Recent advances in T-cell receptor repertoire analysis: Bridging the gap with multimodal single-cell RNA sequencing

Valkiers, Sebastiaan; Vrij, Nicky de; Gielis, Sofie; Verbandt, Sara; Ogunjimi, Benson; Laukens, Kris; Meysman, Pieter

doi:10.1016/j.immuno.2022.100009

Cited by 29 publications

(31 citation statements)

References 194 publications

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“…More generally, we expect clonal dynamics to be linked to the cellular phenotype, as our preliminary analysis showed for CD4 and CD8 cells. Phenotypes can be characterized with increasing resolution using single-cell expression data [37], which also provides paired TCR information [38]. Future work combining longitudinal sampling with single-cell techniques could help explore the relationship between neutral clonal dynamics and cell type.…”

Section: Discussionmentioning

confidence: 99%

Inferring the T-cells repertoire dynamics of healthy individuals

Koraichi

Ferri

Walczak

et al. 2022

Preprint

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The adaptive immune system is a diverse ecosystem that responds to pathogens by selecting cells with specific receptors. While clonal expansion in response to particular immune challenges has been extensively studied, we do not know the neutral dynamics that drive the immune system in absence of strong stimuli. Here we learn the parameters that underlie the clonal dynamics of the T-cell repertoire in healthy individuals of different ages, by applying Bayesian inference to longitudinal immune repertoire sequencing (RepSeq) data. Quantifying the experimental noise accurately for a given RepSeq technique allows us to disentangle real changes in clonal frequencies from noise. We find that the data are consistent with clone sizes following a geometric Brownian motion, and show that its predicted steady state is in quantitative agreement with the observed power-law behaviour of the clone-size distribution. The inferred turnover time scale of the repertoire increases substantially with patient age, and depends on the clone size in some individuals.

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Section: Discussionmentioning

confidence: 99%

Inferring the T-cells repertoire dynamics of healthy individuals

Koraichi

Ferri

Walczak

et al. 2022

Preprint

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“…This allows to identify whether certain sequences were originally present at high abundance—identical sequences with different UMIs—or the same sequence is highly abundant because it was preferentially amplified by the PCR—equal sequences with the same UMI. Recent advances in TCR repertoire analysis now allow to combine single-cell TCR sequencing with transcriptomics analysis [ 24 , 25 , 26 , 27 ]. This has been specially interesting to link signatures of tumour-reactive T cells with their TCRs, although usually the aim of using these technologies is not to analyse diversity but to describe tumour-specific TCRs [ 28 ].…”

Section: T Cell Receptormentioning

confidence: 99%

Evaluation of the TCR Repertoire as a Predictive and Prognostic Biomarker in Cancer: Diversity or Clonality?

Aran

Garrigós²,

Curigliano

et al. 2022

Cancers

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T cells play a vital role in the anti-tumoural response, and the presence of tumour-infiltrating lymphocytes has shown to be directly correlated with a good prognosis in several cancer types. Nevertheless, some patients presenting tumour-infiltrating lymphocytes do not have favourable outcomes. The TCR determines the specificities of T cells, so the analysis of the TCR repertoire has been recently considered to be a potential biomarker for patients’ progression and response to therapies with immune checkpoint inhibitors. The TCR repertoire is one of the multiple elements comprising the immune system and is conditioned by several factors, including tissue type, tumour mutational burden, and patients’ immunogenetics. Its study is crucial to understanding the anti-tumoural response, how to beneficially modulate the immune response with current or new treatments, and how to better predict the prognosis. Here, we present a critical review including essential studies on TCR repertoire conducted in patients with cancer with the aim to draw the current conclusions and try to elucidate whether it is better to encounter higher clonality with few TCRs at higher frequencies, or higher diversity with many different TCRs at lower frequencies.

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“…Repertoire sequencing is one of the most actively developed sequencing technologies. In addition to the conventional procedure described above, single-cell repertoire sequencing has also been developed in recent years (4,31). Using such protocols, for example, the pairing of the TCRa and TCRb chains can be measured (4,31).…”

Section: Repertoire Sequencing and Batch Effectsmentioning

confidence: 99%

“…In addition to the conventional procedure described above, single-cell repertoire sequencing has also been developed in recent years (4,31). Using such protocols, for example, the pairing of the TCRa and TCRb chains can be measured (4,31). Furthermore, dual TCRs can be investigated (32)(33)(34)(35)(36).…”

Section: Repertoire Sequencing and Batch Effectsmentioning

confidence: 99%

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Machine Learning Approaches to TCR Repertoire Analysis

et al. 2022

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Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

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Recent advances in T-cell receptor repertoire analysis: Bridging the gap with multimodal single-cell RNA sequencing

Cited by 29 publications

References 194 publications

Inferring the T-cells repertoire dynamics of healthy individuals

Inferring the T-cells repertoire dynamics of healthy individuals

Evaluation of the TCR Repertoire as a Predictive and Prognostic Biomarker in Cancer: Diversity or Clonality?

Machine Learning Approaches to TCR Repertoire Analysis

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