scholarly journals Data Driven Cell Cycle Model to Quantify the Efficacy of Cancer Therapeutics Targeting Specific Cell-Cycle Phases From Flow Cytometry Results

2021 ◽  
Vol 1 ◽  
Author(s):  
David W. James ◽  
Andrew Filby ◽  
M. Rowan Brown ◽  
Huw D. Summers ◽  
Lewis W. Francis ◽  
...  

Many chemotherapeutic drugs target cell processes in specific cell cycle phases. Determining the specific phases targeted is key to understanding drug mechanism of action and efficacy against specific cancer types. Flow cytometry experiments, combined with cell cycle phase and division round specific staining, can be used to quantify the current cell cycle phase and number of mitotic events of each cell within a population. However, quantification of cell interphase times and the efficacy of cytotoxic drugs targeting specific cell cycle phases cannot be determined directly. We present a data driven computational cell population model for interpreting experimental results, where in-silico populations are initialized to match observable results from experimental populations. A two-stage approach is used to determine the efficacy of cytotoxic drugs in blocking cell-cycle phase transitions. In the first stage, our model is fitted to experimental multi-parameter flow cytometry results from untreated cell populations to identify parameters defining probability density functions for phase transitions. In the second stage, we introduce a blocking routine to the model which blocks a percentage of attempted transitions between cell-cycle phases due to therapeutic treatment. The resulting model closely matches the percentage of cells from experiment in each cell-cycle phase and division round. From untreated cell populations, interphase and intermitotic times can be inferred. We then identify the specific cell-cycle phases that cytotoxic compounds target and quantify the percentages of cell transitions that are blocked compared with the untreated population, which will lead to improved understanding of drug efficacy and mechanism of action.

1997 ◽  
Vol 236 (1) ◽  
pp. 259-267 ◽  
Author(s):  
Brian L. Sailer ◽  
Joseph G. Valdez ◽  
John A. Steinkamp ◽  
Zbigniew Darzynkiewicz ◽  
Harry A. Crissman

1988 ◽  
Vol 36 (9) ◽  
pp. 1147-1152 ◽  
Author(s):  
G Ciancio ◽  
A Pollack ◽  
M A Taupier ◽  
N L Block ◽  
G L Irvin

We developed a rapid technique for preservation of Hoechst 33342/propidium iodide-stained cells, using ethanol as a fixative. Combined staining with these dyes makes possible analysis of cell-cycle phase-specific cell death. The technique relies on exclusion of propidium iodide from the viable cells, whereas Hoechst stains all of the cells. The bivariate histograms resulting from the flow cytometric analysis contain the equivalent of two single-parameter DNA histograms, one of the living and the other of the dead cell population. Preservation of staining involved addition of 25% ethanol in PBS after propidium iodide staining and before Hoechst staining. The separation between the living and the dead cell populations was maintained for over 3 days at 4 degrees C. This technique will be valuable for quantitative evaluation of the cell-cycle phase-specific effects of cytostatic or cytotoxic agents, particularly in situations where a lag period between staining and analysis is unavoidable.


2017 ◽  
Author(s):  
Hui Xiao Chao ◽  
Cere E. Poovey ◽  
Ashley A. Privette ◽  
Gavin D. Grant ◽  
Hui Yan Chao ◽  
...  

ABSTRACTDNA damage checkpoints are cellular mechanisms that protect the integrity of the genome during cell cycle progression. In response to genotoxic stress, these checkpoints halt cell cycle progression until the damage is repaired, allowing cells enough time to recover from damage before resuming normal proliferation. Here, we investigate the temporal dynamics of DNA damage checkpoints in individual proliferating cells by observing cell cycle phase transitions following acute DNA damage. We find that in gap phases (G1 and G2), DNA damage triggers an abrupt halt to cell cycle progression in which the duration of arrest correlates with the severity of damage. However, cells that have already progressed beyond a proposed “commitment point” within a given cell cycle phase readily transition to the next phase, revealing a relaxation of checkpoint stringency during later stages of certain cell cycle phases. In contrast to G1 and G2, cell cycle progression in S phase is significantly less sensitive to DNA damage. Instead of exhibiting a complete halt, we find that increasing DNA damage doses leads to decreased rates of S-phase progression followed by arrest in the subsequent G2. Moreover, these phase-specific differences in DNA damage checkpoint dynamics are associated with corresponding differences in the proportions of irreversibly arrested cells. Thus, the precise timing of DNA damage determines the sensitivity, rate of cell cycle progression, and functional outcomes for damaged cells. These findings should inform our understanding of cell fate decisions after treatment with common cancer therapeutics such as genotoxins or spindle poisons, which often target cells in a specific cell cycle phase.


