Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

Abstract A machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of...

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Autores principales:	Yersultan Mirasbekov, Adina Zhumakhanova, Almira Zhantuyakova, Kuanysh Sarkytbayev, Dmitry V. Malashenkov, Assel Baishulakova, Veronika Dashkova, Thomas A. Davidson, Ivan A. Vorobjev, Erik Jeppesen, Natasha S. Barteneva
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8ea195a6aa8940738d6084217ff462e1
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spelling	oai:doaj.org-article:8ea195a6aa8940738d6084217ff462e12021-12-02T17:15:33ZSemi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom10.1038/s41598-021-88661-22045-2322https://doaj.org/article/8ea195a6aa8940738d6084217ff462e12021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88661-2https://doaj.org/toc/2045-2322Abstract A machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.Yersultan MirasbekovAdina ZhumakhanovaAlmira ZhantuyakovaKuanysh SarkytbayevDmitry V. MalashenkovAssel BaishulakovaVeronika DashkovaThomas A. DavidsonIvan A. VorobjevErik JeppesenNatasha S. BartenevaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yersultan Mirasbekov Adina Zhumakhanova Almira Zhantuyakova Kuanysh Sarkytbayev Dmitry V. Malashenkov Assel Baishulakova Veronika Dashkova Thomas A. Davidson Ivan A. Vorobjev Erik Jeppesen Natasha S. Barteneva Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom
description	Abstract A machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.
format	article
author	Yersultan Mirasbekov Adina Zhumakhanova Almira Zhantuyakova Kuanysh Sarkytbayev Dmitry V. Malashenkov Assel Baishulakova Veronika Dashkova Thomas A. Davidson Ivan A. Vorobjev Erik Jeppesen Natasha S. Barteneva
author_facet	Yersultan Mirasbekov Adina Zhumakhanova Almira Zhantuyakova Kuanysh Sarkytbayev Dmitry V. Malashenkov Assel Baishulakova Veronika Dashkova Thomas A. Davidson Ivan A. Vorobjev Erik Jeppesen Natasha S. Barteneva
author_sort	Yersultan Mirasbekov
title	Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom
title_short	Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom
title_full	Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom
title_fullStr	Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom
title_full_unstemmed	Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom
title_sort	semi-automated classification of colonial microcystis by flowcam imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8ea195a6aa8940738d6084217ff462e1
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Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

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