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

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journal contribution
posted on 2022-07-21, 08:38 authored by Nelio TL Rodrigues, Tom Bland, Joana Borrego-Pinto, KangBo Ng, Nisha Hirani, Ying Gu, Sherman Foo, Nathan W Goehring
Biological systems are increasingly viewed through a quantitative lens that demands accurate measures of gene expression and local protein concentrations. CRISPR/Cas9 gene tagging has enabled increased use of fluorescence to monitor proteins at or near endogenous levels under native regulatory control. However, due to typically lower expression levels, experiments using endogenously-tagged genes run into limits imposed by autofluorescence (AF). AF is often a particular challenge in wavelengths occupied by commonly used 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 FIJI plugin to correct for autofluorescence using standard filter sets and illumination conditions. Validated for use in C. elegans embryos, starfish oocytes and fission yeast, SAIBR is ideal for samples with a single dominant AF source, and 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.


Crick (Grant ID: 10086, Grant title: Goehring FC001086)