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The interface between immune cells and intracellular bacterial pathogens produces complex, diverse interactions. During individual encounters, highly adaptable and dynamic host cells and bacteria vary in their responses, thereby contributing to well-documented heterogeneous outcomes of infection. The challenge now is to break down the multidimensionality of these interactions into informative readouts of population physiology and predictors of responses to infection. We recently reported one approach to this challenge that couples single cell RNA-seq analysis with fluorescent markers to characterize infection phenotype.1 We detected bacterial subpopulations that elicit profoundly different host responses to infection in the specific host cells that they infect. Here we describe how heterogeneity might be maintained in populations of host and pathogens and discuss the advantages that heterogeneity confers to bacteria during infection and to host cells for eradicating a pathogen. We propose that single cell studies will allow the unraveling of host-pathogen biology and lead to an understanding of how the sum of individual encounters leads to the infection outcome of a whole organism.
Intracellular bacterial pathogens, such as Mycobacterium tuberculosis, Salmonella enterica, Legionella pneumophilia, and Neisseria gonorrhea, spend a significant portion of their life-cycle surviving and replicating within host cells. The capacity to survive within host macrophages is broadly considered to be key to the pathogenic success of these bacteria.2 As a result, harnessing the host's immune defenses for pathogen clearance and targeting pathogen virulence processes have been proposed as promising novel approaches to treat infection. To achieve progress toward this end, a comprehensive and systematic understanding of the dynamics that govern encounters between host and pathogen is required. However, the interface between immune cells and intracellular bacterial pathogens constitutes an extremely diverse and complex set of interactions. This diversity confounds our ability to understand the decisive steps that link cellular biochemical and biophysical events and infection outcome. Recently, we approached this challenge using single cell RNA-seq coupled with fluorescent reporters of infection phenotype using the model system of Salmonella in mouse macrophages. We showed that within individual encounters, host and pathogen vary in their transcriptional programs and subsequent infection outcomes.1 Here we outline the evidence that heterogeneity is an important factor in the interaction between macrophages and intracellular bacteria and the implications of this heterogeneity for host-pathogen biology, including how it may influence infection at the organism level. We suggest that this approach1 can be applied to understand how heterogeneity influences in vivo disease progression and to transform our understanding of the molecular details that underlie infection outcome. This addendum will focus on single cell analysis of host-pathogen interactions. For a detailed description of recent advances in single cell technologies, please see.3-5
Virulence programs of pathogenic intracellular bacteria and the macrophage response to infection are intertwined, with each organism perturbing the other and inducing counter-defenses. This dynamic interaction is made complex by the significant cell-to-cell heterogeneity that exists in the populations of both the infecting pathogen as well as the target host cell. Bacteria display significant cell-to-cell variation in attributes such as growth rate, expression of virulence factors, and sensitivity to antibiotics.6,7 Similarly, macrophages and other innate immune cells display extensive cell-to-cell variation in their ability to kill invading pathogens8 or in response to homogeneous bacterial ligands.9 The heterogeneous, stochastic, and dynamic nature of both host and bacterial populations suggests that their interaction is likely to result in a variety of subpopulations with distinct, complex phenotypes.7,10 Indeed, individual host-pathogen encounters generate well-documented diverse outcomes: some macrophages engulf the bacteria, while others remain uninfected11; some macrophages lyse the ingested bacteria, while others are permissive to intracellular bacterial survival11; and some macrophages will undergo cell death with bacterial release,12 while others survive and allow bacteria to multiply or persist intracellularly.13 Importantly, these outcomes occur concurrently from different cellular encounters within a single population (Fig. 1).
To date, many biological experiments have treated all cells of a particular type as though they are identical despite the fact that it is increasingly clear that populations of seemingly identical cells are in fact heterogeneous. The cellular interactions between heterogeneous host and pathogen cells offer an ideal model system to explore the biological significance of single cell interactions. The challenge will be to interrogate the breadth of heterogeneity and to determine which components of cellular heterogeneity serve a biological function that can contribute to the phenotypic outcomes of infection.
