Bright field microscopy as an alternative to whole cell fluorescence in automated analysis of macrophage images

Abstract

Background: Fluorescence microscopy is the standard tool for detection and analysis of cellular phenomena. This technique, however, has a number of drawbacks such as the limited number of available fluorescent channels in microscopes, overlapping excitation and emission spectra of the stains, and phototoxicity.

Methodology: We here present and validate a method to automatically detect cell population outlines directly from bright field images. By imaging samples with several focus levels forming a bright field -stack, and by measuring the intensity variations of this stack over the -dimension, we construct a new two dimensional projection image of increased contrast. With additional information for locations of each cell, such as stained nuclei, this bright field projection image can be used instead of whole cell fluorescence to locate borders of individual cells, separating touching cells, and enabling single cell analysis. Using the popular CellProfiler freeware cell image analysis software mainly targeted for fluorescence microscopy, we validate our method by automatically segmenting low contrast and rather complex shaped murine macrophage cells.

Significance: The proposed approach frees up a fluorescence channel, which can be used for subcellular studies. It also facilitates cell shape measurement in experiments where whole cell fluorescent staining is either not available, or is dependent on a particular experimental condition. We show that whole cell area detection results using our projected bright field images match closely to the standard approach where cell areas are localized using fluorescence, and conclude that the high contrast bright field projection image can directly replace one fluorescent channel in whole cell quantification. Matlab code for calculating the projections can be downloaded from the supplementary site: http://sites.google.com/site/brightfieldorstaining.

Figure 1. Flowchart of the cell segmentation procedure.

Whole cell fluorescent staining is replaced by projection images calculated from bright field image stacks of different focal planes.

Figure 2. Contrast enhancement by standard deviation projection of bright field image stack.

(A) Low contrast bright field image. (B) Fluorescence staining for whole cell and bright spot detection. (C) Standard deviation projection of stack of bright field images. (D) Inverse of the projection for another visualization of the projection result. In addition to increased contrast, the projection also suppresses background nonuniformities.

Figure 3. Whole cell segmentation using different input data.

(A) Fluorescent whole cell staining. (B) Standard deviation projection of bright field stack. (C) The Annulus-method. The segmentation was performed using CellProfiler software, all methods requiring the use of fluorescent nuclei as markers for each cell.

Figure 4. Pixel-by-pixel comparison of whole cell segmentation using bright field projections against fluorescence ground truth.

(A) Median F-scores over all cells for each image group, with all the projection methods. (B) Median F-scores for cell segmentation using standard deviation projection images, each projected from three randomly selected slices.

Figure 5. Spot enumeration, average number of spots per cell.

Spots detected from fluorescence channel, and distributed among the cells based on different whole cell segmentation methods. (A) Spot counts per cell, cells detected from the bright field projections versus cells detected with the fluorescence reference. (B) Spot counts per cell, cells detected with standard deviation projections for five randomly selected slice triples. Only the Annulus method and 3SlicesRandom3 stand out as inferior to the others.

Figure 6. Cell by cell spot enumeration.

Spots detected from fluorescence channel, and distributed among the cells based on different whole cell segmentation methods. (A) Each data point represents the number of spots in one cell, with cell area detected with standard deviation projection compared to cell area detection using fluorescence. The color indicates the number of overlapping data points. (B) Regression curves of spot counts cell by cell, with cell detection by each of the projection methods, the 3Slices method and the Annulus method against fluorescent ground truth. (C) Regression results of spot counts cell by cell, with cell detection of by the standard deviation projections for five randomly selected slice triples against fluorescence (sets 3SlicesRandom1–5).