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  • High-throughput 3D Assays

    Contains 1 Component(s) Recorded On: 02/07/2018

    In this talk we highlight through a series of case studies how these tools enable a variety of applications including organoid models, immune oncology, and hepatotoxicity models.

    Traditional methods for 3D cell culture are often time consuming, display variability, and lack the necessary throughput for screening applications.  To address these concerns we have developed a set of assay plates and flask formats that enable highly reproducible formation and screening of spheroids.  In this talk we highlight through a series of case studies how these tools enable a variety of applications including organoid models, immune oncology, and hepatotoxicity models. Specifically, we show how a novel combination of Spheroid and Transwell plates enables the investigation of immune cell homing, tumor cytotoxicity, and tumor immune evasion in an easy-to-use 3D high throughput assay.  We  highlight the formation of gastrointestinal organoids derived from human induced pluripotent stem cells.  And we demonstrate how primary human hepatocytes models can be improved with these tools to more closely correlate with in vivo toxicity data.

    Anthony Frutos

    Corning Incorporated

    Dr. Anthony G. Frutos was appointed business technology director, Corning Life Science Development in April 2009. In this role, Tony is responsible for Life Sciences product and process development and delivery. Tony holds 19 U.S. patents and is an author on more than 39 technical publications. He holds a bachelor’s degree in chemistry from Brigham Young University and a Ph.D. in analytical chemistry from the University of Wisconsin, Madison. 

  • Pathology from the Molecular Scale on Up.

    Contains 1 Component(s) Recorded On: 02/07/2018

    I will present evidence of deep internal order in immune functionality demonstrating that differentiation and immune activities have evolved with a definable “shape”. Further, specific cellular neighborhoods of immune cells are now definable with unique abilities to affect cellular phenotypes—and these neighborhoods alter in various disease states.

    High parameter single cell analysis has driven deep understanding of immune processes.  Using a next-generation single-cell “mass cytometry” platform we quantify surface and cytokine or drug responsive indices of kinase target with 45 or more parameter analyses (e.g. 45 antibodies, viability, nucleic acid content, and relative cell size).  Similarly, we have developed two advanced technologies that enable deep phenotyping of solid tissue in both fresh frozen and FFPE formats (50 – 100 markers).   I will present evidence of deep internal order in immune functionality demonstrating that differentiation and immune activities have evolved with a definable “shape”.  Further, specific cellular neighborhoods of immune cells are now definable with unique abilities to affect cellular phenotypes—and these neighborhoods alter in various disease states.   These shapes and neighborhoods are altered during immune surveillance and “imprinted” during, and after, pathogen attack, traumatic injury, or auto-immune disease.  Hierarchies of functionally defined trans-cellular modules are observed that can be used for mechanistic and clinical insights in cancer and immune therapies.

    Yury Goltsev

    Stanford University

    More information coming!

  • SLAS2018 Innovation Award Finalist: Pharos – A Torch to Use in Your Journey In the Dark Genome

    Contains 1 Component(s) Recorded On: 02/07/2018

    Given the heterogeneous set of data types available for individual targets, it is useful to quantify how much and what types of data is available for a target. We describe the development of knowledge profiles and a Knowledge Availability Score (KAS), both derived from the Harmonizome, which is a resource that has characterized data availability across different data sources and types in a uniform manner.

    It is well known that a relatively small set of protein targets receive the bulk of research attention and thus funding. However, there are potential (druggable) opportunities in the remaining under-studied and un-studied proteins. To address this the NIH initiated the "Illuminating the Druggable Genome" program to characterize the dark regions of the druggable genome. As part of this program, a Knowledge Management Center (KMC) was created to aggregate and integrate heterogeneous data sources and data types creating a centralized location for information about all protein targets identified as part of the druggable genome. Since then the KMC has expanded to consider the entire human proteome. In this presentation, we describe Pharos, the user interface for the KMC knowledgebase. We provide an overview of the data sources and types made available via Pharos and then describe the architecture of the system and its integration with KMC & external resources. In particular we highlight the rich search facilities that enable a user to drill down to relevant subsets of data but also support the notion of "serendipitous search".  Given the heterogeneous set of data types available for individual targets, it is useful to quantify how much and what types of data is available for a target. We describe the development of knowledge profiles and a Knowledge Availability Score (KAS), both derived from the Harmonizome, which is a resource that has characterized data availability across different data sources and types in a uniform manner. We then highlight how the KAS is concordant with knowledge trends characterized by traditional metrics such as publications and grants.  We discuss the use of the KAS in the Pharos interface and an example of prioritizing understudied targets by computing the similarity of their knowledge availability profiles with that of well-studied targets.

