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  • Small molecule direct binding by use of ASMS for target tractability assessment and high throughput hit identification

    Contains 1 Component(s) Recorded On: 02/05/2018

    This presentation will highlight the ASMS platform developed for hit identification and target tractability assessments and illustrate its application of a kinase screening campaign as a proof of concept.

    Affinity Selection Mass Spectrometry (ASMS), a label free assay that connects a binding event to the accurate mass identity of the ligand involved, is an established HTS triage platform at GSK that has been used to generate hit qualification data on more than 60 targets during the past three years. As part of a paradigm shift to screen novel targets, we are exploring the use of ASMS for hit identification, target tractability assessments and tool compound identification.  The benefits include reduced cycle time through streamlined assay development, and reduced attrition through identification of compounds that directly engage the target protein. A mass-encoded 180,000 compound library has been created for ASMS screening, and is comprised of compounds that represent aspirational chemical space in terms of molecular weight, cLogP and property forecast index. The output of the ASMS platform has been evaluated against existing target-specific biochemical and biophysical data to develop a better methodology that maximizes the identification of biochemically active compounds while minimizing the overall hit rate. Nearly 85% of compounds with known biochemical and/or biophysical activity showed binding to a protein target with our platform. A sub-set of the full library is being used to evaluate target tractability, and has been used to screen 30+ historical targets, with the goal of correlating compound binding to tractability predictions.  Overall, ASMS tractability outcomes align well with Encoded Library Technology (ELT) and HTS tractability observations. From a methods optimization perspective, continued development of the sample preparation protocols and the LC-MS platform are being targeted to maximize sensitivity and increase platform throughput. Furthermore, the development of an end-to-end informatics solution will complement the analytical platform. This presentation will highlight the ASMS platform developed for hit identification and target tractability assessments and illustrate its application of a kinase screening campaign as a proof of concept.

    Geoff Quinque

    GlaxoSmithKline

    I have been in my current group for six years at GlaxoSmithKline within the Screening, Profiling & Mechanistic Biology Department. Our group is responsible for assay development, high throughput screening and compound profiling and mechanistic characterization of small molecules from target validation through pre-clinical candidate selection. 

  • Automating gene editing for deciphering cancer pathways using microfluidics

    Contains 1 Component(s) Recorded On: 02/05/2018

    In my presentation, I will describe our system to automate gene-editing processes specific to the CRISPR-Cas9 editing workflow, namely cell culturing, lipid-mediated transfection, and cellular analysis. Next, I will show results from optimizing our gene-editing platform to assess the impact of variations in several parameters on the efficacy of cell transfection and gene targeting using Cas9. Finally, we will demonstrate the broad applicability of the device showing results from a knockout loss-of-function screen that is tackling several oncogenes. Overall, this study aims at demonstrating that our genome editing-on-a-chip approach will greatly speed up validation of loss-of-function screens, including genome wide arrayed or pooled screens, at relatively low cost, with minute amount of material and without the need for enrichment analysis based on next-generation sequencing profiles as required by pooled screens.

    In recent years, we have witnessed a breakthrough in genome engineering technology, attributed to the gene-editing technique CRISPR-Cas9 (or often called CRISPR) that works like a pair of scissors to cut, insert or reorder specific genetic fragments, creating changes in the biological cell to understand gene function. CRISPR is full of promise and has already been used in a variety of applications such as to help create mosquitoes that do not transmit malaria (Hammond et al. 2016), to eradicate pathogen genomes from infected species (Ebina et al. 2013, Hu et al. 2014), and more recently to test and to battle cancer (Sanchez-Rivera and Jacks 2015, Shi et al. 2015, Platt et al. 2014). However, with the advent of this technology, there is still a lack of new treatments found for cancer.  Progress in this area has been hindered primarily by the lack of automation tools for manipulating, editing, and analyzing large genomes without any bias – this has limited our understanding of the genes and biological processes involved with cancer.  Here, I will describe how we have developed a new automated microfluidic tool that will target a specific set of genes in lung cancer cells (specifically H1299 cells) and determine which genes are modulators of cancer progression.  This new gene-editing tool powered by droplet-based microfluidics is being used to eliminate multiple perturbations within cells while the readouts will depend on cell population measurements.  Such a technology has emerged as a versatile liquid handling platform for automating biology (Shih et al. 2013) (Ng et al. 2015) and screening-based applications (Dressler, Casadevall, and deMello 2017).  In my presentation, I will describe our system to automate gene-editing processes specific to the CRISPR-Cas9 editing workflow, namely cell culturing, lipid-mediated transfection, and cellular analysis.  Next, I will show results from optimizing our gene-editing platform to assess the impact of variations in several parameters on the efficacy of cell transfection and gene targeting using Cas9. Finally, we will demonstrate the broad applicability of the device showing results from a knockout loss-of-function screen that is tackling several oncogenes.  Overall, this study aims at demonstrating that our genome editing-on-a-chip approach will greatly speed up validation of loss-of-function screens, including genome wide arrayed or pooled screens, at relatively low cost, with minute amount of material and without the need for enrichment analysis based on next-generation sequencing profiles as required by pooled screens.  We believe that this new method will further enhance our understanding of mechanisms related to cancers, which we hope can possibly lead to novel therapies options for those suffering from this disease.

