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Article ; Online: B cell phylogenetics in the single cell era.

Hoehn, Kenneth B / Kleinstein, Steven H

2023 Volume 45, Issue 1, Page(s) 62–74

Abstract: The widespread availability of single-cell RNA sequencing (scRNA-seq) has led to the development of new methods for understanding immune responses. Single-cell transcriptome data can now be paired with B cell receptor (BCR) sequences. However, RNA from ... ...

Abstract	The widespread availability of single-cell RNA sequencing (scRNA-seq) has led to the development of new methods for understanding immune responses. Single-cell transcriptome data can now be paired with B cell receptor (BCR) sequences. However, RNA from BCRs cannot be analyzed like most other genes because BCRs are genetically diverse within individuals. In humans, BCRs are shaped through recombination followed by mutation and selection for antigen binding. As these processes co-occur with cell division, B cells can be studied using phylogenetic trees representing the mutations within a clone. B cell trees can link experimental timepoints, tissues, or cellular subtypes. Here, we review the current state and potential of how B cell phylogenetics can be combined with single-cell data to understand immune responses.
MeSH term(s)	Humans ; Phylogeny ; B-Lymphocytes ; Receptors, Antigen, B-Cell/genetics ; Adaptive Immunity ; Mutation/genetics
Chemical Substances	Receptors, Antigen, B-Cell
Language	English
Publishing date	2023-12-27
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2036831-8
ISSN	1471-4981 ; 1471-4906
ISSN (online)	1471-4981
ISSN	1471-4906
DOI	10.1016/j.it.2023.11.004
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Article ; Online: Inferring B Cell Phylogenies from Paired H and L Chain BCR Sequences with Dowser.

Jensen, Cole G / Sumner, Jacob A / Kleinstein, Steven H / Hoehn, Kenneth B

Journal of immunology (Baltimore, Md. : 1950)

2024

Abstract: Abs are vital to human immune responses and are composed of genetically variable H and L chains. These structures are initially expressed as BCRs. BCR diversity is shaped through somatic hypermutation and selection during immune responses. This ... ...

Abstract	Abs are vital to human immune responses and are composed of genetically variable H and L chains. These structures are initially expressed as BCRs. BCR diversity is shaped through somatic hypermutation and selection during immune responses. This evolutionary process produces B cell clones, cells that descend from a common ancestor but differ by mutations. Phylogenetic trees inferred from BCR sequences can reconstruct the history of mutations within a clone. Until recently, BCR sequencing technologies separated H and L chains, but advancements in single-cell sequencing now pair H and L chains from individual cells. However, it is unclear how these separate genes should be combined to infer B cell phylogenies. In this study, we investigated strategies for using paired H and L chain sequences to build phylogenetic trees. We found that incorporating L chains significantly improved tree accuracy and reproducibility across all methods tested. This improvement was greater than the difference between tree-building methods and persisted even when mixing bulk and single-cell sequencing data. However, we also found that many phylogenetic methods estimated significantly biased branch lengths when some L chains were missing, such as when mixing single-cell and bulk BCR data. This bias was eliminated using maximum likelihood methods with separate branch lengths for H and L chain gene partitions. Thus, we recommend using maximum likelihood methods with separate H and L chain partitions, especially when mixing data types. We implemented these methods in the R package Dowser: https://dowser.readthedocs.io.
Language	English
Publishing date	2024-04-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	3056-9
ISSN	1550-6606 ; 0022-1767 ; 1048-3233 ; 1047-7381
ISSN (online)	1550-6606
ISSN	0022-1767 ; 1048-3233 ; 1047-7381
DOI	10.4049/jimmunol.2300851
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Article ; Online: Predictive overfitting in immunological applications: Pitfalls and solutions.

Gygi, Jeremy P / Kleinstein, Steven H / Guan, Leying

Human vaccines & immunotherapeutics

2023 Volume 19, Issue 2, Page(s) 2251830

Abstract: Overfitting describes the phenomenon where a highly predictive model on the training data generalizes poorly to future observations. It is a common concern when applying machine learning techniques to contemporary medical applications, such as predicting ...

