Multiplex Immunofluorescence Image Quality Checking Using DAPI Channel–referenced Evaluation

Abstract

Multiplex immunofluorescence (MxIF) images provide detailed information of cell composition and spatial context for biomedical research. However, compromised data quality could lead to research biases. Comprehensive image quality checking (QC) is essential for reliable downstream analysis. As a reliable and specific staining of cell nuclei, 4′,6-diamidino-2-phenylindole (DAPI) signals were used as references for tissue localization and auto-focusing across MxIF staining–scanning–bleaching iterations and could potentially be reused for QC. To confirm the feasibility of using DAPI as QC reference, pixel-level DAPI values were extracted to calculate signal fluctuations and tissue content similarities in staining–scanning–bleaching iterations for identifying quality issues. Concordance between automatic quantification and human experts’ annotations were evaluated on a data set consisting of 348 fields of view (FOVs) with 45 immune and tumor cell markers. Cell distribution differences between subsets of QC-pass vs QC-failed FOVs were compared to investigate the downstream effects. Results showed that 87.3% FOVs with tissue damage and 73.4% of artifacts were identified. QC-failed FOVs showed elevated regional gathering in cellular feature space compared with the QC-pass FOVs. Our results supported that DAPI signals could be used as references for MxIF image QC, and low-quality FOVs identified by our method must be cautiously considered for downstream analyses.

QUAL-IF-AI: Quality Control of Immunofluorescence Images using Artificial Intelligence

“…The image tiles obtained from the DAPI channel were first categorized based on the presence or absence of artifacts. Annotations were done only on the DAPI channel as it is shown to be sufficient for quality checking in multiplex immunofluorescence images [7]. Further, for images where artifacts were detected, manual artifact segmentation was performed to precisely delineate the areas affected by these artifacts.…”

“…The quality of the input images is crucial to obtain reliable single-cell measurements and meaningful biological insights. Undesired artifacts, such as external artifacts (dust particles, hair shafts, fibers), air bubbles, antibody aggregates, out-of-focus (OOF) areas, and tissue folds are common not only in hematoxylin and eosin (H&E) images [6] but also in fluorescent microscopy [7]. Dust particles, hair shafts, fibers, and air bubbles can get trapped during sample preparation or coverslipping.…”