# ExoJ – a Fiji/ImageJ2 plugin for automated spatiotemporal detection and analysis of exocytosis

Junjun Liu, Frederik Johannes Verweij, Guillaume van Niel, Thierry Galli,

Lydia Danglot, Philippe Bun

► To cite this version:

Junjun Liu, Frederik Johannes Verweij, Guillaume van Niel, Thierry Galli, Lydia Danglot, et al. ExoJ – a Fiji/ImageJ2 plugin for automated spatiotemporal detection and analysis of exocytosis. Journal of Cell Science, 2024, 137 (20), 10.1242/jcs.261938. hal-05212675

HAL Id: hal-05212675

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Submitted on 18 Aug 2025

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Distributed under a Creative Commons Attribution 4.0 International License
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**ExoJ: an ImageJ2/Fiji plugin for automated spatiotemporal detection and analysis of exocytosis**

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**Running title:** ExoJ for Detection of Exocytosis

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LIU Junjun¹, VERWEIJ Frederik Johannes², VAN NIEL Guillaume³,⁴,⁵, GALLI Thierry⁴,⁶

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DANGLOT Lydia⁴,⁶,⁷,*, BUN Philippe⁷*

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¹ Jinan Central Hospital affiliated to Shandong First Medical University, Jinan 250013, China

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² Utrecht University, Faculty of Science, Department of Biology, 3584 CH Utrecht, The

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Netherlands

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³ Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM

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U1266, Team Endosomal dynamics in neuropathies, 75014 Paris, France

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⁴ GHU-Paris Psychiatrie et Neurosciences, Hôpital Saint Anne, F-75014 Paris, France

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⁵ present address: Team 7bis EVICAT, CRCI2NA, quai Moncousu, Nantes, France

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⁶ Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM

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U1266, Team Membrane traffic in healthy and diseased brain, 75014 Paris, France

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⁷ Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM

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U1266, NeurImag Imaging Core Facility, 75014 Paris, France

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* Corresponding authors

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Emails: liujunjun@sdfmu.edu.cn, f.j.verweij@uu.nl, guillaume.van-niel@inserm.fr,

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thierry.galli@inserm.fr, lydia.danglot@inserm.fr, philippe.bun@inserm.fr

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**Keywords**

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Exocytosis, Fluorescent imaging, ImageJ, Live-cell imaging, pH-sensitive probe
24 **Summary statement**

With the advent of pH-sensitive probes, live-cell imaging stood out as a major approach to study exocytosis. Manual analysis is prone to biases and not reproducible. We designed a robust tool to automate the identification of exocytosis.
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**Abstract**

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Exocytosis is a dynamic physiological process that enables the release of biomolecules to the
surrounding environment via the fusion of membrane compartments to the plasma membrane.

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Understanding its mechanisms is crucial, as defects can compromise essential biological
functions. The development of pH-sensitive optical reporters alongside fluorescence
microscopy enables the assessment of individual vesicle exocytosis events at the cellular
level. Manual annotation represents, however, a time-consuming task, prone to selection
biases and human operational errors. Here, we introduce ExoJ, an automated plugin based on
ImageJ2/Fiji. ExoJ identifies user-defined genuine populations of exocytosis events,
recording quantitative features including intensity, apparent size and duration. We designed
ExoJ to be fully user-configurable, making it suitable to study distinct forms of vesicle
exocytosis regardless of the imaging quality. Our plugin demonstrates its capabilities by
showcasing distinct exocytic dynamics among tetraspanins and vesicular SNAREs protein
reporters. Assessment of performance on synthetic data showed ExoJ is a robust tool, capable
to correctly identify exocytosis events independently of signal-to-noise ratio conditions. We
propose ExoJ as a standard solution for future comparative and quantitative studies of
exocytosis.

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45 **Introduction**

46 Exocytosis is a fundamental biological process to convey sets of chemical information to the
47 extracellular environment, and deliver membrane proteins and lipids to the plasma membrane
48 (PM) (Jahn and Südhof, 1999). Briefly, the process consists in the transport, docking, priming
49 and fusion of intracellular compartments to the PM (Verhage and Sørensen, 2008). This
50 eventually leads to the deposition of proteins and lipids into the PM, and the release of their
51 luminal content to sustain important physiological functions and to respond to external
52 stimuli. The variety of released cargo is vast, including neurotransmitters (Antonucci et al.,
53 2012; Budnik et al., 2016; Filannino et al., 2024; Gundelfinger et al., 2003), antibodies and
54 antigens (Buzas, 2023; Marcoux et al., 2021; Zitvogel et al., 1998), enzymes (Steffen et al.,
55 2008), genetic materials (Crescitelli et al., 2013; Ehnfors et al., 2009; Fujita et al., 2016;
56 Hazrati et al., 2022; Mathivanan et al., 2010; Zernecke et al., 2009), extracellular vesicles
57 (EVs) (Bebelman et al., 2018; Minciacchi et al., 2015; van Niel et al., 2022), cytokines
58 (Aiello et al., 2020; Lamichhane et al., 2015; MacKenzie et al., 2001; Mesri and Altieri,
59 1998; Szabó et al., 2014), signaling or adhesion molecules (Dequidt et al., 2007; Park et al.,
60 2021), and receptors and transporters (Bakr et al., 2021; Guček et al., 2019; Heijnen et al.,
61 1999; Jullie et al., 2014; Kassassir et al., 2023; Martinez-Arca et al., 2001; Park et al., 2006;
62 Passafaro et al., 2001). Precise spatiotemporal control of exocytosis is thus essential for
63 regulating diverse physiological and pathological processes. This also underlies the need to
64 characterize the structural dynamics of the fusion machinery, the biochemical profile of the
65 biological contents and the spatiotemporal dynamics of their release. In addition to a
66 collection of biochemical approaches, methods using live-cell imaging of fluorescently-
67 tagged intracellular vesicles allow for spatiotemporal monitoring and quantitative analysis of
68 content release (Ge et al., 2010). In particular, total internal reflection fluorescence
69 microscopy (TIRFM) has become the imaging modality of choice due to its inherent high
signal-to-noise ratio with a reduced phototoxicity while increasing temporal resolution
(Bebelman et al., 2020; Miesenböck et al., 1998). The TIRFM evanescent field of
illumination enables the recording of events close to the PM, minimizing the disturbance
from fluorescently-labeled vesicles in the cytoplasm. Specific labeling of vesicle exocytosis
is achieved by tagging the content and/or the vesicle membrane with a pH-sensitive
fluorescent protein (FP) variant of GFP, known as pHluorin (Miesenböck et al., 1998;
Sankaranarayanan et al., 2000). The FP pHluorin is quenched in the acidic vesicular lumen,
and brightens in the neutral extracellular environment upon vesicle fusion to the PM
(hereafter termed fusion event). Using TIRFM, the fusion event appears as an abrupt
brightening followed by spreading of the fluorescence signal (Miesenböck et al., 1998).
Besides the vesicle-specific protein marker, the choice of the FP greatly influences the
monitoring of the dynamics of exocytosis steps as evidenced by others (Liu et al., 2021;
Martineau et al., 2017; Shen et al., 2014). Taken together, the subsequent amount of data
generated by fluorescent time series poses the need of a robust analysis pipeline to identify
fusion events in an unbiased manner. Manual annotation of each candidate event represents a
time-consuming task, and is prone to selection biases. This is particularly notable for features
like the XY location of fusion events (potentially indicating intracellular hotspots), the
fluorescence intensity (estimating relative protein amount), the apparent size and the duration
of the events. Because these characteristics exhibit significant variability between cells,
achieving consistent measurements from one cell to another through manual analysis
becomes challenging. Numerous algorithms have been developed to address these challenges,
with varying degrees of user involvement throughout the detection workflow (Bebelman et
al., 2020; Huang et al., 2007; Jullie et al., 2014; Mahmood et al., 2023; Moro et al., 2021a;
Sebastian et al., 2006; Urbina and Gupton, 2021; Wang et al., 2018; Yuan et al., 2015). These
solutions are, however, optimized for a particular population of vesicular cargoes and hence
specific applications. Here we present Project-ExoJ (hereafter named ExoJ), a solution developed as an ImageJ2/Fiji (Schindelin et al., 2012) plugin that automates the identification of fluorescently-reported fusion event. We designed ExoJ to be fully user-configurable, with a graphical user interface to set up a series of parameters that define a genuine population of fusion events according to the experimental conditions. To improve user experience, we provided tools for visualizing and further reporting features such as spatial location, intensity over time, apparent size and duration. To illustrate ExoJ capability, we focused on fusions events reported by tetraspanin (TSPAN) and vesicular soluble N-ethylmaleimide-sensitive fusion protein attachment protein receptor (v-SNARE) proteins coupled to pHluorin. Recordings of quantitative features revealed significant differences between and among labeled vesicle populations. Assessment of ExoJ performance using synthetic data revealed a highly robust and reliable identification tool insensitive to noise encountered in experimental settings.
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# Results

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## ExoJ workflow for automated identification of fusion events

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At least three modes of exocytosis have been identified (full-collapse, kiss-and-run and compound exocytosis) and characterized according to their fusion dynamic patterns (Wu et al., 2014). Although each mode has its own fluorescence fluctuation pattern, the intensity decays to a certain extent right after vesicle fusion to the PM (Fig. S1A,B). This makes automatic recognition possible. Hence, we describe a fusion event as a transient diffraction-limited or large object that displays a sudden increase followed by an exponentially decreasing fluorescence intensity into the background. This fluorescence decrease is characterized by the mean lifetime decay $\tau$ (Fig. 1A). With this definition, we designed a processing pipeline broken into three main steps. Each step is managed in its own graphical user interface (GUI) dialog in which every parameter can be manually adjusted (Fig. 1). To facilitate parameter search optimization, the pipeline includes a “Preview” button that enables users to visualize the results for a specific set of parameters before committing to the analysis. Additionally, a “Back” button provides the flexibility to revert to previous steps and refine earlier parameter selections, ensuring a streamlined workflow (Fig. 1B,C,D and Table 1). These parameters can be saved and called in the pipeline to improve reproducibility.

