Abstract
Populations of cells are almost always heterogeneous in function and fate. To understand the plasticity of cells, it is vital to measure quantitatively and dynamically the molecular processes that underlie cell-fate decisions in single cells. Early events in cell signalling often occur within seconds of the stimulus, whereas intracellular signalling processes and transcriptional changes can take minutes or hours. By contrast, cell-fate decisions, such as whether a cell divides, differentiates or dies, can take many hours or days. Multiparameter experimental and computational methods that integrate quantitative measurement and mathematical simulation of these noisy and complex processes are required to understand the highly dynamic mechanisms that control cell plasticity and fate.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Mattick, J. S. Challenging the dogma: the hidden layer of non-protein-coding RNAs in complex organisms. BioEssays 25, 930–939 (2003).
Maddox, J. Is molecular biology yet a science? Nature 355, 201 (1992).
Spencer, S. L., Gaudet, S., Albeck, J. G., Burke, J. M. & Sorger, P. K. Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature 459, 428–432 (2009).
Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006). This paper reports that the expression of four key proteins induces differentiated cells to revert to a pluripotent state, becoming iPS cells.
Nelson, D. E. et al. Oscillations in NF-κB signaling control the dynamics of gene expression. Science 306, 704–708 (2004).
Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).
Merrill, G. F. Cell synchronization. Methods Cell Biol. 57, 229–249 (1998).
Balsalobre, A., Damiola, F. & Schibler, U. A serum shock induces circadian gene expression in mammalian tissue culture cells. Cell 93, 929–937 (1998).
Metivier, R. et al. Estrogen receptor-α directs ordered, cyclical, and combinatorial recruitment of cofactors on a natural target promoter. Cell 115, 751–763 (2003).
Ashall, L. et al. Pulsatile stimulation determines timing and specificity of NF-κB-dependent transcription. Science 324, 242–246 (2009). This imaging and systems biology study showed that pulsatile stimulation to induce synchronous NF-κB oscillations at different frequencies could direct differential gene expression and that cellular heterogeneity results from the stochastic transcription of negative-feedback inhibitor genes.
Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002). This paper analysed gene expression in single bacteria and defined intrinsic and extrinsic noise in transcription.
Gilkey, J. C., Jaffe, L. F., Ridgway, E. B. & Reynolds, G. T. A free calcium wave traverses the activating egg of the medaka, Oryzias latipes . J. Cell Biol. 76, 448–466 (1978).
Woods, N. M., Cuthbertson, K. S. & Cobbold, P. H. Repetitive transient rises in cytoplasmic free calcium in hormone-stimulated hepatocytes. Nature 319, 600–602 (1986).
Grynkiewicz, G., Poenie, M. & Tsien, R. Y. A new generation of Ca2+ indicators with greatly improved fluorescence properties. J. Biol. Chem. 260, 3440–3450 (1985).
Dolmetsch, R. E., Lewis, R. S., Goodnow, C. C. & Healy, J. I. Differential activation of transcription factors induced by Ca2+ response amplitude and duration. Nature 386, 855–858 (1997). This paper provided the first direct evidence that the frequency of calcium oscillations regulated downstream cellular processes.
Li, W., Llopis, J., Whitney, M., Zlokarnik, G. & Tsien, R. Y. Cell-permeant caged InsP3 ester shows that Ca2+ spike frequency can optimize gene expression. Nature 392, 936–941 (1998).
Hamill, O. P., Marty, A., Neher, E., Sakmann, B. & Sigworth, F. J. Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflugers Arch. 391, 85–100 (1981). This is the key description of the development of the patch-clamp technique.
Fertig, N., Blick, R. H. & Behrends, J. C. Whole cell patch clamp recording performed on a planar glass chip. Biophys. J. 82, 3056–3062 (2002).
Borodinsky, L. N. et al. Activity-dependent homeostatic specification of transmitter expression in embryonic neurons. Nature 429, 523–530 (2004). This study challenged the dogma of genetic determinism in neurogenesis by using a combination of the patch-clamp method and calcium imaging to study and manipulate neurotransmitter expression in embryonic neurons.
Chalfie, M., Tu, Y., Euskirchen, G., Ward, W. W. & Prasher, D. C. Green fluorescent protein as a marker for gene expression. Science 263, 802–805 (1994).
