Time‐Lapse Microscopy Approaches to Track Cell Cycle and Lineage Progression at the Single‐Cell Level

Rachel J. Errington1, Sally C. Chappell1, Imtiaz A. Khan1, Nuria Marquez1, Marie Wiltshire1, Victoria D. Griesdoorn1, Paul J. Smith1

1 Institute of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff, United Kingdom
Publication Name:  Current Protocols in Cytometry
Unit Number:  Unit 12.4
DOI:  10.1002/0471142956.cy1204s64
Online Posting Date:  April, 2013
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Abstract

Time‐lapse microscopy can be described as the repeated collection of an image (in n‐dimensions; x, y, z, λ) or field of view from a microscope at discrete time intervals. The duration of the time interval defines the temporal resolution, which in turn characterizes the type of event detected. This unit describes the implementation of time‐lapse microscopy to link initial cell cycle position during acute exposures to anti‐cancer agents with anti‐proliferative consequences for individual cells. The approach incorporates fundamental concepts arising from the ability to capture simple video sequences of cells from which it is possible to extract kinetic descriptors that reflect the interplay of mitosis and cell death in the growth of an unsynchronized tumor population. Utilizing a multi‐well format enables the user to screen different drug derivatives, multiple dose ranges, or cell cultures with unique genetic backgrounds. The objective of this unit is to present the basic methodology for capturing time‐lapse sequences and touch upon subsequent mining of the data for deriving event curves and possible cell lineage maps. Curr. Protoc. Cytom. 64:12.4.1‐12.4.13. © 2013 by John Wiley & Sons, Inc.

Keywords: video microscopy; time‐lapse; data mining; cell cycle dynamics

     
 
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Table of Contents

  • Introduction
  • System Setup
  • Basic Protocol 1: Time‐Lapse Acquisition Using Adherent Cells
  • Alternate Protocol 1: Time‐Lapse Acquisition with Endpoint Assay to Mark S‐Phase Cells
  • Alternate Protocol 2: Time‐Lapse Acquisition Using Suspension Cells
  • Basic Protocol 2: Sequence Analysis for Mitosis Event or Cell Death
  • Basic Protocol 3: Data Mining—Normalized Event Distribution
  • Basic Protocol 4: Data Mining—Time‐to‐Event Curves
  • Basic Protocol 5: Data Mining—Duration of Mitotic Event
  • Basic Protocol 6: Data Mining—G2 Checkpoint Breaching
  • Basic Protocol 7: Data Mining—Deriving Basic Lineage Parameters
  • Commentary
  • Literature Cited
  • Figures
     
 
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Materials

Basic Protocol 1: Time‐Lapse Acquisition Using Adherent Cells

  Materials
  • Adherent mammalian cells of interest or cultures
  • Cell‐specific tissue culture medium
  • Drug of choice
  • Humidified 5% CO 2 supply
  • 6‐well tissue culture dish
  • Humidified 37°C, 5% CO 2 incubator
  • Time‐lapse microscope

Alternate Protocol 1: Time‐Lapse Acquisition with Endpoint Assay to Mark S‐Phase Cells

  • 10 mg/ml BrdU stock in distilled water (32 mM)
  • 70% (v/v) ethanol
  • Phosphate‐buffered saline (PBS; appendix 2A)
  • 2 M HCl
  • PBS/0.5% Tween
  • 0.1 M sodium tetraborate
  • Phosphate‐buffered saline with 0.6% bovine serum albumin (PBS/BSA)
  • Primary anti‐BrdU antibody
  • Secondary peroxidase anti‐mouse antibody
  • 10× diaminobenzidene (DAB) stock solution (Sigma, cat. no. 5637)
  • 30% hydrogen peroxide (H 2O 2)

Alternate Protocol 2: Time‐Lapse Acquisition Using Suspension Cells

  • 10% RPMI
  • SeaPlaque agarose (FMC BioProducts, Flowgen Instruments)
  • 12‐well plates
  • Microwave oven
  • 37°C water bath
  • 5‐ml stripette
  • Saline‐soaked tissue (e.g., Kimwipes)

Basic Protocol 2: Sequence Analysis for Mitosis Event or Cell Death

  Materials
  • Cells to be viewed
  • Video playback software
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Figures

