Murray Evan, Cho Jae Hun, Goodwin Daniel, Ku Taeyun, Swaney Justin, Kim Sung-Yon, Choi Heejin, Park Young-Gyun, Park Jeong-Yoon, Hubbert Austin, McCue Margaret, Vassallo Sara, Bakh Naveed, Frosch Matthew P, Wedeen Van J, Seung H Sebastian, Chung Kwanghun
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Cell. 2015 Dec 3;163(6):1500-14. doi: 10.1016/j.cell.2015.11.025.
Combined measurement of diverse molecular and anatomical traits that span multiple levels remains a major challenge in biology. Here, we introduce a simple method that enables proteomic imaging for scalable, integrated, high-dimensional phenotyping of both animal tissues and human clinical samples. This method, termed SWITCH, uniformly secures tissue architecture, native biomolecules, and antigenicity across an entire system by synchronizing the tissue preservation reaction. The heat- and chemical-resistant nature of the resulting framework permits multiple rounds (>20) of relabeling. We have performed 22 rounds of labeling of a single tissue with precise co-registration of multiple datasets. Furthermore, SWITCH synchronizes labeling reactions to improve probe penetration depth and uniformity of staining. With SWITCH, we performed combinatorial protein expression profiling of the human cortex and also interrogated the geometric structure of the fiber pathways in mouse brains. Such integrated high-dimensional information may accelerate our understanding of biological systems at multiple levels.
对跨越多个层次的多种分子和解剖学特征进行联合测量仍是生物学中的一项重大挑战。在此，我们介绍一种简单方法，该方法能够实现蛋白质组学成像，用于对动物组织和人类临床样本进行可扩展、综合的高维表型分析。这种方法称为SWITCH，通过同步组织保存反应，在整个系统中统一确保组织结构、天然生物分子和抗原性。所得框架的耐热和耐化学性质允许进行多轮（>20轮）重新标记。我们对单个组织进行了22轮标记，并对多个数据集进行了精确的共配准。此外，SWITCH同步标记反应以提高探针穿透深度和染色均匀性。利用SWITCH，我们对人类皮质进行了组合蛋白质表达谱分析，并研究了小鼠大脑中纤维通路的几何结构。这种综合的高维信息可能会加速我们对多个层次生物系统的理解。
