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[Preprint]. 2023 Jun 28:2023.06.27.546744. [Version 1] doi: 10.1101/2023.06.27.546744

Self-extinguishing relay waves enable homeostatic control of human neutrophil swarming

Evelyn Strickland 1,2, Deng Pan 3, Christian Godfrey 4,5, Julia S Kim 1,6, Alex Hopke 4,5, Maureen Degrange 7, Bryant Villavicencio 8, Michael K Mansour 5,9, Christa S Zerbe 10, Daniel Irimia 4,5, Ariel Amir 3,11, Orion D Weiner 1,2
PMCID: PMC10327146  PMID: 37425711

Abstract

Neutrophils exhibit self-amplified swarming to sites of injury and infection. How swarming is controlled to ensure the proper level of neutrophil recruitment is unknown. Using an ex vivo model of infection, we find that human neutrophils use active relay to generate multiple pulsatile waves of swarming signals. Unlike classic active relay systems such as action potentials, neutrophil swarming relay waves are self-extinguishing, limiting the spatial range of cell recruitment. We identify an NADPH-oxidase-based negative feedback loop that is needed for this self-extinguishing behavior. Through this circuit, neutrophils adjust the number and size of swarming waves for homeostatic levels of cell recruitment over a wide range of initial cell densities. We link a broken homeostat to neutrophil over-recruitment in the context of human chronic granulomatous disease.

Keywords: chemotaxis, collective migration, biological self-organization, neutrophil swarming, CGD, self-extinguishing relay

During collective migration, individual organisms coordinate their movement to solve critical tasks. Birds flock, fish school, and insects swarm to escape predation, find food, and move more efficiently Chandra et al. (2021); Gordon (2019); Parrish and Edelstein-Keshet (1999). These organismal collectives rely on individuals acting on local information, including distance to neighbors, alignment with neighbors, and local chemical cues, to generate complex emergent decisions. Collective migration is also organized at the cellular level, where groups of cells coordinate wound healing LaChance et al. (2022); Sun et al. (2023), cancer metastasis Muinonen-Martin et al. (2014), multicellular morphogenesis Donà et al. (2013), and the transition from single cell to multicellular existence Gregor et al. (2010). We are just beginning to understand the molecular logic of cell-cell communication that enables collective behaviors to arise. Here we probe the logic of collective migration in the context of the human immune response. Neutrophils are first responders of the innate immune system that are recruited to sites of injury and infection to neutralize invading pathogens and aid in tissue repair Ley et al. (2018); Kolaczkowska and Kubes (2013); Peiseler and Kubes (2019). Because the primary signals of injury/infection are relatively short ranged, activated neutrophils release chemoattractants such as Leukotriene B4 (LTB4) for positive feedback-based recruitment of additional neutrophils. This self-amplified ‘swarming’ process significantly enhances the speed and range of recruitment Kienle et al. (2021); Sun and Shi (2016); Poplimont et al. (2020); Reátegui et al. (2017); Lämmermann et al. (2013), but this process must be tightly regulated to limit collateral damage Kienle et al. (2021); Uderhardt et al. (2019); Jiwa et al. (2020); Mawhin et al. (2018). While some brakes on swarming are known Kienle et al. (2021); Reátegui et al. (2017); Uderhardt et al. (2019), how neutrophils control the spatiotemporal dynamics of cell-cell communication to recruit the appropriate number of cells is not well understood, and several fundamental questions remain unanswered. Are swarms primarily constrained by the neutrophils themselves Kienle et al. (2021), or are other cells required Uderhardt et al. (2019)? Do different molecular programs control the duration versus the range of swarming? And how robust is swarming to initial conditions?

While many neutrophil swarming studies have been performed in living animals Kienle et al. (2021); Lämmermann et al. (2013); Uderhardt et al. (2019); Poplimont et al. (2020); Isles et al. (2021); Chtanova et al. (2008); Khazen et al. (2022); Ng et al. (2011), the in vivo context presents several challenges for mechanistic dissection of the swarming process. For example, multiple cell types potentially modulate swarm initiation and propagation Uderhardt et al. (2019), there are a multitude of diffusive signals following tissue damage Uderhardt et al. (2019); Lämmermann et al. (2013), and the in vivo migration environment is complex. These challenges make it difficult to mechanistically dissect the regulation of swarming. Furthermore, a focus on model organisms precludes the analysis of swarming for human neutrophils despite known differences in primary human neutrophil behaviors compared to model systems Nauseef (2023); Siwicki and Kubes (2023). To address these limitations, we are leveraging an ex vivo assay for studying human neutrophil swarming in response to controllable, reproducible, well-defined cues. This assay expands upon a previously-developed ex vivo swarming system Reátegui et al. (2017); Hopke et al. (2020) in which a defined grid of heat-killed Candida albicans “targets” are spotted on a slip of glass to act as an array of swarm initiation points. Healthy human primary neutrophils isolated from whole blood are added to the assay, and swarming responses are observed with live cell microscopy.