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## Vesicle detection

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The first step consists of detecting vesicles seen as bright spots from an image series (Fig. 1A,B and Table 2). Before performing spot detection, we apply a custom photobleaching correction algorithm to compensate for the variations in image intensity within fluorescent time sequences, as described in the Materials and Methods section. Although ExoJ does not include a masking algorithm, it provides flexibility for users to define specific regions of interest (ROIs) for analysis using either the drawing selection toolbox or by selecting
previously saved ROIs from ImageJ/Fiji built-in ROI Manager tool (Fig. S1C). Users can choose to perform the spot detection on either the original movie or on its 1st order differential movie (Transform to dF movie; Fig. 1B and Table 2). The dF movie highlights changes between frames by showing the difference between two consecutive frames from the original movie. ExoJ offers two detection algorithms: a wavelet-based method (default option) or a local maximum method (by unchecking the wavelet filter box; Fig. 1B). For the wavelet-based option, we employ the multiscale à trous wavelet transform algorithm (Olivo-Marin, 2002). To selectively keep user-specified sized spots from image series, images are convolved with a set of wavelet functions that are scaled according to the minimum and maximum spot size (Min. and Max. fusion event apparent size; Fig. 1B, see parameter 1). The resulting images are then decomposed into high and low-frequency components. On the low frequency component images, pixel values are now equal to wavelet coefficients $C_{wavelet}$ as a result of wavelet transformation (Show wavelet lowpass image; Fig. 1B). The coefficient $C_{wavelet}$ coarsely translates the similarity between sets of pixels and the user-defined wavelet functions. $C_{wavelet}$ value increases when there is a close resemblance between the intensity signal and the user-defined wavelet functions.

The median absolute deviation (MAD) is a measure of variability similar to standard deviation but less sensitive to outliers (e.g. non-specific cell compartments, noise saturated pixels, ...). We next define a single hard threshold parameter $k\sigma_{wavelet}$ with $\sigma_{wavelet}$ calculated from the MAD of $C_{wavelet}$. The MAD of $C_{wavelet}$ corresponds to the median of the absolute deviations of $C_{wavelet}$ compared to the median value of all $C_{wavelet}$, and explicated as follows:

$$ \sigma_{wavelet} = \text{MAD}(C_{wavelet}) = \text{median}(\text{abs}(C_{wavelet} - \text{median}(C_{wavelet}))) $$

Users set the tolerance of the spot detection algorithm when they set the value of k. Thus, the algorithm sets to zero the pixel coefficients $C_{wavelet}$ whose absolute values are lower than $k\sigma_{wavelet}$ (Fig. 1B, see parameter 2). Hence, setting a high k value would decrease the number
of detected objects. An inverse transform is finally applied to reconstruct single fluorescence intensity images (Fig. 1B and Table 2). Alternatively, users can rely on a custom-written local maximum algorithm on single images (unchecked Wavelet Filter box; Fig. 1B). In this option, each pixel is replaced with its corresponding neighborhood maximum intensity value.

The radius of the neighborhood is defined by users and set to (Min. fusion event apparent size/2) + 1 (Fig. 1B and Table 2). With this approach we also defined $k\sigma_F$ where $\sigma_F$ is now obtained from the MAD of pixel intensity at each image.

The detection result on single images can be previewed to ensure that proper fluorescence spots were accurately detected (Fig. 1B and Table 2). Once determined, the parameter is applied for the whole image series to extract the XY location of all fluorescent spots with a local maxima method within an adaptive window sized to (Min. fusion event apparent size/2) + 1.

## Spot tracking

The second step consists in building individual time-lapse trajectories of previously localized fluorescent spots (Fig. 1C). While the starts of single trajectories are due to the appearance of a bright spot, the ends are not solely due to the fusion to the PM but could result from limitations in imaging conditions (e.g. low signal-to-noise ratio, loss of focus, ...). To account for this caveat, we combine a simple but yet sufficient multi-frame nearest-neighbor approach with a gap-closing algorithm (Chenouard et al., 2014; Crocker and Grier, 1996).

Here, our plugin introduces three cut-off parameters that need to be tailored according to spot behavior, consisting of a spatial searching range, a temporal searching window and a minimal event size (Fig. 1C and Table 3). Spot size, direction and intensity are not considered during the frame-to-frame tracking process. Spots within the user-defined spatial and temporal
searching range are assigned to the same trajectory, minimizing their global lateral displacement.

## Fusion event identification

Various features have been considered to streamline the accurate identification of different types of fusion events in different cellular contexts (Bebelman et al., 2020; Diaz et al., 2010; Moro et al., 2021b; Sebastian et al., 2006; Urbina et al., 2018; Wang et al., 2018; Yuan et al., 2015). Here, we combine advantages of previous methods to define a versatile processing protocol. We reason that all fusion events display a statistically significant and transient fluorescence peak fluctuation F above the local background $F_0$, followed by an exponential fluorescence decay (Fig. 1D). Considering single spot trajectories, we first perform consecutive adjacent image subtraction to normalize the background fluorescence intensity (Jullie et al., 2014; Sebastian et al., 2006). This step results in a high signal-to-noise ratio 1<sup>st</sup>-order differential fluorescent image series dF (Fig. 1D). For each candidate fusion event, we calculate $\sigma_{dF}$ as the MAD of dF instead of solely considering the normalized peak change in fluorescence intensity $\Delta F/F_0$ (Bebelman et al., 2020; Jullie et al., 2014; Moro et al., 2021a; Urbina et al., 2018). To refine the estimation of $\sigma_{dF}$, the trajectories of single candidate events are extended before (resp. after) the appearance (resp. disappearance) of the spot according to user entries (Expanding Frames; Fig. 1D). We eventually proceed with a moving linear regression on the fluorescence peak intensity profile to refine the onset time $t_0$ of candidate fusion events. This step helps considering fluorescence saturation and successive events at the same XY location. The algorithm also evaluates the maximal displacement of candidate events relative to their initial XY location at $t_0$.

Additional measurements are made by our plugin to describe the population of candidate fusing vesicles, including the mean lifetime $\tau$ which relates to the fusion dynamics and serves
203 as a proxy for the fusion duration; the apparent size of the fusion event and the normalized
204 peak change in fluorescence intensity ΔF/F₀ which estimates the relative amount of
205 fluorescently-labeled proteins. In particular, the estimated apparent size reflects the entire
206 fusion event, which could involve one or more vesicles or even larger compartments. Hence,
207 it may not represent the size of individual vesicles within the fusion complex.

208 To account for various types of fusion and/or image series acquired under different
209 experimental conditions, we integrate user-defined entries to modulate the definition of a
210 genuine fusion event and hence the identification requirements of the algorithm (Table 4). In
211 particular, candidate events with dF higher than kσ<sub>dF</sub> (Detection threshold; Fig. 1D), limited
212 displacements (Max. displacement), duration (Upper/Lower decay limit) and estimated size
213 (Upper/Lower apparent size limit) at t<sub>0</sub> that comply with user inputs are seen as genuine
214 fusion events (Fig. 1D). For the last two parameters, the goodness-of-fit, reported by the
215 coefficient of determination R<sup>2</sup>, is set as a threshold value (Min. R<sup>2</sup>) above which events are
216 selectively kept (Fig. 1D, see parameters 8 and 9). Once the identification parameters are set,
217 the plugin summarizes features of user-defined genuine events in a result table (Fig. S2A).
218 We implemented a comprehensive suite of tools for visualizing, curing and exporting events,
219 consisting in responsive pop-up windows (resp. histograms) for individual (resp. population-
220 level) data of identified events, ensuring an expedited review process (Fig. S2) (see Materials
221 and Methods for more details). In particular, the implemented tools automatically generate
222 intensity (F), 1<sup>st</sup>-order differential peak (dF), apparent size (FWHM) and mean radial
223 intensity over time (Spatial dynamics) profiles along with the detected event sequence
224 (Movie, Montage). These visualizations dynamically adjust to the event selected in the result
225 table.
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ExoJ is an adaptative tool to detect fusion events with high accuracy

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To evaluate whether our tool can accurately detect fusion events, we used Hela cells
expressing pHluorin-tagged CD9, CD63 and CD81 TSPANs commonly used to track
extracellular vesicle (EV) exocytosis (Crescitelli et al., 2013; Kowal et al., 2016; Mathieu et
al., 2021; Théry et al., 2018), and v-SNARE VAMP2 and VAMP7 proteins which are
components in the fusion machineries in many cellular contexts (Burgo et al., 2013; Gupton
and Gertler, 2010; Han et al., 2017; Jahn and Scheller, 2006; Vats and Galli, 2022; Verderio
et al., 2012; Wang et al., 2018) (Fig. 2A). To account for different image qualities and/or
fusion reporter signal intensity (Fig. 2A), we iteratively refined the detection threshold values
kσ<sub>wavelet</sub> (Fig. 1B and Table 2), dF/σ<sub>dF</sub> (Fig. 1D and Table 4) and the time window centered
around candidate events at t<sub>0</sub> (Expanding frames; Fig. 1D and Table 4). During the
identification process, we used the Preview button to optimize parameter search until we
reached a plateau in the number of detected TSPAN- and v-SNARE-reported fusion events
(Fig. 1B-D and Table 1). While detection thresholds allow for distinct fusion intensity with
respect to the local background, our algorithm explores a user-set time window to optimize
the capture of fusion events of different dynamics. No further adjustments were made before
running the identification process of each vesicle population in a batch-processed manner.
We eventually reported a total number of 481 and 315 analyzed events for TSPAN and v-
SNARE population respectively. These numbers represent the sum of events identified across
all analyzed cells (Fig. 2B). Next, we used ExoJ interactive visualization tools to review the
features of each individual event and identify potentially missed events (see “Built-in tools”
section in Materials and Methods for more details; Fig. S2A,B). This in-depth review
typically took about 5 to 10 minutes per analyzed cells, depending on the number of detected
events. Ultimately, we removed 10 TSPAN-pHluorin and 2 v-SNARE-pHluorin detection
hits that did not correspond to fusion events but rather to filopodia tips coming in and out of focus, extracellular fluorescent objects and stationary vesicles.

