Transiently heritable fates and quorum sensing drive early IFN-I response dynamics

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1. 

   Version published to 10.7554/elife.83055 on eLife

   Jan 11, 2023
2. 

   eLife

   Dec 30, 2022

   Author Response

   Reviewer #1 (Public Review):

   1. Context and definitions for stochasticity and heritability: The authors provide well-referenced introductions and explanations throughout the manuscript. However, key understanding of concepts for their central hypothesis on transient heritability are not shared until well into the results sections (Lines 215-227), leaving the introduction somewhat unclear on the authors thinking and motivation. The manuscript would benefit by including clear definitions of "stochastic", "transiently heritable", and "heritable" and their relationships to "intrinsic" and "deterministic" in the introduction.

   Regarding the first point, we agree it is important to include clear definitions timely. Therefore, we added much more detail to the introduction (see tracked changes), and added the following …

   Author Response

   Reviewer #1 (Public Review):

   1. Context and definitions for stochasticity and heritability: The authors provide well-referenced introductions and explanations throughout the manuscript. However, key understanding of concepts for their central hypothesis on transient heritability are not shared until well into the results sections (Lines 215-227), leaving the introduction somewhat unclear on the authors thinking and motivation. The manuscript would benefit by including clear definitions of "stochastic", "transiently heritable", and "heritable" and their relationships to "intrinsic" and "deterministic" in the introduction.

   Regarding the first point, we agree it is important to include clear definitions timely. Therefore, we added much more detail to the introduction (see tracked changes), and added the following definitions and additional explanations:

   Multilayered stochasticity: “stochasticity originating from different levels over the course of an infection.“

   “Importantly, although the terms stochasticity and determinism seem highly dichotomous, deterministic features (e.g., epigenetic regulation) are often, if not always, stochastically regulated (Zernicka-Goetz and Huang, 2010). However, in cellular decision-making, the major difference between a stochastic process and a deterministic process boils down to the effects of (varying) inputs on dictating (varying) outputs. In fact, a stochastic process in characterized by the exact same stimulus leading to varying response outcomes, often as a result of varying host-intrinsic factors (Symmons and Raj, 2016). In contrast, a deterministic process is characterized by an outcome (e.g., IFN-I production) that is fixed, or at least to a large degree, while the input can be variable. How cells are epigenetically predispositioned, in turn, can again be a stochastic process, similar to the fundamentals of developmental biology in which cells are randomly pushed towards deterministic outcomes (Zernicka-Goetz and Huang, 2010).”

   “Transient heritability refers to heritable epigenetic profiles [e.g., profiles encoding cellular fates for the production IFN-Is] that only transfer over a couple of generations, as observed across cell types and systems including cancer drug resistance (Shaffer et al., 2020), cancer fitness (Fennell et al., 2022; Oren et al., 2021), NK cell memory (Rückert et al., 2022), HIV reactivation in T cells (Lu et al., 2021), epithelial immunity (Clark et al., 2021), and trained immunity (Katzmarski et al., 2021).”

   “Besides a growing body of evidence on the role of transient heritable fates dictating cellular behaviors, the effects of population density, often referred to as quorum sensing, are getting more established for immune (signaling) systems (Antonioli et al., 2019; Polonsky et al., 2018; Van Eyndhoven and Tel, 2022). On top of the intrinsic features characterized by stochasticity and determinism, individual immune cells can communicate in various ways to elicit appropriate systemic immune responses. Typically, cytokine-mediated communication is categorized into two types: autocrine and paracrine signaling. Autocrine signaling is defined by cells secreting signaling molecules while simultaneously expressing the cognate receptor. Paracrine signaling is defined by cells either secreting signaling molecules without expressing the cognate receptor, or cells expressing the receptor without secreting the molecule. In essence, quorum sensing can be considered a phenomenon in which autocrine cells determine their population density based on cells engaging in neighbor communication, but without self-communication (Doğaner et al., 2016; Van Eyndhoven and Tel, 2022). Especially in the presence of other competitive decision makers [i.e., cytokine consumers and producers], it is critical for individual cells to assess cellular density, and act accordingly (Oyler-Yaniv et al., 2017).”

