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

Biology

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

Y. Li, C. M. Nowak, et al.

This groundbreaking research by Yi Li, Chance M. Nowak, Uyen Pham, Khai Nguyen, and Leonidas Bleris introduces an automated and stain-free method that leverages machine learning to differentiate between healthy and apoptotic cells using flow cytometry data. The multilayer perceptron model demonstrated exceptional performance in classifying live cells, marking a significant advancement over traditional flow cytometry techniques.

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Playback language: English
Abstract
This paper explores the use of machine learning to differentiate healthy from apoptotic cells using only forward (FSC) and side (SSC) scatter flow cytometry data. Colorectal cancer HCT116 cells treated with miR-34a were classified using Annexin V/propidium iodide staining. Six features (FSC and SSC area, height, and width) were used to train various machine learning models. A multilayer perceptron (MLP) model achieved the best performance, with high precision, recall, F-value, and area under the ROC curve for live cell classification. This model offers an automated, reliable, and stain-free alternative to conventional flow cytometry gating.
Publisher
npj Systems Biology and Applications
Published On
May 26, 2021
Authors
Yi Li, Chance M. Nowak, Uyen Pham, Khai Nguyen, Leonidas Bleris
Tags
machine learning
flow cytometry
apoptotic cells
HCT116 cells
miR-34a
automated classification
multilayer perceptron

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