Department of Cell Biology, Yale School of Medicine, New Haven, CT, USA.
Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Nat Biotechnol. 2023 Nov;41(11):1549-1556. doi: 10.1038/s41587-023-01702-1. Epub 2023 Mar 13.
Single-molecule localization microscopy enables three-dimensional fluorescence imaging at tens-of-nanometer resolution, but requires many camera frames to reconstruct a super-resolved image. This limits the typical throughput to tens of cells per day. While frame rates can now be increased by over an order of magnitude, the large data volumes become limiting in existing workflows. Here we present an integrated acquisition and analysis platform leveraging microscopy-specific data compression, distributed storage and distributed analysis to enable an acquisition and analysis throughput of 10,000 cells per day. The platform facilitates graphically reconfigurable analyses to be automatically initiated from the microscope during acquisition and remotely executed, and can even feed back and queue new acquisition tasks on the microscope. We demonstrate the utility of this framework by imaging hundreds of cells per well in multi-well sample formats. Our platform, implemented within the PYthon-Microscopy Environment (PYME), is easily configurable to control custom microscopes, and includes a plugin framework for user-defined extensions.
单分子定位显微镜能够以数十纳米的分辨率进行三维荧光成像,但需要拍摄许多相机帧才能重建超分辨率图像。这限制了典型的通量,每天只能处理数十个细胞。虽然现在可以将帧率提高一个数量级以上,但在现有的工作流程中,大量的数据量仍然是一个限制因素。在这里,我们提出了一个集成的采集和分析平台,利用显微镜特定的数据压缩、分布式存储和分布式分析,实现了每天 10000 个细胞的采集和分析吞吐量。该平台支持图形化的可重新配置分析,这些分析可以在采集过程中从显微镜自动启动,并远程执行,甚至可以在显微镜上反馈和排队新的采集任务。我们通过在多孔样品格式中对数百个细胞进行成像,展示了该框架的实用性。我们的平台在 PYthon-Microscopy Environment (PYME) 中实现,易于配置以控制自定义显微镜,并包括用于用户定义扩展的插件框架。