Two-Photon Intravital Fluorescence Lifetime Imaging of the Kidney Reveals Cell-Type Specific Metabolic Signatures

自体荧光 活体显微镜检查 荧光寿命成像显微镜 双光子激发显微术 体内 显微镜 生物 荧光显微镜 生物物理学 化学 病理 荧光 光学 医学 物理 生物技术
作者
Takashi Hato,Seth Winfree,Richard N. Day,Ruben M. Sandoval,Bruce A. Molitoris,Mervin C. Yöder,Roger C. Wiggins,Yi Zheng,Kenneth W. Dunn,Pierre C. Dagher
出处
期刊:Journal of The American Society of Nephrology [American Society of Nephrology]
卷期号:28 (8): 2420-2430 被引量:82
标识
DOI:10.1681/asn.2016101153
摘要

In the live animal, tissue autofluorescence arises from a number of biologically important metabolites, such as the reduced form of nicotinamide adenine dinucleotide. Because autofluorescence changes with metabolic state, it can be harnessed as a label-free imaging tool with which to study metabolism in vivo . Here, we used the combination of intravital two-photon microscopy and frequency-domain fluorescence lifetime imaging microscopy (FLIM) to map cell-specific metabolic signatures in the kidneys of live animals. The FLIM images are analyzed using the phasor approach, which requires no prior knowledge of metabolite species and can provide unbiased metabolic fingerprints for each pixel of the lifetime image. Intravital FLIM revealed the metabolic signatures of S1 and S2 proximal tubules to be distinct and resolvable at the subcellular level. Notably, S1 and distal tubules exhibited similar metabolic profiles despite apparent differences in morphology and autofluorescence emission with traditional two-photon microscopy. Time-lapse imaging revealed dynamic changes in the metabolic profiles of the interstitium, urinary lumen, and glomerulus—areas that are not resolved by traditional intensity-based two-photon microscopy. Finally, using a model of endotoxemia, we present examples of the way in which intravital FLIM can be applied to study kidney diseases and metabolism. In conclusion, intravital FLIM of intrinsic metabolites is a bias-free approach with which to characterize and monitor metabolism in vivo , and offers the unique opportunity to uncover dynamic metabolic changes in living animals with subcellular resolution.
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