Subcellular glycan-mannose receptor binding kinetics correlate with myeloid cell function

Domain: chemistry
Published: 2025-12-26
Editorial outcome: major_revision

Abstract
Extracting single-molecule lectin binding kinetics from primary cells has not been possible to date. Here, we present Glyco-PAINT-APP (Automated Processing Pipeline), an automated method that enables the extraction of subcellular glycan interaction kinetics using a Points Accumulation for Imaging in Nano-Topography (PAINT)-based approach. This approach leverages an algorithm for precise, high-throughput, subcellular analysis of glycan binding dynamics and facilitates functional correlation studies between glycoform binding patterns and immune cell polarization. Using synthetic glycans and glycosylated antigens, we demonstrate the ability of the technique to automatically correlate glycan binding parameters in subregions of dendritic cell membranes with increased uptake and cross-presentation efficiency of these antigens. Additionally, we show how the method can uncover subtle differences in MR-mediated glycan binding across various MR-expressing primary cells and cell lines. Taken together, Glyco-PAINT-APP enables insights into the cell-intrinsic heterogeneity of glycan-structure-activity relationships in myeloid immune cells.

Introduction
Myeloid immune cells, such as dendritic cells (DCs) and macrophages are key sentinels of the immune system. They sense the presence of microbial species or altered self through pattern recognition receptors (PRRs), which in turn can trigger innate and adaptive immune responses. One important family of PRRs are the carbohydrate-binding proteins of the C-type lectin receptor family (CLRs). CLR ligation can induce various immune functions, including endocytosis, polarization, cytokine secretion, cell adhesion, and motility, which can subsequently trigger downstream immune responses1,2.

The signalling pathways leading to these biological outcomes are intricate and finely tuned, which complicates the study of how ligand recognition leads to signalling, particularly in their native environment on the cell surface. This complexity arises from a variety of factors: binding of the receptors to ligands is weak and often multivalent, multiple receptors have overlapping specificities, and the surface expression patterns of CLRs can be highly dynamic. Myeloid cells express a diverse array of CLRs for sensing aberrant glycosylation. These can exist as monomers, multimers, and even form heterodimeric complexes3,4. Various factors, such as the polarization state of the cell, can modulate the expression levels of the selectins and alter the sub-cellular distribution between the surface and intracellular storage pools5,6. The resulting dynamic and heterogeneous surface expression leads to significant variability in receptor-ligand interactions.

To complicate matters further, myeloid cells also present a repertoire of glycans that can serve as ligands for their own lectins. These can modulate signalling pathways through ligand competition and receptor clustering. Polarization of immune cells also affects the expression of these cis-ligands, adding yet another layer of complexity to carbohydrate-immune lectin biology7,8. The final complicating factor in CLR-binding and signalling is that the affinities of C-type lectin domains (CTLs) for their ligands are weak (KD in the µM to mM range)9. This complicates the detection and precise study of these interactions. Together with the intrinsic chemical complexity of glycan ligands, these factors have made it exceedingly difficult to establish clear relationships between glycan binding properties and lectin function, particularly as studying these lectins out of their native context removes many of these modulating factors.

We have recently reported a technique that took a step towards resolving some of these issues. We showed that carbohydrate-lectin binding kinetics could be quantified on the surface of lectin over-expressing cell lines (called Glyco-PAINT)10. Glyco-PAINT exploits the transient interaction between fluorescently-labelled sugars and their receptors to generate single molecule binding events10, analogous to those induced by the mismatches of DNA-strands in conventional Points Accumulation for Imaging in Nanoscale Topography (DNA-PAINT)-imaging11. Accumulation of these binding events over time allows for the mapping of receptor-ligand interactions on the cell surface and subsequent derivatization of kinetic parameters such as on-rate, off-rate and diffusion coefficients on a per-cell basis derived from single-molecule binding information.

We envisaged that Glyco-PAINT could be a powerful technique to unravel the complexity of myeloid lectin binding, too, as the single molecule as well as the live cell aspects of the technique are ideal for studying these capricious receptors within their native environment on the myeloid cell surface. However, three major hurdles needed to be overcome to achieve this. The first is that Glyco-PAINT had only been demonstrated in an over-expression system. Second, the Glyco-PAINT processing pipeline could only be used to obtain the binding parameters averaged over the surface of a whole cell, which we hypothesized to be insufficient due to the microheterogeneity of receptor expression on primary cells. The final problem relates to the probes used in the experiments. Glyco-PAINT-probes consist of a carbohydrate cluster directly attached to a fluorophore. This precludes the evaluation of how the immunological outcomes change with receptor binding behaviour.

One receptor, which is archetypal in its biological complexity, is the mannose receptor (CD206, MR). This lectin is expressed on cells of the myeloid lineage12–14, where it carries out a variety of functions. It acts as an endocytic receptor to clear soluble glycoproteins and as a phagocytic receptor of glycosylated particles, leading to clearance and antigen presentation15–17. It also acts as a mediator of inflammatory signalling by inducing M2 polarization of macrophages and inducing T cell tolerance18,19. How the MR translates ligand recognition to all these separate functions remains unknown. Structurally, the MR contains multiple functional domains, including an N-terminal cysteine-rich domain capable of binding sulphated glycans (that can inhibit receptor function20), a fibronectin-like domain capable of binding collagen peptides21,22, and eight CTLs, of which only CTL4 and CTL5 exhibit functional calcium-dependent binding of neutral sugars23. These domains are further modulated by glycosylation at seven N-linked sites, influencing receptor binding properties24. Moreover, the intracellular tail lacks classical signalling motifs5,15, though evidence suggests phosphorylation and diaromatic motifs may facilitate endosomal sorting and signalling25–27.

Despite this biological complexity, the MR has been under heavy investigation as vaccine targeting receptor. Ligation of the MR was shown to lead to the enhanced cross-presentation (and thus cytotoxic CD8 T-cell activation) of glycoprotein antigens, which can be employed to enhance the anti-cancer and anti-viral properties of therapeutic vaccines28. However, further pursuit of this phenomenon has led to conflicting results29–37. No studies have yet managed to correlate the binding of specific ligands to the MR to downstream biology (i.e., uptake and/or cross-presentation). Studies using bulk methods, such as surface plasmon resonance (SPR), failed to correlate binding affinities with uptake or cross-presentation, highlighting the need for methodologies capable of quantifying receptor-ligand kinetics in the native cellular context10,30.

Here, we present the re-development of Glyco-PAINT, such that we can obtain binding parameters of glycans to the MR on myeloid cells. We have developed an analysis algorithm to independently process large volumes of Glyco-PAINT recordings of glycan binding events to primary immune cells in such a way that binding event concentration, dwell times, diffusion coefficients and displacement can be determined for the highly heterogeneous and disperse binding observed across the cell basal membrane of DCs and macrophages. We have dubbed this method “Glyco-PAINT-Automated Processing Pipeline” (Glyco-PAINT-APP). In addition, we developed a with probes containing a pendant cross-presentable epitope between the glyco-clusters and fluorophore of the earlier incarnations of the probe. This allowed us to quantify lectin binding kinetics and correlate this with the uptake and cross-presentation of the same construct.

Correlating the binding kinetics to uptake and cross-presentation biology yielded some surprising findings. We found that the binding of the glyco-clusters was very heterogeneously distributed over the surface of the DCs. On a single cell, highly varying binding regions were observed: from areas with no probe binding, to regions with low numbers of binding events to a highly mobile MR, to unique sub-cellular population of areas where probes engaged with the cell surface in an MR-independent manner. We found a strong negative correlation (Pearson’s R: −0.90) between these highly mobile receptors and probe endocytosis by the DC, which was highly MR-dependent. In contrast, cross-presentation of mannosylated antigens showed a strong positive correlation only to the residence time of the specific probe on DCs of both WT and MR−/− origin (Pearson’s R: 0.82 and 0.77, respectively), independent of the amount of surface mobility. In a second application of the approach, we could also quantify changes in mannoside binding during the macrophage polarisation trajectory and found that the cumulative duration of binding events was enhanced for the M2 phenotype in accordance with upregulated MR expression and endocytosis of ligands.

Results
Quantifying glycan-lectin binding on primary DC
Our first aim was to determine whether the Glyco-PAINT methodology could be used to study binding events on the surface of primary bone-marrow derived mouse dendritic cells (BMDCs)38. Unlike the over-expression system used previously, BMDCs are characterized by heterogeneous and dynamic receptor expression of multiple lectins, multiple of which are capable of binding mannosylated ligands, and structural features such as tight focal adhesions and podosomes that may exclude binding events from the TIRF plane39,40.

Preliminary attempts to quantify binding kinetics of a labelled mannose glycan library consisting of previously reported11 glycoclusters8,10,11 and 13 on BMDCs yielded an unexpected result: whereas binding events could clearly be seen in the Glyco-PAINT recordings (Fig. 1b and Supplementary Fig. 1A–D) no significant differences were observed between the probes for either the events µm−2s-1 (a relative measure of kon), nor in the average ligand dwell time (τ, which is the inverse of off-rate, or koff−1) (Supplementary Fig. 1E, F). When comparing control experiments in CHO-MR cells did show the binding behaviour as originally reported (Fig. 1a and Supplementary Figs. 2A–D and 2E, F for quantification).

Interestingly, the BMDC image reconstructions showed a very high variance in the density of binding events (density maps) between individual cells—with some cells devoid of binding events, and others showing large numbers of events—and even between different regions of the basal membrane of a single cell (Fig. 1b). Areas of high binding events were observed overlapping with brightfield-defined cell outlines (Fig. 1b green areas and Supplementary Fig. 1A–D). However, large areas of the cell basal membrane were also devoid of any binding event density (Fig. 1b red areas and Supplementary Fig. 1A–D), and some areas outside the brightfield-defined cell surface appeared to be also engaging in binding events (Fig. 1b, blue areas), suggesting the presence of receptors on the ultrathin dendrites. This heterogeneity was not observed for CHO-MR cells (Fig. 1a, green areas and Supplementary Fig. 2).

These observations meant that the fundamental assumption underpinning the Glyco-PAINT method—that the receptor density [R] is homogeneous over the whole basal membrane of every cell – was incorrect for BMDCs. This in turn implicates that the per-cell relative on-rate was an irrelevant parameter for the study of primary cells. The off-rates, too, are averaged on a per-cell basis as they are integrated over the complete area; only a subsection contains binding-enabled lectins. Together, the observed highly heterogeneous binding event distribution was calling for a different surface area definition to obtain meaningful quantification of kinetic parameters.

Glyco-PAINT-APP
To address the heterogeneity observed on BMDC surfaces, we developed the Glyco-PAINT-Automated Processing Pipeline (Glyco-PAINT-APP), where instead of using the whole cell as the averaging unit, the basal membrane was segmented into squares to account for the subcellular variation of binding events. This allows for a more precise quantification of glycan binding kinetics within localized areas whilst ignoring the areas in which no binding events occur. An additional boon to this approach was that this method enabled automation of the workflow, as compared to the previous requirement for a manual identification of the cell outlines, thereby accelerating analysis speed.

The Glyco-PAINT-APP is schematically outlined in Fig. 1c (and described in full detail in the Supplementary Software Manual in the Supplementary Information).

