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CLADES:用于细胞谱系分析和操作的可编程基因级联

CLADES: A Programmable Cascade of Genes for Cell Lineage Analysis and Manipulation.

作者信息

González-Aspe Inés, Puchol Beatriz Madariaga, Arrabal Blanca López, Bosonac Brooke, García-Marqués Jorge

机构信息

Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain.

出版信息

Methods Mol Biol. 2025;2886:421-437. doi: 10.1007/978-1-0716-4310-5_21.

Abstract

In the Drosophila brain, neuronal diversity originates from approximately 100 neural stem cells, each dividing asymmetrically. Precise mapping of cell lineages at the single-cell resolution is crucial for understanding the mechanisms that direct neuronal specification. However, existing methods for high-resolution lineage tracing are notably time-consuming and labor-intensive. Here, we outline the best practices for lineage tracing using CLADES (cell lineage access driven by an edition sequence), a revolutionary approach to neuronal lineage tracing that addresses the limitations of previous methods. CLADES effectively traces the birth order of neurons using approximately 100 samples. The technique relies on a genetic cascade of reporter activations and deactivations that delineate lineage progression through color-coded markers. This system not only facilitates the detailed mapping of neuronal lineages but also holds the potential to be applied to tracking biological events and producing cell types for therapeutic purposes.

摘要

在果蝇大脑中,神经元多样性源于约100个神经干细胞,每个神经干细胞都进行不对称分裂。以单细胞分辨率精确绘制细胞谱系对于理解指导神经元特化的机制至关重要。然而,现有的高分辨率谱系追踪方法明显耗时且 labor-intensive。在这里,我们概述了使用CLADES(由编辑序列驱动的细胞谱系访问)进行谱系追踪的最佳实践,这是一种革命性的神经元谱系追踪方法,解决了先前方法的局限性。CLADES使用约100个样本有效地追踪神经元的出生顺序。该技术依赖于报告基因激活和失活的遗传级联,通过颜色编码标记描绘谱系进展。该系统不仅有助于详细绘制神经元谱系,还具有应用于追踪生物学事件和生产用于治疗目的的细胞类型的潜力。

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