34th Congress of the International Society for Advancement of Cytometry
Scientific Tutorials are open to all Full Congress
Registrants; no additional fees apply.

Saturday, June 22, 2019

830 – 1000

Spectral Unmixing and Compensation in Flow and Image Cytometry
David Novo, De Novo Software, USA
Bartek Rajwa, Purdue University, USA

1. Basic principles of light
2. Brief introduction to fluorescence spectroscopy
   a.    Photon-counting statistics, photon interactions, photon collection, shot noise
   b.    Poissonian, super-Poissonian, and sub-Poissonian light
   c.    Theory of photodetection. PMTs, APDs, CCDs
3. Multispectral data–acquisition platforms in flow cytometry and imaging
4. Linear spectral mixture analysis (LSMA)
   a.    Spectral overlap and linear mixing model
   b.    Use of compensation in polychromatic cytometry
   c.    Use of unconstrained LSMA in multispectral and hyperspectral systems
   d.    Abundance and non-negativity-constrained LSMA
   e.    Noise models in LSMA
5. Blind signal unmixing
6. Spectral data visualization and dimensionality reduction
7. Questions and open discussion

Bartek Rajwa: brajwa@purdue.edu
David Novo: info@denovosoftware.com

Genomic Cytometry: Using Multi-Omic Approaches to Increase Dimensionality in Cytometry

Rob Salomon, Institutute for Biomedical Materials and Devices (IBMD), University of Technology Sydney, Australia
Thomas Ashhurst, Sydney Cytometry Facility, The University of Sydney and Centenary Institute, Australia

1) Introduction to Genomic Cytometry - what is it and why is important 
2) Common tools and workflows explained.
3) Understanding genomics: In this section we aim to give flow cytometrist (particularly those working in the Cytometry SRL) a better understanding of the genomics aspects of these workflows
4) Case studies and future directions

We expect attendees will gain a better understanding of 1) the rapidly maturing field of Genomic Cytometry 2) how Genomic Cytometry should be leveraged into more traditional cytometry workflows.

Rob Salomon: Robert.salomon@uts.edu.au or Rob@rob-salomon.com Thomas Ashhurst: thomas.ashhurst@sydney.edu.au

Image Cytometry Fundamentals​

Calum MacAulay, BC Cancer Research Centre and University of British Columbia, Canada
Martial Guillaud, BC Cancer Research Centre and University of British Columbia, Canada

1) Overview of operation and set up of an image analysis system based on microscopy
     a. Issues associated with making a transmission microscope a linear shift invariant system
          i.      Uniformity of illumination
          ii.      Pixel sampling density
          iii.     Ideal camera characteristics
          iv.     Image pre and post processing
     b. Automated focusing
     c. Spectral considerations

2) Overview stochiometric absorption stains for cytometry
      a. Different stains
      b. Staining chemistry
      c. Staining stability and reproducibility
      d. Requirements for quantitative analysis

3) Calculating cell nuclei features
      a. Beer-Lambert law
      b. IOD and DNA distribution features
      c. Shape features
      d. DNA distribution texture features
          i.      Co-occurrence features
          ii.      Discrete features
          iii.     Run length features
     e. Feature normalization

4) Cancer Screening based on early detection
     a. Sensitivity and Specificity is not the whole picture
     b. ROC analysis
     c. Malignancy Associated Changes in cytology specimens;
                how they can be measured and how they maybe used to assist
                in identifying subjects at risk of developing cancer

5) Image Cytometry based screening clinical examples
     a. Sputum
     b. Oral
     c. Cervix

Data-Mining Techniques for Single-Cell Data

Yvan Saeys, VIB-Ghent University, Belgium

1. Basics of data mining, data pre-processing and quality control
2. Different types of single-cell data
3. Descriptive models / clustering/ automated gating
4. Predictive models / biomarker discovery
5. Comparing samples / comparing groups of samples
6. Data visualization and dimensionality reduction
7. Modeling cell developmental trajectories

