Overcoming the barrier of graft rejection by the immune system has always been a major concern for the transplantologist. Since the seminal work of Billingham and colleagues1 on acquired immunological tolerance to allogeneic skin transplants in the 1950s, the discovery of functionally distinct immune cell subsets has shaped the field of transplant immunology. We now have a much better picture of how immune cells operate and how graft rejection can be prevented. Paradoxically, the more knowledge we gain about the functional properties of immune cells, the more we are struggling to define these cells, which hamper further progress in the field.
Although we all think we know what the definition of a T cell is, different researchers will consider different aspects to be of importance: they might mention the thymus, cell-mediated immunity, the T-cell receptor. We suggest that you try testing this statement by asking the next 5 immunologists you meet for their definition. As we move to subtypes, these differences in definition may turn to disagreements. What evidence should we accept to demonstrate that a cell is a naturally occurring or an adaptive regulatory T cell, or, dare we say, a dendritic cell or a macrophage? If we do not agree on this, it is hard to make our science reproducible.
Good definitions are surprisingly hard to make and, in fact, there is an entire discipline devoted to it called “ontology.” This field has gained relevance in many areas, particularly as we move toward big data approaches—computers require accurate definitions if they are to support science.2 Here, we interpret “ontology” broadly to mean, any attempt to organize and standardize the way we describe and represent our knowledge, so enabling different scientists to accurately compare their results with each other.
In this article, we will describe several online resources that are useful for the readership of ‘Transplantation’ in organizing or describing their data. These resources include minimum information models (MIMs), databases, and ontologies.
Minimum information models are reporting guidelines that can be used to ensure that all the critical information is available in published data. These models increase the transparency and reusability of data. As such, MIMs are an important first step toward standardization of experimental procedures. The first MIM in the biomedical field was Minimum Information About a Microarray Experiment [A].3 In the field of immunology, Minimal Information About T cell Assays [B] provides a framework for the reporting of experiments that measure characteristics and functions of T cells.4 Minimal Information About T-Cell Assays was originally designed for the immunomonitoring field, aiming to increase the quality and comparability of T cell biomarkers obtained in clinical trials and studies. This approach serves also as a useful resource for the reporting of T cell data from nonclinical research studies. Another recent initiative is in the field of tolerance-inducing antigen-presenting cell therapies, which have been developed for the prevention of graft rejection after transplantation, or for the treatment of autoimmune diseases.5 The Minimum Information about Tolerogenic Antigen-Presenting cells reporting guidelines [C] are very timely considering a number of tolerance-inducing antigen-presenting cell products that are currently being tested or have been tested in clinical trials,6 facilitating a better comparison of the differences and similarities of these therapeutic cells.
A clear example of a standardized nomenclature that improves communication between scientists is the numbering of surface molecules on immune cells. The Human Leucocyte Differentiation Antigen workshops have taken place 10 times since 1980, identifying new cell surface molecules and assigning them a cluster of differentiation (CD) number.7 The full list and new entries can be found on the Human Cell Differentiation Molecules website [D].
The Gene Ontology (GO) [E] is an ontology in the more strict sense of the word: classifications that enable searching and description. The GO classifies the function of gene products at the level of (i) molecular activity, (ii) biological processes and pathways, and (iii) the cellular components in which they are active.8 Importantly, GO describes gene product characteristics in a species-independent manner, facilitating comparisons cross-species. By providing a controlled vocabulary for gene product attributes, published data can be annotated in a standardized and computer-readable way.
Probably more directly relevant is the specialist database, IPD-IMGT/HLA, which includes sequence for the human MHC9 [F]. It is part of the international ImMunoGeneTics (IMGT) information system, which is a resource describing immunoglobulin and T-cell receptor genes10 [G]. It includes nucleotide and protein sequences, polymorphisms and a database of therapeutic antibodies and fusion proteins. In addition to its suite of databases, it also includes many tools accessible through its website.
The Immune Epitope DataBase (IEDB) [H] is a significant resource containing data extracted manually from 15000 journal articles published since 1960.11 Their website allows searching for epitopes by name, species, or disease. The same team also provides a set of tools for epitope prediction from a peptide sequence.
Both the IMGT and IEDB are driven by a number of detailed ontologies. The IMGT ontology, for example, provides a standard nomenclature for IG, TR, and MHC proteins. Likewise, the IEDB uses the MHC restriction ontology [I] and addressed the heterogeneity of naming between different species. The MHC restriction ontology builds on resources including the IMGT ontologies and attempts to harmonize and cross-link terminology cross-species.12
The final problem to be answered is how to find new and updated e-resources. Biosharing.org [J], which is a comprehensive resource describing the (many!) MIMs, databases, and ontologies that exist can be helpful.
Definitions have a long tradition of causing controversies in many different areas of biology—consider the classic article “What, if anything, is a rabbit?”.13 Currently, ontologies are more often a resource for database builders and informaticians than immunology researchers. However, if we wish to share, compare, and reuse data between different laboratories, we will inevitably need to organize and catalogue our data better. Commonly accepted definitions will certainly not solve all the issues of scientific reproducibility, but a common language will undoubtedly be helpful.
The authors would like to thank Bjoern Peters for his feedback on this article.
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13. Wood AE. What, if anything, is a rabbit? Evolution