PO.TB02.01 · 肿瘤生物学

饮食组成——临床前癌症研究荧光成像中的关键变量

Diet composition - A critical variable in fluorescence imaging for preclinical cancer research

海报缩略图:饮食组成——临床前癌症研究荧光成像中的关键变量
编号 2140 展板 12 时间 4/20 09:00–12:00 区域 Section 28 主讲 Steven Yeung
分会场 In Vivo Imaging
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作者与单位 Authors & Affiliations

STEVEN YEUNG1, Phuong Dang2, Jeffrey D. Peterson3, Sridhar Radhakrishnan1

1Research Diets, Inc., New Brunswick, NJ,2The Jackson Laboratory, Bar Harbor, ME,3Revvity, Durham, NC

摘要 Abstract

中文摘要
背景:基于荧光的成像是临床前癌症研究的基石,能够无创、实时地评估肿瘤生长、转移和治疗应答。然而,饮食成分可影响图像质量和数据完整性。基于谷物的饲料含有非营养成分,如植物化学物质和潜在毒素(包括内毒素、霉菌毒素和重金属),这些成分随批次变化,并影响动物表型、代谢和成像结果。常添加至饮食中用于视觉区分的染料(FD&C蓝1号、黄5号、红40号)可能引入背景荧光,而苜蓿及其他富含叶绿素的饮食成分在红光和近红外(NIR)范围内强烈荧光,与许多NIR荧光癌症成像探针所用波长重叠。本研究评估了不同饮食成分对荧光成像质量的影响。 方法:在IVIS™ Spectrum 2上使用640至750 nm的激发波长直接获取饮食颗粒的荧光成像测量值。对于体内研究,先将8周龄雄性C57BL/6J和NU/J小鼠饲喂含苜蓿的饲料一周,然后转为纯化饮食(含或不含染料)或饲料(含或不含苜蓿)两周。使用多个激发波长配以适当的激发/发射滤光片组,以捕获500-750 nm范围内与饮食和染料相关的信号,大部分分析集中于640-750 nm范围。使用Living Image™ 4.8.3系统软件对数据集进行分析以量化胃肠道荧光,采用光谱解混算法将叶绿素/染料信号与相关NIR荧光探针的信号分离。 结果:在体外,苜蓿(640 nm和675 nm)和所掺入的染料(640 nm)均产生强烈的荧光信号。然而,在体内仅含苜蓿的饮食在640和675 nm处产生强烈的背景信号,足以掩盖肿瘤和炎症探针信号。无苜蓿和纯化饮食(无论是否添加染料)在1-2周内清除了背景叶绿素信号,改善了信噪比和图像清晰度。NU/J和C57BL/6J品系的数据相似。 结论:饮食组成强烈影响荧光成像。饲料中苜蓿来源的叶绿素引入了大量NIR自发荧光,而在无苜蓿饮食中这种荧光被最小化。尽管纯化饮食中的染料在体外强烈荧光,但它们在被摄入后可能降解,从而对体内信号贡献极小。标准化饮食提供了一种简单、经济高效的方法,可改善临床前癌症成像的图像质量、可重复性和转化相关性。
查看英文原文 English abstract
Background: Fluorescence based imaging is a cornerstone of preclinical cancer research, enabling noninvasive, real time assessment of tumor growth, metastasis, and therapeutic response. However, dietary ingredients can impact image quality and data integrity. Grain-based chow diets contain nonnutritive components such as phytochemicals and potential toxins including endotoxins, mycotoxins, and heavy metals, that vary by batch and influence animal phenotype, metabolism, and imaging outcomes. Dyes (FD&C Blue #1, Yellow #5, Red #40) commonly added to diets for visual differentiation may introduce background fluorescence, while alfalfa and other chlorophyll-rich dietary ingredients fluoresce strongly in the red and near infrared (NIR) range, overlapping with wavelengths used by many NIR fluorescent cancer imaging probes. The effect of different dietary ingredients on fluorescence imaging quality was evaluated in this study. Methods: Direct fluorescence imaging measurements of diet pellets were acquired on the IVIS TM Spectrum 2 using excitation wavelengths from 640 to 750 nm. For in vivo studies, 8 wk old male C57BL/6J and NU/J mice were initially fed alfalfa containing chow for a week, then transitioned to either purified diets (with or without dyes) or chows (with or without alfalfa) for two weeks. Multiple excitation wavelengths were used with appropriate excitation/emission filter sets to capture the range of diet- and dye-related signal from 500-750 nm with most analysis focused on the 640-750 nm range. Datasets were analyzed using Living Image TM 4.8.3 system software to quantify gastrointestinal fluorescence, using spectral unmixing algorithms to separate the signal from chlorophyll/dye and a relevant NIR fluorescent probe. Results: In vitro , both alfalfa (640 nm and 675 nm) and incorporated dyes (640 nm) contributed to strong fluorescence signal. However, only the alfalfa containing diets produced strong background signal at 640 and 675 nm in vivo , sufficient to obscure tumor and inflammation probe signals. Alfalfa free and purified diets, with or without added dyes, cleared the background chlorophyll signal within 1-2 weeks, improving signal to noise ratios and image clarity. Data were similar in both NU/J and C57BL/6J strains. Conclusions: Diet composition strongly influences fluorescence imaging. Alfalfa derived chlorophyll in chows introduces substantial NIR autofluorescence, which is minimized in alfalfa-free diets. Although dyes in purified diets fluoresce strongly in vitro , they likely degrade upon consumption contributing to minimal in vivo signal. Standardizing diet offers a simple, cost effective approach to improve image quality, reproducibility, and translational relevance in preclinical cancer imaging.
利益披露 Disclosure
S. Yeung, Research Diets, Inc Employment. P. Dang, Jackson Laboratory Employment. J. D. Peterson, Revvity Employment. S. Radhakrishnan, Research Diets Inc. Employment.

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