Simultaneous extraction of proteins and metabolites from cells in culture

Proper sample preparation is an integral part of all omics approaches, and can drastically impact the results of a wide number of analyses. As metabolomics and proteomics research approaches often yield complementary information, it is desirable to have a sample preparation procedure which can yield information for both types of analyses from the same cell population. This protocol explains a method for the separation and isolation of metabolites and proteins from the same biological sample, in order for downstream use in metabolomics and proteomics analyses simultaneously. In this way, two different levels of biological regulation can be studied in a single sample, minimizing the variance that would result from multiple experiments. This protocol can be used with both adherent and suspension cell cultures, and the extraction of metabolites from cellular medium is also detailed, so that cellular uptake and secretion of metabolites can be quantified. Advantages of this technique includes: 1. Inexpensive and quick to perform; this method does not require any kits. 2. Can be used on any cells in culture, including cell lines and primary cells extracted from living organisms. 3. A wide variety of different analysis techniques can be used, adding additional value to metabolomics data analyzed from a sample; this is of high value in experimental systems biology

Metabolic Profiling as Well as Stable Isotope Assisted Metabolic and Proteomic Analysis of RAW 264.7 Macrophages Exposed to Ship Engine Aerosol Emissions: Different Effects of Heavy Fuel Oil and Refined Diesel Fuel

Exposure to air pollution resulting from fossil fuel combustion has been linked to multiple short-term and long term health effects. In a previous study, exposure of lung epithelial cells to engine exhaust from heavy fuel oil (HFO) and diesel fuel (DF), two of the main fuels used in marine engines, led to an increased regulation of several pathways associated with adverse cellular effects, including pro-inflammatory pathways. In addition, DF exhaust exposure was shown to have a wider response on multiple cellular regulatory levels compared to HFO emissions, suggesting a potentially higher toxicity of DF emissions over HFO. In order to further understand these effects, as well as to validate these findings in another cell line, we investigated macrophages under the same conditions as a more inflammationrelevant model. An air-liquid interface aerosol exposure system was used to provide a more biologically relevant exposure system compared to submerged experiments, with cells exposed to either the complete aerosol (particle and gas phase), or the gas phase only (with particles filtered out). Data from cytotoxicity assays were integrated with metabolomics and proteomics analyses, including stable isotope-assisted metabolomics, in order to uncover pathways affected by combustion aerosol exposure in macrophages. Through this approach, we determined differing phenotypic effects associated with the different components of aerosol. The particle phase of diluted combustion aerosols was found to induce increased cell death in macrophages, while the gas phase was found more to affect the metabolic profile. In particular, a higher cytotoxicity of DF aerosol emission was observed in relation to the HFO aerosol. Furthermore, macrophage exposure to the gas phase of HFO leads to an induction of a pro-inflammatory metabolic and proteomic phenotype. These results validate the effects found in lung epithelial cells, confirming the role of inflammation and cellular stress in the response to combustion aerosols

Emissions from a modern log wood masonry heater and wood pellet boiler : Composition and biological impact on air-liquid interface exposed human lung cancer cells

The consumption of wood fuel is markedly increasing in developing and industrialized countries. Known side effects of wood smoke inhalation manifest in proinflammatory signaling, oxidative stress, DNA damage and hence increased cancer risk. In this study, the composition and acute biological impact of emissions of state-of-the-art wood combustion compliances: masonry heater (MH) and pellet boiler (PB) were investigated. Therefore A549 cells were exposed to emission aerosols in an automated air-liquid interface exposure station followed by cytotoxicity, transcriptome and proteome analyses. In parallel, aerosols were subjected to a chemical and physical haracterization. Compared to PB, the MH combustion at the same dilution ratio resulted in a 3-fold higher particle mass concentration (PM2.5) and deposited dose (PB: 27 $\pm$ 2 ng/cm2, MH; 73 $\pm$ 12 ng/cm2). Additionally, the MH aerosol displayed a substantially larger concentration of aldehydes, polycyclic aromatic hydrocarbons (PAH) or oxidized PAH. Gene ontology analysis of transcriptome of A549 cells exposed to MH emissions revealed the activation of proinflammatory response and key signaling cascades MAP kinase and JAK-STAT. Furthermore, CYP1A1, an essential enzyme in PAH metabolism, was induced. PB combustion aerosol activated the proinflammatory marker IL6 and different transport processes. The proteomics data uncovered induction of DNA damage-associated proteins in response to PB and DNA doublestrand break processing proteins in response to MH emissions. Taking together, the MH produces emissions with a higher particle dose and more toxic compounds while causing only mild biological responses. This finding points to a significant mitigating effect of antioxidative compounds in MH wood smoke

