Bao Shunxing, Tang Yucheng, Lee Ho Hin, Gao Riqiang, Chiron Sophie, Lyu Ilwoo, Coburn Lori A, Wilson Keith T, Roland Joseph T, Landman Bennett A, Huo Yuankai
Dept. of Computer Science, Vanderbilt University, USA.
Dept. of Electrical and Computer Engineering, Vanderbilt University, USA.
Proc Mach Learn Res. 2021 Sep;156:36-46.
Multiplex immunofluorescence (MxIF) is an emerging imaging technique that produces the high sensitivity and specificity of single-cell mapping. With a tenet of "seeing is believing", MxIF enables iterative staining and imaging extensive antibodies, which provides comprehensive biomarkers to segment and group different cells on a single tissue section. However, considerable depletion of the scarce tissue is inevitable from extensive rounds of staining and bleaching ("missing tissue"). Moreover, the immunofluorescence (IF) imaging can globally fail for particular rounds ("missing stain"). In this work, we focus on the "missing stain" issue. It would be appealing to develop digital image synthesis approaches to restore missing stain images without losing more tissue physically. Herein, we aim to develop image synthesis approaches for eleven MxIF structural molecular markers (i.e., epithelial and stromal) on real samples. We propose a novel multi-channel high-resolution image synthesis approach, called pixN2N-HD, to tackle possible missing stain scenarios via a high-resolution generative adversarial network (GAN). Our contribution is three-fold: (1) a single deep network framework is proposed to tackle missing stain in MxIF; (2) the proposed "N-to-N" strategy reduces theoretical four years of computational time to 20 hours when covering all possible missing stains scenarios, with up to five missing stains (e.g., "(N-1)-to-1", "(N-2)-to-2"); and (3) this work is the first comprehensive experimental study of investigating cross-stain synthesis in MxIF. Our results elucidate a promising direction of advancing MxIF imaging with deep image synthesis.
多重免疫荧光（MxIF）是一种新兴的成像技术，可实现单细胞图谱绘制的高灵敏度和特异性。秉持“眼见为实”的原则，MxIF能够对大量抗体进行迭代染色和成像，从而提供全面的生物标志物，以便在单个组织切片上对不同细胞进行分类和分组。然而，由于大量的染色和漂白过程，稀缺组织不可避免地会大量消耗（“组织缺失”）。此外，免疫荧光（IF）成像在特定轮次中可能会全面失败（“染色缺失”）。在这项工作中，我们专注于“染色缺失”问题。开发数字图像合成方法以恢复缺失的染色图像，同时不进一步损耗更多组织，这将是很有吸引力 的。在此，我们旨在针对真实样本上的11种MxIF结构分子标记（即上皮和基质）开发图像合成方法。我们提出了一种新颖的多通道高分辨率图像合成方法pixN2N-HD，通过高分辨率生成对抗网络（GAN）来处理可能出现的染色缺失情况。我们的贡献有三个方面：（1）提出了一个单一的深度网络框架来处理MxIF中的染色缺失问题；（2）所提出的“N对N”策略将覆盖所有可能的染色缺失情况（最多五个缺失染色情况，例如“(N - 1)对1”、“(N - 2)对2”）时的理论四年计算时间减少到20小时；（3）这项工作是对MxIF中交叉染色合成进行研究的首次全面实验性研究。我们的结果阐明了通过深度图像合成推进MxIF成像的一个有前景的方向。
