ePlatypus: an ecosystem for computational analysis of immunogenomics data.

MOTIVATION: The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. RESULTS: Here, we present the ePlatypus computational immunology ecosystem for immunogenomics data analysis, with a focus on adaptive immune repertoires and single-cell sequencing. ePlatypus is an open-source web-based platform and provides programming tutorials and an integrative database that helps elucidate signatures of B and T cell clonal selection. Furthermore, the ecosystem links novel and established bioinformatics pipelines relevant for single-cell immune repertoires and other aspects of computational immunology such as predicting ligand-receptor interactions, structural modeling, simulations, machine learning, graph theory, pseudotime, spatial transcriptomics, and phylogenetics. The ePlatypus ecosystem helps extract deeper insight in computational immunology and immunogenomics and promote open science. AVAILABILITY AND IMPLEMENTATION: Platypus code used in this manuscript can be found at github.com/alexyermanos/Platypus.

Generation of a single-cell B cell atlas of antibody repertoires and transcriptomes to identify signatures associated with antigen specificity.

Although new genomics-based pipelines have potential to augment antibody discovery, these methods remain in their infancy due to an incomplete understanding of the selection process that governs B cell clonal selection, expansion, and antigen specificity. Furthermore, it remains unknown how factors such as aging and reduction of tolerance influence B cell selection. Here we perform single-cell sequencing of antibody repertoires and transcriptomes of murine B cells following immunizations with a model therapeutic antigen target. We determine the relationship between antibody repertoires, gene expression signatures, and antigen specificity across 100,000 B cells. Recombinant expression and characterization of 227 monoclonal antibodies revealed the existence of clonally expanded and class-switched antigen-specific B cells that were more frequent in young mice. Although integrating multiple repertoire features such as germline gene usage and transcriptional signatures failed to distinguish antigen-specific from nonspecific B cells, other features such as immunoglobulin G (IgG) subtype and sequence composition correlated with antigen specificity.

Persistent virus-specific and clonally expanded antibody-secreting cells respond to induced self-antigen in the CNS.

B cells contribute to the pathogenesis of both cellular- and humoral-mediated central nervous system (CNS) inflammatory diseases through a variety of mechanisms. In such conditions, B cells may enter the CNS parenchyma and contribute to local tissue destruction. It remains unexplored, however, how infection and autoimmunity drive transcriptional phenotypes, repertoire features, and antibody functionality. Here, we profiled B cells from the CNS of murine models of intracranial (i.c.) viral infections and autoimmunity. We identified a population of clonally expanded, antibody-secreting cells (ASCs) that had undergone class-switch recombination and extensive somatic hypermutation following i.c. infection with attenuated lymphocytic choriomeningitis virus (rLCMV). Recombinant expression and characterisation of these antibodies revealed specificity to viral antigens (LCMV glycoprotein GP), correlating with ASC persistence in the brain weeks after resolved infection. Furthermore, these virus-specific ASCs upregulated proliferation and expansion programs in response to the conditional and transient induction of the LCMV GP as a neo-self antigen by astrocytes. This class-switched, clonally expanded, and mutated population persisted and was even more pronounced when peripheral B cells were depleted prior to autoantigen induction in the CNS. In contrast, the most expanded B cell clones in mice with persistent expression of LCMV GP in the CNS did not exhibit neo-self antigen specificity, potentially a consequence of local tolerance induction. Finally, a comparable population of clonally expanded, class-switched, and proliferating ASCs was detected in the cerebrospinal fluid of relapsing multiple sclerosis (RMS) patients. Taken together, our findings support the existence of B cells that populate the CNS and are capable of responding to locally encountered autoantigens.

Phenotypic determinism and stochasticity in antibody repertoires of clonally expanded plasma cells.

The capacity of humoral B cell-mediated immunity to effectively respond to and protect against pathogenic infections is largely driven by the presence of a diverse repertoire of polyclonal antibodies in the serum, which are produced by plasma cells (PCs). Recent studies have started to reveal the balance between deterministic mechanisms and stochasticity of antibody repertoires on a genotypic level (i.e., clonal diversity, somatic hypermutation, and germline gene usage). However, it remains unclear if clonal selection and expansion of PCs follow any deterministic rules or are stochastic with regards to phenotypic antibody properties (i.e., antigen-binding, affinity, and epitope specificity). Here, we report on the in-depth genotypic and phenotypic characterization of clonally expanded PC antibody repertoires following protein immunization. We find that clonal expansion drives antigen specificity of the most expanded clones (top ∼10), whereas among the rest of the clonal repertoire antigen specificity is stochastic. Furthermore, we report both on a polyclonal repertoire and clonal lineage level that antibody-antigen binding affinity does not correlate with clonal expansion or somatic hypermutation. Last, we provide evidence for convergence toward targeting dominant epitopes despite clonal sequence diversity among the most expanded clones. Our results highlight the extent to which clonal expansion can be ascribed to antigen binding, affinity, and epitope specificity, and they have implications for the assessment of effective vaccines.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.