Selection of organizational features associated with Instantaneous Cell Speed (ICS).

<p>(A) Comparative CVA clustering of the slowest (S1, 1–20%, blue) and fastest (S5, 81–100%, red) moving <i>Instantaneous Cell Speed</i> (<i>ICS)</i>-defined cell subpopulations using all <i>organizational</i> variables achieves strong separation along the 1^st canonical vector (X-axis, captures 99% of total variation). (B) <i>Organizational</i> features driving separation of subpopulations selected by rank ordering according to absolute coefficient values associated with each feature in the 1^st canonical vector. (C) 81 normalized <i>organizational</i> features (background variables, Y-axis, compact feature names defined in Supporting <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090593#pone.0090593.s005" target="_blank">Table S1</a>) contribute to EN-regression modeling of <i>ICS</i> (<i>behavioral</i> feature, response variable) for the entire control cell population. Horizontal color bars indicate coefficient values (−1ICS, respectively, while absolute coefficient values indicate the importance of each variable to the estimation of <i>ICS</i> per model iteration. With progressive model iterations, the sum of all coefficient values is forced non-uniformly towards zero, with coefficients redistributed to optimize the regression model according to adjusted R². (D) Rank ordering of background variables based on their absolute coefficient values in the optimal regression model (adjusted R² = 0.43, horizontal yellow line, iteration 10, vertical yellow line) provides a second list of <i>organizational</i> features associated with variations in <i>ICS</i>. Variables with grey backgrounds were implicated among the top 15 features by both feature selection methods (B and D).</p

Mapping of directed causal influence based on Granger causality.

<p>(A and B) 3D surface plots of adjusted R² values (Y-axes) based on auto-regression (AR) modeling of the response variable, <i>Instantaneous Cell Speed (ICS)</i>, using combinations of one to ten temporal lags (5 min interval) of the response variable (<i>ICS</i>, Z-axes) and one to ten temporal lags of a background variable, either <i>Sum [CMAC Total RubyRed-LifeAct Intensity] per Cell</i> (X-axis, A) or <i>Mean [CMAC Lifetime] per Cell</i> (X-axis, B). Grey arrows along X and Z axes indicate the inclusion of additional temporal lags of the indicated variable (A and B). (C–G) Significance testing of improvements in adjusted R² values caused by the addition of temporal lags of background (X-axes) and response variables (Y-axes) to an AR model based on >2200 cell observations. White indicates no statistically significant improvement in prediction. Blue and red color schemes (indicating negative and positive correlations between background and response variables, respectively) are each divided into 4 levels of significance, with P values <0.05 (*), <0.01 (**), <0.001 (***) or <0.0001 (****), as indicated (color scale-bar, upper right). Causation is tested reciprocally within variable pairs to discern evidence for the causal influence of <i>organizational</i> variables over <i>ICS</i> (left panels, C–G) and <i>vice versa</i> (right panels, C–G). We infer causal influence only where significance-testing patterns are robust and ordered (as in C [left panel], D [left panel], E [left panel], G [right panel]. Stem plots (below X-axes, C–G, left and right panels) indicate the degree of autocorrelation in respective response variables.</p

Causal influence patterns are plastic and contextually dependent.

<p>(A) Granger causality analysis revealed a sequence of causal interactions extending both up-stream and down-stream of <i>Instantaneous Cell Speed (ICS)</i>: Increasing <i>IDR [Change in CMAC Total EGFP-Paxillin intensity] per Cell</i> (indicates per cell variation in the net rate of EGFP-Paxillin recruitment/release per CMAC) caused increased <i>Median [EGFP-Paxillin – RubyRed-LifeAct Colocalization per CMAC] per Cell</i> (A, left panel). Increasing <i>Median [EGFP-Paxillin – RubyRed-LifeAct Colocalization per CMAC] per Cell</i> caused reduced <i>ICS</i> (A, center panel). Increasing <i>ICS</i> caused increased <i>Cell Compactness</i> (A, right panel), indicating that fast moving cells become less round. The causal links between these four variables are summarized schematically (grey boxes), with positive and negative relationships indicated by arrows and capped lines, respectively. Analyses of Granger causality predictions for equivalent inter-feature relationships in ROCK-inhibited cells (B) reveal comparable causal relationships while Rho-activated cells did not (C). Notably, although the final causal relationship (C, right panel) was still detected, the causal effect was reversed such that increasing <i>ICS</i> caused decreasing <i>Cell Compactness,</i> i.e. increased cell speed caused cells to become more round. All variables are defined in Supporting <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090593#pone.0090593.s005" target="_blank">Table S1</a>.</p

Exploration of individual feature correlations.

