# Integration of past caspase activity biases cell elimination in vivo

Tom Cumming, Alexis Villars, Anđela Davidović, Florence Levillayer, Léo Valon, Romain Levayer

► To cite this version:

Tom Cumming, Alexis Villars, Anđela Davidović, Florence Levillayer, Léo Valon, et al. Integration of past caspase activity biases cell elimination in vivo. 2025. pasteur-05347265

HAL Id: pasteur-05347265

https://pasteur.hal.science/pasteur-05347265v1

Preprint submitted on 4 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 **Integration of past caspase activity biases cell elimination in vivo**

2 Tom Cumming¹, Alexis Villars¹,²*, Andela Davidović³*, Florence Levillayer¹, Léo Valon¹, and

3 Romain Levayer¹§

4 1. Department of Developmental and Stem Cell Biology, Institut Pasteur, Université de

5 Paris Cité, CNRS UMR 3738, 25 rue du Dr. Roux, 75015 Paris, France

6 2. Current address: Department of Biosystems Science and Engineering, ETH Zürich,

7 Schanzenstrasse 44, 4056 Basel, Switzerland

8 3. Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015

9 Paris, France

10 * These authors contributed equally to this work

11 § Lead contact, correspondence to: [romain.levayer@pasteur.fr](mailto:romain.levayer@pasteur.fr)

12 **Abstract**

13 The fine tuning of apoptosis in epithelia is essential for regulating tissue size,
14 shape, homeostasis and the maintenance of sealing properties. Regulation of
15 cell death is mostly orchestrated by the activation of Caspases, proteases which
16 were long thought to trigger an irreversible engagement in cell death. However,
17 recent data in vivo and in vitro outline numerous non-apoptotic functions of
18 caspases as well as quite ubiquitous sublethal activation of effector caspases
19 during development. Yet, it remains unclear in many instances what drives the
20 bifurcation between cell death engagement and cell survival upon caspase
21 activation. The existence of a caspase activity threshold was generally
22 considered to underpin this binary decision, but this was never assessed
23 quantitatively in vivo especially at the single cell level. Using quantitative live
24 imaging combined with machine learning and optogenetics in the *Drosophila*
25 pupal notum (a single layer epithelium), we reveal for the first time the existence
26 of a large heterogeneity of caspase sensitivity between cells, as well as the
27 existence of distinct spatial domains with low or high sensitivity to caspases.

28 Using correlative and perturbative experiments, we outline the central role of
29 past exposure to sublethal caspase activity which sensitises cells for apoptosis
30 for several hours. Integrating information about past caspase activation is
31 sufficient to explain most of the global pattern of caspase sensitivity and predict
at the single cell level which cells will engage in apoptosis. Finally, we
demonstrate that past sublethal caspase activation in a subset of cells is
sufficient to bias cell elimination at the clonal and single cell level, thus revealing
an alternative mechanism of physiological cell competition. Altogether, this
work reveals for the first time the existence of a new layer of apoptosis
regulation in vivo downstream of effector caspases which can be
developmentally regulated and bias clonal selection and the spatial pattern of
cell death.

Keywords: caspases, apoptosis, epithelium, optogenetics, systems biology, decision-making, machine learning, cell competition, *Drosophila*

## Introduction

Apoptosis, the most prevalent form of programmed cell death, is essential to modulate tissue shape and size and contributes to development robustness¹, ². This is particularly relevant in epithelia, which form a tight mechano-chemical barrier through cell-cell adhesion, and can undergo massive remodelling during development or adult organ homeostasis³. While the molecular regulators of apoptosis are very well known, and all converge upon the cleavage and activation of the caspase proteases and the subsequent cleavage of thousands of proteins⁴, we still have a relatively limited understanding of apoptosis regulation in a multicellular and tissular context. This is epitomised by our inability to predict precisely when, where, how many and which specific cells will die in an epithelial tissue, most likely revealing the complex multilayered regulation of epithelial cell death⁵. Nonetheless, this level of predictivity appears essential when it comes to understanding the evolution of tissue size and shape, or to anticipating clonal evolution during development or tumour progression⁶.

The activation of the most terminal caspases, so called effector/executioner caspases, was long though to constitute an irreversible point of engagement in cell death⁷⁻¹². As a result, most molecular dissections of apoptosis regulation have focused on identifying factors acting upstream of caspase cleavage. However, studies over the past decade have now characterised an ever-increasing number of unexpected effector caspase functions which are unrelated to programmed cell death¹³⁻¹⁵. Alternatively, quantitative studies in cell culture revealed early on the variability of cell response to cytotoxic stress, so called fractional killing¹⁶, where heterogeneity and
noise related to cell protein content can alter the engagement in apoptosis despite
similar upstream pro-apoptotic cues<sup>17-19</sup>. Similarly, mitochondrial outer membrane
permeabilisation (MOMP), which was long thought to be an all-or-nothing process
systematically leading to apoptosis<sup>7, 20</sup>, was recently shown to be more nuanced due
to the occurrence of partial MOMP that does not lead to cell death<sup>21, 22</sup>. The
development of novel genetic sensors of effector caspase activation revealed quite
systematic exposure of cells to sub-lethal effector caspase activity in vivo in
*Drosophila*<sup>23, 24</sup> and in cancer cell lines<sup>25, 26</sup>. These works clearly question the
previously dominant view of caspase activation as the terminal point of engagement in
apoptosis, and suggest that a decision process takes place downstream of effector
caspase activation. Several alternative models have been proposed to redirect
caspase to alternative functions, including the sequestration of active caspases to
subcellular compartments<sup>27, 28</sup>, or more generally the existence of a well-defined
threshold of activity below which the cell will not enter apoptosis<sup>29</sup>. Accordingly, genetic
evidence in *Drosophila* using a combination of caspase mutants and irradiation
suggested that the susceptibility to engage in apoptosis could be set by procaspase
levels<sup>29</sup>. However, more recent data combining single cell quantification of caspase
activity using a live sensor and optogenetic control of caspase found little correlation
between caspase activity and the probability to enter apoptosis in HeLa cells<sup>30</sup>. Thus,
what determines the decision between survival and death following effector caspase
activation remains largely unclear, especially *in vivo*.

The *Drosophila* pupal notum, a single layer epithelium, is an ideal system to study the
quantitative regulation of apoptosis in epithelia *in vivo*. During metamorphosis, close
to a thousand cells die in less than 24 hours in a highly stereotyped global pattern<sup>31-36</sup>,
which can be visualised through long term live imaging while combined with genetic
modulation (Fig. 1A). Effector caspase activation systematically precedes and is
required for cell extrusion toward the basal side<sup>32, 34-38</sup>, which is in turn systematically
followed by DNA compaction and apoptosis<sup>36</sup>. Using various live sensors of effector
caspases, we previously outlined the large proportion of cells undergoing transient
effector caspase activation without committing to cell extrusion and apoptosis<sup>32, 34, 35</sup>,
which is also related to the reversion of caspase activity triggered by local pulses of
EGFR/ERK activation near extruding cells<sup>35</sup>.
Here, we use the pupal notum to characterise for the first time quantitatively *in vivo* the relationship between effector caspase activity and commitment to apoptosis. Using a live sensor of effector caspases and a large dataset of tracked cells, we first confirmed that caspase activity provides good predictive power regarding the likelihood of cell death using machine learning (time series forest). However, we surprisingly found that cells commit to apoptosis at various levels of effector caspase activity. Using optogenetic sublethal activation of caspases, we also outlined the reproducible global tissue pattern of caspase sensitivity in the notum, thus confirming that commitment to apoptosis downstream of caspase can be developmentally regulated. Using correlative analysis combined with quantitative optogenetic perturbations, we revealed for the first time that past exposure to caspase can prime cells for apoptosis a few hours later. This contribution of past exposure can explain a significant proportion of the global spatial heterogeneity of caspase sensitivity and is sufficient to predict which cells will commit to apoptosis following a pulse of caspase activation. Finally, using machine learning and clonal perturbation, we confirm that past exposure to caspases can bias cell elimination relative to neighbouring cells and modulate clonal selection during development, hence describing an original mode of physiological cell competition.

## 113 Results

### 114 Spatial and cell-cell heterogeneity of caspase thresholds

We first characterised the effector caspase activity levels associated with engagement in apoptosis in the pupal notum. We segmented and tracked more than 15,000 cells using deep learning pipelines³⁹ and a new plugin for segmentation curation and tracking⁴⁰ to measure the temporal dynamics of caspase activity at the single cell level using the GFP-based effector caspase sensor GC3Ai⁴¹, ⁴² (Fig. 1A,B, movie S1). Given the stability of activated GC3Ai⁴², we used the intensity derivative (the rate of production of active GC3Ai) as a proxy for effector caspase activity³⁵ (called “caspase activity” hereafter). Engagement in cell extrusion is systematically followed by DNA compaction and apoptosis³⁶. We therefore used the initiation of extrusion as a proxy for engagement in apoptosis and systematically detected the onset of area reduction in extruding cells to define cell death engagement (see Methods and ³⁷, Fig. 1C, movie S2). We first used machine learning to identify relevant single cell parameters predictive of engagement in apoptosis. To avoid bias coming from unbalanced data
and the impact of global tissue patterning on cell shape and behaviour, we trained the models systematically using one dying cell and one of its direct surviving neighbours as a control. To integrate temporal features (mean, standard deviation, derivative) we used a time series forest classifier (TSF<sup>43</sup>, see **Methods**) on backward temporal windows of one hour starting at least 10 minutes prior to the onset of extrusion. We first confirmed that cell apical area and perimeter could provide a good prediction of cell death engagement prior to the onset of extrusion (**Fig. S1 A,C,D**), as previously demonstrated<sup>36</sup>. While cell eccentricity had little predictive value (**Fig. S1B**), we found however that caspase activity could accurately predict cell death (**Fig. S1E,F**), especially in the hour preceding the initiation of extrusion. This suggested that instantaneous caspase activity is relevant to predict the engagement in apoptosis and that the decision to die or survive is taken close to the onset of cell extrusion. We therefore explored more extensively the quantitative behaviour of caspase activity close to the cell death engagement point.

We systematically measured the levels of caspase activity before and after the onset of cell extrusion in dying cells, as well as measuring caspase activity in non-dying cells. Doing so, we obtained the probability of being in apoptosis for a given level of cumulative caspase activity (absolute GC3Ai intensity) or instantaneous caspase activity (derivative of GC3Ai signal, **Fig 1D,E**). We first found that absolute GC3Ai signal on its own provides little predictive information on the engagement in cell death, as exemplified by the large proportion of living cells with high intensity levels (**Fig. 1D**, **Fig. S1 G,H**, logistic fit Tjur's R<sup>2</sup>=0.016) and as previously reported in HeLa cells<sup>30</sup>. We observed however a good logistic fit when plotting the proportion of cells engaged in apoptosis as a function of caspase activity (**Fig. 1E**, **Fig. S1 G,I** logistic fit Tjur's R<sup>2</sup>=0.301). While this result suggests that effector caspase activity is partially predictive of cell death engagement, it also outlines the significant cell-to-cell variability in the caspase threshold associated with commitment to apoptosis, as the range of caspase activity levels at which cells engage in apoptosis spans nearly two orders of magnitude (**Fig. 1E**). We confirmed that the variability of caspase threshold does not correlate with cell-to-cell heterogeneity of Gal4 expression levels as there was no correlation between caspase at the onset of extrusion and level of expression of another constitutively expressed UAS-driven fluorescent sensor (**Fig S1J,K**, Spearman correlation r= - 0.124, p=0.58).
We then tested whether this cell-to-cell heterogeneity in death-inducing caspase threshold had any developmental relevance. To assess the variability of caspase sensitivity in space, we used an optogenetic initiator caspase previously developed by our group (optoDronc³⁵) to trigger mild and uniform activation of caspases in a large band encompassing most of the notum (using the pnr-Gal4 driver, **Fig. 1F**). We then systematically tracked the occurrence of extrusion in the domain expressing optoDronc, and registered the position of macrochaetae (large bristles) from each mapped notum as spatial landmarks for a procrustes transformation, allowing us to scale and align death events from several movies to a single spatial map (see **Methods** and **Fig. S2A**). The same procedure was applied to pupae with the same genotype in the absence of blue light pulses as controls. We first revealed that the vast majority of additional deaths occurs in the 90 minutes following mild activation of optoDronc (**Fig. S2B, movie S3**). We therefore created maps of cell death using extrusions occurring in the first 90 minutes following blue light pulses. We subtracted the distribution of spontaneous death in the control map from the blue light-stimulated map to obtain the spatial distribution of additional deaths triggered by the activation of optoDronc (**Fig. 1G, movie S3**). This revealed a clear heterogenous pattern of death induction with a higher susceptibility to caspase in the midline and in bands at the anterior and posterior edges of the tissue (**Fig. 1G, movie S3**). This heterogeneity is not explained by spatial variability of the driver (**Fig 1F bottom**), nor by differences in the kinetics of effector caspase activity downstream of optoDronc (no spatial difference of GC3Ai dynamics upon prolonged blue light activation, **Fig S2C, movie S4**). Moreover, the comparison of the cumulative number of deaths over 3 hours in the controls and the nota exposed to blue light clearly shows that optoDronc pulses trigger a net increase in the number of cell deaths (**Fig. S2B**, 956 versus 482 extrusions, 4 pupae each). This confirmed that the pattern we observed are "additional" deaths events and not a simple "acceleration" of the elimination of cells that would have died during normal development. Thus, we revealed a stereotypical spatial pattern of caspase sensitivity, which suggests that the engagement in apoptosis downstream of effector caspases can be developmentally regulated.

## *Past caspase activity primes cells for apoptosis*

We next set out to identify parameters that could explain the heterogeneity of caspase sensitivity. We isolated the 1,130 dying cells from our dataset, extracted the level of
caspase activity at the onset of extrusion of each cell, and assessed the strength of the correlation between this value and various morphometric and caspase-related parameters in windows of time prior to extrusion (Fig. 2A,B). Doing so, we outlined an interesting negative correlation between GC3Ai intensity in the past (up to 4-5 hours prior to extrusion) and caspase activity at the onset of extrusion (Fig. 2C). This suggested that previous exposure to caspase activity may decrease the instantaneous caspase threshold required to enter apoptosis, hence priming the cell for death. We therefore experimentally tested whether previous exposure to sublethal caspase could indeed prime cells for apoptosis. We used optoDronc to locally expose a subdomain of the notum (lateral to the midline where there is no physiological cell death³³, ³⁵, ³⁶) to two pulses of caspase stimulation separated by 1, 3 or 5 hours, and used as a control the contralateral region exposed only to the latter stimulation (Fig. 2D, movie S5). We observed that cells were significantly more likely to die in the region with prior caspase exposure compared to the single exposure region, with a more than 4-fold increase in cell death number after 1h, and ~25% increase after 3 hours (Fig. 2E, movie S5). This confirmed that past exposure to caspase can indeed prime cells for apoptosis for a few hours.

