Parallel metabolomics and lipidomics enables the comprehensive study of mouse brain regional metabolite and lipid patterns

Effect of chronic intermittent hypoxia on hippocampal lipid metabolism in mice: A targeted lipidomics study

Evidence suggests that cognitive impairments due to obstructive sleep apnea (OSA) are related to abnormal lipid metabolism. Abnormal lipid metabolism in the brain might be a potential cause of neurodegeneration. Nonetheless, there is limited clarity regarding the comprehensive lipid metabolism changes in the hippocampus induced by chronic intermittent hypoxia (CIH). Targeted lipidomic analysis of mouse hippocampus and HT22 cells was essential to exploring changes in lipid metabolism after CIH exposure. The approach was used to quantify 575 lipid species in mouse hippocampus and 1285 lipid species in HT22 cells, belonging to 6 different classes. Among those lipid classes, glycerophospholipids (GP), fatty acyls (FA), sphingolipids (SP), glycerolipid (GL), and prenol lipids (PR) were not significantly changed in mouse hippocampus under CIH conditions, but steroid (ST) levels were significantly reduced. Based on in vitro results, CIH exposure significantly raises the levels of several lipids, including GP, PR, SP, and ST. However, GL and FA did not change significantly. Notably, in vivo as well as in vitro experiments showed down-regulation of LPI (16:0), LPI (18:0), PC (18:1/20:4), PE (O-18:0/16:0), PE (O-16:0/22:6), PI (18:0/18:0), and up-regulation of PI (18:0/19:2). Additionally, in CIH conditions, lipid droplets (LDs) typically accumulate as a result of metabolism disorders. Disorders of lipid metabolism and the abnormal accumulation of LDs in neurons cause neuronal damage and behavioral disorders. The altered levels of LPI, PC, PE, and PI in OSA patients might provide new insights into understanding lipid metabolism disorders in the nervous system.

Dysregulated glycerophospholipid metabolism in amygdala may mediate favipiravir-induced anxiety-like behaviors in mice

Favipiravir, the first RNA polymerase inhibitor approved to treat resistant influenza, has been reported to be associated with central nervous system (CNS) side effects, particularly anxiety-like behavior; nevertheless, the underlying mechanism remains largely unknown. In this study, we investigated the effect of favipiravir on the neurobehavior of mice, and combined lipidomics and transcriptomics analysis to explore the mechanism underlying this effect. In behavioral tests, the mice displayed anxiety-like behaviors after oral favipiravir administration (200 mg/kg) for 7 days continuously. By lipidomics analysis, we observed that favipiravir induced a dysregulation of glycerophospholipid metabolism in the amygdala. Moreover, favipiravir significantly reduced the mRNA level of glycerol-3-phosphate acyltransferase 2 (Gpat2), the rate-limiting enzyme of glycerophospholipid synthesis. Notably, favipiravir markedly reduced the levels of docosahexaenoic acid-enriched phosphatidylethanolamine or phosphatidylcholine (DHA-PE/PC) and arachidonic acid-enriched phosphatidylethanolamine or phosphatidylcholine (AA-PE/PC), two components of glycerophospholipids, in the amygdala. The increased expression of phospholipase A2 (Pla2) may attribute to the enhanced release of arachidonic acid (AA) from AA-PE/PC. Furthermore, favipiravir altered neurite morphology and reduced neurophysiological activity in amygdala neurons in vitro. Collectively, dysregulated glycerophospholipid metabolism in the amygdala may contribute to the adverse effect of favipiravir.

Measurement of cellular MDA content through MTBE-extraction based TBA assay by eliminating cellular interferences

Malondialdehyde (MDA) has long been served as a crucial indicator for assessing cellular oxidative stress levels. In this study, we introduce a new approach to determine cellular MDA levels based on a methyl tert-butyl ether (MTBE) extraction, aimed at eliminating interferences from cellular components during thiobarbituric acid (TBA) derivatization of MDA. By leveraging the effective MTBE extraction, we identified that the determination of the MDA-TBA adduct formed from the MTBE extraction layer can effectively eliminate the interferences from cellular proteins and metabolites. This method demonstrated acceptable linearity and precision in cellular samples and showed significant differences in H2O2 treated cellular oxidative stress models. The MTBE extraction-based MDA-TBA approach provides a reliable, cost-effective, and feasible method to determine cellular MDA levels using batch microplate reader approach for the assessment of cellular oxidative stress.

