An optimized PDMS microfluidic device for ultra-fast and high-throughput imaging flow cytometry.

Imaging flow cytometry (IFC) is a powerful tool for cell detection and analysis due to its high throughput and compatibility in image acquisition. Optical time-stretch (OTS) imaging is considered as one of the most promising imaging techniques for IFC because it can realize cell imaging at a flow speed of around 60 m s-1. However, existing PDMS-based microchannels cannot function at flow velocities higher than 10 m s-1; thus the capability of OTS-based IFC is significantly limited. To overcome the velocity barrier for PDMS-based microchannels, we proposed an optimized design of PDMS-based microchannels with reduced hydraulic resistance and 3D hydrodynamic focusing capability, which can drive fluids at an ultra-high flow velocity (of up to 40 m s-1) by using common syringe pumps. To verify the feasibility of our design, we fabricated and installed the microchannel in an OTS IFC system. The experimental results first proved that the proposed microchannel can support a stable flow velocity of up to 40 m s-1 without any leakage or damage. Then, we demonstrated that the OTS IFC is capable of imaging cells at a velocity of up to 40 m s-1 with good quality. To the best of our knowledge, it is the first time that IFC has achieved such a high flow velocity just by using a PDMS-glass chip. Moreover, high velocity can enhance the focusing of cells on the optical focal plane, increasing the number of detected cells and the throughput. This work provides a promising solution for IFC to fully release its capability of advanced imaging techniques by operating at an extremely high screening throughput.

Optical time-stretch imaging flow cytometry in the compressed domain.

Imaging flow cytometry based on optical time-stretch (OTS) imaging combined with a microfluidic chip attracts much attention in the large-scale single-cell analysis due to its high throughput, high precision, and label-free operation. Compressive sensing has been integrated into OTS imaging to relieve the pressure on the sampling and transmission of massive data. However, image decompression brings an extra overhead of computing power to the system, but does not generate additional information. In this work, we propose and demonstrate OTS imaging flow cytometry in the compressed domain. Specifically, we constructed a machine-learning network to analyze the cells without decompressing the images. The results show that our system enables high-quality imaging and high-accurate cell classification with an accuracy of over 99% at a compression ratio of 10%. This work provides a viable solution to the big data problem in OTS imaging flow cytometry, boosting its application in practice.

Cell damage evaluation by intelligent imaging flow cytometry.

Essential thrombocythemia (ET) is an uncommon situation in which the body produces too many platelets. This can cause blood clots anywhere in the body and results in various symptoms and even strokes or heart attacks. Removing excessive platelets using acoustofluidic methods receives extensive attention due to their high efficiency and high yield. While the damage to the remaining cells, such as erythrocytes and leukocytes is yet evaluated. Existing cell damage evaluation methods usually require cell staining, which are time-consuming and labor-intensive. In this paper, we investigate cell damage by optical time-stretch (OTS) imaging flow cytometry with high throughput and in a label-free manner. Specifically, we first image the erythrocytes and leukocytes sorted by acoustofluidic sorting chip with different acoustic wave powers and flowing speed using OTS imaging flow cytometry at a flowing speed up to 1 m/s. Then, we employ machine learning algorithms to extract biophysical phenotypic features from the cellular images, as well as to cluster and identify images. The results show that both the errors of the biophysical phenotypic features and the proportion of abnormal cells are within 10% in the undamaged cell groups, while the errors are much greater than 10% in the damaged cell groups, indicating that acoustofluidic sorting causes little damage to the cells within the appropriate acoustic power, agreeing well with clinical assays. Our method provides a novel approach for high-throughput and label-free cell damage evaluation in scientific research and clinical settings.

Typing of acute leukemia by intelligent optical time-stretch imaging flow cytometry on a chip.

