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High-throughput cell-based assays

High-throughput cell-based assays

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High-throughput cell-based assays

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  1. High-throughput cell-based assays Wolfgang Huber European Molecular Biology Laboratory European Bioinformatics Institute

  2. Signaling pathways Drosophila antibacterial signalling Drosophila Toll/antifungalsignaling

  3. Interference/Perturbation tools Small compounds Full-length cDNA (over-)expression RNAi

  4. RNAi Initiation Execution He and Hannon, 2004

  5. RNAi as a loss of function perturbator degradation protein gene-sequence specific reagents (eg siRNAs) easy to make for any gene (there are caveats...) translation mRNA transcription gene living cells

  6. RNAi as a loss of function perturbator degradation protein gene-sequence specific reagents (eg siRNAs) easy to make for any gene (there are caveats...) mRNA transcription gene living cells

  7. RNAi as a loss of function perturbator gene-sequence specific reagents (eg siRNAs) easy to make for any gene (there are caveats...) mRNA transcription gene living cells

  8. C. elegans Drosophila Mammals E. coli dsRNA dsRNA siRNA T7 > 200bp > 200bp 21bp Injection and soaking Bathing Transfection Feeding bacteria Cell-culture Cell-culture Worms Precursor dsRNA Dicer siRNAs Degradation of target message RNAi experiments in different organisms

  9. Differential Expression vs Signaling Function RIP/IMD pathways RNAi phenotypes Differentially regulated genes R RIP Tak1 280 genes ~ 70 IKK Rel Targets Michael Boutros

  10. Most pathway targets are not required for pathway function RIP/IMD pathways RNAi phenotypes Differentially regulated genes R RIP Tak1 280 genes ~ 70 IKK Rel 3 Targets Michael Boutros

  11. What is a phenotype: it all depends on the assay Any cellular process can be probed. - (de-)activation of a signaling pathway - cell differentiation - changes in the cell cycle dynamics - morphological changes - activation of apoptosis Similarly, for organisms (e.g. fly embryos, worms) Phenotypes can be registered at various levels of detail - yes/no alternative - single quantitative variable - tuple of quantitative variables - image - time course

  12. Monitoring tools for automated phenotyping Plate reader 96 or 384 well plates, 1…4 measurements per well FACS; Acumen Explorer ca. 2000 x 4…8 measurements per well Automated Microscopy practically unlimited. many MB

  13. Plate reader Assays Cells are seeded on-top of pre-aliquoted siRNA pools RNAi by reverse-transfection 2x 68 384-well plates +/- Compound treatment (48h pt) 72h Cell viability (‘CellTiterGlo’ Assay) Computational analysis Secondary assays

  14. cellHTS2 workflows for analysing a cell-based assay experiment

  15. NChannelSet D Sample-ID red R Sample-ID green G Physical coordinates Sample-ID blue B Sequence Array-ID _ALL_ Target gene ID Physical coordinates assayData can contain N=0, 1, 2, ..., matrices of the same size Sequence Target gene ID Sample-ID red Sample-ID green Sample-ID blue Array ID “pheno”Data (AnnotatedDataframe)‏ featureData (AnnotatedDataframe)‏ labelDescription channelDescription labelDescription varMetaData

  16. The data Numeric values xijk i = wells (e.g. 20,000)‏ j = different reporters (e.g. 2) k = different assays (e.g. 5)‏ Metadata about wells pi = plate in which is well i ri = row (within plate) of well i ci = column (within plate) of well I siRNA sequence, target gene, .... Metadata about reporters Fluc, Rluc, ... Metadata about assays k replicate number different variants of the assay date it was done

  17. Between plate effects kth well ith plate

  18. package splots

  19. "Normalisation" xijki = wells (e.g. 20,000)‏ j = different reporters, dyes k = different assays Plate median normalisation - can use other estimators of location, e.g. mean, midpoint of shorth; or shift and scale according to values of positive and negative controls - maintains the dimensions of x

  20. Normalization: Plate effects k-th well i-th plate Percent of control Normalized percent inhibition z-score

  21. Spatial normalization B-score: two-way medianpolish before fitted row and column effects rth row cth column ith plate after Malo et al., Nat. Biotech. 2006

  22. How to estimate the normalization parameters? From which data points: • Based on the intensities of the controls • if they work uniformly well across all plates • Based on the intensities of the samples • invoke assumptions such as "most genes have • no effect", or "same distribution of effect sizes" • Which estimator: mean vs median vs shorth standard deviation vs MAD vs IQR No universally optimal answer, it depends on the data. In the best case, it doesn't matter.

  23. Estimators of location mean

  24. Estimators of scale

  25. Channel summarisation xijki = wells (e.g. 20,000)‏ j = different reporters, dyes k = different assays (log-)ratio collapses the second dimension of x

  26. Replicate Summarisation xijki = wells (e.g. 20,000)‏ j = different reporters, dyes k = different assays replicate summarization changes third dimension of x

  27. Processing steps xijki = wells (e.g. 20,000)‏ j = different reporters, dyes k = different assays normalisation of whole plate effects dim unchanged normalisation of within plate effects dim unchanged summarization of channels change 2nd dim replicate summarization change 3rd dim scoring (transformation into z-scores) can change 1st dim contrasts (as in linear models) change 3rd dim NchannelSet provides a robust and powerful infrastructure for these operations (and keeping the metadata aligned and intact)‏

  28. Zhang JH, Chung TD, Oldenburg KR, "A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays." J Biomol Screen.1999;4(2):67-73. NB: would be more efficient

  29. show example report

  30. Thanks Ligia Bras Florian Hahne Michael Boutros Thomas Horn, Tina Büchling, Dorothee Nickles, Dierk Ingelfinger Elin Axelsson Gregoire Pau Martin Morgan