Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

Ben-Moshe, Noa Bossel; Hen-Avivi, Shelly; Levitin, Natalia; Yehezkel, Dror; Oosting, Marije; Joosten, Leo A. B.; Netea, Mihai G.; Avraham, Roi

doi:10.1038/s41467-019-11257-y

Cited by 71 publications

(68 citation statements)

References 71 publications

(76 reference statements)

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“…To test the feasibility of identifying microbial reads from scRNA-seq datasets, we applied CSI-Microbes to two "gold-standard" datasets where immune cells were infected ex vivo with the intracellular bacteria Salmonella enterica and subsequently sequenced using scRNA-seq (Aulicino et al, 2018;Ben-Moshe et al, 2019). In the first dataset, where ~7,000 peripheral blood mononuclear cells (PBMCs) were infected with Salmonella enterica serovar Typhimurium and sequenced using 10x 3' sequencing, we identified very few reads that mapped to the Salmonella genus.…”

Section: Resultsmentioning

confidence: 99%

scRNA-seq analysis of colon and esophageal tumors uncovers abundant microbial reads in myeloid cells undergoing proinflammatory transcriptional alterations

Robinson

Stone²,

Ruppin

et al. 2020

Preprint

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Microbial taxa that are differentially abundant between cell types are likely to be intracellular. Here we describe a new computational pipeline called CSI-Microbes (computational identification of Cell type Specific Intracellular Microbes) that aims to identify such putative intracellular species from single cell RNA-seq data in a given tumor sample. CSI-microbes also includes additional steps that can be applied to filter out microbial contaminants from the bona fide microbial residents of cells in the patients. We first test and validate CSI-microbes on a dataset of immune cells deliberately infected with Salmonella. We then apply CSI-microbes to identify intracellular microbes in breast cancer and melanoma. We identify Streptomyces as differentially abundant in the tumor cells of one breast cancer sample. We further identify three bacterial genera and four fungal genera that are differentially abundant and hence likely to be intracellular in the tumor cells in melanoma samples. No cell type specific bacteria were identified in our analysis of brain tumor samples. In sum, CSI-Microbes offers a new way to identify likely intracellular microbes living within specific cell populations in malignant tumors, markedly extending upon previous studies aimed at inferring microbial abundance from bulk tumor expression data.

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Section: Resultsmentioning

confidence: 99%

scRNA-seq analysis of colon and esophageal tumors uncovers abundant microbial reads in myeloid cells undergoing proinflammatory transcriptional alterations

Robinson

Stone²,

Ruppin

et al. 2020

Preprint

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“…(G) Host immune cells subjected to pathogens can be sorted by cell outcomes and characterized by scRNA-seq to understand host heterogeneity in the context of pathogenic microbes (Avraham et al, 2015 ; Saliba et al, 2016 ). (H) Human peripheral blood mononuclear cells (PBMC) can be studied in response to pathogenic microbes to generate predictive models of disease outcomes for patients (Bossel Ben-Moshe et al, 2019 ). (I) The intestinal epithelium is a classic example of a host-microbiota interface.…”

Section: Microbiome Studies At Single-cell Resolutionmentioning

confidence: 99%

Host-Microbiome Interactions in the Era of Single-Cell Biology

Sharma

Thaiss

2020

Front. Cell. Infect. Microbiol.

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Microbes are the most prevalent form of life yet also the least well-understood in terms of their diversity. Due to a greater appreciation of their role in modulating host physiology, microbes have come to the forefront of biological investigation of human health and disease. Despite this, capturing the heterogeneity of microbes, and that of the host responses they induce, has been challenging due to the bulk methods of nucleic acid and cellular analysis. One of the greatest recent advancements in our understanding of complex organisms has happened in the field of single-cell analysis through genomics, transcriptomics, and spatial resolution. While significantly advancing our understanding of host biology, these techniques have only recently been applied to microbial systems to shed light on their diversity as well as interactions with host cells in both commensal and pathogenic contexts. In this review, we highlight emerging technologies that are poised to provide key insights into understanding how microbe heterogeneity can be studied. We then take a detailed look into how host single-cell analysis has uncovered the impact of microbes on host heterogeneity and the effect of host biology on microorganisms. Most of these insights would have been challenging, and in some cases impossible, without the advent of single-cell analysis, suggesting the importance of the single-cell paradigm for progressing the microbiology field forward through a host-microbiome perspective and applying these insights to better understand and treat human disease.

