NINDS Screen – inactive control compounds.

<p>NINDS Screen – inactive control compounds.</p

NINDS Screen - Negative Hits.

<p>NINDS Screen - Negative Hits.</p

Data acquisition and feature extraction.

<p>A “High Content Screening” data acquisition pipeline was developed using the <b><i>Cell</i></b> <b><i>Profiler</i></b> software package: (<b>A</b>) <i>Primary</i> <i>object</i> <i>recognition</i>. DAPI stained cell nuclei (I) are detected and classified as primary objects (II). Nuclei touching the border, as well as nuclei outside the typical diameter range are excluded from the analysis (III). (<b>B</b>) <i>Secondary and tertiary object recognition</i>. Cell borders are detected in the Phalloidin channel (I) through a propagation function, that uses the DAPI nuclei (primary objects) as seeds to propagate outwards to the region of highest intensity (cell border). Again, cells touching the border of the image are excluded from analysis (II). Tertiary objects (cytoplasm) are generated by subtracting primary from secondary objects (III). (<b>C</b>) <i>Neighbor analysis and filtering of separated cells</i>. The amount of cell-cell interactions is measured by analyzing each cell’s fraction of membrane in contact with other cells (I) as well as the number of contacting neighbors (II). A filter is applied to extract all “separated cells” (no contact with other cells) (III). (<b>D</b>) <i>Feature extraction and machine learning</i>. All separated cells are subjected to a multi-parameter feature extraction. The information as well as compressed images of all channels is stored in an SQLite library and imported into <b><i>Cell</i></b> <b><i>Analyst</i></b>. Here, machine learning algorithms are generated to automatically discriminate spread (S) from unspread (U) cells. Thereby, the algorithms are built on a subset of cells that are categorized manually. The plot displays the typical cross-validation accuracy of a 3-class classifier (using 20 rules) applied to such a subset. - Magnification: Calibration bars in A(I) are applicable to all images of A-C and correspond to 50µm.</p

High Content Screen (HCS) for cell spreading of 3T3 fibroblasts.

<p>The major steps of the screen are illustrated: (<b>A</b>) Swiss 3T3 cells are incubated with different compounds in 96-well plates on two different substrates (yellow/red). Each column contains the same compound. The outer wells are left empty. (<b>B</b>) After 1h incubation at 37°C/5% CO₂, cells are fixed with 4% paraformaldehyde in isotonic phosphate buffer and stained with DAPI (nuclei) and Phalloidin (F-actin). (<b>C</b>) 24 images are acquired per channel from each well using the 10x objective. No images are taken in the center or at the border of the well. (<b>D</b>) Image file names are being padded with meta-tags containing location and treatment information. (<b>E</b>) Using a custom developed software pipeline in <b><i>Cell</i></b> <b><i>Profiler</i></b>, cell bodies and cell nuclei are automatically detected. Phenotypic features are extracted and stored in an SQLite database, annotated with its corresponding meta-tags and linked to its compressed image files. (<b>F</b>) The SQLite database can be imported into <b><i>Cell</i></b> <b><i>Analyst</i></b>. Machine learning algorithms can be used to detect the most prominent cell features that are changed in a certain condition. Furthermore, it allows for an automated categorization of cells by their phenotypes (e.g. spread vs. unspread cells). (<b>G</b>) A data analysis pipeline was constructed in <b><i>KNIME</i></b> to automatically import, normalize and visualize the data. Scatter plots/matrices allow for outlier removals and hit selections. Pivot tables of normalized data / hit lists are generated and exported into .csv files for direct import into statistical analysis software. (<b>H</b>) All hits were validated in dose-response assays with 6 instead of 3 replicates.</p

NINDS Screen - Positive Hits.

<p>NINDS Screen - Positive Hits.</p

Data normalization and hit selection.

