Abstract
Brain slice preparations are widely used for research in neuroscience. However, a high-quality preparation is essential and there is no consensus regarding stable parameters that can be used to define the status of the brain slice preparation after its collection at different time points. Thus, it is critical to fully characterize the experimental conditions for ex vivo studies using brain slices for electrophysiological recording. In this study, we used a multiplatform (LC-MS and GC-MS) untargeted metabolomics-based approach to shed light on the metabolome and lipidome changes taking place at different time intervals during the brain slice preparation process. We have found significant modifications in the levels of 300 compounds, including several lipid classes and their derivatives, as well as metabolites involved in the GABAergic pathway and the TCA cycle. All these preparation-dependent changes in the brain biochemistry related to the time interval should be taken into consideration for future studies to facilitate non-biased interpretations of the experimental results.
Similar content being viewed by others
Data Availability
The data that support the findings of this study are available from the corresponding authors upon request.
Abbreviations
- ARA:
-
arachidonic acid
- Cer:
-
ceramides
- CV:
-
coefficient of variation
- DAG:
-
diradylglycerols
- DHA:
-
docosahexaenoic acid
- ESI:
-
electrospray ionization
- FbI:
-
find by ion
- GABA:
-
gamma-aminobutyric acid
- GC-MS:
-
gas chromatography-mass spectrometry
- GPLs:
-
glycerophospholipids
- h:
-
hour
- HMDB:
-
human metabolome database
- IS:
-
internal standard
- LC-MS:
-
liquid chromatography-mass spectrometry
- LPC:
-
lysoglycerophosphocholines
- LPE:
-
lysoglycerophosphoethanolamines
- MAG:
-
monoradylglycerols
- MFE:
-
molecular feature extraction
- min:
-
minutes
- MVDA:
-
multivariate data analysis
- NIST:
-
National Institute of Standards and Technology
- NMDA:
-
N-methyl-d-aspartate
- NMDG:
-
N-methyl-d-glucamine
- ns:
-
not significant
- OPLS-DA:
-
orthogonal partial least square-discriminant analysis
- PC:
-
glycerophosphocholines
- PCA:
-
principal component analysis
- PE:
-
glycerophosphoethanolamines
- PI:
-
glycerophosphoinositols
- PLS-DA:
-
partial least square-discriminant analysis
- PS:
-
glycerophosphoserine
- PUFA:
-
polyunsaturated fatty acid
- QCs:
-
quality controls
- QC-SVRC:
-
quality control-support vector regression
- QTOF:
-
quadrupole time-of-flight
- RFE:
-
recursive feature extraction
- RT:
-
retention time
- RTL:
-
retention time lock
- SD:
-
standard deviation
- SEM:
-
standard error of mean
- SM:
-
sphingomyelins
- TAG:
-
triradylglycerols
- TIC:
-
total ion chromatogram
- TCA:
-
tricarboxylic acid cycle
- UVDA:
-
univariate data analysis
- VIP:
-
variable influence on projection
References
Yamamoto C, McIlwain H (1966) Electrical activities in thin sections from the mammalian brain maintained in chemically-defined media in vitro. J Neurochem 13(12):1333–1343
Llinas RR (1988) The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242(4886):1654–1664
Cho S, Wood A, Bowlby M (2007) Brain slices as models for neurodegenerative disease and screening platforms to identify novel therapeutics. Curr Neuropharmacol 5(1):19–33
Babiloni C, Blinowska K, Bonanni L, Cichocki A, de Haan W, del Percio C, Dubois B, Escudero J et al (2020) What electrophysiology tells us about Alzheimer's disease: a window into the synchronization and connectivity of brain neurons. Neurobiol Aging 85:58–73
Varela C, Llano DA, Theyel BB (2011) An introduction to in vitro slice approaches for the study of neuronal circuitry. Neuromethods 65:103–1250
Steriade M (2003) The corticothalamic system in sleep. Front Biosci 8:d878–d899
Stein LR, Zorumski CF, Izumi Y (2017) Dissection method affects electrophysiological properties of hippocampal slices. Oruen: CNS Jl 3(2):94–101