<p>(A) A heat map of Spearman’s rank correlation coefficients (rs) summarizes the pairwise correlative relationships between all 88 recorded variables (<i>organizational</i> and <i>behavioral,</i> compact feature names defined in Supporting <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090593#pone.0090593.s005" target="_blank">Table S1</a>) based on ranked observation values (blue = negative rs; red = positive rs; green = rs ∼ 0). (B–G) Selected correlations to ICS (indicated in heat map by lines (B)–(G), corresponding to panels B–G) plotted as ranked values of <i>ICS</i> (X-axes) vs ranked values of <i>organizational</i> features (Y-axes): <i>Cell Area</i> (B); <i>Cell Compactness</i> (C); <i>Mean [CMAC Lifetime] per Cell</i> (D); <i>Sum [CMAC Total RubyRed-LifeAct Intensity] per Cell</i> (E); <i>Median [EGFP-Paxillin – RubyRed-LifeAct Colocalization per CMAC] per Cell</i> (F); <i>Median [CMAC Area] per Cell</i> (G). Red dotted trend lines represent linear best fits.</p

Disentangling Membrane Dynamics and Cell Migration; Differential Influences of F-actin and Cell-Matrix Adhesions

<div><p>Cell migration is heavily interconnected with plasma membrane protrusion and retraction (collectively termed “membrane dynamics”). This makes it difficult to distinguish regulatory mechanisms that differentially influence migration and membrane dynamics. Yet such distinctions may be valuable given evidence that cancer cell invasion in 3D may be better predicted by 2D membrane dynamics than by 2D cell migration, implying a degree of functional independence between these processes. Here, we applied multi-scale single cell imaging and a systematic statistical approach to disentangle regulatory associations underlying either migration or membrane dynamics. This revealed preferential correlations between membrane dynamics and F-actin features, contrasting with an enrichment of links between cell migration and adhesion complex properties. These correlative linkages were often non-linear and therefore context-dependent, strengthening or weakening with spontaneous heterogeneity in cell behavior. More broadly, we observed that slow moving cells tend to increase in area, while fast moving cells tend to shrink, and that the size of dynamic membrane domains is independent of cell area. Overall, we define macromolecular features preferentially associated with either cell migration or membrane dynamics, enabling more specific interrogation and targeting of these processes in future.</p></div

Quantitative imaging and analysis reveals that slow migrating cells increase in area while fast migrating cells decrease in area.

<p>(A) Live H1299 P/L cells expressing EGFP-paxillin and RubyRed-LifeAct were imaged and segmented in order to identify individual cells and their cohort of Cell-Matrix Adhesion Complexes (CMACs). EGFP-paxillin and RubyRed-LifeAct channels are displayed in inverted gray scale (high intensity is black). The segmentation image shows the EGFP-paxillin channel with the cell border identified in blue, CMAC borders in red and CMAC major axes in cyan. Scale bar: 10 μm. (B) By comparing consecutive frames, protrusions (green), retractions (red), short-lived (blue) and stable (gray) regions were identified, as described in Materials and Methods. White circles indicate the locations of CMACs. (C) The average size of each type of dynamic cell region (per frame) in the dataset was calculated and stratified per quintile of Cell Speed. The total height of each bar (the sum of protrusion, retraction and short-lived areas per frame) corresponds to the Dynamic Cell Area. We observed that this quantity increases with Cell Speed. (D) Scatter Plot: The net value of protrusion minus retraction areas (delta (Δ) Cell Area, μm²) is shown as a function of Cell Speed. The density of observations at a given Cell Speed (Cell Speed conditional density) is color-coded following log transformation, enabling better observation of trends in Δ Cell Area values given changing Cell Speed. A linear fit (Pearson’s correlation coefficient <i>r</i> = -0.27, <i>P</i> = 4.39·10⁻²³³) of the relationship is indicated (black line). Probability distributions: of Cell Speed (X axis), and; Δ Cell Area (Y axis, right) show a heavy-tailed distribution of Cell Speed where most cells are slow moving and few are fast moving, while Cell Area changes are approximately symmetrical overall. Notice, however, that the relatively few fast moving cells are decreasing in area substantially (retraction area is much larger than protrusion area), while the numerous slow moving cells only grow slightly, on average. (E) Mean Cell Speed autocorrelation coefficients (Y axis) are plotted conditioned upon the mean Cell Speed (X axis) of each cell. Autocorrelation values were calculated per cell trajectory with a maximum time lag of 1 h (12 frames). A linear fit (Pearson’s correlation coefficient <i>r</i> = -0.32, <i>P</i> = 0.00037) of the relationship indicates that autocorrelation of Cell Speed is lower in cells with higher average Cell Speeds. This indicates that the temporal persistence of Cell Speed correlates negatively to Cell Speed itself. See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135204#pone.0135204.s005" target="_blank">S1 Movie</a>, showing the same cell as in A-B.</p

Analysis of relationship structures between features and Cell Speed.

