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


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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  • Abreu L, Borges L, Marangoni J, Abreu PC (2012) Cryopreservation of some useful microalgae species for biotechnological exploitation. J Appl Phycol 24:1579–1588

  • Aghaeepour N, Finak G, Hoos H, Mosmann TR, Brinkman R, Gottardo R, Scheuermann RH (2013) Critical assessment of automated flow cytometry data analysis techniques. Nature Meth 10:228–238

  • American Public Health Association, Greenberg AE, Jenkins D, Connors JJ, American Water Works Association, Water Pollution Control Federation (1980) Standard methods for the examination of water and wastewater. APHA-AWWAWPCF, Washington, D.C.

    Google Scholar 

  • Andersen RA (2005) Algal culturing techniques appendix A—recipes for freshwater and seawater media. In: Algal culturing techniques, 1st edn. Academic Press, Burlington

    Google Scholar 

  • Bajerski F, Stock J, Hanf B, Darienko T, Heine-Dobbernack E, Lorenz M, Naujox L, Keller ERJ, Schumacher HM, Friedl T, Eberth S, Mock HP, Kniemeyer O, Overmann J (2018) ATP content and cell viability as indicators for cryostress across the diversity of life. Front Physiol 9:2018.00921

    Article  Google Scholar 

  • Brennan L, Owende P (2010) Biofuels from microalgae—a review of technologies for production, processing, and extractions of biofuels and co-products. Renew Sust Energ Rev 14:557–577

  • Christensen JM, Tiersch TR (1997) Cryopreservation of channel catfish spermatozoa: effect of cryoprotectant, straw size, and formulation of extender. Theriogenology 47:639–645

  • Cordero B, Voltolina D (1997) Viability of mass algal cultures preserved by freezing and freeze-drying. Aquac Eng 16:205–211

  • Crippen RW, Perrier JL (1974) The use of neutral red and Evans blue for live-dead determinations of marine plankton (with comments on the use of rotenone for inhibition of grazing). Stain Technol 49:97–104

    CAS  Article  Google Scholar 

  • Darzynkiewicz Z, Robinson JP, Roederer M (2009) Essential cytometry methods. Academic Press, Amsterdam

    Google Scholar 

  • Day JG (2004) Cryopreservation: fundamentals, mechanisms of damage on freezing/thawing and application in culture collections. Nova Hedwigia 79:191–205

  • Day JG, Watanabe MM, Morris GJ, Fleck RA, McLellan MR (1997) Long-term viability of preserved eukaryotic algae. J Appl Phycol 9:121–127

  • Day JG, Fleck RA, Benson EE (2000) Cryopreservation-recalcitrance in microalgae: novel approaches to identify and avoid cryo-injury. J Appl Phycol 12:369–377

  • Dressel DM, Heinle DR, Grote MC (1972) Vital staining to sort dead and live copepods. Chesap Sci 13:156–159

    Article  Google Scholar 

  • Enamala MK, Enamala S, Chavali M, Donepudi J, Yadavalli R, Kolapalli B, Aradhyula TV, Velpuri J, Kuppam C (2018) Production of biofuels from microalgae - a review on cultivation, harvesting, lipid extraction, and numerous applications of microalgae. Renew Sust Energ Rev 94:49–68

  • Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 1st ACM SIGKDD, Montreal, Canada pp 226–231

  • Fachet M, Hermsdorf D, Rihko-Struckmann L, Sundmacher K (2016) Flow cytometry enables dynamic tracking of algal stress response: a case study using carotenogenesis in Dunaliella salina. Algal Res 13:227–234

    Article  Google Scholar 

  • Fleming KK, Hubel A (2007) Cryopreservation of hematopoietic stem cells: emerging science, technology and issues. Transfus Med Hemother 34:268–275

  • Franklin NM, Adams MS, Stauber JL, Lim RP (2001) Development of an improved rapid enzyme inhibition bioassay with marine and freshwater microalgae using flow cytometry. Arch Environ Contam Toxicol 40:469–480

