Abstract
Measurement of cell surface coverage has become a common technique for the assessment of growth behavior of cells. As an indirect measurement method, this can be accomplished by monitoring changes in electrode impedance, which constitutes the basis of electric cell-substrate impedance sensing (ECIS). ECIS typically yields growth curves where impedance is plotted against time, and changes in single cell growth behavior or cell proliferation can be displayed without significantly impacting cell physiology. To provide better comparability of ECIS curves in different experimental settings, we developed a large toolset of R scripts for their transformation and quantification. They allow importing growth curves generated by ECIS systems, edit, transform, graph and analyze them while delivering quantitative data extracted from reference points on the curve. Quantification is implemented through three different curve fit algorithms (smoothing spline, logistic model, segmented regression). From the obtained models, curve reference points such as the first derivative maximum, segmentation knots and area under the curve are then extracted. The scripts were tested for general applicability in real-life cell culture experiments on partly anonymized cell lines, a calibration setup with a cell dilution series of impedance versus seeded cell number and finally IPEC-J2 cells treated with 1% and 5% ethanol.
Highlights
A large number of external and environmental influences, including drug treatments under laboratory conditions, exert a promoting or inhibiting effect on the overall growth of cell populations [1,2,3,4], which is typically represented and visualized as biological growth curves [5,6,7,8,9,10]
The best results are often achieved with 3–10 knots for electric cell-substrate impedance sensing system (ECIS) growth curves with largely linear subregions, but this parameter can be fine-tuned
We demonstrated in a real-life cell culture experiment on IPEC-J2 dilution and ethamethods correlatecurve well and are nonlinear with the from amount of initially seeded cells, but less nol treatment data that defined reference points obtained three implemented with thewell counted number on theofelectrode surface.cells, These observations fitting methodsso correlate and are nonlinear withresiding the amount initially seeded conclude that ECIS
Summary
A large number of external and environmental influences, including drug treatments under laboratory conditions, exert a promoting or inhibiting effect on the overall growth of cell populations [1,2,3,4], which is typically represented and visualized as biological growth curves [5,6,7,8,9,10]. A common feature of growth curves is the partition into five essential and chronological subregions: (1) a flat baseline, either before any growth has started or on which growth occurs but is not detectable by the instrumental system due to the lack of sufficient sensitivity, (2) an exponential growth phase, (3) a subsequent linear growth phase, (4) a negative exponential transition phase (5) and the saturation or plateau phase in which growth is stalled [13] Using these curves, it is possible to assess growth behavior using certain parameters as endpoints, such as the comparison of populations at one selected timepoint [14,15]. Various methods have been established for the statistical evaluation of growth curves derived from the population observation of plants [19], animal species [17], microbial growth [18] and cell cultures [16]
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