Glyco-PAINT-APP starts by subjecting sets of microscope recordings of Glyco-PAINT experiments to single particle tracking analysis (see also Supplementary Video 1). This yields output files containing spatial information belonging to each spot in each frame of the Glyco-PAINT recording. Tracks, consisting of closely linked spots through multiple consecutive frames, can then be identified and defined by their duration, and movement-based parameters (such as distance travelled, start-point, end-point, and displacement). The Glyco-PAINT-APP then processes the recordings by segmenting the field of view in a raster of squares (typically 20×20 squares per field of view), rather than manually outlining cells. The kinetic information is then quantified for each of these squares, rather than per cell. This allowed us to select only those regions of the cell surface on which binding events take place based on the number of events that occur for a given set of quality control parameters. Squares are selected based on the quality control parameters derived from the TrackMate output. In addition, we have included new quality control measures, namely the “Density Ratio” (the ratio of events in a square divided by the number of events in the lowest binding 10% of squares that contain at least one event), and the “Maximum Variability” (a score for event homogeneity within a single square). In addition, “Connectivity” (a measure for how isolated a square can be within the image) can also be included but is set to “free” in these experiments. Further selection takes place via the “RSquared” value that relates to the quality (R2) of the one-phase exponential decay fit or “Minimum number of Tracks To Calculate Tau” of (see Supplementary Software Manual in the Supplementary Information for a detailed description of quality control parameters). The interactive tuning of these parameters is done using the “SelectSquares” window and the effects on the recording are assessed by direct visual feedback from the “RecordingViewer” (Fig. 1c and Supplementary Video 2), where the tracking overlay with grids and brightfield image are displayed side-by-side. This allows for user-informed data curation.

Once the squares are selected, kinetic parameters including glycan on- and off-rates, diffusion coefficients, speed, displacement and maximum, average, or total binding event duration can be displayed for each square per recording using the heatmap toggle function in the “RecordingViewer” (Fig. 1c). The “CompileProject” built-in modality can integrate kinetic data from multiple recordings obtained from multiple biological replicates within a single project and can generate a merged output file to which statistical analysis can be applied with scientific graphing software of choice (RStudio41, GraphPad Prism, etc.).

With this automated analysis pipeline in place, we first validated the approach by analysis of the binding of glycoclusters 8, 10, 11 and 13. These ligands cover a range of affinities from our earlier Glyco-PAINT work and helped determine how variation of each parameter affected τ (and thus off-rate), track mobility, the relative on-rate and density ratio. This sweep included varying spot detection thresholds, track reconstruction parameters, and grid size. The parameter sensitivity analysis for tracking and spot detection parameters is depicted with heatmaps of each scenario in Supplementary Fig. 5A, B. From this analysis, we first determined how varying the parameters affected the number of squares that passed selection criteria per image for CHO-MR versus the MR-negative CHO-WT (Supplementary Fig. 6A). We found that any of the parameter values for Gap Closing Max Distance, Max Frame Gap and Linking Max Distance were able to select squares engaging in MR-mediated binding events from CHO-WT background. For the Minimum Nr Of Spots, we found that tracks should consist of a minimum of 3 spots, since the number of selected squares when allowing minimum track lengths of 2 spots in CHO-WT were boasted close to CHO-MR levels. This implicates that binding events spanning only 2 frames (100ms) can be considered MR-unrelated background, whereas a minimum of 4 spots appeared to result in too stringent filtering (Supplementary Fig. 6A). For the Threshold, we found that values >4 were resulting in accurate separation between the two cell types.

Next, we further analysed the sensitivity of track and spot detection parameters on the kinetic parameters off-rate, on-rate and diffusion coefficient. Here, it was found that of the tracking parameters, Min Nr Of Spots had a large influence on the average τ since short duration tracks are discarded. In contrast, median off-rates were largely independent of threshold settings above 3 (Supplementary Fig. 6B), median number of detected events (on-rate) remained constant above 5 (Supplementary Fig. 6C) and also diffusion coefficients did not vary above 4 (Supplementary Fig. 6D). However, the density ratio appeared to remain constant above a threshold of 8 (Supplementary Fig. 6E). To summarize, the track reconstruction parameters as indicated in the method section were kept constant for all analysis and the spot detection threshold was initially set at 5 since for this value the detection bias was minimized. If >1,500,000 spots per recording were detected, the threshold was increased to a maximum of 20 in steps of 5 due to computational limitations.

Subcellular analysis of immune cell lectin binding
We next applied Glyco-PAINT-APP with these settings to the profiling of glycan binding events on DCs. For these intrinsically heterogeneous cells, we optimized the grid size to avoid averaging artefacts. For this we used the complete dataset of mannose ligands binding to BMDC, which consists of approximately 400 different recordings (each of standard 82×82 µm², 512×512 pixels, and 2000 frames at 20 fps). Our analysis demonstrated that 20 squares per axis provided an optimal balance between statistical robustness of the off-rate fit (R2 > 0.9), the number of binding events per square, and the minimization of averaging artifacts (Supplementary Fig. 7A). Off-rates (τ) did not vary with square size (Supplementary Fig. 7B), but the density of event (rel. kon) values, and the density ratio diverged with square size (Supplementary Fig. 7C, D).

With this analysis pipeline in hand, we set out to reprocess the recordings of the mannose probe library on primary DCs for which the original Glyco-PAINT procedure had not yielded any significant differences in binding between the different glycoforms. The square-based analysis for DCs is displayed in Fig. 2 (for the same recordings as in Fig 1a, Supplementary Fig. 1A–D, and extended in Supplementary Fig. 3A, B for mono- and trimannosides 8, 10, 11 and 13). Figure 2b, f show that the APP-algorithm was able to independently distinguish areas with binding event density from background using filtering based on the previously optimized parameter settings. The added advantage of this approach is that binding events that fell outside brightfield-invisible features of the DC are now also included in the analysis. The thus observed binding rates in different areas of the cell basal membrane can be visualised as heatmaps in Fig. 2c, d, g, h. The grid-based analysis by Glyco-PAINT-APP also improved the effective selection of cell areas engaged in binding events for mono- and trimannoside-based probe binding to CHO-MR (Supplementary Fig. 4), highlighting the omnipresence of microheterogeneity even in over-expression systems. Taken together, this method allows for precise analysis of glycan-lectin interactions on live immune cells with intrinsically heterogenic expression, avoiding the emergence of averaging artefacts as in the original Glyco-PAINT analysis. Some interesting parameters began to emerge from the approach. For example, a discrepancy between areas of high number of binding events (i.e., high rel. kon; Fig. 2c, g) and areas with long average duration of binding (τ, koff−1, Fig. 2d, h) within the same cell could be seen on DCs.

Uptake and antigen presentation of SLP glycoforms
We next wanted to determine whether any of the on-cell kinetic parameters could prove predictive for some of the previously reported roles (see introduction) of the MR and related CLRs on BMDCs, namely its capacity as an uptake receptor and a cross-presentation enhancing receptor. We therefore designed Synthetic Long Peptide (SLP) versions of the fluorophore-labelled mannose clusters that also contained the model cross-presentable epitope Ovalbumin 247-264 (OVASLP)42, as it has been hypothesized that different glycoforms of these SLPs show different cross-presentation behaviour33,43. This additional feature would allow us to monitor the various binding parameters (by Glyco-PAINT-APP), uptake (by flow cytometry), and antigen cross-presentation (using the cognate OT-I T-cell), thereby having readouts for these three aspects of MR biology from a single probe.

The constructs were synthesized using in-line solid-phase peptide synthesis of the peptide antigen and an oligo-azidolysine (6-azido-norleucine) cluster, followed by on-resin modification with fluorophore. Copper-catalysed azide-alkyne cycloaddition (CuAAC)-mediated44,45 coupling of the mannose glycans to the azidolysine clusters in solution yielded the target glycosylated SLPs. This resulted in mono-, bi- and hexavalent versions of mono-(1-3) or trimannosylated (4-6) SLPs and a non-glycosylated control molecule (7) that are displayed in Fig. 3a (synthetic procedures and characterization can be found in the supplementary information and Supplementary Fig. 8). These glycan motifs span a KD range for MR-binding from 3 µM to more than 100 µM (based on SPR studies10,30), thereby enabling investigation of glycan-structure-binding-activity relationships over a large affinity range.

To verify the absence of artefacts resulting from the SLP antigenic cargo on kinetics, we compared the median on- and off-rates of OVA SLP 1-6 to glycoclusters 8-13 on CHO-MR cells and plotted the correlation between the two in Fig. 3b, c. Neither extension of the peptide core nor the change of the fluorophore from ATTO655 to sulfo-Cy5 significantly affected koff. A decrease in the median number of binding events per square was detected for the glycan SLPs with respect to the glycans 8-13, potentially originating from modestly altered physical-chemical properties of the peptide cargo.

The binding parameters of these glycosylated SLPs were assessed for their binding to live WT BMDCs and BMDCs derived from MR-/- mice This analysis enabled us to precisely determine the contribution of glycan SLP binding events mediated by the MR, and not by any of the other lectins present on the DCs. A population of squares engaging in binding event with high mobility (high diffusion coefficient) and low event density (kon) were uniquely present for the WT cells that were not observed on DCs lacking MR (Fig. 4a, b). This population of binding events appeared most prominent for glycan SLPs 2, 3 and 6 but not for 1, 5 and 7. To ensure that uptake (and resulting removal of a ligand from the TIRF-plane) affected track length, cells were also treated with the uptake inhibitor Cytochalasin D (CytD, which works through inhibition of actin polymerisation)46,47. Treatment of cells with this inhibitor confirmed that uptake resulting from most ligand binding was reliant on endocytic mechanisms involving actin polymerisation, as event density, dwell time and diffusion coefficient increased significantly for probes 1 Tri (4) and 2 Tri (5) after CytD treatment of BMDC (Supplementary Fig. 9 for probe pairwise comparisons, Supplementary Fig. 14A, B for comparisons between WT, MR−/− and CytD conditions, and in Supplementary Table 1 numeric values are listed). These probes were also the most effectively taken up by DCs (vide infra).

Next, the uptake of these SLPs was measured by incubating BMDCs with SLP 1-7 for 1h, followed by quantification of the sCy5 signal by flow cytometry. The histograms in Fig. 4c show that a specific subpopulation of MR+ CD11c+ DCs (Supplementary Fig. 10B, E, for gating strategy and MR co-staining) are mostly engaged in this MR-mediated intracellular uptake (Supplementary Fig. 10D, for competition with broad-spectrum ligand mannan and Supplementary Fig. 11A, B for intracellular localization of SLP 5 and 6), highlighting the intrinsic heterogeneity of these cells. Quantification of this effect using phagocytic index (PI)48 revealed a glycoform-specific profile with divalent trisaccharide-modified SLP 5 being most effectively taken up in contrast to its hexavalent counterpart 6. Uptake of the non-glycosylated control 7 was near baseline levels, emphasizing the glycan-mediated engagement of endocytic lectins. Similarly, all uptake was abolished MR−/− cells (Supplementary Fig. 10B).

