Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, Umeå, Sweden.
Sartorius Corporate Research, Sartorius Stedim Data Analytics AB, Umeå, Sweden.
PLoS One. 2022 May 19;17(5):e0264241. doi: 10.1371/journal.pone.0264241. eCollection 2022.
Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many fluorescent microscopy techniques exist, due to its cost-effectiveness and accessibility, widefield fluorescent imaging remains one of the most widely used. To accomplish imaging of 3D samples, conventional widefield fluorescence imaging entails acquiring a sequence of 2D images spaced along the z-dimension, typically called a z-stack. Oftentimes, the first step in an analysis pipeline is to project that 3D volume into a single 2D image because 3D image data can be cumbersome to manage and challenging to analyze and interpret. Furthermore, z-stack acquisition is often time-consuming, which consequently may induce photodamage to the biological sample; these are major barriers for workflows that require high-throughput, such as drug screening. As an alternative to z-stacks, axial sweep acquisition schemes have been proposed to circumvent these drawbacks and offer potential of 100-fold faster image acquisition for 3D-samples compared to z-stack acquisition. Unfortunately, these acquisition techniques generate low-quality 2D z-projected images that require restoration with unwieldy, computationally heavy algorithms before the images can be interrogated. We propose a novel workflow to combine axial z-sweep acquisition with deep learning-based image restoration, ultimately enabling high-throughput and high-quality imaging of complex 3D-samples using 2D projection images. To demonstrate the capabilities of our proposed workflow, we apply it to live-cell imaging of large 3D tumor spheroid cultures and find we can produce high-fidelity images appropriate for quantitative analysis. Therefore, we conclude that combining axial z-sweep image acquisition with deep learning-based image restoration enables high-throughput and high-quality fluorescence imaging of complex 3D biological samples.
荧光显微镜是可视化和量化复杂生物过程时空动态的核心方法。虽然存在许多荧光显微镜技术，但由于其成本效益和可及性，宽场荧光成像仍然是最广泛使用的技术之一。为了对 3D 样品进行成像，传统的宽场荧光成像需要沿着 z 方向获取一系列间隔的 2D 图像，通常称为 z 堆叠。在分析流水线的第一步中，通常将该 3D 体积投影到单个 2D 图像中，因为 3D 图像数据难以管理，并且分析和解释具有挑战性。此外，z 堆叠采集通常很耗时，这可能会导致生物样本光损伤；这些是需要高通量的工作流程的主要障碍，例如药物筛选。作为 z 堆叠的替代方案，已经提出了轴向扫描采集方案来规避这些缺点，并提供了与 z 堆叠采集相比，对 3D 样品进行 100 倍更快图像采集的潜力。不幸的是，这些采集技术生成的低质量 2D z 投影图像需要使用复杂的、计算量大的算法进行恢复，然后才能对图像进行分析。我们提出了一种新的工作流程，将轴向 z 扫描采集与基于深度学习的图像恢复相结合，最终实现使用 2D 投影图像对复杂 3D 样品进行高通量和高质量成像。为了展示我们提出的工作流程的能力，我们将其应用于大 3D 肿瘤球体培养物的活细胞成像，并发现我们可以生成适用于定量分析的高保真图像。因此，我们得出结论，将轴向 z 扫描图像采集与基于深度学习的图像恢复相结合，可以实现复杂 3D 生物样品的高通量和高质量荧光成像。
