Discrimination of tumor cell type based on cytometric detection of dielectric properties

Functionalized DNA nanoplatform for multi-target simultaneous imaging: Establish the atlas of cancer cell species

Detection and imaging of cell membrane receptor proteins have gained widespread interest in recent years. However, recognition based on a single biomarker can induce false positive feedback, including off-target phenomenon caused by the absence of tumor-specific antigens. In addition, nucleic acid probes often cause nonspecific and undesired cell internalization during cell imaging. In this work, we constructed a logic gate DNA nano-platform (LGDP) for single-molecule imaging of cell membrane proteins to synergistically diagnose cancer cells. The traffic light-like color response of LGDP facilitates the precise discrimination among different cell lines. Combined with single molecule technology, the target proteins were qualitatively and quantitatively analyzed synergistically. Logic-gated recognition integrated in aptamer-functionalized molecular machines will prompt fast cells analysis, laying the foundation of cancer early diagnosis and treatment.

Machine learning implementation strategy in imaging and impedance flow cytometry

Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.

A Perturbed Asymmetrical Y-TypeSheathless Chip for Particle Control Based on Adjustable Tilted-Angle Traveling Surface Acoustic Waves (ataTSAWs)

The precise control of target particles (20 µm) at different inclination angles θi is achieved by combining a perturbed asymmetric sheathless Y-type microchannel and a digital transducer (IDT). The offset single-row micropillar array with the buffer area can not only concentrate large and small particles in a fixed region of the flow channel, but also avoid the large deflection of some small particles at the end of the array. The addition of the buffer area can effectively improve the separation purity of the chip. By exploring the manufacturing process of the microchannel substrate, an adjustable tilted-angle scheme is proposed. The use of ataTSAW makes the acoustic field area in the microchannel have no corner effect region. Through experiments, when the signal source frequency was 33.6 MHz, and the flow rate was 20 µL/min, our designed chip could capture 20 µm particles when θi = 5°. The deflection of 20 µm particles can be realized when θi = 15°–45°. The precise dynamic separation of 20 µm particles can be achieved when θi = 25°–45°, and the separation purity and efficiency were 97% and 100%, respectively.