Reference-based comparison of adaptive immune receptor repertoires

Weber, Cédric R.; Rubio, Teresa; Wang, Longlong; Zhang, Wei; Robert, Philippe A.; Akbar, Rahmad; Snapkov, Igor; Wu, Jinghua; Kuijjer, Marieke L.; Tarazona, Sonia; Conesa, Ana; Sandve, Geir Kjetil; Liu, Xiao; Greiff, Victor

doi:10.1016/j.crmeth.2022.100269

Cited by 13 publications

(18 citation statements)

References 109 publications

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“…Further, Eq (10) will be large for a TCR with many neighbors in R 2 but few in R 1 , since there will be many nearby TCRs all with relatively high transport values. Because of these properties, we use Eq (10) as the core scoring mechanism for our methods and analyses presented here, and will simply call this value loneliness. The expression in Eq (10) relies on a neighborhood radius parameter δ which requires tuning: setting δ too small will lead to unstable results as there will rarely be neighbors for a given t, and setting δ too large will assign too many neighbors to each TCR and grossly inflate the scores.…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…Because of these properties, we use Eq (10) as the core scoring mechanism for our methods and analyses presented here, and will simply call this value loneliness. The expression in Eq (10) relies on a neighborhood radius parameter δ which requires tuning: setting δ too small will lead to unstable results as there will rarely be neighbors for a given t, and setting δ too large will assign too many neighbors to each TCR and grossly inflate the scores. However, we show that Eq (10) consistently behaves better than Eq (9) as an indicator of lonely groups of TCRs for each sensible radius δ.…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…The expression in Eq (10) relies on a neighborhood radius parameter δ which requires tuning: setting δ too small will lead to unstable results as there will rarely be neighbors for a given t, and setting δ too large will assign too many neighbors to each TCR and grossly inflate the scores. However, we show that Eq (10) consistently behaves better than Eq (9) as an indicator of lonely groups of TCRs for each sensible radius δ. For example, Eq (10) does a significantly better job discriminating "spiked-in" epitope-specific TCRs from a background of naive TCRs in simulation experiments (see S1 Fig and the results section on simulation-based benchmarking).…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…Most current methods of repertoire comparison involve reducing the TCR sequences into simpler summaries and then comparing these summaries [2][3][4][5][6][7][8][9][10]. As comparing full CDR3 sequences can be highly involved, one approach is to simply compare CDR3 length distributions [11,12].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparing T cell receptor repertoires using optimal transport

et al. 2022

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The complexity of entire T cell receptor (TCR) repertoires makes their comparison a difficult but important task. Current methods of TCR repertoire comparison can incur a high loss of distributional information by considering overly simplistic sequence- or repertoire-level characteristics. Optimal transport methods form a suitable approach for such comparison given some distance or metric between values in the sample space, with appealing theoretical and computational properties. In this paper we introduce a nonparametric approach to comparing empirical TCR repertoires that applies the Sinkhorn distance, a fast, contemporary optimal transport method, and a recently-created distance between TCRs called TCRdist. We show that our methods identify meaningful differences between samples from distinct TCR distributions for several case studies, and compete with more complicated methods despite minimal modeling assumptions and a simpler pipeline.

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Section: Plos Computational Biologymentioning

confidence: 99%

Section: Plos Computational Biologymentioning

confidence: 99%

Section: Plos Computational Biologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparing T cell receptor repertoires using optimal transport

et al. 2022

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“…Several groups have developed metrics to assess similarity among entire BCR repertoires [8,9], but few opensource tools enable facile, multi-dimensional visualization and exploration of these repertoires. An overview of existing tools and their features [10][11][12][13][14][15][16][17][18][19][20][21] is provided in Table 1. Notable examples of higher-level BCR repertoire visualizations (e.g.…”

Section: Introductionmentioning

confidence: 99%

AIRRscape: An interactive tool for exploring B-cell receptor repertoires and antibody responses

et al. 2022

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The sequencing of antibody repertoires of B-cells at increasing coverage and depth has led to the identification of vast numbers of immunoglobulin heavy and light chains. However, the size and complexity of these Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) datasets makes it difficult to perform exploratory analyses. To aid in data exploration, we have developed AIRRscape, an R Shiny-based interactive web browser application that enables B-cell receptor (BCR) and antibody feature discovery through comparisons among multiple repertoires. Using AIRR-seq data as input, AIRRscape starts by aggregating and sorting repertoires into interactive and explorable bins of germline V-gene, germline J-gene, and CDR3 length, providing a high-level view of the entire repertoire. Interesting subsets of repertoires can be quickly identified and selected, and then network topologies of CDR3 motifs can be generated for further exploration. Here we demonstrate AIRRscape using patient BCR repertoires and sequences of published monoclonal antibodies to investigate patterns of humoral immunity to three viral pathogens: SARS-CoV-2, HIV-1, and DENV (dengue virus). AIRRscape reveals convergent antibody sequences among datasets for all three pathogens, although HIV-1 antibody datasets display limited convergence and idiosyncratic responses. We have made AIRRscape available as a web-based Shiny application, along with code on GitHub to encourage its open development and use by immuno-informaticians, virologists, immunologists, vaccine developers, and other scientists that are interested in exploring and comparing multiple immune receptor repertoires.

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Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling

Lê Quý,

Chernigovskaya,

Stensland

et al. 2024

npj Syst Biol Appl

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Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial for understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy human donors and sequenced BCRs at bulk and single-cell levels, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.

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Reference-based comparison of adaptive immune receptor repertoires

Cited by 13 publications

References 109 publications

Comparing T cell receptor repertoires using optimal transport

Comparing T cell receptor repertoires using optimal transport

AIRRscape: An interactive tool for exploring B-cell receptor repertoires and antibody responses

Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling

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