To begin to untangle how population heterogeneity might be relevant for infection outcomes, we recently coupled single cell genomics and phenotypic analysis.1 We developed a pipeline that integrates (1) single-cell massively parallel sequencing of host cells; (2) quantitative microscopy of infection phenotypes; and (3) transcriptomics and subpopulation analysis of pathogen populations. To characterize infection phenotype, we used a two2-color fluorescence system to differentially label live and dead S. typhimurium. This labeling enabled mouse bone marrow macrophages to be classified as uninfected, infected but containing dead bacteria, or infected containing live bacteria. As expected, host cells varied widely in their abilities to phagocytose bacteria and to restrict bacterial growth after internalization. Applying single-cell RNA-seq to exposed macrophages, we demonstrated that transcriptional profiles could distinguish between exposed and unexposed cells, and between infected and uninfected cells. We identified sets of genes that distinguished between the different observed infection phenotypes. One set responded simply to exposure to extracellular bacteria regardless of whether bacteria were internalized. Meanwhile, another set was induced only in cells with intracellular bacteria; notably, this set showed significant variation in expression from cell to cell. For example, we found that genes involved in the type I IFN response were upregulated in infected macrophages, but only in one-third of the population. This subpopulation heterogeneity would not have been observed using population analysis and in fact implies that there is extensive cell-to-cell variation in the response to bacterial invasion. We found that this variable host IFN response is due to the variation in a bacterial factor of the infecting bacterial population. Using a fluorescent reporter of host cell type I IFN expression simultaneously with RNAseq to characterize bacterial gene expression, we found that the phoP regulon is upregulated in those bacteria that elicited a strong host type I IFN response. phoP and phoQ encode, respectively, the response regulator and sensor kinase of a 2-component response regulator system that is activated upon macrophage invasion.14 We showed that variation in the activity of PhoPQ in individual bacteria drives variation in the type I IFN response of the individual host cells taking up a particular bacterium; this variation is mediated by the modification of bacterial LPS. This response could be recapitulated using a more homogenous population of bacteria either deficient in or constitutively expressing phoP. Interestingly, we found that PhoP is only active in a fraction of invading wild-type bacteria, and infected macrophages differentially recognized and responded to these bacterial subsets. In summary, this study demonstrates a causative link between bacterial phenotypic diversity and a variable host immune response.
Other cases of variable pathogen subsets important for infection outcome have also been described, indicating that this is a more general phenomenon. Examples include the identification of subsets of Mycobacterium marinum in infected fish larvae that express high levels of drug efflux pumps resulting in antimicrobial tolerance15; minor non-dividing Salmonella subsets that persist in infected mice13,16; and subsets of Mycobacterium tuberculosis which are able to evade autophagy targeting through variation in the ESX-1 secretion system.17 We hypothesize that bacterial diversity is advantageous in the arms race between host and pathogen, as it increases the chance of bacterial survival in varied host environments.18
Two possible explanations have been proposed for coexisting subpopulations of pathogens: bet-hedging and division of labor.19 According to the bet-hedging theory, in a fluctuating environment, heterogeneity within the population ensures that some part of the population expresses a phenotype well-suited for survival in one environment, while another subpopulation is poised for optimal survival in a different environment. According to the latter theory, division of labor allows a population to perform different functions simultaneously that would be costly or impossible to combine within a single individual. Of note, these models are not mutually exclusive but may in fact be simultaneously at play in a population of pathogens.
In multi-cellular organisms, the potential advantage of heterogeneity of clonal population of cells is a more complicated issue. It has previously been suggested that eukaryotic cells might favor, in contrast to a heterogeneous cellular response, a more predictable, synchronized cellular response20 to ensure a robust whole-tissue reaction to infection. However, extensive theoretical and experimental work has started to challenge this deterministic view. Single cell studies have shown that within tissues, cells in fact demonstrate intrinsic fluctuations in their responses, which then act as a source of extrinsic heterogeneity for other cells within the same tissue. For example, several recent studies have shown that subsets of innate immune cells respond differentially to homogenous ligands in vitro9 and in vivo.21 It seems therefore that cell-to-cell variation can, in some cases, be integral to the normal responses of mammalian cells, due to different mechanisms governing their differentiation, tissue distribution, and responsiveness to stimuli.
Studies using bulk population analysis have created our current paradigms for host-pathogen encounters, which entail temporally coordinated, sequential programs that culminate in a discrete phenotype (see Box 1 and Fig. 2A). Recent indications from single cell studies of heterogeneity challenge this concept of a final discrete phenotype and uniform response of host or pathogen.