    Rajarshi Guha

    NIH

    Rajarshi Guha is Group Leader (Research Informatics) in the Division of Pre-Clinical Innovation at NCATS. With over 10 years of experience in handling, analysing and visualizing chemical information, he brings a diverse range of skills and experience to his current role at NCATS. He is involved in small molecule development projects in a variety of therapeutic areas including rare cancers and infectious diseases. He is also involved in software and algorithm development in the areas of cheminformatics methods and large scale infrastructure projects including Pharos (http://pharos.nih.gov/) BARD (http://bard.nih.gov/). His research interests focus on methodology development to analyze and visualize chemical biology data sets, with specific focus on techniques to link chemical structure information to molecular, bibliographic, genomic and clinical covariates.   He has held multiple leadership roles in the American Chemical Society’s Division of Chemical Information and is currently a co-Editor in Chief of the Journal of Cheminformatics.

  • Identifying new allosteric sites on PTP1B using fragment-based tether scanning

    Contains 1 Component(s) Recorded On: 02/07/2018

    We have been applying a disulfide-tethering fragment-based approach to identify and characterize new binding and allosteric sites on PTP1B. Here we report our progress to date, and show that this “Tether Scanning” approach has allowed us to identify new binding sites on PTP1B, as well as new disulfide fragments that modulate PTP1B activity.

    Due to its role in regulating insulin receptor kinase, protein-tyrosine phosphatase 1B (PTP1B) has been a long sought after drug target for the treatment of diabetes and other metabolic disorders. Unfortunately, due to the high homology between PTP family members and the charged nature of substrate mimics, developing selective and cell-permeable active site inhibitors of PTP1B has proven notoriously difficult. For this reason, there has been great interest in developing compounds that allosterically modulate PTP1B activity. Towards this goal, we have been applying a disulfide-tethering fragment-based approach to identify and characterize new binding and allosteric sites on PTP1B. Here we report our progress to date, and show that this “Tether Scanning” approach has allowed us to identify new binding sites on PTP1B, as well as new disulfide fragments that modulate PTP1B activity. 

    Zachary Hill

    University of California, San Francisco

    Zachary is currently a postdoctoral fellow in Jim Wells' lab at UCSF, where he works on the development of chemical biology approaches to identify and study new bioactive small molecules. Prior to coming to UCSF, Zachary completed his PhD in Organic Chemistry with Dustin Maly at the University of Washington, where he worked on the development of bivalent kinase inhibitors. During his postdoc, Zachary has been fortunate enough to be supported by a fellowship from the Helen Hay Whitney Foundation, as well as a K99 Transition award from the NIH-NCI. In the next year, Zachary hopes to transition to an independent position where he will continue applying ligand-based approaches to study biological systems. 

  • Inflammation-on-a-chip – High-throughput microscale arrays for human neutrophil swarming

    Contains 1 Component(s) Recorded On: 02/07/2018

    Neutrophil swarms protect healthy tissues by sealing off sites of infection. In the absence of swarming, microbial invasion of surrounding tissues can result in severe infections. Recent observations in animal models have shown that swarming requires rapid neutrophil responses and well-choreographed neutrophil migration patterns.

