Improve the accuracy of cell metrics using EVE Analytics

Objective: cell segmentation in EVE Analytics

Nanolive’s label-free technology makes it possible to image cells for long periods of time, at high temporal resolution. The quantity and complexity of the images generated allows us to visualize biological processes in unprecedented detail, but also magnifies the challenges associated with image analysis. Manual image registration and analysis is impossible and so computer-aided processing must be used to harness data complexity (1). In this technical note, we introduce the key elements involved in cell segmentation, which are essential to understand the novelty of EVE Analytics (EA), Nanolive’s software solution for quantitative cell analysis. We then evaluate the performance of EA segmentation against fluorescence-based segmentation and compare how metrics produced by both approaches differ.

How are cell metrics impacted?

EA automatically calculates numerous cell metrics. Here we compare cell area, perimeter, compactness, and extent calculated using EA (green) and compare them to the values generated by low Draq5 and Cell traceviolet (D+C) conditions (orange). In addition, we report one novel cell metric: dry mass, and exploit the full potential of a texture measurement called granularity, which are unique advantages of Nanolive imaging.

How are cell metrics impacted?
How are cell metrics impacted?

Comparison between values generated by low D+C conditions (orange) and EVE Analytics values (green) for the metrics cell area, perimeter, compactness and extent. Examples of dry mass and granularity.

Observations

  • The first striking observation is that the EA captures much greater cell-to-cell variability, as is demonstrated by the large interquartile spread of single cells values at each time point. This is of crucial importance as cell-to-cell variability reflects biological processes at work (2).
  • The second fundamental observation concerns the inverse trends in compactness and extent. The values produced by the fluo-segmentation suggest cells are well spread (and by deduction healthy) but we know from the images that this is far from the case. In comparison, the values quantified using EA accurately reflect the stress the cells are experiencing.
  • The final two plots represent metrics that are either inaccessible (dry mass) or that make little sense (granularity) using fluo-segmentations, further reinforcing the advantages of using EA.
References

[1] Meijering, E. IEEE Signal Process. Mag. 29, 140-145 (2012).

[2] Frechin M. et al. Nature 523, 88-91 (2015)

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