The cross-presentation efficiency of these glycosylated SLPs was tested next by pulsing differentiated DCs, which were matured for 2h with TLR4 ligand MPLA, with 40 nM SLP (which is in a similar concentration range as Glyco-PAINT binding studies, see Supplementary Fig. 10C, D for different antigen dosages and alternative quantification of T cell proliferation) for 2h followed by a 3-day coculture with cognate CD8 T cells that were freshly isolated from OT-I mice. The non-mannosylated control SLP 7 was most efficiently cross-presented, whereas, SLPs decorated with mannosides of increasing complexity and valency 1-6 showed an unexpected decrease in T-cell proliferation induction. We also evaluated the cross-presentation efficiency in MR−/− cells and found an identical glycoform-dependent pattern. To validate correct differentiation and functional response of the MR−/− cells as compared to WT cells we characterized the change in cell surface marker expression levels in response to LPS stimulus and found no aberrant expression patterns Supplementary Fig. 10A. Additionally, we verified DC origin (see Supplementary Fig. 11F for steady-state cross-presentation of antigens in splenic DCs33) and the effect of timing of DC maturation and found an identical pattern for all timepoints in agreement with the observed diminishing effect of antigen mannosylation on T cell proliferation (Supplementary Fig. 11E, G).

Having observed this non-linear (Supplementary Fig. 12A) and, to us at least, counter-intuitive relationship between binding, the amounts of endocytosed antigen, and MHC-I-restricted cross-presentation, we next determined whether any of the subcellular kinetic parameters of SLP binding to DCs were predictive of either of these biological functions. A correlation was made using the median values of the following 10 kinetic parameters as derived by Glyco-PAINT-APP per analysed square: τ (ms), rel. kon (events s−1 µm−2), total track duration (s), long track duration (10% longest, s), short track duration (90% shortest, s), diffusion coefficient (nm2s−1), speed (µms-1) and max. speed (µms-1). From this analysis, glycan-SLP residence time (τ) on the DC surface was the best predictor for cross-presentation and this correlation upheld for the MR−/− cells. Furthermore, high diffusion coefficients (and accordingly larger displacement of the track) correlated with low uptake of the SLP (Fig. 4g, h). Both correlations were disrupted after cell-treatment with CytD (Supplementary Fig. 12B). These observations imply a mechanism where binding events of long duration and high on-membrane mobility are less likely to result in receptor-mediated endocytosis but more likely to enter a cytosolic cross-presentation pathway. Taken together, the correlations between on-cell kinetics and cellular functionality demonstrate the potential of the Glyco-PAINT-APP to uncover functional glycan-structure-activity relationships.

MR binding on macrophages
Thus far, we have found strong discrepancies in detected glycan binding parameters in the CHO-MR overexpression system versus native lectin-expressing cells. We next studied whether such discrepancies also extended across different myeloid cells. For this, we studied the changes in carbohydrate binding upon macrophage polarization. Certain pro- and anti-inflammatory stimuli are known to affect absolute levels of the MR on the cell surface upon polarisation of the macrophage from its “naïve” M0-like state to either the inflamed M1-like or wound-healing M2-like phenotype49. It is, however, not known whether this change in MR (and related CLR) levels also alters binding preferences of the macrophage. We attempted to investigate this by quantifying the binding parameters of mono- and hexavalent mono- and trimannoside 8, 10, 11 and 13 MR ligands, respectively, to murine bone marrow-derived macrophages (BMDM) treated with either M1 inducing (LPS and IFN-γ) or M2-inducing (IL-4) stimuli (Fig. 5a–c). Here, we observed a glycoform-specific trend with increased binding for the hexavalent clusters in comparison to the monovalent clusters, much alike the CHO-MR binding distribution (Supplementary Fig. 4). Additionally, the detected binding events also resulting in endocytic glycan uptake as determined by flow cytometry, where the M2 macrophages appeared to be more endocytic (Supplementary Fig. 13A, B). Surprisingly, a uniform increase in number of binding events (rel. kon) across all glycans upon M2 polarization, which is known to upregulate MR expression levels50, was not apparent from these data. However, for the total binding as quantified by the total track duration (number of events multiplied with their duration) within a square, there appeared to be a vast increase for the M2 phenotype compared to M0 and M1 (Fig. 5c) for all glycans except 13 (when square selection criteria were adjusted to R_Squared > 0.1 to accommodate long duration binding events, as illustrated in Supplementary Fig. 15A–C). This suggests the emergence of a distinct population of long duration binding events that are potentially the result of CLR upregulation by M2 macrophages, analogous to increase in highly mobile long tracks that were seen to be MR-specific on DCs. To evaluate effects of polarization on the spatial clustering of binding events, a clustering analysis was performed using the x, y coordinates of each track. This analysis did not result in glycoform-specific trends for clustering of events on macrophage cell surface (Supplementary Fig. 13C).

Next, binding data for polarizing macrophages were pooled with the previously acquired binding data on CHO, CHO-MR, BMDC WT, BMDC MR−/− and BMDM MR−/− to precisely evaluate the contribution of MR-mediated binding events in a single analysis. In Fig. 5d, representative examples of recordings with 13-binding events are shown, and in Fig. 5e the relative contributions of four main kinetic parameters are visualized in radar plots for the combinations of MR-expressing or MR-lacking cell types and BMDM polarization states. From these plots, it is immediately apparent that indeed the majority of 10 and 13 binding events detected on CHO-MR and BMDM are MR mediated. This contrasts with BMDC, for which there seem to be highest degree of redundancy in non-MR binding partners.

Methods
Mice
Male C57BL/6J and OT-I (C57BL/6-Tg(TcraTcrb)1100Mjb/J) mice were purchased from the Jackson Laboratory (CA, USA), MR−/− mice on a C57BL/6 background were originally provided by Lee et al.66. The animals were bred and housed under specific pathogen-free conditions and provided with water and food ad libitum under a 12:12 day/night cycle. Mice ranging from 8 to 15 weeks old were euthanized by cervical dislocation before harvest of lymphoid organs and/or thigh bones, femur, and tibia.

Cell culture
Bone Marrow-Derived Dendritic Cells (BMDC). Bone marrow (BM) was isolated from femurs, tibias, and thigh bones of WT or MR−/− C57Bl/6J mice via centrifugation (1900 rcf, 4.5 min) of scissor-cut bones that were placed in a 1.5 mL tube. The resulting pellet was subjected to red blood cell (RBC) lysis by resuspending in 0.5 mL of ammonium chloride-potassium (ACK) lysis buffer (Gibco, A1049201). After 3 min incubation at rt, the suspension was filtered over a 70 µm filter (Falcon, 352350), rinsed with 5 mL PBS and washed once with PBS by 5 min centrifugation at 300 rcf at rt. Thus obtained BM was cryopreserved in 10% DMSO in FCS according to standard methods67 or directly resuspended at 1x106 cells/mL in 15 cm uncoated culture dishes (Sarstedt, 82.1184.500) in complete RPMI-1640 (Capricorn, RPMI-A) supplemented with 10% heat-inactivated fetal calf serum (FCS, Gibco, A5670701), penicillin (100 I.U./mL) and streptomycin (50 µg/mL) (Gibco, 15140148), 2 mM GlutaMAX (Gibco, 35050061), 50 µM 2-mercaptoethanol (Gibco, 31350010) and 20 ng/mL mGM-CSF (Peprotech, 315-03) and cultured in a humidified incubator at 37 °C and 5% CO2. On day two, 5 mL of the above-described fresh medium was added, and on day 4, cells were reseeded in fresh medium at 1E6/mL. Cells were used for microscopy and T cell activation experiments on day 7 or 8.

Bone Marrow-Derived Macrophages (BMDM). BM was isolated from femurs, tibias, and thigh bones of WT or MR−/− C57Bl/6J mice via centrifugation (1900 rcf, 4.5 min) of scissor-cut bones that were placed in a 1.5 mL tube. The resulting pellet was subjected to RBC lysis by resuspending in 0.5 mL of ACK lysis buffer. After 3 min incubation at rt, the suspension was filtered over a 70 µm filter, rinsed with 5 mL PBS and washed once with PBS by 5 min centrifugation at 300 rcf at rt. Thus obtained BM was cryopreserved in 10% DMSO in FCS according to standard methods67 or directly resuspended at 0.8 x 106 cells/mL in 15 cm uncoated culture dishes (Sarstedt, 82.1184.500) in complete RPMI-1640 (Capricorn, RPMI-A) supplemented with 10% heat-inactivated fetal calf serum (FCS, Gibco, A5670701), penicillin (100 I.U./mL) and streptomycin (50 µg/mL) (Gibco, 15140148), 2 mM GlutaMAX (Gibco, 35050061), 50 µM 2-mercaptoethanol (Gibco, 31350010) and 20 ng/mL M-CSF (Biolegend, 576404) and cultured in a humidified incubator at 37 °C and 5% CO2. On day 2, 5 mL fresh medium was added, and on day 4 medium was aspirated and replenished with 15 mL fresh medium. Cells were used for experiments on day 7 or 8.

CHO-MR. The CHO-MR cell line was kindly provided by Luisa Martinez-Pomares68 and cultured in DMEM/F12 (Capricorn, DMEM-12-A), supplemented with 10% FCS, penicillin (100 I.U./mL), streptomycin (50 µg/mL) and selection antibiotic G418 (0.6 mg/mL). A layer of adherent cells was washed with PBS, and cells were harvested by 10 min incubation with 2 mM EDTA in PBS and subcultured approximately twice per week at a 1:5 split when cells reached 70-80% confluency.

Statistical analysis and sample size. Statistical analyses were conducted to compare glycan ligand binding kinetics across probes using the Glyco-PAINT square-based subsampling technology. Subsampling subcellular regions (squares) within fields of view increased the number of independent data points, enhancing statistical power compared to treating entire fields of view or cells as single units. For all Glyco-PAINT experiments, at least 3 biological replicates (independent experiments with fresh mouse material or new passage number for cell lines) with three technical replicates (fields of view per condition) were recorded. For flow cytometry assays, at least three biological replicates with two technical replicates per condition were conducted. A two-way ANOVA was used to assess differences among probes with respect to kinetic parameters derived from Glyco-PAINT experiments and flow cytometric assays, followed by Tukey’s Honest Significant Difference (HSD) test for post-hoc comparisons to control Type I error. Significance is reported as: ns (not significant), p≥0.05; p<0.05 (*); p<0.01 (**); p<0.001 (***); and p<0.0001 (****).

Fluorescent glycan probes. Glycan and glycan SLP probes were stored as lyophilized powders at −20°C. Upon thawing, vials were reconstituted in DMSO and concentration was determined by measurement of absorbance using Nanodrop apparatus with extinction coefficients εsCy5 = 250.000 M−1 cm−1 at 641 nm and εATTO655 = 125.000 M−1 cm−1 at 663 nm. Small aliquots were stored at −20°C until use.

OT-I T cell isolation. A 70 µm filter was placed on a 50 mL tube and pre-wetted with PBS supplemented with 2 mM EDTA and 2% FCS (single-cell suspension buffer, SCSB). Freshly harvested spleens from OT-I transgenic mice were placed on the filter and disrupted with the back end of a syringe. After thorough washing, the suspension was centrifuged (10 min., 300 rcf, rt). Next, the pellet was gently resuspended in 2 mL of ACK lysing buffer (Gibco, A1049201). After 3 min incubation, the suspension was diluted with 10 mL PBS, filtered once more, and centrifuged again (300 rcf, 10 min, rt). The pellet was resuspended and subjected to magnetically-activated depletion of non-target cells using the “Naïve CD8a+ T cell isolation Kit” (Miltenyi, 130-096-543) according to manufacturer’s protocol. The obtained T cells were counted and resuspended at a density of 5-15 x 106/mL in 1 mL PBS with 5 µM CFSE (Biolegend) and incubated for 15 min. at 37°C. After incubation, cells were spun down (10 min, 300 rcf, rt), washed once more with complete RPMI-1640 and were ready for downstream use.