   1. Generalizability of findings to other cell types, systems, and triggers: The cell line and Poly(I:C) delivery method used by the authors lacks sufficient characterization to extend the conclusions derived from its use. Notably, the NIH3T3-IRF7-CFP cell line expresses IRF7 constitutively and thus may only be a good model for cells with similar expression levels; many primary cells only express IRF7 at low levels or not at all until stimulated (PMID: 2140621). The conclusions would be greatly strengthened by demonstrating similar first responder dynamics/heritability in other cell types. The experiments measuring the efficiency of Poly(I:C) delivery by transfection lack sufficient resolution to determine if the Poly(I:C) is intracellular or membrane bound. IFN-I response kinetics, and potentially quality, would likely be distinct between cytosolic and endosomal sensing and may impact the likelihood of becoming a first responder.

   Regarding the generalizability of findings to other cell types, systems, and triggers, we thank reviewer 1 for binging up this crucial point. About the IRF7 expression, IRF7 is expressed at a low amount in most cells and is strongly induced by type I IFN-mediated signaling (Marie et al., 1998; Sato et al., 1998b; Honda et al., 2006). How we used the word “constitutively” refers to the IRF7 molecules always being fluorescent, not that IRF7 is always highly expressed in these cells. Therefore, NIH3T3 is similar to all other cells, except for plasmacytoid dendritic cells, which are known for their high background levels of IRF7. We changed the revised manuscript accordingly:

   “Accordingly, we used a NIH3T3:IRF7-CFP reporter cell line, expressing low, physiological background levels of IRF7-CFP fusion proteins, to monitor signaling dynamics during early phase IFN-I response dynamics (Figure 1b).”

   Regarding the comparison with other cell types, we emphasized the similar responders numbers observed in plasmacytoid dendritic cells (an argument that the intrinsic factor of IRF7 background differences is not determining responders). We changed the revised manuscript accordingly:

   “In short, IFN-I responses are elicited by fractions of so-called first responding cells, also referred to as ‘precocious cells’ or ‘early responding cells’, which start the initial IFN-I production upon viral detection, both validated in vitro, in vivo, and across cell types (Bauer et al., 2016; Hjorton et al., 2020; Patil et al., 2015; Shalek et al., 2014; Van Eyndhoven et al., 2021a; Wimmers et al., 2018).”

   “This percentage is in line with what has been found across literature, species [i.e., human and mice] and cell types [i.e., fibroblasts, monocyte derived dendritic cells, plasmacytoid dendritic cells], which ranges from 0.8 to 10% of early responders, emphasizing the elegant yet robust feature of only a fraction of first responding cells driving the population-wide IFN-I system (Bauer et al., 2016; Drayman et al., 2019; Patil et al., 2015; Shalek et al., 2014; Van Eyndhoven et al., 2021a; Wimmers et al., 2018).”

   Regarding the hypothesis brought up by the reviewer on the role of cytosolic versus endosomal sensing impacting IFN-I response kinetics, and potentially quality, we hypothesize otherwise. Shalek and colleagues tested LPS (TLR4 ligand), PIC (TLR3 ligand, endosomal), and PAM (TLR2 ligand), all eliciting similar early responding cells, which they called precocious cells. This argues that the phenomenon of first responders is independent of the type of stimulation. Besides, for plasmacytoid dendritic cells, both R848 (TLR7/8 ligand), and CpG-C (TLR9 ligand) elicit very similar early IFN-I responses. In contrast, R848 and CpG-C elicit very different late IFN-I response dynamics, reflected by the fraction and activation dynamic of second responders (yet unpublished). We clarified accordingly:

   “Moreover, various stimuli (live and synthetic) targeted membrane, cytosolic, and endosomal receptors, arguing that the mode of activation is not driving the discrepancies in responder fates.”

   1. Epigenetic regulation of transient heritability: To test the contribution of epigenetic regulation on first responder fate, the authors treat their cells with DNMTi. While treatment with this drug does increase the proportion of first responder cells, the authors don't provide evidence that the mechanism of action is mediated by inhibiting DNA methylation. This is further confounded by the reduced responder frequencies in DNMTi treated cells transduced with Poly(I:C) (Fig 4g). The authors offer an explanation for this observation, but their reported data (Fig 4h) doesn't measure whether DNMTi, leads to latent retrovirus activation, broader demethylation, or a combination of the two.

