Estimation of T-cell repertoire diversity and clonal size distribution by Poisson abundance models

Sepúlveda, Nuno; Paulino, Carlos Daniel; Carneiro, Jorge

doi:10.1016/j.jim.2009.11.009

Cited by 29 publications

(47 citation statements)

References 58 publications

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“…These models were used to describe global features of the sequence ensemble, such as the probability distribution following Zipf’s law (220) – the observation that the probability of sequences is inversely proportional to their frequency-rank, or the observation of peaks of frequency in sequence landscape as possible signatures of past pathogenic challenges. Recently, the estimation of repertoire diversity and clonal size distribution were analyzed by Poisson abundance models (221) and simple bivariate-Poisson-lognormal (BPLN) parametric model for fitting and analyzing TR repertoire data was proposed (222). Similarly, network analysis of IG repertoire from Weinstein et al study revealed the possibility to identify subgroups of individuals on the basis of IG network similarity (223).…”

Section: Statistical Analysis and Modeling Of Immune Repertoire Datamentioning

confidence: 99%

The Past, Present, and Future of Immune Repertoire Biology – The Rise of Next-Generation Repertoire Analysis

et al. 2013

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T and B cell repertoires are collections of lymphocytes, each characterized by its antigen-specific receptor. We review here classical technologies and analysis strategies developed to assess immunoglobulin (IG) and T cell receptor (TR) repertoire diversity, and describe recent advances in the field. First, we describe the broad range of available methodological tools developed in the past decades, each of which answering different questions and showing complementarity for progressive identification of the level of repertoire alterations: global overview of the diversity by flow cytometry, IG repertoire descriptions at the protein level for the identification of IG reactivities, IG/TR CDR3 spectratyping strategies, and related molecular quantification or dynamics of T/B cell differentiation. Additionally, we introduce the recent technological advances in molecular biology tools allowing deeper analysis of IG/TR diversity by next-generation sequencing (NGS), offering systematic and comprehensive sequencing of IG/TR transcripts in a short amount of time. NGS provides several angles of analysis such as clonotype frequency, CDR3 diversity, CDR3 sequence analysis, V allele identification with a quantitative dimension, therefore requiring high-throughput analysis tools development. In this line, we discuss the recent efforts made for nomenclature standardization and ontology development. We then present the variety of available statistical analysis and modeling approaches developed with regards to the various levels of diversity analysis, and reveal the increasing sophistication of those modeling approaches. To conclude, we provide some examples of recent mathematical modeling strategies and perspectives that illustrate the active rise of a “next-generation” of repertoire analysis.

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Section: Statistical Analysis and Modeling Of Immune Repertoire Datamentioning

confidence: 99%

The Past, Present, and Future of Immune Repertoire Biology – The Rise of Next-Generation Repertoire Analysis

et al. 2013

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“…Following the famous paper by Fisher and his colleagues (Fisher et al, 1943), many researchers have adopted a gamma density as a mixing model. Other parametric models include, among others, the log-normal (Bulmer, 1974), inverse-Gaussian (Ord and Whitmore, 1986), and generalized inverse-Gaussian (Sichel, 1997) distributions (Sepúlveda et al, 2010). An obvious advantage of such parametric models is that the inference problem reduces to estimating only a few relatively low-dimensional parameters for which the traditional estimation procedures can be typically applied.…”

Section: Poisson Models Of Abundancementioning

confidence: 99%

“…To account for this heterogeneity, the homogeneous model has been expanded to a variety of mixture models, typically under the assumption of the Poisson distribution of the TCR clones. These Poisson abundance mixture models (Chao, 2006) assume that each TCR variant (i.e., each clone family or clonotype) is sampled according to the Poisson distribution with a specific sampling rate, itself varying according to a prescribed parametric (mixing) distribution e.g., exponential, gamma, or lognormal (Ord and Whitmore 1986; Sepúlveda et al 2010; Bulmer 1974). The recent detailed comparative study of Sepúlveda et al (2010) identified one of such models, the Poisson-lognormal mixture (PLN), as particularly well suited for modeling clonal diversity.…”

Section: Introductionmentioning

confidence: 99%

Model for comparative analysis of antigen receptor repertoires

Rempała¹,

Seweryn²,

Ignatowicz³

2011

Journal of Theoretical Biology

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In modern molecular biology one of the standard ways of analyzing a vertebrate immune system is to sequence and compare the counts of specific antigen receptor clones (either immunoglobulins or T-cell receptors) derived from various tissues under different experimental or clinical conditions. The resulting statistical challenges are difficult and do not fit readily into the standard statistical framework of contingency tables primarily due to the serious under-sampling of the receptor populations. This under-sampling is caused, on one hand, by the extreme diversity of antigen receptor repertoires maintained by the immune system and, on the other, by the high cost and labor intensity of the receptor data collection process. In most of the recent immunological literature the differences across antigen receptor populations are examined via non-parametric statistical measures of the species overlap and diversity borrowed from ecological studies. While this approach is robust in a wide range of situations, it seems to provide little insight into the underlying clonal size distribution and the overall mechanism differentiating the receptor populations. As a possible alternative, the current paper presents a parametric method that adjusts for the data under-sampling as well as provides a unifying approach to a simultaneous comparison of multiple receptor groups by means of the modern statistical tools of unsupervised learning. The parametric model is based on a flexible multivariate Poisson-lognormal distribution and is seen to be a natural generalization of the univariate Poisson-lognormal models used in the ecological studies of biodiversity patterns. The procedure for evaluating a model's fit is described along with the public domain software developed to perform the necessary diagnostics. The model-driven analysis is seen to compare favorably vis a vis traditional methods when applied to the data from T-cell receptors in transgenic mice populations.

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“…One argument is the need for a large number of TCR V genes for generating the maximum repertoire diversity. In fact, the number of V genes is used directly in standard formulas for estimating the immunological repertoire that an organism has for responding to the large diversity of antigens (Sepulveda et al 2010). Moreover, here, we showed evidence that all mammalian TRBV genes are derived from four ancestral genes and from five TRAV genes, some of which have not diversified over evolutionary time, remaining a single gene within each species.…”

Section: Discussionmentioning

confidence: 57%

Evolution of V genes from the TRV loci of mammals

2015

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Information concerning the evolution of T lymphocyte receptors (TCR) can be deciphered from that part of the molecule that recognizes antigen presented by major histocompatibility complex (MHC), namely the variable (V) regions. The genes that code for these variable regions are found within the TCR loci. Here, we describe a study of the evolutionary origin of V genes that code for the α and β chains of the TCR loci of mammals. In particular, we demonstrate that most of the 35 TRAV and 25 TRBV conserved genes found in Primates are also found in other Eutheria, while in Marsupials, Monotremes, and Reptiles, these genes diversified in a different manner. We also show that in mammals, all TRAV genes are derived from five ancestral genes, while all TRBV genes originate from four such genes. In Reptiles, the five TRAV and three out of the four TRBV ancestral genes exist, as well as other V genes not found in mammals. We also studied the TRGV and TRDV loci from all mammals, and we show a relationship of the TRDV to the TRAV locus throughout evolutionary time.

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Estimation of T-cell repertoire diversity and clonal size distribution by Poisson abundance models

Cited by 29 publications

References 58 publications

The Past, Present, and Future of Immune Repertoire Biology – The Rise of Next-Generation Repertoire Analysis

The Past, Present, and Future of Immune Repertoire Biology – The Rise of Next-Generation Repertoire Analysis

Model for comparative analysis of antigen receptor repertoires

Evolution of V genes from the TRV loci of mammals

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