CELLS 2023 Workshop

7th International Cells in Experimental Life Science Workshop, CELLS 2023

A hybrid workshop associated with ICBO 2023

August 29, 2023, 13:30 – 17:45 BRT

12:30 pm - 4:45 pm EDT, 9:30 am - 1:45 pm PDT, 5:30 pm - 9:45 pm BST, 6:30 pm -10:45 pm CEST

CELLS 2023 Workshop will be held as hybrid event, in person at ICBO 2023 at the University of Brasilia, Brasilia, Brazil, and virtually via Zoom.

Participation is free through the following Zoom link:

https://buffalo.zoom.us/j/94857154189?pwd=VTR4UmVQUHN0SHhjY3BWUEFXMkhNdz09

If you want to attend the rest of ICBO 2023, please register via the ICBO 2023 registration site.

CELLS 2023 Program (all times BRT, see above for conversion)

13:30–40 Introduction, Alexander Diehl, University at Buffalo

13:40–55 “The interplay of Wikidata and the Cell Ontology,” Tiago Lubiana, Universtity of São Paulo

Wikidata is a collaborative and comprehensive knowledge base, for enhancing biomedical ontologies, particularly the Open Biological and Biomedical Ontologies (OBO) Foundry ontologies. One important OBO Foundry ontology is the Cell Ontology (CL), which has grown since its inception in 2005 to encompass over 2,600 cell classes. While contributing to CL can be challenging due to the need for significant technical expertise, Wikidata offers a more accessible platform for crowd contributions. Our work demonstrates the benefits of integrating both endeavours, with Wikidata's multilingual capability fostering inclusivity and its links to Wikipedia providing textual descriptions and aiding in content validation. Through our curation workflow, we established over 2,600 cross-references between Wikidata and CL, revealing cell types present on Wikipedia but absent from CL, thus indicating potential areas for CL's expansion. Our findings suggest that integrating Wikidata with the Cell Ontology can significantly enhance the ontology ecosystem by lowering barriers to entry and promoting broader collaboration, paving the way for improving other ontologies in the future.

13:55–14:25 “Development of a standard for the automated annotation of flow cytometry cell populations with ontology linked labels,” Justin Meskas, Dotmatics

If two scientists working on two different experiments label a cell population with the same phenotype, how sure can we be that they are the same population? Defining adjectives such as low, dim, bright and ++ are inexact and not standardized, and are not used in consistent ways to uniquely identify the same cell populations. We believe there is an opportunity for a data-driven, community-built standard for automated annotation of flow cytometry cell populations based on a pixel map atlas that will enable automated, accurate and quantitative labelling of flow cytometry cell populations. This approach will have the ability to compare the same populations across any gating approach of choice (e.g., by hand, supervised, ML/AI, any dimensionality reduction method). It will also enable the automated linking to cell ontology terms.

14:25–40 “Ontological modeling of cell evolution in multicellular organisms,” Oliver He, University of Michigan

Understanding how different cell types are evolutionarily and developmentally related and tracing the emergence of new cell types can provide a deeper understanding of morphological evolution and the origin of biodiversity. In this study, we propose a systematic ontological modeling of cell evolution. First, we propose there are three types of cell evolution: evolution of cellular differentiation, evolution of cell type lineage, and evolution of related cell types among species. Evolution of cellular differentiation is about evolution of novel cell types within a species, which is derived from the cellular differentiation process. Evolution of cell type lineage, a related process, is about how related cell lineages evolve within a species, and it traces cell types upwards until the original ancestor cell. Evolution of related cell types among species is about evolution of homologous cell types in separate species. We will justify our modeling and provide examples to illustrate the modeling. Furthermore, we have initiated an ontology-based CellCards system to systematically represent and analyze various cell evolution events.

14:40–55 “Defining cell type taxonomies for the brain and across species,” Nelson Johansen, Allen Institute

The mammalian brain contains many cell types that are segregated into distinct layers and regions and are associated with distinct functions including motor control and reward. To study the diversity of cell types at scale, the Allen Institute has been using single cell and single nucleus transcriptomics and epigenetics. Measuring molecular variation across single cells allows for the identification of cell types that can be organized in a cellular hierarchy or taxonomy. Defining cell type taxonomies relies on our ability to robustly cluster cells, annotate cell labels across studies, identify conserved features across species, and associate types with previous work using genetic markers. It is an open question how to optimize each of these steps, and best practices are emerging from taxonomy building efforts at the Allen Institute that integrate information across species, including human, non-human primate, and mouse.

