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Related Experiment Videos

Focus-drift correction in time-lapse confocal imaging.

Marko Kreft1, Matjaz Stenovec, Robert Zorec

  • 1Laboratory of Neuroendocrinology-Molecular Cell Physiology, Institute of Pathophysiology, Medical Faculty, University of Ljubljana, Slovenia.

Annals of the New York Academy of Sciences
|September 13, 2005
PubMed
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Focus drift in long-term cell imaging can be corrected using a novel software approach. This method analyzes image stacks to maintain focus, ensuring clearer analysis of cellular processes over time.

Area of Science:

  • Cell Biology
  • Microscopy Techniques
  • Biophysics

Background:

  • Long-term time-lapse imaging of living cells is crucial for observing dynamic cellular processes.
  • Focus drift, caused by mechanical instability or thermal gradients, significantly degrades image quality in extended recordings.
  • Existing autofocus methods are often unsuitable for confocal microscopy due to multi-plane imaging.

Purpose of the Study:

  • To develop and validate a software-based autofocus method for correcting focus drift in long-term time-lapse confocal microscopy.
  • To ensure high-quality imaging for accurate analysis of cellular dynamics during extended experiments.

Main Methods:

  • Periodically acquiring image stacks (z-stacks) of the specimen during time-lapse recording.
  • Calculating Pearson's correlation coefficient between images in the z-stack and a reference image.

Related Experiment Videos

  • Identifying the optimal focal plane by locating the maximal correlation coefficient of pixel intensities.

    • Main Results:

    • The developed software approach successfully identified the correct focal plane in confocal image stacks.
    • Demonstrated effective correction of focus drift in time-lapse imaging of living rat lactotroph cells.
    • Validated the method using simultaneous imaging of secretory granule discharge and membrane labeling.

    Conclusions:

    • The proposed method provides an effective solution for mitigating focus drift in long-term time-lapse confocal imaging.
    • This technique enhances the reliability and accuracy of live-cell imaging analysis.
    • The approach is suitable for studying dynamic cellular events over extended periods.