Mosaic Expression
Mosaic Expression - Related Videos
10:31
The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics
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Cited by 11 •
2016
This protocol demonstrates how to generate fluorescently marked, genetically defined clonal tumors in the Drosophila eye/antennal imaginal discs (EAD). It describes how to dissect the EAD and brain from the third instar larvae and how to process them to visualize and quantify gene expression changes and tumor invasiveness.
07:23
Mosaic Zebrafish Transgenesis for Evaluating Enhancer Sequences
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Cited by 6 •
2010
We demonstrate our approach to finding potential enhancer elements from developmentally regulated genes and evaluating their function through mosaic zebrafish transgenesis.
09:33
The Tomato/GFP-FLP/FRT Method for Live Imaging of Mosaic Adult Drosophila Photoreceptor Cells
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Cited by 24 •
2013
The Tomato/GFP-FLP/FRT method involves visualizing mosaic photoreceptor cells in living Drosophila. It can be used to follow individual photoreceptor cell fates in the retina for days or weeks. This method is ideal for studies of retinal degeneration and neurodegenerative diseases or photoreceptor cell development.
01:34
Fluid Mosaic Model
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2026
The fluid mosaic model was first proposed as a visual representation of research observations. The model comprises the composition and dynamics of membranes and serves as a foundation for future membrane-related studies. The model depicts the structure of the plasma membrane with a variety of components, which include phospholipids, proteins, and carbohydrates. These integral molecules are loosely bound, defining the cell’s border and providing fluidity for optimal function.LipidsThe most...
01:19
Fluid Mosaic Model
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2025
Scientists identified the plasma membrane in the 1890s and its principal chemical components (lipids and proteins) by 1915. The model for plasma membrane structure, proposed in 1935 by Hugh Davson and James Danielli, was the first model to be widely accepted in the scientific community. The model was based on the plasma membrane's "railroad track" appearance in early electron micrographs. Davson and Danielli theorized that the plasma membrane's structure resembled a sandwich with the analogy of...
FAQs
Random X-chromosome inactivation causes different cells to retain activity from different X chromosomes. Once established during development, these cellular states can produce neighboring populations with different gene activity despite sharing much of the same genetic background. This mechanism is especially useful for interpreting how chromosome-level regulation creates distinct patterns across an otherwise similar tissue.
Epigenetic silencing can turn a gene off through regulatory changes that do not alter its DNA sequence. Position-effect variegation links expression differences to a gene’s regulatory or genomic context. Both mechanisms can create patches of active and inactive cells, but they highlight different sources of control: cellular silencing states versus the influence of genomic position.
Stochastic transcription introduces random variation in whether or how strongly a gene is transcribed in individual cells. If that difference persists as cells divide, it can be propagated into groups of related descendants. The resulting pattern helps explain how development can generate distinct cellular states without requiring every cell to undergo a different inherited DNA change.
Regulatory elements control when and where a gene is active, so linking them to a reporter gene makes expression changes observable. Differences in reporter activity can identify the timing and location of cellular state changes. This approach allows investigators to connect regulatory control with developmental patterns rather than examining gene activity only as an organism-wide average.
A typical strategy links a reporter gene to regulatory elements associated with the expression pattern being studied, then examines where reporter activity appears within the tissue or organism. Spatial differences reveal cellular variation, while changes across development indicate when states emerge. The resulting pattern can support analysis of tissue organization and regulatory behavior.
Stable differences in gene activity can mark groups of cells that share a developmental history. By following where those expression states occur, researchers can infer relationships among descendants and examine how tissues become organized. This makes mosaic patterns useful for connecting early regulatory events with later cellular arrangements, even when the cells occupy different regions.
A genetic mutation may produce disease-related effects in only some cells when regulation differs across a tissue. Comparing affected and unaffected cellular regions can therefore reveal how gene activity modifies the mutation’s consequences. Mosaic expression provides a framework for relating cellular variation to disease patterns and for studying how regulatory mechanisms influence phenotype during development and inheritance.