    Hugo Sinha

    Concordia University

    I am French and American and grew up between both continents, following a bilingual curriculum my entire life. I moved to Montreal in 2012 and obtained an BSc in Biology with Distinction in 2016 from Concordia University. I immediately started a research-based MAsc in Electrical and Computer Engineering, attempting to bridge the gap between biology and engineering specifically by automating synthetic biology with digital microfluidics for high-throughput screenings. With the current stalling of traditional biological technique, I am excited by the avenues that this field is opening and look forward to continuing to work with microfluidics.

  • Addressing the Scalability of Human iPSC-derived Neurons for HTS Implementation and Phenotypic Screening

    Contains 1 Component(s) Recorded On: 02/05/2018

    Traditional high throughput screening (HTS) assays for neuronal targets employ non-primary non-human neuronal cells due to the scale necessary for HTS as well as the unreliable and economically demanding nature of primary neurons. The discovery of new drugs for neuropsychiatric disorders have further been hampered by lack of access to disease-relevant human primary neurons and appropriate disease models.

    Traditional high throughput screening (HTS) assays for neuronal targets employ non-primary non-human neuronal cells due to the scale necessary for HTS as well as the unreliable and economically demanding nature of primary neurons. The discovery of new drugs for neuropsychiatric disorders have further been hampered by lack of access to disease-relevant human primary neurons and appropriate disease models. Human induced pluripotent stem cell (hiPSC) technology can address some of the obstacles by allowing the generation of human neurons through (1) embryoid body (EB) formation, (2) cultivation on stromal feeder cells, and, (3) employing lineage specific differentiation factors. The former techniques for hiPSC reprogramming (1&2) are slow, variable, and not yet scalable for HTS applications. Straightforward methods to reproducibly differentiate hiPSCs to functional cortical induced neurons (iN) in less than two weeks by forced expression of a single transcription factor has been demonstrated but, never taken to the HTS scale (3). We have successfully recapitulated the aforementioned technique and leveraged the CRISPR technology to define the path to a plate-compatible format amenable for large-scale HTS implementation. The resulting iN cells exhibit appropriate genetic and fluorescent markers that give confidence of bonafide neuronal differentiation. Imminently, we intent to test the preliminary iN cells for their ability to post-mitotically increase synaptogenesis following treatment with LOPAC test compounds via staining for synaptophysin. Ultimately, we will determine reliability and reproducibility over time with industrial scale robotics. Furthermore, we also intend to leverage the CRISPR technology to create a library of disease-relevant-phenotypes from hiPSC-derived cellular models that will provide more opportunities for all biologists to study epigenetic mechanisms and scale-up screening initiatives with Scripps Research Institute Molecular Screening Center (SRIMSC).

    BanuPriya Sridharan

    The Scripps Research Institute Molecular Screening Center, Scripps Florida

    Postdoctoral-Associate at the Molecular Medicine Department in the Scripps Research Institute (Florida), a Ph. D. in Bioengineering with over 6 years of experience with 3D cell models for diagnostic and regenerative medicine applications. Proven record of publications with stem cell applications and enthusiastic about long-term, multidisciplinary collaborations geared towards hit identification and lead optimization. Strong personal interest in the development of the next generation of more physiologically relevant cell-based CRISPR assays relying on stem cells and either high-content or functional readouts.

