FLINO: a new method for immunofluorescence bioimage normalization.

Area of Science:

• Biomedical imaging
• Computational pathology
• Bioinformatics

Background:

• Multiplexed immunofluorescence bioimaging enables single-cell analysis and spatial organization studies for precision diagnostics.
• Current multi-round staining pipelines introduce experimental batch effects, potentially obscuring biological signals.
• Robust algorithms are needed to correct batch effects without introducing bias.

Purpose of the Study:

• To present and evaluate a novel immuno-fluorescence image normalization (FLINO) method.
• To compare FLINO against existing normalization methods and workflows.
• To establish a ground truth dataset for evaluating normalization performance.

Main Methods:

• A ground truth dataset was created using multi-round DAPI staining on the same tissue slides.
• Multiple normalization methods (median, quantile, etc.) were evaluated on grid and segmented cell objects.
• The FLINO method, a grid-based technique with on-slide controls, was developed and applied.

Main Results:

• An upper quartile normalization of grid objects achieved performance comparable to segmented cell normalization.
• Ten or more on-slide controls robustly corrected batch effects, while fewer introduced bias.
• The FLINO R-scripts and data are publicly available for reproducible research.

Conclusions:

• The developed grid-based normalization technique offers a robust solution for batch effect correction in multiplexed immunofluorescence.
• FLINO provides a valuable tool for enhancing the accuracy and reliability of bioimaging data.
• This work facilitates the development of more precise diagnostics and therapeutics.