Friday, April 26, 2024
4/26/2024
Flow cytometry is the tool of choice for high-speed analysis of large cell populations, with the tradeoff of lacking intracellular spatial information. Imaging flow cytometry (IFC) has emerged as a new tool that combines advantages of microscopy with the high speed of flow cytometry. However, they can only provide 2D images to determine three-dimensional (3D) distribution of cellular features, have a limited field of view (FOV), and require precise control of the fluidic system to minimize image blurring due to uncontrolled cell rotation or translation across the FOV. The absence of 3D imaging results in ambiguity of object locations and blurring by focal depth due to the projection of a 3D cell into a 2D image. Although in the last decades flow cytometry systems that can actually acquire three-dimensional (3D) spatial information were developed, constraints related to resolution and samples size remained as their biggest limitation. Therefore, the goal of this proposal is to develop the next generation 3D imaging flow cytometers with high-throughput and high-content capabilities for 3D imaging of hundreds to thousands of cells and spheroids per second with high resolution, for the first time. We propose to develop such a cytometry method, using a novel microscopy method, Line Excitation Array Detection microscopy (LEAD), that can image objects in large field of views at the rate of current 1D cytometers, but with high 3D resolution and high signal-to-noise ratios (SNR). Our proposed LEAD cytometer is a fast-scanned light-sheet microscope capable of MHz frame rates. We will develop the fastest MHz line-scanning method using a longitudinal acousto-optic deflector driven by a chirped frequency signal. We will image the scanned light sheet using a linear silicon photomultiplier array, which will provide the sensitivity required when scanning so quickly, and the parallel readout required for such high frame rates. First, we will develop linear LEAD 3D imaging flow cytometry at sub-micron scale resolution and small FOVs. Although our preliminary data indicates we will be able to image at 100 kHz – MHz frame rates at such high resolution with high SNR, we will perform experiments measuring the SNR to determine the operating range of LEAD cytometry. In the second aim, we will increase the FOV by developing two-photon LEAD imaging flow cytometry with Bessel beams. To support the larger FOV, we will develop a 128-channel data acquisition system using eight 16-channel data acquisition cards. In the third aim, we will develop a state-of-the-art computational infrastructure that allows for file transfers up to 25 GB/s, storage (>100 TB), and analysis that only takes 3x the imaging time. We will use 2 deep learning models for analysis. If successful, this high-risk/high-reward proposal would alter the imaging flow cytometry landscape. The proposed 3D imaging flow cytometer can offer improved cell and spheroid analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. Improved sensitivity will help users to improve research outcomes or diagnose patients with higher statistical power.
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