Spatiotemporal analysis of glucagon secretory granule dynamics

Spatiotemporal analysis of glucagon secretory granule dynamics | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Traffic This is a preprint and has not been peer reviewed. Data may be preliminary. 21 April 2025 V1 Latest version Share on Spatiotemporal analysis of glucagon secretory granule dynamics Authors : SAMUELE GHIGNOLI 0000-0002-5057-6986 , Valentina De Lorenzi 0000-0002-7429-6524 , Gianmarco Ferri , Licia Anna Pugliese , Marta Tesi , Piero Marchetti , Stefano Luin 0000-0003-2673-366X , and Francesco Cardarelli 0000-0003-3049-5940 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174523174.47414370/v1 Published Traffic Version of record Peer review timeline 264 views 137 downloads Contents Abstract Abstract Introduction Discussion Materials and Methods References Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The secretion of insulin and glucagon by pancreatic β and α cells, respectively, is critical for glucose homeostasis. While the insulin granule dynamics is well-characterized, the intracellular behavior of glucagon secretory granules (GSG) remains poorly understood. Here, we analyze the mobility of GSGs in α cells and insulin secretory granules (ISG) in β cells using spatiotemporal correlation spectroscopy and single-particle tracking (SPT), with a focus on the role of the cytoskeleton. Under basal conditions, SPT classification reveals that GSGs predominantly exhibit diffusive motion (57.6 ± 10%), with smaller fractions categorized as immobile (35.8 ± 10.6%) or drifted (6.6 ± 3%), resembling ISGs dynamics. By disrupting microtubules, we confirmed their role as active tracks for directed granule transport in both cell types. However, actin depolymerization, which enhances ISGs directed motion, has little effect on GSGs and instead increases their immobile fraction. This differential behavior persists under physiological secretory stimuli - high glucose for β cells and low glucose for α cells - indicating that, while both systems employ the cytoskeleton for secretion, α and β cells exhibit distinct recruitment and utilization patterns. These findings suggest that α and β cells may modulate granule dynamics differently to meet their specific secretory demands. Spatiotemporal analysis of glucagon secretory granule dynamics Authors: Samuele Ghignoli a , Valentina De Lorenzi a , Gianmarco Ferri b , Licia Anna Pugliese a , Marta Tesi c , Piero Marchetti c , Stefano Luin a , Francesco Cardarelli a,* Affiliations: a NEST Laboratory - Scuola Normale Superiore, Piazza San Silvestro 12, Pisa, Italy. b Fondazione Pisana per la Scienza, Pisa, Italy. c Department of Clinical and Experimental Medicine, Islet Cell Laboratory, University of Pisa, Pisa, Italy. * To whom correspondence should be addressed: [email protected] Keywords: α cells, β cells, Single Particle Tracking, imaging-derived Mean Square Displacement, fluorescence, secretion, cytoskeleton “‘latex Abstract The secretion of insulin and glucagon by pancreatic β and α cells, respectively, is critical for glucose homeostasis. While the insulin granule dynamics is well-characterized, the intracellular behavior of glucagon secretory granules (GSG) remains poorly understood. Here, we analyze the mobility of GSGs in α cells and insulin secretory granules (ISG) in β cells using spatiotemporal correlation spectroscopy and single-particle tracking (SPT), with a focus on the role of the cytoskeleton. Under basal conditions, SPT classification reveals that GSGs predominantly exhibit diffusive motion (57.6 ± 10%), with smaller fractions categorized as immobile (35.8 ± 10.6%) or drifted (6.6 ± 3%), resembling ISGs dynamics. By disrupting microtubules, we confirmed their role as active tracks for directed granule transport in both cell types. However, actin depolymerization, which enhances ISGs directed motion, has little effect on GSGs and instead increases their immobile fraction. This differential behavior persists under physiological secretory stimuli - high glucose for β cells and low glucose for α cells - indicating that, while both systems employ the cytoskeleton for secretion, α and β cells exhibit distinct recruitment and utilization patterns. These findings suggest that α and β cells may modulate granule dynamics differently to meet their specific secretory demands. Introduction Glucose homeostasis is tightly regulated by the opposing actions of insulin and glucagon, secreted by pancreatic β and α cells, respectively, within the islets of Langerhans 1–3 . These hormones are stored in secretory granules and released in response to metabolic cues, following a well-coordinated process of biogenesis, maturation, intracellular trafficking, and exocytosis 4–6 . Understanding the intracellular dynamics of these granules is essential for deciphering the regulatory mechanisms that maintain glucose homeostasis and their dysfunction in metabolic diseases such as diabetes 7,8 . While insulin granule dynamics in β cells have been extensively characterized, research on glucagon secretory granules (GSG) in α cells remains limited. The process of insulin secretion begins with glucose metabolism, leading to ATP production, closure of ATP-sensitive K⁺ channels, membrane depolarization, Ca²⁺ influx, and the mobilization of insulin-containing granules toward the plasma membrane 5 . High-resolution imaging techniques, including electron microscopy (EM) 9,10 , total internal reflection fluorescence (TIRF) microscopy 9,11–13 , spatiotemporal fluorescence correlation spectroscopy (FCS) 14 , and single-particle tracking (SPT) 15–18 , have provided a detailed picture of insulin secretory granule (ISG) trafficking. These studies revealed that upon glucose stimulation, ISGs mobilization occurs in two phases: an initial rapid release of docked granules, followed by a sustained phase involving the transport of a reserve pool from deeper cytoplasmic regions 8,19,20 . Additionally, cytoskeletal elements were demonstrated to play distinct roles in regulating granule trafficking. Cortical F-actin was demonstrated to act as a barrier that primarily restricts ISGs motion under basal conditions 15 ; indeed, F-actin extensive remodeling and/or depolymerization (e.g. induced by Latrunculin-B) is thought to facilitate granule exocytosis upon glucose stimulation 21–24 . Regarding α cells, limited data are available on the effect of F-actin depolymerization on glucagon release as pro-secretory stimulus 25,26 . On the other hand, microtubules were well demonstrated to serve as tracks for active ISGs transport, specifically to promote the sustained secondary phase of secretion 15 . For what concerns α cells, Yokawa and co-workers measured reduced GSGs velocity upon NCZ-induced microtubule depolymerization in α-TC6 cells by spinning-disk microscopy 27 . The limited understanding of GSGs dynamics must be attributed to