<p>A data analysis pipeline was developed using the <b><i>KNIME</i></b> software package. The steps of data normalization and hit selection are shown: (<b>A</b>) <i>Data</i> <i>normalization</i> <i>using</i> <i>virtual</i> <i>row</i> <i>shuffling</i>. A schematic representation of the data normalization procedure is shown for the three lower and upper plates of each experimental stack (9 plates total). First, a percentage of control (POC) normalization is used to normalize each plate to its corresponding DSMO control on ctrl substrate (1). Next, a virtual row shuffling algorithm is applied to allow for median averaging (B-Scoring): Hereby, rows are virtually being “shuffled” with corresponding rows from other plates of the experiment. It is made sure, that each row`s position on a virtual plate corresponds to its position on the original plate. Furthermore, only rows from plates in comparable stack positions are being mixed to prevent stack position effects to influence normalization (2). Finally, normalized data are reverse-shuffled to their original layout and subjected to hit selection (3). (<b>B</b>-<b>D</b>) Representative heatmaps (cytoplasm areas, increasing from green via black to red) are shown for raw data (B), POC normalized data (C) and B-Score normalized data (D). (<b>E</b>) <i>Hit Selection</i>. Hits are selected manually from scatter matrices generated in KNIME. Any compound, selected as hit in either plot is being marked yellow in all plots of the scatter matrix. All hits are automatically exported into hit lists for further analysis. Black arrowheads mark positive control (ROCK inhibitor Y-27632) on ctrl (left) and Nogo-A-Δ20 substrate (right). </p

Assay optimization and validation.

<p>To ensure assay reliability different cell culture conditions were tested: (<b>A</b>) <i>Cell</i> <i>type</i>. Graph shows cell area after one hour of cell spreading on control vs. Nogo-A-Δ20 substrate for two different 3T3 fibroblast lines (red dotted lines mark IC₅₀ of Nogo-A-Δ20). (<b>B</b>) <i>Positive control</i>. ROCK inhibitor (Y-27632) dose-response curve for 3T3 cell spreading on control (plastic) vs. Nogo-A-Δ20 substrate (10 pmol/cm²). (<b>C/D/E</b>) Representative pictures of cells incubated with either 0.1% DMSO (ctrl), 10 pmol/cm² Nogo-A-Δ20 (inhibitory substrate) or 5μM Y-27632 (positive control). (<b>F/G</b>) <i>Cell Number</i>. The amount of imaged, well separated cells (cells without contact to neighboring cells) per total cells plated is shown in (F) while the number of cells characterized as spread per total cells plated is shown in (G). (<b>H</b>) <i>DMSO toxicity</i>. A dose-response curve for increasing DMSO concentrations on cell size (cytoplasm area) is plotted. <i>All experiments were performed at least in triplicate (n=3)</i>. <i>For all graphs: standard errors of the means are shown</i>. <i>Statistical</i> <i>analysis</i> <i>was</i> <i>performed</i> <i>in</i> <b><i>GraphPad</i></b> <b><i>Prism</i><i> </i><i>6</i></b> <i>using</i> <i>an</i> <i>ordinary</i> <i>One-Way</i> <i>ANOVA</i> <i>test</i> <i>followed</i> <i>by</i> <i>a</i> <i>Tukey multiple</i> <i>comparison</i> <i>test</i> or <i>by</i> <i>using</i> <i>an</i> <i>unpaired</i> <i>Student’s</i> <i>t-test; p-values: ns>0.05; *<0.05, **<0.005, ***<0.0005, ****<0.00005</i> .</p

An open source based high content screening method for cell biology laboratories investigating cell spreading and adhesion

BACKGROUND: Adhesion dependent mechanisms are increasingly recognized to be important for a wide range of biological processes, diseases and therapeutics. This has led to a rising demand of pharmaceutical modulators. However, most currently available adhesion assays are time consuming and/or lack sensitivity and reproducibility or depend on specialized and expensive equipment often only available at screening facilities. Thus, rapid and economical high-content screening approaches are urgently needed. RESULTS: We established a fully open source high-content screening method for identifying modulators of adhesion. We successfully used this method to detect small molecules that are able to influence cell adhesion and cell spreading of Swiss-3T3 fibroblasts in general and/or specifically counteract Nogo-A-Δ20-induced inhibition of adhesion and cell spreading. The tricyclic anti-depressant clomipramine hydrochloride was shown to not only inhibit Nogo-A-Δ20-induced cell spreading inhibition in 3T3 fibroblasts but also to promote growth and counteract neurite outgrowth inhibition in highly purified primary neurons isolated from rat cerebellum. CONCLUSIONS: We have developed and validated a high content screening approach that can be used in any ordinarily equipped cell biology laboratory employing exclusively freely available open-source software in order to find novel modulators of adhesion and cell spreading. The versatility and adjustability of the whole screening method will enable not only centers specialized in high-throughput screens but most importantly also labs not routinely employing screens in their daily work routine to investigate the effects of a wide range of different compounds or siRNAs on adhesion and adhesion-modulating molecules