Funding an SRL
Franziska Grieder, National Institutes of Health, USA
Kylie Price, Malaghan Institute of Medical Research, New Zealand

There is often a lot of pressure on SRL managers to run a partial or full cost-recovery core facility which presents many challenges.  Identifying and securing external funding (in any form) can alleviate this pressure.  This tutorial will cover various funding strategies for SRLs, looking at diversifying funding streams.  Dr. Franziska Grieder will focus on grants and federal funding options to support the core facility.  Kylie Price will look at how to find, maintain and grow philanthropic relationships and how to maximise opportunities with high-net-worth individuals/groups.

After attending this tutorial, participants should have a better understanding of:

• Potential available funding streams
• How best to present core technologies and core facility services to entice funding
• How to secure new and returning customers
• How to engage stakeholders

Franziska Grieder: griederf@mail.nih.gov
Kylie Price: kprice@malaghan.org.nz

Moving from IP to Commercialization

Bill Hyun, University of California, San Franciso, USA
Betsy Ohlsson-Wilhelm, SciGro, Inc./North Central Office, USA

One of the major assets of a small business is its intellectual property (IP). In this tutorial we will follow the life cycle of the most common form of IP, the patent, from its initiation as a patent disclosure to its end, 20 years later. During the course of the cycle we will discuss the following questions:

IP Types: What is patentable and what is not? Is the appropriate type of IP for your innovation/invention a patent, a copyright, a trademark, a trade secret or know how? What are the characteristics of each and what type of protection does each offer?
IP Fundability: What is fundable IP?  Are the claims enforceable? Do they make it difficult for others to find non-covered workarounds? What gives one freedom to operate (FTO – absolutely critical)? What do angels/investors/VCs look for? Is the IP single purpose or does it protect a platform requiring sub-license strategies? If licenses are required – what type(s) of license(s) and what are good terms (royalty rates, etc)? Does IP protect first product? Future products?
Commercial Protection afforded by IP: How does one assess competitive IP strength/blocking IP for a given field of use (FOU)? How can one deal with competitor patents? What types of patent strategies are possible and what are the risk/benefits of each? Decision of patent vs. publication only.
Commercial Value of IP: How does the timing and cost of patent maintenance change with time and affect your business strategy? In what geographic locations should you patent and what are the associated costs? How does one optimize cost? How will this impact your strategic action plan against piracy (STRAP)? How much is really needed to convert IP to minimal viable product (MVP)? How does one handle patent infringements? Is your IP counsel as innovative as you are? How does one choose the right one?

Betsy M. Ohlsson-Wilhelm, Ph.D., CEO
SciGro, Inc./North Central Office
Foster Plaza 5, Suite 300/PMB 20
651 Holiday Drive
Pittsburgh, PA 15220-2740
E-mail: bmow@scigro.com

William Hyun, PhD
Director, Laboratory for Advanced Cytometry
Director, SONY Biotechnology Center
Department of Laboratory Medicine
University of California, San Francisco
PO Box 0451
San Francisco, CA 94143-0451

1030 - 1200

Multiplexing in Tissue Image Cytometry
Er Liu, Lyra Biomedical, Inc., USA

1.  Introduction of cytometry (flow vs image).
- Brief overview of flow cytometry and image cytometry/tissue cytometry
- Multiplexing applications in image cytometry/tissue cytometry

2. Overview of Fluorescence based Multiplexing Workflow for image cytometry and tissue cytometry
2.1  Multiplexed tissue sample prep
 -  Multi-marker staining techniques (including probe and assay development, and staining methodoligy)
 -  Enabling automation instrument platforms). 
2.2 Multi-color tissue imaging
-  Multi-color tissue imaging techniques
-  Challenges in imaging instrumentation design for high-plex applications. 
2.3 Quantitative image analysis and informatics

3. Other multiplexing techniques for image cytometry and tissue cytometry

4. Summary
Best t-SNE on the Block: How to Achieve a Meaningful Low-Dimensional Embedding and Interpret it Correctly

Anna Belkina, Boston University, USA
Josef Spidlen, FlowJo, USA

1. How to approach dimensionality reduction of cytometry data.
2. Review of t-distributed Stochastic Neighborhood Embedding (t-SNE).
3. Strategies to improve traditional t-SNE embedding; t-SNE modifications.
4. Overview of t-SNE alternatives.
5. Conclusions/Discussion.

