Architecture and permeability of post-cytokinesis plasmodesmata lacking cytoplasmic sleeves

This work was supported by the grants by the Region Aquitaine (to E.M.B) and PEPS (Initial Support for Exploratory Projects to E.M.B) and National Agency for Research (Grant ANR-14-CE19-0006-01 to E.M.B).Plasmodesmata are remarkable cellular machines responsible for the controlled exchange of proteins, small RNAs and signalling molecules between cells. They are lined by the plasma membrane (PM), contain a strand of tubular endoplasmic reticulum (ER), and the space between these two membranes is thought to control plasmodesmata permeability. Here, we have reconstructed plasmodesmata three-dimensional (3D) ultrastructure with an unprecedented level of 3D information using electron tomography. We show that within plasmodesmata, ER-PM contact sites undergo substantial remodelling events during cell differentiation. Instead of being open pores, post-cytokinesis plasmodesmata present such intimate ER-PM contact along the entire length of the pores that no intermembrane gap is visible. Later on, during cell expansion, the plasmodesmata pore widens and the two membranes separate, leaving a cytosolic sleeve spanned by tethers whose presence correlates with the appearance of the intermembrane gap. Surprisingly, the post-cytokinesis plasmodesmata allow diffusion of macromolecules despite the apparent lack of an open cytoplasmic sleeve, forcing the reassessment of the mechanisms that control plant cell-cell communication.PostprintPeer reviewe

Bloom’s Syndrome and PICH Helicases Cooperate with Topoisomerase IIα in Centromere Disjunction before Anaphase

Centromeres are specialized chromosome domains that control chromosome segregation during mitosis, but little is known about the mechanisms underlying the maintenance of their integrity. Centromeric ultrafine anaphase bridges are physiological DNA structures thought to contain unresolved DNA catenations between the centromeres separating during anaphase. BLM and PICH helicases colocalize at these ultrafine anaphase bridges and promote their resolution. As PICH is detectable at centromeres from prometaphase onwards, we hypothesized that BLM might also be located at centromeres and that the two proteins might cooperate to resolve DNA catenations before the onset of anaphase. Using immunofluorescence analyses, we demonstrated the recruitment of BLM to centromeres from G2 phase to mitosis. With a combination of fluorescence in situ hybridization, electron microscopy, RNA interference, chromosome spreads and chromatin immunoprecipitation, we showed that both BLM-deficient and PICH-deficient prometaphase cells displayed changes in centromere structure. These cells also had a higher frequency of centromeric non disjunction in the absence of cohesin, suggesting the persistence of catenations. Both proteins were required for the correct recruitment to the centromere of active topoisomerase IIα, an enzyme specialized in the catenation/decatenation process. These observations reveal the existence of a functional relationship between BLM, PICH and topoisomerase IIα in the centromere decatenation process. They indicate that the higher frequency of centromeric ultrafine anaphase bridges in BLM-deficient cells and in cells treated with topoisomerase IIα inhibitors is probably due not only to unresolved physiological ultrafine anaphase bridges, but also to newly formed ultrafine anaphase bridges. We suggest that BLM and PICH cooperate in rendering centromeric catenates accessible to topoisomerase IIα, thereby facilitating correct centromere disjunction and preventing the formation of supernumerary centromeric ultrafine anaphase bridges

The impact of cyclin-dependent kinase 5 depletion on poly(ADP-ribose) polymerase activity and responses to radiation

Cyclin-dependent kinase 5 (Cdk5) has been identified as a determinant of sensitivity to poly(ADP-ribose) polymerase (PARP) inhibitors. Here, the consequences of its depletion on cell survival, PARP activity, the recruitment of base excision repair (BER) proteins to DNA damage sites, and overall DNA single-strand break (SSB) repair were investigated using isogenic HeLa stably depleted (KD) and Control cell lines. Synthetic lethality achieved by disrupting PARP activity in Cdk5-deficient cells was confirmed, and the Cdk5KD cells were also found to be sensitive to the killing effects of ionizing radiation (IR) but not methyl methanesulfonate or neocarzinostatin. The recruitment profiles of GFP-PARP-1 and XRCC1-YFP to sites of micro-irradiated Cdk5KD cells were slower and reached lower maximum values, while the profile of GFP-PCNA recruitment was faster and attained higher maximum values compared to Control cells. Higher basal, IR, and hydrogen peroxide-induced polymer levels were observed in Cdk5KD compared to Control cells. Recruitment of GFP-PARP-1 in which serines 782, 785, and 786, potential Cdk5 phosphorylation targets, were mutated to alanines in micro-irradiated Control cells was also reduced. We hypothesize that Cdk5-dependent PARP-1 phosphorylation on one or more of these serines results in an attenuation of its ribosylating activity facilitating persistence at DNA damage sites. Despite these deficiencies, Cdk5KD cells are able to effectively repair SSBs probably via the long patch BER pathway, suggesting that the enhanced radiation sensitivity of Cdk5KD cells is due to a role of Cdk5 in other pathways or the altered polymer levels

