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Biospecimen Commons: A Tool for Encouraging Openness and Transparency in Biospecimen Sample CollectionContains 2 Component(s) Recorded On: 10/26/2016
Collecting this information in one place enables researchers to use samples from different biobanks with confidence that comparisons between them are valid and will hold up to scrutiny over time. By improving transparency in specimen collection procedures, we can improve reproducibility while decreasing wasted resources in preclinical research.
One major problem facing preclinical research, and especially the development of Precision Medicine, is irreproducibility caused by the limited information available regarding the collection of biological specimens. While publishing groups require that researchers divulge the details of how biological specimens are analyzed to obtain their results, there are currently no requirements on describing the details of how the specimens were collected. Numerous reports have begun to arise demonstrating that differences in initial sample preparation which are not often reported can have important impacts in the downstream analytical results. These effects cannot be accounted for and corrected without this knowledge.
Biospecimen Commons hosts both the protocols used in specimen collection and the associated collections in two interconnected databases. Both protocols and collections have meta-data attached regarding the types of specimens they pertain, to in order to facilitate browsing and searching. Protocols have unique identifiers in order to clearly delineate changes in protocols over time in the literature. Profile pages for biorepositories enable researchers to find the samples which have been prepared using protocols appropriate for a given research endeavor. Collecting this information in one place enables researchers to use samples from different biobanks with confidence that comparisons between them are valid and will hold up to scrutiny over time. By improving transparency in specimen collection procedures, we can improve reproducibility while decreasing wasted resources in preclinical research.
Joseph Miceli, Ph.D.
Head of Development for Biospecimen Commons
Joe Miceli heads the development of Biospecimen Commons, an open access database developed between Arizona State University and Global Biological Standards Institute. The goal of Biospecimen Commons is to increase reproducibility and transparency in preclinical research by making protocols used in specimen collection publicly available. Joe received his Ph.D. from the Biological Design Graduate Program at ASU, an interdisciplinary training program. His scientific expertise ranges across the areas of microbial community dynamics, bioinformatics, molecular biology, environmental biotechnology, and electrochemistry.
From the Journal of Laboratory Automation (JALA)
SLAS Electronic Laboratory Neighborhood E-Zine
Coming Soon at SLAS2017
Compound Screening and Profiling in Cultured Human (3D) TissuesContains 2 Component(s) Recorded On: 09/26/2016
In this webinar, we discuss how this can be exploited, allowing drugs to be classified based on their target specificity and discriminating polypharmacology and off-target toxicities.
Cells grown in monolayer (2D) culture display a frantic rate of proliferation not seen in the most malignant of tumors. Aberrant gene expression profiles of cells grown on tissue culture plastic also reflect their loss of tissue-specific functions. But the culture of even highly transformed cancer cell lines in three dimensional extracellular matrix protein-rich environment can tame the cell cycle and restore some phenotypic and functional characteristics of the original patient tissue. For these reasons, 3D tissue cultures provide a more physiologically relevant context for the screening of compounds, compared to 2D cell cultures. In this webinar, however, we will skip over these important aspects to focus on an under-exploited characteristic of 3D tissues – their increased phenotypic complexity. The examination of tissue morphology by histopathologists is invaluable in the diagnosis of disease. Similarly, the morphology of 3D cultured tissues can also provide rich insight into the diseased state and the impact of drug treatment – both therapeutic and adverse. However, most users of 3D cell culture use simple readouts of cell viability which neglect the rich information that can be gained from image-based analysis. Just as different mutations can drive characteristically different phenotypes, disruption of different cellular pathways with drugs can also induce characteristic phenotypic responses. In this webinar, we discuss how this can be exploited, allowing drugs to be classified based on their target specificity and discriminating polypharmacology and off-target toxicities.
Leo Price, PhD.
Leo Price completed his PhD in Cell Physiology at University College, London (1995). After a postdoc at The Scripps Research Institute, he continued his research on cell adhesion signaling at the Netherlands Cancer Institute and University of Utrecht. From 2006, he was group leader at the Leiden Academic Centre for Drug Research at Leiden University in The Netherlands, where he further developed this research making extensive use of 3D cell culture models. In 2011 he founded OcellO, which provides high content tissue-based screening services to the drug discovery industry.
