A centrally positioned cluster of multiple centrioles in antigen-presenting cells fosters T cell activation

Domain: physics
Published: 2026-01-13
Editorial outcome: major_revision

Abstract
Cellular polarization plays a crucial role in regulating immunological processes and is often associated with reorientation of the centrosome. During immune synapse formation, centrosome repositioning in lymphocytes assists in T cell activation. While a single centrosome, consisting of two centrioles, is present in T cells, antigen-presenting cells such as dendritic cells amplify centrioles during maturation and immune activation. How centriole amplification in antigen-presenting cells affects immune synapse formation and T cell activation is unclear. In this study, we combine experimental data with mathematical and computational modelling to provide evidence that extra centrioles in dendritic cells form over-active microtubule organizing centers, which cluster during dendritic cell-T cell interactions and, unlike in T cells, localize close to the cell center. Perturbing either centrosome integrity or centriole numbers and configuration in dendritic cells results in impaired T cell activation. Collectively, our results highlight a crucial role for centriole amplification and optimal centrosome positioning in antigen-presenting cells for controlling T cell responses.

Introduction
The centrosome is a key organelle in eukaryotic cells, serving as the primary microtubule-organizing center (MTOC) in most animal cells. Structurally, centrosomes consist of two centrioles connected by a flexible linker, and surrounded by a dense layer of pericentriolar material (PCM)1,2. This organization allows the centrosome to effectively nucleate and anchor microtubule (MT) filaments, which are essential for maintaining cell shape, enabling intracellular transport, and facilitating cell division. Typically, centriole numbers are tightly regulated during the cell cycle, leading to two centrioles (one centrosome) in G1 phase and four centrioles (two centrosomes) before mitosis3–5. Yet, centriole numbers can vary significantly during organismal development and differentiation into specialized cell types, leading to cells that contain either no centrosome or multiple6–8.

While originally named according to its position at the geometrical center of the cell, it is now well established that centrosome repositioning to the cell cortex is a prerequisite for various fundamental cellular processes such as polarized secretion, directional migration, asymmetric cell division, and immune surveillance9–12. Within the adaptive immune system, T cells play a central role in orchestrating antigen-specific immune responses. T cell activation is facilitated by the formation of the immune synapse (IS), a specialized signaling interface between antigen-presenting cells (APCs) and T cells. The formation of the IS starts with the recognition of a cognate antigen by the T cell receptor, which is presented via major histocompatibility (MHC) complexes on the surface of the APC. This interaction drives the spatial organization of receptors, adhesion molecules, and signaling complexes into distinct supramolecular activation clusters (SMACs), structuring the IS into a highly ordered, functional domain13,14. Formation of the IS is further coupled to a major reorganization of the T cell’s MT cytoskeleton: antigen recognition results in the repositioning of the T cell’s centrosome from the uropod to a position adjacent to the IS15,16. Centrosome reorientation toward the IS strictly depends on antigen recognition by the cognate T cell receptor17 and is accompanied by the movement of other organelles, such as the Golgi apparatus, toward the IS18–20. Polarization of Golgi-derived vesicles is considered to facilitate directional vesicle release toward the target cell19,21. Moreover, MTOC reorientation in T cells has been demonstrated to be required for sustained T cell receptor signaling downstream of IS formation22.

While the role of centrosome polarization in T cells is well-characterized, far less is understood about centrosome function and dynamics in APCs. Dendritic cells (DCs) represent the most potent APCs of the innate immune system, which have the unique ability to activate T cells in vivo23. They are strategically located in peripheral tissues, where they patrol the environment for invading microbial pathogens. Upon antigen encounter, DCs become highly migratory and translocate to draining lymph nodes to present peripherally acquired and processed antigens to naïve T cells. Besides antigen presentation, DCs shape T cell responses by secreting large amounts of soluble cytokines that activate T cells24.

There is accumulating evidence that functional specialization of the DC MT cytoskeleton plays a pivotal role in determining their immunostimulatory function25,26. Upon antigen encounter, DCs enter a robust cell cycle arrest, which can be accompanied by the acquisition of additional centrioles in G1 phase26. Extra centrioles facilitate persistent migration of cells along chemotactic cues and enhance cytokine secretion, yet how these additional centrioles influence IS formation and T cell activation remains largely unknown. In particular, it is unclear how multiple centrioles are organized during the formation of antigen-specific immune synapses, and how the resulting MT architecture in APCs influences T cell activation.

In this study, we investigate the functional organization of multiple centrioles and centrosomal MTs in murine DCs during antigen-specific T cell responses in single cells and within lymphoid tissues. Our interdisciplinary approach, combining experimental data with computational modeling, reveals optimal centrosome positioning in APCs and how centrosome integrity and function impact efficient T cell activation.

Results
Centrosome and MT integrity in DCs are required for T cell activation
To address whether an intact centrosome in APCs is a prerequisite for efficient T cell activation, we first sought to pharmacologically interfere with centrosome integrity specifically in APCs prior to T cell conjugation. To this end, we used antigen-presenting DCs as a model cell type for studying antigen-specific T cell activation. To obtain sufficiently large numbers of cells, we generated DCs from the bone marrow of Centrin-2 (CETN2)-GFP expressing mice in the presence of granulocyte-macrophage colony-stimulating factor (GM-CSF) and stimulated the cells with lipopolysaccharide (LPS) overnight to induce cell maturation. As mature bone marrow-derived DCs (BMDCs) are a heterogenous population of cells consisting of diploid and tetraploid cells as a result of an incomplete mitosis26, we separated BMDCs based on DNA content into diploid (2N) and tetraploid (4N) cells by flow cytometry and concentrated exclusively on the 2N cell fraction for all further experiments (Supplementary Fig. 1a). Sorted 2N DCs were further loaded or not loaded with different concentrations of the model antigen ovalbumin-peptide (OVAp) and incubated with naïve CD4+, OVA-specific T cells, which express a transgenic T cell receptor recognizing the OVAp (OT-II T cells). Under these experimental conditions, OVAp-loaded DCs efficiently activated naïve T cells as measured by surface levels of early activation markers such as CD69 and CD62L downregulation, as well as T cell proliferation assessed by proliferation-mediated dilution of the fluorescent dye carboxyfluorescein succinimidyl ester (CFSE)27 (Supplementary Fig. 1b-e).

To specifically target centrioles in DCs, we further treated BMDCs with the polo-like kinase 4 (PLK4) inhibitor Centrinone during differentiation and maturation28 (Fig. 1a). Centrinone treatment efficiently depleted CETN2-GFP+ foci in more than 60% of all mature cells but did not interfere with terminal differentiation of DCs as determined by upregulation of DC-specific cell surface markers such as CD11c and MHCII (Fig. 1b and Supplementary Fig. 2a,b). Note that Centrinone-treatment solely inhibits the generation of new procentrioles but does not deplete existing centrioles leading to a mixed population of cells that contain either no or one centriole, two, three or four, or more than four centrioles (Fig. 1b). When probing cells with antibodies against γ-tubulin to monitor PCM proteins surrounding the centrioles, we found residual staining present in Centrinone-treated cells, which appeared less intense than in untreated controls (Fig. 1c). This indicates some degree of residual PCM organization after Centrinone treatment. To confirm the lack of centrioles at higher resolution, we carried out expansion microscopy of Centrinone-treated and control cells. Mature DCs were immobilized on poly-L-lysine-coated coverslips and stained against acetylated (ac)-tubulin to identify centrioles, γ-tubulin to monitor PCM proteins, and N-Hydroxysuccinimide (NHS) ester to nonspecifically label free amino groups of proteins. In control samples, we routinely found cells with two, four, and more than four barrel-shaped centrioles identified by ac-tubulin and NHS ester staining, that were surrounded by PCM proteins (Fig. 1d, left). In the presence of PLK4-inhibition, ac-tubulin staining was not detectable, but cells had pronounced PCM structures shown by the presence of γ-tubulin (Fig. 1d, right). Similarly, NHS ester staining did not yield prominent barrel-shaped structures that co-localized with ac-tubulin staining in Centrinone-treated cells. These results indicate that pharmacological inhibition of PLK4 leads to depletion of centrioles but maintains part of the centrosomal PCM in DCs. In line with these findings, residual PCM organization has recently been described in cytotoxic T lymphocytes after genetic depletion of centrioles29.

The presence of γ-tubulin in the absence of centrioles prompted us to further examine whether MT organization and architecture was affected by the lack of centrioles. Immunostaining of Centrinone-treated and control cells against α-tubulin revealed that acentriolar cells exhibited the same number of MT filaments within defined areas around PCM proteins compared to control cells (Fig. 1e and Supplementary Fig. 2c). Moreover, the overall number of MTOCs was unaltered in the presence of Centrinone (Fig. 1e), demonstrating that under our assay conditions, DCs lacking centrioles are able to nucleate and organize MT filaments to form a functional MTOC. When co-culturing antigen-loaded Centrinone-treated cells with OT-II T cells, we found that cells lacking centrioles exhibited a significantly diminished capacity for inducing CD4+ T cell proliferation compared to control cells (Fig. 1f). These results reveal that centrioles and PCM integrity in APCs are essential for efficient T cell activation.

To further analyze the role of the MT cytoskeleton during T cell activation, we next perturbed MT growth in DCs by pre-treating cells with the MT destabilizing agent pretubulysin30,31. While permanent presence of pretubulysin for 24h efficiently depolymerized MT filaments in DCs, drug wash-out led to the regrowth of single filaments after 24h post-treatment (Fig. 2a). Of note, pretubulysin wash-out did not restore MT networks to the level of control cells, but led to fewer MT filaments growing predominantly from a single MTOC (Fig. 2b). Regrowing MTs appeared significantly shorter and less straight compared to control-treated cells (Fig. 2c) indicating that pretubulysin treatment is, to some extent, irreversible. This allowed us to study the role of MTs specifically in DCs, while leaving the T cell’s MT cytoskeleton unaffected (Supplementary Fig. 2d,e).

To this end, we first loaded mature DCs with OVAp and subsequently treated the cells for 1h with pretubulysin to disassemble MT filaments after antigen loading (Fig. 2d). Pre-treated and control DCs were either washed two times (wash-out) or directly (w/o wash-out) co-cultured with OT-II T cells. To assess T cell activation, we first measured IL-2 cytokine secretion, which is predominantly produced by activated T cells32, after 24h of co-culture by ELISA. We found that IL-2 levels were markedly reduced in the presence of or after pretubulysin wash-out indicating reduced T cell activation after MT perturbation in DCs (Fig. 2e). Note that IL-2 levels were still below the detection limit after 24h of co-culture when loading the cells with 0.1 μg/mL OVAp. Similar to IL-2 secretion, OX40 cell surface levels, a prominent T cell activation marker33, were significantly reduced on T cells after pretubulysin treatment further confirming diminished T cell stimulation after MT perturbation in APCs (Fig. 2e). To address whether reduced T cell activation impacts subsequent T cell proliferation, we measured T cell expansion by proliferation-mediated dilution of CFSE. In accordance with reduced IL-2 levels, T cell proliferation decreased from 62% in control samples to 27% after pretubulysin treatment and wash-out in the presence of 0.1 μg/mL OVAp (Fig. 2f). These results highlight that MT integrity in DCs is an immediate prerequisite for efficient T cell activation. In summary, our results emphasize the importance of an intact centrosome and centrosomal MT array in APCs for eliciting CD4+ T cell responses.

Enhanced MTOC activity in DCs with multiple centrioles during IS formation
Our data highlight a crucial role of the centrosome and the MT cytoskeleton for efficient T cell priming. While centriole numbers are generally limited to two in G1 phase and four prior to mitosis, DCs amplify centrioles upon antigen encounter leading to G1-arrested cells containing more than two centrioles26. We refer to this condition as multiple or extra centrioles. To address whether extra centrioles in DCs affect MTOC function in DCs, we visualized centrioles, PCM proteins and MT arrays in fixed samples. As extra centrioles in DCs arise due to overduplication of centrioles and by mitotic defects, which are accompanied by tetraploidization, we focused exclusively on diploid (2N) cells. 82% of mature BMDCs showed a 2N DNA profile of which 20% of cells contained more than two centrioles (Fig. 3a). Sorted 2N cells were further immuno-stained against either pericentrin (PCNT), γ-tubulin or CDK5RAP2 to visualize PCM proteins surrounding the centrioles and α-tubulin to trace centrosomal MT filaments (Fig. 3b–d and Supplementay Fig. 3a,b). We found that the levels of all three PCM proteins were increased in cells with multiple centrioles indicating that extra centrioles are able to recruit PCM proteins (Fig. 3c,d; right panels and Supplementay Fig. 3a). To further address whether and how extra centrioles in DCs impact antigen-specific DC-T cell interactions, we next visualized PCM recruitment during IS formation focusing on γ-tubulin as crucial MT nucleating component. OVAp loaded CETN2-GFP expressing DCs were co-cultured with OT-II T cells and cell-cell conjugates were fixed after 2h. Similar to unconjugated cells, we found enhanced γ-tubulin recruitment in DCs with multiple centrioles in the presence of T cells (Fig. 3e,f). High-resolution deconvolution microscopy further allowed us to identify MT filaments growing from the centrosome and quantify filament numbers within defined areas around the centrosome (Supplementary Fig. 2c). According to the role of γ-tubulin in promoting MT nucleation, we found increased numbers of MT filaments in the presence of extra centrioles during antigen-specific DC-T cell interactions, while the number of MTOCs was undistinguishable (Fig. 3g). These results demonstrate that centrosomal MT nucleation capacity in DCs is increased in the presence of additional centrioles, leading to one over-active MTOC with the capacity to nucleate a larger number of cytoplasmic MT filaments.

DCs form multiple T cell contacts independently of centriole numbers
To address whether enhanced centrosomal MT nucleation in DCs with multiple centrioles correlates with a higher capacity to form multiple T cell contacts, we determined the frequency distribution of bound T cells in relation to DC centriole numbers. In the presence of excess T cells (1:5 DC/T cell ratio), DCs engaged with either one T cell (mono-conjugated) or several T cells simultaneously (multi-conjugated) (Supplementary Fig. 3c). Cell-cell contacts were also formed in the absence of OVAp as described before34,35. As CD4+ T helper synapses comprise at least four distinct stages that proceed over several hours and are associated with morphological shape changes as well as reorganization of cytoskeletal components in both cell types20, we analysed conjugate formation after 1, 2 and 4h after conjunction. One BMDC bound on average two to three T cells simultaneously in the presence of excess T cells (Supplementary Fig. 3c). We did not detect significant differences in the number of interacting T cells between DCs with two or multiple centrioles at distinct time points of conjugation (Supplementary Fig. 3d, e). Thus, we concluded that the capacity to form cell-cell contacts between DCs and T cells occurs independently of centriole numbers and the levels of centrosomal MT nucleation in DCs.

Enhanced T cell activation in the presence of multiple centrioles in DCs
To further address whether the presence of additional centrioles and enhanced centrosomal MT nucleation impacts T cell stimulation, we sought to simultaneously visualize T cell activation and centriole numbers. To this end, we made use of Nur77GFP transgenic mice, which express GFP under the control of the Nur77 promotor36. Nur77 gene expression is up-regulated by antigen-dependent T cell receptor stimulation but not by other homeostatic or inflammatory signals37. Accordingly, in this mouse model, the levels of GFP expression directly reflect T cell receptor signal strength and reach a maximum expression after 12-24h of T cell receptor stimulation36. To estimate the timing of T cell activation after DC encounter in the context of MHCII antigen presentation, we crossed Nur77GFP mice with OT-II transgenic mice. Flow cytometric analysis revealed that Nur77-dependent GFP expression in T cells was low in the absence of antigen at all time points analyzed and after 1h of co-culture with OVAp-loaded CETN2-GFP expressing DCs. By contrast, 2h after mixing with OVAp-loaded DCs, a clear GFP signal appeared, which strongly increased after 4h, 6h, and 20h of incubation (Fig. 4a and Supplementary Fig. 4a). At 20h of co-culture in the presence of antigen, 67% of all T cells showed a prominent GFP signal, indicating efficient activation. In summary, Nur77GFP levels in T cells effectively reflect antigen-dependent T cell activation (Fig. 4a).