both the historical focus on β cells (due to their central role in diabetes) as well as to the limited availability of α-cell models and suitable live-cell labeling techniques for GSGs. To address these challenges, we utilized αTC1-9 cells as an α-cell model (hereafter α cells) and INS-1E cells as a β-cell reference (hereafter β cells) and we exploited the ability of the fluorescent Zn²⁺ chelator ZIGIR to label both granule types 28 . We employed spatiotemporal fluorescence correlation spectroscopy (FCS) and single-particle tracking (SPT) to characterize granule mobility under basal conditions, upon cytoskeletal perturbations, and in response to pro-secretory stimuli. By comparing ISGs and GSGs trafficking, we aimed to identify shared and distinct regulatory mechanisms that could shed light on α-cell physiology and its role in glucose homeostasis. Results Labeling of glucagon and insulin granules by ZIGIR. To fluorescently label secretory granules in living α and β cells, we utilized a recently developed method based on the Zn²⁺-chelator ZIGIR 28 . According to the manufacturer’s protocol, the ZIGIR-based labeling involves administering a single dose of 1 μM, waiting at least 15 minutes before live-cell imaging (Fig. 1A, see Materials and Methods for further details). Upon applying this protocol to our system, nearly all cells in the petri dish exhibited punctate fluorescent staining, predominantly near the plasma membrane, in both α cells (Fig. 1B) and β cells (Fig. 1C). As a reference, we used transfection of EGFP-tagged neuropeptide Y (NPY-EGFP) to label GSGs in α cells (Fig. S1) 29 . Notably, ZIGIR and NPY-EGFP signals in live imaging overlapped in punctate structures throughout the cytoplasm. In addition, sparse NPY-EGFP signal was detected, likely due to the presence of fully mature NPY-EGFP proteins within the Golgi network (Fig. S2A-B). Moreover, we confirmed that the labelled vesicles are indeed GSGs by performing anti-glucagon immunofluorescence on NPY-EGFP transfected α cells (Fig. S2C-D). “‘latex Fig. 1: Granules labelling method. (A) Schematic representation of ZIGIR labelling protocol in which a single administration of ZIGIR 15 minutes prior to confocal microscopy imaging results in a punctate cellular labelling pattern. Servier Medical Art (https://smart.servier.com/) was used to create part of this figure. (B-C) Representative images of α cells (B) and β cells (C) in which GSGs and ISGs were labelled by ZIGIR, respectively. Validation of live cell imaging protocol via spatiotemporal fluorescence correlation spectroscopy. To evaluate the time-resolved live-cell imaging protocol used to assess granule dynamics, we acquired confocal images stacks of 500 frames of ZIGIR-labeled granules in living cells (i.e., ~5 images per second or ~200 ms temporal resolution). Then, we performed iMSD (imaging-derived Mean Square Displacement) analysis on these image stacks (Fig. 2A) 14,30,31 . iMSD, in fact, is a rapid, robust spatiotemporal correlation spectroscopy technique that provides a real-time quantitative estimate of organelle diffusivity D m (D micro represents the diffusivity at short time and space scales, reflecting the effective free diffusion that may occur within confined areas) and of the overall mode of motion (through the Alpha anomalous coefficient), while simultaneously probing average granules size information through the offset (the y-axis intercept) of the iMSD plot. As extensively discussed elsewhere 14,30,31 , this offset corresponds to the squared waist of the spatial correlation function at zero lag time (σ 0 ²). This value incorporates contributions from both the squared width of the point spread function and the squared average particle size 14,30,31 . Consequently, the offset increases with granule dimension. “‘latex Fig. 2: iMSD and STORM comparison between α cells and β cells. (A) 500 -frames movies of confocal images of labelled GSGs and ISGs in α cells and β cells, respectively, were acquired at ~200 ms per frame; the movies were processed by a custom made iMSD script for average dynamical and structural analysis. A summary graph of the results obtained by the iMSD script: the alpha anomalous coefficient, the diffusion parameter at short length scale D m and the apparent size parameter (offset). See materials and methods for details. (B-C-D) Comparison of the alpha anomalous coefficient, D m , and offset (squared apparent size) obtained for α cells (N = 34) and β cells (N = 27) labelled by ZIGIR. Each dot shown in the scatter plots represents the outcome from a single acquisition; horizontal gray lines with error bars are Mean ± SD. (B) In both cells types the motion of the granules was sub-diffusive, even if the alpha anomalous coefficient for GSGs in α cells (0.82 ± 0.14) was closer to 1 than for ISGs in β cells (0.64 ± 0.14; Mean ± SD). (C) No significant differences were ascertained for the diffusivity, despite some outliers in both cases but mostly in β cells. α cells D m = 1.5*10 -3 ± 0.9*10 -3 µm 2 /s and β cells D m = 2.3*10 -3 ± 1.9*10 -3 µm 2 /s (Mean ± SD). (D) A difference was present for the offset, highlighting a difference in apparent size, indicating that GSGs on average resulted smaller than ISGs. (E) Size results from STORM imaging of super-resolved granules comparing GSGs diameter (132.5 ± 39.0 nm) and ISGs diameter (169.7 ± 56.4 nm). Each dot shown in the scatter plot represents a single granule measurement; horizontal gray lines with error bars are Mean ± SD. We checked normality and performed t test for the normally distributed data and Mann-Whitney nonparametric test for the non-normally distributed data. We compared ZIGIR-labeled GSGs in α cells with ISGs in β cells (Fig. 2B-D) through iMSD. A couple of observations can be made: first, ZIGIR proved to be a reliable marker for Zinc-rich organelles, showing consistency in the dynamic properties of GSGs labeled with NPY-EGFP 29 (Fig. S3A), as well as ISGs labeled with C-pep-EGFP 14 (Fig. S3B). Second, as expected, iMSD analysis shows that GSGs are principally diffusive, with average properties comparable to those of ISGs (see Alpha anomalous parameter and D m in Fig. 2B-C); indeed, as discussed in 32 , an average Alpha anomalous coefficient slightly lower than 1 can be expected for mostly-diffusive motion within cells, being the diffusive motion hindered by obstacles like membranes or cytoskeleton elements. Statistical analysis of the offset values reveals difference in size between GSGs and ISGs, with ISGs generally bigger than GSGs (Fig. 2D). It is important to note that inadequate temporal resolution and the point spread function (PSF) width can lead to an overestimation of the granule sizes 30 , so we decided to further validate this difference by direct measurements of granule size in both cell types using Stochastic Optical Reconstruction Microscopy (STORM), as reported in Fig. 2E and S4. The