Integration of past caspase activity is predictive of the spatial pattern of death and single cell engagement in apoptosis

So far our results suggested that past caspase activity provides relevant information to predict the engagement in apoptosis upon further caspase activation. We therefore revisited our experiments triggering global mild caspase activation in the notum (Fig. 1F,G), and noticed that the pattern of apoptosis induction closely matched the pattern of absolute GC3Ai intensity (compare Movie S1, Fig. 1B with Fig. 1F). We therefore checked whether the pattern of cumulative past caspase activity (using absolute GC3Ai intensity as a proxy) could indeed explain the preferential localisation of cell death following a pulse of optoDronc activation. The pattern of GC3Ai intensity changes during pupal development, with early pupae showing a strong signal in the anterior domain, mild signal in the midline and no signal in the posterior region (~18 hours APF), while at late stage there is a strong increase in signal in the midline and the posterior region (~24 hours APF, movie S1, Fig. 3A). We therefore used optoDronc to trigger mild global caspase activation in early and late pupae to compare the pattern of cell elimination and the pattern of GC3Ai intensity. Strikingly, caspase
227 activation in the early pupae mostly triggers death in the anterior and midline domain
228 and has little impact in the posterior region, while induction of caspase in late pupae
229 triggered death in the anterior, midline and posterior regions, correlating well with the
230 corresponding pattern of GC3Ai signals at the relevant developmental stage (Fig. 3A,
231 movie S6 Pearson correlation comparing the spatial pattern of GC3Ai intensity and
232 local extrusion number r=0.31, p=1.60.10⁻⁶, for early pupae and r=0.65, p=1.67.10⁻²⁸
233 for late pupae). This suggested that the pattern of death induction by optoDronc may
234 be largely explained by past exposure to caspase activation. To directly confirm the
235 link between past activation and the spatial pattern of caspase sensitivity, we erased
236 physiological caspase activity by depleting the pro-apoptotic gene *hid* using RNAi³⁴, 37
237 (see the almost absence of GC3Ai signal Fig. 3B left, movie S7), and triggered a pulse
238 of caspase activity in the *pnr* domain with optoDronc (Fig. 3B, movie S6). Epistatically,
239 optoDronc can directly trigger caspase activation downstream of the pro-apoptotic
240 genes *hid*, *grim* or *reaper*⁵ and bypass the depletion of *hid* by RNAi. We therefore
241 characterised the pattern of death induction by optoDronc in the *hid* RNAi background.
242 Interestingly, the pattern of cell elimination was much more homogeneous in the
243 context of *hid* RNAi compared to optoDronc pulses in WT background (Fig. 3B,
244 compare to Fig 3A). The diminution of spatial patterning is reflected by the
245 considerable reduction in clustering of cell death distribution in the *hid* RNAi context
246 compared to late controls (Fig. 3C, reduced global Moran's I). This confirmed that past
247 caspase activation (here regulated by *hid*) is essential to generate the spatial bias of
248 cell elimination. We noticed however that there is still a slight increase in the probability
249 to observe cell death near the midline and in the anterior domain, accordingly the
250 spatial autocorrelation is still higher than that expected from a random spatial
251 distribution (Fig. 3C, global Moran's I for a random distribution ≈ 0). This suggests that
252 other unknown spatially patterned factors can also modulate the susceptibility to
253 caspases in the notum independently of past caspase activity.

254 We then checked whether the history of cells and their previous exposure to caspase
255 could also be predictive of the engagement in apoptosis at the single cell level.
256 Combining both optoDronc and GC3Ai under the expression of the notum-wide pnr
257 driver, we used a pulse of blue light to simultaneously image the levels of GC3Ai at
258 time 0 and globally activate caspase. We then assessed which cells will die in the
259 midline, and found a striking correlation at the single cell level between the probability
to die and the levels of GC3Ai intensity at time 0 (Fig. 3D,E, movie S8). This suggests that cumulative past exposure to caspase is predictive of which cell will engage in apoptosis following a pulse of caspase activity. While we previously showed that absolute GC3Ai levels on their own are poorly predictive of apoptosis engagement (Fig. 1D), we show here that cumulative past caspase activation (GC3Ai levels) can significantly modulate cell elimination probability when caspase becomes active at a given time. Altogether, these results suggest that the decision to die is first dominated by the current status of caspase activity, which is then fine-tuned by cumulative past exposure.

Relative past and current caspase activity can bias clonal elimination and the death probability relative to neighbours

We previously showed that the initiation of cell extrusion triggers a pulse of ERK activity in the neighbouring cells which shuts down caspase and protects them from apoptosis³⁵. Accordingly, when neighbouring cells activate caspase simultaneously, the first cell to enter extrusion will inhibit the death of its neighbours³⁵ (Fig 4A,), in a similar fashion to cell selection by lateral inhibition⁴⁴. As a result, cell death decision-making is not fully cell autonomous and rather relies on which cells in the group will be the fastest to enter extrusion/apoptosis. We therefore tested whether relative differences in caspase activity and past exposure to caspase could bias the selection of the cell to die among neighbours. We compared dying cells and their neighbours and checked which parameter is predictive of the first cell to die. Among 1,130 groups of individual dying cells and their neighbours, the first cell to die had the highest GC3Ai signal in 52% of cases (random assignment would be close to 15%) and had the highest caspase activity in 77% of cases (Fig 4B, comparison at the onset of the first extrusion). This suggested that relative caspase activity and relative previous exposure to caspase could both bias cell elimination among neighbours. To confirm that our capacity to predict the engagement in death among a group of cells is not just restricted to the timepoint at the onset of extrusion, we again used TSF machine learning to test the predictive strength of relative differences in caspase between neighbouring cells over time. We first used the TSF model to assign a death probability value for each cell in a group of neighbours, and assumed that the one with the relative higher probability score should be the one to die first (Fig. 4C. see Methods). We used the relative GC3Ai and caspase activity values between the dying cell and its neighbours for
training the TSF model. This led to very good prediction of which cell will die first in a group when using the first hour window preceding cell extrusion onset with GC3Ai intensity or caspase activity (Fig. 4C, F1 scores>0.7, note that TSF of GC3Ai also includes information about the derivative), and stronger predictive capacity for raw GC3Ai starting 1 hour prior to the onset of extrusion and lasting up to 3 hours (Fig. 4C). Altogether, these results confirmed that the combination of the relative values of caspase activity and past exposure are sufficient to predict which cell will die first in a group of neighbours.

This non-cell autonomous death decision is reminiscent of cell competition⁴⁵, ⁴⁶, a context-dependent cell elimination process which triggers the elimination of suboptimal cells by their neighbours through short range communication. We therefore checked whether past exposure to caspase could indeed bias clonal selection during normal development. We triggered mild/sublethal activation of optoDronc in clones of the posterior domain of the notum and quantified the probability of cell death from 90min to 5 hours after blue light pulses (hence not counting deaths directly triggered by the first pulse of activation, see Fig S2B). We found a striking increase in death probability relative to neighbouring WT cells (Fig 4D, movie S9, 3.5-fold increase of cell death probability), confirming that past exposure to caspase is sufficient to generate “loser”-like clones which are more likely to be eliminated in regions where caspases become physiologically activated later. Altogether, we showed that past exposure to caspase can bias single cell elimination and clonal dynamics, suggesting that the history of caspase activation could be sufficient to generate physiological cell competition by priming a subset of cells for apoptosis. This novel mechanism of physiological cell competition would not require direct comparison between cells and could be sufficient to bias cell elimination.

## Discussion

We outlined here for the first time the large diversity and spatial heterogeneity of caspase sensitivity in vivo. The existence of reproducible large spatial patterns of caspase sensitivity suggests for the first time that the decision to engage in apoptosis downstream of effector caspases can be developmentally regulated. Using machine learning, optogenetics and correlative analysis, we demonstrated that past exposure to caspase activation can prime cells for apoptosis in the following hours. This
"memory" of past activation explains a significant proportion of the spatial bias of caspase sensitivity and can predict which cell will engage in apoptosis following a pulse of caspase activation. Globally, our work demonstrates that while instantaneous caspase activity is the primary predictor of cell death engagement (Fig. 1E and Fig. S1), information provided by past exposure generates a significant proportion of the heterogeneity that can bias cell elimination. Of note, we still found a significant spatial bias of cell death distribution upon optoDronc activation despite erasure of past caspase activity (Fig. 3B,C). This suggests that other parameters, yet to be identified, can also modulate the susceptibility to enter apoptosis following caspase activation in the notum.

Previously, quantitative characterisation of caspase dynamics in HeLa cells using the same GC3Ai sensor suggested that caspase activity was poorly predictive of apoptosis engagement³⁰, in contrast with our system where the combination of present and past caspase activity is sufficient to predict cell death. This apparent discrepancy may be explained by various differences between the biological systems and the assays. For instance, we focus here on epithelial cells where cell death decision is intrinsically connected to cell extrusion. Moreover, while we can use the engagement in extrusion to temporally align dying cells, there is no trivial criteria to define the onset of apoptosis in HeLa cells and align the temporal profiles, which may have masked relevant information about the utility of caspase activity to predict cell death. Since our machine learning approach suggests that the information provided by caspase activity is mostly relevant in the few hours preceding cell extrusion, such temporal alignment is therefore essential to identify relevant signatures. Finally, the intrinsic resistance of HeLa cells to apoptosis⁴⁷⁻⁴⁹ may mask the instructive role of caspases. Accordingly, caspase signals become predictive in HeLa cells when they are primed by stress conditions³⁰. Irrespective of these differences, these two studies clearly outline the variability of cell death decision-making following caspase activation and the existence of downstream parameters that can modulate apoptosis engagement. Of note, the influence of past caspase activity on the spatial pattern of caspase sensitivity does not seem to be a peculiarity of the pupal notum. Similar spatial bias can be observed in the wing imaginal disc upon homogeneous and transient pulses of pro-apoptotic genes, which preferentially trigger cell death in regions which were already physiologically exposed to caspases⁵⁰ (Fig. S3). This study relies on the relationship between engagement in
extrusion and engagement in apoptosis. Interestingly, we observed a systematic
358
exponential increase in caspase activity as individual cells progress in extrusion (³⁵ and
359
**Fig. 1C**), which may be compatible with a positive feedback loop whereby extrusion
360
remodelling promotes caspase activity which then promotes extrusion progression.
361
This amplification loop could explain the binary decision associated with extrusion and
362
apoptosis engagement. Future work will help to identify the factors promoting this rapid
363
increase of caspase activity and its relationship with the well-described process of
364
anoikis, whereby loss of adhesion drives cells to irreversibly commit to die⁵¹.
365

What, then, are the molecular mechanisms encoding the memory of past caspase
366
activation, and what sets this characteristic timescale of a few hours? This memory is
367
unlikely to be explained by the perdurance of active caspases which were previously
368
shown to be very short-lived⁹, ⁵²⁻⁵⁴, in agreement with our previous measurement of the
369
short time delay between pulses of ERK and inflection of caspase activity (averaged
370
delay of 15min³⁵) which is not compatible with long-lived activated caspases.
371
Alternatively, partial cleavage of caspase substrates facilitating the engagement in
372
extrusion may explain this apoptotic priming. In this scenario, the timescale of memory
373
should be related to the characteristic turnover of the targeted proteins. Accordingly,
374
we tested different pathways known to be modulated downstream of caspases that
375
could feed-back on apoptosis susceptibility. For instance, JNK pathway activity can be
376
tuned by caspase⁵⁵ and directly impact apoptosis engagement. However, while a
377
fluorescent reporter of JNK (TRE-RFP⁵⁶) shows activation in the midline, there was
378
absolutely no sign of activation in the posterior domain which is exposed to caspase
379
activity for several hours during development of the notum (**Fig. S4A, movie S10**).
380
Alternately, reactive oxygen species (ROS) can be triggered by caspases²⁸, ³⁸, ⁵⁷⁻⁶⁰ and
381
could modulate apoptosis by increasing cellular toxicity. However, we could not detect
382
clear activation of ROS in the posterior region using a transcriptional reporter (GstD-
383
GFP, **Fig. S4B, movie S11**), nor any induction of ROS following a sublethal pulse of
384
optoDronc in one heminotum (**Fig. S4C**). Proteasome inhibition was also shown to
385
facilitate apoptosis engagement through the stabilisation of active caspases⁹, and
386
several proteasome components were shown to be cleaved by caspases in S2 cells⁶¹.
387
However, a live sensor of proteasome activity (proteoflux⁶²) did not reveal distinctive
388
change of protein degradation rate in the posterior domain (**Fig. S4D, movie S12**).
389
Sublethal caspase activity can also modulate cytoskeleton organisation¹³, hence
390
391 modulating the probability to enter extrusion. Accordingly, sublethal Dronc activity was
392 shown to downregulate Myoll in the pupal notum<sup>63</sup>, and global tension reduction was
393 recently shown to be permissive for cell extrusion<sup>64</sup>. However, we could not detect clear
394 modulation of Myoll intensity following sublethal pulses of optoDronc (Fig. S4E, movie
395 S13). We also previously showed that disassembly of microtubules by caspases
396 promotes cell deformability and the initiation of cell extrusion<sup>37</sup>. However, we could not
397 detect any clear change in microtubule organisation upon sublethal optoDronc
398 activation (Fig. S4F, movie S14). Finally, we tested whether the pattern of Diap1 (the
399 orthologue of XIAP1, a core inhibitor of apoptosis which can directly tune effector
caspase activity<sup>65-67</sup>) could partially explain the spatial variability of optoDronc
400 sensitivity. We designed a knock-in line of Diap1 tagged with fluorescent mScarlet to
401 assess the variation of protein levels (since Diap1 can be regulated by protein
degradation<sup>66</sup>). We observed a narrow band in the midline with low Diap1, as
402 previously reported using a reporter of Diap1 degradation<sup>68</sup>, and a decrease in Diap1
403 levels at the posterior edge of the tissue at late stage of development (Fig. S4G, movie
404 S15). However, we saw no change in Diap1 levels following a sublethal pulse of
405 optoDronc (Fig. S4H, movie S16). Thus, we could not identify a single obvious
406 candidate explaining how past caspase activation sensitises cells for apoptosis, which
407 may be compatible with a subtle and combinatorial contribution of the modulation of
408 many caspase targets.
409

410
411 Interestingly, this work outlined the numerous cells which transiently activate effector
412 caspases and yet survive. It remains unclear at this point whether such transient
413 activations represent a failure to engage in apoptosis, a process akin to anastasis<sup>24, 69</sup>,
414 <sup>70</sup>, or whether they may also have a relevant physiological function in regulating
415 cytoskeleton/shape or other cellular properties. What, then, could be the physiological
416 relevance of the heterogeneity of caspase sensitivity? At the population level, the
417 existence of heterogeneity and an almost linear correlation between probability to enter
418 extrusion and caspase activity (Fig. 1E) suggests that caspase acts like a rheostat
419 rather than a switch at the population level. The heterogeneity of caspase threshold
420 would therefore be physiologically relevant as it would avoid massive simultaneous
421 elimination of cells passing a threshold of caspase activity (hence leading to tissue
422 rupture<sup>35, 71</sup>) and instead progressively eliminate increasing proportions of cells.
423
Our work also suggests that the engagement in apoptosis is not totally cell-autonomous and relies on which cells among neighbours will engage in extrusion first. In this context, cells that were previously exposed to caspases and/or having relatively higher caspase activity are more likely to die first, either in a physiological context, or upon mild caspase activation in clones. This non-autonomous decision is reminiscent of the process of cell competition whereby neighbouring cells trigger the elimination of suboptimal cells<sup>72</sup>. Interestingly, previous work in mouse embryonic stem cells already suggested that an increased susceptibility to apoptosis is sufficient to generate so-called "loser" cells that will be preferentially eliminated in a developmental context with high apoptosis rate<sup>73, 74</sup>. While *Drosophila* has been a central system to decipher the concepts and mechanisms of cell competition<sup>75</sup>, up till now there has been very little characterisation of spontaneous cell competition taking place during normal development. We propose that the relative modulation of caspase sensitivity by past exposure could constitute one example of such physiological cell competition which could bias clonal selection and the genetic make-up of adult tissues. Future work will help to characterise the contribution of such bias in pre-tumoural clone expansion and cancer progression.

