Scores attributed by patients for the explored modalities of information delivery.

<p>Within each dimension (What, Where, When, or How), any two bars with different shades of grey correspond to two items for which the scores were identified as significantly different by the multiple comparison procedure, whereas all bars with a given identical shade of grey correspond to items for which the scores were identified as non-significantly different. *Issued from the Wilcoxon-Nemenyi-MacDonald-Thompson test procedure. ^†Intermediate item: neither significantly different from “community pharmacy” nor from “Patient’s home” (n.b. the P value for the comparison of the two latters groups is 0.01). CI indicates confidence interval.</p

Scores attributed by patients for the explored modalities of information delivery.

<p>Within each dimension (What, Where, When, or How), any two bars with different shades of grey correspond to two items for which the scores were identified as significantly different by the multiple comparison procedure, whereas all bars with a given identical shade of grey correspond to items for which the scores were identified as non-significantly different. *Issued from the Wilcoxon-Nemenyi-MacDonald-Thompson test procedure. ^†Intermediate item: neither significantly different from “community pharmacy” nor from “Patient’s home” (n.b. the P value for the comparison of the two latters groups is 0.01). CI indicates confidence interval.</p

Patients’ perceptions of the importance of several domains related to the content of educational programs.

<p>Any two Bars with different shades of grey correspond to subdomains for which the scores were identified as significantly different by the multiple comparison procedure, whereas all bars with a given identical shade of grey correspond to subdomains for which the scores were identified as non-significantly different.</p

Dynamic Proteomics of Human Protein Level and Localization across the Cell Cycle

<div><p>Regulation of proteins across the cell cycle is a basic process in cell biology. It has been difficult to study this globally in human cells due to lack of methods to accurately follow protein levels and localizations over time. Estimates based on global mRNA measurements suggest that only a few percent of human genes have cell-cycle dependent mRNA levels. Here, we used dynamic proteomics to study the cell-cycle dependence of proteins. We used 495 clones of a human cell line, each with a different protein tagged fluorescently at its endogenous locus. Protein level and localization was quantified in individual cells over 24h of growth using time-lapse microscopy. Instead of standard chemical or mechanical methods for cell synchronization, we employed in-silico synchronization to place protein levels and localization on a time axis between two cell divisions. This non-perturbative synchronization approach, together with the high accuracy of the measurements, allowed a sensitive assay of cell-cycle dependence. We further developed a computational approach that uses texture features to evaluate changes in protein localizations. We find that 40% of the proteins showed cell cycle dependence, of which 11% showed changes in protein level and 35% in localization. This suggests that a broader range of cell-cycle dependent proteins exists in human cells than was previously appreciated. Most of the cell-cycle dependent proteins exhibit changes in cellular localization. Such changes can be a useful tool in the regulation of the cell-cycle being fast and efficient.</p> </div

Schematic overview of dynamic proteomics for exploring cell cycle dependent changes in level and localization.

<p>(A) CD tagging was used to insert YFP as an exon into the introns of genes on the chromosome of a human cell line clone (H1299), resulting in a full length protein fused to YFP expressed from its endogenous locus. (B) A panel of 6 representative clones with different tagged proteins from the LARC library (C) Time-lapse microscopy and automated image analysis allow capturing proteins levels and localizations in individual cells over time. Yellow arrow indicates a cell in mitosis, green arrow indicates cells post mitosis. (D) Fluorescence traces of individual cells over a 40 hours movie (tagged protein is DDX5). Sharp decreases are at division events (E) In silico synchronization is done by plotting cell dynamics on a time axis which indicates time from previous or next division. Time is divided by mean cell cycle duration, to provide fraction of cell cycle elapsed. G, S and G2 phases are estimated from Sigal <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048722#pone.0048722-Sigal2" target="_blank">[15]</a>. Grey lines- cells with two mitosis events in the movie, blue, red lines: cells with one mitotic event. The fluorescence level is normalized to the maximal level before cell division. (F) In silico synchronized dynamics are used to examine cell-cycle dependence on levels and localizations. On the top panel, Protein profile (blue) that is significantly different from the average profile (black) is considered cell cycle dependent. On the bottom panel : nuclear protein shows a nuclear ratio (nuc/total) profile close to 1 most of the cell cycle, while cytoplasmic protein, shows a nuclear ratio close to 0 most of the cell cycle (besides during the mitosis, where the nucleus and cytoplasm are hard to segment apart). Protein that change its localization from the cytoplasm to the nucleus in a cell cycle dependent manner, present a nuclear ratio that is variable across the cell cycle.</p

Cell cycle dependence of protein levels.

<p>(A) Fluorescence averaged over all cells for 495 proteins, as a function of fraction of cell cycle. Each profile was normalized by the initial level of protein before mitosis to factor out different absolute expression levels. (B) Average profile of over all proteins. Dashed lines represent the standard deviation. (C) Fluorescence profiles normalized by the average profile were clustered using hierarchical clustering. (D) Examples for different clusters of proteins are shown. (E) Normalized profiles of the 56 cycling proteins are presented.</p

Cell cycle dependence of protein localization using texture features.

<p>(A) Illustrations of different cells texture. Note that while the energy ), doesn’t take into account the absolute differences in intensity, those are taken into account in the contrast calculation (, and so, similar cells can have same energy, but different contrast (the 2 cells on the left). (B) Normalized profiles of the contrast along the cell cycle are shown for a few proteins. (C) A series of images from a single cell from a clone that expresses HDAC2 fused to YFP. (D) Normalized profiles of the energy along the cell cycle are shown for a few proteins. (E) A series of images from a single cell from a clone that expresses ARID1B fused to YFP.</p

Distribution of cell cycle dependent proteins with changes in level and localization.

<p>The number of proteins that were found to be cell cycle dependent in each of the tested categories is shown and so are the number of proteins that were found to be cell cycle dependent in several categories.</p

Noise Genetics: Inferring Protein Function by Correlating Phenotype with Protein Levels and Localization in Individual Human Cells

<div><p>To understand gene function, genetic analysis uses large perturbations such as gene deletion, knockdown or over-expression. Large perturbations have drawbacks: they move the cell far from its normal working point, and can thus be masked by off-target effects or compensation by other genes. Here, we offer a complementary approach, called noise genetics. We use natural cell-cell variations in protein level and localization, and correlate them to the natural variations of the phenotype of the same cells. Observing these variations is made possible by recent advances in dynamic proteomics that allow measuring proteins over time in individual living cells. Using motility of human cancer cells as a model system, and time-lapse microscopy on 566 fluorescently tagged proteins, we found 74 candidate motility genes whose level or localization strongly correlate with motility in individual cells. We recovered 30 known motility genes, and validated several novel ones by mild knockdown experiments. Noise genetics can complement standard genetics for a variety of phenotypes.</p></div

Overview of the noise genetics approach.

<p>(a) We used a library of endogenously tagged proteins in human H1299 cells. Briefly, a retrovirus introduced YFP as an artificial exon into the introns of genes. Fluorescent clones were selected and sequenced. The clones express full length protein under endogenous control, with an internal YFP tag. (b) We selected 566 unique proteins with high quality movies and correct localization, and performed, or used existing, 24 h time-lapse movies(c) under controlled conditions. (d) Automated image analysis enabled by a mCherry tag in the parental clone enabled automatic tracking of protein level and localization as well as motility of each individual cell over time. (e) To find candidate motility genes, we sought proteins with high absolute correlation between protein level or localization (contrast, texture) and motility parameters (velocity, persistence). We tested a sample of the candidate motility genes by siRNA knockdown.</p