Huang S, Uusisaari MY (2013) Physiological temperature during brain slicing enhances the quality of acute slice preparations. Front Cell Neurosci 7:48
Ivanov A, Zilberter Y (2011) Critical state of energy metabolism in brain slices: the principal role of oxygen delivery and energy substrates in shaping neuronal activity. Front Neuroenerg 3:9
Siklos L et al (1997) Intracellular calcium redistribution accompanies changes in total tissue Na+, K+ and water during the first two hours of in vitro incubation of hippocampal slices. Neuroscience 79(4):1013–1022
Whittingham TS, Lust WD, Christakis DA, Passonneau JV (1984) Metabolic stability of hippocampal slice preparations during prolonged incubation. J Neurochem 43(3):689–696
Schurr A, Reid KH, Tseng MT, Edmonds HL Jr (1984) The stability of the hippocampal slice preparation: an electrophysiological and ultrastructural analysis. Brain Res 297(2):357–362
Kirov SA, Petrak LJ, Fiala JC, Harris KM (2004) Dendritic spines disappear with chilling but proliferate excessively upon rewarming of mature hippocampus. Neuroscience 127(1):69–80
Trivino-Paredes JS, Nahirney PC, Pinar C, Grandes P, Christie BR (2019) Acute slice preparation for electrophysiology increases spine numbers equivalently in the male and female juvenile hippocampus: a DiI labeling study. J Neurophysiol 122(3):958–969
Fiala JC, Kirov SA, Feinberg MD, Petrak LJ, George P, Goddard CA, Harris KM (2003) Timing of neuronal and glial ultrastructure disruption during brain slice preparation and recovery in vitro. J Comp Neurol 465(1):90–103
Buskila Y et al (2014) Extending the viability of acute brain slices. Sci Rep 4:5309
Grøndahl TØ, Langmoen IA (1993) Epileptogenic effect of antibiotic drugs. J Neurosurg 78(6):938–943
Hertz L (2012) Metabolic studies in brain slices–past, present, and future. Front Pharmacol 3:26
Ting JT, Lee BR, Chong P, Soler-Llavina G, Cobbs C, Koch C, Zeng H, Lein E (2018) Preparation of acute brain slices using an optimized N-methyl-D-glucamine protective recovery method. J Vis Exp (132):53825. https://doi.org/10.3791/53825
Gonzalez-Riano C, Tapia-González S, García A, Muñoz A, DeFelipe J, Barbas C (2017) Metabolomics and neuroanatomical evaluation of post-mortem changes in the hippocampus. Brain Struct and Funct 222(6):2831–2853
Gonzalez-Riano C, León-Espinosa G, Regalado-Reyes M, García A, DeFelipe J, Barbas C (2019) Metabolomic study of hibernating syrian hamster brains: in search of neuroprotective agents. J Proteome Res 18(3):1175–1190
Dudzik D, Barbas-Bernardos C, García A, Barbas C (2017). Quality assurance procedures for mass spectrometry untargeted metabolomics. a review. J Pharm Biomed Anal 147:149–173
Si-Hung L, Causon TJ, Hann S (2017) Comparison of fully wettable RPLC stationary phases for LC-MS-based cellular metabolomics. Electrophoresis 38(18):2287–2295
Kuligowski J, Sánchez-Illana Á, Sanjuán-Herráez D, Vento M, Quintás G (2015) Intra-batch effect correction in liquid chromatography-mass spectrometry using quality control samples and support vector regression (QC-SVRC). Analyst 140(22):7810–7817
de la Fuente AG et al (2018) Knowledge-based metabolite annotation tool: CEU mass mediator. J Pharm Biomed Anal 154:138–149
Gil-De-La-Fuente A et al (2019) CEU mass mediator 3.0: a metabolite annotation tool. J Proteome Res 18(2):797–802
Han X (2016) Lipidomics: Comprehensive mass spectrometry of lipids. John Wiley & Sons Inc., Hoboken, New Jersey
Tsugawa H, Ikeda K, Takahashi M, Satoh A, Mori Y, Uchino H, Okahashi N, Yamada Y, Tada I, Bonini P et al (2020) MS-DIAL 4: accelerating lipidomics using an MS/MS, CCS, and retention time atlas. https://doi.org/10.1101/2020.02.11.944900
Mohamed A, Molendijk J, Hill MM (2020) lipidr: a software tool for data mining and analysis of lipidomics datasets. J Proteome Res 19:2890–2897
Santello M, Toni N, Volterra A (2019) Astrocyte function from information processing to cognition and cognitive impairment. Nat Neurosci 22(2):154–166