Anna Belkina: belkina@bu.edu

Current Standards in Flow Cytometry Cell Sorter Biosafety
Geoffrey Lyon, Yale University, USA
Steve Perfetto, National Institutes of Health, USA
Evan Jellison, University of Connecticut Health Center

Novel Validation Method for Evaluating Biocontainment of Cell Sorting Flow Cytometers

Aerosol containment validation is an essential component of the risk assessment and risk management process for evaluating the containment of biohazards used on flow cytometer cell sorters.  The ISAC Biosafety Committee has recently published a new protocol for containment testing of cell sorters using fluorescent microspheres and disposable Cyclex-d impaction sampling cassettes.  This new standard operating procedure also provides significant advantages over previous containment testing methodologies. 

This tutorial will cover:
• An introduction and description of the new containment test;
• A comparison of the advantages of the new fluorescent bead test compared to previous methods;
• How the microsphere assay can also confirm the containment of secondary containment devices (e.g. biological safety cabinets and HEPA-filtered aerosol management systems); and
• Information on the role of containment testing as part of a comprehensive biosafety program for sorting biohazard.

This new containment validation test is a new tool for those sorting biohazards and will supplement the components of a biosafety program for sorting cells, which should also include a detailed registration process to capture known and potential biohazards; biosafety training for cell sorting operators; defined standard operating procedures for sorting biohazards and for addressing emergencies such as clogs and deflections; and the selection of appropriate personal protective equipment and engineering controls (containment equipment) commensurate with the identified risks.  The panel will include members of the ISAC Biosafety Committee who will be present to answer any questions about the new assay or the current cell sorter biosafety standards following the presentation.

Geoffrey Lyon
Lab Phone:   203-737-6471
Office Phone:  203-737-5959
Email:  geoffrey.lyon@yale.edu

Steve Perfetto
Phone:  301-761-6992

Evan Jellison
Phone: 860-679-6595

Deep Learning for Image Analysis

Devin Sullivan, KTH Royal Institute of Technology, Sweden
Casper Winsnes, Proteomics, KTH Royal Institute of Technology, Sweden

Course Overview Deep learning has become an essential tool in biology, particularly in the field of image analysis. This rapidly changing field has revolutionized many aspects of biological data analysis. Given the ubiquity of these methods it is imperative that scientists (both computational and bench biologists) know and understand the fundamentals of how and when to use such tools. This tutorial aims to arm scientists with these tools and teach them how to appropriately use them in their research. Particular emphasis will be put on image analysis methods, the pitfalls of deep learning, and when deep learning may or may not provide biological insight. After completion attendees should: Be comfortable discussing a variety of deep learning and image analysis methods Be familiar with tools to effectively implement such solutions to aid their research Be able to assess work done in the field of deep learning particularly with respect to image analysis

Bring It On! Challenging Samples Within an SRL Core, From A to Z: Acellular Organelles to Zooplankton
Nicole Poulton, Facility for Aquatic Cytometry, Bigelow Laboratory for Ocean Sciences, USA 
Rachael Sheridan, Van Andel Institute, USA

Life in a Shared Resource Flow Cytometry Laboratory (SRL) is never boring. In addition to routine samples, we are often faced with challenging samples. In a biomedical research setting these could be anything from subcellular organelles, such as, nuclei and mitochondria, debris-ridden tissue preps, non-mammalian organisms including plant cells, plankton, as well as, bacteria and viruses. Each of these samples present unique challenges to the SRL cytometrist. In this tutorial we will discuss and present our experiences working with these samples in both a biomedical and aquatic cytometry core facility, and provide some approaches and tips to keep in mind when you confront these types of samples.  We will address some of the following issues:

