1st International Workshop on

Cells in ExperimentaL Life Science

CELLS-2017

Copthorne Hotel, Newcastle, UK 

September 13th, 2017

in conjunction with the

International Conference on Biomedical Ontology (2017)

Contact: icbo.cells@gmail.com


Program

Wednesday 13th September 2017 14:00 - 17:30

14:00 - 14:10        Welcome                                                                             Sirarat Sarntivijai         

14:10 - 14:30        Experimental cells - Past/Present/Future (click for slides)        Alexander Diehl    


Session 1 : Challenges in semantic representation of cell biology

Chris Pacheco Rivera, Rowland Mosbergen, Othmar Korn, Tyrone Chen, Isha Nagpal and Christine A. Wells   
Stemformatics (www.stemformatics.org) is a web-based pocket dictionary used by stem cell biologists. The platform hosts a growing collection of manually-curated and high-quality stem cell datasets. It allows easy visualisation and comparison of gene expression profiles across different platforms from different laboratory sources in mouse and human samples. Stemformatics hosts >344 public datasets, with >7060 human and >1853 mouse samples. Data integration in the Stemformatics platform consists predominantly of horizontal integration of many experiments run on different platforms, but all addressing the same data type - primarily transcriptome, including microarray and RNAseq. Here the major ontological challenge is consistency of experimental metadata, to facilitate rapid identification of related cell types. Stemformatics also curates “omics” platforms such as ChIPSeq, miRNA, proteomics, and metabolomics data. Comparison of the same sample types across many different molecular species presents additional ontology challenges, related to ambiguous relationships between data types. Our motivation is to accommodate easy access to expression profiles of genes of interest across different stem cell experiments. We provide an easy interface for stem cell researchers with lack of bioinformatics background to complex gene expression datasets throughout several “omics” data. Stemformatics supports microarray and RNAseq data, and host platforms for ChIPSeq, miRNA and proteomics. Here, we review the challenges of adapting ontology standards to fit a stem cell framework.

Vladimir Kiselev and Martin Hemberg
Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. Here, we present scmap, a method (source code available at https://github.com/hemberg-lab/scmap and the application can be run from http://www.hemberg-lab.cloud/scmap) for projecting cells from a scRNA-seq experiment on to the cell-types identified in a different experiment.

15:00 - 15:15        Usage of Cell Nomenclature in Biomedical Literature (click for slides)
Şenay Kafkas and Sirarat Sarntivijai
Cell lines and cell types are extensively studied in biomedical research yielding to a significant amount of publications each year. Identifying cell lines and cell types precisely in publications is crucial for science reproducibility and knowledge integration. There are efforts for standardisation of the cell nomenclature based on ontology development to support FAIR principles of the cell knowledge. However, it is important to analyse the usage of cell nomenclature in publications at a large scale for understanding the level of uptake of cell nomenclature in literature by scientists. In this study, we analyse the usage of cell nomenclature, both in Vivo, and in Vitro in biomedical literature by using text mining methods and present our initial results. The findings provide an insight to understand how experimental cells are described in publication to allow for the better establishment of cell types and cell lines standardisation.

Edison Ong, Sirarat Sarntivijai, Simon Jupp, Helen Parkinson, and Yongqun He
The Experimental Factor Ontology (EFO) is an application ontology driven by experimental variables including cell lines. The Cell Line Ontology (CLO) is an OBO community-based ontology that contains information of cell lines and relevant experimental components. EFO’s principle is to be the bridge between different experimental factors by reusing the knowledge from the bio-ontology community when possible. It is sensible that EFO aligns its cell line information with OBO-preferred CLO. There are, however, areas of challenges to be addressed with a common design pattern that are compatible with both EFO and CLO for a fully cooperative alignment. In this study, we developed a strategy to compare, align, and map cell line terms between EFO and CLO. We mined Cellosaurus resources for EFO-CLO cross-references. Text labels of cell lines from both ontologies were aligned then verified by biological information axiomatized in each source. The preliminary results revealed a set of cell lines shared between the two ontologies, and a set of cell lines unique to each resource. A design pattern that integrates EFO and CLO was also developed. The final updated CLO will be examined as the candidate ontology to import and replace EFO cell line classes to support the interoperability in the bio-ontology domain.