Abstract	Overfitting describes the phenomenon where a highly predictive model on the training data generalizes poorly to future observations. It is a common concern when applying machine learning techniques to contemporary medical applications, such as predicting vaccination response and disease status in infectious disease or cancer studies. This review examines the causes of overfitting and offers strategies to counteract it, focusing on model complexity reduction, reliable model evaluation, and harnessing data diversity. Through discussion of the underlying mathematical models and illustrative examples using both synthetic data and published real datasets, our objective is to equip analysts and bioinformaticians with the knowledge and tools necessary to detect and mitigate overfitting in their research.
MeSH term(s)	Machine Learning ; Vaccination
Language	English
Publishing date	2023-09-12
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2664176-8
ISSN	2164-554X ; 2164-5515
ISSN (online)	2164-554X
ISSN	2164-5515
DOI	10.1080/21645515.2023.2251830
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Article ; Online: A supervised bayesian factor model for the identification of multi-omics signatures.

Gygi, Jeremy P / Konstorum, Anna / Pawar, Shrikant / Aron, Edel / Kleinstein, Steven H / Guan, Leying

Bioinformatics (Oxford, England)

2024

Abstract: Motivation: Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a ...

Abstract	Motivation: Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful. Results: We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of COVID-19 severity and breast cancer tumor subtypes. Availability: SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR. Supplementary information: Supplementary data are available at Bioinformatics online.
Language	English
Publishing date	2024-04-11
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btae202
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Article: Inferring B cell phylogenies from paired heavy and light chain BCR sequences with Dowser.

Jensen, Cole G / Sumner, Jacob A / Kleinstein, Steven H / Hoehn, Kenneth B

bioRxiv : the preprint server for biology

2023

Abstract: Antibodies are vital to human immune responses and are composed of genetically variable heavy and light chains. These structures are initially expressed as B cell receptors (BCRs). BCR diversity is shaped through somatic hypermutation and selection ... ...

Abstract	Antibodies are vital to human immune responses and are composed of genetically variable heavy and light chains. These structures are initially expressed as B cell receptors (BCRs). BCR diversity is shaped through somatic hypermutation and selection during immune responses. This evolutionary process produces B cell clones, cells that descend from a common ancestor but differ by mutations. Phylogenetic trees inferred from BCR sequences can reconstruct the history of mutations within a clone. Until recently, BCR sequencing technologies separated heavy and light chains, but advancements in single cell sequencing now pair heavy and light chains from individual cells. However, it is unclear how these separate genes should be combined to infer B cell phylogenies. In this study, we investigated strategies for using paired heavy and light chain sequences to build phylogenetic trees. We found incorporating light chains significantly improved tree accuracy and reproducibility across all methods tested. This improvement was greater than the difference between tree building methods and persisted even when mixing bulk and single cell sequencing data. However, we also found that many phylogenetic methods estimated significantly biased branch lengths when some light chains were missing, such as when mixing single cell and bulk BCR data. This bias was eliminated using maximum likelihood methods with separate branch lengths for heavy and light chain gene partitions. Thus, we recommend using maximum likelihood methods with separate heavy and light chain partitions, especially when mixing data types. We implemented these methods in the R package Dowser: https://dowser.readthedocs.io.
Language	English
Publishing date	2023-10-02
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.09.29.560187
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Article ; Online: Phylogenetic analysis of migration, differentiation, and class switching in B cells.

Hoehn, Kenneth B / Pybus, Oliver G / Kleinstein, Steven H

PLoS computational biology

2022 Volume 18, Issue 4, Page(s) e1009885

Abstract: 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, ... ...

Abstract	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://dowser.readthedocs.io.
MeSH term(s)	B-Lymphocytes ; Cell Differentiation/genetics ; HIV Infections ; Humans ; Immunoglobulin Class Switching ; Phylogeny ; Receptors, Antigen, B-Cell/genetics
Chemical Substances	Receptors, Antigen, B-Cell
Language	English
Publishing date	2022-04-25
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1009885
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Position-Dependent Differential Targeting of Somatic Hypermutation.

