(ECHA), E. C. A. Know more about the effects of the chemicals we use in Europe (ECHA/PR/16/01). https://echa.europa.eu/de/-/know-more-about-the-effects-of-the-chemicals-we-use-in-europe (2016).
Liu, W. J., Nie, H. X., Liang, D. P., Bai, Y. & Liu, H. W. Phospholipid imaging of zebrafish exposed to fipronil using atmospheric pressure matrix-assisted laser desorption ionization mass spectrometry. Talanta https://doi.org/10.1016/j.talanta.2019.120357 (2020).
Google Scholar
Sparvero, L. J. et al. Mapping of phospholipids by MALDI imaging (MALDI-MSI): Realities and expectations. Chem. Phys. Lipid. 165, 545–562. https://doi.org/10.1016/j.chemphyslip.2012.06.001 (2012).
Google Scholar
Koizumi, S. et al. Imaging mass spectrometry revealed the production of lyso-phosphatidylcholine in the injured ischemic rat brain. Neuroscience 168(1), 219–225. https://doi.org/10.1016/j.neuroscience.2010.03.056 (2010).
Google Scholar
Hankin, J. A. et al. MALDI mass spectrometric imaging of lipids in rat brain injury models. J. Am. Soc. Mass Spectrom. 22(6), 1014–1021. https://doi.org/10.1007/s13361-011-0122-z (2011).
Google Scholar
Zhao, C. et al. MALDI-MS imaging reveals asymmetric spatial distribution of lipid metabolites from bisphenol s-induced nephrotoxicity. Anal. Chem. 90(5), 3196–3204. https://doi.org/10.1021/acs.analchem.7b04540 (2018).
Google Scholar
Barbacci, D. C. et al. Mass spectrometric imaging of ceramide biomarkers tracks therapeutic response in traumatic brain injury. ACS Chem. Neurosci. 8(10), 2266–2274. https://doi.org/10.1021/acschemneuro.7b00189 (2017).
Google Scholar
Rompp, A. et al. Histology by mass spectrometry: Label-free tissue characterization obtained from high-accuracy bioanalytical imaging. Angew. Chem. Int. Ed. 49, 3834–3838. https://doi.org/10.1002/anie.200905559 (2010).
Google Scholar
Zemski Berry, K. A. et al. MALDI imaging of lipid biochemistry in tissues by mass spectrometry. Chem. Rev. 111, 6491–6512. https://doi.org/10.1021/cr200280p (2011).
Google Scholar
Cornett, D. S., Reyzer, M. L., Chaurand, P. & Caprioli, R. M. MALDI imaging mass spectrometry: Molecular snapshots of biochemical systems. Nat. Methods 4, 828–833. https://doi.org/10.1038/nmeth1094 (2007).
Google Scholar
Römpp, A. & Spengler, B. Mass spectrometry imaging with high resolution in mass and space. Histochem. Cell Biol. 139, 759–783. https://doi.org/10.1007/s00418-013-1097-6 (2013).
Google Scholar
Monroe, E. B. et al. SIMS and MALDI MS imaging of the spinal cord. Proteomics 8(18), 3746-3754. https://doi.org/10.1002/pmic.200800127 (2008).
Google Scholar
Chaurand, P., Cornett, D. S., Angel, P. M. & Caprioli, R. M. From whole-body sections down to cellular level, multiscale imaging of phospholipids by MALDI mass spectrometry. Mol. Cell. Proteom. https://doi.org/10.1074/mcp.O110.004259 (2011).
Google Scholar
Lee, H.-B. & Peart, T. E. Determination of bisphenol A in sewage effluent and sludge by solid-phase and supercritical fluid extraction and gas chromatography/mass spectrometry. J. AOAC Int. 83, 290–298. https://doi.org/10.1093/jaoac/83.2.290 (2000).
Google Scholar
Desbenoit, N., Walch, A., Spengler, B., Brunelle, A. & Römpp, A. Correlative mass spectrometry imaging, applying time-of-flight secondary ion mass spectrometry and atmospheric pressure matrix-assisted laser desorption/ionization to a single tissue section. Rapid Commun. Mass Spectrometry 32, 159–166. https://doi.org/10.1002/rcm.8022 (2018).
Google Scholar
Meding, S. et al. Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging. J. Proteome Res. 11, 1996–2003. https://doi.org/10.1021/pr200784p (2012).
Google Scholar
Ritschar, S. et al. Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning. Histochem. Cell Biol. https://doi.org/10.1007/s00418-021-02037-1 (2021).
Google Scholar
Altshuler, I. et al. An integrated multi-disciplinary approach for studying multiple stressors in freshwater ecosystems: Daphnia as a model organism. Integr. Comp. Biol. 51(4), 623–633. https://doi.org/10.1093/icb/icr103 (2011).
