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Machine learning processing of microalgae flow cytometry readings: illustrated with Chlorella vulgaris viability assays

Journal of Applied Phycology volume 32, pages 2967–2976 (2020)Cite this article

Abstract

A flow cytometry viability assay protocol is proposed and applied to model microalgae Chlorella vulgaris. The protocol relies on concomitant dual staining of the cells (fluorescein diacetate (FDA), propidium iodide (PI)) and machine learning processing of the results. Protocol development highlighted that working at 4 °C allows to preserve the stained sample for 15 min before analysis. Furthermore, the inclusion of an extracellular FDA washing step in the protocol improves the signal-to-noise ratio, allowing better detection of active cells. Once established, this protocol was validated against 7 test cases (controlled mixtures of active and non-viable cells). Its performances on the test cases are good: − 0.19%abs deviation on active cell quantification (processed by humans). Furthermore, a machine learning workflow, based on DBSCAN algorithm, was introduced. After a calibration procedure, the algorithm provided very satisfactorily results with − 0.10%abs deviation compared to human processing. This approach permitted to automate and speed up (15 folds) cytometry readings processing. Finally, the proposed workflow was used to assess Chlorella vulgaris cryostorage procedure efficiency. The impact of freezing protocol on cell viability was first investigated over 48-h storage (− 20 °C). Then, the most promising procedure (pelleted, − 20 °C) was tested over 1 month. The observed trends and values in viability loss correlate well with literature. This shows that flow cytometry is a valid tool to assess for microalgae cryopreservation protocol efficiency.

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Authors and Affiliations

  1. LGPM, CentraleSupélec, SFR Condorcet FR CNRS 3417, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), Université Paris-Saclay, 3 rue des Rouges Terres, 51110, Pomacle, France

    Victor Pozzobon, Wendie Levasseur, Elise Viau, Emilie Michiels & Patrick Perré

  2. URD Agro Biotechnologies Industrielles (ABI)-AgroParisTech, 3 rue des Rouges Terres, 51110, Pomacle, France

    Tiphaine Clément

Authors
  1. Victor Pozzobon
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  2. Wendie Levasseur
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  3. Elise Viau
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  4. Emilie Michiels
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  5. Tiphaine Clément
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  6. Patrick Perré
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Contributions

WL, PP and VP initiated and designed the study. EV led the experimental work with the help of WL, EM, TC and VP. All the authors critically interpreted the results. EV and VP drafted the manuscript; the other authors corrected it. All authors approve the manuscript.

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Correspondence to Victor Pozzobon.

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Fig. S1:

Sample temporal stability. 100 % active cells, dual stained. Storage at 4 °C, in the dark. (PNG 431 kb)

Fig. S2:

The five acquired parameters for 50 % active, 50 % non-viable cells test case and FL2 vs. FL1 map gated by FSC > 7. FCS profile: gray area discarded cells (FSC < 7) for drawing the map (PNG 410 kb)

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Pozzobon, V., Levasseur, W., Viau, E. et al. Machine learning processing of microalgae flow cytometry readings: illustrated with Chlorella vulgaris viability assays. J Appl Phycol 32, 2967–2976 (2020). https://doi.org/10.1007/s10811-020-02180-7

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  • DOI: https://doi.org/10.1007/s10811-020-02180-7

Keywords

  • Flow cytometry
  • Dual staining
  • Machine learning
  • DBSCAN
  • Cryopreservation