The capacity to modulate the identification algorithm ensures our plugin to be adaptive in detecting distinct types of fluorescently-reported fusion event with high accuracy.

### ExoJ feature evaluation provides quantitative insights into distinct forms of vesicle-mediated exocytosis

Next, we investigated the potential of ExoJ for quantitative analysis of exocytosis. TSPAN CD9, CD63 and CD81 are known to form protein microdomains at the PM (Hemler, 2005; Le Naour et al., 2006). Recent studies revealed that these TSPAN proteins are enriched to varying degrees in the membrane of EV subpopulations, including exosomes and ectosomes (Crescitelli et al., 2013; Escola et al., 1998; Kowal et al., 2016; Mathieu et al., 2021; Verweij et al., 2018). Exosomes are formed within late endosomes as intraluminal vesicles, and further released upon fusion with the PM. Their secretion can be monitored using pHluorin-tagged proteins (Fig. 2A). Previous studies suggest that TSPAN CD9 is primarily enriched in other EV subtypes that directly bud from the PM, making them undetected with our current approach (Kowal et al., 2016; Mathieu et al., 2021; Verweij et al., 2018). We examined post-fusion features of TSPAN subpopulations and compared them to transport vesicles reported with v-SNARE-pHluorin (Fig. 2A). In Hela cells, the detection algorithm recorded a statistically similar fusion activity between individual reporter populations (Fig. 2B) with no correlation with the intensity and fusion apparent size (Fig. 2C,D). Despite TSPAN-pHluorin-reported events displaying two to three-fold higher fusion intensity than the v-SNAREs, our unbiased automatic detection method was able to capture fusion events reported by both, highlighting the effectiveness of our approach even in the presence of significantly varying signal intensity (Fig. 2A,C). The recorded difference among v-SNARE-pHluorin
subpopulations is consistent with previous findings in various cellular contexts, where v-SNAREs VAMP2 and VAMP7 were observed to be differently localized in endosomes at different stages (Gupton and Gertler, 2010; Wang et al., 2018) (Fig. 2C,E). Furthermore, this difference in fusion intensity strongly suggests a difference in the number of fluorescently-labeled protein reporters associated with each fusion event. The estimated apparent size of TSPAN-pHluorin events aligns with previous qualitative analysis at supra-optical electron microscopy (EM) resolution using a dynamic correlative light electron microscopy approach (Verweij et al., 2018) and falls within the range reported for v-SNARE-pHluorin events (Fig. 2D) (Altick et al., 2009). In addition, this closely matches early quantitative EM studies in the central nervous system (Roizin et al., 1967). It is important to consider that our system resolution limit is 246 nm when interpreting the apparent size results (see **Practical considerations for live-cell imaging analysis**). Therefore, even if there are actual size differences in fusion events smaller than 246 nm, we would not be able to detect them. Focusing on the fusion duration, we first noted the short-lived fluorescence signal of v-SNARE-pHluorin, corresponding to a rapid either post-fusion lateral diffusion at the PM or endocytic process (Alberts et al., 2006; Miesenböck et al., 1998; Urbina et al., 2018; Verweij et al., 2018) (Fig. 2E). We also showed that subpopulations of TSPAN-pHluorin displayed a significantly distinct fusion event duration (Fig. 2E). The signal duration at the PM of CD81-pHluorin was almost two- and three-fold longer than CD63- and CD9-pHluorin respectively. This could not previously be measured with this level of accuracy (Verweij et al., 2018). The divergence in fusion duration could not be explained by a noticeable difference in apparent size, nor a significantly higher amount of TSPANs (Fig. 2C,D). For CD63 and CD81, we hypothesized that the observed difference could either reflect the various regulatory fusion machineries and/or the heterogeneity in the properties of TSPAN-containing vesicles released by the cell, potentially reflecting differences in MVB fusion machinery or cargo composition
300 (Edgar et al., 2016; Larios et al., 2020). Indeed, TSPANs are known to organize in distinct
301 TSPAN-enriched microdomains that consist primarily of less than 120 nm homotypic
302 TSPAN-TSPAN interactions and specific partner proteins (Charrin et al., 2009; Rubinstein et
303 al., 1996; van Deventer et al., 2021). It is important to note that these observations don't
304 definitively distinguish between MVB fusion and other fusion events involving TSPANs.
305 Further investigation employing complementary techniques like immunoelectron microscopy
306 or cargo-specific assays would provide a more conclusive classification of the released
307 vesicles. Furthermore, the signal duration of CD9-pHluorin was similar to v-SNARE-labeled
308 population of vesicles, as previously observed (Verweij et al., 2018). This suggested that
309 CD9-pHluorin bursts of fluorescence may reflect post-fusion lateral diffusion over the PM or
310 endocytosis similarly to v-SNARE-pHluorin rather than exosome secretion in most events.

311 Altogether, our computer vision-assisted tool enabled us to record and evaluate features of
312 different types of cargo and/or vesicles, providing quantitative insights into post-fusion
313 dynamics.

315 Performance assessment of ExoJ evidences a highly robust solution to detect fusion
316 events

317 To fully assess the performance of ExoJ, we simulated movies with synthetic data
318 corresponding to fluorescence signal of fusion events randomly distributed at the cell surface
319 (Fig. 3A). In our simulation, we modeled a random number of events across a wide range of
320 normalized intensity ΔF/F₀, apparent size and duration τ features including data collected in
321 this study and in different cellular contexts and/or protein reporters from others (Persoon et
322 al., 2019; Urbina et al., 2018; Verweij et al., 2018; Wang et al., 2018; Yuan et al., 2015). We
323 also simulated events featured as non-relevant, exhibiting distinct signal decay behaviors (see
Materials and Methods for details) (Fig. S3A). We additionally tested the influence of increasing Gaussian noise signal which coarsely recapitulates local background variation due to noise sources (Fig. 3A). ExoJ was able to accurately capture simulated events within the range of typical signal-to-noise ratio (SNR) observed experimentally, with an identification error rate (also equates to 100 – accuracy) as low as 1.5% up to 3% which remains comparable to Urbina and colleagues' work (Urbina et al., 2018) (Fig. 3B). At each noise increment, we thoroughly adjusted the algorithm requirements by refining $k\sigma_{wavelet}$ (step 1), $dF/\sigma_{dF}$ (step 3), both goodness-of-fit $R^2$ and the time window to optimize event identification (Fig. 1D and Table 4). These parameters were optimized for three randomly selected simulated movies, saved and further applied to all remaining time series. Altogether, we spent on average 15 minutes for the analysis of each simulated movie. In response to incremental Gaussian noise, ExoJ detection capability was significantly degraded with an error rate up to 7% ± 1.4%, which still performs at a higher level of accuracy under equal noise condition (Urbina et al., 2018). We further compared ExoJ to AMvBE (Automated Multivesicular Body Exocytosis), a manual detection in ImageJ/Fiji (Bebelman et al., 2020). We proceeded with the AMvBE-assisted manual detection of all simulated movies, which took from 30 to 40 minutes per movie depending on the incremental Gaussian noise condition. We optimized the identification parameters to maximize the capture of all relevant events. In simulated movies with typical experimental SNR, manual detection exhibited a significantly lower accuracy, with error rates ranging from 19% to 25%. Even when relaxing the detection criteria to improve capture rate, manual detection with AMvBE still worsened, reaching up to 34.2% ± 8.0% (see Materials and Methods for more details). This result demonstrated that ExoJ offered a time-efficient and accurate alternative to manual analysis method.

Error rate alone is an incomplete measure on simulated movies with disparate fusion activity.

Thus, we introduced standard metrics such as sensitivity, precision, $F_1$ score and specificity
which altogether score and provide a more balanced assessment on ExoJ ability to correctly detect events while accurately discarding those featured as non-relevant (see Materials and Methods for details). Results on simulated without added noise demonstrated that ExoJ is a highly robust tool with scoring metrics above 99% (Fig. 3C) which bettered previously published algorithms (Sebastian et al., 2006; Urbina et al., 2018). Increasing the contribution of Gaussian noise significantly impaired the ability to correctly capture fusion events. On average, the sensitivity was down to 89.6% in the lowest condition. However, these captured events were accurately identified (mean precision of 97.8%) while avoiding those non-relevant (mean specificity of 97.3%) (Fig. 3C). Notably, the F₁ score decreased from 0.99 to 0.93, reflecting the trade-off between sensitivity and precision under increasing noise. While the highest sensitivity, precision and specificity scores achieve 100%, the impact of increasing Gaussian noise signal was significant with a sensitivity, precision and specificity score as low as 66.7%, 80% and 75%, respectively. The reported lowest scoring results occurred with the strongest added Gaussian noise signal. In addition, there was no correlation between ExoJ scoring results and the ratio of simulated events (non-relevant versus relevant) featured per movie (Fig. S3B). The flexibility of ExoJ enabled us to relax the algorithm requirements to account for changes in imaging signal-to-noise ratio without compromising its ability to effectively capture fusion events.

