The traditional diagnosis of leukemia relies on pathologists to observe and classify cells on bone marrow smears, which is low-throughput, time-consuming, and subject to human bias. To overcome these limitations, we demonstrate intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging (OTS-QPI) that acquires bright-field and quantitative phase images of white blood cells (WBCs) containing leukemia cells with high throughput (15,000 cells/s) for deep-learning-based classification. After trained with 64,000 images, a convolutional neural network (CNN) distinguishes three different types of leukemia cells from WBCs with an accuracy of over 96%. Our method provides new possibilities for high-throughput, label-free, and intelligent leukemia diagnosis.
Platelets participate in both physiological hemostasis and pathological thrombosis by forming aggregates activated by various agonists. However, it has been considered impossible to identify the stimuli and classify the aggregates. Here we present an intelligent method for classifying platelet aggregates by agonist type based on the combination of high-throughput imaging flow cytometry and a convolutional neural network. It morphologically identifies the contributions of different agonists to platelet aggregation with high accuracy. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to develop a new class of clinical diagnostics and therapeutics.
In the near future, single-molecule surface-enhanced Raman spectroscopy (SERS) is expected to expand the family of popular analytical tools for single-molecule characterization. We provide a roadmap for achieving single molecule SERS through different enhancement strategies for diverse applications. We introduce some characteristic features related to single-molecule SERS, such as Raman enhancement factor, intensity fluctuation, and data analysis. We then review recent strategies for enhancing the Raman signal intensities of single molecules, including electromagnetic enhancement, chemical enhancement, and resonance enhancement strategies. To demonstrate the utility of single-molecule SERS in practical applications, we present several examples of its use in various fields, including catalysis, imaging, and nanoelectronics. Finally, we specify current challenges in the development of single-molecule SERS and propose corresponding solutions.
Optofluidic time-stretch microscopy is a powerful tool in imaging flow cytometry as it enables continuous image acquisition at a frame rate higher than 10,000 frames per second. In addition to bright-field imaging that provides morphological information, attempts have been made to integrate quantitative phase imaging (QPI) with optofluidic time-stretch microscopy to acquire information related to subcellular structure, such as the refractive index and thickness. However, the applicability of such methods is hindered by errors introduced during phase unwrapping and the need for a high-bandwidth photodetector. To overcome these limitations, here we demonstrate optofluidic time-stretch QPI based on an acousto-optic modulator (AOM) that acquires intensity and phase image with a low-bandwidth photodetector without phase-unwrapping errors. In our system, the signal beam that carries cellular information interferes with the reference beam, the frequency of which is shifted by 1/4 of the repetition frequency of the laser by an AOM. The beat note is then detected by a normal photodetector, and its waveform that consists of groups of four successive pulses is converted into phase and intensity images with simple calculations. Therefore, we lower the requirement of the photodetector bandwidth and eliminate the errors in phase unwrapping while maintaining a throughput of 10,000 cells per second. These advantages of our system offer new possibilities for high-throughput label-free cancer cell detection in blood by looking at cellular phase information including structural features, enabling early cancer detection and improving the effectiveness of treatment.