Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning

Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluorophores, and the situation is complicated even further when imaged under a multiphoton microscope. Several methods have been developed and commonly used by biologists for fluorescence microscopy spectral unmixing, such as linear unmixing, non-negative matrix factorization, deconvolution, and principal component analysis. However, they either require pre-knowledge of emission spectra or restrict the number of fluorophores to be the same as detection channels, which highly limits the real-world applications of those spectral unmixing methods. In this paper, we developed a robust and flexible spectral unmixing method: Learning Unsupervised Means of Spectra (LUMoS), which uses an unsupervised machine learning clustering method to learn individual fluorophores’ spectral signatures from mixed images, and blindly separate channels without restrictions on the number of fluorophores that can be imaged. This method highly expands the hardware capability of two-photon microscopy to simultaneously image more fluorophores than is possible with instrumentation alone. Experimental and simulated results demonstrated the robustness of LUMoS in multi-channel separations of two-photon microscopy images. We also extended the application of this method to background/autofluorescence removal and colocalization analysis. Lastly, we integrated this tool into ImageJ to offer an easy to use spectral unmixing tool for fluorescence imaging. LUMoS allows us to gain a higher spectral resolution and obtain a cleaner image without the need to upgrade the imaging hardware capabilities.由于荧光团（fluorophores）的发射光谱存在重叠，荧光显微镜图像常出现荧光串色问题，进而引发假阳性检测。当样本标记有3种及以上荧光团时，该问题几乎无法避免；而在多光子显微镜下成像时，情况会愈发复杂。目前已开发出多种被生物学家广泛使用的荧光显微镜光谱解混方法，例如线性解混、非负矩阵分解、反卷积以及主成分分析。然而，这些方法要么需要预先已知发射光谱，要么要求荧光团数量与检测通道数保持一致，这极大限制了此类光谱解混方法的实际应用。本文提出了一种鲁棒且灵活的光谱解混方法：光谱无监督学习方法（Learning Unsupervised Means of Spectra，LUMoS）。该方法借助无监督机器学习聚类技术，从混合图像中学习各荧光团的光谱特征，可在无需限制可成像荧光团数量的前提下实现盲通道分离。该方法极大拓展了双光子显微镜的硬件性能，使其可同时成像的荧光团数量远超仪器本身的理论上限。实验与仿真结果验证了LUMoS在双光子显微镜图像多通道分离中的鲁棒性。此外，我们还将该方法的应用范围拓展至背景/自发荧光去除与共定位分析领域。最后，我们将该工具集成至ImageJ中，为荧光成像领域提供了一款易用的光谱解混工具。LUMoS可帮助用户在无需升级成像硬件的前提下，获得更高的光谱分辨率与更清晰的图像。