Biology subjects

Kleinstein, S. H.

Publications and source records attributed to Kleinstein, S. H..

23 records · Page 2Linked to original sources

bioRxiv2020

Phylogenetic analysis of migration, differentiation, and class switching in B cells

B cells undergo rapid mutation and selection for antibody binding affinity when producing antibodies capable of neutralizing pathogens. This evolutionary process can be intermixed with migration between tissues, differentiation between cellular subsets, and switching between functional isotypes. B cell receptor (BCR) sequence data has the potential to elucidate important information about these processes. However, there is currently no robust, generalizable framework for making such inferences from BCR sequence data. To address this, we develop three parsimony-based summary statistics to characterize migration, differentiation, and isotype switching along B cell phylogenetic trees. We use simulations to demonstrate the effectiveness of this approach. We then use this framework to infer patterns of cellular differentiation and isotype switching from high throughput BCR sequence datasets obtained from patients in a study of HIV infection and a study of food allergy. These methods are implemented in the R package dowser, available at https://bitbucket.org/kleinstein/dowser. Author summaryB cells produce high affinity antibodies through an evolutionary process of mutation and selection during adaptive immune responses. Migration between tissues, differentiation to cellular subtypes, and switching between different antibody isotypes can be important factors in shaping the role B cells play in response to infection, autoimmune disease, and allergies. B cell receptor (BCR) sequence data has the potential to elucidate important information about these processes. However, there is currently no robust, generalizable framework for making such inferences from BCR sequence data. Here, we develop three parsimony-based summary statistics to characterize migration, differentiation, and isotype switching along B cell phylogenetic trees. Using simulations, we confirm the effectiveness of our approach, as well as identify some caveats. We further use these summary statistics to investigate patterns of cellular differentiation in three HIV patients, and patterns of isotype switching in an individual with food allergies. Our methods are released in the R package dowser: https://bitbucket.org/kleinstein/dowser.

immunology↗

bioRxiv2019

Immunoglobulin heavy chains are sufficient to determine most B cell clonal relationships

B cell clonal expansion is vital for adaptive immunity. High-throughput B cell receptor (BCR) sequencing enables investigating this process, but requires computational inference to identify clonal relationships. This inference usually relies on only the BCR heavy chain, as most current protocols do not preserve heavy:light chain pairing. The extent to which paired light chains aids inference is unknown. Using human single-cell paired BCR datasets, we assessed the ability of heavy chain-based clonal clustering to identify clones. Of the expanded clones identified, <20% grouped cells expressing inconsistent light chains. Heavy chains from these misclustered clones contained more distant junction sequences and shared fewer V segment mutations than the accurate clones. This suggests that additional heavy chain information could be leveraged to refine clonal relationships. Conversely, light chains were insufficient to refine heavy chain-based clonal clusters. Overall, the BCR heavy chain alone is sufficient to identify clonal relationships with confidence.

immunology↗

bioRxiv2018

Identification of subject-specific immunoglobulin alleles from expressed repertoire sequencing data

The adaptive immune receptor repertoire (AIRR) contains information on an individuals immune past, present and potential in the form of the evolving sequences that encode the B cell receptor (BCR) repertoire. AIRR sequencing (AIRR-seq) studies rely on databases of known BCR germline variable (V), diversity (D) and joining (J) genes to detect somatic mutations in AIRR-seq data via comparison to the best-aligning database alleles. However, it has been shown that these databases are far from complete, leading to systematic misidentification of mutated positions in subsets of sample sequences. We previously presented TIgGER, a computational method to identify subject-specific V gene genotypes, including the presence of novel V gene alleles, directly from AIRR-seq data. However, the original algorithm was unable to detect alleles that differed by more than 5 single nucleotide polymorphisms (SNPs) from a database allele. Here we present and apply an improved version of the TIgGER algorithm which can detect alleles that differ by any number of SNPs from the nearest database allele, and can construct subject-specific genotypes with minimal prior information. TIgGER predictions are validated both computationally (using a leave-one-out strategy) and experimentally (using genomic sequencing), resulting in the addition of three new immunoglobulin heavy chain V (IGHV) gene alleles to the IMGT repertoire. Finally, we develop a Bayesian strategy to provide a confidence estimate associated with genotype calls. All together, these methods allow for much higher accuracy in germline allele assignment, an essential step in AIRR-seq studies.

bioinformatics↗

bioRxiv2017

Performance-optimized partitioning of clonotypes from high-throughput immunoglobulin repertoire sequencing data

MotivationDuring adaptive immune responses, activated B cells expand and undergo somatic hypermutation of their immunoglobulin (Ig) receptor, forming a clone of diversified cells that can be related back to a common ancestor. Identification of B cell clonotypes from high-throughput Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) data relies on computational analysis. Recently, we proposed an automate method to partition sequences into clonal groups based on single-linkage clustering of the Ig receptor junction region with length-normalized hamming distance metric. This method could identify clonally-related sequences with high confidence on several benchmark experimental and simulated data sets. However, this approach was computationally expensive, and unable to provide estimates of accuracy for new data. Here, a new method is presented that address this computational bottleneck and also provides a study-specific estimation of performance, including sensitivity and specificity. The method uses a finite mixture modeling fitting procedure for learning the parameters of two univariate curves which fit the bimodal distributions of the distance vector between pairs of sequences. These distribution are used to estimate the performance of different threshold choices for partitioning sequences into clonotypes. These performance estimates are validated using simulated and experimental datasets. With this method, clonotypes can be identified from AIRR-seq data with sensitivity and specificity profiles that are user-defined based on the overall goals of the study.\n\nAvailabilitySource code is freely available at the Immcantation Portal: www.immcantation.com under the CC BY-SA 4.0 license.\n\nContactsteven.kleinstein@yale.edu

immunology↗

bioRxiv2017

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

Tumor-infiltrating lymphocytes (TILs) are critical to anti-cancer immune responses, but their diverse phenotypes and functions remain poorly understood and challenging to study. We therefore developed a single-cell barcoding technology for deep characterization of TILs without the need for cell-sorting or culture. Our emulsion-based method captures full-length, natively paired B-cell and T-cell receptor (BCR and TCR) sequences from lymphocytes among millions of input cells. We validated the method with 3 million B-cells from healthy human blood and 350,000 B-cells from an HIV elite controller, before processing 400,000 cells from an unsorted dissociated ovarian adenocarcinoma and recovering paired BCRs and TCRs from over 11,000 TILs. We then extended the barcoding method to detect DNA-labeled antibodies, allowing ultra-high throughput, simultaneous protein detection and RNA sequencing from single cells.

immunology↗