Multiplex Immunofluorescence Image Quality Checking Using DAPI Channel-referenced Evaluation.

Area of Science:

• Biomedical Imaging
• Computational Pathology
• Immunohistochemistry

Background:

• Multiplex immunofluorescence (MxIF) provides critical spatial and cellular data in biomedical research.
• Compromised MxIF image quality can introduce significant bias into research findings.
• Robust quality control (QC) is vital for ensuring the reliability of downstream MxIF analyses.

Purpose of the Study:

• To evaluate the feasibility of using 4',6-diamidino-2-phenylindole (DAPI) staining as a reference for automated quality control (QC) in MxIF imaging.
• To develop and validate a DAPI-based method for identifying compromised MxIF image quality.
• To assess the downstream impact of excluding low-quality MxIF images on cellular distribution analysis.

Main Methods:

• Pixel-level DAPI signal intensity was extracted from MxIF datasets.
• Signal fluctuations and tissue content similarity were quantified across staining-scanning-bleaching cycles to detect quality issues.
• Automated QC results were compared against human expert annotations on 348 fields of view (FOVs).
• Cellular distribution differences were analyzed between QC-passed and QC-failed FOVs.

Main Results:

• The DAPI-based QC method successfully identified 87.3% of FOVs with tissue damage and 73.4% with artifacts.
• A significant difference in cellular distribution patterns was observed between QC-passed and QC-failed FOVs.
• High concordance was achieved between automated DAPI-based QC and expert annotations.

Conclusions:

• 4',6-diamidino-2-phenylindole (DAPI) signals serve as a reliable and effective reference for automated quality control in multiplex immunofluorescence (MxIF) imaging.
• Excluding low-quality FOVs identified by DAPI-based QC is crucial for mitigating downstream analytical biases.
• This automated QC approach enhances the reliability and reproducibility of MxIF-based research.