To elucidate whether T cell activation changes in the presence of different centriole numbers, we first sorted BMDCs according to CETN2-GFP signal intensities, which leads to two DC subpopulations enriched for either cells with two centrioles (CETN2-GFPlow) or cells with multiple centrioles (CETN2-GFPhigh)26 (Supplementary Fig. 4b,c). The percentage of cells carrying multiple centrioles ranged from 13–26% in the CETN-GFPlow population and 30–49% within the CETN2-GFPhigh population, leading to an average enrichment of cells with multiple centrioles by a factor of 2.0 (Supplementary Fig. 4d). Sorted cells were further loaded with OVAp and incubated for either 6h or 20h with Nur77GFP/OT-II transgenic T cells. After both time points of co-culture, the frequency of Nur77GFP-positive cells was significantly higher when T cell priming was accomplished with DCs enriched for multiple centrioles, indicating that these cells activate a larger number of T cells within the same time period compared to cells with only two centrioles (Fig. 4b).

To directly observe T cell activation on a single cell level and in relation to centriole numbers, we immobilized DC-T cell conjugates at distinct time points of co-culture and determined fluorescence intensities of GFP levels in T cells. To distinguish between distinct numbers of centrioles, we used CETN2-GFP expressing DCs and counterstained cells with antibodies against γ-tubulin or CDK5RAP2. This approach allows simultaneous visualization of DC centriole numbers, T cell MTOC positioning as well as T cell activation via Nur77. We observed polarization of the T cell’s centrosome to the nascent IS in an antigen-dependent manner at all time points analyzed, indicating efficient IS formation (Fig. 4c and Supplementary Fig. 5a). Similar to our flow cytometric analysis, GFP levels were significantly higher in the presence of antigen and steadily increased from 2h to 20h of co-culture demonstrating T cell receptor activation and antigen-specific T cell priming (Fig. 4d and Supplementary Fig. 5b). Nur77-dependent GFP expression was elevated in T cells that had already reoriented its centrosome compared to cells with a centrally localized MTOC further demonstrating that centrosome reorientation in T cells correlates with Nur77-dependent GFP expression and T cell priming (Supplementary Fig. 5c). We next determined T cell activation in relation to the number of centrioles present in the conjugated DC. To this end, we defined a threshold for T cell activation based on the frequency distribution of GFP signal intensities in T cells co-cultured in the absence of antigen (Supplementary Fig. 5d,e). We found that as early as 2h of co-culture, the number of GFP+ T cells was elevated after priming with DCs that contain multiple centrioles and the same increase was observed after 4, 6, and 20h of mixing (Fig. 4e). In accordance to our flow cytometric analysis, these results demonstrate that the frequency of activated T cells is higher when the APC contains multiple centrioles. Altogether, our findings provide evidence that cells with multiple centrioles activate T cells faster compared to cells with only two centrioles confirming optimized T cell responses in the presence of extra centrioles in APCs.

Multiple centrioles cluster and localize close to the cell center in DCs during IS formation
Centrosome polarization has been reported in DCs during the interaction with naïve CD8+ T cells. MTOC reorientation in DCs depends on the Rho GTPase Cdc42 and enables targeted delivery of vesicles containing T cell stimulatory molecules38. Based on these observations, we reasoned that the observed accelerated T cell priming capacity in the presence of multi-numerous centrioles may result from de-clustering of extra centrioles. Subsequently, centrioles could move and reorient to distinct contact sites forming additional MTOCs, which efficiently deliver stimulatory molecules to the respective IS (Fig. 5a). To test this hypothesis, we measured intracentrosomal distances (between centrioles of one centrosome) and average distances between all centrioles in DCs with multiple centrioles after conjugate formation in the presence or absence of OVAp (Fig. 5b, c, schemes). We further distinguished whether cells form mono- or multi-conjugated synapses. Analysis of intracentrosomal distances in cells with two centrioles treated with or without OVAp for distinct time points of co-culture (1, 2, 4, 6 and 20h) revealed distances of 0.8-1.1 μm between individual centrioles independently of the number of T cells attached (Fig. 5b and Supplementary Fig. 6a). Similarly, average distances in cells with multiple centrioles ranged between 0.9–1.3 μm and did not show prominent differences between OVAp loaded and unloaded cells (Fig. 5c and Supplementary Fig. 6b), suggesting that multiple centrioles remain clustered during antigen-specific DC-T cell contacts. Averaged distances were indistinguishable not only when cells formed single contacts but also in multi-conjugated cells.

To further analyze centriole positioning in DCs, we quantified the ratio of distances between the DC centrioles and the T cell, which was normalized to the distance between the DC center point (CP) and the T cell in the presence or absence of antigen (Fig. 5d). Our analysis revealed that 1, 2, 4, 6 and 20h after co-culture, all centrioles in DCs were still located in close proximity to the cell center and the nucleus (Fig. 5e, f and Supplementary Fig. 6c–f). This behavior was not only observed in multi-conjugated cells but also when single synapses were formed. These results suggest that MTOC positioning in DCs was independent of the number of centrioles and T cells conjugated. Moreover, our findings demonstrate that centrosome polarization toward the IS in DCs is dispensable for T cell activation.

To further study DC centriole dynamics with high spatio-temporal resolution, in particular during early DC-T cell encounters, we recorded time-lapse images of DC-T cell conjugates. To this end, CETN2-GFP expressing DCs were loaded with OVAp, mixed with OVA-specific OT-II T cells and imaged directly after mixing. To monitor efficient DC-T cell interaction, we visualized intracellular calcium (Ca2+) influx in T cells and considered only interactions with long-lasting Ca2+ responses. In cells containing two centrioles, intracentrosomal distances did not increase upon contacts with one or several T cells (Supplementary Fig. 7a, Supplementary movies 1, 2). Similarly, average distances in cells with multiple centrioles remained unaltered in conjunction with one or several T cells (Supplementary Fig. 7b, Supplementary movies 3, 4). Moreover, no net movement of centrioles in DCs could be observed within the imaging period (0-1h after conjugation) in the presence or absence of antigen (Supplementary Fig. 7c, and Supplementary movies 1–4). Instead, centrioles were found near the nucleus independently of the number of T cells bound, similar to our fixed samples (Supplementary Fig. 7d).

As cellular behavior can vary significantly depending on whether cells are cultured on flat 2D surfaces or under confined 3D conditions39, we further sought to study centriole configuration in physiologically relevant environments and carried out adoptive transfer experiments40. Naive OVAp-specific OT-II-specific T cells labelled with CellTrace Far Red Cell Proliferation dye were seeded into wild type recipient mice and, after 24h immunized by injection of OVAp-loaded CETN2-GFP expressing BMDCs (Fig. 6a). Mice were sacrificed 48h after BMDC injection to isolate popliteal and inguinally lymph nodes (LNs). Immunostaining of frozen LN sections revealed antigen-specific DC-T cell interactions within T cell areas (Fig. 6b). 3D rendering of DC-T cell contacts within LNs identified intracentrosomal distances of 0.9±0.2 μm (1 T cell) or 0.8±0.2 µm (>1 T cell) and average distances between centrioles of 1.1±0.2 μm (1 T cell) or 1.2±0.3 µm (>1 T cell) (Fig. 6c,d, and Supplementary movie 5) highlighting a clustered centriole configuration in vivo. Similar to our 2D analysis, all centrioles localized closely to the cell center during DC-T cell contacts independent of the number of T cells attached (Fig. 6e). In summary, our results demonstrate that multiple centrioles cluster during antigen-specific DC-T cell contacts in vitro and in vivo and collectively stay close to the cell center when mono- or multi-conjugated contacts are formed.

Centriole de-clustering impairs T cell activation
As we observed tight centriole clustering during DC-T cell contacts, we next addressed the importance of this phenomenon for the induction of T cell priming. Therefore, DCs were treated with the de-clustering agent PJ-34, a poly-ADP-ribose polymerase inhibitor with no MT binding activity in proliferating cells41,42. PJ-34 was administered for 3h on mature DCs to test the effect on centriole organization and to rule out effects on transcriptional regulation which might impact T cell activation. MHCII cell surface expression was unaltered after PJ-34 treatment (Supplementary Fig. 8a,b). In addition, mRNA expression levels of DC-specific cytokines such as Ccl5, Cxcl1 and Il-6 were analysed as they are involved in attracting and activating immune cells43–45. mRNA levels of Ccl5, Cxcl1 and Il-6 were unaffected by PJ-34 treatment (Supplementary Fig. 8c). Also, intracellular protein levels were undistinguishable in PJ-34-treated cells compared to control cells (Supplementary Fig. 8d). Thus, we concluded that PJ-34 treatment does not perturb antigen presentation or cytokine production in DCs. Next, centriolar distances were measured in fixed samples. Under control conditions (HO), cells containing two centrioles displayed mean intracentrosomal distances of 0.7±0.3 μm, while intercentrosomal distances in cells with multiple centrioles were slightly larger, with average distances of 1.3±0.7 μm (Fig. 7a,b). PJ-34 treatment led to a significant shift to larger distances, in particular of intercentrosomal distances in cells carrying multiple centrioles (2.2±1.6 μm), indicating that pairs of centrioles get dispersed (Fig. 7b, and Supplementary Fig. 8e). Immunostaining against γ- and α-tubulin and analysis of MTOC numbers and MT filaments emanating from either clustered or dispersed centrioles revealed that PJ-34-treated cells harbor significantly more MTOCs per cell compared to control cells while the number of MT filaments was unaltered upon PJ-34 treatment (Fig. 7c). Intriguingly, when co-cultering either PJ-34-treated or control OVAp-loaded DCs with OT-II T cells, we found that in the presence of de-clustered centrioles, T cell activation was significantly diminished compared to control cells (Fig. 7d, e), demonstrating that de-clustering of multi-numerous centrioles and formation of multiple MTOCs impairs T cell activation. In summary, our findings highlight a crucial role for pro-centriole and MTOC arrangement in APCs to efficiently activate T cells.

Modeling T cell priming with geometrically optimal centriole positioning in DCs
Since centrioles are integral to the centrosome, their location directly corresponds to that of the centrosome. Our experimental data emphasize a crucial role of centriole positioning close to the cell center in DCs and provide evidence that T cell activation is increased in the presence of multiple clustered centrioles compared to DCs containing two centrioles. These observations prompt queries into whether the centrosome’s proximity to the cell center is advantageous for DCs. For instance, a centrosomal position geometrically optimal to the entire cell surface may expedite the delivery of immune signals to the interacting T cells. The optimal position will also provide dynamic MTs faster access to the IS. Therefore, a key task is to examine the optimal centrosome localization and how MTs contribute to centrosome positioning. Further, we address the physical consequences of centriole clustering. In other words, does clustering of centrioles facilitate efficient MT search for the IS over dispersed centrioles acting as separate MTOCs in distinct locations? And whether the optimal position also corresponds to the mechanically stable position of the centriole cluster. To further elaborate on this, we resort to mathematical and computational models to systematically approach these questions.

First, we rationalized with simple geometric considerations the physiological consequences of centralized centrioles during T cell priming. We first seek to explore the geometrically optimal position of the centriole cluster or a single centrosome with two centrioles in DCs relative to the entire cell surface, without explicitly delving into the complexities of MT dynamics. Since the position of the target - the center of the IS on the cell surface - is not predetermined, a centrosomal position with minimal average distance to all possible target points on the cell surface may facilitate efficient delivery of stimulatory molecules to the IS. For cell surface regions directly accessible to the centrosome, the shortest distance is a straight line from the centrosome to the target point on the cell surface. However, for regions no longer visible due to nuclear hindrance, the minimum geometric distance would follow the scheme presented in Fig. 8a. This distance corresponds to the path length of MTs on optimal trajectories between the centrosome and the target point on the cell surface. For a detailed calculation, we refer to the “Methods” section.

In the absence of a nucleus, the optimal centrosome position coincides with the geometric center of the cell, as determined through analytical calculations in both circular and spherical cellular geometries and further validated by computational analysis (see Supplementary Information; Supplementary Fig. 9a). However, the presence of a nucleus covering the central region of the cell prevents the centrosome from locating at the cell center. Using computational analysis, we first explore the optimal centrosome position in the presence of a centrally located nucleus and estimate the average geometric distance, <d_short>, as a function of the centrosome’s distance from the cell center, h_CS, or from the nuclear surface, d_CS-NS. Our data indicate an optimal perinuclear positioning of the centrosome, marginally shifted from the nuclear surface by <1 μm (Fig. 8b). We further investigate the centrosome’s position for various off-centered positions of the nucleus. Note that, for a centrally located nucleus, spherical symmetry of the cell and the nucleus ensures that estimating <d_short> with random target points on the cell surface is independent of the direction of the centrosome placed away from the nucleus. It solely depends on the centrosome’s distance from the nucleus. However, for an off-centered nucleus, <d_short> depends on the specific three-dimensional positioning of the centrosome relative to the nucleus. We discovered that the global minimum of <d_short> is achieved when the centrosome lies on a fixed axis pointed from the nuclear center toward the cell center (shown as Z-axis in the plots; Supplementary Fig. 9b–e). Therefore, by placing the centrosome at various locations along that axis, we estimate the optimized values of h_CS and d_CS-NS, corresponding to the minimum of <d_short>, and plotted for three different nuclear positions: d_offset = 0 μm, representing a centrally located nucleus; d_offset = 6 μm, where the cell center is just outside the nuclear surface and d_offset = 12 μm, indicating the maximally off-centered nucleus touching the cell surface (Fig. 8c). Our findings suggest that positioning the centrosome close to the nuclear periphery is advantageous when the nucleus is at the cell center. The optimal centrosome position shifts toward the cell center as the nucleus becomes off-centered. Overall, our model provides a rational accounting for the geometric positioning of centrosomes or the clustered centrioles near the cell center leading to efficient priming of T cells on the cell surface.

Modeling highlights a critical role of MT dynamics for centrosome (or centriole) positioning in DCs
Next, we sought to understand how MT dynamics may affect centrosome positioning in DCs and aimed to determine the optimal centrosome position that minimizes the time required for searching the IS in DCs. To explore this, we adopted the well-established “search and capture” hypothesis, initially proposed in the context of mitotic spindle assembly, which leads to the binding of MT filaments to the kinetochores of sister chromatids46–48. This framework was further extended in a recent study to examine the docking efficiency of MTs at the IS in T cells49 and involves dynamically unstable MTs originating from the centrosome, exploring the three-dimensional cellular volume to locate and capture the target IS. We accommodate this established model for MT dynamics in T cells with morphological differences of the MT network of T cells and DCs: T cells are spherical (radius, r ≈ 5 μm), much smaller than DCs, with a relatively large nucleus (r ≈ 3 μm), such that the centrosome is generally located close to cell surface and MTs emanating tangentially to the surface50,51. DCs are large cells (r ≈ 18 μm) with a relatively smaller nucleus (r ≈ 6 μm) and MTs typically nucleated from a clustered centriole assembly positioned close to the cell center. This specific arrangement allows MTs to radiate astrally towards the surface, rarely touching it tangentially as in T cells (Fig. 8d). Consequently, we incorporate a reduced MT gliding probability into our model: When growing MTs hit the cell surface, they can undergo instant catastrophe determined by the angle of contact with the surface or glide along the surface with catastrophe frequency increasing with gliding distance, as depicted in Fig. 8e (ii), (iii). Note that such cortex-induced changes in MT growth direction were reported in animal cells52,53, plant cells54, yeast55, and HeLa cells56 and are well established in theoretical modeling49,57. We further hypothesized that MT growth can be similarly directed by the nuclear surface since we frequently observe MTs in the “geometric shadow” behind the nucleus (the region not accessible to straight MTs growing from the centrosome; see also Fig. 3e) and incorporated it into our model as sketched in Fig. 8e (i): When MTs reach the nuclear surface, they first glide along it and subsequently detach at a rate that increases with MT curvature along the nuclear surface. After detachment, MTs grow tangentially from the nuclear surface toward the cell surface seeking the target.