  • SLAS2018 Innovation Award Finalist: Ultrafast all-optical laser-scanning imaging - Enabling deep single-cell imaging and analysis

    Contains 1 Component(s) Recorded On: 02/05/2018

    ​Studying cell populations, their transition states and functions at the single cell level is critical for understanding in normal tissue development and pathogenesis of disease. However, current platforms for single-cell analysis (SCA) lack the practical combination of throughput and precision that is limited by the prohibitive costs and time in performing SCA, very often involving thousands to millions individual cells – largely explaining the limited applications of SCA to date.

    Studying cell populations, their transition states and functions at the single cell level is critical for understanding in normal tissue development and pathogenesis of disease. However, current platforms for single-cell analysis (SCA) lack the practical combination of throughput and precision that is limited by the prohibitive costs and time in performing SCA, very often involving thousands to millions individual cells – largely explaining the limited applications of SCA to date. For creating new scientific insights and enriching the diagnostic toolsets, it is valuable to explore alternative biomarkers, notably biophysical markers, which maximizes the cost-effectiveness of SCA because of its label-free nature. Also, as it is closely tied with many cellular behaviours, biophysical markers can complement and correlate with the information retrieved by existing biochemical markers with high statistical precision – providing a comprehensive catalogue of single-cell properties and thus a new landscape of “Cell Altas”. Optical microscopy is an effective tool to visualize cells with high spatiotemporal resolution. However, its full adoption for high-throughput SCA has been hampered by the intrinsic speed limit imposed by the prevalent image capture strategies, which involve the laser scanning technologies (e.g. galvanometric mirrors), and/or the image sensors (e.g. CCD and CMOS). The laser scanning speed is fundamentally limited by the mechanical inertia of the mirrors whereas the image capture rate of CCD/CMOS sensor is fundamentally limited by the required image sensitivity. Notably, this speed-versus-sensitivity trade-off of the image sensor explains why the throughput of flow cytometry has to be scaled down from 100,000 cells/sec to 1,000 cells/sec when the imaging capability is incorporated. To address these challenges, we adopt two related techniques to enable imaging flow cytometry with the unprecedented combination of imaging resolution and speed. Sharing a common concept of all-optical laser-scanning by ultrafast spatiotemporal encoding of laser pulses, these techniques, time-stretch imaging and free-space angular-chirp-enhanced delay (FACED) imaging enable ultrahigh-throughput single-cell imaging with multiple image contrasts (e.g. quantitative phase and fluorescence imaging) at a line-scan rate beyond 10’s MHz (i.e. an imaging throughput up to ~100,000 cells/sec). Moreover, they also enable quantification of intrinsic biophysical markers of individual cells – a largely unexploited class of single-cell signatures that is known to be correlated with the overwhelmingly investigated biochemical markers. All in all, these ultrafast single-cell imaging platforms could find new potentials in deep machine learning complex biological processes from such an enormous size of image data (from molecular signatures to biophysical phenotypes), especially to unveil the unknown heterogeneity between different single cells and to detect (and even quantify) rare aberrant cells.

    Kevin Tsia

    The University of Hong Kong

    Kevin Tsia received his Ph.D. degree at the Electrical Engineering Department, at UCLA, in 2009. He is currently an Associate Professor in Department of Electrical and Electronic Engineering at the University of Hong Kong. His research interest covers a broad range of subject matters, including ultra-fast real-time spectroscopy and microscopy for biomedical applications such as imaging flow cytometry, MHz optical coherence tomography. He received Early Career Award 2012-2013 by the Research Grants Council (RGC) in Hong Kong. He also received the Outstanding Young Research Award 2015 at HKU as well as 14th Chinese Science and Technology Award for Young Scientists in 2016. His recent research on ultrafast imaging technology, dubbed “ATOM” and "FACED", has been covered by local media and scientific magazines. He is author/coauthor of over 130 journal/conference papers and book chapters. He holds 2 granted and 3 pending US patents on ultrafast imaging technologies.