## Acknowledgements

We would like to thank members of the CDEH group for constructive comments on the manuscript. We would also like to thank Anne Loap for her contribution at the start of the project during her internship, Vaishnav Manoj for testing the role of protein stability during his internship, and Georgy Sapozhnikov for initiating the machine learning approach during his internship. We would like to thank Eugenia Piddini for sharing the hs-CL1::GFP line, the Bloomington Drosophila Stock Center and their NIH funding (P40 OD018537), the Drosophila Genetic Resource Center and the Vienna Drosophila Resource Center, Flybase for sharing essential information, stocks and reagents. We thank Gaëlle Letort for developing and sharing the EpiCure pipeline, Alexis Matamoro-Vidal for help with the analysis of apoptosis pattern in the wing discs. We would like to thank the Image Analysis Hub (IAH) of Institut Pasteur for developing the local z-projector plugin and Zellige. TC was supported by a grant from the PPU program of Institut Pasteur and a 4th year PhD grant from the Ligue Nationale Contre le Cancer. Work in RL lab is supported by the Institut Pasteur, the ANR PRC CoECECa, the ANR PRC MAPEFLU, the ANR-10-LABX-0073, the CNRS (UMR 3738) and the European
Union (ERC, PrApEDoC, 101085444). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. A CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions.

## 463 Authors contributions

TC, AV and RL designed the project. AV performed the preliminary experiments showing the heterogeneity of caspase threshold and the role of past activation, and participated in large movie segmentation. TC did all the other experiments and analysis. AD performed the statistical analysis of the segmented dataset and the machine learning. FL designed crosses and fly lines. RL and LV supervised the work, RL obtained funding. All the authors participated in the article writing and editing.

## 470 Star Methods

### 471 Resource availability

#### 472 Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Romain Levayer (romain.levayer@pasteur.fr).

#### 475 Material availability

All the reagents generated in this study will be shared upon request to the lead contact without any restrictions.

#### 478 Data and Code availability

All code generated in this study and the raw data corresponding to each figure panel (including images and local projection of movies) will be accessible on a repository upon final publication of the article. Further information about the dataset can be requested from the lead contact.

### 483 Experimental model and subject details

#### 484 *Drosophila melanogaster* husbandry
All the experiments were performed with *Drosophila melanogaster* fly lines with regular husbandry techniques. The fly food used contains agar agar (7.6 g/l), saccharose (53 g/l) dry yeast (48 g/l), maize flour (38.4 g/l), propionic acid (3.8 ml/l), Nipagin 10% (23.9 ml/l) all mixed in one litre of distilled water. Flies were raised at 25°C in plastic vials with a 12h/12h dark light cycle at 60% of moisture unless specified in the legends and in the table below (alternatively raised at 18°C or 29°C). Females and males were used without distinction for all the experiments. We did not determine the health/immune status of pupae, adults, embryos and larvae, they were not involved in previous procedures, and they were all drug and test naïve.

### *Drosophila melanogaster* strains

The strains used in this study and their origin are listed in the table below.

<table><thead><tr><td>Fly line</td><td>Chromosome location</td><td>Origin (citation)</td><td>Source</td></tr></thead><tbody><tr><td>E-cad::3XmKate (KI)</td><td>II</td><td>76</td><td>BDSC_58789</td></tr><tr><td>E-cad::GFP (KI)</td><td>II</td><td>76</td><td>BDSC_60584</td></tr><tr><td>sqh-Sqh:3XmKate2</td><td>II</td><td>77</td><td>Yohanns Bellaïche</td></tr><tr><td>UAS-his3.3 mIFP-T2A-HO1</td><td>III</td><td>78</td><td>BDSC_64184</td></tr><tr><td>UAS-hid dsRNA</td><td>III</td><td>VDRC</td><td>VDRC:v8269</td></tr><tr><td>UAS-GC3Ai</td><td>III</td><td>79</td><td>N/A</td></tr><tr><td>UAS-GC3Ai</td><td>III</td><td>80</td><td>BDCS_28817</td></tr><tr><td>UAS-optoDronc::GFP</td><td>II</td><td>35</td><td>35</td></tr><tr><td>UAS-optoDronc</td><td>II</td><td>This study</td><td>N/A</td></tr><tr><td>UAS-Cd4-mIFP-T2A-HO1</td><td>III</td><td>Bloomington</td><td>BDSC_64182</td></tr><tr><td>hs-flp22; E-cad::3xmKate ; act<cd2<gal4,UAS-Cd4-mIFP-T2A-HO1</td><td>I; II; III</td><td>This study</td><td>This study</td></tr><tr><td>GMR11F02-gal4 (pdm2-Gal4)</td><td>III</td><td>Bloomington</td><td>BDCS_48928</td></tr><tr><td>UAS-reaper; tub-Gal80ts/Cyo</td><td>X; II</td><td>Eduardo Moreno</td><td>N/A</td></tr><tr><td>hs-CL1::GFP</td><td>II</td><td>62</td><td>62</td></tr><tr><td>TRE-dsRed-40</td><td>II</td><td>Dirk Bohman<sup>56</sup></td><td>56</td></tr><tr><td>GstD-GFP</td><td>III</td><td>Bloomington</td><td>BDCS_26926</td></tr><tr><td>Diap1::mScarlet-I (KI)</td><td>III</td><td>This study</td><td>N/A</td></tr><tr><td>UAS-αtub::mCherry</td><td>II</td><td>37</td><td>37</td></tr></tbody></table>

The exact genotype used for each experiment is listed in the next table. ACI: time After Clone Induction, APF: After Pupal Formation, AEL: After Egg Laying.
<table><thead><tr><th>Figure</th><th>Genotype</th><th>Heat shock 37°C</th><th>Stage</th></tr></thead><tbody><tr><td>1A-E</td><td>w; E-cad::3xmKate(KI)/UAS-GC3Ai; pnr-Gal4/+</td><td>N/A</td><td>16h APF</td></tr><tr><td>1F,G</td><td>w; E-cad:: 3x mKate(KI)/UAS-optoDronc::GFP ; pnr-Gal4/+</td><td>N/A</td><td>20h APF</td></tr><tr><td>S1A-H</td><td>w; E-cad:: 3x mKate(KI)/UAS-GC3Ai; pnr-Gal4/+</td><td>N/A</td><td>16h APF</td></tr><tr><td>S1J</td><td>w; E-cad:: 3x mKate(KI)/UAS-GC3Ai; pnr-Gal4/UAS-His3::mIFP-T2A-HO1</td><td>N/A</td><td>16h APF</td></tr><tr><td>S2A,B</td><td>w; E-cad:: 3x mKate(KI)/UAS-optoDronc::GFP ; pnr-Gal4/+</td><td>N/A</td><td>20h APF</td></tr><tr><td>S2C,D</td><td>w; E-cad:: 3x mKate(KI)/UAS-optoDronc ; pnr-Gal4/UAS-GC3Ai</td><td>N/A</td><td>20h APF</td></tr><tr><td>2A-C</td><td>w; E-cad:: 3x mKate(KI)/UAS-GC3Ai; pnr-Gal4/+</td><td>N/A</td><td>16h APF</td></tr><tr><td>2D,E</td><td>w; E-cad:: 3x mKate(KI)/UAS-optoDronc::GFP ; pnr-Gal4/+</td><td>N/A</td><td>20h APF</td></tr><tr><td>3A</td><td>w; E-cad:: 3x mKate(KI)/UAS-GC3Ai; pnr-Gal4/+</td><td>N/A</td><td>18h and 24h APF</td></tr><tr><td>3A,C</td><td>w; E-cad:: 3x mKate(KI)/UAS-optoDronc::GFP ; pnr-Gal4/+</td><td>N/A</td><td>18h and 24h APF</td></tr><tr><td>3B</td><td>w; E-cad:: 3x mKate(KI)/UAS-GC3Ai; pnr-Gal4/UAS-hid dsRNA</td><td>N/A</td><td>18h and 24h APF</td></tr><tr><td>3B,C</td><td>w; E-cad:: 3x mKate(KI)/UAS-optoDronc::GFP ; pnr-Gal4/UAS-hid dsRNA</td><td>N/A</td><td>18h and 24h APF</td></tr><tr><td>3D,E</td><td>w; E-cad:: 3x mKate(KI)/UAS-optoDronc ; pnr-Gal4/UAS-GC3Ai</td><td>N/A</td><td>20h APF</td></tr><tr><td>4B,C</td><td>w; E-cad:: 3x mKate(KI)/UAS-GC3Ai; pnr-Gal4/+</td><td>N/A</td><td>16h APF</td></tr><tr><td>4D</td><td>hs-flp22; E-cad:: 3x mKate(KI)/UAS-optoDronc::GFP ; act&lt;cd2&lt;Gal4, UAS-Cd4::mIFP-T2A-HO1/+</td><td>48h ACI</td><td>24h APF</td></tr><tr><td>S3A,B</td><td>UAS-reaper/+ ; tub-Gal80<sup>s</sup>/+; pdm2-Gal4/+</td><td>N/A</td><td>96h AEL</td></tr><tr><td>S4A</td><td>w; E-cad::GFP(KI)/TRE-dsRed</td><td>N/A</td><td>16h APF</td></tr><tr><td>S4B</td><td>w; E-cad::GFP(KI)/+; GstD-GFP/+</td><td>N/A</td><td>16h APF</td></tr><tr><td>S4C</td><td>w; E-cad:: 3x mKate(KI)/UAS-optoDronc; pnr-Gal4, UAS-Cd4::mIFP-T2A-HO1/GstD-GFP</td><td>N/A</td><td>20h APF</td></tr><tr><td>S4D</td><td>w; E-cad:: 3x mKate(KI)/hs-CL1::GFP</td><td>N/A</td><td>16h APF</td></tr><tr><td>S4E</td><td>w; sqh:: 3x mKate /UAS-optoDronc; pnr-Gal4, UAS-Cd4::mIFP-T2A-HO1/+</td><td>N/A</td><td>20h APF</td></tr><tr><td>S4F</td><td>w; UAS-αtub::mCherry/UAS-optoDronc; pnr-Gal4, UAS-Cd4::mIFP-T2A-HO1/+</td><td>N/A</td><td>20h APF</td></tr><tr><td>S4G</td><td>w; E-cad::GFP(KI)/+; diap1::mScarlet1(KI)/+</td><td>N/A</td><td>16h APF</td></tr><tr><td>S4H</td><td>w; UAS-optoDronc/+; pnr-Gal4, UAS-Cd4::mIFP-T2A-HO1/diap1::mScarlet1(KI)</td><td>N/A</td><td>20h APF</td></tr></tbody></table>

## Generation of fly lines

### Knock-In Diap1::mScarlet-1
CRSPR-mediated homologous recombination was achieved by injecting three
503
plasmids, one corresponding to the donor template and two encoding the guide RNAs
504
(gRNAs), into Cas9-expressing embryos (BL55821) by BestGene Inc and screened for
505
3xP3-DsRed insertion. The following gRNAs were selected using the Target Finder
506
tool (https://flycrispr.org/target-finder/). The guide sequences (with PAM sequence
507
underlined) were:

gRNA1, 5'-GGCTCCGTCCGTAGCTCCCACGG -3' 509

gRNA2, 5'- GGTTGATGAGTAAGCTACATGGG -3' . 510

Corresponding oligonucleotides containing the guide were annealed and cloned into
511
Bsbl digested pU6.3-gRNA
512

(DGRC Stock 1362; https://dgrc.bio.indiana.edu//stock/1362 ; RRID:DGRC_1362). 513

To produce the homologous repair construct pScarlessHD-Diap1-mScarlet-DsRed ,
514
all cloning was performed by using NEBuilder HiFi DNA Assembly Method. Linear DNA
515
fragments were amplified by PCR and inserted into pScarlessHD-DsRed (DGRC
516
#1364) see https://flycrispr.org/scarless-gene-editing/. 517

518

The final construct was checked by sequencing. 519

520

The homology arms (1kb long) flanking the target sites were amplified from genomic
521
DNA extracted from line BL55821, the mScarlet-I was amplified from pmScarlet-i_C1
522
(Addgene 85044). The candidate lines were sequenced to confirm successful editing.
523
The -Dsred lines were obtained by crossing to line PBac-transposase (BL32070) and
524
the removal of the DsRed cassette was confirmed by sequencing.
525

526

527
Design of UAS-optoDronc without GFP

The p3xUAS-DRONC-linker-CRY2PHR was generated by amplifying DRONC-linker-
528
CRY2PHR from UAS-optoDronc::GFP plasmid, generated in ³⁵, by PCR and inserted
529
in pJFRC4-3XUAS-IVS-mCD8::GFP (Addgene 26217) cut by NotI and Xbal (to excise
530
mCD8::GFP) using NEBuilder HiFi DNA Assembly Method to generate p3xUAS-
531
DRONC-linker-CRY2PHR. The construct was checked by sequencing and inserted at
532
the attp site attp40A after injection by Bestgene.
533

The following primers were used for amplification: 534
535 DRONC-Linker-CRY2PHR F:

