Issue 14, 2017

Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy

Abstract

According to WHO, about 10 million new cases of thrombotic disorders are diagnosed worldwide every year. Thrombotic disorders, including atherothrombosis (the leading cause of death in the US and Europe), are induced by occlusion of blood vessels, due to the formation of blood clots in which aggregated platelets play an important role. The presence of aggregated platelets in blood may be related to atherothrombosis (especially acute myocardial infarction) and is, hence, useful as a potential biomarker for the disease. However, conventional high-throughput blood analysers fail to accurately identify aggregated platelets in blood. Here we present an in vitro on-chip assay for label-free, single-cell image-based detection of aggregated platelets in human blood. This assay builds on a combination of optofluidic time-stretch microscopy on a microfluidic chip operating at a high throughput of 10 000 blood cells per second with machine learning, enabling morphology-based identification and enumeration of aggregated platelets in a short period of time. By performing cell classification with machine learning, we differentiate aggregated platelets from single platelets and white blood cells with a high specificity and sensitivity of 96.6% for both. Our results indicate that the assay is potentially promising as predictive diagnosis and therapeutic monitoring of thrombotic disorders in clinical settings.

Graphical abstract: Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy

Supplementary files

Article information

Article type
Paper
Submitted
12 Apr 2017
Accepted
08 Jun 2017
First published
19 Jun 2017

Lab Chip, 2017,17, 2426-2434

Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy

Y. Jiang, C. Lei, A. Yasumoto, H. Kobayashi, Y. Aisaka, T. Ito, B. Guo, N. Nitta, N. Kutsuna, Y. Ozeki, A. Nakagawa, Y. Yatomi and K. Goda, Lab Chip, 2017, 17, 2426 DOI: 10.1039/C7LC00396J

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