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Wednesday, September 20 • 9:00am - 11:30am
Session 3: Data Analysis
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The analysis of the large amount of data generated high content screening experiments represents a significant challenge and is currently a bottleneck in many small molecules and genetic screening projects.  This section is an exciting opportunity for reviewing the main challenges in interpreting complex high-content screening data, including key informatics and data analysis approaches such as image descriptors computations and classification algorithms.

9:00 - 9:30 
Computational methods for fluorescence microscopy and quantitative bioimaging
Charles Kervrann, Senior Researcher, Inria Rennes - Bretagne AtlantiqueSEPICO Project-Team 

During the past two decades, biological imaging has undergone a revolution in the development of new microscopy techniques that allow visualization of tissues, cells, proteins and macromolecular structures at all levels of resolution. Thanks to recent advances in optics, digital sensors and labeling probes, one can now visualize sub-cellular components and organelles at the scale of a few dozens nanometers to several hundreds of nanometers. As a result, fluorescent microscopy and multimodal imaging has become the workhorse of modern biology. All these technological advances in microscopy, created new challenges for researchers in quantitative image processing and analysis. Therefore, dedicated efforts are necessary to develop and integrate cutting-edge approaches in image processing and optical technologies to push the limits of the instrumentation and to analyze the large amount of data being produced.In this talk, we present image processing methods, mathematical models, and algorithms to build an integrated imaging approach that bridges the resolution gaps between the molecule and the whole cell, in space and time. The presented methods are dedicated to the analysis of proteins in motion inside the cell, with a special focus on Rab protein trafficking observed in time-lapse confocal microscopy or total internal reflection fluorescence microscopy. Nevertheless, the proposed image processing methods and algorithms are flexible in most cases, with a minimal number of control parameters to be tuned. They can be applied to a large range of problems in cell imaging and can be integrated in generic image-based workflows, including for high content screening applications.

9:30 - 10:00 
Big data approaches for computational phenotyping
Thomas Walter, MINES ParisTech 

In High Content Screening (HCS) we dispose of the technological tools to perform imaging experiments at an unprecedented scale and thus to generate extremely large and complex image data sets, that can be readily qualified as “big data”. In this context, methods from computer vision and machine learning have been instrumental to deal with the generated large-scale image data sets. After a short review of computer vision techniques for computational phenotyping for live cell imaging data, I will present recent developments in the field of spatial transcriptomics, where we aim at understanding the spatial aspects of gene expression at a large scale. This poses interesting and challenging questions for the analysis of such data: single RNAs can be automatically detected and their spatial distribution analyzed with newly designed features and machine learning methods. Importantly, as there is little prior knowledge available for this type of data, we have built a virtual cell environment in order to simulate RNA localization patterns inside cells to validate the developed methods. More generally, simulation frameworks are becoming increasingly important for computational phenotyping. Finally, I will present new approaches to analyze and computationally phenotype cells in their tissular context. Segmentation is one of the major bottlenecks in computational phenotyping of histopathology data. Here, I present a new technique for the segmentation and classification of nuclei based on deep learning. This method allows us to analyze large cohorts of patient data with respect to their cellular and tissular phenotypes and to relate these descriptors to clinical variables.

10:00 - 10:30
Active-learning strategies for high content imaging screening using complex in vitro models 
Alejandro Amador, GSK

The analysis of physicochemical properties during compound optimization is critical to identify candidate drug quality and safety issues. Without the proper biological systems in place and ADME (absorption, distribution, metabolism and excretion) characteristics, many clinical leads have failed to achieve desired target exposure, pharmacological response and/or safety margins in humans. Numerous parameters affect the prediction of the efficacy of a drug on the body. However, the persistent failures to translate promising preclinical candidates into clinical success highlight detachment from clinical practice during assay validation. In fact, much of the drug screening process is done with cells plated as two-dimensional (2D) monolayer on the bottom of microplate wells. A growing amount of work has shown that the use of three-dimensional (3D) in vitro models in combination and active learning processes present a novel and potentially high-value de-risking strategy. To help address these issues, our group is engaging into multidisciplinary collaborations to develop novel preclinical models and cell-based screening technologies to improve clinical relevance. We are designing and building an intelligent, automated platform to accelerate the identification of high value small molecule leads, and the delivery of quality candidates into clinical development. In this presentation, I will introduce a high-content imaging based 3D tumor in vitro model that our group is using for testing oncology drugs. In addition, I will talk about some of the challenges we are facing when analyzing multiparameter data of large number of images with various computational active-learning approaches.