Development of a Global Metabo-Lipid-Prote-omics Workflow to Compare Healthy Proximal and Distal Colonic Epithelium in Mice

A multimetabo-lipid-prote-omics workflow was developed to characterize the molecular interplay within proximal (PC) and distal (DC) colonic epithelium of healthy mice. This multiomics data set lays the foundation to better understand the two tissue types and can be used to study, for example, colon-related diseases like colorectal cancer or inflammatory bowel disease. First, the methyl tert-butyl ether extraction method was optimized, so that from a single tissue biopsy >350 reference-matched metabolites, >1850 reference-matched lipids, and >4500 proteins were detected by using targeted and untargeted metabolomics, untargeted lipidomics, and proteomics. Next, each omics-data set was analyzed individually and then merged with the additional omics disciplines to generate a deep understanding of the underlying complex regulatory network within the colon. Our data demonstrates, for example, differences in mucin formation, detected on substrate level as well as on enzyme level, and altered lipid metabolism by the detection of phospholipases hydrolyzing sphingomyelins to ceramides. In conclusion, the combination of the three mass spectrometry-based omics techniques can better entangle the functional and regional differences between PC and DC tissue compared to each single omics technique.

An important role for triglyceride in regulating spermatogenesis

Drosophila is a powerful model to study how lipids affect spermatogenesis. Yet, the contribution of neutral lipids, a major lipid group which resides in organelles called lipid droplets (LD), to sperm development is largely unknown. Emerging evidence suggests LD are present in the testis and that loss of neutral lipid- and LD-associated genes causes subfertility; however, key regulators of testis neutral lipids and LD remain unclear. Here, we show LD are present in early-stage somatic and germline cells within the Drosophila testis. We identified a role for triglyceride lipase brummer (bmm) in regulating testis LD, and found that whole-body loss of bmm leads to defects in sperm development. Importantly, these represent cell-autonomous roles for bmm in regulating testis LD and spermatogenesis. Because lipidomic analysis of bmm mutants revealed excess triglyceride accumulation, and spermatogenic defects in bmm mutants were rescued by genetically blocking triglyceride synthesis, our data suggest that bmm-mediated regulation of triglyceride influences sperm development. This identifies triglyceride as an important neutral lipid that contributes to Drosophila sperm development, and reveals a key role for bmm in regulating testis triglyceride levels during spermatogenesis.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates, such as fatty acids, carbohydrates, lipids, and amino acids, to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities, which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection: direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF-MS/MS). Using DI-FTICR MS, 9644 metabolic features were detected where 7156 were assigned a molecular formula and 1107 were annotated by accurate mass assignment. Using LC-Q-TOF-MS/MS, 21,428 metabolic features were detected where 285 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 1340 heart metabolites were identified in this study, which span a wide range of polarities including polar (benzenoids, carbohydrates, and nucleosides) as well as nonpolar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results from this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

A study on cholesterol-cholesteryl ester metabolic homeostasis and drug intervention in hyperlipidemic hamsters using UHPLC-MS/MS

Hyperlipidemia is a global metabolic disorder characterized by dysregulation of lipid metabolism. This dysregulation is closely associated with the altered homeostasis of cholesterol-cholesteryl ester (CE) metabolism in systemic circulation, and some organs. Additionally, the relationship between oxidized cholesteryl ester (oxCE) and the disease has also gained attention. Currently, there is a lack of comprehensive research on the alterations in cholesterol-CE metabolism in the context of hyperlipidemia, as well as the characteristics of lipid-lowering agents in regulating this metabolic state. Therefore, 40 oxCEs were identified in the hamster liver sample, and novel ultrahigh performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) methods were established for simultaneous analysis of cholesterol, 57 CEs, and 40 oxCEs in the serum, liver, adipose tissue, and intestine of hyperlipidemic hamsters. This study investigated the metabolic alterations between cholesterol-CE/oxCE in hyperlipidemic hamsters and those treated with lipid-lowering agents, including the Niemann-Pick-C1 like-1 protein (NPC1L1) inhibitor ezetimibe and the acyl coenzyme A: cholesterol acyltransferase (ACAT) inhibitor avasimibe. The study findings demonstrate metabolic disorders in cholesterol-CE/oxCE homeostasis in hyperlipidemic hamsters. Lipid-lowering agent therapy can improve the metabolic dysregulation caused by hyperlipidemia, with distinct characteristics: ezetimibe is more effective in reducing cholesterol, while avasimibe is more effective in reducing CEs/oxCEs. Eight potential biomarkers indicating the dysregulation of cholesterol-CE metabolism caused by hyperlipidemia and its improvement by lipid-lowering agents have been identified in the serum. This study offers new insights into the hyperlipidemia pathophysiology and the mechanisms of lipid-lowering agents from a novel perspective on cholesterol-CE/oxCE metabolic homeostasis.

