SAIBR

SAIBR performs regression-based spectral autofluorescence correction to enable accurate quantification of fluorescent proteins such as GFP and mNeonGreen in imaging of endogenously tagged proteins (e.g., via CRISPR/Cas9).

Key Features:

• Regression-based correction: Uses regression to model and remove autofluorescence contributions from spectral image data.
• Standard filter sets and illumination: Operates using standard filter sets and illumination conditions for spectral measurements.
• Platform independence: Applies across different microscopy setups without reliance on specialized hardware.
• Enhanced signal quantification: Improves detection and accurate quantitation of weak fluorophore signals obscured by autofluorescence.
• Validation across model systems: Has been validated in C. elegans embryos, starfish oocytes, and fission yeast.

Scientific Applications:

• Quantitative endogenous protein measurement: Enables accurate measurement of protein expression from endogenously tagged genes, including low-expression targets.
• Fluorescent protein imaging: Improves signal separation and quantitation for GFP and mNeonGreen where emission overlaps with tissue autofluorescence.
• Developmental and cell biology imaging: Facilitates fluorescence-based assays in embryos, oocytes, and yeast by reducing autofluorescence interference.
• CRISPR/Cas9-based tagging studies: Supports imaging studies that rely on endogenous fluorescent tagging to monitor proteins under native regulation.

Methodology:

Performs spectral autofluorescence correction by regression using measurements from standard filter sets and illumination conditions.