# Germination and outgrowth of Bacillus mycoides KBAB4 spores are impacted by environmental pH, quantitatively analyzed at single cell level with sporetracker

C. Trunet, N. Vischer, Louis Coroller, S. Brul

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C. Trunet, N. Vischer, Louis Coroller, S. Brul. Germination and outgrowth of Bacillus mycoides KBAB4 spores are impacted by environmental pH, quantitatively analyzed at single cell level with sporetracker. Food Microbiology, 2024, 121, pp.104509. 10.1016/j.fm.2024.104509. hal-04809314

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# Germination and outgrowth of *Bacillus mycoides* KBAB4 spores are impacted by environmental pH, quantitatively analyzed at single cell level with sporetracker

C. Trunet<sup>a, *</sup>, N. Vischer<sup>b</sup>, L. Coroller<sup>a</sup>, S. Brul<sup>b</sup>

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

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## ARTICLE INFO

### Keywords:

- Spore-forming bacteria
- Acidic environment
- Heterogeneity
- Modelling

## ABSTRACT

Quantifying spore germination and outgrowth heterogeneity is challenging. Single cell level analysis should provide supplementary knowledge regarding the impact of unfavorable conditions on germination and outgrowth dynamics. This work aimed to quantify the impact of pH on spore germination and outgrowth, investigating the behavior of individual spore crops, produced under optimal and suboptimal conditions. *Bacillus mycoides* (formerly *B. weihenstephanensis*) KBAB4 spores, produced at pH 7.4 and at pH 5.5 were incubated at different pH values, from pH 5.2 to 7.4. The spores were monitored by microscopy live imaging, in controlled conditions, at 30 °C. The images were analyzed using SporeTracker, to determine the state of single cells. The impact of pH on germination and outgrowth times and rates was estimated and the correlation between these parameters was quantified. The correlation between germination and outgrowth times was significantly higher at low pH. These results suggest that an environmental pressure highlights the heterogeneity of spore germination and outgrowth within a spore population. Results were consistent with previous observations at population level, now confirmed and extended to single cell level. Therefore, single cell level analyses can be used to quantify the heterogeneity of spore populations, which is of interest in order to control the development of spore-forming bacteria, responsible for food safety issues.

## 1. Introduction

Germination and outgrowth of bacterial spores is a highly heterogeneous phenomenon (Chen et al., 2006; Indest et al., 2009; Setlow, 2013). This heterogeneity can be approached at different levels, from population to single cell level. In addition, suboptimal culture conditions, such as temperature, pH or weak acid concentration, lead to significantly lower rates or extended times of germination and outgrowth (Eijlander et al., 2011; van Melis et al., 2014).

Quantifying the impact of environmental factors, in addition to providing better knowledge of germination and outgrowth process, should lead to better control of the development of spore-forming bacteria in foods. Previous works have aimed to quantify the impact of heat treatment and/or suboptimal incubation conditions, in terms of temperature, pH or organic acid presence, on the ability of spores to germinate and restore growth (den Besten et al., 2012; Mtimet et al., 2015; Sun et al., 2021; Trunet et al., 2015, 2018). These studies were

based on cultural methods and quantified the population of spores that were ultimately able to form a colony on solid media or an increased $A_{600}$ in broth.

Discrimination between the physiological stages, observable in particular during the germination and outgrowth process, and, at the least, dormant spores, germinated spores and vegetative cells is possible using direct observation methods. These methods are necessary in order to take into account the impact of environmental factors on germination and outgrowth dynamics. Studies based on observation by phase contrast microscopy (Kong et al., 2014; Pandey et al., 2013; Stringer et al., 2011) or flow cytometry (Mathys et al., 2007; Trunet et al., 2020) have enabled the discrimination of the Impact of treatment and/or unfavorable incubation conditions on germination or outgrowth. Notably, recent work by Trunet et al. (2020) showed a higher heterogeneity of spore germination and outgrowth due to low incubation pH, based on the quantification of the evolution of spore physiological stages over time. In the same study, mathematical models were developed, allowing

<footnote><sup>a</sup> Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29000, Quimper, France</footnote>

<footnote><sup>b</sup> Laboratory for Molecular Biology and Microbial Food Safety, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands</footnote>

<footnote>* Corresponding author.
E-mail address: clement.trunet@univ-brest.fr (C. Trunet).</footnote>
the quantification of each spore subpopulation as well as the impact of temperature and pH on the evolution of each subpopulation.

