1. Cell Biology
  2. Immunology and Inflammation
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Tight nanoscale clustering of Fcγ receptors using DNA origami promotes phagocytosis

  1. Nadja Kern
  2. Rui Dong
  3. Shawn M Douglas
  4. Ronald D Vale  Is a corresponding author
  5. Meghan A Morrissey  Is a corresponding author
  1. Department of Cellular and Molecular Pharmacology, University of California San Francisco, United States
  2. Howard Hughes Medical Institute, University of California San Francisco, United States
  3. Howard Hughes Medical Institute Janelia Research Campus, United States
  4. Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, United States
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Cite this article as: eLife 2021;10:e68311 doi: 10.7554/eLife.68311

Abstract

Macrophages destroy pathogens and diseased cells through Fcγ receptor (FcγR)-driven phagocytosis of antibody-opsonized targets. Phagocytosis requires activation of multiple FcγRs, but the mechanism controlling the threshold for response is unclear. We developed a DNA origami-based engulfment system that allows precise nanoscale control of the number and spacing of ligands. When the number of ligands remains constant, reducing ligand spacing from 17.5 nm to 7 nm potently enhances engulfment, primarily by increasing efficiency of the engulfment-initiation process. Tighter ligand clustering increases receptor phosphorylation, as well as proximal downstream signals. Increasing the number of signaling domains recruited to a single ligand-receptor complex was not sufficient to recapitulate this effect, indicating that clustering of multiple receptors is required. Our results suggest that macrophages use information about local ligand densities to make critical engulfment decisions, which has implications for the mechanism of antibody-mediated phagocytosis and the design of immunotherapies.

eLife digest

The word ‘phagocytosis’ means cellular eating. It is the process by which cells extend their membranes around foreign particles and engulf them. Macrophages, a type of immune cell found in every tissue of the body, perform phagocytosis to eat pathogens and diseased cells. To avoid eating healthy cells, macrophages focus on targets marked by proteins called antibodies. They look for cells coated with high levels of a type of antibody called immunoglobulin G, or IgG for short, but only eat cells coated with enough IgG, raising the question, can macrophages count?

Macrophages recognize IgG antibodies using cell surface receptors called Fc-gamma Receptors. When these receptors bind to IgG, they cluster together. Researchers do not yet know how the number of IgG antibodies per cluster, or the spacing between them, affects phagocytosis. To find this out, researchers need to be able to manipulate the clustering experimentally. One way to do this is using a technique called DNA origami. This technique creates nanoscale patterns of DNA strands on a target surface. If the part of a receptor that interacts with its target is then replaced with a complementary DNA strand to the strands on the target surface, the receptor will bind the surface following the nanoscale pattern. This allows researchers to generate synthetic targets with specific patterns of receptor-target interaction.

Kern et al. replaced the part of the macrophage Fc-gamma Receptor that interacts with IgG with a strand of DNA. They then used DNA origami to arrange complementary DNA strands on pegboards and attached these pegboards to silica beads. The different arrangements of DNA on these pegboards mimicked the types of antibody clusters macrophages might encounter on the surfaces of the cells and particles they have to engulf in the body. Kern et al. found that tight clusters of the DNA targets on the pegboards made the macrophages most likely to begin phagocytosis, particularly clusters of eight or more DNA strands spaced less than seven nanometers apart. Macrophages encountering these tight clusters showed an increase in Fc-gamma receptor activation, which is crucial for macrophage attack.

Whether or not macrophages can count, they can at least sense the level of clustering of IgG antibodies to determine if a target should be engulfed. Doctors use antibody therapies that rely on Fc-gamma receptor engagement to treat cancer, autoimmune and neurodegenerative diseases. Understanding how clustering affects phagocytosis could aid in the design of new antibody treatments. It could also help improve the design of synthetic receptors to create designer immune cells that can attack specific targets. The next step will be to recreate the results from the synthetic system used by Kern et al. with natural receptors and antibodies.

Introduction

Immune cells eliminate pathogens and diseased cells while limiting damage to healthy cells. Macrophages, professional phagocytes and key effectors of the innate immune system, play an important role in this process by engulfing opsonized targets bearing ‘eat me’ signals. One of the most common ‘eat me’ signals is the immunoglobulin G (IgG) antibody, which can bind foreign proteins on infected cells or pathogens. IgG is recognized by Fcγ receptors (FcγR) in macrophages that drive antibody-dependent cellular phagocytosis (ADCP) (DiLillo et al., 2014; Erwig and Gow, 2016; Nimmerjahn and Ravetch, 2008). ADCP is a key mechanism of action for several cancer immunotherapies including rituximab, trastuzumab, and cetuximab (Chao et al., 2010; Uchida et al., 2004; Watanabe et al., 1999; Weiskopf et al., 2013; Weiskopf and Weissman, 2015). Exploring the design parameters of effective antibodies could provide valuable insight into the molecular mechanisms driving ADCP.

Activation of multiple FcγRs is required for a macrophage to engulf a three-dimensional target. FcγR-IgG must be present across the entire target to drive progressive closure of the phagocytic cup that surrounds the target (Griffin et al., 1975). In addition, a critical antibody threshold across an entire target dictates an all-or-none engulfment response by the macrophage (Zhang et al., 2010). Although the mechanism of this thresholded response remains unclear, receptor clustering plays a role in regulating digital responses in other immune cells (Berger et al., 2020; Davis and van der Merwe, 2006; Holowka and Baird, 1996; Kato et al., 2020; Ma et al., 2020; Veneziano et al., 2020). FcγR clustering may also regulate phagocytosis (Goodridge et al., 2012). High-resolution imaging of macrophages has demonstrated that IgG-bound FcγRs form clusters (resolution of >100 nm) within the plasma membrane (Lin et al., 2016; Lopes et al., 2017; Sobota et al., 2005). These small clusters, which recruit downstream effector proteins such as Syk-kinase and phosphoinositide 3-kinase, eventually coalesce into larger micron-scale patches as they migrate towards the center of the cell-target synapse (Jaumouillé et al., 2014; Lin et al., 2016; Lopes et al., 2017; Sobota et al., 2005).

Prior observational studies could not decouple ligand clustering from other parameters, such as ligand number or receptor mobility. As a result, we do not have a clear picture of how ligand number or molecular spacing regulates signal activation. To directly assess such questions, we have developed a reconstituted system that utilizes DNA origami to manipulate ligand patterns on a single-molecule level with nanometer resolution. We found that tightly spaced ligands strongly enhanced phagocytosis compared to the same number of more dispersed ligands. Through manipulating the number and spacing of ligands on individual origami pegboards, we found that eight or more ligands per cluster maximized FcγR-driven engulfment, and that macrophages preferentially engulfed targets that had receptor-ligand clusters spaced ≤7 nm apart. We demonstrated that tight ligand clustering enhanced receptor phosphorylation, and the generation of PIP3 and actin filaments – critical downstream signaling molecules – at the phagocytic synapse. Together, our results suggest that the nanoscale clustering of receptors may allow macrophages to discriminate between lower density background stimuli and the higher density of ligands on opsonized targets. These results have implications for the design of immunotherapies that involve manipulating FcγR-driven engulfment.

Results

Developing a DNA-based chimeric antigen receptor to study phagocytosis

To study how isolated biochemical and biophysical ligand parameters affect engulfment, we sought to develop a well-defined and tunable engulfment system. Our lab previously developed a synthetic T cell signaling system, in which we replaced the receptor-ligand interaction (TCR-pMHC) with complimentary DNA oligos (Taylor et al., 2017). We applied a similar DNA-based synthetic chimeric antigen receptor to study engulfment signaling in macrophages. In our DNA-CARγ receptor, we replaced the native extracellular ligand-binding domain of the FcγR with an extracellular SNAP-tag that covalently binds a benzyl-guanine-labeled single-stranded DNA (ssDNA) (receptor DNA; Figure 1a; Morrissey et al., 2018). The SNAP-tag was then joined to the CD86 transmembrane domain followed by the intracellular signaling domain of the FcRγ chain (Nimmerjahn and Ravetch, 2008). We expressed the DNA-CARγ in the macrophage-like cell line RAW264.7 and the monocyte-like cell line THP-1.

Figure 1 with 1 supplement see all
A DNA-based system for controlling engulfment.

(A) Schematic shows the endogenous (left box) and DNA-based (middle and right boxes) engulfment systems. Engulfment via endogenous FcγRs (left box) is induced through anti-biotin IgG bound to 1-oleoyl-2-(12-biotinyl(aminododecanoyl))-sn-glycero-3-phosphoethanolamine (biotin-PE) lipids incorporated into the bilayer surrounding the silica bead targets. Engulfment induced via the DNA-based system uses chimeric antigen receptors (CAR) expressed in the macrophage and biotinylated ligand DNA that is bound to the lipid bilayer surrounding the silica bead. The DNA-CARγ (middle box) consists of a single-stranded DNA (ssDNA) (receptor DNA) covalently attached to an extracellular SNAP-tag fused to a CD86 transmembrane domain, the intracellular domain of the FcRγ chain, and a fluorescent tag. The DNA-CARadhesion (right box) is identical but lacks the signaling FcRγ chain. (B) Example images depicting the engulfment assay. Silica beads were coated with a supported lipid bilayer (magenta) and functionalized with neutravidin and the indicated density of ligand DNA (Figure 1—figure supplement 1A). The functionalized beads were added to RAW264.7 macrophages expressing either the DNA-CARγ or the DNA-CARadhesion (green) and fixed after 45 min. The average number of beads engulfed per macrophage was assessed by confocal microscopy. Scale bar denotes 5 µm here and in all subsequent figures. Internalized beads are denoted with a white sphere in the merged images. (C) The number of beads engulfed per cell for DNA-CARγ (blue) or DNA-CARadhesion (gray) macrophages was normalized to the maximum bead eating observed in each replicate. Dots and error bars denote the mean ± SEM of three independent replicates (n ≥ 100 cells analyzed per experiment). (D) DNA-CARγ-expressing macrophages were incubated with bilayer-coated beads (gray) functionalized with anti-biotin IgG (magenta), neutravidin (black), or neutravidin and saturating amounts of ssDNA (blue). The average number of beads engulfed per cell was assessed. Full data representing the fraction of macrophages engulfing specific numbers of IgG or ssDNA beads is shown in Figure 1—figure supplement 1. Each data point represents the mean of an independent experiment, denoted by symbol shape, and bars denote the mean ± SEM. n.s. denotes p>0.05, * indicates p<0.05, ** indicates p<0.005, and **** indicates p<0.0001 by a multiple t-test comparison corrected for multiple comparisons using the Holm–Sidak's method (C) or Student’s t-test (D).

As an engulfment target, we used silica beads coated with a supported lipid bilayer to mimic the surface of a target cell. The beads were functionalized with biotinylated ssDNA (ligand DNA) containing a sequence complementary to the receptor DNA via biotin-neutravidin interactions (Figure 1A). We used a ligand DNA strand that has 13 complementary base pairs to the receptor DNA, which we chose because the receptor-ligand dwell time (~24 s; Taylor et al., 2017) was comparable to the dwell time of IgG-FcγR interactions (~30–150 s; Li et al., 2007).

To test whether this synthetic system can drive specific engulfment of ligand-functionalized silica beads, we used confocal microscopy to measure the number of beads that were engulfed by each cell (Figure 1B, C). The DNA-CARγ drove specific engulfment of DNA-bound beads in both RAW264.7 and THP-1 cells (Figure 1C, Figure 1—figure supplement 1). The extent of engulfment was similar to IgG-coated beads, and the ligand density required for robust phagocytosis was also comparable to IgG [Figure 1D, Figure 1—figure supplement 1; Bakalar et al., 2018; Morrissey et al., 2020]. As a control, we tested a variant of the DNA-CAR that lacked the intracellular domain of the FcRγ chain (DNA-CARadhesion). Cells expressing the DNA-CARadhesion failed to induce engulfment of DNA-functionalized beads (Figure 1C), demonstrating that this process depends upon the signaling domain of the FcγR. Together, these data show that the DNA-CARγ can drive engulfment of targets in a ligand- and FcγR-specific manner.

DNA origami pegboards activate DNA-CARγ macrophages

DNA origami technology provides the ability to easily build three-dimensional objects that present ssDNA oligonucleotides with defined nanometer-level spatial organization (Hong et al., 2017; Rothemund, 2006; Seeman, 2010; Shaw et al., 2019; Veneziano et al., 2020). We used DNA origami to manipulate the spatial distribution of DNA-CARγ ligands in order to determine how nanoscale ligand spacing affects engulfment. We used a recently developed two-tiered DNA origami pegboard that encompasses a total of 72 ssDNA positions spaced 7 nm and 3.5 nm apart in the x and y dimensions, respectively (Dong et al., 2021Figure 2A, Figure 2—figure supplement 1). Each of the 72 ligand positions can be manipulated independently, allowing for full control over the ligand at each position (Figure 2—figure supplement 1). The DNA origami pegboard also contains fluorophores at each of its four corners to allow for visualization, and 12 biotin-modified oligos on the bottom half of the pegboard to attach it to a neutravidin-containing supported lipid bilayer or glass coverslip (Figure 2A, B, Figure 2—figure supplement 1).

