Exploiting Cell-To-Cell Variability To Detect Cellular Perturbations

<div><p>Any single-cell-resolved measurement generates a population distribution of phenotypes, characterized by a mean, a variance, and a shape. Here we show that changes in the shape of a phenotypic distribution can signal perturbations to cellular processes, providing a way to screen for underlying molecular machinery. We analyzed images of a Drosophila S2R+ cell line perturbed by RNA interference, and tracked 27 single-cell features which report on endocytic activity, and cell and nuclear morphology. In replicate measurements feature distributions had erratic means and variances, but reproducible shapes; RNAi down-regulation reliably induced shape deviations in at least one feature for 1072 out of 7131 genes surveyed, as revealed by a Kolmogorov-Smirnov-like statistic. We were able to use these shape deviations to identify a spectrum of genes that influenced cell morphology, nuclear morphology, and multiple pathways of endocytosis. By preserving single-cell data, our method was even able to detect effects invisible to a population-averaged analysis. These results demonstrate that cell-to-cell variability contains accessible and useful biological information, which can be exploited in existing cell-based assays.</p></div

Well-to-well and cell-to-cell variability.

<p>(<b>A,B</b>) Population distributions (histograms) of features I3 (A) and G3 (B), for three negative control wells from a single slide. Hollow bars show raw distributions; solid bars show the data when distributions are normalized to have zero mean and unit variance. (<b>C,D</b>) Heat maps of population-averaged mean values for features I3 (C) and G3 (D). The positional effects in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090540#pone-0090540-g002" target="_blank">Figure 2C</a> likely arise from labeling and imaging artifacts. (<b>E</b>) ANOVA F-statistic for inter-row variance versus within-row variance of distribution means (x-axis) or skewnesses (y-axis), for all 27 features. Each point shows the median F-statistic over 84 slides; intensity features are colored orange, geometric features are colored purple. Data for each slide are shown in Figure S1B,C in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090540#pone.0090540.s001" target="_blank">File S1</a>. (<b>F</b>) Cumulative distributions of feature I3, from a negative control well (grey) and a positive control well (Arf1; blue). The left panel shows raw data; the right panel shows that cumulative distributions are still distinguishable after normalization.</p

Statistical performance of shape-based scoring.

<p>(<b>A</b>) The number of genes that occur two or more times above each Z-score threshold, for three representative features. The green curve shows the number of genes selected from the screen; the grey band represents the upper and lower limits of number of genes selected from 1000 randomly permuted datasets. We used a Z-score cutoff of 3 (red line) to select hits. (<b>B</b>) For feature I3, distribution of Z-scores for negative control wells (top panel), and positive control wells (second panel: Shibire; third panel: Arf1; fourth panel: Sec23). At a given Z-score threshold (red line), the false-positive rate (FP  =  <i>α</i>) is the fraction of negatives above threshold (solid grey bars), while true-positive rate (TP  =  1-<i>β</i>) is the fraction of positives above threshold (hollow blue bars). (<b>C</b>) For feature I3, the upper left panel shows the fraction of genes occurring zero, one, two, or three times above a Z-score threshold of 3; circles show actual data, bars show the inferred composition of each bin, in terms of positives (green) and negatives (grey). The lower-left panel shows the fraction of positives in each bin; genes occurring two or more times above threshold are strongly enriched in positives. The two right panels show the performance when hits are selected from a single measurement rather than using triplicates. (<b>D</b>) The grey band shows the range of inferred FP rates for 25 features (excepting features G11 and G15 for which the inference procedure fails to converge); the black line shows the mean of the measured FP rates for the same features. (<b>E</b>) Inferred TP rates. Green bands show the range of inferred true positive rates (1–<i>β₀</i> ± <i>σ</i>; see Methods<b>:</b> Assessing statistical power from triplicate data) as a function of inferred false positive rates (<i>α</i>); blue lines show the observed TP and FP rates among positive control genes (light: Shibire; medium: Sec23; dark: Arf1). The box to the right of each graph shows the inferred average TP rate (1–<i>β₀</i>) at FP  =  0.1; the solid dot shows the performance using normalized distributions, the hollow dot shows the performance using un-normalized distributions.</p

Comparing shape-based scoring to other scoring methods.

