Automated quantitative cytological analysis using portable microfluidic microscopy

Field of view scanning based quantitative interferometric microscopic cytometers for cellular imaging and analysis

Microimaging is of great significance in the biological and medical fields, since it can realize observations acting as important references for cellular research and disease diagnosis. However, traditional microscopy only offers qualitative sample contours; moreover, it is difficult to reach large-amount sample observations limited by the fixed field of view (FoV). To realize massive cellular measurements quantitatively, three designed quantitative interferometric microscopic cytometers based on the FoV scanning are introduced and compared in details in this article. These devices not only retrieve the quantitative sample phase distributions in the extended FoV, but also provide the detailed information of massive cells, such as cellular volume, area, and roundness. Considering their capabilities as quantitative imaging and large-amount sampling, it is believed that these quantitative interferometric microscopic cytometers (QIMCs) can be potentially adopted in high-throughput cell imaging and statistical analysis for both the biological and medical applications.

Cytopathological image analysis using deep-learning networks in microfluidic microscopy

Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing process. Several neural network architectures were designed to provide human expertise to machines. In this paper, we explore and propose the feasibility of using deep-learning networks for cytopathologic analysis by performing the classification of three important unlabeled, unstained leukemia cell lines (K562, MOLT, and HL60). The cell images used in the classification are captured using a low-cost, high-throughput cell imaging technique: microfluidics-based imaging flow cytometry. We demonstrate that without any conventional fine segmentation followed by explicit feature extraction, the proposed deep-learning algorithms effectively classify the coarsely localized cell lines. We show that the designed deep belief network as well as the deeply pretrained convolutional neural network outperform the conventionally used decision systems and are important in the medical domain, where the availability of labeled data is limited for training. We hope that our work enables the development of a clinically significant high-throughput microfluidic microscopy-based tool for disease screening/triaging, especially in resource-limited settings.

Single-layer microfluidic device to realize hydrodynamic 3D flow focusing

The recent rapid growth of microfluidic applications has witnessed the emergence of several particle flow focusing techniques for analysis and/or further processing. The majority of flow focusing techniques employ an external sheath fluid to achieve sample flow focusing independent of the flow rate, in contrast to sheath-free techniques. However, the introduction of a sheath fluid to surround the sample fluid has complicated the device design and fabrication, generally involving multi-layer fabrication and bonding of multiple polydimethylsiloxane (PDMS) layers. Several promising efforts have been made to reduce the complexity of fabrication. However, most of these methods involved the use of inertial/Dean effects, which in turn demanded the use of higher sample flow rates. In this paper, we report a method of flow focusing that uses a sheath fluid to enclose the sample in a single layer of PDMS, and that possesses applicability for a wide range of sample flow rates. This method of flow focusing uses abrupt channel depth variation and a shift of one of the sample-sheath junctions (termed as 'junction-shift') against the direction of the sample flow. This configuration serves to manipulate the sample fluid with respect to the sheath fluid and achieve the desired flow focusing. This design facilitates the attainment of 3D flow focusing in two sequential steps (depth-wise and then along the lateral direction) and in distinct regions, hence enabling the regions to be used in imaging and non-imaging flow cytometric applications, respectively. Simulations were performed to characterize and determine the optimum set of design parameters. Experimental demonstrations of this technique were carried out by focusing fluorescein dye and blood cells in flow.