•    What types of samples can be analyzed by flow cytometry (biomedical to environmental)?
•    Why are SRLs observing more challenging samples?
•    How do operators prepare samples for cytometric analysis?
•    What steps should be considered during instrument setup for analysis or sorting?
o    Small particle detection
o    Alternative sheath solutions
o    Additional detectors and lasers
•    How can we use non-antibody conjugated dyes to our advantage?
•    Is autofluorescence a friend or foe?

The tutorial and discussion will provide participants with a better understanding of how to handle and prepare for different types of samples as the biomedical field expands, and use of a core facility changes.  We will provide a link to a ‘tips and tricks’ webpage addressing how to handle a variety of samples.  At the end of tutorial, we hope you feel more comfortable saying, “Bring it on!” to researchers seeking to analyze or sort a variety of samples both within the biomedical field and beyond.

Nicole Poulton: npoulton@bigelow.org
Rachael Sheridan: Rachael.Sheridan@vai.org

Panel Design – A Practical Guide for Successful Flow Cytometry Panels from 10-30 Parameters

Florian Mair, Fred Hutchinson Cancer Research Center, USA
Kelly Lundsten, BioLegend, USA

Over the past decade, technical improvements and new reagents have permitted fluorescent-based flow cytometry assays to measure up to 30 parameters, with 40 on the horizon. These complex assays require robust controls and thorough experimental planning, but there are currently few resources that provide a systematic approach for reliable panel design. Also, historical notions as to how fluorophores and controls should be chosen are sometimes at odds with the reality of modern panel design.

In this tutorial, we will provide a practical guide for successful fluorescent panel design for any complex panel from 10-30 (or more) parameters, both for conventional compensation-based as well as spectral cytometry. Specifically, we will cover the following topics:

-    Brief overview of signal detection in conventional and spectral flow cytometers
-    The concept and underlying cause of spreading error (SE)
-    How the spillover spreading matrix (SSM) can be efficiently used to guide panel design
-    Relevant relationships between SE, fluorophore brightness and antigen expression level
-    Step-by-step approaches towards building a new panel
-    Similarities and distinctions between panel design for compensation-based instruments vs spectral cytometers
-    An overview of essential controls and typical caveats
-    Examples of successful 25-30 parameter experiments

This tutorial is targeted to help participants avoid the frustrating experience of designing cytometry experiments solely by trial-and-error, and teach strategies to minimize wasting time and reagents on improperly designed panels. Participants will gain a solid base for tackling panel design in an efficient way on any fluorescent cytometry platform.

Florian Mair: fmair@fredhutch.org
Kelly Lundsten: klundsten@biolegend.com

1300 - 1430

An Introduction to Imaging Flow Cytometry and its Data Analysis
Dominic Jenner, Defense Science Technology Laboratory, UK
Orla Maguire Roswell Park Comprehensive Cancer Center, USA

1. Introduction to IFC
   a. What is it and how does it work
2. IFC vs conventional flow vs image cytometry
   a. Which one when
3. IFC Experimental design
4. IFC basic data analysis – with worked examples
5. IFC advance data analysis – with worked examples

