Special Interest Group (SIG)

HCS/HCA Data and Informatics Special Interest Group (SIG)

Wednesday, February 7
12:00 PM - 1:15 PM
Location: 2

SLAS 2018 and SBI2 HCS/HCA Data and Informatics Special Interest Group (SIG)

The Society for Biomolecular Imaging and Informatics (SBI2, http://www.sbi2.org/ ) and SLAS will co-host the 2018 HCS/HCA Data and Informatics SIG on Wednesday, February 7th, 12 noon to 1:15 PM in the San Diego Convention Center, San Diego, CA.

The format will be a guided discussion of three HCS/HCA Data and Informatics topics led by discussion leaders who will present a brief introduction with some background and data slides on their themes to prompt the audience to both engage and participate in a lively discussion.

Theme: “Concerned about the Analysis of Multiparameter HCS/HCA Data: find out what Deep Learning can do to help!”

Topic 1: "Beyond the conventional information in images"

Discussion Leader: Minh Doan, Ph.D. Imaging Platform, Broad Institute of MIT and Harvard, Cambridge

Modern bioimaging is rapidly changing, including the expansion of dimensionality at both image acquisition and data analytics stages, and the arrival of deep learning that reshapes the feature space. We will present recent efforts to leverage these techniques in a variety of applications.

Topic 2: “Deep Learning Analysis of High Content Imaging Screens”

Discussion Leader: Dana Nojima, Ph.D. Genome Analysis Unit, Amgen, Inc.

High Content Imaging screens produce phenotypically rich data sets. To leverage this complexity, detailed image analysis measuring hundreds of features with subsequent multivariate analysis have been utilized. Recently Deep Learning workflows based on Convolutional Neural Networks (CNN) have demonstrated their usefulness as a tool for analysis of High Content image data.

Topic 3: “Deep Learning for HCS: quick understanding of phenotypic space, reliable classification results and easy analysis transfer”.

Discussion Leaders: Stephan Steigele & Matthias Fassler, Genedata AG, Basel, Switzerland.

We’ll provide a 5-minute introduction on the main steps for applied deep learning in the HCS domain illustrating the associated paradigm shift. We’ll depict its huge potential and together with the audience we’ll challenge three aspects: generation of highly resolved maps of phenotypic space, the reliable generation of pharmacologically relevant results and the transfer of image analysis protocols across different specimens and/or imaging modalities.

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Paul A. Johnston

Research Associate Professor
Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh

Paul Johnston is a research Associate Professor in the department of Pharmaceutical Sciences, School of Pharmacy at the University of Pittsburgh. He obtained a B.Sc. with Honors (1978) and a Ph.D. (1983) in Biochemistry, from the University of East Anglia, Norwich, England. Subsequent postdoctoral positions in the department of Pharmacology at the University of North Carolina, the Pathology department of Duke University, and at the Howard Hughes Institute of the University of Texas Southwestern have provided a diversity of experience in biochemistry, molecular biology, cell biology, immunology, protein purification and recombinant protein expression. He has twenty six years of drug discovery experience in the Pharmaceutical (American Cyanamid, Sphinx Pharmaceuticals & Eli Lilly), Biotechnology (Embrex) and academic sectors (Pitt). Throughout his career Dr. Johnston has been an innovator of cell based approaches to lead generation and optimization, and pioneered the development and implementation of high content imaging technology to drug discovery.

Presentation(s):

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Minh Doan

Assay developer
Broad Institute of MIT and Harvard

I've trained as a medical doctor (2004-2010) and did PhD in molecular cell biology (2010-2015) in University of Debrecen, Hungary. During this time, my research topic was examining biophysical and biochemical features of adipocytes during adipogenesis.
I then did my first postdoc in microbiology in Pasteur Institute, Paris, France for about 2 years, focusing on genomic interaction between the host cell and Chlamydial microbes. From 2016, I join Dr. Anne Carpenter's Imaging Platform at the Broad Institute of MIT and Harvard, leading the effort to analyze Imaging flow cytometric data in clinical studies. Here we focus on developing machine learning methodology, especially deep learning, to automate the analytic processing of single cell images, from not only Imaging flow cytometry but may potentially applicable for general bioimaging. We recently exemplified this machine learning application in the studies of leukemia and eosinophil activation.

Presentation(s):

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Dana Nojima

Senior Scientist
Amgen

Dana Nojima is a Senior Scientist in the BioAssay Technology group, Genome Assessment Unit of Amgen. He received his Ph.D. (1995) in Cell Biology from the University of Minnesota. He held postdoctoral positions in the Department of Pharmacology at the University of Minnesota and the Department of Urology at San Francisco Veterans Affairs Medical Center. He began his work with High Content Imaging at Hyseq (2002) and continued in this area as an application scientist with Vitra BioSciences, Molecular Devices, Evotec Technology and PerkinElmer. He has been with Amgen since 2010 leading efforts in High Content Screening and imaging technology development.

Presentation(s):

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Stephan Steigele

Head of Science
Genedata AG

Dr. Stephan Steigele received his Dipl. Biol. from the the University of Hohenheim, Germany (main focus in neurophysiology / biochemistry / genetics) and his Dr. rer. nat. (PhD) from the computer science department at the University of Leipzig, Germany; followed by a postdoc position in RNA/protein informatics and comparative genomics at the same university and at Fraunhofer Institute for Cell Therapy and Immunology (IZI).

He works for Genedata since 2009; He is an initiator and leader on various projects, e.g. for enabling complex technologies (APC, Label-Free, HCS, DMPK, TSA, SPR) with a recent focus on Deep Learning and Machine Learning and their application in pharma relevant areas; for prototyping and formulating production ready solutions.

Presentation(s):

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Matthias Fassler

Imaging Project Lead
Genedata AG

Dr Matthias Fassler is the Imaging Project Lead at Genedata AG in Basel, Switzerland. Matthias has 10 years of experience in HCS, starting with a PhD in Cell Biology where he established an HCS assay to study protein trafficking in the secretory pathway. He then moved to PerkinElmer in Hamburg, Germany, where he worked as an application scientist. In this role he has run very different assays on PerkinElmer instruments and developed a sound expertise in image analysis. He joined Genedata in 2014 as a Scientific Account Manager and since 2017 he is responsible for the different activities around image-based screening at Genedata.

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