The aim of this work was to quantify the impact of pH on spore germination and outgrowth at single cell level, investigating the behavior of individual spores of *Bacillus mycoides* (formerly *Bacillus weihenstephanensis* (Liu et al., 2018)), produced in optimal and suboptimal conditions, in adverse growth conditions. Finally, the aim was to compare the results with methods based on population or subpopulation observations and determine whether mathematical models based on population or subpopulation quantification were suitable for single cell-based quantification.

# 2. Material and methods

## 2.1. Strain

The *Bacillus mycoides* (formerly *weihenstephanensis*) strain KBAB4 (INRA, Avignon, France) isolated from a forest soil (Guinebretière et al. 2008) was used in this work. The strain was stored at -80 °C in 1 mL aliquots of Brain Heart Infusion (BHI, Biokar Diagnostics, Beauvais, France) mixed with 50% glycerol (v/v) at a concentration of approximately 2 × 10⁶ CFU mL⁻¹. A 100 mL volume of BHI was inoculated with 1 mL of the stock suspensions and incubated for 8 h at optimal growth temperature, namely 30 °C, then 1 mL volume was transferred into 100 mL of BHI for 16 h of incubation at the same temperature. Finally, 0.1 mL of bacterial suspension was added to 100 mL of BHI and was incubated for 6 h. The final cell concentration in the pre-culture was approximately 10⁸ CFU mL⁻¹; the number of spores estimated by the number of cells surviving a 5 min, 70 °C heat treatment was lower than 100 spores.mL⁻¹.

## 2.2. Spore production

Spores were produced through a two-step sporulation process (Baril et al., 2012). Volumes of 100 mL of the previously described pre-culture were centrifuged (6000×g, 10 min, 12 °C) and suspended in 100 mL of sporulation mineral buffer (SMB) made of K2HPO4 at 4.5 g.L⁻¹, KH2PO4 at 1.8 g.L⁻¹, CaCl2, H2O at 8.0 mg.L⁻¹ and MnSO4, H2O at 1.5 mg.L⁻¹, filter-sterilized using 0.2 µm pore size filters (Baril et al., 2012). The suspension of *B. mycoides* cells was incubated with shaking at 30 °C, at pH 7.00 or at pH 5.50. Spores in SMB were harvested when free spores represented more than 95% of cells under ×1000 magnification in phase-contrast microscopy (Olympus BX50, Olympus Optical Co., Ltd, Hamburg, Germany), i.e. after 1–2 days at optimal growth temperature and up to 10 days at suboptimal temperature. The spore suspensions were centrifuged (6000×g, 10 min, 12 °C). Spore pellets were suspended in 5 mL of sterile distilled water. The 5 mL suspensions were divided into 1 mL aliquots and stored for 1 month at 4 °C before use. Laboratory observations consistently show that spore heat resistance does not change for at least a six-month storage time (unpublished data). The final concentrations of the stock suspensions were 10⁸ spores.mL⁻¹.

## 2.3. Slide preparation and germination conditions

A chamber was prepared by attaching a 65 µL volume Gene Frame (1.5 × 1.6 cm, Thermo Scientific) to a standard microscope slide as described by Pandey et al. (2013). The spores were incubated on semi-solid Brain Heart Infusion (BHI, Oxoid) at different pH values: pH 7.40, pH 7.00, pH 6.40, pH 6.00, pH 5.50, pH 5.20, mixed with agarose (Sigma - Aldrich) in the medium in a 1:1 ratio. The pH was adjusted with a solution of HCl 10 M.

## 2.4. Time-lapse microscopy

Time-lapse series were made by making use of a temperature-controlled boxed incubation system for live imaging set at 30 °C and

observation was performed using a Nikon Eclipse Ti-E microscope (Nikon Instruments, Tokyo, Japan) equipped with a sCmos420 camera (Hamamatsu Flash 4.0 V2, Hamamatsu City, Japan), driven by NS1 Nikon software. Phase-contrast time-lapse series were recorded at a sample frequency of 1 picture per 1 min for 4–12 h, depending on the incubation conditions. A minimum number of 5 different fields were recorded in parallel per experiment. In each field, on average 40 isolated spores were identified and monitored over time. Three biological replicates were performed for incubation at pH 7.40, pH 6.40 and pH 5.50.

