Cell Cycle Synchronization and Time-Lapse Imaging of Cytokinetic Tobacco BY-2 Cells

2021 ◽  
pp. 245-252
Author(s):  
Keisho Maeda ◽  
Takumi Higaki
Methods ◽  
2018 ◽  
Vol 133 ◽  
pp. 81-90 ◽  
Author(s):  
Katja M. Piltti ◽  
Brian J. Cummings ◽  
Krystal Carta ◽  
Ayla Manughian-Peter ◽  
Colleen L. Worne ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hiroaki Shimono ◽  
Atsushi Kaida ◽  
Hisao Homma ◽  
Hitomi Nojima ◽  
Yusuke Onozato ◽  
...  

AbstractIn this study, we examined the fluctuation in radioresponse of HeLa cells during the cell cycle. For this purpose, we used HeLa cells expressing two types of fluorescent ubiquitination-based cell cycle indicators (Fucci), HeLa-Fucci (CA)2 and HeLa-Fucci (SA), and combined this approach with the micronucleus (MN) assay to assess radioresponse. The Fucci system distinguishes cell cycle phases based on the colour of fluorescence and cell morphology under live conditions. Time-lapse imaging allowed us to further identify sub-positions within the G1 and S phases at the time of irradiation by two independent means, and to quantitate the number of MNs by following each cell through M phase until the next G1 phase. Notably, we found that radioresponse was low in late G1 phase, but rapidly increased in early S phase. It then decreased until late S phase and increased in G2 phase. For the first time, we demonstrated the unique fluctuation of radioresponse by the MN assay during the cell cycle in HeLa cells. We discuss the difference between previous clonogenic experiments using M phase-synchronised cell populations and ours, as well as the clinical implications of the present findings.


2011 ◽  
Vol 23 (1) ◽  
pp. 245
Author(s):  
I. Faerge ◽  
A. Egeskov-Madsen ◽  
P. Holm

Porcine neural progenitor cells (pNPC) derived from embryonic stem cells are capable of self-renewal and differentiation into neural and glia lineages, rendering them promising candidates for cell-based therapy of neurodegenerative diseases in a large animal biomedical model. A prerequisite for the successful future therapeutic use of pNPC is a comprehensive characterisation and understanding of the neurogenic process. This is important for learning how to direct cell fates into required proportions of the cell type wanted for the specific brain disease to be treated, and it is crucial for avoiding uncontrolled cell proliferation leading to fatal tumour formations. Time-lapse analysis is a powerful tool to obtain live cell characterisation by analysing individual cell fate. Information on cellular development, division, and differentiation can be composed into a pedigree-like structure denoted as cellular genealogy giving an overview of the proliferation profile of a cell culture and the duration of each cell cycle (Al-Kofani et al. 2006). The aim of the study was to construct cellular genealogies of pNPC and differentiated neural lineages, respectively, by time-lapse imaging to evaluate the effect of external variables observed by changes in the topology of the cellular genealogy. Porcine NPC were derived from epiblast cells isolated from day-9 porcine blastocysts and cultured in DMEM/12, Pen/strep, B27 and N2 with basic fibroblast growth factor and epidermal growth factor, and differentiation was obtained by withdrawal of basic fibroblast growth factor and epidermal growth factor. The state of cellular development of undifferentiated and differentiated pNPC was verified immunohistochemically by the presence of SOX2, NESTIN, TUJI, and GFAB (Rasmussen et al. 2010). The time-lapse images were captured by a Nikon Biostation with a 10× resolution under phase contrast in a humidified chamber at 38°C with 5% CO2, 5% O2, and 90% N2. For each sequence, images were captured at intervals of 10 min in 16 frames. Sequences 1, 2, and 3 constituted passage 15 pNPC, passage 4 pNPC, and presumably differentiated cells, respectively. For each sequence, cell cycle length was calculated after manual tracking of selected cells. The cell cycle length of pNPC is shown in Table 1. Based on these data, cellular genealogies characteristic of each individual cell type have been constructed. Table 1.Cell cycle length of porcine neutral progenitor cells (pNPC) before and after differentiation


2013 ◽  
Vol 368 (1629) ◽  
pp. 20130006 ◽  
Author(s):  
Chi-Fang Wu ◽  
Natasha S. Savage ◽  
Daniel J. Lew

Saccharomyces cerevisiae yeast cells polarize in order to form a single bud in each cell cycle. Distinct patterns of bud-site selection are observed in haploid and diploid cells. Genetic approaches have identified the molecular machinery responsible for positioning the bud site: during bud formation, specific locations are marked with immobile landmark proteins. In the next cell cycle, landmarks act through the Ras-family GTPase Rsr1 to promote local activation of the conserved Rho-family GTPase, Cdc42. Additional Cdc42 accumulates by positive feedback, creating a concentrated patch of GTP-Cdc42, which polarizes the cytoskeleton to promote bud emergence. Using time-lapse imaging and mathematical modelling, we examined the process of bud-site establishment. Imaging reveals unexpected effects of the bud-site-selection system on the dynamics of polarity establishment, raising new questions about how that system may operate. We found that polarity factors sometimes accumulate at more than one site among the landmark-specified locations, and we suggest that competition between clusters of polarity factors determines the final location of the Cdc42 cluster. Modelling indicated that temporally constant landmark-localized Rsr1 would weaken or block competition, yielding more than one polarity site. Instead, we suggest that polarity factors recruit Rsr1, effectively sequestering it from other locations and thereby terminating landmark activity.


