(M1430-05-25) Dynamic Monitoring of Cancer Cell Growth in Monolayer and 3D Spheroid Cultures by Fluorescence – Correlation with Volume, Cell Viability, and Cellular Protein Measurements

Purpose: Two-dimensional (2D) monolayer cancer cells and three-dimensional (3D) multicellular spheroids (MCS) serve as common cell culture models to assess the activity of anticancer drug candidates. Common parameters to monitor the cancer cell growth in these models include volume, cell viability, total cellular protein, and more recently, fluorescence of cancer cells that express fluorescent proteins. Although the last method carries the advantage of non-destructive, dynamic monitoring of cell growth over time, it still needs further validation with other more established methods. In this study, we aimed (1) to construct 2D monolayers and 3D MCS of fluorescent human lung cancer cells (A549-iRFP); (2) to monitor the growth of both 2D and 3D A549-iRFP cells using four different quantitative measurements (volume, cell viability, cellular protein, and iRFP fluorescence); and (3) to correlate the fluorescent signal from A549-iRFP cells with the other quantitative measurements.

Methods: A549-iRFP monolayer cells were cultured in 96-well flat-bottom microplates at different seeding densities. A549-iRFP MCS were cultured in Corning ULA 96-well round-bottom spheroid microplates at a seeding density of 4000 cells/well. The iRFP fluorescent signal was measured by an Odyssey® Infrared Imaging 205 System at the 700 nm channel; cell viability was quantified by CellTiter-Glo 3D Cell Viability Assay; the cellular protein was quantified by BCA assay after digestion. The morphology of the 3D MCS was imaged by Keyence BZ-X700 fluorescence microscope and simulated by ReViSP software from MATLAB. The volume of 3D MCS was estimated both by formula (Volume = π/6 * length * width²) and by ReViSP software from MATLAB

Results: The iRFP fluorescence, cell viability, and cellular protein level of A549-iRFP monolayer showed strong linear relationship with cell seeding density, and the iRFP fluorescent signal is well-correlated (Pearson r > 0.99, p < 0.0001) with cell viability and cellular protein level in the monolayer cells. (Figure 1) The morphology of 3D MCS can be monitored under the Keyence BZ-X700 microscope. The images can be used to simulate the MCS structure using ReViSP software from MATLAB. The volume estimation of MCS by the formula and ReViSP software showed strong correlation. The fluorescent images of MCS by the Odyssey® Infrared Imaging 205 System showed similar morphology to that by microscopy. (Figure 2) The iRFP fluorescence, volume, cell viability, and cellular protein level of A549-iRFP MCS increased with the culturing time. The iRFP fluorescent signal was well-correlated (stat) with volume, cell viability, and cellular protein level in the 3D MCS model. Compared with the 2D monolayers, correlations between the iRFP fluorescence and the measurements of destructive assays (cell viability assay and BCA assay) were much weaker in 3D MCS (Pearson r > 0.73, p < 0.0001), possibly due to incomplete digestion of the MCS structure. The iRFP fluorescent signals showed the best correlation with volume (Pearson r > 0.93, p < 0.0001); however, compared to volume estimation, fluorescent measurement is more reliable for MCS with irregular morphology at later stages. (Figure 3)

Conclusion: The iRFP fluorescent signal from A549-iRFP lung cancer cells correlates strongly with cell viability and cellular protein level in both monolayer and MCS cultures.  The iRFP fluorescent signal also showed a strong correlation with volume in the 3D MCS. Compared to the other three measurements, the quantification by iRFP fluorescence does not need to break cell structure; and compared to volume estimation, iRFP fluorescent measurement is more reliable for MCS with irregular morphology. Thus, the fluorescent signal of iRFP in A549-iRFP cells can be used to reliably and continually monitor cancer cell growth. Due to its long wavelength, the iRFP fluorescent signal could also be used to quantify cancer progression in small animals.

Acknowledgments: This work was supported by Scholarly and Artistic Activity Grant of University of the Pacific.

Figure 1. (A) The linear relationship between iRFP fluorescent signal, cell viability, and cellular protein level with cell seeding density in A549-iRFP 2D monolayers, N = 3. (B) The fluorescent signal of A549-iRFP 2D monolayer cells at different seeding densities, N = 3. (C) Pearson correlation analysis between fluorescent signal with cell viability and cellular protein in A549-iRFP 2D monolayers, N = 18.



Figure 3. (A) The linear relationship between iRFP fluorescent signal, simulated MCS volume by ReViSP software, cell viability, and cellular protein level with time in A549-iRFP 3D MCS (N = 10 for fluorescent measurement and volume estimation, N = 5 for cell viability and cellular protein quantification at each time point) (B) The fluorescent signal of A549-iRFP 3D MCS at different time points, N = 10. (C) Pearson correlation analysis between iRFP fluorescent signal with simulated MCS volume by ReViSP software, cell viability, and cellular protein level in A549-iRFP 3D MCS model. (N = 60 for fluorescent measurement and volume estimation, N = 30 for cell viability and protein quantification)



Figure 2. Representative A549-iRFP 3D MCS imaged by (A) Keyence BZ-X700 fluorescence microscope, (B) Odyssey® Infrared Imaging 205 System, and (C) simulated by ReViSP software from MATLAB at different time points. (D) Pearson correlation analysis between volume estimation by the formula and volume estimation by the ReViSP software from MATLAB, N = 60.