SAIBR: A simple, platform-independent method for spectral autofluorescence correction

Biological systems are increasingly viewed through the lens of mathematics, physics, and systems approaches that demand accurate quantification of gene expression and local protein concentrations. Such approaches have benefited greatly from the revolution in genetic engineering sparked by CRISPR/Cas9. By facilitating the tagging of genes at their genomic loci, CRISPR/Cas9 allows us to use fluorescence to monitor proteins that are expressed at or near endogenous levels under native regulatory control. However, due to their typically lower expression levels, quantitative experiments using endogenously-tagged genes can run into limits imposed by autofluorescence (AF). AF is often a particular challenge in the illumination bands occupied by the most efficient fluorescent proteins (GFP, mNeonGreen). Stimulated by our work in C. elegans, we describe and validate Spectral Autofluorescence Image correction By Regression (SAIBR), a simple, platform-independent protocol, and associated GUI-based FIJI plugin to correct for autofluorescence using standard filter sets and illumination conditions. Fully validated for use in C. elegans embryos and tested in diverse systems, including starfish oocytes and fission yeast, SAIBR achieves accurate quantitation of fluorophore signal and enables reliable detection and quantification of even weakly expressed proteins. Thus, SAIBR provides a highly accessible, low barrier way to incorporate AF correction as standard for researchers working on a broad variety of cell and developmental systems. Summary StatementImplemented as an easy-to-use Fiji Plugin, SAIBR provides effective autofluorescence correction for cells and tissues using standard imaging conditions.