Automated Cell Tracking and Analysis in Phase-Contrast Videos (iTrack4U): Development of Java Software Based on Combined Mean-Shift Processes

Cell migration is a key biological process with a role in both physiological and pathological conditions. Locomotion of cells during embryonic development is essential for their correct positioning in the organism; immune cells have to migrate and circulate in response to injury. Failure of cells to migrate or an inappropriate acquisition of migratory capacities can result in severe defects such as altered pigmentation, skull and limb abnormalities during development, and defective wound repair, immunosuppression or tumor dissemination. The ability to accurately analyze and quantify cell migration is important for our understanding of development, homeostasis and disease. In vitro cell tracking experiments, using primary or established cell cultures, are often used to study migration as cells can quickly and easily be genetically or chemically manipulated. Images of the cells are acquired at regular time intervals over several hours using microscopes equipped with CCD camera. The locations (x,y,t) of each cell on the recorded sequence of frames then need to be tracked. Manual computer-assisted tracking is the traditional method for analyzing the migratory behavior of cells. However, this processing is extremely tedious and time-consuming. Most existing tracking algorithms require experience in programming languages that are unfamiliar to most biologists. We therefore developed an automated cell tracking program, written in Java, which uses a mean-shift algorithm and ImageJ as a library. iTrack4U is a user-friendly software. Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U. Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences. Finally, iTrack4U is adapted for phase contrast and fluorescent cells. © 2013 CORDELIERES et al.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Automated cell tracking and analysis in phase-contrast videos (iTrack4U): development of Java software based on combined mean-shift processes.

Cell migration is a key biological process with a role in both physiological and pathological conditions. Locomotion of cells during embryonic development is essential for their correct positioning in the organism; immune cells have to migrate and circulate in response to injury. Failure of cells to migrate or an inappropriate acquisition of migratory capacities can result in severe defects such as altered pigmentation, skull and limb abnormalities during development, and defective wound repair, immunosuppression or tumor dissemination. The ability to accurately analyze and quantify cell migration is important for our understanding of development, homeostasis and disease. In vitro cell tracking experiments, using primary or established cell cultures, are often used to study migration as cells can quickly and easily be genetically or chemically manipulated. Images of the cells are acquired at regular time intervals over several hours using microscopes equipped with CCD camera. The locations (x,y,t) of each cell on the recorded sequence of frames then need to be tracked. Manual computer-assisted tracking is the traditional method for analyzing the migratory behavior of cells. However, this processing is extremely tedious and time-consuming. Most existing tracking algorithms require experience in programming languages that are unfamiliar to most biologists. We therefore developed an automated cell tracking program, written in Java, which uses a mean-shift algorithm and ImageJ as a library. iTrack4U is a user-friendly software. Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U. Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences. Finally, iTrack4U is adapted for phase contrast and fluorescent cells

Architecture and permeability of post-cytokinesis plasmodesmata lacking cytoplasmic sleeves

Plasmodesmata are remarkable cellular machines responsible for the controlled exchange of proteins, small RNAs and signalling molecules between cells. They are lined by the plasma membrane (PM), contain a strand of tubular endoplasmic reticulum (ER), and the space between these two membranes is thought to control plasmodesmata permeability. Here, we have reconstructed plasmodesmata three-dimensional (3D) ultrastructure with an unprecedented level of 3D information using electron tomography. We show that within plasmodesmata, ER-PM contact sites undergo substantial remodelling events during cell differentiation. Instead of being open pores, post-cytokinesis plasmodesmata present such intimate ER-PM contact along the entire length of the pores that no intermembrane gap is visible. Later on, during cell expansion, the plasmodesmata pore widens and the two membranes separate, leaving a cytosolic sleeve spanned by tethers whose presence correlates with the appearance of the intermembrane gap. Surprisingly, the post-cytokinesis plasmodesmata allow diffusion of macromolecules despite the apparent lack of an open cytoplasmic sleeve, forcing the reassessment of the mechanisms that control plant cell-cell communication

Time benefit of automatic tracking <i>vs</i>. manual tracking.