2013 ◽  
Vol 65 (10) ◽  
pp. 2603-2615 ◽  
Author(s):  
Pascal Genschik ◽  
Katia Marrocco ◽  
Lien Bach ◽  
Sandra Noir ◽  
Marie-Claire Criqui

1997 ◽  
Vol 12 (2) ◽  
pp. 111-115 ◽  
Author(s):  
Tatsurou Koide ◽  
Hideo Kamei ◽  
Yoko Hashimoto ◽  
Takashi Kojima ◽  
Keisuke Terabe ◽  
...  

2020 ◽  
Vol 31 (13) ◽  
pp. 1346-1354 ◽  
Author(s):  
Yukiko Nagao ◽  
Mika Sakamoto ◽  
Takumi Chinen ◽  
Yasushi Okada ◽  
Daisuke Takao

By applying convolutional neural network-based classifiers, we demonstrate that cell images can be robustly classified according to cell cycle phases. Combined with Grad-CAM analysis, our approach enables us to extract biological features underlying cellular phenomena of interest in an unbiased and data-driven manner.


10.4081/838 ◽  
2009 ◽  
Vol 47 (4) ◽  
pp. 289 ◽  
Author(s):  
G Mazzini ◽  
C Ferrari ◽  
E Erba

The discrimination of live/dead cells as well as the detection of apoptosis is a frequent need in many areas of experimental biology. Cell proliferation is linked to apoptosis and controlled by several genes. During the cell life, specific events can stimulate proliferation while others may trigger the apoptotic pathway. Very few methods (i.e. TUNEL) are now available for studies aimed at correlation between apoptosis and proliferation. Therefore, there is interest in developing new methodological approaches that are able to correlate apoptosis to the cell cycle phases. Recently new approaches have been proposed to detect and enumerate apoptotic cells by flow cytometry. Among these, the most established and applied are those based on the cell membrane modifications induced in the early phases of the apoptotic process. The dye pair Hoechst 33342 (HO) and Propidium Iodide (PI), thanks to their peculiar characteristics to be respectively permeable and impermeable to the intact cell membrane, seems to be very useful. Unfortunately the spectral interaction of these dyes generates a consistent “energy transfer” from HO to PI. The co-presence of the dyes in a nucleus results in a modification in the intensity of both the emitted fluorescences. In order to designate the damaged cells (red fluorescence) to the specific cell cycle phases (blue fluorescence), we have tested different staining protocols aimed to minimize the interference of these dyes as much as possible. In cell culture models, we are able to detect serum-starved apoptotic cells as well as to designate their exact location in the cell cycle phases using a very low PI concentration. Using a Partec PAS flow cytometer equipped with HBO lamp and argon ion laser, a double UV/blue excitation has been performed. This analytical approach is able to discriminate live blue cells from the damaged (blue-red) ones even at 0.05 ?g/mL PI. The same instrumental setting allows performing other multi-colour analyses including AnnexinV-FITC as well as the possibility to make a correlated analysis to phenotype markers.


Cell Cycle ◽  
2018 ◽  
Vol 17 (21-22) ◽  
pp. 2496-2516 ◽  
Author(s):  
Gavin D. Grant ◽  
Katarzyna M. Kedziora ◽  
Juanita C. Limas ◽  
Jeanette Gowen Cook ◽  
Jeremy E. Purvis

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