美国约翰霍普金斯大学Chuan-Hsiang Huang、Jr-Ming Yang等研究人员合作，利用大规模复用生物传感器条形码破译细胞信号网络。该项研究成果于2021年11月26日在线发表在《细胞》杂志上。

据研究人员介绍，基因编码的荧光生物传感器是监测活细胞内生化活动的有力工具，但其复用能力受到可用光谱空间的限制。

通过开发一套条码蛋白，其可产生超过100个条码，并且在光谱上可与常用的生物传感器分离，研究人员克服了这个问题。表达不同生物传感器的条码细胞混合物被同时成像，并通过深度学习模型进行分析，来实现信号事件的大规模复用跟踪。

重要的是，细胞混合物中的不同生物传感器显示出高度协调的活动，从而促进了对其时间关系的划分。同时追踪受体酪氨酸激酶信号网络中的多个生物传感器，结果揭示了效应器适应的不同机制、KRAS突变的细胞自主和非自主效应，以及网络中复杂的相互作用。生物传感器条码提出了一种可扩展的方法，可用于扩大多路复用能力，并解读信号网络的复杂性及其在细胞间的相互作用。

附：英文原文

Title: Deciphering cell signaling networks with massively multiplexed biosensor barcoding

Author: Jr-Ming Yang, Wei-Yu Chi, Jessica Liang, Saki Takayanagi, Pablo A. Iglesias, Chuan-Hsiang Huang

Issue&Volume: 2021-11-26

Abstract: Genetically encoded fluorescent biosensors are powerful tools for monitoring biochemical activities in live cells, but their multiplexing capacity is limited by the available spectral space. We overcome this problem by developing a set of barcoding proteins that can generate over 100 barcodes and are spectrally separable from commonly used biosensors. Mixtures of barcoded cells expressing different biosensors are simultaneously imaged and analyzed by deep learning models to achieve massively multiplexed tracking of signaling events. Importantly, different biosensors in cell mixtures show highly coordinated activities, thus facilitating the delineation of their temporal relationship. Simultaneous tracking of multiple biosensors in the receptor tyrosine kinase signaling network reveals distinct mechanisms of effector adaptation, cell autonomous and non-autonomous effects of KRAS mutations, as well as complex interactions in the network. Biosensor barcoding presents a scalable method to expand multiplexing capabilities for deciphering the complexity of signaling networks and their interactions between cells.

DOI: 10.1016/j.cell.2021.11.005

Source: https://www.cell.com/cell/fulltext/S0092-8674(21)01320-9