期刊
ACTA BIOMATERIALIA
卷 80, 期 -, 页码 85-96出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.actbio.2018.09.034
关键词
Biomaterials; Biosensor; FLIM; Live cell imaging; Organoid; Scaffold
资金
- Science Foundation Ireland (SFI) [13/SIRG/2144, 12/RC/2276]
- Russian Science Foundation (RSF) [18-15-00407]
- Russian academic excellence project 5-100
- Russian Science Foundation [18-15-00407] Funding Source: Russian Science Foundation
Quantitative measurement of pH and metabolite gradients by microscopy is one of the challenges in the production of scaffold-grown organoids and multicellular aggregates. Herein, we used the cellulose-binding domain (CBD) of the Cellulomonas fimi CenA protein for designing biosensor scaffolds that allow measurement of pH and Ca2+ gradients by fluorescence intensity and lifetime imaging (FLIM) detection modes. By fusing CBD with pH-sensitive enhanced cyan fluorescent protein (CBD-ECFP), we achieved efficient labeling of cellulose-based scaffolds based on nanofibrillar, bacterial cellulose, and decellularized plant materials. CBD-ECFP bound to the cellulose matrices demonstrated pH sensitivity comparable to untagged ECFP (1.9-2.3 ns for pH 6-8), thus making it compatible with FLIM-based analysis of extracellular pH. By using 3D culture of human colon cancer cells (HCT116) and adult stem cell-derived mouse intestinal organoids, we evaluated the utility of the produced biosensor scaffold. CBD-ECFP was sensitive to increases in extracellular acidification: the results showed a decline in 0.2-0.4 pH units in response to membrane depolarization by the protonophore FCCP. With the intestinal organoid model, we demonstrated multiparametric imaging by combining extracellular acidification (FLIM) with phosphorescent probe-based monitoring of cell oxygenation. The described labeling strategy allows for the design of extracellular pH-sensitive scaffolds for multiparametric FLIM assays and their use in engineered live cancer and stem cell-derived tissues. Collectively, this research can help in achieving the controlled biofabrication of 3D tissue models with known metabolic characteristics. (C) 2018 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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