Figure 2 with 2 supplements see all
DNA origami pegboard induces ligand-dependent signaling.

(A) Schematic shows the DNA origami pegboard used in this study (right) and the components used to create it using a one-pot assembly method (left, Figure 2—figure supplement 1). The top of the two-tiered DNA origami pegboard has 72 positions spaced 7 nm and 3.5 nm apart in the x and y dimensions, which can be modified to expose a single-stranded ligand DNA (red) or no ligand (light blue). A fluorophore is attached at each corner of the pegboard for visualization (pink). The bottom tier of the pegboard displays 12 biotin molecules (yellow) used to attach the origami to neutravidin-coated surfaces. Full representation of the DNA origami pegboard assembly is shown in Figure 2—figure supplement 1. (B) Schematic portraying the Total Internal Reflection Fluorescence (TIRF) microscopy setup used to image THP-1 cells interacting with origami pegboards functionalized to glass coverslips in (C) and (D) (left). On the right is a zoomed-in side view of an origami pegboard functionalized to a biotin (yellow) and neutravidin (gray) functionalized glass coverslip and interacting with a single DNA-CARγ receptor. (C) TIRF microscopy images of THP-1 cells show that the DNA-CARγ (BFP; fifth panel; black in linescan), the receptor DNA bound to the DNA-CARγ (Cy5; fourth panel; green in linescan), and Syk (mNeonGreen; third panel; cyan in merge and linescan) are recruited to individual 72-ligand (72L) origami pegboards (Atto-647; second panel; magenta in merge and linescan). Each diffraction-limited magenta spot represents an origami pegboard. The top panels show a single cell (outlined in yellow), and the bottom insets (orange box in top image) show three origami pegboards at higher magnification. The linescan (right, area denoted with a white arrow in merged inset) shows the fluorescence intensity of each of these channels. Intensity was normalized so that 1 is the highest observed intensity and 0 is background for each channel. (D) TIRF microscopy images show DNA-CARγ-expressing THP1s interacting with 72L origami pegboards (pink) and origami pegboards presenting the indicated number of ligands (pegboards labeled in green). Left schematics represent origami pegboard setups for each row of images where red dots denote the presence of a ligand DNA. Middle images depict a single macrophage (outlined in yellow), and right images show the area indicated with an orange box on the left. Examples of DNA-CARγ-mNeonGreen (gray) recruitment to individual origami pegboards is marked by pink (72L origami pegboard) and green (origami pegboard with the indicated ligand number) arrowheads (right). (E) Quantification of experiment shown in (D). Top graph shows the DNA-CARγ intensity at the indicated origami pegboard type normalized to the average DNA-CARγ intensity at 72L origami pegboards in the same well. Each dot represents one origami pegboard, and red lines denote the mean ± SEM of pooled data from three separate replicates. n.s. denotes p>0.05, * indicates p<0.05, and **** indicates p<0.0001 by an ordinary one-way ANOVA with Holm–Sidak’s multiple comparison test. A linear regression fit (bottom) of the average fluorescence intensities of each of the origami pegboards suggests that the mean DNA-CARγ fluorescent intensities are linearly proportional to the number of ligands per DNA origami pegboard. The black dots represent the mean normalized DNA-CARγ intensity, the red line denotes the linear regression fit, and the gray lines show the 95% confidence intervals.

To determine if the DNA origami pegboards could successfully activate signaling, we first tested whether receptors were recruited to the origami pegboard in a ligand-dependent manner. Using TIRF microscopy, we quantified the fluorescence intensity of the recruited GFP-tagged DNA-CARγ receptor to origami pegboards presenting 0, 2, 4, 16, 36, or 72 ligands (Figure 2B–E). Using signal from the 72 ligand (72L) origami pegboard as an internal intensity standard of brightness, and thus correcting for differences in illumination between wells, we found that the average fluorescence intensity correlated with the number of ligands presented by individual origami pegboards (Figure 2D, E). In addition, we measured Syk recruitment to individual DNA origami pegboards and found that Syk intensity also increased as a function of the number of ligands present on each origami pegboard (Figure 2C, Figure 2—figure supplement 2). These results confirmed that our DNA origami system provides a platform that allows quantitative receptor recruitment and the analysis of downstream signaling pathways.

Nanoscale clustering of ligand enhances phagocytosis

FcγR cluster upon ligand binding, but the functional importance of such clustering for phagocytosis has not been directly addressed, and whether a critical density of receptor-ligand pairs is necessary to initiate FcγR signaling is unclear (Duchemin et al., 1994; Jaumouillé et al., 2014; Lin et al., 2016; Lopes et al., 2017; Sobota et al., 2005). To address these questions, we varied the size of ligand clusters by designing DNA origami pegboards presenting 2–36 ligands. To ensure a constant total number of ligands and origami pegboards on each bead, we mixed the signaling origami pegboards with 0-ligand ‘blank’ origami pegboards in appropriate ratios (Figure 3A). We confirmed that the surface concentration of origami pegboards on the beads was comparable using fluorescence microscopy (Figure 3—figure supplement 1). We found that increasing the number of ligands per cluster increased engulfment, but that engulfment plateaued at a cluster size of eight ligands (Figure 3b). We confirmed that the observed engulfment phenotype was both ligand, receptor, and FcγR signaling dependent (Figure 3C, D). Together, these data reveal that FcγR clustering strongly enhances engulfment up to a cluster size of eight ligands.

Figure 3 with 1 supplement see all
Nanoscale clustering of ligand enhances phagocytosis.

(A) Schematic showing an origami pegboard functionalized to a lipid bilayer surrounding a silica bead (left) and the origami pegboard mixtures used to functionalize the bilayer-coated silica beads for experiment quantified in (B) (right). Blue squares represent origami pegboards with the indicated number of ligands (schematics below, red dot denotes ligand DNA and light blue dot denotes no ligand), and gray squares represent 0-ligand ‘blank’ origami pegboards. Pie charts above describe the ratios of ligand origami presenting pegboards to ‘blank’ pegboards. (B) Beads were functionalized with mixtures of origami pegboards containing the indicated ligand-presenting origami pegboard and the 0-ligand ‘blank’ origami pegboards in amounts designated in (A). The graph depicts the number of beads internalized per DNA-CARγ-expressing macrophage normalized to the maximum bead eating in that replicate. Each dot represents an independent replicate (n ≥ 100 cells analyzed per experiment), denoted by symbol shape, with red lines denoting mean ± SEM. Data is normalized to the maximum bead eating in each replicate. (C) Example image showing the DNA-CARγ (green) drives engulfment of beads (bilayer labeled in magenta) functionalized with 4-ligand DNA origami pegboards. A cross section of the z plane indicated in the inset panel (white line, bottom) shows that beads are fully internalized. (D) Bilayer-coated silica beads were functionalized with neutravidin, neutravidin and DNA origami pegboards presenting 0 DNA ligands, or neutravidin and 4-ligand DNA origami pegboards. The graph depicts normalized bead eating per cell of the indicated bead type for cells expressing the DNA-CARγ or the DNA-CARadhesion. Each dot represents an independent replicate, denoted by symbol shape (n ≥ 100 cells analyzed per experiment), with red lines denoting mean ± SEM. The data are normalized to the maximum bead eating in each replicate. * denotes p<0.05, ** denotes p<0.005, **** denotes p<0.0001, and n.s. denotes p>0.05 in (B) and (D) as determined by an ordinary one-way ANOVA with Holm–Sidak’s multiple comparison test.

Spatial organization of ligands in nanoclusters regulates engulfment

Next, we examined whether distance between individual receptor-ligand molecules within a signaling cluster impacts engulfment. For this experiment, we varied the spacing of four ligands on the origami pegboard. The 4-ligand tight origami (4T) contains four ligands clustered at the center of the pegboard (7 nm by 3.5 nm square), the medium origami (4M) has ligands spaced 21 nm by 17.5 nm apart, and the spread origami (4S) has four ligands positioned at the four corners of the pegboard (35 nm by 38.5 nm square) (Figure 4A). We found that the efficiency of macrophage engulfment was approximately twofold higher for the 4T-functionalized beads when compared to the 4M or 4S beads (Figure 4A). We confirmed via fluorescence microscopy that the concentration of origami pegboards on the surface was similar, and therefore ligand numbers on the beads were similar (Figure 4—figure supplement 1). Human THP-1 cells expressing the DNA-CARγ showed the same ligand spacing dependence (Figure 4—figure supplement 1). In addition, we generated DNA-CAR constructs containing the FcRγ and α chain transmembrane domains that would be present in the endogenous receptor complex (Figure 4—figure supplement 1). To minimize dimerization between FcRγ transmembrane domains, we either made a C25Aγ chain mutation, as this cysteine forms a disulfide bridge between y chains, or truncated the transmembrane domain before this residue. We found that the efficiency of macrophage engulfment was dependent on ligand spacing for all constructs tested (Figure 4—figure supplement 1). Expression of the various DNA-CARs at the cell cortex was comparable, and engulfment of beads functionalized with both the 4T and the 4S origami platforms was dependent on the FcγR signaling domain (Figure 4—figure supplement 1). Together, these results demonstrate that macrophages preferentially engulf targets with tighter ligand clusters.

Figure 4 with 1 supplement see all
Spatial arrangement of ligands within nanoclusters regulates engulfment.

(A) Schematics (top) depict 4-ligand origami pegboards presenting ligands at the positions indicated in red. Beads were functionalized with 0-ligand ‘blank’ (gray) origami pegboards, 4T (orange) origami pegboards, 4M (green) origami pegboards, or 4S (cyan) origami pegboards at equal amounts and fed to DNA-CARγ-expressing macrophages. Representative confocal images (middle) depict bead (bilayer in magenta) engulfment by macrophages (green). Internalized beads are denoted with a white sphere. Quantification of the engulfment assay is shown in the graph below depicting the number of beads engulfed per macrophage normalized to the maximum observed eating in that replicate. (B) Schematics of the receptor DNA (blue) paired with the medium-affinity 13 base pair DNA-ligand (red) used in all previous experiments including (A) and the high-affinity 16 base pair ligand-DNA (yellow) used for experiment shown in the graph below. Beads were functionalized with 0-ligand ‘blank’ (gray), high-affinity 4T (orange), high-affinity 4M (green), or high-affinity 4S (cyan) origami pegboards and fed to DNA-CARγ-expressing macrophages. Graph shows the number of beads engulfed per macrophage normalized to the maximum observed eating in that replicate. Each data point represents the mean of an independent experiment, shapes denote data from the same replicate, and bars show the mean ± SEM (A, B). * denotes p<0.05, *** denotes p<0.0005, **** denotes p<0.0001, and n.s. denotes p>0.05 as determined by an ordinary one-way ANOVA with Holm–Sidak’s multiple comparison test (A, B).

Tightly spaced ligands could potentially increase phagocytosis by enhancing the avidity of receptor-ligand interactions within each cluster. Such a hypothesis would predict that tightly spaced ligands increase DNA-CARγ-BFP occupancy at the phagocytic cup. However, when we measured the total fluorescence intensity of receptors at the phagocytic cup, we did not detect a difference in DNA-CARγ-BFP recruitment to 4T and 4S beads (Figure 6A, B). However, to eliminate any potential contribution of avidity, we created 4T and 4S origami pegboards with very high-affinity 16mer DNA ligands that are predicted to dissociate on a time scale of >7 hr (Taylor et al., 2017; Figure 4B). Using these 16mer high-affinity ligands, we found that 4T origami beads were still preferentially engulfed over 4M or 4S origami beads (Figure 4B, Figure 4—figure supplement 1). These results suggest that an avidity effect is not the cause of the preferential engulfment of targets having tightly spaced ligands.