Fast-moving multicellular Ca2+ waves define the zone of recruitment in human neutrophil swarming

To monitor cell-cell communication in conjunction with more traditional swarming readouts like motility, we used a cytosolic calcium dye. Neutrophils increase cytosolic calcium following exposure to primary chemoattractants as well as swarming cues such at LTB4 Molski et al. (1981), and we envisioned that monitoring calcium influx would enable us to follow the propagation of swarming signals and correlate these to the regulation of directed cell movement (Figure 1a). Furthermore, calcium signals at the core of injury sites in vivo have been shown to be linked to key swarm signaling events Poplimont et al. (2020), and the simpler planar environment and higher signal-to-noise imaging of our assay should enable a more sensitive detection of swarm signal propagation to neutrophils far from the heat-killed Candida albicans target. Calcium signaling has been studied during mice and zebrafish swarming Poplimont et al. (2020); Khazen et al. (2022) but not during human neutrophil swarming. Following neutrophil introduction to the assay, we observe a calcium influx of the neutrophils that are in contact with the fungal target as well as multiple multicellular waves of calcium activity that radially propagate away from the target to surrounding neutrophils (Supplemental Movie SM1). An example time course of these waves is shown in Figure 1b with the overlayed tracked boundary of the calcium wave. Wave tracks are calculated by fitting a circle to a cloud of cells that are determined to be active via calcium signal binning and grouped using the ARCOS algorithm Gagliardi et al. (2022) (Supplemental Figure S1a,b; Supplemental Movie SM2). Wave movement is roughly isotropic, and the fitting of waves to a circle enables the tracking of wave propagation kinetics (Supplemental Figure S1c,d). Immediately following the passage of a calcium wave, cells rapidly polarize and radially migrate towards the wave origin site. Cells within the wave perimeter move towards the fungal target, while cells outside the wave perimeter lack coordinated movement (Figure 1c,d). By analyzing cell responses across many targets and donors, we observe that most, if not all, movement occurs within the boundaries of the tracked calcium wave, indicating that these wave fronts represent an effective boundary of the swarming guidance cue reception (Figure 1e). Though the propagating calcium waves delimit the zone of recruited neutrophils, they operate at very different timescales with respect to neutrophil movement. Calcium waves move an order of magnitude faster than the resulting neutrophil chemotaxis towards the wave origin (Figure 1f), and calcium waves propagate in an opposite direction to cell movement during swarming.

Figure 1. Fast-moving multicellular Ca2+ waves define the zone of recruitment in human neutrophil swarming.

Figure 1.

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(A) Ex vivo assay for neutrophil swarming with self-amplified recruitment of cells in response to local site of heat-killed Candida albicans. Human neutrophils are isolated via immunomagnetic selection and dyed with CalBryte 520-AM (cytosolic Ca2+ reporter, used to monitor neutrophil detection of pathogen and swarming cues) and Hoechst 3334 (nuclear marker, used to track cell movement). Cells are placed on printed targets of heat-killed Candida albicans, and neutrophil swarming responses are monitored via confocal microscopy. (B) Time lapse sequence of a multicellular wave of neutrophil Ca2+ activity following detection of the fungal target. Dotted line represents wave boundary as tracked by our analysis software; scale bar = 200 µm. (C) Ca2+ wave defines the zone of neutrophil recruitment towards the fugal target. Final radius of Ca2+ wave from cells in 1b is shown in blue. Cell tracks for 3 minutes following wave initiation are indicated with color corresponding to radial velocity towards the wave center; scale bar = 100 µm. (D) Average radial velocity for cells inside versus outside the final wave boundary is plotted over 4 minutes for a population of cells during a single Ca2+ wave (Cell tracks inside the wave n = 3720, outside wave tracks n = 9499). Wave boundary predicts zone of recruitment during swarming. (E) Comparison of mean radial velocity for cells inside versus outside largest Ca2+ wave boundary in each target ROI. Tracks were averaged over the duration of the wave event, and lines connect mean radial velocities of outer versus inner cells for each target ROI. Dependent paired t-test p-value = 5.8 × 10−27 (Target ROIs n = 56) (F) Average velocity of Ca2+ waves propagated across the field of cells during swarming compared to the average cell velocity for the cells within these wave boundaries. Propagated Ca2+ waves are approximately one order of magnitude faster than cell movement during swarming.