    CAS  Article  Google Scholar 

  • Herzenberg LA, Parks D, Sahaf B, Perez O, Roederer M, Herzenberg LA (2002) The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford. Clin Chem 48:1819–1827

  • Jochem FJ (1999) Dark survival strategies in marine phytoplankton assessed by cytometric measurement of metabolic activity with fluorescein diacetate. Mar Biol 135:721–728

    CAS  Article  Google Scholar 

  • Kapoore RV, Huete-Ortega M, Day JG, Okurowska K, Slocombe SP, Stanley MS, Vaidyanathanl S (2019) Effects of cryopreservation on viability and functional stability of an industrially relevant alga. Sci Rep 9:2093

  • Levasseur W, Perré P, Pozzobon V (2020) A review of high value-added molecules production by microalgae in light of the classification. Biotech Adv

  • Lin C, Chong G, Wang LH, Kuo FW, Tsai S (2019) Use of luminometry and flow cytometry for evaluating the effects of cryoprotectants in the gorgonian coral endosymbiont Symbiodinium. Phycol Res 67:320–326

  • Liu H, Yu Y, Kong F, He L, Yu H, Giesy JP, Wang X (2008) Effects of tetrabromobisphenol A on the green alga Chlorella pyrenoidosa. J Env Sci Health A 43:1271–1278

    CAS  Article  Google Scholar 

  • MortainBertrand A, Etchart F, de Boucaud MT (1996) A method for the cryoconservation of Dunaliella salina (Chlorophyceae): effect of glycerol and cold adaptation. J Phycol 32:346–352

  • Ohnuma M, Kuroiwa T, Tanaka K (2011) Optimization of cryopreservation conditions for the unicellular red alga Cyanidioschyzon merolae. J Gen Appl Microbiol 57:137–143

  • Ormerod MG, Imrie PR (1990) Flow cytometry. In: Walker JM, Pollard JW, Walker JM (eds) Animal cell culture, methods in molecular biology. Humana Press, Totowa, pp 543–558

    Chapter  Google Scholar 

  • Pancha I, Chokshi K, George B, Ghosh T, Paliwal C, Maurya R, Mishra S (2014) Nitrogen stress triggered biochemical and morphological changes in the microalgae Scenedesmus sp. CCNM 1077. Bioresour Technol 156:146–154

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

  • Pereira H, Schulze PSC, Schüler LM, Santos T, Barreira L, Varela J (2018) Fluorescence activated cell-sorting principles and applications in microalgal biotechnology. Algal Res 30:113–120

  • Rizwan M, Mujtaba G, Memon SA, Lee K, Rashid N (2018) Exploring the potential of microalgae for new biotechnology applications and beyond: a review. Renew Sust Energ Rev 92:394–404

  • Selvin R, Reguera B, Bravo I, Yentsch CM (1989) Use of fluorescein diacetate (FDA) as a single-cell probe of metabolic activity in dinoflagellate cultures. Biol Oceanogr 6:505–511

  • Singh M, Rajoriya JS, Kumar A, Ghosh SK, Prasad JK (2018) Cryopreservation of buffalo (Bubalus bubalis) semen: current status and future prospective. Buffalo Bull 37:109–128

  • Taylor R, Fletcher RL (1998) Cryopreservation of eukaryotic algae - a review of methodologies. J Appl Phycol 10:481–501

  • Valeri CR, Fowler K, Sobucki J (1965) The in vivo survival, mode of removal of the non-viable cells, and the total amount of supernatant hemoglobin in deglycerolized, resuspended erythrocytes. Transfusion 5:273–285

    Article  Google Scholar 

  • Widholm JM (1972) The use of fluorescein diacetate and phenosafranine for determining viability of cultured plant cells. Stain Technol 47:189–194

    CAS  Article  Google Scholar 

  • Ye X, Ho JWK (2019) Ultrafast clustering of single-cell flow cytometry data using FlowGrid. BMC Syst Biol 13:35

    Article  Google Scholar 

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Authors and Affiliations



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).

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  • Flow cytometry
  • Dual staining
  • Machine learning
  • Cryopreservation