Real-Time Assessment of the Brain

“If the human brain were so simple that we could understand it, we would be so simple that we couldn’t.”  

This quote, attributed to the late physicist, Emerson M. Pugh, succinctly highlights the daunting challenges of neuroscience research. The complexity of the nervous system lies not only in its structure but also in its functions and interactions. It is a system that controls our memories, emotions, behaviors, and movements, with a biological architecture comprising billions of neurons, glia, and non-neural cells. These cells form trillions of dynamic connections which do not act in isolation, rather they are constantly responding to signals emanating from throughout the body. Additionally, each cell body, axon, and dendrite contain a variety of intracellular signaling pathways which, in turn, are modulated by hundreds of neurotransmitters, neuropeptides, extracellular matrix and adhesion molecules, lipids, and growth factors. Elucidating how these pathways operate and how cells respond to these signals is paramount to understanding the nervous system’s function in healthy, neuropsychiatric, and neuropathologic conditions.

The development and widespread adoption of “omics” technologies over the past few decades have made significant contributions to neuroscience research by providing comprehensive insights into the molecular mechanisms of the brain. Genomics, transcriptomics, and proteomics are by far the most widely employed technologies utilized in neuroscience research today and have collectively enhanced our understanding of the molecular mechanisms governing brain functionality, revealing the intricate interplay between genes, their transcripts, and the resultant proteins. However, these technologies do not provide a real-time assessment of the signaling and metabolic pathways in the brain in response to normal and pathological stimuli. This is a critical limitation considering the brain accounts for more than 20% of the human body’s total metabolism while only constituting approximately 2% of our body’s total mass.¹ To address this void, the adoption of metabolomics—the study of small molecules known as metabolites—becomes essential. 

Global Metabolomic Profiling in Neuroscience Research

The innate power of omics technologies is in their ability to cast a wide, relatively comprehensive net to identify subtle differences in multiple biological pathways from a single sample. Global untargeted metabolomics, such as Metabolon’s Global Discovery Panel, allows for the simultaneous analysis of thousands of metabolites, including amino acids, lipids, nucleotides, vitamins, and xenobiotics from multiple metabolic pathways such as glycolysis, the citric acid cycle, oxidative stress, fatty acid oxidation, and others. This technology therefore can provide pivotal insight into the current phenotype of a sample and applies to a wide range of neuroscience-related areas of interest (Table 1).² Moreover, by integrating metabolomics with other omics technologies, researchers can achieve a more holistic view of how genetics and the environment interact to influence neurobiological processes and health outcomes. This integration allows for a comprehensive understanding of the complex interplay between genes, proteins, metabolites, and environmental factors, leading to more effective strategies for neurological disease prevention, diagnosis, and treatment.³

Lipidomic Profiling in Neuroscience Research 

While untargeted metabolomics allows for a wide assessment of the biological changes occurring within the nervous system, lipidomics focuses entirely on lipids, which play a crucial role in the structure and function of the nervous system. It has been estimated that approximately 50% of the human brain’s dry weight is made up of lipids.⁴ Several classes of lipids are found in the nervous system, including phospholipids, glycolipids, cholesterol and cholesteryl esters, sphingolipids, triacylglycerols (TAGs), eicosanoids, and endocannabinoids. These molecules not only provide structural support for cellular membranes, synapses, and myelin sheaths but can also serve as signaling molecules that help modulate brain development and maintain homeostasis during normal and pathological conditions.^5,6 Through lipidomics, researchers can better comprehend brain structure, function, injury processes, and repair mechanisms, offering insights into more effective treatments for brain-related injuries and diseases.^7,8

In addition to the Global Discovery Panel, Metabolon also offers quantified lipid analysis capabilities with specialized panels. The Complex Lipids Targeted Panel assesses a range of crucial brain lipids, including phosphatidylcholines, phosphatidylethanolamines, cholesteryl esters, sphingomyelins, and TAGs. Moreover, the Cannabinoids Targeted Panel delivers a detailed quantification of a wide array of cannabinoid compounds, including both endocannabinoids and phytocannabinoids, along with their human metabolites.

