Literature DB >> 32359651

Analysis of vitamin A and retinoids in biological matrices.

Lindsay C Czuba1, Guo Zhong1, King C Yabut1, Nina Isoherranen2.   

Abstract

Vitamin A signaling pathways are predominantly driven by the cellular concentrations of all-trans-retinoic acid (atRA), as the main mechanism of retinoid signaling is via activation of retinoic acid receptors. atRA concentrations are in turn controlled by the storage of vitamin A and enzymatic processes that synthesize and clear atRA. This has resulted in the need for robust and highly specific analytical methods to accurately quantify retinoids in diverse biological matrices. Tissue-specific differences in both the quantity of retinoids and background matrix interferences can confound the quantification of retinoids, and the bioanalysis requires high performance instrumentation, such as liquid chromatography mass-spectrometry (LC-MS). Successful bioanalysis of retinoids is further complicated by the innate structural instability of retinoids and their relatively high lipophilicity. Further, in vitro experiments with retinoids require attention to experimental design and interpretation to account for the instability of retinoids due to isomerization and degradation, sequential metabolism to numerous structurally similar metabolites, and substrate depletion during experiments. In addition, in vitro biological activity is often confounded by residual presence of retinoids in common biological reagents such as cell culture media. This chapter identifies common biological and analytical complexities in retinoid bioanalysis in diverse biological matrices, and in the use of retinoids in cell culture and metabolic incubations. In addition, this chapter highlights best practices for the successful detection and quantification of the vitamin A metabolome in a wide range of biological matrices.
© 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Liquid chromatography; Mass spectrometry; Retinoic acid; Retinoid; Retinoid bioanalysis

Mesh:

Substances:

Year:  2020        PMID: 32359651      PMCID: PMC7323900          DOI: 10.1016/bs.mie.2020.02.010

Source DB:  PubMed          Journal:  Methods Enzymol        ISSN: 0076-6879            Impact factor:   1.600


  55 in total

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