14:55–15:15 Coffee Break

15:15–15:45 “A single-cell transcriptional timelapse of mouse embryonic development, from gastrula to pup,” Chengxiang (CX) Qiu, University of Washington

The house mouse, Mus musculus, is an exceptional model system, combining genetic tractability with close homology to human biology. Gestation in mouse development lasts just under three weeks, a period during which its genome orchestrates the astonishing transformation of a single cell zygote into a free-living pup composed of >500 million cells. Towards a global framework for exploring mammalian development, we applied single cell combinatorial indexing (sci-*) to profile the transcriptional states of 12.4 million nuclei from 83 precisely staged embryos spanning late gastrulation (embryonic day 8 or E8) to birth (postnatal day 0 or P0), with 2-hr temporal resolution during somitogenesis, 6-hr resolution through to birth, and 20-min resolution during the immediate postpartum period. From these data (E8 to P0), we annotate dozens of major cell clusters and hundreds of cell types and perform deeper analyses of the unfolding of the posterior embryo during somitogenesis as well as the ontogenesis of the kidney, mesenchyme, retina, and early neurons. Finally, we leverage the depth and temporal resolution of these whole embryo snapshots, together with other published data, to construct and curate a rooted tree of cell type relationships that spans mouse development from zygote to pup. Throughout this tree, we systematically nominate sets of transcription factors (TFs) and other genes as candidate drivers of the in vivo differentiation of hundreds of mammalian cell types.

15:45–16:15 “Species and cell types: Elephants in the room (and how to describe them),”Jeff Doyle, Cornell University

Species and cell types exist at very different levels in the hierarchy of life as key elements in the fields of evolutionary biology and cell biology, respectively. Researchers in both fields “know them when they see them”, but both defy simple characterization, and “solutions” in both cases include ignoring the elephant in the room. Philosophically, are these entities simply human constructs (a nominalist view), or do they have real meaning? If they are “real”, are they classes, individuals, or a “something else” that combines elements of both; in modern philosophy this is a homeostatic property cluster (HPC), with “fuzzy” boundaries but defined by its emergent properties. In systematics (the study of the kinds and diversity of organisms) the distinction has been made between species concepts and the criteria for recognizing them. Operationally, much of the disagreement over the definition of species arises from the prioritization of different properties (e.g., reproductive isolation, phenotypic differentiation, ecological specialization) by different researchers—the “blind men and the elephant” problem. Systematics has been revolutionized by the ready availability of genomic data and analytical tools for reconstructing relationships from such data at an ever-finer level. Because the prevailing species concept in the phylogenomics era is one of connected population lineages, there was some hope that species might be recognized objectively using sequence data alone. Any hope for unanimity was fleeting: It is now argued that phylogenomic methods resolve genetic lineages at a level below species. Analogies exist in cell biology, where the fine-scale resolution of single cell RNA-seq has led to the “transcriptomic cell type” definition, in which state and type are conflated. Although “lineages” exist in several senses in cell biology, the transcriptomic cell type concept, like some species concepts, is based on static phenotypic relationships rather than lineages; a recently proposed lineage-based approach for cell atlases would represent another parallel between cell biology and systematics. Many similar challenges exist in defining species and cell types, but a solution may at least in theory be more attainable for the latter, if there is something (e.g., a “core regulatory complex” involving a set of transcription factors) that defines and perpetuates each cell type within species (however those are defined!) and is shared by homologous cell types among species. No such tangible defining entity has been proposed for species.