<p>(A) A Venn diagram summarizes the frequencies of particular relationship structures between features and changing values of Cell Speed. Each circle of the Venn diagram contains two colors, indicating the Cell Speed quintiles (red, slow; yellow, moderate; green, fast) between which feature values were compared via pairwise testing (slow <i>versus</i> moderate; moderate <i>versus</i> fast; slow <i>versus</i> fast). Segments of the Venn diagram indicate which combinations of pairwise tests (Wilcoxon rank sum test with Bonferroni correction) resulted in statistically discernable differences. The number of features is indicated for which a given combination of tests showed significance. To aid interpretation, schematic archetypes are included to indicate the type of correspondence that is observed between each feature and Cell Speed. Where boxes do not overlap in the Y-axis, statistically significant differences were detected between feature values in the corresponding Cell Speed groups. Note that the actual sign of feature responses may be inverted compared to these generalized archetypes. (B-K) The observed archetypes from (A) are illustrated to the left and examples of features corresponding to each archetype are shown in boxes to the right. Comparison brackets in each panel indicate significant differences (P<0.001 after Bonferroni correction, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135204#sec012" target="_blank">Materials and Methods</a>). Box plots show quartiles. Outliers are not shown. Notches are placed at the median value , where <i>n</i> is the number of observations in each quintile (approximation of the 95% confidence interval of the median). (B) Mean Cell-Matrix Adhesion Complex (CMAC) Lifetime and (C) median of CMAC Mean paxillin intensity per cell both show stably monotonic decreases across all Cell Speeds. (D) Quartile dispersion (QD) of CMAC compactness is significantly lower in fast than in slow cells. (E) The median rate of change in CMAC area is negative meaning that CMACs are shrinking. This shrinking is more rapid in fast than in slow cells. (F) Cell major Axis is significantly higher in moderate than in slow cell observations, but not between any other groups. (G) QD of CMAC to cell border distance at each time point increases significantly between slow and moderate but not between moderate and fast cells; (H) paxillin-actin colocalization on a cell level decreases in a corresponding way. (I) Coefficient of variation (CoV) of CMAC Speed is significantly lower in fast than in moderate cell observations. (J) The number of CMACs per cell at each time point and (K) the total area of the cell at each time point both show a monotonic decrease between moderate and fast cells.</p

Stringent selection of features related to Cell Speed and or Corrected Membrane Dynamics.

<p>Canonical Vector Analysis (CVA) was used for multivariate separation of slow (red), moderate (yellow) and fast (green) Cell Speed groups (A), and low (blue), intermediate (gray) and high (pink) Corrected Membrane Dynamics (CMD) groups (B), respectively. (C) The features were categorized by whether they contributed to each separation, as well as whether they showed a significant difference between groups (determined via Kruskal-Wallis multiple group testing). According to this two-step criteria, 15 variables contributed to a difference only between Cell Speed related groups, 7 variables contributed to the difference between CMD related groups only and 33 variables contributed to both Cell Speed and CMD related differences. (D-E) Cell Speed related responses. (D) The coefficient of variation indicates heterogeneity in Cell-Matrix Adhesion Complex (CMAC) perimeter distribution. This heterogeneity decreases with Cell Speed but is not significantly changed with CMD. (E) The median rate of change in CMAC paxillin intensity (frame-to-frame difference) shows a concerted decrease in relation to increased Cell Speed, but not in response to changing CMD. (F-G) Responses related to CMD. (F) Median LifeAct intensity per CMAC. This feature is independent of Cell Speed, but decreases with higher CMD. (G) Mean LifeAct intensity per cell is also independent of Cell Speed but decreases with increased CMD. (H-I) Responses related to both Cell Speed and CMD. (H) Median of mean CMAC paxillin intensity per CMAC decreases with both increased Cell Speed and CMD. (I) Number of CMACs per cell decreases with increased Cell Speed but increases with CMD.</p

Methodological overview.

