Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Long-term time series analysis of quantum dot encoded cells by deconvolution of the autofluorescence signal

Brown, Martyn Rowan, Summers, Huw D., Rees, Paul, Chappell, Sally Claire, Silvestre, Oscar Ricardo, Khan, Imtiaz A., Smith, Paul James and Errington, Rachel Jane 2010. Long-term time series analysis of quantum dot encoded cells by deconvolution of the autofluorescence signal. Cytometry Part A 77A (10) , pp. 925-932. 10.1002/cyto.a.20936

Full text not available from this repository.

Abstract

The monitoring of cells labeled with quantum dot endosome-targeted markers in a highly proliferative population provides a quantitative approach to determine the redistribution of quantum dot signal as cells divide over generations. We demonstrate that the use of time-series flow cytometry in conjunction with a stochastic numerical simulation to provide a means to describe the proliferative features and quantum dot inheritance over multiple generations of a human tumor population. However, the core challenge for long-term tracking where the original quantum dot fluorescence signal over time becomes redistributed across a greater cell number requires accountability of background fluorescence in the simulation. By including an autofluorescence component, we are able to continue even when this signal predominates (i.e., >80% of the total signal) and obtain valid readouts of the proliferative system. We determine the robustness of the technique by tracking a human osteosarcoma cell population over 8 days and discuss the accuracy and certainty of the model parameters obtained. This systems biology approach provides insight into both cell heterogeneity and division dynamics within the population and furthermore informs on the lineage history of its members.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Neuroscience and Mental Health Research Institute (NMHRI)
Subjects: R Medicine > R Medicine (General)
Uncontrolled Keywords: Flow cytometry ; Cell-cycle ; Quantum dot Nano-toxicity ; Systems biology ; Proliferation ; In-silico modeling
Publisher: Wiley-Blackwell
ISSN: 1552-4922
Last Modified: 10 Oct 2017 13:58
URI: http://orca-mwe.cf.ac.uk/id/eprint/22306

Citation Data

Cited 11 times in Google Scholar. View in Google Scholar

Cited 14 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item