Dominic Jenner: dcjenner@dstl.gov.uk
Orla Maguire: orla.maguire@roswellpark.org

Photodetection in Flow Cytometry: Key Considerations for the Latest Detectors Including High QE PMTs, APDs, SiPMs and Cameras
Slawomir Piatek, New Jersey Institute of Technology, USA
Earl Hergert, Hamamatsu Corporation, USA
James Butler, Hamamatsu Corporation, USA
  1. A brief introduction to the optics and photodetection of a flow cytometer
    1. Illumination optics
    2. Interrogation point
    3. Interaction of light with cells and microparticles; fluorescence
    4. Light detection optics
    5. Photodetection: conversion of light energy to electrical signal
    6. Front-end electronics
    7. A/D conversion
    8. Scatter plots
  2. First look at the four photodetectors
    1. Introduction to a photodiode
    2. Introduction to avalanche photodiode
    3. Introduction to a photomultiplier tube
    4. Introduction to a silicon photomultiplier
  3. Opto-electronic characteristics of photodetectors
    1. Photosensitivity
    2. Intrinsic gain
    3. Dark current
    4. Dynamic range
    5. Bandwidth
    6. Stability
  4. Introduction to noise
    1. Types of noise: photon and dark current shot noise, multiplication, Johnson, amplifier
    2. Importance of intrinsic gain and bandwidth on noise
    3. Signal-to-noise ratio
  5. A more-in-depth look at the four photodetectors; a side-by-side comparison
    1. Operation principles
    2. Overall photosensitivity; detection signal-to-noise ratio
    3. Dynamic range
    4. Detection bandwidth
    5. Temperature effects
    6. Stability
  6. How the scatter plots are affected by:
    1. Random and systematic noise
    2. Gain and temperature drifts
    3. Limited dynamic range
  7. Photodetection in flow cytometry using a TDI camera
    1. Principles of operation of a TDI camera
    2. When would TDI be an optimal choice
Slawomir Piatek - piatek@njit.edu James Butler - jbutler@hamamatsu.com, M: (408) 315-4717

Advanced Data Analysis in a Shared Resource Laboratory
Sofie Van Gassen, VIB-UGent Center for Inflammation Research, Ghent University, Belgium
Dagna Sheerar, SCYM(ASCP), University of Wisconsin Comprehensive Cancer Center Flow Cytometry Laboratory, USA

Current data analysis tools
        1. Overview of the current state of computational cytometry
                    a. Visualization techniques (e.g. SPADE, tSNE, UMAP)
                    b. Automated gating (e.g. flowDensity, flowLearn)
                    c. Population discovery (e.g. Citrus, FlowSOM, CellCNN)
        2. Commercially available platforms vs. Programming
                   a. Options available in FlowJo, Cytobank, FCS Express
                   b. Pros and cons of using R
        3. Considerations for the use of advanced data analysis platforms
                   a. Choosing the right platform for the right job
                   b. Quality control procedures for data files
                   c. Resources for computational analysis
                   d. Experimental planning for advanced data analysis
        4. Experimental design considerations
                   a. Power calculations – include enough samples to cover sample loss
                   b. Quality control criteria for sample quality
                   c. Length of study and instrument stability
                Fresh samples or batched frozen?
                   d. Detailed protocol(s)
                   e. Contact information
                   f. Rigorous record keeping
        5. Instrument considerations
                   a. Proper QC and maintenance
                   b. Stability over time
                   c. Instrument characterization?
        6. Assay design considerations
                   a. Panel optimized for best sensitivity
                   b. Internal standards
               Reference beads
               Reference sample
                   c. Proper controls
               FMO, compensation, biological controls
        7. Sample preparation considerations
                   a. Stability of reagents and consumables over life of experiment
                   b. Reagent quality control
                   c. Consumable quality control (plastic ware)
                   d. Detailed protocols and rigorous cross training of technicians
        8. Data acquisition considerations
                   a. Daily instrument QC
                   b. Standardization
                   c. Annotation
                   d. Proper number of events collected per data file
                   e. Resources for rigor and reproducibility

Sofie Van Gassen: sofie.vangassen@ugent.be 
Dagna Sheerar: dsheerar@wisc.edu

Cell Image Classification: An Overview of Methods with Software Examples

Gustavo Rohde, University of Virginia, USA
Mohammed Shifat E. Rabbi, University of Virginia, USA

Overview: We shall explain the importance of cell image classification in science and medicine. Then, we shall describe three main approaches of cell image classification highlighting their strengths and weaknesses. We shall also demonstrate how to classify cell images to understand cellular mechanisms using a python software.