Isotope Cluster-Based Compound Matching in Gas Chromatography/ Mass Spectrometry for Non-Targeted Metabolomics

Gas chromatography coupled to mass spectrometry (GC/MS) has emerged as a powerful tool in metabolomics studies. A major bottleneck in current data analysis of GC/MS-based metabolomics studies is compound matching and identification, as current methods generate high rates of false positive and false -negative identifications. This is especially true for data sets containing a high amount of noise. In this work, a novel spectral similarity measure based on the specific fragmentation patterns of electron impact mass spectra is proposed. An important aspect of these algorithmic methods is the handling of noisy data. The performance of the proposed method compared to the dot product, the current gold standard, was evaluated on a complex biological data set. The analysis results showed significant improvements of the proposed method in compound matching and chromatogram alignment compared to the dot product

Data on the implementation of Narrative CV captured ahead of the 2023 Recognition and Rewards Festival

Created on 05 Feb 2024 - 18:02 by Noémie Aubert BonnAs part of the 2023 Dutch Recognition and Rewards Festival, James Morris, Sean Sapcariu, Karen Stroobants, and Noémie Aubert Bonn hosted a workshop to gather perspectives on Narrative CVs, including whether and how they may contribute to shifts in research culture that are needed to support research assessment reform. A short 12-question survey was circulated ahead of the workshop to representatives from research organisations via the following forums: the DORA Funders Group; the Global Research Council Responsible Research Assessment Working Group; and the Science Europe Working Group on Research Culture.The survey was launched between March and April 2023 and received 24 full responses from representatives of 23 research organisations, covering 18 countries across 4 continents and one European-wide organisation. The aggregated data were presented during the workshop at the Recognition and Rewards Festival in Utrecht, NL on 13 April 2023 to provoke discussion among participants.Given the high potential of re-identification of the survey data, we summarised narrative information in key themes of responses. Here, we share the de-identified data to accompany the manuscript which reports findings from the workshops. We also share a codebook which explains each question from the survey and details whether any changes were conducted to minimise re-identification.</p

Isotope Cluster-Based Compound Matching in Gas Chromatography/Mass Spectrometry for Non-Targeted Metabolomics

Gas chromatography coupled to mass spectrometry (GC/MS) has emerged as a powerful tool in metabolomics studies. A major bottleneck in current data analysis of GC/MS-based metabolomics studies is compound matching and identification, as current methods generate high rates of false positive and false -negative identifications. This is especially true for data sets containing a high amount of noise. In this work, a novel spectral similarity measure based on the specific fragmentation patterns of electron impact mass spectra is proposed. An important aspect of these algorithmic methods is the handling of noisy data. The performance of the proposed method compared to the dot product, the current gold standard, was evaluated on a complex biological data set. The analysis results showed significant improvements of the proposed method in compound matching and chromatogram alignment compared to the dot product

Fragment Formula Calculator (FFC): Determination of chemical formulas for fragment ions in mass spectrometric data

