PT  - JOURNAL ARTICLE
AU  - Julienne LaChance
AU  - Daniel J. Cohen
TI  - Practical Fluorescence Reconstruction Microscopy for Large Samples and Low-Magnification Imaging
AID  - 10.1101/2020.03.05.979419
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.03.05.979419
4099  - http://biorxiv.org/content/early/2020/05/12/2020.03.05.979419.short
4100  - http://biorxiv.org/content/early/2020/05/12/2020.03.05.979419.full
AB  - Fluorescence reconstruction microscopy (FRM) describes a class of techniques where transmitted light images are passed into a convolutional neural network which 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, current FRM benchmarks are abstractions that are difficult to relate to how valuable or trustworthy an FRM prediction 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 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 the code, sample data, and user manual to enable more widespread adoption of FRM.Competing Interest StatementThe authors have declared no competing interest.