results confirmed the trend observed by the offset in the iMSD and are consistent with previous transmission electron microscopy (TEM) and scanning electron microscopy (SEM) studies 10,33 . SPT-based classification of granule dynamics under upkeeping conditions. The intrinsic heterogeneity of dynamic parameters within the population of diffusing granules is inevitably averaged out by the iMSD algorithm. Additionally, iMSD analysis is primarily sensitive to the mobile, diffusing population of granules, while it tends to underrepresent or overlook stationary granules 34–36 . With iMSD, granules with varying mobilities and diffusion characteristics are analyzed collectively along with stationary granules, or those with minimal displacement, which mitigates the underlying differences in their dynamic parameters. To capture all relevant information, we employed single-particle tracking (SPT) analysis using the TrackMate plugin for ImageJ to construct the tracks and custom MATLAB scripts to analyze them (Fig. 3A, see Materials and Methods for further details). Briefly, we used TrackMate for particle recognition, spatial localization, and trajectory reconstruction 37,38 ; granules were detected using a Laplacian of Gaussians (LoG) filter, with an estimated object diameter of 0.3 µm, consistent with our observations, considering PSF deformation due to confocal imaging (Fig. 2D-E). The tracking algorithm was based on a linear assignment problem (LAP) 39 , without accounting for splitting or merging events. Following TrackMate analysis, a custom MATLAB script was used for data analysis and trajectory classification (see Materials and Methods) 36 . This approach enabled us to separate the trajectories of the granules into sub-trajectories characterized by a single motion mode, when needed, and to classify them into three categories: blocked’, ‘diffusive’, and ‘drifted’. The ‘blocked’ category represents immobile or strongly confined granules, the ‘diffusive’ category pertains to granules diffusing freely, possibly within a confined region, and the ‘drifted’ category includes granules moving in fast, directed steps (Fig. 3B). In brief, starting from trajectories obtained from the TrackMate analysis, the first step of classification looks for (parts of) trajectories characterized by a fast directed motion, in a sort of “stop-and-go” analysis 40–42 . The remaining (sub)trajectories (meaning both trajectories and sub-trajectories) are often still multimodal, therefore in the second step of classification they are segmented looking for “transient arrest of diffusion” (TAD) zones. The resulting (sub)trajectories are classified according to the type of motion through MSS-TAD analysis, which differentiates blocked or slow (sub)trajectories from diffusive or fast ones 32,36 . The drifted sub-trajectories resulting from this second step of the classification are merged with the faster drifted ones obtained previously. Classification criteria have been optimized through parameter tests (see Materials and Methods). A key parameter was the threshold velocity, v t , set at 0.2 μm/s, which defined the lower velocity limit for classifying particles as drifted in the first step of analysis. This threshold value proved to be optimal for separating drifted trajectories from diffusive and/or confined ones, highlighting the distinction between fast/directed and slow/confined movement. “‘latex Fig. 3: SPT analysis for particle trajectories classification of α cells and β cells in upkeeping conditions. (A) Summary scheme of the principal steps of the analysis for the trajectory segmentation and classification, leading to the final division into blocked, diffusive and drifted (sub)trajectories. (B) Representative output of the custom-made MATLAB script, highlighting the results of segmenting the trajectories and the classification into blocked, diffusive and drifted parts (for details see material and methods): gray (sub)trajectories are blocked, red (sub)trajectories are diffusive and both cyan and green ones are drifted (from the first and second step of the analysis, respectively); black segments are connections between different sub-trajectories within the same trajectory. (C-D) Resulting fraction of granules undergoing the specified motion mode for different α cells (N = 24) ( C ) or β cells (N = 35) ( D ) in upkeep conditions. The graph represents the percentage (%) of the total length of sub-trajectories classified in the three different modes. “Drifted” y-axis on the right (% Length Drifted). Scatter plots with Mean ± SD with each dot representing the outcome from a single acquisition. This SPT approach enabled us to classify the dynamics of both GSGs and ISGs in upkeeping condition (Fig. 3C-D, respectively). We defined the upkeeping condition as standard cell culture maintenance condition, without external stimuli or perturbation. The SPT classification of GSGs in α cells revealed a pattern in which diffusive behavior dominates (57.6 ± 10%), over blocked (35.8 ± 10.6%) and drifted ones (6.6 ± 3%, Fig. 3C, Tab. 1). These percentages represent the proportion of granules exhibiting each type of motion relative to the total number of granules tracked across all trajectories and frames, effectively quantifying the fraction of time that the granules spend in a motion category. Noteworthy, GSGs exhibited a notably higher propensity for directed motion compared to ISGs (6.6% vs. 2.9%, Tab. 1), a trend also observed through iMSD analysis of the entire mobile granule population (Fig. 2B), despite the lesser sensibility of this technique. Notably, the classification results for ISGs in β cells under upkeeping conditions closely align with findings from other studies using similar SPT-based approaches 9,15 , showing a clear predominance of diffusive behavior (64.3 ± 7.7%) over blocked (32.8 ± 7.2%) and drifted (2.9 ± 1.3%) ones (Fig. 3D, Tab. 1). To characterize motion dynamics, we computed the joint distribution of the short-lag-time diffusion coefficient (D 12 , corresponding to D m but for each (sub)trajectory) and the anomalous diffusion parameter γ 32 , which is functionally analogous to (half of) the Alpha anomalous coefficient obtained from iMSD analysis. Therefore, it provides equivalent insight into dynamic properties (Fig. S5). In α cells under upkeeping conditions, the resulting graph shows a peak slightly under γ = 0.5, indicating a motion very close to Brownian but with a tendency of sub-diffusive behavior (Fig. S5A). In β cells under upkeeping condition, the peak appears quite below γ = 0.5, indicating a more strongly sub-diffusive motion (Fig. S5B). In summary, γ analysis from SPT script confirms the findings of the iMSD-derived alpha anomalous coefficient; the similarity between α and β cells for the distributions of D 12 values, and for their peaks, reflects the lack of significant differences among the D m population as