Splenic DC isolation. Spleens isolated from C57Bl/6J mice were placed on a 10 cm petri dish (Sarstedt, 83.3902) containing 5 mL HBSS (Gibco, 14025092) supplemented with 1 mg/mL collagenase IV (Sigma, NC2115693) and 20 U/mL DNAse (Thermo Scientific, EN0525) and minced into small pieces. After incubating for 30 min at 37°C, tissue digestion was stopped by addition of 2 mM EDTA. The remaining homogenate was disrupted and RBC-lysed as described above and subjected to magnetically-activated depletion of non-target cells using the “Pan Dendritic Cell isolation Kit” (Miltenyi, 130-100-875) according to manufacturer’s protocol.

Glycan uptake and cell surface staining. 2.5 x 105 WT, MR−/− BMDC or BMDM were seeded after treatment with polarizing stimulus as described below in a 96 well v-bottom plate (Sarstedt, 82.1583001). The next day, Glycan SLP probes were added to the cells at 250 nM and incubated for 1 h at 37°C. Active uptake was halted by addition of ice-cold PBS and washed twice with PBS + 2% FCS + 2 mM EDTA (300 rcf, 5 min., rt). Cells were stained with a selection of the following stains and antibodies: Zombie Yellow (Biolegend, 423103, 1:500), TruStain FcX (Biolegend, cat no 101319, 1:100), CD11c-eFluor450 (clone: N418, eBioscience, cat no 48-0114-82, dilution 1:200), CD206-AF488 or AF647 (clone: MR5D3, Biorad, MCA2235A488T, 1:20), CD86-PerCP (Biolegend, cat no 105025, clone GL-1, 1:200), F4/80-APC-Cy7 (Biolegend, cat no 123117, clone BM8, dilution 1:400), MHC-II-AF488 (Biolegend, cat no 107615, clone M5/114.15.2, dilution 1:1000) for 30 min. on ice. Then cells were washed twice with PBS + 2% FCS + 2 mM EDTA and acquired on Guava EasyCyte 12HT. For BMDM uptake and characterization, an identical procedure was followed but acquisition was performed on Sony ID7000 spectral flow cytometer. Analysis was performed using FlowJo v8. Phagocytic index (PI) was calculated according to equation 1. Statistical analysis and plotting were performed using GraphPad Prism V10.

PI = % sCy5+ cells x MFI sCy5+ cells / % sCy5− cells x MFI sCy5− cells (1)

Confocal imaging. BMDC were cultured as described above and plated on Ibidi Treat µ-Slide 8 Well High dishes (Ibidi, 80806) at 0.5 x 106 cells per well. Cells were incubated with Cy5-labeled SLP 250 nM for 1 h at 37°C in complete RPMI. Following ligand incubation, cells were washed twice with ice-cold PBS and fixed incubated with CD11c-BV421 (BioLegend, cat no 117329, clone N418, dilution 1:100) antibody for 30 min. on ice. Then cells were washed twice and incubated with 4% paraformaldehyde (PFA) in PBS for 15 min at room temperature. Fixed cells were permeabilized with 0.1% Triton X-100 in PBS for 20 min and blocked with and washed twice with blocking buffer (PBS + 2% FCS + 2 mM EDTA). Then, cells were incubated with LAMP1-AF488 (BioLegend, cat no 121607, clone 1D4B, dilution 1:100) and EEA1-AF594 (MBL Life Sciences, cat no M176-A59, clone 3C10, dilution 1:100) antibodies for 1 h on ice. After final washes, coverslips were mounted in glycerol/DABCO mounting medium. Images were acquired using a Leica Stellaris 8 White Light Laser (WLL) confocal microscope equipped with a 63×/1.40 NA oil-immersion objective. Excitation was performed at 638 nm for Cy5, 488 nm for AF488 and 561 nm for AF594, with detection windows set according to manufacturer recommendations to avoid channel crosstalk. Line averaging (n = 2–4) and sequential scanning were applied where appropriate. All acquisition parameters (laser power, detector gain, pinhole size) were kept constant between conditions. Images were processed in LASX software (Leica Microsystems) and analyzed in Fiji (ImageJ) without nonlinear contrast adjustments.

T cell proliferation assay. 5 x 104 WT, MR−/− BMDC or 1 x 105 splenic DC were seeded in 96-well Nunc U-bottom plates (Thermo Scientific, 168136) and pulsed with Glycan SLP at 40 nM for indicated time. In indicated experiments, DC were pretreated with 1 µg/mL MPLA (Avanti, 699800P) for 2 h before antigen pulse. After antigen pulse was finished, the plate was spun down for 3 min at 600 rcf, washed once with complete RPMI-1640 and 1-1.5E5 freshly isolated, CFSE-stained OT-I T cells were added to the pulsed DCs in 200 µL complete RPMI-1640 and incubated at 37°C. After 3 d of coculture, the plate was spun down (3 min, 600 rcf, 4°C) and supernatant was removed and stored at −80°C. Cells were washed with FACS buffer (PBS with 2 mM EDTA, 2% FCS and 7.4 mM NaN3) and stained with Aqua Live/DEAD (Invitrogen, L34957, 1:500), TruStain FcX (Biolegend, 101319, 1:100), CD8a—APC (clone: 53-7.6, Biolegend, 100711, 1:200), and TCR Vbeta 5.1/5.2—eFluor450 (clone: MR9-4, eBioscience, 48-5796-82) for 30 min on ice, washed two times and acquired on a BD Fortessa I flow cytometer and analyzed using FlowJo v8. To calculate mean cycle, the CFSE dilution factor was obtained by dividing the MFI of the antigen pulsed condition by the DMSO pulsed condition expressed as Log269. Statistical analysis and plotting were performed using GraphPad Prism V10.

BMDM polarization. At day 6, adherent macrophages were harvested by aspiration of medium, washing once with PBS and incubation with 10 mL of 2 mM EDTA in PBS for 10 min at 37°C. Cells were reseeded in complete medium with addition of polarizing stimulus, 20 ng/mL IFN-γ (Peprotech, 315-05-100UG) + 100 ng/mL LPS-EB (Invivogen, tlrl-eblps) for M1 or 20 ng/mL IL-4 (Peprotech, 214-14-20UG) for M2 for 16 h.

Glyco-PAINT optical setup. Single molecule imaging was performed on a Nikon Ti2 N-STORM system equipped with TIRF module, Z piezo element, perfect focus system for axial drift correction and an OkoLab incubator with temperature and CO2 controller (37°C and 5% CO2) for live-cell imaging. Recordings were acquired using the 647 nm excitation laser (160 mW, 1.9 kW/cm2). Upon laser excitation, fluorescence was collected by a 100x 1.49 NA oil-immersion objective, passed through a quad-band dichroic mirror (97335 Nikon), and detected by a Hamamatsu ORCA Flash 4.0 CMOS camera with 160 nm pixel size. The signal was collected using the following settings: 512×512 pixel region, no binning, pixel depth 16-bit, exposure time 50 ms, live-cell observation and 2D-STORM (lens out), zoom 1x, lens x0.4, and for live-cell observation at 37°C, the correction collar was set to position 8160.

Acquisition of Glyco-PAINT recordings. 5 x 104 CHO-MR or 1 x 105 WT, MR−/− BMDC or BMDM were seeded in 8-well glass-bottomed microscopy slides (Ibidi, 80827) in complete medium. After equilibration in the microscope incubator, fluorescent glycan was added at 5 nM for CHO-MR and 10 nM for BMDC or BMDM experiments. Then, cells were brought into focus using brightfield illumination and 2000 frames (at 50 ms intervals) were recorded within a single field-of-view at 40–60% of maximum 647 nm laser power using TIRF illumination. For indicated experiments, Cytochalasin D (Focus Biomolecules) was added to a final concentration of 10 µM for 30 min prior to acquisition.

ROI-based analysis of Glyco-PAINT recordings. TrackMate was run manually using the Fiji plugin70. The LoG spot detection algorithm was applied with an object radius of 0.5 µm, with pre-processing with median filter and sub-pixel localization unchecked. Threshold values were set to 5, 10 or 15 such that no more than 1.500.000 spots were detected and kept identical per experimental condition. Next, single-particle tracking was performed using the SimpleLAP tracker algorithm with a maximum frame gap of 3, a max linking distance of 0.6 μm and a gap closing max distance of 1.2 μm. Tracks with only two spots were discarded. Then, manual Regions of Interest (ROIs) were drawn in individual recordings using Fiji based on brightfield-defined cell outlines. The number of tracks residing within the ROI was compared with the number of tracks outside the ROI to establish a ratio of cell/glass density. Only recordings for which that ratio exceeded 2 were considered for further analysis. For all remaining recordings, the kon, koff and MSD were calculated for tracks within the ROI according to the procedure as described by Riera et al.10. Statistical analysis and plotting were performed using GraphPad Prism v10.

Glyco-PAINT-APP analysis of recordings. Processing of recordings using Glyco-PAINT-APP was performed as described in a step-by-step procedure in the Supplementary Software Manual in the Supplementary Information and accompanying Supplementary Videos 1 and 2. For the TrackMate processing, a batch file (ExperimentInfo.csv) containing the experiment metadata and tracking parameters was created. Threshold values were set to 5, 10 or 15 such that no more than 1.500.000 spots were detected. Recordings were then processed in TrackMate using the ‘Run TrackMate Batch’ plugin provided by the Glyco-PAINT-APP. Through this plugin, spot detection and tracking by TrackMate70 was performed as indicated in the batch file using the SimpleLAP tracker algorithm with a maximum frame gap of 3, a max linking distance of 0.6 μm and a gap closing max distance of 1.2 μm. Tracks with only two spots were discarded. For the parameter sensitivity analysis, each tracking or spot detection was varied as indicated, whilst the others were kept at aforementioned values (Basis scenario).

With the Glyco-PAINT-APP utility ‘Generate Squares’, a grid of squares was overlaid, and kinetic properties for each square were calculated. Default parameters for grid processing are (deviations are mentioned in figure captions): Nr of Squares in row 20, Minimum Tracks to Calculate Tau 20, Min allowable RSquared 0.1, Min Required Density Ratio 2, Maximum Allowable Variability 10 and Neighbour Mode Free.

For every recording, a background track count was calculated by averaging the track count of the 40 (10% of the total number of squares) least dense squares. Only squares for which the track count exceeded the Min Required Density Ratio of 2 were considered (and the remaining ignored as background). For each square, the variability was calculated and only squares for which the variability was less than the Maximum Allowable Variability of 10 were considered. For squares meeting both the Minimum Required Density Ratio and Maximum Allowable Variability criteria, and containing at least the Minimum Tracks to Calculate Tau kinetic parameters, including kon, koff and MSD were calculated as in Riera et al.10, or copied from the TrackMate Tracks table output (for velocity, displacement, and track duration). Summary files were created using the “CompileProject” utility, creating an “AllRecordings.csv” file, containing information for all 1119 recordings, an “AllSquares.csv” file containing information on 389,200 (973×400) squares and an “AllTracks.csv” file containing information on approximately 16 million tracks (binding events) in the project. Statistical analysis and plotting using these merged files were performed using the ggplot2 package in R41,71.