Google Scholar
Bambino, K. & Chu, J. in Zebrafish at the Interface of Development and Disease Research Vol. 124 Current Topics in Developmental Biology (ed K. C. Sadler) 331–367 (2017).
Seda, J. & Petrusek, A. Daphnia as a model organism in limnology and aquatic biology: Introductory remarks. J. Limnol. 70, 337–344. https://doi.org/10.4081/jlimnol.2011.337 (2011).
Google Scholar
de Souza Anselmo, C., Sardela, V. F., de Sousa, V. P. & Pereira, H. M. G. Zebrafish (Danio rerio): A valuable tool for predicting the metabolism of xenobiotics in humans? Comp. Biochem. Physiol. Part C: Toxicol. Pharmacol. 212, 34–46. https://doi.org/10.1016/j.cbpc.2018.06.005 (2018).
Google Scholar
Panula, P. et al. The comparative neuroanatomy and neurochemistry of zebrafish CNS systems of relevance to human neuropsychiatric diseases. Neurobiol. Dis. 40, 46–57. https://doi.org/10.1016/j.nbd.2010.05.010 (2010).
Google Scholar
Korn, H. & Faber, D. S. The Mauthner cell half a century later: A neurobiological model for decision-making?. Neuron 47, 13–28. https://doi.org/10.1016/j.neuron.2005.05.019 (2005).
Google Scholar
Schirmer, E., Schuster, S. & Machnik, P. Bisphenols exert detrimental effects on neuronal signaling in mature vertebrate brains. Commun. Biol. https://doi.org/10.1038/s42003-021-01966-w (2021).
Google Scholar
Flößner, D. Book review: Cladocera: The genus Daphnia (including Daphniopsis). Int. Rev. Hydrobiol. 90, 637. https://doi.org/10.1002/iroh.200590003 (2005).
Google Scholar
OECD. Test No. 211: Daphnia magna Reproduction Test. (2012).
Muyssen, B. T. A. & Janssen, C. R. Multigeneration zinc acclimation and tolerance in Daphnia magna: Implications for water-quality guidelines and ecological risk assessment. Environ. Toxicol. Chem. 20, 2053–2060. https://doi.org/10.1002/etc.5620200926 (2001).
Google Scholar
Blewett, T. A. et al. Sublethal and reproductive effects of acute and chronic exposure to flowback and produced water from hydraulic fracturing on the water flea Daphnia magna. Environ. Sci. Technol. 51, 3032–3039. https://doi.org/10.1021/acs.est.6b05179 (2017).
Google Scholar
Yang, J. H., Kim, H. J., Lee, S. M., Kim, B. M. & Seo, Y. R. Cadmium-induced biomarkers discovery and comparative network analysis in Daphnia magna. Mol. Cell. Toxicol. 13, 327–336. https://doi.org/10.1007/s13273-017-0036-3 (2017).
Google Scholar
Ferain, A. et al. Body lipid composition modulates acute cadmium toxicity in Daphnia magna adults and juveniles. Chemosphere 205, 328–338. https://doi.org/10.1016/j.chemosphere.2018.04.091 (2018).
Google Scholar
Ritschar, S., Narayana, V. K. B., Rabus, M. & Laforsch, C. Uncovering the chemistry behind inducible morphological defences in the crustacean Daphniamagna via micro-Raman spectroscopy. Sci. Rep. 10(1), 22408. https://doi.org/10.1038/s41598-020-79755-4 (2020).
Google Scholar
Machnik, P., Schirmer, E., Glück, L. & Schuster, S. Recordings in an integrating central neuron provide a quick way for identifying appropriate anaesthetic use in fish. Sci. Rep. 8, 17541. https://doi.org/10.1038/s41598-018-36130-8 (2018).
Google Scholar
Luzio, A. et al. Copper induced upregulation of apoptosis related genes in zebrafish (Danio rerio) gill. Aquat. Toxicol. 128, 183–189. https://doi.org/10.1016/j.aquatox.2012.12.018 (2013).
Google Scholar
Macirella, R. & Brunelli, E. Morphofunctional alterations in zebrafish (Danio rerio) gills after exposure to mercury chloride. Int. J. Mol. Sci. https://doi.org/10.3390/ijms18040824 (2017).
Google Scholar
Mansouri, B. & Johari, S. A. Effects of short-term exposure to sublethal concentrations of silver nanoparticles on histopathology and electron microscope ultrastructure of zebrafish (Danio rerio) gills. IJT 10, 15–20. https://doi.org/10.32598/IJT.10.1.60.4 (2016).
Google Scholar
Perez, C. J., Tata, A., de Campos, M. L., Peng, C. & Ifa, D. R. Monitoring toxic ionic liquids in zebrafish (Danio rerio) with desorption electrospray ionization mass spectrometry imaging (DESI-MSI). J. Am. Soc. Mass Spectrom. 28, 1136–1148. https://doi.org/10.1007/s13361-016-1515-9 (2017).