<p>Cells were followed either manually (full circles) or using iTrack4U (white circles). It took about three minutes to track each single cell, corresponding to 181 frames, by manual tracking. Twenty cells can therefore be manually tracked in 1 hour and 200 cells during 10 hours of active work. Automatic tracking is performed in two major steps: (i) the establishment of the parameters for both pre-processing and tracking requires about two hours and (ii) the automatic tracking requires about four seconds to fully track a single cell. Using iTrack4U is beneficial for following over about 50 cells. </p

Geometric characteristics of cell trajectories associated with distances and extracted by manual and automatic tracking.

<div><p>Cells were imaged every four minutes for 12 hours and experiments were repeated three times. The same 40 independent cells were tracked manually (M) and automatically (A). The following variables were extracted from the manually and automatically retrieved sets of coordinates:.</p> <p><i>A</i>. <i>Total distance of migration by WM852 human melanoma cells</i>. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.0774).</p> <p><i>B</i>. <i>Euclidian distance (start-end distance) of WM852 human melanoma cells</i>. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.9672).</p> <p><i>C</i>. <i>Persistence of migration by WM852 human melanoma cells</i>. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.5012).</p> <p><i>D</i>. <i>Definition of migration variables used in this figure</i>. Total distance = d_ttl, Euclidian distance = d_S-E, persistence = d_ttl / d_S-E, minimum travelled distance = d_min, maximum travelled distance = d_max.</p> <p><i>E</i>. <i>Average distance of migration by WM852 human melanoma cells</i>. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.0774 and p = 0.3913 for the average distance and standard deviation, respectively). </p> <p><i>F</i>. <i>Extreme values (minimum and maximum distances) of migration for WM852 human melanoma cells</i>. Manual and automatic methods were not statistically significant for the maximum distance (standard unpaired t-test, p = 0.2611). A significant difference for the minimum distance has no real meaning, as explained in the text (standard unpaired t-test, p = 0.001).</p></div

Illustration of the mean-shift model.

<div><p><i>A</i>. Initialization of the generic kernel based on the user-defined position (x’₀,y’₀).</p> <p>The kernel (here an octagon, ndir = 8) is divided into sectors (S1 to S8), each one containing two nested triangle-shaped regions (R_b and R_w), one sensitive to dark and the other to bright pixels (“b” means “black” and “w” means “white”). Here, R_b6 and R_w6 are shown, with a total of 16 regions (R_b1 to R_b8 and R_w1 to R_w8). Only the contours of sectors 2-5 are shown in order to lighten and better visualize the figure. The cell is not presented for clarity.</p> <p><i>B</i>. <i>Adjustment of the position of the center at t₀</i>. </p> <p>Sixteen mass centers (g_b1 to g_b8 and g_w1 to g_w8) are first calculated from the intensities of the pixels from each region, (g_b1 and g_w1 are shown). Sector mass centers (C) are calculated from g_wn and g_bn, (C₈ is shown as an example). The center (x₀,y₀) is defined as the centroid of the mass centers C₁ to C₈.</p> <p><i>C</i>. <i>Adaptation of the kernels to cell morphology at t₀</i>. </p> <p>The distances (d_i) between each mass center (g_wn) and kernel center (x₀,y₀) are calculated (d₈ is shown as an example). The new outer radii (r_wn) are calculated based on d_n, the average d_n distances, the expansion factor and the anisotropy factor. r_bn is assigned according to the ratio (r_b / r_w), which is initially defined by the user.</p> <p><i>D</i>. <i>Representation of the kernels at t₁</i>. </p> <p>Information is obtained applying the processes explained in B and C. The size of the sectors will increase or decrease (indicated by the arrows) as a function of cell shape modifications. </p></div