Finally, we asked whether the event detection procedure is reliable enough to handle biological variability between individual cells, particularly under the most challenging noise conditions. To mimic this, we took advantage of the random spatial distribution of Gaussian noise added to simulated timeseries. We added a high noise level with a sigma value of 1, followed by event identification using constant set of parameters previously optimized for single simulated movies. We repeated this procedure ten times, scored ExoJ performance at each round and reported the overall relative variation using the MAD. The scoring results
374 were grouped into four categories based on the simulated fusion activity (see Materials and Methods). The average MAD values indicated a well-maintained performance with a range of 375 variations of 3.1%-4.1%, 3.8%-6.9% and 4.5%-7.8%, for accuracy, F₁ score and specificity, 376 respectively. While the F₁ score and specificity exhibited variations up to 20% in some 377 individual measurements under the challenging noise condition (σG = 1), the reported average 378 MAD values remained consistently low. This result suggested that ExoJ can handle 379 biological variability as shown by the reported average MAD values of ExoJ performance. 380

381 Taken together, assessment of ExoJ performance revealed a robust tool in identifying fusion 382 events insensitive to experimental noise.
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Discussion

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Here we presented ExoJ, a computer-vision assisted tool for the automated identification and analysis of exocytosis events marked by a pH-sensitive probe. The identification algorithm was designed as a fully user-configurable processing pipeline, making it suitable to detect and analyze various forms of fusion events independently of the imaging condition. Built-in options were implemented for visualization and manual curation of event features.

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We demonstrated ExoJ ability in monitoring fusion events reported by TSPAN- and v-SNARE-pHluorin proteins with high accuracy. Reporting of spatiotemporal features provided quantitative insights with unique details between and among subpopulations of vesicle exocytosis. Using simulated data covering a wide range of feature data, performance assessment underscored ExoJ as a robust and reliable identification tool. As it is, ExoJ usage has no specific constraints on vesicle exocytosis features including normalized intensity ΔF/F₀, duration of signal decay τ and apparent size. In response to changing condition in imaging, the sensitivity of event detection could be adjusted by lowering the detection threshold value kσ right from the first step (Fig. 1B). This will increase the number of captured spots along with the processing time in the subsequent steps (up to 12 minutes per movie on average for +σG condition; Fig. 3A). We derived the duration of fusion events by evaluating the mean lifetime τ of fluorescence peak intensity profiles F fitted with single mono-exponential decay functions (Fig. 1A). However, fusion events can also exhibit fluorescence decay patterns that deviate from a single exponential function, as shown in epithelial cells (Mahmood et al., 2023) and neurons (Hiester et al., 2017; Jullie et al., 2014;
Roman-Vendrell et al., 2014) for instances. To address this limitation, ExoJ allows users to export the raw data to facilitate manual curation. This enables users to fit alternative mathematical models to their data. In contrast to fusion intensity and duration, apparent size measurements reported by FWHM were, however, limited due to the resolution of TIRFM (Fig. 2D). This limitation can lead to underestimations of the actual size of fusion events due to several factors. Events with low fluorescent intensity or an incomplete capture within the field of view will have a smaller FWHM, underestimating their actual size. Additionally, elongated or irregularly-shaped events can appear smaller in one dimension and background noise and camera imperfections can introduce further inaccuracies in FWHM measurements. The advent of fast-imaging Structured Illumination Microscopy and novel fluorescent markers offer promising avenues for overcoming these limitations (Huang et al., 2018; Li et al., 2015; Liu et al., 2021; Roth et al., 2020). Such advancements could enable the recording of the structural dynamics of exocytosis with unprecedented resolution, providing more accurate information about the size of fusion events. In combination with ExoJ, we are confident that these developments will certainly help providing insights into exocytosis-associated protein dynamics, and exploring machinery involved in EV biogenesis and secretion (Verweij et al., 2022).

424

425 **Relevance to existing methods**

Numerous bioinformatics tools on single or cross-platforms have previously been developed for specific applications which make fair comparison difficult (Bebelman et al., 2020; Diaz et al., 2010; Huang et al., 2007; Jullie et al., 2014; Mahmood et al., 2023; Moro et al., 2021a; Sebastian et al., 2006; Urbina and Gupton, 2021; Wang et al., 2018; Yuan et al., 2015). In addition, some image formats are not accepted, file size limit could hinder the time of
analysis, and feature extraction approach could vary. Our goal in developing ExoJ was to
provide a common, yet robust computer-vision assisted solution regardless of the
experimental condition. To this end, we designed a GUI-based tool as a plugin for
ImageJ2/Fiji, a well-established platform for biological image analysis (Schindelin et al.,
2012). As a result, ExoJ accepts all type of image formats and bit depths recognized by
ImageJ2/Fiji. Prior to running ExoJ if necessary, experimental data could be pre-processed
using built-in filter toolboxes (see Materials and Methods for more details). In the last
decade, supervised methods approaches have achieved great success in providing solutions
for detecting subcellular structures in fluorescent microscopy images (Boland and Murphy,
2001; Hu et al., 2010; Johnson et al., 2015). In particular, machine learning approaches were
successfully applied for spot detection upon a training phase (Jiang et al., 2007; Lin et al.,
2019). Careful selection and label of training datasets is, however, an essential prerequisite,
either manually or using other detection methods. Instead, we opted for a feature-based
approach. This method relies on simple user-configurable features such as fluorescently
patterned vesicular spots to achieve good performance, eliminating the need for extensive
labeled training data (Basset et al., 2015; Smal et al., 2010). We illustrated the capacity of
ExoJ to detect distinct fusion events, illustrated here by TSPAN- and SNARE-pHluorin,
using a limited number of biological parameters (Fig. 1B-D). We based ExoJ detection on a
proven wavelet transform algorithm (Lagache et al., 2018; Olivo-Marin, 2002; Püspöki et al.,
2016; Ruusuvuori et al., 2010; Toonen et al., 2006; Yuan et al., 2015). We also introduced
the statistical measure MAD to hone the capture of candidate fusion events which could
partly explain the performance difference with Yuan and colleagues' wavelet-based tool
(Yuan et al., 2015). Our detection algorithm also explored a user-set time window centered
around fusion events, whereas previously published tools had it fixed or hardly modifiable
(Mahmood et al., 2023; Urbina et al., 2018; Yuan et al., 2015). In our study, we directly
compared ExoJ to AMvBE-assisted manual detection using simulated movies. Manual
detection proved to be time-consuming and substantially less accurate than ExoJ (Fig. 3B).
Even attempts to improve capture rate by relaxing detection criteria with AMvBE resulted in
a further decline in accuracy. ExoJ offers functionalities to facilitate the identification of user-
defined genuine events as well as implementing options for data visualization, manual
curation and export (Fig. S2). Throughout the workflow, users can go back and forth to
readjust parameters and preview results (Table 1). In addition to our study, ExoJ has recently
demonstrated its capability in providing quantitative insights on the role of SNARE protein
SNAP29 in CD63-pHluorin-labelled EVs in PC-3 cells imaged with a confocal spinning-disk
microscope (Hessvik et al., 2023). We are convinced that ExoJ could become a standard tool
for quantitative review of comparative studies of vesicle exocytosis in an unbiased manner.
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Materials and Methods

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Installation and system requirements

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ExoJ was developed as an ImageJ2/Fiji (Schindelin et al., 2012) plugin for the automated
detection and analysis of fusion events in 8 or 16-bit grayscale fluorescent image series. The
plugin is published under the GPLv3 license. It uses ImageJ2/Fiji capabilities to open a wide
range of image formats using the plugin Bio-Formats (Linkert et al., 2010). The plugin ExoJ
can be downloaded on the following website https://www.project-exoj.com/ with the code
source available at https://github.com/zs6e/excytosis-analyzer-plugin. To install the plugin,
place the .jar file in the ImageJ2/Fiji plugin directory. After a restart, ExoJ will be available
in the ImageJ2/Fiji plugin menu. ExoJ is compatible with ImageJ v1.53t or newer version,
and has been tested on Windows and MacOS platforms. To ensure smooth operation, ExoJ
required a 64-bit operating system and Java 8. While 4 GB of RAM is the minimum, it may
limit ExoJ performance when working with large datasets (image files and/or time series).
For optimal performance, consider increasing the allocated memory for ImageJ/Fiji within its
settings. This option is accessible by navigating to Edit > Options > Memory & Threads... in
the ImageJ/Fiji menu. To avoid potential plugin errors, we strongly recommend setting “.” as
the decimal separator. This can be changed in the “Region and Language” format options for
both MacOS and Windows platforms.

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Photobleaching correction

The main challenges posed by reliable vesicle detection are associated with prolonged live-
cell acquisition, such as cell migration, focus drift and photobleaching. While cell migration
and focus drift lead to unpredictable fluorescent changes at a given pixel, the photobleaching effect can be theoretically compensated to a certain extent (Miura, 2020). The variations of fluorescent signals over time within cell compartments are occurring at slower dynamics compared to single fusion events. To compensate for this effect, we implement an optional custom-written correction based on a non-fitting, pixel-by-pixel weighted detrending method. The option is available during the spot detection step (Fig. 1B). If “Correct photobleaching” is selected in the setting menu, the correction is applied on the whole time series and can be reversed upon deselection. In detail, for each pixel P<sub>xy</sub>, we measured the fluorescence intensity difference between the first P<sub>xy0</sub> and last P<sub>xyt</sub> frame in the image series. These values were then normalized by the difference between the maximal m<sub>max</sub> and minimal m<sub>min</sub> intensity, also calculated throughout the image series to generate a weight map (W<sub>xy</sub>) such as:

$$W_{xy} = (P_{xy0} - P_{xyt})/(\mu_{max} - \mu_{min})$$

Next, for each frame i we calculate the difference d<sub>i</sub> between the mean intensity of the entire image series μ and the mean intensity of that frame μ<sub>i</sub>. Finally, the fluorescence intensity of every pixel at each frame is compensated by W<sub>xy</sub> d<sub>i</sub> to obtain the final detrended image series.

