Biosynthetic potential of the global ocean microbiome.

Natural microbial communities are phylogenetically and metabolically diverse. In addition to underexplored organismal groups1, this diversity encompasses a rich discovery potential for ecologically and biotechnologically relevant enzymes and biochemical compounds2,3. However, studying this diversity to identify genomic pathways for the synthesis of such compounds4 and assigning them to their respective hosts remains challenging. The biosynthetic potential of microorganisms in the open ocean remains largely uncharted owing to limitations in the analysis of genome-resolved data at the global scale. Here we investigated the diversity and novelty of biosynthetic gene clusters in the ocean by integrating around 10,000 microbial genomes from cultivated and single cells with more than 25,000 newly reconstructed draft genomes from more than 1,000 seawater samples. These efforts revealed approximately 40,000 putative mostly new biosynthetic gene clusters, several of which were found in previously unsuspected phylogenetic groups. Among these groups, we identified a lineage rich in biosynthetic gene clusters ('Candidatus Eudoremicrobiaceae') that belongs to an uncultivated bacterial phylum and includes some of the most biosynthetically diverse microorganisms in this environment. From these, we characterized the phospeptin and pythonamide pathways, revealing cases of unusual bioactive compound structure and enzymology, respectively. Together, this research demonstrates how microbiomics-driven strategies can enable the investigation of previously undescribed enzymes and natural products in underexplored microbial groups and environments.

Profiling the specificity of clonally expanded plasma cells during chronic viral infection by single-cell analysis.

Plasma cells and their secreted antibodies play a central role in the long-term protection against chronic viral infection. However, due to experimental limitations, a comprehensive description of linked genotypic, phenotypic, and antibody repertoire features of plasma cells (gene expression, clonal frequency, virus specificity, and affinity) has been challenging to obtain. To address this, we performed single-cell transcriptome and antibody repertoire sequencing of the murine BM plasma cell population following chronic lymphocytic choriomeningitis virus infection. Our single-cell sequencing approach recovered full-length and paired heavy- and light-chain sequence information for thousands of plasma cells and enabled us to perform recombinant antibody expression and specificity screening. Antibody repertoire analysis revealed that, relative to protein immunization, chronic infection led to increased levels of clonal expansion, class-switching, and somatic variants. Furthermore, antibodies from the highly expanded and class-switched (IgG) plasma cells were found to be specific for multiple viral antigens and a subset of clones exhibited cross-reactivity to nonviral and autoantigens. Integrating single-cell transcriptome data with antibody specificity suggested that plasma cell transcriptional phenotype was correlated to viral antigen specificity. Our findings demonstrate that chronic viral infection can induce and sustain plasma cell clonal expansion, combined with significant somatic hypermutation, and can generate cross-reactive antibodies.

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.

Platypus: an open-access software for integrating lymphocyte single-cell immune repertoires with transcriptomes.

High-throughput single-cell sequencing (scSeq) technologies are revolutionizing the ability to molecularly profile B and T lymphocytes by offering the opportunity to simultaneously obtain information on adaptive immune receptor repertoires (VDJ repertoires) and transcriptomes. An integrated quantification of immune repertoire parameters, such as germline gene usage, clonal expansion, somatic hypermutation and transcriptional states opens up new possibilities for the high-resolution analysis of lymphocytes and the inference of antigen-specificity. While multiple tools now exist to investigate gene expression profiles from scSeq of transcriptomes, there is a lack of software dedicated to single-cell immune repertoires. Here, we present Platypus, an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. Platypus provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology. To showcase the capabilities of Platypus, we use it to analyze and visualize single-cell immune repertoires and transcriptomes from B and T cells from convalescent COVID-19 patients, revealing unique insight into the repertoire features and transcriptional profiles of clonally expanded lymphocytes. Platypus will expedite progress by facilitating the analysis of single-cell immune repertoire and transcriptome sequencing.