Day, R. N. & Davidson, M. W. The fluorescent protein palette: tools for cellular imaging. Chem. Soc. Rev. 38, 2887–2921 (2009). This review presents a clear description of the range of fluorescent proteins available, together with their properties and applications.
Bray, D. Cell Movements: From Molecules To Motility (Garland Science, 2000).
Carrero, G., McDonald, D., Crawford, E., de Vries, G. & Hendzel, M. J. Using FRAP and mathematical modeling to determine the in vivo kinetics of nuclear proteins. Methods 29, 14–28 (2003).
Kim, S. A., Heinze, K. G. & Schwille, P. Fluorescence correlation spectroscopy in living cells. Nature Methods 4, 963–973 (2007).
Cai, L., Dalal, C. K. & Elowitz, M. B. Frequency-modulated nuclear localization bursts coordinate gene regulation. Nature 455, 485–490 (2008).
White, M. R. & Spiller, D. G. Is frequency-encoding of information a major theme in cellular processes? Cell Cycle 8, 2677–2678 (2009).
Lahav, G. et al. Dynamics of the p53–Mdm2 feedback loop in individual cells. Nature Genet. 36, 147–150 (2004).
McNally, J. G., Muller, W. G., Walker, D., Wolford, R. & Hager, G. L. The glucocorticoid receptor: rapid exchange with regulatory sites in living cells. Science 287, 1262–1265 (2000).
Fields, S. & Song, O. K. A novel genetic system to detect protein–protein interactions. Nature 340, 245–246 (1989).
Gu, Y., Di, W. L., Kelsell, D. P. & Zicha, D. Quantitative fluorescence resonance energy transfer (FRET) measurement with acceptor photobleaching and spectral unmixing. J. Microsc. 215, 162–173 (2004).
Suhling, K., French, P. M. & Phillips, D. Time-resolved fluorescence microscopy. Photochem. Photobiol. Sci. 4, 13–22 (2005).
Ai, H. W., Henderson, J. N., Remington, S. J. & Campbell, R. E. Directed evolution of a monomeric, bright and photostable version of Clavularia cyan fluorescent protein: structural characterization and applications in fluorescence imaging. Biochem. J. 400, 531–540 (2006).
Ganesan, S., Ameer-Beg, S. M., Ng, T. T., Vojnovic, B. & Wouters, F. S. A dark yellow fluorescent protein (YFP)-based resonance energy-accepting chromoprotein (REACh) for Förster resonance energy transfer with GFP. Proc. Natl Acad. Sci. USA 103, 4089–4094 (2006).
Miyawaki, A. et al. Fluorescent indicators for Ca2+ based on green fluorescent proteins and calmodulin. Nature 388, 882–887 (1997).
VanEngelenburg, S. B. & Palmer, A. E. Fluorescent biosensors of protein function. Curr. Opin. Chem. Biol. 12, 60–65 (2008).
Nagai, T., Sawano, A., Park, E. S. & Miyawaki, A. Circularly permuted green fluorescent proteins engineered to sense Ca2+ . Proc. Natl Acad. Sci. USA 98, 3197–3202 (2001).
Wiseman, P. W. et al. Spatial mapping of integrin interactions and dynamics during cell migration by image correlation microscopy. J. Cell Sci. 117, 5521–5534 (2004).
Davis, I. The 'super-resolution' revolution. Biochem. Soc. Trans. 37, 1042–1044 (2009).
Sakon, J. J. & Weninger, K. R. Detecting the conformation of individual proteins in live cells. Nature Methods 7, 203–205 (2010).
de Wet, J. R., Wood, K. V., DeLuca, M., Helinski, D. R. & Subramani, S. Firefly luciferase gene: structure and expression in mammalian cells. Mol. Cell. Biol. 7, 725–737 (1987).
Li, X. et al. Generation of destabilized green fluorescent protein as a transcription reporter. J. Biol. Chem. 273, 34970–34975 (1998).
Castano, J. P., Kineman, R. D. & Frawley, L. S. Dynamic monitoring and quantification of gene expression in single, living cells: a molecular basis for secretory cell heterogeneity. Mol. Endocrinol. 10, 599–605 (1996).
Rutter, G. A., White, M. R. & Tavare, J. M. Involvement of MAP kinase in insulin signalling revealed by non-invasive imaging of luciferase gene expression in single living cells. Curr. Biol. 5, 890–899 (1995).
