Laser-based wide-field coherent imaging methods suffer from low image contrast due to the speckle noise as well as poor lateral resolution using collimated illumination. To improve the image contrast and spatial resolution for label-free cell imaging applications, we propose a new dynamic speckle illumination scheme using perfect optical vortex (POV) beams that can provide finer speckles with more uniform distributions. The low spatial coherence from the POV speckle field has significantly improved the signal-to-noise ratio (SNR) and the image contrast, thus contributing to a high spatial resolution that matches the diffraction limit in our cell imaging experiments. Importantly, the depth-resolved imaging capability has been realized which has allowed us to visualize fine subcellular structures at different focal planes.
Migrasome is a type of recently discovered organelle that plays a vital role in the release of cytosolic contents, regulation of zebrafish embryo formation, mitochondria quality control process, etc. Fluorescence microscopy is widely used to investigate biological specimens, including migrasomes. However, the labelling of fluorescence probes not only requires additional preparation steps, but also may interfere with cellular functions and potentially result in phototoxicity, while only a limited number of labelled structures can be observed at one time. Optical diffraction tomography, as a label-free imaging technique complementary to fluorescence imaging tools, is able to characterize the biophysical properties of organelles. Here we propose to apply optical diffraction tomography for three-dimensional (3D) imaging of migrasome and monitoring its dynamics in living cells.
We have recently demonstrated a high throughput three-dimensional (3D) image flow cytometry method, in which a machine-learning algorithm is used to retrieve the 3D refractive index maps of cells from one angle-multiplexing interferogram. Using this system, we have imaged flowing red blood cells and NIH/3T3 cells with a throughput of more than < 10,000 volumes/second. To further demonstrate its potential on cell phenotyping for clinical testing, we plan to apply this platform to image large populations of various cell types and extracting their morphological and biophysical parameters.
Optical diffraction tomography (ODT) has demonstrated its potential for revealing subcellular structures and quantitative compositions in living cells without chemical staining. Recently, we developed a deep-learning based algorithm to reconstruct the 3D refractive index (RI) maps of cells using a single raw interferogram, measured from an angle-multiplexed ODT system. Using this system, we demonstrated a high throughput 3D image cytometry method, in which a microfluidic chip for controlling cell flow is integrated in the ODT system. By flowing the cells in the chip and minimizing the camera exposure time, we can achieve 3D imaging of over 6,000 cells per second.
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