- A Novel Automated High-Content Analysis Workflow Capturing Cell Population Dynamics from Induced Pluripotent Stem Cell Live Imaging Data
- High-Throughput Platform for Identifying Molecular Factors Involved in Phenotypic Stabilization of Primary Human Hepatocytes In Vitro
- Development of a 3D Tissue Culture–Based High-Content Screening Platform That Uses Phenotypic Profiling to Discriminate Selective Inhibitors of Receptor Tyrosine Kinases
- Reproducibility of Uniform Spheroid Formation in 384-Well Plates: The Effect of Medium Evaporation
Also from the Journal of Biomolecular Screening (JBS)
- iScreen: Image-Based High-Content RNAi Screening Analysis Tools
- Increasing the Content of High-Content Screening: An Overview
- Phenotypic Profiling of Raf Inhibitors and Mitochondrial Toxicity in 3D Tissue Using Biodynamic Imaging
- Biosensor-Expressing Spheroid Cultures for Imaging of Drug-Induced Effects in Three Dimensions
From the Journal of Laboratory Automation (JALA)
- Delivering an Automated and Integrated Approach to Combination Screening Using Acoustic-Droplet Technology
- Fully Automated One-Step Production of Functional 3D Tumor Spheroids for High-Content Screening
SLAS Electronic Laboratory Neighborhood E-Zine
Addressing the Challenges of 1536-Well Cell-Based ScreeningContains 1 Component(s) Recorded On: 05/17/2016
Many phenotypic cell-based assays are limited to 384-well formats due to the challenges of liquid handling, and the need for consistency during microplate washing and media change steps. Use of expensive assay formats in 384 format impacts the number of compounds that can be screened through such assays. To address these challenges, Plant explains how the BlueCatBio centrifugal plate washer integrates centrifugal emptying with the individual addition of up to four separate solutions for complex phenotypic assays.
Many phenotypic cell-based assays are limited to 384-well formats due to the challenges of liquid handling, and the need for consistency during microplate washing and media change steps. Use of expensive assay formats in 384 format impacts the number of compounds that can be screened through such assays.To address these challenges, Plant explains how the BlueCatBio centrifugal plate washer integrates centrifugal emptying with the individual addition of up to four separate solutions for complex phenotypic assays. The achievement of near zero residual volume by using centrifugation rather than aspiration is enabling for 1536-well formats. Typically phenotypic imaging assays are limited to 384-well formats due to the need for consistent washing steps after fixation and staining. To miniaturize to a 1536-well format the centrifugal plate washer is used to perform washing cycles. Similar high-throughput screening (HTS) quality metrics are obtained in 1536 formats compared to that obtained in the original HTS using 384 format.The Corning Epic system is a label-free detection system that uses resonant waveguide grating to measure the drug-induced response of cells using dynamic mass redistribution (DMR). DMR is measured by the refraction of light from a biosensor integral to an Epic plate, but the cost of these high value plates has limited the use of this technology at scale. To enable its use in HTS, Dr. Plant hasdeveloped a 1536 format assay, but many challenges are encountered throughout this process.When using smaller assay volumes, lengthy incubations at 37oC can result in issues with evaporation leading to edge effects. These are often more apparent when scaling up to larger batches of plates. Testing various types of plate seals shows that edge effects are dramatically reduced.Successful conversion of complex phenotypic assays to 1536-well formats results in up to a 50 percent reduction in cell number requirements and approximately a four-fold reduction in time taken to perform a full HTS screening campaign. It also allows screening of expensive assay formats and cell types in high-content phenotypic HTS screens.
Scientist, AstraZeneca Pharmaceuticals, Macclesfield, United Kingdom
Having obtained her BSc in Biochemistry at the University of Manchester UK, Helen Plant has worked in the pharmaceutical industry for 23 years. Dr. Plant is an experienced drug-discovery bioscientist, working in the fields of Biochemical & Cell Assay Development, High Throughput Screening and Laboratory Automation. Helen Plant has been employed in her current role since 2009 as a senior research scientist within the AstraZeneca Global High Throughput Screening centre, with a responsibility for delivering screening data to a global internal & external customer base.
Fully Automated 3D Cell Culture Provides Standardized, Biologically Relevant, and High Production for Human CellsContains 1 Component(s) Recorded On: 04/06/2016
Next generation 3D cell culture systems will virtually eliminate cell misidentification, contamination and infection, while optimizing cell growth and phenotype in order to provide the biotechnology and the reparative/regenerative medical industries with the highest quality products.