To explore how the MT’s gliding ability along the cell surface affects centrosome positioning in DCs, we plot the average search time (<T_search>) against the distance of the centrosome from the surface of a centrally located nucleus, d_CS-NS, while varying the parameter λ_l (Fig. 8f) regulating the sensitivity of the catastrophe frequency with MT’s gliding distance (see Fig. 8e, iii). A higher λ_l corresponds to a faster MT catastrophe and vice versa. We find that for higher λ_l, the average search time is minimized when the centrosome is located near the nuclear periphery. This observation correlates with the scenario where MTs are not allowed to glide along the cell surface, resulting in instant catastrophe upon hitting the cell surface. However, for very small λ_l values, the optimal centrosome position shifts adjacent to the cell surface. With restricted MT gliding along the cell surface, the search progresses via two pathways: MTs can directly reach the target from the centrosome, and MTs that encounter the nucleus glide along its surface and reach the target on the cell surface after detaching from the nuclear surface. In this case, MTs can capture the target most efficiently when they are close to the nuclear surface, minimizing the average distance between the centrosome and the target IS. For a centrosome located far off from the nucleus, capturing IS located on the cell surface well below the cell’s equatorial plane (hidden below the nucleus) relies on the MTs originating from the centrosome hitting the nuclear surface and gliding along it before reaching the target IS. This in turn increases the overall search time for centrosomes distant from the nucleus. Conversely, if MTs can glide along the cell surface (small λ_l), placing the centrosome near the cell surface accelerates the target capture. Note that, given the experimentally consistent optimal centrosome positioning without MT gliding along the cell surface (or with larger λ_l), henceforth, we proceeded with simulations, preventing the MTs from gliding along the cell surface.

Next, we explored the sensitivity of our findings to off-centered nuclear positioning (Fig. 8g). In line with geometric predictions, our results indicate the optimal perinuclear position of the centrosome for a centrally located nucleus and in the vicinity of the cell center for an off-centered nucleus (Fig. 8g, h). Surprisingly, even with severely shorter MTs (~30% of the average MT length considered in the model), the optimal centrosome position remains consistent, with prolonged search times (Supplementary Fig. 9f–h). Evidently, for a largely off-centered nucleus (d_offset = 12 μm), a perinuclear centrosome results in a significant increase in the distance to targets on the cell surface away from the nucleus, leading to extended search times. In contrast, MT arrays from a centrally located centrosome efficiently capture targets across the cell surface due to the minimum average distance to the centrosome. Interestingly, like spherical cells, flattened DCs display optimal centrosome positioning at the cell center for an off-centered nucleus and in the perinuclear region along the short axis of the cell for a centrally located nucleus (Supplementary Fig. 10a–d).

Clustering of multiple centrioles enhances MT search efficiency
Our experiments show that PJ-34-induced de-clustering of centrioles, resulting in distant MTOCs within the cell, reduces DCs’ T cell activation efficiency (Fig. 7). Therefore, to further investigate how clustering of multiple centrioles through centrosomal aggregation affects the search process, we performed simulations involving four independent clusters of centrioles (i.e., four centrosomes) acting as independent MTOCs, with an evenly distributed array of MTs (Fig. 9a–c). These centrosomes were separated and randomly positioned around the optimal centrosomal position determined earlier for a centrally located and off-centered nucleus (see Fig. 8g, h). Strikingly, our findings revealed that irrespective of the nuclear positioning, the average search time is minimized when all the centrosomes are close together, forming clustered centrioles - a trend that holds even in largely flattened cells (Supplementary Fig. 10e–g). Note that, to ensure unrestricted MT growth along nuclear periphery, we flattened the cell up to the point such that the nuclear envelope does not contact the cell boundary. Clustered centrioles promote efficient capture of the IS by a unified astral array of MTs. The collective search conducted by MTs nucleating from all the centrosomes originating from a single optimal position increases the likelihood that at least one MT captures the target. However, in the case of dispersed centrosomes, the capture process is not as efficient since the MTs do not originate from the same optimal position. Fewer MTs from each of the centrosomes searching independently for distant targets on the cell surface decrease the chance of a successful capture which effectively increases the overall search time.

Noting that our experiments revealed a ~10–20% increase in the total number of MTs in DCs containing multiple clustered centrioles compared to DCs with only two centrioles, we further employ our computational model to investigate the correlation between MT search time and the number of MTs (see Fig. 3g, left). Interestingly, we find a decrease in the search time with an increasing number of MTs (Fig. 9d). This behavior is consistent with our analytical prediction of average search time without a nucleus, estimated within the model framework introduced earlier49 (see Supplementary Information). Note that, dispersing the centrosomes may also facilitate faster target capture when MTs are allowed to glide freely along the cell surface rather than restricting their growth (Supplementary Fig. 11a–c). Under such conditions, the dispersed centrosomes allow MTs to efficiently capture the targets by gliding along the cell surface, resulting in a reduced search time. Overall, our model highlights the pivotal role of multiple clustered centrioles, coupled with increased MT numbers, efficiently activating T cells by facilitating quicker access to the IS through dynamic MTs.

A plausible mechanistic force-balance model describing centriole cluster positioning and centrosome integrity in DCs
To find the mechanically stable configuration of multiple centrioles, irrespective of a putative attractive force between them, we used an established mechanistic model of MT-generated forces resulting from interactions of centrosomal MTs with the nucleus and the cell surface58–60. We considered initially four closely spaced centrosomes (each containg centriole doublets) with independent microtubule nucleation activity. While pushing against the cell or nuclear surfaces, a polymerizing MT is buckled, and the corresponding buckling force is applied on the centrosomes. The resultant force causes the centrosomes to move through the cytoplasm59–61 as sketched in Fig. 9e. Similar to the search and capture model described earlier with frequent observations of MTs at the “geometrical shadow“ behind the nucleus (see also Fig. 3e), we hypothesize that MT growth can be directed by the nuclear surface. Accordingly, our model assumes that the majority of MTs slide along the nuclear surface and only a small fraction (~10%) buckles at the nuclear surface, whereas all MTs hitting the cell surface generate buckling-induced-pushing force on the centrosomes.

We observe that all centrosomes stayed together and positioned along the line joining the nuclear center and the cell center. The centrosomes remain close to the nuclear surface (~1 μm away from the nuclear surface) when the nucleus is fixed at the cell center (d_offset = 0) and near the cell center when the nucleus is off-centered (d_offset = 6 μm and 12 μm) (Fig. 9f, Supplementary movies 6-8). These findings qualitatively corroborate our experimental observations and simulation results of the optimal centrosome (or centrioles) positions estimated geometrically and by the search-and-capture scheme. As expected, when more MTs are assumed to buckle at the nuclear surface, the distance between the centroid of the four closely spaced centrosomes (or centriole cluster) and the nuclear surface increases due to the higher pushing forces generated on the centrosomes from the nuclear surface (Supplementary Fig. 11d).

Altogether, our computational approach highlights a beneficial role for multiple clustered centrioles in APCs, which nucleate a larger number of MT filaments and collectively stay close to the cell center to optimally form cell-cell contacts and ultimately activate T cells.

Methods
Mice
All mice used in this study were bred on a C57BL/6J background and maintained at the institutional animal facilities in accordance with the German law for animal experimentation. Permission of all experimental procedures involving animals was granted and approved by the local authorities (Landesamt für Verbraucherschutz und Ernährung North Rhine-Westphalia [LAVENRW under AZ 81-02.05.40.19.022 and AZ 81-02.04.2021.A319]). CETN2-GFP and Nur77GFP mice were purchased from Jackson (CB6-Tg(CAG-EGFP/CETN2)3-4Jgg/J and B6N.B6-Tg(Nr4a1-EGFP/cre)820Khog/J). OVA-specific OT-II mice were a gift of Sven Burgdorf. Alternatively, OT-II mice were purchased from Charles River and intercrossed with B6.SJL-Ptprca Pepcb/BoyJ (congenic CD45.1 mice, also purchased from Charles River) for one generation to obtain heterozygous OT-II CD45.1/2 mice that were then used as donors for the in vivo experiments. Mice from both sexes were used and sexes matched for in vivo experiments.

Housing conditions: Animals are provided food and autoclaved water ad libitum. Cages are ventilated via a central air conditioning system and kept at a temperature of 21 ± 1 °C and a relative humidity of 55 ± 15%. The air is exchanged 70 times per hour. Furthermore, animals are kept at a 12h/12h day/night rhythm. During the day 400 lux of light are provided whereas during the night a red light is switched on.

Dendritic cell culture
Femurs and tibias from legs of 3–5 month-old CETN2-GFP expressing mice were removed and placed in 70 % ethanol for 2 min. Bone marrow was flushed with PBS using a 26 gauge needle. 2 × 10^6 cells were seeded per 100mm petri dish containing 9ml of complete medium (Roswell Park Memorial Institute (RPMI) 1640 supplemented with 10% Fetal Calf Serum, 2 mM L-Glutamine, 100U/mL Penicillin, 100 μg/mL Streptomycin, 50 μM ß-Mercaptoethanol; all Gibco) and 1mL of Granulocyte-Monocyte Colony Stimulating Factor (GM-CSF, supernatant from hybridoma culture). On day 3 and 6 complete medium supplemented with 20% GM-CSF was added to each dish. To induce maturation, cells were stimulated overnight with 200 ng/mL lipopolysaccharide (LPS) and used for experiments on day 8-9. Alternatively, DCs were frozen in FCS containing 10% DMSO on day 7. For experimental use they were thawed the day before the experiment and stimulated with 200 ng/mL LPS overnight.

To prevent new pro-centriole formation cells were cultured in the presence of the PLK4 inhibitor Centrinone (Tocris; 250 nM or 500 nM) or control (solvent DMSO) during differentiation and maturation. To induce microtubule depolymerization, cells were treated with 1 µM pretubulysin or control (solvent DMSO) for 1h after antigen-loading. Cells were washed (wash-out) or not (w/o wash-out) with full media before T cell addition. To induce centrosome de-clustering PJ-34 (Sigma-Aldrich) was used. Cells were loaded with OVAp for 2h and subsequently treated with 50, 100 or 200 µM of PJ-34 or control (solvent H2O) for 3h. Cells were washed two times with full media and incubated with OT-II-specific T cells at the indicated time points.

Flow cytometry
For flow cytometric analysis, cells were washed with PBS once and incubated 10 min with anti-CD16/CD32 antibody (1:100) in blocking buffer (1x PBS, 1% BSA, 2 mM EDTA). Staining with fluorescently labelled antibodies diluted in blocking buffer was carried out for 20 min at 4 °C. For intracellular cytokine staining, cells were fixed and permeabilized with the Foxp3/Transcription Factor Staining Buffer Set (eBioscience) according to the manufacturer’s protocol. The following antibodies were used: hamster anti-mouse CD11c-PE (N418, BioLegend 117308, 1:500), rat anti-mouse MHCII (I-A/I-E)-APC-Cy7 (M5/114.15.2, BioLegend 100222, 1:800), rat anti-mouse MHCII (I-A/I-E)-eFluor450 (M5/114.15.2, Invitrogen 48-5321-82, 1:800), rat anti-mouse CD4-APC (RM4-5, BioLegend 100516, 1:500), rat anti-mouse CD19-PE (6D5, BioLegend 115507, 1:500), rat anti-mouse CD19-Pacific Blue (6D5, BioLegend 115523, 1:500), rat anti-mouse MHCII (I-A/I-E)-PE-Dazzle (M5/114.15.2, BioLegend 107648, 1:600), hamster anti-mouse CD69-FITC (H1.2F3, BioLegend 104505, 1:200), hamster anti-mouse CD69-PE-Dazzle (H1.2F3, BioLegend 104536, 1:200), rat anti-mouse CD62L-PE-Cy7 (MEL-14, BioLegend 104417, 1:500), rat anti-mouse OX40 (CD134)-BV711 (OX-86, BioLegend 119421, 1:300), rat anti-mouse IL-6-PE (MP5-20F3, BioLegend 504503, 1:300), mouse anti-mouse CCL5-PE (2E9/CCL5, BioLegend 149103, 1:300), rabbit anti-mouse CXCL1-Alexa Fluor 594 (1174A, R&D Instruments IC4532T, 1:300). For live dead staining DRAQ7 (BioLegend 424001, 1:500) or the Zombie Aqua Fixable Viability Kit (BioLegend 423101) was used. After staining, cells were washed once with blocking buffer and data acquired at the LSRII flow cytometer (BD Bioscience). Data analysis was performed using FlowJo v10.8.1.

To determine T cell activation via CD69 upregulation and CD62L downregulation, DC co-cultures with splenocytes were analysed after 20-22h via flow cytometry as described above. OX40 upregulation on the T cell surface was determined after 2.5 days. T cell proliferation rates were assessed by dilution of CFSE. To this end, prior to incubation with DCs, splenocytes were stained with a final concentration of 0.5 µM CFSE (Invitrogen) for 7 min at 37 °C in PBS and washed with complete medium. Co-cultures were incubated for 2.5 days. Nur77-dependent GFP upregulation was also determined via flow cytometry after the indicated time points of naïve CD4+ T cell co-culture with DCs.

Sorting of 2N BMDCs
To sort DCs based on their DNA content, mature BMDCs were harvested and stained with Vybrant DyeCycle Violet Stain (1:1000, Thermofisher; V35003) in RPMI without phenol red for 20 min at 37 °C. Cells were sorted at the ARIA III Sorter (BD Bioscience) according to their ploidy level with focusing on diploid cells (2N) and dismissing polyploid cells. Cells were re-analysed after the sort to ensure purity of the individual subpopulations. Afterwards, cells were recovered in full medium at 37 °C for at least 30 min.

Mixed lymphocyte reactions (MLR)
For DC-T cell co-culturing assays sorted DCs (1−2 × 10^4 cells/well) were seeded in 96-well U-bottom plates. After recovering time of 30 min, cells were incubated with OVAp antigen (OVA_323-339: specifically recognized by CD4+ OT-II T cells; 0.01 µg/mL, 0.1 µg/mL, 1 µg/mL) or without OVAp (controls) for 2h. In the meantime, splenocytes were isolated from OT-II mice or Nur77GFP/OT-II mice. Therefore, splenic, inguinal, axillary and brachial lymph nodes were removed and smashed through a 70 µm filter using PBS and a syringe piston. After centrifugation (400xg 5 min), ACK lysis buffer (Gibco) was added for 5 min at RT before stopping the red blood cell lysis with PBS containing 2% FCS and 2 mM EDTA. Cells were filtered through a 40 µm filter, centrifuged and adjusted to the respective cell number to co-culture with DCs or for subsequent naïve CD4+ T cell isolation. Naïve CD4+ T cell isolation was performed according to the manual of the EasySep Mouse Naïve CD4+ T Cell Isolation Kit (STEMCELL Technologies). After removing the antigen from the DCs, T cells were added to DCs in a ratio of 1:2 or 1:5.

