Xiaomei Tang, Zhenye Hou, Li Lu, Yige Song, Changdong Chai, Lanfang Li, Yuhang Shen, Shuo Wang, Ya Zhao, Jinyu Zeng, Yiqing Guo, Fuliang Jiang, Zhigao Xiang, Hao Li, Aodi He, Bing Zhang, Youming Lu, Xinyan Li
The dense packing of cells within tissues poses challenges for studying cellular properties. Sparse labeling, genetically targeting a small subset of densely distributed cells, provides a powerful approach to investigate cellular morphology, connectivity, dynamics, and functions, especially in neuroscience. However, current sparse labeling methods are generally restricted to fixed labeling densities and often exhibit a decline in sparsity over time. In this study, by maximizing the utility of commonly used recombinases and the relational recognition sites, we constructed a versatile sparse labeling system, termed Tri-M (Multi-recombinase, Multi-recognition site, and Multi-nested), built upon competitive recombination. The Tri-M system, which integrates transgenic mice with viral injection, enables tunable and graded sparse labeling of specific cell types within specific brain regions with long-term stability. This system significantly enhances the ability to track, analyze, and manipulate cells within tissues characterized by dense cellular packing, from single cells to populations.