Wave propagation during neutrophil swarming is consistent with active relay but not core diffusion.

LTB4, an inflammatory lipid of the leukotriene family, is thought to be one of the key secreted molecules that regulates neutrophil swarming Lämmermann et al. (2013); Reátegui et al. (2017). Neutrophils release LTB4 in response to various damage-associated pattern molecules and pathogen-associated pattern molecules Reátegui et al. (2017); Afonso et al. (2012); McDonald et al. (1994), including Candida albicans Fischer et al. (2021). To test whether LTB4 reception is required for the rapid long-range calcium waves in our ex vivo swarming assay, we blocked LTB4 reception with the LTB4 receptor antagonist BIIL315 Birke et al. (2001). Blocking LTB4 receptors inhibited the rapidly propagating long-range calcium waves that accompany swarming. In the absence of LTB4 reception, a much smaller range, slow-moving Ca2+ wave was observed (Supplemental Figure S2b,c; Supplemental Movie SM3). Quantitative analysis of wave propagation in both settings indicate that the rapid, long-range calcium waves that accompany swarming are dependent on LTB4 reception (Figure 2a).

Figure 2. Wave propagation during human neutrophil swarming is consistent with active relay but not core diffusion.

Figure 2.

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(A) Ca2+ wave propagation across a field of neutrophils during swarming for cells in the absence (50 waves) or presence (17 waves) of an LTB4 receptor inhibitor (1 µM BIIL315); averages shown with 95% confidence interval shaded. LTB4 reception is required for rapid, long-range Ca2+ wave propagation. (B) Two proposed models of LTB4 propagation from a site of infection to the rest of the field with the predicted kinetics of signal propagation shown for each. In a relay model of swarming Afonso et al. (2012); Lämmermann et al. (2013); Dieterle et al. (2019), each activated neutrophil releases LTB4, which stimulates the adjacent cell to release LTB4; this wave should spread with a constant velocity over time, giving an α of 2. For a core production model of swarming Poplimont et al. (2020), only neutrophils at the center of a swarm produce LTB4, which moves across the field through passive diffusion; this wave should spread with an α of 1 (linear on R2 plot). (C) Average early wave kinetics for control cells with mean and 95% confidence interval. Individual tracks are fit to a power law equation to determine the α of wave propagation for each experiment. (D) Wave propagation for LTB4R-inhibited cells averaged with 95% confidence interval in grey are individually fit to a power law equation for the first two minutes following swarm initiation. (E) Resulting curve fit alpha parameters for both unperturbed and LTB4R blockade conditions are plotted, with control cells showing wave propagation consistent with active relay. LTB4 reception-inhibited cells showed residual wave propagation consistent with core diffusion. Welch’s t-test P-value = 1.7 × 10−7 (F) Summary of this figure. LTB4-mediated waves propagate with kinetics consistent with active relay (and not core diffusion), while the zone of cell activation for LTB4-blockaded cells propagates away from the site of heat-killed Candida albicans with kinetics consistent with simple diffusion.

