Back to Search
Start Over
A mathematical insight into cell labelling experiments for clonal analysis
- Source :
- Journal of Anatomy
- Publication Year :
- 2019
- Publisher :
- Wiley, 2019.
-
Abstract
- Studying the progression of the proliferative and differentiative patterns of neural stem cells at the individual cell level is crucial to the understanding of cortex development and how the disruption of such patterns can lead to malformations and neurodevelopmental diseases. However, our understanding of the precise lineage progression programme at single‐cell resolution is still incomplete due to the technical variations in lineage‐tracing approaches. One of the key challenges involves developing a robust theoretical framework in which we can integrate experimental observations and introduce correction factors to obtain a reliable and representative description of the temporal modulation of proliferation and differentiation. In order to obtain more conclusive insights, we carry out virtual clonal analysis using mathematical modelling and compare our results against experimental data. Using a dataset obtained with Mosaic Analysis with Double Markers, we illustrate how the theoretical description can be exploited to interpret and reconcile the disparity between virtual and experimental results.
- Subjects :
- cortical neurogenesis
0301 basic medicine
Histology
Lineage (genetic)
Computer science
Neurogenesis
branching processes
Computational biology
Models, Biological
Clonal analysis
Mice
03 medical and health sciences
0302 clinical medicine
Lineage tracing
Animals
Cell Lineage
birth‐death stochastic process
QA
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Cerebral Cortex
QM
clonal analysis
Experimental data
Original Articles
Cell Biology
QP
Clone Cells
030104 developmental biology
Mosaic Analysis with Double Markers
Original Article
Anatomy
030217 neurology & neurosurgery
Developmental Biology
Subjects
Details
- Language :
- English
- ISSN :
- 00218782
- Database :
- OpenAIRE
- Journal :
- Journal of Anatomy
- Accession number :
- edsair.doi.dedup.....8cc3c4dac072eb7e04487722a95a7de1