Systems Biology

The phototransduction cascade allows photoreceptors to detect light across a wide range of intensities without saturation, with cGMP serving as the second messenger and calcium feedback as the key regulatory mechanism. While experimental evidence suggests that cAMP may also play a role in modulating this cascade, such regulation would necessitate rapid changes in cAMP levels on a timescale of seconds. However, data on the dynamics of intracellular cAMP changes in photoreceptors remain scarce, primarily due to the limitations of conventional fluorescence-based methods in this specialized sensory system. To address this gap, we developed a methodology combining rapid cryofixation of retinal samples following light stimulation with the isolation of outer segment preparations. The rapid cryofixation setup comprises six computer-controlled sections, each with a high-speed stepper motor-driven lever that rapidly moves the specimen in a 180° arc within ~80 ms to press it against a liquid nitrogen-cooled copper cylinder for fixation. Using highly sensitive metabolomics techniques, we measured cAMP levels in these samples. This approach enables the investigation of rapid cAMP dynamics and its potential regulatory role in phototransduction, providing a foundation for understanding the interplay between cAMP and PKA signaling in photoreceptor function.

Trypanosoma cruzi, the causative agent of Chagas disease, faces significant metabolic challenges due to fluctuating nutrient availability and oxidative stress within its insect vector. Metabolomic techniques, such as gas chromatography–mass spectrometry (GC–MS), have been widely used to study the adaptive mechanisms of the parasite. This article describes a standardized method for the untargeted metabolomics analysis of T. cruzi epimastigote, covering parasite cultivation, sample deproteinization with methanol, metabolite extraction, derivatization with BSTFA, and GC–MS analysis. To ensure robustness and reproducibility, statistical analysis uses univariate tests, as well as multivariate approaches such as principal component analysis (PCA) and partial least squares (PLS) regression. The protocol offers a reliable and sensitive method to study metabolic responses in T. cruzi under environmental stress, with low biological variability and high reproducibility.

Stable isotopes have frequently been used to study metabolic processes in live cells both in vitro and in vivo. Glutamine, the most abundant amino acid in human blood, plays multiple roles in cellular metabolism by contributing to the production of nucleotides, lipids, glutathione, and other amino acids. It also supports energy production via anaplerosis of tricarboxylic acid cycle intermediates. While ¹³C-glutamine has been extensively employed to study glutamine metabolism in various cell types, detailed analyses of specific lipids derived from ¹³C-glutamine via the reductive carboxylation pathway are limited. In this protocol, we present a detailed procedure to investigate glutamine metabolism in human glioblastoma (GBM) cells by conducting ¹³C-glutamine tracing coupled with untargeted metabolomics analysis using liquid chromatography–mass spectrometry (LC–MS/MS). The method includes step-by-step instructions for the extraction and detection of polar metabolites and long-chain fatty acids (LCFAs) derived from ¹³C-glutamine in GBM cells. Notably, this approach enables the distinction between isomers of two monounsaturated FAs with identical masses: palmitoleic acid (16:1n-7) (cis-9-hexadecenoic acid) and palmitelaidic acid (16:1n-7) (trans-9-hexadecenoic acid) derived from ¹³C-glutamine through the reductive carboxylation process. In addition, using this protocol, we also unveil previously unknown metabolic alterations in GBM cells following lysosome inhibition by the antipsychotic drug pimozide.

Metabolite modifications play a critical role in enhancing plants’ adaptability to environmental changes and serve as a major source of functional diversity in metabolites. However, current metabolomics approaches are limited to targeted analyses of a small number of known modified metabolites and lack comprehensive, large-scale studies of plant metabolite modifications. Here, we describe a widely targeted metabolite modificomics (WTMM) strategy, developed using ultra-high-performance liquid chromatography–quadrupole linear ion trap (UHPLC-Q-Trap) and ultra-high-performance liquid chromatography–Q-Exactive Orbitrap (UHPLC-QE-Orbitrap) technologies. This strategy enables high-throughput identification and sensitive quantification of modified metabolites. Using tomato as a model, we conducted a metabolite modificomics study and constructed a WTMM database, identifying 165 novel modified metabolites. The WTMM strategy is broadly applicable and can be extended to the study of other plant species.

