Comparison of Multiple Injections with Continuous Infusion of Tritiated Thymidine, and Estimation of the Cell Cycle Time

1969 ◽  
Vol 5 (3) ◽  
pp. 575-582
Author(s):  
W. K. BLENKINSOPP ◽  
C. W. GILBERT
Keyword(s):  
Cell Cycle ◽  
Cell Proliferation ◽  
Continuous Infusion ◽  
Cycle Time ◽  
Squamous Epithelium ◽  
Tritiated Thymidine ◽  
Intraperitoneal Administration ◽  
Cell Cycle Time ◽  
Multiple Injections ◽  
Stratified Squamous Epithelium

Labelled nuclei were counted in stratified squamous epithelium in mice killed after 24 h intraperitoneal administration of tritiated thymidine to label cells synthesizing deoxyribonucleic acid. Multiple injections produced the same result as an infusion of tritiated thymidine given after 24 h infusion of saline, but infusion of tritiated thymidine alone produced a different result. Thus, cell proliferation was depressed during the first 24 h of continuous infusion but was normal during the second 24 h. Comparison of proliferation of the oesophageal epithelium at the level of the thyroid and at the level of the diaphragm showed no difference between the two. Comparison of male with female mice given 72-h infusions of tritiated thymidine showed that cell proliferation occurred at the same rate in both. The cell cycle time was estimated in the epithelium of the oesophagus and tongue by comparison of mice given a single injection with mice given multiple injections of tritiated thymidine.

CELL PROLIFERATION IN THE PREPUBERTAL MALE RAT ADRENAL CORTEX: AN AUTORADIOGRAPHIC STUDY

1971 ◽  
Vol 49 (4) ◽  
pp. 599-609 ◽  
Author(s):  
N. A. WRIGHT
Keyword(s):  
Cell Cycle ◽  
Cell Proliferation ◽  
Adrenal Cortex ◽  
Tritiated Thymidine ◽  
Autoradiographic Study ◽  
Male Rats ◽  
Male Rat ◽  
General Increase ◽  
Cortical Cells ◽  
Cell Cycle Time

SUMMARY On the basis of labelling indices measured with tritiated thymidine at intervals throughout its thickness, the adrenal cortex of prepubertal male rats has been divided into four compartments. These are called the glomerular, proliferative, fascicular and reticular compartments, respectively. Labelling indices measured for each compartment showed highest values in the glomerular and proliferative compartments, with values of 6·73% and 7·09% respectively. The fascicular compartment showed a lower index of 3·16% while the reticular compartment gave the lowest value of 1·15%. These differences are further reflected in measurements of the mitotic indices for each compartment. The phases of the cell cycle have been measured by pulse-chase analysis in each compartment, and all phases estimated showed an increase in duration as the inner compartments were approached. The duration of interphase DNA synthesis (ts) was found to be shortest in the glomerular and proliferative compartments, with values of 7·45 and 7·73 h, respectively. The fascicular compartment showed lengthening of ts to 8·56 h, and the reticular compartment gave the highest value of 9·21 h. Similarly, the values obtained for G2 (the post-DNA synthetic interval) and tm (the duration of mitosis), and a calculated value of the cell cycle time all showed a general increase in duration from the outer to the inner compartments. The relation of these results to theories of adrenocortical cytogenesis is discussed, and it is suggested that the differences in cell cycle components can best be explained by the inward migration of cortical cells from the outer compartments.

CHANGES IN CELL PROLIFERATION RATE IN MOUSE UTERINE EPITHELIUM DURING CONTINUOUS OESTROGEN TREATMENT

1974 ◽  
Vol 61 (1) ◽  
pp. 117-121 ◽  
Author(s):  
AUDREY E. LEE ◽  
L. A. ROGERS ◽  
GAIL TRINDER
Keyword(s):  
Cell Cycle ◽  
Cell Proliferation ◽  
Dna Synthesis ◽  
Mitotic Index ◽  
Cycle Time ◽  
Luminal Epithelium ◽  
Cell Cycle Time ◽  
Oestrogen Treatment ◽  
Probability Of Transition ◽  
The Mean

SUMMARY Fraction of labelled mitoses (FLM) curves were constructed for mouse uterine luminal epithelium during oestradiol treatment; on day 2 when mitosis was high, and on days 4 and 9 when mitosis was low. No difference was found between the duration of DNA synthesis on these 3 days. The distance between the first and second peaks, usually taken as an estimate of the mean cell cycle time, did not change significantly between days 2 and 4, although the labelling index fell from 38 to 8%. The second peaks of the FLM curves became progressively lower on the three days examined, which was consistent with the interpretation that there was a reduction in the probability of transition of cells from G1 (the post-mitotic period) into the replicative phase of the cell cycle, resulting in the observed fall in mitotic index.