Variations in metabolite profiles of serum coronas produced around PEGylated liposomal drugs by surface property

Understanding biomolecular coronas that spontaneously occur around nanocarriers (NCs) in biological fluids is critical to nanomedicine as the coronas influence the behaviors of NCs in biological systems. In contrast to extensive investigations of protein coronas over the past decades, understanding of the coronas of biomolecules beyond proteins, e.g., metabolites, has been rather limited despite such biochemicals being ubiquitously involved in the coronas, which may influence the bio-nano interactions and thus exert certain biological impacts. In this study, serum biomolecular coronas, in particular the coronas of metabolites including lipids, around PEGylated doxorubicin-loaded liposomes with different surface property were investigated. The surface properties of liposomal drugs varied in terms of surface charge and PEGylation density by employing different ionic lipids such as DOTAP and DOPS and different concentrations of PEGylation lipids in liposome formulation. Using the liposomal drugs, the influence of the surface property on the serum metabolite profiles in the coronas was traced for target molecules of 220 lipids and 88 hydrophilic metabolites. From the results, it was found that metabolites rather than proteins mainly constitute the serum coronas on the liposomal drugs. Most of the serum metabolites were found to be retained in the coronas but with altered abundances. Depending on their class, lipids exhibited a different dependence on the surface property. However, overall, lipids appeared to favor corona formation on more negatively charged and PEGylated surfaces. Hydrophilic metabolites also exhibited a similar propensity for corona formation. This study on the surface dependence of metabolite corona formation provides a fundamental contribution toward attaining a comprehensive understanding of biomolecular coronas, which will be critical to the development of efficient nanomedicine.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates such as fatty acids, carbohydrates, lipids, and amino acids to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic extractions) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection - direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF MS/MS). Using DI-FTICR MS, 9,521 metabolic features were detected where 7,699 were assigned a chemical formula and 1,756 were assigned an annotated by accurate mass assignment. Using LC-Q-TOF MS/MS, 21,428 metabolic features were detected where 626 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 2276 heart metabolites were identified in this study which span a wide range of polarities including polar (benzenoids, alkaloids and derivatives and nucleosides) as well as non-polar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results of this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER

Mass spectrometry (MS)-based untargeted metabolomic and lipidomic approaches are being used increasingly in biomedical research. The adoption and integration of these data are critical to the overall multi-omic toolkit. Recently, a sample extraction method called Multi-ABLE has been developed, which enables concurrent generation of proteomic and untargeted metabolomic and lipidomic data from a small amount of tissue. The proteomics field has a well-established set of software for processing of acquired data; however, there is a lack of a unified, off-the-shelf, ready-to-use bioinformatics pipeline that can take advantage of and prepare concurrently generated metabolomic and lipidomic data for joint downstream analyses. Here we present an R pipeline called MultiABLER as a unified and simple upstream processing and analysis pipeline for both metabolomics and lipidomics datasets acquired using liquid chromatography-tandem mass spectrometry. The code is available via an open-source license at https://github.com/holab-hku/MultiABLER.

The role of selenium in shaping mice brain metabolome and selenoproteome through the gut-brain axis by combining metabolomics, metallomics, gene expression and amplicon sequencing

Selenium (Se) is a trace element crucial for human health. Recently, the impact of Se supplementation on gut microbiota has been pointed out as well as its influence on the expression of certain selenoproteins and gut metabolites. This study aims to elucidate the link between Se supplementation, brain selenoproteins and brain metabolome as well as the possible connection with the gut-brain axis. To this end, an in vivo study with 40 BALB/c mice was carried out. The study included conventional (n=20) and mice model with microbiota depleted by antibiotics (n=20) under a regular or Se supplemented diet. Brain selenoproteome was determined by a transcriptomic/gene expression profile, while brain metabolome and gut microbiota profiles were accomplished by untargeted metabolomics and amplicon sequencing, respectively. The total content of Se in brain was also determined. The selenoproteins genes Dio and Gpx isoenzymes, SelenoH, SelenoI, SelenoT, SelenoV and SelenoW and 31 metabolites were significantly altered in the brain after Se supplementation in conventional mice, while 11 selenoproteins and 26 metabolites were altered in microbiota depleted mice. The main altered brain metabolites were related to glyoxylate and dicarboxylate metabolism, amino acid metabolism, and gut microbiota that have been previously related with the gut-brain axis (e.g., members of Lachnospiraceae and Ruminococcaceae families). Moreover, specific associations were determined between brain selenoproteome and metabolome, which correlated with the same bacteria, suggesting an intertwined mechanism. Our results demonstrated the effect of Se on brain metabolome through specific selenoproteins gene expression and gut microbiota.