<p>(<b>A</b>) The difference in the true-positive rate TP_N–TP_U represents the increase in performance derived from normalization. Upper panels: black histograms show the distribution of TP_N–TP_U values for 25 features (excepting G11 and G15); colored bars represent the fraction of intensity (orange) and geometric (purple) features in each bin. The left panel shows the observed performance for positive controls; the right panel shows the inferred performance over all genes. Lower panels: same as upper, but now the normalization strategy (TP_N) is compared to partial normalization (TP­_P), when bins are normalized to have the average mean and variance of their eight nearest neighbors. (<b>B</b>) The ‘traditional’ Z-score is defined based only on the mean values of feature distributions. The figure shows the cumulative distributions of traditional Z-score values for genes that have been validated as hits for intensity features (orange) or geometric features (purple) in an independent experimental assay. Less than 10% of these Z-scores have an absolute value greater than unity.</p

Results and biological significance.

<p>(<b>A</b>) We validated hits using an independent experimental assay based on the population-averaged mean value of each phenotype <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090540#pone.0090540-Gupta1" target="_blank">[14]</a>. We carried out this measurement both for hits as well as for a number of non-hits (genes with below-threshold Z-scores). The y-axis shows the fraction of original hits validated (FP  =  0.1); the x-axis shows the fraction of sub-threshold genes validated. Each dot gives the result for a single feature type; hits for features G11 and G15 were not included in the secondary measurement. Horizontal and vertical dotted lines show the FP rate. The distinction between hits and sub-threshold genes was based on shape-based scoring alone, but the former are detected at a much higher rate than the latter using the population-average-based assay. This demonstrates a strong correlation between the ability of a gene to influence the mean value of a phenotype and the shape of a phenotypic population distribution. (<b>B</b>) Relationships of hit subsets to cell density. We show the cell number per imaging field as a vertical histogram. The median (horizontal line), mean (box), and percentiles (5%, 25%, 75%, 95%) of the cell number distribution are overlaid. Histograms are separately shown for negative control wells, all test wells, and for fluid uptake, Transferrin uptake, and nuclear morphology hits. (<b>C</b>) Area-proportional Venn diagram of hits that influence fluid-phase uptake (F), Transferrin-receptor-mediated uptake (T), or nuclear and cell morphology (N). Of the 26 genes that influence cell size, 21 which do not influence other features have been omitted. Numbers give the sizes of non-overlapping subsets. The total number of hits is 1051 (shown) + 21 (not shown). (<b>D</b>) Functional enrichment. We annotated genes according to the Gene Ontology (GO) ‘cellular component’ classification system, using only the most specific term for each gene. We used the one-tailed Fisher’s exact test to determine an enrichment p-value for each teach GO term among the 1072 hits, given its background occurrence among the 7216 RNAi probes. To correct p-values for multiple hypotheses, we used 1000 simulated datasets in which GO terms had been randomly permuted. The table shows the seven GO terms with corrected p-values < 0.1, along with the observed and expected number of genes among the seven non-overlapping gene subsets of the Venn diagram.</p

An image-based RNAi screen for endocytic and cell morphological features.

