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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O10 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O10. PREDICTION OF CELL RESPONSES TO SURFACE TOPOGRAPHIES USING MACHINE LEARNING TECHNIQUES Aliaksei S Vasilevich 1 *,Shantanu Singh 2 , Aurélie Carlier 1 & Jan de Boer 1 . Laboratory for Cell Biology-inspired Tissue Engineering, Merln Institute, Maastricht University 1 , Imaging Platform, Broad Institute of MIT and Harvard 2 . *a.vasilevich@maastrichtuniversity.nl Topographical cues have been repeatedly shown to influence cell fate dramatically (Bettinger et. al., 2009). This phenomenon opens new opportunities to design the interaction between biomaterials and biological tissues in a predictable manner. Unfortunately, the exact mechanism of topographical control of cell behavior remains largely unknown. We have therefore developed a technology in our laboratory to determine an optimal surface topography for virtually any application in biomedical field. Previously we have reported that we can control cell shape by our surfaces in a predictable manner (Hulsman et.al., 2015). Here we demonstrate that we can successfully predict not only cell shape, but also cell response on protein level based on the properties of our topographies. The results of our study show that we are able to design materials for biomedical applications that require a particular cell behavior. INTRODUCTION The TopoChip, a micro topography screening platform, enables the assessment of cell response to 2176 unique topographies in a single high-throughput screen. The topographical features were randomly selected from an in silico library of more than 150 million of topographies, which were designed from algorithm that synthesized patterns based on simple geometric elements – circles, triangles and rectangles (Unadkat et al, 2011). In our previous studies, we have demonstrated that these surface topographies exert a mitogenic effect on hMSCs (Unadkat et al, 2011), as well as on cell shape (Hulsman et. al., 2015). In this paper, we show that these topographies can also be used to modulate the ALP expression in human mesenchymal stromal cells, as well as pluripotency in human induced pluripotent stem (iPS) cells. We further show that computational models can be build to predict these protein levels using surface topography parameters. METHODS Cell response to topography was captured by high-content imaging. Using image analysis and data mining methods described previously (Hulsman et.al., 2015), multiparametric “profiles” of cellular response were obtained. Multiple replicates of each topography were used to estimate the median level of a cellular response of interest – either ALP in human mesenchymal stromal cells (hMSCs), or the median number of Oct4 positive cells in population of human induced pluripotent stem cell (hIPSCs). We aimed to predict the cellular response based on surface topography parameters using machine learning methods. To learn and validate these methods (specifically, classifiers), the data were split into training and testing sets in a 3:1 proportion respectively. In the training step, we performed a 10-fold cross-validation to obtain optimal parameters for each classifier. The caret package (Kuhn M., 2008) in R (R core team, 2015) was used to perform the analysis. RESULTS & DISCUSSION In the first project, we conducted a screening on the TopoChip with hMSCs in order to find topographies that would be able to increase the ALP level, a protein that is an early marker of osteogenesis. We were able to successfully find such surfaces and confirm results experimentally (publication in preparation). To move further we decided to check how accurately we can make a prediction of ALP level in hMSCs based on topographical features. Focussing only on extreme examples, we selected 100 high- and and low-scoring topographies and used the model validation scheme described in Methods to find the most accurate binary classifier for our data set. We tested several classifiers and identified random forest as most precise, which obtained an accuracy of 96% on the held-out test set. In a second project, we aim to find a topography that will increase proliferation and pluripotency of hIPSCs. We used Oct4 as a marker of pluripotency. The screening was performed on one half of the Topochip (1000+ surfaces), which were then ranked based on the number of Oct4 positive cells. One hundred high- and low-scoring surfaces were chosen to train a classifier. Using logistic regression , we obtained 72% accuracy on a held-out test set. We used this model to predict surfaces that would increase pluripotency in hIPSCs among surfaces that were not included in the initial screening. Topographies were ranked according to their predicted probability score and top 30 surfaces were chosen for experimental validation. We found that 79% of selected surfaces were predicted accurately. In summary, the combination of our screening methods and machine learning algorithms open new avenues to design surfaces with desired properties for variable applications. Our next step will be to find a surface with maximum ALP level from our virtual library based on our screening data. REFERENCES Bettinger C J, Langer R, & Borenstein J T. “Engineering Substrate Micro- and Nanotopography to Control Cell Function.” Angewandte Chemie (International ed. in English) 48.30 (2009). Hulsman M et. al., Analysis of high-throughput screening reveals the effect of surface topographies on cellular morphology, Acta Biomaterialia, 15, (2015). Kuhn M. “Building Predictive Models in R Using the caret Package” Journal of Statistical Software, Vol. 28, (2008) R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/. (2015) Unadkat H V. et al. “An Algorithm-Based Topographical Biomaterials Library to Instruct Cell Fate.” Proceedings of the National Academy of Sciences of the United States of America 108.40 (2011). 30