Spatial clustering of single-molecule tracks was quantified as L(r) – r at r = 2 µm from Ripley’s L-function, computed using spatstat.explore and spatstat.geom packages in R on filtered x/y track coordinates from pre-selected squares, with differences between adjuvant conditions assessed by pairwise Wilcoxon tests with Benjamini–Hochberg correction.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors propose a technique to assess glycan-lectin interactions at the subcellular level in live cells, which is based on the collection of a series of fluorescence images (using fluorescently tagged carbohydrates) and their post processing. Interestingly, using the selected cell types that the authors study, the results show a non-uniform distribution of binding events at the cell surface and the authors claim that, using this technique, it is possible to quantify the kinetics of the detected interactions, namely, on and off rates, as well as diffusion coefficients of the binding complexes on the surface of individual cells. Overall, the concept of the manuscript is appealing, and the proposed methodology has conditions to be used by the scientific community. However, there are two main concerns related to this manuscript that I would like to point out:\n1) A proper validation of the methodology would require the authors to compare their results to known/established methods. I understand that in the context of the work, it might be difficult to find adequate data or techniques that could be used as a reference. However, as the authors mention, there are other techniques that can quantify the glycan-lectin interactions, as for example SPR, ELISA, etc. While these techniques are not the same as doing the experiments in live cells (as the authors propose here), the biophysical parameters (on, off rates, etc.) should not be dramatically different. I recommend that the authors compare their results with the ones obtained from those techniques to validate their methodology.\n2) The results are interesting, showing some surprising data related with the mobility of the glycan/lectin complexes and a correlation with internalization or their cross-presentation on the cell surface. The ability to do this type of assessment is important and challenging to do with the current methodologies, supporting the relevance of the manuscript. However, the work, as it is presented, focus more on a methodological perspective and less in the study of a particular biological/biophysical system, leading to my belief that the work would be more suitable for the methodological journal that focus on the publication of protocols.\nMinor Comments:\n1) CTL is mentioned for the first time in line 56, and DC in line 126, but neither has been defined previously. Please correct.\n2) Line 84, reformulate the phrase.\n3) Line 143, check the units at the end of the line\n4) Please check the whole text for grammar and typos", "reviewer_2": "The paper by Steuten et al., “Quantification of single molecule glycan-mannose receptor binding kinetics on myeloid cells reveals high subcellular binding heterogeneity” introduces Glyco-PAINT-APP. This is an automated processing pipeline aimed at quantifying glycan binding dynamics at the subcellular level using a PAINT-based single-molecule imaging technique. The authors demonstrate its application in profiling glycan-lectin binding analysis in immune cells, particularly dendritic cells and macrophages, and correlate these interactions to immune receptor function. The new methodology enhances existing Glyco-PAINT techniques by implementing an automated analysis workflow, improved spatial resolution and therefore enabling biological function correlation.\nThis is one of the first automated high-resolution analyses of glycan-lectin interactions. The study successfully maps glycan binding event density, dwell times, and diffusion coefficients at the subcellular level allows new insights into receptor biology what is quite exciting.\nOverall, the paper is well written, and the work is technically sound and well-structured, but several issues must be clarified to strengthen the study conclusions and ensure a more robust interpretation of the data.\nHere are the points:\n1. For the single-particle tracking, the authors used the 'Simple LAP tracker algorithm with a maximum frame gap of 3, a max linking distance of 0.6 μm, and a gap closing max distance of 1.2 μm. Tracks with only two spots were discarded' (lines 1022, 1038). Can these parameters be considered reliable across all experimental conditions? Did the authors benchmark the LAP tracker against alternative segmentation or tracking methods (e.g., Otsu thresholding, machine learning-based segmentation) to validate its robustness? To what extent do these LAP tracking parameters require optimization based on fluorophore brightness, molecular mobility, or frame rate? Was any experimental or manual validation performed to ensure tracking accuracy under the specific conditions used in this study?\n2. Spot detection is critical in the Glyco-PAINT-APP study, as accurate identification of glycan binding events directly impacts tracking reliability and the resulting kinetic measurements. Did the authors manually verify a subset of detected single-molecule events and compare them to the automated output? Additionally, were small changes in detection threshold tested for their effect on event counts or kinetics? The authors mention using thresholds of 5, 10, and 15, but a direct comparison of the resulting datasets is not provided. Since threshold selection can introduce detection bias - either by excluding weak but valid events or by including noise - it is important to explicitly assess the robustness of the analysis across different threshold settings and address potential bias in spot detection.\n3. Regarding the figure 2: What kind of “select squares criteria”—free or strict—was chosen in b and c? After watching the videos, the images confuse me a bit. It seems like in figure 2b, the square areas are quite noisy with high background in comparison to 2f. Was the same background correction applied to both images, or were different noise reduction methods used?\nAdditionally, the squares represented in g and h do not correspond to the squares in f; rather, they are additional squares in different positions. Why were different squares analyzed instead of using the same ones as in f? Wouldn't it be more consistent to show and analyze the same squares (in f, g and h) as was done for the Trimannose Cluster (13) analysis? If different squares were intentionally chosen in f, g and h, what was the rationale for this selection? Given that the square selection method can significantly influence the interpretation of spatial binding behavior, it is important to clarify the selection criteria, ensure consistent background processing, and maintain comparability across panels.\n4. Regarding figure 4 and figure S7, where the binding parameters of glycosylated SLPs to cells were tested: The authors stated (lines 277-280) that the on-rates, off-rates, and diffusion coefficients were significantly increased for all glycosylated probes (1-6) but not for the non-glycosylated control (7). However, this does not fully align with the data presented in Figure S7.\nSpecifically, in the CHO-MR treated condition (figure S7, g and h) and the BMDC-treated condition (figure S7, a and c), on-rates and off-rates do not consistently increase - in some cases, they partially decrease, contradicting the claim of a uniform increase. Additionally, the on-rate for the non-glycosylated control antigen 7 also increases in certain conditions (figure S7, c, d, and h), which is inconsistent with the statement that only glycosylated probes exhibit this effect.\nPlease comment on the discrepancy between the reported binding kinetics in figures 4 and S7 and the expected trends based on previous studies. Studies on mannose receptor (MR) and DC-SIGN binding kinetics (Feinberg et al., 2001; Taylor et al., 1992) suggest that trimannosylated ligands should exhibit lower off-rates than monomannosylated ones due to multivalent binding effects. However, figure 4 shows no such difference. Additionally, prior work on actin-dependent receptor dynamics (Lau et al., 2007) indicates that Cytochalasin D should disrupt clustering and reduce binding stability, rather than uniformly increasing on- and off-rates. Can the authors clarify how their findings align with these previously reported trends?\n5. The manuscript uses different Density_Ratio thresholds (>10 in case of macrophages and >2 for BMDCs). Can the authors clarify why different thresholds were chosen and whether these values were optimized based on a specific criterion? How does varying the Density_Ratio threshold affect the interpretation of the results? A direct comparison of results using different Density_Ratio values would help assess the robustness of the findings and eliminate bias in data interpretation.\n6. Regarding figure 5: In figure 5b, the square areas appear quite noisy with a high background compared to Figures 5a and 5c. This raises concerns about the high Density_Ratio threshold used for this dataset.\nWould it be possible that the polarization towards M1 macrophages primarily detects strong clustering or receptor hotspots, rather than capturing the full range of receptor behavior? Would broader changes in receptor behavior, including moderate enrichment, be more effectively detected using Density_Ratio >2 instead of >10?\nA Density_Ratio >2 might be more suitable for analyzing broader spatial patterns, allowing for more variation in binding behavior to be captured, rather than focusing exclusively on the most intense binding regions.\nCan the authors comment on this? I may be misinterpreting the reasoning behind the choice of Density_Ratio thresholds, so clarification on whether different thresholds influence the biological conclusions would be valuable.\n7. Additionally, the figure 5 provides interesting insights into how macrophage polarization affects MR ligand binding, particularly with hexatrimannoside. However, testing only one ligand does not fully establish whether polarization alters MR binding specificity. Would testing additional glycan ligands (e.g., mono- and trimannosylated ligands) help determine whether MR binding preferences shift in M2 macrophages?\nAdditionally, could receptor clustering contribute to prolonged dwell times in M2 macrophages rather than just increased MR expression? A fluorescence-based receptor clustering analysis could clarify whether M2 macrophages exhibit larger receptor aggregates, leading to longer binding events.\nThe authors also state that binding events in the M2 state exhibited longer diffusion coefficients compared to the M1 or M0 state. However, from the data, the log diffusion coefficient appears nearly the same between M1 and M2 macrophages, with only the frequency of events increasing. This suggests that binding kinetics are altered by the number of MR-positive events rather than by an inherent difference in receptor diffusion behavior. Can the authors clarify this point?\nFurthermore, in Figure 5B, it is noticeable that some tracks exhibit long track lengths, but these tracks are unfortunately not covered by the grids used in the analysis. Could this influence the interpretation of the results? If long tracks are systematically excluded from the analysis, the impact of extended receptor-ligand interactions may be underestimated.\nWould an alternative grid size or adaptive segmentation approach help capture these events more effectively?\nLastly, since M1 macrophages exhibit increased receptor shuttling, would comparing MR internalization rates clarify whether ligand binding is affected by receptor turnover rather than diffusion alone?\n8. For a balanced interpretation of the data, the discussion section should more transparently address study limitations. In particular, potential confounding variables such as receptor trafficking, glycan degradation, and pH effects need to be considered, as they could influence the observed binding kinetics. Additionally, the authors should explicitly acknowledge that correlation does not automatically imply causation, and alternative explanations for the reported effects should be discussed.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The authors describe a very interesting development of the Glyco-paint method to discriminate in a space resolved manner different kinetics of binding of glycans to myeloid cells. The pipeline analysis allowing glycans analysis is of high potential impact fo the study of cells-glycan interactions. Nevertheless, there is a lack of evidence to suggest that the binding observed is on Mannose Receptor and not a mixture of different C-Type lectin receptors on the cells. Similarly, the uptake of glycoclusters is not sufficiently supported.\nMain comments\nThe authors mention the specific Mannose receptor in their study but other CLRs are able to recognize mannoses (like DC-SIGN for example), especially clustered mannose that might induce a lot of binding to other lectins. Is it possible to confirm that the binding observed is only on MR through labelling of endogenous MR (with antibody?) or MR knockout? Otherwise, the manuscript should be altered to reflect that the lectin binders are unknown.\nCan the heterogeneity of binding observed be ascribed to the binding to different lectins?\nThe experiments on BMDC and CHO were performed with different concentrations of glycan probes, respectively 5 and 1nM. Could you comment this difference?\nThe uptake of the SLP-glycomolecules is measured by flow cytometry, which could also show binding to the cell surface. It would be interesting to show by confocal microscopy that the SLP-molecules are located in vesicles inside the cells to confirm the uptake.\nMinor comments\nPage 5 lines 140-147. Please explicit why the early observations on BMDC are surprising in more detail as it is the basis of the study and will help the reader to understand the questions asked.\nCould you also explicit which probes were used here and the rationale behind this probe choice. Figure 3 showing the structures of the fluorescent sugars is introduced only later.\nFigure 1 c) data inspection and curation figure is too small to be informative. The same applies to the schemes of resting and polarised cells that is maybe a bit oversimplistic. It would be helpful for the reader to introduce the structure of the glycan probes and parts of the averaged data to show the rationale behind the study (fig S1 E-F).