  • A highly-reproducible automated protein sample preparation workflow for quantitative mass spectrometry in plasma or blood

    Contains 1 Component(s) Recorded On: 02/05/2018

    Sample preparation for protein quantification by mass spectrometry requires multiple processing steps including denaturation, reduction, alkylation, protease digestion, and peptide cleanup. Scaling these procedures for the analysis of numerous complex biological samples, such as plasma, can be tedious and time-consuming, as there are many liquid transfer steps and timed reactions where technical variations can be introduced and propagated. Therefore, we have automated this digestion workflow and adapted it to include the preparation of dried blood obtained from remote sampling devices, allowing high throughput analysis of both archived and “real-time” sampling of our pathological surveillance biomarkers.

    Sample preparation for protein quantification by mass spectrometry requires multiple processing steps including denaturation, reduction, alkylation, protease digestion, and peptide cleanup. Scaling these procedures for the analysis of numerous complex biological samples, such as plasma, can be tedious and time-consuming, as there are many liquid transfer steps and timed reactions where technical variations can be introduced and propagated. Therefore, we have automated this digestion workflow and adapted it to include the preparation of dried blood obtained from remote sampling devices, allowing high throughput analysis of both archived and “real-time” sampling of our pathological surveillance biomarkers.  Our pathological surveillance biomarker assay is composed of 72 plasma proteins that screen for 8 pathological signatures. METHODS. We established an automated sample preparation workflow with a total processing time for 96 plasma or blood samples of 5 hours, including a 2-hour incubation with trypsin. Peptide cleanup is accomplished by online diversion during the LC/MS/MS analysis.  RESULTS. In a selected reaction monitoring (SRM) assay targeting 6 plasma biomarkers and spiked β-galactosidase, mean intra-day CVs for 5 samples ranged from 5.5%-8.9% for serum and 3.9%-7.2% for plasma, and mean inter-day CVs over 5 days ranged from 5.8%-10.6% for serum and 3.9%-6.0% for plasma. As well for the highly multiplex surveillance biomarker assay, 90% of the transitions from 6 plasma samples repeated on 3 separate days had total CVs below 20%. Similar results were obtained when the workflow was transferred to a second site: 93% of peptides had CVs below 20%. In an analysis of plasma samples from 48 individuals (disease and healthy), the average CVs for spiked β-galactosidase was < 15%. The workflow was adapted for the direct processing of remote blood sampling devices (Neoteryx) and achieved equivalent high performance for spiked β-galactosidase when part of a 10 and 72 protein SRM assays. 

    Jennifer Van Eyk

    Cedar Sinai Medical Center

    PhD in Biochemistry

  • 1-D High-Throughput Screening Assays for Primary Human T Cells

    Contains 1 Component(s) Recorded On: 02/05/2018

    The parallel microfluidic cytometer (PMC) is an imaging flow cytometer that operates on statistical analysis of low-pixel-count, one-dimensional (1-D) line scans. It is efficient in data collection and operates on suspension cells. Our 1-D instrument leverages both the high throughput aspects of traditional flow cytometry and the high spatial content of 2-D imaging cytometers. In this talk, we present a supervised automated pipeline for the PMC that minimizes operator intervention by incorporating automated multivariate logistic regression for data scoring.

    The parallel microfluidic cytometer (PMC) is an imaging flow cytometer that operates on statistical analysis of low-pixel-count, one-dimensional (1-D) line scans. It is efficient in data collection and operates on suspension cells. Our 1-D instrument leverages both the high throughput aspects of traditional flow cytometry and the high spatial content of 2-D imaging cytometers. In this talk, we present a supervised automated pipeline for the PMC that minimizes operator intervention by incorporating automated multivariate logistic regression for data scoring. The approach quantifies biomarker localization of activated T cells into a single descriptive ‘activity score’ readout. Reducing complex phenotypes into a simple readout has many advantages for drug screening and characterization. We test the self-tuning statistical algorithms in human primary T cells in flow with various drug response assays. We readily achieve an average Z’ of 0.55 and SSMD of 13. The PMC is volume efficient, needing only 4 µL of sample volume per well. Anywhere from 3000 to 9000 independent sample tests can be processed from a single 15 mL blood donation. The parallel nature of our laser scanning system enables high well throughput and is extremely scalable. We conclude that the new technology will support primary cell protein localization assays and “on-the-fly” data scoring at a sample throughput of more than 100,000 wells per day. This is, in principle, consistent with large-scale primary pharmaceutical screens. We demonstrate that 1-D imaging provides many advantages for rapid development of primary T cell assays in flow.