   We are well aware that the hypothesis on retrovirus activation are inconclusive. Unfortunately, we currently do not have the ability to utilize the tools to properly assess this hypothesis. Instead, we can only speculate. However, we were able to assess the effects of a different epigenetic drug [i.e., HDACi], as suggested later by the reviewer. Therefore, to strengthen our data interpretation, we added the following additional information and experimental data to the revised manuscript:

   “Also the treatment with varying dosages and durations of Trichostatin A, an histone deacetylase inhibitor (HDACi), increased the number of responding cells (Supplementary Figure 5).”

   “The rather long timescales of switching from responders to non-responders, and the other way around, imply epigenetic mechanisms at play, and indeed, prior work has indicated an important role for epigenetics dictating IFN-I response dynamics (reviewed in (Barrat et al., 2019)).”

   “Both methylation and histone acetylation have been suggested in dictating transient heritable cellular fates (Clark et al., 2021; Lu et al., 2021; Shaffer et al., 2020).”

   1. Temporal experimental data to validate and extend transient heritability and quorum sensing: Developing a model for cellular-decision making during early IFN-I responses, the authors formalize and test the hypothesis of transient heritability. While the data largely fit the model proposed (Fig 6D-F), the reported data points lack sufficient temporal resolution to validate the model during the earlier and more variable generations. Given that by generation 9 variability in first responder frequency has almost stabilized, there is only one data point (generation 6) to evaluate the fit of the ODE described. More densely sampled data points below generation 10 are necessary to validate the model. Moreover, a discussion of Kon calculation/observation, meaning, and validation is missing. To partially test their claim that Kon is a function of density (i.e., quorum sensing), the authors plate cells at different densities and measure the responder frequency at generation 6. This analysis lacks contextualization of other autocrine and paracrine signals potentially impacting IFN-I response. Moreover, these signals will be diverse in different cell types and could impact Kon and/or the overall model.

   We agree that our first model validation was suboptimal, indeed because of lacking sufficient temporal resolution. Therefore, we performed additional experiments on clones of generation 1, 2, 3, 4, 5, of which the results turned out to be remarkably robust. We changed the revised manuscript accordingly:

   “Surprisingly, the data obtained from clones of generation one through nine resulted in a mean higher than 2.134% (Figure 6d; Supplementary Figure 9), and a fluctuating CV (Figure 6e). From generation 13 onwards, both the mean and the CV start to meet the data obtained from the regular cultures again, which are similar to the theoretical outcomes of a stochastic process. Accordingly, we modeled first responders as a binary switch, where individual cells are either responding (ON) or nonresponding (OFF), similar to the transient heritable fates characterized and modeled before (Shaffer et al., 2020). Details on the ODE model are provided in the Materials and Methods section. We could fit the transient heritability model to the data when starting from 100% responders at generation zero [i.e., a single cell isolated from the regular culture]. Cells switch OFF after 5 generation on average, with a constant kon rate throughout. Interestingly, in generation zero we observed (nearly) only IFN-I responders, which we believe might be caused by single cells being deprived from any paracrine cues, which could include inhibitory factors that normally limited responsiveness. However, single IFN-I-producing cells [i.e., plasmacytoid dendritic cells and monocyte derived dendritic cells] encapsulated in picoliter droplets or captured in small microfluidic chambers did not display this behavior (Shalek et al., 2014; Wimmers et al., 2018). Instead, one could argue that single cells establish a different microenvironment, compared to a situation in which cells are close to neighboring cells, which elicits behavioral changes accordingly. The dimensions of microfluidic droplets and chambers are in the same range of cell-to-cell contacts in vitro, while single cells seeded for cloning are surrounded by rather massive areas and volumes without other cells present. Therefore, we hypothesize that these single cells lack biochemical, and perhaps biomechanical cues provided by dense cell populations, which result in behavioral changes in these cells, in our case, making them more responsive. Similarly, in quorum sensing, cells secrete soluble signaling molecules (called autoinducers), which enables cells to get a sense of their cell density (Postat and Bousso, 2019; Waters and Bassler, 2005). Without signaling of these molecules, cells perceive being isolated from the rest. In microfluidic droplets and chambers, these molecules accumulate, given the relatively small volumes.”

