Dynamic Epitope Expression from Static Cytometry Data: Principles and Reproducibility

Background: An imprecise quantitative sense for the oscillating levels of proteins and their modifications, interactions, and translocations as a function of the cell cycle is fundamentally important for a cartoon/narrative understanding for how the cell cycle works. Mathematical modeling of the same cartoon/narrative models would be greatly enhanced by an openended methodology providing precise quantification of many proteins and their modifications, etc. Here we present methodology that fulfills these features. Methodology: Multiparametric flow cytometry was performed on Molt4 cells to measure cyclins A2 and B1, phospho-S10histone H3, DNA content, and light scatter (cell size). The resulting 5 dimensional data were analyzed as a series of bivariate plots to isolate the data as segments of an N-dimensional ‘‘worm’ ’ through the data space. Sequential, unidirectional regions of the data were used to assemble expression profiles for each parameter as a function of cell frequency. Results: Analysis of synthesized data in which the true values where known validated the approach. Triplicate experiments demonstrated exceptional reproducibility. Comparison of three triplicate experiments stained by two methods (single cyclin or dual cyclin measurements with common DNA and phospho-histone H3 measurements) supported the feasibility of combining an unlimited number of epitopes through this methodology. The sequential degradations of cyclin A2 followed by cyclin B1 followed by de-phosphorylation of histone H3 were precisely mapped. Finally, a two phase expression rat

Curcumin rescues breast cells from epithelial‑mesenchymal transition and invasion induced by anti‑miR‑34a

Breast cancer is the most prevalent type of cancer among women worldwide and it is characterized by a high morbidity. Curcumin is a naturally occurring compound derived from the rhizome of Curcuma longa and is known to have antioxidant and anticarcinogenic properties. Emerging evidence has indicated that microRNAs (miRNAs or miRs) function as oncogenes or tumor suppressor genes to control invasion and migration. The aim of this study was to evaluate the effects of curcumin on genes implicated in epithelial-mesenchymal transition (EMT) and to examine the involvement of Rho-A in the migration and invasion of MCF-10F and MDA-MB-231 breast cell lines. Furthermore, to the best of our knowledge, this is the first study to examine the effects of curcumin on Rho-A and on genes involved in EMT, such as Axl, Slug and CD24 in order to determine whether the compound is able to prevent migration and invasion by targeting miRNAs as a regulator of such genes. Specifically, we focused on miR-34a which acts as a tumor suppressor gene in human breast cell lines. The present study demonstrated that the Axl, Slug and CD24 genes were implicated in EMT, and Rho-A was also involved in the migration and invasion of MCF-10F and MDA-MB-231 cell lines. Curcumin also acted upon the miRNA as a regulator of genes implicated in EMT and upon Rho-A as well, affecting the migration and invasion of the cells. This occurred independently of their estrogen receptor (ER), progesterone receptor (PgR) and human epidermal growth factor receptor 2 (HER2) receptors in the non-malignant MCF-10F and malignant MDA-MB-231 breast cell lines, which are both negative for such receptors.Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) CONICYT FONDECYT 1120006 1161219 3190744 UTA-MINEDUC UTA1117 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDAP 1513001

Defining ambiguity.

<p>Molt4 cells were stained for phospho-S10-histone H3 (PHH3-A488), cyclin A2 (Cyclin A2-A647), and DNA content. A: typical view for counting mitotic cells (green dots). This view provides ambiguous data vis-à-vis profile extraction since the cells between the G2 cluster and the bulk of mitotic events at the highest PHH3 levels represent cells that were in the processes of net gain and net loss of PHH3 (A, dual arrows). The ambiguity can be resolved by plotting PHH3 versus cyclin A2 (B). Arrows show the direction of movement through the data space of cell traversing the cell cycle.</p

Segmentation of multiparametric data.

<p>Samples from the same Molt4 population as shown in previous figures were stained for cyclins A2, B1, PHH3, and DNA content. Segmentation is more complex then single cyclin stained samples, requiring four bivariate views. The first segment (labeled “1”) begins in A (cyclin A2 vs. PHH3) then jumps to B, which is a plot of cyclin A2 vs. cyclin B1 for interphase cells (NOT gated for mitotic cells identified by a region (not shown) set as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030870#pone.0030870.s001" target="_blank">Figure S1</a>), and begins with “2” and ends with “21”. The next segment is back in A, labeled “22” and ends at “34”. The next segment, “35” is in C, which is a plot of cyclin A2 vs. cyclin B1 for mitotic cells (AND gated for mitotic cells as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030870#pone.0030870.s001" target="_blank">Figure S1</a>). The final cluster is segmented in a bivariate of PHH3 vs. cyclin B1 (not shown). D shows resolution of the last mitotic cluster by color (cyan, magenta), labeled “39” and “40” in a 3D plot of PHH3, cyclin A2, and cyclin B1 to show the orthogonal nature of the segment continuum for mitotic cells.</p

Segmentation of cytometric data.

<p>A simulated DNA content distribution composed of the sum (A, dashed black line) of multiple normal Gaussian components (A, thin blue lines) provides a means to extract a perfect DNA content expression profile from idealize typical cytometry histograms (B, blue circles). Segmenting the same histogram by contiguous regions yields the same expression profile (B, orange circles). The segmentation needs to account for measurement variation at the ends of the histogram.</p

Expression profiles from multiparametric data.

<p>Joined PHH3 profiles extracted from single cyclin sample data (both cyclins A2 and B1) and multi-cyclin data are co-plotted (left). A cyclin B1 expression profile extracted from single cyclin data (SC) is co-plotted with an expression profile extracted from multi-cyclin data (MC).</p

DNA expression profile extraction.

<p>A: profile extraction by indirect means. The DNA values were obtained for the cells falling within regions from cyclin A2 vs PHH3 plots as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030870#pone-0030870-g004" target="_blank">Figure 4</a>. Error bars are SEM. B: direct extraction compared to indirect. The line traces the data in A; the error bars are the 95% confidence intervals. Circles represent values calculated from each of three Molt4 data sets, derived by directly segmenting DNA histograms as demonstrated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030870#pone-0030870-g002" target="_blank">Figure 2</a>.</p

Segmentation for cyclin B1 and PHH3.

<p>The approach is as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030870#pone-0030870-g004" target="_blank">Figure 4</a> with differences due to the pattern created by the relationship between cyclin B1 and PHH3. A:interphase segmentation. B: mitotic segmentation.</p

Expression profiles from multi-parametric data.

<p>PHH3 (green) and cyclin A2 (red) profiles, extracted from single cyclin sample data, are co-plotted with PHH3 (blue) and cyclin A2, extracted from multi-cyclin sample data, in three views that emphasize the entire cell cycle (top), G2 and M (lower left) and mitosis (lower right).</p

Segmentation in two dimensions.

<p>Molt4 cells were stained as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030870#pone-0030870-g003" target="_blank">Figure 3</a>. A: segmentation of interphase; B: segmentation of mitosis and the beginning of interphase (R48). For a description of the logic, see the text.</p