Phenotypic analysis with trans-recombination-based genetic mosaic models

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved..

Mosaicism refers to the presence of genetically distinct cell populations in an individual derived from a single zygote, which occurs during the process of development, aging, and genetic diseases. To date, a variety of genetically engineered mosaic analysis models have been established and widely used in studying gene function at exceptional cellular and spatiotemporal resolution, leading to many ground-breaking discoveries. Mosaic analysis with a repressible cellular marker and mosaic analysis with double markers are genetic mosaic analysis models based on trans-recombination. These models can generate sibling cells of distinct genotypes in the same animal and simultaneously label them with different colors. As a result, they offer a powerful approach for lineage tracing and studying the behavior of individual mutant cells in a wildtype environment, which is particularly useful for determining whether gene function is cell autonomous or nonautonomous. Here, we present a comprehensive review on the establishment and applications of mosaic analysis with a repressible cellular marker and mosaic analysis with double marker systems. Leveraging the capabilities of these mosaic models for phenotypic analysis will facilitate new discoveries on the cellular and molecular mechanisms of development and disease.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:299

Enthalten in:

The Journal of biological chemistry - 299(2023), 11 vom: 21. Nov., Seite 105265

Sprache:

Englisch

Beteiligte Personen:

Zhang, Yu [VerfasserIn]
Zeng, Jianhao [VerfasserIn]
Xu, Bing [VerfasserIn]

Links:

Volltext

Themen:

Cancer
Cell–cell interaction
Drosophila
Genomic imprinting
Journal Article
Lineage tracing
MADM
MARCM
Mouse
Research Support, Non-U.S. Gov't
Review
Zebrafish

Anmerkungen:

Date Completed 27.11.2023

Date Revised 06.02.2024

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1016/j.jbc.2023.105265

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM362311544