Complex interactions between different host immune cell types can determine the outcome of pathogen infections. Advances in single cell RNA-sequencing (scRNA-seq) allow probing of these immune interactions, such as cell-type compositions, which are then interpreted by deconvolution algorithms using bulk RNA-seq measurements. However, not all aspects of immune surveillance are represented by current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella , we develop a deconvolution algorithm for inferring cell-type specific infection responses from bulk measurements. We apply our dynamic deconvolution algorithm to a cohort of healthy individuals challenged ex vivo with Salmonella , and to three cohorts of tuberculosis patients during different stages of disease. We reveal cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and human infection outcomes.

Key to the success of intracellular pathogens is the ability to sense and respond to a changing host cell environment. Macrophages exposed to microbial products undergo metabolic changes that drive inflammatory responses. However, the role of macrophage metabolic reprogramming in bacterial adaptation to the intracellular environment has not been explored. Here, using metabolic profiling and dual RNA sequencing, we show that succinate accumulation in macrophages is sensed by intracellular Salmonella Typhimurium (S. Tm) to promote antimicrobial resistance and type III secretion. S. Tm lacking the succinate uptake transporter DcuB displays impaired survival in macrophages and in mice. Thus, S. Tm co-opts the metabolic reprogramming of infected macrophages as a signal that induces its own virulence and survival, providing an additional perspective on metabolic host–pathogen cross-talk.

Reproducible and robust data on antibody repertoires are invaluable for basic and applied immunology. Next-generation sequencing (NGS) of antibody variable regions has emerged as a powerful tool in systems immunology, providing quantitative molecular information on antibody polyclonal composition. However, major computational challenges exist when analyzing antibody sequences, from error handling to hypermutation profiles and clonal expansion analyses. In this work, we developed the ASAP (A webserver for Immunoglobulin-Seq Analysis Pipeline) webserver (). The input to ASAP is a paired-end sequence dataset from one or more replicates, with or without unique molecular identifiers. These datasets can be derived from NGS of human or murine antibody variable regions. ASAP first filters and annotates the sequence reads using public or user-provided germline sequence information. The ASAP webserver next performs various calculations, including somatic hypermutation level, CDR3 lengths, V(D)J family assignments, and V(D)J combination distribution. These analyses are repeated for each replicate. ASAP provides additional information by analyzing the commonalities and differences between the repeats (“joint” analysis). For example, ASAP examines the shared variable regions and their frequency in each replicate to determine which sequences are less likely to be a result of a sample preparation derived and/or sequencing errors. Moreover, ASAP clusters the data to clones and reports the identity and prevalence of top ranking clones (clonal expansion analysis). ASAP further provides the distribution of synonymous and non-synonymous mutations within the V genes somatic hypermutations. Finally, ASAP provides means to process the data for proteomic analysis of serum/secreted antibodies by generating a variable region database for liquid chromatography high resolution tandem mass spectrometry (LC-MS/MS) interpretation. ASAP is user-friendly, free, and open to all users, with no login requirement. ASAP is applicable for researchers interested in basic questions related to B cell development and differentiation, as well as applied researchers who are interested in vaccine development and monoclonal antibody engineering. By virtue of its user-friendliness, ASAP opens the antibody analysis field to non-expert users who seek to boost their research with immune repertoire analysis.

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Encounters between host cells and intracellular bacterial pathogens lead to complex phenotypes that determine the outcome of infection. Single-cell RNA sequencing (scRNA-seq) is increasingly used to study the host factors underlying diverse cellular phenotypes but has limited capacity to analyze the role of bacterial factors. Here, we developed scPAIR-seq, a single-cell approach to analyze infection with a pooled library of multiplex-tagged, barcoded bacterial mutants. Infected host cells and barcodes of intracellular bacterial mutants are both captured by scRNA-seq to functionally analyze mutant-dependent changes in host transcriptomes. We applied scPAIR-seq to macrophages infected with a library of Salmonella Typhimurium secretion system effector mutants. We analyzed redundancy between effectors and mutant-specific unique fingerprints and mapped the global virulence network of each individual effector by its impact on host immune pathways. ScPAIR-seq is a powerful tool to untangle bacterial virulence strategies and their complex interplay with host defense strategies that drive infection outcome.

Encounters between host cells and intracellular bacterial pathogens lead to complex phenotypes that determine the outcome of infection. Single-cell RNA-sequencing (scRNA-seq) are increasingly used to study the host factors underlying diverse cellular phenotypes. But current approaches do not permit the simultaneous unbiased study of both host and bacterial factors during infection. Here, we developed scPAIR-seq, an approach to analyze both host and pathogen factors during infection by combining multiplex-tagged mutant bacterial library with scRNA-seq to identify mutant-specific changes in host transcriptomes. We applied scPAIR-seq to macrophages infected with a library of Salmonella Typhimurium secretion system effector mutants. We developed a pipeline to independently analyze redundancy between effectors and mutant-specific unique fingerprints, and mapped the global virulence network of each individual effector by its impact on host immune pathways. ScPAIR-seq is a powerful tool to untangle bacterial virulence strategies and their complex interplay with host defense strategies that drive infection outcome.

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