## 2.5. Image analysis

The images acquired by phase contrast microscopy were analyzed using SporeTracker, a macro running with ImageJ described by Pandey et al. (2013). In the present study, three populations were defined regarding their characteristics in phase contrast microscopy allowing the quantification of germination and outgrowth of *B. mycoides* KBAB4: dormant spores, germinated spores and vegetative cells. Germination was detected by a drop in the brightness value and two parameters were calculated for this phenomenon: germination lag time and the time of germination (corresponding to the time at which the lower brightness value is reached, named phase-dark). Outgrowth was characterized by the enlargement of the spore until it reached the surface area of a vegetative cell. The time taken to reach the surface area of a single vegetative cell was set as the outgrowth time.

## 2.6. Modeling germination and outgrowth curves

Germination and outgrowth curves were obtained representing the distribution of cumulative numbers of germinated spores and vegetative cells respectively.

As described in Trunet et al. (2020), the germination and outgrowth curves were fitted using the model inspired by the Weibull cumulative distribution.

$$P_i(t) = 1 - 10^{\left(\frac{t-t_0}{t_i}\right)^{S_i}} \quad (1)$$

where, $P_i$ corresponds to the Weibull distribution of time, $t_i$ was the time to reach 90% of the maximum number of cells for each cell type i (either germinated spores or vegetative cells), $t_0$ is the time taken for the first spore to germinate or the first germinated cell to outgrow, and $S_i$ expresses the scattering of $t_i$ within the population (the higher $S_i$ is, the lower the scattering is).

$$N_G(t) = N_{Gmax} \cdot (1 - \tau_{\text{germ}} \cdot P_{\text{germ}}(t)) \quad (2)$$

where $N_G(t)$ is the germinated spore number, $N_{Gmax}$ is the maximum number of germinated spores, $\tau_{\text{germ}}$ is the proportion of dormant spores able to germinate and $P_{\text{germ}}(t)$ is the cumulative Weibull distribution as described previously (equation (1)).

$$N_{VC}(t) = N_{VCmax} \cdot (1 - \tau_{\text{outgr}} \cdot P_{\text{outgr}}(t)) \quad (3)$$

where $N_{VC}(t)$ is the vegetative cell number, $N_{VCmax}$ is the maximum number of outgrown cells, $\tau_{\text{outgr}}$ is the proportion of germinated cells able to outgrow and $P_{\text{outgr}}(t)$ is the cumulative Weibull distribution as described previously (equation (1)).

Secondary modeling was performed to quantify the impact of incubation pH on the time of first germination ($t_0_{\text{germ}}$), the time taken to reach 90% germination ($t_{\text{germ}}$), the germination rate ($\tau_{\text{germ}}$), the time of first outgrowth ($t_0_{\text{outgr}}$), the time taken to reach 90% outgrowth ($t_{\text{outgr}}$), and the germination rate ($\tau_{\text{outgr}}$) using the model developed by Trunet et al. 2020, based on growth parameters.
## 2.7. Statistics

The models were fitted on the observations by minimizing the sum of squared errors (SSE) using the lsqcurvefit function from MatlabR2012b (MathWorks, Natick, MA). The goodness of fit of the models was checked by the root mean square error (RMSE) (Huet et al., 2010, p. 2; Scherrer et al., 2009). The smaller the RMSEs were, the better the model fitted the data. The 95% confidence intervals were calculated using the nlparci function from MatlabR2012b. The fitting performance of each model was statistically evaluated by the F test, comparing the MSE of the model to the MSE of the data. The computed *f* value was compared to the F table value (0.05 significance level). If the *f* value was lower than the *F* value from the table, the *F* test was accepted, indicating that the model fitting was statistically acceptable. Moreover, variance comparison tests were performed (anova function, MatlabR2012b).

Pearson coefficients were calculated in order to estimate the correlation between germination and outgrowth times of individual spores using the Analysis Tool of Excel (2013) (Microsoft, Redmond, USA). To determine if two Pearson coefficient scores were significantly different, a comparison test was performed using the cocor package in R (R 3.1.1) (Diedenhofen and Musch, 2015).

# 3. Results

## 3.1. Correlation between germination and outgrowth times

The germination time and outgrowth time of individual spores was determined, respectively as the time taken to turn phase-dark and the time taken to reach the surface area of a vegetative cell. The correlation between these times was estimated thanks to the Pearson coefficient for the replicates performed by incubation at pH 7.40, pH 6.40 and pH 5.50 (Table 1). Basically, the correlation between germination and outgrowth times determines whether the first spore able to germinate will be the first to form a new vegetative cell. All Pearson coefficient scores were significant with *p* < 0.01. The Pearson correlation obtained for incubation at pH 5.50 was significantly higher than for pH 7.40 and pH 6.40, for spores produced in optimal conditions. These results demonstrated that germination and outgrowth times of individual spores showed a significantly higher correlation at low pH.

