Supplementary Discussion:

Transcription

1 Supplementary Discussion: I. Controls: Some important considerations for optimizing a high content assay Edge effect: The external rows and columns of a 96 / 384 well plate are the most affected by evaporation during incubations at 37ºC, leading to solutes in the media reaching concentrations different than in the internal positions. This might affect the behaviour of the cells. In order to minimize this effect, we prefer to exclude the external rows and columns from the screening. As an additional measure, we fill them with the same media as the other wells. Other possible alternatives include using breathable foils to cover the plate, addition of water to the affected wells or using statistical correction 1. Needless to say, every approach needs to be validated before it is used in a screening campaign. Positioning of controls: Another important factor in designing an optimal layout for chemical / RNAi screening is the positioning of the negative and positive controls in the assay plate. The negative and positive controls reflect the system states in its basal and perturbed conditions, respectively. Hence to capture these states in a robust manner, it is important to have as many replicates of these conditions as possible. The number of replicates is an empiric choice. In our case, we prefer to have a larger representation of the negative controls in our assay plates since data normalizations are done with respect to them. The spatial distribution of the negative controls in the plate is also an important factor since they can identify artefacts associated with specific rows or columns arising from a fault in automatic dispensing. Our experience shows that plate effect could be partially compensated during normalization procedures. For this, the best layout requires a ring shaped distribution of the negative controls in the outer perimeters of the plate plus few control points in its center (Layout 1). Since this may not be practical at all times, this could be reduced to a U shaped distribution of the negative controls (Layout 2) or to two strips of negative controls (Layout 3). In the case of our endocytosis chemical screen, we used the layout 3. We had AG-1478 and Nocodazole as the positive controls and DMSO as the negative control. (AG-1478 is a specific inhibitor of EGF receptor tyrosine kinase and affects mostly EGF trafficking, whereas Nocodazole interferes with the polymerization of microtubules and affects both EGF and Tfn trafficking.) The two positive controls are used in triplicates wells in column 4 while the rest of the control wells are used for DMSO treatment. We stress again that before embarking on a screening campaign, pilot experiments should be performed to estimate plate positioning-effects and make a trade-off between the number of controls required and the necessity to correct plate positioning-effects.

2 Layout 1: An ideal ring-like distribution of the negative controls around the experimental wells. Layout 2: An alternative U shaped distribution of negative controls. Layout 3: Another alternative of multiple strips of negative controls. For Layouts 1-3, N Con refers to the negative controls, P Con refers to the positive controls and Lib refers to the compounds from the library.

3 II. Some generic metrics used for the quality control of high-content screening datasets HeLa endocytosis assay Number of images per well: 12 Mean Number of cells per (Opera) view-field: 30 Images with number of cells <5 or >65 were discarded Number of images per well: 12 Correlations between biological replicates: Mycobacteria assay Correlations between biological replicates: 0.44 Number of images per well: 30 Mean Number of cells per (Opera) view-field: 30 Images with number of cells <5 or >65 were discarded Number of images per well: 30 Correlations between biological replicates: The correlation between biological replicates is an important criterion for assessing the robustness of the assay. The correlation between biological replicates was lower in case of the Mycobacteria assay as it involved the use of human donors primary cells and bacterial infections, which are important sources of variations. III. Choosing the optimal number of parameters for the multi-parametric profile The common way for comparison of two phenotypes is correlation, which considers the phenotypic trait independently of its strength 2. The main goal of hit selection is the discrimination of true positive (i.e. correlated) phenotypes. The selectivity of discrimination depends on the probability to get a correlation above the threshold value x for a true-noncorrelated phenotype with n independent parameters by matter of chance. Following the geometrical reasoning of the classical paper by Fisher 3, the p-value of sample correlation for non-correlated phenotypes (probability of false-positive) is: p value ( x) = arccos( x) n 2 sin ( α) 0 π 2 n 2 0 sin ( α) dα dα, where x Pearson correlation between phenotypes, n - number of independent parameters in the phenotypic profile.