Growing living human cells in vitro for bio-production, drug discovery and regenerative medicine is challenged by the difficulty in developing methods for reproducibly and cost effectively growing large number of human cells in a way that represents in vivo cellular environment. 3D cell culture is a discipline that will ultimately replace 2D cell culture (culture performed on flat plastic surfaces) since the use of biologically relevant surfaces, substances, geometries, and stresses produces cells which more reliably express their in vivo phenotypes and physiology. Automation and robotics will be necessary to provide parallel and/or random access processing of many cell lines simultaneously so that they will meet the FDA “good laboratory practice" (GLP) and “good manufacturing practice" (GMP) standards under development for cultured cells. Automation and robotics will be used in conjunction with new cell culture methods including the use of 3D technologies incorporating biomimetic substrates, xeno free cell culture media, shear forces, and oxygen concentrations that more closely mimics the in vivo environment. Next generation 3D cell culture systems will virtually eliminate cell misidentification, contamination and infection, while optimizing cell growth and phenotype in order to provide the biotechnology and the reparative/regenerative medical industries with the highest quality products.
Robin A. Felder
PhD, Professor of Pathology, Associate Director Laboratory Medicine, The University of Virginia
Dr. Robin Felder is a Professor of Pathology and Associate Director of Laboratory Medicine at the University of Virginia-UVA, and is Chair of Medical Automation.org. Dr. Felder received his PhD in Biochemistry from Georgetown University. He has published over 300 papers, reviews, chapters, and co-edited three books on medical automation. He has been awarded 22 patents and has founded 9 biotech companies, including 2 non-profit organizations including the Association for Laboratory Automation and its journal(JALA) and Medical Automation.org. He has received numerous awards including the Engelberger Robotics Award, UVA's Innovator of the Year, and the AACC/NACB Annual Research Award.
The SmartLab of the Future: Bench-top lab-automation with the PetriJet and NutriJet platformsContains 1 Component(s) Recorded On: 03/24/2016
This webinar will give a brief overview on available bench-top lab automation (BTLA) systems and the philosophy behind. In the SmartLab-Systems group at the Chair of Bioprocess Engineering at the Technische Universität Dresden two BTLA devices have already been developed – the PetriJet platform and the NutriJet.
Laboratory standard procedures such as sample handling or medium preparation are often characterized by manual processing steps. In order to improve the overall lab efficiency, to cut cost or to increase the sample throughput automation proved to be a feasible mean. Due to the constant development in automation engineering innovative components offer endless possibilities for an engineering of bench-top devices for laboratory automation which often already found their way into small and middle sized laboratories.This webinar will give a brief overview on available bench-top lab automation (BTLA) systems and the philosophy behind. In the SmartLab-Systems group at the Chair of Bioprocess Engineering at the Technische Universität Dresden two BTLA devices have already been developed – the PetriJet platform and the NutriJet.The PetriJet platform provides a lab with a compact device for the automated handling of culture dishes in batches of 20 or continuously. Together with exchangeable processing stations nearly all tasks associated with culture dishes e. g. identification, filling, imaging can be effectively automated.The automated imaging of biological samples in culture dishes for example facilitates the documentation in clinical routine analysis and can also be used together with automatic image analysis as a powerful tool for e. g. the routine checks for Legionella in drinking water.Another important issue is medium preparation in biological laboratories as it involves many manual processing steps and is the basis for reliable experimental results. NutriJet provides a lab with a fully automated solution for medium composition right from the container of the ingredient manufacturer and is fully GMP-compliant.
Dr. Felix Lenk
Group head SmartLab-Systems, TU Dresden, Institute of Food Technology and Bioprocess Engineering, Chair of Bioprocess Engineering
Felix Lenk is currently a group head of the research group “SmartLab-Systems" at the Technische Universität Dresden, Germany.
Felix studied Automation & Control and Electrical Engineering at the Technische Universität Dresden, Germany and the University of Calgary, Calgary, Alberta, Canada and received his PhD from the Technische Universität Dresden, Germany in 2014. His research includes the fields of laboratory automation especially the topics work flow optimization, automatic imaging and automatic image analysis as well as the modeling of growth processes in biotechnology. He co-ordinates several national BMBF-joint research projects and has been recognized with the InterLabTec-Award in the category sustainability.