IL-2 ELISA of MLR
For quantification of IL-2 cytokine levels via ELISA, DCs and T cells were co-cultured as described above (MLR). 5 × 10^4 DCs and 10 × 10^4 T cells were co-cultured in 200 µL culture volume for 20h. Supernatants were harvested and incubated with the mouse IL-2 ELISA Kit (Invitrogen) according to the manufacturer’s instructions.

mRNA expression levels
For mRNA quantification after PJ-34 treatment, 1 × 10^6 BMDCs were harvested in 350 µL Lysis Buffer (RNeasy Lysis Buffer + 1% ß-Mercaptoethanol), and RNA isolation was carried out using the RNeasy Mini Kit (all products purchased from Qiagen). RNA concentration was determined using Nanodrop 1000 spectrophotometer. Gene expression was assessed using the TaqMan RNA-to-CT 1-Step Kit (Thermo Fisher Scientific) with a reaction volume of 20 µL containing 250 ng RNA template and 1 µL of TaqMan Gene Expression Assay (Thermo Fisher Scientific; duplicates performed). Samples were run on a CFX96 Real-Time System (BioRad) according to the manufacturer’s instructions. Data were normalized according to the expression of a housekeeping gene in DCs (TATA-binding protein). Analysis of relative gene expression was carried out using the CFX Manager Software Version 3.1 (BioRad).

Under-agarose interaction assay
To allow visualization of centrioles during live cell imaging, DC-T cell co-cultures were injected under a block of agarose to prevent cell floating during the imaging period. Therefore, a custom-made chamber was built by gluing a 1-cm plastic ring with paraffin into a glass-bottom dish. 1 % agarose solution was prepared by mixing 0.2g UltraPure agarose (Invitrogen, 16500-100) with 5mL water, 5mL 2x Hanks’ Balanced Salt Solution (HBSS 10x, Gibco, 14185052) and 10mL phenol red-free RPMI 1640 Medium supplemented with 20% FCS and 1% penicillin 100 U/mL/streptomycin 100 µg/mL. For live cell imaging, ascorbic acid was added to a final concentration of 50 µM. 500 µL of the heated agarose was poured into each chamber. After polymerization, the dishes were filled with water around the agarose and incubated for 45–60 min at 37 °C, 5% CO2 to equilibrate the agarose. For the experiment, cells were injected with a small tip under the agarose in a volume of 0.4–0.6 µL. To allow visualization of interaction during live cell imaging, T cells were stained with the calcium-sensitive dye Cal520 (Abcam) prior to injection. For efficient staining, cells were incubated with 3 µM Cal520 in phenol red-free RPMI 1640 medium supplemented with 20% FCS for 30 min at 37 °C. To avoid toxic effects the dye was efficiently removed by washing two times. Live cell imaging was started directly after DC and T cell injection. Alternatively, to allow immunofluorescence staining, cells were fixed after 60-90 min in the incubator with 4% paraformaldehyde (PFA) solution overnight at 4 °C. The next day, agarose was removed carefully and the cells were washed with PBS two times before staining.

Adoptive transfer experiments
For in vivo DC-T cell interaction experiments, naïve OT-II T cells were isolated using the EasySep Mouse Naive CD4+ T Cell Isolation Kit (STEMCELL Technologies) according to the manufacturer’s instructions. Naïve CD4+ OT-II T cells were labelled with the CellTrace Far Red Cell Proliferation dye (Invitrogen) at a concentration of 0.5 µM and 2 × 10^6 naïve OT-II cells were injected into the tail vein of C57BL6/J wild type mice. After 24h, 1 × 10^6 mature CETN2-GFP expressing BMDCs, which were loaded with 1 µg/mL OVAp, were injected subcutaneously into the hocks of both hind legs. Popliteal and inguinal lymph nodes were harvested after 48h and incubated in 1% PFA in PBS over night at 4 °C. Subsequently, LNs were washed with PBS and dehydrated in a sucrose gradient (10%-30% sucrose in PBS). LNs were embedded in cryomedium (Tissue-Tek O.C.T. Compound, Sakura) and stored at -80 °C until they were cut into 20 µm thick sections.

Staining of cryosections. Staining of cryosections was carried out at room temperature. Sections were permeabilized with 0.3% Triton X-100 in PBS for 10 min and then blocked with 1% BSA in 0.3% Triton X-100 in PBS for 30 min. All primary and secondary antibodies were used in a dilution of 1:200 in 1% BSA in 0.3% Triton X-100 in PBS. Sections were incubated with primary antibodies overnight. After washing 3×10 min with PBS, secondary antibodies were added for 2h. DAPI staining (1:1000 in PBS, Stock 1 mg/mL, Sigma) was carried out for 30 min. After 3×10 min washing in PBS, sections were incubated in Ce3D tissue clearing solution (BioLegend) for at least 2h and then mounted with mounting medium (Invitrogen).

Immunofluorescence
Cells were immobilized by incubating 1-2 µL of cell suspension on uncoated cover slips for 5 min at 37 °C before adding 4% PFA for 20 min. For MT staining, cells were fixed after injection under agarose as described above (section: Under-agarose interaction assay). Fixed cells were washed twice with PBS for 10 min. To allow intracellular antibody staining cells were permeabilized adding 0.2% Triton X-100 (Sigma) in PBS for 30 min at room temperature. After washing 3×10 min with PBS samples were incubated in blocking solution (5% BSA (Roth) in PBS) for 1h to prevent unspecific binding of the antibodies. Next, samples were incubated with primary antibodies diluted in blocking solution overnight at 4 °C. Staining for PCNT was carried out for 1h at room temperature. Afterwards, coverslips were washed 3×10 min with PBS and stained with secondary antibodies diluted in blocking solution in the dark for 1h at room temperature. After three times washing 10 min with PBS coverslips were mounted with DAPI-containing mounting medium (Invitrogen) and sealed with nail polish before imaging.

The following primary antibodies were used: rat anti-mouse alpha-tubulin (YL1/2, Invitrogen MA1-80017, 1:500), mouse anti-mouse acetylated-tubulin (6-11B-1, Sigma-Aldrich T7451, 1:500), mouse anti-mouse γ-tubulin (GTU-88, Sigma-Aldrich T6557, 1:500), rabbit anti-mouse γ-tubulin (polyclonal, Abcam AB11317, 1:500), rabbit anti-mouse CDK5RAP2 (polyclonal, Sigma-Aldrich 06-1398, 1:500), rabbit anti-mouse pericentrin (EPR21987, Abcam AB4448, 1:200), hamster anti-mouse TCRβ chain-Biotin (H57-597, BD Bioscience 553168, 1:100). The following secondary antibodies were used in a dilution of 1:400: Donkey Anti-Mouse Alexa Fluor 647 AffiniPure F(ab’)2 Fragment IgG (H+L) (715-606-150), Donkey Anti-Mouse Cy3 AffiniPure F(ab’)2 Fragment IgG (H+L) (715-166-151), Donkey Anti-Rat Cy3 AffiniPure F(ab’)2 Fragment IgG (H+L) (712-165-150), Donkey Anti-Rabbit Alexa Fluor 647 AffiniPure F(ab’)2 Fragment IgG (H+L) (711-606-152), Goat Anti-Rabbit Cy3 AffiniPure F(ab’)2 Fragment IgG (H+L) (111-165-144), Streptavidin-Cy3 (016-160-084) (all from Jackson ImmunoResearch).

Microscopy
Confocal microscopy was performed on a motorized stage at RT with an inverted microscope equipped with an Airyscan module; an EC Plan-Neofluar 10×/0.30 objective; a Plan-Apochromat 63×/1.4 oil DIC objective; 488, 561, and 633 laser lines; and a photomultiplier tube (all Zeiss). For fixed samples 0.2 µm sections were acquired leading to z-stacks of mostly 4-8 µm height (cells) or 16-20 µm height (tissue samples). To analyse MT filaments, images were acquired using the Airy module and posttreated by deconvolution. The same confocal imaging setup was used for live cell imaging. During live cell acquisition dishes were placed in a 37 °C chamber and cells imaged at 10 to 20 sec interval for 30-60 min. The auto-focus option was used to keep the centrioles in focus. For all experiments, imaging software ZEN Black 2.3 SP1 was deployed.

Image analysis. Image processing and data analysis was performed using ImageJ and Imaris 10.2.0. To prepare images for display in the manuscript, brightness and contrast were adjusted and smoothing was applied. Number of centrioles was determined from multi-z-stack images by counting individual CETN2-GFP foci co-localizing with a PCM marker (γ-tubulin, CDK5RAP2, PCNT) or ac-tubulin. Quantitative intensity measurements were carried out on maximum z-projections measuring RAW integrated density or integrated density of selected regions of interest (ROIs). ROIs around the centrioles were selected by keeping the same size (~8 µm²) (see Fig. 1c). Overlapping cells were excluded from the analysis.

For determining intracellular MT numbers, MT filaments were counted manually at a defined round-shaped area (25 µm²) around the centrioles or acentriolar MTOCs (see Supplemantary Fig. 2c). All z-planes were used to precisely detect individual MT filaments. Tracing of MTs was carried out by using the NeuronJ plugin using maximum z-projections. MTOCs were defined by the following criteria: i) a clear PCM foci (γ-tubulin, CDK5RAP2 or PCNT) is visible, which ii) nucleates MT filaments from the respective region.

For measurements of distances between or from centrioles, maximum z-projections of fixed samples were used to determine centriole coordinates in two dimensions leading to x, y values. However, whole confocal stacks were used to determine the position of the T cell’s centrosome and identify cell-cell contacts. In timelapse videos, centrioles were tracked by using the Manual Tracking plugin. Obtained coordinates were used to calculate the centriole center point coordinates using the following formulas: x_CP = x_n and y_CP = y_n with n: number of single centriole.

In maximum z-projections of fixed samples as well as in timelapse videos, cell outlines were marked with the ‘freehand selection’ tool. Subsequent measurement of this ROI revealed area and centroid. Distances between two points were calculated using the formula distance d = sqrt((x2-x1)^2 + (y2-y1)^2).

3D reconstruction of DC-T cell contacts in lymph nodes. 3D TIF files were processed and analyzed using Imaris 10.2.0 (Bitplane) for quantitative analysis. T cells and DCs and their respective nuclei were segmented based on DAPI staining, cytoplasmic fluorescence signals from the CellTrace far red cell proliferation dye (T cells) or thresholding GFP expression (DCs). The built-in watershed algorithm in Imaris was used for cell boundary delineation, and centriole detection utilized the “spots” function with automated size filtering, followed by 3D distance calculations between paired structures using Imaris’ measurement function. All volumetric reconstructions were generated through Imaris’ integrated rendering engine.

Expansion microscopy (ExM)
Control or Centrinone-treated BMDCs were seeded on poly-L-lysine (0.1 mg/mL, Sigma) coated coverslips and allowed to adhere for 1h at 37 °C. U-ExM protocol was carried out as described previously92,93 with mild adjustments. Cells were fixed with 4% PFA in PBS at 37 °C for 15 min, following post-fixation with 0.7% PFA and 2% acrylamide (AA) at RT overnight followed by PBS wash prior to gelation. Gelation was carried out in a wet chamber placed on ice and covered with Parafilm. Washed coverslips were transferred promptly on a small drop of gelation solution (19 % sodium acrylate, 10 % AA, and 0.1 % N, N’-methylenebisacrylamide initiated with 0.5% tetramethylethylenediamine and 0.5% ammonium persulfate). Gelation was initiated on ice for 5 min and then transferred to 37 °C for 30 min to allow polymerization. Gels were then detached from the coverslips in a denaturation buffer (50 mM Tris-base, 200 mM NaCl, 200 mM SDS in ddH2O, pH 9) and denatured at 95 °C for 1h. Gels were expanded with 3x ddH2O wash and a small piece of gel was cut out for staining. Staining was performed sequentially overnight at RT in a staining buffer (2 % BSA, 10 % sodium azide in PBS) with 3x ddH2O wash between each step with the following primary: mouse anti-mouse acetylated-tubulin (C3B9, Sigma-Aldrich 00020913, 1:10), rabbit anti-mouse γ-tubulin (polyclonal, Abcam AB11317, 1:200), rabbit anti-mouse CDK5RAP2 (polyclonal, Sigma-Aldrich 06-1398, 1:200) and secondary: goat anti-rabbit Alexa Fluor 488 IgG H+L (Invitrogen A11008, 1:500) and goat anti-mouse Alexa Fluor 555 IgG H+L (1:500, Invitrogen A21422) antibodies. NHS Ester Atto 425 (20 mg mL−1 in PBS, ATTO-TEC) staining was done in PBS at RT for 1.5h and gels were expanded with 3x ddH2O wash before imaging. Microscopy was performed on a Nikon Eclipse Ti2 microscope equipped with Yokogawa CSU-W1 spinning disc module using the CF Plan-Apochromat VC 60×/1.2 water objective.

Statistical analysis
Data analysis was carried out with GraphPad Prism 10 (GraphPad Software, San Diego, CA, USA). Samples were tested for Gaussian distribution using D’Agostino-Pearson omnibus normality test to fulfil the criteria for performing Student’s t-tests. Welch’s correction was applied when two samples had unequal variances. When data distribution was not normal, Mann-Whitney test was carried out. For small datasets, Gaussian distribution was assumed but could not be formally tested. For analysis of Nur77 GFP expression, CETN2-GFPlow and CETN2-GFPhigh samples from individual experiments were paired. For multiple comparisons where data distribution was normal, one-way Anova was used followed by Dunnett’s multiple comparisons as post-hoc test. When data distribution was not normal, Kruskal-Wallis test with Dunn’s multiple comparisons was used. For multiple comparisons with more than two independent variables two-way Anova was used with Dunnett’s or Šidák’s multiple comparisons. All graphs display mean values ± s.d. (95% Confidence Interval). No statistical method was used to predetermine sample size. The experiments were not randomized and investigators were not blinded to allocation during experiments and outcome assessment. Individual experiments were validated separately and only pooled if showing the same trend. All P values are indicated in the figures.

Computational model
DCs were modeled as spheres with radius R_cell containing a spherical nucleus of radius R_nu as depicted in Fig. 8a. The line connecting the nucleus center and cell center defines the z-axis. The centrosome is placed along the z-axis at a distance h_CS, above the cell center and d_CS-NS (= h_CS - R_nu) away from the nuclear surface. The parameter d_offset determines the distance of the nucleus center from the cell center as depicted in Fig. 8c, inset.

Average geometrical distance between centrosomes and the target points on cell surface
The optimal geometric centrosome position is determined by minimizing the average distance, <d_short>, between the centrosome and all target points on the cell surface. In the absence of a nucleus, this distance is the shortest straight geometric distance between the centrosome and the target points on the cell surface. In the presence of a nucleus, a region of the cell surface lies in the “geometric shadow” behind the nucleus, which is the region not accessible to straight MTs growing from the centrosome. In such configurations, two tangents are drawn on the nuclear surface, one originating from the centrosome and the other from the target point on the cell surface, ensuring that the corresponding tangent points on the nuclear surface, centrosome, and the centers of the nucleus and target remain on the same plane. The total distance is then determined as the sum of the lengths of the tangential segments and the intermediate arc length along the nuclear surface (see schematic in Fig. 8a). The average geometric distance, <d_short>, is then computed by averaging over 10^6 random target points on the cell surface.

Average search time
The optimal centrosome position is determined by the minimum of the search time, T_search, required for dynamic MTs to capture a target located randomly on the cell surface. The target zone is represented as a circular disk of radius R_tau embedded in the cell surface, as illustrated in Fig. 8e. The target is located on the cell surface at an arbitrary position, specified by the polar angle theta_tau (0° - 180°) and azimuthal angle phi_tau (0° - 360°), measured from the positive z axis and x axis, respectively. A specific number of N_MTs nucleate from the centrosome and explore the surrounding three-dimensional cellular space searching for the target. In the presence of supernumerary centrosomes with multiple dispersed centriole assemblies, the total N_MTs are distributed evenly among all the centrosomes. The dynamically unstable MTs exhibit consistent growth at a velocity, v_g, switch to a shrinking phase with a catastrophe frequency, f_c, and shortening at a different velocity, v_s. The catastrophe frequency is chosen such that, on average, MTs could cover a distance equivalent to half of the cell perimeter. The simulations are performed with zero rescue frequency (f_r), preventing shrinking MTs from switching to the growth phase. A zero-rescue frequency is optimal since it minimizes the search time that MTs spend exploring directions lacking the target49,94–96. A Monte Carlo algorithm is implemented to simulate individual MTs. At each computational timestep (Δt), a uniform random number between 0 and 1 was compared with the probability 1 - exp(-f_c Δt) that an MT switches from growth to shortening. If the random number is found to be less than this probability, the MT begins shortening. Once a MT shrinks back to the centrosome, a new growth cycle starts in another random direction.