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“…While such metabolic models aim to represent the behavior of individual cells, their contextualization has generally relied on information collected from bulk population data. However, the advent of single-cell RNA-Seq (scRNA-Seq) has highlighted the substantial extent of cell-to-cell diversity that is often missed by bulk profiles [15], [16], and can be especially 3 / 27 prominent in immune cells and associated with their functional diversity [8], [17]- [28]. One of the earliest examples has been the diversity among T helper 17 (Th17) cells [29].…”

Section: Introductionmentioning

confidence: 99%

In Silico Modeling of Metabolic State in Single Th17 Cells Reveals Novel Regulators of Inflammation and Autoimmunity

Wagner¹,

Wang

DeTomaso³

et al. 2020

Preprint

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Cellular metabolism, a key regulator of immune responses, is difficult to study with current technologies in individual cells Here, we present Compass, an algorithm to characterize the metabolic state of cells based on single-cell RNA-Seq and flux balance analysis. We applied Compass to associate metabolic states with functional variability (pathogenic potential) amongst Th17 cells and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known differences between Th17 and Treg cells, as well as novel targets in amino-acid pathways, which we tested through targeted metabolic assays. Compass further predicted a particular glycolytic reaction (phosphoglycerate mutase -PGAM) that promotes an anti-inflammatory Th17 phenotype, contrary to the common understanding of glycolysis as pro-inflammatory. We demonstrate that PGAM inhibition leads non-pathogenic Th17 cells to adopt a pro-inflammatory transcriptome and induce autoimmunity in vivo. Compass is broadly applicable for characterizing metabolic states of cells and relating metabolic heterogeneity to other cellular phenotypes. / 27 RESULTS Compass -an algorithm for comprehensive characterization of single-cell metabolismWe reasoned that even though the mRNA expression of individual enzymes does not necessarily provide an accurate proxy for their metabolic activity, a global analysis the entire metabolic network (as enabled by RNA-Seq) in the context of a large sample set (as offered by single cell genomics) coupled with strict criteria for hypotheses testing, would provide an effective framework for predicting cellular metabolic status of the cell. This led us to develop the Compass algorithm, which integrates scRNA-Seq profiles with prior knowledge of the metabolic network to infer a metabolic state of the cell (Figure 1A).The metabolic network is encoded in a Genome-Scale Metabolic Model (GSMM) that includes reaction stoichiometry, biochemical constraints such as reaction irreversibility and nutrient availability, and gene-enzyme-reaction associations. Here, we use Recon2, which comprises of 7,440 reactions and 2,626 unique metabolites [41]. To explore the metabolic capabilities of each cell, Compass solves a series of constraint-based optimization problems (formalized as linear programs) that produce a set of numeric scores, one per reaction (Methods). Intuitively, the score of each reaction in each cell reflects how well adjusted is the cell's overall transcriptome to maintaining high flux through that reaction. Henceforth, we refer to the scores as quantifying the "potential activity" of a metabolic reaction (or "activity" in short when it is clear from the context that Compass predictions are discussed).Compass belongs to the family of Flux Balance Analysis (FBA) algorithms that model metabolic fluxes, namely the rate by which chemical reactions convert substrates to products and apply constrained optimization methods to find flux distributions that satisfy desired properties (a flux distribution is an assignment of flux value to ever...

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Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

Cited by 71 publications

References 71 publications

scRNA-seq analysis of colon and esophageal tumors uncovers abundant microbial reads in myeloid cells undergoing proinflammatory transcriptional alterations

scRNA-seq analysis of colon and esophageal tumors uncovers abundant microbial reads in myeloid cells undergoing proinflammatory transcriptional alterations

Host-Microbiome Interactions in the Era of Single-Cell Biology

In Silico Modeling of Metabolic State in Single Th17 Cells Reveals Novel Regulators of Inflammation and Autoimmunity

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