Tight ligand spacing enhances engulfment initiation and downstream signaling

We next determined how ligand spacing affects the kinetics of engulfment. Using data from live-cell imaging, we subdivided the engulfment process into three steps: bead binding, engulfment initiation, and engulfment completion (Figure 5A, Video 1). To compare engulfment dynamics mediated by 4T and 4S origami pegboards in the same experiment, we labeled each pegboard type with a different colored fluorophore, functionalized a set of beads with each type of pegboard, and added both bead types to macrophages at the same time (Figure 5B, Video 2). Macrophages interacted with beads functionalized with the 4T and 4S pegboards with comparable frequency (46 ± 7% total bead-cell contacts vs. 54 ± 7% total bead-cell contacts, respectively). However, the probability of engulfment initiation was significantly higher for the 4T (95 ± 5% of bead contacts) versus 4S (61 ± 9% of bead contacts) beads, and the probability that initiation events resulted in successful completion of engulfment was higher for 4T (69 ± 9% of initiation events) versus 4S (39 ± 11% of initiation events) beads (Figure 5A). Initiation events that failed to induce successful engulfment either stalled after progressing partially over the bead or retracted the extended membrane back to the base of the bead. In addition, for beads that were engulfed, the time from contact to engulfment initiation was ~300 s longer for beads functionalized with 4S origami pegboards than beads containing 4T origami pegboards (Figure 5C). However, once initiated, the time from initiation to completion of engulfment did not differ significantly for beads coated with 4T or 4S origami (Figure 5D). Overall, 66 ± 8% of 4T bead contacts resulted in successful engulfment compared to 24 ± 8% for 4S beads (Figure 5E). The DNA-CARadhesion macrophages rarely met the initiation criteria, suggesting that active signaling from the FcγR is required (Figure 5—figure supplement 1). Together, these data reveal that tighter spacing between ligands within a cluster enhances the probability and kinetics of initiating engulfment, as well as the overall success frequency of completing engulfment, but does not affect the rate of phagosome closure once initiated.

Figure 5 with 1 supplement see all
Nanoscale ligand clustering controls engulfment initiation.

(A) Schematic portraying origami pegboards used to analyze the steps in the engulfment process quantified in (C–E). Bead binding is defined as the first frame the macrophage contacts a bead; initiation is the first frame in which the macrophage membrane has begun to extend around the bead, and completion is defined as full internalization. The macrophage membrane was visualized using the DNA-CARγ, which was present throughout the cell cortex. The % of beads that progress to the next stage of engulfment (% success) is indicated for 4T (orange, origami labeled with Atto550N) and 4S (cyan, origami labeled with Atto647N) beads. **** denotes p<0.0001 as determined by Fisher’s exact test. (B) Still images from a confocal microscopy timelapse showing the macrophage (green) interacting with both the 4T origami pegboard-functionalized beads (orange) and the 4S origami pegboard-functionalized beads (cyan), but preferentially engulfing the 4T origami pegboard-functionalized beads. In the bottom panel (DNA-CARγ channel), engulfed beads have been indicated by a sphere colored to match its corresponding origami type. (C) Graph depicts quantification of the time from bead contact to engulfment initiation for all beads that were successfully engulfed. Each dot represents one bead with red lines denoting mean ± SEM. (D) Graph depicts the time from engulfment initiation to completion. Each dot represents one bead with red lines denoting mean ± SEM. (E) Graph shows the fraction of contacted 4T and 4S beads engulfed (orange and cyan, respectively) by the macrophages. Data represent quantification from four independent experiments, denoted by symbol shape, and bars denote the mean ± SEM. n.s. denotes p>0.05 and ** indicates p<0.005 by Student’s t-test comparing the 4T- and 4S-functionalized beads (C–E).

Video 1
The engulfment program broken into three steps: bead binding, engulfment initiation, and engulfment completion.

A macrophage infected with the DNA-CARγ (green) engulfs a 5 μm silica bead coated in a supported lipid bilayer (magenta) and functionalized with 4T origami pegboards. The movie is a maximum intensity projection of z-planes and depicts the bead binding, initiation, and completion steps of the engulfment process. Time is indicated at the top left, and scale bar denotes 5 μm.

Video 2
DNA-CARγ macrophages preferentially engulf beads functionalized with tightly spaced ligands.

A DNA-CARγ-expressing macrophage (green) interacts with 4T origami pegboard-functionalized beads (orange) and 4S origami pegboard-functionalized beads (cyan) that were added simultaneously and in equal amounts to the well of cells. The macrophage engulfs only 4T origami pegboard-functionalized beads. The movie is a maximum intensity projection of z-planes acquired every 20 s for 28 min. Time is indicated at the top left.

Tightly spaced ligands enhance receptor phosphorylation

We next determined how the 4T or 4S origami pegboards affect signaling downstream of FcγR binding by measuring fold enrichment at the phagocytic cup compared to the rest of the cortex of (1) a marker for receptor phosphorylation (the tandem SH2 domains of Syk; Bakalar et al., 2018; Morrissey et al., 2018), (2) PIP3 (via recruitment of the PIP3 binding protein Akt-PH-GFP), and (3) filamentous actin (measured by rhodamine-phalloidin binding, Figure 6A, B). We found that 4T phagocytic cups recruited more tSH2-Syk than the 4S beads, indicating an increase in receptor phosphorylation by nanoclustered ligands. Generation of PIP3 and actin filaments at the phagocytic cup also increased at 4T relative to 4S synapses (Figure 6B). This differential recruitment of downstream signaling molecules to 4T versus 4S origami beads was most apparent in early and mid-stage phagocytic cups; late-stage cups showed only a slightly significant difference in tSH2-Syk recruitment and no significant differences in generation of PIP3 or actin filaments (Figure 6—figure supplement 1). Together, these data demonstrate that nanoscale ligand spacing affects early downstream signaling events involved in phagocytic cup formation.

Figure 6 with 1 supplement see all
Nanoscale ligand spacing controls receptor activation.

(A) Beads were functionalized with 4T (orange) or 4S (cyan) origami pegboards at equal amounts, added to macrophages expressing the DNA-CARγ (magenta) and the indicated signaling reporter protein (green; grayscale on top). Phagocytic synapses were imaged via confocal microscopy. Asterisks indicate whether a 4T (orange) or a 4S (cyan) bead is at the indicated phagocytic synapse in the upper panel. (B) Schematic (left) depicts the areas measured from images shown in (A) to quantify the fluorescence intensity (yellow outlines). Each phagocytic synapse measurement was normalized to the fluorescence intensity of the cell cortex at the same z-plane. Graphs (right) depict the ratio of fluorescence at 4T- or 4S-functionalized bead synapses to the cortex for the indicated reporter. Each dot represents one bead with red lines denoting mean ± SEM. (C) Schematic portraying the CAR constructs and origami used in the experiment quantified in (D). The DNA-CAR-4xγ construct (left) consists of four repeats of the intracellular domain of the DNA-CARγ connected by a GGSG linker. The DNA-CAR-1xγ−3xΔITAM (right) is identical to the DNA-CAR-4xγ except that the tyrosines composing the immune receptor tyrosine-based activation motif (ITAM) domains (purple circles) are mutated to phenylalanines in the three C-terminal repeats (gray). Cells expressing either of these constructs were fed beads functionalized with either high-affinity 1-ligand origami pegboards (left), high-affinity 4T origami pegboards (right), or 0-ligand ‘blank’ origami pegboards (not shown), and engulfment was assessed after 45 min. (D) Graph shows the number of beads engulfed per macrophage normalized to the maximum observed eating in that replicate. Each data point represents the mean from an independent experiment, denoted by symbol shape, and bars denote the mean ± SEM. Blue points represent a condition where 16 ITAMs are available per origami, orange points represent conditions where 4 ITAMs are available per origami, purple points represent a condition where 1 ITAM is available per origami, and gray points represent conditions where no ITAM is available. n.s. denotes p>0.05, *** denotes p<0.0005, and **** denotes p<0.00005 as determined by the Student’s t-test (B) or an ordinary one-way ANOVA with Holm–Sidak’s multiple comparison test (D).

We next sought to understand why distributing ligands into tight clusters enhanced receptor phosphorylation and engulfment. One possibility is that the clustering of four complete receptors is needed to drive segregation of the inhibitory phosphatase CD45 and allow sustained phosphorylation of the FcγR immune receptor tyrosine-based activation motif (ITAM) (Bakalar et al., 2018; Freeman et al., 2016; Goodridge et al., 2012; Schmid et al., 2016). Alternatively, the 4-ligand cluster may be needed to obtain a critical intracellular concentration of FcγR ITAM signaling domains. To test for the latter possibility, we designed a synthetic receptor (DNA-CAR-4xγ) that contains four repeats of the intracellular domain of the DNA-CARγ connected by a GGSG linker between each repeat (Figure 6C). We confirmed that this DNA-CAR-4xγ receptor was more potent in activating engulfment than an equivalent receptor (DNA-CAR-1xγ−3xΔITAM) in which the three C-terminal ITAM domains were mutated to phenylalanines (Figure 6C, D). Keeping the number of intracellular ITAMs constant, we compared the engulfment efficiency mediated by two different receptors: (1) the DNA-CAR-4xγ that interacted with beads functionalized with 1-ligand origami and (2) the DNA-CAR-1xγ−3xΔITAM that interacted with beads coated with equivalent amounts of 4T origami (Figure 6C). While the DNA-CAR-1xγ−3xΔITAM-expressing macrophages engulfed 4T origami beads, the DNA-CAR-4xγ macrophages failed to engulf the high-affinity 1-ligand origami beads (Figure 6D, Figure 6—figure supplement 1). To ensure that all four ITAM domains on the DNA-CAR-4xγ were signaling competent, we designed two additional DNA-CARs that placed the functional ITAM at the second and fourth position (Figure 6—figure supplement 1). These receptors were able to induce phagocytosis of 4T origami beads, indicating that the DNA-CAR-4xγ likely contains four functional ITAMs. Collectively, these results indicate that the tight clustering of multiple receptors is necessary for engulfment and increasing the number of intracellular signaling modules on a single receptor is not sufficient to surpass the threshold for activation of engulfment.

Discussion

Macrophages integrate information from many FcγR-antibody interactions to discriminate between highly opsonized targets and background signal from soluble antibody or sparsely opsonized targets. How the macrophage integrates signals from multiple FcγR binding events to make an all-or-none engulfment response is not clear. Here, we use DNA origami nanostructures to manipulate and assess how the nanoscale spatial organization of receptor-ligand interactions modulates FcγR signaling and the engulfment process. We found that tight ligand clustering increases the probability of initiating phagocytosis by enhancing FcγR phosphorylation.

Phagocytosis requires IgG across the entire target surface to initiate local receptor activation and to ‘zipper’ close the phagocytic cup (Freeman et al., 2016; Griffin et al., 1975). Consistent with this zipper model, incomplete opsonization of a target surface, or micron-scale spaces between IgG patches, decreases engulfment (Freeman et al., 2016; Griffin et al., 1975). Initially suggested as an alternative to the zipper model, the trigger model proposed that engulfment occurs once a threshold number of receptors interact with IgG (Ben M'Barek et al., 2015; Griffin et al., 1975; Swanson and Baer, 1995). While this model has largely fallen out of favor, more recent studies have found that a critical IgG threshold is needed to activate the final stages of phagocytosis (Zhang et al., 2010). Our data suggest that there may also be a nanoscale density-dependent trigger for receptor phosphorylation and downstream signaling. Taken together, these results suggest that both tight nanoscale IgG-FcγR clustering and a uniform distribution of IgG across the target are needed to direct signaling to ‘zipper’ close the phagocytic cup. Why might macrophages use this local density-dependent trigger to dictate engulfment responses? Macrophages constantly encounter background ‘eat me’ signals (Gonzalez-Quintela et al., 2008). This hyper-local density measurement may buffer macrophages against background stimuli and weakly opsonized targets that are unlikely to have adjacent bound antibodies, while still robustly detecting and efficiently engulfing highly opsonized targets.

Our findings are consistent with previous results demonstrating that FcγR crosslinking correlates with increased ITAM phosphorylation (Huang et al., 1992; Kwiatkowska and Sobota, 2001; Lin et al., 2016; Sobota et al., 2005). While our data pinpoints a role for ligand spacing in regulating receptor phosphorylation, it is possible that later steps in the phagocytic signaling pathway are also directly affected by ligand spacing. The mechanism by which dense-ligand clustering promotes receptor phosphorylation remains an open question, although our data rule out a couple of models. Specifically, we demonstrate that nanoscale ligand clustering does not noticeably affect the amount of ligand-bound receptor at the phagocytic cup, and that ligand spacing continues to affect engulfment when avidity effects are diminished through the use of high-affinity receptor-ligands. Collectively, these data reveal that changes in receptor binding or recruitment caused by increased avidity are unlikely to account for the increased potency of clustered ligands. Our data also exclude the possibility that receptor clustering simply increases the local intracellular concentration of FcγR signaling domains as arranging FcγR ITAMs in tandem did not have the same effect as clustering multiple receptor-ligand interactions. However, it remains possible that the geometry of the intracellular signaling domains could be important for activating or localizing downstream signaling, and that tandem ITAMs on the same polypeptide cannot produce the same engulfment signals as ITAMs on separate parallel polypeptides.