536 TAACCCTAATTCTTATCCTTTACTTCAGGCGGCCGCAACATGCAGCCGCCGGAGCTCGA

537 DRONC-Linker-CRY2PHR R:

538 CCACAGAAGTAAGGTTCC

539 **Live imaging and movie preparation**

540 Notum live imaging was performed as followed: the pupae were collected at the fluid
541 stage (0-10 hours after pupal formation), aged at 22°C or 29°C, glued on double-sided
542 tape on a slide and surrounded by two home-made steel spacers (thickness: 0.64 mm,
543 width: 20x20mm). The pupal case was opened up to the abdomen using forceps and
544 mounted with a 20x40mm #1.5 coverslip buttered with halocarbon oil 10S. The
545 coverslip was then attached to spacers and the slide with two pieces of tape. Pupae
546 were collected 48 after clone induction and dissected usually at 16 to 18 hours APF
547 (after pupal formation). The time of imaging for each experiment is provided in the table
548 above. Pupae were dissected and imaged on a confocal spinning disc microscope
549 (Gataca systems) with a 40X oil objective (Nikon plan fluor, N.A. 1.30), a Zeiss LSM880
550 (Zen Black software) equipped with a fast Airyscan using an oil 40X objective (N.A.
551 1.3), or a Zeiss LSM980 (Zen Blue software) equipped with a fast Airyscan 2 using an
552 oil 40X objective (N.A. 1.3). Z-stacks (0.5 or 1 µm/slice) were captured every 1, 2, 5 or
553 10 min using autofocus at 22°C. The autofocus was performed using the plane of E-
554 cad or other apical membrane marker as a reference, using a Zen Macro developed
555 by Jan Ellenberg laboratory (MyPic) on the LSM880, the built-in autofocus function of
556 Zen Blue on the LSM980, or a custom made Metamorph journal on the spinning disc.
557 Movies were performed in the nota close to the scutellum region containing the midline
558 and the aDC and pDC macrochaetae. Movies shown are adaptive local Z-projections.
559 The plane of E-cad or other apical membrane marker was used as a reference to locate
560 the surface of interest on spatial windows of the image (using the maximum in Z of
561 average intensity or the maximum of the standard deviation). Other channels were then
562 projected with a maximum intensity z-projection using an offset and projection stack
563 size appropriate for the localisation of the signal of interest. This procedure was either
564 applied using a custom Matlab routine³⁴, ⁸¹. On some occasions where this local Z-
565 projection pipeline could not accurately resolve folds in the tissue or would project the
566 signal of the cuticle above the apical cell surface of interest, the Zellige⁸² Fiji plugin
was used to locate and project the surface channel, and the resulting z-height maps
were saved and imported into the custom local Z-projector Matlab routine as a
reference surface from which other channels could be projected if necessary.
Automation of the Zellige plugin to run on multiple timepoints was achieved with a
custom ImageJ macro.

572 **Optogenetic control of caspase with optoDronc**

Crosses involving expression of optoDronc were maintained in the dark at 25°C.
Collection of pupae and dissection were performed on a binocular with LEDs covered
by a home-made red filter (Lee colour filter set, primary red) after checking that blue
light was effectively cut using a spectrometer.

577 *Mild induction of optoDronc*

For experiments involving mild induction of optoDronc, either globally, in regions of the
tissue or in clones, blue light pulses were administered by acquiring images using the
488 nm laser either as a short time series (5 or 10 single planes within 5 minutes) or a
single z-stack (10 planes of variable interval to cover the apical surface of the notum).
optoDronc pulse images were acquired on a Zeiss LSM880 in fast airyscan imaging
mode using an oil 40X objective with 488 nm laser power 2.8% and pixel dwell of
1.08µsec (for global activation of optoDronc) or 1.02µsec (for local and clonal
activation).

586 Full activation of optoDronc

To assess the response of the effector caspase sensor GC3Ai to full and prolonged
activation of caspase with optoDronc, z-stacks of 22 slices (1 µm/slice) were captured
on a Zeiss LSM880 in fast airyscan mode every 2 mins using the 488 nm laser (2.8%
power, pixel dwell 0.54µsec) to excite optoDronc and image GC3Ai intensity, and the
561 nm laser to image E-cad::mKate at the cell apical junctions.

592 **Image analysis**

593 *Cell segmentation*

In order to extract quantitative data from individual cells over time, cells were
segmented using the EPySeg Python package on the Z-projected E-cad surface. The
model used for segmentation was either the built-in v2 model from EPySeg³⁹, or a
version of this model after re-training on our own previously corrected segmentation
masks. Correction of segmentation masks was performed either using the Fiji plugin
TissueAnalyzer, or the Napari plugin EpiCure⁴⁰. Corrected segmentation masks were
then exported for cell tracking.

Tracking using skimage and trackpy

For each of the large segmented GC3Ai movies, the input is an array of segmented
images ($I_k)_{k=1,...,N}$, where k is a time-frame, and each image $I_k$ is a snapshot of a tissue
at time $t = k \times 5$ min. Each image is a binary mask, where the value 1 (or 255)
represents the boundary between two cells, and 0 is either intracellular space or
background. The 'skimage' library of python was used to detect and label cells as
connected regions in each image. Furthermore, the x,y position of each connected
region in the image was saved as well as its geometric properties such as area,
perimeter, eccentricity, solidity, orientation, and the coordinates of each pixel belonging
to that connected region. All this was saved in one dataframe structure of the 'pandas'
python library.

The 'trackpy' library of python was then used to track the connected regions over time.
A pixel range of 15 was used with the adaptive step 0.95. Additionally, it was permitted
for a cell to 'disappear' from the tracking during one time frame. This was done in order
to compensate for some errors that occurred in the segmentation, such as when a real
boundary between two cells was not detected in one image. The result was a
dataframe as before, with a new label named 'particle', that maintains the same value
for different time frames for the same connected region.

We eventually removed border cells, sensory organ precursor cells (SOP), and too
short-lived cells from the analysis. Border cells were identified by pixels touching the
edges of the image and were flagged as background. Then, using the 'skimage' library,
the background regions were dilated with 3 x 3 kernels, and cells that overlapped with
this dilated background were then flagged as border cells. Sensory organ precursor
(SOP) cells are specialized mechanosensory cells that behave differently from their
epidermal neighbours in the notum. Since SOPs grow to a larger size than their
epidermal neighbours at late developmental stage, any cell that in any moment
reached an area of 3000 pixels (89.79µm²) was removed from analysis. Any particle
that lived less than 15 time frames (75 min) was also removed from the analysis.
629

Following this curation, the remaining tracked particles were those that lived long enough, were not too large (not SOPs) and were never at the edge of the tissue. These relevant cells were used for all further analysis.

633

### Detecting dying cells using morphological features

The identification of dying cells was based on the identification of cells which do not appear in the final time frame, and which undergo apical constriction. The raw signal of apical area was smoothed to remove noise. As some tracked cells disappeared over one time frame, the ' numpy ' library was used to interpolate the signal over those time frames which lack information about the cell. Then, a median filter of kernel size 15, from 'scipy.signal' library of python, was used to smooth the signal. Cells whose smoothed area shrank at least 50% over the last 75 min of their life were tagged as dying cells. Their raw area was then assessed to ensure that the area was in progressive decline in the last 4 time points, i.e. 20 min prior to disappearance.

642

643

### Detection of area inflection point as the onset of cell extrusion

The onset of extrusion was defined as the moment when a dying cell starts reducing in size before its full delamination and disappearance from the tissue. A 'two-line fit' procedure was used to detect this moment automatically for each dying cell.

645

646

A given dying cell lives on a time interval $[t_0, t_{end}]$, and its area is recorded on every 5-min time step in this interval. At times $\{t_0, t_1, t_2, ..., t_{end}\}$, it has a recorded area, $\{a_0, a_1, a_2, ..., a_{end}\}$. As the cell does not exist after $t_{end}$, one more time step is defined: $t_{end+1} = t_{end} + 5$ min, and the final size of the cell $a_{end+1} = 0$. The time observations were then split in two sets $\{t_0, ..., t_k\}$ and $\{t_{k+1}, ..., t_{end+1}\}$, and using the python library 'statsmodels.api', two separate ordinary least squares models were fit on the time-area points. From both models the sum of all residuals was extracted, and then the residuals from the left-line and right-line fit were summed. This value was saved as $r_k$.

648

649

650

651

652

653

654

655

656

657

658

659

This process was repeated for every $k = 1, ..., t_{end}$, producing a set of residuals $\{r_1, ..., r_{end}\}$. At the minimum value in this set, $r_m$, the corresponding $t_m$ was defined as the inflection point of the cell area and the onset of extrusion for the given cell, $t_{onset} = t_m$.
660

661

662

663

664

Some dying cells divide in the course of their life, and this method could wrongly attribute the moment of division as the onset of extrusion. For this reason, the inflection point analysis was restricted to the last hour (last 12 recorded points) of a dying cell life, so the time interval of interest was in fact [t<sub>end</sub> − 60 min, t<sub>end</sub>].

665

### Interpolation and smoothing of signals

666

667

668

669

670

It is worth noting that raw recordings of signals (area, perimeter, gfp levels) are noisy. Even though the quality of imaging is high, the raw signals will always have some amount of technical error. Parameters such as area, perimeter, eccentricity were smoothed using a median filter with kernel size 15, which was chosen preferentially over a mean filter due to the fact that it shortens the signal for the size of its kernel.

671

672

673

674

675

676

677

The GC3Ai signal (mean GFP intensity) was smoothed using a Savitzky-Golay filter, which fits successive subsets of adjacent data points with a low degree polynomial using the linear least squares. This was implemented using the 'scipy.signal' library of python with kernel size 15 and the 3rd degree polynomial. Furthermore, such smoothing enables for an easy calculation of the derivative of the signal, in this case - the caspase levels. The derivative of the signal would be a derivative of the polynomial, and in practice, we only need to specify the parameter of the function.

678

679

680

While GC3Ai signal should cumulate in the cell, signal can also fluctuate due to adaptive z-projection which can generate some negative values in the GC3Ai derivative remaining close to 0. We decided to set all these values to 0.

681

682

683

684

After running the full image analysis pipeline on both movies separately and confirming that the same relationships and conclusions were drawn from each, the two datasets were pooled for greater statistical power and more information for machine learning after rescaling raw GC3Ai signal distribution using the median.

685

### Caspase threshold search and logistic regression

686

687

688

689

690

To investigate the existence of an overall threshold that determines if a cell in the tissue will extrude, all values of smoothed GC3Ai intensity and caspase activity from all cells, dying and non-dying, at all timepoints, were taken into account. This analysis was performed on more than 15 000 cells, where each cell lives at least 15 time frames and at most 192 time frames.
691

692

693

694

695

For a cell, c, we have values of a signal $x = \{x_k\}_{k=t_0,...,t_{end}}$, where $x_k$ is a value of a signal (GC3Ai or caspase) at time frame k, $t_0$ is a time frame when the cell c is first observed, and $t_{end}$ is its last time frame.

Each $x_k$ of every cell is assigned a value, labelled with

696

$$y_k = \begin{cases} 0, & \text{if cell is extruding,} \\ 1, & \text{otherwise.} \end{cases}$$

697

If a cell does not die during the movie, all values {$y_k\}_{k=t_0,...,t_{end}}$ are equal to 0.

698

If a cell dies during the movie, we identify its extrusion time $t_{onset}$, then

699

700

$$y_k = \begin{cases} 0, & k < t_{onset}, \\ 1, & k \ge t_{onset}. \end{cases}$$

701

702

703

704

705

706

707

A logistic regression was performed on ($x_k, y_k$) pairs of values, fitting a logistic model (sigmoid function) to the data. This function takes real values and outputs a value between 0 and 1. We interpret it as probability to have a certain value of $x_k$ to be labeled with 0 or 1. A good fit of the model and a sharp transition between the two states (not extruding and extruding) would indicate the existence of a threshold of the signal (either GC3Ai or caspase activity) that determines if the cell is extruding or not.

708

709

710

711

The goodness of fit of each logistic model to the real data was assessed by calculating the Tjur pseudo R-squared (Tjur's $R^2$, also known as the coefficient of discrimination). Tjur's $R^2$ compares the average fitted probability of the two outcomes, and is calculated as follows:

712

$$R^2_{Tjur} = | \hat{\pi}(y = 1) - \hat{\pi}(y = 0) |,$$

713

714

715

where $\hat{\pi}(y = 1)$ is the average predicted outcome from the model when $y = 1$, and $\hat{\pi}(y = 0)$ is the average predicted outcome when $y = 0$. The value of Tjur's $R^2$ can be interpreted as follows (see Fig. S1 for more details):

716

717

$R^2_{Tjur} \approx 1$: The model has absolute predictive power, values of the y variable are segregated perfectly by the x variable.
718 $R_{Tjur}^2 \approx 0$: The model has no predictive power, values of the y variable are not segregated by the x variable.

720 $0 < R_{Tjur}^2 < 1$: The model has some predictive power, values of the y variable are somewhat segregated by the x variable.

722

723 Testing correlation between caspase activity at the onset of extrusion and GAL4 expression
724

725 To assess whether cell-to-cell variability in measured caspase activity at the point of commitment to death could actually be explained by differences in expression of the caspase sensor under the control of the GAL4-UAS system, both the UAS-GC3Ai caspase sensor and the constitutively expressed UAS-His3-IFP far-red histone marker were expressed in the same notum under the control of the notum-wide *pnr-G4* driver. Extrusion events were automatically detected using DeXtrusion⁸³ and segmented using the TissueAnalyzer Fiji plugin⁸⁴, and ROIs of segmented extruding cells were used to measure the evolution of perimeter and GC3Ai intensity over time. Cell perimeter was smoothed using a mean filter of kernel size 5 datapoints, and a custom MATLAB routine was used to automatically detect the perimeter inflection point using a two-line fit and assign this as the onset of extrusion (using the same logic as the approach detailed in methods section "Detection of area inflection point as the onset of cell extrusion"). Raw GC3Ai intensity of extruding cells was smoothed over 3 datapoints, the derivative of this signal was calculated to quantify the rate of change, and this derivative was smoothed over 3 datapoints (hereafter "caspase activity"). The cytoplasmic GC3Ai signal was used to follow the cell shape of extruding cells in z from their apical to nuclear planes and identify their respective His3-IFP-marked nuclei, maximum intensity local z-projections of which were segmented using the StarDist Fiji plugin⁸⁵. Caspase at the onset of extrusion was plotted against His3-IFP mean intensity at the onset of extrusion, and the correlation was calculated using Spearman's R.

