Kinetic cell-based morphological screening: prediction of mechanism of compound action and off-target effects

Abstract

We describe a cell-based kinetic profiling approach using impedance readout for monitoring the effect of small molecule compounds. This noninvasive readout allows continuous sampling of cellular responses to biologically active compounds and the ensuing kinetic profile provides information regarding the temporal interaction of compounds with cells. The utility of this approach was tested by screening a library containing FDA approved drugs, experimental compounds, and nature compounds. Compounds with similar activity produced similar impedance-based time-dependent cell response profiles (TCRPs). The compounds were clustered based on TCRP similarity. We identified novel mechanisms for existing drugs, confirmed previously reported calcium modulating activity for COX-2 inhibitor celecoxib, and identified an additional mechanism for the experimental compound monastrol. We also identified and characterized a new antimitotic agent. Our findings indicate that the TCRP approach provides predictive mechanistic information for small molecule compounds.

Figure 1

Real-Time monitoring of adherent cells by the RT-CES system (A) The RT-CES System is composed of a plate station accommodating up to six 96 well E-Plates, an electronic analyzer and a computer which runs the software for automatic and real-time data acquisition and display. The E-Plates are integrated with interdigitated gold microelectrodes. A Schematic representation of microelectrodes and the principle of utilizing cell electrode impedance to non-invasively measure adherent cells are shown. In the absence of cells, the baseline impedance (Z₀) at time t₀ represents the impedance of the gold microelectrodes. Addition of cells to the sensor microelectrodes leads to changes in impedance signal at time t₁ (Z_cellt1) that is directly proportional to the number of cells seeded on the sensors and is displayed as Cell Index (CI). The CI value changes with time (Z_cellt2) and reflects the morphology, adhesion and number of cells inside the well. (B) Real-time monitoring of the density-dependent growth and proliferation of four cell lines on the RT-CES platform. The cells suspensions were transferred to E-Plates and placed on the RT-CES reader for real-time monitoring every 30 minutes for the duration of the assay. (C) Time-dependent cell response profiles (TCRP) of two different cytotoxic agents using A549 cells. The arrow indicates the point of compound addition.

Figure 2

Clustering analysis and TCRPs of compounds associated different mechanistic groups (A) Agglomerative, hierarchical clustering analysis of TCRPs in the long-term response. The descriptor is the change in CI as a function of time. A complete list of the compounds is shown in Supplementary Figure 2. (B) Typical A549 TCRPs of short-term response group together with compound classes. Most compounds in this sub-group appear to be antagonists of GPCRs and modulators of calcium. The light blue arrow on the x-axis in this graph and proceeding graphs indicate that the short term data was collected every 2 min. (C) Typical A549 TCRP associated with compounds and compound classes which modulate the nuclear hormone receptors. (D) Typical A549 TCRP for tubulin modulating compounds and compound classes. (E) Typical A549 TCRP for compounds which affect nucleotide and DNA synthesis. (F) Typical A549 TCRP for compounds which affect protein synthesis.

Figure 2

Clustering analysis and TCRPs of compounds associated different mechanistic groups (A) Agglomerative, hierarchical clustering analysis of TCRPs in the long-term response. The descriptor is the change in CI as a function of time. A complete list of the compounds is shown in Supplementary Figure 2. (B) Typical A549 TCRPs of short-term response group together with compound classes. Most compounds in this sub-group appear to be antagonists of GPCRs and modulators of calcium. The light blue arrow on the x-axis in this graph and proceeding graphs indicate that the short term data was collected every 2 min. (C) Typical A549 TCRP associated with compounds and compound classes which modulate the nuclear hormone receptors. (D) Typical A549 TCRP for tubulin modulating compounds and compound classes. (E) Typical A549 TCRP for compounds which affect nucleotide and DNA synthesis. (F) Typical A549 TCRP for compounds which affect protein synthesis.

Figure 3

TCRPs can be predictive of mechanism of action. (A) COX-2 inhibitors, valdecoxib, rofecoxib, deracoxib and celecoxib were tested in A549 cells at different doses. Only celecoxib resulted in a short-term response TCRP, indicating it might affect calcium levels inside the cell. (B) Direct measurement of calcium levels in the cell using fura-2 demonstrated that celecoxib can induce an increase in calcium levels inside the cell. (C) The TCRP for rifampin co-clustered with well known modulators of nuclear hormone receptors, such as hydrocortisone. (D) The TCRP for estradiol co-clustered with those of other tubulin modulating agents such as colchicine. (E) Compounds with similar backbone structure as digoxin resulted in TCRP similar to camptothecin, indicating that they may affect DNA synthesis or repair as predicted by the TCRP.

Figure 4

Using the time resolution offered by TCRP to identify off-target effect for compounds with established activity. (A) Monastrol and S-trityl-cysteine result in a long term TCRP indicative of mitotic arrest. Monastrol (100 μM final concentration) but not S-trityl-cysteine (11 μM final concentration) leads to a short-term response profile indicative of modulation of calcium. The right panel shows staining of A549 cells with phospho-histone H3 antibody after treatment with monastrol for 16 hours. (B) Treatment HEK 293 cells stably expressing the human Cav1.2 voltage-gated L type calcium channel with monastrol (100μM) leads to inhibition of calcium uptake (* ρ value < 0.001 compared to control), while S-trityl-L-Cysteine (20 μM) does not have a significant effect. The error bars represent standard deviations (N of at least 4 samples) and the data is representative of at least 3 independent experiments.