Machine learning processing of microalgae flow cytometry readings: illustrated withChlorella vulgarisviability assays

A flow cytometry viability assay protocol is proposed and applied to model microalgaeChlorella 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 degrees 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 assessChlorella vulgariscryostorage procedure efficiency. The impact of freezing protocol on cell viability was first investigated over 48-h storage (- 20 degrees C). Then, the most promising procedure (pelleted, - 20 degrees 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.