Commentary
How Long Does it Take a Cell to Divide? James H. Jett*
Key terms cell division time; microscopy; non-Poisson analysis; mitotic cell identification
HOW LONG DOES IT TAKE A CELL TO DIVIDE?
THIS
is one of the oldest questions asked in cell biology. Some bacterial cells can divide in as few as 20 min. The life cycle of many mammalian cells in culture takes >10 h to complete. One of the earliest techniques for determining cell growth rates is by measuring the number of cells in a culture versus time. The number of adherent cells can be measured at inoculation of a culture and again at a single succeeding time point. For suspension cultures, the culture can be sampled at numerous time points to provide a better estimate of the growth rate. These methods assume that all of the cells are playing the game, that is, that they are all growing and that none are in Go or are dying/dead. Newer methods focus on labeling of molecules within cells that are associated with specific stages of the cell cycle (1–4). The article by Summers et al. appearing in this issue of Cytometry Part A (page 385) presents a novel approach to determining cell division times. It involves recording over time a series of images of a field in a monolayer cell culture, identifying mitotic cells, and applying a non-homogeneous Poisson-based analysis to determine the inter-mitotic times. Low magnification images of a region of cells growing in a tissue culture flask are recorded at 15 min intervals. The only information extracted from the images is the presence and location of mitotic cells. This is accomplished by identifying the morphology of the mitotic cells, which is circular, as they lift off of the surface of the dish. The circular morphology is easily recognized manually as it uniquely identifies mitotic
cells. An image analysis protocol was also developed to automate identification of the mitotic cells. It located 87% of the mitotic cells found manually. This approach eliminates tedious analytical methods often necessary to extract biologically relevant information from cellular images. The second novel aspect of this article is the analytical procedure—non-homogeneous Poisson time series analysis— used to determine the inter-mitotic times. When cytometrists think of Poisson statistics they usually think of counting statistics: the standard deviation of the number of events. That measure of uncertainty is applicable to the number of counts in each channel of a histogram—be it univariate or bivariate. However, that is not the Poisson statistical analysis used in this article. In order to understand the non-homogeneous Poisson analysis, it is illustrative to first describe a situation in which homogeneous Poisson analysis applies. A homogeneous Poisson process is one in which events occur at a non-varying rate and there is no memory in the system, i.e., the events are independent. An example of this is the decay of a very large number of radioactive nuclei with a very long lifetime. The decay rate is constant over the course of any measurement. The data recorded are the times of detected decay events. The lifetime is then determined by forming the distribution of times between detected decays. This time interval distribution is a negative exponential whose exponent is proportional to the radioactive lifetime. Thus, it is possible to measure very long lifetimes by recording the times of detected decays and transforming that data into an inter-time distribution. Direct measurement of the decay rate is impossible for long lifetimes, say a million years. For a phenomenon described by a non-homogeneous Poisson time series, the event rate is not constant. For the data analyzed in this article, the time series of the appearance of mitotic figures in a cell culture, the rate of detection of the mitotic figures increases with time as the measurements
Department of Cell Biology and Physiology, University of New Mexico Albuquerque, New Mexico
Published online 26 March 2015 in Wiley Online Library (wileyonlinelibrary.com)
Received 1 March 2015; Accepted 6 March 2015
DOI: 10.1002/cyto.a.22665
*Correspondence to: James H. Jett, Department of Cell Biology and Physiology, University of New Mexico, Albuquerque, NM, USA. E-Mail: [email protected]
C 2015 International Society for Advancement of Cytometry V
Cytometry Part A 87A: 383 384, 2015
Commentary progress. That is, the event rate is not constant over time and the homogeneous Poisson approach does not apply. The analysis presented determines the inter-mitotic division times and is used to detect cell synchronization and quantify the degree of synchronization. What future developments might we expect? In the area of data acquisition, a fully automated identification of mitotic figures with 100% efficiency should be possible (5–7). The mitotic cell detection method presented is only applicable to growth of cells attached to a substrate. What about suspension cultures? By sequentially analyzing a fixed volume of such a culture by flow cytometry and identifying the mitotic cells, a similar analysis could be performed.
384
LITERATURE CITED 1. Jacobberger JW, Frisa PS, Sramkoski RM, Stefan S, Shults KE, Soni DV. A new biomarker for mitotic cells. Cytometry A 2008;73A:5–15. 2. Juan G, Traganos F, Darzynkiewicz Z. Methods to identify mitotic cells by flow cytometry. Methods Cell Biol Part A 2001;63:343–354. 3. Darzynkiewicz Z, Zhao H. Cell cycle analysis by flow cytometry. In: eLS. Chichester: Wiley; 2014. http://www.els.net [doi: 10.1002/9780470015902.a0002571. pub2] 4. Juan G. In silico analysis of cell cycle progression. Cytometry A 2014;85A:741–742. 5. Furia L, Pelicci PG, Faretta M. A computational platform for robotized fluorescence microscopy (I): High-content image-based cell-cycle analysis. Cytometry A 2013; 83A:333–343. 6. Furia L, Pelicci PG, Faretta M. A computational platform for robotized fluorescence microscopy (II): DNA damage, replication, checkpoint activation, and cell cycle progression by high-content high-resolution multiparameter image-cytometry. Cytometry A 2013;83A:344–355. 7. Yoo HJ, Park J, Yoon TH. High throughput cell cycle analysis using microfluidic image cytometry (lFIC). Cytometry A 2013;83A:356–362.