4 The p value calculation is summarized in the following table (significant values [p<0.05] are highlighted in yellow): x/ n Hence, the statistical confidence increases with increasing the number of parameters, assuming the parameters are independent of each other. If the parameters are partially dependent, then the effective number of independent parameters may be used instead of the total number of parameters. A crude estimation of the effective number of independent parameters n is given by the formula: 2 ( 1 ) n = n ρ, where n is the number of parameters and ρ is the mean correlation between parameters. IV. Covariation The parameter spaces of biological assays are, in general, non-orthogonal and unequally scaled. Generalization of Euclidian distance in the multi-dimensional non-orthogonal space is called the Mahalanobis distance ( which is given by: (( ) ) 1 T 1 2 ( ) d = x y Σ x y, where x and y are multi-dimensional vectors (profiles) and Σ is the covariation matrix. In the special case of an equally scaled orthogonal space, the covariance matrix becomes an identity matrix, i.e. it has 1 on diagonal and zero on off-diagonal elements: Σ=

5 Covariation matrix could be calculated on the basis of a training dataset that is non-biased. This could be the total reference dataset (assuming that majority of the reference dataset is neutral) or a dedicated negative control (such as mock, scrambled sirna, etc). MotionTracking offers to choose either estimated covariance matrix from the reference dataset or assign it to identity matrix. In the former case, the mask is used to select experimental conditions, which will be used for covariation estimation. The special symbol asterisk ( * ) means wildcard. For example: * means: all dataset. MOCK* means: only lines where condition name begins on MOCK. IMPORTANT: The estimated covariation matrix is singular if the size of the training dataset is smaller than the number of parameters. This compromises the calculation of Mahalanobis distance resulting in erroneous results. If one wants to estimate non-equality of parameters scaling but is are convinced that offdiagonal elements (correlation of parameters) are a result of noise, one can force to zero the off-diagonal elements of covariation matrix by selecting this option. V. Data normalization using in MotionTracking The normalization of data is the conversion of the original parameter values into z-scores relative to the negative control: z = x µ σ where x is parameter value, µ is mean value of the negative controls, σ is standard deviation of the negative controls We provide an example of a normalized dataset in comma-separated format (csv) (Supplementary Data 3) derived using the normalization procedure in the MotionTracking software. The content of data is described in [Collinet et al, 2010] 4. Normalization protocol: 1. Import data from the csv file into the set analysis table of MotionTracking by clicking menu Statistic->Batch Statistic->Open Batch Statistic Window:

6 Result of the import is presented below: 2. In the Batch Statistic Window, select the menu Process Data->Normalize->Normalize: 3. In the popup Naming for Normalization window, choose Gene Name and press the OK button: 4. In the Normalize window, set the parameters for the negative control selection using the following steps:

7 a. Choose normalization relative to Control b. Do not use normalization by group, unless the dataset has clear group marking (for details see the MotionTracking manual). c. Choose a method for Control calculation. Possible options are Mean, Median, Mode and Simple Mean. Since imported data have no SEM, there is no N xi 2 i= 1 σ i 1 N difference between Mean ( µ = ) and Simple Mean ( µ = N 1 xi N ). i= 1 2 σ d. Press OK button. 5. In the Naming of Control windows, choose Gene Name and press button OK : i= 1 i 6. A new window containing the total list of names will appear. Select the name of the negative control and click OK :

8 7. In the window Statistics List for Normalization select the parameters that have to be normalized and click the OK button: 8. The result of Normalization will appear in the Image Set Analysis window:

9 References 1. Fava et al., High-Content Phenotypic Cell-Based Assays, Imaging Cellular and Molecular Biological Functions Principles and Practice, 2007 Springer 2. Maulik U., Bandyopadhyay S., Mukhopadhyay A., Multiobjective Genetic Algorithms for Clustering, Springer-Verlag, Fisher R., Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Indefinitely Large Population, Biometrika, v.10(4), pp , Collinet C et al., Systems survey of endocytosis by multiparametric image analysis. Nature. 464, (2010). 5. Sundaramurthy V et al., Integration of chemical and RNAi multiparametric profiles identifies triggers of intracellular mycobacterial killing. Cell Host Microbe. 13, (2013).