Dynamic Proteomics: a database for dynamics and localizations of endogenous fluorescently-tagged proteins in living human cells

Recent advances allow tracking the levels and locations of a thousand proteins in individual living human cells over time using a library of annotated reporter cell clones (LARC). This library was created by Cohen et al. to study the proteome dynamics of a human lung carcinoma cell-line treated with an anti-cancer drug. Here, we report the Dynamic Proteomics database for the proteins studied by Cohen et al. Each cell-line clone in LARC has a protein tagged with yellow fluorescent protein, expressed from its endogenous chromosomal location, under its natural regulation. The Dynamic Proteomics interface facilitates searches for genes of interest, downloads of protein fluorescent movies and alignments of dynamics following drug addition. Each protein in the database is displayed with its annotation, cDNA sequence, fluorescent images and movies obtained by the time-lapse microscopy. The protein dynamics in the database represents a quantitative trace of the protein fluorescence levels in nucleus and cytoplasm produced by image analysis of movies over time. Furthermore, a sequence analysis provides a search and comparison of up to 50 input DNA sequences with all cDNAs in the library. The raw movies may be useful as a benchmark for developing image analysis tools for individual-cell dynamic-proteomics. The database is available at http://www.dynamicproteomics.net/

Generation of double-labeled reporter cell lines for studying co-dynamics of endogenous proteins in individual human cells.

Understanding the dynamic relationship between components of a system or pathway at the individual cell level is a current challenge. To address this, we developed an approach that allows simultaneous tracking of several endogenous proteins of choice within individual living human cells. The approach is based on fluorescent tagging of proteins at their native locus by directed gene targeting. A fluorescent tag-encoding DNA is introduced as a new exon into the intronic region of the gene of interest, resulting in expression of a full-length fluorescently tagged protein. We used this approach to establish human cell lines simultaneously expressing two components of a major antioxidant defense system, thioredoxin 1 (Trx) and thioredoxin reductase 1 (TrxR1), labeled with CFP and YFP, respectively. We find that the distributions of both proteins between nuclear and cytoplasmic compartments were highly variable between cells. However, the two proteins did not vary independently of each other: protein levels of Trx and TrxR1 in both the whole cell and the nucleus were substantially correlated. We further find that in response to a stress-inducing drug (CPT), both Trx and TrxR1 accumulated in the nuclei in a manner that was highly temporally correlated. This accumulation considerably reduced cell-to-cell variability in nuclear content of both proteins, suggesting a uniform response of the thioredoxin system to stress. These results indicate that Trx and TrxR1 act in concert in response to stress in regard to both time course and variability. Thus, our approach provides an efficient tool for studying dynamic relationship between components of systems of interest at a single-cell level

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

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

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

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 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