The direction of MT nucleation from the centrosome was governed by two angles, polar angle, theta (0° - 180°) and azimuthal angle, phi (0° - 360°), in the standard spherical polar coordinate system. Depending on the direction of nucleation and subsequent interactions with the cell or nuclear surface, two distinct scenarios arise:
(i) The model assumes that when MTs interact with the nuclear surface, they can move along the nuclear surface maintaining the same azimuthal angle phi at which they originated from the centrosome. During this movement along the nuclear surface, the increasing curvature of the MT can promote their detachment at a rate K_NS_detach. After detachment, the MTs grow tangentially from the nuclear surface and approach the cell surface searching for the target. The rate of detachment, K_NS_detach, is assumed to be an exponentially increasing function of the MTs’ curvature along the nuclear surface, as depicted in Fig. 8e, (i). Two parameters, k_0 (the prefactor of the exponential term) and alpha_d (a phenomenological constant within the exponential term) regulate the sensitivity of the MTs’ detachment rate (see Fig. 8e, (i)). Smaller values of k_0 and/or larger values of alpha_d promotes the MTs to glide along the nuclear surface, minimizing the chances of rapid detachment. MTs that extend beyond half of the nuclear perimeter from their initial contact points are immediately detached from the nuclear surface.
(ii) If the MTs reach the cell surface either after detaching from the nuclear surface or directly from the centrosome, they can eventually capture the target if the MT tip hits the target. Otherwise, they move along the cell surface seeking the target or experience catastrophe induced by the surface curvature. This cell surface induced MT catastrophe is incorporated in the model based on previous studies demonstrating that the distribution of MTs along the cell surface is influenced by the cell shape, and ruggedness of the cell surface regulating the MT bending and catastrophe56,77,97. The model assumed a probabilistic catastrophe of the MTs at the cell surface depending on the angle of incidence (beta) of the MT with respect to the local normal. The probability is chosen such that the tangential incidence (beta = pi/2) of MTs would promote their gliding along the cell surface and normal incidence (beta = 0) would induce catastrophe at a certain rate (Fig. 8e, (ii)). Specifically, the catastrophe probability P_surf_cat = P_b cos(beta) and the gliding probability P_surf_glide = 1 - P_surf_cat are considered. The value of P_b is chosen to be 1 to ensure that MTs experience instant catastrophe for normal incidence on the cell surface (beta = 0). If the MTs begin moving along the cell surface overcoming the cell surface-induced catastrophe, the MTs’ movement along the cell surface is further restrained by modulating the catastrophe frequency of gliding MTs according to f_surf_c = f_c exp(lambda_l L_surf_MT), where L_surf_MT represents the segment of the MT’s length gliding along the cell surface, and lambda_l is a phenomenological constant determining the rate at which the catastrophe frequency increased per unit length of the MT (Fig. 8e, (iii)). This additional consideration of catastrophe modulation is motivated by experimental observations of MT organization in DCs, which revealed that a very small fraction of the MTs or almost no MTs appeared to be gliding along the rugged cell surface26. The simulation is continued until a successful target-MT attachment is formed. The average search time, <T_search>, is calculated using 10^5 different random positions of the target on the cell surface. The parameters used in the simulation are tabulated in Supplementary Table 1 in Supplementary Information.