McFerran, D. W. et al. Persistent synchronized oscillations in prolactin gene promoter activity in living pituitary cells. Endocrinology 142, 3255–3260 (2001).
Takasuka, N., White, M. R., Wood, C. D., Robertson, W. R. & Davis, J. R. Dynamic changes in prolactin promoter activation in individual living lactotrophic cells. Endocrinology 139, 1361–1368 (1998).
White, M. R. et al. Real-time analysis of the transcriptional regulation of HIV and hCMV promoters in single mammalian cells. J. Cell Sci. 108, 441–455 (1995).
Ko, M. S., Nakauchi, H. & Takahashi, N. The dose dependence of glucocorticoid-inducible gene expression results from changes in the number of transcriptionally active templates. EMBO J. 9, 2835–2842 (1990).
Newlands, S. et al. Transcription occurs in pulses in muscle fibers. Genes Dev. 12, 2748–2758 (1998).
Wijgerde, M., Grosveld, F. & Fraser, P. Transcription complex stability and chromatin dynamics in vivo . Nature 377, 209–213 (1995). This study used RNA FISH to investigate transcription in cells undergoing the switch from expression of fetal globin genes to adult globin ones and found that expression stochastically flipped between the fetal and adult genes, implying that the probability of transcription determines the phenotype of each cell.
Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y. & Tyagi, S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 4, e309 (2006). This study used RNA counting at fixed time points to analyse the burst size of transcription in single mammalian cells.
Chubb, J. R., Trcek, T., Shenoy, S. M. & Singer, R. H. Transcriptional pulsing of a developmental gene. Curr. Biol. 16, 1018–1025 (2006).
Janicki, S. M. et al. From silencing to gene expression: real-time analysis in single cells. Cell 116, 683–698 (2004).
Levsky, J. M., Shenoy, S. M., Pezo, R. C. & Singer, R. H. Single-cell gene expression profiling. Science 297, 836–840 (2002).
Chiang, M. K. & Melton, D. A. Single-cell transcript analysis of pancreas development. Dev. Cell 4, 383–393 (2003).
Tietjen, I. et al. Single-cell transcriptional analysis of neuronal progenitors. Neuron 38, 161–175 (2003).
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods 6, 377–382 (2009).
Huh, W. K. et al. Global analysis of protein localization in budding yeast. Nature 425, 686–691 (2003).
Sigal, A. et al. Variability and memory of protein levels in human cells. Nature 444, 643–646 (2006).
Heintz, N. BAC to the future: the use of bac transgenic mice for neuroscience research. Nature Rev. Neurosci. 2, 861–870 (2001).
Semprini, S. et al. Real-time visualization of human prolactin alternate promoter usage in vivo using a double-transgenic rat model. Mol. Endocrinol. 23, 529–538 (2009).
Reid, G., Gallais, R. & Metivier, R. Marking time: the dynamic role of chromatin and covalent modification in transcription. Int. J. Biochem. Cell Biol. 41, 155–163 (2009).
Jackson, D. A., Hassan, A. B., Errington, R. J. & Cook, P. R. Visualization of focal sites of transcription within human nuclei. EMBO J. 12, 1059–1065 (1993).
Schoenfelder, S. et al. Preferential associations between co-regulated genes reveal a transcriptional interactome in erythroid cells. Nature Genet. 42, 53–61 (2010).
Bhutani, N. et al. Reprogramming towards pluripotency requires AID-dependent DNA demethylation. Nature 463, 1042–1047 (2010).
Harper, C. V. et al. Dynamic organisation of prolactin gene expression in living pituitary tissue. J. Cell Sci. 123, 424–430 (2010).
Harlow, E. & Lane, D. Using Antibodies: A Laboratory Manual (Cold Spring Harbor Laboratory Press, 1998).
Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003).
Beynon, R. J., Doherty, M. K., Pratt, J. M. & Gaskell, S. J. Multiplexed absolute quantification in proteomics using artificial QCAT proteins of concatenated signature peptides. Nature Methods 2, 587–589 (2005).
Schubert, W., Gieseler, A., Krusche, A. & Hillert, R. Toponome mapping in prostate cancer: detection of 2000 cell surface protein clusters in a single tissue section and cell type specific annotation by using a three symbol code. J. Proteome Res. 8, 2696–2707 (2009).
