Application of xCELLigence RTCA Biosensor Technology for Revealing the Profile and Window of Drug Responsiveness in Real Time

Abstract

The xCELLigence technology is a real-time cellular biosensor, which measures the net adhesion of cells to high-density gold electrode arrays printed on custom-designed E-plates. The strength of cellular adhesion is influenced by a myriad of factors that include cell type, cell viability, growth, migration, spreading and proliferation. We therefore hypothesised that xCELLigence biosensor technology would provide a valuable platform for the measurement of drug responses in a multitude of different experimental, clinical or pharmacological contexts. In this manuscript, we demonstrate how xCELLigence technology has been invaluable in the identification of (1) not only if cells respond to a particular drug, but (2) the window of drug responsiveness. The latter aspect is often left to educated guess work in classical end-point assays, whereas biosensor technology reveals the temporal profile of the response in real time, which enables both acute responses and longer term responses to be profiled within the same assay. In our experience, the xCELLigence biosensor technology is suitable for highly targeted drug assessment and also low to medium throughput drug screening, which produces high content temporal data in real time.

Figure 1

xCELLigence technology; impedance recording and cell adhesion. Summary of xCELLigence system technology and impedance, which shows: (a) the high density gold array on the E-plates; and (b) the initial phases of seeding and adhesion, which is recorded by the biosensor and represented as the Cell Index (adhesion).

Figure 2

Interpretation of xCELLigence biosensor Cell Index curves: determination of assay parameters influencing experimental design. After obtaining a standard Cell Index adhesion curve for a particular cell type, it can be used to interpret specific behaviours of the cells relevant to the experimental design. Using the HMEC-1 endothelial cells as an example, the initial period of the Cell Index curve reveals the initial phase of cell adhesion and spreading (0–8 h), followed by a plateau phase prior to a gradual period of proliferation. The arrows represent potential time points for drug treatment; note that the behaviour or response of the cells may vary at each. The curve represents the mean Cell Index value from >4 wells ± SD.

Figure 3

Optimisation of ideal culture conditions. Different Cell Index adhesion curves are obtained from different cells types. The adhesion of (a) HMEC-1 endothelial cells and (b) NTera2/D1 (NT2)-astrocytes is shown. Both cell types were seeded at a range of titrations to identify the ideal seeding density and reveal time windows for drug treatment. Curves represent the mean Cell Index value from >4 wells ± SD.

Figure 4

Optimisation of matrix coating for best growth conditions. There are a multitude of factors that can influence the growth and viability of cells. This figure highlights the influence of collagen (red curve) on the adhesion of human brain cerebral microvascular cell (hCMVECs) endothelial cells. Collagen is a major component of the basal lamina of the blood brain barrier structure. Here, the endothelial cells achieve a higher level of adhesion faster than those not grown on a matrix. Mean Cell Index value from >4 wells ± SD.

Figure 5

Direct comparison of growth characteristics of NT2-astrocytes from different cultures. Here, xCELLigence was used to assess the consistency and growth characteristics of NT2-derived astrocytes produced from different differentiations (and by different students). This example shows that the astrocytes in Differentiation C had a very low Cell Index, which is inconsistent with the strong level of adhesion typical of these cells (Differentiations A and B).

Figure 6

Measurement of rapid or transient cellular responses using xCELLigence. The usefulness of xCELLigence for monitoring acute transient responses is exemplified with the response of microvascular endothelial cells to sphingosine-1-phosphate (S1P). The response to S1P is immediate and transient, and xCELLigence reveals the precise timing and magnitude of the response. S1P was added at several concentrations (addition indicated by black arrow, 1 µM to 1 nM), which reveals a concentration-dependent response. The Normalised Cell Index plot is also used in conjunction with the Cell Index plots to determine the extent (%) of the response. Curves represent the Mean Cell Index value from >4 wells ± SD.

Figure 7

Use of xCELLigence for measuring drug effects on cell viability, compromise and death. Here, NT2 astrocytes were treated with the pro-inflammatory cytokine IL1β at a range of concentration to assess the acute and long-term effects on the cells. During the initial 24-h period following cytokine treatment, there is a pronounced increase in the Cell Index, which is consistent with inflammatory activation of the cell and increased astrocytic size as a consequence. This is followed by a progressive and continual loss of adhesion. IL1β was added 24 h after seeding, and each curve represents the mean ± SD of four wells. The images in (b) show the viable astrocytes remaining on the E-plate at the end of the experiment. The white dotted circles reveal the position of the non-transparent electrode array. These images were acquired using an inverted microscope. The array can be seen more easily in the bright-field insert. Note that ACEA have developed plates with view strips for imaging of cells during xCELLigence experiments.

Figure 8

xCELLigence reveals the differential temporal responsiveness of skin endothelial cells to various TLR ligands across a six-day time course. CL075 (5 μg/mL) induces an immediate and sustained increase in endothelial adhesion, whereas the R837 (5 μg/mL) effects are not obvious until at least 48 h after drug addition. The cells did not appear to respond to R848 (10 μg/mL). The black arrow indicates the time of TLR agonist addition, and the acute response period, highlighted by the red box, is shown in the lower panel. The lower panel reveals clearly the immediacy of the CL075 response. Each curve represents the mean of four wells ± SD. Note that the lower panel is normalised to the time of drug addition.

Figure 9

xCELLigence SP “Well Graph” view for drug discovery. An additional component of the ACEA software is the Well Graph view, which provides an overview of the entire time course for each well. This is useful for drug discovery applications or for experimental quality control. This snap-shot allows easy identification of specific or unusual responses, as highlighted by the encircled responses. These have been enlarged with the time of drug addition marked with the asterisk (24 h post seeding). The response in these highlighted wells is immediate and reveals substantial loss of adhesion, which would be consistent with a cytotoxic drug effect.

Figure A1

Screenshots of key software functionality. (a) The default software page showing the various page tabs; (b) highlighting how to switch between the Cell Index, Normalised Cell Index and the Delta Cell Index view in the Plot tab.

Figure A2

xCELLigence provides quality control and assay variation. The Cell Index curves in (a) show the normal variation that we observe across an E96 well plate, which is usually around 10%–15%. However, xCELLigence can also reveal poor seeding practice (b), which is indicative of such a substantial spread in the Cell Index curves. (c) An example where too few cells were seeded into the E-plate. These exemplify useful teaching and quality control aspects of the technology.

Figure A3

Cell Index vs. Normalised Cell Index. (a,b) show exactly the same dataset, where different densities of endothelial cell were grown for 144 h. The top panel shows the raw Cell Index data, whereas the bottom panel is normalised at 24 h (used here as a hypothetical drug treatment time point). Note the very different shapes of the Cell Index curves following normalisation. As the normalisation function converts all values to a proportion of 1.0, important information is lost in the transformation.