A mechanistic force-balance model demonstrating centrosomal positioning in DCs
The centrosomes (CSs) are considered as small spherical objects with r_CS (~0.5 µm), free to move within the cellular space between the cell surface and a nucleus fixed in space at various positions within the cell. MTs are cylinders of vanishing radii and nucleated from the centrosome uniformly in all directions. The number of MTs is evenly distributed among the centrosomes, and the dynamics of each MT is governed by v_g, v_s, f_c and f_r discussed earlier. Based on earlier studies of centrosome positioning and stability in interphase cells, the model assumes a pushing force-dominated regime arising from MT interaction with the cell and nucleus (Fig. 9e)59,60,98. In our model, a growing MT of length L_MT hitting the cell or nuclear surface buckles as per first-order Euler’s buckling and translates a pushing force (~200/L_MT^2) to the corresponding centrosome59,60. The model assumes that only a fraction of MTs hitting the nucleus buckles, while the others continue to slide along the nuclear surface. The buckling force is inversely proportional to the MT length and hence only short MTs generate significant buckling force and also undergo instant catastrophe58,59 leading to short-lived MT pushing forces. The resultant total force on the centrosomes can move them through the viscous cytoplasm following Stokes’ law59,61. If F_CS is the net force acting on a centrosome, V_CS the instantaneous velocity and mu the effective viscous drag on centrosome, then as per Stokes’ law F_CS = mu V_CS (with mu = 6 pi eta r_CS; eta is the coefficient of cytoplasmic viscosity)59,61. The position of centrosome is updated using the coarse-grained timestep delta_t = 10^-2 s. The parameter values are mentioned in Supplementary Table 1 in Supplementary Information.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Point-by-point response Nature Communications NCOMMS-24-49110\nPoint-by-point response on NCOMMS-24-49110:\n‘Multiple clustered centrosomes in antigen-presenting cells\nfoster T cell activation without MTOC polarization’\nWe like to thank the reviewers for the time and effort dedicated to evaluating our manuscript\nentitled ‘Multiple clustered centrosomes in antigen-presenting cells foster T cell activation\nwithout MTOC polarization’. We greatly appreciate the insightful comments and the\nopportunity to clarify and further support our findings. We believe that this constructive criticism\ngreatly improved our initial manuscript.\nWe understood that one major concern raised by two of the expert reviewers was the\nphysiological relevance of our findings and the differences between 2D and 3D\nmicroenvironments that could affect cellular behavior and centriole/centrosome configuration.\nWe agree that this is an important point and a major weakness of our initial manuscript. We\ncarefully addressed this issue by conducting adoptive transfer experiments that allowed us to\nimage DC-T cell interactions within lymph nodes. These experiments mimic more precisely\nthe physiological environment of cells and represent a major advancement of our current\nmanuscript.\nMoreover, we restructured our revised manuscript (in particular the introduction and\ndiscussion section) to make it more concise and to focus on the major findings of our studies.\nIn this context, we also slightly modified the title to ’Multiple clustered centrioles in antigen-\npresenting cells foster T cell activation without centrosome polarization’ to better distinguish\nbetween centrioles and centrosome (as well as their function) as recommended by reviewer\n2. Please find below our detailed point-to-point response to the specific comments and\nconcerns raised by the reviewer experts.\nResponse to Reviewer 1\nReviewer #1 (Comments to the Authors):\nThe manuscript “Multiple clustered centrosomes in antigen-presenting cells foster T cell\nactivation without MTOC polarization” examines how centrosomal organization in dendritic\ncells contributes to immune synapse formation and activation of T cells. The authors combine\nimaging, cell biological manipulations and mathematical modeling to study how centrosome\nnumber, integrity and spatial positioning influence efficient T cell activation. Overall, the\nmanuscript is well presented and the conclusions appear reasonable.\nWe thank the reviewer for the thorough evaluation of our work and the insightful comments.\nWe greatly appreciate her/his expertise in the field and hope that we can sufficiently respond\nto the major (and minor) concerns raised.\nSome specific comments for the presented results\nFigure 1:\n1\nPoint-by-point response Nature Communications NCOMMS-24-49110\nAuthors should specify whether the images are single-plane or maximum intensity projections.\nIf single-plane images were used, the authors should justify how the imaging plane was\nselected.\nIn most cases, we show maximum z-projections. We have now clearly stated this in the figure\nlegends and/or in the ‘Materials and Methods’ section (e.g., figure legend of Fig. 1b, p. 36,\nline 1197 and image analysis section p. 22-23, lines 785-807). Moreover, we extended our\nimage analysis section to explain in more detail how we analyzed the different parameters and\nshow representative images for clarification (when helpful) (e.g., Fig. 1c, Supplementary Fig.\n2c). We hope that such detailed explanations of image analysis will improve data\nreproducibility.\nB: (Right) Quantification of centriole numbers after Centrinone treatment: It is unclear how the\nauthors define centriole detection through intensity thresholds. A description of how\ncentrosomes were identified based on fluorescence intensity should be included.\nWe apologize for the confusion. We did not count centrioles based on fluorescence intensity\nthresholds after Centrinone treatment. Instead, centriole numbers were determined according\nto the number of CETN2-GFP+ foci that co-localize with a PCM marker such as γ-tubulin,\nCDK5RAP2 or pericentrin. In Fig. 1c we used signal intensities when quantifying γ-tubulin\nlevels. We now added a representative image and marked the respective ROIs in which we\ndetermined integrated density levels of γ-tubulin signals. In addition, we describe more\nprecisely how we quantified γ-tubulin levels at the centrosome in the ‘Material and Methods’\nsection (p. 22, lines 786-791).\nAlso, what was the rationale for testing two Centrinone concentrations (250 nM, 500 nM)?\nWas there a reason why certain panels only report results for one of the two concentrations?\nFor instance, Panel C: Plotted results for 3 conditions (control, 250 nM, 500 nM Centrinone),\nbut only showed montages of 2 conditions (control & 250 nM Centrinone). Perhaps this was\ndone because there was no significant difference between the 250 nM and 500 nM conditions?\nThis is not apparent from the presented results, since the effect of the two concentrations are\nnot compared to each other in the beeswarm plot.\nThe initial study carried out by Wong et al (Wong et al, 2015; PMID: 25931445) used 125 nM\nand 300 nM Centrinone on cancer cell lines. Therefore, we started to use concentrations\nranging from 125 nM (data not shown) to 250 nM. We were surprised that we found perfect\nMTOCs in the presence of Centrinone. To make sure, that we really target centrioles, we used\na higher concentration and decided to double the amount (500 nM), which is still in the range\nof what has been published before. We included exemplary pictures for 250 nM and 500 nM\nCentrinone-treatment (Fig. 1c) and tested for statistical significance in the beeswarm plot as\nsuggested by the reviewer. Indeed, the γ-tubulin intensity is even significantly decreased in\nthe 500 nM Centrinone condition in comparison to 250 nM Centrinone\nE: Quantification of MTOCs (left) and MT filaments emanating from defined regions around\ncentrosomes (right) in mature CETN2-GFP expressing BMDCs after Centrinone treatment\nQuantification of MTOCs and microtubule filaments is not clearly described. The authors\nshould provide details on the image analysis methods used, including how regions were\nselected and how filament numbers were counted.\n2\nPoint-by-point response Nature Communications NCOMMS-24-49110\nWe apologize for not being precise here. We quantified MT numbers in defined regions around\nthe centrosome (CETN2-GFP+ foci surrounded by PCM makers [either γ-\ntubulin/CDK5RAP2/PCNT]). Whole z-stack planes were used to identify individual filaments.\nIn the revised version of the manuscript, we now show an exemplary picture to illustrate this\nbetter (Supplementary Fig. 2c). Additionally, we included the respective information in the\n‘Materials and Methods’ section (p. 22, lines 792-797).\nPerhaps the authors could elaborate on how the gamma-tubulin intensity was normalized? It\ncould just be my lack of understanding, but I found this a bit vague. For instance, lines 1054-\n1055: “Graph shows normalized values relative to cells with two centrioles” but does not\nelaborate on the normalization procedure. Perhaps the authors wish to account for the fact\nthat all three treatments yield a mixed population of cells, all with different numbers of\ncentrioles (as shown in panel B) but this should be specified.\nWe completely agree with the reviewer that we want to account for the fact that we are\nanalyzing a mixed population of cells. Therefore, we calculated mean values of γ-tubulin\nintensities of all cells with two centrioles. Next, we normalized/divided the intensities of all cells\n(independent on their centriole number) to this mean. The respective information can be found\nin the figure legend (p. 36, line 1207).\nF: (Left) Quantification of proliferating T cells after Centrinone treatment according to CFSE\nlabeling. There seems to be a clear interaction effect between the centrinone concentration\nand OVA loading (high OVA loading reduces the difference between the untreated and\ncentrinone treated conditions for T cell proliferation. The interaction terms in the ANOVA\nshould be examined more carefully to delineate how enhanced loading can overcome lack of\ncentrioles.\nAs far as we interpret our data, T cell proliferation drops in the 0.1 µg/mL OVAp condition from\nthe ctrl 74.38% ±17.79% to 62.04% ±16.98% after 250 nM Centrinone treatment and to\n61.99% ±18.95% after 500 nM Centrinone treatment (please see also table below for better\noverview of all numbers). In the 1 µg/mL OVAp condition the exact values are in the ctrl\n80.06% ±14.79%, after 250 nM Centrinone treatment 66.22% ±25.36% and after 500 nM\nCentrinone treatment 64.35% ±26.51%. The absolute reduction in proliferation is in the same\nrange with both concentrations of OVAp (ctrl vs. 250 nM or ctrl vs. 500 nM) as the values at 1\nµg/mL OVAp loading condition display partially higher standard deviation the statistical\nsignificance is reduced. We feel that we cannot clearly answer whether enhanced OVAp\nloading can overcome lack of centrioles. This would require further investigations.\nDrop between ctrl Drop between ctrl\nValues 250 nM vs 250 nM 500 nM vs 500 nM\n[%] ctrl Centrinone Centrinone Centrinone Centrinone\nMean SD Absolute Relative Absolute Relative\nMean SD values values Mean SD values values\nw/o\nOVAp 2,93 1,80 5,70 6,25 7,54 6,40\n0.1 12.34 16.59 12.39 16.65\nµg/mL\nOVAp 74,38 17,79 62,04 16,98 61,99 18,95\n3\nPoint-by-point response Nature Communications NCOMMS-24-49110\n1 µg/mL 13.84 17.28 15.71 19.62\nOVAp 80,06 14,79 66,22 25,36 64,35 26,51\nThe meaning of the dotted box and the axis units in panel F are unclear and should be clarified\nto improve the reader's understanding of the data presentation.\nThe dotted box in former Fig. 1F represents the gate that we analyzed. We excluded the\nhistograms from the revised Figure 1f to make the figures less crowded (as suggested by\nreviewer 3). The gating strategy is now shown in Supplementary Fig. 1d to clearly\ndemonstrate which gates were analyzed.\nFigure 2:\nF: Quantification of T cell proliferation after co-culture with pretubulysin-treated BMDCs\nLine 155: “T cell activation was markedly reduced in the presence of pretubulysin (Fig. 2f)”\nIn line 155, the authors seem to equate T cell activation with CFSE dilution in Figure 2f, which\nmeasures proliferation. It is unclear that CFSE dilution can be used as a proxy for T cell\nactivation, as proliferation and activation are distinct processes. Ideally, they should present\ndata on early activation markers to strengthen their argument that pretubulysin impacts\nactivation, rather than just proliferation.\nWe thank the reviewer for this comment. In the revised manuscript we distinguish more\ncarefully between T cell proliferation and activation. Briefly, we measured IL-2 levels (Smith &\nKendall, 1988; PMID: 3131876) in our DC-T cell co-cultures and OX40 upregulation on T cells\n(Gramaglia et al, 1998; PMID: 9862675) as two prominent T cell activation markers (Fig. 2e;\nplease see also comment by Reviewer 2).\nThe authors state that DC MTs had \"not fully recovered\" by 24h after tubulysin wash-out. Is\nthere any quantitative data to support this statement? The right-hand plot in Figure 2C does\nprovide evidence that the tubulysin wash-out worked, but it's unclear what time point this plot\nrepresents. The authors should mention how much time passed between wash-out and\nquantification of MTs (Fig. 2C).\nThe quantification of MT architecture was carried out 24 h after drug wash-out. We included\nthis information directly in the figure panel (Fig. 2a) and the figure legend (for Fig. 2b and c;\np. 37, lines 1234 and 1236). Moreover, we included the parameters ‘MT length’ and ‘MT\nstraightness’ in our revised version of the manuscript to quantify MT recovery (Fig. 2c). ‘MT\nstraightness’ is defined as Euclidean distance of start to end point of one MT divided by whole\nMT length. This is explained in a scheme above the graphs. MT length and straightness are\nsignificantly reduced after 24 h of pretubulysin wash-out indicating that the drug acts to some\nextent irreversibly. This further allows us to study the role of MTs specifically in DCs.\nWe included these results in the main text of the manuscript (p. 5, lines 153-161).\nIs there any supplementary data that could show MT recovery at different time points? This\npoint may be a bit nit-picky, but since the long duration of MT destabilization is what justified\nthe authors’ use of a wash-out prior to co-culture experiments (which is preferable because it\nallowed them to co-culture treated APCs and T cells without exposing T cells to the treatment).\n4\nPoint-by-point response Nature Communications NCOMMS-24-49110\nThe quantification of MT architecture was carried out after 24\nh of wash-out as this was also the time point that we used for\nassessing T cell activation (Fig. 2e). MT length and\nstraightness are significantly reduced after 24 h of pretubulysin\nwash-out. Additionally, beforehand we carried out a time\ncourse experiment to determine MT recovery, which we did not\ninclude in the initial version of the manuscript (Fig. R1). After 6\nh of pretubulysin wash-out, the number of MT filaments is even\nmore drastically reduced than after 24 h of wash-out. This\nresult indicates that pretubulysin treatment is - to some extend\n– reversible, but does not lead to a full recovery of MT\narchitecture after 24 h, which we quantified by additional\nparameters such as MT length and straightness (Fig. 2c) in the Figure R1: Quantification of MT\nrevised version of the manuscript. We did not include the 6 h filaments after 6 h and 24 h of\npretubulysin wash-out.\ntimepoint in the manuscript as we felt that this rather leads to\nover-crowding of our figures. Nevertheless, if the reviewer believes that we should include this\ninformation we are more than happy to show Fig. R1 in the main manuscript.\nFigure 2C: Is there a reason why no data is available for the no wash-out condition? Perhaps\nfilaments too extensively disrupted to reliably count filaments?\nAs the reviewer assumes correctly, we were hardly able to detect any MT filaments in the\npresence of 1 µM pretubulysin, which is why we did not quantify parameters such as MT\nnumber, length or straightness under this condition.\nFigure 3: (multiple centrioles are not needed for enhanced conjugate formation but are\nmore effective?)\nB/E: Immunostaining and quantification of MT filaments\nThe authors should clarify the method used to quantify microtubule filaments and explain how\nindividual filaments were distinguished in crowded cellular regions. Additionally, details on the\narea or region where MT filaments were counted should be provided for reproducibility. What\nis the “defined area around the centrosome”?\nWe quantified MT numbers in a round-shaped region around the centrosome (25µm²). Whole\nz-stack planes were used to identify individual filaments in MT dense regions. In\nSupplementary Fig. 2c, we now show an exemplary picture to illustrate this better.\nAdditionally, we included the respective information in the ‘Materials and Methods’ section (p.\n22, lines 792-797). We thank the reviewer for this important comment and generally extended\nthe image analysis section to improve reproducibility.\nFigure 4:\nD: Quantification of MTOC polarization towards the IS in T cells after different timepoints of\nco-culture\nHow is it determined which T cells have a “reoriented centrosome”? Is the whole confocal\nstack used?\n5\nPoint-by-point response Nature Communications NCOMMS-24-49110\nAs pointed out by the reviewer, whole confocal stacks were used to determine the position of\nthe T cell’s centrosome. We included this information in the image analysis section of the\n‘Materials and Methods’ part (p. 23, lines 799-801).\nF. This is a bit confusing as the T cells express Nurr-GFP while the DCs express CEN2-GFP.\nHow were the two signals differentiated to unambiguously identify T cells and DCs.\nWe thank the reviewer for pointing this out and carefully considered this issue. Importantly, in\nthe T cell, GFP is expressed in the nucleus (and the GFP signal was quantified in the nucleus\n– defined via DAPI staining), while in the DC there is background GFP present in the cytoplasm\nand additionally – even higher signal intensity – at the centrioles. Whole z-stack planes were\nused to identify DCs and T cells. Additionally, both cell types differ in size and shape. T cell\nareas for quantification were carefully chosen to avoid overlap with the DC. All overlapping\ncells, e.g. T cells lying on top of the DC, were excluded from the analysis.\nFigure 5:\nB/C: Quantifications of intracentrosomal and average distances in DCs with one and multiple\ncentrosome(s) at different time points of co-culture\nThe authors mention using a manual tracking plug-in to calculate intracentrosomal and\nintercentrosomal distances. However, they do not provide detailed criteria for defining what\nconstitutes the boundaries of individual centrosomes or how the centroid positions were\nselected, which could introduce user bias.\nThe manual tracking plugin provided by ImageJ was only used in live cell imaging videos to\nextract the coordinates of the centrioles (x,y). Based on these coordinates, the distances were\ncalculated in excel using the formular: 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = ,(𝑥 −𝑥 )!+(𝑦 −𝑦 )! . The centroid of\n! \" ! \"\nthe DC was identified according to the boundaries of the CETN2-GFP signal (marking the DC\nmembrane). We explain this in more detail in the ‘Materials and Methods’ section (p. 23, lines\n798-807) and refer to the schemes in Fig. 5b,c.\nLine 1171 (figure caption): “Each data point represents on cell derived from 5 independent\nexperiments.”\nMinor spelling error– “Each data point represents one cell”\nWe corrected the spelling mistake in the revised version of the manuscript.\nD: Sketches and confocal CETN2-GFP images indicating distances between centrosome\ncenter point (CP) and T cell CP, and DC CP and T cell CP in cells with one (upper) and multiple\n(lower) centrosomes.\nWhat is the quantitative criteria for defining center points of the centrosome/multiple\ncentrosomes, T cell, and dendritic cell? Also unclear whether centrosomal distance was\ncomputed in 3D from confocal stacks or from the maximum intensity projection.\nFor DCs and T cells, center points were measured in ImageJ using the centroid measurement\nafter marking the DC boundaries with the ‘freehand selection’ tool on maximum z-projection\nimages. The centroid displays the geometric center point without considering any signal\n6\nPoint-by-point response Nature Communications NCOMMS-24-49110\nintensities. The centriole center point coordinates were calculated with the formulas: 𝑥 =\n#$\n∑&!\nand 𝑦 =\n∑(!\nwith n: centriole number. We included this in the image analysis section of\n#$\n' '\nthe manuscript (p. 23, lines 798-807).\n• Lines 262-264: Th is is a minor detail, but I wonder whether the phrasing of this assertion\nmakes sense:\n\"… average distances in cells with multiple centrosomes […] did not show prominent\ndifferences between OVAp loaded and unloaded cells suggesting that multiple centrosomes\ncongregate together and cluster during antigen-specific DC-T cell contacts.\"\n◦ Does it make sense to assert that centrosomes actively congregated together, given that,\nto my understanding: (1) they did not actually observe this movement -- only the end result --\nand (2) no clear differences were noted in the presence or absence of OVAp? If not, perhaps\nan alternate description could be \"centrosomes remained clustered together…\".\n◦ Then again, perhaps the final centrosome position provides sufficient evidence to assert that\nclustering must have occurred, if clustering is not the dominant morphology in non-conjugated\nDCs.\nWe agree with the reviewer that our formulation is misleading and changed it to ‘….centrioles\nremain clustered…’ (p. 8, line 288) as suggested by the reviewer.\nFigure 6:\nThe confinement of DCs and T cells under an agarose block may mechanically restrict their\nmovement and potentially alter the physiological dynamics of centrosome reorientation and\nimmune synapse formation. The authors should comment on how/if this could impact\ncentrosome configurations.