To analyse time-lapse video microscopy experiments of the immune system, quantification of cell migration is required. A toolbox of quantitative measures, such as cell speed, motility coefficient, confinement ratio and various angles of migration is available for that purpose.
Time-lapse imaging of the immune system is associated with various artefacts than can affect the estimated cell positions over time. As a result, measures of cell migration are also affected by such artefacts, and this may obscure biologically relevant differences between experimental settings or generate spurious results.
Imaging artefacts are related either to cell tracking (switching or splitting of tracks, double tracking of cells and errors of tracking near borders) or to imaging itself (imprecise calibration of the axial dimension and small tissue drift).
Detection and correction for artefacts can be done using various migration angles. For example, plotting the average angle to the axial border plane versus the distance to that border can help to detect border tracking errors, calibration errors in the axial dimension and small tissue drift.
Cell-based measurements are better at detecting distinct subpopulations among cells than step-based approaches. However, an important disadvantage of cell-based versus step-based parameters is that the shape of the imaged space affects the results, and this makes comparison between experiments problematic.
Determining contact times between cells in imaging experiments is non-trivial because observed (underestimated) rather than exact contact time is known for most contacts. However, the true contact time distribution can be estimated using a mathematical approach.
The visualization of the dynamic behaviour of and interactions between immune cells using time-lapse video microscopy has an important role in modern immunology. To draw robust conclusions, quantification of such cell migration is required. However, imaging experiments are associated with various artefacts that can affect the estimated positions of the immune cells under analysis, which form the basis of any subsequent analysis. Here, we describe potential artefacts that could affect the interpretation of data sets on immune cell migration. We propose how these errors can be recognized and corrected, and suggest ways to prevent the data analysis itself leading to biased results.
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We would like to thank C. Allen, U. von Andrian, S. Ariotti, J. Cyster, S. Henrickson, J. Lynch, T. Mempel, M. Miller and T. Schumacher for discussing various aspects of cell migration and cellular interactions, and M. Miller for letting us use previously published data from his group to illustrate some of the issues discussed. This work was supported by the Netherlands Organization for Scientific Research (NWO), grants 916.86.080 (J.B.B) and 016.048.603 (R.J.d.B).
The authors declare no competing financial interests.
(PDF 630 kb)
Examples of cell migration measurements in an experimental data set. (PDF 394 kb)
Examples of artifact detection and correction in experimental data sets (compare to Figure 5 in main text). (PDF 493 kb)
- Time-lapse video microscopy
A microscopy technique in which sequential static images from multiple time frames are combined into a video displayed at a faster rate than the images were acquired.
- Two-photon excitation
A technique by which fluorescent markers are excited by the nearly simultaneous absorption of two photons of low energy, resulting in the emission of fluorescent light that is collected by a detector.
- Image volume
The three-dimensional volume, typically in the shape of a box, from which emitted fluorescence, and thus fluorescently labelled cells, can be detected.
- Motility coefficient
A measure for how fast cells displace from their starting positions during a random walk process. It is identical to a diffusion coefficient.
- Persistent motion
The phenomenon that cells generally travel in relatively straight lines on a short timescale (usually of minutes).
- Frequency distribution
Enumeration of all values that a variable can take and the relative number of times each value occurs.
- Uniform distribution
A distribution in which there is a defined maximum and minimum that a value can take and in which the occurrence of each value in between is equally probable.
- Sine distribution
A distribution for which the relative number of occurrences of each of the possible angles between 0 and 180 degrees is proportional to the sine function.
- Cosine distribution
A distribution for which the relative number of occurrences of each of the possible angles between 0 and 90 degrees is proportional to the cosine function.
A volume element on a regular lattice in three-dimensional space.
Cell death and other artefactual cell behaviour associated with illumination during fluorescence microscopy.
- Refractive index
A measure of how much the speed of light is reduced inside a medium, causing the light to change direction at the interface of two different media.
- Tissue drift
The artefactual movement of an entire specimen during an imaging experiment. It gives the impression that cells within the tissue are moving collectively in a certain direction.
- Second harmonic generation
An optical process in which photons are formed that have twice the energy of the initial photons (and thus half of the original wavelength) when interacting with nonlinear materials such as collagens.
- Optimization algorithm
A numerical method for finding the values of a set of parameters such that the value of a function of those parameters is as small or large as possible. In a data-fitting procedure those parameter values represent the best fit.
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Beltman, J., Marée, A. & de Boer, R. Analysing immune cell migration. Nat Rev Immunol 9, 789–798 (2009). https://doi.org/10.1038/nri2638
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