Machine learning processing of microalgae flow cytometry readings: illustrated with Chlorella vulgaris viability assays
- PDF / 1,594,559 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 43 Downloads / 152 Views
Machine learning processing of microalgae flow cytometry readings: illustrated with Chlorella vulgaris viability assays Victor Pozzobon 1
&
Wendie Levasseur 1 & Elise Viau 1 & Emilie Michiels 1 & Tiphaine Clément 2 & Patrick Perré 1
Received: 24 March 2020 / Revised and accepted: 5 June 2020 # Springer Nature B.V. 2020
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. Keywords Flow cytometry . Dual staining . Machine learning . DBSCAN . Cryopreservation
Introduction Mankind is currently confronted to several challenges, the most striking ones being dwindling resources and climate change. In both cases, microalgae can be of assistance thanks to their variety of applications ranging from food and feed to carbon neutral biofuel productions (Brennan and Owende, 2010; Enamala et al., 2018). In addition, over recent years, microalgae have shown promise for the production of high
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10811-020-02180-7) contains supplementary material, which is available to authorized users. * Victor Pozzobon [email protected] 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
2
URD Agro Biotechnologies Industrielles (ABI)-AgroParisTech, 3 rue des Rouges Terres, 51110 Pomacle, France
added value molecules such as pigments, essential fatty acids, specific carbohydrates and antioxidants
Data Loading...