745 Correlation between cell parameters and caspase activity at the onset of extrusion
746

747 To assess the strength of the correlation between caspase activity at the onset of extrusion and other features of dying cells over time, the area inflection point of each dying cell was detected as previously described, and the cell's value of caspase activity
(smoothed derivative of mean GC3Ai intensity) at that timepoint was taken as its caspase activity at the onset of extrusion. All dying cells were aligned in time by their onset of extrusion ($t_{onset}$), and their values of various morphological and caspase features in windows of time prior to the onset of extrusion were extracted. Specifically, for each dying cell, values of caspase activity, GC3Ai intensity, area, perimeter, derivative of area, derivative of perimeter and eccentricity were averaged in a 20-minute sliding window moved in 5-minute increments over the period covering -10hrs to 0hrs relative to the onset of extrusion. Using python, linear correlations were then calculated between the values of each parameter in each time window and caspase activity at the onset of extrusion, such that the correlation coefficient R could be computed for each relationship and used to populate a matrix showing the strength of the association between the level of active caspase at the onset of extrusion and the value of each parameter across time prior to extrusion.

### Mapping deaths following optoDronc activation

Coordinates of cell death events (x,y,t) following global mild optoDronc activation were registered manually by placing a point ROI at the coordinates of closure of an extruding cell in Fiji. The coordinates of the 4 anterior and posterior dorsocentral macrochaetae (aDC and pDC) in each notum were also manually registered to be used as landmarks for Procrustes transformation. The same process was carried out for control nota which expressed optoDronc but received no blue light pulses. Procrustes transformation was carried out using the built-in procrustes function in Matlab to take the landmark positions from each mapped notum, translate them to the same origin, scale them to the same size and rotate them until the coordinates of corresponding landmark positions aligned as closely as possible. This allowed the procrustes-transformed coordinates of extrusions from each notum to be superimposed on a common spatial map of the notum. The number of extrusions in discrete spatial windows was calculated by sorting points into bins by their Procrustes-transformed coordinates on a 15x15 density heatmap (covering roughly 5x5cells). A single density heatmap was generated summing all values for each group of nota. In the case of the density histograms shown in Fig 1G, the control map was then subtracted from the blue light pulses map through simple matrix subtraction to generate a map showing the spatial distribution of additional optoDronc-induced deaths.
To compare the spatial distribution of optoDronc-induced deaths at different developmental stages with the pattern of GC3Ai at the relevant stage, single images of GC3Ai distribution from the same notum at early and late stage of development were used to generate maps by averaging the fluorescent intensity of the sensor in spatial windows on a 15x15 grid (each box in the grid has width, height = 23.45µm). Maps of optoDronc-induced deaths from early and late pupae were generated as described above, this time using Procrustes transformation to align the coordinates of extrusions to the notum from which the GC3Ai map was generated as a reference. Furthermore, the limits of the extrusion density map were this time restricted to the limits of the GC3Ai reference image, to ensure a more like-for-like comparison of spatial windows between the two maps. Simple pairwise correlation between bins on the heatmaps of GC3Ai and extrusions at the relevant developmental stage was then calculated using Matlab to assess the strength of the relationship between the two classes of spatial map.

The extent to which optoDronc-induced deaths were clustered in the case of early, late and *hid* RNAi nota was assessed by measuring spatial autocorrelation of the summed maps of extrusion events from all nota in each group. Spatial autocorrelation was measured by calculating global Moran's *I* using the moran.test function as implemented in the 'spdep' R package<sup>86</sup>. Global Moran's *I* reports the extent to which the value of each position in the grid is related to the value of the neighbouring positions (here considering 'rook' neighbours, i.e. adjacent neighbours but diagonal ones), and therefore provides an index of clustering/dispersion. The value can be interpreted as follows:

*   0 < *I* < 1: data are more spatially clustered than expected from a random distribution

*   *I* ≈ 1: data are randomly spatially distributed

*   -1 < *I* < 0: data are more spatially dispersed than expected from a random distribution

### Calculating death probability following optoDronc pulse as a function of GC3Ai at t0

To assess the probability of cells in regions of high endogenous caspase to undergo cell death following mild optoDronc activation as a function of their cumulative caspase activity at t0, the GC3Ai sensor was combined with a non-GFP-tagged version of optoDronc. A single z-stack was captured at t0 to image E-cad::mKate and GC3Ai, and
simultaneously trigger mild optoDronc activation (see methods section 'mild induction of optoDronc' for precise conditions of optogenetic activation). Cell contour was then imaged and projected over the following 90 minutes, and extrusions were manually tracked by placing a point ROI in the centre of each dying cell at the start of the movie. The image of E-cad::mKate and GC3Ai at t0 was segmented using EPySeg and corrected using the Fiji plugin TissueAnalyzer. The segmentation mask was then exported as a set of Fiji ROIs which were used to measure the mean intensity of GC3Ai within each cell. Extrusion probabilities were assigned by taking the coordinates of each manually tracked extrusion and measuring the Euclidean distance between this point and the centroid of each cell in the mask. The cell with the minimum distance between its centroid and this point was considered to be the dying cell, and its extrusion probability was set to 1, and after this was done for all extrusions, all other cells were assigned extrusion probability 0. Normalised GC3Ai values were calculated by dividing all GC3Ai intensity measurements by the maximum GC3Ai value from their respective movie, to allow data from multiple movies to be pooled. Values of normalised GC3Ai intensity were binned and the mean extrusion probability at each bin was plotted to show the relationship between cumulative caspase activity at the onset of optoDronc activation and probability of death in the following 90 minutes.

### Triggering past caspase activity in clones and calculating rate of death

To assess the difference in rate of death between WT clones and clones with past caspase activity within the same tissue, optoDronc was expressed in clones. Virgin female flies with the genotype *hsflp; E-cad::mKate/CyO; act>cd2>GAL4,UAS-cd4-mIFP* were crossed with males of the genotype *UAS-optoDroncGFP/CyO; MKRS/TM6B*. Clones were induced through a 15 minute heat shock in a 37°C water bath, and tubes were then maintained in the dark at 25°C. Fluid stage pupae were collected around 32 hours after clone induction and placed at 22°C, then imaged at 48 hours after clone induction. Mild optoDronc activation in clones was induced by capturing a single z-stack exposing the notum to blue light (see methods section 'mild induction of optoDronc' for precise conditions of activation), then the cell contour and clones were imaged for the following 5 hours using the E-cad::mKate and mcd4-IFP markers, respectively. All extrusions in the period of 1.5hrs – 5hrs following optoDronc activation were tracked manually by detecting the closure of delaminating cells, and each event was scored as either a WT or clonal death. We focused on the posterior
region were caspase gets developmentally activated later (see **movie S1**). The image
846
of cell contour at 1.5hrs following optoDronc pulses was segmented using the python
847
package EPySeg, and corrected using the napari plugin EpiCure. Cells at the border
848
of the image were removed from the segmented cell mask, the number of clone cells
849
was detected manually by counting cells in the mask expressing the mcd4-IFP marker,
850
and the number of WT cells was calculating by subtracting the number of clone cells
851
from the total number of cells in the mask. Data from two movies was pooled, and
852
death rate was calculated for both WT and clonal populations by dividing the number
853
of deaths in each population by cell number at 1.5hrs, then multiplying by 100 to be
854
expressed as a percentage.
855

856

Assessing rate of protein turnover with Proteoflux

To investigate any difference in the rate of protein turnover in distinct regions of the
857
pupal notum, a pulse-chase experiment was carried out with the Proteoflux tool. Fluid-
858
stage pupae expressing the Proteoflux tool under the control of a heat-shock promoter
859
were selected, aged overnight at 22°C, then mounted on double-sided tape on a slide
860
and expression of Proteoflux was induced by placing the slide in an incubator at 37°C
861
for either 60 or 90 minutes. The pupae were then removed from the incubator,
862
immediately dissected and imaged on a confocal spinning disc microscope (Gataca
863
systems) with a 40X oil objective (Nikon plan fluor, N.A. 1.30) to capture the decay of
864
the fluorescent signal.
865

866

Machine learning analysis

867

Defining cell groups

To define cell groups for death prediction analysis, all cells touching a dying cell at its
868
onset of extrusion ($t_{onset}$) were designated as neighbours, and the collection of
869
neighbour and dying cell was designated as a group. With 1,130 dying cells in the
870
segmented dataset, as many groups were defined. The groups contain between 5 and
871
11 neighbours.
872

To define groups of neighbours, for a dying cell $c^D$:

1. Find the time of the onset of extrusion, $t_{onset}$

2. From the time frame at $t_{onset}$
876

877

878

879

I. Get coordinates of all pixels of the dying cell $c^D$, and create a mask $M^D$, such that $M^D$ is a matrix of the full image size, where each element of a matrix corresponds to a given pixel in the image and $M_{ij}^D = 1$ if a pixel (i, j) belongs to $c^D$, and 0 otherwise.

880

881

II. Dilate the mask $M^D$ and find all cells in the same time frame that have some intersection with $M^D$.

882

883

III. Verify if the intersecting cells are in the set of relevant cells, if yes - add them to the list of neighbours of $c^D$.

884

## Predicting dying cell using instant GC3Ai and caspase levels

885

886

887

888

Among groups containing a dying cell and all of its neighbours, we compared absolute GC3Ai intensity and caspase activity at the onset of the first extrusion and counted the proportion of groups where the first dying cell have the highest relative GC3Ai or caspase activity.

889

## Time series forest (TSF)

890

891

892

893

894

895

The question of predicting which cell in the group of cells will be the first to die can be formulated as a time series classification problem, i.e. a machine learning problem. Due to the temporal structure of the input data, standard machine learning algorithms that are designed for structured data are usually not well suited. In particular, the order of the values is an essential part of time series, and consecutive time points are likely to be highly correlated.

896

897

898

899

900

901

902

903

Time series forest<sup>43</sup> is a relatively intuitive algorithm. It derives summary features for all time series by dividing them into intervals and summarising each interval by its mean, standard deviation and gradient. Then a Random forest-like strategy is employed to select between a random subset of these features at each node in each of an ensemble of trees. A novel selection criterion is used that considers both entropy gain and the margin by which a feature separates the classes. This continues until the entropy gain ceases to improve, at which stage the node is defined as a leaf. TSF has been shown to be accurate classifier while being computationally efficient.

904

## Training and testing data set

905

906

We want to investigate if the past behaviour of some cell variable could be informative in predicting which cell in a given group of cells is going to die first.
The groups of neighbours are defined as above - one dying cell and its neighbours at the time of onset. Now we look at the past behaviour of these cells. We have opted to use sliding-window strategy to investigate if and which signal is predictive of a dying cell. We define a time interval $I_{w,z}$, with window size, w and ending position with respect to the onset of extrusion of a dying cell of a group, $t_{onset} - z$, so that

$$I_{w,z} = [t_{onset} - z - w, t_{onset} - z].$$

For example, for a time window of 1 hour that start 90 min prior to the onset of extrusion and ends 30 min prior to the onset, the interval we will look at is $I_{w,z} = [t_{onset} - 90 \text{ min}, t_{onset} - 30 \text{ min}]$. Now, if the dying cell of a group entered extrusion at, for example $t_{onset} = 100 \text{ min}$, the time interval of interest for us will be $I_{w,z} = [10 \text{ min}, 70 \text{ min}]$, i.e. in time frames [2, 14]. In absolute terms, each group of neighbours is analyzed based its own time of onset, so the actual time interval is group-specific, so the analysis is given in terms of 'how far are we from the onset'.

For training TSF models we use only the information from one signal on the given interval. The algorithm to train one TSF model for given parameters w and z is as follows:

**for** each group of neighbours

find the onset of extrusion of the dying cell, $t_{onset}$

define $I_{w,z}$

**for** each cell c in the group

$X_c$ = the signal from cell c on $I_{w,z}$.

if a cell is a dying cell of the group

$$Y_c = 1$$

else

$$Y_c = 0$$

The full dataset is a list of all time series, $X = \{X_c\}$, for all cells from all groups of neighbours, with corresponding classifications $Y = \{Y_c\}$. If no other transformation is made, the neighbouring cells are related to each other through the definition of the time
interval used for the extraction of the signal. Note that for every new set of parameters w and z we train different TSF model.

We use 70% of randomly picked data for training and the remaining 30% of data for testing. This means that if a given dying cell is selected to be used for training, all of its neighbours will be used for training.

### Hyperparameters

We use the implemented version of TSF algorithm in python from **pyts** library [4]. There are a few hyper-parameters that need to be set for good model fitting. Most notably we need to set **n_estimators**, the number of trees in the forest. Following an example given in<sup>87</sup>, we use the *out-of-bag* error in order to estimate a suitable value for n_estimators at which the error stabilises. Finally, we use n_estimators = 125.

### Training

We firstly trained TSF models using all neighbours in training datasets. For each TSF<sub>w,z</sub> model, that is defined by the window size w and the final time point prior to onset of extrusion z, we train models based on the:

- GC3Ai signal, caspase activity, area, perimeter and eccentricity.

We vary parameters w and z as follows:

- w ∈ [1 hr, 2 hr, 3 hr, 4 hr],

- z ∈ [10 min, 20 min, 30 min, 1 hr, 2 hr, 3 hr, 4 hr].

Note that for larger values of parameters w and z we have less cells in datasets.

### Balanced datasets

In a movie 1 we detected 514 dying cells. In a movie 2 we detected 616 dying cells. Each of these dying cells has between 5 and 11 neighbours at the onset of extrusion. For an easier interpretation we can use balanced datasets - for one dying, take only one non-dying neighbour. For balanced datasets we add one layer of randomisation of our dataset as we choose one random neighbour from each group of neighbours.

### Scores
For testing how well the trained model performs on a new, never-seen, dataset we use previously selected test dataset. Then we ask a model to predict if the given cell in a test dataset is dying or living, i.e. the model is classifying the given time series into two classes, 0 or 1. The success can be measured with different scores. Firstly we can count how many times the model predicted positive outcome (class 1, i.e. dying cell) correctly - true positives TP, or wrongly - false positives FP. Then we count how many times the model predicted negative outcome (class 0, i.e. non-dying cell) correctly - true negatives TN, or wrongly - false negatives FN. Then the scores are defined as follows:

1) Accuracy = $\frac{TP + TN}{N}$, tells us how many true predictions have been made overall.

2) Precision = $\frac{TP}{TP + FP}$, tells us how many true predictions have been made among all positive predictions.

3) Recall = $\frac{TP}{TP + FN}$, tells us how many true predictions have been made among all cases where the prediction should have been positive.

4) F1 score = $2 \times \text{precision} \times \text{recall} / (\text{precision} + \text{recall})$, tells us how effectively a model makes a trade-off between precision and recall. Notice that F1 score goes to zero if any of the components goes to zero.

