Yu Wan Wendy, Liu Lina, Liu Xiaoxuan, Wang Wei, Zahurul Islam Md, Dong Chunhua, Garen Craig R, Woodside Michael T, Gupta Manisha, Mandal Mrinal, Rozmus Wojciech, Yin Tsui Ying
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.
Authors with equal contribution.
Biomed Opt Express. 2021 May 19;12(6):3512-3529. doi: 10.1364/BOE.424357. eCollection 2021 Jun 1.
Light scattering has been used for label-free cell detection. The angular light scattering patterns from the cells are unique to them based on the cell size, nucleus size, number of mitochondria, and cell surface roughness. The patterns collected from the cells can then be classified based on different image characteristics. We have also developed a machine learning (ML) method to classify these cell light scattering patterns. As a case study we have used this light scattering technique integrated with the machine learning to analyze staurosporine-treated SH-SY5Y neuroblastoma cells and compare them to non-treated control cells. Experimental results show that the ML technique can provide a classification accuracy (treated versus non-treated) of over 90%. The predicted percentage of the treated cells in a mixed solution is within 5% of the reference (ground-truth) value and the technique has the potential to be a viable method for real-time detection and diagnosis.
光散射已被用于无标记细胞检测。基于细胞大小、细胞核大小、线粒体数量和细胞表面粗糙度，细胞的角向光散射模式对其而言是独特的。然后可以根据不同的图像特征对从细胞收集的模式进行分类。我们还开发了一种机器学习（ML）方法来对这些细胞光散射模式进行分类。作为一个案例研究，我们使用这种光散射技术与机器学习相结合，分析了星形孢菌素处理的SH-SY5Y神经母细胞瘤细胞，并将其与未处理的对照细胞进行比较。实验结果表明，ML技术可以提供超过90%的分类准确率（处理组与未处理组）。混合溶液中处理细胞的预测百分比在参考（真实）值的5%以内，该技术有潜力成为一种可行的实时检测和诊断方法。