One possible model to explain the observed ligand-density dependence of signaling involves the ordering of lipids around the FcγR. Segregated liquid-ordered and liquid-disordered membrane domains around immune receptor clusters have been reported to promote receptor phosphorylation (Bag et al., 2020; Dinic et al., 2015; Eggeling et al., 2009; Kabouridis, 2006; Simons and Ikonen, 1997; Sohn et al., 2006; Stone et al., 2017). FcγR clusters are associated with liquid-ordered domains (Beekman et al., 2008; Katsumata et al., 2001; Kwiatkowska and Sobota, 2001). Liquid-ordered domains recruit Src family kinases, which phosphorylate FcγRs, while liquid-disordered domains are enriched in the transmembrane phosphatase CD45, which dephosphorylates FcγRs (Bag et al., 2020; Sohn et al., 2006; Stone et al., 2017). Thus, lipid ordering could provide a mechanism that leads to receptor activation if denser receptor-ligand clusters are more efficient in nucleating or associating with ordered lipid domains.

As an alternative model, a denser cluster of ligated receptors may enhance the steric exclusion of the bulky transmembrane proteins like the phosphatases CD45 and CD148 (Bakalar et al., 2018; Goodridge et al., 2012; Zhu et al., 2008). CD45 is heavily glycosylated, making the extracellular domain 25–40 nm tall (Davis and van der Merwe, 2006; McCall et al., 1992; Woollett et al., 1985). Because of its size, CD45 is excluded from close cell-cell contacts, such as those mediated by IgG-FcγR, which have a dimension of 11.5 nm (Bakalar et al., 2018; Burroughs et al., 2011; Carbone et al., 2017; Chung et al., 2013; Lu et al., 2011; Schmid et al., 2016). IgG bound to antigens ≤ 10.5 nm from the target surface induces CD45 exclusion and engulfment (estimated total intermembrane distance of ≤22 nm Bakalar et al., 2018). Our DNA origami structure is estimated to generate similar intermembrane spacing, consisting of hybridized receptor-ligand DNA (~9.4 nm), the origami pegboard (6 nm), and neutravidin (4 nm) (Rosano et al., 1999). A higher receptor-ligand density constrains membrane shape fluctuations (Krobath et al., 2009; Krobath et al., 2011; Różycki et al., 2010), and this constraint may increase CD45 exclusion (Schmid et al., 2016). Both the lipid ordering and the steric exclusion models predict at least a partial exclusion of the CD45 from the zone of the receptor cluster. However, the dimension of the tight cluster in particular is very small (7 by 3.5 nm) and measurement of protein concentration at this level is currently not easily achieved, even with super-resolution techniques. Overall, our results establish the molecular and spatial parameters necessary for FcγR activation and demonstrate that the spatial organization of IgG-FcγR interactions alone can affect engulfment decisions.

How does our synthetic DNA-CARγ receptor compare to endogenous FcγRs? Our DNA-CARs are single-chain receptors that recruit one intracellular signaling domain per ligand, similar to the single-chain human FcγRIIA receptor (Nimmerjahn and Ravetch, 2006). FcγRIIA is ubiquitously expressed on human myeloid cells, and high-affinity FcRIIA alleles correlate with an increase in effectiveness of the ADCP-inducing drug rituximab and lupus susceptibility (Bruhns and Jönsson, 2015; Nimmerjahn and Ravetch, 2006). The majority of FcγR family members, including all activating mouse FcγRs and the human FcγRI and FcγRIIIA, are multimeric complexes composed of a ligand-binding α chain and a dimerized signaling γ chain. This results in two signaling γ chains recruited to each IgG ligand. The different stoichiometry between ligand-binding and intracellular signaling domains may affect some parameters like optimal cluster size. A second difference between the DNA-CARγ and the endogenous system is the presence of the CD86 transmembrane domain. We found that ligand spacing had a similar effect on phagocytosis when we replaced the CD86 transmembrane domain with the Fcα or Fcγ transmembrane domain. However, the Fcγ transmembrane domain construct triggered more bead internalization across all conditions. We hypothesize this could be because the transmembrane domain retains some ability to dimerize, recruiting more signaling domains to each ligand, or because it is better able to associate with lipid-ordered domains. Future studies that pattern either endogenous Fc receptor complex or IgG ligand could clarify these questions.

How does the spacing requirements for FcγR nanoclusters compare to other signaling systems? Engineered multivalent Fc oligomers revealed that IgE ligand geometry alters Fcε receptor signaling in mast cells (Sil et al., 2007). DNA origami nanoparticles and planar nanolithography arrays have previously examined optimal inter-ligand distance for the T cell receptor, B cell receptor, NK cell receptor CD16, death receptor Fas, and integrins (Arnold et al., 2004; Berger et al., 2020; Cai et al., 2018; Deeg et al., 2013; Delcassian et al., 2013; Dong et al., 2021; Veneziano et al., 2020). Some systems, like integrin-mediated cell adhesion, appear to have very discrete threshold requirements for ligand spacing while others, like T cell activation, appear to continuously improve with reduced intermolecular spacing (Arnold et al., 2004; Cai et al., 2018). Our system may be more similar to the continuous improvement observed in T cell activation as our most spaced ligands (36.5 nm) are capable of activating some phagocytosis, albeit not as potently as the 4T. Interestingly, as the intermembrane distance between T cell and target increases, the requirement for tight ligand spacing becomes more stringent (Cai et al., 2018). This suggests that IgG bound to tall antigens may be more dependent on tight nanocluster spacing than short antigens. Planar arrays have also been used to vary inter-cluster spacing, in addition to inter-ligand spacing (Cai et al., 2018; Freeman et al., 2016). Examining the optimal inter-cluster spacing during phagosome closure may be an interesting direction for future studies.

Our study on the spatial requirements of FcγR activation could have implications for the design of therapeutic antibodies or chimeric antigen receptors. Antibody therapies that rely on FcγR engagement are used to treat cancer, autoimmune, and neurodegenerative diseases (Chao et al., 2010; Nimmerjahn and Ravetch, 2005; Uchida et al., 2004; Watanabe et al., 1999; Weiskopf et al., 2013; Weiskopf and Weissman, 2015). Multimerizing Fc domains or targeting multiple antibodies to the same antigen may increase antibody potency (Zhang et al., 2016). Interestingly, rituximab, a successful anti-CD20 therapy that potently induces ADCP, has two binding sites on its target antigen (Zhao et al., 2020). Selecting clustered antigens or pharmacologically inducing antigen clustering may also increase antibody potency (Chew et al., 2020). These results suggest that oligomerization may lead to more effective therapy; however, a systematic study of the spatial parameters that affect FcγR activation has not been undertaken (Bakalar et al., 2018). Our data suggest that antibody engineering strategies that optimize spacing of multiple antibodies through leucine zippers, cysteine bonds, DNA hybridization (Delcassian et al., 2013; Seifert et al., 2014; Sil et al., 2007), or multimeric scaffolds (Divine et al., 2020; Fallas et al., 2017; Huang et al., 2021; Ueda et al., 2020) could lead to stronger FcγR activation and potentially more effective therapies.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
AntibodyAlexa Fluor 647 anti-biotin IgG (mouse monoclonal)Jackson Immuno LabsCat# 200-602-211
RRID:AB_2339046
AntibodyAlexa Fluor 488 anti-biotin IgG
(mouse monoclonal)
Jackson Immuno LabsCat# 200-542-211
RRID:AB_2339040
Sequence-based reagentReceptor DNA strandThis paperBenzylguanine-5'- AATATGATGTATGTGG-3'Oligonucleotide was ordered from IDT with a 5' terminal amine. Conjugation to benzyl-guanine was performed as described (Farlow et al., 2013).
Sequence-based reagentDNA ligand strandIDTBiotin-5'-TTTT-TTTCATACATCATATT- 3'-Atto647
Sequence-based reagentp8064 DNA scaffoldIDTCat# 1081314
Chemical compound, drugAlexa Fluor 488 PhalloidinThermo/Molecular ProbesCat# A12379
Commercial assay, kitLipofectamine LTXThermoFisherCat# 15338030
Commercial assay, kitLenti-X ConcentratorTakara BiosciencesCat# 631231
Peptide, recombinant proteinPierce Biotinylated Bovine Serum Albumin (Biotin-LC-BSA)ThermoScientificCat# 29130
Peptide, recombinant proteinNeutravidinThermoScientificCat# 31050
Cell line (human)Lenti-X 293 T cell lineTakara BiosciencesCat# 632180For lentivirus production.
Cell line (human)HEK293T cellsUCSF Cell Culture FacilityFor lentivirus production.
Cell line (mouse)Raw264.7 MacrophagesATCCCat# ATCC TIB-71
RRID:CVCL_0493
Cell line (human)THP1 MonocytesATCCCat# ATCC TIB-202
RRID:CVCL_0006
Transfected construct (mouse)pHR-DNA-CARγThis paperIn PhR vector. Signal peptide: (MQSGTHWRVLGLCLLSVGVWGQD) derived from CD3ε Extracellular: HA tag plus a linker (LPETGGGGGG), SNAPf (from the pSNAPf plasmid, New England Biolabs) Linker: GGSGGSGGS, TM and intracellular: CD86TM (aa 236–271), cytoplasmic domain (aa 45–86) of the Fc γ-chain UniProtKB - P20491 (FCERG_MOUSE) linker: GSGS, Fluorophore: mGFP or BFP.
Transfected construct (mouse, human)pHR-Syk-BFPAdapted from DOI: 10.1016/j.immuni.2020.07.008CDS: aa1-629 UniProtKB - P48025 (KSYK_MOUSE), Linker: ADPVAT, Fluorophore: BFP.
Transfected construct (mouse, human)pHR-DNA-CARadhesionDOI: 10.1016/j.immuni.2020.07.008In PhR vector. Signal peptide: (MQSGTHWRVLGLCLLSVGVWGQD) derived from CD3ε Extracellular: HA tag plus a linker (LPETGGGGGG), SNAPf (from the pSNAPf plasmid, New England Biolabs) Linker: GGSGGSGGS, TM and intracellular: CD86TM (aa 236–271), linker: SADASGG, fluorophore: eGFP.
Transfected construct (mouse)pHR-DNA-CARγTM-C25AThis paperIn PhR vector. Signal peptide: (MQSGTHWRVLGLCLLSVGVWGQD) derived from CD3ε
Extracellular: HA tag plus a linker (LPETGGGGGG), SNAPf (from the pSNAPf plasmid, New England Biolabs)
Linker: GGSGGSGGS, TM and intracellular: (aa 19–86) of the Fcγ-chain UniProtKB – P20491 (FCERG_MOUSE) with aa25 mutated from C to A linker: GSGS, fluorophore: mGFP or BFP.
Transfected construct (mouse)pHR-DNA-CARγTM-aa26-86This paperIn PhR vector. Signal peptide: (MQSGTHWRVLGLCLLSVGVWGQD) derived from CD3ε
Extracellular: HA tag plus a linker (LPETGGGGGG), SNAPf (from the pSNAPf plasmid, New England Biolabs)
Linker: GGSGGSGGS, TM and intracellular: (aa 26–86) of the Fcγ-chain UniProtKB – P20491 (FCERG_MOUSE) linker: GSGS, fluorophore: mGFP or BFP.
Transfected construct (mouse)pHR-DNA-CARγ-αTM (aa291–404)This paperIn PhR vector. Signal peptide: (MQSGTHWRVLGLC
LLSVGVWGQD) derived from CD3ε
Extracellular: HA tag plus a linker (LPETGGGGGG), SNAPf (from the pSNAPf plasmid, New England Biolabs)
Linker: GGSGGSGGS, TM and intracellular: FcGR1 α-chain (aa 291–404) UniProtKB – P26151 (FCGR1_MOUSE) followed by cytoplasmic domain (aa 45–86) of the Fcγ-chain UniProtKB - P20491 (FCERG_MOUSE) linker: GSGS, fluorophore: mGFP or BFP.
Transfected construct (mouse, human)pHR-mNeonGreen-tSH2 SykAdapted from DOI: 10.1016/j.cell.2018.05.059CDS: aa2-261 UniProtKB - P48025 (KSYK_MOUSE), linker: GGGSGGGG, fluorophore: mNeonGreen.
Transfected construct (mouse, human)pHR-Akt PH domainThis paperCDS: aa1–164 UniProtKB – P31749 (AKT1_HUMAN), linker: HMTSPVAT, fluorophore: mGFP.
Transfected construct (mouse)pHR-DNA-CAR4xγThis paperIn PhR vector. Signal peptide: (MQSGTHWRVLGLCLLSVGVWGQD) derived from CD3ε
Extracellular: HA tag plus a linker (LPETGGGGGG), SNAPf (from the pSNAPf plasmid, New England Biolabs)
Linker: GGSGGSGGS, TM and intracellular: CD86TM (aa 236–271), four repeats of the cytoplasmic domain (aa 45–86) of the Fc γ-chain UniProtKB – P20491 (FCERG_MOUSE) with a GSGS linker between each repeat, linker: GSGS, fluorophore: mGFP.
Transfected construct (mouse)pHR-DNA-CAR-N1xγ−3xΔITAMThis paperIn PhR vector. Signal peptide: (MQSGTHWRVLGLCLLSVGVWGQD) derived from CD3ε
Extracellular: HA tag plus a linker (LPETGGGGGG), SNAPf (from the pSNAPf plasmid, New England Biolabs)
Linker: GGSGGSGGS, TM and intracellular: CD86TM (aa 236–271), the cytoplasmic domain (aa 45–86) of the Fcγ-chain UniProtKB – P20491 (FCERG_MOUSE) followed by three repeats of the cytoplasmic domain (aa 45–86) of the Fc γ-chain UniProtKB – P20491 (FCERG_MOUSE) with aa65 and aa76 mutated from YtoF and a GSGS linker between each repeat, linker: GSGS, fluorophore: mGFP.
Transfected construct (mouse)pHR-DNA-CAR-3xΔITAM-C1xγThis paperIn PhR vector. Signal peptide: (MQSGTHWRVLGLCLLSVGVWGQD) derived from CD3ε
Extracellular: HA tag plus a linker (LPETGGGGGG), SNAPf (from the pSNAPf plasmid, New England Biolabs)
Linker: GGSGGSGGS, TM and intracellular: CD86TM (aa 236–271), three repeats of the cytoplasmic domain (aa 45–86) of the Fc γ-chain UniProtKB – P20491 (FCERG_MOUSE) with aa65 and aa76 mutated from YtoF and a GSGS linker between each repeat followed by the cytoplasmic domain (aa 45–86) of the Fc γ-chain UniProtKB – P20491 (FCERG_MOUSE), linker: GSGS, fluorophore: mGFP.
Transfected construct (mouse)pHR-DNA-CAR-1xΔITAM-1xγ−2xΔITAMThis paperIn PhR vector. Signal peptide: (MQSGTHWRVLGLCLLSVGVWGQD) derived from CD3ε
Extracellular: HA tag plus a linker (LPETGGGGGG), SNAPf (from the pSNAPf plasmid, New England Biolabs)
Linker: GGSGGSGGS, TM and intracellular: CD86TM (aa 236–271), the cytoplasmic domain (aa 45–86) of the Fc γ-chain UniProtKB – P20491 (FCERG_MOUSE) with aa65 and aa76 mutated from YtoF the cytoplasmic domain, GSGS linker (aa 45–86) of the Fcγ-chain UniProtKB – P20491 (FCERG_MOUSE), GSGS linker, followed by two more repeats of the Fc γ-chain UniProtKB – P20491 (FCERG_MOUSE) with aa65 and aa76 mutated from YtoF the cytoplasmic domain and a GSGS linker between each repeat, linker: GSGS, fluorophore: mGFP.
Transfected construct (human)pHR-DNA-CARγ humanThis paperIn PhR vector. Signal peptide: (MQSGTHWRVLGLCLLSVGVWGQD) derived from CD3ε
Extracellular: HA tag plus a linker (LPETGGGGGG), SNAPf (from the pSNAPf plasmid, New England Biolabs)
Linker: GGSGGSGGS, TM and intracellular: CD86TM (aa 236–271), cytoplasmic domain (aa 45–86) of the Fc γ-chain UniProtKB – P30273 (FCERG_HUMAN) linker: GSGS, fluorophore: mGFP or BFP.
Recombinant DNA reagentpMD2.G lentiviral plasmidD. Stainier, Max Planck; VSV-G envelopeRRID:addgene_12259
Recombinant DNA reagentpCMV-dR8.91DOI: 10.1038/nature11220.Current RRID:addgene_8455
Recombinant DNA reagentpHRSIN-CSGWDOI: 10.1038/nature11220.
Software, algorithmImageJNIH
Software, algorithmAffinity Designer
Software, algorithmFijihttps://fiji.sc/
Software, algorithmPrismGraphPad8
Software, algorithmMicromanagerDOI:10.14440/jbm.2014.36
Other5 μm silica microspheresBangsCat# SS05N
OtherBiotinyl Cap PEAvantiCat# 870273
OtherPOPCAvantiCat# 850457
OtherPEG5000-PEAvantiCat# 880230
OtherAtto390 DOPEATTO-TEC GmbHCat# AD 390-161
OtherMatriPlateBrooksCat# MGB096-1-2-LG-L
Other96-well round bottomed platesCorningCat# 38018
OtherIllustra NAP-5 columnsCytivaCat# 17085301