In addition, the correlation between germination and outgrowth time is significantly lower at pH 5.5 for spores produced at low pH.

The same *B. mycoides* KBAB4 spore population incubated at optimal pH (pH 7.40 and pH 6.40, Trunet et al. 2020)) or low pH (pH 5.50) showed significantly different correlation between germination time and outgrowth times of individual spores. These results suggest a high heterogeneity in the spore population, observable thanks to an environmental pressure applied to the spores, due to low pH.

## 3.2. Modelling spore germination and outgrowth using single cell data

*B. mycoides* KBAB4 spores were individually monitored providing 21 germination and outgrowth curves, each fitted with equations (2) and

<caption>**Table 1**</caption>

Pearson correlation coefficient between germination time and outgrowth time of individual spores of *Bacillus mycoides* KBAB4 produced at 30 °C and pH 7.40 or 30 °C pH 5.50, at incubation pH 7.40, pH 6.40 and pH 5.40.

<table><thead><tr><th colspan="3">Pearson coefficient between time to 90% germination and time to 90% outgrowth (95% CI)</th></tr><tr><th>Incubation pH</th><th>Optimal sporulation conditions [CI]</th><th>Sub-optimal sporulation conditions [CI]</th></tr></thead><tbody><tr><td>pH 7.40</td><td>0.38 [0.26, 0.48]*</td><td>0.38 [0.24, 0.51]*</td></tr><tr><td>pH 6.40</td><td>0.21 [0.09, 0.32]*</td><td>0.68 [0.56, 0.77]*</td></tr><tr><td>pH 5.50</td><td>0.82 [0.75, 0.87]*</td><td>0.64 [0.49, 0.75]*</td></tr></tbody></table>

(3) respectively (Fig. 1). These equations provided a satisfactory description of both germination and outgrowth curves.

Equations (2) and (3) provided the time of first germination ($t_0$ germ), the time taken to reach 90% germination ($t_{germ}$), the germination rate ($\tau_{germ}$), the time of first outgrowth ($t_{outgr}$), the time taken to reach 90% outgrowth ($\tau_{outgr}$), and the outgrowth rate ($\tau_{outgr}$). A significant impact was observed on all these parameters for spores incubated at pH 5.50 compared to spores incubated at pH 7.40 (ANOVA, *p* = 0.05%). No significant differences could be pointed out between spores incubated at pH 6.40 and spores incubated at pH 7.40 (ANOVA, *p* = 0.05%). Special attention must be paid to the outgrowth time parameter as it was not possible to estimate it at the lower pH tested (pH 5.20). Indeed, outgrowth was a rare event in this condition and no applicable data could be obtained (Fig. 2).

The obtained data were fitted using the secondary model developed by Trunet et al. (2020), based on physiological parameters, namely the minimum and optimal pH for growth. This model provided a satisfactory description of the set of parameters obtained with equations (1) and (2) regarding the incubation pH. Thus, this model, developed to describe the evolution of a high number of cells observed by flow cytometry, is suitable to describe the evolution of spore and cell populations at single cell level. The estimated values of the optimal time of first germination ($t_0$ germ), the time taken to reach 90% germination ($t_{germ}$), the germination rate ($\tau_{germ}$), the time of first outgrowth ($t_{outgr}$), and the outgrowth rate ($\tau_{outgr}$) were consistent with the values estimated in Trunet et al. (2020). Only the time taken to reach 90% outgrowth ($t_{outgr}$) was significantly higher in this work (142.66 min CI 95% [135.05, 151.18]) compared to Trunet et al. (2020) (73.83 min CI 95% [64.47, 83.20]) (Table 2).

