CRI, Inc., Woburn, MA 01801 , USA.
Biotechniques. 2005 Dec;39(6 Suppl):S33-7. doi: 10.2144/000112093.
Noninvasive in vivo imaging is a rapidly growing field with applications in basic biology, drug discovery and clinical medicine. Because of the high cost of magnetic resonance (MR)- and computed tomography (CT)-based systems, a great deal of effort has gone into developing optical imaging methods, which offer, in some modalities, the promise of high spatial resolution and the ability to detect multiple markers simultaneously However, the ability to image and quantitate fluorescently labeled tumors and other fluorescently labeled markers in vivo has generally been limited by the autofluorescence of the tissue, which reduces the sensitivity of detection and accuracy of quantitation of the labeled target. Multispectral imaging methodology, which spectrally characterizes and computationally eliminates autofluorescence, enhances signal-to-background dramatically, revealing otherwise invisible labeled targets. Signal-to-noise considerations can guide the choice of appropriate sensors for fluorescence-based imaging, which generally does not benefit from the use of highly cooled (and expensive) cameras. Effective use of spectral tools to remove autofluorescence signal requires accurate spectra of the individual components. Using manual and automated algorithms to generate these spectra, it is possible to detect as many as three fluorescent protein-labeled tumors and two separate autofluorescent signals in a single subject.
非侵入式活体成像技术是一个快速发展的领域,在基础生物学、药物发现和临床医学中有广泛的应用。由于磁共振(MR)和计算机断层扫描(CT)成像系统的成本较高,人们投入了大量精力来开发光学成像方法,这些方法在某些模式下具有高空间分辨率和同时检测多个标记物的潜力。然而,活体荧光标记肿瘤和其他荧光标记物的成像和定量能力通常受到组织的自发荧光的限制,这降低了检测的灵敏度和标记靶标的定量准确性。多光谱成像方法学对自发荧光进行光谱特征描述和计算消除,极大地提高了信号与背景的对比度,揭示了原本不可见的标记靶标。信噪比的考虑因素可以指导荧光成像中合适传感器的选择,而荧光成像通常不会受益于使用高度冷却(且昂贵)的相机。有效利用光谱工具去除自发荧光信号需要获得各个组件的准确光谱。通过使用手动和自动算法生成这些光谱,可以在单个对象中检测多达三个荧光蛋白标记的肿瘤和两个单独的自发荧光信号。