Perea G, Navarrete M, Araque A (2009) Tripartite synapses: astrocytes process and control synaptic information. Trends Neurosci 32(8):421–431
Czéh B, Varga ZKK, Henningsen K, Kovács GL, Miseta A, Wiborg O (2015) Chronic stress reduces the number of GABAergic interneurons in the adult rat hippocampus, dorsal-ventral and region-specific differences. Hippocampus 25(3):393–405
Roth FC, Draguhn A (2012) GABA metabolism and transport: effects on synaptic efficacy. Neural Plast 2012:1–12
Molnár E (2016) Investigation of neurotransmitter receptors in brain slices using cell surface biotinylation. In: Luján R, Ciruela F (Eds). Receptor and Ion Channel Detection in the Brain. Neuromethods, Humana Press, New York, pp 39-48
Collingridge GL, Peineau S, Howland JG, Wang YT (2010) Long-term depression in the CNS. Nat Rev Neurosci 11(7):459–473
Zhang Y, Cudmore RH, Lin DT, Linden DJ, Huganir RL (2015) Visualization of NMDA receptor–dependent AMPA receptor synaptic plasticity in vivo. Nat Neurosci 18(3):402–407
Facecchia K, Fochesato LA, Ray SD, Stohs SJ, Pandey S (2011) Oxidative toxicity in neurodegenerative diseases: role of mitochondrial dysfunction and therapeutic strategies. J Toxicol 2011:1–12
Mederos S, González-Arias C, Perea G (2018) Astrocyte–neuron networks: a multilane highway of signaling for homeostatic brain function. Front Synaptic Neurosci 10:45
Mathiisen TM, Lehre KP, Danbolt NC, Ottersen OP (2010) The perivascular astroglial sheath provides a complete covering of the brain microvessels: an electron microscopic 3D reconstruction. Glia 58(9):1094–1103
Deitmer JW, Theparambil SM, Ruminot I, Noor SI, Becker HM (2019) Energy dynamics in the brain: contributions of astrocytes to metabolism and pH homeostasis. Front Neurosci 13:1301
Caesar K, Hashemi P, Douhou A, Bonvento G, Boutelle MG, Walls AB, Lauritzen M (2008) Glutamate receptor-dependent increments in lactate, glucose and oxygen metabolism evoked in rat cerebellum in vivo. J Physiol 586(5):1337–1349
Magistretti PJ, Pellerin L (1999) Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging. Philos Trans R Soc Lond B Biol Sci 354(1387):1155–1163
Gallagher CN, Carpenter KLH, Grice P, Howe DJ, Mason A, Timofeev I, Menon DK, Kirkpatrick PJ et al (2009) The human brain utilizes lactate via the tricarboxylic acid cycle: a 13C-labelled microdialysis and high-resolution nuclear magnetic resonance study. Brain 132(10):2839–2849
Dienel GA (2019) Brain glucose metabolism: integration of energetics with function. Physiol Rev 99(1):949–1045
Dienel GA (2013) Astrocytic energetics during excitatory neurotransmission: what are contributions of glutamate oxidation and glycolysis? Neurochem Int 63(4):244–258
Bergles DE, Jahr CE (1997) Synaptic activation of glutamate transporters in hippocampal astrocytes. Neuron 19(6):1297–1308
Spanaki C, Plaitakis A (2012) The role of glutamate dehydrogenase in mammalian ammonia metabolism. Neurotox Res 21(1):117–127
Norenberg MD, Martinez-Hernandez A (1979) Fine structural localization of glutamine synthetase in astrocytes of rat brain. Brain Res 161(2):303–310
Schousboe A (1981) Transport and metabolism of glutamate and GABA in neurons and glial cells. In Int Rev Neurobiol 22:1–45
Schousboe A et al (2013) Astrocytic control of biosynthesis and turnover of the neurotransmitters glutamate and GABA. Front Endocrinol 4:102
Hussain G, Wang J, Rasul A, Anwar H, Imran A, Qasim M, Zafar S, Kamran SKS et al (2019) Role of cholesterol and sphingolipids in brain development and neurological diseases. Lipids Health Dis 18(1):26
Buskila Y, Bellot-Saez A, Kékesi O, Cameron M, Morley J (2020) Extending the life span of acute neuronal tissue for imaging and electrophysiological studies. In: Wright N (ed.) Basic Neurobiology Techniques, Neuromethods. Humana, New York, pp 235–259
Wolosker H, Blackshaw S, Snyder SH (1999) Serine racemase: a glial enzyme synthesizing D-serine to regulate glutamate-N-methyl-D-aspartate neurotransmission. Proc Natl Acad Sci 96(23):13409–13414
