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Drug susceptibility (also called chemosensitivity) is an important criterion for developing a therapeutic strategy for various cancer types such as breast cancer and leukemia. Recently, functional assays such as high-content screening together with genomic analysis have been shown to be effective for predicting drug susceptibility, but their clinical applicability is poor since they are time-consuming (several days long), labor-intensive, and costly. Here we present a highly simple, rapid, and cost-effective liquid biopsy for ex vivo drug-susceptibility testing of leukemia. The method is based on an extreme-throughput (>1 million cells per second), label-free, whole-blood imaging flow cytometer with a deep convolutional autoencoder, enabling image-based identification of the drug susceptibility of every single white blood cell in whole blood within 24 hours by simply flowing a drug-treated whole blood sample as little as 500 μL into the imaging flow cytometer without labeling. Our results show that the method accurately evaluates the drug susceptibility of white blood cells from untreated patients with acute lymphoblastic leukemia. Our method holds promise for affordable precision medicine.

Euglena gracilis (E. gracilis) has recently been attracting attention as a potential renewable source for the production of biofuels, livestock feed, cosmetics, and dietary supplements. Research has focused on strain isolation, productivity improvement, nutrient and resource allocation, and co-product production, key steps that ultimately determine the economic viability and compatibility of the biomass produced. To achieve these characteristics, approaches to select E. gracilis mutants with desirable properties, such as high wax ester content, high growth rate, and high environmental tolerance for biodiesel and biomass production, are needed. Flow-based analysis and sorting can be rapid and highly automated but calls for techniques that can precisely control the position of E. gracilis with varying sizes and shapes in a tightly focused stream in a high-throughput manner. In this work, we use a stepped microchannel consisting of a low-aspect-ratio straight channel and a series of expansion regions along the channel height. We study horizontal and vertical focusing, orientation, rotational, and translational behaviors of E. gracilis as a function of aspect ratio (AR) and channel Reynolds number (Re). By making use of inertial focusing and local secondary flows, E. gracilis with diverse shapes are directed to a single equilibrium position in a single focal stream. As an application of on-chip flow cytometry, we integrate a focusing microchip with a custom laser-two-focus (L2F) optical system and demonstrate the detection of chlorophyll autofluorescence as well as the measurement of the velocity of E. gracilis cells flowing through the microchannel.

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.