shown in Fig. 2C. α 35.8 ± 10.6 57.6 ± 10 6.6 ± 3 24 β 32.8 ± 7.2 64.3 ± 7.7 2.9 ± 1.3 35 α NCZ 56.1 ± 10.7 40.4 ± 10.2 3.5 ± 1.7 17 β NCZ 48.2 ± 9.8 50.1 ± 9.8 1.7 ± 0.6 17 α Lat-B 58.3 ± 7.2 33.7 ± 8.1 8.1 ± 4.5 17 β Lat-B 19.2 ± 6.4 71.0 ± 6.9 9.8 ± 3.1 16 α ctrl Hypo (2.5 mM glucose) 26.2 ± 6.3 70.8 ± 6.5 2.9 ± 1.5 19 β ctrl High Gluc (2.5 mM glucose) 44.8 ± 10.6 53.1 ± 10.5 2 ± 0.9 22 α Hypoglycemia (1 mM glucose) 29.3 ± 8.6 66.4 ± 8 4.4 ± 2 16 β High Gluc (16.7 mM glucose) 35 ± 9.2 61.5 ± 9.3 3.4 ± 2 25 α ctrl High Gluc (2.5 mM glucose) 28.2 ± 5.7 67.2 ± 5.3 4.6 ± 2.2 23 β ctrl Hypo (2.5 mM glucose) 42.4 ± 11.1 55.1 ± 10.5 2.5 ± 1.8 31 α High Gluc (16.7 mM glucose) 28.4 ± 6.3 67.0 ± 6.5 4.5 ± 1.8 24 β Hypoglycemia (1 mM glucose) 39.6 ± 9.8 58.4 ± 9.4 2.1 ± 1.7 35 Tab. 1: Summary table of the classified categories for different conditions. To better read the table, α cells and β cells are alternated with the first on gray rows and the seconds on white rows. Nomenclature abbreviations: Nocodazole (NCZ), Latrunculin-B (Lat-B), hypoglycemia condition (Hypo), high glucose stimulus (High Gluc). N is the numerosity of samples and all the data are expressed as length percentage with mean ± SD. SPT-based classification of granule dynamics: the role of cytoskeleton components. To evaluate the influence of cytoskeleton components on granule dynamics in upkeeping condition, we performed SPT analysis in the presence of cytoskeleton-disrupting agents, i.e. 10 μM Nocodazole (NCZ) to induce microtubule depolymerization and 10 µM Latrunculin-B (Lat-B) to induce F-actin depolymerization (Fig. 4A-B). As mentioned in the introduction, previous studies have well characterized the role of cytoskeleton in β cells: microtubules serve as tracks for active granule transport, especially supporting the sustained secondary phase of secretion 15 , while cortical F-actin primarily acts as a barrier to ISG motion under basal conditions and it’s subjected to depolymerization to facilitate exocytosis upon glucose stimulation 21–23 . In line with these expectations, NCZ treatment in β cells produced a significant increase in the proportion of ISGs exhibiting blocked trajectories compared to control condition, at the expense of mobile trajectories (See Tab. 1). Notably the drifted trajectories decreased (from 2.9 ± 1.3% to 1.7 ± 0.6%, mean ± SD, Fig. 4C, S6A-B, Tab. 1). β cells responded as expected to the pro-secretory stimulus of Lat-B-induced F-actin depolymerization, showing substantial reduction of the fraction of blocked ISGs trajectories, and a corresponding increase in drifted-motion trajectories (Fig. 4D, S6A-C, Tab. 1). These changes in dynamics are evident also upon iMSD analysis: we observed a significant increase in D m , from 3.35*10 -3 µm 2 /s to 12.8*10 -3 µm 2 /s (Fig. S7A). Considering GSGs dynamics, the α cells responded to NCZ-induced microtubule depolymerization similarly to β cells, with a neat increase in the fraction of blocked granules and a concomitant reduction in both diffusive and drifted categories (Fig. 4E, S6D-E, Tab. 1). Their response to F-actin depolymerization, instead, did not match the results from β cells. Indeed, the fraction of immobile GSGs increased significantly compared to the control (blocked from 35.8 ± 10.6% to 58.3 ± 7.2%, mean ± SD, Fig. 4F, S6D-F, Tab. 1), mostly at the expense of diffusive trajectories (from 57.6 ± 10% to 33.7 ± 8.1%, mean ± SD, Fig. 4F, S6D-F, Tab. 1), with no significant shift in the fraction of drifted ones. This kind of behavior could also be highlighted with iMSD by analyzing the Alpha anomalous coefficient, whose average decreased from 0.82 for untreated cells to 0.45 for Lat-B treated cells (Fig. S7B), highlighting a very sub-diffusive motion in this last case. Fig. 4: SPT classification for ISGs and GSGs trajectories after Nocodazole (NCZ) and Latrunculin-B (Lat-B) treatment. (A) Tubulin is labelled with sirTubulin (SirTUB) in green in control condition (Ctrl) and after NCZ treatment, in which the cytoskeleton appears fragmented. (B) Actin is labelled with sirActin (SirACT) in violet in control condition (Ctrl) and after Lat-B treatment, in which the actin filaments appear fragmented. (C-F) Box plots for 3 categories of granules trajectories in which we compare the upkeep condition (ctrl) with respect to cytoskeletal component depolymerization. (C) Comparison between ISGs ctrl condition (n = 35, same data of Fig. 3 D) and microtubule depolymerization (NCZ, n = 17). NCZ treatment caused an increase in blocked trajectories to the detriment of both diffusive and drifted ones, denoting a reduction of general motility. “Drifted” y-axis on the right (% Length Drifted). (D) Comparison between ISGs ctrl condition (n = 35, same data of Fig. 3D) and disrupted actin filaments one (Lat-B, n = 16). Lat-B induced a net decrease in blocked trajectories and raised up both diffusive and drifted ones. “Drifted” y-axis on the right (% Length Drifted). (E) Comparison between GSGs ctrl condition (n = 24, same data of Fig. 3C) and microtubules disruption one (NCZ, n = 17). Here the treatment increased the blocked trajectories with a concomitant decrease of the motility fraction of the trajectories. “Drifted” y-axis on the right (% Length Drifted). (F) Comparison between ctrl condition (n = 24, same data of Fig. 3C) and disrupted actin filaments one (Lat-B, n = 17). The result is almost the opposite of the one for ISGs, with an increase of blocked trajectories and a concomitant decrease in diffusive trajectories. “Drifted” y-axis on the right (% Length Drifted). We checked normality and performed t test for the normally distributed data and Mann-Whitney nonparametric test for the non-normally distributed data for each trajectories category. Scatter plots with Mean ± Standard deviation with each dot shown represents the outcome from a single acquisition. SPT-based classification of granule dynamics under secretory stimuli. The discordant behavior of α and β cells in response to Lat-B-induced F-actin depolymerization, a putative pro-secretory stimulus for both cell types 24–26,43 , prompted us to evaluate whether the same difference persisted under their respective secretory stimuli induced by glucose concentration. Thus, we investigated α and β cells responses to low (hereafter hypoglycemia) and high glucose concentration, respectively. The analyzed granule population includes both pre-existing and newly produced granules following stimulation 9,29,44 , and we examined how their mobility changed under these conditions. In the β cells model, exposure to its standard secretory stimulus (increasing glucose concentration from 2.5 mM to 16.7 mM) resulted in a clear decrease in the fraction of immobile ISGs (from 44.8 ± 10.6% to 35.0 ± 9.2%, Fig. 5A, Tab. 1), accompanied by an increase in mobile granules, particularly those exhibiting drifted motion (from 2.0 ± 0.9% to 3.4 ± 2.0%, Fig. 