We next investigated how the LTB4 ligands are propagated from the target site to the rest of the field during swarming. Two models have been proposed for the signal amplification observed during swarming. For the relay model of swarming, pioneer neutrophils at the site of infection secrete LTB4, which activates surrounding neutrophils to secrete additional LTB4, thereby continuing the relay Lämmermann et al. (2013); Afonso et al. (2012); Dieterle et al. (2019). This active relay mechanism could enable neutrophils to collectively signal across significant distances from the injury/infection site, analogous to cell-cell signal propagation in aggregating Dictyostelium Shaffer (1975); van Oss et al. (1996). In contrast, for the core production model of swarming, only neutrophils in direct contact with the site of injury/infection produce significant LTB4, which then passively diffuses into the tissue to attract more neutrophils Poplimont et al. (2020). These two swarming models give very different predictions for the dynamics of propagation of the LTB4 wavefront as it moves away from the site of infection. The relay model predicts a wave that travels at a fixed velocity because of continuous signal re-amplification at the traveling front. This produces an activation zone whose area (wave fit circle radius squared / R2) scales quadratically in time. In contrast, since the core diffusion model predicts LTB4 production restricted to the central source, this system is limited by diffusion, and therefore its activation area (R2) scales linearly in time (Figure 2b). We tracked the radius2 versus time for multiple waves across many healthy donors in control cells (Figure 2c) versus LTB4-blockaded cells (Figure 2d) and fit a power law to each individual wave trajectory. This analysis reveals that control cells exhibit an LTB4 reception-dependent wave propagation with an alpha close to 2, consistent with the active relay model of swarming and inconsistent with the core production model of swarming (Figure 2e,f). In the absence of LTB4 signal reception, cells exhibit a slower, smaller pattern of signal propagation with an alpha close to 1, which is consistent with core diffusion. These data suggest that the residual release of non-LTB4 ligands from cells on the target passively diffuse to the rest of the field (Figure 2e,f). These two examples establish that we can distinguish between diffusive waves and relay-mediated ones, and LTB4-mediated waves exhibit relay-like ballistic spreading in the early phase of wave propagation.

Swarming relay waves self-extinguish through an NADPH Oxidase negative feedback loop

Our observation that neutrophil swarming cues radiate from killed yeast targets via active relay might be expected to produce waves that continue to propagate as long as there are nearby receptive cells to continue the wave. This is the expected behavior of actively relayed systems including action potentials, mitotic waves, and Dictyostelium aggregation Gelens et al. (2014). We previously modeled an active relay model for swarming in which cells that pass a threshold amount of LTB4 themselves create more LTB4, as diagramed in Figure 3a Dieterle et al. (2019). Once initiated, these waves continue to propagate indefinitely given a field of responsive cells. In contrast with the predictions of a simple relay model, our experimentally-observed waves propagate and then abruptly stop (Figure 3b,c), indicating a more complex mode of regulation than positive feedback alone. While negative feedback loops could theoretically collaborate with positive feedback to generate self-extinguishing relay, we were only able to find one such example in the literature. In this work, a 1D model of two separately-generated waves of diffusing activator and inhibitor were able to generate a fixed radius of response in a cell-free system Ataullakhanov et al. (1998). We sought a simpler potential mechanism of wave stopping that does not depend on two independently propagated waves (see Supplementary Modeling Text, Supplemental Figure S3) and that is consistent with the dynamics of wave termination in our experimental context. This self-extinguishing relay model depends on two different responses initiated at two different concentrations of LTB4. Cells begin generating a non-diffusive internal inhibitor once they pass a low extracellular threshold of LTB4. After passing a higher threshold of extracellular LTB4, cells begin to release LTB4 themselves (Figure 3d). In this system, cells farther from an initiation event will have an increasing time delay between crossing the lower ‘inhibition circuit initiation’ threshold and the higher LTB4 production threshold. When a cell passes the threshold for inhibition, it begins accumulating the inhibitory cue that limits the relay strength of that cell. Close to the source of the activating cue at the target, only a small amount of inhibitor is generated prior to LTB4 production, and relay is still possible. Further from the source, the inhibitor accumulates for longer periods before the LTB4 production threshold is passed, thereby terminating further signal relay. This model can recapitulate the experimentally-observed wavefront dynamics (Figure 3d).

Figure 3. Neutrophils employ a NADPH-oxidase-based negative feedback loop to generate a self-extinguishing relay during swarming.

Figure 3.

