Livecyte's Automated Tracking exposes a global research dilemma
Cell migration is an essential and highly regulated process involved in many areas of biology, including embryonic development, tissue homeostasis and regeneration. Cell migration also plays a key role in cancer, where it drives tumour metastasis.
Monitoring cell migratory behaviour over long periods of time requires imaging techniques with very low phototoxicity. Conventionally techniques such as brightfield or phase contrast imaging are used, but these modalities are poorly suited to automated cell identification. Thus, many researchers are forced to track cell motion by hand in order to understand their migratory behaviour.
Why is that a problem?
Manually tracking cells is not only extremely time-consuming and laborious — https://vimeo.com/553476933 — perhaps even more critically, manually tracking cells is not as accurate as people assume and significant variability can occur from person to person.
Whilst manual tracking is commonly deployed throughout the time-lapse, it is time and labour intensive, and suffers from inter-operator variability, ill-defined cell centroid positioning, and an intrinsic lack of morphological data. In many cases, the vast number of cell images collected during a time-lapse means only a subset of cells is tracked within a population, leading to a poor approximation of migration rates. Multiple available tracking tools offer a certain level of image pre-processing and background filers which may also perturb tracking measurements from one user to the next, depending on the method of tracking used.
What is the dilemma?
The promise of time saving and accuracy with automated tracking isn’t as easy as simply writing an algorithm; a fundamental change to the way cells are analysed is essential. This may be a rethink of our reliance on fluorescence microscopy. Adherence to the doctrine that accurate live cell tracking needs time, minimal photoperturbation and excellent contrast is paramount.
How does Livecyte solve this?
Automated tracking allows for the analysis of large time-lapse data sets to truly understand and analyse cell behaviour in an efficient, reproducible, and statistically robust way. Most simple automated tracking approaches, however, are dependent on high contrast images (as seen in fluorescence) where cells may be segmented by thresholding, i.e., pixels above an intensity threshold are seen as cell and the rest is background.
Livecyte uses a quantitative phase imaging modality called ptychography which is both ideally suited to automated cell tracking and extremely low phototoxicity. Additionally, we show Livecyte’s single-cell tracking is accurate and provides outputs consistent with averaging the manual tracking of many users. Livecyte’s Analyse software has easy-to-use algorithms that automatically segment and track all cells in a field of view, removing the subjective nature of the manual approach by standardising the tracking process as articulated in the application note “Uncovering the inconvenient truth behind manual tracking”.
How do you learn more?
ATA Scientific can assist with demonstrations, seminars, information, papers, application scientists and importantly facilitate a conversation with Phasefocus if required. Please contact Peter Davis at ATA Scientific: firstname.lastname@example.org
- Huth, J., Buchholz, M., Kraus, J.M., Schmucker, M., Von Wichert, G., Krndija, D., Seufferlein, T., Gress, T.M. and Kestler, H.A., 2010. Significantly improved precision of cell migration analysis in time-lapse video microscopy through use of a fully automated tracking system. BMC cell biology, 11(1), p.24.
- Meijering, E., Dzyubachyk, O. and Smal, I., 201 Methods for cell and particle tracking. In Methods in enzymology (Vol. 504, pp. 183-200). Academic Press.
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