Zhou, Julian Q / Kleinstein, Steven H

Journal of immunology (Baltimore, Md. : 1950)

2020 Volume 205, Issue 12, Page(s) 3468–3479

Abstract: Somatic hypermutation (SHM) generates much of the Ab diversity necessary for affinity maturation and effective humoral immunity. The activation-induced cytidine deaminase-induced DNA lesions and error-prone repair that underlie SHM are known to exhibit ... ...

Abstract	Somatic hypermutation (SHM) generates much of the Ab diversity necessary for affinity maturation and effective humoral immunity. The activation-induced cytidine deaminase-induced DNA lesions and error-prone repair that underlie SHM are known to exhibit intrinsic biases when targeting the Ig sequences. Computational models for SHM targeting often model the targeting probability of a nucleotide in a motif-based fashion, assuming that the same DNA motif is equally likely to be targeted regardless of its position along the Ig sequence. The validity of this assumption, however, has not been rigorously studied in vivo. In this study, by analyzing a large collection of 956,157 human Ig sequences while controlling for the confounding influence of selection, we show that the likelihood of a DNA 5-mer motif being targeted by SHM is not the same at different positions in the same Ig sequence. We found position-dependent differential SHM targeting for about three quarters of the 38 and 269 unique motifs from more than half of the 292 and 1912 motif-allele pairs analyzed using productive and nonproductive Ig sequences, respectively. The direction of the differential SHM targeting was largely conserved across individuals with no allele-specific effect within an IgH variable gene family, but was not consistent with general decay of SHM targeting with increasing distance from the transcription start site. However, SHM targeting did correlate positively with the mutability of the wider sequence neighborhood surrounding the motif. These findings provide insights and future directions for computational efforts toward modeling SHM.
MeSH term(s)	Alleles ; Computer Simulation ; Humans ; Immunoglobulin Heavy Chains/genetics ; Models, Genetic ; Nucleotide Motifs ; Somatic Hypermutation, Immunoglobulin
Chemical Substances	Immunoglobulin Heavy Chains
Language	English
Publishing date	2020-11-13
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	3056-9
ISSN	1550-6606 ; 0022-1767 ; 1048-3233 ; 1047-7381
ISSN (online)	1550-6606
ISSN	0022-1767 ; 1048-3233 ; 1047-7381
DOI	10.4049/jimmunol.2000496
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Article ; Online: Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data.

Nouri, Nima / Kleinstein, Steven H

PLoS computational biology

2020 Volume 16, Issue 6, Page(s) e1007977

Abstract: 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 ... ...

Abstract	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 B cell receptor (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 junction 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 junction sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture junction similarity and shared mutations, and combine these in a spectral clustering framework to infer the BCR clonal relationships. Using both simulated and experimental data, we show that this model improves both the sensitivity and specificity for identifying B cell clones. 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 AGPLv3 license.
MeSH term(s)	B-Lymphocytes/immunology ; Complementarity Determining Regions ; Computational Biology/methods ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Mutation ; Receptors, Antigen, B-Cell/genetics ; Receptors, Antigen, B-Cell/immunology ; Recombination, Genetic
Chemical Substances	Complementarity Determining Regions ; Receptors, Antigen, B-Cell
Language	English
Publishing date	2020-06-23
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1007977
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Article ; Online: Cutting Edge: Ig H Chains Are Sufficient to Determine Most B Cell Clonal Relationships.

Zhou, Julian Q / Kleinstein, Steven H

Journal of immunology (Baltimore, Md. : 1950)

2019 Volume 203, Issue 7, Page(s) 1687–1692

Abstract: B cell clonal expansion is vital for adaptive immunity. High-throughput BCR sequencing enables investigating this process but requires computational inference to identify clonal relationships. This inference usually relies on only the BCR H chain, as ... ...

