Kim Gyuri, Shin Hyejin, Eom Minho, Kim Hyunwoo, Chang Jae-Byum, Yoon Young-Gyu
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea.
Department of Materials Science and Engineering, KAIST, Daejeon, Republic of Korea.
Commun Biol. 2025 Jun 14;8(1):928. doi: 10.1038/s42003-025-08357-5.
Three-dimensional multiplexed fluorescence imaging is an indispensable technique in neuroscience. For two-dimensional multiplexed imaging, cyclic immunofluorescence, which involves repeating staining, imaging, and signal removal over multiple cycles, has been widely used. However, the application of cyclic immunofluorescence to three dimensions poses challenges, as a single staining process can take more than 12 hours for thick specimens, and repeating this process for multiple cycles can be prohibitively long. Here, we propose SEPARATE (Spatial Expression PAttern-guided paiRing And unmixing of proTEins), a method that reduces the number of cycles by half by imaging two proteins using a single fluorophore. This is achieved by labeling two proteins with the same fluorophores and unmixing their signals based on their three-dimensional spatial expression patterns, using a neural network. We employ a feature extraction network to quantify the spatial distinction between proteins, with these quantified values, termed feature-based distances, used to identify protein pairs. We then validate the feature extraction network with ten proteins, showing a high correlation between spatial pattern distinction and signal unmixing performance. We finally demonstrate the volumetric multiplexed imaging of six proteins using three fluorophores, pairing them based on feature-based distances and unmixing their signals through protein separation networks.
三维多重荧光成像在神经科学中是一项不可或缺的技术。对于二维多重成像，循环免疫荧光法，即通过在多个循环中重复染色、成像和信号去除，已被广泛使用。然而，将循环免疫荧光法应用于三维成像存在挑战，因为对于厚标本，单个染色过程可能需要超过12小时，而重复此过程多个循环可能会长得令人望而却步。在这里，我们提出了SEPARATE（空间表达模式引导的蛋白质配对和解混）方法，该方法通过使用单个荧光团对两种蛋白质进行成像，将循环次数减少一半。这是通过用相同的荧光团标记两种蛋白质，并使用神经网络根据它们的三维空间表达模式对其信号进行解混来实现的。我们采用一个特征提取网络来量化蛋白质之间的空间差异，这些量化值称为基于特征的距离，用于识别蛋白质对。然后，我们用十种蛋白质验证了特征提取网络，结果表明空间模式差异与信号解混性能之间具有高度相关性。我们最终展示了使用三种荧光团对六种蛋白质进行体积多重成像，根据基于特征的距离对它们进行配对，并通过蛋白质分离网络对其信号进行解混。