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O11 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O11. ANALYSIS OF MASS SPECTROMETRY QUALITY CONTROL METRICS Wout Bittremieux 1 , Pieter Meysman 1 , Lennart Martens 2 , Bart Goethals 1 , Dirk Valkenborg 3 & Kris Laukens 1 . Advanced Database Research and Modeling (ADReM) & Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp / Antwerp University Hospital 1 ; Department of Biochemistry & Department of Medical Protein Research, Ghent University / VIB 2 ; Flemish Institute for Technological Research (VITO) 3 . * wout.bittremieux@uantwerpen.be Mass-spectrometry-based proteomics is a powerful analytical technique to identify complex protein samples, however, its results are still subject to a large variability. Lately several quality control metrics have been introduced to assess the performance of a mass spectrometry experiment. Unfortunately these metrics are generally not sufficiently thoroughly understood. For this reason, we present a few powerful techniques to analyse multiple experiments based on quality control metrics, identify low-performance experiments, and provide an interpretation of outlying experiments. INTRODUCTION Mass-spectrometry-based proteomics is a powerful analytical technique that can be used to identify complex protein samples. Despite many technological and computational advances, performing a mass spectrometry experiment is still a highly complicated task and its results are subject to a large variability. To understand and evaluate how technical variability affects the results of an experiment, lately several quality control (QC) and performance metrics have been introduced. Unfortunately, despite the availability of such QC metrics covering a wide range of qualitative information, a systematic approach to quality control is often still lacking. As most quality control tools are able to generate several dozens of metrics, any single experiment can be characterized by multiple QC metrics. Therefore it is often not clear which metrics are most interesting in general, or even which metrics are relevant in a specific situation. To take into account the multidimensional data space formed by the numerous metrics, we have applied advanced techniques to visualize, analyze, and interpret the QC metrics. METHODS Outlier detection can be used to detect deviating experiments with a low performance or a high level of (unexplained) variability. These outlying experiments can subsequently be analyzed to discover the source of the reduced performance and to enhance the quality of future experiments. However, it is insufficient to know that a specific experiment is an outlier; it is also of vital importance to know the reason. To understand why an experiment is an outlier, we have used the subspace of QC metrics in which the outlying experiment can be differentiated from the other experiments. This provides crucial information on how to interpret an outlier, which can be used by domain experts to increase interpretability and investigate the performance of the experiment. RESULTS & DISCUSSION Figure 1 shows an example of interpreting a specific experiment that has been identified as an outlier. As can be seen, two QC metrics mainly contribute to this experiment being an outlier. The explanatory subspace formed by these QC metrics can be extracted, which can then be interpreted by domain experts, resulting in insights in relationships between various QC metrics. FIGURE 1. QC metrics importances for interpreting an outlying experiment. Next, by combining the explanatory subspaces for all individual outliers, it is possible to get a general view on which QC metrics are most relevant when detecting deviating experiments. When taking the various explanatory subspaces for all different outliers into account, a distinction between several of the outliers can be made in terms of the number of identified spectra (PSM’s). As can be seen in Figure 2, for some specific QC metrics (highlighted in italics) the outliers result in a notably lower number of PSM's compared to the nonoutlying experiments. Because monitoring a large number of QC metrics on a regular basis is often unpractical, it is more convenient to focus on a small number of user-friendly, well-understood, and discriminating metrics. As the QC metrics highlighted in Figure 2 are shown to indicate low-performance experiments, these metrics are prime candidates to monitor on a continuous basis to quickly detect faulty experiments. FIGURE 2. Comparison of the number of PSM’s between the non-outlying and the outlying experiments. 31
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