Stem cell studies of human malignant brain tumors

1985 ◽  
Vol 63 (3) ◽  
pp. 426-432 ◽  
Author(s):  
Bertrand Pertuiset ◽  
Dolores Dougherty ◽  
Carlos Cromeyer ◽  
Takao Hoshino ◽  
Mitchel Berger ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Proliferation ◽  
Brain Tumors ◽  
Tritiated Thymidine ◽  
Early Passage ◽  
Cell Cycle Time ◽  
Content Type ◽  
Cell Kill ◽  
Fine Print

✓ The proliferation kinetics were studied in early-passage cultures of cells from 13 human malignant brain tumors and two specimens of normal brain under conditions similar to those used in clonogenic cell-survival studies. Autoradiography was performed in all but four cases to estimate the fraction of cells actively replicating deoxyribonucleic acid (DNA), the approximate cell cycle time, and the effect of low-dose tritiated thymidine on cell proliferation. The mean tumor cell doubling time (TD) was 53 hours for five glioblastomas, 46 hours for two ependymomas, and 83 hours for two medulloblastomas. A gliosarcoma grew fastest (TD = 22 hours) in culture and a pilocytic astrocytoma grew slowest (TD = 144 hours). The approximate cell cycle time ranged from 1 to 2.5 days for all tumors tested. This suggests that chemotherapeutic agents that predominantly kill proliferating cells should be administered in vitro for at least 2 to 2.5 days to achieve maximum cell kill. The approximate growth fraction ranged from 0.65 to 0.96 for all tumors except for the two medulloblastomas and the pilocytic astrocytoma, which had growth fractions of 0.34 and 0.35, respectively. Most laboratories investigating the chemosensitivity of primary or early-passage human tumor cells require that 40% to 70% of cells be killed to consider a drug active in vitro. The results of this study suggest that the cell-cycle-specific agents cannot achieve a high enough cell kill to be considered active for some tumors that grow slowly in culture. An estimate of the in vitro growth rate is necessary to reliably interpret cell-survival results with such agents. Tritiated thymidine appeared to slow cell proliferation in some of the cultures, presumably as a result of radiation-induced DNA damage caused by tritium that had been incorporated into DNA. The degree to which cell growth was slowed in individual tumors correlated with the patient's clinical response to radiation therapy and postoperative survival time.

The measurement of the cell cycle time in squamous epithelium using the metaphase arrest technique with vincristine

1977 ◽  
Vol 96 (5) ◽  
pp. 493-502 ◽  
Author(s):  
MARK B. Duffill ◽  
DAVID R. APPLETON ◽  
PAUL DYSON ◽  
SAM SHUSTER ◽  
NICHOLAS A. WRIGHT
Keyword(s):  
Cell Cycle ◽  
Cycle Time ◽  
Squamous Epithelium ◽  
Metaphase Arrest ◽  
Cell Cycle Time

scholarly journals Connexin 37 profoundly slows cell cycle progression in rat insulinoma cells

2008 ◽  
Vol 295 (5) ◽  
pp. C1103-C1112 ◽  
Author(s):  
Janis M. Burt ◽  
Tasha K. Nelson ◽  
Alexander M. Simon ◽  
Jennifer S. Fang
Keyword(s):  
Cell Cycle ◽  
Cell Proliferation ◽  
Cycle Time ◽  
Cell Cycle Progression ◽  
Serum Deprivation ◽  
Cell Cycle Time ◽  
Cycle Progression ◽  
Suppressive Function ◽  
Connexin 37 ◽  
Rat Insulinoma

In addition to providing a pathway for intercellular communication, the gap junction-forming proteins, connexins, can serve a growth-suppressive function that is both connexin and cell-type specific. To assess its potential growth-suppressive function, we stably introduced connexin 37 (Cx37) into connexin-deficient, tumorigenic rat insulinoma (Rin) cells under the control of an inducible promoter. Proliferation of these iRin37 cells, when induced to express Cx37, was profoundly slowed: cell cycle time increased from 2 to 9 days. Proliferation and cell cycle time of Rin cells expressing Cx40 or Cx43 did not differ from Cx-deficient Rin cells. Cx37 suppressed Rin cell proliferation irrespective of cell density at the time of induced expression and without causing apoptosis. All phases of the cell cycle were prolonged by Cx37 expression, and progression through the G1/S checkpoint was delayed, resulting in accumulation of cells at this point. Serum deprivation augmented the effect of Cx37 to accumulate cells in late G1. Cx43 expression also affected cell cycle progression of Rin cells, but its effects were opposite to Cx37, with decreases in G1 and increases in S-phase cells. These effects of Cx43 were also augmented by serum deprivation. Cx-deficient Rin cells were unaffected by serum deprivation. Our results indicate that Cx37 expression suppresses cell proliferation by significantly increasing cell cycle time by extending all phases of the cell cycle and accumulating cells at the G1/S checkpoint.