## Built-in tools

A series of built-in options are implemented to visualize and manage single and/or the population of fusing vesicles (Fig. S2). For each analyzed fusion event, users can visualize the corresponding sequence of cropped images as a movie (button Movie) or a montage of stills (button Montage) along with associated fluorescence peak intensity F (button Peak Intensity (F) Plot), 1st order differential intensity dF (button (dF) Plot) and estimated apparent size (button Size) fitting plots. Note that a visual smooth effect could persist while generating montages of still frames. To avoid such effect, users can rely on ImageJ's built-in “Make
Montage" function. We also record the fluorescence spreading over (button Spatial dynamics; Fig. S2A) as initially proposed by Bowser and colleagues (Bowser and Khakh, 2007). We allow the possibility to discard detected events using the appropriate button in the result table (Remove; Fig. S2A). Similarly, candidate fusion events can be manually added using ImageJ's built-in drawing selection tools (button Add; Fig. S2B). Upon region selection around the potential event, the plugin will display the fluorescence peak intensity profile F for a user-defined timeframe. Right after pressing the button Add in the profile window, the newly-added event is immediately reviewed. The exact size and shape of region selection are not crucial for adding events. However, we strongly recommend drawing a precise region around the candidate event. This helps avoid capturing other nearby potential events that might occur within the same timeframe. Three options are made available to generate histograms for the temporal occurrence (button Event Counts), apparent size (button Size Stat.) and mean lifetime (button Tau Stat.) of fusion events. A spatiotemporal map distribution of fusion events is available upon selection in the result table (Fig. S2C), and can further be exported as an RGB image. We designed three action buttons for exporting the result table (Export current table), fluorescence peak intensity profile F (Export all intensity plots) and feature table (Export all Table) for all individual events.

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## Reporters of fusion events in live cells

We used TSPAN and v-SNARE proteins as fusion event reporters. Specifically, we focused on TSPAN CD9, CD63 and CD81 (Escola et al., 1998; Kowal et al., 2016; Théry et al., 2018; Yáñez-Mó et al., 2015), and v-SNARE VAMP2/synaptobrevin2 (vesicle-associated membrane protein 2) and VAMP7/TI-VAMP (vesicle-associated membrane protein 7 / Tetanus neurotoxin-Insensitive vesicle-associated membrane protein) (Chaineau et al., 2009;
Galli et al., 1998) proteins coupled to pHluorin (Miesenböck et al., 1998). CD9-pHluorin, CD63-pHluorin and CD81-pHluorin plasmids were constructed as previously described by Verweij and colleagues (Verweij et al., 2018). VAMP2-pHluorin was a kind gift from Dr. T. Ryan (Cornell University, USA). VAMP7-pHluorin construction corresponds to an improved version (Chaineau et al., 2008) previously described (Martinez-Arca et al., 2000) and further characterized (Burgo et al., 2012; Wang et al., 2018). We used Hela cells cultured in DMEM supplemented with 10% FBS (Perbio Sciences; HyClone), 100 U/ml penicillin G, 100 mg/ml streptomycin sulfate and 2 mM glutamax (Invitrogen, Thermofischer Scientific). Cells at 50% to 60% confluence were transfected using Lipofectamine 2000 reagent (Invitrogen) on either 35-mm glass bottom petri dishes (Ibidi) scaled with 500 ng of TSPAN-plasmids or 18-mm round glass coverslips deposited in 12-well plates scaled with 1 mg of VAMP plasmid. Glass coverslips were further mounted on a Chamlide EC magnetic chamber (LCI).

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## 550 Live cell imaging

Prior to imaging, cell medium was replaced with homemade Hepes-buffered Krebs-Ringer as previously used by Danglot and colleagues (Danglot et al., 2012) or Leibovitz's L-15 solution (Gibco). Hela cells were imaged 24 h after transfection on an inverted microscope (Axio Observer 7, Zeiss) equipped with a TIRF module and a top stage imaging chamber (Tokai Hit STX-CO2) ensuring a constant temperature at 37°C. All imaging experiments were carried out with a 100x 1.46 NA oil objective (Zeiss), an air-cooled 488 nm laser line at 0.4-0.6% power, with a TIRF angle set for 300-400-nm penetration depth and an additional optovar 1.6x to reach a pixel size of 100 nm. We opted for a slightly deeper penetration depth to capture events potentially occurring slightly beyond the immediate membrane surface. Images were acquired with Zen Black (SP2.3, Zeiss) onto an electron-multiplying charge-
coupled device camera (iXon, Andor) at a frame rate of 5 Hz. Fluorescent timeseries were saved as tiff or czi files. Fusion activity was defined as the number of detected events throughout the cell surface over the course of a time-lapse experiment, which was typically 3 minutes (Fig. 2B). Thus, experimental data in this study consisted in single fluorescent timeseries of 901 images. Cell surface measurement was carried out using ImageJ/Fiji segmentation tools (Schindelin et al., 2012). Number of analyzed cells (N) per labeled population is indicated.

## 569 Procedure for TSPAN- and v-SNARE-reported fusion events

The detection parameters are adapted to the different types of fusion event. To find optimal parameters, we analyzed three time series per subpopulation. We stopped the optimization process when the number of detected events reached a plateau, taking approximately 45 minutes for each TSPAN and 1 hour for each v-SNARE reporter. Once optimized, these parameters were saved (Save button; Table 1) and applied (Load button; Table 1) to all remaining time series within each subpopulation. This streamlines analysis, significantly reducing the average processing time per time series to approximately 10 minutes. This time primarily reflects the computational demands of analysis, user review of detected events and verification of potentially missed events (see “Built-in tools” section for details; Fig. S2A,B). In details, we set the detection threshold values $k\sigma_{wavelet}$ to 4.0-5.0 (resp. 3.0) and $dF/\sigma_{dF}$ to 4.0-5.0 (resp. 3.0-3.5) for fusion events reported by TSPAN-pHluorin (resp. v-SNARE-pHluorin). We imposed a minimal signal duration of two frames (Fig. 1D). We completed the detection of candidate events with additional frames corresponding to 1 s (resp. 3-4 s) before (resp. after) the maximal peak fluorescence at $t_0$ (Expanding frames; Fig. 1C and Table 4). For v-SNARE-pHluorin, we limited the temporal window to 0.6 s (resp. 2-3 s) before (resp. 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584
after) the maximal peak. The fluorescence profiles were fitted with a minimal number of five
points and a minimal goodness-of-fit R² of 0.75 were set as the fitting threshold. The initial
vesicle apparent size (range from 4 to 10 pixels; step 1; Fig. 1B) and the tracking parameters
(step 2; Fig. 1C) do not vary between TSPAN and v-SNARE protein reporters. We reported
the number of identified events n for each protein reporters as follows: CD9, n = 129; CD63,
n = 200; CD81, n = 152; Vamp2, n = 232; Vamp7, n = 83.

591

592 **Practical considerations for live-cell imaging analysis**

593 We first focused on three time series per condition and/or reporter proteins. The time taken
594 for optimal parameter search depends on many factors such as memory allocation for
595 ImageJ/Fiji (see **Installation and system requirements**), data size and image quality. Large
596 datasets potentially contain more fusion events for analysis which can extend the time for
597 parameter search and analysis. Poorly resolved or low SNR images necessitate more relaxed
598 detection threshold parameter (σ<sub>wavelet</sub>, step 1), leading to longer computation times. To
599 optimize the search of optimal detection parameter using exoJ, users can leverage the
600 extensive capabilities of ImageJ/Fiji for pre-processing fluorescent time series using. Existing
601 plugins and built-in filters offer convenient options to address common image quality issues.
602 Plugins like “rolling ball” (Sternberg, 1983) and convoluted background subtraction (Peng et
603 al., 2017) and bilateral filter (Chaudhury et al., 2011) can help reducing noise, background
604 signal and non-uniform illumination. Additionally, the deep learning tool CARE (Boothe et
605 al., 2023) can be used for more advanced noise reduction. To account for photobleaching,
606 ExoJ includes a custom-written tool (see “Photobleaching correction” section), which does
607 not assume a specific bleaching behavior.
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For highly motile cells, registering time series with plugins like TurboReg and StackReg helps mitigate spatial movement that could lead to detection errors (Thevenaz et al., 1998).

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ExoJ offers increased flexibility in defining the analysis area. Users can either draw a custom ROI using ImageJ/Fiji drawing tools or select previously segmented areas saved in the ROI Manager. This allows for focusing on specific cellular regions, excluding irrelevant spots like those on cell sides. While defining the ROI may increase computation time during spot detection due to sorting and excluding irrelevant areas, it significantly speeds up subsequent steps (tracking and identification).

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Users have the possibility to perform the spot detection on a 1st order differential movie rather than the original one (dF movie; Fig. 1B and Table 2). To ensure that all pixels in the final dF movie have non-negative values, a constant offset is added, determined by finding the minimum intensity across all pixels after the difference is calculated.

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To account for the limitations imposed by the diffraction limit on apparent size (FWHM) measurements, we strongly recommend measuring the point spread function (PSF) of the optical system. In our work, as described by Faklaris and colleagues (Faklaris et al., 2022), the PSF was measured to be 246 nm ± 0.03 nm at 488 nm. This information can be optionally entered in ExoJ workflow from step 1 (Theoretical resolution input box; Fig. 1B and Table 2). ExoJ will then compare the user-provided PSF value to the estimated size of each detected event. Events with an estimated FWHM smaller than the specified PSF value will be flagged in red within the "ExoJ: Detected exocytosis list" table (Fig. S2A). This helps users identify potential limitations in size accuracy for these events.

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ExoJ currently detects events with a single exponential decay pattern in their fluorescence intensity profiles (Fig. 1A and Fig. S1A,B). To account for events exhibiting fluorescence decay patterns that deviate from a single exponential function, we suggest lowering the min R² (Fig. 1D parameter and Table 4) input value, deactivate "the lower decay limit" box (Fig.

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1D parameter 5 and Table 4) and further export features tables of all detected events for manual curation (Export all table action button, Fig. S2A). These suggestions may allow ExoJ to detect a wider range of events but may also increase the number of false positive that require manual review.

