Machine Learning to Quantitate Neutrophil NETosis

Laila Elsherif,Joshua A Walker,Noah Sciaky,Stephen P Holly,Nadeem Ramadan,Ioannis Rekleitis,Christine A Burris,Martis W Cowles,Tina M Leisner,Kenneth I Ataga,Leslie V Parise,Md Modasshir,Joshua N Cooper,Carrington A Metts

doi:10.1038/s41598-019-53202-5

Abstract

We introduce machine learning (ML) to perform classification and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered process involved in multiple human diseases. CNNs achieved >94% in performance accuracy in differentiating NETotic from non-NETotic cells and vastly facilitated dose-response analysis and screening of the NETotic response in neutrophils from patients. Using only features learned from nuclear morphology, CNNs can distinguish between NETosis and necrosis and between distinct NETosis signaling pathways, making them a precise tool for NETosis detection. Furthermore, by using CNNs and tools to determine object dispersion, we uncovered differences in NETotic nuclei clustering between major NETosis pathways that is useful in understanding NETosis signaling events. Our study also shows that neutrophils from patients with sickle cell disease were unresponsive to one of two major NETosis pathways. Thus, we demonstrate the design, performance, and implementation of ML tools for rapid quantitative and qualitative cell analysis in basic science.

Highlights

Machine learning (ML) is a branch of artificial intelligence built on the idea that computers can acquire knowledge through data and observations without explicit programming; they can learn to generalize from examples and make predictions
The other main strategy to quantitate NETosis capitalizes on the discernable change that occurs in nuclear shape, which involves assessing the fraction of NETotic nuclei in a mixed cell population
The improvement we introduce is the use of convolutional neural networks to classify NETotic and non-NETotic adherent neutrophils; efficiently process thousands of cell images captured by an automated microscope system; facilitate graded dose-response analyses; predict which of the two major NETosis pathways is involved; and distinguish NETotic from necrotic cells

Summary

Introduction

Machine learning (ML) is a branch of artificial intelligence built on the idea that computers can acquire knowledge through data and observations without explicit programming; they can learn to generalize from examples and make predictions. The earliest attempts to quantitate NETosis involved measuring changes in the size and shape of neutrophil nuclei using fully-supervised image processing methods which suffer from the weaknesses of rule-based modeling[40,41,42,43].

Results

Conclusion