Details
| Original language | English |
|---|---|
| Pages (from-to) | 213-223 |
| Number of pages | 11 |
| Journal | SLAS Discovery |
| Volume | 24 |
| Issue number | 3 |
| Early online date | 25 Jan 2019 |
| Publication status | Published - Mar 2019 |
Abstract
Mode of action (MoA) identification of bioactive compounds is very often a challenging and time-consuming task. We used a label-free kinetic profiling method based on an impedance readout to monitor the time-dependent cellular response profiles for the interaction of bioactive natural products and other small molecules with mammalian cells. Such approaches have been rarely used so far due to the lack of data mining tools to properly capture the characteristics of the impedance curves. We developed a data analysis pipeline for the xCELLigence Real-Time Cell Analysis detection platform to process the data, assess and score their reproducibility, and provide rank-based MoA predictions for a reference set of 60 bioactive compounds. The method can reveal additional, previously unknown targets, as exemplified by the identification of tubulin-destabilizing activities of the RNA synthesis inhibitor actinomycin D and the effects on DNA replication of vioprolide A. The data analysis pipeline is based on the statistical programming language R and is available to the scientific community through a GitHub repository.
Keywords
- actinomycin D, impedance spectroscopy, mode of action, natural products, target identification
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biotechnology
- Chemistry(all)
- Analytical Chemistry
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Biochemistry, Genetics and Molecular Biology(all)
- Molecular Medicine
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In: SLAS Discovery, Vol. 24, No. 3, 03.2019, p. 213-223.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - xCELLanalyzer: A Framework for the Analysis of Cellular Impedance Measurements for Mode of Action Discovery
AU - Franke, Raimo
AU - Hinkelmann, Bettina
AU - Fetz, Verena
AU - Stradal, Theresia
AU - Sasse, Florenz
AU - Klawonn, Frank
AU - Brönstrup, Mark
N1 - Funding Information: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been supported by the President’s Initiative and Networking Funds of the Helmholtz Association of German Research Centres (HGF) under contract number VH-GS-202, and by the EU-funded European Marine Biological Research Infrastructure Cluster (EMBRIC, code 654008).
PY - 2019/3
Y1 - 2019/3
N2 - Mode of action (MoA) identification of bioactive compounds is very often a challenging and time-consuming task. We used a label-free kinetic profiling method based on an impedance readout to monitor the time-dependent cellular response profiles for the interaction of bioactive natural products and other small molecules with mammalian cells. Such approaches have been rarely used so far due to the lack of data mining tools to properly capture the characteristics of the impedance curves. We developed a data analysis pipeline for the xCELLigence Real-Time Cell Analysis detection platform to process the data, assess and score their reproducibility, and provide rank-based MoA predictions for a reference set of 60 bioactive compounds. The method can reveal additional, previously unknown targets, as exemplified by the identification of tubulin-destabilizing activities of the RNA synthesis inhibitor actinomycin D and the effects on DNA replication of vioprolide A. The data analysis pipeline is based on the statistical programming language R and is available to the scientific community through a GitHub repository.
AB - Mode of action (MoA) identification of bioactive compounds is very often a challenging and time-consuming task. We used a label-free kinetic profiling method based on an impedance readout to monitor the time-dependent cellular response profiles for the interaction of bioactive natural products and other small molecules with mammalian cells. Such approaches have been rarely used so far due to the lack of data mining tools to properly capture the characteristics of the impedance curves. We developed a data analysis pipeline for the xCELLigence Real-Time Cell Analysis detection platform to process the data, assess and score their reproducibility, and provide rank-based MoA predictions for a reference set of 60 bioactive compounds. The method can reveal additional, previously unknown targets, as exemplified by the identification of tubulin-destabilizing activities of the RNA synthesis inhibitor actinomycin D and the effects on DNA replication of vioprolide A. The data analysis pipeline is based on the statistical programming language R and is available to the scientific community through a GitHub repository.
KW - actinomycin D
KW - impedance spectroscopy
KW - mode of action
KW - natural products
KW - target identification
UR - http://www.scopus.com/inward/record.url?scp=85061065298&partnerID=8YFLogxK
U2 - 10.1177/2472555218819459
DO - 10.1177/2472555218819459
M3 - Article
C2 - 30681906
AN - SCOPUS:85061065298
VL - 24
SP - 213
EP - 223
JO - SLAS Discovery
JF - SLAS Discovery
SN - 2472-5552
IS - 3
ER -