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Metabolic Changes in Brain Slices over Time: a Multiplatform Metabolomics Approach

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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.

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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

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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.).

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Authors and Affiliations

  1. Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Spain

    Carolina Gonzalez-Riano & Coral Barbas

  2. Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Politécnica de Madrid, Madrid, Spain

    Silvia Tapia-González & Javier DeFelipe

  3. Instituto Cajal (CSIC), Avenida Doctor Arce 37, 28002, Madrid, Spain

    Silvia Tapia-González, Gertrudis Perea, Candela González-Arias & Javier DeFelipe

  4. Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, Madrid, Spain

    Silvia Tapia-González & Javier DeFelipe

Authors
  1. Carolina Gonzalez-Riano
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  2. Silvia Tapia-González
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  3. Gertrudis Perea
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  4. Candela González-Arias
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  5. Javier DeFelipe
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  6. Coral Barbas
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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

Correspondence to Coral Barbas.

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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.

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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)

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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

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