Google Scholar
Stutts, W. L. et al. Methods for cryosectioning and mass spectrometry imaging of whole-body zebrafish. J. Am. Soc. Mass Spectrom. 31, 768–772. https://doi.org/10.1021/jasms.9b00097 (2020).
Google Scholar
Purves, D. & Williams, S. M. Neuroscience. 2nd edition. Vol. Chapter 11, Vision: The Eye (Sinauer Associates, 2001).
Strungaru, S. A. et al. Toxicity and chronic effects of deltamethrin exposure on zebrafish (Danio rerio) as a reference model for freshwater fish community. Ecotoxicol. Environ. Saf. 171, 854–862. https://doi.org/10.1016/j.ecoenv.2019.01.057 (2019).
Google Scholar
Mishra, A. & Devi, Y. Histopathological alterations in the brain (optic tectum) of the fresh water teleost Channa punctatus in response to acute and subchronic exposure to the pesticide Chlorpyrifos. Acta Histochem. 116, 176–181. https://doi.org/10.1016/j.acthis.2013.07.001 (2014).
Google Scholar
Jia, W., Mao, L., Zhang, L., Zhang, Y. & Jiang, H. Effects of two strobilurins (azoxystrobin and picoxystrobin) on embryonic development and enzyme activities in juveniles and adult fish livers of zebrafish (Danio rerio). Chemosphere 207, 573–580. https://doi.org/10.1016/j.chemosphere.2018.05.138 (2018).
Google Scholar
Seyoum, A., Pradhan, A., Jass, J. & Olsson, P. E. Perfluorinated alkyl substances impede growth, reproduction, lipid metabolism and lifespan in Daphnia magna. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.139682 (2020).
Google Scholar
Scanlan, L. D. et al. Gene transcription, metabolite and lipid profiling in eco-indicator Daphnia magna indicate diverse mechanisms of toxicity by legacy and emerging flame-retardants. Environ. Sci. Technol. 49, 7400–7410. https://doi.org/10.1021/acs.est.5b00977 (2015).
Google Scholar
Heinlaan, M. et al. Changes in the Daphnia magna midgut upon ingestion of copper oxide nanoparticles: A transmission electron microscopy study. Water Res. 45, 179–190. https://doi.org/10.1016/j.watres.2010.08.026 (2011).
Google Scholar
Abe, T., Saito, H., Niikura, Y., Shigeoka, T. & Nakano, Y. Embryonic development assay with Daphnia magna: Application to toxicity of aniline derivatives. Chemosphere 45, 487–495. https://doi.org/10.1016/s0045-6535(01)00049-2 (2001).
Google Scholar
Sengupta, N., Gerard, P. D. & Baldwin, W. S. Perturbations in polar lipids, starvation survival and reproduction following exposure to unsaturated fatty acids or environmental toxicants in Daphnia magna. Chemosphere 144, 2302–2311. https://doi.org/10.1016/j.chemosphere.2015.11.015 (2016).
Google Scholar
Huber, K. et al. Approaching cellular resolution and reliable identification in mass spectrometry imaging of tryptic peptides. Anal. Bioanal. Chem. 410, 5825–5837. https://doi.org/10.1007/s00216-018-1199-z (2018).
Google Scholar
White, R. M. et al. Transparent adult zebrafish as a tool for in vivo transplantation analysis. Cell Stem Cell 2, 183–189. https://doi.org/10.1016/j.stem.2007.11.002 (2008).
Google Scholar
Nagayoshi, S. et al. Insertional mutagenesis by the Tol2 transposon-mediated enhancer trap approach generated mutations in two developmental genes: tcf7 and synembryn-like. Development 135, 159–169. https://doi.org/10.1242/dev.009050 (2008).
Google Scholar
Perciedu Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. Exp. Physiol. 105, 1459–1466. https://doi.org/10.1113/EP088870 (2020).
Google Scholar
Elendt, B. P. Selenium deficiency in Crustacea. Protoplasma 154, 25–33. https://doi.org/10.1007/BF01349532 (1990).
Google Scholar
Sud, M. et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 35, D527–D532. https://doi.org/10.1093/nar/gkl838 (2007).
Google Scholar
Race, A. M., Styles, I. B. & Bunch, J. Inclusive sharing of mass spectrometry imaging data requires a converter for all. J. Proteom. 75, 5111–5112. https://doi.org/10.1016/j.jprot.2012.05.035 (2012).
Google Scholar
Robichaud, G., Garrard, K. P., Barry, J. A. & Muddiman, D. C. MSiReader: An open-source interface to view and analyze high resolving power MS imaging files on Matlab platform. J. Am. Soc. Mass Spectrom. 24, 718–721. https://doi.org/10.1007/s13361-013-0607-z (2013).
Google Scholar
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