    Neutrophil swarms protect healthy tissues by sealing off sites of infection. In the absence of swarming, microbial invasion of surrounding tissues can result in severe infections. Recent observations in animal models have shown that swarming requires rapid neutrophil responses and well-choreographed neutrophil migration patterns. However, in animal models, physical access to the molecular signals coordinating neutrophil activities during swarming is limited. Here, we report the development and validation of large microscale arrays of targets for the study of human neutrophils during swarming ex vivo. We characterized the synchronized growth of thousands of swarms at once, towards live-microbe and microbe-like synthetic particles simulating infections.  We took advantage of the synchronized swarming in small volumes to analyze in detail the mediators released at different phases of human-neutrophil swarming against various targets. We found that the mediators coordinating human-neutrophil swarming form a complex network, with multiple levels of redundancy, which includes more than 40 signaling proteins, i.e. stimulatory of neutrophil activity, proteolytic enzymes and enzyme inhibitors, activators of other immune and non-immune cells (monocytes, lymphocytes, endothelial cells, adipocytes, etc).  We identified only one mediator that limits the growth of neutrophil swarms, LAX4, which is a lipid and has been associated before with the restoration of immune homeostasis.  We compared the swarming behavior of neutrophils from patients following major trauma and healthy individuals and found various deficiencies that resolve over time.  Overall, we report a new platform technology for studying neutrophil swarming, a behavior that is relevant to various disease and physiologic processes, and which could serve as discovery and validation platform for novel anti-inflammatory and anti-microbial treatments. 

    Daniel Irimia

    Harvard Medical School - Massachusetts General Hospital

    microfluidics, neutrophils, inflammation, sepsis

  • Tumor Organoids for Therapeutic Discovery and Personalized Medicine

    Contains 1 Component(s) Recorded On: 02/07/2018

    This presentation will discuss our recent innovative tumor organoid models of various cancers, which we have engineered for 3D HCS drug discovery targeting EMT. Our tumor organoid models feature an innovative dual fluorescent biomarker reporter of EMT that can effectively track the forward and reverse EMT transition in live tumor organoids.

    The past decade has seen a revolution in developing 3D tissue models of organ function, anatomy, and disease which can be employed for markedly improved drug screening approaches. These models are referred to as organoid, organotypic, or spheroid and these terms are used interchangeably within the literature. Organoids are defined by their ability to mimic in vivo organ function and/or disease, they can be engineered with multiple cell types and microenvironment components, and organoids have the distinct ability to self-assemble. Like organoids, tumor organoids mimic in vivo tumor biology and recapitulate key interactions between extracellular matrix (ECM) molecules and tumor cell receptors that initiate signaling events regulating and promoting cancer. Tumor organoids prove to be very useful for modeling epithelial-mesenchymal transition (EMT), a reversible process that allows adherent epithelial cells to undergo morphological changes acquiring the motile mesenchymal cell phenotype. This phenotypic plasticity is essential for human and animal body development and wound healing, allowing cells to shed from the epithelium and invade and migrate through the microenvironment to specific locations where mesenchymal cells differentiate or induce differentiation of other cells into specialized cell types and stem cells to produce tissues, organs and bones. However, aberrant EMT is linked as a major driving force in the pathology of the most prominent human diseases: fibrosis, cardiovascular disease, inflammatory disease, eye disease, and cancer progression and metastasis. Therefore, drug discovery targeting EMT has become an attractive strategy towards more effective therapies, particularly for the treatment malignant cancers. This presentation will discuss our recent innovative tumor organoid models of various cancers, which we have engineered for 3D HCS drug discovery targeting EMT. Our tumor organoid models feature an innovative dual fluorescent biomarker reporter of EMT that can effectively track the forward and reverse EMT transition in live tumor organoids. Moreover, we will discuss hit confirmation approaches and secondary assays used to validate compounds that specifically modulate or reverse EMT. Finally, we will discuss our most recent approaches to develop patient derived tumor organoid (PDTO) models suitable for high-content analysis and screening towards achieving the goal of personalized medicine in cancer.

    Dan LaBarbera

    University of Colorado Anschutz Medical Campus

    Dr. LaBarbera is an associate professor of drug discovery and medicinal chemistry at the University of Colorado Anschutz Medical Campus. He received his PhD from Arizona State University in organic and medicinal chemistry focused on cancer drug design and development. He completed a NIH National Research Service Award postdoctoral training fellowship award focused on multidisciplinary cancer research. Dr. LaBarbera’s laboratory is engaged in preclinical drug discovery and development aimed at translating effective therapies targeting cancer, diabetes, and microbial infection. A major focus of his research is the molecular and cancer biology controlling epithelial-mesenchymal transition (EMT) and the
    identification of novel therapeutics that specifically target these mechanisms. Dr. LaBarbera's laboratory has developed pioneering approaches to HTS/HCS drug discovery using 3D tumor organoids and we are applying our innovative approaches to develop patient derived tumor organoid models for drug discovery and personalized medicine.