Abstract	B cell clonal expansion is vital for adaptive immunity. High-throughput BCR sequencing enables investigating this process but requires computational inference to identify clonal relationships. This inference usually relies on only the BCR H chain, as most current protocols do not preserve H:L chain pairing. The extent to which paired L chains aids inference is unknown. Using human single-cell paired BCR datasets, we assessed the ability of H chain-based clonal clustering to identify clones. Of the expanded clones identified, <20% grouped cells expressing inconsistent L chains. H chains from these misclustered clones contained more distant junction sequences and shared fewer V segment mutations than the accurate clones. This suggests that additional H chain information could be leveraged to refine clonal relationships. Conversely, L chains were insufficient to refine H chain-based clonal clusters. Overall, the BCR H chain alone is sufficient to identify clonal relationships with confidence.
MeSH term(s)	B-Lymphocytes/cytology ; B-Lymphocytes/immunology ; Databases, Genetic ; Gene Rearrangement, B-Lymphocyte, Heavy Chain ; Humans ; Immunoglobulin Heavy Chains/genetics ; Immunoglobulin Heavy Chains/immunology ; Immunoglobulin Light Chains/genetics ; Immunoglobulin Light Chains/immunology ; Receptors, Antigen, B-Cell/genetics ; Receptors, Antigen, B-Cell/immunology
Chemical Substances	Immunoglobulin Heavy Chains ; Immunoglobulin Light Chains ; Receptors, Antigen, B-Cell
Language	English
Publishing date	2019-09-04
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	3056-9
ISSN	1550-6606 ; 0022-1767 ; 1048-3233 ; 1047-7381
ISSN (online)	1550-6606
ISSN	0022-1767 ; 1048-3233 ; 1047-7381
DOI	10.4049/jimmunol.1900666
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Article ; Online: A pipeline for the retrieval and extraction of domain-specific information with application to COVID-19 immune signatures.

Newton, Adam J H / Chartash, David / Kleinstein, Steven H / McDougal, Robert A

BMC bioinformatics

2023 Volume 24, Issue 1, Page(s) 292

Abstract: Background: The accelerating pace of biomedical publication has made it impractical to manually, systematically identify papers containing specific information and extract this information. This is especially challenging when the information itself ... ...

Abstract	Background: The accelerating pace of biomedical publication has made it impractical to manually, systematically identify papers containing specific information and extract this information. This is especially challenging when the information itself resides beyond titles or abstracts. For emerging science, with a limited set of known papers of interest and an incomplete information model, this is of pressing concern. A timely example in retrospect is the identification of immune signatures (coherent sets of biomarkers) driving differential SARS-CoV-2 infection outcomes. Implementation: We built a classifier to identify papers containing domain-specific information from the document embeddings of the title and abstract. To train this classifier with limited data, we developed an iterative process leveraging pre-trained SPECTER document embeddings, SVM classifiers and web-enabled expert review to iteratively augment the training set. This training set was then used to create a classifier to identify papers containing domain-specific information. Finally, information was extracted from these papers through a semi-automated system that directly solicited the paper authors to respond via a web-based form. Results: We demonstrate a classifier that retrieves papers with human COVID-19 immune signatures with a positive predictive value of 86%. The type of immune signature (e.g., gene expression vs. other types of profiling) was also identified with a positive predictive value of 74%. Semi-automated queries to the corresponding authors of these publications requesting signature information achieved a 31% response rate. Conclusions: Our results demonstrate the efficacy of using a SVM classifier with document embeddings of the title and abstract, to retrieve papers with domain-specific information, even when that information is rarely present in the abstract. Targeted author engagement based on classifier predictions offers a promising pathway to build a semi-structured representation of such information. Through this approach, partially automated literature mining can help rapidly create semi-structured knowledge repositories for automatic analysis of emerging health threats.
MeSH term(s)	Humans ; COVID-19 ; SARS-CoV-2
Language	English
Publishing date	2023-07-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-023-05397-8
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