Cell culture

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RAW264.7 macrophages were purchased from the ATCC and cultured in DMEM (Gibco, Cat# 11965-092) supplemented with 1× penicillin-streptomycin-L-glutamine (Corning, Cat# 30-009 Cl), 1 mM sodium pyruvate (Gibco, Cat# 11360-070), and 10% heat-inactivated fetal bovine serum (Atlanta Biologicals, Cat# S11150H). THP1 cells were also purchased from the ATCC and cultured in RPMI 1640 Medium (Gibco, Cat# 11875-093) supplemented with 1× Pen-Strep-Glutamine and 10% heat-inactivated fetal bovine serum. All cells were certified mycoplasma-free and discarded after 20 passages to minimize variation.

Constructs and antibodies

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All relevant information can be found in the Key resources table, including detailed descriptions of the amino acid sequences for all constructs.

Lentivirus production and infection

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Lentiviral infection was used to express constructs described in the Key resources table in either RAW264.7 or THP1 cells. Lentivirus was produced by HEK293T cells or Lenti-X 293 T cells (Takara Biosciences, Cat# 632180) transfected with pMD2.G (a gift from Didier Tronon, Addgene plasmid # 12259 containing the VSV-G envelope protein), pCMV-dR8.91 (since replaced by second generation compatible pCMV-dR8.2, Addgene plasmid #8455), and a lentiviral backbone vector containing the construct of interest (derived from pHRSIN-CSGW, see Key resources table) using lipofectamine LTX (Invitrogen, Cat# 15338-100). The HEK293T media was harvested 60–72 hr post-transfection, filtered through a 0.45 µm filter, and concentrated using Lenti-X (Takara Biosciences, Cat# 631232) via the standard protocol. Concentrated virus was added directly to the cells, and the plate was centrifuged at 2200×g for 45 min at 37°C. Cells were analyzed a minimum of 60 hr later. Cells infected with more than one viral construct were FACs sorted (Sony SH800) before use to enrich for double infected cells.

DNA origami preparation

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The DNA origami pegboard utilized for all experiments was generated as described in Figure 2—figure supplement 1. The p8064 DNA scaffold was purchased from IDT (Cat# 1081314). All unmodified oligonucleotides utilized for the origami were purchased from IDT in 96-well plates with standard desalting purification and resuspension at 100 µM in water. Fluorophore and biotin-conjugated oligonucleotides were also purchased from IDT (HPLC purification). All oligonucleotide sequences are listed in Supplementary file 1, the assembly is schematized in Figure 2—figure supplement 1, and the Cadnano strand diagram for the pegboard with 72 medium-affinity ligands is included in Figure 2—figure supplement 1. Core staple oligonucleotides (200 nM) (plates 1 and 2), ligand oligonucleotides (200 nM) (plates 3L, 3MA, and 3 HA), biotinylated oligonucleotides (200 nM), DNA scaffold (20 nM final concentration), and fluorophore-labeled oligonucleotides (200 nM final concentration) were mixed in 1× folding buffer (5 mM Tris pH 8.0, 1 mM EDTA, 5 mM NaCl, 20 mM MgCl2). Origami folding reaction was performed in a PCR thermocycler (Bio-Rad MJ Research PTC-240 Tetrad), with initial denaturation at 65°C for 15 min followed by cooling from 60°C to 40°C with a decrease of 1°C/hr. To purify excess oligonucleotides from fully folded DNA origami, the DNA folding reaction was mixed with an equal volume of PEG precipitation buffer (15% (w/v) PEG-8000, 5 mM Tris-Base pH 8.0, 1 mM EDTA, 500 mM NaCl, 20 mM MgCl2) and centrifuged at 16,000× rcf for 25 min at room temperature (RT). The supernatant was removed, and the pellet was resuspended in 1× folding buffer. PEG purification was repeated a second time, and the final pellet was resuspended at the desired concentration in 1× folding buffer and stored at 4°C.

Preparation of benzylguanine-conjugated DNA oligonucleotides

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5′-amine modified (5AmMC6) DNA oligonucleotides were ordered from IDT and diluted in 0.15 M HEPES pH 8.5 to a final concentration of 2 mM. N-hydroxysuccinimide ester (BG-GLA-NHS)-functionalized benzylguanine was purchased from NEB (Cat# S9151S) and freshly reconstituted in DMSO to a final concentration of 83 mM. To functionalize the oligonucleotides with benzylguanine, the two solutions were mixed so that the molar ratio of oligonucleotide-amine:benzylguanine-NHS is 1:50 and the final concentration of HEPES is between 50 mM and 100 mM. The reaction was left on a rotator overnight at RT. To remove excess benzylguanine-NHS ester, the reaction product was purified the next day with illustra NAP-5 Columns (Cytiva, Cat# 17085301), using H2O for elution. The molar concentration of the benzylguanine-conjugated oligonucleotides was determined by measuring the absorbance of the purified reaction at 260 nm with a Nanodrop. This reaction was further condensed with the Savant SpeedVac DNA 130 Integrated Vacuum Concentrator System, resuspended in water to a final concentration of 100 µM, aliquoted, and stored at −20°C until use.

Functionalization of glass surface with DNA origami

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96-well glass-bottom MatriPlates were purchased from Brooks (Cat# MGB096-1-2-LG-L). Before use, plates were incubated in 5% (v/v) Hellmanex III solution (Z805939-1EA; Sigma) overnight, washed extensively with Milli-Q water, dried under the flow of nitrogen gas, and covered with sealing tape (ThermoFisher, Cat# 15036). Wells used for experiment were unsealed, incubated with 200 µL of Biotin-BSA (ThermoFisher, Cat# 29130) at 0.5 mg/mL in PBS pH 7.4 at RT for 2 hr overnight. Wells were washed 6× with PBS pH 7.4 to remove excess BSA and incubated for 30 min at RT with 100 μL neutravidin at 250 μg/mL in PBS pH 7.4 for origami quantification and 50 μg/mL for cellular experiments. Wells were again washed 6× with PBS pH 7.4 supplemented with 20 mM MgCl2 and incubated for 1–2 hr with the desired amount of DNA origami diluted in PBS pH 7.4 with 20 mM MgCl2 and 0.1% BSA.

DNA origami quantification

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Five wells of a 96-well glass-bottom MatriPlate per origami reaction were prepared as described in ‘Functionalization of glass surface with DNA origami’. The purified DNA origami reaction was serially diluted into PBS pH 7.4 with 20 mM MgCl2 and 0.1% BSA, and five different concentrations were plated and incubated for 1.5 hr before washing 5× with PBS pH 7.4 with 20 mM MgCl2 and 0.1% BSA. Fluorescent TIRF images were acquired in the channel with which the origami was labeled. 100 sites per well were imaged using the High Content Screening (HCS) Site Generator plug-in in µManager (Stuurman et al., 2010). The number of individual DNA origami per µm2 in each well was quantified using the Spot Counter plug-in in Fiji. This was repeated for all concentrations of origami plated. The final concentration of the origami reaction was measured as number of origami/µm2 and was calculated from a linear fit including all concentrations in which individual origami could be identified by the plug-in.

TIRF imaging

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96-well glass-bottom MatriPlates were functionalized with DNA origami as described and then washed into engulfment imaging media (20 mM HEPES pH 7.4, 135 mM NaCl, 4 mM KCl, 1 mM CaCl2, 10 mM glucose) containing 20 mM MgCl2. Approximately 100,000 dual-infected mNeonGreen-DNA-CARγ and BFP-Syk THP1 cells per well were pelleted via centrifugation, washed into engulfment imaging media, re-pelleted, and resuspended into 50 µL of engulfment imaging media. 1 µL of 100 μM benzylguanine-labeled-receptor DNA stock was added per ~50,000 cells pelleted, and the cell-DNA mixture was incubated at RT for 15 min. Cells were subsequently washed twice via centrifugation with 10 mL of imaging buffer to remove excess benzylguanine-labeled DNA and resuspended in 200 μL per 100,000 cells of imaging buffer containing 20 mM MgCl2. Cells were then immediately added to each well and imaged. Data was only collected from a central region of interest (ROI) in the TIRF field. The origami fluorescent intensities along the x and y axes were plotted to ensure there was no drop off in signal and thus no uniformity of illumination.