On the pathogen side: Initially, contact stimulates adherence of the bacteria to host cells by pili and fimbriae.15 In non-phagocytic cells, cell contact also activates injection of effector molecules directly into the host cell cytoplasm that stimulates entrapping of the bacteria and invasion.41 The intracellular environment triggers a global modulation of gene expression that activates diverse virulence strategies, including alterations of pathogen-associated molecular patterns (PAMPs) and secretion of compounds to alter macrophage response and allows the bacteria to persist within the host.42 Secretion of effector molecules affects the properties of host vacuoles by remodelling locally the actin cytoskeleton, block the recruitment of NADPH oxidase and alter the proteic and lipidic composition of vacuoles.43 If this is successful, the bacteria can then multiply in the intracellular compartment.
On the host side: Host surface receptors recognize bacterial PAMPs to initiate phagocytosis of the bacteria and induction of a transcriptional response that leads to the production of inflammatory cytokines and a variety of effector defense mechanisms.44 Shortly after pathogen uptake, the phagosome undergoes maturation via its sequential interaction with subcompartments of the endocytic pathway.45 During the course of maturation, a full arsenal of antimicrobial features is induced, resulting in a phago-lysosome containing a highly acidic, oxidative and degradative milieu that is sufficient to clear the bacteria.8 If instead, the bacteria survive, epithelial-associated macrophages and other phagocytes such as dendritic cells carry engulfed bacteria to deeper tissues and further to draining lymph nodes,16 resulting in dissemination of infection. In lymph nodes, antigens are presented to T cells by professional antigen-presenting cells, such as dendritic cells and macrophages (as well as B cells), to initiate specific cellular immunity and generate specific T cells.46
Classical models have previously described defined temporal activation programs of macrophages.22 According to these, interferon-γ and bacterial LPS induce a program that includes increased expression of pro-inflammatory genes,23 while the administration of IL-4 and IL-13 results in increased expression of genes involved in tissue remodeling.24 This terminal polarization of mutually exclusive populations of macrophages is termed “M1” or “M2”, respectively.25 According to this model, M1 macrophages are exclusively important for clearance of intracellular pathogens and M2 macrophages for allergy and extracellular parasitic defense. However, recent findings from several independent groups suggest that this concept oversimplifies the in vivo complexity.26 In tissues, infection of pre-existing subpopulations of resident macrophages with intracellular bacteria27 do not induce simply macrophage populations with M1 signatures; instead, infection results in mixed macrophage phenotypes with simultaneous expression of M1 and M2 genes. This complexity has led investigators to examine a model of macrophage plasticity in vivo. According to this model, upon infection, the interplay between inhibitory and activating signals and the internal state of the cell before infection will prompt diverse terminal phenotypes.
Similarly, in bacteria, classical models assumed that virulence programs are activated in a concerted manner in a bacterial population, by regulators such as quorum sensing systems.28 Recently it has been shown that because virulence gene expression is costly, virulence genes are variably expressed from bacterium to bacterium,29 as a means to mitigate the cost of its expression in the population. Further, subpopulations of bacteria growing slowly in association with virulence gene expression can support local growth of more rapidly growing non-virulent subpopulations that are necessary for successful establishment of infection, a behavior termed cooperative virulence.30
Of note, a trait that promotes virulence within one cell type might confer a disadvantage in different cell types, in which the bacterium may benefit from expression of a different set of genes. For example, the two 2 Salmonella secretion systems (SPI-1 and SPI-2) promote virulence within the context of a specific host cell type (i.e. epithelial cells and macrophages, respectively).31 Using single cell analyses of host and bacterial factors, can expose the strategies the bacterium uses to tune the variation of multiple virulence traits and create subpopulations that ensure that some pathogen subsets prevail at different stages of infection or in different host cell environments.32
The bistability of virulence gene expression can be the consequence of variable induction by stimuli or microenvironments, or, it may be the result of a purely stochastic process, occurring even in uniform in vitro environments. Thus, the existence of these heterogeneous subpopulations of host and bacteria that are observed as the result of their mutual encounters raises the important question of whether the heterogeneity stochastically preexists or is induced by their encounter.
In the classic, commonly accepted model of a linear, orchestrated response to infection (Box 1), the observed heterogeneity and discrete outcomes could be determined by the kinetics of induction of a response in each individual encounter. The existence of heterogeneity would then be due to an underlying non-uniform rate of induction of either the immune response or the pathogen virulence programs, which results in differences in infection progression between individual encounters and the co-existence of different subpopulations (Fig. 2A). Heterogeneous populations are thus presumed to be induced and representative of different stages through which infection progresses linearly.
