10:30-10:35 Chairperson’s Opening Remarks
10:35-11:00 High-Content Imaging in Oncology Discovery: Identification and Characterization of Novel Targets in Cancer Stem Cells
Jonathan Low, Ph.D., Post-Doctoral Scientist, Cancer Cell Growth and Survival, Lilly Corporate Center
Although the cycling of eukaryotic cells has long been a primary focus for cancer therapeutics, recent advances in imaging and data analysis allow even further definition of cellular events as they occur in individual cells and cellular subpopulations in response to treatment. High-content imaging (HCI) has been an effective tool to elucidate cellular responses to a variety of agents, however, these data were most frequently observed as averages of the entire captured population, unnecessarily decreasing the resolution of each assay. Here we dissect the eukaryotic cellular subpopulations in response to treatment using HCI in conjunction with unsupervised K means clustering. We first generate distinct phenotypic fingerprints for each major cell cycle and mitotic compartment and use those fingerprints to characterize chemotherapeutic agents. We determine that the cell cycle arrest phenotypes caused by these agents are similar to, though distinct from, those found in untreated cells both in vitro and in vivo, and that these distinctions frequently suggest the mechanism of action. Further, we demonstrate the power of this technique to identify novel targets and detect the differential effects of target knockdown on cancer stem cells through the use of shRNA libraries. High-content data are then integrated with additional discovery tools to link phenotypic changes with cellular pathways. HCI analysis of imaging data, obtained from individual cells under all of these research conditions, grouped into cellular subpopulations, and multiplexed with additional tools represents a powerful method to discern both cellular events and treatment effects.
11:00-11:25 Characteristics and Re-Programming of Breast Cancer Stem Cells
Fredika M. Robertson, Ph.D., Professor, Department of Experimental Therapeutics; Director, Translational Research, The Morgan Welch Inflammatory Breast Cancer Research Program, The University of Texas M.D. Anderson Cancer Center
Very aggressive tumor types contain a high percentage of cells defined as cancer stem cells (CSCs) with characteristics similar to those of embryonic stem cells including a slow turnover time that can be imaged and quantitated by their retention of the nucleoside analog ethynyl deoxyuridine (EDU). CSCs form 3-dimensional (3D) tumor spheroids that differentially express specific surface markers including stage specific embryonic antigens 1 and 4 (SSEA1/4), CD133, and CD44+/CD24-/low. CSCs have characteristic patterns of gene expression of molecules in signaling pathways that regulate survival, self-renewal, pleuripotency, and multi-drug resistance. Agents that can either stimulate the normally quiescent CSCs to re-enter the cell cycle or agents that target transcription factors regulating self renewal and survival result in re-programming CSCs for their elimination. The effects of exposure to these agents that can re-program activities of CSCs will be discussed in the context of image-based analysis.
11:25-11:40 Illuminating your Stem Cell Research with the IN Cell Analyzer 2000: A focus on Cell Colony Analysis
Stephen Minger, Ph.D., Head Research & Development, Cell Technologies, GE Healthcare
Understanding Stem Cell colony growth, status and progression through differentiation routes has become a major factor in harnessing the potential of Stem Cell technologies for the future. High Content Analysis of whole well images from the IN Cell Analyzer 2000 using Investigator image analysis software will be discussed which provide new approaches and valuable insights into these important evaluations.
11:40-11:55 Using Embryonic Stem Cells for Drug Discovery
Amy Sinor, Ph.D., Assay Development Scientist, Department of Stem Cell and Regenerative Biology, Harvard Stem Cell Institute, Harvard University
Many neurodegenerative diseases involve the selective death of specific types of cells. Scientists interested in understanding selective neural degeneration have been hampered by the lack of experimentally convenient in vitro systems. In the last few years, it has become possible to generate large numbers of motor neurons from mouse embryonic stem (ES) cells. This has enabled us to carry out new types of studies directed at identifying therapeutics for two different motor neuron diseases: Spinal Muscular Atrophy (SMA) and Amyotrophic Lateral Sclerosis (ALS). To address this issue, we have carried out high-content screens in ES derived motor neurons from both wildtype, SMN deficient, and mutant human SOD1 ES cells. All the ES cell lines used carry a transgene in which GFP expression is regulated by the HB9 promoter, a motor neuron specific marker. This marker allowed us to identify motor neurons in order to determine protein expression of SMN and the number of motor neurons. Our goal was to identify small molecule compounds that could increase SMN levels and promote neuronal survival.
10:30-10:35 Chairperson’s Opening Remarks
10:35-11:00 Quantifying Challenging Phenotypes in Images
Mark-Anthony Bray, Ph.D., Computational Biologist, Imaging Platform, Broad Institute
Many challenging image-based phenotypes have recently become quantifiable due to advances in image analysis and machine learning algorithms. Our recent work in the area has enabled high-content analysis of phenotypes relevant to multiple basic biological processes and clinically relevant diseases. For example, our recent work has enabled screens of phenotypes in physiologically relevant co-culture systems, where cell types with diverse morphologies are present in each sample. The variety of phenotypes that can be accurately quantified using software continues to grow.
11:00-11:25 Quantitative Analysis of High-Content Screens: How Can Machine Intelligence Help?
Peter Horvath, Ph.D., Image Processing Scientist, Light Microscopy Centre, ETH Zurich
Accurate quantitative analysis is essential for high-content screens. We will show cell-based classification with machine learning techniques. A novel semi-supervised learning-based method will be presented to speed up the learning process with orders of magnitude. Finally, we will present new machine intelligence methods for accurate quality control.
11:25-11:50 Application of Pattern Recognition to Image-Based Small Molecule Screening Data for Phenotypic Analysis
John McLaughlin, Ph.D., Scientist & Manager, Biology, Rigel Pharmaceuticals, Inc.