Quantification of receptor and Syk recruitment to individual origami

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Cells that expressed both the mNeonGreen-tagged DNA-CARγ receptor and the BFP-tagged Syk and had interactions with the 72-ligand origami were chosen for analysis in Fiji. An ROI was drawn around the perimeter of the cell-glass surface interaction, which was determined by the presence of receptor fluorescence. The ‘Spot Intensity in All Channel’ plug-in in Fiji (https://github.com/nicost/spotIntensityAnalysis/; Stuurman, 2020) was used to identify individual origami pegboards, measure fluorescence intensity of the DNA-CARγ receptor and Syk at each origami pegboard, and subtract local background fluorescence. The intensity at each origami pegboard was normalized to the average intensity measured at 72-ligand origami pegboards in each well.

Supported lipid bilayer-coated silica bead preparation

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Chloroform-suspended lipids were mixed in the following molar ratios: 96.8% POPC (Avanti, Cat# 850457), 2.5% biotinyl cap PE (Avanti, Cat# 870273), 0.5% PEG5000-PE (Avanti, Cat# 880230), and 0.2% atto390-DOPE (ATTO-TEC GmbH, Cat# AD 390-161) for labeled lipid bilayers, or 97% POPC, 2.5% biotinyl cap PE, and 0.5% PEG5000-PE for unlabeled lipid bilayers. The lipid mixes were dried under argon gas and desiccated overnight to remove chloroform. The dried lipids were resuspended in 1 mL PBS, pH 7.2 (Gibco, Cat# 20012050) and stored under argon gas. Lipids were formed into small unilamellar vesicles via ≥30 rounds of freeze-thaws and cleared via ultracentrifugation (TLA120.1 rotor, 35,000 rpm/53,227×g, 35 min, 4°C). Lipids were stored at 4°C under argon gas in an Eppendorf tube for up to 2 weeks. To form bilayers on beads, 8.6 × 108 silica beads with a 4.89 µm diameter (10 µL of 10% solids, Bangs Labs, Cat# SS05N) were washed 2× with water followed by 2× with PBS by spinning at 300 rcf and decanting. Beads were then mixed with 1 mM SUVs in PBS, vortexed for 10 s at medium speed, covered in foil, and incubated in an end-over-end rotator at RT for 0.5–2 hr to allow bilayers to form over the beads. The beads were then washed 3× in PBS to remove excess SUVs and resuspended in 100 μL of 0.2% casein (Sigma, Cat# C5890) in PBS for 15 min at RT to block nonspecific binding. Neutravidin (Thermo, Cat# 31000) was added to the beads at a final concentration of 1 μg/mL for 20–30 min, and the beads were subsequently washed 3× in PBS with 0.2% casein and 20 mM MgCl2 to remove unbound neutravidin. The indicated amounts of biotinylated ssDNA or saturating amounts of DNA origami pegboards were added to the beads and incubated for 1 hr at RT with end-over-end mixing to allow for coupling. Beads were washed two times and resuspended in 100 μL PBS with 0.2% casein and 20 mM MgCl2 to remove uncoupled origami pegboards or ssDNA. When functionalizing SUV-coated beads with anti-biotin Alexa Fluor 647-IgG (Jackson ImmunoResearch Laboratories Cat# 200-602-211, Lot# 137445), the IgG was added to the beads at 1 μM immediately following the casein blocking step, and beads were incubated for 1 hr at RT with end-over-end mixing.

Quantification of ssDNA, IgG, or origami on beads

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To estimate the amount of ssDNA bound to each bead, we compared the fluorescence of Atto647-labeled DNA on the bead surface to calibrated fluorescent beads (Quantum Alexa Fluor 647, Bangs Lab) using confocal microscopy (Figure 1—figure supplement 1). To determine saturating conditions of IgG and origami pegboards, we titrated the amount of IgG or origami in the coupling reaction and used confocal microscopy to determine the concentration at which maximum coupling was achieved. A comparable amount of origami pegboard coupling was also confirmed with confocal microscopy for beads used in the same experiment.

Quantification of engulfment

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30,000 RAW264.7 macrophages were plated in one well of a 96-well glass bottom MatriPlate (Brooks, Cat# MGB096-1-2-LG-L) between 12 and 24 hr prior to the experiment. Immediately before adding beads, 100 μL of a 1 μM solution of benzylguanine-conjugated receptor DNA in engulfment imaging media was added, incubated for 10 min at RT, and washed out four times with engulfment imaging media containing 20 mM MgCl2, making sure to leave ~100 μL of media covering the cells between washes, and finally leaving the cells in ~300 μL of media. Approximately 8 × 105 beads were added to the well and engulfment was allowed to proceed for 45 min in the cell incubator. Cells were fixed with 4% PFA for 10 min and washed into PBS. For Figures 4C and 6D, 10 nM Alexa Fluor 647 anti-biotin IgG (Jackson Immuno Labs, Cat# 200-602-211) diluted into PBS containing 3% BSA was added to each well for 10 min to label non-internalized beads. Wells were subsequently washed three times with PBS. Images were acquired using the HCS Site Generator plug-in in µManager and at least 100 cells were scored for each condition. When quantifying bead engulfment, cells were selected for analysis based on a threshold of GFP fluorescence, which was held constant throughout analysis for each individual experiment. For Figures 3, 4, and 6, and Figure 4—figure supplement 1, the analyzer was blinded during engulfment scoring using the position randomizer plug-in in µManager. For the THP1 cells, ~100,000 cells per condition were spun down, washed into engulfment imaging media, and coupled to benzylguanine-labeled receptor DNA as described under TIRF imaging. Cells were resuspended into 300 μL engulfment imaging media containing 20 mM MgCl2 in an Eppendorf tube, ~8 × 105 beads were added to the tube, and the tube was inverted 8× before plating the solution into a round-bottomed 96-well plate (Corning, Cat# 38018). Engulfment was allowed to proceed for 45 min in the cell incubator before the plate was briefly spun and the cells were fixed in 4% PFA for 10 min. Cells were subsequently washed 3× with PBS by briefly centrifuging the plate and removing the media, and finally moved into a 96-well glass-bottom MatriPlate for imaging.

Quantification of engulfment kinetics

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RAW264.7 macrophages were plated and prepared in wells of a 96-well glass bottom MatriPlate as described in ‘Quantification of engulfment’. Using Multi-Dimensional Acquisition in µManager, four positions in the well were marked for imaging at 20 s intervals through at least seven z-planes. Approximately 4 × 105 Atto647N-labeled 4S origami functionalized beads and ~4 × 105 Atto550N-labeled 4T origami functionalized beads were mixed in an Eppendorf tube, added to the well, and immediately imaged. Bead contacts were identified by counting the number of beads that came into contact with the cells throughout the imaging time. Initiation events were identified by active membrane extension events around the bead. Engulfment completion was identified by complete internalization of the bead by the macrophage. The initiation time was quantified as the amount of time between bead contact (the first frame in which the bead contacted the macrophage) and engulfment initiation (the first frame in which membrane extension around the bead was visualized) and was only measured for beads that were completely internalized by the end of the imaging time. The engulfment time was quantified as the amount of time between engulfment initiation and engulfment completion (the first frame in which the bead has been fully internalized by the cell).

Quantification of synapse intensity of DNA-CARγ receptor, tSH2 Syk, PIP3 reporter, and actin filaments

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Phagocytic cups were selected for analysis based on clear initiation of membrane extension around the bead visualized by GFP fluorescence from the DNA-CARγ receptor. The phagocytic cup and the cell cortex (areas indicated in schematic in Figure 6B) were traced with a line (six pixels wide for DNA-CARγ receptor and the tSH2 Syk reporter, and eight pixels wide for the Akt-PH reporter and phalloidin) at the Z-slice with the clearest cross section of the cup.

Microscopy and analysis

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Images were acquired on a spinning disc confocal microscope (Nikon Ti-Eclipse inverted microscope with a Yokogawa CSU-X spinning disk unit and an Andor iXon EM-CCD camera) equipped with a 40 × 0.95 NA air and a 100 × 1.49 NA oil immersion objective. The microscope was controlled using µManager. For TIRF imaging, images were acquired on the same microscope with a motorized TIRF arm using a Hamamatsu Flash 4.0 camera and the 100 × 1.49 NA oil immersion objective.

Statistics

Statistical analysis was performed in Prism 8 (GraphPad, Inc). The statistical test used is indicated in each relevant figure legend.

Data availability

All relevant data is in the paper or source data files.

References

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    IECON Proceedings. pp. 1104–1109.

Decision letter

  1. Michael L Dustin
    Reviewing Editor; University of Oxford, United Kingdom
  2. Carla V Rothlin
    Senior Editor; Yale School of Medicine, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

[Editors' note: this paper was reviewed by Review Commons.]

Acceptance summary:

You have utilised DNA origami and synthetic biology tools to convincingly demonstrate that phagocytosis is dependent upon the nanoscale spacing of ligands. Your DNA origami approach is able to operate in a range of intermolecular spacing that sits in between what can be achieved with protein oligomerization and methods based on arraying of metallic particles on planar substrates, which were the previous state of the art. The improved understanding of the physical requirements for phagocytosis that you have generated may also have applications in immunotherapy.

Decision letter after peer review:

Thank you for submitting your article "Tight nanoscale clustering of Fc γ-receptors using DNA origami promotes phagocytosis" for consideration by eLife. Your article has been reviewed by 3 peer reviewers at Review Commons, and the evaluation at eLife has been overseen by a Reviewing Editor and Carla Rothlin as the Senior Editor.

This is a very nicely done synthetic biology/biophysics study on the effect of ligands spacing on phagocytosis. They use a DNA based recognition system similar to that previously use to investigate T cell signaling, but express the SNAP tag linked transmembrane receptor in a macrophage cell line and present the ligands using DNA origami mats to control the number and spacing of complementary ligands that are designed to be in the typical range for low or high affinity FcR, a receptor that can trigger phagocytosis. The study offers valuable quantitative data sets that will be of immediate interest to groups working in this area to understand principled of how this class of receptors work, and in the future, for design of synthetic receptors for immunotherapy applications.

Congratulations on your compelling exploration of the role of ligand spacing in phagocytosis using a DNA based chimeric receptor system and DNA origami.

The original reviewers concur that you have addressed their concerns. There is one issue to clarify.

1. One of the reviewers had asked for experiments with a "receptor fusion construct" (see 10.1038/s41467-019-10097-0) where you retain the alpha1-gamma2 architecture of the native FcR, but add the SNAP-DNA complex to the FcgR1 to determine if this changes the potency or spacing threshold. We understand that this might pose a number of challenges and its OK that you didn't do this. However, the new experiments using the FcgRI α TM and the FcR γ TM without the disulfides may need just a bit more explanation to not confuse readers. The reviewer noted that the FcR γ TM based constructs were more potent at phagocytosis, although, as you point out, they retained the trend of the spacing dependence. Even though the disulfides were not included, it's expected that the FcR γ construct will have a tendency to dimerise and that this may make it more potent. If this is the case, it still doesn't quite address the original question as you now have a synthetic receptor with two ligand binding and two signal transduction modules, whereas the "receptor fusion" would have just one ligand binding unit with two signal transduction modules. The nuances of this need to be discussed better and in relation to the configuration of most natural activating immunoreceptors. I hope this is clear and that you can easily address this.

https://doi.org/10.7554/eLife.68311.sa1

Author response

Overall, we were pleased that the reviewers found our study carefully designed and interesting. We have addressed their comments below.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The manuscript by Kern, et al., demonstrates that phagocytosis in macrophages is regulated in part by the intermolecular distance of phagocytosis-promoting receptors engaging phagocytic targets. Cells expressing chimeric receptors containing cytosolic domains of Fc receptors (FcR) and defined ligand-binding DNA domains were used to drive phagocytosis of opsonized glass beads coated with complementary DNA ligands of defined spacing and number. These so-called origami ligands allowed manipulation of receptor spacing following engagement, which allowed the demonstration that tight spacing of ligands (7 nm or 3.5 nm) optimized signaling for phagocytosis. The study is carefully performed and convincing. I have a few technical concerns and minor suggestions.

1. It is assumed that the origami preparations were entirely uniform. How much variation was there? Is that supported by TIRF microscopy of origami preparations? Was the TIRF microscopy calibrated for uniformity of fluorescence (ie., shade correction)?

Our laboratory, Dong et al., has extensively characterized the origami uniformity and robustness of these exact pegboards. This paper was just posted on bioRxiv (Dong et. al, 2021). We have also cited this paper in our revised manuscript in reference to the characterization of the DNA origami (Line 117).

We did not use any shade correction. Instead we only collected data from a central ROI in our TIRF field. To check for uniformity of illumination, we plotted the origami pegboard fluorescent intensity along the x and y axis. We observed very modest drop off in signal – the average signal intensity of origamis within 100 pixels of the edge is 76 ± 6% the intensity of origamis in a 100 pixel square in the center of the ROI. Fitting this data with a Gaussian model resulted in very poor R values. While this may account for some of the variation in signal intensity at individual points, we expect the normalized averages of each condition to be unaffected. We have amended the methods to describe this strategy (Lines 851-854).