## TSF prediction in a group of neighbouring cells using relative values

Another way to test and score a given TSF model is to ask it to predict the dying cell among a given group of cells. Rather than the overall classification of the time series, we are in fact interested to know which of the given cells (typically neighbouring cells) is the most likely to die first. Our metric will take a group of cells and instead of asking a model which class each cell belongs to, we ask the model which cell has the highest probability to be a dying cell.

### for each group of neighbours in test dataset

#### for each cell in the group

##### $P_{cell}$ = probability that the cell is a dying cell

##### $c_{best}$ = the cell with the highest $P_{cell}$ in the group

##### if $c_{best}$ is the dying cell of the neighbourhood
good predictions += 1

The maximum value for goodpredictions is the total number of groups we have in the
test dataset. The score is then defined as

local accuracy = goodpredictions / total number of groups in dataset.

For this task, we used relative GC3Ai and caspase activity values between neighbours
for training the TSF model. The modified datasets are created as follows,

for each group of neighbours

find the onset of extrusion of the dying cell, t_onset

define I<sub>w,z</sub>

MEAN = mean signal from all cells in the group on I<sub>w,z</sub>

for each cell c in the group

X<sub>c</sub> = the signal from cell c on I<sub>w,z</sub>

X<sub>c,modified</sub> = MEAN - X<sub>c</sub>.

if a cell is a dying cell of the group

Y<sub>c</sub> = 1

else

Y<sub>c</sub> = 0

Then the procedure is the same as described above. We train TSF models on balanced
datasets, using various signals, using w = 1hr and various values of parameter z. We
calculate the scores and do local prediction on the test datasets.

1013 Statistics

Data were not analysed blindly. No specific method was used to predetermine the
number of samples. The definition of n and the number of samples is given in each
figure legend and in the table of the Experimental model section. Error bars are
1017 standard error of the mean (s.e.m.) or 95% confidence interval. p-values are calculated
1018 through t-test if the data passed normality test (Shapiro-Wilk test), or Mann-Whitney
1019 test/Rank sum test if the distribution was not normal. Statistical tests were performed
1020 on Graphpad Prism 8 or Matlab.

1021 References

1022 1. Fuchs, Y. & Steller, H. Programmed cell death in animal development and disease. *Cell* **147**, 742-758 (2011).

1023 2. Ambrosini, A. et al. Apoptotic forces in tissue morphogenesis. *Mechanisms of Development* **144**, 33-42 (2017).

1024 3. Guillot, C. & Lecuit, T. Mechanics of epithelial tissue homeostasis and morphogenesis. *Science* **340**, 1185-1189 (2013).

1025 4. Crawford, E.D. & Wells, J.A. Caspase substrates and cellular remodeling. *Annu Rev Biochem* **80**, 1055-1087 (2011).

1026 5. Cumming, T. & Levayer, R. Toward a predictive understanding of epithelial cell death. *Semin Cell Dev Biol* **156**, 44-57 (2024).

1027 6. Greaves, M. & Maley, C.C. Clonal evolution in cancer. *Nature* **481**, 306-313 (2012).

1028 7. Goldstein, J.C., Waterhouse, N.J., Juin, P., Evan, G.I. & Green, D.R. The coordinate release of cytochrome c during apoptosis is rapid, complete and kinetically invariant. *Nat Cell Biol* **2**, 156-162 (2000).

1029 8. Rehm, M. et al. Single-cell fluorescence resonance energy transfer analysis demonstrates that caspase activation during apoptosis is a rapid process. *Role of caspase-3*. *J Biol Chem* **277**, 24506-24514 (2002).

1030 9. Albeck, J.G. et al. Quantitative analysis of pathways controlling extrinsic apoptosis in single cells. *Mol Cell* **30**, 11-25 (2008).

1031 10. Rehm, M., Huber, H.J., Dussmann, H. & Prehn, J.H. Systems analysis of effector caspase activation and its control by X-linked inhibitor of apoptosis protein. *EMBO J* **25**, 4338-4349 (2006).

1032 11. Bagci, E.Z., Vodovotz, Y., Billiar, T.R., Ermentrout, G.B. & Bahar, I. Bistability in apoptosis: roles of bax, bcl-2, and mitochondrial permeability transition pores. *Biophys J* **90**, 1546-1559 (2006).

1033 12. Ziraldo, R. & Ma, L. A mathematical model for apoptotic switch in Drosophila. *Phys Biol* **12**, 056003 (2015).

1034 13. Nakajima, Y.I. & Kuranaga, E. Caspase-dependent non-apoptotic processes in development. *Cell Death Differ* **24**, 1422-1430 (2017).

1035 14. Feinstein-Rotkopf, Y. & Arama, E. Can't live without them, can live with them: roles of caspases during vital cellular processes. *Apoptosis* **2009** **14**:8 **14**, 980-995 (2009).

1036 15. Eskandari, E. & Eaves, C.J. Paradoxical roles of caspase-3 in regulating cell survival, proliferation, and tumorigenesis. *J Cell Biol* **221** (2022).

1037 16. Flusberg, D.A., Roux, J., Spencer, S.L. & Sorger, P.K. Cells surviving fractional killing by TRAIL exhibit transient but sustainable resistance and inflammatory phenotypes. *Mol Biol Cell* **24**, 2186-2200 (2013).

1038 17. Spencer, S.L., Gaudet, S., Albeck, J.G., Burke, J.M. & Sorger, P.K. Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. *Nature* **459**, 428-432 (2009).

1039 18. Roux, J. et al. Fractional killing arises from cell-to-cell variability in overcoming a caspase activity threshold. *Mol Syst Biol* **11**, 803 (2015).

1040 19. Albeck, J.G., Burke, J.M., Spencer, S.L., Lauffenburger, D.A. & Sorger, P.K. Modeling a snap-action, variable-delay switch controlling extrinsic cell death. *PLoS Biol* **6**, 2831-2852 (2008).
1063 20. Rehm, M., Dussmann, H. & Prehn, J.H. Real-time single cell analysis of Smac/DIABLO release during apoptosis. *J Cell Biol* **162**, 1031-1043 (2003).

1065 21. Tait, S.W.G. et al. Resistance to caspase-independent cell death requires persistence of intact mitochondria. *Developmental cell* **18**, 802-802 (2010).

1067 22. Ichim, G. et al. Limited Mitochondrial Permeabilization Causes DNA Damage and Genomic Instability in the Absence of Cell Death. *Molecular Cell* **57**, 860-860 (2015).

1069 23. Ding, A.X. et al. CasExpress reveals widespread and diverse patterns of cell survival of caspase-3 activation during development in vivo. *Elife* **5** (2016).

1071 24. Tang, H.L., Tang, H.M., Fung, M.C. & Hardwick, J.M. In vivo CaspaseTracker biosensor system for detecting anastasis and non-apoptotic caspase activity. *Sci Rep* **5**, 9015 (2015).

1073 25. Sun, L. et al. Anastasis confers ovarian cancer cells increased malignancy through elevated p38 MAPK activation. *Cell Death Differ* **30**, 809-824 (2023).

1075 26. Wang, R. et al. Anastasis enhances metastasis and chemoresistance of colorectal cancer cells through upregulating cIAP2/NFkappaB signaling. *Cell Death Dis* **14**, 388 (2023).

1077 27. Kang, Y., Neuman, S.D. & Bashirullah, A. Tango7 regulates cortical activity of caspases during reaper-triggered changes in tissue elasticity. *Nat Commun* **8**, 603 (2017).

1079 28. Amcheslavsky, A. et al. Plasma Membrane Localization of Apoptotic Caspases for Non-apoptotic Functions. *Dev Cell* **45**, 450-464 e453 (2018).

1081 29. Florentin, A. & Arama, E. Caspase levels and execution efficiencies determine the apoptotic potential of the cell. *J Cell Biol* **196**, 513-527 (2012).

1083 30. Nano, M., Mondo, J.A., Harwood, J., Balasanyan, V. & Montell, D.J. Cell survival following direct executioner-caspase activation. *Proc Natl Acad Sci U S A* **120**, e2216531120 (2023).

1085 31. Marinari, E. et al. Live-cell delamination counterbalances epithelial growth to limit tissue overcrowding. *Nature* **484**, 542-545 (2012).

1087 32. Levayer, R., Dupont, C. & Moreno, E. Tissue Crowding Induces Caspase-Dependent Competition for Space. *Curr Biol* **26**, 670-677 (2016).

1089 33. Guirao, B. et al. Unified quantitative characterization of epithelial tissue development. *Elife* **4** (2015).

1091 34. Moreno, E., Valon, L., Levillayer, F. & Levayer, R. Competition for Space Induces Cell Elimination through Compaction-Driven ERK Downregulation. *Curr Biol* **29**, 23-34 e28 (2019).

1093 35. Valon, L. et al. Robustness of epithelial sealing is an emerging property of local ERK feedback driven by cell elimination. *Dev Cell* **56**, 1700-1711 e1708 (2021).

1095 36. Cachoux, V.M.L. et al. Epithelial apoptotic pattern emerges from global and local regulation by cell apical area. *Curr Biol* **33**, 4807-4826 e4806 (2023).

1097 37. Villars, A., Matamoro-Vidal, A., Levillayer, F. & Levayer, R. Microtubule disassembly by caspases is an important rate-limiting step of cell extrusion. *Nat Commun* **13**, 3632 (2022).

1099 38. Fujisawa, Y., Shinoda, N., Chihara, T. & Miura, M. ROS Regulate Caspase-Dependent Cell Delamination without Apoptosis in the Drosophila Pupal Notum. *iScience* **23**, 101413 (2020).

1101 39. Aigouy, B., Cortes, C., Liu, S. & Prud'Homme, B. EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning. *Development* **147** (2020).

1103 40. Letort, G. et al. https://gitlab.pasteur.fr/gletort/epicure. (2024).

1104 41. Schott, S. et al. A fluorescent toolkit for spatiotemporal tracking of apoptotic cells in living Drosophila tissues. *Development* **144**, 3840-3846 (2017).

1106 42. Zhang, J. et al. Visualization of caspase-3-like activity in cells using a genetically encoded fluorescent biosensor activated by protein cleavage. *Nat Commun* **4**, 2157 (2013).

1108 43. Deng, H.T., Runger, G., Tuv, E. & Vladimir, M. A time series forest for classification and feature extraction. *Inform Sciences* **239**, 142-153 (2013).

1110 44. Simpson, P. Lateral inhibition and the development of the sensory bristles of the adult peripheral nervous system of Drosophila. *Development* **109**, 509-519 (1990).

1112 45. Nichols, J., Lima, A. & Rodriguez, T.A. Cell competition and the regulative nature of early mammalian development. *Cell Stem Cell* **29**, 1018-1030 (2022).
1114 46. Levayer, R. & Moreno, E. Mechanisms of cell competition: themes and variations. *J Cell Biol* **200**, 689-698 (2013).

1115 47. Matlashewski, G., Banks, L., Pim, D. & Crawford, L. Analysis of human p53 proteins and mRNA levels in normal and transformed cells. *Eur J Biochem* **154**, 665-672 (1986).

1116 48. Scheffner, M., Werness, B.A., Huibregtse, J.M., Levine, A.J. & Howley, P.M. The E6 oncoprotein encoded by human papillomavirus types 16 and 18 promotes the degradation of p53. *Cell* **63**, 1129-1136 (1990).

1117 49. Lalaoui, N. et al. TRAIL-R4 promotes tumor growth and resistance to apoptosis in cervical carcinoma HeLa cells through AKT. *PLoS One* **6**, e19679 (2011).

1118 50. Matamoro-Vidal, A., Cumming, T., Davidovic, A., Levillayer, F. & Levayer, R. Patterned apoptosis has an instructive role for local growth and tissue shape regulation in a fast-growing epithelium. *Curr Biol* **34**, 376-388 e377 (2024).

1119 51. Paoli, P., Giannoni, E. & Chiarugi, P. Anoikis molecular pathways and its role in cancer progression. *Biochim Biophys Acta* **1833**, 3481-3498 (2013).

1120 52. Tawa, P. et al. Catalytic activity of caspase-3 is required for its degradation: stabilization of the active complex by synthetic inhibitors. *Cell Death Differ* **11**, 439-447 (2004).

1121 53. Suzuki, Y., Nakabayashi, Y. & Takahashi, R. Ubiquitin-protein ligase activity of X-linked inhibitor of apoptosis protein promotes proteasomal degradation of caspase-3 and enhances its anti-apoptotic effect in Fas-induced cell death. *Proc Natl Acad Sci U S A* **98**, 8662-8667 (2001).

1122 54. Choi, Y.E. et al. The E3 ubiquitin ligase cIAP1 binds and ubiquitinates caspase-3 and -7 via unique mechanisms at distinct steps in their processing. *J Biol Chem* **284**, 12772-12782 (2009).

1123 55. Ryoo, H.D., Gorenc, T. & Steller, H. Apoptotic cells can induce compensatory cell proliferation through the JNK and the Wingless signaling pathways. *Dev Cell* **7**, 491-501 (2004).

1124 56. Chatterjee, N. & Bohmann, D. A versatile PhIC31 based reporter system for measuring AP-1 and Nrf2 signaling in Drosophila and in tissue culture. *PLoS One* **7**, e34063 (2012).

1125 57. Farrell, L. et al. Actin remodeling mediates ROS production and JNK activation to drive apoptosis-induced proliferation. *PLoS Genet* **18**, e1010533 (2022).

1126 58. Santabarbara-Ruiz, P. et al. Ask1 and Akt act synergistically to promote ROS-dependent regeneration in Drosophila. *PLoS Genet* **15**, e1007926 (2019).

1127 59. Sanchez, J.A. et al. Eiger/TNFalpha-mediated Dilp8 and ROS production coordinate intra-organ growth in Drosophila. *PLoS Genet* **15**, e1008133 (2019).

1128 60. Santabarbara-Ruiz, P. et al. ROS-Induced JNK and p38 Signaling Is Required for Unpaired Cytokine Activation during Drosophila Regeneration. *PLoS Genet* **11**, e1005595 (2015).

1129 61. Crawford, E.D. et al. Conservation of caspase substrates across metazoans suggests hierarchical importance of signaling pathways over specific targets and cleavage site motifs in apoptosis. *Cell Death Differ* **19**, 2040-2048 (2012).

1130 62. Baumgartner, M.E., Dinan, M.P., Langton, P.F., Kucinski, I. & Piddini, E. Proteotoxic stress is a driver of the loser status and cell competition. *Nature Cell Biology* **23**, 136-146 (2021).

1131 63. Fujisawa, Y., Kosakamoto, H., Chihara, T. & Miura, M. Non-apoptotic function of Drosophila caspase activation in epithelial thorax closure and wound healing. *Development* **146** (2019).

1132 64. Mann, Z. et al. Preexisting tissue mechanical hypertension at adherens junctions disrupts apoptotic extrusion in epithelia. *Mol Biol Cell* **35**, br3 (2024).