   Regarding the contextualization of autocrine and paracrine signaling impacting IFN-I response dynamics in these studies, we added the following additional information:

   “On top of the intrinsic features characterized by stochasticity and determinism, individual immune cells can communicate in various ways to elicit appropriate systemic immune responses. Typically, cytokine-mediated communication is categorized into two types: autocrine and paracrine signaling. Autocrine signaling is defined by cells secreting signaling molecules while simultaneously expressing the cognate receptor. Paracrine signaling is defined by cells either secreting signaling molecules without expressing the cognate receptor, or cells expressing the receptor without secreting the molecule. In essence, quorum sensing can be considered a phenomenon in which autocrine cells determine their population density based on cells engaging in neighbor communication, but without self-communication (Doğaner et al., 2016; Van Eyndhoven and Tel, 2022).”

   Regarding the point that signals will be diverse in different cell types and could impact Kon and/or the overall model, yes, but we expect this to be only minor. Besides, the model can be easily adjusted to the different parameters per cell type (see Saint-Antoine et al., 2022).

   Reviewer #3 (Public Review):

   1. For the small fraction of cells that respond in the absence of Poly(I:C), are these cells just showing IRF7 translocation or are they fully responding with IFNB production? Has this been observed in other experimental systems or contexts? Do you also observe secondary responders in the unstimulated samples (as shown in the stimulated in Fig. 2G-I)?

   Regarding the first point on the unstimulated translocated cells, excellent point. Although we have not experimentally validated it, we hypothesize that cells are able to produce constitutive levels of IFN-Is, as thoroughly described in literature, so we assume that these translocated cells produce IFN-Is. We provided additional speculation in the revised manuscript:

   “Besides, the background numbers of translocated cells possibly reflect the intrinsic feature of the IFN-I system to ensure basal IFN-I expression and IFNAR signaling to equip immune cells to rapidly mobilize effective antiviral immune responses, and homeostatic balance through tonic signaling (Gough et al., 2012; Ivashkiv and Donlin, 2014).”

   1. Do the second responders only arise through direct IFN-I production by first responders? Is it possible that this response has any relationship with the initial transfection with Poly(I:C)?

   From the droplet-based experiments with plasmacytoid dendritic cells performed before (Wimmers et al., 2018; Van Eyndhoven et al., 2021), we could conclude that the second responders indeed required the activation and subsequent early IFN-I production of first responders. Whereas droplet-based microfluidics is a very stable, and controlled method, producing thousands of homogeneous droplets, we concluded that the difference between first and second responders is not elicited upon variations in activation (e.g., transfection discrepancies).

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3. 

   eLife

   Nov 10, 2022

   eLife assessment

   This study presents a valuable finding that adds to a growing body of evidence reporting heritable cell states that can guide fate choices in single cells, in this case the fate of early IFN-I response. The evidence supporting the claims of the authors is solid, although testing the generalizability of the result to other cell types or contexts and strengthening the link to epigenetic regulation would have strengthened the study. Overall, this work will be of interest to a wide set of scientists, including cell biologists, immunologists, and systems biologists.

   Read the original source
4. 

   eLife

   Nov 10, 2022

   Reviewer #1 (Public Review):

   In the work by Van Eyndhoven et al., the authors aim to determine if the cell state present in the cells that first produce Type I Interferon (IFN-I, an antiviral cytokine) is stochastically regulated or may be epigenetically inheritable. This work builds from previous studies demonstrating that IFN-I responses occur in two waves: a small proportion of early responding "precocious" cells which induce population-wide responses through autocrine and paracrine signaling. The authors contextualize their study well within the literature, and discuss the hypotheses of stochasticity or determinism driving early responding cell fate. Within this context, the authors set out to characterize and model the nature of these "first responder" cells during IFN-I antiviral signaling. Developing a quantitative imaging …

   Reviewer #1 (Public Review):