Berglund, L. et al. A genecentric Human Protein Atlas for expression profiles based on antibodies. Mol. Cell. Proteomics 7, 2019–2027 (2008).
Beck, M. et al. Visual proteomics of the human pathogen Leptospira interrogans . Nature Methods 6, 817–823 (2009).
Adams, S. R. et al. New biarsenical ligands and tetracysteine motifs for protein labeling in vitro and in vivo: synthesis and biological applications. J. Am. Chem. Soc. 124, 6063–6076 (2002).
Keppler, A. et al. A general method for the covalent labeling of fusion proteins with small molecules in vivo . Nature Biotechnol. 21, 86–89 (2003).
Yamanaka, S. Elite and stochastic models for induced pluripotent stem cell generation. Nature 460, 49–52 (2009).
Sakaue-Sawano, A. et al. Visualizing spatiotemporal dynamics of multicellular cell-cycle progression. Cell 132, 487–498 (2008). This paper describes the development of FUCCI technology, which allows the progression of cell division to be visualized by using fluorescent proteins that are stable (and visible) only at specific cell-cycle stages.
Freudiger, C. W. et al. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science 322, 1857–1861 (2008).
Evans, C. L. & Xie, X. S. Coherent anti-Stokes Raman scattering microscopy: chemical imaging for biology and medicine. Annu. Rev. Anal. Chem. 1, 883–909 (2008).
Min, W. et al. Imaging chromophores with undetectable fluorescence by stimulated emission microscopy. Nature 461, 1105–1109 (2009).
Briggs, R. & King, T. J. Transplantation of living nuclei from blastula cells into enucleated frogs' eggs. Proc. Natl Acad. Sci. USA 38, 455–463 (1952).
Gurdon, J. B. Adult frogs derived from the nuclei of single somatic cells. Dev. Biol. 4, 256–273 (1962).
Wilmut, I., Schnieke, A. E., McWhir, J., Kind, A. J. & Campbell, K. H. Viable offspring derived from fetal and adult mammalian cells. Nature 385, 810–813 (1997).
Zhou, H. et al. Generation of induced pluripotent stem cells using recombinant proteins. Cell Stem Cell 4, 381–384 (2009).
Fire, A. et al. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans . Nature 391, 806–811 (1998).
Carpenter, A. E. & Sabatini, D. M. Systematic genome-wide screens of gene function. Nature Rev. Genet. 5, 11–22 (2004).
Ellis-Davies, G.C. Caged compounds: photorelease technology for control of cellular chemistry and physiology. Nature Methods 4, 619–628 (2007).
See, V., Rajala, N. K., Spiller, D. G. & White, M. R. Calcium-dependent regulation of the cell cycle via a novel MAPK–NF-κB pathway in Swiss 3T3 cells. J. Cell Biol. 166, 661–672 (2004).
Machacek, M. et al. Coordination of Rho GTPase activities during cell protrusion. Nature 461, 99–103 (2009).
Wu, Y. I. et al. A genetically encoded photoactivatable Rac controls the motility of living cells. Nature 461, 104–108 (2009).
Banaszynski, L. A., Chen, L. C., Maynard-Smith, L. A., Ooi, A. G. & Wandless, T. J. A rapid, reversible, and tunable method to regulate protein function in living cells using synthetic small molecules. Cell 126, 995–1004 (2006).
Liu, P. & Mathies, R. A. Integrated microfluidic systems for high-performance genetic analysis. Trends Biotechnol. 27, 572–581 (2009).
Bennett, M. R. & Hasty, J. Microfluidic devices for measuring gene network dynamics in single cells. Nature Rev. Genet. 10, 628–638 (2009).
Wlodkowic, D., Faley, S., Zagnoni, M., Wikswo, J. P. & Cooper, J. M. Microfluidic single-cell array cytometry for the analysis of tumor apoptosis. Anal. Chem. 81, 5517–5523 (2009).
Kumaresan, P., Yang, C. J., Cronier, S. A., Blazej, R. G. & Mathies, R. A. High-throughput single copy DNA amplification and cell analysis in engineered nanoliter droplets. Anal. Chem. 80, 3522–3529 (2008).
Bontoux, N. et al. Integrating whole transcriptome assays on a lab-on-a-chip for single cell gene profiling. Lab Chip 8, 443–450 (2008).