Author response image 1

2. Likewise, how much variation was there in the expression of the chimeric receptors? Large variation in receptor numbers per cell could significantly alter the quantitative studies. Aside from the flow sorting for cells expressing two different molecules, how were cells selected for analysis?

We thank the reviewer for bringing up this point. We confirmed comparable receptor expression levels at the cell cortex of the DNA CAR-𝛾 and the DNA CAR-adhesion used throughout the paper. We also have confirmed that receptor levels at the cell cortex were similar for the large DNA CAR constructs used in Figure 6C-D. This data is now included in Figures S5 and S7. We have also altered the text to include this (lines 169-172):

“Expression of the various DNA CARs at the cell cortex was comparable, and engulfment of beads functionalized with both the 4T and the 4S origami platforms was dependent on the Fc𝛾R signaling domain (Figure S5).”

When quantifying bead engulfment, cells were selected for analysis based on a threshold of GFP fluorescence, which was held constant throughout analysis for each individual experiment. We have amended the “Quantification of engulfment” methods section to convey this (​ lines 921-923).

3. The scale of the origami relative to the cells is difficult to discern in Figures 2C and D. Additional text would be helpful to indicate, for example, that the spots on the Figure 2D inset indicate entire origami rather than ligand spots on individual origami particles.

Thank you for pointing this out, we see how the legend was unclear and have corrected it (lines 453-454), including specifically noting “Each diffraction limited magenta spot represents an origami pegboard.” We have also outlined the cell boundary in yellow to make the cell size more clear.

4. Figure 5 legend, line 482: How was macrophage membrane visualized for these measurements?

We have added the following clarification (line 535-536): “The​ macrophage membrane was visualized using the DNA CAR𝛾, which was present throughout the cell cortex.”

5. Line 265: "our data suggest that there may be a local density-dependent trigger for receptor phosphorylation and downstream signaling". This threshold-dependent trigger response was also indicated in the study of Zhang, et al. 2010. PNAS.

The Zhang et al. study was influential in our study design, and we wish to give the appropriate credit. Zhang et al. found that a sufficient amount of IgG is necessary to activate late (but not early) steps in the phagocytic signaling pathway. In contrast, our study addresses IgG concentration in small nanoclusters. We find that this nanoscale density affects receptor phosphorylation. Thus, we think these two studies are distinct and complementary. Lines 283-287 now read:

“While this model has largely fallen out of favor, more recent studies have found that a critical IgG threshold is needed to activate the final stages of phagocytosis (Zhang et al., 2010). Our data suggest that there may also be a nanoscale density-dependent trigger for receptor phosphorylation and downstream signaling.”

6. Line 55: Rephrase, “we found that a minimum threshold of 8 ligands per cluster maximized FcgR-driven engulfment.” It is difficult to picture how a minimum threshold maximizes something.

We now state “we found that 8 or more ligands per cluster maximized FcgR-driven engulfment.”

7. Line 184: Rephrase, "we created… pegboards with very high-affinity DNA ligands that are predicted not to dissociate on a time scale of >7 hr". Remove "not".

Thank you for pointing this out, it is now correct.

Reviewer #1 (Significance (Required)):

This study provides a significant advance in understanding about the molecular mechanisms of signaling for particle ingestion by phagocytosis.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The manuscript on “Tight nanoscale clustering of Fcg-receptors using DNA origami promotes phagocytosis" studies how clustering and nanoscale spacing of ligand molecules for a chimeric Fcg-receptors influence the phagocytosis of functionalized silicon beads by macrophage cell lines. The basis of this study is the design of a chimeric Fc-receptor (DNA-CARg) comprising an extracellular SNAP-tag domain that can be loaded with single-stranded (ss) DNA, the transmembrane part of CD86 and the cytosolic part of the Fc-receptor g-chain containing an immunoreceptor tyrosine-based activation motif (ITAM) as well as a C-terminal green fluorescent protein (GFP). As control the authors used a similar designed DNA-CAR that is lacking the intracellular ITAM-containing FCg tail. The chosen target for this chimeric DNA-CAR, are silicon beads covered by a lipid bilayer that contains biotin-labelled lipids that, via Neutravidin, can be loaded with a biotinylated DNA origami pegboard displaying complimentary ss-DNA as ligand for the DNA-CAR. The DNA origami pegboard contains four ATTO647N fluorescence for visualization and the ssDNA ligand in different quantities and spacing.

Using these principles, the authors study how ligand affinity, concentration and spacing influence the activation of the DNA-CARg and the engulfment of the loaded beads.

The authors show that bead engulfment is increased between 2 till 8 ssDNA ligands on the pegboard. After this, ligand numbers do not play a role anymore in the engulfment. They then study the role of the ligand spacing using pegboards that either contain 4 single strand DNA ligands in close (7nm/3,5nm) proximity or a more spaced version using 21/17,5 nm or 35/38,5 nm. The authors find that the bead engulfment is maximally and positively affected by the close spacing of the ssDNA ligands. In their final experiments the authors vary the design of the DNA-CARs by tetramerization of the ITAM-containing Fcg-signaling subunit. In their discussion the authors mention different possibilities for the effect of spacing on the engulfment process.

I think that, in general, this is an interesting study. However, it has some caveats and open issues that should be clarified before its publication.

Major comments

1. As a general comment, it is somewhat a pity that the authors did not use the endogenous FcR as a control. It would have been quite easy for the authors to place the SNAP-tag domain on the Fcg extracellular domain which would allow to do all their experiments in parallel, not only with the DNA-CAR, but also with a DNA-containing wild type receptor. Such a control would be important because, by using a CD86 transmembrane domain, the authors do not know whether the nanoscale localization of their chimeric receptors is reflecting that of the endogenous Fcg receptor.

We agree with the reviewer completely. We have repeated experiments shown in Figure 4A with a DNA-CAR containing the Fc𝛾 transmembrane domain instead of CD86 as the reviewer suggests. We also included a DNA-CAR version of the Fc𝛾R1 α chain, although this construct was not expressed as well as the others. These data are now included in Figure S5, and referenced in ​ lines 167-168.​

2. An important issue that is discussed by the authors but not addressed in this manuscript is whether the different amount and spacing of the ligand is only impacting on signaling or also on the mechanical stress of the cells. Indeed, mechanical stress on the cytoskeleton arrangement could influence the engulfment process. For this, it would be very important to test that the different bead engulfment, for example, those shown in Figure 4, is strictly dependent on signaling kinases. The authors should repeat the experiment of Figure 4 a and b in the presence or absence of kinase inhibitors such as the Syk inhibitor R406 or the Src inhibitor PP2 to show whether the different phase of engulfment is dependent on the signaling function of these kinases. This crucial experiment is clearly missing from their study.

We agree this is an interesting point. We find that ligand spacing affects receptor phosphorylation; however this does not preclude effects on downstream aspects of the signaling pathway. We will clarify this by adding the following comment to the manuscript (line 299-301): “While our data pinpoints a role for ligand spacing in regulating receptor phosphorylation, it is possible that later steps in the phagocytic signaling pathway are also directly affected by ligand spacing.”

The DNA-CAR-adhesion in Figure 1 strongly suggests that intracellular signaling is essential for phagocytosis. We have now included additional controls using this construct as detailed in our response to point 3 below. Unfortunately, Src and Syk inhibitors or knockout abrogate Fc𝛾R mediated phagocytosis (for example, PMIDs 11698501, 9632805, 12176909, 15136586) and thus would eliminate phagocytosis in both the 4T and 4S conditions.​ This precludes analysis of downstream steps in the phagocytic signaling pathway.

3. Another problem of this study is that the authors show in Figure 1A the control DNA-CAR-adhesion but then hardly use it in their study. For example, the crucial experiments shown in Figure 4 should be conducted in parallel with DNA-CAR-adhesion expressing macrophage cells. This study could provide another indication whether or not ITAM signaling is important for the engulfment process.

We have added this control. It is now included in Figure S5 and S7. Figure 3D also shows that the DNA-CAR-adhesion combined with the 4T origami pegboards does not activate phagocytosis and we have amended the text to make this more clear (line 152).

4. Another important aspect is how the concentration of the loaded origami pegboard is influencing the engulfment process. In particular, it would be interesting to show the padlocks with different spacings such as the 4T closed spacing versus 4s large spacing show a different dependency on the concentration of this padlock loading on the beads. This would be another important experiment to add to their study.

We agree that this is an interesting question. We suspect that at a very high origami density, 4S signaling would improve, and potentially approach the 4T. However, we are currently coating the beads in saturating levels of origami pegboards. Thus we cannot increase origami pegboard density and address this directly.

Minor comments:

1. The definition of the ITAM is Immunoreceptor Tyrosine-based Activation Motif and not "Immune Tyrosine Activation Motif" as stated by the authors.

We have corrected this.

2. The authors discuss that it is the segregation of the inhibitory phosphatase CD45 from the clustered Fc receptors is the major mechanism explaining their finding that 4T closed spacing is more effective than 4s large spacing. With the event of the CRISPR/Cas9 technology it is trivial to delete the CD45 gene in the genome of the RAW264.7 macrophage cell line used in this study and I am puzzled why they author are not conducting such a simple but for their study very important experiment (it takes only 1-2 month to get the results).

This experiment may be informative but we have two concerns about its feasibility. First, CD45 is a phosphatase with many different roles in macrophage biology, including activating Src family kinases by dephosphorylating inhibitory phosphorylation sites (PMID 8175795, 18249142, 12414720). Second, CD45 is not the only bulky phosphatase segregated from receptor nanoclusters. For example, CD148 is also excluded from the phagocytic synapse (PMID 21525931). CD45 and CD148 double knockout macrophages show hyperphosphorylation of the inhibitory tyrosine on Src family kinases, severe inhibition of phagocytosis, and an overall decrease in tyrosine phosphorylation (PMID 18249142). CD45 knockout alone showed mild phenotypes in macrophages. We anticipate that knocking out CD45 alone would have little effect, and knocking out both of these phosphatases would preclude analysis of phagocytosis. Because of our feasibility concerns and the lengthy timeline for this experiment, we believe this is outside of the scope of our study.

In our discussion, we simplistically described our possible models in terms of CD45 exclusion, as the mechanisms of CD45 exclusion have been well characterized. This was an error and we have amended our discussion to read (lines 335-343):

“As an alternative model, a denser cluster of ligated receptors may enhance the steric exclusion of the bulky transmembrane proteins like the phosphatases CD45 and CD148 (Bakalar et al., 2018; Goodridge et al., 2012; Zhu, Brdicka, Katsumoto, Lin, and Weiss, 2008).”

Reviewer #2 (Significance (Required)):

The innovative part of this study is the combination of SNAP-tag attached, chimeric Fc-receptor with the DNA origami pegboard technology to address important open question on receptor function.

Referees cross-commenting

I find most of my three reviewing colleagues reasonable

I also agree to Reviewer #1 comments 2

Likewise, how much variation was there in the expression of the chimeric receptors? Large variation in receptor numbers per cell could significantly alter the quantitative studies. Aside from the flow sorting for cells expressing two different molecules, how were cells selected for analysis?

But I want to add it is not only the amount of receptors but ils the nanoscale location that is key to receptor function

We have ensured that all receptors are trafficked to the cell surface. We have also measured their intensity at the cell cortex as discussed in response to Reviewer 1.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

This is a very nicely done synthetic biology/biophysics study on the effect of ligands spacing on phagocytosis. They use a DNA based recognition system that the group has previously use to investigate T cell signaling, but express the SNAP tag linked transmembrane receptor in a macrophage cell line and present the ligands using DNA origami mats to control the number and spacing of complementary ligands that are designed to be in the typical range for low or high affinity FcR, a receptor that can trigger phagocytosis. The study offers some very nice quantitative data sets that will be of immediate interest to groups working in this area and, in the future, for design of synthetic receptors for immunotherapy applications. Other groups are working on similar platform for TCR. I don't feel there is any need for more experiments, but I have some questions and suggestions. Answering and considering these could clarify the new biological knowledge gained.

We thank the reviewer for their support of our manuscript. Given the reviewer’s statement that no new experiments are required, we have answered their questions to the best of our ability given the current data. Should the editor decide that any of these topics require experimental data to enhance the significance of the paper, we are happy to discuss new experiments.

Reviewer #3 (Significance (Required)):

I think the significance would be increased by addressing these questions, that would help understand how the synthesis system described related to other system directed as similar questions and more natural settings.

1. The densities of the freely mobile DNA ligands required to trigger phagocytosis is quite high. Was the length of the DNA duplexes optimized? The entire complex for both the intermediate and high affinity duplexes seems quite short, perhaps <10 nm. Might the stimulation be more efficient if a short stretch of DS DNA is added to increase the length to 12-13 nm?