2003 ◽  
Vol 14 (1) ◽  
pp. 107-117 ◽  
Author(s):  
Laura Trinkle-Mulcahy ◽  
Paul D. Andrews ◽  
Sasala Wickramasinghe ◽  
Judith Sleeman ◽  
Alan Prescott ◽  
...  

Protein phosphatase 1 (PP1) is a ubiquitous serine/threonine phosphatase that regulates many cellular processes, including cell division. When transiently expressed as fluorescent protein (FP) fusions, the three PP1 isoforms, α, β/δ, and γ1, are active phosphatases with distinct localization patterns. We report here the establishment and characterization of HeLa cell lines stably expressing either FP-PP1γ or FP alone. Time-lapse imaging reveals dynamic targeting of FP-PP1γ to specific sites throughout the cell cycle, contrasting with the diffuse pattern observed for FP alone. FP-PP1γ shows a nucleolar accumulation during interphase. On entry into mitosis, it localizes initially at kinetochores, where it exchanges rapidly with the diffuse cytoplasmic pool. A dramatic relocalization of PP1 to the chromosome-containing regions occurs at the transition from early to late anaphase, and by telophase FP-PP1γ also accumulates at the cleavage furrow and midbody. The changing spatio-temporal distribution of PP1γ revealed using the stable PP1 cell lines implicates it in multiple processes, including nucleolar function, the regulation of chromosome segregation and cytokinesis.


2014 ◽  
Vol 72 ◽  
pp. 241-249 ◽  
Author(s):  
Hisayuki Hashimoto ◽  
Shinsuke Yuasa ◽  
Hidenori Tabata ◽  
Shugo Tohyama ◽  
Nozomi Hayashiji ◽  
...  

2016 ◽  
Author(s):  
Sarah M. Mangiameli ◽  
Brian T. Veit ◽  
Houra Merrikh ◽  
Paul A. Wiggins

The positioning of the DNA replication machinery (replisome) has been the subject of several studies. Two conflicting models for replisome localization have been proposed: In the Factory Model, sister replisomes remain spatially colocalized as the replicating DNA is translocated through a stationary replication factory. In the Track Model, sister replisomes translocate independently along a stationary DNA track and the replisomes are spatially separated for the majority of the cell cycle. Here, we used time-lapse imaging to observe and quantify the position of fluorescently labeled processivity-clamp (DnaN) complexes throughout the cell cycle in two highly-divergent bacterial model organisms: Bacillus subtilis and Escherichia coli. Because DnaN is a core component of the replication machinery, its localization patterns should be an appropriate proxy for replisome positioning in general. We present automated statistical analysis of DnaN positioning in large populations, which is essential due to the high degree of cell-to-cell variation. We find that both bacteria show remarkably similar DnaN positioning, where any potential separation of the two replication forks remains below the diffraction limit throughout the majority of the replication cycle. Additionally, the localization pattern of several other core replisome components is consistent with that of DnaN. These data altogether indicate that the two replication forks remain spatially colocalized and mostly function in close proximity throughout the replication cycle.The conservation of the observed localization patterns in these highly divergent species suggests that the subcellular positioning of the replisome is a functionally critical feature of DNA replication.Author SummaryCell proliferation depends on efficient replication of the genome. Bacteria typically have a single origin of replication on a circular chromosome. After replication initiation, two replisomes assemble at the origin and each copy one of the two arms of the chromosome until they reach the terminus. There have been conflicting reports about the subcellular positioning and putative co-localization of the two replication forks during this process. It has remained controversial whether the two replisomes remain relatively close to each other with the DNA being pulled through, or separate as they translocate along the DNA like a track. Existing studies have relied heavily on snapshot images and these experiments cannot unambiguously distinguish between these two models: i.e. two resolvable forks versus two pairs of co-localized forks. The ability of replication to re-initiate before cell division in bacterial cells further complicates the interpretation of these types of imaging studies. In this paper, we use a combination of snapshot imaging, time-lapse imaging, and quantitative analysis to measure the fraction of time forks are co-localized during each cell cycle. We find that the forks are co-localized for the majority ( 80%) of the replication cycle in two highly-divergent model organisms: B. subtilis and E. coli. Our observations are consistent with proximal localization of the two forks, but also some transient separations of sister forks during replication. The conserved behavior of sub-cellular positioning of the replisomes in these two highly divergent species implies a potential functional relevance of this feature.


2015 ◽  
Vol 26 (22) ◽  
pp. 3898-3903 ◽  
Author(s):  
Richard John Wheeler

Tools to analyze cyclical cellular processes, particularly the cell cycle, are of broad value for cell biology. Cell cycle synchronization and live-cell time-lapse observation are widely used to analyze these processes but are not available for many systems. Simple mathematical methods built on the ergodic principle are a well-established, widely applicable, and powerful alternative analysis approach, although they are less widely used. These methods extract data about the dynamics of a cyclical process from a single time-point “snapshot” of a population of cells progressing through the cycle asynchronously. Here, I demonstrate application of these simple mathematical methods to analysis of basic cyclical processes—cycles including a division event, cell populations undergoing unicellular aging, and cell cycles with multiple fission (schizogony)—as well as recent advances that allow detailed mapping of the cell cycle from continuously changing properties of the cell such as size and DNA content. This includes examples using existing data from mammalian, yeast, and unicellular eukaryotic parasite cell biology. Through the ongoing advances in high-throughput cell analysis by light microscopy, electron microscopy, and flow cytometry, these mathematical methods are becoming ever more important and are a powerful complementary method to traditional synchronization and time-lapse cell cycle analysis methods.


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