Estimating T-cell repertoire diversity: limitations of classical estimators and a new approach
Open accessAbstract
A highly diverse T-cell receptor (TCR) repertoire is a fundamental property of an effective immune system, and is associated with efficient control of viral infections and other pathogens. However, direct measurement of total TCR diversity is impossible. The diversity is high and the frequency distribution of individual TCRs is heavily skewed; the diversity therefore cannot be captured in a blood sample. Consequently, estimators of the total number of TCR clonotypes that are present in the individual, in addition to those observed, are essential. This is analogous to the ‘unseen species problem’ in ecology. We review the diversity (species richness) estimators that have been applied to T-cell repertoires and the methods used to validate these estimators. We show that existing approaches have significant shortcomings, and frequently underestimate true TCR diversity. We highlight our recently developed estimator, DivE, which can accurately estimate diversity across a range of immunological and biological systems.
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The distribution of TCR frequencies in an analyzed sample may not reflect the entire repertoire and likely changes with age, which hampers inferential estimates of overall richness.
“The distribution of TCR frequencies in the analyzed sample does not necessarily reflect the distribution in the entire repertoire; moreover, the distribution is likely to change with age, hampering inferential estimates of overall richness”
From Successful and Maladaptive T Cell Aging (2017) · cites this paper as Laydon et al., 2015
No optimal solution currently exists for extrapolating TCR richness from small samples to the whole repertoire.
“Currently, an optimal solution for the latter barrier does not exist”
From Successful and Maladaptive T Cell Aging (2017) · cites this paper as Laydon et al., 2015
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Epstein, M. R., Barenco, M., Klein, N. J., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086986 · Cited by 9 · see citing works
Bayesian multivariate Poisson abundance models for T-cell receptor data
Greene, J., Birtwistle, M. R., Ignatowicz, L., et al. (2013) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2013.02.009 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
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Molecular mimicry and autoimmunity
Rojas, M., Restrepo-Jiménez, P., Monsalve, D. M., Pacheco, Y., Acosta-Ampudia, Y., Ramírez-Santana, C., Leung, P. S. C., Ansari, A. A., Gershwin, M. E., & Anaya, J.-M. (2018) · Journal of Autoimmunity · doi:10.1016/j.jaut.2018.10.012 · Cited by 627 · see citing works
Mechanisms underlying T cell ageing
Goronzy, J. J., & Weyand, C. M. (2019) · Nature Reviews Immunology · doi:10.1038/s41577-019-0180-1 · Cited by 506 · see citing works
Successful and Maladaptive T Cell Aging
Goronzy, J. J., & Weyand, C. M. (2017) · Immunity · doi:10.1016/j.immuni.2017.03.010 · Cited by 362 · see citing works
Singh, M., Al-Eryani, G., Carswell, S., Ferguson, J. M., Blackburn, J., Barton, K., Roden, D., Luciani, F., Giang Phan, T., Junankar, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11049-4 · Cited by 308 · see citing works
Bioinformatic and Statistical Analysis of Adaptive Immune Repertoires
Greiff, V., Miho, E., Menzel, U., & Reddy, S. T. (2015) · Trends in Immunology · doi:10.1016/j.it.2015.09.006 · Cited by 210 · see citing works
Miho, E., Yermanos, A., Weber, C. R., Berger, C. T., Reddy, S. T., & Greiff, V. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.00224 · Cited by 189 · see citing works
Borràs, D. M., Verbandt, S., Ausserhofer, M., Sturm, G., Lim, J., Verge, G. A., Vanmeerbeek, I., Laureano, R. S., Govaerts, J., Sprooten, J., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00605-4 · Cited by 67 · see citing works
High-throughput sequencing insights into T-cell receptor repertoire diversity in aging
Goronzy, J. J., Qi, Q., Olshen, R. A., & Weyand, C. M. (2015) · Genome Medicine · doi:10.1186/s13073-015-0242-3 · Cited by 36 · see citing works
- Authors
- Laydon, D. J., Bangham, C. R. M., & Asquith, B.
- Affiliations
- Imperial College London (GB)
- Corresponding author
- Asquith, Becca
- Venue
- Philosophical Transactions of the Royal Society B: Biological Sciences
- Publisher
- The Royal Society
- Published
- 2015-08-19
- Volume
- 370
- Issue
- 1675
- Pages
- 20140291
- ISSN
- 0962-8436 (ISSN-L)
- 1471-2970
- PMC
- PMC4528489
- OpenAlex
- W2173617309
- Cited by
- 215
- References
- 94
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 18 · 2023: 23 · 2022: 22 · 2021: 19 · 2020: 24 · 2019: 29 · 2018: 23 · 2017: 15 · 2016: 13 · 2015: 4
- FWCI
- 1.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Philosophical Transactions of the Royal Society B Biological Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (100291, 103865/Z/14/Z, 103865)
- Blood Cancer UK (12038)
- Medical Research Council (MR/J007439/1, MR/K019090/1)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- T cell receptor repertoire, species richness estimators, TCR clonotypes, immune diversity, viral infection control
- MeSH terms
- Animals, Humans, Models, Genetic, Receptors, Antigen, T-Cell, T-Lymphocytes, Genetic Variation, Gene Rearrangement, T-Lymphocyte, Models, Immunological, Lymphocyte Count, Statistics, Nonparametric, Host-Pathogen Interactions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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