Following nanoparticle uptake by cells using high-throughput microscopy and the deep-learning based cell identification algorithm Cellpose

Frontiers in Nanotechnology

Nanotechnology

Itxaso Aguirre-Zuazo¹  Else Niemeijer¹  Isa de Boer¹  Timea B. Gandek¹  Boxuan Yang¹  Ceri J. Richards¹  Christoffer Åberg² 

[1]Pharmaceutical Analysis, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, Netherlands

[2]null

关键词: nanoparticles;    cell uptake;    fluorescence microscopy;    high-throughput;    machine learning;    image segmentation;    modelling;    Cellpose;   

DOI  :  10.3389/fnano.2023.1181362

 received in 2023-03-07, accepted in 2023-04-26,  发布年份 2023

来源: Frontiers

【 摘 要 】

How many nanoparticles are taken up by human cells is a key question for many applications, both within medicine and safety. While many methods have been developed and applied to this question, microscopy-based methods present some unique advantages. However, the laborious nature of microscopy, in particular the consequent image analysis, remains a bottleneck. Automated image analysis has been pursued to remedy this situation, but offers its own challenges. Here we tested the recently developed deep-learning based cell identification algorithm Cellpose on fluorescence microscopy images of HeLa cells. We found that the algorithm performed very well, and hence developed a workflow that allowed us to acquire, and analyse, thousands of cells in a relatively modest amount of time, without sacrificing cell identification accuracy. We subsequently tested the workflow on images of cells exposed to fluorescently-labelled polystyrene nanoparticles. This dataset was then used to study the relationship between cell size and nanoparticle uptake, a subject where high-throughput microscopy is of particular utility.

【 授权许可】

Unknown   
Copyright © 2023 Yang, Richards, Gandek, de Boer, Aguirre-Zuazo, Niemeijer and Åberg.