Bennett, M. R. et al. Metabolic gene regulation in a dynamically changing environment. Nature 454, 1119–1122 (2008).
Li Jeon, N. et al. Neutrophil chemotaxis in linear and complex gradients of interleukin-8 formed in a microfabricated device. Nature Biotechnol. 20, 826–830 (2002).
Mettetal, J. T., Muzzey, D., Gomez-Uribe, C. & van Oudenaarden, A. The frequency dependence of osmo-adaptation in Saccharomyces cerevisiae . Science 319, 482–484 (2008).
Locke, J. C. & Elowitz, M. B. Using movies to analyse gene circuit dynamics in single cells. Nature Rev. Microbiol. 7, 383–392 (2009).
Roenneberg, T., Chua, E. J., Bernardo, R. & Mendoza, E. Modelling biological rhythms. Curr. Biol. 18, R826–R835 (2008).
Tyson, J. J. & Novak, B. Temporal organization of the cell cycle. Curr. Biol. 18, R759–R768 (2008).
Shankaran, H. et al. Rapid and sustained nuclear–cytoplasmic ERK oscillations induced by epidermal growth factor. Mol. Syst. Biol. 5, 332 (2009).
Yoshiura, S. et al. Ultradian oscillations of Stat, Smad, and Hes1 expression in response to serum. Proc. Natl Acad. Sci. USA 104, 11292–11297 (2007).
Wilkinson, D. J. Stochastic Modelling for Systems Biology (Chapman & Hall/CRC, 2006).
Finkenstadt, B. et al. Reconstruction of transcriptional dynamics from gene reporter data using differential equations. Bioinformatics 24, 2901–2907 (2008).
Meinhardt, H. Models of biological pattern formation: from elementary steps to the organization of embryonic axes. Curr. Top. Dev. Biol. 81, 1–63 (2008).
Rand, D. A. Mapping global sensitivity of cellular network dynamics: sensitivity heat maps and a global summation law. J. R. Soc. Interface 5, S59–S69 (2008).
Locke, J. C. W. et al. Extension of a genetic network model by iterative experimentation and mathematical analysis. Mol. Syst. Biol. 1, 2005.0013 (2005).
Hucka, M. et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003).
Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
Shen, H. et al. Automated tracking of gene expression in individual cells and cell compartments. J. R. Soc. Interface 3, 787–794 (2006).
Pepperkok, R. & Ellenberg, J. High-throughput fluorescence microscopy for systems biology. Nature Rev. Mol. Cell Biol. 7, 690–696 (2006).
Swedlow, J. R., Goldberg, I., Brauner, E. & Sorger, P. K. Informatics and quantitative analysis in biological imaging. Science 300, 100–102 (2003).
Keller, P. J., Schmidt, A. D., Wittbrodt, J. & Stelzer, E. H. Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322, 1065–1069 (2008).
McMahon, A., Supatto, W., Fraser, S. E. & Stathopoulos, A. Dynamic analyses of Drosophila gastrulation provide insights into collective cell migration. Science 322, 1546–1550 (2008).
Shimojo, H., Ohtsuka, T. & Kageyama, R. Oscillations in notch signaling regulate maintenance of neural progenitors. Neuron 58, 52–64 (2008).
Pourquie, O. The segmentation clock: converting embryonic time into spatial pattern. Science 301, 328–330 (2003).
Liu, A. C., Lewis, W. G. & Kay, S. A. Mammalian circadian signaling networks and therapeutic targets. Nature Chem. Biol. 3, 630–639 (2007).
Liu, A. C. et al. Intercellular coupling confers robustness against mutations in the SCN circadian clock network. Cell 129, 605–616 (2007).
Novak, B., Tyson, J. J., Gyorffy, B. & Csikasz-Nagy, A. Irreversible cell-cycle transitions are due to systems-level feedback. Nature Cell Biol. 9, 724–728 (2007).
Michnick, S. W., Ear, P. H., Manderson, E. N., Remy, I. & Stefan, E. Universal strategies in research and drug discovery based on protein-fragment complementation assays. Nature Rev. Drug Discov. 6, 569–582 (2007).
Ozawa, T., Natori, Y., Sato, M. & Umezawa, Y. Imaging dynamics of endogenous mitochondrial RNA in single living cells. Nature Methods 4, 413–419 (2007).