Session 2 : Community consensus on the semantics of cell biology - direction forward of the work in progress

15:30 - 16:00        Cell Ontology in an age of data-driven cell classification (click for slides)
David Osumi-Sutherland  
Data driven cell classification is becoming increasingly common. This poses both an opportunity and a challenge for ontologies that define and classify cell types. One major opportunity comes from the need for mechanisms to relate new, data-driven classifications back to classical nomenclatures and the vast amounts of data and knowledge described using them. Cell ontologies can also provide a bridge between different data-driven classifications and make use of the outputs of these technologies to provide mechanisms for classification of cell types from minimal data. Well-structured cell ontologies also have great potential for driving biologically meaningful, integrative queries that cross multiple bulk data-types. Cell ontologies will only be able to play these roles if they are sufficiently accurate, flexible and scalable enough to provide practical ways to work with the flood of new data and classifications.  Here I discuss the challenges and map out potential solutions using examples from the mouse retina and the Drosophila olfactory system.

Trygve Bakken, Lindsay Cowell, Brian Aevermann, Mark Novotny, Rebecca Hodge, Jeremy Miller, Alexandra Lee, Ivan Chang, Jamison McCorrison, Bali Pulendran, Yu Qian, Nicholas Schork, Roger Lasken, Ed Lein and Richard Scheuermann  
A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology. In this paper, we present examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and discuss strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies.

Edison Ong, Jiangan Xie, Zhaohui Ni, Qingping Liu, Sirarat Sarntivijai, Yu Lin, Vasileios Stathias, Daniel Cooper, Caty Chung, Stephan Schürer and Yongqun He
The NIH Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program involves many groups and laboratories working on over 1000 cell lines. The community-based Cell Line Ontology (CLO) is the default ontology for LINCS cell line representation and integration. CLO has consistently represented all 1,097 LINCS cell lines and included information extracted from the LINCS Data Portal and ChEMBL. Using MCF 10A cell line cells as example, we also demonstrated how to ontologically model their cellular signatures such as their non-tumorigenic epithelial cell type, three-dimensional growth, latrunculin-A-induced actin depolymerization and apoptosis, and cell line transfection. A CLO subset view of LINCS cell lines, named LINCS-CLOview, was also generated to support systematic LINCS cell line analysis and queries. Overall, LINCS cell lines are associated with 43 cell types, 131 tissues and organs, and 121 cancer types. The LINCS-CLOview information can be queried using SPARQL scripts.

17:00 - 17:30        Discussion & Wrap up - Where do we go from here?

                             

Workshop Theme and Topics

CELLS is the latest addition to the workshop series at the International Conference on Biomedical Ontology (ICBO) that aims to cover topics of data and metadata representation, standardization, management, and analysis of experimental cells in the biological and biomedical context. The rise of single-cell RNA sequencing technologies has brought the challenge of metadata handling to the field of experimental biology and biomedical ontology. More urgent than ever now, biologists and ontologists have to reach the consensus on the agreement on various topics such as naming convention and standard for novel cell type nomenclature, and modeling the biological reality into an ontology and semantic framework.

The rise of cell technologies has provided science with a fast lane to advance discovery in biomedical research. Experimental cell cultures and cell lines are widely used and often generated in a de Novo fashion at the laboratory. Normalization of experimental cell data produced in different laboratory settings is sometimes difficult, even when the cell types studied are nominally the same. It has also become unclear where the separation between data and metadata is due to the level of granularity of the details. Furthermore, there are no real unified modeling solutions that are universal to all experimental questions and situations. Therefore, data representation and modeling is very much driven by individual experiment. Consolidation of heterogeneous metadata in a large central data repository is a real challenge. New knowledge obtained by high-resolution technologies such as CyTOF and single-cell RNA sequencing introduces even more data that require robust representation, especially regarding novel cell populations that have never been seen before and do not belong to any existing classes of well-established OBO ontologies.