How Long Does it Take a Cell to Divide? James H. Jett*
Key terms cell division time; microscopy; non-Poisson analysis; mitotic cell identification
HOW LONG DOES IT TAKE A CELL TO DIVIDE?
THIS
is one of the oldest questions asked in cell biology. Some bacterial cells can divide in as few as 20 min. The life cycle of many mammalian cells in culture takes >10 h to complete. One of the earliest techniques for determining cell growth rates is by measuring the number of cells in a culture versus time. The number of adherent cells can be measured at inoculation of a culture and again at a single succeeding time point. For suspension cultures, the culture can be sampled at numerous time points to provide a better estimate of the growth rate. These methods assume that all of the cells are playing the game, that is, that they are all growing and that none are in Go or are dying/dead. Newer methods focus on labeling of molecules within cells that are associated with specific stages of the cell cycle (1–4). The article by Summers et al. appearing in this issue of Cytometry Part A (page 385) presents a novel approach to determining cell division times. It involves recording over time a series of images of a field in a monolayer cell culture, identifying mitotic cells, and applying a non-homogeneous Poisson-based analysis to determine the inter-mitotic times. Low magnification images of a region of cells growing in a tissue culture flask are recorded at 15 min intervals. The only information extracted from the images is the presence and location of mitotic cells. This is accomplished by identifying the morphology of the mitotic cells, which is circular, as they lift off of the surface of the dish. The circular morphology is easily recognized manually as it uniquely identifies mitotic
cells. An image analysis protocol was also developed to automate identification of the mitotic cells. It located 87% of the mitotic cells found manually. This approach eliminates tedious analytical methods often necessary to extract biologically relevant information from cellular images. The second novel aspect of this article is the analytical procedure—non-homogeneous Poisson time series analysis— used to determine the inter-mitotic times. When cytometrists think of Poisson statistics they usually think of counting statistics: the standard deviation of the number of events. That measure of uncertainty is applicable to the number of counts in each channel of a histogram—be it univariate or bivariate. However, that is not the Poisson statistical analysis used in this article. In order to understand the non-homogeneous Poisson analysis, it is illustrative to first describe a situation in which homogeneous Poisson analysis applies. A homogeneous Poisson process is one in which events occur at a non-varying rate and there is no memory in the system, i.e., the events are independent. An example of this is the decay of a very large number of radioactive nuclei with a very long lifetime. The decay rate is constant over the course of any measurement. The data recorded are the times of detected decay events. The lifetime is then determined by forming the distribution of times between detected decays. This time interval distribution is a negative exponential whose exponent is proportional to the radioactive lifetime. Thus, it is possible to measure very long lifetimes by recording the times of detected decays and transforming that data into an inter-time distribution. Direct measurement of the decay rate is impossible for long lifetimes, say a million years. For a phenomenon described by a non-homogeneous Poisson time series, the event rate is not constant. For the data analyzed in this article, the time series of the appearance of mitotic figures in a cell culture, the rate of detection of the mitotic figures increases with time as the measurements
Department of Cell Biology and Physiology, University of New Mexico Albuquerque, New Mexico
Published online 26 March 2015 in Wiley Online Library (wileyonlinelibrary.com)
Received 1 March 2015; Accepted 6 March 2015
DOI: 10.1002/cyto.a.22665
*Correspondence to: James H. Jett, Department of Cell Biology and Physiology, University of New Mexico, Albuquerque, NM, USA. E-Mail: [email protected]
C 2015 International Society for Advancement of Cytometry V
Cytometry Part A 87A: 383 384, 2015
Commentary progress. That is, the event rate is not constant over time and the homogeneous Poisson approach does not apply. The analysis presented determines the inter-mitotic division times and is used to detect cell synchronization and quantify the degree of synchronization. What future developments might we expect? In the area of data acquisition, a fully automated identification of mitotic figures with 100% efficiency should be possible (5–7). The mitotic cell detection method presented is only applicable to growth of cells attached to a substrate. What about suspension cultures? By sequentially analyzing a fixed volume of such a culture by flow cytometry and identifying the mitotic cells, a similar analysis could be performed.
384
LITERATURE CITED 1. Jacobberger JW, Frisa PS, Sramkoski RM, Stefan S, Shults KE, Soni DV. A new biomarker for mitotic cells. Cytometry A 2008;73A:5–15. 2. Juan G, Traganos F, Darzynkiewicz Z. Methods to identify mitotic cells by flow cytometry. Methods Cell Biol Part A 2001;63:343–354. 3. Darzynkiewicz Z, Zhao H. Cell cycle analysis by flow cytometry. In: eLS. Chichester: Wiley; 2014. http://www.els.net [doi: 10.1002/9780470015902.a0002571. pub2] 4. Juan G. In silico analysis of cell cycle progression. Cytometry A 2014;85A:741–742. 5. Furia L, Pelicci PG, Faretta M. A computational platform for robotized fluorescence microscopy (I): High-content image-based cell-cycle analysis. Cytometry A 2013; 83A:333–343. 6. Furia L, Pelicci PG, Faretta M. A computational platform for robotized fluorescence microscopy (II): DNA damage, replication, checkpoint activation, and cell cycle progression by high-content high-resolution multiparameter image-cytometry. Cytometry A 2013;83A:344–355. 7. Yoo HJ, Park J, Yoon TH. High throughput cell cycle analysis using microfluidic image cytometry (lFIC). Cytometry A 2013;83A:356–362.
