Constrained fits of the 1dPCH data to two-species allows for the examination of monomer and oligomer populations.

<p>A. A model was assumed where Ste50 could exist as either a monomer with fixed brightness, or oligomer with unconstrained brightness and number (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0001931#s4" target="_blank">Materials and Methods</a>, and main text). Results are displayed to show the percentage of each component. Error bars are the standard error of the mean. B. Average brightness values for autofluorescence, GFP, GFP-GFP, and GFP-GFP-GFP from the data in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0001931#pone-0001931-g003" target="_blank">Figure 3</a>, fit to a line. Error bars are the standard deviation. The line represents the best fit of the data to a linear model with a slope of 1959 and intercept 1193, which was then extrapolated toward higher brightness. Average brightness observed for the Ste50 oligomer from the analysis described above are marked on the extrapolated part of the plot.</p

2dPCH analysis of Ste50-mCherry and Ste11-GFP detects binding stoichiometry.

<p>50 µs bins were used. Data were fit to a one-component model or two-component model, as explained in the text. Symbols and bars represent the averages and standard deviations, respectively. Schematic representations of average stoichiometry observed; possible geometries of the interactions (see main text) are displayed next to the corresponding regions of the graph.</p

Fluctuation data can probe protein-protein interactions.

<p>A. Example traces of fluctuation data for dual-color experiments. B. Data can be analyzed by correlation analysis to examine concentration, diffusion, and co-diffusion of red and green particles. C. 1dPCH examines the distribution of photon events per time interval, and reports concentration and ‘brightness’, or oligomeric status. D. 2dPCH reports simultaneously concentration, interaction, oligomerization, and binding stoichiometry of heterogeneous complexes. An example two-dimensional PCH histogram is shown, with frequency versus number of green photons and number of red photons per time bin. E. Example, two-dimensional plot of a fit of modeled 2dPCH data. If a monomer red or green probe has a brightness of 3000 CPSM, for example, the plot demonstrates points one would expect to find values for with non-interacting monomeric species, or interacting monomeric species, or interacting dimeric species, as labeled.</p

1dPCH analysis of Ste50-GFP and Ste11-GFP probes homo-oligomerization.

<p>A. Example curves for GFP and mCherry (mCH.) controls in live yeast. B. Notched box plots of PCH fits, ranging from 18 to 30 individual, 7 second data traces from 5 to 10 cells. For auto-fluorescence measurements, data represents 7 measurements for mCherry and 15 measurements for GFP. 50 µs bins were used to generate the PCH distributions. C. Notched box plots of 1dPCH fits of GFP tagged species, with lines (same color scheme as in B) representing average brightness values of monomer, dimer, and trimer controls for a basis of comparison.</p

Yeast strains used in this study.

<p>all strains are S288C background, his3Δ1;leu2Δ0;met15Δ0;ura3Δ0.</p

CellProfiler 3.0: Next-generation image processing for biology

<div><p>CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler’s infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.</p></div

Segmentation steps for the quantification of transcripts per cell within a 3D blastocyst.

<p>Images were captured of a mouse embryo blastocyst cell membrane stained with WGA and FISH for GAPDH transcripts. (A) Original 3D image of blastocyst cell membrane prior to analysis. (B) CellProfiler 3.0 image processing modules used for membrane image processing. Figure labels: RH (“RemoveHoles”), Close (“Closing”), Erode (“Erosion”), Mask (“MaskImage”), Math (“ImageMath”), EorS Features (“EnhanceOrSuppressFeatures”). (C) Nuclei after segmentation by CellProfiler, as viewed in Fiji. (D) Segmentation of cells after setting nuclei as seeds by CellProfiler, as viewed in Fiji. (E) Segmentation of GAPDH transcript foci using CellProfiler, as viewed in Fiji. (F) Examples of analysis that can be done by CellProfiler: (top) cell volume relative nucleus volume, (middle) GAPDH transcript quantity in each cell using CellProfiler’s “RelateObjects” module, (bottom) number of GAPDH transcripts in Z-plane (bin size = 2.5 μm). The underlying measurements may be downloaded as <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s012" target="_blank">S1 File</a>. <i>Images were provided by Javier Frias Aldeguer and Nicolas Rivron from Hubrecht Institute</i>, <i>Netherlands</i>, <i>and are available from the Broad Bioimage Benchmark Collection (<a href="https://data.broadinstitute.org/bbbc/BBBC032/" target="_blank">https://data.broadinstitute.org/bbbc/BBBC032/</a></i>). 3D, three-dimensional; FISH, fluorescent in situ hybridization; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; WGA, wheat germ agglutinin.</p

Examples of 3D image segmentation produced by CellProfiler 3.0, across two experimental systems and two sets of synthesized images.

<p>Three focal planes shown for each. Raw images (left) and CellProfiler outputs (right) showing nuclei of mouse embryo blastocyst (A), mouse trophoblast stem cells (B), and synthetic images of HL60 cell lines (C) and (D). More information about segmentation steps used for these images can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s002" target="_blank">S2</a>–<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s005" target="_blank">S5</a> Figs. (E) Comparison of the segmentation accuracy of CellProfiler 3.0 and Fiji’s plugin MorphoLibJ, based on the Rand index of the processed image and its ground truth (out of a total of 1.0). Object accuracy comparisons of these same images may be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s017" target="_blank">S6 Fig</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s014" target="_blank">S3</a> File. 3D, three-dimensional; hiPSC, human induced pluripotent stem cell.</p

Segmentation and analysis of 3D hiPSC images using CellProfiler 3.0.

<p>DNA channel showing nuclei (A), CellMaskDeepRed channel showing membrane (B), and GFP channel showing beta-actin (C) at the center (left) and edge (right) of the hiPSC colony. (D) Various measurements obtained from the samples are shown; note that cells touching the edge of each image are excluded from this analysis. The underlying measurements may be downloaded as <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s013" target="_blank">S2 File</a>. <i>Images are from the Allen Institute for Cell Science</i>, <i>Seattle</i>, <i>and are available from the Broad Bioimage Benchmark Collection (<a href="https://data.broadinstitute.org/bbbc/BBBC034/" target="_blank">https://data.broadinstitute.org/bbbc/BBBC034/</a>)</i>. 3D, three-dimensional; GFP, green fluorescent protein; hiPSC, human induced pluripotent stem cell.</p