Moreover, the known structure of clonal populations may be leveraged for model-based inference using RepSeq, potentially providing advantages over existing RNAseq-based approaches. The differences between RNAseq and RepSeq data, however, means that direct translation of these methods is questionable. There, approaches are becoming standardized (DESEQ2, EdgeR, etc.) and technical problems have been formulated and partly addressed. Inference of frequency variation from sequencing data has been intensely researched in other areas of systems biology, such as in RNAseq studies. Finally, both the underlying clonal population dynamics and the transformation applied by the measurement is stochastic, each contributing its own variability, making inferences based on sample ratios of molecule counts inaccurate. We also only observe a small fraction of the total number of clones, so some extrapolation is necessary. In either case, a measurement model is needed since what is observed (molecule counts) is indirect. Another regime for model-based approaches is the response to a single, strong perturbation, such as a vaccine, giving rise to a stereotyped, transient response dynamics. Model-based approaches, in contrast, can in principle capture features of the actual repertoire response to, for instance ongoing, natural stimuli, modeled as a point process of infections, and giving rise to diffusion-like response dynamics. ) quantify repertoire response properties using measurement statistics that are limited to what is observed in the sample, rather than what transpires in the individual. Despite the large number of samples (clones) in these datasets lending it to model-based inference, there are few existing model-based approaches to this analysis. Longitudinal repertoire sequencing (RepSeq) makes possible the characterization of repertoire dynamics. Despite large-scale efforts, how repertoire statistics respond to such acute perturbations is unknown. Next generation sequencing allows us to gain access to repertoire-wide data supporting more comprehensive repertoire analysis and more robust vaccine design. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the manuscript and its Supporting Information files.įunding: This work was supported by the European Research Council () Consolidator Grant n.
Received: DecemAccepted: ApPublished: April 29, 2020Ĭopyright: © 2020 Puelma Touzel et al. Applying our approach to yellow fever vaccination as a model of acute infection in humans, we identify candidate clones participating in the response.Ĭitation: Puelma Touzel M, Walczak AM, Mora T (2020) Inferring the immune response from repertoire sequencing. We then use that null model as a baseline to infer a model of clonal expansion from two repertoire time points taken before and after an immune challenge.
#IMMUNE REPERTOIRE SEQUENCING HOW TO#
Using replicate experiments, we first show how to learn the natural variability of read counts by inferring the distributions of clone sizes as well as an explicit noise model relating true frequencies of clones to their read count. Here, we present a general Bayesian approach to disentangle repertoire variations from these stochastic effects. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. High-throughput sequencing of B- and T-cell receptors makes it possible to track immune repertoires across time, in different tissues, and in acute and chronic diseases or in healthy individuals.
0 kommentar(er)