This presentation will describe an image based phenotypic screen for AuroraB Kinase inhibitors that we have developed, which lead to the discovery and subsequent development of a small molecule R763/AS703569 currently in clinical trials for cancer. This screen is a proliferation type assay in which cancer cell lines are treated with small molecules for 48hrs then fixed and stained for the presence of DNA and Actin. We create training sets from treatments with control compounds and use them to create support vector machine classifiers that are subsequently used to mine our data for interesting phenotypes in addition to AuroraB. Our large annotated data set with many well-characterized controls has provided an excellent opportunity to validate and improve the predictive capabilities of the classifiers. Various strategies for increasing training set robustness have demonstrated an impressive ability to productively mine screening data collected on a weekly basis over many years. We have found that pattern recognition can significantly enhance and speed attempts to quantify what are often overwhelmingly large and complex image data sets produced by image-based screening.
11:50-12:15 High-Performance Image Analysis for High-Content Screening
Dadong Wang, Ph.D., Project Leader, Biotech Imaging, CSIRO
Large image datasets and fast turnaround requirements have made efficient High Content Screening (HCS) a challenging task. With the enormous progress in high performance computing, computers with multi-core CPUs have become standard and GPUs are being used more widely in data and compute-intensive environments. This talk will report some of our studies in high performance image analysis and its applications in HCA, including GPU based image analysis and multi-core based batch processing for the quantitative High Content Analysis of neurite outgrowth. With the multi-core based batch processing on a quad-core machine, the time for our neuron body detection algorithm has been reduced to 38% of the original, and 46% for neurite analysis. The results show that the high performance image analysis can significantly increase the throughput of HCS and improve the workflow in laboratories.
10:30-10:35 Chairperson’s Opening Remarks
10:35-11:00 Implications of High-Throughput Flow Cytometry on Drug Discovery
J. Paul Robinson, Ph.D., SVM, Professor, Cytomics & Deputy Director, Bindley Bioscience Center, Cytomics & Imaging, Purdue University
The time has come for high-content tools such as flow cytometry to also move into the high-throughput domain. This requires both hardware and software changes. It is not easy to move a technology that has a 40-year history of operating under the same conditions, to change its basic operational rationale. However, that is happening. One of the major changes is the radical change in analytical tools becoming available. This presentation will outline these recent tool-sets that will transform the field of flow cytometry.
11:00-11:25 Phospho Flow Cytometry in Drug Discovery: From Screening to Clinical Trials
Peter Krutzik, Ph.D., Senior Scientist, Baxter Lab in Genetics Pharmacology, Microbiology & Immunology, Stanford University
Flow cytometry is a powerful tool for analyzing 10 or more parameters at the single cell level. Recently, the use of phospho-specific antibodies has allowed us to measure intracellular signaling events in addition to classical surface markers. This enables us to analyze kinase signaling cascades in complex primary cell populations such as human peripheral blood. Using phospho flow, we performed a small molecule drug screen in primary cells, in both 96 and 384 well format, to search for inhibitors of immunological pathways. The screen yielded pathway- and novel cell type-specific inhibitors of cytokine-induced type-specific inhibitors of cytokine-induced Jak-Stat signaling. The method was used both in vitro and in vivo to confirm drug activity. To improve sample throughput, we employed Fluorescent Cell Barcoding (FCB), a multiplexing method that enables combination of samples prior to antibody staining. We will also discuss preliminary work in automating the phospho flow method for large scale screening projects.
11:25-11:50 High-Content High-Throughput Flow Cytometry for Small Molecule Discovery
Eric Prossnitz, Ph.D., Professor, Cell Biology and Physiology, University of New Mexico
The University of New Mexico Center for Molecule Discovery continues to innovate in the application of the HyperCyt flow cytometry platform for high-content high throughput small molecule discovery. The platform is evolving for 1536 well plates and direct sample delivery. Recently, we have demonstrated HTS applications with primary cells and yeast multiplex model systems for TOR pathway analysis, as well as innovative molecular assays for intracellular trafficking pathways. The flow cytometry platform is well-suited to fill a unique niche in small molecule identification for cell and molecular assays in suspension, especially in complex cell suspensions for primary cells, hematopoietic stem cells, and leukemia.
11:50-12:15 SERS Cytometry for High-Content Analysis: More Parameters for Less
John P. Nolan, Ph.D., Professor, La Jolla Bioengineering Institute
Fluorescence methods dominate the field of cytometry, providing sensitive and quantitative measurements of molecules, cells, and other particles. In flow cytometry especially, multiple light sources, filters and detectors enable as many as 20 different fluorescence probes to be detected and measured simultaneously and rapidly on individual cells. However, this requires the use of multiple lasers and fluorophores with emission spectra that fill the optical spectrum from the UV to the near IR, and significant increases in this number are unlikely with existing light sources, fluorophores, and detectors. To make more efficient use of this spectral range, we have developed instruments and probes that take advantage of surface-enhanced Raman scattering (SERS). SERS occurs at the surface of metal nanoparticles and offers sensitivity comparable to fluorescence, but with much more efficient use of the optical spectrum, providing the potential of hundreds of tags to be resolved with a single laser line and less than 100 nm of spectral space. We use nanoparticle probes with distinctive SERS spectra functionalized with antibodies or other targeting molecules to measure multiple targets simultaneously. Raman flow cytometers use spectrographs and array detectors to measure high resolution SERS spectra from hundreds of individual particles per second. Simultaneous Raman and fluorescence flow cytometry provide the best of both worlds, with fluorescence measurements of both functionally and antigenic markers combined with very highly multiparameter measurements of antigens or other targets.