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(A) Previously-proposed simple diffusive relay model of swarming Dieterle et al. (2019). As cells exceed a threshold amount of extracellular LTB4, they begin to release more LTB4 to their surroundings. This wave propagates indefinitely once initiated in a homogenous field of cells. (B) Comparing the simple diffusive relay prediction with an average of experimentally-observed swarming wavefronts shows how wave behavior diverges, as experimental relay waves come to a stop, whereas simple relay propagates indefinitely (Axes in µm. Wave n = 5. Volunteer N = 2). (C) While experimental waves initially propagate with a relay-like R2 relation, in the latter phase the wave decelerates then stops. (D) With the introduction of an activation-dependent inhibitor, it is possible to generate models of a self-extinguishing relay. Here different extracellular LTB4 thresholds for production of a local inhibitor (low threshold) versus release of more LTB4 (high threshold) generate an area of local inhibition that limits the wave propagation. Since the local inhibition requires accumulation over time to inhibit relay, wave termination is only achieved at further distances from the source. Our self-extinguishing relay model yields wavefront kinetics that closely resemble the experimentally observed Ca2+ swarming wavefront. The red X denotes where the model predicts the wave extinguishing (2D panel axes in µm.). (E) Here we test a candidate activation-dependent inhibitor for the self-extinguishing relay during swarming. In neutrophils, LTB4-stimulated activation of the NADPH Oxidase complex limits the Ca2+ influx that potentiates LTB4 synthesis Song et al. (2020). Inhibition of NADPH oxidase activity via the NADPH oxidase inhibitor DPI produce swarming waves that no longer stop in the observable microscopic field (lower panels, scale: 200 µm) and mimic simple relay dynamics throughout their propagation (right panel) (Volunteer N = 3, Wave n = 11).

What might serve as the activation-dependent inhibitor in this self-extinguishing relay mechanism? The inhibitor should be produced downstream of LTB4 reception, and the accumulation of this inhibitor should attenuate LTB4 production. NADPH oxidase activation satisfies both conditions. NADPH oxidase is used in ROS-dependent pathogen killing downstream of LTB4 and other chemoattractants Song et al. (2020); Hopke et al. (2020), and the inhibition of NADPH oxidase activation potentiates LTB4 production and neutrophil accumulation during swarming Song et al. (2020); Hopke et al. (2020); Henrickson et al. (2018); Roxo-Junior and Simão (2014); Hamasaki et al. (1989). If NADPH oxidase activity is a critical component in self-extinguishing relay, we would expect NADPH oxidase inhibition to prevent swarming wave termination, thereby reverting the swarming process to a simple relay system (as in Figure 3a). Indeed, the NADPH oxidase inhibitor DPI (Figure 3e, Supplemental Movie SM4) abolished the ability of swarming waves to self-extinguish, with swarming waves continuing to propagate from the targets out of the microscopic field of view. To ensure that this effect is not an off-target of DPI, we analyzed neutrophils from Chronic Granulomatosis Disease (CGD) patients with a genetic defect in neutrophil NADPH oxidase machinery. CGD neutrophils exhibit similar non-extinguishing wave dynamics as DPI-treated healthy donor cells (Supplemental Figure S4a). While both DPI based NADPH Oxidase-inhibited and a CGD donor wave activity led to strong recruitment of the cells within a calcium event, they only generated a single large wave compared to the multiple waves observed in unperturbed cells (Supplemental Figure S4b; Supplemental Movie SM4).

Neutrophils tune the size and number of swarming waves for homeostatic recruitment to targets

To investigate the physiological significance of multiple self-extinguishing waves (control neutrophils) versus a single uncontrolled relay wave (DPI-treated or CGD neutrophils), we next sought to investigate the relation between wave size/number and the resulting cell movement and recruitment. We hypothesized that these wave parameters may be actively adjusted to ensure robust homeostatic control of neutrophil recruitment across a wide range of initial conditions. We created quantitative metrics for both the propagating Ca2+ wave as well as neutrophil chemotaxis response that could be extended to multiple wave sizes and intensities. First, we aligned single-cell tracks for neutrophils entering the zone of recruitment and averaged their radial migration velocity profile for single, well-separated waves (Figure 4a, Supplemental Figure S5a). Upon entering a wave, neutrophils execute a discrete ‘run’ of movement towards the wave center but then revert to random, slower movement. Such a ‘step’ of movement inwards is consistent with the expected behavior from our self-extinguishing model where the chemotactic gradient decays behind the wavefront as the relay strength becomes more dominated by the local inhibitory mechanism. These data indicate that a single wave event does not recruit all cells to the center in one burst. To expand our analysis to multiple waves, we integrated the radial movement of all cells within a wave event (Figure 4b, Supplemental Figure S5b). Each wave independently recruited of a bolus of neutrophils toward a given target, with larger wave events more potently stimulating cell recruitment than smaller wave events (Supplemental Movie SM5). When looking across many experiments and donors, the total integrated wave area highly correlated with the Integrated Radial Movement of the cells within wave events (Figure 4c).