16:15–45 “Concepts of cell types and how they relate to the Cell Ontology,” David Osumi-Sutherland, Sanger Institute

The explosion of single cell biology has brought the problem of defining cell type to the fore, with a plethora of papers discussing the issue and many promoting their "one true way" to define cell types - by lineage, by transcriptomic profile, by evolutionary origin... The developers of cell ontologies are inevitably constrained by more practical concerns. They need to build artefacts that are practically useful for annotating diverse data types at many different levels of granularity. Once their ontologies have been used for annotation, they need to be useful for reliable retrieval of annotated data by users with very different starting points from those of the original annotator. They may use different names, they may be interested in different properties or focussed on classification at different granularities. I argue that this requires taking a liberal, inclusive view of defining cell types. OBO cell ontologies, such as The Cell Ontology (CL) and the Drosophila Anatomy Ontology (DAO) already do this in that they support multiple axes of classification including classification based on lineage, location, structure and function, managing this, in part, through automated inference. The Provisional Cell Ontology supports definition and classification based on single cell transcriptomic data analysis while the DAO has neuronal classes defined by morphological similarity derived from morphological data. Future versions might incorporate, for example, evolutionary conceptions of cell type or data-driven definitions from lineage data. I will discuss: why I think this is possible; the inevitable compromises required; potential pitfalls and how to avoid them; and how we can make the resulting artefacts easy for biologists to use.

16:45–17:30 Panel Discussion, "Practical Definitions of Cell Types"

CELLS 2023 Workshop Themes

(i) knowledge representation for newly-discovered and known cell types, particularly in light of single cell RNA sequencing data and other high-throughput data types

(ii) understanding the relationships of cell types to their anatomical contexts

(iii) the use of machine learning approaches for automatically assigning cell type data to ontology classes

(iv) knowledge representation of cell types in disease states

(v) evolutionary and differentiation relationships among cell types

Background

Current high throughput methods such as single cell RNA sequencing and flow and mass cytometry are producing a large amount of data related to existing and novel cell types in health and disease. At the same time, experimental approaches such as microscopy, genomics, and metabolomics are expanding understanding of cellular functioning in relation to neighboring cells and the whole organism. Ontologies are being increasingly used as a tool for integrating and analyzing these diverse data types. The Cell Ontology (CL) and Cell Line Ontology (CLO) have long been established as reference ontologies in the OBO framework for representing cell type and cell line information, and additional ontologies such as the Gene Ontology, Protein Ontology, and the Ontology for Biomedical Investigation are also important for representing not only experimental data about cell types but also the methods used to produce that data. There is a continuing need for improve automated analysis techniques to link data about cells with appropriate ontologies.

The 7th International Cells in Experimental Life Science Workshop, CELLS 2023, will provide a venue for discussions of the application of biomedical ontologies to represent and analyze cell-related knowledge and data. The workshop will also cover the extension of CL for ontological representation of cell types based on new methodologies and experiments. In addition it will cover real-world use cases which may require other ontological adaptations beyond CL and CLO.

Submission Formats for CELLS 2023

(i) abstracts or extended abstracts (1-3 pages) for presentations

(ii) papers (5-10 pages)

Abstracts are one page, references optional. Extended abstracts are formatted as mini-papers, and should include references. Abstracts and papers will be considered equally for presentations.

The paper template for CELLS 2023 is the same used in ICBO 2023, and will be submitted via EasyChair using the CEURART one column template. All submissions will go through peer reviews by at least two reviewers.

The workshop is planned as a four hour event and will be organized into sections based on the topics of presentations. Presentation time allocation will be determined after the number of each submission type is finalized. A panel discussion will be arranged at the end of the workshop.

EasyChair Submission Link

Organizers

Alexander D. Diehl, PhD

Associate Professor of Biomedical InformaticsJacobs School of Medicine and Biomedical Sciences, University at Buffalo77 Goodell St, Suite 540Buffalo, New York, 14203, USAaddiehl@buffalo.edu

Yongqun "Oliver" He, DVM, PhD

Associate ProfessorUnit for Laboratory Animal Medicine, Department of Microbiology and ImmunologyCenter for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center University of Michigan Medical SchoolOffice: 2511F ARF, 1150 W. Medical Center Dr.Ann Arbor, MI 48109-0168, USAyongqunh@med.umich.edu

David Osumi-Sutherland, PhD

Ontologies CoordinatorEMBL-EBI, Wellcome Genome CampusHinxton, Cambridgeshire, CB10 1SD, UK.davidos@ebi.ac.uk

Tiago Lubiana

Universtity of São Paulo, São Paulo - Braziltiago.lubiana.alves@usp.br

Previous CELLS Workshops

2022, 2021, 2020, 2019, 2018, 2017

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