5A, Tab. 1), consistent with previous findings 15 and with the pro-secretory effect observed using Lat-B. As a control, β cells subjected to hypoglycemic conditions exhibited no changes in ISGs dynamics (Fig. 5B, Tab. 1). To assess GSGs mobility under secretory stimulation, the α cells were incubated in KRBH supplemented with 1 mM glucose for 45-60 minutes after a 45-60 minutes preincubation period in KRBH supplemented with 2.5 mM glucose, as previously reported to simulate hypoglycemia in this α cells model 45 (see Materials and Methods). The fractions of blocked and diffusive trajectories remained substantially unchanged as compared to control conditions, with only a minor increase in drifted trajectories, as expected under a secretory stimulus (from 2.9 ± 1.5% for α ctrl in 2.5 mM glucose to 4.4 ± 2% for α cells in hypoglycemia, 1 mM glucose, Fig. 5C, Tab. 1). Increasing glucose concentration (up to 16.7 mM) on α cells had no detectable effect on GSGs dynamics (Fig. 5D, Tab. 1). Taken together, these findings suggest that under secretory stimuli, ISGs show an increase in both drifted and diffusive motion, similar to the effect observed after F-actin disruption. In contrast, GSGs exhibit a milder response to hypoglycemia, with an increase only in drifted motion. However, following Lat-B treatment on α cells, drifted trajectories did not further increase, and instead, blocked trajectories became more prominent, suggesting a distinct response of α cells compared to β cells. “‘latex Fig. 5: Alteration of granules trajectories after secretory stimuli (High Glucose) for β cells and after Hypoglycaemia stimulus for α cells, and the related counterpart. Scatter plots showing the 3 categories (blocked, diffusive, and drifted) of GSGs and ISGs motion modes with schematic representation of cells during the event of secretion or with no secretion. We collected the images within one hour from the stimulus to check the motility alteration. “Drifted” y-axis on the right (% Length Drifted) for each graph. (A) ISGs motility after high glucose stimulus. Comparison between the control condition in KRBH buffer (2.5 mM Gluc, n = 22) with respect to secretory stimulus of high glucose (16.7 mM Gluc, n = 25). (B) ISGs motility in hypoglycaemia condition. Comparison between control condition in KRBH buffer (2.5 mM Gluc, n = 31) and hypoglycemia treatment (1 mM Gluc, n =35) showing no alteration in any category of trajectories, as expected. (C) GSGs motility in hypoglycaemia condition. Comparison between control condition in KRBH buffer (2.5 mM Gluc, n = 19) and hypoglycemia treatment (1 mM Gluc, n =16). Only the drifted portion of granules seemed to increase, confirming a higher motility linked to directional transport. We collected the images within one hour starting from the treatment with 1mM glucose media. (D) GSGs motility in α cells after high glucose stimulus. We perform the same glucose stimulus used for the data in panel A, confirming the absence of alteration in motility (2.5 mM Gluc, n = 23; 16.7 mM Gluc, n = 24). We checked normality and performed t test for the normally distributed data and Mann-Whitney nonparametric test for the non-normally distributed data for each trajectories category. Servier Medical Art (https://smart.servier.com/) was used to create part of this figure. Scatter plots with Mean ± SD with each dot shown represent the outcome from a single acquisition. Discussion The intracellular trafficking of secretory granules is a key determinant of hormone release, yet while insulin granules have been extensively studied, glucagon granules remain poorly characterized. Here, we provide a quantitative analysis of GSGs mobility in α cells using a combination of advanced time-resolved fluorescence microscopy techniques. In brief, spatiotemporal FCS allowed us to extract the average dynamic properties of GSGs, while a custom MATLAB-based SPT approach enabled high-resolution (~200 ms/frame) tracking of individual granules, classifying them into ‘blocked’ (immobile), ‘diffusive’ (random motion), and ‘drifted’ (directed transport) categories. The SPT classification allowed us to resolve distinct motion categories, revealing that under basal conditions, GSGs predominantly exhibit diffusive motion (57.6 ± 10%), with smaller fractions categorized as almost immobile (35.8 ± 10.6%) or drifted (6.6 ± 3%). Interestingly, these distributions closely mirror those of ISGs, suggesting that the two granule types may share similar trafficking strategies in upkeeping conditions. The classification results for ISGs in β cells are consistent with previous studies using similar SPT-based approaches 9,15 . We then explored the role of cytoskeletal components in regulating granule motion. Microtubules disruption significantly reduced directed motion for both granule types, potentially extending to α cells the same conclusions on microtubules role as active tracks for long-range granule transport already characterized in β cells 46 . Worthy of mention, this is consistent also with previous observations in α cells: Yokawa and co-workers measured reduced GSGs velocity upon NCZ-induced microtubule depolymerization in α-TC6 cells by spinning-disk microscopy 27 . On the other hand, Lat-B-induced actin depolymerization, thought to act as a pro-secretory stimulus in both cell types 24–26,43 , produced apparently contrasting effects: ISGs showed increased drifted motion and overall enhanced mobility, while GSGs exhibited no significant increase in directed motion, with an overall decrease in mobility. This response difference persisted under physiological secretory stimuli, with ISGs substantially increasing directed (and diffusive) motion upon high glucose stimulation, and GSGs displaying a lower response under hypoglycemia. These findings suggest that, while both cell types utilize the cytoskeleton for granule trafficking, their reliance on specific cytoskeletal components may differ, likely reflecting distinct regulatory mechanisms tailored to their secretory demands. Notably, previous studies have reported differences in the kinetics of exocytosis between α and β cells. Although both cell types have been proven to display biphasic hormone secretion, as measured by TIRF microscopy, α cells exhibit markedly reduced exocytosis rates as compared to β-cells 29 . This difference in secretory dynamics could be linked to the distinct granule motion patterns observed here, potentially reflecting adaptations to their respective physiological roles. The differential responses of GSGs and ISGs to cytoskeletal perturbations raise intriguing questions about the molecular machinery governing their trafficking. It is possible that distinct motor proteins or scaffolding interactions play specialized roles in α and β cells, reflecting their distinct functional needs. Further studies are needed to dissect the molecular basis of these differences and