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

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

RHenriques FOM2018 Live- and Fixed-Cell Super-Resolution Microscopy Enabled by Open-Source Analytics in ImageJ.pdf

<div> <div> <div> <p>ImageJ is one of the main platforms for algorithms enabling super-resolution microscopy approaches that depend on an analytical step. In this tutorial, I have given an overview of how ImageJ-based image analysis is being employed to generate, qualify and quantify super- resolution microscopy data. I have focused on super-resolution methods that are purely enabled by analysis of imaging data without the optical modification of microscopes, such as Single Molecule Microscopy methods (e.g.: PALM, STORM and DNA-PAINT) and the recently developed Super-Resolution Radial Fluctuations (SRRF) approach [1] developed by our laboratory. Throughout the tutorial, I have given walkthrough examples of how to acquire and analyse data with these methods, as well as discuss how to optimise image quality [2] and discuss pitfalls. The tutorial was setup so that participants will be able to quickly translate the discussed approaches into their own research. </p><p><br></p><p> </p><div> <div> <div> <ol> <li> <p>Gustafsson, N., Culley, S., Ashdown, G., Owen, D. M., Pereira, P. M. & Henriques, R. Fast live-cell conventional fluorophore nanoscopy with ImageJ through super- resolution radial fluctuations. Nat. Commun. 7, 12471 (2016). </p> </li> <li> <p>Culley, S., Albrecht, D., Jacobs, C., Pereira, P. M., Leterrier, C., Mercer, J. & Henriques, R. NanoJ-SQUIRREL: quantitative mapping and minimisation of super- resolution optical imaging artefacts. bioRxiv (2017). doi:10.1101/158279 </p> </li> </ol> </div> </div> </div> </div> </div> </div

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

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

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

Role of lysosomal genes.

<p>(A) Network map depicting known and predicted interactions (green lines: genetic; blue lines: physical; brown lines: predicted based on conserved data) between the ‘Granule group’ set of eye colour mutants (pink) and selected hits (gray). In this network, genes encoding Carnation (<i>car</i>; the fly homolog of VPS33), Deep orange (<i>dor</i>), Carmine (<i>cm</i>) and Rab7 were identified with roles in CG endocytosis in this study (denoted by black asterisks), while White (<i>w</i>) depletion affected at least one Tf pathway feature (white asterisk). (B) Localization of Carnation on early fluid endosomes. <i>Drosophila</i> S2R+ cells were pulsed with TMR-Dextran for two minutes and fixed and labeled with antibodies to Carnation (αCar). Micrographs show a representative cell imaged in two channels and a pseudo colour merge image (labeled TMRdex and αCar), in red, green and merge respectively). Carnation (green) is seen enriched on peripheral, small, early fluid endosomes (red). Three examples of such endosomes (white arrows in merge panel) are shown in the magnified inset. (C) Fluorescent micrographs depict the levels of fluid uptake in representative S2R+ cells treated with dsRNA against <i>car</i> (first lower panel) or <i>syx1A</i> (last lower panel) or in hemocytes from <i>car¹</i> mutant flies (middle lower panel), with their respective controls (upper panels). Bar graph represents mean and SD of normalized fluorescent integrated intensity per cell from 2–3 experiments, with 100–150 cells per treatment (S2R+ cells) or 40 cells per genotype (hemocytes). (D) Representative fluorescent micrographs depict fluid uptake measured in hemocytes as in (C), in flies that were: homozygous for a mutant allele of <i>car</i> (<i>car¹</i>); a hetero-allelic combination of <i>car¹</i>/+;<i>syx1</i>/+;or wild type (CS). Also tested were flies heterozygous for <i>syx1</i>/+ and <i>car¹</i>/+. Bar graph represents mean and SD of normalized fluorescent integrated intensity per hemocyte from 2–3 experiments with 40 cells per genotype. (E) Representative micrographs show human AGS cells treated with control siRNA or siRNA to hSYX1A and hVPS33A/B and pulsed with FITC-Dextran for 5 min. Right panel - Bar graphs show population averaged mean fluorescence intensity uptake per cell (representative experiment with n>50 cells per replicate, 2 replicates). Scale bar in (B–E) main panel = 5 µm, inset = 1 µm. Double asterisks denote significance <i>p</i> values lower than 0.01 with the Student's T-Test.</p

Endocytic phenotypes in mutant primary hemocytes from <i>Drosophila</i>.

<p>(A–D) dsRNA treated S2R+ cells phenocopy corresponding allelic mutants in primary hemocyte cultures in a secondary assay. Scatter plots (A, B) show normalized fold change in fluorescence intensity of dextran that was pulsed (A) or chased (B) in S2R+ cells treated with different dsRNAs (y axis) or in hemocytes (x axis) from the corresponding mutant flies. In all cases, representative values were normalized to those from negative controls (CS hemocytes or zeo dsRNA treated S2R+ cells) and are plotted as mean± SEM. (n>30 for hemocyte assays, n>200 for S2R+ assays in all cases). For the chase assay in (B), we utilized <i>dor⁴</i> and <i>car¹</i> mutant hemocytes as positive controls (shown in light blue; Sriram et al., 2003). (C) Representative micrographs of hemocyte cultures from flies carrying hypomorphic alleles of <i>vps35</i>, <i>epac</i>, <i>α-cop</i> and <i>CG1418</i> assayed as in (B). (D) Summary of the experiment in (A–B) displaying statistically significant (Student's T-test, p<0.05) changes in uptake/retention of mutant hemocytes or gene-depleted S2R+ cells as colour coded maps. Scale bar in (C) = 5 µm.</p

Quantitative profiling of two endocytic routes at single cell resolution.

<p>(A) Experimental workflow outline for cell seeding, transfection and multiplex endocytic assays to obtain multifeature data across 7131 gene depletions. The entire procedure was performed on a cell array (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100554#pone.0100554.s001" target="_blank">Figure S1A</a>; details in SOM) and the positions of negative and positive (dsRNA against <i>sec23</i>, <i>arf1</i>, <i>shi</i>) controls are highlighted in their respective colours. (B) Table grouping the 27 quantitative features into categories. The top half of the table contains direct measurements of intensity, while the lower half contains geometric parameters of the cell, endosomes and nucleus. Various measurements are made from each fluorescent channel, including those utilizing different pixel radii for local background subtraction while detecting endosomes. (C) Representative brightfield (bf) and fluorescent micrographs of a field of view of individual cells (zoomed in insets) labeled with four different fluorescent probes: Hoechst; FITC-Dextran (Fdex); Alexa568-Tf (Tf); Alexa647-αOkt9 (Okt9); (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100554#pone.0100554.s018" target="_blank">Methods S1</a> for details). The psuedocolour merge image is a composite of the Fdex (green), TfR (red) and Okt9 (blue) channels. Scale bar = 15 µm; inset = 3×. (D) Grayscale heatmap representing the fraction of four control genes (<i>arf1</i> (<i>arf79f</i>); <i>shi</i>; <i>sec23</i>; <i>chc</i>) picked up as hits (above a Z-score threshold of 3) across all 27 features in the entire dataset. Higher values on the grayscale bar denote higher pick-up rates. The features with higher pick-up rates correspond to the known endocytic roles of these genes.</p