CELLS-2017 workshop aims to provide a venue for discussion in various hot topics in the single-cell and multi-cell technology domains. This will include the input from both biology and ontology metadata sides of life sciences. The organizers will lay the background on the development, maintenance, and application of the Cell Ontology (CL) and the Cell Line Ontology (CLO) before expanding the presentations and discussions from the submissions which will:
    1.    Bring together the participants from all tiers in experimental cell research and data analysis to facilitate communications and collaboration towards a consensus data representation in experimental cell research.
    2.    Identify research problems and challenges in the ontological representation and applications of experimental cells, and related topics via the real use cases.
    3.    Identify and discuss possible solutions for the problems and challenged presented in the workshop.
    4.    Initiate a sustainable venue for future collaborations and discussions among the participants.



Submission

For the paper submission, we will allow three submission formats:

  • full research papers (6-8 pages) format
  • work in progress / late breaking results (2-4 pages), and
  • a statement of interest (one page) for podium presentation.

The paper format will be the same as the format used in ICBO.

Templates can be found here.

All the papers will be submitted and handled through Easy Chair.  https://easychair.org/conferences/?conf=cells2017

After the full papers are accepted, we will work with BMC Bioinformatics editors and reviewers to decide which papers will be formally invited for extension to be included in a thematic series in the  journal. All full-length (10 pages maximum) and short-length (4 pages maximum) submissions will go through peer reviews by at least two reviewers. The one-page statement-of-interest submissions will be reviewed by the workshop organizers.

We invite the submission of research papers, work in progress/late-breaking results and statement of interest for presentation at CELLS-2017. Papers are invited in areas, such as availability and interoperability of existing resources for cell and cell line terminologies and catalogues, applications and challenges of cell modeling, and improvement and best practices of the current experimental cell ontology landscape. Example topics include (but not limited to):
    •    Collaborative ontology development for experimental cell modeling.
    •    Ontologies in cell type and cell culture metadata and standards.
    •    Knowledge representation and knowledge discovery for novel discovery.
    •    Biocuration of experimental cell data
    •    The usage of standard cell and cell line nomenclatures in literature.
    •    Updates on work in progress and statement of interest of cell modeling questions.
Selected submissions will also be published in the CELLS thematic issue of BMC Bioinformatics. Should the authors accept the offer to publish BMC Bioinformatics, they will agree to a secondary review-for-publication process and the publishing fee.


Workshop Schedule/Important Dates

  • Individual Workshop Papers due date extended to July 9, 2017
  • Notification of Acceptance: July 22, 2017
  • Camera Ready: July 30, 2017
  • Workshop: Sept. 13, 2017
  • First Revision due to BMC Bioinformatics: Sept 30, 2017


    Workshop Organizers

    • Sirarat Sarntivijai, PhD Samples, Phenotypes, and Ontologies Team, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire, UK. 
    • Yongqun “Oliver” He, DVM, PhD Department of Microbiology and Immunology Unit for Laboratory Animal Medicine Center for Computational Medicine and Bioinformatics University of Michigan Medical School, Ann Arbor, MI, USA. 
    • Alexander Diehl, PhD University at Buffalo, The State University of New York, Buffalo, NY, USA.
    • Contact: icbo.cells@gmail.com

    Program Committee (PC) Members

    • Liwei Wang, Jilin University
    • Jie Zheng, Department of Genetics and Institute of Biomedical Informatics, Parelman School of Medicine University of Pennsylvania
    • Matthew Brush, Oregon Health and Science University
    • Terrence Meehan, Samples, Phenotypes, and Ontologies Team, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory
    • Nicole Vasilevsky, Oregon Health and Science University
    • Chris Mungall, Lawrence Berkeley National Laboratory
    • David Osumi-Sutherland, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory
    • Lindsay Cowell, University of Texas Southwestern Medical Center at Dallas
    • Sebastian Köhler, Charité Berlin