to determine whether they contribute to dysregulated hormone secretion in metabolic disorders such as diabetes. By providing a comparative framework for studying ISG and GSG mobility, our findings lay the groundwork for future investigations into α cell physiology and its role in glucose homeostasis. Materials and Methods Cell culture. Alpha TC1 clone 9 (αTC1-9) cells (ATCC, cat#CRL-2350, RRID: CVCL_0150) were cultured at 37 °C with 10% CO 2 in a controlled environment. They were maintained in DMEM low-glucose medium (1 g/l) (Gibco, cat#11880036) supplemented with 10% heat-inactivated fetal bovine serum (FBS), 15 mM HEPES, 2 mM L-glutamine, 100 U/mL penicillin-streptomycin, 0.1 mM non-essential amino acids (NEAA), and 0.02% bovine serum albumin (BSA). For β-cell-like cultures, we used INS-1E cells, kindly provided by Prof. C. Wollheim (University of Geneva, Medical Center, RRID: CVCL_0351). INS-1E cells were grown in RPMI 1640 medium (Gibco, cat#11835105) containing 11.1 mM D-glucose, supplemented with 10 mM HEPES, 100 U/mL penicillin-streptomycin, 1 mM sodium pyruvate, 2 mM L-glutamine, 10% heat-inactivated FBS, and 50 μM tissue culture-grade β-mercaptoethanol. They were maintained at 37 °C with 5% CO 2 in a controlled environment. These cell culture mediums were used in upkeeping conditions and for cytoskeleton-disrupting experiments. When needed, αTC1-9 cells were transfected with NPY-EGFP (EGFP-tagged neuropeptide Y plasmid, a gift from Justin Taraska; Addgene plasmid #74629 [http://n2t.net/addgene:74629; RRID: Addgene_74629]) 29 . INS-1E cells were transfected with C-pep-EGFP 47 . The transfections were performed by plating αTC1-9 cells on 22-mm glass-bottom dishes (WillCo Wells, cat#HBST-3522) and INS-1E cells on ibiTreat μ-Dish 35 mm plates (Ibidi, cat#81156). The following day, transfection was carried out using Lipofectamine 2000 (Life Technologies) according to the manufacturer’s instructions. Cells were imaged 24 hours post-transfection. Live-cell imaging. For iMSD and SPT experiments, GSGs and ISGs were labeled with ZIGIR, a fluorescent probe that targets zinc-rich granules 28 . αTC1-9 and INS-1E cells were plated on 22-mm glass-bottom dishes and ibiTreat μ-Dish 35 mm plates, respectively, and allowed to adhere overnight before labeling and imaging. For labeling, cells were incubated with 1 μM ZIGIR in upkeeping medium and after 15 minutes of incubation, unless otherwise specified, imaging was performed. For assessing cytoskeletal components depolymerization, cells were treated for 30 minutes with 10 μM SiR-Tubulin (Spirochrome, cat#SC002 48 ) to label microtubules or with 10 μM SiR-Actin (Spirochrome, cat#SC001 48 ) to label actin filaments. For these treatments, 10 μM Nocodazole (Sigma-Aldrich, cat#487928) and 10 μM Latrunculin-B (Sigma-Aldrich, cat#428020) were added to upkeeping medium 30 minutes before imaging to disrupt microtubules and actin filaments, respectively. To label granules in these experiments, 1 μM ZIGIR was added concomitantly with the cytoskeletal polymerization inhibitors. In both cases, image acquisition was completed within 1 hour and 30 minutes starting from imaging. For the labelling comparison experiments (Fig. 2 and S3) between ZIGIR and EGFP-tagged markers for both α cells and β cells through iMSD, image stacks were acquired using a Leica SP5 inverted confocal microscope. The labelling method comparison was conducted in cells labeled with ZIGIR or transfected with C-pep-EGFP (β cells) or NPY-EGFP (α cells) exciting ZIGIR at 514 nm and EGFP-tagged probes at 488 nm and collecting the signal between 525 nm and 700 nm and between 500 nm and 600 nm, respectively. A 100× oil-immersion objective (N.A. 1.3) was used, with the pinhole aperture set to 1 Airy. Each stack consisted of 500 frames, acquired with a 204 ms acquisition time per frame and a 1400 Hz line frequency. Other iMSD and SPT experiments were conducted using two additional confocal microscopes with equivalent tracking capabilities. Imaging was performed on an inverted Zeiss LSM 800 confocal microscope (Jena, Germany), exciting ZIGIR at 561 nm and collecting fluorescence between 590 and 700 nm. The pinhole aperture was set to 1 Airy (53 μm), using a 63× (N.A. 1.4) oil objective. Each stack consisted of 500 frames, acquired with a 204 ms acquisition time per frame. Moreover, Zeiss confocal microscope was used to perform colocalization study in live cells exciting sequentially ZIGIR at 561 nm and EGFP at 488 collecting the signal between 550 nm and 700 nm and between 400 nm and 550 nm, respectively. Additionally, a Leica Stellaris 8 confocal microscope was used, exciting ZIGIR at 545 nm and collecting fluorescence between 565-732 nm. Each stack consisted of 500 frames at 256×256 pixels resolution, with an acquisition time of 198 ms per frame. Live-cell imaging was performed at 37 °C under controlled CO 2 conditions. Immunostaining and colocalization analysis. αTC1-9 cells were transfected with NPY-EGFP and plated on glass coverslips. After 24 hours, the cells were fixed with 4% paraformaldehyde (PFA) for 30 minutes at room temperature (RT), followed by permeabilization with PBS containing 0.1% Triton X-100 (PBST) for 10 minutes at RT. To block non-specific binding, cells were incubated with 2% bovine serum albumin (BSA) for 30 minutes at RT. For immunostaining, cells were incubated with a mouse anti-glucagon primary antibody (Boster, cat# MA1047) diluted 1:200 for 2 hours at 37 °C, followed by incubation with an anti-mouse Alexa Fluor 647 secondary antibody (ThermoFisher, cat# A31571) diluted 1:500 for 1 hour at RT in the dark. Colocalization analysis was performed to validate the specificity of glucagon granule labeling. Colocalization comparison was first performed between ZIGIR and NPY-EGFP in live α cells, followed by a comparison between NPY-EGFP and anti-glucagon in fixed samples. This two-step approach was necessary because ZIGIR cannot be used on fixed samples 28 . Pearson correlation and Manders coefficients were calculated using the BIOP JACoP plugin for ImageJ, applying the Otsu method for thresholding. For STORM imaging, the same fixation protocol previously described was applied to both αTC1-9 cells and INS-1E cells. For STORM immunostaining, αTC1-9 cells were incubated with anti-glucagon primary antibody as previously described, while INS-1E cells were incubated with an anti-insulin primary antibody (Immunological Sciences, cat# AB-84377) diluted 1:200 at 4 °C overnight. Both primary antibody incubations were followed by incubation with an anti-mouse Alexa Fluor 647 secondary antibody (ThermoFisher, cat# A31571), diluted 1:500, for 1 hour at RT in the dark. dSTORM Imaging of insulin and glucagon secretory granules. A Nikon N-STORM TIRF microscope (Nikon Instruments), equipped with a 100× oil immersion objective (CFI Apo TIRF 100×, NA 1.49, oil; Nikon), was used to acquire 20,000 frames in TIRF