## Computer simulation of exocytosis

To fairly assess the robustness of ExoJ, we generated 20 movies of 901 images. These movies simulated fusion events using randomly-distributed gaussian-shaped pHluorin signal intensities on the cell surface, with a fixed pixel size of 100 nm, a frame rate of 5 Hz and no pre-processing filter applied. We simulated two groups of synthetic events, labeled as relevant and non-relevant according to the features we imposed. A series of parameters was defined to encompass distinct features of relevant fusion events including those approximated by users in this study as well as previous works (Persoon et al., 2019; Urbina et al., 2018; Verweij et al., 2018; Wang et al., 2018; Yuan et al., 2015). In details, we imposed to the simulated events a random distribution of: (1) normalized fluorescence peak intensity $\Delta F/F_0$ as low as 10% up to 200% above the local background (centered circle of 9-pixel radius) and (2) apparent size over a range of gaussian widths from 3 to 7 pixels. Events featured as relevant exhibited single exponential decay behavior with a mean lifetime $\tau$ from 1 to 50 timeframes (i.e. 0.2 s to 10 s). In contrast, we modified the transient nature of fusion events featured as non-relevant where the signal decay could be (1) 1-frame long, (2) long-lived and constant fluorescence peak intensity, or (3) a damped sine wave along with (4) a random spatial displacement. The maximal number of simulated relevant and non-relevant events was both fixed to 50, and randomly set for each movie. The simulated fusion activity eventually ranged from 1.2 to 9.8 $\mu m^{-2}.min^{-2}.10^3$ which matches with previously recorded frequency rate (Persoon et al., 2019; Urbina et al., 2018; Verweij et al., 2018; Wang et al., 2018; Yuan et al.,
2015). The identification pipeline was optimized to capture simulated events labeled as relevant. To represent random noise, we added fluorescent intensity variations following a Gaussian distribution to the simulated movies. We achieved this using the ImageJ built-in function "Add Specified Noise". We added noise intensity with standard deviations (σ<sub>G</sub>) of 0.25, 0.5 or 1 times the standard deviation calculated for each frame of the simulated time series.

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## Comparison with manual detection

To compare ExoJ accuracy with manual detection, we used the AMvBE macro, a tool within ImageJ/Fiji for analyzing events in fluorescent time series (Bebelman et al., 2020). We optimized AMvBE identification parameters to maximize the capture of all relevant events. In details, we impose a time window for event correction of 5 frames, a duration of at least 1 frame after peak fluorescence, a minimal event size fixed to 0.2 mm, a rescue threshold fixed to 3 and a spot movement up to twice the FHWM. Notably, with increasing Gaussian noise added to the simulated movies, we relaxed the minimum goodness-of-fit R<sup>2</sup> value associated with the estimation of mean lifetime. This parameter can be adjusted within the macro itself. We lowered it as low as 0.5 for the noisiest condition (σ<sub>G</sub> = 1).

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## Performance evaluation metrics

To benchmark the performance of ExoJ several metrics were used including identification error rate, sensitivity, precision, specificity and F1 score. These metrics were recorded for analysis on movies with simulated events with relevant (resp. non-relevant) features considered as true positive (TP) (resp. true negative, TN) attribute. When running ExoJ, each
identified event was considered with either relevant (TP) or non-relevant (labeled as false positive, FP) attributes. Finally, undetected relevant event was considered with false negative (FN) attribute. Considering these definitions, metrics are measured based on direct count of true and false positives and negatives such as:

$$ \text{Identification error rate} = 100 * (1 - ((TP + TN)/\text{all simulated events})) $$

which corresponds to the proportion of correctly identified relevant and non-relevant events out of all the simulated events.

$$ \text{Sensitivity} = 100 * (TP/(TP + FN)) $$

which reports the proportion of correctly identified relevant events out of the total number of simulated relevant events;

$$ \text{Precision} = 100 * (TP/(TP + FP)) $$

which reports the proportion of correctly identified relevant events out of the total number of identified fusion events;

$$ \text{Specificity} = 100 * (TN/(TN + FP)) $$

which reports the proportion of correctly identified non-relevant events out of the total number of simulated non-relevant events.

We additionally introduced F₁ score (Fawcett, 2006) which reports the capability to both capture relevant events (sensitivity) and be accurate with the events ExoJ does capture (precision) as:

$$ F_1 \text{ score} = 2 * ((\text{sensitivity} * \text{precision})/(\text{sensitivity} + \text{precision})) $$

with 1.0 as the highest possible value.
698 Statistical data analysis

699 We performed statistical analysis using Prism 9.0 (GraphPad). For multiple comparisons
700 between populations of labeled vesicles, Kruskal-Wallis non-parametric ANOVA was used
701 to determine significance, followed by Dunn's post hoc test for multiple comparisons
702 between populations of labeled vesicles and simulated movies with increasing Gaussian noise
703 intensity. Statistical significance was considered for α = 0.05. Data are reported as box and
704 whisker plots. Boxes show the median +/- interquartile range and the whiskers go down to
705 the minimum and up to the maximal value in each dataset. Median values are displayed in
706 brackets below each box and whisker plot unless otherwise specified in the figure caption
707 (e.g. mean values are used in Fig. 3B,C,D and Fig. S3B).
708 Acknowledgements

709 The authors thank the NeurImag imaging facility of the Institute of Psychiatry and
710 Neurosciences of Paris (Inserm U1266) where the imaging experiments were carried out.

711 NeurImag is part of GIS IBISA and the national infrastructure France BioImaging supported
712 by the French National Research Agency (ANR-10-INBS-04). We thank Dr. Liangyi Chen
713 and Dr. Yongdeng Zhang for sharing their Matlab executable tool for exocytosis detection.

714 We are grateful to Julie N'guyen for maintaining and providing HeLa cells. We thank Dr.

715 Sebastien Nola, Dr. Cédric Blouin and Brett Davis for their insightful comments on the
716 manuscript. Zeiss Confocal LSM 880 Elyra PS.1 equipped with a TIRF module was
717 purchased with a Région Ile-de-France grant awarded to Lydia Danglot DIM Cerveau et
718 Pensée project. Guillaume Van Niel would like to acknowledge Institut National du Cancer,
719 Fondation ARC pour la Recherche sur le Cancer and the ANR for their financial support.

720

721 Fundings

722 This work was supported by Agence Nationale de la Recherche (ANR-19-HBPR-0003,

723 ANR-19-CE16-0012) and FLAG-ERA (Sensei grant 19-CE16-0012-01) to L.D., by a

724 European Molecular Biology Organization grant (EMBO ALTF 1383-2014), a Fondation

725 ARC pour la Recherche sur le Cancer fellowship (PGA1RF20190208474) to F.J.V., a

726 Fondation pour la Recherche Médicale grant (AJE20160635884) to G.V.N, and an Institut

727 National Du Cancer grant (INCA N°2019-125 PLBIO19-059) and ANR (ANR-20-CE18-0026-01) to F.J.V. and G.V.N.
729 **Author contributions**

730 **JL**: Conceptualization, Methodology, Software, Data curation, Resources, Visualization, Writing- Original draft, Writing- Review and Editing. **FJV**: Methodology, Resources, Investigation, Validation, Writing-Review and Editing. **GVN**: Supervision, Funding acquisition, Writing-Review and Editing. **TG**: Resources, Writing-Review and Editing. **LD**: Conceptualization, Supervision, Methodology, Funding acquisition, Resources, Investigation, Writing-Review and Editing. **PB**: Conceptualization, Supervision, Project administration, Methodology, Software, Data curation, Investigation, Formal analysis, Visualization, Writing-Original draft, Writing-Review and Editing.

738

739 **Availability of data and Material**

740 The datasets used and/or analysed during the current study are available from the corresponding authors on reasonable request. Time series including the one used in this report as well as five simulated movies are available in the Zenodo repository: https://doi.org/10.5281/zenodo.6610894 and https://doi.org/10.5281/zenodo.7595198. The latest version of ExoJ as well as a tutorial can be found on the following website https://www.project-exoj.com. The source codes for ExoJ and for the generation of simulated movies was deposited in a GitHub repository: https://github.com/zs6e/excytosis-analyzer-plugin.

748

749 **Competing interests**

750 The authors declare that they have no competing interests.
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Verweij, F. J., Bebelman, M. P., Jimenez, C. R., Garcia-Vallejo, J. J., Janssen, H., Neefjes, J., Knol, J. C., de Goeij-de Haas, R., Piersma, S. R., Baglio, S. R., et al. (2018). Quantifying exosome secretion from single cells reveals a modulatory role for GPCR signaling. *Journal of Cell Biology* **217**, 1129–1142.

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Verweij, F. J., Bebelman, M. P., George, A. E., Couty, M., Bécot, A., Palmulli, R., Heiligenstein, X., Sirés-Campos, J., Raposo, G., Pegtel, D. M., et al. (2022). ER membrane contact sites support endosomal small GTPase conversion for exosome secretion. *Journal of Cell Biology* **221**(12), e202112032.

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Zernecke, A., Bidzhekov, K., Noels, H., Shagdarsuren, E., Gan, L., Denecke, B., Hristov, M., Köppel, T., Jahantigh, M. N., Lutgens, E., et al. (2009). Delivery of MicroRNA-126 by Apoptotic Bodies Induces CXCL12-Dependent Vascular Protection. *Sci Signal* **2**(100), ra81.

