Lactation-associated macrophages exist in murine mammary tissue and human milk.

Macrophages are involved in immune defense, organogenesis and tissue homeostasis. Macrophages contribute to the different phases of mammary gland remodeling during development, pregnancy and involution postlactation. Less is known about the dynamics of mammary gland macrophages in the lactation stage. Here, we describe a macrophage population present during lactation in mice. By multiparameter flow cytometry and single-cell RNA sequencing, we identified a lactation-induced CD11c+CX3CR1+Dectin-1+ macrophage population (liMac) that was distinct from the two resident F4/80hi and F4/80lo macrophage subsets present pregestationally. LiMacs were predominantly monocyte-derived and expanded by proliferation in situ concomitant with nursing. LiMacs developed independently of IL-34, but required CSF-1 signaling and were partly microbiota-dependent. Locally, they resided adjacent to the basal cells of the alveoli and extravasated into the milk. We found several macrophage subsets in human milk that resembled liMacs. Collectively, these findings reveal the emergence of unique macrophages in the mammary gland and milk during lactation.

Cell-binding IgM in CSF is distinctive of multiple sclerosis and targets the iron transporter SCARA5.

Intrathecal IgM production in multiple sclerosis is associated with a worse disease course. To investigate pathogenic relevance of autoreactive IgM in multiple sclerosis, CSF from two independent cohorts, including multiple sclerosis patients and controls, were screened for antibody binding to induced pluripotent stem cell-derived neurons and astrocytes, and a panel of CNS-related cell lines. IgM binding to a primitive neuro-ectodermal tumour cell line discriminated 10% of multiple sclerosis donors from controls. Transcriptomes of single IgM producing CSF B cells from patients with cell-binding IgM were sequenced and used to produce recombinant monoclonal antibodies for characterization and antigen identification. We produced five cell-binding recombinant IgM antibodies, of which one, cloned from an HLA-DR + plasma-like B cell, mediated antigen-dependent complement activation. Immunoprecipitation and mass spectrometry, and biochemical and transcriptome analysis of the target cells identified the iron transport scavenger protein SCARA5 as the antigen target of this antibody. Intrathecal injection of a SCARA5 antibody led to an increased T cell infiltration in an experimental autoimmune encephalomyelitis (EAE) model. CSF IgM might contribute to CNS inflammation in multiple sclerosis by binding to cell surface antigens like SCARA5 and activating complement, or by facilitating immune cell migration into the brain.

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.

Single-cell immune repertoire sequencing of B and T cells in murine models of infection and autoimmunity.

Adaptive immune repertoires are composed by the ensemble of B and T-cell receptors within an individual, reflecting both past and current immune responses. Recent advances in single-cell sequencing enable recovery of the complete adaptive immune receptor sequences in addition to transcriptional information. Here, we recovered transcriptome and immune repertoire information for polyclonal T follicular helper cells following lymphocytic choriomeningitis virus (LCMV) infection, CD8+ T cells with binding specificity restricted to two distinct LCMV peptides, and B and T cells isolated from the nervous system in the context of experimental autoimmune encephalomyelitis. We could relate clonal expansion, germline gene usage, and clonal convergence to cell phenotypes spanning activation, memory, naive, antibody secretion, T-cell inflation, and regulation. Together, this dataset provides a resource for immunologists that can be integrated with future single-cell immune repertoire and transcriptome sequencing datasets.

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.

Epigenetic dosage identifies two major and functionally distinct ß cell subtypes.

The mechanisms that specify and stabilize cell subtypes remain poorly understood. Here, we identify two major subtypes of pancreatic ß cells based on histone mark heterogeneity (ßHI and ßLO). ßHI cells exhibit ∼4-fold higher levels of H3K27me3, distinct chromatin organization and compaction, and a specific transcriptional pattern. ßHI and ßLO cells also differ in size, morphology, cytosolic and nuclear ultrastructure, epigenomes, cell surface marker expression, and function, and can be FACS separated into CD24+ and CD24- fractions. Functionally, ßHI cells have increased mitochondrial mass, activity, and insulin secretion in vivo and ex vivo. Partial loss of function indicates that H3K27me3 dosage regulates ßHI/ßLO ratio in vivo, suggesting that control of ß cell subtype identity and ratio is at least partially uncoupled. Both subtypes are conserved in humans, with ßHI cells enriched in humans with type 2 diabetes. Thus, epigenetic dosage is a novel regulator of cell subtype specification and identifies two functionally distinct ß cell subtypes.

Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes.

Motivation: Single-cell sequencing now enables the recovery of full-length immune receptor repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate single-cell datasets incorporating immune receptor repertoires and gene expression. Results: We developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, transcriptional phenotypes and spatial location. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. We demonstrated the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Finally, we simulated immune repertoire information onto existing spatial transcriptomic experiments, thereby generating novel datasets that could be used to develop and integrate methods to profile clonal selection in a spatially resolved manner. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies. Availability and implementation: The R package and code used in this manuscript can be found at github.com/alexyermanos/echidna and also in the R package Platypus (Yermanos et al., 2021). Installation instructions and the vignette for Echidna is described in the Platypus Computational Ecosystem (https://alexyermanos.github.io/Platypus/index.html). Publicly available data and corresponding sample accession numbers can be found in Supplementary Tables S2 and S3. Supplementary information: Supplementary data are available at Bioinformatics Advances online.