Identifying Druggable Cells: Automated Methods for High-content Single-cell ScreeningContains 1 Component(s) Recorded On: 01/21/2016
This presentation was recorded at SLAS2016.Historically, cancer therapy screening has been rooted in the action of the drug itself, leveraging either the identification of new drug targets, or an overall measure of cancer cell death. However, drug resistance and cancer stem cell research has revealed that a critical set of cancer cells are extremely potent, wreaking havoc on patients even when the majority of proliferating cells have been eliminated. Identification and targeting of unique cell subtypes remains a critical challenge for the fight against cancer and immunotherapy.Single-cell technologies such as flow and mass cytometry allow for efficient sorting and profiling of millions of cells. The two primary challenges, however are scalability of both processing these cellular subsets as well as identification of all of the cellular subtypes from a patient in response to treatment.To this end, we have developed an automated pipeline leveraging robotics, as well as algorithmic and machine learning methods to process and computationally identify cancer cell subsets in response to therapeutic perturbations. From this pipeline, we have derived a universal measure for therapeutic similarity based on the systematic mechanism of a drug. Not only are we able to detect and identify cell subsets that respond to therapeutics as early as 1 hour, but the DNA damage response which defines these cells. Furthermore, we find that the mechanisms of cell cycle and cell signaling have a much stronger signal than cell death in stratifying cellular responses to therapy.In this talk I will discuss my initial applications of this workflow to cells treated with 89 therapeutics currently approved for cancer therapy, creating a new landscape for classification of cancer therapeutics, from canonical checkpoint to kinase inhibitors. In addition I will highlight newer applications of our pipeline to high-content mass cytometry analysis, as well as extensions of our machine learning methods for identification of rare and differential cell subsets.
Tiffany J. Chen
Director of Informatics, Cytobank, Inc and Stanford University, Mountain View, California
Tiffany J. Chen (Ph.D., M.S. Stanford, B.S. Duke) is currently Director of Informatics at Cytobank, Inc and focuses on integration of new algorithms, machine learning tools, and experimental methods for single cell biology. In this role, she focuses on coordination and management of cross-functional teams across computational, statistical, and biomedical collaborations as well as the management of data. Her current research (affiliate, Stanford University) focuses on computational modeling of the mechanisms of action for cancer drugs and immunotherapy, as well as contributions to analysis of solid tumor profiling and immune biomarker discovery.
Droplet Microfluidics: Amphiphilic Nanoparticles as Droplet Stabilizers for High-fidelity and Ultrahigh-throughput Droplet AssaysContains 1 Component(s) Recorded On: 01/21/2016
This presentation was recorded at SLAS2016.
Droplet microfluidics, in which nanoliter- to picoliter-sized drops are used to encapsulate and compartmentalize molecules or cells, has enabled a wide range of biochemical applications. Examples include digital PCR and directed evolution of enzymes. The first part of the talk will focus on the design and synthesis of amphiphilic silica nanoparticles for the stabilization of aqueous drops in fluorinated oils for applications in droplet microfluidics. The success of droplet microfluidics has thus far relied on one type of surfactant for the stabilization of drops. However, surfactants are known to cause interdrop transport of small, hydrophobic molecules. Such transport leads to the cross-contamination of droplet contents. The use of nanoparticles mitigates this transport as particles are irreversibly adsorbed to the liquid–liquid interface. They do not form micelles as surfactants do, and thus, a major pathway for interdrop transport is eliminated. The second part of the talk will describe a high-throughput optofluidic droplet interrogation device capable of counting fluorescent drops at a throughput of 254,000 drops per second. The device consists of 16 parallel microfluidic channels bonded directly to a filter-coated two-dimensional Complementary Metal-Oxide-Semiconductor (CMOS) sensor array. Fluorescence signals emitted from the drops are collected by the sensor that forms the bottom of the channel. The proximity of the drops to the sensor facilitates efficient collection of fluorescence emission from the drops, and overcomes the trade-off between light collection efficiency and field of view in conventional microscopy. The interrogation rate of the device is currently limited by the acquisition speed of CMOS sensor, and is expected to increase further as high-speed sensors become increasingly available.
Sindy KY. Tang
Assistant Professor, Stanford University, Stanford, California
Sindy Tang received her B.S. degree in Electrical Engineering from California Institute of Technology in 2003, M.S. from Stanford in 2004, and Ph.D. from Harvard in Engineering Sciences in 2010. Sindy's research interests include microfluidics, optofluidics and nanophotonics for the development of tools for biology and smart materials. She joined the faculty of Stanford University in September 2011 as an assistant professor in the Department of Mechanical Engineering.
