Annual Meeting of the Society for Biomolecular Imaging and Informatics
Education Day, Sep. 17, 2019
At the 2019 Annual Meeting in Boston, the Society will offer three tutorial tracks during the Education Day on Sep. 17, 2019 (the first day of the conference). The participants will have an opportunity to learn about biology-related data-science methods (including image processing, data reduction, and visualization), assay development strategies, as well as familiarize themselves with various technologies used in high-content screening and analysis (cytometry, modern imaging modalities, etc.). The educational program is intended for beginners and experienced users of the technology. The flexible schedule is designed to allow participation in three presentations and practical session. The Data Track includes a hands-on image analysis session employing CellProfiler. The attendees are encouraged to bring their laptop computers with pre-installed CellProfiler software to follow along with the demonstration by the instructor.
The Education Day lunch and the practical session are sponsored by
Assay Development and Standardization Track
Introduction to image analysis (Mark Bray)
Basic Concepts in Imaging-Based High-Throughput Screening and High-Throughput Profiling Assay Development (Joshua Harrill)
Introduction to flow cytometry for microscopists (Dave Gebhard)
Data dimensionality reduction: PCA, t-SNE, UMAP (Anna Belkina)
Introduction to statistics for HCS/HTS (Bartek Rajwa)
3D optical microscopy - methods, techniques, developments (Seungil Kim)
Advanced image analysis: feature extraction, machine learning (Beth Cimini)
Assay Guidance Manual - content, use, contributions (Nathan Coussens)
Imaging mass spectroscopy (Jared Burks)
Lunch break sponsored by Fujifilm
Fujifilm-sponsored lunch and laptop set-up for the practical sessions
Practical image analysis with CellProfiler (Santosh Hariharan) [till 2:45 PM]
HCS of Protein-Protein Interactions (Paul Johnston)
Development of 3D culture based assays (Olivier Frey)
Both human expert-analysts and many machine learning algorithms struggle with multidimensional datasets. However, such datasets often contain partially redundant features, and with so-called dimensionality-reduction methods, one can create a low-dimensional representation of high-dimensional data while retaining most of the information. In this tutorial, the participants will be first presented with a general overview of dimensionality reduction concepts, following by an exploration of various popular dimensionality reduction methods such as PCA, t-SNE, and UMAP. Using several toy and real-life datasets, we will demonstrate and discuss each of these techniques and their applicability for biological data analysis. Although we will briefly touch on feature extraction approaches for cell imaging, the tutorial will focus mainly on visualization-enabling data processing methods. This tutorial is intended for biologists who are interested in computational approaches designed to explore their data in a comprehensive and unbiased manner. The participants will learn how various classes of single-cell data (including microscopy, flow and mass cytometry, and single-cell transcriptomics) can be processed for 2- and 3-D representation, allowing convenient visualization and easier interpretation.
This introduction will acquaint attendees with the concepts, methods, software and workflows behind automated image analysis. We will introduce the researcher to the basic principles behind determining which pixels in an image below to each cell and/or cellular compartments and measuring properties of interest, with the intent of providing a fuller understanding of the rich information available for discerning phenotypes of interest. No prior knowledge is assumed, though attending the companion introductory sessions is recommended.
Image analysis can be a powerful tool for biologists due to its adaptability and flexibility. In addition to finding and counting objects, it can create hundreds or thousands of measurements for every object found, allowing users to classify objects on either simple or complex criteria. In this session, we will cover some classes of measurements frequently used in bioimaging assays, tools for capturing them, and cases where this sort of learning can be particularly powerful. The tutorial will expand the topics covered in the introductory image analysis session presented by Mark Bray.
The NCATS Assay Guidance Manual (AGM) is an eBook of best practices for the design, development, and implementation of robust assays for early drug discovery. Initiated by pharmaceutical company scientists, the manual provides guidance for designing a “testing funnel” of assays to identify genuine hits using high-throughput screening (HTS) and advancing them through preclinical development. With contributions from more than 100 scientists, much of this information was previously "tribal knowledge" within the pharmaceutical industry and is not readily found in a classroom or the literature. Combined with a workshop/tutorial component, the overall goal of the AGM is to provide a valuable resource for training translational scientists. Expected Educational Benefits: The NCATS Assay Guidance Manual eBook is intended to benefit the worldwide drug discovery community by providing guidelines and best practices for the successful design, optimization, implementation, and interpretation of robust assays suitable for early stage discovery. The eBook is complimented by a workshop series, data analysis tools, and educational videos. This presentation will highlight the wide range of topics, tools, and events enabled by the AGM, as well as the AGM history and future perspectives. Targeted Audience Level: This presentation will benefit students, early-career researchers, and experienced investigators, who are interested in robust assay design, development, and implementation to support early-stage drug discovery.
Imaging Mass Cytometry was developed from a suspension based mass cytometry (CyTOF) founded in inductively coupled plasma (ICP) mass spectroscopy. Using carrier molecules, isotopically enriched lanthanide metals are attached to antibodies. These metal labeled antibodies are then used to detect target proteins in tissues, cells, or most anything attached to a microscope slide. Detection of these metal labeled antibodies occurs when a laser is used to ablate the sample material from the slide thus introducing the lanthanide metals to the ICP. Once the material and lanthanide metals are ionized in the ICP torch, 5500-7500 Kelvin, the lanthanide metals (high mass) are separated by their relative masses in a time of flight (TOF) chamber. The sample is interrogated via a raster scan, introducing the sample material into the detector a pixel at a time. When the pixel data is reassembled a quantitative image is generated. Come to the talk for the details, high-plex imaging that results, and what and why we employ this technology.