Many small molecules require derivatization to increase their volatility and to be amenable to gas chromatographic (GC) separation. Derivatization is usually time-consuming, and typical batch-wise procedures increase sample variability. Sequential automation of derivatization via robotic liquid handling enables the overlapping of sample preparation and analysis, maximizing time efficiency and minimizing variability. Herein, a protocol for the fully automated, two-stage derivatization of human blood–based samples in line with GC–[Orbitrap] mass spectrometry (MS)-based metabolomics is described. The protocol delivers a sample-to-sample runtime of 31 min, being suitable for better throughput routine metabolomic analysis.

Capturing produced, consumed, or exchanged metabolites (metabolomics) and the result of gene expression (transcriptomics) require the extraction of metabolites and RNA. Multi-omics approaches and, notably, the combination of metabolomics and transcriptomic analyses are required for understanding the functional changes and adaptation of microorganisms to different physico-chemical and environmental conditions. A protocol was developed to extract total RNA and metabolites from less than 6 mg of a kind of phototrophic biofilm: oxygenic photogranules. These granules are aggregates of several hundred micrometers up to several millimeters. They harbor heterotrophic bacteria and phototrophs. After a common step for cell disruption by bead-beating, a part of the volume was recovered for RNA extraction, and the other half was used for the methanol- and dichloromethane-based extraction of metabolites. The solvents enabled the separation of two phases (aqueous and lipid) containing hydrophilic and lipophilic metabolites, respectively. The ¹H nuclear magnetic resonance (NMR) analysis of these extracts produced spectra that contained over a hundred signals with a signal-to-noise ratio higher than 10. The quality of the spectra enabled the identification of dozens of metabolites per sample. Total RNA was purified using a commercially available kit, yielding sufficient concentration and quality for metatranscriptomic analysis. This novel method enables the co-extraction of RNA and metabolites from the same sample, as opposed to the parallel extraction from two samples. Using the same sample for both extractions is particularly advantageous when working with inherently heterogeneous complex biofilm. In heterogeneous systems, differences between samples may be substantial. The co-extraction will enable a holistic analysis of the metabolomics and metatranscriptomics data generated, minimizing experimental biases, including technical variations and, notably, biological variability. As a result, it will ensure more robust multi-omics analyses, particularly by improving the correlation between metabolic changes and transcript modifications.

Stable-isotope resolved metabolomics (SIRM) is a powerful approach for characterizing metabolic states in cells and organisms. By incorporating isotopes, such as ¹³C, into substrates, researchers can trace reaction rates across specific metabolic pathways. Integrating metabolomics data with gene expression profiles further enriches the analysis, as we demonstrated in our prior study on glioblastoma metabolic symbiosis. However, the bioinformatics tools for analyzing tracer metabolomics data have been limited. In this protocol, we encourage the researchers to use SIRM and transcriptomics data and to perform the downstream analysis using our software tool DIMet. Indeed, DIMet is the first comprehensive tool designed for the differential analysis of tracer metabolomics data, alongside its integration with transcriptomics data. DIMet facilitates the analysis of stable-isotope labeling and metabolic abundances, offering a streamlined approach to infer metabolic changes without requiring complex flux analysis. Its pathway-based "metabologram" visualizations effectively integrate metabolomics and transcriptomics data, offering a versatile platform capable of analyzing corrected tracer datasets across diverse systems, organisms, and isotopes. We provide detailed steps for sample preparation and data analysis using DIMet through its intuitive, web-based Galaxy interface. To showcase DIMet's capabilities, we analyzed LDHA/B knockout glioblastoma cell lines compared to controls. Accessible to all researchers through Galaxy, DIMet is free, user-friendly, and open source, making it a valuable resource for advancing metabolic research.

