Sparse-labeling means that the labeling density is about 1% and labeled neurons do not obscure each other. It is important for visualizing synaptic structures as well as following axons or dendrites across long distances.
In neuroscience, to clarify the developmental principles and functional logic of neural circuits, it is essential to access individual neurons. There are three main strategies to realize the analysis of a single nerve cell or its precursor, including sparse-labeling, the introduction of more labels, and improved image resolution. Among them, the labeling strategy still occupies a key advantage despite the current progress in nanoscale imaging. In general, sparse-labeling can be achieved by locating labels to specific cells or distributing them in a random manner or a combination of the two. Golgi straining remains a benchmark for sparse-labeling. Sparseness and randomness are the main characteristics of sparse-labeling and they are related because the labeling probability directly affects the labeling density. The sparseness of the labeling can be controlled by adjusting the random process.
It has been proved that compared with random labeling, the use of transgenic animals expressing reporters from cell-type specific promoter fragments is more efficient for many scientific problems. Some neuron types are sufficiently sparsely distributed for single-cell analysis, but spatial clustering neurons are more typical, and subdivision of the expression pattern is required. In this case, there are two types of transcriptional strategies, including deterministic sparse transcription and stochastic sparse transcription.
Fig.1 MORF3 Mice Enable Brainwide Sparse Labeling of Microglia and Astrocytes to Reveal Their Detailed Morphology. (Veldman, 2020)
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References
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