  • Routine lab automation with culture dishes made easy - How the PetriJet platform technology helps to makes drinking water analysis faster and better

    Contains 1 Component(s) Recorded On: 02/07/2018

    ​Digitalization, Automation and Miniaturization currently change the way we live and work. It also affects daily work in laboratories. The disruptive development of new technologies such as open source automation technology, the Internet of Things (IoT) and 3D-printing offer endless possibilities for an in-house engineering of new laboratory devices which are compact, adaptable and smart.

    Digitalization, Automation and Miniaturization currently change the way we live and work. It also affects daily work in laboratories. The disruptive development of new technologies such as open source automation technology, the Internet of Things (IoT) and 3D-printing offer endless possibilities for an in-house engineering of new laboratory devices which are compact, adaptable and smart.

    At the SmartLab systems department of the Technische Universitaet Dresden, Germany approaches for the laboratory of the future have been developed and implemented. This includes the PetriJet platform technology which was developed the automate all processes associated with culture dishes in environments such as routine laboratories for drinking water or blood samples as well as culture development for the next generation of antibiotics. The device technically is a x-y-robot consisting of two linear axles enabled to transport variable sizes and shapes of culture dishes from A to B through a 3D-printed gripper-system which can also remove the lid of the culture dish. Core part of the programming is a self-learning control software that does not need any teaching – the most time-consuming part of setting up a typical robot. With the presented solution an experiment conducted on samples is planned only once and executed for all culture dishes in the machine with the right processing stations – e. g. sample imaging – installed. It is not necessary to specify locations for culture dish piles and treated dishes are allocated dynamically while user interactions are directed by LED-lighting. The system can process more than 1.200 culture dishes in an 8-hour shift and is equipped with a storage unit for these culture dishes. Several processing stations e. g. for sample plating or drug discovery are under development

    The first PetriJet platform designed and build in the SmartLab systems lab in Dresden, Germany now operates in a routine laboratory for drinking water analysis in Hamburg, Germany and currently visually inspects a four-digit number of drinking water samples subject to infection with Legionella bacteria every day.

    The systems integrated image analysis software counts colonies on direct samples as well as on filters, sorts the samples and is linked to the LIMS of the company speeding up to inspection process by factor 4 and making the data available to customers just in time even after a night shift. Inspection quality and throughput has increased significantly and stored proof images are available even weeks after sample treatment enabling a completely new approach to data mining and infection tracking.

    Felix Lenk

    TU Dresden INT

    Felix Lenk is a Postdoc at the Institute of Natural Materials Technology at the TU Dresden, Germany, and head of the SmartLab systems department working on the next generation of laboratory devices and systems. He studied Automation & Control and Electrical Engineering at the TU Dresden, Germany and at the University of Calgary, Calgary, Alberta, Canada and graduated in 2009. In 2014 he received his PhD in Bioprocess Engineering at the TU Dresden in the field of growth modeling of plant in vitro cultures. He currently works in the field of autonomous sensor systems, laboratory automation and assistance systems as well as biological sample imaging and automatic image analysis for different biotech applications.

  • NGS-based Genome-wide Genetic Screens for RNA Processing Regulators

    Contains 1 Component(s) Recorded On: 02/07/2018

    High throughput genetic and chemical screens have been powerful tools to comprehensively identify regulators in specific cellular pathways or drug leads in both industry and academia. However, most screens rely on one or a few functional readouts, even with so-called high content screens. We have developed a robust and fully automatable screen platform that couples with NGS (next-generation sequencing) to monitor the expression of hundred-to-thousand endogenous genes associated with a phenotype without need to purify RNA.