\nWe absolutely agree with the reviewer that the microenvironment and the level of confinement\nmight have a strong impact on cellular behaviour and the physiological dynamics of\ncentrioles/centrosomes. To more precisely mimic the conditions cells are facing in tissues, we\nperformed adoptive transfer experiments which are presented in Figure 6. Briefly, we injected\nOT-II T cells intravenously into WT mice and after 24 h CETN2-GFP expressing BMDCs into\nthe hock. After 48 h, popliteal and inguinal lymph nodes were collected. Cryosections were\nstained with PCM markers such as CDK5RAP2 or pericentrin. 3D image analysis shows that\nalso within lymph nodes, centrioles are clustered and localize near the cell centre in close\nproximity to the nucleus. We added these new results in the main text of our revised\nmanuscript (p. 9, lines 311-325).\nLine 625 (methods): “...DC-T cell co-cultures were injected under a block of agarose as\npreviously described”\nThe cited paper describes a different set-up that includes two glass surfaces spaced by PDMS\npillars.\nWe thank the reviewer for this notification and apologize for the mistake. We removed the\nrespective citation and corrected the paragraph to ‘….DC-T cell co-cultures were injected\nunder a block of agarose to prevent cell floating during the imaging period’ (p. 20, line 713).\n7\nPoint-by-point response Nature Communications NCOMMS-24-49110\nFigure 7:\nPARP enzymes play crucial roles in DNA repair, transcriptional regulation, and cellular\nmetabolism. PARP inhibition could disrupt these processes in DCs, impairing antigen\npresentation or cytokine production, which would independently affect T cell activation. Is the\nobserved reduction in T cell activation solely due to centrosome declustering, or might it also\ninvolve broader PARP-related cellular changes?\nThe reviewer raises important points here. In the revised version of our manuscript, we\ncarefully checked for potential side effects of PJ-34 treatment using different concentrations\nof PJ-34. We determined the capacity to present antigens by staining for cell surface MHCII\nlevels (Supplementary Fig. 7a,b) and measured mRNA levels of DC-specific cytokines\n(Cxcl1, Il-6, Ccl5) in the presence or absence of PJ-34 (Supplementary Fig. 7c,d). All mRNA\nlevels seem unaffected by PJ-34 treatment. Moreover, intracellular protein levels measured\nby intracellular cytokine staining showed no significant differences between the treatments.\nThese results strongly suggest, that PJ-34 treatment for 3 h does not lead to large changes in\ncytokine levels or impaired antigen presentation in DCs.\nThe authors should clarify why intracentrosomal distance was affected by PJ-34 treatment in\nnon-cocultured cells (panel B), but not in co-cultured cells (panel D). There is one way I could\nsee this being relevant: in order to confidently assert that activation is impaired upon de-\nclustering of multi-numerous centrosomes, it seems important to emphasize that the de-\nclustering agent only affects intercentrosomal distances, not intracentrosomal distances (and\nthus, the results arise due to declustering of centrosomes, rather than abnormal spacing\nbetween centrioles in each individual centrosome). Still, as long as the authors can show that\nintracentrosomal distance was not affected in the context of the coculture experiment, I think\nthey should be able to defend their assertion.\nWe agree with the reviewer that the observed phenotype on T cell activation in the presence\nof PJ-34 can only be assigned to de-clustering of centrioles, when the linker between the two\ncentrioles (reflected as intracentrosomal distances) is intact. As pointed out by the reviewer,\nintracentrosomal distances in DC-T cell co-culture experiments were largely unaffected by PJ-\n34 treatment, which is why we feel confident about our conclusion that predominantly de-\nclustering of multiple centrioles causes the observed phenotype. We cannot really explain the\ndifference between co-cultured and single cells.\nE: The authors should clarify whether these results drawn only from 2N cells with multiple\ncentrosomes, or was data collected from all 2N cells (single and multiple centrosomes)?\nLines 303-306: “Interestingly, when co-culturing with either PJ-34-treated or control OVAp-\nloaded DCs with OT-II T cells, we found that in the presence of de-clustered centrosomes,\nT cell activation was significantly diminished compared to control cells.”\nThis is relevant because when centrosomes were counted in figure 3A, it seemed as though\nmulticentrosome cells only comprised ~25% of all 2N cells (centriole number >=3). Therefore,\nin a population of single- and multi-centrosome cells, I wonder whether any effects on multi-\ncentrosome cells would be prominent enough to yield the significant differences shown in\nFigure 7E. If not, perhaps there is some other mechanism at play by which PJ-34 could affect\nthe ability of DCs to stimulate T cell proliferation?\n8\nPoint-by-point response Nature Communications NCOMMS-24-49110\nData from Figure 7e were collected on sorted 2N cells. We included this information in the\nrespective figure legend. In addition to sorting on 2N cells, we also slightly enriched for cells\nwith multiple centrioles leading to 30% of the cells containing >2 centrioles. This enrichment\nallows to monitor differences in the presence of de-clustering agents as more cells with\nmultiple centrioles are present. Furthermore, we want to highlight that the PJ-34 treated cells\nshow significantly increased MTOCs which is the functional effect of de-clustered centrioles\nand very likely to account for decreased T cell activation capacity by those DCs. In addition,\nwe show in the revised version of the manuscript that we tested for potential side effects of\nPJ-34: antigen-presentation (MHCII surface expression) as well as cytokine production\n(Supplementary Figure 7a-d). We apologize for the confusion and now clearly state BMDC\nsorting in the figure legend (p. 42, line 1361).\nFigure 8:\nB: Plot of < 𝑑short > vs hCS in the presence of a nucleus located centrally to the cell\nLine 340: “Our data indicate an optimal perinuclear positioning of the centrosome, marginally\nshifted from the nuclear surface by < 1 𝜇𝑚”\nA visual indicator of the minimum < 𝑑short > on plot B would better emphasize the optimal\npositioning, as the x-axis scale makes it difficult to identify.\nWe thank the reviewer for this constructive suggestion. To improve clarity, we have added a\nred vertical dotted line on the x-axis in Fig. 8 b to indicate the optimal centrosome position -\ndefined by its distance from the cell centre (ℎ ) and from the nuclear surface (𝑑 ) - that\n#) #)*+)\ncorresponds to the minimum value of < 𝑑 > for a centrally located nucleus (p. 43, lines\n,-./0\n1372-1373).\nGeneral comments: While the authors report that DCs with multiple centrosomes exhibit\nincreased cytokine secretion, the referenced study lacks temporal data on when these\ncytokines are released relative to IS formation. To establish a direct link between MT contact\nand T cell activation, the authors should investigate the time-scale of cytokine release using\nreal-time measurements. Alternatively, they should suggest this as a future direction to\ndetermine whether early MT contacts trigger cytokine exocytosis or if other mechanisms\ncontribute to the observed increase in T cell activation.\nWe agree with the reviewer that real-time measurements of cytokine secretion and\nsimultaneous T cell activation would be very informative and establish a more detailed link\nbetween elevated MT numbers and enhanced T cell activation. We believe that these\nexperiments are technically very challenging and might go beyond the scope of this study.\nTo directly link enhanced cytokine trafficking and/or secretion in the presence of multiple\ncentrioles and increased MT nucleation with T cell activation, we would need a robust T cell\nactivation marker, that can be detected instantaneously. Calcium signalling is fast, but whether\nit allows to detect differences on the time scale of seconds in cells with different numbers of\ncentrioles is questionable. In parallel, we would need to monitor centrioles, MT filaments and\nfluorescent cytokines that are secreted. For all structures, we would need to record z-stacks\nover time, which means high photo stress and requires superfast imaging techniques. All\nexperiments require single cell resolution as we are dealing with a mixed population of cells\nregarding centriole numbers. As suggested by the reviewer, we rephrased our claims on\ncytokine secretion and vesicle transport as missing mechanistic link between multiple\n9\nPoint-by-point response Nature Communications NCOMMS-24-49110\ncentrioles, increased numbers of MT filaments and enhanced T cell activation. Moreover, we\nincluded a sentence in the discussion where we propose future studies on this topic (p. 17,\nlines 602-605).\n• Minor typos –\n◦ Line 61-62: “Yet, B cells have a rather week T cell priming capacity” → “weak”\n◦ Line 119: poly-L-lysin -> poly-L-lysine\n◦ Line 150-151: “thus allowing to study the role of MTs…” → “thus allowing us to\nstudy…\n◦ Line 205: promotor -> promoter\nWe thank the reviewer for thoroughly reading the manuscript and corrected the mentioned\ntypos in the revised version of the manuscript.\n10\nPoint-by-point response Nature Communications NCOMMS-24-49110\nResponse to Reviewer 2\nReviewer #2 (Comments to the Authors):\nIn the manuscript entitled “Multiple clustered centrosomes in antigen-presenting cells\nfoster T cell activation without MTOC polarization”, by Stötzel et al. explored how centrosome\namplification in DCs affects immune synapse formation and T cell activation. They integrated\nexperimental data with computational modeling to demonstrate that extra centrioles in DCs\nlead to the formation of overactive centrosomes. These centrosomes cluster during DC-T cell\ninteractions and are positioned near the cell center. Disrupting either the number of centrioles\nor the centrosomes configuration results in impaired T cell activation.\nThe entire study is quite interesting, as it establishes the relationship between optimal\ncentrosome positioning, centrosome numbers, and T cell activation.\nHowever, the author seems to have a limited understanding of the concepts, distinctions, and\ngeneral functions between centrosomes and centrioles, leading to some misinterpretations of\nexperimental data. Furthermore, I believe that using computational modeling to explain\ncentrosome positioning may be somewhat ambitious. A more comprehensive model should\nincorporate additional intracellular variables and factors. If most of the issues below are\nresolved, I recommend that this manuscript be accepted.\nWe thank the reviewer for her/his interest in our work and for the insightful comments. We\nunderstand the concerns raised, particularly on the distinction between ‘centrioles’ and\n‘centrosomes’ and a specific part of our computational analysis. We recognize the importance\nof these points and addressed them by carefully rephrasing the respective passages in the\nrevised version of the manuscript. Briefly, we now use the term ‘centrioles’ in the first part of\nour manuscript as we are counting CETN2-GFP+ foci that are part of the centriole (please see\nalso our answer to your comment on Fig. 4G). Later, after showing the functionality of extra\ncentrioles by pericentrin, γ-tubulin and CDK5RAP2 staining, we define the condition >2\ncentrioles as ‘over-active MTOCs’. When we talk about numbers, we use the term multiple or\nextra ‘centrioles’ instead of ‘centrosomes’ as we detect only one MTOC.\nRegarding our computational model please find our detailed response and extension of the\nmodel below.\nSpecific comments:\nThe author raises several detailed scientific questions in the Introduction and Results sections,\nbut it appears that few of them are thoroughly addressed.\nThe Introduction section is somewhat redundant, and the presentation of background\nknowledge is a bit disorganized and lacks logical coherence. It is recommended to succinctly\npresent the background information and clearly state the key scientific question.\nWe thank the reviewer for this comment. We completely rephrased the introduction to make it\nmore concise and logical, discarded redundant passages and elaborated in more detail on\nnovel aspects of our work. Similarly, in the results section we anticipated to further strengthen\nour results on having a centrally localized centriole-cluster and included in vivo data on DC-T\ncell interactions within lymph nodes (Fig. 6). We hope that these new data address more\nthoroughly the importance of centriole configuration during DC-T cell interactions.\n11\nPoint-by-point response Nature Communications NCOMMS-24-49110\nPericentrin is the most typical marker of the PCM; it would be best if the author included\nadditional staining for it.\nWe thank the reviewer for this important comment. We included pericentrin (PCNT) staining\nas an additional PCM marker in the revised version of our manuscript in Figure 3. We re-\nanalyzed centriole numbers (CENT2-GFP+ foci co-localizing with PCNT staining) in diploid\ncells and show this in Figure 3a. Moreover, we quantified PCNT intensity around centrioles\nwhich showed increased PCNT levels at multiple centrioles in comparison to only two\ncentrioles (Fig. 3c). In addition, we performed PCNT staining on cryosections of popliteal and\ninguinal lymph nodes (Fig. 6). This allowed us to counterstain centrioles in the injected\nCENT2-GFP expressing BMDCs while at the same time also the T cell’s centrosome is\nmarked.\nThe assertion that \"MT organization was not affected by the loss of centrioles\" is a perspective\nheld by only some researchers. If the author wishes to make this claim, they should cite 2-3\nhigh-quality references.\nWe agree with the reviewer that this statement is controversially discussed in the community\nand cannot be generalized. We now emphasize that we only refer to our own study, where we\nshow that MT numbers per cell are not altered in Centrinone-treated vs. control cells (Fig. 1e).\nWe rephrased the respective paragraph to ‘….demonstrating that under our assay conditions\nDCs lacking centrioles are able to nucleate and organize MT filaments to form a functional\nMTOC.’ (p. 4-5, lines 146-148).\nI am not an expert in the field of immunology, but I still feel that using only one experimental\nmethod, flow cytometry (Fig. 2F), to support such an important conclusion “We found that T\ncell activation was markedly reduced in the presence of pretubulysin”, is insufficient and not\nsolid.\nAs suggested by the reviewer, we included additional T cell activation measurements in our\nrevised version of the manuscript such as IL-2 secretion (measured by ELISA; Smith &\nKendall, 1988; PMID: 3131876) and OX40 upregulation on the T cell - a prominent activation\nmarker (Gramaglia et al., 1998; PMID: 9862675) (Figure 2e, p. 5, lines 164-171). We hope\nthat we can convince the reviewer, that these additional read-outs emphasize the importance\nof the MT cytoskeleton in DCs for T cell activation and strengthen our initial claims.\nFor Figure 4G and its related data: Centrin-2 is primarily associated with centrioles and is a\nmarker for centrioles. If the centrosome is concentrated in a single focus, it is defined as \"one\ncentrosome,\" even if it contains multiple centrioles. It is defined as \"multiple centrosomes\" only\nwhen there are multiple foci or if it appears dispersed. For identifying multiple centrosomes,\nPCNT (Pericentrin) should be used as the marker instead of Centrin-2.\nWe thank the reviewer for this important comment. We rephrased all sections and paid\nattention on the nomenclature of centrioles and centrosomes as outlined above. In addition,\nwe included PCNT staining as a marker for the PCM and thereby assess precisely centriole\nvs. centrosome numbers. In most cases where more than two centrioles are present, only one\nclear pericentrin signal can be detected, which appears more intense compared to cells with\n12\nPoint-by-point response Nature Communications NCOMMS-24-49110\nonly two centrioles. Similar results were obtained for γ-tubulin and CDK5RAP2 staining.\nMoreover, MTs emanating from multiple clustered centrioles are significantly increased.\nTherefore, we refer to this condition as one over-active MTOC (Fig. 3a-g) (p. 6, lines 184-\n205).\nI understand the novelty the author is aiming for in the final section of the Results, but\nexplaining centrosome positioning using a geometric model seems somewhat far-fetched\nand unusual. If a computational model is to be used for this explanation, the evidence\nprovided by the author is not sufficient. For instance, there are many interacting factors with\nthe centrosome within the cell, including various organelles and variables such as fluidity.\nI believe this represents a highly complex computational process, unlike the current model in\nthe paper.\nWe appreciate the reviewer’s thoughtful critique and acknowledge that centrosome\npositioning is governed by a complex interplay of cellular components and mechanical\nforces - including interactions with organelles, cytoplasmic viscosity, cortical tension, and\nactomyosin contractility. However, our primary focus in this study was to understand how a\nspecific centrosome location affects the efficiency of the microtubule-based search process\nfor the synapse region important for T cell activation, rather than to investigate the\nmechanisms underlying centrosome positioning. Our simulations show that a centrally\npositioned centrosome (or centriole cluster) minimizes search time by dynamic MTs, thereby\npotentially enhancing activation of T cells attached to the dendritic cell membrane.\nComplementing this, our experiments highlight the critical role of centriole clustering and\ncentral positioning in antigen-presenting cells (APCs) for modulating T cell responses. To\nexplore a possible physical basis for this centring, we introduced a simplified geometric\nmodel that considers MT-generated pushing forces from the cell cortex and nuclear surface.\nThis minimal model yields a mechanically stable configuration near the nuclear periphery\nand close to the cell centre, offering a plausible explanation for centrosome positioning\nwithout invoking detailed cytoplasmic modelling.\nWe fully agree that centrosome positioning likely arises from multiple, overlapping\nmechanisms. Prior work has implicated MT pushing against the cell boundary (PMID:\n9177199; PMID: 9346483; PMID: 30780383), cortical dynein pulling (PMID: 20980619;\nPMID: 22304918; https://www.nature.com/articles/s41567-023-02223-z), pushing forces\ndue to MT-organelle interactions (PMID: 20980619), cytoplasmic dynein-mediated pulling\nvia vesicles attachment (PMID: 21866267; PMID: 20980619), and actomyosin-driven\ncentripetal flows (PMID: 20980619; PMID: 40243666). Our model suggests that MT-\ngenerated pushing forces play a leading role under specific conditions, promoting\ncentrosome centring in dendritic cells, while an alternative cortical pulling-dominated regime\ncould lead to peripheral positioning and even declustering of centrioles. While a detailed\nintegration of these mechanisms is beyond the scope of this study, we now include a brief\nsummary in the revised discussion section (p. 16-17, lines 583-590) and propose such\nextensions as a direction for future work.\n13\nPoint-by-point response Nature Communications NCOMMS-24-49110\nResponse to Reviewer 3\nReviewer #3 (Remarks to the Author):\nThe authors have tried to understand how the MTOC (Microtubule organization center)\norganization in APC (antigen-presenting cell) has an effect on T-cell activation. It is well\nestablished that cellular polarization is essential for immune regulation, often involving\ncentrosome reorientation. However, how centrosomes are oriented from the perspective of\nAPCs is not entirely known. Here, the authors consider murine DCs (Dendritic cells) and\nobserve, using experimental and mathematical models, that DCs’ additional centrioles create\nover-active centrosomes that cluster near the cell center during DC-T cell interactions.\nDisrupting centriole count or centrosome positioning in DCs impairs T cell activation,\nemphasizing the critical role of centrosome amplification and positioning in APCs for effective\nT cell responses.\nThe manuscript details an important and often overlooked aspect of T cell activation. The\ncomplementary nature of the study (experiments supported with computational models) is\ncommendable (please note that the reviewer is not an expert in computational modeling).\nWhile the enthusiasm for the topic is high, the reviewer is less excited about the manuscript\nin its present form. Mainly, the loss of enthusiasm stems from overcrowding of the figures\n(which makes it very challenging to follow and understand the novelty of work), low sample\nnumbers, and a few irregularities noted below.\nWe are grateful that reviewer 3 describes our study as important and has notified the current\nlack of data on the DC side of the immune synapse. Moreover, we are happy that this reviewer\nexplicitly acknowledges the interdisciplinary nature of our study, which we believe is a great\nstrength of our work. We highly appreciate her/his thorough and insightful evaluation of our\nwork.\nWe also understand the importance of addressing the issues raised regarding figure\npresentation and increasing N/n numbers, which we thoroughly addressed in the revised\nversion of the manuscript. Please find below our detailed responses to the individual\ncomments.\nMajor comments:\n1. The figures are very dense, making them hard to follow. The figure captions are very\nlengthy, and some require a single page. The authors are encouraged to reconsider how they\nshould present their data.\nWe rearranged each individual figure and shifted parts of the main figures to the\nsupplementary section (former Fig. 3f-h and former Fig. 6) to make the figures less crowded\nand thereby hopefully easier for the reader to capture the results. Large histogram panels of\nindividual conditions were removed as exemplary gating strategies are shown in the respective\nsupplementary figure panels (please see new Fig. 1f and 2f and Supplementary Fig. 2a).\nMoreover, we included a detailed description of image analysis in the ‘Materials and Methods’\nsection which further allowed us to shorten some figure legends without impacting the reader’s\nunderstanding.