TY  - GEN
Y1  - 2013///
TI  - LifeXplorer Improves Image Acquisition and Analysis in Systems Biology

CY  - Heidelberg
AV  - public
KW  - Intelligent Microscopy
Automated Microscopy
Highthroughput Microscopy
ID  - heidok15556
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/15556/
A1  - Moßler, Jonas Carl
N2  - Improvements in the capacity to perform and analyze time-resolved experiments are a fundamental challenge in quantitative biology, with automation providing the eventual solution. To that end the author developed an algorithm, termed Automated Result / Cost Optimization (ARCO), as a universally-applicable approach based on an operator-defined experiment result. ARCO increased the information density of high-resolution microscopy experiments while minimizing the associated costs, including the loss of biosensor sensitivity and phototoxicity. Several derivations of the ARCO algorithm provided automatic optimization of microscopy experiments via dynamic re-adjustments of parameters, including XY-positions and light exposure during run time. Furthermore, to integrate high-throughput microscopy with single-cell, high-sampling online data analysis, the author developed LifeXplorer, an adaptable processing and hardware control platform, here applied to the Olympus IX81 ScanR and Nikon Ti-E NIS Elements imaging systems. All applications of the ARCO algorithm were benchmarked against two intracellular events which are well known to be influenced by phototoxic stress; mitochondrial energetics and the reassembly of the Golgi. ARCO optimization significantly increased the information content and accuracy of experimental results, while reducing phototoxicity by several fold. The LifeXplorer platform integrates commonly-used tools (ImageJ, CellProfiler, Matlab, etc.), allowing for adoption and development of new ARCO applications. The application of ARCO to both image acquisition and analysis is an important step towards automation and integration of microscopy and data analysis for the emergence of quantitative biology via computer vision.
ER  -