1133 65. Hay, B.A., Wassarman, D.A. & Rubin, G.M. Drosophila homologs of baculovirus inhibitor of apoptosis proteins function to block cell death. *Cell* **83**, 1253-1262 (1995).

1134 66. Yoo, S.J. et al. Hid, Rpr and Grim negatively regulate DIAP1 levels through distinct mechanisms. *Nature Cell Biology* **4**, 416-424 (2002).

1135 67. Ditzel, M. et al. Inactivation of effector caspases through nondegradative polyubiquitylation. *Mol Cell* **32**, 540-553 (2008).

1136 68. Koto, A., Kuranaga, E. & Miura, M. Temporal regulation of Drosophila IAP1 determines caspase functions in sensory organ development. *J Cell Biol* **187**, 219-231 (2009).

1137 69. Tang, H.L. et al. Cell survival, DNA damage, and oncogenic transformation after a transient and reversible apoptotic response. *Mol Biol Cell* **23**, 2240-2252 (2012).
1166 70. Tang, H.L., Tang, H.M., Hardwick, J.M. & Fung, M.C. Strategies for tracking anastasis, a cell survival phenomenon that reverses apoptosis. *J Vis Exp* (2015).

1168 71. Levayer, R. Staying away from the breaking point: Probing the limits of epithelial cell elimination. *Curr Opin Cell Biol* **86**, 102316 (2024).

1170 72. Di Gregorio, A., Bowling, S. & Rodriguez, Tristan A. Cell Competition and Its Role in the Regulation of Cell Fitness from Development to Cancer. *Developmental Cell* **38**, 621-634 (2016).

1173 73. Valverde-Lopez, J.A. et al. P53 and BCL-2 family proteins PUMA and NOXA define competitive fitness in pluripotent cell competition. *PLoS Genet* **20**, e1011193 (2024).

1175 74. Pernaute, B. et al. DRP1 levels determine the apoptotic threshold during embryonic differentiation through a mitophagy-dependent mechanism. *Dev Cell* **57**, 1316-1330 e1317 (2022).

1178 75. Baker, N.E. Mechanisms of cell competition emerging from Drosophila studies. *Curr Opin Cell Biol* **48**, 40-46 (2017).

1180 76. Huang, J., Zhou, W., Watson, A.M., Jan, Y.N. & Hong, Y. Efficient ends-out gene targeting in Drosophila. *Genetics* **180**, 703-707 (2008).

1182 77. Pinheiro, D. et al. Transmission of cytokinesis forces via E-cadherin dilution and actomyosin flows. *Nature* **545**, 103-107 (2017).

1184 78. Yu, D. et al. Rational design of a monomeric and photostable far-red fluorescent protein for fluorescence imaging in vivo. *Protein Sci* **25**, 308-315 (2016).

1186 79. Karim, F.D. & Rubin, G.M. Ectopic expression of activated Ras1 induces hyperplastic growth and increased cell death in Drosophila imaginal tissues. *Development* **125**, 1-9 (1998).

1188 80. Oh, H. & Irvine, K.D. In vivo analysis of Yorkie phosphorylation sites. *Oncogene* **28**, 1916-1927 (2009).

1190 81. Herbert, S. et al. LocalZProjector and DeProj: a toolbox for local 2D projection and accurate morphometrics of large 3D microscopy images. *BMC Biol* **19**, 136 (2021).

1192 82. Trebeau, C., de Monvel, J.B., Altay, G., Tinevez, J.Y. & Etournay, R. Extracting multiple surfaces from 3D microscopy images in complex biological tissues with the Zellige software tool. *BMC Biol* **20**, 183 (2022).

1195 83. Villars, A., Letort, G., Valon, L. & Levayer, R. DeXtrusion: Automatic recognition of epithelial cell extrusion through machine learning in vivo. *Development* (2023).

1197 84. Etournay, R. et al. TissueMiner: A multiscale analysis toolkit to quantify how cellular processes create tissue dynamics. *Elife* **5** (2016).

1199 85. Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell Detection with Star-Convex Polygons. *Lect Notes Comput Sc* **11071**, 265-273 (2018).

1201 86. Bivand, R.S. & Wong, D.W.S. Comparing implementations of global and local indicators of spatial association TEST. **27(3)**, 716-748 (2018).

1203 87. https://scikit-learn.org/stable/autoexamples/ensemble/plot_ensemble_oob.html.

1204
<caption>Figure 1</caption>

bioRxiv preprint doi: https://doi.org/10.1101/2025.05.18.654727; this version posted May 20, 2025. The copyright holder for this preprint which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

<figure><img src="image_2.png" /></figure>
**Figure 1: Cell-to-cell and spatial heterogeneity of caspase threshold associated with cell death (associated with Supplementary Figure 1 and 2).**

**A:** Schematic of the *Drosophila* pupal notum (dotted square) and the GC3Ai effector caspase sensor, which folds and emits fluorescence upon cleavage of the DEVD consensus cleavage site. **A:** Anterior, **P:** Posterior, **L:** Left, **R:** Right. **B:** Local z-projection of a live pupal notum expressing E-cad::mKate (magenta) and GC3Ai (green) in the pnr domain. Middle panel shows the cell contour upon segmentation with EPySeg, curation with EpiCure, and cell tracking with trackpy. Right panel shows GC3Ai intensity measurement in single cells (blue: low, yellow: high). Scale bar=50µm. **C:** Snapshots of a dying cell before (left, negative time) and after the onset of extrusion (right, positive time), E-cad::mKate in magenta and GC3Ai in green. Yellow polygon shows the cell contour. Scale bars=5µm. Bottom graphs show the corresponding apical area (left), GC3Ai intensity (middle) and estimated caspase activity (right) based on the GC3Ai intensity derivative. The dark blue zone corresponds to the time where the cell is engaged in extrusion, after the area inflection point shown by the black dots. **D,E:** Estimation of the probability to be engaged in cell death (corresponding with measurements taken where the cell is engaged in extrusion, see dark blue area in the curves in **C**) as a function of the current GC3Ai intensity (**D**), or current effector caspase activity, estimated with the GC3Ai intensity derivative (**E**). n= 2 fully segmented pupal nota, 15,568 cells and 1,130 dying cells. While there is a high proportion of non-dying cells at high GC3Ai, there is a good correlation between probability to die and levels of GC3Ai derivative. However, the curve does not correspond to a single threshold value and outlines a large range of values at which cells engage in apoptosis. **F:** Schematic of the opto-Dronc construct, an optogenetic Caspase9 allowing tunable control of caspase activity in *Drosophila*³⁵. The *Drosophila* Caspase 9 (Dronc) is fused to GFP in N-ter and CRY2-PHD in C-ter which oligomerises upon blue light exposure (green dots, middle scheme), triggering Dronc cleavage and caspase activation. Bottom scheme illustrates the zone of mild activation of optoDronc used to analyse the spatial pattern of cell death (red dots). The consensus maps of resulting deaths are obtained by Procrustes alignment of several tissues using the aDC and pDC macrochaetae as landmarks (pink crosses). Bottom left shows the expression pattern of optoDronc in the pnr-Gal4 domain and the bottom right picture shows one example of cell death distribution upon optoDronc activation (red dots, cell contour shown with E-cad::mKate, grey). Scale bars=50µm. **G:** Summed map of the extrusions counted in nota for 90 minutes following mild optoDronc activation with blue light pulses, n=4 nota aligned and scaled, 737 extrusions. Bottom map shows the extrusion in control nota (same genotype, no blue pulse, 222 extrusions). The estimation of the distribution of cell death triggered by optoDronc (right map) is obtained by subtracting the control death map (b) from the blue light-exposed map (a). The total local number of deaths is colour coded (red high, blue low). **A:** Anterior, **P:** Posterior, **L:** Left, **R:** Right.
<caption>Figure 2</caption>

bioRxiv preprint doi: https://doi.org/10.1101/2025.05.18.654727; this version posted May 20, 2025. The copyright holder for this preprint which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

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

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

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

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

E-cad::mKate

<figure><img src="image_8.png" /></figure>
**Figure 2: Past caspase activation primes cells for apoptosis.**

**A:** Cell area (blue) and caspase activity (GC3Ai intensity derivative, red) of a single dying cell. The green dotted line shows the onset of cell extrusion. The blue and red dots show the area and caspase activity at the onset of apoptosis engagement. We tracked multiple parameters backward in time (green squares, 20 minutes sliding window shifted every 5 minutes) related to cell geometry and caspase dynamics on 1,130 dying cells (2 nota). **B:** Examples of correlations calculated between caspase activity at the onset of apoptosis engagement and perimeter from 70 to 50 min prior to apoptosis engagement (top) and GC3Ai intensity from 70 to 50 min prior to apoptosis engagement (bottom). **C:** Matrix of correlations of various geometrical (cell apical area, perimeter, rate of change of area, rate of change of perimeter, eccentricity) and caspase-related parameters (GC3Ai intensity, caspase-derivative of GC3Ai intensity) with caspase activity at the onset of engagement in apoptosis. Correlations are calculated on time windows of 20 minutes prior to the onset of cell extrusion (t=0 hours). **Blue:** positive correlation, **Red:** negative correlation. Note the clear global negative correlation between GC3Ai intensity in the past and caspase at the onset. n=1,130 dying cells, 2 nota. **D:** Schematic of the experiment directly testing the influence of past caspase activation on apoptosis engagement. *pnr-Gal4* drives *UAS-optoDronc* which is activated with a short blue light pulse in the lateral right part of the notum (top, regions with very limited spontaneous cell death). After different waiting times (Δt; 1, 3 or 5 hours), the left and right regions of the notum are exposed to blue light. The number of extrusions is then compared in the left and right region for the next 120 min. Bottom pictures show an example of a notum with the distribution of cell extrusions in the left and right regions over 120min following the second blue light pulse (one red dot=one cell extrusion) Scale bars=50μm. **E:** Quantification of the proportion of cell extrusions in the right (twice-exposed) and left (once-exposed) regions, as a percentage of the total extrusions per notum in the 120 minutes following the second blue light pulse, with a waiting time of 1, 3 and 5 hours after the first blue light exposure (n = 5 nota per condition). Error bars show s.e.m..
<caption>Figure 3</caption>

bioRxiv preprint doi: https://doi.org/10.1101/2025.05.18.654727; this version posted May 20, 2025. The copyright holder for this preprint which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

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

<figure><img src="image_10.png" /></figure>
Figure 3: Past caspase activation is predictive of apoptosis spatial pattern and single cell decision.

**A,B:** Left, local projections of an early (top, ~18h APF), and late (middle, ~24h APF) pupal notum (**A**), or late pupal notum upon depletion of the pro-apoptotic gene *hid* by RNAi (bottom, **B**). E-cad::mKate (magenta), *pnr-Gal4 UAS-GC3Ai* (green, same intensity scale for all) and the spatial map of the GC3Ai intensity (right, white to dark red, same scale in the three conditions). Scale bars=50µm. Middle: schematic of the global optoDronc activation performed and the tracking of cell death over 90 minutes. Right: Sum maps of cell death distribution observed in the 90min following blue light pulse obtained after the rescaling/alignment of 3 young (top, **A**), 3 old (middle, **A**), and 3 *UAS-hid dsRNA* pupae (bottom, **B**), total extrusion number: 238 in the young, 644 in the old pupae, 345 in old *UAS-hid dsRNA* pupae. Colour scale (white to dark red, low to high number of extrusions) is the same in the 3 conditions. **C:** Spatial autocorrelation of the extrusion pattern observed over 90min after optoDronc activation in young, old, or *UAS-hid dsRNA* pupae (see Methods). **D:** Top, schematic of the experiment, local activation of optoDronc in the midline is combined with the imaging of GC3Ai intensity at t0. Cell extrusion is then mapped in the next 90min. Bottom, local projection of an early pupa at the onset of blue light exposure (GC3Ai signal, green, E-cad::mKate, magenta). The localisation of cell death in the next 90min is shown with red dots. Scale bar=50µm. **E:** Single cell probability of cell extrusion during the 90min following optoDronc activation in the midline as a function of the normalised GC3Ai raw intensity at t0. Grey area is the standard error of the mean (note that probability measurement is poorly reliable above 0.7 due to the low number of cells showing these extreme values).
<caption>Figure 4</caption>

bioRxiv preprint doi: https://doi.org/10.1101/2025.05.18.654727; this version posted May 20, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

Caspase active

<figure><img src="image_13.png" /></figure>

Caspase inhibition via ERK

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

Apoptotic cell

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

Protected for 1hr

<figure><img src="image_16.png" /></figure>

N = 2 movies
2078 WT cells
471 clone cells

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

hsf1p; Ecad::mKate, optoDroncGFP; mcd4-IFP

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

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>
**Figure 4: Past caspase activation biases single and clonal cell elimination.**

**A:** Schematic of local ERK negative feedback on epithelial cell death. Upon initiation of cell extrusion, the activation of ERK in the neighbouring cells (orange) blocks caspase activity (green) and protect them from cell death for at least 1 hour (see ³⁵). **B:** Percentage of prediction success regarding which cell will die first among a group of neighbouring cells based on comparison of GC3Ai/caspase parameters at the onset of the first extrusion event. The first dying cell has the highest GC3Ai signal in 52% of cases, and the highest caspase activity in 77% of cases. n=1,130 groups of cells. **C:** Proportion of correct prediction of the first cell dying among a group of neighbours based on a Time Series Forest model. The cell with the highest probability score in the group is predicted to be the first to die. TSF is trained either on relative GC3Ai intensity (blue) or on relative caspase activity (orange) comparing a dying cell and a surviving neighbour as control (see Methods). The black dotted line shows the expected score for random prediction, the grey area shows the 95% confidence interval (prediction is tested on a balanced dataset of dying cells and surviving neighbours, a random prediction will be correct in 50% of cases). The time windows (starting from 10 minutes prior to extrusion onset) used for TSF prediction and training are shown on the x-axis (time in minutes prior to extrusion onset). **D:** Schematic of the experiment triggering sublethal caspase activation in clones. Clones expressing optoDronc (green) located in the posterior region of the pupal notum are exposed to mild blue light pulses. Rate of death is then calculated in the clonal cells and outside the clones from 1.5hrs to 5hrs after blue light pulses. In this time window, cell death is mostly triggered by spontaneous developmental activation of caspase in the posterior region. Bottom panels show local projection of a pupal notum expressing E-cad::mKate (magenta) and UAS-mCD4::mIFP (green/grey) at the onset of blue light pulses, 1.5hrs and 5hrs later. Red dots show cell extrusion events counted from 1.5hrs to 5hrs. The death probability in control and optoDronc clones is shown in the bar graph (n=471 clonal and 2078 WT cells, 2 nota, p=5.31e-17, Fisher's exact test). Error bars are 95% confidence interval. Scale bars=30µm.
bioRxiv preprint doi: https://doi.org/10.1101/2025.05.18.654727; this version posted May 20, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