10:30 - 11:00
The analysis of image-based CRISPR-Cas9 gene perturbation screens
Reinoud de Groot, IMLS, University of Zürich

CRISPR-Cas9 has emerged as a powerful tool for functional genomic screening. The large-scale CRISPR-Cas9 screens performed to date have used a pooled screening format, precluding image-based phenotyping of individual cells. We set out to develop methods for arrayed, image-based CRISPR-Cas9 screening. To this end, we developed a pipeline for high throughput generation of CRISPR-Cas9 targeting plasmids to construct an arrayed screening library of approximately 2200 plasmids targeting ubiquitin ligases, kinases and phosphatases. We performed an image-based screen for genes affecting the subcellular localization of a marker of the nuclear pore complex. I will discuss how we exploited the experimentally induced genetic mosaicism for the analysis of this large-scale, image-based, functional genomic screening experiment. Our data analysis strategy allowed us to identify the perturbations that cause phenotypic changes in individual cells and subsequently select the phenotypically perturbed cells for further analysis.

11:00 - 11:30
Exploring Toxicity Profiling for Drug Screening using Deep Learning Strategies
Daniel Jimenez, Centro Nacional de Investigaciones Cardiovasculares (CNIC)

Toxicity is a major factor of failure in drug development, causing costly withdrawals of drugs from the market. Efficienct drug discovery could be greatly improved if compounds with cytotoxic characteristics were identified during primary screening campaigns. Although apoptotic & necrotic processes involve dramatic changes in cell morphology, these have never been exploited for systematic and quantitative assessment of cellular toxicity due to lack of methods to identify them in a reproducible manner. The development of high-content imaging platforms has allowed the incorporation of complex toxicity counter-screens. However, this requires expensive and lengthy labelling of cytotoxicity specific reporters. There is thus an urgent need to develop cost-effective methods for toxicity assessment qualified for high throughput screening (HTS). We hypothesize that toxicity could be detected without toxicity specific labelling by using state of the art computer vision methods, thus reducing significantly the cost of drug screens. In this work we develop a framework to determine cell cytotoxicity status as a HTS readout based on the analysis of nuclear fluorescence microscopy images by using deep learning strategies. Multiple cell-based assays including concentration-response curves for drugs with different cytotoxic effects are used for training and validation of the system. Preliminary results support our hypothesis, reporting high correlations between toxicity predictions, dose-response curves, and well-established cytotoxicity reporters; suggesting that the automatic pattern recognition performed by deep neural networks is able to detect differential structural and nuclear-based features related with the cytotoxic state of the cells. Therefore, this framework has the potential to become a new scientific breackthrough for toxicity assessment in drug screening.

Vendor Snapshots: 
  • GENEDATA, Presenter: Matthias Fassler


avatar for Alejandro Amador

Alejandro Amador

Scientific Leader - Platform Biology Automation, GSK
Alejandro Amador studied biology at the Autonomous University of Barcelona (Barcelona, Spain). He obtained his PhD in the laboratory of Dr. Mara Dierssen at the Center for Genomic Regulation (CRG, Barcelona, Spain), where he worked with mouse models of Panic disorder and... Read More →
avatar for Reinoud de Groot

Reinoud de Groot

Reinoud de Groot studied biology at Utrecht University, the Netherlands. He joined the lab of prof. H.C. Korswagen to investigate the mechanisms of Wnt signaling and Wnt secretion and obtained his PhD in 2014. He subsequently joined the lab of prof. L. Pelkmans at the University of... Read More →
avatar for Charles Kervrann

Charles Kervrann

Inria Senior Researcher, Inria Rennes - Bretagne Atlantique
Charles Kervrann received the M.Sc. (1992), the PhD (1995) and the HDR (2010) in Signal Processing and Telecommunications from the University of Rennes 1, France. From 1997 to 2010, he was researcher at the INRA Applied Mathematics and Informatics Department (1997-2003) and he... Read More →
avatar for Thomas Walter

Thomas Walter

Thomas Walter has been working in the field of biomedical image analysis for more than 15 years. His most visible scientific contributions have been in the field of High Content Screening (HCS). He has pioneered methods in the field of cellular phenotyping for live cell imaging d... Read More →

Wednesday September 20, 2017 9:00am - 11:30am
Rosales I Courtyard by Marriot Madrid Princesa Hotel