High Throughput Screening of Metagenomic DNA LibrariesContains 1 Component(s)
This presentation was recorded at SLAS2016.The trillions of bacteria that make up the human microbiome are believed to encode functions that are important to human health; however, little is known about the specific effectors that commensal bacteria use to interact with the human host. Functional metagenomics provides a systematic means of surveying commensal DNA for genes that encode effector functions. Here we examine 3,000 megabases of metagenomic DNA cloned from three phenotypically distinct patients for effectors that activate NF-κB, a transcription factor known to play a central role in mediating responses to environmental stimuli. This screen led to the identification of 26 unique commensal bacteria effector genes (Cbegs) that are predicted to encode proteins with diverse catabolic, anabolic and ligand binding functions and most frequently interact with either glycans or lipids. Detailed analysis of one effector gene family (Cbeg12) recovered from all three patient libraries found that it encodes for the production of N-acyl-3-hydroxypalmitoyl-glycine (commendamide). This metabolite was also found in culture broth from the commensal bacterium Bacteroides vulgatus, which harbors a gene highly similar to Cbeg12. Commendamide resembles long-chain N-acyl-amides that function as mammalian signaling molecules through activation of GPCRs, which led us to the observation that commendamide activates the GPCR G2A/GPR132. G2A has been implicated in disease models of autoimmunity and atherosclerosis. This study shows the utility of functional metagenomics for identifying potential mechanisms used by commensal bacteria for host interactions and outlines a functional metagenomics-based pipeline for the systematic identification of diverse commensal bacteria effectors that impact host cellular functions.
Instructor, Icahn School of Medicine at Mount Sinai, New York City, New York
Louis Cohen is an instructor in the department of medicine and division of gastroenterology at the Icahn School of Medicine at Mount Sinai Hospital. He has an adjunct appointment in the Laboratory of Genetically Encoded Small Molecules at Rockefeller University. His clinical interest in the treatment of patients with inflammatory bowel disease and in the laboratory he studies host-microbial interactions using functional metagenomic methods.
Genome Engineering with Zinc Finger NucleasesContains 1 Component(s) Recorded On: 01/21/2016
This presentation was recorded at SLAS2016.Proteins that can be designed to cleave user-chosen sites in a living genome provide powerful tools for engineering eukaryotic cells with new and useful properties. By provoking break repair of the targeted locus, such proteins can mediate highly efficient rates of gene disruption, gene editing or gene addition at levels that allow ready isolation of cells or organisms bearing a desired genetic change. These capabilities can enable diverse applications in research, medicine, and biotechnology, including the creation of customized cell and animal models for drug development. Realizing the full potential of these technologies, however, requires methods for generating sufficiently active nucleases for the widest range of sequence targets while minimizing off-target effects. This talk will describe strategies and recent work that use zinc finger nucleases to address these considerations. It will also summarize examples of genome engineering in animal and cellular models including iPSCs.
Vice President, Technology, Sangamo BioSciences, Richmond, California
Edward Rebar, Ph.D. is Vice President of Technology at Sangamo BioSciences, where he directs the design and characterization of zinc finger nucleases for therapeutic applications. He has held this position since December 2007, and has worked at Sangamo since 1998. Prior to joining Sangamo, Ed earned his Ph.D. from MIT in the laboratory of Dr. Carl Pabo, where he developed phage display methods for engineering zinc fingers with novel DNA-binding specificities. Ed has authored over 50 publications relating to the development of customized DNA binding proteins and nucleases for genome editing, as well as numerous patents.
Drug-Target Residence Time: Target Engagement, Target Vulnerability and Predictions of in Vivo Drug ActivityContains 1 Component(s) Recorded On: 01/21/2016
This presentation was recorded at SLAS2016.Predicting drug efficacy in humans remains a major barrier to the development of novel therapeutics. To improve the prediction of in vivo drug activity we propose that the kinetics of drug-target interactions, and in particular the life-time of the drug-target complex (residence time), should be integrated into predictive models since drug and target are not at equilibrium in vivo. In particular, drugs that dissociate slowly from their targets will have extended activity at low drug concentration thus mitigating a reduction in the frequency of dosing and hence an increase in therapeutic index. We have consequently developed a mechanistic PK/PD model that incorporates drug-target kinetics and have used this model to successfully predict efficacy in models of bacterial infection caused by Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus. We believe that our approach, which is relevant across all disease areas, will have a profound impact on the development of new drugs.
Professor, Stony Brook University, Stony Brook, New York
Peter Tonge is a Professor of Chemistry and of Radiology (by courtesy), and the Director of Infectious Disease Research in the Institute for Chemical Biology & Drug Discovery at Stony Brook University. He is also the co-Director or the Chemical Biology Training Program, and the Director of the Biomolecular Imaging Cluster. He joined Stony Brook in 1996 where his research program combines kinetic, structural, synthetic, computational and biophysical approaches to (i) develop inhibitors of enzyme drug targets and (ii) to understand the mechanism of photoreceptors and optogenetic devices. A primary focus of his program involves the integration of drug-target binding kinetics into predictions of drug activity to improve the selection and development of drug candidates. Correlations between drug pharmacokinetics and pharmacodynamics are aided by the synthesis of radiotracers and the use of positron emission tomography to non-invasively image drug biodistribution.