The extracellular domain of the DNA-CAR (SNAP tag and ssDNA strand) are approximately 10 nm (PMID 28340336). The biotinylated ligand ssDNA is attached to the bilayer via neutravidin, resulting in a predicted 14 nm intermembrane spacing. The endogenous IgG FcR complex is 11.5 nm. Bakalar et al. (PMID 29958103) tested the effect of antigen height on phagocytosis and found that the shortest intermembrane distance tested (approximately 15 nm) was the most effective. As the reviewer notes, the optimal distance between macrophage and target may be larger than our DNA-CAR. However we think the intermembrane spacing in our system is within the biologically relevant range.

We saw robust phagocytosis at 300 molecules/micron of ssDNA, which is similar to the IgG density used on supported lipid bilayer-coated beads in other phagocytosis studies (PMID 29958103, 32768386). As the reviewer noticed, this is significantly higher than ligand density necessary to activate T cells (PMID 28340336).​ We have added a comment on ligand density to lines 96-97.

2. Are the origami mats generally laterally mobile on the bilayers. If so, what is the diffusion coefficient? Can one detect the mats accumulating in the initial interface between the bead and cell, particularly in cased where there is no phagocytosis? Would immobility of the mats make them more efficient at mediating phagocytosis compared to the monodispersed ligands, which I assume are highly mobile and might even be "slippery".

We have confirmed that our bead protocol generally produces mobile bilayers, where his-tagged proteins can freely diffuse to the cell-bead interface (see accumulation of a his-tagged FRB binding to a transmembrane FKBP receptor at the cell-bead synapse below). We can qualitatively say that the origamis appear mobile on a planar lipid bilayer (see Dong et. al 2021 and images below). Directly measuring the diffusion coefficient on the beads is extremely difficult because the beads themselves are mobile (both diffusing and rotating), and cannot be imaged via TIRF. We do not see much accumulation of the origami at cell-bead synapses. This could reflect lower mobility of the origamis, or could be because the relative enrichment of origamis is difficult to detect over the signal from unligated origamis.

Overall, we expect the origami pegboards (tethered by 12 neutravidins) are less mobile than single strand DNA (tethered by a single neutravidin, supported by qualitative images below). We are uncertain whether this promotes phagocytosis. At least one study suggests that increased IgG mobility promotes phagocytosis (PMID 25771017). However, the zipper model would suggest that tethered ligands may provide a better foothold for the macrophage as it zippers the phagosome closed (PMID 14732161). Hypothetically, ligand mobility could affect signaling in two ways – first by promoting nanocluster formation, and second by serving as a stable platform for signaling as the phagosome closes. Since our system has pre-formed nanoclusters, the effect of ligand mobility may be quite different than in the endogenous setting.

In Author response image 2, a 10xHis-FRB labeled with AlexaFluor647 was conjugated to Ni-chelating lipids in the bead supported lipid bilayer. The macrophages express a synthetic receptor containing an extracellular FKBP and an intracellular GFP. Upon addition of rapamycin, FRB and FKBP form a high affinity dimer, and FRB accumulates at the bead-macrophage contact sites.

Author response image 2

In Author response image 3, single molecules were imaged for 3 sec. The tracks of each molecule are depicted by lines, colored to distinguish between individual molecules. The scale bar represents5 microns in both panels.

Author response image 3

3. Breaking down the analysis into initiation and completion is interesting. When using the non-signalling adhesion constructs, would they get to the initiation stage or would that attachment be less extensive than the initiation phase.

This is an interesting question. While we did not include the DNA-CAR-adhesion in our kinetic experiments, we have now quantified the frequency of cups that would match our ‘initiation’ criteria in 3 representative data sets where macrophages were fixed after 45 minutes of interaction with origami pegboard-coated beads. We found that an average of 16/125 of 4T beads touching DNA-CAR-adhesion macrophages met the ‘initiation’ criteria and an average of 2/125 were eaten (14% total). In comparison, we examined 4T beads touching DNA CAR𝛾 macrophages and found that on average 23/125 met the ‘initiation’ criteria, and 45/125 were already engulfed (54%). This suggests that the DNA-CAR-adhesion alone may induce enough interaction to meet our initiation criteria, but without active signaling from the FcR this extensive interaction is rare. We have added this data in a new Figure S6 and commented on this in lines 213-215.

4. It would be interesting to put these results in perspective of earier work on spacing with planar nanoarrays, although these can't be applied to beads. For integrin mediated adhesion there was a very distinct threshold for RGD ligand spacing that could be related to the size of some integrin-cytoskeletal linkers (PMID: 15067875). On the other hand, T cell activation seemed more continuous with changes in spacing over a wide range with no discrete threshold (PMID: 24117051, 24125583) unless the spacing was increased to allow access to CD45, in which case a more discrete threshold was generated (PMID: 29713075). The results here for phagocytosis with the very small ligands that would likely exclude CD45 seems to be more of a continuum without a discrete threshold, although high densities of ligand are needed. This issue of continuous sensing vs sharp threshold is biologically interesting so would be good assess this by as consistent standards are possible across systems.

We agree that this is an interesting body of literature worth adding to our discussion. We have added a paragraph that puts our study in the context of prior work on related systems, including these nanolithography studies (Line 364-382):

“How does the spacing requirements for Fc𝛾R nanoclusters compare to other signaling systems? Engineered multivalent Fc oligomers revealed that IgE ligand geometry alters Fcε receptor signaling in mast cells (Sil, Lee, Luo, Holowka, and Baird, 2007). ​DNA origami nanoparticles and planar nanolithography arrays have previously examined optimal inter-ligand distance for the T cell receptor, B cell receptor, NK cell receptor CD16, death receptor Fas, and integrins (Arnold et al., 2004; Berger et al., 2020; Cai et al., 2018; Deeg et al., 2013; Delcassian et al., 2013; Dong et al., 2021; Veneziano et al., 2020). ​Some systems, like integrin-mediated cell adhesion, appear to have very discrete threshold requirements for ligand spacing while others, like T cell activation, appear to continuously improve with reduced intermolecular spacing (Arnold et al., 2004; Cai et al., 2018). Our system may be more similar to the continuous improvement observed in T cell activation, as our most spaced ligands (36.5 nm) are capable of activating some phagocytosis, albeit not as potently as the 4T. Interestingly, as the intermembrane distance between T cell and target increases, the requirement for tight ligand spacing becomes more stringent (Cai et al., 2018). This suggests that IgG bound to tall antigens may be more dependent on tight nanocluster spacing than short antigens. Planar arrays have also been used to vary inter-cluster spacing, in addition to inter-ligand spacing (Cai et al., 2018; Freeman et al., 2016). Examining the optimal inter-cluster spacing during phagosome closure may be an interesting direction for future studies.”

Additional experiments performed in revision

In addition to these reviewer comments, we have added additional controls validating the DNA-CAR-4x𝛾 used in Figure 6c,d. We compared the DNA-CAR-4x𝛾 to versions of the DNA-CAR-1x𝛾 -3x𝛥 ITAM construct with the functional ITAM in the second and fourth positions (see the schematics now included Figure S7). We found that four individual receptors with a single ITAM each were able to induce phagocytosis regardless of which position the ITAM was in. However the DNA-CAR-4x𝛾 construct, which also contains 4 ITAMs, was not. This further validates the experiment presented in 6c,d. We also fixed minor errors we discovered in the presentation of data for Figures 1C and S1A.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The original reviewers concur that you have addressed their concerns. There is one issue to clarify.

1. One of the reviewers had asked for experiments with a "receptor fusion construct" (see 10.1038/s41467-019-10097-0) where you retain the alpha1-gamma2 architecture of the native FcR, but add the SNAP-DNA complex to the FcgR1 to determine if this changes the potency or spacing threshold. We understand that this might pose a number of challenges and its OK that you didn't do this. However, the new experiments using the FcgRI α TM and the FcR γ TM without the disulfides may need just a bit more explanation to not confuse readers. The reviewer noted that the FcR γ TM based constructs were more potent at phagocytosis, although, as you point out, they retained the trend of the spacing dependence. Even though the disulfides were not included, its expected that the FcR γ construct will have a tendency to dimerise and that this may make it more potent. If this is the case, it still doesn't quite address the original question as you now have a synthetic receptor with two ligand binding and two signal transduction modules, whereas the "receptor fusion" would have just one ligand binding unit with two signal transduction modules. The nuances of this need to be discussed better and in relation to the configuration of most natural activating immunoreceptors. I hope this is clear and that you can easily address this.

We apologize for misunderstanding this concern in the original comment. We did unsuccessfully try to generate an Fc ⍺ chain DNA CAR. We thought this CAR may be able to recruit the endogenous Fc γ chains and signal with the 1 ligand:2 signaling domain architecture present in most, but not all, activating FcγRs. However, this construct failed to induce engulfment of beads coated in ssDNA. Upon further examination we found that the majority of the protein was not trafficked to the cell surface. The TRuC strategy may be a better approach for generating a receptor with the correct architecture, although the position of the SNAP tag, the expression levels of the modified ⍺ chain, and the assembly into a multimeric receptor would all need to be controlled or optimized to ensure each ligand binding event correlates with a fully assembled complex and the overall architecture of the phagocytic synapse is maintained (ie, intermembrane spacing). Thus, we believe this is outside the scope of our study.

Instead, we have taken the suggestion to clarify the nuances of our study. We have amended the Results section to better describe the CARs with the Fc γ or ⍺ chain transmembrane domains (lines 163-169). We have also added the following paragraph to the discussion clarifying how our DNA CAR compares to the native FcγR complex:

“How does our synthetic DNA-CAR𝛾 receptor compare to endogenous FcγRs? Our DNA CARs are single chain receptors that recruit one intracellular signaling domain per ligand, similar to the single chain human FcγRIIA receptor (Nimmerjahn and Ravetch, 2006). FcγRIIA is ubiquitously expressed on human myeloid cells, and high affinity FcRIIA alleles correlate with an increase in effectiveness of the ADCP-inducing drug Rituximab and lupus susceptibility (Bruhns and Jönsson, 2015; Nimmerjahn and Ravetch, 2006). The majority of FcγR family members, including all activating mouse FcγRs and the human FcγRI and FcγRIIIA are multimeric complexes composed of a ligand binding ⍺ chain and a dimerized signaling 𝛾 chain. This results in two signaling 𝛾 chains recruited to each IgG ligand. The different stoichiometry between ligand binding and intracellular signaling domains may affect some parameters like optimal cluster size. A second difference between the DNA-CAR𝛾 and the endogenous system is the presence of the CD86 transmembrane domain. We found that ligand spacing had a similar effect on phagocytosis when we replaced the CD86 transmembrane domain with the Fc ⍺ or Fc γ transmembrane domain. However, the Fc γ transmembrane domain construct triggered more bead internalization across all conditions. We hypothesize this could be because the transmembrane domain retains some ability to dimerize, recruiting more signaling domains to each ligand, or because it is better able to associate with lipid-ordered domains. Future studies that pattern either endogenous Fc Receptor complex or IgG ligand could clarify these questions.”

https://doi.org/10.7554/eLife.68311.sa2

Article and author information

Author details

  1. Nadja Kern

    1. Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, United States
    2. Howard Hughes Medical Institute, University of California San Francisco, San Francisco, United States
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1313-5890
  2. Rui Dong

    1. Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, United States
    2. Howard Hughes Medical Institute, University of California San Francisco, San Francisco, United States
    Contribution
    Resources, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9118-3636
  3. Shawn M Douglas

    Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, United States
    Contribution
    Resources, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5398-9041
  4. Ronald D Vale

    1. Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, United States
    2. Howard Hughes Medical Institute, University of California San Francisco, San Francisco, United States
    3. Howard Hughes Medical Institute Janelia Research Campus, Ashburn, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Writing - review and editing
    For correspondence
    Ron.Vale@ucsf.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3460-2758
  5. Meghan A Morrissey

    1. Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, United States
    2. Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing
    For correspondence
    morrissey@ucsb.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0531-4864

Funding

Howard Hughes Medical Institute

  • Ronald D Vale

National Institute of General Medical Sciences (F32GM120990)

  • Meghan A Morrissey

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank N Stuurman for help with microscopy and developing the ‘image randomizer’ plug-in for blinding our analysis as well as the ‘Spot Intensity in All Channel’ plug-in for quantification of our TIRF experiments. We also thank K McKinley, T Skokan, C Gladkova, and J Sheu-Gruttadauria for discussions and critical feedback on this manuscript. MAM was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number F32GM120990. Funding was provided by the Howard Hughes Medical Institute to RDV.

Senior Editor

  1. Carla V Rothlin, Yale School of Medicine, United States

Reviewing Editor

  1. Michael L Dustin, University of Oxford, United Kingdom

Publication history

  1. Received: March 11, 2021
  2. Accepted: May 1, 2021
  3. Version of Record published: June 3, 2021 (version 1)
  4. Version of Record updated: June 7, 2021 (version 2)

Copyright

© 2021, Kern et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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