    High throughput genetic and chemical screens have been powerful tools to comprehensively identify regulators in specific cellular pathways or drug leads in both industry and academia.  However, most screens rely on one or a few functional readouts, even with so-called high content screens.  We have developed a robust and fully automatable screen platform that couples with NGS (next-generation sequencing) to monitor the expression of hundred-to-thousand endogenous genes associated with a phenotype without need to purify RNA.  The platform is based on annealing a cohort of specific oligonucleotides to specific transcripts and/or their isoforms followed by solid phase selection, RNA-templated oligonucleotide ligation, and PCR amplification using bar-coded primers. We refer this assay strategy as RASL-seq (RNA Annealing, Selection, Ligation coupled with NGS).  Pooled samples from up to 1500 reactions in 384 wells can then be sequenced in a single lane of an Illumina sequencing flowcell to obtain quantitative information on targeted transcripts. We have previously identified a gene signature associated with activated androgen receptor (AR) on prostate cancer cells and applied RASL-seq to identify chemicals that can specific inactivate the AR pathway, thus establishing the proof-of-concept for gene signature-based chemical screens, which are equally applicable to both druggable and non-druggable targets.  We have also utilized the RASL-seq platform to screen for regulators involved in the regulation of pre-mRNA splicing and alternative polyadenylation in mammalian cells.  Coupled with our efforts in technology development, we have developed a bioinformatics pipeline to process the data for quantitative and network analyses.  The RASL-seq platform thus offers a general solution to pathway dissection in both genetic and chemical screens.

    Hai-Ri Li

    University of California, San Diego

    Name: Hai-Ri Li    Affiliation: University of California, San Diego
    Position Institution Year(s)
    Project Scientist UC, San Diego 2007-Present
    Postdoc Fellow UC, San Diego 2002-2006
    Postdoc Fellow Sanford-Burnham Medical Research Institute 2000-2001
    EDUCATION / TRAINING:
    Institution or Location Degree Year(s) Field of Study
    China Medical University MS 1986-1989 Endocrinology, Medicine
    Jiamushi Medical College, China MD 1977-1983 Medicine
    EXPERTISE AND RELEVANT EXPERIENCE
    I have been involved in genomics, especially in RNA. I developed two simple RNA-seq methods, one for whole transcriptome profiling and another for 3’ end of RNA profiling. In addition, I developed a target gene profiling method, i.e. RASL-seq (RNA-mediated Annealing, Selection and Ligation, combined with high-throughput sequencing). We applied it to chemical screening in prostate cancer cells. We selected hundred genes in androgen receptor pathway as signatures and observe which chemical compounds affect androgen receptor pathway genes. We sequenced 1536 samples in single lane of Illumina flowcell and found a few promising candidates for prostate cancer therapy. Recently we also applied it to identification of RNA processing regulators and found new previously undescribed RNA processing regulators, which will be helpful in exploring new mechanisms in different diseases.

  • SLAS2018 Innovation Award Finalist: Combinatorial Drug Screening, High-Throughput Flow Cytometry, and Agile Integration: a Modern Platform for Personalized Treatment Discovery for Cancer Patients

    Contains 1 Component(s) Recorded On: 02/07/2018

    ​Our mission at Notable Labs is to identify actionable, personalized treatments for cancer patients. To help us achieve this goal, we have developed a platform that combines combinatorial testing, drug repurposing, and several high-throughput technologies to automate our phenotypic screens on primary patient samples. Our current focus is in acute myeloid leukemia (AML) and other hematological malignancies, although the platform and assay can be extended to other indications.

    Our mission at Notable Labs is to identify actionable, personalized treatments for cancer patients. To help us achieve this goal, we have developed a platform that combines combinatorial testing, drug repurposing, and several high-throughput technologies to automate our phenotypic screens on primary patient samples. Our current focus is in acute myeloid leukemia (AML) and other hematological malignancies, although the platform and assay can be extended to other indications.

    Our automation platform includes our custom-made laboratory information management system (LIMS) working in tandem with our robotic workcell, which handles all screening and assay operation. The architecture of this system is designed to allow for separation between conceptualization and execution; scientists can plan their screens and experiments through the LIMS, then walk to the workcell and start an automated run that executes their plan, with minimal preparation.