\nIt is surprising on Fig S1 F that the difference in koff between glycans 1 and 6 is not judged significant.\nFig 3 b,c please move labels (eg 1tri ) so they are visible.", "reviewer_5": "- What are the noteworthy results?\nIn the manuscript titled “Quantification of single molecule glycan-mannose receptor binding kinetics on myeloid cells reveals high subcellular binding heterogeneity” by Kas Steuten, Johannes J.A. Bakker, Ward Doelman, Diana Torres-García, Roger Riera, Lorenzo Albertazzi and Sander I. van Kasteren, the authors\ndescribe an automated image processing pipeline they refer to as Glyco-Paint-APP that aims to extract sub-cellular glycan interaction kinetics. The work describes a spatially resolved quantitative image analysis approach that is applied to a method described in a previous study by Riera et al. (2021) Nature Chem. Biol. titled “Single-Molecule Imaging of Glycan–Lectin Interactions on Cells with Glyco-PAINT”. The application of their pipeline allows them to quantify carbohydrate lectin binding kinetics based with single molecule resolution.\nWhile I am not an expert in the field of myeloid cells or lectin binding, I find the method description for quantiative spatially resolved kinetic measure of glycan interaction with receptors from glyco-PAINT experiment to be interesting and of wider relevance. Steuten et al. demonstrate using the image-analysis pipeline developed in FIJI with interface to TrackMate (Glyco-PAINT-APP), that receptor kinetics are indeed spatially inhomogeneous. Thus the technical development and the finding both form novel aspects of their work, improving on previous approaches. Using gylcolyated synthetic long peptides (SLPs) they quantify immune cell uptake of SLPs and the role of actin polymerization. They find the residence time of glycan-SLPs to be the best positive predictor of cross-presentation in dendritic cell co-culture with CD8 T-cells.They also find the on-rates and diffusion coefficients in carbohydrate binding of mannose receptors (MR) correlate with polarization state of macrophages, Taken together these results suggest a utility for sub-cellular analysis using the tools developed for immune-cell dynamics and the role of carobohydrate ligand-binding kinetics.\n- Will the work be of significance to the field and related fields? How does it compare to the established literature? If the work is not original, please provide relevant references.\nThe application of a pipleline for single molecule tracking and extracting binding and mobility kinetics from STORM microscopy is likely indeed to have wider impact beyond the cellular systems described. The use of such a tool through standard FIJI plugins is likely to allow for it’s wider use.\n- Does the work support the conclusions and claims, or is additional evidence needed?\nThe claims made through the manuscript indeed are supported by sufficient data. Some minor changes in the manuscript that would help clarity of representation of the data are added.\n- Are there any flaws in the data analysis, interpretation and conclusions? - Do these prohibit publication or require revision?\nThis reviewer has noted minor changes that could help make some of the figures clearer. These need to be addressed in order to support the manuscript’s claims.\n1) Figure 1c: Please add units to the diffusion coefficient and track duration plots. Unless they are normalized, in which case the legend needs to clarify this.\n2) Figure 1 plots appear to be screenshots of the outputs and the software interface and could be improved upon with high resolution figures.\n3) Multiple figure legends have font sizes that are quite variable making them at times hard to read (e.g. in Fig. 4).\n4) The brightfield images in Fig. 5 lack contrast. It might help to outline the features that the authors wish to highlight by outlining cells, similar to what has been done by them in Fig. 1.\n- Is the methodology sound? Does the work meet the expected standards in your field?\nThe methodology adopted is based on fairly standard algorithms of single particle tracking implemented in TrackMate under FIJI and used by the authors. The approach taken is a good combination of existing ideas applied to a novel problem.\n- Is there enough detail provided in the methods for the work to be reproduced?\nYes"}, "author_response": "Please find attached our revised manuscript titled ‘Quantification of single molecule glycan-mannose receptor binding kinetics on myeloid cells reveals high subcellular binding heterogeneity’. We want to thank the reviewers for the detailed and thorough review of the paper and have as a result significantly altered the manuscript, including multiple additional experiments that we have detailed in a point-by-point below.\nYours sincerely,\nSander I. van Kasteren, on behalf of all co-authors\nPoint-by-Point Response to Reviewer's Comments\nReviewer #1 (Remarks to the Author):\nThe authors propose a technique to assess glycan-lectin interactions at the subcellular level in live cells, which is based on the collection of a series of fluorescence images (using fluorescently tagged carbohydrates) and their post processing. Interestingly, using the selected cell types that the authors study, the results show a non-uniform distribution of binding events at the cell surface and the authors claim that, using this technique, it is possible to quantify the kinetics of the detected interactions, namely, on and off rates, as well as diffusion coefficients of the binding complexes on the surface of individual cells. Overall, the concept of the manuscript is appealing, and the proposed methodology has conditions to be used by the scientific community. However, there are two main concerns related to this manuscript that I would like to point out:\nWe thank the reviewer for the positive assessment of our work. We will address each of the comments in the following paragraphs.\n1) A proper validation of the methodology would require the authors to compare their results to known/established methods. I understand that in the context of the work, it might be difficult to find adequate data or techniques that could be used as a reference. However, as the authors mention, there are other techniques that can quantify the glycan-lectin interactions, as for example SPR, ELISA, etc. While these techniques are not the same as doing the experiments in live cells (as the authors propose here), the biophysical parameters (on, off rates, etc.) should not be dramatically different. I recommend that the authors compare their results with the ones obtained from those techniques to validate their methodology.\nWe apologise for not mentioning the fact that we had previously done this comparison in the first Glyco-PAINT manuscript (https://pubmed.ncbi.nlm.nih.gov/34764473/). Here we found that the binding by SPR and Glyco-PAINT on the CHO cell lines correlated relatively well. However, as the current study extends this approach to primary cells with native receptor expression, we have taken the binding data of the CHO-MR cells as the benchmark, rather than re-benchmarking with SPR for the comparison of the primary cell lectin binding, as the complexity of the glycan-binding environment—including heterogeneous lectin repertoires and potential cis-ligand interactions – already gave such big differences, that we focused on solving those.\n2) The results are interesting, showing some surprising data related with the mobility of the glycan/lectin complexes and a correlation with internalization or their cross-presentation on the cell surface. The ability to do this type of assessment is important and challenging to do with the current methodologies, supporting the relevance of the manuscript. However, the work, as it is presented, focus more on a methodological perspective and less in the study of a particular biological/biophysical system, leading to my belief that the work would be more suitable for the methodological journal that focus on the publication of protocols.\nWe agree with the assessment of the reviewer that the first iteration of the paper was ‘heavy on the methodology’. Whereas we think that the method an sich is of high value to the scientific community, we have placed extra emphasis on the biological findings this new method has allowed us to obtain. For this, we have performed extra experiments with MR-knockout DCs, to more clearly tease apart the correlation between binding parameters, uptake and cross-presentation, in a manner not accessible through existing approaches. Figure 4A, B and 5E show that specific binding sub-populations on the DCs and macrophages are MR-specific events, thereby for the first time identifying specific lectin binding within a complex ‘lectinome’ (apologies for the neologism) on a primary cell and correlating these specific binding events to the functions ascribed to this receptor.\nMinor Comments:\n1) CTL is mentioned for the first time in line 56, and DC in line 126, but neither has been defined previously. Please correct.\n2) Line 84, reformulate the phrase.\n3) Line 143, check the units at the end of the line\n4) Please check the whole text for grammar and typos\nAll corrections agreed and adapted in revised manuscript and can be seen in the marked manuscript.\nReviewer #2 (Remarks to the Author):\nThe paper by Steuten et al., “Quantification of single molecule glycan-mannose receptor binding kinetics on myeloid cells reveals high subcellular binding heterogeneity” introduces Glyco-PAINT-APP. This is an automated processing pipeline aimed at quantifying glycan binding dynamics at the subcellular level using a PAINT-based single-molecule imaging technique. The authors demonstrate its application in profiling glycan-lectin binding analysis in immune cells, particularly dendritic cells and macrophages, and correlate these interactions to immune receptor function. The new methodology enhances existing Glyco-PAINT techniques by implementing an automated analysis workflow, improved spatial resolution and therefore enabling biological function correlation.\nThis is one of the first automated high-resolution analyses of glycan-lectin interactions. The study successfully maps glycan binding event density, dwell times, and diffusion coefficients at the subcellular level allows new insights into receptor biology what is quite exciting.\nOverall, the paper is well written, and the work is technically sound and well-structured, but several issues must be clarified to strengthen the study conclusions and ensure a more robust interpretation of the data.\nWe thank the reviewer for the positive assessment of our work. We will address each of the comments in the following paragraphs.\nFor the single-particle tracking, the authors used the 'Simple LAP tracker algorithm with a maximum frame gap of 3, a max linking distance of 0.6 μm, and a gap closing max distance of 1.2 μm. Tracks with only two spots were discarded' (lines 1022, 1038). Can these parameters be considered reliable across all experimental conditions?\nYes, they can. We have performed a sweep of LAP tracker parameters to determine variance of the results with different tracking parameters and found that the chosen values corresponding to the ‘basis scenario’ (and as cited by the reviewer) were able to effectively discriminate MR-mediated binding events from background in the CHO-MR vs CHO cell line pair (see figure S5A, B). When deviating from the ‘basis scenario’ parameter set, it appeared that mostly the minimum number of spots for a track had effects on the separation between CHO-MR and CHO-WT squares. The new data underpinning these findings have been included as Figure S5-6 and as additional text under subheading Glyco-PAINT-APP of the main text.\nDid the authors benchmark the LAP tracker against alternative segmentation or tracking methods (e.g., Otsu thresholding, machine learning-based segmentation) to validate its robustness?\nWe have not done this. As it was our aim to create a broadly accessible method, we opted to use the features offered by TrackMate. In the future, we do hope to evaluate the Otsu/machine-learning based segmentation approaches, but it was beyond us for this revision.\nTo what extent do these LAP tracking parameters require optimization based on fluorophore brightness, molecular mobility, or frame rate?\nThey do. We have optimized the tracking parameters for both fluorophore brightness and molecular mobility, based on the binding of 4 different glycans to the CHO-MR/CHO cell line pair (please see response to earlier comment for a discussion regarding the results of this sensitivity analysis). These four glycans capture a binding range and therefore reflect varying molecular mobility and brightness. We have completely rewritten the section of the manuscript that describes this analysis to more clearly reflect our choices in this manner. In addition, we have expanded the discussion to more clearly describe these limitations. Regarding, the frame rate, we have kept it the same as in our previous study on CHO-MR to remain consistent with this work (https://pubmed.ncbi.nlm.nih.gov/34764473/).\nWas any experimental or manual validation performed to ensure tracking accuracy under the specific conditions used in this study?\nFor the CHO-MR cells, we have correlated all our data to data obtained by our manual Glyco-PAINT method. In addition, we also used biological validation of the tracking parameters. Firstly, we used the cell pairs of CHO-MR and CHO (that does not have the MR) as a positive and negative control. This allowed us to strictly isolate MR-only binding events. We defined the range of acceptable settings based as those in which our negative controls were negative and even our weakest binding positive controls were positive. On the DCs, we have added additional experiments (Figure 4 and 5) using dendritic cells and macrophages of a MR-knockout mouse. This allowed us to also look at MR-specific binding within the context of the other lectins present on DCs. These controls allowed us to experimentally validate the parameter choices.\nSpot detection is critical in the Glyco-PAINT-APP study, as accurate identification of glycan binding events directly impacts tracking reliability and the resulting kinetic measurements.\nDid the authors manually verify a subset of detected single-molecule events and compare them to the automated output?