Figure 4. Neutrophils achieve homeostatic recruitment to sites of heat-killed Candida albicans by modulating the size and frequency of swarming waves.

Figure 4.

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(A) Averaged radial velocities of neutrophil movement towards a target following time of exposure to swarming Ca2+ wave with 95% confidence interval plotted for 1758 cell tracks. A single wave produces a transient bolus of neutrophil recruitment. (B) All cellular tracks inside a wave are integrated for their radial movement (Cell track n = 5813); multiple Ca2+ wave events for the same target are plotted as shaded areas colored by maximum final area for an example ROI. Each wave induces a bolus of neutrophil movement towards the target, with larger waves inducing larger neutrophil responses. (C) Integrated radial movement of all cell tracks and integrated wave max area measurements calculated across 57 target ROIs demonstrate the strong positive correlation between Ca2+ wave signaling area and movement towards a target. Linear fit slope: 7.8 µm ± 0.8, 95% confidence interval. A control where all cell tracks are considered is in Supplemental Figure S5C. (D) Relation of seeding density to cell accumulation at target. Integrated wave max area measurements taken for each target ROI over 45min. Linear fit slope: −6.3 × 104 µm2 per 1000 cells/mm2 ± 1 × 104, 95% confidence interval. Lower cell densities elicit larger and more numerous swarming waves. (Scale bar: 200 µm). (E) Z-stack measurements taken across multiple ROIs per well 60 min after seeding swarming assays at different cell densities. Accumulation measured as integrated fluorescence at target site divided by median single-cell nuclear fluorescence. Low Density ROI Estimation: 402 cells, High Density ROI Estimation: 551 cells. Linear fit slope: 27 cells accumulate at target site per 1000 cells/mm2 ± 4 cells; 95% confidence interval. Only a small change in final neutrophil target accumulation is seen over a 6x range of cell swarming densities, compared to expectation for swarming directly proportional to cell density (linear scaling, orange line) (Scale bar: 100 µm). (F) Widefield measurements taken across multiple ROIs per well 60min after seeding swarming assays with different cell densities of either control donor cells or CGD (NADPH oxidase-defective) human donor cells. Accumulation measured similar to E, see Methods. Control ROI Estimation: 295 cells, CGD ROI Estimation: 4780 cells. CGD Linear fit slope: 297 cells accumulate at a target per 1000 cells/mm2 ± 19 cells, 95% confidence interval (Volunteer N=4, Target n = 253). Healthy control linear fit slope: 40 cells accumulate at a target per 1000 cells/mm2 ± 4 cells (Volunteer N = 3, Target n = 157)(Scale bar: 100 µm).

We asked next whether cells tune the number or size of swarming waves for robust homeostatic control of swarming over a range of initial conditions. Towards this end, we seeded our swarming assay at a range of initial neutrophil densities. As seeding density increased, neutrophils generated smaller and fewer swarming waves, resulting in a decreased overall integrated calcium area (Figure 4d, Supplemental Figure S5d, e). This reduction in swarming waves with increasing cell density could potentially enable a homeostatic recruitment of the same number of neutrophils across a range of initial cell densities. To investigate this possibility, we monitored neutrophil recruitment to sites of heat-killed Candida albicans at a range of seeding densities over 60 min (Supplemental Figure S5f, g; Supplemental Movie SM6). Over a 6-fold change in initial cell density, there was a nearly constant accumulation of neutrophils at the target, with targets at the lowest densities attracting approximately 85% of the cells attracted at the highest cell densities (compared to 6x differences that would be expected for a non-homeostatic system) (Figure 4e, Supplemental Figure S5h). Because CGD cells are defective in the negative feedback loop that constrains wave size, we predicted that these cells should exhibit a defective homeostat and therefore recruit cells proportional to their surrounding density. Indeed, CGD cells exhibit a much stronger density dependence on recruitment than healthy donor cells (Figure 4f, Supplemental Figure S4e, f).

