To the Editor:
Advances in high-throughput fluorescence microscopy (HTFM) and image informatics have allowed single-cell phenotypic variability to be characterized and related to putative biological functions. However, estimates of single-cell variability may be skewed by inaccurate image correction. Furthermore, methods used in the HTFM literature do not generally take advantage of shared image properties, instead correcting each image independently. Here, we revisit the basic mathematical framework underlying image correction to show how the true distributions of commonly measured single-cell features are altered in uncorrected images. We also report an observation that allows the use of a simple method for accurate image correction in HTFM.
The intensity of a pixel, I, can be modeled by I = S(F + B), where S represents shading due to uneven illumination, F is the biologically relevant foreground fluorescence and B is the background fluorescence1,2,3 (Supplementary Note). Image correction is a data preprocessing step whose goal is to obtain F. (Subsequent normalization can address systematic experimental errors, such as row or column biases in microtiter plates4,5.)
To investigate how the apparent cellular variability (for example, s.d. or coefficient of variation) is affected when S or B is not removed from images, we analyzed such effects for the distributions of commonly used single-cell features: average, total or ratiometric intensities of biomarkers (Fig. 1a and Supplementary Note). For these simple features, background and shading can alter apparent phenotypic variability in surprising ways. For example, the effects of either on the ratiometric feature are generally unpredictable, whereas background increases total feature variability owing to underlying differences in cell size. Such changes to distributions of single-cell features can be particularly important in screening, where variation is commonly used to determine whether experimental conditions differ statistically from control conditions.
In practice, the challenge in correction lies in the estimation of shading within an image, as subsequent background subtraction is relatively simple. How should shading in large HTFM image data sets be estimated? Every image from a microtiter plate could, in principle, have a unique shading pattern and background. Indeed, this is an implicit assumption of common correction methods in HTFM studies. However, we find that shading is not unique to every image but rather is predictable by image position within a well (Fig. 1b). This observation suggests a practical correction strategy for HTFM that estimates one reference shading pattern per in-well position (Fig. 1b and Supplementary Note). This empirical approach is simpler than commonly used methods that estimate correction parameters for each individual image and more accurate than applying one set of correction parameters to all images.
A.D.C. performed the experiments, analyzed the data and prepared the figures. C.W. and S.R. provided critical interpretations of the data and revisions of the manuscript. A.D.C., L.F.W. and S.J.A. wrote the manuscript. S.R., L.F.W. and S.J.A. prepared the general mathematical formulations. All authors reviewed the final manuscript.
Schultz, M.L., Lipkin, L.E., Wade, M.J., Lemkin, P.F. & Carman, G.M. J. Histochem. Cytochem. 22, 751–754 (1974).
Likar, B., Maintz, J.B.A., Viergever, M.A. & Perus, F. J. Microsc. 197, 285–295 (2000).
Madisetti, V. & Williams, D.B. The Digital Signal Processing Handbook. (CRC Press LLC, 1998).
Bray, M.-A. & Carpenter, A. in Assay Guidance Manual (eds. Sittampalam, G.S. et al.) (Eli Lilly and Company and the National Center for Advancing Translational Sciences, 2012).
Dragiev, P., Nadon, R. & Makarenkov, V. BMC Bioinformatics 12, 25 (2011).
We thank J. Shay (University of Texas Southwestern Medical Center) for kindly providing human colonic epithelial cells. This work was supported by US National Cancer Institute R01CA133253 (S.J.A.); US National Institutes of Health R01 GM071794 (S.J.A.), R01 GM081549 (L.F.W.) and T32 GM008203 (A.D.C.); the Welch Foundation I-1619 (S.J.A.) and I-1644 (L.F.W.) and the Howard Hughes Medical Institute Med into Grad Initiative (A.D.C.).
The authors declare no competing financial interests.
About this article
Cite this article
Coster, A., Wichaidit, C., Rajaram, S. et al. A simple image correction method for high-throughput microscopy. Nat Methods 11, 602 (2014). https://doi.org/10.1038/nmeth.2971
This article is cited by
The Visual Computer (2022)
Nature Communications (2017)
Adaptive and Background-Aware GAL4 Expression Enhancement of Co-registered Confocal Microscopy Images
Nature Chemical Biology (2015)