illumination mode. Images were captured at a digital resolution of 256×256 pixels (crop), with a pixel size of 158.7 nm. Acquisitions consisted of 5,000 cycles, each including one activation frame with a 405 nm excitation laser, followed by three readout frames with a 647 nm excitation laser. Excitation intensities, measured at objective, were ~0.5–1 KW/cm² for the 647 nm readout laser (MPB Communications) and ~35 W/cm² for the 405 nm activation laser (Oxius LBX-405). Images were detected using an EMCCD camera (Andor iXon DU-897; Andor Technologies) with EM gain set to 300 and a temporal resolution of 30 ms. The STORM imaging buffer was prepared by filtering and storing at 4°C a solution of 690 μL of 50 mM Tris buffer (pH 8.0), containing 10 mM NaCl and 10% w/v glucose. Immediately before use, 3.5 μL of GLOX solution (described below) and 3.5 μL of 2-mercaptoethanol were added. The final solution was then applied to the petri dishes, which was sealed with aluminum tape. The GLOX solution was prepared by dissolving 14 mg of glucose oxidase and 50 μL of catalase (17 mg/mL) in 200 μL of STORM imaging buffer. It was stored at 4°C for a maximum of 14 days. Acquired dSTORM stacks were processed using ThunderStorm, a Fiji (ImageJ) plugin for PALM and STORM data analysis. First, acquisition properties were configured in the “Camera setup” menu: pixel size = 158.7 nm, photoelectrons per A/D count = 2.5, base level = 100 counts, and EM gain = 300. Localization was performed using the default algorithm, and results were refined through post-filtering in ThunderStorm, including a) Removal of the first 500 frames; b) drift correction by correlation; c) merging of reactivated molecules (max distance: 20 nm, max off frames: 1, unlimited frames per molecule) d) Removal of localizations with sigma > 130 nm or uncertainty > 40 nm. Filtered molecule localizations were first analyzed using a ClusDoC algorithm for clustering 49 installed in MATLAB, based on the DBSCAN algorithm for clusterization. The parameters for clusterization were set as: epsilon = 50 nm and minPts = 8, respectively representing the maximum distance between two points for them to be considered neighbors and the minimum number of neighboring points required to form a cluster. Each identified cluster was considered a secretory granule. The cluster contour is determined by creating a 2D histogram of localization points within the cluster as described in the ClusDoc reference paper 49 . The boundary of this region is used as the cluster contour for measuring single granule diameter, approximating it as a circle through a custom-made MATLAB script. Data analysis: iMSD. A custom MATLAB (MathWorks Inc., Natick, MA) script was adapted to extract dynamic and structural information from iMSD image stacks. This script, fully described in Ferri et al. 14 , calculates a spatiotemporal correlation function and determines its width at each lag time, in order to extract the iMSD curve. The resulting iMSD curve describes the average motion law of granules, and can be characterized by two diffusion parameters: Anomalous diffusion coefficient (Alpha anomalous), which indicates the type of motion: 1 to super-diffusion, likely due to guided drifted motion. Average diffusion coefficient (D m ): Represents the local (or short-lag time) free diffusivity of granules, potentially within confined regions. Additionally, the offset of the iMSD curve provides information about granule size. While the offset does not correspond to the actual physical size of the object, it serves as a squared apparent size metric that can be compared across different experimental conditions. Data analysis: Single Particle Tracking. SPT analysis was performed using TrackMate, an open-source ImageJ plugin that enables particle detection and localization, and trajectory reconstruction 37,38 . TrackMate identified labeled granules in time-lapse videos, applying a Laplacian of Gaussians (LoG) filter for particle detection and localization. The LoG detector settings included an estimated object diameter of 0.3 µm, a quality threshold optimized per dataset, median filter preprocessing, and subpixel localization. For trajectory reconstruction, we used the linear assignment problem (LAP) tracker 39 , which allows for gap-closing events, but without considering splitting or merging. The tracking parameters were: • Maximum frame-to-frame linking distance: 0.5 µm • Maximum gap-closing distance: 0.5 µm • Maximum frame gap: 3 frames Custom MATLAB scripts converted TrackMate results (exported as spot statistics) and performed further data analysis and classification (Tab. 1S for parameters summary). Only trajectories spanning more than five frames were considered for the analysis. Trajectories Segmentation and Motion Classification To segment trajectories and classify motion types, we performed two steps: first, we applied a stop-and-go analysis based on a velocity threshold (v t ). The velocity of each granule was calculated within a moving 5-frame window across the 500-frame image stack. If needed, the trajectories were divided into sub-trajectories with at least 5 spots having this velocity above and below v t , and the (sub)trajectories (meaning both trajectories and sub-trajectories) were classified as follows: Drifted motion (“isdrifted” / go motion 32,36,40 ): (sub)trajectories with velocities above v t . Mixed / stop trajectories: (sub)trajectories with velocity below v t , often containing segments with different motion types. Based on empirical tests, a velocity threshold v t = 0.2 µm/s was used. In the second step, MSS-TAD (moment scaling spectrum - transient arrest of diffusion) analysis 32 was applied to these mixed (sub)trajectories to distinguish between diffusive and blocked motion. Briefly, as in Durso et al. 36 and Marchetti et al. 32 , the mean square displacement (MSD) and different order displacement moments (1 st to 6 th order) were computed up to a lag time corresponding to the maximum between one-quarter of the number of trajectory frames and five frames. If the trajectory was considered “mixed” based on the behavior of these functions, the algorithm looked for “confinement zones” by checking the likelihood L c that the local diffusivity was too low with respect to the average. To identify these confinement zones, we adopted a seed-and-threshold strategy 50 (https://svi.nl/SeedAndThreshold) rather than setting hard thresholds for minimum L c and minimum duration in frames (t c ). The segmentation process included: Seed zone selection: Using an initial likelihood threshold L c1 = 1.8 and keeping only segments longer than t c1 = 5 (including first and last frames). Region growing: Extending seed zones using a second, lower likelihood threshold L c2 = 1.4, retaining segments longer than t c2 (7). The final sub-trajectory classification was performed according to the MSS-TAD framework, as