Brain lipidomics: From functional landscape to clinical significance

Lipids are crucial components of cellular function owing to their role in membrane formation, intercellular signaling, energy storage, and homeostasis maintenance. In the brain, lipid dysregulations have been associated with the etiology and progression of neurodegeneration and other neurological pathologies. Hence, brain lipids are emerging as important potential targets for the early diagnosis and prognosis of neurological diseases. This review aims to highlight the significance and usefulness of lipidomics in diagnosing and treating brain diseases. We explored lipid alterations associated with brain diseases, paying attention to organ-specific characteristics and the functions of brain lipids. As the recent advances in brain lipidomics would have been impossible without advances in analytical techniques, we provide up-to-date information on mass spectrometric approaches and integrative analysis with other omic approaches. Last, we present the potential applications of lipidomics combined with artificial intelligence techniques and interdisciplinary collaborative research for treating brain diseases with clinical heterogeneities.

Addressing big data challenges in mass spectrometry-based metabolomics

Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.

Impacts of Formula Supplemented with Milk Fat Globule Membrane on the Neurolipidome of Brain Regions of Piglets

The milk fat globule membrane (MFGM) appears to play an important role in infant neurocognitive development; however, its mechanism(s) of action remains unclear. This study aimed to investigate the role of a dietary MFGM supplement on the lipid profiles of different neonatal brain regions. Ten-day-old male piglets (4−5 kg) were fed unsupplemented infant formula (control, n = 7) or an infant formula supplemented with low (4%) or high (8%) levels of MFGM (n = 8 each) daily for 21 days. Piglets were then euthanized, and brain tissues were sectioned. Untargeted liquid chromatography-mass spectrometry lipidomics was performed on the cerebellum, hippocampus, prefrontal cortex, and the rest of the brain. The analyses identified 271 and 171 lipids using positive and negative ionization modes, respectively, spanning 16 different lipid classes. MFGM consumption did not significantly alter the lipidome in most brain regions, regardless of dose, compared to the control infant formula. However, 16 triacylglyceride species were increased in the hippocampus (t-test, p-value < 0.05) of the high-supplemented piglets. Most lipids (262 (96.7%) and 160 (93.6%), respectively) differed significantly between different brain regions (ANOVA, false discovery rate corrected p-value < 0.05) independent of diet. Thus, this study highlighted that dietary MFGM altered lipid abundance in the hippocampus and detected large differences in lipid profiles between neonatal piglet brain regions.

MS-FINDER Assisted in Understanding the Profile of Flavonoids in Temporal Dimension during the Fermentation of Pu-erh Tea

Flavonoid represents a significant class of secondary metabolites in Pu-erh tea with benefits to human health. For a rapid and complete discovery of such compounds, we established a data mining workflow that integrates software MS-DIAL, MS-FINDER, and molecular networking analysis. As a result, 181 flavonoids were tentatively annotated including 22 first found in Pu-erh tea, and two of them were potentially new molecules. The dynamic alteration of these flavonoids during Pu-erh fermentation was further investigated. They all showed a trend of first increasing and then decreasing. Moreover, statistical analysis showed that the first to third pile turnings of the fermentation process had a greater impact on the changes of flavonoids. Partial metabolic pathways were proposed. This study provides a quick and automatic strategy for flavonoid profiling. The temporal dimension of flavonoids during fermentation may serve as a theoretical basis for Pu-erh tea manufacturing technology and study on substance foundation.

MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities

MOTIVATION

Post-acquisition sample normalization is a critical step in comparative metabolomics to remove the variation introduced by sample amount or concentration difference. Previously reported approaches are either specific to one sample type or built on strong assumptions on data structure, which are limited to certain levels. This encouraged us to develop MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected on mass spectrometry (MS) platforms.

RESULTS

MAFFIN calculates normalization factors using maximal density fold change (MDFC) computed by a kernel density-based approach. Using both simulated data and 20 metabolomics data sets, we showcased that MDFC outperforms four commonly used normalization methods in terms of reducing the intragroup variation among samples. Two essential steps, overlooked in conventional methods, were also examined and incorporated into MAFFIN. (1) MAFFIN uses multiple orthogonal criteria to select high-quality features for normalization factor calculation, which minimizes the bias caused by abiotic features or metabolites with poor quantitative performance. (2) MAFFIN corrects the MS signal intensities of high-quality features using serial quality control (QC) samples, which guarantees the accuracy of fold change calculations. MAFFIN was applied to a human saliva metabolomics study and led to better data separation in principal component analysis (PCA) and more confirmed significantly altered metabolites.

