Pinkard Henry, Baghdassarian Hratch, Mujal Adriana, Roberts Ed, Hu Kenneth H, Friedman Daniel Haim, Malenica Ivana, Shagam Taylor, Fries Adam, Corbin Kaitlin, Krummel Matthew F, Waller Laura
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
Computational Biology Graduate Group, University of California, Berkeley, CA, USA.
Nat Commun. 2021 Mar 26;12(1):1916. doi: 10.1038/s41467-021-22246-5.
Multiphoton microscopy is a powerful technique for deep in vivo imaging in scattering samples. However, it requires precise, sample-dependent increases in excitation power with depth in order to generate contrast in scattering tissue, while minimizing photobleaching and phototoxicity. We show here how adaptive imaging can optimize illumination power at each point in a 3D volume as a function of the sample's shape, without the need for specialized fluorescent labeling. Our method relies on training a physics-based machine learning model using cells with identical fluorescent labels imaged in situ. We use this technique for in vivo imaging of immune responses in mouse lymph nodes following vaccination. We achieve visualization of physiologically realistic numbers of antigen-specific T cells (~2 orders of magnitude lower than previous studies), and demonstrate changes in the global organization and motility of dendritic cell networks during the early stages of the immune response. We provide a step-by-step tutorial for implementing this technique using exclusively open-source hardware and software.
多光子显微镜是一种用于散射样本体内深度成像的强大技术。然而，为了在散射组织中产生对比度，同时将光漂白和光毒性降至最低，它需要根据样本情况精确地随着深度增加激发功率。我们在此展示了自适应成像如何根据样本形状在三维体积中的每个点优化照明功率，而无需专门的荧光标记。我们的方法依赖于使用原位成像的具有相同荧光标记的细胞来训练基于物理的机器学习模型。我们将此技术用于疫苗接种后小鼠淋巴结免疫反应的体内成像。我们实现了对抗抗原特异性T细胞生理现实数量的可视化（比以前的研究低约2个数量级），并证明了免疫反应早期树突状细胞网络的整体组织和运动性的变化。我们提供了一个使用完全开源硬件和软件实施此技术的分步教程。
