2025年3月25日出版的《自然—生物技术》杂志发表了以色列科学家的一项最新研究成果。来自魏茨曼科学研究所的Leeat Keren课题组报道了高维成像使用组合通道复用和深度学习。

课题组研究人员提出了组合多路复用（CombPlex），这是一个组合染色平台，结合了一个算法框架，以指数方式增加测量蛋白质的数量。每个蛋白质可以在几个通道中成像，每个通道包含几个蛋白质的聚集图像。然后将这些组合压缩图像解压缩为以深度学习为主题的单个蛋白质图像。研究团队将22种蛋白质的染色压缩到5个成像通道，实现了精确的重建。研究组在荧光显微镜和基于质量的成像中展示了这种方法，并成功地应用于多种组织和癌症类型。CombPlex可以通过任何成像方式增加测量蛋白质的数量，而不需要专门的仪器。

据介绍，了解组织结构和功能需要在保留空间信息的同时，以单细胞分辨率量化多种蛋白质表达的工具。目前的成像技术为每种蛋白质提供了单独的通道，限制了吞吐量和可扩展性。

附：英文原文

Title: High-dimensional imaging using combinatorial channel multiplexing and deep learning

Author: Ben-Uri, Raz, Ben Shabat, Lior, Shainshein, Dana, Bar-Tal, Omer, Bussi, Yuval, Maimon, Noa, Keidar Haran, Tal, Milo, Idan, Goliand, Inna, Addadi, Yoseph, Salame, Tomer Meir, Rochwarger, Alexander, Schrch, Christian M., Bagon, Shai, Elhanani, Ofer, Keren, Leeat

Issue&Volume: 2025-03-25

Abstract: Understanding tissue structure and function requires tools that quantify the expression of multiple proteins at single-cell resolution while preserving spatial information. Current imaging technologies use a separate channel for each protein, limiting throughput and scalability. Here, we present combinatorial multiplexing (CombPlex), a combinatorial staining platform coupled with an algorithmic framework to exponentially increase the number of measured proteins. Every protein can be imaged in several channels and every channel contains agglomerated images of several proteins. These combinatorically compressed images are then decompressed to individual protein images using deep learning. We achieve accurate reconstruction when compressing the stains of 22 proteins to five imaging channels. We demonstrate the approach both in fluorescence microscopy and in mass-based imaging and show successful application across multiple tissues and cancer types. CombPlex can escalate the number of proteins measured by any imaging modality, without the need for specialized instrumentation.

DOI: 10.1038/s41587-025-02585-0

Source: https://www.nature.com/articles/s41587-025-02585-0