# 4. Discussion

## 4.1. Correlation between germination and outgrowth times

Spore germination is triggered by the binding of germinant molecules, such as amino acids, to specific germinant receptors that differ between bacterial strains (Artzi et al., 2021; Warda et al., 2017). Moreover, germination commitment, linked to the binding of the germinant to a receptor (Zhang et al., 2014), seems to be pH-dependent (Broussolle et al., 2008; Wang et al., 2015; Yi and Setlow, 2010) or modulated by pre-treatment such as heat treatment or high pressure treatment (Delbrück et al., 2022; Trunet et al., 2015; Wang et al., 2011a;2011a). This modulation of germination depends on the strain as well as the nature of the germinant molecule. The extended time of germination and outgrowth may be explained by the pH-dependent binding of germinants to their germinant receptors. The results obtained in the present work suggest that the pH of the spore production environment also has an impact on this heterogeneity. Indeed, spores produced at low pH showed a significantly higher correlation coefficient between germination time and outgrowth time starting at incubation pH 6.40 (only at pH 5.50 for spores produced in optimal conditions) compared to spores incubated at pH 7.40. This result showed a more heterogeneous spore population when sporulation was performed at suboptimal pH (Table 1). However, germination time and outgrowth were not significantly impacted by the pH of sporulation (data not shown; supplemental data), which is consistent with previous work (Trunet et al., 2020). Thus, even if the spores produced in different pH conditions seemed to be characterized by the same germination and outgrowth times as spores produced in optimal conditions, the spore population formed at low pH is significantly more heterogeneous. This heterogeneity, due to an additional stress corresponding to low sporulation pH, may be linked to a “less efficient” structuration of the germinosome as the germinosome assembly is key in the germination ability of bacterial spores, notably for *B. cereus* (Wang et al., 2021) or linked to a lower level of germinant receptors as observed in *B. subtilis* (Ghosh et al., 2012, p. 201) as GerA proteins were previously less

<footnote>*p < 0.01.</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 1. Germination (a) and outgrowth (b) curves of *Bacillus mycoides* KBAB4 spores at pH 7.40 (A) and pH 5.50 (B).</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 2. Effect of incubation pH on time of germination initiation ($t_0$ germ) (A), time taken to reach 90% germination ($t_{germ}$) (B), the proportion of germinated spores ($r_{germ}$) (C), the outgrowth initiation time ($t_0$ outgr) (D), the time taken to reach 90% outgrowth ($t_{outgr}$) (E) and the proportion of outgrowing cells ($r_{outgr}$) (F). The line correspond to the model described in the Material and Methods section.</figcaption></figure>
<caption>**Table 2**</caption>

Germination and outgrowth parameters for *Bacillus mycoides* KBAB4 spores based on microscopy observations and flow cytometry observation (Trunet et al. 2020).

<table><thead><tr><th rowspan="2"></th><th colspan="3">Estimated values in this study Based on microscopy observations</th><th colspan="3">Estimated values in Trunet et al., (2020) Based on flow cytometry observations</th></tr><tr><th>optimal value (95% CI) (min)</th><th>pH<sub>min</sub> (95% CI)</th><th>pH<sub>opt</sub> (95% CI)</th><th>optimal value (95% CI)</th><th>pH<sub>min</sub> (95% CI)</th><th>pH<sub>opt</sub> (95% CI)</th></tr></thead><tbody><tr><td>t<sub>0 germ</sub></td><td>4.42 (3.63, 5.64)</td><td>4.63 (4.17, 5.10)</td><td>6.69 (6.46, 6.92)</td><td></td><td></td><td></td></tr><tr><td>t<sub>germ</sub></td><td>8.53 (7.88, 9.29)</td><td>3.53 (2.96, 4.10)</td><td>7.11 (6.76, 7.47)</td><td>6.67 (5.66, 7.74)</td><td>4.79 (4.53, 5.07)</td><td>6.78 (6.39, 7.17)</td></tr><tr><td>τ<sub>germ</sub></td><td>0.87 (0.75, 0.99)</td><td>4.95 (2.09, 7.81)</td><td>7.02 (5.99, 8.06)</td><td>0.96 (0.93, 0.99)</td><td>4.91 (4.82, 5.01)</td><td>7.98 (6.81, 8.96)</td></tr><tr><td>t<sub>0 outgr</sub></td><td>103.58 (113.56, 95.21)</td><td>-7.89 (-12.07, -3.71)</td><td>7.06 (6.57, 7.56)</td><td></td><td></td><td></td></tr><tr><td>t<sub>outgr</sub></td><td>142.66 (135.05, 151.18)</td><td>-14.09 (-21.43, -6.74)</td><td>7.20 (6.64, 7.77)</td><td>73.83 (64.47, 83.20)</td><td>4.76 (4.57, 4.95)</td><td>6.50 (6.36, 6.66)</td></tr><tr><td>τ<sub>outgr</sub></td><td>0.83 (0.74, 0.92)</td><td>5.05 (4.95, 5.14)</td><td>7.06 (6.27, 7.86)</td><td>0.88 (0.83, 0.94)</td><td>4.95 (4.89, 5.00)</td><td>6.82 (6.54, 7.09)</td></tr></tbody></table>

detected in *Bacillus mycoides* KBAB4 spores produced at pH 5.50 (Trunet et al. 2020). The same study showed a lower detection of the SpoVA protein family in spores produced at pH 5.50, which can also help to explain this heterogeneous population of spores.

