Cell morphology-based machine learning models for human cell state classification

Abstract Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to m...

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Autores principales:	Yi Li, Chance M. Nowak, Uyen Pham, Khai Nguyen, Leonidas Bleris
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f1e3a60d60654b6bbac2e007e799a270
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spelling	oai:doaj.org-article:f1e3a60d60654b6bbac2e007e799a2702021-12-02T15:49:54ZCell morphology-based machine learning models for human cell state classification10.1038/s41540-021-00180-y2056-7189https://doaj.org/article/f1e3a60d60654b6bbac2e007e799a2702021-05-01T00:00:00Zhttps://doi.org/10.1038/s41540-021-00180-yhttps://doaj.org/toc/2056-7189Abstract Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 live f value and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information.Yi LiChance M. NowakUyen PhamKhai NguyenLeonidas BlerisNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 7, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Yi Li Chance M. Nowak Uyen Pham Khai Nguyen Leonidas Bleris Cell morphology-based machine learning models for human cell state classification
description	Abstract Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 live f value and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information.
format	article
author	Yi Li Chance M. Nowak Uyen Pham Khai Nguyen Leonidas Bleris
author_facet	Yi Li Chance M. Nowak Uyen Pham Khai Nguyen Leonidas Bleris
author_sort	Yi Li
title	Cell morphology-based machine learning models for human cell state classification
title_short	Cell morphology-based machine learning models for human cell state classification
title_full	Cell morphology-based machine learning models for human cell state classification
title_fullStr	Cell morphology-based machine learning models for human cell state classification
title_full_unstemmed	Cell morphology-based machine learning models for human cell state classification
title_sort	cell morphology-based machine learning models for human cell state classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f1e3a60d60654b6bbac2e007e799a270
work_keys_str_mv	AT yili cellmorphologybasedmachinelearningmodelsforhumancellstateclassification AT chancemnowak cellmorphologybasedmachinelearningmodelsforhumancellstateclassification AT uyenpham cellmorphologybasedmachinelearningmodelsforhumancellstateclassification AT khainguyen cellmorphologybasedmachinelearningmodelsforhumancellstateclassification AT leonidasbleris cellmorphologybasedmachinelearningmodelsforhumancellstateclassification
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Cell morphology-based machine learning models for human cell state classification

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