Simple SummaryMultispectral, multiplex immunofluorescence (mIF) microscopy is an emerging technology for characterization of the tumour microenvironment. Achieving high-throughput collection and analysis of mIF microscopy images often requires the use of multiple microscopes, but it is not guaranteed that data from one microscope can be compared to data from another. We used a set of eight melanoma tissue samples to measure and correct for data differences between three microscopes. We scanned the samples twice on each microscope and measured the average tissue flux densities in the resulting sets of images. By applying a relatively simple calibration model accounting for sample- and microscope-specific effects, we were able to reduce the variations in raw image brightness and immune marker expression measurements by 79% and 72%, respectively. This shows that simple procedures can be used to effectively standardize mIF data from multiple microscopes for potential use in both research and clinical diagnostic settings.AbstractMultispectral, multiplex immunofluorescence (mIF) microscopy has been used to great effect in research to identify cellular co-expression profiles and spatial relationships within tissue, providing a myriad of diagnostic advantages. As these technologies mature, it is essential that image data from mIF microscopes is reproducible and standardizable across devices. We sought to characterize and correct differences in illumination intensity and spectral sensitivity between three multispectral microscopes. We scanned eight melanoma tissue samples twice on each microscope and calculated their average tissue region flux intensities. We found a baseline average standard deviation of 29.9% across all microscopes, scans, and samples, which was reduced to 13.9% after applying sample-specific corrections accounting for differences in the tissue shown on each slide. We used a basic calibration model to correct sample- and microscope-specific effects on overall brightness and relative brightness as a function of the image layer. We tested the generalizability of the calibration procedure and found that applying corrections to independent validation subsets of the samples reduced the variation to 2.9 ± 0.03%. Variations in the unmixed marker expressions were reduced from 15.8% to 4.4% by correcting the raw images to a single reference microscope. Our findings show that mIF microscopes can be standardized for use in clinical pathology laboratories using a relatively simple correction model.