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.

提出了流式细胞术活力检测方案并应用于微藻小球藻的建模。。该协议依赖于细胞的双重染色（荧光素二乙酸盐（FDA），碘化丙啶（PI））和结果的机器学习处理。方案开发突出表明，在4°C下工作可使分析前的染色样品保存15分钟。此外，方案中包括细胞外FDA洗涤步骤，可改善信噪比，从而更好地检测活性细胞。建立后，该协议针对7个测试用例（活动和非活动细胞的受控混合物）进行了验证。它在测试用例上的表现良好：−主动细胞定量分析（人工处理）偏差为0.19％abs。此外，介绍了一种基于DBSCAN算法的机器学习工作流程。经过校准程序后，该算法提供的结果非常令人满意，结果为− 0。与人工相比，偏差为10％。这种方法允许自动化并加速（15倍）细胞计数读数处理。最后，建议的工作流程用于评估小球藻冷冻程序的效率。首先研究了冷冻方案对细胞存活力的影响，其储存时间为48小时（− 20°C）。然后，在1个月内测试了最有前途的程序（制粒，-20°C）。观察到的生存力丧失趋势和价值与文献有很好的相关性。这表明流式细胞仪是评估微藻冷冻保存方案效率的有效工具。