Tumor-infiltrating immune repertoires captured by single-cell barcoding in emulsion

Briggs, Adrian W.; Goldfless, Stephen J.; Timberlake, Sonia; Belmont, Brian J.; Clouser, Christopher R.; Koppstein, David; Sok, Devin; Heiden, Jason Vander; Tamminen, Manu; Kleinstein, Steven H.; Burton, Dennis R.; Church, George M.; Vigneault, François

doi:10.1101/134841

Cited by 29 publications

(48 citation statements)

References 62 publications

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“…While the methodology presented here was developed and tested for sequencing data from the H chain only, cutting-edge technologies, including single-cell sequencing, provide paired IGH-and IGL-chain data (DeKosky et al, 2015;Macosko et al, 2015;Briggs et al, 2017). These paired data can be incorporated into the proposed method by extending the criteria for the initial grouping of sequences (i.e., VJ( )-groups) to include Number of Processors Speedup Fig.…”

Section: Resultsmentioning

confidence: 99%

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

Nouri

Kleinstein

2019

Preprint

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Motivation: Adaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their BCR sequence may differ due to the accumulation of somatic hypermutations (SHMs). Clonal relationships are learned from AIRR-seq data by analyzing the BCR sequence, with the most common methods focused on the highly diverse CDR3 region. However, clonally related cells often share SHMs which have been accumulated during affinity maturation. Here, we investigate whether shared SHMs in the V and J segments of the BCR can be leveraged along with the CDR3 sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture shared mutations and CDR3 similarity, and combine these in a spectral clustering framework. Using simulated data, we show that this model improves both the sensitivity and specificity for identifying clonal relationships. Availability: Source code for this method is freely available in the SCOPer (Spectral Clustering for clOne Partitioning) R package (version 0.2 or newer) in the Immcantation framework: www.immcantation.org under the CC BY-SA 4.0 license.

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Section: Resultsmentioning

confidence: 99%

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

Nouri

Kleinstein

2019

Preprint

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“…Recently, single‐cell emulsion‐barcoding workflows such as InDrop or DropSeq have been developed for whole‐transcriptome RNA‐seq. A similar approach has lately been adapted for sequencing human B cells, notably from an HIV elite controller . In brief, single cells were encapsulated in emulsion droplets, lysed and cDNA was tagged with both unique molecular identifiers for error correction as well as droplet‐specific barcodes to infer chain‐pairing.…”

Section: Antibody Discovery By Sequencing Immune Repertoiresmentioning

confidence: 99%

Integrating high‐throughput screening and sequencing for monoclonal antibody discovery and engineering

2017

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OTHER ARTICLES PUBLISHED IN THIS SERIES SummaryMonoclonal antibody discovery and engineering is a field that has traditionally been dominated by high-throughput screening platforms (e.g. hybridomas and surface display). In recent years the emergence of highthroughput sequencing has made it possible to obtain large-scale information on antibody repertoire diversity. Additionally, it has now become more routine to perform high-throughput sequencing on antibody repertoires to also directly discover antibodies. In this review, we provide an overview of the progress in this field to date and show how high-throughput screening and sequencing are converging to deliver powerful new workflows for monoclonal antibody discovery and engineering.

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“…Both simulations start with a single naive sequence as a starting point for the tree simulation; this is evolved a number of generations to a population of BCR sequences from which a sample is drawn and used for inference. To get realistic starting sequences for the simulations we created a set of 288 naive sequences inferred by partis (56) from the healthy donor human single cell dataset in (57) and selected to be of high confidence. When a simulation run is initialized a naive sequence is drawn randomly from this set.…”

Section: Simulationmentioning

confidence: 99%

Benchmarking tree and ancestral sequence inference for B cell receptor sequences

Davidsen

Matsen

2018

Preprint

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B cell receptor sequences evolve during affinity maturation according to a Darwinian process of mutation and selection. Phylogenetic tools are used extensively to reconstruct ancestral sequences and phylogenetic trees from affinity-matured sequences. In addition to using general-purpose phylogenetic methods, researchers have developed new tools to accommodate the special features of B cell sequence evolution. However, the performance of classical phylogenetic techniques in the presence of B cell-specific features is not well understood, nor how much the newer generation of B cell specific tools represent an improvement over classical methods. In this paper we benchmark the performance of classical phylogenetic and new B cell-specific tools when applied to B cell receptor sequences simulated from a forward-time model of B cell receptor affinity maturation towards a mature receptor. We show that the currently used tools vary substantially in terms of tree structure and ancestral sequence inference accuracy. Furthermore, we show that there are still large performance gains to be achieved by modeling the special mutation process of B cell receptors. These conclusions are further strengthened with real data using the rules of isotype switching to count possible violations within each inferred phylogeny.

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Tumor-infiltrating immune repertoires captured by single-cell barcoding in emulsion

Cited by 29 publications

References 62 publications

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

Integrating high‐throughput screening and sequencing for monoclonal antibody discovery and engineering

Benchmarking tree and ancestral sequence inference for B cell receptor sequences

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