In another model, the simultaneous existence of different subpopulations prior to infection could also explain the observed heterogeneity as infection could proceed via distinct but parallel programs within each sub-population. Each different, pre-existing host cell subpopulations could make a commitment to a distinct cellular program after encountering a particular bacterium, wherein the state of the specific bacterium can also contribute to the outcome (Fig. 2B). In this model, infection outcome is predetermined by the individual traits of the host and bacteria before their encounter.
Analyzing the infection process through detailed time courses using single cell analysis of both host and bacterial cells, before and after infection, will help determine between these two 2 models. Importantly, we hypothesize that the two 2 models are not necessarily mutually exclusive, and a combination of these two2—a temporal linear process of preexisting subpopulations of cells taking divergent, parallel commitment paths—may be possible. Thus, the existence of heterogeneous subpopulations even within a clonal population of cells introduces a new complexity to the study of host-pathogen in vitro; in vivo, this complexity is even greater due to the numerous different host cell types within a single tissue and the different microenvironments that a pathogen encounters throughout infection.
The heterogeneous states of a single type of bacteria and host cell are evident at early stages of infection, upon initial inoculation with the bacteria. Adding to the complexity of this heterogeneity at late stages of infection, the bacteria encounters many different tissue types organized in complex anatomy, thereby resulting in numerous, diverse microenvironments and host cell types in which a pathogen must survive. Because we lack the sensitivity to query individual host and pathogen encounters, one of the least-understood periods in the course of infectious disease lies between the initial inoculation with a dose of pathogens and the appearance of the first symptoms of disease, or the lack of appearance thereof.33
The early stages of infection, when bacterial numbers are small, play a critical role in determining infection outcome. Several lines of evidence indicate that the fate of the infection process can be determined by just a few single individual encounters: Invasion of body surfaces by as little 10 pathogenic bacteria in some cases, can establish infection34; From the initial site of infection, immune cells carry even small numbers of engulfed bacteria to deeper tissues35; Infection bottlenecks allows only very few individual bacteria to survive, these can then disseminate to cause more widespread, systemic infection, often from clonal growth of an individual bacterium.36 Single cell analysis can now provide the much-needed resolution to query individual host and pathogen encounters at these critical junctions of infection.
In late stages of infection, when bacterial numbers are high and spread, the different host cell types and fluctuating microenvironments play an important role in effective elimination of the bacteria and resolution of infection.37 For example, spatial segregation occurs when microcolonies of pathogens are surrounded by different types of host cells to form discrete, infected lesions.38 Paradoxically, the same host factors that can eliminate some bacteria, can also provide a protected niche in which some intracellular bacteria can survive, replicate, and ultimately disseminate to other tissues.39 This functional heterogeneity among the different lesions can result in phenotypic heterogeneity, where the host can sterilize the infection in some lesions while in others the bacteria can colonize and persist. However, studies measuring population averages obscure our understanding of these heterogeneous processes of host and pathogen and neglects where individual bacteria are located or how they spread and interact during infection. Better understanding of this heterogeneity, through analysis of individual encounters, can help us to identify the strategies of host and pathogen at local interactions that determine the outcome of the entire infection of the organism.40
Bacterial pathogens represent a serious public health concern, with the rise of antibiotic resistant bacterial pathogens constituting one of the most serious threats to human health (http://www.who.int/drugresistance/documents/surveillancereport/en/). Yet, crucial aspects of infection remain poorly understood. An appreciation of population dynamics at the bulk level provides almost no information on the inherent heterogeneity that underlies host-pathogen encounters: from host cells that internalize bacteria to the early and late processes of in vivo infection. Application of novel single cell approaches can help us determine how heterogeneous processes of host macrophages and pathogenic bacteria gives rise to well-documented deterministic infection outcomes (Fig. 1). In vivo, application of novel single cell approaches to both early and later stages of infection can finally provide the much-needed resolution to analyze determinants of in vivo infection, individual cellular encounters, pathogen dissemination, formation of new infection foci, and infiltration of immune cells to form inflammatory lesions. Understanding this rich biology could significantly improve our ability to predict the outcomes of local host-pathogen encounters and overall disease progression, and enhance our ability to suggest potential novel antimicrobial treatments.
No potential conflicts of interest were disclosed.