Videos

Literature Cited

   Allen, R.D. 1985. New observation on cell architecture and dynamics by video‐enhanced contrast optical microscopy. Annu. Rev. Biophys. Chem. 14:265‐290.
   Allen, R.D., Allen, N.S., and Travis, J.L. 1981. Video‐enhanced contrast, differential interference contrast (AVEC‐DIC) microscopy: A new method capable of analyzing microtubule‐related motility in the reticulopodial network of Allogromia laticollaris. Cell Motil. 1:291‐302.
   Allman, R., Errington, R.J., and Smith, P.J. 2003. Delayed expression of apoptosis in human lymphoma cells undergoing low‐dose taxol‐induced mitotic stress. Br. J. Cancer 88:1649‐1658.
   Brugmans, N., Cassiman, J.J., Van der Heydt, L., Oosterlinck, A.J.J., Vlietinck, R., and Vanden Berghe, H. 1982. Quantification of the degree of cell spreading of human fibroblasts by semi‐automated analyses of the cell perimeter. Cytometry 3:262‐268.
   Bond, J., Haughton, M., Blaydes, J., Gire, V., Wynford‐Thomas, D., and Wyllie, F. 1996. Evidence that transcriptional activation by p53 plays a direct role in the induction of cellular senescence. Oncogene 13:2097‐2104.
   Chu, K., Teele, N., Dewey, M.W., Albright, N., and Dewey, W.C. 2004. Computerized video time lapse study of cell cycle delay and arrest, mitotic catastrophe, apoptosis and clonogenic survival in irradiated 14‐3‐3sigma and CDKN1A (p21) knockout cell lines. Radiat. Res. 162:270‐286.
   Dover, R. and Potten, C.S. 1988. Heterogeneity and cell cycle analyses from time‐lapse studies of human keratinocytes in vitro. J. Cell Sci. 89:359‐364.
   Feeney, G.P., Errington, R.J., Wiltshire, M., Marquez, N., Chappell, S.C., and Smith, P.J. 2003. Tracking the cell cycle origins for escape from topotecan action by breast cancer cells. Br. J. Cancer 88:1310‐1317.
   Khan, I.A., Husemann, P., Campbell, L., White, N.S., White, R.J., Smith, P.J., and Errington, R.J. 2007. ProgeniDB: A novel cell lineage database for generation associated phenotypic behavior in cell‐based assays. Cell Cycle 6:868‐874.
   Marquez, N., Chappell, S.C., Sansom, O.J., Clarke, A.R., Court, J., Errington, R.J., and Smith, P.J. 2003. Single cell tracking reveals that Msh2 is a key component of an early‐acting DNA damage‐activated G2 checkpoint. Oncogene 22:7642‐7648.
   Marquez, N., Chappell, S.C., Sansom, O.J., Clarke, A.R., Teesdale‐Spittle, P., Errington, R.J., and Smith, P.J. 2004. Microtubule stress modifies intra‐nuclear location of Msh2 in mouse embryonic fibroblasts. Cell Cycle 3:662‐671.
   Pawley, J.B. (ed.) 1995. Handbook of Biological Confocal Microscopy. Plenum, New York.
   Smith, P.J., Khan, I.A., and Errington, R.J. 2009. Cytomics and cellular informatics—coping with asymmetry and heterogeneity in biological systems. Drug Discov. Today 14:271‐277.
   Stephens, P., Grenard, P., Aeschlimann, P., Langley, M., Blain, E., Errington, R., Kipling, D., Thomas, D., and Aeschlimann, D. 2004. Crosslinking and G‐protein functions of trans‐glutaminase 2 contribute differentially to fibroblast wound healing responses. J. Cell Sci. 117:3389‐3403.
   Therneau, T.M. and Grambsch, P.M. 2000. Modeling Survival Data. Extending the Cox Model. Springer‐Verlag, New York.
   Weiss, D.G., Maile, W., and Wick, R.A. 1989. Video microscopy. In Light Microscopy in Biology. A Practical Approach. (A.J. Lacey, ed.) pp. 221‐278. IRL Press, London.
   White, N.S. and Errington, R.J. 2005. Fluorescence techniques for drug delivery research: Theory and practice. Adv. Drug Deliv. Rev. 57:17‐42.
Internet Resources
   http://www.moleculardevices.com/
  Wide‐field high‐content‐screening instrument
  http://www.gelifesciences.com
  Automated microscopes.
  http://www.leica‐microsystems.com/products/digital‐microscopes/
  Timelapse imaging that sits in your incubator.
  http://www.zeiss.com/
  Incubators for microscopes.
  http://www.nikoninstruments.com/en_GB/Information‐Center/Time‐Lapse
  The above Web sites are for typical instruments from the typical black‐box screening instruments to bespoke automated microscopes.
  http://www.olympusamerica.com/seg_section/seg_microscopes.asp?section=inverted
  http://essenbioscience.com/
  http://www.solentsci.com/
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