<p>(<b>A</b>) A single <i>Drosophila</i> S2R+ cell, fixed and imaged at 20X and 0.75 NA; the scale bar is 3 µm. FITC-Dextran (green) labels the GEEC pathway responsible for fluid-phase uptake; Alexa568-Transferrin (red) labels the clathrin-dependent receptor-mediated endocytic pathway; the Alexa647-Okt9 antibody (blue) labels steady-state cell surface levels of the Transferrin receptor; the nucleus (not shown) was imaged with a Hoechst stain. Region 1: pure GEEC endosome; Region 2: pure Transferrin endosome; Region 3: colocalization signature, marking a heterotypic fusion product between the two types of endosomes; Region 4: surface cluster of Okt9. (<b>B</b>) Schematic representation of the cell from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090540#pone-0090540-g001" target="_blank">Figure 1A</a>. (<b>C</b>) Schematic representation of intensity features (orange), which track the cell-averaged intensity of the various fluorescent labels; and geometric features (purple), which track the sizes and shapes of the cell, of the nucleus, and of endosomes. (<b>D</b>) 27 single-cell features. The two rows correspond to intensity and geometric features; each column relates to individual endocytic pathways or cell-morphological features. See Table S1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090540#pone.0090540.s001" target="_blank">File S1</a> for detailed feature descriptions. (<b>E</b>) The screen was carried out on glass slides printed with 300 wells in a 10×30 format, each well containing dsRNA targeted against different genes. Colors represent negative (black) or positive (blue) control wells, while white represents test wells. Details of the image analysis and the experimental conditions are provided in the companion paper <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090540#pone.0090540-Gupta1" target="_blank">[14]</a>.</p

N-Cadherin Relocalizes from the Periphery to the Center of the Synapse after Transient Synaptic Stimulation in Hippocampal Neurons

<div><p>N-cadherin is a cell adhesion molecule which is enriched at synapses. Binding of N-cadherin molecules to each other across the synaptic cleft has been postulated to stabilize adhesion between the presynaptic bouton and the postsynaptic terminal. N-cadherin is also required for activity-induced changes at synapses, including hippocampal long term potentiation and activity-induced spine expansion and stabilization. We hypothesized that these activity-dependent changes might involve changes in N-cadherin localization within synapses. To determine whether synaptic activity changes the localization of N-cadherin, we used structured illumination microscopy, a super-resolution approach which overcomes the conventional resolution limits of light microscopy, to visualize the localization of N-cadherin within synapses of hippocampal neurons. We found that synaptic N-cadherin exhibits a spectrum of localization patterns, ranging from puncta at the periphery of the synapse adjacent to the active zone to an even distribution along the synaptic cleft. Furthermore, the N-cadherin localization pattern within synapses changes during KCl depolarization and after transient synaptic stimulation. During KCl depolarization, N-cadherin relocalizes away from the central region of the synaptic cleft to the periphery of the synapse. In contrast, after transient synaptic stimulation with KCl followed by a period of rest in normal media, fewer synapses have N-cadherin present as puncta at the periphery and more synapses have N-cadherin present more centrally and uniformly along the synapse compared to unstimulated cells. This indicates that transient synaptic stimulation modulates N-cadherin localization within the synapse. These results bring new information to the structural organization and activity-induced changes occurring at synapses, and suggest that N-cadherin relocalization may contribute to activity dependent changes at synapses.</p> </div

Figure S1 from CEP19 cooperates with FOP and CEP350 to drive early steps in the ciliogenesis programme

Primary cilia are microtubule-based sensory organelles necessary for efficient transduction of extracellular cues. To initiate cilia formation, ciliary vesicles (CVs) are transported to the vicinity of the centrosome where they dock to the distal end of the mother centriole and fuse to initiate cilium assembly. However, to this date, the early steps in cilia formation remain incompletely understood. Here, we demonstrate functional interplay between CEP19, FOP and CEP350 in ciliogenesis. Using three-dimensional structured-illumination microscopy (3D-SIM) imaging, we mapped the relative spatial distribution of these proteins at the distal end of the mother centriole and show that CEP350/FOP act upstream of CEP19 in their recruitment hierarchy. We demonstrate that CEP19 CRISPR KO cells are severely impaired in their ability to form cilia, analogous to the loss of function of CEP19 binding partners FOP and CEP350. Using GFP-tagged deletion constructs of CEP19, we show that the C-terminus of CEP19 is required for both its localization to centrioles and for its function in ciliogenesis. Critically, this region also mediates the interaction between CEP19 and FOP/CEP350. Interestingly, a morbid obesity-associated R82* truncated mutant of CEP19 cannot ciliate nor interact with FOP and CEP350, indicative of a putative role for CEP19 in ciliopathies. Finally, analysis of CEP19 KO cells using thin-section electron microscopy revealed marked defects in the docking of CVs to the distal end of the mother centrioles. Together, these data demonstrate a role for the CEP19, FOP and CEP350 module in ciliogenesis and the possible effect of disrupting their functions in ciliopathies