Acute leukemia (AL) is one of the top life-threatening diseases. Accurate typing of AL can significantly improve its prognosis. However, conventional methods for AL typing often require cell staining, which is time-consuming and labor-intensive. Furthermore, their performance is highly limited by the specificity and availability of fluorescent labels, which can hardly meet the requirements of AL typing in clinical settings. Here, we demonstrate AL typing by intelligent optical time-stretch (OTS) imaging flow cytometry on a microfluidic chip. Specifically, we employ OTS microscopy to capture the images of cells in clinical bone marrow samples with a spatial resolution of 780 nm at a high flowing speed of 1 m s-1 in a label-free manner. Then, to show the clinical utility of our method for which the features of clinical samples are diverse, we design and construct a deep convolutional neural network (CNN) to analyze the cellular images and determine the AL type of each sample. We measure 30 clinical samples composed of 7 acute lymphoblastic leukemia (ALL) samples, 17 acute myelogenous leukemia (AML) samples, and 6 samples from healthy donors, resulting in a total of 227 620 images acquired. Results show that our method can distinguish ALL and AML with an accuracy of 95.03%, which, to the best of our knowledge, is a record in label-free AL typing. In addition to AL typing, we believe that the high throughput, high accuracy, and label-free operation of our method make it a potential solution for cell analysis in scientific research and clinical settings.

Adversarial Multiscale Feature Learning Framework for Overlapping Chromosome Segmentation.

Chromosome karyotype analysis is of great clinical importance in the diagnosis and treatment of diseases. Since manual analysis is highly time and effort consuming, computer-assisted automatic chromosome karyotype analysis based on images is routinely used to improve the efficiency and accuracy of the analysis. However, the strip-shaped chromosomes easily overlap each other when imaged, significantly affecting the accuracy of the subsequent analysis and hindering the development of chromosome analysis instruments. In this paper, we present an adversarial, multiscale feature learning framework to improve the accuracy and adaptability of overlapping chromosome segmentation. We first adopt the nested U-shaped network with dense skip connections as the generator to explore the optimal representation of the chromosome images by exploiting multiscale features. Then we use the conditional generative adversarial network (cGAN) to generate images similar to the original ones; the training stability of the network is enhanced by applying the least-square GAN objective. Finally, we replace the common cross-entropy loss with the advanced Lovász-Softmax loss to improve the model's optimization and accelerate the model's convergence. Comparing with the established algorithms, the performance of our framework is proven superior by using public datasets in eight evaluation criteria, showing its great potential in overlapping chromosome segmentation.

High-throughput and high-accuracy diagnosis of multiple myeloma with multi-object detection.

Multiple myeloma (MM) is a type of blood cancer where plasma cells abnormally multiply and crowd out regular blood cells in the bones. Automated analysis of bone marrow smear examination is considered promising to improve the performance and reduce the labor cost in MM diagnosis. To address the drawbacks in established methods, which mainly aim at identifying monoclonal plasma cells (monoclonal PCs) via binary classification, in this work, considering that monoclonal PCs is not the only basis in MM diagnosis, for the first we construct a multi-object detection model for MM diagnosis. The experimental results show that our model can handle the images at a throughput of 80 slides/s and identify six lineages of bone marrow cells with an average accuracy of 90.8%. This work makes a step further toward full-automatic and high-efficiency MM diagnosis.

Analysis of signal detection configurations in optical time-stretch imaging.

Optical time-stretch (OTS) imaging is effective for observing ultra-fast dynamic events in real time by virtue of its capability of acquiring images with high spatial resolution at high speed. In different implementations of OTS imaging, different configurations of its signal detection, i.e. fiber-coupled and free-space detection schemes, are employed. In this research, we quantitatively analyze and compare the two detection configurations of OTS imaging in terms of sensitivity and image quality with the USAF-1951 resolution chart and diamond films, respectively, providing a valuable guidance for the system design of OTS imaging in diverse fields.

Temporally interleaved optical time-stretch imaging.

Optical time-stretch imaging has shown potential in diverse fields for its capability of acquiring images at high speed and high resolution. However, its wide application is hindered by the stringent requirement on the instrumentation hardware caused by the high-speed serial data stream. Here we demonstrate temporally interleaved optical time-stretch imaging that lowers the requirement without sacrificing the frame rate or spatial resolution by interleaving the high-speed data stream into multiple channels in the time domain. Its performance is validated with both a United States Air Force (USAF)-1951 resolution chart and a single-crystal diamond film. We achieve a 101 Mfps 1D scanning rate and 3 µm spatial resolution with only a 2.5 GS/s sampling rate by using a two-channel-interleaved system.