Fluorescence reconstruction microscopy (FRM) describes a class of techniques where transmitted light images are passed into a convolutional neural network that then outputs predicted epifluorescence images. This approach enables many benefits including reduced phototoxicity, freeing up of fluorescence channels, simplified sample preparation, and the ability to re-process legacy data for new insights. However, FRM can be complex to implement, and current FRM benchmarks are abstractions that are difficult to relate to how valuable or trustworthy a reconstruction is. Here, we relate the conventional benchmarks and demonstrations to practical and familiar cell biology analyses to demonstrate that FRM should be judged in context. We further demonstrate that it performs remarkably well even with lower-magnification microscopy data, as are often collected in screening and high content imaging. Specifically, we present promising results for nuclei, cell-cell junctions, and fine feature reconstruction; provide data-driven experimental design guidelines; and provide researcher-friendly code, complete sample data, and a researcher manual to enable more widespread adoption of FRM.

荧光重建显微镜 (FRM) 描述了一类技术，其中透射光图像被传递到卷积神经网络，然后输出预测的落射荧光图像。这种方法具有许多优点，包括降低光毒性、释放荧光通道、简化样品制备以及重新处理遗留数据以获得新见解的能力。然而，FRM 的实施起来可能很复杂，并且当前的 FRM 基准是抽象的，很难与重建的价值或可信度联系起来。在这里，我们将传统的基准和演示与实际和熟悉的细胞生物学分析联系起来，以证明 FRM 应该在上下文中进行判断。我们进一步证明，即使使用较低放大倍率的显微镜数据（如筛选和高内涵成像中经常收集的数据），它也表现得非常好。具体来说，我们在细胞核、细胞-细胞连接和精细特征重建方面提出了有希望的结果；提供数据驱动的实验设计指南；并提供研究人员友好的代码、完整的样本数据和研究人员手册，以促进 FRM 的更广泛采用。