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.

Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

“…The use of a uniformly fluorescent reference image (“white-referencing”), while common, is not suitable to high-throughput screening. A retrospective method to correct all acquired images on a per-channel, per-plate basis is therefore recommended 41 ; the illumination pipeline takes this approach.…”

“…The illumination correction pipeline aggregates the fluorescent images on a per-plate basis to produce a post-hoc estimate of the 2-D illumination distribution, one for each channel, per plate. We have found that this corrective step improves the ability to detect subtle phenotypic differences in profiling applications 41 .…”

Quantifying co-cultured cell phenotypes in high-throughput using pixel-based classification

“…To account for systematic bias due to non-homogeneous illumination across the image field, all images were illumination-corrected [12]. A dedicated CellProfiler pipeline loaded all the images from the plate and averaged them.…”

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

“…In imaging based systems, such as HCS, spatial variation of illumination intensity or detection sensitivity (or both), often seen as reduced intensity in the corners, can contribute to apparent variation in the cell-to-cell measurements. Methods for correcting non-uniform illumination or detection sensitivity are available [52, 53], but often are not used because variation across the field, especially when it is stable from field-to-field, has little impact on the population average values that are most often used as assay readouts. However, for heterogeneity analysis it is important to understand the impact of the detection system on cell-to-cell variation.…”