Modern biomarker and translational research as well as personalized health care studies rely heavily on powerful omics’ technologies, including metabolomics and lipidomics. However, to translate metabolomics and lipidomics discoveries into a high-throughput clinical setting, standardization is of utmost importance. Here, we compared and benchmarked a quantitative lipidomics platform. The employed Lipidyzer platform is based on lipid class separation by means of differential mobility spectrometry with subsequent multiple reaction monitoring. Quantitation is achieved by the use of 54 deuterated internal standards and an automated informatics approach. We investigated the platform performance across nine laboratories using NIST SRM 1950–Metabolites in Frozen Human Plasma, and three NIST Candidate Reference Materials 8231–Frozen Human Plasma Suite for Metabolomics (high triglyceride, diabetic, and African-American plasma). In addition, we comparatively analyzed 59 plasma samples from individuals with familial hypercholesterolemia from a clinical cohort study. We provide evidence that the more practical methyl-tert-butyl ether extraction outperforms the classic Bligh and Dyer approach and compare our results with two previously published ring trials. In summary, we present standardized lipidomics protocols, allowing for the highly reproducible analysis of several hundred human plasma lipids, and present detailed molecular information for potentially disease relevant and ethnicity-related materials.

Background: Laser Interstitial ThermoTherapy (LITT) is a well established surgical method. The use of LITT is so far limited to homogeneous tissues, e.g. the liver. One of the reasons is the limited capability of existing treatment planning models to calculate accurately the damage zone. The treatment planning in inhomogeneous tissues, especially of regions near main vessels, poses still a challenge. In order to extend the application of LITT to a wider range of anatomical regions new simulation methods are needed. The model described with this article enables efficient simulation for predicting damaged tissue as a basis for a future laser-surgical planning system. Previously we described the dependency of the model on geometry. With the presented paper including two video files we focus on the methodological, physical and mathematical background of the model. Methods: In contrast to previous simulation attempts, our model is based on finite element method (FEM). We propose the use of LITT, in sensitive areas such as the neck region to treat tumours in lymph node with dimensions of 0.5 cm – 2 cm in diameter near the carotid artery. Our model is based on calculations describing the light distribution using the diffusion approximation of the transport theory; the temperature rise using the bioheat equation, including the effect of microperfusion in tissue to determine the extent of thermal damage; and the dependency of thermal and optical properties on the temperature and the injury. Injury is estimated using a damage integral. To check our model we performed a first in vitro experiment on porcine muscle tissue. Results: We performed the derivation of the geometry from 3D ultrasound data and show for this proposed geometry the energy distribution, the heat elevation, and the damage zone. Further on, we perform a comparison with the in-vitro experiment. The calculation shows an error of 5% in the x-axis parallel to the blood vessel. Conclusions: The FEM technique proposed can overcome limitations of other methods and enables an efficient simulation for predicting the damage zone induced using LITT. Our calculations show clearly that major vessels would not be damaged. The area/volume of the damaged zone calculated from both simulation and in-vitro experiment fits well and the deviation is small. One of the main reasons for the deviation is the lack of accurate values of the tissue optical properties. In further experiments this needs to be validated.

Targeting metabolism through bioactive key metabolites is an upcoming future therapeutic strategy. We questioned how modifying intracellular lipid metabolism could be a possible means for alleviating inflammation. Using a recently developed chemical probe (SH42), we inhibited distal cholesterol biosynthesis through selective inhibition of Δ²⁴-dehydrocholesterol reductase (DHCR24). Inhibition of DHCR24 led to an antiinflammatory/proresolving phenotype in a murine peritonitis model. Subsequently, we investigated several omics layers in order to link our phenotypic observations with key metabolic alterations. Lipidomic analysis revealed a significant increase in endogenous polyunsaturated fatty acid (PUFA) biosynthesis. These data integrated with gene expression analysis, revealing increased expression of the desaturase Fads6 and the key proresolving enzyme Alox-12/15. Protein array analysis, as well as immune cell phenotype and functional analysis, substantiated these results confirming the antiinflammatory/proresolving phenotype. Ultimately, lipid mediator (LM) analysis revealed the increased production of bioactive lipids, channeling the observed metabolic alterations into a key class of metabolites known for their capacity to change the inflammatory phenotype. Significance Lipid metabolism is crucial to many (patho-)physiological processes, including inflammation. In particular, cholesterol biosynthesis has emerged as an exciting novel therapeutic target in numerous recent studies. Much is known about the early steps in cholesterol biosynthesis; however, the later steps have hitherto largely been neglected. Here, we investigated the druggability of distal cholesterol biosynthesis using a selective and potent chemical probe (SH42). This inhibition leads to the accumulation of the bioactive metabolite desmosterol, boosting the biosynthesis of polyunsaturated fatty acids (PUFA) and the downstream production of antiinflammatory mediators. Our report integrates distal cholesterol biosynthesis and its intermediate desmosterol with endogenous PUFA biosynthesis and resolution of inflammation, rendering this pathway attractive for further developments in the context of proresolving therapies.

Multiple reaction monitoring (MRM) is a key tool for biomarker validation and the translation of potential biomarkers into the clinic.

The use of protein biomarkers as surrogates for clinical endpoints requires extensive multilevel validation including development of robust and sensitive assays for precise measurement of protein concentration. Multiple reaction monitoring (MRM) is a well-established mass-spectrometric method that can be used for reproducible protein-concentration measurements in biological specimens collected via microsampling. The dried blood spot (DBS) microsampling technique can be performed non-invasively without the expertise of a phlebotomist, and can enhance analyte stability which facilitate the application of this technique in retrospective studies while providing lower storage and shipping costs, because cold-chain logistics can be eliminated. Thus, precise, sensitive, and multiplexed methods for measuring protein concentrations in DBSs can be used for de novo biomarker discovery and for biomarker quantification or verification experiments. To achieve this goal, MRM assays were developed for multiplexed concentration measurement of proteins in DBSs. The lower limit of quantification (LLOQ) was found to have a median total coefficient of variation (CV) of 18% for 245 proteins, whereas the median LLOQ was 5 fmol of peptide injected on column, and the median inter-day CV over 4 days for measuring endogenous protein concentration was 8%. The majority (88%) of the assays displayed parallelism, whereas the peptide standards remained stable throughout the assay workflow and after exposure to multiple freeze-thaw cycles. For 190 proteins, the measured protein concentrations remained stable in DBS stored at ambient laboratory temperature for up to 2 months. Finally, the developed assays were used to measure the concentration ranges for 200 proteins in twenty same sex, same race and age matched individuals. Graphical Abstract Highlights Repeatable quantification of 200 proteins in dried blood spots. Determined lower limit of quantification, repeatability, parallelism and stability. Protein stability in DBS stored at ambient temperatures for up to 2 months. Concentration ranges for 200 proteins in 20 healthy individuals.