   In the work by Van Eyndhoven et al., the authors aim to determine if the cell state present in the cells that first produce Type I Interferon (IFN-I, an antiviral cytokine) is stochastically regulated or may be epigenetically inheritable. This work builds from previous studies demonstrating that IFN-I responses occur in two waves: a small proportion of early responding "precocious" cells which induce population-wide responses through autocrine and paracrine signaling. The authors contextualize their study well within the literature, and discuss the hypotheses of stochasticity or determinism driving early responding cell fate. Within this context, the authors set out to characterize and model the nature of these "first responder" cells during IFN-I antiviral signaling. Developing a quantitative imaging approach to measure IRF7 translocation, the authors measure the proportion of first responder cells as defined by higher ratios of nuclear/cytosolic IRF7 expression. Transfection of Poly(I:C) induces IFN-I signaling and leads to ~2% first responders, in line with previously published work. The authors then show that responder frequencies increase following treatment with a DNA methyltransferase inhibitor, suggesting a relationship between epigenetic regulation and responder potential. To test the hypothesis that the first responder cell state occurs stochastically, the authors adapted the Luria-Delbruck fluctuation test by evaluating responder frequency as a function of cell division or generation. First witnessing high variability of responder frequencies using limiting dilution clonal expansion followed by low stable frequencies after 100 divisions (similar to regular cultures), the authors suggest that the first responder state may be partially heritable and develop a mathematical model of transient heritability. Finally, to assess whether cell density and quorum sensing contribute to this transient heritability, cells plated at different densities were interrogated for responder frequencies after a fixed number of divisions; only low density seeding led to high and variable responder frequencies.

   The interrogation of IFN-I early responding cells by Van Eyndhoven et al. is well executed and supports the claim that first responder events are non-stochastic. However, the use of transgenic reporter cells in vitro may limit the findings reported in the manuscript to this system, and awaits further experimentation to assess the generalizability of these findings to overall cellular decision-making during inflammatory responses. Identifying the mechanisms responsible for transient heritability and the density-dependent regulation will be of high interest.

   1. Context and definitions for stochasticity and heritability: The authors provide well-referenced introductions and explanations throughout the manuscript. However, key understanding of concepts for their central hypothesis on transient heritability are not shared until well into the results sections (Lines 215-227), leaving the introduction somewhat unclear on the authors thinking and motivation. The manuscript would benefit by including clear definitions of "stochastic", "transiently heritable", and "heritable" and their relationships to "intrinsic" and "deterministic" in the introduction.

   2. Generalizability of findings to other cell types, systems, and triggers: The cell line and Poly(I:C) delivery method used by the authors lacks sufficient characterization to extend the conclusions derived from its use. Notably, the NIH3T3-IRF7-CFP cell line expresses IRF7 constitutively and thus may only be a good model for cells with similar expression levels; many primary cells only express IRF7 at low levels or not at all until stimulated (PMID: 2140621). The conclusions would be greatly strengthened by demonstrating similar first responder dynamics/heritability in other cell types. The experiments measuring the efficiency of Poly(I:C) delivery by transfection lack sufficient resolution to determine if the Poly(I:C) is intracellular or membrane bound. IFN-I response kinetics, and potentially quality, would likely be distinct between cytosolic and endosomal sensing and may impact the likelihood of becoming a first responder.

   3. Epigenetic regulation of transient heritability: To test the contribution of epigenetic regulation on first responder fate, the authors treat their cells with DNMTi. While treatment with this drug does increase the proportion of first responder cells, the authors don't provide evidence that the mechanism of action is mediated by inhibiting DNA methylation. This is further confounded by the reduced responder frequencies in DNMTi treated cells transduced with Poly(I:C) (Fig 4g). The authors offer an explanation for this observation, but their reported data (Fig 4h) doesn't measure whether DNMTi, leads to latent retrovirus activation, broader demethylation, or a combination of the two.

   4. Temporal experimental data to validate and extend transient heritability and quorum sensing: Developing a model for cellular-decision making during early IFN-I responses, the authors formalize and test the hypothesis of transient heritability. While the data largely fit the model proposed (Fig 6D-F), the reported data points lack sufficient temporal resolution to validate the model during the earlier and more variable generations. Given that by generation 9 variability in first responder frequency has almost stabilized, there is only one data point (generation 6) to evaluate the fit of the ODE described. More densely sampled data points below generation 10 are necessary to validate the model. Moreover, a discussion of Kon calculation/observation, meaning, and validation is missing. To partially test their claim that Kon is a function of density (i.e., quorum sensing), the authors plate cells at different densities and measure the responder frequency at generation 6. This analysis lacks contextualization of other autocrine and paracrine signals potentially impacting IFN-I response. Moreover, these signals will be diverse in different cell types and could impact Kon and/or the overall model.