Discussion

By leveraging a cell-cell signaling readout in a highly controllable ex vivo swarming assay, our work reveals self-extinguishing waves of human neutrophil recruitment to sites of heat-killed Candida albicans. The active relay is based on an LTB4-positive feedback loop (Figure 2), and the ability to self-extinguish depends on LTB4-based NADPH-oxidase activation (Figure 3, 4). The active relay enables cells to transmit guidance cues much farther and faster than would be possible if only the cells directly on the target secreted swarming signals. Furthermore, the propagated waves may generate temporally-evolving guidance cues that enable more effective chemotaxis than static spatial gradients Geiger et al. (2003); Tweedy et al. (2016); Aranyosi et al. (2015); Skoge et al. (2014). Behind a wavefront, decaying gradients could further prevent cells from over accumulating at the core of swarms as cellular memory mechanisms lose strength Skoge et al. (2014) and as chemotaxis-inhibitory LTB4 metabolites accumulate Archambault et al. (2019); Pettipher et al. (1993); Tweedy et al. (2016). The self-extinguishing nature of the relay enables the system to adjust the number and size of swarming waves to achieve robust homeostatic control of neutrophil recruitment over a wide range of initial cell concentrations. Disruption of this homeostat has severe consequences for neutrophil recruitment in vivo. Previous work has shown that CGD patient neutrophils (that are defective in NADPH-oxidase activation) overproduce LTB4 and hyperaccumulate at sites of injury/infection in vitro and in vivo Song et al. (2020); Hamasaki et al. (1989); Henrickson et al. (2018); Hopke et al. (2020); Dinauer (2019). Our work shows that these cells are also defective in the negative feedback arm that limits the range of swarming signals and therefore lack the homeostat that normally constrains cell swarming.

Our ex vivo system demonstrates that human neutrophils can modulate their cell-cell signaling to limit the range of cell swarming without feedback from other cell types or environmental cues. In future work, it will be interesting to probe how other cell types influence the initiation, termination, and resolving phases of swarming. We have focused on killed yeast-mediated neutrophil swarming (whose closest parallel is likely an infected lymph node Chtanova et al. (2008); Lämmermann et al. (2013)), but it will be interesting to compare how sterile injury Lämmermann et al. (2013); Ng et al. (2011); Poplimont et al. (2020); Park et al. (2018) and combined injury/infection Poplimont et al. (2020); Chtanova et al. (2008) change the dynamics of the swarming process. Other swarming terminators (such as Grk2 activation Kienle et al. (2021)) control the duration of swarming, whereas the NADPH oxidase negative feedback circuit studied in our work also controls the spatial range of swarming signal propagation. It will be interesting to probe how these swarming termination programs relate to one another. Finally, as there are a number of other human diseases with known swarming defects Knooihuizen et al. (2021); Alexander et al. (2021); Barros et al. (2021), it will be interesting to determine how these disease states interact with our model parameters and effect the relay system we study in this work.

Methods

For a detailed description of all the methods and code used in this work, please see Supplemental Methods.

Supplementary Material

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Supplement 2
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Supplement 3
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Acknowledgements

We thank members of the Weiner, Amir, and Irimia lab for helpful discussions, particularly Nick Martin and Henry De Belly for a critical reading of the manuscript. We thank Paul Dieterle, Wencheng Ji, and Allyson Sgro for their detailed feedback and advice regarding the theoretical model. We also thank the blood donors and phlebotomists without whom this project would have been impossible. This work was supported by an AHA predoctoral fellowship (ES), the National Institutes of Health grants (GM118167 to ODW, GM092804 to DI, R01 AI132638 to MKM), the National Science Foundation/Biotechnology and Biological Sciences Research Council grant (2019598 to ODW), the National Science Foundation Center for Cellular Construction Grant (DBI-1548297 to ODW), the Department of Defense (HU00012020011 to MKM) the Novo Nordisk Foundation grant for the Center for Geometrically Engineered Cellular Systems (NNF17OC0028176 to ODW), the Clore Center for Biological Physics (AA), the NSF CAREER Grant (1752024 to AA), and the Shriners Hospitals for Children (71010-BOS-22 to DI). CZ was supported by the Intramural Research Program of the National Institutes of Health Clinical Center and the National Institute of Allergy and Infectious Diseases. Finally, we would like to thank the Center for Advanced Light Microscopy-Nikon Imaging Center at UCSF for their support in using the CREST/C2 Confocal microscope. BIIL 315 was kindly provided by Boehringer Ingelheim via its open innovation platform opnMe, available at https://opnme.com.

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