Bright Field Microscopy as an Alternative to Whole Cell Fluorescence in Automated Analysis of Macrophage Images

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.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.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

Epigenome-guided analysis of the transcriptome of plaque macrophages during atherosclerosis regression reveals activation of the Wnt signaling pathway.

We report the first systems biology investigation of regulators controlling arterial plaque macrophage transcriptional changes in response to lipid lowering in vivo in two distinct mouse models of atherosclerosis regression. Transcriptome measurements from plaque macrophages from the Reversa mouse were integrated with measurements from an aortic transplant-based mouse model of plaque regression. Functional relevance of the genes detected as differentially expressed in plaque macrophages in response to lipid lowering in vivo was assessed through analysis of gene functional annotations, overlap with in vitro foam cell studies, and overlap of associated eQTLs with human atherosclerosis/CAD risk SNPs. To identify transcription factors that control plaque macrophage responses to lipid lowering in vivo, we used an integrative strategy--leveraging macrophage epigenomic measurements--to detect enrichment of transcription factor binding sites upstream of genes that are differentially expressed in plaque macrophages during regression. The integrated analysis uncovered eight transcription factor binding site elements that were statistically overrepresented within the 5' regulatory regions of genes that were upregulated in plaque macrophages in the Reversa model under maximal regression conditions and within the 5' regulatory regions of genes that were upregulated in the aortic transplant model during regression. Of these, the TCF/LEF binding site was present in promoters of upregulated genes related to cell motility, suggesting that the canonical Wnt signaling pathway may be activated in plaque macrophages during regression. We validated this network-based prediction by demonstrating that β-catenin expression is higher in regressing (vs. control group) plaques in both regression models, and we further demonstrated that stimulation of canonical Wnt signaling increases macrophage migration in vitro. These results suggest involvement of canonical Wnt signaling in macrophage emigration from the plaque during lipid lowering-induced regression, and they illustrate the discovery potential of an epigenome-guided, systems approach to understanding atherosclerosis regression

Whole cell segmentation using different input data.

<p>(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.</p

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

<p>(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.</p

Spot enumeration, average number of spots per cell.

<p>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.</p

Slopes and biases of spot per cell counts for all methods.

<p>Spots are detected from fluorescence channel, but distributed among individual cells by whole cell detection based on the different methods. All methods except Annulus and 3SlicesRandom3 resulted in a near perfect match.</p

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

<p>(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.</p

Cell by cell spot enumeration.

<p>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).</p

Flowchart of the cell segmentation procedure.

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

Predicted molecular interaction network for upregulated genes that possess CTTTGA elements connects cholesterol to motility.

<p>Red ellipses represent genes whose expression levels are elevated in plaque macrophages from <i>Mttp</i>-inactivated vs. vehicle-treated animals (darker color indicates a greater differential expression ratio; see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004828#pgen.1004828.s010" target="_blank">Table S2</a>). Yellow hexagons and blue edges represent the upstream signaling pathway based on the literature (the blunt-ended edge indicates inhibition of signaling), with green rectangles representing lipids (<i>cholesterol</i>: membrane cholesterol; <i>lipoprotein</i>: apoB-containing lipoprotein). Blue diamonds represent transcription factor binding site motifs. Gray edges connect the transcription factors to the downstream genes that possess corresponding binding site sequence matches.</p