\nYes. We have extensively compared the manual (Figure S2) output to the automated output for CHO-MR (Figure S4) and have confirmed that this is in line with the original study. As mentioned in the text, the manual version of the method is not effective for primary cells due to the introduction of averaging artefacts.\nAdditionally, were small changes in detection threshold tested for their effect on event counts or kinetics? The authors mention using thresholds of 5, 10, and 15, but a direct comparison of the resulting datasets is not provided. Since threshold selection can introduce detection bias - either by excluding weak but valid events or by including noise - it is important to explicitly assess the robustness of the analysis across different threshold settings and address potential bias in spot detection.\nYes, they were. We have now included a spot detection threshold sweep in Figure S5 and S6 as part of the parameter sensitivity analysis for the CHO/CHO-MR dataset. From this analysis we concluded that thresholds above 4 can reliably discriminate MR-mediated binding events from background and therefore we concluded that any value above 4 would not result in detection biases arising from inclusion of background noise as suggested by the reviewer. Off-rates, on-rates and diffusion coefficients were independent of the threshold parameters. A detailed description and interpretation of the parameter sensitivity analysis is also added in lines under the subheading Glyco-PAINT-APP in the revised manuscript main text.\n3. Regarding the figure 2: What kind of “select squares criteria”—free or strict—was chosen in b and c? After watching the videos, the images confuse me a bit. It seems like in figure 2b, the square areas are quite noisy with high background in comparison to 2f. Was the same background correction applied to both images, or were different noise reduction methods used?\nFor both the indicated images, select squares criteria was set to “free”. We thank the reviewer for raising this point as the squares selection criterion was not mentioned in the methods section. We have now also included this in the experimental method detail of the revised manuscript.\nAdditionally, the squares represented in g and h do not correspond to the squares in f; rather, they are additional squares in different positions. Why were different squares analyzed instead of using the same ones as in f? Wouldn't it be more consistent to show and analyze the same squares (in f, g and h) as was done for the Trimannose Cluster (13) analysis? If different squares were intentionally chosen in f, g and h, what was the rationale for this selection? Given that the square selection method can significantly influence the interpretation of spatial binding behavior, it is important to clarify the selection criteria, ensure consistent background processing, and maintain comparability across panels.\nThank you for pointing out this mistake in the squares selection criteria for generating these images. We have reevaluated the squares selection criteria for all figures and revised the figures.\n4. Regarding figure 4 and figure S7, where the binding parameters of glycosylated SLPs to cells were tested: The authors stated (lines 277-280) that the on-rates, off-rates, and diffusion coefficients were significantly increased for all glycosylated probes (1-6) but not for the non-glycosylated control (7). However, this does not fully align with the data presented in Figure S7.\nSpecifically, in the CHO-MR treated condition (figure S7, g and h) and the BMDC-treated condition (figure S7, a and c), on-rates and off-rates do not consistently increase - in some cases, they partially decrease, contradicting the claim of a uniform increase. Additionally, the on-rate for the non-glycosylated control antigen 7 also increases in certain conditions (figure S7, c, d, and h), which is inconsistent with the statement that only glycosylated probes exhibit this effect.\nThank you for pointing at this inconsistency in the textual description of the data. In the current version of the manuscript we have revised this analysis and corrected data filtering mistakes in the original figures as mentioned by the reviewer. We have revised the text belonging to this statement under subheading Uptake and antigen presentation of SLP glycoforms and added additional figures to display the precise effects of CytD on kinetic parameters which are shown in Figure S14, and also in Figure S9 and Table S1 for the numeric values.\nPlease comment on the discrepancy between the reported binding kinetics in figures 4 and S7 and the expected trends based on previous studies. Studies on mannose receptor (MR) and DC-SIGN binding kinetics (Feinberg et al., 2001; Taylor et al., 1992) suggest that trimannosylated ligands should exhibit lower off-rates than monomannosylated ones due to multivalent binding effects. However, figure 4 shows no such difference. Additionally, prior work on actin-dependent receptor dynamics (Lau et al., 2007) indicates that Cytochalasin D should disrupt clustering and reduce binding stability, rather than uniformly increasing on- and off-rates. Can the authors clarify how their findings align with these previously reported trends?\nIn their 2001 paper, Feinberg et al. (https://www.jbc.org/article/S0021-9258(21)00140-X/fulltext#fig3), show the binding of a CfG-array to CTL-Domain-4 of the MR in steady state. What confuses me is two aspects of this study, namely that this is a steady state binding study with no off rates given, and that mannose still binds relatively well compared to some other high mannose structures (e.g. 264, 499, and 601). An additional factor that makes these data hard to compare is because their lectin reagent is a streptavidin tetramer of a single lectin domain, suggesting that the multivalent binding effect could be the result of the lectin reagent. The 1992 paper by Taylor and co-workers, again does not give off rates, but steady state binding of mannosylated-BSA, invertase and mannan to various binding domains. That aside, we too had expected to see a lower off rate for the trimannoside compared to a monomannoside and expected a lengthening of these off rates with CytD treatment. Whereas we did not observe significant differences in off rate between the mono and the trimannoside, we did see lowering of the off rate going from 1 to 2 to 6 copies of each of these sugars, suggesting a multivalent effect on a slightly larger distance scale. The fact that we did not see a uniform lowering of the off rate (as a proxy for reduced binding stability) is in line with our observations of our previous paper where we showed that the MR did not bind our ligand library in cluster form, instead it was single MR-ligand interactions responsible for binding. This combined with the lack of an cytosolic actin anchor of the MR and the off targets had us ascribe these observations to those parameters. However, to further clarify some of these issues (particularly on the DCs that also have other lectins), we have included the same set of experiments and correlation with MR-/- BMDCs (Figure 4/5) rather than the CytD experiments, which have now been moved to the SI.\n5. The manuscript uses different Density_Ratio thresholds (>10 in case of macrophages and >2 for BMDCs). Can the authors clarify why different thresholds were chosen and whether these values were optimized based on a specific criterion? How does varying the Density_Ratio threshold affect the interpretation of the results? A direct comparison of results using different Density_Ratio values would help assess the robustness of the findings and eliminate bias in data interpretation.\nThere was no specific optimization criterion behind the use of different Density_Ratio thresholds; the choice of >10 for macrophages arose from the observation that BMDMs exhibit substantially higher levels of ligand binding events (by virtue of their higher MR-expression), which is also apparent in the absolute density values. To address this, the same figure with filtering at Density Ratio 2 is now included in the manuscript. As found for the optimisations described earlier, values do not deviate significantly upon this change in Density_Ratio. In the revised version of the manuscript we describe that not Density_Ratio but R_Squared is limiting parameter for squares selection in BMDM recordings. A supplementary figure is added that visually displays the discrepancy in square selection for three scenario’s, R_Squared > 0.1, R_Squared > 0.9 and Density_Ratio > 10 (Figure 14A, C). Here it can be seen that typically for BMDM long duration tracks do not meet the criteria for good fit and Tau derivatization. Therefore it may be considered to reduce the selection criteria here and then for example look at different parameters that are not dependent on fitting but do reflect track lengths. We have thus picked Total Track Duration as the parameter to display, as it meets this requirement.\n6. Regarding figure 5: In figure 5b, the square areas appear quite noisy with a high background compared to Figures 5a and 5c. This raises concerns about the high Density_Ratio threshold used for this dataset. Would it be possible that the polarization towards M1 macrophages primarily detects strong clustering or receptor hotspots, rather than capturing the full range of receptor behavior? Would broader changes in receptor behavior, including moderate enrichment, be more effectively detected using Density_Ratio >2 instead of >10? A Density_Ratio >2 might be more suitable for analyzing broader spatial patterns, allowing for more variation in binding behavior to be captured, rather than focusing exclusively on the most intense binding regions.\nCan the authors comment on this? I may be misinterpreting the reasoning behind the choice of Density_Ratio thresholds, so clarification on whether different thresholds influence the biological conclusions would be valuable.\nAs explained in previous comment, it is not so much the Density_Ratio but mostly the R_Squared that results in this square selection. For clarity additional tracking overlay figures with Density_Ratio 10 (instead of 2) have been added to Figure S13A.\n7. Additionally, the figure 5 provides interesting insights into how macrophage polarization affects MR ligand binding, particularly with hexatrimannoside. However, testing only one ligand does not fully establish whether polarization alters MR binding specificity. Would testing additional glycan ligands (e.g., mono- and trimannosylated ligands) help determine whether MR binding preferences shift in M2 macrophages?\nThank you for this helpful suggestion. In response, we have now performed additional experiments including glycans 1 Mono, 6 Mono, 1 Tri, and 6 Tri—matching the probe selection used in earlier parts of the manuscript. This expanded dataset provides a more comprehensive view of how macrophage polarization influences glycan binding. Figure 5 has been revised accordingly to reflect these new findings.\nAdditionally, could receptor clustering contribute to prolonged dwell times in M2 macrophages rather than just increased MR expression? A fluorescence-based receptor clustering analysis could clarify whether M2 macrophages exhibit larger receptor aggregates, leading to longer binding events.\nThank you for this helpful suggestion. In both our earlier paper and this work, we have performed K-means clustering analysis based on the average x,y positions of the detected tracks for the macrophage experiments across all four glycan probes and the three adjuvants. From this analysis we could not detect a significant difference in clustering of the binding events for the polarization states of most of the tested ligands. Except for 1 Mono (8), which appeared to have a slightly higher clustering score for the M1 phenotype. This observation is somewhat analogous to what was observed for CHO-MR in our earlier work. These data support the hypothesis that – for soluble ligands at least – the MR does not require clustering for binding. See Figure S13C for the results of the clustering analysis.\nThe authors also state that binding events in the M2 state exhibited longer diffusion coefficients compared to the M1 or M0 state. However, from the data, the log diffusion coefficient appears nearly the same between M1 and M2 macrophages, with only the frequency of events increasing. This suggests that binding kinetics are altered by the number of MR-positive events rather than by an inherent difference in receptor diffusion behavior. Can the authors clarify this point?\nWe have removed this statement.\nFurthermore, in Figure 5B, it is noticeable that some tracks exhibit long track lengths, but these tracks are unfortunately not covered by the grids used in the analysis. Could this influence the interpretation of the results? If long tracks are systematically excluded from the analysis, the impact of extended receptor-ligand interactions may be underestimated. Would an alternative grid size or adaptive segmentation approach help capture these events more effectively?\nWe indeed found that some tracks fall outside the selected grid regions, particularly in macrophage images where extended binding duration events are more commonly found. Our intention was to use an automated, grid-based segmentation approach, which applies stringent selection criteria to distinguish true signal from background noise. We have however in response to comment 5 of this reviewer reevaluated the square selection criteria for macrophages specifically and found that by lowering the R_Squared criteria to 0.1 there tracks are more efficiently captured. This is also illustrated in Figure S15A, C and described under subheading MR binding on macrophages\nLastly, since M1 macrophages exhibit increased receptor shuttling, would comparing MR internalization rates clarify whether ligand binding is affected by receptor turnover rather than diffusion alone?\nThank you for this insightful suggestion. To address the potential role of receptor turnover in ligand binding dynamics, we analyzed the uptake of the glycan probes by macrophages. We do however find that uptake of the glycans is merely identical for the various polarization states so have updated this in the manuscript (Figure S12B).\n8. For a balanced interpretation of the data, the discussion section should more transparently address study limitations. In particular, potential confounding variables such as receptor trafficking, glycan degradation, and pH effects need to be considered, as they could influence the observed binding kinetics. Additionally, the authors should explicitly acknowledge that correlation does not automatically imply causation, and alternative explanations for the reported effects should be discussed.