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<figure><img src="image_2.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 1.** ExoJ facilitates the automated identification of pHluorin-reported exocytosis.</figcaption></figure>

(A) The framework of the algorithm comprises three main steps to identify and analyze fusion events from fluorescent time series. A prompt of available files appears at the start of ExoJ. Users can load, refresh and select image series of interest (Table 1). A typical fusion
event labeled by CD9-pHluorin in Hela cell is shown as a time-lapse montage. Before fusion (gray-colored inset frame), the fluorescence is quenched in acidic environment. Upon fusion, the pHluorin is exposed to neutral pH. A bright diffraction-limited spot appears and diffuses over time (blue-colored inset frames). Signal intensity is fitted using a mono-exponential decay function to extract the mean lifetime, denoted as τ. (B) Detection of vesicles (seens as spots) are first defined by setting (1) the minimal and maximal event apparent size (Min. and Max. fusion event apparent size). Users can optionally activate and fill in the spatial resolution of the optical system they used for live-cell imaging (see Materials and Methods for more details). Spots are detected using either a local maximum (unchecked box) or à trous wavelet transform algorithm (default option, checked box). The detection threshold value is manually set by users to permit the algorithm to detect spots on single images (here in red).

The threshold value is (2) a multiple of σ (MAD) of either wavelet coefficients $C_{wavelet}$ (à trous wavelet transform) or pixel intensity calculated for each fluorescent image (local maximum). Increasing the detection threshold ultimately leads to a decreased number of detected spots as illustrated for both algorithms here. Insets (blue box) display the low-pass images derived from the à trous wavelet transform (inverted LUT). User-input parameters are described in Table 2. (C) Previously detected spots are connected to reconstruct time-lapse trajectories. The parameters considered for reconstruction are illustrated, and include the maximal distance (spatial searching range) and the time gap (temporal searching depth) between two successive spots as well as the minimal number of connected spots (event duration) (see also Table 3). Tracking results including intensity profile and fluorescent montage for single candidate events can be reviewed upon selection (Show Tracking list button). (D) For each candidate fusion event, a series of user-defined parameters are applied to identify fusion events (see Table 4). (1) The number of data points used for fitting a mono-exponential decay function to derive the mean lifetime τ can be adjusted. (2) To refine the
analysis of the local background and mean lifetime τ of a detected fusion event (peak intensity), users can adjust the number of additional frames included before and after the event. (3) These expanded frames can be built in two ways: starting from the center coordinates at the beginning and end of the detected event, (ticked box) the frames are fixed or (unticked box) dynamically adjust based on where the peak intensity occurs. (4) The detection threshold σ<sub>dF</sub> is the MAD of the 1st order differential fluorescence intensity profile set to sort fusion events. (5) Upper and lower decay limit entries enable users to set the range of mean lifetime τ of fusion events. The fluorescence peak intensity profiles at different timepoints were fitted with a two-dimensional Gaussian fit to estimate the apparent size of fusion event and track the XY position. Fusion events can be identified by accordingly setting the range of apparent size (6) and the position XY over time (7). Evaluation of both mean lifetime τ and apparent size is associated with the goodness-of-fit R<sup>2</sup> (8-9).
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>Figure 2. Feature evaluation of fusion events evidences distinct dynamics of vesicle-mediated exocytosis.</figcaption></figure>

(A) Schematic model illustrating the markers used in this work to monitor vesicle fusion to the PM. The content and/or the vesicle membrane are labeled with pHluorin. Time-lapse
1108 montages of TSPAN- and v-SNARE-mediated fusion events are shown (invert LUT). (B-E)
1109 Combined scatter dot (black circle) and box and whisker (red border) plots are shown for
1110 individual population of labeled vesicles. (B) The median values of fusion activity (in µm⁻²·min⁻¹·10³) are displayed in brackets. Number of analyzed cells (N) for TSPAN and v-SNARE proteins are indicated on top of each box and whisker plots and represented with black circles. (C-E) Similarly, the median values of (C) normalized peak intensity ΔF/F₀, (D) apparent size (FWHM in µm) and (E) event duration τ (in s) are displayed in brackets. Each black circle corresponds to an event. (D) The blue line indicates the resolution limit of our optical system, as determined by the point spread function measurement (see Materials and Methods for more details). Median apparent size values were calculated only for events larger than this limit (Black circles). Fusion events smaller than the measured resolution limit are color-coded in blue. Feature comparisons between TSPAN- and v-SNARE-labeled vesicular population were performed using a Kruskal-Wallis's test followed with a post hoc Dunn's multiple comparison test. Only statistically significant differences are shown. (C) For fusion activity reported by ΔF/F₀: ****, P < 0.0001; ***, P < 0.0002; (D) For fusion estimated apparent size, *, P < 0.05; (E) For fusion duration reported by the mean lifetime τ, ****, P < 0.0001; ***, P < 0.0002; *, P < 0.03.

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<figure><img src="image_12.png" /><figcaption>Figure 3. ExoJ is a highly robust tool to identify distinct forms of fusion events.</figcaption></figure>

(A) Examples of simulated fusion event signal at the cell surface with increasing Gaussian noise intensity (Invert LUT). The inset (blue box) shows four simulated relevant (resp. non-relevant) events indicated by green (resp. pink) arrowhead(s). The total number of simulated fusion events as well as their XY location, normalized peak intensity $\Delta F/F_0$, apparent size and duration $\tau$ are randomly set (see Materials and Methods). Brightness and contrast were

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adjusted for event visualizations. (B) A total of 20 movies were simulated with an overall balanced number of randomly generated relevant (green) and non-relevant (pink) fusion events as indicated. The XY plot corresponds to the evaluation of identification error rate with increasing Gaussian noise intensity using either ExoJ or AMvBE script (manual detection). The estimated noise range of experimental image series is indicated in yellow. Data represent mean ± SEM. For each dataset, Kruskal-Wallis's test was performed (P < 0.0001) followed with a post hoc Dunn's multiple comparison test with 0 as the control: ****, P < 0.0001; **, P < 0.002 for ExoJ and **, P < 0.007 for AMvBE. Only statistical differences are indicated. (C) Assessment of ExoJ sensitivity (proportion of correctly identified relevant events), precision (proportion of identified events as relevant ones that were actually correct), specificity (proportion of correctly identified non-relevant events) with increasing Gaussian noise intensity are shown as combined scatter dot (black) and box and whisker (red) plots. Mean value as well as average F₁ score are reported for each condition. Black circles correspond to the scoring results of individual simulated movies. Note that the median values for all metrics except F₁ score, are 100% across noise conditions. The median F₁ score are equal to 1, 0.99, 0.97 and 0.95 for σG = 0, + σG/4, + σG/2 and + σG, respectively. Kruskal-Wallis's test was performed (P < 0.0001) followed with a post hoc Dunn's multiple comparison test with 0 as the control (****, P < 0.0001; **, P < 0.002; n.s., non-significant). (D) Assessment of ExoJ repeatability on individual simulated movies with added Gaussian noise (σG = 1), and reported by the MAD of accuracy, F₁ score and specificity. The data is presented as combined scatter triangular shape (black) and whisker (red) plots for each scoring metric. Simulated movies were grouped into four categories based on their simulated fusion activity (total number of relevant and non-relevant events per µm² per second): 1-5, 5-6.5, 6.5-7, 7-10. Each category comprises five simulated movies (indicated in brackets). Mean values of MAD are displayed for each group, reflecting the
variability of ExoJ performance across 50 repeated scoring measurements (10 repeated
measurements per simulated movie). The median values within each category are displayed
here: accuracy (3.4%, 2.5%, 3.8%, 2.6%), F₁ score (3.6%, 5.9%, 6.3%, 3.6%) and specificity
(3.3%, 4.7%, 3.8%, 4.2%).
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<figure><img src="image_13.png" /></figure>

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<figure><img src="image_14.png" /></figure>

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<figure><img src="image_15.png" /></figure>

<caption>**Figure S1. ExoJ detection of fusion events.**</caption>

(A) Example of the fluorescence peak intensity profiles over time for a full fusion and a kiss-and-run-like event in a CD9-expressing Hela cell. Data were recorded using ExoJ. Purple block highlights the time window displayed in panel (B). (B) Time-lapse galleries showcasing events at different time scales. Both events are characterized by a sudden
1171 increase in fluorescent intensity, but can be distinguished based on the post-fusion dynamics.

1172 Full fusion event shows an exponential decay while kiss-and-run-like event exhibits an

1173 irregular decrease. (C) ExoJ detection workflow can be confined to a user-defined region of

1174 interest (ROI) during the first step (Spot detection; Fig. 1B, Table 2) using either the drawing

1175 selection toolbox (image of ImageJ/Fiji toolbar with relevant tools, e.g., freehand selection)

1176 or by selecting a previously segmented areas stored in the ROI Manager tool. In this example,

1177 the area (yellow contour) was defined to encompass most of the CD9-expressing Hela cell

1178 interior while discarding the fluorescent signal at the cell periphery. The result after à trous

1179 wavelet transform for a threshold set to 2σ is shown with (left image) and without (right

1180 image) the region selection option. The number of detected spots (red circles) is drastically

1181 reduced when using the region selection option, especially at the cell periphery.

