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Classification of dead and living microalgae Chlorella vulgaris by bioimage informatics and machine learning

Reimann R, Zeng B, Jakopec M, Burdukiewicz M, Petrick I, Schierack P, Rödiger S (2020)

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Publication Type: Journal article

Publication year: 2020

Journal

Book Volume: 48

Article Number: 101908

DOI: 10.1016/j.algal.2020.101908

Abstract

The ratio between living and dead cells is an important parameter in microalgae culture and environmental monitoring. Fast, robust and automated analytical methods for monitoring microalgae growth for biotechnological and pharmaceutical applications and for optimizing production strains are needed. We developed a pipeline for the automatic binary classification of living and dead microalgae. We compared multispectral fluorescence microscopy and flow cytometry as readout platforms. Images of Chlorella vulgaris suspension cultures were captured and features of microalgaes (e.g., size) were extracted by bioimage informatics. We classified the microalgae cells with seven machine learning algorithms, including Naive Bayes, Random Forest and Neural Networks. Random Forest was a particularly suitable method for classifying the living/dead microalgae population, the label-free classifier can reach 86% of the area under the curve (AUC) if only morphological characteristics are used. The AUC rose to 99.6% if the fluorescence signal of Syto 9 (dye for all cells) and Propidium iodide (dye for damaged cells) were added as features, and the prediction accuracy reached 96.6% which is over the accuracy of 95% from the cell viability analysis using the staining method. This means that our classifier is useful as it improves the staining method by correcting 30% of the false positive/negative cells. Instead of only predicting single cells, we developed a Random Forest model to classify the distribution of living and dead microalgae populations using the fingerprint vector features, which gives an overall AUC of 94.5% and accuracy of 82%. We found that dead cells are significantly larger in average diameter (1.4-fold, p < 0.01) and area (1.5-fold, p < 0.01) when compared to living cells. In summary, combinations of statistical methods, bioimage informatics and machine learning are useful approaches for automated investigation in analyzing population dynamics of microalgae cultures.

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How to cite

APA:

Reimann, R., Zeng, B., Jakopec, M., Burdukiewicz, M., Petrick, I., Schierack, P., & Rödiger, S. (2020). Classification of dead and living microalgae Chlorella vulgaris by bioimage informatics and machine learning. Algal Research, 48. https://dx.doi.org/10.1016/j.algal.2020.101908

MLA:

Reimann, Ronny, et al. "Classification of dead and living microalgae Chlorella vulgaris by bioimage informatics and machine learning." Algal Research 48 (2020).

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