AVAILABILITY AND IMPLEMENTATION

The MAFFIN algorithm was implemented in an R package named MAFFIN. Package installation, user instruction, and demo data are available at https://github.com/HuanLab/MAFFIN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Optimized integration of metabolomics and lipidomics reveals brain region-specific changes of oxidative stress and neuroinflammation in type 1 diabetic mice with cognitive decline

INTRODUCTION

Type 1 diabetes (T1D) causes cognitive decline and has been associated with brain metabolic disorders, but its potential molecular mechanisms remain unclear.

OBJECTIVES

The purpose of this study was to explore the molecular mechanisms underlying T1D-induced cognitive impairment using metabolomics and lipidomics.

METHODS

We developed an optimized integration approach of metabolomics and lipidomics for brain tissue based on UPLC-Q-TOF-MS and analyzed a comprehensive characterization of metabolite and lipid profiles in the hippocampus and frontal cortex of T1D male mice with cognitive decline (T1DCD) and age-matched control (CONT) mice.

RESULTS

The results show that T1DCD mice had brain metabolic disorders in a region-specific manner relative to CONT mice, and the frontal cortex exhibited a higher lipid peroxidation than the hippocampus in T1DCD mice. Based on metabolic changes, we found that microglia was activated under diabetic condition and thereby promoted oxidative stress and neuroinflammation, leading to neuronal injury, and this event was more pronounced in the frontal cortex than the hippocampus.

CONCLUSION

Our results suggest that brain region-specific shifts in oxidative stress and neuroinflammation may contribute to diabetic cognitive decline, and the frontal cortex could be the more vulnerable brain region than the hippocampus.

Comprehensive Serum Lipidomics for Detecting Incipient Dementia in Parkinson's Disease

While a number of methods are available for analyzing lipids, unbiased untargeted lipidomics with high coverage remains a challenge. In this work, we report a study of isotope-standard-assisted liquid chromatography mass spectrometry lipidomics of serum for biomarker discovery. We focus on Parkinson's disease (PD), a neurodegenerative disorder that often progresses to dementia. Currently, the diagnosis of PD is purely clinical and there is limited ability to predict which PD patients will transition to dementia, hampering early interventions. We studied serum samples from healthy controls and PD patients with no clinical signs of dementia. A follow-up 3 years later revealed that a subset of PD patients had transitioned to dementia. Using the baseline samples, we constructed two biomarker panels to differentiate (1) PD patients from healthy controls and (2) PD patients that remained cognitively stable from PD patients with incipient dementia (diagnosed 3 years after sample collection). The proposed biomarker panels displayed excellent performance and may be useful for detecting prodromal PD dementia, allowing early interventions and prevention efforts. The biochemistry of significantly changed lipids is also discussed within the current knowledge of neurological pathologies. Our results are promising and future work using a larger cohort of samples is warranted.

Integrated Metabolomics and Transcriptomics Using an Optimised Dual Extraction Process to Study Human Brain Cancer Cells and Tissues

The integration of untargeted metabolomics and transcriptomics from the same population of cells or tissue enhances the confidence in the identified metabolic pathways and understanding of the enzyme–metabolite relationship. Here, we optimised a simultaneous extraction method of metabolites/lipids and RNA from ependymoma cells (BXD-1425). Relative to established RNA (mirVana kit) or metabolite (sequential solvent addition and shaking) single extraction methods, four dual-extraction techniques were evaluated and compared (methanol:water:chloroform ratios): cryomill/mirVana (1:1:2); cryomill-wash/Econospin (5:1:2); rotation/phenol-chloroform (9:10:1); Sequential/mirVana (1:1:3). All methods extracted the same metabolites, yet rotation/phenol-chloroform did not extract lipids. Cryomill/mirVana and sequential/mirVana recovered the highest amounts of RNA, at 70 and 68% of that recovered with mirVana kit alone. sequential/mirVana, involving RNA extraction from the interphase of our established sequential solvent addition and shaking metabolomics-lipidomics extraction method, was the most efficient approach overall. Sequential/mirVana was applied to study a) the biological effect caused by acute serum starvation in BXD-1425 cells and b) primary ependymoma tumour tissue. We found (a) 64 differentially abundant metabolites and 28 differentially expressed metabolic genes, discovering four gene-metabolite interactions, and (b) all metabolites and 62% lipids were above the limit of detection, and RNA yield was sufficient for transcriptomics, in just 10 mg of tissue.