The Importance of Study Design 

The unique complexities and limited accessibility of the nervous system, coupled with the difficulties in acquiring various sample types like brain tissue and cerebrospinal fluid (CSF), make the adoption of metabolomics in neuroscience particularly challenging compared to other biological systems. Obtaining CSF, which is often preferred over plasma or serum for its closer representation of the brain’s metabolic environment, is invasive and poses ethical and practical challenges, particularly in vulnerable populations such as children or patients with severe neurological disorders.⁹ Additionally, the limited volume of CSF that can be safely extracted can also impact data yield. Similarly, the extreme difficulty in acquiring human brain tissue, due to its invasive nature and ethical considerations, further hinders direct studies of human brain metabolism, often limiting analysis to post-mortem samples. Moreover, the heterogeneity of brain tissue, with different anatomical regions having vastly different metabolic profiles, requires precise sampling and adds to the complexity of data interpretation in relation to overall brain function and pathology. Metabolomic analysis of other neuroscience model organisms such as rodents can introduce similar challenges, as the small sizes can severely limit sample volumes, potentially requiring pooled analysis or increased animal numbers.¹⁰ This is why we recommend discussing your planned metabolomic analysis with Metabolon scientists in the study design stage so that we can leverage our extensive neuroscience experience and knowledge to achieve an adequately powered and focused analysis tailored to your areas of interest.

References

1. Raichle ME, Gusnard DA. Appraising the brain’s energy budget. Proc Natl Acad Sci U S A. 2002;99(16):10237-10239. doi:10.1073/pnas.172399499
2. Vasilopoulou CG, Margarity M, Klapa MI. Metabolomic Analysis in Brain Research: Opportunities and Challenges. Front Physiol. 2016;7:183. doi:10.3389/fphys.2016.00183
3. Kaddurah-Daouk R, Kristal BS, Weinshilboum RM. Metabolomics: a global biochemical approach to drug response and disease. Annu Rev Pharmacol Toxicol. 2008;48:653-683. doi:10.1146/annurev.pharmtox.48.113006.094715
4. Hamilton JA, Hillard CJ, Spector AA, Watkins PA. Brain uptake and utilization of fatty acids, lipids and lipoproteins: application to neurological disorders. J Mol Neurosci. 2007;33(1):2-11. doi:10.1007/s12031-007-0060-1
5. Litvinchuk A, Suh JH, Guo JL, et al. Amelioration of Tau and ApoE4-linked glial lipid accumulation and neurodegeneration with an LXR agonist. Neuron. 2024;112(3):384-403.e8. doi:10.1016/j.neuron.2023.10.023
6. Hussain G, Wang J, Rasul A, et al. Role of cholesterol and sphingolipids in brain development and neurological diseases. Lipids Health Dis. 2019;18(1):26. doi:10.1186/s12944-019-0965-z
7. Yoon JH, Seo Y, Jo YS, et al. Brain lipidomics: From functional landscape to clinical significance. Sci Adv. 2022;8(37):eadc9317. doi:10.1126/sciadv.adc9317
8. Fitzner D, Bader JM, Penkert H, et al. Cell-Type- and Brain-Region-Resolved Mouse Brain Lipidome. Cell Rep. 2020;32(11):108132. doi:10.1016/j.celrep.2020.108132
9. Panyard DJ, Kim KM, Darst BF, et al. Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations. Commun Biol. 2021;4(1):63. doi:10.1038/s42003-020-01583-z
10. Chinopoulos C, Zhang SF, Thomas B, Ten V, Starkov AA. Isolation and functional assessment of mitochondria from small amounts of mouse brain tissue. Methods Mol Biol. 2011;793:311-324. doi:10.1007/978-1-61779-328-8_20