions in homeostatic as well as disease states. This paper presents a step-by-step protocol for a shotgun lipid analysis method that supports the simultaneous detection and quantification of a few hundred molecular lipid species in different tissue and biofluid samples at high throughput. This method leverages automated nano-flow direct injection of a total lipid extract spiked with labeled internal standards without chromatographic separation into a high-resolution mass spectrometry instrument. Starting from sub-microgram amounts of rodent tissue, the MS analysis takes 10 min per sample and covers up to 400 lipids from 14 lipid classes in mouse lung tissue. The method presented here is well suited for studying disease mechanisms and identifying and quantifying biomarkers that indicate early toxicity or beneficial effects within rodent tissues.

Here, as representative results, we present data illustrating how shotgun lipidomics can be used to study the effects of CS on the lungs of mice. The shotgun lipid analysis protocol was successfully applied for an in vivo study for comparative analysis of two groups of samples: lung tissues dissected from nine female Apoe−/− mice exposed to CS (3R4F group; positive control) and an equal number of mice maintained under fresh air conditions (sham group) as negative control collected at 3 months, 4 months, and 6 months exposure time points. Animals were kept under filtered and conditioned fresh air at 22 °C ± 2 °C temperature and 55% ± 15% humidity and fed with a gamma-irradiated pellet diet. The light regime was maintained at 12 h per day. Up to eight mice were housed per each cage. 3R4F reference cigarettes for the exposure treatment were purchased from the University of Kentucky to generate the 3R4F CS aerosol (600 µg of total particulate matter TPM/L aerosol) for a target exposure concentration equivalent to 28 µg of nicotine/L. CS smoke from 3R4F cigarettes was produced using 30 port rotary smoking machines according to Phillips et al.27. Based on the Health Canada Intensive Smoking Protocol, 55 mL of puff volume, one puff per 30 s, and 100% blockage of ventilation holes were applied to 3R4F cigarettes to provide sufficient exposure. All additional details on the study design have been reported previously27,28.
Overall, ~400 molecular lipid species from the 14 most abundant lipid classes were identified and quantified (Figure 3). The total normalized lipid concentration per class was slightly elevated for the PE, PEO, PC, PCO, PI, and PG lipid classes, and some downregulation was observed for SM, LPE, and PA lipids (Figure 3A), while no difference could be seen for TAGs and PS. Interestingly, elevated levels of PCO and PEO plasmalogen lipids in the CS group have been reported in inflammatory cells29. One of the intracellular functions of plasmalogen lipids is antioxidant activity. Under exposure to reactive oxygen (ROS) and nitrogen species (RNS), selective oxidation of plasmalogens in comparison with diacyl phospholipids was reported30. The enyl-ether bond in the chemical structure of plasmalogens is sensitive to radical attack upon oxidative stress31. The major products of radical attack are hydroxylated eicosatetraenoic acids, 2-monoacylglycerol phospholipids, pentadecanol, formic acids, α-hydroxyaldehydes of various chain lengths, 1-formyl-2-arachidonoyl glycerophospholipid, and lysophospholipids.
These results show that, among the molecular features based on the total molecular formula, 100-120 compounds were significantly impacted by CS exposure (Figure 3D). The detailed deconvolution of the MS2 fragmentation information and normalization of the fragment signal intensities allowed the approximate definition of the total amount of each conjugated fatty acid (FA) represented in each lipid class (Figure 3E) and comparison of these data between exposed and control groups. A clear decrease in both fully saturated FAs and ones with only a few unsaturations, mostly in the context of lyso-lipids, was observed. In contrast, polyunsaturated fatty acids (PUFA) in the composition of PC, PE, and PG phospholipid classes were elevated in the CS group for all time points. The extreme cases of PC and PG conjugated with eicosapentanoic (EPA) and docosahexaenoic acid (DHA) were exclusively detected in the 3R4F CS exposure group (Figure 3E, red color).
<img alt="Microplate setup for assay with standards, blanks, and sample analysis; data evaluation context." class="xfigimg" src="/files/ftp_upload/63726/63726fig01.jpg" data-altupdatedat="1788282455000" data-alt="Figure 1"> 
Figure 1: Plate layout for sample distribution in the Bradford assay. Blank and standard samples are placed in triplicate in columns 1-3; unknown samples are placed in duplicate in columns 4-11. <a href="https://www.jove.com/files/ftp_upload/63726/63726fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Microtiter plate layout showing sample positions, QC controls, and TB/ISB tests for assay results." class="xfigimg" src="/files/ftp_upload/63726/63726fig02.jpg" data-altupdatedat="1788282455000" data-alt="Figure 2"> 
Figure 2: Plate layout of the 96-well microtiter plate for shotgun mass spectrometry analysis. The distribution of blank, standard, unknown, and QC samples for acquisition in the positive ionization mode is mapped on the left side of the plate (columns 1-6); all samples for the negative ionization mode are placed on the right (columns 7-12). Abbreviations: pos = positive; QC = quality control; neg = negative; TB = total blank. <a href="https://www.jove.com/files/ftp_upload/63726/63726fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Lipidomic analysis results, bar chart and scatter plots of fold change, heatmap correlation analysis." class="xfigimg" src="/files/ftp_upload/63726/63726fig03.jpg" data-altupdatedat="1788282455000" data-alt="Figure 3"> 
Figure 3: Shotgun lipidomics analysis of mouse lungs exposed to cigarette smoke. Apoe−/− mice were exposed to CS from the 3R4F reference cigarette or fresh air (sham). (A) Lipid class concentrations and the number of lipid species per class. For each class, the number of quantified lipid species is given in brackets. Mean and 95% confidence intervals for each lipid class, separately for each exposure type (aggregated across three time points). (B) Volcano plots showing the effect size (log2 fold change) and significance (-log10 FDR-adjusted p value) of the quantified lipid species for the 3R4F CS versus sham comparison at the three time points. Significantly elevated lipids are marked in yellow; significantly decreased lipids are marked in cyan (FDR-adjusted p value &lt; 0.05). (C) Differential abundance of the 25 lipid species with the largest absolute mean fold changes. Log2 fold changes for the 3R4F CS versus sham comparison are color-coded, and statistical significance is indicated: *: FDR-adjusted p value &lt; 0.01; X: FDR-adjusted p value &lt; 0.05. (D) Comparison plots. Correlation coefficients for the fold-change comparisons are color-coded, and the number of shared differentially abundant lipids is indicated (total numbers in the margins). Pie charts show the percentage of shared differentially abundant lipids with the same direction of change (FC sign). Asterisk indicates a significant overlap of the differentially abundant lipids. (E) Differential abundance of conjugated FAs per lipid class. Heatmap as in panel C for conjugated fatty acids with significant abundance differences between 3R4F and sham exposure in all three time points (FDR-adjusted p value &lt; 0.05). The amount of each fatty acid per lipid class was estimated based on the MS2 intensities of the fatty-acid fragments. Abbreviations: CS = cigarette smoke; FDR = false discovery rate; FC = fold change; FA = fatty acids; PC = phosphatidylcholine; PS = phosphatidylserine; TAG = triacylglycerol; SM = sphingomyelin; PEO = plasmalogen phosphatidylethanolamine; PE = phosphatidylethanolamine; PG = phosphatidyl glycerol; PI = phosphatidylinositol; DAG = diacylglycerol; SE = sterol/cholesteryl esters; PCO = plasmalogen phosphatidylcholine; LPC = lyso phosphatidylcholine; LPE = lyso phosphatidylethanolamine; PA = phosphatidic acid. <a href="https://www.jove.com/files/ftp_upload/63726/63726fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>BSA final concentration (mg/mL)</td>
			<td>BSA solution at 2 mg/mL (µL)</td>
			<td>Ammonium bicarbonate buffer (µL)</td>
		</tr>
		<tr>
			<td>1</td>
			<td>50</td>
			<td>50</td>
		</tr>
		<tr>
			<td>0.75</td>
			<td>37.5</td>
			<td>62.5</td>
		</tr>
		<tr>
			<td>0.5</td>
			<td>25</td>
			<td>75</td>
		</tr>
		<tr>
			<td>0.25</td>
			<td>12.5</td>
			<td>87.5</td>
		</tr>
		<tr>
			<td>0.125</td>
			<td>12.5</td>
			<td>187.5</td>
		</tr>
		<tr>
			<td>0.0625</td>
			<td>50 of 0.125 mg/mL solution</td>
			<td>50</td>
		</tr>
		<tr>
			<td>0</td>
			<td>0</td>
			<td>100</td>
		</tr>
	</tbody>
</table>
Table 1: Dilution scheme of the BSA internal standard for the calibration curve for the Bradford assay. Abbreviation: BSA = bovine serum albumin.
Supplementary Information: MFQL files used for LipidXplorer lipid identification. The .zip package consists of the individual required MFQL files, each named following this pattern: &lt;lipid class abbreviation&gt;_&lt;ionization mode&gt;_MS2.mfql (e.g., PE_neg_MS2.mfql). The file names for labeled internal standard identification include the D7(D9) tag in the &lt;lipid abbreviation&gt; (e.g., PED7_neg_MS2.mfql). These MFQL files are used to verify the amount of deuterium in the internal standard. <a href="https://www.jove.com/files/ftp_upload/63726/-_Supplementary%20Information%20JoVE%20Review.zip" target="_blank">Please click here to download this File.</a>

