Multiplexed Spectral Imaging of 120 Different Fluorescent Labels
Multiplexed Spectral Imaging of 120 Different Fluorescent Labels
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DOI：
10.1371/journal.pone.0158495
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发表时间：
2016-07-08
期刊：
影响因子：
3.7
通讯作者：
Borisy, Gary G.
中科院分区：
文献类型：
--
作者：
Valm, Alex M.;Oldenbourg, Rudolf;Borisy, Gary G.
当通过带通滤光器获取时，能够在单个图像中明确区分的荧光标记的数量受到可用荧光团光谱重叠的严重限制。光谱显微镜的最新发展和对光谱记录图像数据的线性分解算法的应用使得光谱高度重叠的荧光团的同时成像成为可能。然而，当荧光信号在多个波长上被分离时，可区分的荧光团的数量仍然受到不可避免的信噪比下降的限制。在这里，我们提出了一种光谱图像分析算法，以极大地扩展用荧光团的二进制组合标记的可区分对象的数量。我们的算法利用了关于已标记样本的先验知识，并对分解解施加了二进制标记约束。我们已经应用我们的标记和分析策略来识别荧光原位杂交标记的微生物，并在这里展示了在一张图像中区分120个不同标记的微生物的能力。
The number of fluorescent labels that can unambiguously be distinguished in a single image when acquired through band pass filters is severely limited by the spectral overlap of available fluorophores. The recent development of spectral microscopy and the application of linear unmixing algorithms to spectrally recorded image data have allowed simultaneous imaging of fluorophores with highly overlapping spectra. However, the number of distinguishable fluorophores is still limited by the unavoidable decrease in signal to noise ratio when fluorescence signals are fractionated over multiple wavelength bins. Here we present a spectral image analysis algorithm to greatly expand the number of distinguishable objects labeled with binary combinations of fluorophores. Our algorithm utilizes a priori knowledge about labeled specimens and imposes a binary label constraint on the unmixing solution. We have applied our labeling and analysis strategy to identify microbes labeled by fluorescence in situ hybridization and here demonstrate the ability to distinguish 120 differently labeled microbes in a single image.