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<figure><img src="image_16.png" /><figcaption>Interactive time series
"Secretion Preview"</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Feature table</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Pop-up window displaying corresponding profile plots</figcaption></figure>

<figure><img src="image_19.png" /></figure>

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<figure><img src="image_20.png" /><figcaption>"Secretion Preview" window</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>Fluorescence peak intensity profile F</figcaption></figure>

<figure><img src="image_22.png" /></figure>

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<figure><img src="image_23.png" /><figcaption>Map settings</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>Mean lifetime distribution</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>Apparent size (FWHM) distribution</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>Temporal occurrence</figcaption></figure>

<figure><img src="image_27.png" /></figure>

1184 **Figure S2. Suite of built-in tools for visualizing and managing events.**

1185 (A) Following ExoJ analysis, a result table ("ExoJ: Detected exocytosis list" window) listing
1186 the detected events and an interactive time series ("Secretion preview" window)
1187 automatically appear, allowing users to review the data. The result table provides a summary
1188 of individual quantitative features such as the spatial and temporal information (Position XY,
1189 Begin Frame, Duration, Peak Frame), the maximal displacement during the process (Max.
1190 displacement in pixel), the detection threshold value (dF/σ<sub>dF</sub>), the local background (F<sub>0</sub>) and
normalized peak fluorescence intensity (ΔF/F₀), the estimated apparent size (FWHM) and the mean lifetime decay (τ) along with the associated goodness-of-fit parameters (R²). The “Secretion Preview” window displays identified events marked with colored-circles. Double-clicking on selected events in the result table opens a new table that features the XY position, the peak intensity F, the estimated apparent size and its associated goodness-of-fit R² over time. ExoJ features responsive visualization tools that generates the intensity (F), the 1st order differential peak (dF), the apparent size and mean radial intensity F over time (Spatial dynamics) profiles as well as the detected event sequence (Movie, Montage). These are automatically adjusted to the selected event in the result table (highlighted in blue). The result table (Export current table), fluorescence peak intensity plot (Export all intensity plots) and feature table (Export all table) for each detected event can be exported using the dedicated action buttons. (B) ExoJ allows users to manage the list of detected fusion events. For potentially missed events, users can leverage ImageJ built-in selection tool to draw around the location of candidate events on the “Secretion Preview” window. The yellow square corresponds to a drawn region around a candidate event. After pressing the button Add of the result table, a prompt window corresponding to the fluorescence peak intensity F profile of the event pops-up. Users are then asked to define the event timeframe and press on the action button “Add” to incorporate the event into the analysis. (C) A spatial occurrence map can be displayed (Distribution map button), adjusted (Map settings button) and further exported as an RGB image. Upon corresponding button activation, users can visualize the distribution of fusion event features: duration (Tau Stat.), apparent size (Size Stat.) and temporal occurrence (Event counts) throughout the fluorescent time series.
A

Montages of synthetic events

<figure><img src="image_28.png" /></figure>

Mono-exponential decay

<figure><img src="image_29.png" /></figure>

1-frame long

<figure><img src="image_30.png" /></figure>

long-lived

<figure><img src="image_31.png" /></figure>

damped sine wave decay
+/- displacement

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<figure><img src="image_32.png" /></figure>

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Figure S3. ExoJ performance under varying simulated fusion events and noise conditions

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(A) Montages of still frames depicting typical patterns of simulated fluorescence intensity over time (invert LUT). The representative relevant (R) event is highlighted with a green frame, while Non-relevant (Nr) events are shown in pink frames. The fluorescence pattern for each event type is indicated next to the montage (refer to the "Computer simulation of exocytosis" section in Materials and Methods for more details). Numbers correspond to the time frame within the montage. Brightness and contrast were linearly adjusted for a better display of events. (B) Assessment of ExoJ performance under varying simulated conditions
are shown as combined scatter dot (black) and box and whisker (red) plots. Average values of corresponding scoring metrics are indicated. The 20 simulated movies were grouped into four categories based on their Non-relevant (Nr) to Relevant (R) event ratio: 0 - 0.5 (n = 6), 0.5 - 1 (n = 4), 1 - 2 (n = 4) and > 2 (n = 6). Each panel shows the scoring results (black circles) of ExoJ sensitivity (left), precision (center) and specificity (right) plotted against increasing Gaussian noise signal ($\sigma_G$).
<table><tr><td>(Pop-up window)<br>List of available<br>files</td><td>Upon starting the plugin, a pop-up window lists opened image-type files.<br>To start (resp. stop) the analysis, press Open (resp. Close). If an image<br>series does not appear, Refresh the pop-up window.<br>After pressing Open, the selected image series will be duplicated for<br>further processing and analysis. Note that the plugin can run in parallel<br>multiple instances.</td></tr><tr><td>Save, Load,<br>Restore</td><td>Parameters are saved (in .dat format), loaded (.dat file) or restored to the<br>metadata of the image series.</td></tr><tr><td>Label color</td><td>Detected vesicles (seen as spots) are marked by circles of selected color.</td></tr><tr><td>Preview, Run,<br>Back</td><td>At each step, processing results can be reviewed for different set of<br>parameters (Preview).<br>During the spot detection step, single fluorescent images are displayed.<br>During the spot tracking step, the whole sequence is shown.<br>During the event identification step, the whole sequence and the table<br>result are displayed.<br>To revert to the previous step, press Back.<br>To proceed to the next step, press Run.</td></tr></table>
<table><caption>**Table 2.** Parameters and options for vesicle (seen as spot) detection</caption><tr><td>Pixel size, Frame rate, Frame time interval</td><td>Pixel size, frame rate (and time interval) are automatically extracted from the metadata of selected image series. Correct if necessary.</td></tr><tr><td>Min. and Max. vesicle apparent size</td><td>Initial user-entries for the estimated apparent size (FWHM in pixel) of the vesicular population of interest. We suggest setting a wide range of apparent size to capture all vesicles.</td></tr><tr><td>(Checked) Use wavelet filter</td><td>Apply à trous wavelet transform algorithm on fluorescent image series where pixel values now correspond to wavelet coefficient (C<sub>wavelet</sub>). Result on single images can be reviewed (Preview button).</td></tr><tr><td>Detection threshold</td><td>Critical integer value that corresponds to a multiple of the MAD of C<sub>wavelet</sub> (noted σ<sub>wavelet</sub>). For a high (resp. low) value, number of detected spots is decreasing (resp. increasing) along with the calculation time.</td></tr><tr><td>Show wavelet lowpass image</td><td>When activated, this option displays the result of the à trous wavelet transform algorithm on single frames including fluorescent spots (colored circles) with C<sub>wavelet</sub> above the detection threshold.</td></tr><tr><td>(Unchecked) Use wavelet filter</td><td>The spot detection relies on the local maximum algorithm with Min. and Max. vesicle apparent size as initial entries.</td></tr><tr><td>Detection threshold</td><td>Critical integer value that corresponds to a multiple of the MAD of all pixel intensity (noted σ<sub>intensity</sub>) at each image.</td></tr><tr><td>Theoretical resolution (if any)</td><td>Optional user input. Apparent size analysis is limited by the optical system resolution. To account for this, consider measuring the PSF (see Materials and Methods for more details). If filled in, ExoJ will flag in red events with an apparent size below the specified value.</td></tr><tr><td>Correct Photobleaching</td><td>A pixel-by-pixel detrending method is immediately applied to the image series. Strongly recommend to correct photobleaching when applicable to improve wavelet-based spot detection. Note that the correction can introduce bright or saturated pixels. The process can be reversed by deactivating the option.</td></tr><tr><td>Transform to dF movie</td><td>The new movie displays the difference between each frame and the one following it from the original timeseries. This option can</td></tr></table>
<table><tr><td></td><td>be deactivated to revert to the original movie.</td></tr></table>
<table><caption>**Table 3.** Parameters and options for vesicle (seen as spot) tracking</caption><tr><td>Spatial searching range (in pixel)</td><td>This integer value corresponds to the maximal acceptable displacement of the spot (flagged by its fluorescence peak) between two consecutive frames. We recommend setting the value similar to <b>Min. vesicle apparent size</b> (from Step 1) to account for possible movements during image acquisition (cell, imaging setup, ...).</td></tr><tr><td>Temporal searching depth (in frame number)</td><td>This integer value corresponds to the maximal allowed between frames when tracking a spot. If the tracked spot disappears from one frame to the next, the algorithm will suppose that the given spot remains at the same location and generate virtual spots to bridge the gap and maintain the track. By default, the value 1 corresponds to a <u>no gap situation</u>. To maximize the detection of consecutive events at the same location, we advise setting the parameter to 1. This ensures spot tracking even if they disappear for a single frame. However, this might lead to the inclusion of background noise and/or tracking artifacts as potential events.</td></tr><tr><td>Minimal event size (in frame number)</td><td>This threshold value corresponds to the minimal duration of each individual track that will be processed and analyzed (Event identification step). Note that gaps are not counted in the track size.</td></tr><tr><td>Show tracking list</td><td>When activated, the list of all vesicle trajectories with a user-specified <b>Minimal event size</b> is displayed for review. Traces of F and dF, montages of still images can be generated and saved.</td></tr></table>
<table><caption>**Table 4.** Parameters and options for the identification of vesicle fusion events</caption><tr><td>Min. points for fitting procedure</td><td>Integer value that corresponds to the minimal number of points needed to fit the fluorescence decay intensity over time to derive the mean lifetime. By default, 5 data points are recommended.</td></tr><tr><td>Expanding frames (pre/post peak)</td><td>Frame addition before and after the tracked candidate fusion events. The two integer (pre and post) values influence the evaluation of MAD of the 1st order differential intensity peak (dF), noted σ. Note that a large window after peak detection (post) improves the evaluation of mean lifetime decay but might hinder the detection of consecutive fusion events at the same location. To maximize the detection of consecutive events at the same location, we recommend setting the post Expanding frames to 0 and adjusting the Min. R2 parameter to account for the reduced accuracy in mean lifetime decay calculations.</td></tr><tr><td>Fixed position</td><td>If ticked, the expanded frames use fixed coordinates derived from the beginning and end of the detected event. If unticked, the expanded frames dynamically adjust their coordinates to track peak intensity.</td></tr><tr><td>Detection threshold (dF/σ)</td><td>Integer value above which the 1st order differential intensity peak dF is seen as a fusion event.</td></tr><tr><td>Upper/Lower decay limit</td><td>Integer range for the mean lifetime decay of candidate fusion events. A minimal value of 2 timepoints is recommended.</td></tr><tr><td>Upper/Lower apparent size limit</td><td>Integer range for the estimated apparent size of candidate fusion events (disabled by default). Tick a checkbox to activate apparent size estimation.</td></tr><tr><td>Max. displacement</td><td>Maximal lateral shift of the peak intensity (F) recorded throughout the fusion event process.</td></tr><tr><td>Min. R<sup>2</sup></td><td>Minimal value of goodness-of-fit R<sup>2</sup> set as a threshold and evaluated from fitted curves for deriving the mean lifetime and the estimated of the fusion event.</td></tr></table>