The accurate determination of mass isotopomer distributions (MID) is of great significance for stable isotope-labeling experiments. Most commonly, MIDs are derived from gas chromatography/electron ionization mass spectrometry (GC/EI-MS) measurements. The analysis of fragment ions formed during EI, which contain only specific parts of the original molecule can provide valuable information on the positional distribution of the label. The chemical formula of a fragment ion is usually applied to derive the correction matrix for accurate MID calculation. Hence, the correct assignment of chemical formulas to fragment ions is of crucial importance for correct MIDs. Moreover, the positional distribution of stable isotopes within a fragment ion is of high interest for stable isotope-assisted metabolomics techniques. For example, 13C-metabolic flux analyses (13C-MFA) are dependent on the exact knowledge of the number and position of retained carbon atoms of the unfragmented molecule. Fragment ions containing different carbon atoms are of special interest, since they can carry different flux information. However, the process of mass spectral fragmentation is complex, and identifying the substructures and chemical formulas for these fragment ions is nontrivial. For that reason, we developed an algorithm, based on a systematic bond cleavage, to determine chemical formulas and retained atoms for EI derived fragment ions. Here, we present the fragment formula calculator (FFC) algorithm that can calculate chemical formulas for fragment ions where the chemical bonding (e.g., Lewis structures) of the intact molecule is known. The proposed algorithm is able to cope with general molecular rearrangement reactions occurring during EI in GC/MS measurements. The FFC algorithm is able to integrate stable isotope labeling experiments into the analysis and can automatically exclude candidate formulas that do not fit the observed labeling patterns.1 We applied the FFC algorithm to create a fragment ion repository that contains the chemical formulas and retained carbon atoms of a wide range of trimethylsilyl and tert-butyldimethylsilyl derivatized compounds. In total, we report the chemical formulas and backbone carbon compositions for 160 fragment ions of 43 alkylsilyl-derivatives of primary metabolites. Finally, we implemented the FFC algorithm in an easy-to-use graphical user interface and made it publicly available at http://www.ffc.lu

Additional file 1 of Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis

Supplemental information. List of isotopically enriched compounds and their mass isotopomer distributions; selected mass spectra. (PDF 308 kb

Fragment Formula Calculator (FFC): Determination of Chemical Formulas for Fragment Ions in Mass Spectrometric Data

The accurate determination of mass isotopomer distributions (MID) is of great significance for stable isotope-labeling experiments. Most commonly, MIDs are derived from gas chromatography/electron ionization mass spectrometry (GC/EI-MS) measurements. The analysis of fragment ions formed during EI, which contain only specific parts of the original molecule can provide valuable information on the positional distribution of the label. The chemical formula of a fragment ion is usually applied to derive the correction matrix for accurate MID calculation. Hence, the correct assignment of chemical formulas to fragment ions is of crucial importance for correct MIDs. Moreover, the positional distribution of stable isotopes within a fragment ion is of high interest for stable isotope-assisted metabolomics techniques. For example, ¹³C-metabolic flux analyses (¹³C-MFA) are dependent on the exact knowledge of the number and position of retained carbon atoms of the unfragmented molecule. Fragment ions containing different carbon atoms are of special interest, since they can carry different flux information. However, the process of mass spectral fragmentation is complex, and identifying the substructures and chemical formulas for these fragment ions is nontrivial. For that reason, we developed an algorithm, based on a systematic bond cleavage, to determine chemical formulas and retained atoms for EI derived fragment ions. Here, we present the fragment formula calculator (FFC) algorithm that can calculate chemical formulas for fragment ions where the chemical bonding (e.g., Lewis structures) of the intact molecule is known. The proposed algorithm is able to cope with general molecular rearrangement reactions occurring during EI in GC/MS measurements. The FFC algorithm is able to integrate stable isotope labeling experiments into the analysis and can automatically exclude candidate formulas that do not fit the observed labeling patterns. We applied the FFC algorithm to create a fragment ion repository that contains the chemical formulas and retained carbon atoms of a wide range of trimethylsilyl and <i>tert</i>-butyldimethylsilyl derivatized compounds. In total, we report the chemical formulas and backbone carbon compositions for 160 fragment ions of 43 alkylsilyl-derivatives of primary metabolites. Finally, we implemented the FFC algorithm in an easy-to-use graphical user interface and made it publicly available at http://www.ffc.lu