Pipeline for illumination correction of images for high-throughput microscopy

Abstract

The presence of systematic noise in images in high-throughput microscopy experiments can significantly impact the accuracy of downstream results. Among the most common sources of systematic noise is non-homogeneous illumination across the image field. This often adds an unacceptable level of noise, obscures true quantitative differences and precludes biological experiments that rely on accurate fluorescence intensity measurements. In this paper, we seek to quantify the improvement in the quality of high-content screen readouts due to software-based illumination correction. We present a straightforward illumination correction pipeline that has been used by our group across many experiments. We test the pipeline on real-world high-throughput image sets and evaluate the performance of the pipeline at two levels: (a) Z'-factor to evaluate the effect of the image correction on a univariate readout, representative of a typical high-content screen, and (b) classification accuracy on phenotypic signatures derived from the images, representative of an experiment involving more complex data mining. We find that applying the proposed post-hoc correction method improves performance in both experiments, even when illumination correction has already been applied using software associated with the instrument. To facilitate the ready application and future development of illumination correction methods, we have made our complete test data sets as well as open-source image analysis pipelines publicly available. This software-based solution has the potential to improve outcomes for a wide-variety of image-based HTS experiments.

Keywords: Fluorescence microscopy; high-throughput microscopy; illumination correction; shading correction; vignetting.

Figure 1

Estimating illumination correction functions. Sample ICFs for DAPI (top-left), actin (top-right) and tubulin (bottom-left). The image dimensions are 1280 × 1024 pixels. To generate an ICF, the mean image across all images in a plate is smoothed using a median filter with window size = 500 pixels. The value of this parameter was chosen manually as follows. Start with filter dimensions approximately 25% that of the image, then increase the dimensions in increments of about 10% of the image size until the ICFs appear smooth overall; the presence of bright ‘blotches’ in the ICF indicates that the local cellular intensities still dominate the global illumination pattern or that artefacts are present. The density of cells across the field-of-view is uniform (bottom-right). This check is important to confirm that there are no systematic uneven cell distribution patterns that could lead to erroneous ICFs.

Figure 2

Influence of illumination correction on image-based profiling. Illumination correction improves accuracy of mechanism-of-action classification of compounds by 6% even though these images have already been white-referenced using the microscope's software. Confusion matrices show classification accuracy without the proposed method of correction (top) and with correction (bottom). ICFs were computed by plate-wise grouping of images.