Hida, N. et al. High-sensitivity real-time imaging of dual protein–protein interactions in living subjects using multicolor luciferases. PLoS ONE 4, e5868 (2009).
Acknowledgements
We thank Z. Seymour, C. Harper, A. Adamson, R. Awais, J. Ankers, S. Semprini and J. Cooper for providing data for the figures, as well as the many colleagues who provided suggestions, comments and assistance with the manuscript. Work in our laboratories has been funded by the Biotechnology and Biological Sciences Research Council (grants BBD0107481, BBF0059381, BBE0136001, BBE0042101, BBE0129651, BBF0052611, BBF0053181 and BBF0058061), the Medical Research Council (grant G0500346), the Wellcome Trust (grant 67252), the Engineering and Physical Sciences Research Council, the BioSim Network of Excellence (part of the European Union's Sixth Framework Programme; grant 005137) and PAPIIT (Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica, Mexico; grant IN223810). We apologize to the authors of the many excellent papers that were omitted because of space limitations.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Additional information
Reprints and permissions information is available at http://www.nature.com/reprints.
Supplementary information
Supplementary Movie 1
This movie shows early signalling events. Calcium imaging of pituitary cells (of the GH3 cell line) is shown before and after treatment with thyrotropin-releasing hormone, which was added to the cultured cells one-third of the way into the time series. Fluctuations in the amount of calcium in the cytoplasm over 400 s (at 1 frame s–1) were visualized by using fluo-4 dye (green), which increases in intensity with increasing calcium concentration (see Fig. 2a, which illustrates a short section of the movie after addition of thyrotropin-releasing hormone). (AVI 8190 kb)
Supplementary Movie 2
This movie shows transcription-factor translocation. Fluorescent protein imaging for 579 min of neuroblastoma cells (of the SK-N-AS cell line) treated with tumour-necrosis factor-α is shown. The protein RELA (which is a subunit of the transcription factor nuclear factor-κB) was fused to the fluorescent protein DsRed-Express (red). RELA oscillates between the cytoplasm and the nucleus of cells with a period of about 100 min. Concurrently, the RELA inhibitor IκBα, labelled with enhanced green fluorescent protein (green), shows cycles of synthesis and degradation that have an inverse phase to the cycles of RELA translocation. (AVI 1626 kb)
Supplementary Movie 3
This movie shows transcription analysis. Low-light-level imaging of pituitary cells (of the GH3 cell line) expressing firefly luciferase under the control of the stably transfected promoter of the human prolactin gene is shown. The substrate of luciferase, luciferin, was added to the medium, and images were taken at 15-min intervals over 40 h. Luminescence intensity increases from blue to green to yellow to red. The cycles of transcription are heterogeneous across the cells. (AVI 1573 kb)
Supplementary Movie 4
This movie shows cell division. Imaging of epithelial cells (of the HeLa cell line) by using fluorescent, ubiquitylation-based cell-cycle indicator (FUCCI) technology, over 22 h, is shown. Cells transiently express FUCCI proteins, depending on their differing stability at different phases of the cell cycle: G1 phase (red), S phase (green), G2 phase (reduced green fluorescence) and M phase (no fluorescent signal). Each of the cells in the field of view at the start of the experiment undergoes division. (AVI 3401 kb)
Rights and permissions
About this article
Cite this article
Spiller, D., Wood, C., Rand, D. et al. Measurement of single-cell dynamics. Nature 465, 736–745 (2010). https://doi.org/10.1038/nature09232
Published:
Issue Date:
DOI: https://doi.org/10.1038/nature09232
This article is cited by
-
Trigger-activated autonomous DNA machine for amplified liver cancer biomarker microRNA21 imaging
Analytical Sciences (2023)
-
Simplifying biochemical signal transport in steady flows within a single-cell-trapping microchannel by linear low-order systems
Microfluidics and Nanofluidics (2023)
-
Optimal control of bioproduction in the presence of population heterogeneity
Journal of Mathematical Biology (2023)
-
Utilising NV based quantum sensing for velocimetry at the nanoscale
Scientific Reports (2020)
-
Monitoring cell endocytosis of liposomes by real-time electrical impedance spectroscopy
Analytical and Bioanalytical Chemistry (2020)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