<p>(A) Schematic of live H1299 cells expressing EGFP-paxillin (Green, upper left) and RubyRed-LifeAct (red, upper right) that were imaged at 5 min intervals for 8 h. (Lower left) Segmentation identified the cell border (blue) and Cell-Matrix Adhesion Complexes (CMACs) (red). These were tracked over time, allowing extraction of static and dynamic <i>features</i> describing cell, CMAC and F-actin characteristics per cell, per time point. (Lower right) Consecutive frames were compared and protrusive (green), retractive (red), short-lived (blue) and stable (gray) regions were identified. The identification of these regions allowed for per cell quantification of both <i>processes</i> of interest, membrane dynamics and cell migration. (B) Dynamic Cell Area, defined as the total non-stable area (total area of protrusions, retractions and short-lived regions), is linearly dependent on Cell Speed. Observations were stratified into equally sized Cell Speed groups: slow (red); moderate (yellow); or fast (green). (C) To establish a Cell Speed-independent measure of membrane activity, Corrected Membrane Dynamics (CMD) was calculated by subtracting the linear dependence between Cell Speed and Dynamic Cell Area. CMD data was also stratified into equal groups with: low (blue); intermediate (gray), or; high (pink) activity. (D-E) Venn diagrams summarize the frequencies of particular relationship structures between features and changing values of Cell Speed (D) or CMD (E). Each circle of the Venn diagrams contain two colors (as defined in B-C), indicating the pairs of Cell Speed or CMD groups between which feature values were statistically compared, e.g. slow (red) vs moderate (yellow) migrating cells in D. Segments of the Venn diagram indicate which combinations of these pairwise statistical comparisons revealed significant differences in feature values. To aid interpretation, schematic archetypes (small graphical insets) are included to indicate the generalized relationship structure that is observed between each feature (Y-axes) and either Cell Speed or CMD (X-axes). Where boxes do not overlap in the Y-axis, statistically significant differences were detected between feature values in the corresponding Cell Speed or CMD groups. For example, features associated with the category represented in the lower segment of D would have significantly different values when comparing cells migrating slow (red) <i>versus</i> fast (green), but not between slow and moderate, or moderate and fast migrating cells. Hence, change in the values of such features would proceed slowly but progressively over the full range of observed Cell Speeds. Note that the actual sign of feature responses may be inverted compared to these generalized archetypes. (F) Finally, Cell Speed- and CMD-related features were identified and compared using a stringent approach. This resulted in lists of features that are related to Cell Speed, to CMD or to both processes. Of those features related to both processes, some showed equivalent responses (feature 1 example), while others showed distinct and even opposite dependencies to each process (feature 2 example).</p

Speed and Corrected Membrane Dynamics are independent.

<p>(A) Values for Cell Speed and Dynamic Cell Area are plotted. The density of observations at a given Cell Speed (Cell Speed conditional density) is color-coded following log transformation, enabling better observation of trends in Dynamic Cell Area values given changing Cell Speed. A linear fit (Pearson’s correlation coefficient <i>r</i> = 0.74) of the relationship between Cell Speed and Dynamic Cell Area is indicated (black line). (B) Scatter plot of Cell Speed versus Dynamic Cell Area. Cell observations are divided into Cell Speed quintiles as indicated by color scaling: red (slow, 0%-20%); orange (20%-40%); yellow (moderate, 40%-60%); light-green (60%-80%); green (fast, 80%-100%). (C) Box plots show the median and variability of Dynamic Cell Area per speed quintile. The box shows the quartiles and the whiskers show 1.5 times the interquartile range (IQR). Outliers are not shown. Notches are placed at the median value , where <i>n</i> is the number of observations in each quintile (approximation of the 95% confidence interval of the median). By comparing these measures for each Cell Speed quintile we observed a monotonic increase of Dynamic Cell Area with Cell Speed. Colors as in (B). (D) Corrected Membrane Dynamics (CMD) is defined by subtracting the linear relationship between Cell Speed and Dynamic Cell Area from all observations. Conditional density color-coding and linear fit are calculated and displayed as in (A). (E) Scatter plot of Cell Speed versus CMD, color-coded by Cell Speed quintiles as in B. (F) Box plots as in (C) based on Cell Speed quintiles show that there is no trend in CMD as a function of Cell Speed. Box plots structured as in C. Colors as in (B). (G) Scatter plot of Cell Speed versus CMD. The observations were divided into equally sized quintiles of CMD as indicated by color scaling: blue (low, 0%-20%); purple (20%-40%); grey (intermediate, 40%-60%); dark-pink (60%-80%); pink (high, 80%-100%). (H) Box plots as in (C) showing median and variability of Cell Speed per CMD quintile. Colors as in (G). (I) Box plots as in (C) showing median and variability of Dynamic Cell Area per CMD quintile. Colors as in (G).</p