\n2. Following up on the above comment, the authors should clearly highlight the novelty of their\n14\nPoint-by-point response Nature Communications NCOMMS-24-49110\nwork. Does the work provide a new interpretation of T cell activation, a new method, or a new\ncomputational model? How are the experiments and model tied together?\nWe thank the reviewer for this constructive comment. We modified the introduction section to\nhighlight the novelty of our work and made the content more concise as also suggested by\nreviewer 2. Briefly, we aimed to center our research focus on the role of the centrosome and\nthe MT cytoskeleton in antigen-presenting cells for efficient T cell activation. The process of T\ncell activation is key for the induction of an adaptive immune response and fundamental\nintracellular processes shape this cell-cell interaction. Our results highlight the crucial role of\nthe MT cytoskeleton (including centrioles and MTs) for immune-related functions in\ninterphase. We tackle the arising question on intracellular processes by using wet lab\nexperiments and computational modelling, because we believe that this gives a more\ncomprehensive understanding.\nWe also aimed to tie our experiments and computational model closer together by extending\nthe model for flattened cells to make it comparable to the 2D situation (confinement)\nduring\nlive cell imaging and vice versa, we analyzed centriole configuration in a 3D tissue\nmicroenvironment (Fig. 6).\n3. The reviewer was puzzled in interpreting the data in the following figures:\na. In Fig 1E (left graph), what is the percentage of cells with two centrioles and no centrioles\nin the presence of 500 nM Centrinone?\nWe find around 60% of cells without any centriole and 12% of cells with two centrioles when\ntreating cells with 500 nM Centrinone (quantified now in Fig. 1b). In total, >130 cells have\nbeen analyzed per condition. We included the number of cells analyzed in the figure legend.\nAs described by Wong et al, 2015, Centrinone-treatment does not ‘destroy’ existing centrioles\nbut prevents the formation of new daughter centrioles. We treated our cells directly after\nisolating the bone marrow, when we started to differentiate into the DC lineage. The cells\nundergo roughly 6-7 rounds of division until we stimulate them with LPS. This implies that the\ncells can proliferate even in the absence of centrioles (most likely by forming acentriolar\nMTOCs). This is a very interesting phenomenon, that we did not comment on further as we\nare not focusing on the role of centrioles during cell division.\nb. For Fig 2, the low sample size from only two experiments raises doubts about the claims of\nno changes in MTOC.\nWe repeated the experiments for DC MTOC and MT analysis in the presence of pretubulysin\nto increase sample sizes (former Fig. 2C and 2E; now presented in Fig. 2b, c). In total, we\nanalyzed N = 33 (ctrl) /34 (pretubulysin wash-out) cells (left: MT number); N = 39 (ctrl) /43\n(pretubulysin wash-out) cells (right: MTOC number) derived from n = 3 independent\nexperiments. Cells were differentiated from three different mice. We included all n/N numbers\nin the figure legend according to the Nature Communications policy on samples sizes (e.g.:\nFigure legend Figure 2b,c, p. 37, lines 1234-1241).\nIn line with this, the analysis of T cell MT and MTOC numbers was extended as well. Now\nshowing in Supplementary Figure 2e four independent experiments with total cells analyzed:\nN = 93 (ctrl) /183 (pretubulysin wash-out) /60 (pretubulysin w/o wash-out) (left: MT number);\nN = 103 (ctrl) /184 (pretubulysin wash-out) /61 (pretubulysin w/o wash-out) (right: MTOC\nnumber).\n15\nPoint-by-point response Nature Communications NCOMMS-24-49110\nc. The authors must provide high-resolution magnified images (insets and similar to image\nquality in subsequent figures) and supporting extra images in the supplementary materials (for\nall figures). From Figure 2b, the MTs look intact in the representative image w/o pre-tubulysin\nwashout and with washout, which is contrary to the claims made by the authors in Line 149.\nWe thank the reviewer for this comment. In the revised version of the manuscript, we now\nprovide high-resolution images for all figures either in the main part or in the supplementary\ninformation. For former Fig. 2B (now Fig. 2a) we show a-tubulin staining separately from the\nmerged image, which will hopefully make the differences in MT architecture more visible. We\nalso included additional parameters to assess MT structure such as length and straightness\n(Fig. 2c) to demonstrate that after pretubulysin wash-out not only MT numbers are affected\nbut also MT structure.\n4. Following up on comment 2b, the number of MT filaments shows high variability in control\nexperiments (Fig 1F and 2C). These numbers should be the same or similar. How are the\nauthors defining an MT filament? Does it have a minimum length?\nWe thank the reviewer for this comment. In former Fig 1F and 2C MT numbers have been\nanalyzed by different people in the lab, which is our explanation why MT numbers vary\nbetween different experiments. We re-analyzed all pictures/experiments for our pretubulysin\nexperiment by one single person and used a precise ROI with a defined area (25 um2) to count\nMT filaments (Supplementary Fig. 2c). In Fig. 2c also MT length is determined after\npretubulysin wash-out.\n5. The authors mentioned that tubulin intensity is higher during IS (Immune synapse)\nformation\nin the presence of two or more centrosomes (Figure 3b). However, the provided images do\nnot clearly show that IS formation corresponds to the centrosome number. Hence, the authors\nshould provide magnified microscopic images of the same.\nWe apologize for this confusion. We now show an exemplary image how we quantified γ-\ntubulin intensities (Fig. 1c). Our data demonstrate that γ-tubulin intensity (and also PCNT and\nCDK5RAP2 intensity, Fig. 3c,d and Supplementary Fig. 3a) is higher in cells with >2\ncentrioles. We also added magnified microscopic images of cells bearing 2, 4 or more than 4\ncentrioles. The increased γ-tubulin intensity in cells with >2 centrioles could be shown in DCs\nwithout T cells and during IS formation.\nA separate finding from our study shows that the formation of contacts between DCs and T\ncells does not correspond to the centriole number as shown in Supplementary Fig 3d,e.\nHowever, T cell activation, which was measured by Nur77-GFP upregulation (Fig. 4b, e),\ncorrelates with centriole numbers. We hope we could explain our results more precisely and\nalso re-ordered this figure for a better understanding. In addition, we added a more detailed\ndescription of our image analysis section (p. 22-23, lines 785-807) and representative images\nof image analysis in Fig. 1c.\n6. In Fig 4d (graph), the authors have plotted the MTOC reorientation during the co-culture at\ndifferent time points. Authors must provide representative microscopic images of the\ncentrosome alignment at other time points (4, 6, 20h) in their supporting information.\n16\nPoint-by-point response Nature Communications NCOMMS-24-49110\nWe included representative images of MTOC reorientation in T cells at 4, 6, 20 h after co-\nculture (now Fig. 4c).\n7. Since Figures 3 and 6 are inconclusive findings, the authors are encouraged to move them\nto supplementary materials.\nAs suggested by the reviewer, we shifted former Fig. 3F-H (now Supplementary Fig. 3c-e)\nand former Fig. 6 (now Supplementary Fig. 6) to the supplementary information.\n8. Similar to the irregularity mentioned in comment 3, in Figure 3F, the relative frequency of\nmono conjugation between T cell and DC cells does not reflect the centrosome numbers in\nFigure 3G in the same condition.\nWe apologize for the confusion. In former figure 3F (now Supplementary Fig. 3c) we do not\ndistinguish between cells with two or multiple centrioles. Just the overall frequency distribution\nof mono- and multi-conjugated cells is shown depending whether DCs were loaded with OVAp\nor not. The data from former figure 3F+3G results from the same analysis (now\nSupplementary Fig. 3c,d). Data is displayed as frequencies from the respective condition\ne.g., Suppl. Fig. 3d left: from all the contacts formed in the w/o OVAp condition, Suppl. Fig. 3d\nright: from all the contacts formed in the + OVAp condition. Please find all details on cell\nnumbers and frequencies in the figure legend (Supplementary information p.10, lines 158-\n167) and in the source sheet.\n9. In Figure 4C, It is unclear how the GFP values are observed to be below the threshold in\n20h coculture. What does this signify? The reviewer is also confused on how the authors are\nclaiming that multiple centrosomes are more efficient in activating T cells than with only one\ncentrosome (lines 246-247), when there is no data provided for the latter.\nIn previous Fig. 4C (now Fig. 4b) we quantified T cell activation via Nur77 expression by flow\ncytometry after 6 h and 20 h of co-culture. We distinguished between co-culture with CETN2-\nGFPhigh cells (enriched for >2 centrioles) and with CETN2-GFPlow cells (enriched for two\ncentrioles). After co-culture, the frequency of Nur77+ T cells of all CD4+ T cells was assessed\naccording to the gating strategy shown in Supplementary figure 4a. We normalized data to the\npercentage of activated cells in the CETN2-GFPlow cell-condition. Thus, values below one\nindicate less T cell activation by DCs enriched for cells with multiple centrioles. To make this\nclearer, we changed the respective figure legend (p. 39, lines 1287-1288).\n10. In Figures 5b and 5e, the authors have provided intercentrosomal and intracentrosomal\ndistances for 1, 2, and 4h of co-culture. Why did the authors not included the data for 6h and\n20h where the maximum T cell activation was observed (Line 215 and Figure 4b)?\nWe thank the reviewer for this comment and repeated the respective experiments. We now\nincluded 6 h and 20 h data points in our analysis (Fig. 5b-c and 5e,f and Supplementary Fig.\n5a-d and 5f). We initially chose earlier timepoints, as we expected IS formation and MTOC\nreorientation to occur before Nur77-GFP gene expression. In summary, we did not detect\nchanges in intercentrosomal and intracentrosomal distances at all time points tested and\ntherefore concluded, that the DC MTOC stays at the cell center. To further elaborate on this\n17\nPoint-by-point response Nature Communications NCOMMS-24-49110\npoint, we now also included in vivo data to strengthen our claim in physiologically more\nrelevant environments (Fig. 6).\n11. In Fig 6A and 6C (graphs), the mean values can be misleading as the individual data points\nare highly inconsistent. The authors should reconsider increasing the ‘N’ values to determine\nthe distances.\nWe increased the number of cells (N) for the respective experiment and included the dataset\nin the revised version of the manuscript (now Supplementary Fig. 6a and c). The following\nnumber of cells were analyzed: 6a: N = 17/13/24/13 (left to right) and 6c (left): N = 17/14/57/34\n(left to right). Overall, we do not observe changes in centrosome position within the first hour\nof DC-T cell contact.\n12. In Figure 7b", "reviewer_2": "In the manuscript entitled “Multiple clustered centrosomes in antigen-presenting cells foster T cell activation without MTOC polarization”, by Stötzel et al. explored how centrosome amplification in DCs affects immune synapse formation and T cell activation. They integrated experimental data with computational modeling to demonstrate that extra centrioles in DCs lead to the formation of overactive centrosomes. These centrosomes cluster during DC-T cell interactions and are positioned near the cell center. Disrupting either the number of centrioles or the centrosomes configuration results in impaired T cell activation. The entire study is quite interesting, as it establishes the relationship between optimal centrosome positioning, centrosome numbers, and T cell activation. However, the author seems to have a limited understanding of the concepts, distinctions, and general functions between centrosomes and centrioles, leading to some misinterpretations of experimental data. Furthermore, I believe that using computational modeling to explain centrosome positioning may be somewhat ambitious. A more comprehensive model should incorporate additional intracellular variables and factors. If most of the issues below are resolved, I recommend that this manuscript be accepted. Specific comments:\nThe author raises several detailed scientific questions in the Introduction and Results sections, but it appears that few of them are thoroughly addressed.\nThe Introduction section is somewhat redundant, and the presentation of background knowledge is a bit disorganized and lacks logical coherence. It is recommended to succinctly present the background information and clearly state the key scientific question.\nPericentrin is the most typical marker of the PCM; it would be best if the author included additional staining for it.\nThe assertion that \"MT organization was not affected by the loss of centrioles\" is a perspective held by only some researchers. If the author wishes to make this claim, they should cite 2-3 high-quality references.\nI am not an expert in the field of immunology, but I still feel that using only one experimental method, flow cytometry (Fig. 2F), to support such an important conclusion “We found that T cell activation was markedly reduced in the presence of pretubulysin”, is insufficient and not solid.\nFor Figure 4G and its related data: Centrin-2 is primarily associated with centrioles and is a marker for centrioles. If the centrosome is concentrated in a single focus, it is defined as \"one centrosome,\" even if it contains multiple centrioles. It is defined as \"multiple centrosomes\" only when there are multiple foci or if it appears dispersed. For identifying multiple centrosomes, PCNT (Pericentrin) should be used as the marker instead of Centrin-2.\nI understand the novelty the author is aiming for in the final section of the Results, but explaining centrosome positioning using a geometric model seems somewhat far-fetched and unusual. If a computational model is to be used for this explanation, the evidence provided by the author is not sufficient. For instance, there are many interacting factors with the centrosome within the cell, including various organelles and variables such as fluidity. I believe this represents a highly complex computational process, unlike the current model in the paper.", "reviewer_3": "The authors have tried to understand how the MTOC (Microtubule organization center) organization in APC (antigen-presenting cell) has an effect on T-cell activation. It is well established that cellular polarization is essential for immune regulation, often involving centrosome reorientation. However, how centrosomes are oriented from the perspective of APCs is not entirely known. Here, the authors consider murine DCs (Dendritic cells) and observe, using experimental and mathematical models, that DCs’ additional centrioles create over-active centrosomes that cluster near the cell center during DC-T cell interactions. Disrupting centriole count or centrosome positioning in DCs impairs T cell activation, emphasizing the critical role of centrosome amplification and positioning in APCs for effective T cell responses. The manuscript details an important and often overlooked aspect of T cell activation. The complementary nature of the study (experiments supported with computational models) is commendable (please note that the reviewer is not an expert in computational modeling).\nWhile the enthusiasm for the topic is high, the reviewer is less excited about the manuscript in its present form. Mainly, the loss of enthusiasm stems from overcrowding of the figures (which makes it very challenging to follow and understand the novelty of work), low sample numbers, and a few irregularities noted below.\nMajor comments:\n1. The figures are very dense, making them hard to follow. The figure captions are very lengthy, and some require a single page. The authors are encouraged to reconsider how they should present their data.\n2. Following up on the above comment, the authors should clearly highlight the novelty of their work. Does the work provide a new interpretation of T cell activation, a new method, or a new computational model? How are the experiments and model tied together?\n3. The reviewer was puzzled in interpreting the data in the following figures:\na. In Fig 1E (left graph), what is the percentage of cells with two centrioles and no centrioles in the presence of 500 nM Centrinone?\nb. For Fig 2, the low sample size from only two experiments raises doubts about the claims of no changes in MTOC.\nc. The authors must provide high-resolution magnified images (insets and similar to image quality in subsequent figures) and supporting extra images in the supplementary materials (for all figures). From Figure 2b, the MTs look intact in the representative image w/o pre-tubulysin washout and with washout, which is contrary to the claims made by the authors in Line 149.\n4. Following up on comment 2b, the number of MT filaments shows high variability in control experiments (Fig 1F and 2C). These numbers should be the same or similar. How are the authors defining an MT filament? Does it have a minimum length?\n5. The authors mentioned that tubulin intensity is higher during IS (Immune synapse) formation in the presence of two or more centrosomes (Figure 3b). However, the provided images do not clearly show that IS formation corresponds to the centrosome number. Hence, the authors should provide magnified microscopic images of the same.\n6. In Fig 4d (graph), the authors have plotted the MTOC reorientation during the co-culture at different time points. Authors must provide representative microscopic images of the centrosome alignment at other time points (4, 6, 20h) in their supporting information.\n7. Since Figures 3 and 6 are inconclusive findings, the authors are encouraged to move them to supplementary materials.\n8. Similar to the irregularity mentioned in comment 3, in Figure 3F, the relative frequency of mono conjugation between T cell and DC cells does not reflect the centrosome numbers in Figure 3G in the same condition.\n9. In Figure 4C, It is unclear how the GFP values are observed to be below the threshold in 20h coculture. What does this signify? The reviewer is also confused on how the authors are claiming that multiple centrosomes are more efficient in activating T cells than with only one centrosome (lines 246-247), when there is no data provided for the latter.\n10. In Figures 5b and 5e, the authors have provided intercentrosomal and intracentrosomal distances for 1, 2, and 4h of co-culture. Why did the authors not included the data for 6h and 20h where the maximum T cell activation was observed (Line 215 and Figure 4b)?\n11. In Fig 6A and 6C (graphs), the mean values can be misleading as the individual data points are highly inconsistent. The authors should reconsider increasing the ‘N’ values to determine the distances.\n12. In Figure 7b, c: Why did the authors use two different concentrations of PJ34 to study α-tubulin and centrosomal distance?\n13. The reviewer is not an expert in computational modeling. However, the conditions outlined in the model for DCs (spherical cells) do not match the shape and culture conditions in experiments (2D). How does this impact the findings in Figures 8 and 9 and, generally, the force balance model with MT sliding?\n14. A general question (and a perceived weakness of the manuscript) is how flat 2D environments recover in vivo behavior. How would the accumulation of T cells change in more physiologically relevant environments?\nMinor comments:\nThe reviewer has some minor questions (mostly clarifications) on data organization.\n1) Is the 500 nM Centrinone effect on depleting the 2N cells % statistically significant in supp fig 1E?\n2) The authors should mention the different colors in the figure legends, especially for Figure 2a.\n3) Line 62: Please correct the typo in “… week T cell priming…”?\n4) clarify the Fig 4D legend: “…each datapoint is one individual experiment, whereas N> 100 cells analyzed per condition”. What does this mean?\n5) In Figures 3c and d, the Y-axis should be the same for easy interpretation of data.\n6) The authors should clarify the sample size information in Figure 4G- the authors state that analyzed cells are greater than 100 (line 1156), yet the data point shows only ~4.\n7) The numbering of figures in the text is out of order.\n8) Authors should modify the Y-axis label of Figure 5e. indicating the centrosome numbers (as per the figure legend).", "reviewer_4": "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_5": "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."}, "author_response": "Point-by-point response Nature Communications NCOMMS-24-49110B\nPoint-by-point response on NCOMMS-24-49110B:\n‘A centrally positioned cluster of multiple centrioles in antigen-\npresenting cells fosters T cell activation’\nWe thank all reviewers for the time and effort invested in evaluating our revised manuscript\nentitled ‘A centrally positioned cluster of multiple centrioles in antigen-presenting cells fosters\nT cell activation’. We are pleased that our manuscript is accepted (in principle) for publication\nby Nature Communications.\nBelow, we provide our response to the remaining comment from Reviewer 3.\nResponse to Reviewer 3\nReviewer #3 (Remarks to the Authors):\nThe authors have, for the most part, addressed the issues. They might want to look into the\nfollowing minor comment:\nThe authors propose (comment 1) that cell division may be facilitated by acentriolar\nmicrotubule-organizing center (MTOC) formation in cells lacking centrioles (Fig. 1e), as\npreviously demonstrated (Wong et al. 2015; https://doi.org/10.1126/science.aaa5111). In\nFigure 1E, the authors do not present evidence of cell division in acentriolar cells. Furthermore,\nif the authors are citing Wang et al. (2015) in Science, the cited authors did not provide\nevidence of acentriolar division in immune cells. Moreover, normal cells enter a quiescent\nphase rather than dividing.\nWe agree with the reviewer that we did not provide evidence for acentriolar cell divisions in\ndendritic cells. Therefore, we did not include this statement in our final manuscript but we still\ncite Wong et al. 2015 who were the first ones using Centrinone for centriole depletion in cells.\n1"}]

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