    Steve Wang

    Boston University

    Previous research assistant in Daniel Ehrlich's group at Boston University. Developed 1-D cytometry methods and high throughput automated statistical approaches to screening in primary T cells.

  • Applying 'NewSQL' technologies to scientific data to enable self-guided data discovery and analysis

    Contains 1 Component(s) Recorded On: 02/05/2018

    Scientific data organization and analysis remains a significant impediment to drug discovery particularly in late-stage animal studies, despite years of effort and ongoing “data lake” projects. Recent shifts to more heavily employ outsourced research have further fragmented data standards, increased reliance on ad hoc reports, and yielded single-use data. We have developed a novel approach to data curation and aggregation that enables scientists to self-serve scientific data regardless of its originating source and deposit those data into self-guided and open-ended analysis.

    Scientific data organization and analysis remains a significant impediment to drug discovery particularly in late-stage animal studies, despite years of effort and ongoing “data lake” projects.   Recent shifts to more heavily employ outsourced research have further fragmented data standards, increased reliance on ad hoc reports, and yielded single-use data.  We have developed a novel approach to data curation and aggregation that enables scientists to self-serve scientific data regardless of its originating source and deposit those data into self-guided and open-ended analysis.  Our approach relies on NoSQL database technologies to connect to structured existing data source(s) (like internally developed data lakes) or ad hoc sources like folders of Excel spreadsheets.  All the results, regardless of source, are indexed into a common data shape that drive performance and ensures a consistent user experience.  Discovered results are presented through RESTful web services or a “NewSQL” front-end.  During the course of the past year, we have refined this approach through a collaborative program with a large drug discovery company.  In this presentation, we will describe the motivation of our approach, show the results, and provide metrics for how much this novel approaches speed data discovery and utilization.

    Daniel Weaver

    PerkinElmer

    Dr. Daniel C. Weaver is a Senior Product Manager for Research Informatics at PerkinElmer Informatics.  Prior to joining PerkinElmer, Dr. Weaver was the Director of Scientific Computing at Array Biopharma, Inc. in Boulder, Colorado, where he led all aspects of scientific software development and acquisition.  Over the course of the last decade, Dr. Weaver’s team delivered systems to support scientific endeavors ranging from target identification though drug discovery and into clinical development and translational medicine.  In a previous life, Dr. Weaver was the Lead Scientist for Gene Expression Analysis at Genomica.  He received his doctorate in developmental genetics from the University of Colorado, Boulder under the direction of Dr. William B. Wood where he studied patterning in early development. 

  • Development of an Automated High Throughput CHO Stable Pool Platform for Generating Large Protein Collections

    Contains 1 Component(s) Recorded On: 02/05/2018

    Innovative solutions ranging from a new software Dashboard to manage projects and execute processes, a recently developed non-invasive Flask Density Reader and an upgraded harvest and purification system compatible with magnetic beads will be presented.

    Recombinant protein expression and purification is a central process in biomedical research and Chinese hamster ovary (CHO) cells are a primary workhorse for protein production from mammalian cells.  GNF has developed a robust suite of software and automated systems to support high throughput CHO (HT-CHO) stable pool establishment, archive of cell banks and protein purification.  Pools are established in 96-well plates, maintained until they are ready for scale up, and then expanded into an AutoFlask™.  Once cells reach the desired density, cell bank archives are created and one or more batch production AutoFlasks™ are inoculated depending on the amount of protein requested.  As an example, a single 50mL culture expressing a human IgG1 antibody typically yields 10 milligrams of protein.  Innovative solutions ranging from a new software Dashboard to manage projects and execute processes, a recently developed non-invasive Flask Density Reader and an upgraded harvest and purification system compatible with magnetic beads will be presented.  This platform enables cost-effective, facile production of proteins at quantities and quality useful for early stage drug discovery tasks such as screening, protein engineering and even in vivo studies. 