previously described in Marchetti et al. 32 , excluding the “TAD” category for sub-trajectories, and the (sub)trajectories were finally summarized into three categories (Tab. 1S for parameters): • Blocked: stationary, almost immobile trajectories. The “blocked” category includes the “immobile” (sub)trajectories in Marchetti et al. 32 . • Diffusive: exploratory motion possibly within a confined region. The “diffusive” category includes all the (sub)trajectories classified as “slow” and “fast” in Marchetti et al. 32 , which is different with respect to Durso et al. 36 . However, in this work we consider higher limits to discern “immobile” vs. “slow” vs. “fast” (sub)trajectories for diffusivity: 2×10 -3 and 10 -2 µm 2 /s here (D_block and D-lim in Tab. 1S, respectively) vs. 10 -3 and 2×10 -3 µm 2 /s in Durso et al. 36 , respectively. • Drifted: fast, directional transport-associated motion. The “drifted” category includes the “drifted” (sub)trajectories deriving from both analysis steps. Finally, we expressed results as “length” percentages, representing the fraction of the total number of spots within (sub)trajectories classified as blocked, diffusive, or drifted. Moreover, in some cases we determined the experimental diffusivity – γ anomalous parameter joint distribution (see the final part of the section “SPT-based classification of granule dynamics under upkeeping conditions”) starting from all the (sub)trajectories, like in Marchetti et al. 32 . Secretion protocols. Granule motion dynamics have been analyzed under secretion conditions as by protocols contained in literature 14,45 . These protocols require a KRBH working buffer, based on the Krebs-Ringer physiological buffer, composed of 140 mM NaCl, 3.6 mM KCl, 0.5 mM NaH 2 PO 4 , 0.5 mM MgSO 4 , 10mM HEPES, 5mM NaHCO 3 , 1.5 mM CaCl 2 , 0.1% BSA, pH 7.4. INS-1E Cell Insulin Secretion assay: INS-1E cells were seeded onto Ibidi plates and cultured in upkeeping conditions until they reached 75–80% confluence on the day of the experiment. Cells were washed twice with KRBH supplemented with 2.5 mM glucose and pre-incubated in this low-glucose medium with 1 μM ZIGIR for granule labeling for 30 minutes at 37°C and 5% CO₂. Following pre-incubation, confocal imaging was performed under this low-glucose conditions for up to 30 minutes. To induce secretion, glucose was added to the KRBH medium to reach 16.7 mM (high-glucose condition), and, after 3 minutes incubation, imaging continued for an additional 30 minutes. For a negative control, the same protocol was applied to αTC1-9 cells, which were plated on WillCo plates instead of Ibidi plates. αTC1-9 Cell Glucagon Secretion Assay: For glucagon secretion experiments 45 , αTC1-9 cells were seeded on WillCo plates two days before the experiment (Day 0) to reach 75–80% confluence by the day of the experiment. Day 1 (Starvation): Growth medium (DMEM, see Cell Culture section) was replaced by DMEM (Pan Biotech, cat# P04-01548) supplemented as described above but, with 2.5 mM glucose. Day 2 (Experiment): Cells were washed twice with KRBH + 2.5 mM glucose and pre-incubated in this medium at 37 °C and 5% CO 2 for 45–60 minutes. Image stacks were acquired during this period for the control condition, with 1 μM ZIGIR used for granule labeling 15 minutes before imaging. To stimulate glucagon release, after pre-incubation, cells were washed with KRBH (0 mM glucose), then incubated in KRBH supplemented with 1 mM glucose, along with 1 μM ZIGIR for granule labeling. For negative control, the same protocol was applied to INS1-E cells. Statistical analysis. To assess data distribution, we applied the Shapiro-Wilk normality tests. For comparing normally distributed data, an unpaired t-test was used, while for non-normally distributed data, we employed the Mann-Whitney nonparametric tests. In cases involving multiple group comparisons, one-way ANOVA was performed after confirming normality. *: p<0.05; ***: p<0.001; ****: p<0.0001; non-significant differences are not shown. All tests were performed using GraphPad Prism version 8.0.1 for Windows, GraphPad Software, San Diego, California USA, www.graphpad.com. Acknowledgments This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 866127, project CAPTUR3D). Servier Medical Art (https://smart.servier.com/) was used to create the Fig. 1A, Fig. 5 and Fig. S1A, which is licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Competing interests The authors declare no competing interests. Authors contributions SG : Investigation, Formal analysis, Visualization, writing - review & editing; VDL : Investigation, Formal analysis, Visualization, Writing - original draft, writing - review & editing; GF: Investigation, Formal analysis; LAP: Investigation, Formal analysis; MT: Investigation, Formal analysis; PM: Conceptualization, Writing - original draft, writing - review & editing; SL: Conceptualization, Software development, Formal analysis, Writing - original draft, writing - review & editing; FC : Conceptualization, Funding acquisition, Supervision, Writing - original draft, writing - review & editing. Ethic statement The present study did not require ethical review or approval Data availability statement The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. References 1. 1. Hartig SM, Cox AR. Paracrine signaling in islet function and survival. J Mol Med . 2020;98(4). 2. 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Collection Traffic Keywords cytoskeleton fluorescence imaging-derived mean square displacement secretion single particle tracking α cells β cells Authors Affiliations SAMUELE GHIGNOLI 0000-0002-5057-6986 Scuola Normale Superiore Laboratorio NEST View all articles by this author Valentina De Lorenzi 0000-0002-7429-6524 Scuola Normale Superiore Laboratorio NEST View all articles by this author Gianmarco Ferri Fondazione Pisana per la Scienza View all articles by this author Licia Anna Pugliese Scuola Normale Superiore Laboratorio NEST View all articles by this author Marta Tesi Universita degli Studi di Pisa Dipartimento di Medicina Clinica e Sperimentale View all articles by this author Piero Marchetti Universita degli Studi di Pisa Dipartimento di Medicina Clinica e Sperimentale View all articles by this author Stefano Luin 0000-0003-2673-366X Scuola Normale Superiore Laboratorio NEST View all articles by this author Francesco Cardarelli 0000-0003-3049-5940 [email protected] Scuola Normale Superiore Laboratorio NEST View all articles by this author Metrics & Citations Metrics Article Usage 264 views 137 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation SAMUELE GHIGNOLI, Valentina De Lorenzi, Gianmarco Ferri, et al. Spatiotemporal analysis of glucagon secretory granule dynamics. Authorea . 21 April 2025. DOI: https://doi.org/10.22541/au.174523174.47414370/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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