   Read the original source
5. 

   eLife

   Nov 10, 2022

   Reviewer #2 (Public Review):

   In this manuscript, Eyndhoven and colleagues develop an experimental and analytical setup to test the role of cell-intrinsic factors in guiding fate decisions to viral infections. The study is motivated by the observations that early antiviral response mediated by type 1 interferon (IFN-1) is not fully penetrant in response to virus, and is initiated only in 1-3% of the cells. Using a combination of IFN-1 reporter system, automated image segmentation, DNMT inhibitors, and Luria-Delbrück fluctuation test in a murine cell line model, the authors state that cell intrinsic factors guide IFN-1 response in rare cells. This response (measured with IRF7 translocation) is predetermined and heritable over several generations. Lastly, the authors report cell density effects on IFN-1 response, a phenomena the authors …

   Reviewer #2 (Public Review):

   In this manuscript, Eyndhoven and colleagues develop an experimental and analytical setup to test the role of cell-intrinsic factors in guiding fate decisions to viral infections. The study is motivated by the observations that early antiviral response mediated by type 1 interferon (IFN-1) is not fully penetrant in response to virus, and is initiated only in 1-3% of the cells. Using a combination of IFN-1 reporter system, automated image segmentation, DNMT inhibitors, and Luria-Delbrück fluctuation test in a murine cell line model, the authors state that cell intrinsic factors guide IFN-1 response in rare cells. This response (measured with IRF7 translocation) is predetermined and heritable over several generations. Lastly, the authors report cell density effects on IFN-1 response, a phenomena the authors refer to as "quorum sensing", and rationalize their observations with an ODE-based mathematical model.

   Overall, this is a well-designed, well-controlled, and timely study, given the rapidly increasing reports documenting heritable cell states that can guide fate choices in single cells. The manuscript has elegant experiments and is generally clear to follow and the figures are easy to understand. While the authors largely state what they find, some of their claims and terminology are not supported by their experiments. Additionally, many figures lacked scale bars, axis, labels, and detailed captions. The authors are also encouraged to cite a wider set of seminal studies, acknowledging their contributions to transient cell states guiding fate choices.

   Read the original source
6. 

   eLife

   Nov 10, 2022

   Reviewer #3 (Public Review):

   In this paper, Van Eyndhoven et al. use a quantitative and system immunology approach to dissect the factors contributing to the fate of early IFN-I responders. Overall, this manuscript is quite elegant and technically very strong. My questions/comments are limited to (1) the fraction of cells that respond in the absence of Poly(I:C), (2) the source of stimulation for the second responders in this system.

   1. For the small fraction of cells that respond in the absence of Poly(I:C), are these cells just showing IRF7 translocation or are they fully responding with IFNB production? Has this been observed in other experimental systems or contexts? Do you also observe secondary responders in the unstimulated samples (as shown in the stimulated in Fig. 2G-I)?

   2. Do the second responders only arise through direct …

   Reviewer #3 (Public Review):

   In this paper, Van Eyndhoven et al. use a quantitative and system immunology approach to dissect the factors contributing to the fate of early IFN-I responders. Overall, this manuscript is quite elegant and technically very strong. My questions/comments are limited to (1) the fraction of cells that respond in the absence of Poly(I:C), (2) the source of stimulation for the second responders in this system.

   1. For the small fraction of cells that respond in the absence of Poly(I:C), are these cells just showing IRF7 translocation or are they fully responding with IFNB production? Has this been observed in other experimental systems or contexts? Do you also observe secondary responders in the unstimulated samples (as shown in the stimulated in Fig. 2G-I)?

   2. Do the second responders only arise through direct IFN-I production by first responders? Is it possible that this response has any relationship with the initial transfection with Poly(I:C)?

   Read the original source
7. 

   Version published to 10.1101/2022.09.11.507479v1 on bioRxiv

   Sep 13, 2022