MADM-ML, a mouse genetic mosaic system with increased clonal efficiency.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 24143253.
- Also identified by DOI 10.1371/journal.pone.0077672 and PMC identifier PMC2716028.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Mosaic Analysis with Double Markers (MADM) is a mouse genetic system that allows simultaneous gene knockout and fluorescent labeling of sparse, clonally-related cells within an otherwise normal mouse, thereby circumventing embryonic lethality problems and providing single-cell resolution for phenotypic analysis in vivo. The clonal efficiency of MADM is intrinsically low because it relies on Cre/loxP-mediated mitotic recombination between two homologous chromosomes rather than within the same chromosome, as in the case of conditional knockout (CKO). Although sparse labeling enhances in vivo resolution, the original MADM labels too few or even no cells when a low-expressing Cre transgene is used or a small population of cells is studied. Recently, we described the usage of a new system, MADM-ML, which contains three mutually exclusive, self-recognizing loxP variant sites as opposed to a single loxP site present in the original MADM system (referred to as MADM-SL in this paper). Here we carefully compared the recombination efficiency between MADM-SL and MADM-ML using the same Cre transgene, and found that the new system labels significantly more cells than the original system does. When we established mouse medulloblastoma models with both the original and the new MADM systems, we found that, while the MADM-SL model suffered from varied tumor progression and incomplete penetrance, the MADM-ML model had consistent tumor progression and full penetrance of tumor formation. Therefore MADM-ML, with its higher recombination efficiency, will broaden the applicability of MADM for studying many biological questions including normal development and disease modeling at cellular resolution in vivo.