Coyle JT, Balu D, Wolosker H (2020) D-serine, the shape-shifting NMDA receptor co-agonist. Neurochem Res 45(6):1344–1353
Rapoport SI (2008) Arachidonic acid and the brain. J Nutr 138(12):2515–2520
Attwell D, Miller B, Sarantis M (1993) Arachidonic acid as a messenger in the central nervous system. Sem Neurosci 5(3):159–169
Leaf A (2001) The electrophysiologic basis for the antiarrhythmic and anticonvulsant effects of n − 3 polyunsaturated fatty acids: Heart and brain. Lipids 36(1):S107–S110
Voskuyl RA, Vreugdenhil M, Kang JX, Leaf A (1998) Anticonvulsant effect of polyunsaturated fatty acids in rats, using the cortical stimulation model. Eur J Pharmacol 341(2-3):145–152
Dyall SC (2015) Long-chain omega-3 fatty acids and the brain: a review of the independent and shared effects of EPA, DPA and DHA. Front Aging Neurosci 7:52
Cravatt BF et al (1995) Chemical characterization of a family of brain lipids that induce sleep. Science 268(5216):1506–1509
Hiley CR, Hoi PM (2007) Oleamide: a fatty acid amide signaling molecule in the cardiovascular system? Cardiovasc Drug Rev 25(1):46–60
Acknowledgments
We would like to thank Nick Guthrie for his excellent text editing, and Vanesa Alonso for her technical assistance.
Funding
This work was supported by grants from the following entities: FEDER Program 2014-2020 of the Community of Madrid (Ref.S2017/BMD3684), Centro de Investigación en Red sobre Enfermedades Neurodegenerativas (CIBERNED, CB06/05/0066, Spain) and the Spanish “Ministerio de Ciencia, Innovación y Universidades” (grant PGC2018-094307-B-I00 to J.D and RTI2018-095166-B-I00 to C.B; the Cajal Blue Brain Project [the Spanish partner of the Blue Brain Project initiative from EPFL, Switzerland to J.D]; the PhD fellowship program from MINECO (Spain) (BES-2017-080303) to CG-A; and MINECO grant (BFU2016-75107-P, PID2019-106579RB-I00) and CSIC PIE grant (2019AEP152) to G.P.).
Author information
Authors and Affiliations
Contributions
Conceptualization: Silvia Tapia-González, Gertrudis Perea, and Javier DeFelipe. Data acquisition: Carolina Gonzalez-Riaño and Coral Barbas. Data analysis: Carolina Gonzalez-Riaño and Coral Barbas. Data interpretation: Carolina Gonzalez-Riaño, Gertrudis Perea, Silvia Tapia-González, Candela González-Arias, Javier DeFelipe, and Coral Barbas. Supervision: Coral Barbas and Javier DeFelipe. Writing—original draft: Carolina Gonzalez-Riaño, Gertrudis Perea, and Javier DeFelipe. Writing—review and editing: Carolina Gonzalez-Riaño, Gertrudis Perea, Silvia Tapia-González, Candela González-Arias, Javier DeFelipe, and Coral Barbas.
Corresponding author
Ethics declarations
Competing Interests
The authors declare that they have no competing interests.
Ethics Approval and Consent to Participate
All experimental protocols involving the use of animals were performed in accordance with recommendations for the proper care and use of laboratory animals and under the authorization of the regulations and policies governing the care and use of laboratory animals from the Cajal Institute (Madrid, Spain), in accordance with the European Commission (2010/63/EU), FELASA, and ARRIVE guidelines. Special care was taken to minimize animal suffering and to reduce the number of animals used to the minimum required for statistical accuracy.
Consent for Publication
Not applicable.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Additional file 1
Supplementary Methods. Table 1 Metabolites found as statistically significant at any of the comparisons performed at different time points. (PDF 1120 kb)
Rights and permissions
About this article
Cite this article
Gonzalez-Riano, C., Tapia-González, S., Perea, G. et al. Metabolic Changes in Brain Slices over Time: a Multiplatform Metabolomics Approach. Mol Neurobiol 58, 3224–3237 (2021). https://doi.org/10.1007/s12035-020-02264-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12035-020-02264-y