Dietary saturated fatty acids (SFAs) are upregulated in the blood circulation following digestion. A variety of circulating lipid species have been implicated in metabolic and inflammatory diseases; however, due to the extreme variability in serum or plasma lipid concentrations found in human studies, established reference ranges are still lacking, in addition to lipid specificity and diagnostic biomarkers. Mass spectrometry is widely used for identification of lipid species in the plasma, and there are many differences in sample extraction methods within the literature. We used ultra-high performance liquid chromatography (UPLC) coupled to a high-resolution hybrid triple quadrupole-time-of-flight (QToF) mass spectrometry (MS) to compare relative peak abundance of specific lipid species within the following lipid classes: free fatty acids (FFAs), triglycerides (TAGs), phosphatidylcholines (PCs), and sphingolipids (SGs), in the plasma of mice fed a standard chow (SC; low in SFAs) or ketogenic diet (KD; high in SFAs) for two weeks. In this protocol, we used Principal Component Analysis (PCA) and R to visualize how individual mice clustered together according to their diet, and we found that KD-fed mice displayed unique blood profiles for many lipid species identified within each lipid class compared to SC-fed mice. We conclude that two weeks of KD feeding is sufficient to significantly alter circulating lipids, with PCs being the most altered lipid class, followed by SGs, TAGs, and FFAs, including palmitic acid (PA) and PA-saturated lipids. This protocol is needed to advance knowledge on the impact that SFA-enriched diets have on concentrations of specific lipids in the blood that are known to be associated with metabolic and inflammatory diseases.

Key features

• Analysis of relative plasma lipid concentrations from mice on different diets using R.

• Lipidomics data collected via ultra-high performance liquid chromatography (UPLC) coupled to a high-resolution hybrid triple quadrupole-time-of-flight (QToF) mass spectrometry (MS).

• Allows for a comprehensive comparison of diet-dependent plasma lipid profiles, including a variety of specific lipid species within several different lipid classes.

• Accumulation of certain free fatty acids, phosphatidylcholines, triglycerides, and sphingolipids are associated with metabolic and inflammatory diseases, and plasma concentrations may be clinically useful.

Graphical overview

Non-alcoholic steatohepatitis (NASH) is a condition characterized by inflammation and hepatic injury/fibrosis caused by the accumulation of ectopic fats in the liver. Recent advances in lipidomics have allowed the identification and characterization of lipid species and have revealed signature patterns of various diseases. Here, we describe a lipidomics workflow to assess the lipid profiles of liver homogenates taken from a NASH mouse model. The protocol described below was used to extract and analyze the metabolites from the livers of mice with NASH by liquid chromatography–mass spectrometry (LC-MS); however, it can be applied to other tissue homogenate samples. Using this method, over 1,000 species of lipids from five classes can be analyzed in a single run on the LC-MS. Also, partial elucidation of the identity of neutral lipid (triacylglycerides and diacylglycerides) aliphatic chains can be performed with this simple LC-MS setup.

Key features

• Over 1,000 lipid species (sphingolipids, cholesteryl esters, neutral lipids, phospholipids, fatty acids) are analyzed in one run.

• Analysis of liver lipids in non-alcoholic steatohepatitis (NASH) mouse model.

• Normal-phase chromatography coupled to a triple quadrupole mass spectrometer.

Graphical overview

Schematic procedure for the homogenization and extraction of mouse liver tissue in preparation for LC-MS analysis (Created with BioRender.com)

Magnetic resonance spectroscopy (MRS) can be used to measure in vivo concentrations of neurometabolites. This information can be used to identify neurotransmitter involvement in healthy (e.g., perceptual and cognitive processes) and unhealthy brain function (e.g., neurological and psychiatric illnesses). The standard approach for analyzing MRS data is to combine spectral transients acquired over a ~10 min scan to yield a single estimate that reflects the average metabolite concentration during that period. The temporal resolution of metabolite measurements is sacrificed in this manner to achieve a sufficient signal-to-noise ratio to produce a reliable estimate. Here we introduce two analyses that can be used to increase the temporal resolution of neurometabolite estimates produced from MRS measurements. The first analysis uses a sliding window approach to create a smoothed trace of neurometabolite concentration for each MRS scan. The second analysis combines transients across participants, rather than time, producing a single “group trace” with the highest possible temporal resolution achievable with the data. These analyses advance MRS beyond the current “static” application by allowing researchers to measure dynamic changes in neurometabolite concentration and expanding the types of questions that the technique can be used to address.