\nWe have significantly altered the discussion to more transparently show the limitation of the method and of the analysis and interpretation of the data on the points suggested.\nReviewer #2 (Remarks on code availability):\nThe GlycoPaint-Pipeline repository provides a structured and well-documented analysis pipeline for single-molecule glycan-receptor interaction data. The authors supply detailed installation instructions through a dedicated Installation.md file, guiding users through the setup of Fiji, TrackMate, Python, and R environments. The codebase includes both Python and R scripts and is organized with clear folder structures and metadata.\nImportantly, the repository also includes test datasets (Demo/ folder), such as All Tracks.csv and Experiment Info.csv, which allow users to verify installation and explore example outputs. This is a valuable addition that enhances the reproducibility of the pipeline.\nThat said, I found the large number of files and subfolders a bit overwhelming to navigate, and it’s possible I missed some details despite reviewing the materials thoroughly. While I’m not a programming expert, I do have more experience than a typical user, and from that perspective, the pipeline is moderately accessible. However, the overall usability would improve with better integration between the data, code, and documentation to make it easier to understand how the components relate and how to get started.\nTo support broader use and reproducibility, the authors are encouraged to:\n• Add a walkthrough or script that demonstrates how to use the demo dataset to generate key outputs or figures,\n• Clearly link specific scripts to corresponding analyses or figures in the manuscript,\n• Providing a requirements.txt or environment file to streamline setup.\nOverall, the pipeline is a valuable and technically well-executed resource for specialized users and offers a strong foundation for reproducible glycan-binding analysis.\nWe thank the reviewer for critically assessing the code associated with our manuscript. Since reusability of the software is, besides sharing the biological data, one of the main points of the manuscript, we have in response to these comments revised the software manual and included more walkthrough scripts and also scripts that correspond to figures in the manuscript itself to further clarify the software. We had non-experts (i.e., undergraduate students) use and run the software to ensure its useability, but we gladly accept any further suggestions for improvement.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nThank you for your time taken to review our manuscript.\nReviewer #4 (Remarks to the Author):\nThe authors describe a very interesting development of the Glyco-paint method to discriminate in a space resolved manner different kinetics of binding of glycans to myeloid cells. The pipeline analysis allowing glycans analysis is of high potential impact fo the study of cells-glycan interactions. Nevertheless, there is a lack of evidence to suggest that the binding observed is on Mannose Receptor and not a mixture of different C-Type lectin receptors on the cells. Similarly, the uptake of glycoclusters is not sufficiently supported.\nWe thank the reviewer for assessing our manuscript and acknowledging the potential high impact of the work. We will address the comments made by the reviewer regarding lectin specificity and uptake in detail in the below paragraphs.\nMain comments\nThe authors mention the specific Mannose receptor in their study but other CLRs are able to recognize mannoses (like DC-SIGN for example), especially clustered mannose that might induce a lot of binding to other lectins. Is it possible to confirm that the binding observed is only on MR through labelling of endogenous MR (with antibody?) or MR knockout? Otherwise, the manuscript should be altered to reflect that the lectin binders are unknown.\nWe fully agree that multiple C-type lectins, including the murine homologues of DC-SIGN can also bind these clusters. To allow the separation of the contribution of the binding of these lectins to the overall biology, we have repeated all experiments with MR-/- BMDCs and macrophages to allow the specific identification of MR-specific binding events not seen in the knockout. These experiments show that the MR-binding events are essential for the uptake of the probes (as analysed by flow cytometry, but also by microscopy in Figure S11A, B). To our surprise, but fitting earlier data, there is some correlation with MR-binding and cross-presentation, but it is much weaker than we hypothesized. This offers a numerical confirmation of earlier literature discussions on this complex phenomenon.\nCan the heterogeneity of binding observed be ascribed to the binding to different lectins?\nYes, but pinpointing exactly which lectin (other than the MR), or combination of lectins, is responsible for which set of binding events is beyond us at this point in time.\nThe experiments on BMDC and CHO were performed with different concentrations of glycan probes, respectively 5 and 1nM. Could you comment this difference?\nThese concentrations have been selected to obtain a sufficient number of binding events per recording. The BMDCs have lower levels of MR than the CHO overexpression system used. This required a higher ligand concentration to obtain a suitable number of events as this parameter is dependent on both receptor and ligand concentration.\nThe uptake of the SLP-glycomolecules is measured by flow cytometry, which could also show binding to the cell surface. It would be interesting to show by confocal microscopy that the SLP-molecules are located in vesicles inside the cells to confirm the uptake.\nWe have confirmed vesicular intracellular uptake for two of glycan SLPs using confocal imaging. This data can be found in Figure S11A, B.\nMinor comments\nPage 5 lines 140-147. Please explicit why the early observations on BMDC are surprising in more detail as it is the basis of the study and will help the reader to understand the questions asked.\nWe have rewritten this section of the text to make the weirdness of our first BMDCs observations more explicit.\nCould you also explicit which probes were used here and the rationale behind this probe choice.\nWe chose the probes to cover the extremes and middle of the binding ranges to the MR observed on the CHO-MR cells.\nFigure 3 showing the structures of the fluorescent sugars is introduced only later.\nWe also struggled with this, as our chemist’s instinct told us that these figure should mentioned beforehand. However, for the order of the narrative, we wanted to mention the controversy that started this paper first, before going into details. To somewhat solve this, a specific reference to Figure 3 is still added at the correct point in the main text. As we have also introduced these probes before in Riera et al. 2021., we also refer to them via this reference in the text. If this is insufficient, please let us know and we will add a supplementary figure in the correct chronological position. Otherwise, we hope this suffices.\nFigure 1 c) data inspection and curation figure is too small to be informative. The same applies to the schemes of resting and polarised cells that is maybe a bit oversimplistic.\nIn the revised version of the manuscript the figures are added with substantial higher resolution to allow zooming in to observe more detail. The illustrations of resting and polarized cells is replaced with a schematic drawing of the glycans also in response to previous comment.\nIt would be helpful for the reader to introduce the structure of the glycan probes and parts of the averaged data to show the rationale behind the study (fig S1 E-F).\nWe have added a clear reference to the structures of the glycan probes in the first paragraphs of the text, referring to the correct figure. We wish to highlight that these structures have been reported before by Riera et al. We also are showing the averaged data and resulting averaging errors in Figures 1 and S1.\nIt is surprising on Fig S1 F that the difference in koff between glycans 1 and 6 is not judged significant.\nWe agree that this is surprising at first sight. We did verify the analysis underlying this figure repeatedly but came to the same conclusion. To help understand this fact we have added the p-value to the plot instead of ‘ns’. Additionally, we wish to emphasize that the data in Figure S1F is based on identical data as Figure 2J\nFig 3 b,c please move labels (eg 1tri ) so they are visible.\nCorrected.\nReviewer #5 (Remarks to the Author):\nIn the manuscript titled “Quantification of single molecule glycan-mannose receptor binding kinetics on myeloid cells reveals high subcellular binding heterogeneity” by Kas Steuten, Johannes J.A. Bakker, Ward Doelman, Diana Torres-García, Roger Riera, Lorenzo Albertazzi and Sander I. van Kasteren, the authors describe an automated image processing pipeline they refer to as Glyco-Paint-APP that aims to extract sub-cellular glycan interaction kinetics. The work describes a spatially resolved quantitative image analysis approach that is applied to a method described in a previous study by Riera et al. (2021) Nature Chem. Biol. titled “Single-Molecule Imaging of Glycan–Lectin Interactions on Cells with Glyco-PAINT”. The application of their pipeline allows them to quantify carbohydrate lectin binding kinetics based with single molecule resolution.\nWhile I am not an expert in the field of myeloid cells or lectin binding, I find the method description for quantiative spatially resolved kinetic measure of glycan interaction with receptors from glyco-PAINT experiment to be interesting and of wider relevance. Steuten et al. demonstrate using the image-analysis pipeline developed in FIJI with interface to TrackMate (Glyco-PAINT-APP), that receptor kinetics are indeed spatially inhomogeneous. Thus the technical development and the finding both form novel aspects of their work, improving on previous approaches. Using gylcolyated synthetic long peptides (SLPs) they quantify immune cell uptake of SLPs and the role of actin polymerization. They find the residence time of glycan-SLPs to be the best positive predictor of cross-presentation in dendritic cell co-culture with CD8 T-cells. They also find the on-rates and diffusion coefficients in carbohydrate binding of mannose receptors (MR) correlate with polarization state of macrophages, Taken together these results suggest a utility for sub-cellular analysis using the tools developed for immune-cell dynamics and the role of carobohydrate ligand-binding kinetics.\nWe thank the reviewer for careful reading of the manuscript and positive assessment.\n1) Figure 1c: Please add units to the diffusion coefficient and track duration plots. Unless they are normalized, in which case the legend needs to clarify this.\nWe have purposely not put the units in the subfigures of Figure 1 because they are meant as representative data to showcase the technique and do not necessarily have to be interpreted with units. If this is deemed fundamentally incorrect, we can however include the standard units as we did further in the paper, if the reviewer would so like.\n2) Figure 1 plots appear to be screenshots of the outputs and the software interface and could be improved upon with high resolution figures.\nIn the revised version of the manuscript figures have been included as high-resolution files that maintain text and image quality.\n3) Multiple figure legends have font sizes that are quite variable making them at times hard to read (e.g. in Fig. 4).\nThis has been addressed in the current version.\n4) The brightfield images in Fig. 5 lack contrast. It might help to outline the features that the authors wish to highlight by outlining cells, similar to what has been done by them in Fig. 1.\nWe thank the reviewer for this comment and have addressed the resolution and image quality.\n5) I found the instructions run and install the script somewhat confusing beginning with system configuration, paths and getting the FIJI plugin to work. The authors could clarify the installation procedure for usability of their code. I have not reviewed the code line-by-line since I was unable to actually run it.\nIn response to comments by this and other reviewers the software manual and instruction have been heavily revised and proofread by users that er not related to the project."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors followed most of the recommendations from the reviewers. Importantly, the included modifications significantly increased the clarity of the discussion and conclusions. The recommendations that the authors decided not to follow are well justified in the comments to the reviewers. Overall, the quality of the revised version of the manuscript, when compared to the original version, has significantly improved and, in my opinion, the present version is at a scientific level compatible with the high standards of Nature Communications.", "reviewer_2": "Dear Authors,\nThank you for your thoughtful and well-structured rebuttal letter, and for the substantial revisions made to your manuscript. I appreciate the care with which you addressed each of the reviewer points.\nYour parameter sensitivity analyses, additional experiments on macrophage polarization, the clarification of figure inconsistencies, and the improvements in both the manuscript text and supplementary material have significantly strengthened the work. I particularly welcome your transparent acknowledgment of limitations (e.g., interpretation of diffusion kinetics, MR clustering assumptions, and correlation vs causation). The additional data (e.g., MR-knockout controls, density ratio comparisons, and extended glycan panel) enhance the robustness and biological relevance of your findings.\nWhile the processing pipeline remains technically complex, the revised documentation now offers much clearer guidance and structure, and the study represents a valuable contribution to the field of single-molecule glycan analysis.\nThank you for your responsiveness and efforts to improve the quality of the manuscript. I have no remaining major concerns.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The authors have correctly answered to the questions raised during the first round of review."}, "author_response": ""}]

Decision letter