## 4.2. Modelling spore germination and outgrowth using single cell data

The set of parameters describing germination and outgrowth, namely the time of first germination ($t_0$), the time taken to reach 90% germination ($t_{germ}$), the germination rate ($\tau_{germ}$), the time of first outgrowth ($t_0$), the time taken to reach 90% outgrowth ($t_{outgr}$) and the outgrowth rate ($\tau_{outgr}$), was estimated using the mathematical model, based on population observations, developed by Trunet et al. (2020). The data processing and the whole set of estimated parameters is presented in supplemental data (Supplemental data 1). This set of parameters was set according to the methodology used to obtain the data and the model used to fit the germination and outgrowth curves. For example, using this model, which is based on the Weibull distribution, to fit log-transformed data involves estimating 90% germination or outgrowth ratios. However, other parameters can be used, such as the time between the mixing of the dormant spores with the germinant and the initiation of CaDPA release, the time taken for CaDPA to be released, the time taken for the cortex to be lysed or the time taken for the spore to emerge from the spore coat. These different parameters can be defined from data acquired by different methods such as phase contrast microscopy, differential interference contrast microscopy or Raman spectrometry (He et al., 2018a, 2018b; Kong et al., 2010, 2014; Wang et al., 2011). Thus, the choice of parameters used to describe and quantify sprouting and outgrowth is highly dependent on the nature of the data acquired (images, OD600, flow cytometry data, Raman spectra, etc.) and the use of the output data (qualitative data, quantitative data, modelling).

The results obtained in this study showed that the impact of pH on germination and outgrowth of *Bacillus mycoides* KBAB4 was consistent with previous work, based on population observations, thus extended to and confirmed at single cell level. In addition, it has been showed that environmental stresses, such as temperature, pH or heat treatment, could affect time of germination at single cell level (Kong et al., 2014; Wang et al., 2011; Zhang et al., 2010).

In addition, the estimated minimum and optimal pH of germination and outgrowth, were not significantly different between the results obtained in the present work and the results obtained by Trunet et al. (2020). Again, particular attention must be paid to the minimum pH of germination and outgrowth. Particularly, the minimum pH estimation based on outgrowth parameters was challenging as the probability of the observation of rare events near the growth limit is low.

## 5. Conclusion

In conclusion, the present work showed that the correlation between germination time and outgrowth time was impacted by low pH, highlighting a high heterogeneity of spore populations, observable when they are under low-pH stress. This observation is of high interest as it is now clear that unfavorable incubation conditions allow discrimination

between early germinating and late germinating spores, which is not possible in optimal conditions for the same spore sample, in the conditions tested.

This work supported and extended the possibility of modelling spore germination and outgrowth from population to single cell level and demonstrated that the Trunet et al., 2020 model is suitable to describe the evolution of spore and cell populations at single cell level. Indeed, the state of individual spores can be analyzed and linked to the outgrowth mechanisms (Wang et al., 2022; Zhang et al., 2009). This work showed that the impact of pH on spore germination and outgrowth at single cell level is consistent with observations at sub-population level and population level. However, microscopy-based observation did not allow the quantification of the impact of incubation pH near the germination limits, as rare event observation is challenging due to the number of experiments needed to assess the variability within the cell population. High-throughput methods, such as flow cytometry, and single cell level analysis methods, such as microscopy, are complementary and allow a better comprehension of spore behavior in adverse conditions. While flowcytometry is superior at a coarse grain level in capturing germination and outgrowth of large spore quantities, live-imaging is especially suited to address at single spore level, in a detailed time-resolved manner, which sequential germination and outgrowth stages are most affected by the adverse conditions tested. In addition, it allows for the interrogation of these processes at the molecular mechanistic level for instance making use of fluorescent reporter proteins (Wen et al., 2022).

### CRediT authorship contribution statement

C. Trunet: Writing – review & editing, Writing – original draft, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. N. Vischer: Software, Methodology, Investigation, Formal analysis. L. Coroller: Writing – review & editing, Validation, Supervision, Methodology, Formal analysis, Conceptualization. S. Brul: Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Clement Trunet reports financial support was provided by Erasmus Plus. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

This work was supported by the European Commission under the Erasmus+ program.

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
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