scholarly journals A multi-stage representation of cell proliferation as a Markov process

2017 ◽  
Author(s):  
Christian A. Yates ◽  
Matthew J. Ford ◽  
Richard L. Mort
Keyword(s):  
Cell Cycle ◽  
Cell Proliferation ◽  
Markov Process ◽  
Cycle Time ◽  
Time Distribution ◽  
Cellular Proliferation ◽  
Gillespie Algorithm ◽  
Biological Processes ◽  
Cell Cycle Time ◽  
Gillespie’S Algorithm

AbstractThe stochastic simulation algorithm commonly known as Gillespie’s algorithm (originally derived for modelling well-mixed systems of chemical reactions) is now used ubiquitously in the modelling of biological processes in which stochastic effects play an important role. In well mixed scenarios at the sub-cellular level it is often reasonable to assume that times between successive reaction/interaction events are exponentially distributed and can be appropriately modelled as a Markov process and hence simulated by the Gillespie algorithm. However, Gillespie’s algorithm is routinely applied to model biological systems for which it was never intended. In particular, processes in which cell proliferation is important (e.g. embryonic development, cancer formation) should not be simulated naively using the Gillespie algorithm since the history-dependent nature of the cell cycle breaks the Markov process. The variance in experimentally measured cell cycle times is far less than in an exponential cell cycle time distribution with the same mean.Here we suggest a method of modelling the cell cycle that restores the memoryless property to the system and is therefore consistent with simulation via the Gillespie algorithm. By breaking the cell cycle into a number of independent exponentially distributed stages we can restore the Markov property at the same time as more accurately approximating the appropriate cell cycle time distributions. The consequences of our revised mathematical model are explored analytically as far as possible. We demonstrate the importance of employing the correct cell cycle time distribution by recapitulating the results from two models incorporating cellular proliferation (one spatial and one non-spatial) and demonstrating that changing the cell cycle time distribution makes quantitative and qualitative differences to the outcome of the models. Our adaptation will allow modellers and experimentalists alike to appropriately represent cellular proliferation - vital to the accurate modelling of many biological processes - whilst still being able to take advantage of the power and efficiency of the popular Gillespie algorithm.

scholarly journals A Multi-stage Representation of Cell Proliferation as a Markov Process

2017 ◽  
Vol 79 (12) ◽  
pp. 2905-2928 ◽  
Author(s):  
Christian A. Yates ◽  
Matthew J. Ford ◽  
Richard L. Mort
Keyword(s):  
Cell Cycle ◽  
Cell Proliferation ◽  
Markov Process ◽  
Cycle Time ◽  
Time Distribution ◽  
Cellular Proliferation ◽  
Gillespie Algorithm ◽  
Biological Processes ◽  
Cell Cycle Time ◽  
Gillespie’S Algorithm

Abstract The stochastic simulation algorithm commonly known as Gillespie’s algorithm (originally derived for modelling well-mixed systems of chemical reactions) is now used ubiquitously in the modelling of biological processes in which stochastic effects play an important role. In well-mixed scenarios at the sub-cellular level it is often reasonable to assume that times between successive reaction/interaction events are exponentially distributed and can be appropriately modelled as a Markov process and hence simulated by the Gillespie algorithm. However, Gillespie’s algorithm is routinely applied to model biological systems for which it was never intended. In particular, processes in which cell proliferation is important (e.g. embryonic development, cancer formation) should not be simulated naively using the Gillespie algorithm since the history-dependent nature of the cell cycle breaks the Markov process. The variance in experimentally measured cell cycle times is far less than in an exponential cell cycle time distribution with the same mean. Here we suggest a method of modelling the cell cycle that restores the memoryless property to the system and is therefore consistent with simulation via the Gillespie algorithm. By breaking the cell cycle into a number of independent exponentially distributed stages, we can restore the Markov property at the same time as more accurately approximating the appropriate cell cycle time distributions. The consequences of our revised mathematical model are explored analytically as far as possible. We demonstrate the importance of employing the correct cell cycle time distribution by recapitulating the results from two models incorporating cellular proliferation (one spatial and one non-spatial) and demonstrating that changing the cell cycle time distribution makes quantitative and qualitative differences to the outcome of the models. Our adaptation will allow modellers and experimentalists alike to appropriately represent cellular proliferation—vital to the accurate modelling of many biological processes—whilst still being able to take advantage of the power and efficiency of the popular Gillespie algorithm.

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