Applications of cell image classification: Cell image classification boosts many aspects of cell biology and medicine: from drug discovery to genetic screening; from determining the malignancy of cancer to understanding the roles of subcellular organelles. We shall demonstrate few of these applications with real data. Those are diagnosis of cancer using thyroid cells, determining effectiveness of a drug using human osteosarcoma cells, understanding the roles of subcellular proteins using HeLa cells, and differentiating different staining patterns using human epithelial cells.

Cell image classification techniques: This tutorial will explain three main approaches of cell image classification:

- Numerical feature extraction based methods. Extracted features can be valuable in the context of cell image classification.

- End-to-end neural networks. The state of the art technology of neural networks can also be used to classify cell images. They can be especially useful in applications where the number of images per class is large.

- Transport-based morphometry. This image representation method facilitated by the mathematics of optimal transport can also be used to classify cell images. In addition, this method provides visual information relating to the image class differences enabling the scope for interpretability.

Summary: This tutorial will explain three main cell image classification techniques with comparative evaluations. We shall also explain how the real world problems in science and medicine can be solved using cell image classification methods, providing suggestions in regards to which classification method should be used in the context of which cell imaging. Software examples using python codes will also be demonstrated. Source code and slides will be provided to the participants for free.

Gustavo K. Rohde - gustavo@virginia.edu
M Shifat-E-Rabbi - mr2kz@virginia.edu

Writing, Publishing and Reviewing: Advice, Tips and News from Cytometry Part A – The Journal of Quantitative Cell Science

Attila Tarnok, University of Leipzig, Germany
Julia Kostova, John Wiley & Sons, USA

Scientific journals require certain quality standards from manuscripts to be acceptable for further reviewing and publication. There are some very common reasons why a paper gets reviewed and accepted or rejected. This tutorial aims to highlight all major aspects of manuscript writing, submission and communication with the reviewers, points out what can (and very often does) go wrong and how to do it right in order to improve your chances to get your paper published. Special emphasis will be taken to focus on the needs for publishing cytometry data in biomedical and technical oriented journals such as Cytometry Part A. The process will be shown from the Editors and the publishers point-of-view.

How to write a good manuscript: Here the most important aspects of writing a good manuscript and the most common mistakes made in writing a manuscript will be explained and discussed.
Manuscript processing and reviewing process: The processing of manuscript within the journal will be presented and discussed from the editors, reviewers and publishers perspective.

Attila Tarnok: cytometry_part_a@hotmail.com
Julia Kostova: jkostova@wiley.com

Flow Cytometry of Extracellular Vesicles: Guidelines for Reporting Methods and Results​

John Nolan, Scintillon Institute, USA
Joshua Welsh, National Institutes of Health, USA
Joanne Lannigan, University of Virginia, USA

Tutorial Description:
Extracellular vesicles (EVs) are of interest because of the wide range of potential roles they can play in normal physiology and disease. Their heterogeneity in biofluids motivate single particle analysis approaches such as flow cytometry, but their small size makes them difficult to measure, producing a literature that is rife with artifacts and irreproducible results. In order to promote improved rigor and reproducibility of EV FC measurements, a Working Group of researchers from ISAC, the International Society for Extracellular Vesicles (ISEV), and the International Society for Thrombosis and Hemostasis (ISTH) is developing a framework and guidelines for the reporting of EV FC methods and results. In this tutorial we will review this framework and the current state of reporting guidelines, with examples and suggestions for best practices that might improve the values of FC-based measurement of EVs.

Tutorial Topics:
  • Extracellular vesicles: what needs to be measured and why
  • Minimum information:  MISEV and MIFlowCyt
  • Flow cytometry: instruments and assays
  • EV sample processing and preanalytical variables
  • EV experimental design, standards and controls
  • EV staining and measurement 
  • EV data analysis, reporting and presentation
  • EV analysis: Recommendations for investigators and SRLs

John Nolan: jnolan@scintillon.org
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