Figure S4 from CEP19 cooperates with FOP and CEP350 to drive early steps in the ciliogenesis programme

Primary cilia are microtubule-based sensory organelles necessary for efficient transduction of extracellular cues. To initiate cilia formation, ciliary vesicles (CVs) are transported to the vicinity of the centrosome where they dock to the distal end of the mother centriole and fuse to initiate cilium assembly. However, to this date, the early steps in cilia formation remain incompletely understood. Here, we demonstrate functional interplay between CEP19, FOP and CEP350 in ciliogenesis. Using three-dimensional structured-illumination microscopy (3D-SIM) imaging, we mapped the relative spatial distribution of these proteins at the distal end of the mother centriole and show that CEP350/FOP act upstream of CEP19 in their recruitment hierarchy. We demonstrate that CEP19 CRISPR KO cells are severely impaired in their ability to form cilia, analogous to the loss of function of CEP19 binding partners FOP and CEP350. Using GFP-tagged deletion constructs of CEP19, we show that the C-terminus of CEP19 is required for both its localization to centrioles and for its function in ciliogenesis. Critically, this region also mediates the interaction between CEP19 and FOP/CEP350. Interestingly, a morbid obesity-associated R82* truncated mutant of CEP19 cannot ciliate nor interact with FOP and CEP350, indicative of a putative role for CEP19 in ciliopathies. Finally, analysis of CEP19 KO cells using thin-section electron microscopy revealed marked defects in the docking of CVs to the distal end of the mother centrioles. Together, these data demonstrate a role for the CEP19, FOP and CEP350 module in ciliogenesis and the possible effect of disrupting their functions in ciliopathies

N-cadherin has a spectrum of localization patterns at the synaptic cleft.

<p>(<b>A</b>) N-cadherin is localized between the pre- and post-synaptic compartments, represented by vGlut1 and PSD95 respectively. (<b>B</b>) N-cadherin is localized at or adjacent to the active zone, represented by bassoon. <i>Arrows</i>, N-cadherin puncta associated with synapses; <i>arrowheads</i>, N-cadherin puncta not associated with synapses. (<b>C</b>) N-cadherin localization at synapses varies from punctate, often flanking one side of the synapse, to uniform along the synaptic cleft. (<b>D</b>) Classification of N-cadherin localization patterns relative to bassoon into five categories (single puncta, double puncta, cleft with puncta, cleft, and round(bassoon)/puncta). Representative images and schematics of the five different N-cadherin localization patterns. (<b>E</b>) Percentage of synapses (mean±s.e.m) in 17-20 DIV hippocampal neurons in each N-cadherin pattern category. n=7 experiments, ≥77 synapses per experiment, 650 synapses total. p<0.0001, one-way ANOVA, Tukey’s post-test (p<0.01 for single puncta vs. double puncta; p<0.01 for single puncta vs. cleft; p>0.05 for single puncta vs. cleft with puncta; p<0.001 for single puncta vs. round/puncta). (<b>F</b>) Percentage of synapses (mean±s.e.m) in each N-cadherin pattern category in 11 DIV hippocampal neurons compared to matched cultures at 17-20 DIV. N-cadherin at synapses from neurons at 11 DIV is distributed more evenly along the synaptic cleft and less as puncta compared to synapses from neurons at 17-20 DIV. Two-way ANOVA with matched values and Bonferroni post-test. n=2 experiments, ≥103 synapses per experiment.</p