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

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

<figure><img src="image_24.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>D</figcaption></figure>

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

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

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

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

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

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

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

<figure><img src="image_33.png" /><figcaption>pnr-GAL4; UAS-His3::mIFP</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>K</figcaption></figure>

<figure><img src="image_35.png" /></figure>
**Supplementary Figure 1: Identification of parameters and time windows relevant for cell death prediction (associated with Figure 1).**

**A:** Summary of the metrics shown in each graph of the Time Series Forest (TSF) prediction. Solid curve, precision, dotted curve, recall, dotted-dash curve, F1 score on the y axis. x axis shows the time windows used to train the model (in minutes prior to extrusion onset, red dotted line). **B-F:** Precision, recall and F1 score of the prediction of dying cells in the pupal notum using TSF models trained on the corresponding time window (x-axis) using a dying cell and one of its neighbours as control measuring cell eccentricity (**B**), cell apical perimeter (**C**), cell apical area (**D**), GC3Ai raw intensity (**E**) and caspase activity (GC3Ai intensity derivative, **F**). The baseline (random prediction) is at 0.5. **G:** Schematic of the segregation of data based on logistic regression and sorting of the dying and non-dying cells based on caspase activity. Left shows a perfect prediction (step-like function), middle the absence of predictive value, and right a partial predictive value. **H,I:** Logistic regression of the probability of cell death as a function of raw GC3Ai intensity (**H**) and caspase activity (**I**) based on the curves shown in **Fig. 1D** and **E**. The discrimination power is estimated using Tjur's R². **J:** Snapshots of a single cell undergoing cell extrusion showing E-cad::mKate (top, magenta), UAS-GC3Ai signal (middle, green) and UAS-His3::mIFP (bottom, cyan) both driven by the pnr-Gal4 driver. Time 0 is the onset of cell extrusion. Scale bars=10µm. Blue curve shows the perimeter over time, green curve the caspase activity (GC3Ai derivative) and cyan curve the His3::mIFP intensity. **K:** Correlation between caspase activity at the onset of extrusion (x-axis) and His3::mIFP intensity (y-axis). n=22 extruding cells (one dot=one extruding cell). Note the absence of correlation between the far-red signal (a proxy of Gal4 transcriptional activity) and the caspase activity at the onset, r=-0.12, p=0.58.
<caption>**Supplementary Figure 2**</caption>

bioRxiv preprint doi: https://doi.org/10.1101/2025.05.18.654727; this version posted May 20, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

<figure><img src="image_36.png" /></figure>
**Supplementary Figure 2: Susceptibility to caspase is spatially patterned (associated with Figure 1 and Figure 2).**

**A:** Schematic of the process used to align pupae upon optoDronc pulses to produce a consensus spatial map of optoDronc-induced deaths. Procrustes transformation is used to align, scale and rotate pupae and their corresponding mapped extrusions using 4 posterior macrochaetae (aDC and pDC macrochaetae) as spatial landmarks (pink crosses). **B:** Bar plots of the absolute number of cell extrusions counted at various time windows (minutes post-blue light activation) in *pnr-Gal4 UAS-optoDronc* pupae following mild and global activation of optoDronc (left, (a)) or in absence of blue light (right, (b)) (associated with **Fig. 1F,G**). n=4 pupae, 956 cell death after blue light activation, 482 in the controls. Note that the additional deaths observed in the blue light-activated pupae mostly occur in the first 90 minutes (black dotted lines) and death rate returns to control levels after 90 minutes. **C:** Snapshots of a pupal notum expressing *UAS-optoDronc* (no GFP tag) and *UAS-GC3Ai* with *pnr-Gal4* upon global and persistent blue light exposure. E-cad::mKate in magenta, GC3Ai in green. Note the global and homogeneous increase of GC3Ai signal (despite the initial heterogeneity corresponding to the normal pattern of caspase activation. The coloured squares show the regions of intensity measurement quantified in **D**. Scale bars=50µm. See also **movie S4**. Anterior, left, posterior, right. **D:** Time evolution of GC3Ai intensity in different ROIs of the pupae shown in **C**, blue: midline, pink: anterior, purple: posterior, green: lateral.
A

pdm2-Gal4 UAS-GC3Ai

GC3Ai (junction plane)

GC3Ai

DSRF

pdm2-Gal4 UAS-RFP
UAS-RFP

E-cad

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

B

<figure><img src="image_39.png" /></figure>

pdm2-Gal4 UAS-reaper

cDcp1

DSRF

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

UAS-reaper induction duration

0h

4h

8h

24h

<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>
**Supplementary Figure 3: Susceptibility to caspase is also spatially patterned in the larval wing imaginal disc (associated with Discussion).**

**A:** Top, pattern of expression of the pdm2-Gal4 driver in the larval wing imaginal disc (green, UAS-RFP, grey, E-cad). Middle: local projection of DSRF staining (intervein regions, magenta), GC3Ai signal in the plane of adherens junctions (green). Bottom: Pseudo colour of GC3Ai intensity in the adherens junction plane. White arrows show regions of high GC3Ai signal.

**B:** Representative examples of cell death distribution (cleaved Dcp1, green) upon induction of Reaper in the *pdm2-Gal4* domain for different activation time (duration of 29°C incubation in hours releasing Gal80ts repression). Cell death is mostly located in the previously characterised apoptotic hotspots at 0h (see ⁵⁰), is first enriched in regions already showing some cumulated GC3Ai signal after 4 and 8h of activation, and eventually is distributed throughout the wing disc. n=23, 24, 28 and 14 wing discs. Scale bars=100µm.
Supplementary Figure 4

bioRxiv preprint doi: https://doi.org/10.1101/2025.05.18.654727; this version posted May 20, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /></figure>

<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>
**Supplementary Figure 4: Testing putative regulators of the memory of past caspase activation (associated with Discussion).**

**A:** Snapshots of a pupal notum expressing the JNK reporter TRE-RFP (middle, magenta) and E-cad::GFP (top, green) at different developmental times (hours After Pupal Formation, APF). Anterior, left, posterior, right. Note the absence of signal in the posterior region. Scale bar=50µm. Representative of 2 pupae. **B:** Snapshots of a pupal notum expressing the ROS reporter GstD1-GFP (middle, magenta) and E-cad::GFP (top, green) at different developmental times (hours After Pupal Formation, APF). Anterior, left, posterior, right. Note the absence of clear signal upregulation in the posterior region. Scale bar=50µm. Representative of 3 pupae.

**C:** Snapshots of a pupal notum expressing the ROS reporter GstD1-GFP (greyscale) and *pnr-Gal4 UAS-optoDronc* upon mild activation of caspase in the left region of the notum (see left schematic, dotted blue rectangle) 4 hours after blue light activation (note that GstD1-GFP transcriptional reporter should perdure for hours upon activation). No clear difference can be observed between the activated and control regions.

Anterior, left, posterior, right. Scale bar=50µm. Representative of 2 pupae. **D:** Snapshots of a pupal notum expressing the proteasome activity reporter (ProteoFLUX, hs-CL1::GFP, magenta) and E-cad::mKate (top, green) over time after heat shock induction of the proteasome reporter (hours After Heat Shock, AHS). Anterior, left, posterior, right. Note the absence of clear pattern of speed of intensity decrease (a proxy of proteasome activity), especially in the posterior region. Scale bar=50µm. Representative of 7 pupae. **E:** Snapshots of a pupal notum expressing squh::mKate (greyscale) and *pnr-Gal4 UAS-optoDronc* upon mild activation of caspase in the right region of the notum (see left schematic, dotted blue rectangle) over time after blue light activation (minutes). No clear differences of junctional squh::mKate levels could be observed between the activated and control regions. Anterior, left, posterior, right. Scale bar=50µm. Representative of 5 pupae. **F:** Snapshots of a pupal notum expressing UAS-α-tub::mCherry (greyscale) and *pnr-Gal4 UAS-optoDronc* upon mild activation of caspase in the left region of the notum (see left schematic, dotted blue rectangle) over time after blue light activation (minutes). Close up views of the two regions in yellow rectangles are shown above and below. No clear differences of cortical microtubule organisation could be observed between the activated and control regions. Anterior, left, posterior, right. Scale bar=50µm in main image and 20µm in cropped insets. Representative of 2 pupae. **G:** Snapshots of a pupal notum expressing the knock-in fusion of Diap1::mScarlett (magenta, middle) and E-cad::GFP (top, green) at different developmental times (hours After Pupal Formation, APF). Anterior, left, posterior, right. Note the absence of clear downregulation of Diap1 concentration in the posterior region where cell deaths occur from 16 to 26 hours APF. Scale bar=50µm. Representative of 2 pupae. **H:** Snapshots of a pupal notum expressing the
knock-in of Diap1::mScarlett (middle, magenta), *pnr-Gal4 UAS-optoDronc* and UAS-mCd4::mIFP (top, green) upon mild activation of caspase in the left region of the notum (see left schematic, dotted blue rectangle) over time after blue light activation of caspases (minutes). No clear differences of Diap1 levels could be observed between the activated and control regions. Anterior, left, posterior, right. Scale bar=50µm. Representative of 2 pupae.
## Supplementary movie legends

**Movie S1:** Movie related to Fig. 1A-E, 2A-C, 4A-C, S1A-I. ~16h APF notum local z-projection with E-cad::mKate (top left, magenta) and UAS-GC3Ai (top middle, green) driven by the pnr-Gal4 driver. Bottom left shows the skeletonised tissue. Bottom middle the cell tracking (1 colour per cell), the bottom right the colour coded cell average GC3Ai intensity (dark blue low, yellow high). Anterior, left, posterior, right. Scale bar is 50µm.

**Movie S2:** Movie related to Fig. 1C. Example of an extruding cell from a notum local z-projection with E-cad::mKate (middle, magenta) and UAS-GC3Ai (right, green) driven by the pnr-Gal4 driver. The skeletonised contour of cells is shown on the left, the dying cell is shown in white and with yellow contours. Anterior, left, posterior, right. Scale bar is 5µm.

**Movie S3:** Movie related to Fig. 1F,J. ~20h APF notum local z-projection with E-cad::mKate (greyscale) expressing UAS-optoDronc::GFP driven by the pnr-Gal4 driver after global mild activation of optoDronc (left) or without blue light activation (control, right). Red dots show the dying cells. Anterior, left, posterior, right. Scale bars are 50µm.

**Movie S4:** Movie related to Fig. S2C,D. ~20h APF notum local z-projection with E-cad::mKate (left, magenta) expressing UAS-optoDronc and UAS-GC3Ai (middle, green) driven by the pnr-Gal4 during a strong and global activation of optoDronc. Anterior, left, posterior, right. Scale bar is 50µm.

**Movie S5:** Movie related to Fig. 2D. ~20h APF notum local z-projection with E-cad::mKate (greyscale) expressing UAS-optoDronc::GFP driven by the pnr-Gal4 after a local activation of optoDronc (in the red and blue square regions). The blue square was already exposed to a pulse of blue light 1 hour before. Dying cells in the regions of interest are shown with red dots. Anterior, left, posterior, right. Scale bar is 50µm.

**Movie S6:** Movie related to Fig. 3A. Nota local z-projection with E-cad::mKate (greyscale) expressing UAS-optoDronc::GFP driven by the pnr-Gal4 driver after global mild activation of optoDronc in a young pupae (left, 18h APF), late pupae (middle, 24h APF) or upon inhibition of *hid* (UAS-*hid* dsRNA, right). Red dots show the dying cells. Anterior, left, posterior, right. Scale bars are 50µm.
**Movie S7:** Movie related to Fig. 3B. ~16h APF notum local z-projection with E-cad::mKate (left, magenta) expressing UAS-GC3Ai (middle, green) and UAS-hid dsRNA driven by the pnr-Gal4 driver. Compare with Movie S1 for control. Anterior, left, posterior, right. Scale bar is 50µm.

**Movie S8:** Movie related to Fig. 3D,E. ~16h APF notum local z-projection with E-cad::mKate (magenta) expressing UAS-GC3Ai (green) and UAS-optoDronc driven by the pnr-Gal4 driver after global mild activation of optoDronc (also used to image GC3Ai at T0). White dots show the dying cells. Anterior, left, posterior, right. Scale bar is 20µm.

**Movie S9:** Movie related to Fig. 4D. ~24h APF notum local z-projection with E-cad::mKate (left, magenta), UAS-optoDronc::GFP and UAS-mCd4::mIFP-T2A-HO1 (green, middle) expressed in clones after mild activation of optoDronc. Red dots show the dying cells detected between 1.5hrs and 5hrs after the blue light pulse. Anterior, left, posterior, right. Scale bar is 30µm.

**Movie S10:** Movie related to Fig. S4A. ~18h APF notum local z-projection with E-cad::mKate (left, green) and the JNK pathway sensor TRE-RFP (middle, magenta). Anterior, left, posterior, right. Scale bar is 50µm.

**Movie S11:** Movie related to Fig. S4B. ~18h APF notum local z-projection with E-cad::mKate (left, green) and the ROS sensor GstD-GFP (middle, magenta). Anterior, left, posterior, right. Scale bar is 50µm.

**Movie S12:** Movie related to Fig. S4D. ~16h APF notum local z-projection with E-cad::mKate (left, green) and the proteasome activity sensor Proteoflux (hs-CL1::GFP, middle, magenta) levels after a pulse of transcriptional activation. Anterior, left, posterior, right. Scale bar is 50µm.

**Movie S13:** Movie related to Fig. S4D. ~20h APF notum local z-projection with sqh::mKate (greyscale) and UAS-optoDronc::GFP driven by the pnr-Gal4 driver after mild blue light stimulation in the blue square region. Anterior, left, posterior, right. Scale bar is 50µm.

**Movie S14:** Movie related to Fig. S4F. ~20h APF notum local z-projection with UAS-αtub::mCherry (greyscale) and UAS-optoDronc::GFP driven by the pnr-Gal4 driver
after mild blue light stimulation in the blue square region. Anterior, left, posterior, right.
Scale bar is 50µm.

**Movie S15:** Movie related to Fig. S4G. ~16h APF notum local z-projection with E-cad::GFP (green, left) and Diap1::mScarlet (KI) (magenta, middle). Anterior, left, posterior, right. Scale bar is 50µm.

**Movie S16:** Movie related to Fig. S4H. ~20h APF notum local z-projection expressing UAS-optoDronc::GFP and UAS-mCd4::mIFP-T2A-HO1 (green, left) and Diap1::mScarlet (KI) (magenta, middle) with the pnr-Gal4 driver after mild activation of optoDronc in the blue square region. Anterior, left, posterior, right. Scale bar is 50µm.