The use of 3D cell culture models and organ-on-chip systems is rapidly expanding because they are recognized as representing more structurally and physiologically relevant models of in vivo biology. Making 3D cell structures accessible to high content imaging and screening poses a next level of multi-disciplinary challenges on development teams. This course will provide a short overview of available scalable 3D cell culture models ranging from individual scaffold-free culture approaches to complex multi-organ devices. The advantages and limitations of the various culture models, assays and imaging approaches will be addressed and discussed.
Flow cytometry is a legacy technology for single cell analysis that shares many of the same underlying fundamental principles with quantitative image analysis. Flow cytometry and quantitative image analysis are both used to derive high content data from single cells. This tutorial will review the concepts and fundamentals of flow cytometry, terms, operations and processes, and will compare and contrast flow and image cytometry to help attendees better understand how flow and image cytometry can complement and inform each other. No prior knowledge of flow cytometry is required. The material assumes a working knowledge of fluorescence applications.
Automated imaging and analysis have become the workhorse for the current high content screening strategy. In combination with machine learning methods, biologists can now get additional insights regarding fundamental biological processes. CellProfiler is one of the most widely used open-source software for automated analysis of cell images. With its easy to use user interface biologists can build advanced analysis pipeline without the need for in-depth knowledge of image processing. In this workshop, we will demo the installation and usage of CellProfiler. We will go through all the basic steps necessary to build pipeline, extract numerical features from cell images as well as analyze high dimensional data using CellProfiler Analyst through sample image datasets. Attendees can bring their laptops and work alongside using instructions from the workshop.
This session introduces basic concepts of imaging-based high-throughput screening (HTS) and high-throughput profiling (HTP) assay development. HTS assays are designed to evaluate a discrete cellular process and produce a single, or small number of quantitative outputs. In contrast, HTP assays measures dozens to thousands of features and provide highly-multiplexed quantitative outputs. Either type of approach may be used to evaluate the effects of chemicals or other perturbagens on cellular biology. Topics for this session include (but are not limited to) considerations for model selection, endpoint selection, HCS assay design, identification and use of positive control and reference chemicals, methods for evaluating assay dynamic range and approaches for evaluating assay reproducibility. Attendees will gain a basic foundational knowledge of guiding principles underlying the development of imaging-based HTS and HTP assays. The views expressed in this presentation are those of the author and do not necessarily reflect USEPA policy.
Protein-protein interactions (PPIs) are obligatory for all cellular functions and represent potential therapeutic targets for drug discovery. Never-the-less, the relative paucity of approved PPI inhibitor/disruptor drugs indicates that the discovery of such molecules remains challenging and the prevailing perception has been that PPI targets are essentially “undruggable”. However, the structural elucidation of several PPI complexes has revealed that protein-binding interfaces contain discrete “hot spots” that may preferentially facilitate binding interactions. It’s been proposed that a relatively small number of amino acids at the PPI interface contribute most of the binding energy, and that the contact surfaces exhibit some degree of flexibility with cavities, pockets and grooves available for small molecule binding. The enormous potential of PPI inhibitors/disruptors as therapeutics has prompted the development and implementation of many biochemical and cell-based assay formats compatible with HTS and/or HCS. In cell-based PPI formats, the interacting partners are generated in situ and PPIs occur within the cellular milieu where cofactors or post-translational modifications are available. This course will describe three distinct PPI assays compatible with HCS: fluorescence resonance energy transfer (FRET) based assays, protein complementation assays (PCA), and positional biosensor assays.
Phenotypic image analysis of traditional 2D cell cultures has afforded large-scale drug screenings in the pre-clinical setting. However, high failure rates of lead compounds in clinical testing suggests we need better models during the drug development process. 3D biomimetic models such as spheroids and organoids have increased in popularity because they can provide a 3D microenvironment that more closely recapitulates in vivo conditions compared to 2D monolayer cultures. There are multiple imaging platforms and available image analyses to elucidate interesting and dynamic biological processes in 3D. In this educational session, we will introduce basic concepts for different imaging techniques such as confocal, 2-photon and light-sheet microscopy, considerations and limitations in designing 3D imaging approaches, integration with high-throughput and high-content applications, and the types of analyses available for specific examples. This session will be beneficial for those who want to gain a basic knowledge on 3D imaging and for advanced users interested in discussing potential challenges associated with scaling current imaging workflows for large scale drug screening applications.
This tutorial will briefly review the statistical approaches used to analyze, visualize and interpret the HT/HC screening data, and formulate conclusion regarding the screening results. We will discuss the measures of effect sizes (Cohen’s d and its multivariate generalization), the dedicated metrics of assay quality such as Z' (Z-prime) and Sw (assay window), and demonstrate the relationship between them. The presentation will explain the conceptual origins of the common HT/HC assay quality indices, the logic behind the formulas, as well as their applicability, implicit assumptions, and limitations. The talk will address the relationship between the traditional measures used in screening, and classification performance measures employed in machine-learning (sensitivity, specificity, predictive values, F1 score, and AUC). The tutorial will also touch upon other essential concepts of data analysis in phenotypic screening: the notions of significance, replication, statistical power, fixed and random effects, and meta-analysis, and link those ideas to the everyday praxis of assay design, optimization, and execution. The intended audience includes the screening practitioners working with all the types of HT or HC screens (bulk assays, image-based system, and flow cytometry instruments).