Shotgun mass spectrometry-based lipidomics delivers a sensitive quantitative snapshot of a broad spectrum of lipid classes simultaneously in a single measurement from various rodent tissues.

Lipids play a vital role as essential components of all prokaryotic and eukaryotic cells. Constant technological improvements in mass spectrometry have ...

Research

JoVE Journal

Biochemistry

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Mouse Adipose Tissue Collection and Processing for RNA Analysis

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Lipidomics and Transcriptomics in Neurological Diseases

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Preparation, Administration, and Assessment of In Vivo Tissue-Specific Cellular Uptake of Fluorescent Dye-Labeled Liposomes

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Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

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Sample Preparation for Rapid Lipid Analysis in Drosophila Brain Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

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Here, we describe a protocol to use in vivo four-dimensional ultrasound imaging and ex vivo mass spectrometry imaging to assess biomechanical and biomolecular alterations in the murine cardiovascular system. This technique is applied to analyze cardiac remodeling in surgically induced myocardial infarction and vascular changes in aging animals.

Multimodal Study of Murine Cardiovascular Remodeling: Four-Dimensional Ultrasound and Mass Spectrometry Imaging

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A Method for Measuring RNA N6-methyladenosine Modifications in Cells and Tissues

Learn shotgun lipidomics for quantifying over 400 molecular lipids across 14 lipid classes in rodent tissues. This rapid 10-minute method enables toxicity biomarker identification and lipid mechanism research.

Shotgun Lipidomics of Rodent Tissues

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