    The core assay in this platform is a high-throughput flow cytometry assay that generates a wealth of phenotypic data on the primary patient samples that are run through the system. Over the course of the assay, the robotic scheduler serves as a middleman for information flow between the LIMS and the workcell instruments themselves. The LIMS sends relevant data about the screen to the scheduler, which in turn acts on the data and directly controls the instruments to run the assay.

    After completion of an assay, raw data flows back from the workcell instruments to the scheduling software, which consolidates the data and uploads it to our cloud-based LIMS. From there, a number of in-house software tools are used to streamline our flow cytometry data analysis, allowing scientists to analyze complex, multi-dimensional flow data across thousands of wells.

    The platform has been validated across a number of patients. Our data has led to actionable treatment options for relapsed and refractory patients that has, in some instances, resulted in complete remission in AML patients. The platform and architecture that we have developed brings together our collective hardware, software, and biological knowledge, and demonstrates the predictive power of our data-driven approach to personalized medicine for cancer patients.

    Transon Nguyen

    Notable Labs

    Transon Nguyen is the lead automation engineer at Notable Labs, where he develops hardware and software systems to advance Notable's high-throughput screening capabilities.  He was previously a biomedical engineer at the Charles Stark Draper Laboratory, developing advanced in vitro organ systems for drug discovery.  Transon holds an M.S. in Mechanical Engineering from the Massachusetts Institute of Technology, and an M.S. in Biomedical Engineering from the University of California, Irvine.

  • Spatial neuron cell-type mapping in mouse brain by in situ sequencing

    Contains 1 Component(s) Recorded On: 02/07/2018

    Single-cell RNA-seq (scRNA-seq) is a powerful tool to classify cells to molecularly defined cell types. However, the information about absolute frequency of cells and exact spatial location the within the original tissue is lost.

    Single-cell RNA-seq (scRNA-seq) is a powerful tool to classify cells to molecularly defined cell types. However, the information about absolute frequency of cells and exact spatial location the within the original tissue is lost. The brain is the most complex tissue in mammals with respect to the number of different cell-types and the way they are arranged locally and through long range cell-to-cell connections. Here, we demonstrate that in situ sequencing (Ke et al., Nat.Meth., 2013) can be used to build a cell-type spatial map of 100 000s of cells in sections of mouse brain. We use in situ sequencing to map the activity of 99 marker genes within single cells across sections of mouse brains. The marker genes are selected to identify neurons in cortex and hippocampus as defined by scRNAseq. In a single experiment on a single standard microscopy slide, we can analyse four coronal brain sections from adult mice. Each section contains around 100,000 cells and we generate about 3 million reads per section. The read distribution for individual marker genes matches well with the Allen Brain Atlas. To turn the 99 molecular distributions into cell-types, we use a probabilistic approach to assign identity to individual cells based on comparison with the profiles of 35 cell types as defined by scRNA-seq. The sensitivity of the approach is demonstrated by our identification of rare Pvalb-expressing cells among pyramidal cells in stratum pyramidale, and Cck-positive cells, in stratum radiatum.


    Ke, R., Mignardi, M., Pacureanu, A., Svedlund, J., Botling, J., Wahlby, C. & Nilsson, M. In situ sequencing for RNA analysis in preserved tissue and cells Nat. Methods 10, 857-860 (2013).

    Mats Nilsson

    Science for Life Laboratory, Stockholm University

    My research is focused on development and application of novel molecular analysis tools and systems. I have pioneered the development of methods based on DNA circularization, i.e., padlock and selector probes, as well as rolling circle amplification (RCA). The work involves fundamental studies of nuclease- and nucleic acid hybridization mechanisms, design assays based on them and integrate these into methods and systems. I have a large international network of collaborators in academia and industry, ranging from basic molecular biology and physics to clinicians and engineers. The aim is to enable new research and to bring some technologies towards diagnostic use. My group is based at Science for Life Laboratory, which is a joint cross-disciplinary research center at the Karolinska Campus, formed by Stockholm University, Karolinska Institutet, and KTH. I have authored more than 140 scientific articles that have been cited more than 7 000 times (h-index 40).