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 -