Compared with flow cytometry, the flux of optical microscope is limited
In order to identify small cell subsets and detect rare cell events, large-scale analysis of cells is required. Usually, flow cytometry based on fluorescence intensity is used, which can analyze 104-106 cells at the single cell level in a few minutes. However, this flow cytometry analysis requires markers to identify cell populations of interest in order to improve the efficiency of analysis. Light microscopy is also used to identify specific stages of cellular processes (such as mitosis), butCompared with flow cytometry, the flux of optical microscopy and the ability to conduct large-scale cell analysis are limited, so it is difficult to find a statistically sufficient number of cells.
The rapid development of imaging flow cytometry and the latest progress of high-throughput 3D IFCM
Recently, imaging flow cytometry (IFCM) has been developed rapidly to overcome the throughput and quantity limitations of image analysis. However, most IFCM technologies are based on 2D images and cannot accurately analyze and understand the key cell structures and processes of tissues in three dimensions, including the morphology of dividing nuclei, intracellular biochemical pathways and co localization events. Compared with 2D IFCM, high-throughput 3D IFCM requires additional image acquisition time, so it is difficult to achieve. For example, using a traditional confocal scanning microscope for 3D imaging, each point in the space is obtained in sequence, which usually takes several minutes to obtain the image. Although the high sampling rate can be achieved by using an acoustooptic deflector to improve the scanning speed, the high bandwidth required limits the number of photons per sampling time, resulting in a reduction in signal-to-noise ratio.
In order to promote the application of 3D IFCM in cell analysis,The sadao OTA team of the University of Tokyo recentlySmall SciencePublished research articles on, a high-speed IFCM method is reported. This method can obtain 3D cell images with a detection flux of more than 2000 cells per second, so as to analyze the intracellular structure in detail.
In this paper, the author first introduces the working principle of the developed 3D film IFCM (as shown in the above figure):the cells flow parallel at a constant speed in the y-axis direction, and the channel centers are aligned in the z-axis direction; By using the inclined microscope, the image of the cross section of the channel can be obtained at the plane intersecting the diagonal of the channel, and then the 3D scanning of the translational cells can be completed based on the continuously obtained images of the static inclined plane.
Then, the author used a single cell for testing. In 100 consecutive frames, the 3D image of a single cell was successfully reconstructed, in which the structure of cell membrane and nucleus was clearly identifiable, and the subcellular structure in all directions could be spatially analyzed. In the high-throughput imaging experiment, the author used 600 frames to reconstruct 724 × four hundred and thirty-three × thirty-eight μ The 3D image of the cell population in the region m, and the cell segmentation algorithm was used to calculate the 2682 cell information obtained in the acquisition time of 833 Ms. With a high detection throughput of 3220 cells per second, cells overlapping in the depth direction can still be clearly identified in the cross-sectional image (Figure f above).
In addition, in this work, the author also studied the parallel flow of cells with acoustic focusing, and conducted high-throughput parallel 3D IFCM experiments. The results show that the 3D IFCM carried out by this device can achieve the detection flux of 2312 cells per second, and canfiveLarge scale 3D image analysis is carried out on the order of cells. It can be expected that this method is expected to be used for rapid and large-scale 3D cell structure screening to help researchers accurately analyze cells at the single cell level.
High-Throughput Parallel Optofluidic 3D-Imaging Flow Cytometry
Masashi Ugawa, Sadao Ota*
DOI:10.1002/smsc. two hundred and two million one hundred thousand one hundred and twenty-six