    Paul Anderson

    Gnf Systems

    BS Chemical Engineering, UCSB, 2003; MS Biomedical Engineering, Case Western Reserve University, 2005; Senior Automation Engineer, Genomics Institute of the Novartis Research Foundation, 2005-Present

  • Modeling the contribution of common variants to schizophrenia risk.

    Contains 1 Component(s) Recorded On: 02/05/2018

    Schizophrenia (SZ) is a debilitating psychiatric disorder for which the complex genetic mechanisms underlying the disease state remain unclear. Whereas highly penetrant variants have proven well-suited to human induced pluripotent stem cell (hiPSC)-based models, the power of hiPSC-based studies to resolve the much smaller effects of common variants within the size of cohorts that can be realistically assembled remains uncertain.

    Schizophrenia (SZ) is a debilitating psychiatric disorder for which the complex genetic mechanisms underlying the disease state remain unclear. Whereas highly penetrant variants have proven well-suited to human induced pluripotent stem cell (hiPSC)-based models, the power of hiPSC-based studies to resolve the much smaller effects of common variants within the size of cohorts that can be realistically assembled remains uncertain. We identified microRNA-9 as having significantly downregulated levels and activity in a subset of SZ hiPSC-derived neural progenitor cells NPCs, a finding that was corroborated by a larger replication cohort and further validated by an independent gene-set enrichment analysis of the largest SZ genome-wide association study (GWAS) to date. Overall, this demonstrated a remarkable convergence of independent hiPSC- and genetics-based discovery approaches.  In developing this larger case/control SZ hiPSC cohort of hiPSC-derived NPCs and neurons, we identified a variety of sources of variation, but by reducing the stochastic effects of the differentiation process, we observed a significant concordance with two large post mortem datasets. We predict a growing convergence between hiPSC and post mortem studies as both approaches expand to larger cohort sizes. Meanwhile, we have been integrating CRISPR-mediated gene editing, activation and repression technologies with our hiPSC-based neural platform, in order to develop a scalable system for testing the effect of a manipulating the growing number of SZ-associated variants and genes in NPCs, neurons and astrocytes. Altogether, our objective is to understand the cell-type specific contributions of SZ risk variants to disease predisposition.

    Kristen Brennand

    ISMMS

    Kristen Brennand, PhD is an Associate Professor of Genetics and Genomics, Neuroscience and Psychiatry at the Icahn School of Medicine at Mount Sinai, in New York, New York. She trained in developmental and stem cell biology at Harvard University and in neurobiology during postdoctoral at the Salk Institute for Biological Studies. By combining expertise in stem cell biology and neurobiology, she has pioneered a new approach by which to study psychiatric disease. Dr. Brennand’s work is funded by the National Institutes of Health, the New York Stem Cell Foundation, the Brain Research Foundation and the Brain and Behavior Research Foundation.

  • MALDI-TOF-MS - A label free technology for high throughput screening

    Contains 1 Component(s) Recorded On: 02/05/2018

    In the past, the throughput of MS-based assay technologies was limited, but recent developments in the field of MALDI-TOF-MS devices and spotting technologies substantially increased the ability for miniaturization and speed of such approaches. The talk will shed light on challenges in this process and provides results of this application in high throughput screening projects.

    Mass spectrometry (MS) is an emerging technology for identifying and characterizing molecules that modulate biological targets, offering a label free, direct detection method. This technology enables the application of more physiologically relevant assays and reduces time and costs compared to current classical approaches increasing the efficiency of the drug discovery process.

    In the past, the throughput of MS-based assay technologies was limited, but recent developments in the field of MALDI-TOF-MS devices and spotting technologies substantially increased the ability for miniaturization and speed of such approaches. However, the application of MALDI is based on a matrix-compatible sample preparation step and is limited to a certain space of analytes. This requires the identification of MALDI compatible, physiological relevant assay conditions, as well as development of fast and reproducible liquid handling procedures. The talk will shed light on challenges in this process and provides results of this application in high throughput screening projects.

    Frank Buettner

    Boehringer-Ingelheim Pharma GmbH & Co.KG

    Laboratory Leader