PhotoFiTT: a quantitative framework for assessing phototoxicity in live-cell microscopy experiments

Domain: biology
Published: 2025-12-13
Editorial outcome: major_revision

Abstract
Phototoxicity in live-cell fluorescence microscopy can compromise experimental outcomes, yet quantitative methods to assess its impact remain limited. Here, we present PhotoFiTT (Phototoxicity Fitness Time Trial), an integrated framework combining a standardised experimental protocol with advanced image analysis to quantify light-induced cellular stress in label-free settings. PhotoFiTT leverages machine learning and cell cycle dynamics to analyse mitotic timing, cell size changes, and overall cellular activity in response to controlled light exposure. Using adherent mammalian cells, we demonstrate PhotoFiTT’s ability to detect wavelength- and dose-dependent effects, showcasing that near-UV light induces significant mitotic delays at doses as low as 0.6 J/cm2, while longer wavelengths require higher doses for comparable deleterious effects. PhotoFiTT enables researchers to establish quantitative benchmarks for acceptable levels of photodamage, facilitating the optimisation of imaging protocols that balance image quality with sample health.

Introduction
Live-cell fluorescence microscopy has revolutionised our ability to study dynamic biological processes in near-native conditions1. However, the intense illumination required can induce phototoxicity, disrupting cellular functions2–5. These effects, often subtle and cumulative, can lead to significant changes in cell behaviour, organelle integrity, and developmental processes6–10. Yet, establishing optimal imaging protocols that balance high-quality data acquisition with minimal biological interference remains a complex challenge11. Traditional methods for assessing phototoxicity include viability assays and morphological observations2,3. Historically, photobleaching has been used as a proxy reporter of phototoxicity2,5,12, but with the advent of highly photostable fluorescent proteins like mStayGold13, this approach has become less reliable. Importantly, specimen type, developmental stage, and experimental conditions influence phototoxicity tolerance, complicating the development of replicable imaging protocols14,15. Previous studies have offered valuable quantitative assessments of phototoxicity3,10,12,14,16–19, including the use of advanced deep-learning algorithms as classifiers20 and mitosis analysis21. Yet, a critical need remains for standardised and generalisable methods able to quantitatively link cell damage to high-irradiance light exposure across different fluorescence microscopy techniques. Addressing this gap, we introduce PhotoFiTT (Phototoxicity Fitness Time Trial), a quantitative imaging-based framework designed to assess phototoxicity effects on cellular behaviour in live-cell microscopy experiments (Fig. 1) for adherent cell lines in traditional coverslip-based setups. Here, we show how PhotoFiTT provides a rigorous, label-free, and quantitative approach to evaluate phototoxicity, enhancing the reliability and reproducibility of live-cell imaging studies. It is designed as an integrated framework comprising a standardised experimental protocol and an advanced image analysis pipeline. PhotoFiTT leverages the predictable nature of cell division22 as a 'biological clock' to quantify phototoxicity-induced perturbations (Fig. 1). The framework uses low-irradiance illumination brightfield microscopy to monitor cell populations that have been exposed to controlled light damage events. It employs deep-learning algorithms, including virtual staining and cell segmentation, to aid in tracking abnormal light-induced cellular behaviour. PhotoFiTT employs three key measurements: (1) Mitosis monitoring: assess delays in cell division by mapping cell rounding in time (Fig. 1a); (2) Cell size dynamics: track changes in cell size to discriminate between cell division and cell cycle arrest (Fig. 1b); (3) Cellular activity: quantify overall cellular changes over time as a measure of overall cellular health (Fig. 1c). Cellular activity reflects general dynamic changes in cellular shape across time. By analysing these factors, PhotoFiTT enables researchers to extract numerical constraints for optimising live-cell-compatible illumination conditions.

Results
In this study, we applied the PhotoFiTT concept using synchronised cell populations in a simplified framework. This approach allowed us to examine multiple mitotic events under highly controlled conditions. We also measured unsynchronised populations as controls to identify any anomalies that might arise from the synchronisation process and its possible influence on the phototoxicity sensitivity of the examined cell populations23. This implementation (Fig. 2a), followed these key steps: (1) Synchronise cells using the CDK1 inhibitor RO-330624, which arrests cells in the G2/M interface, or study unsynchronised cells that can be tracked from cell rounding until division; (2) Expose cells to an illumination event replicating the pattern of the imaging experiment to be analysed for phototoxicity; (3) Identify undergoing cell rounding and division to separate mitotic rounding from stress-induced cell rounding (Fig. 2b) using a Stardist25 deep-learning model purposely-trained for brightfield round cell detection; (4) Analyse three key parameters: mitotic timing, cell diameter dynamics, and cellular activity. Supplementary Movies 1 and 2 provide in-detail tutorials to reproduce these procedures. We conducted a comprehensive analysis of phototoxicity primarily using Chinese Hamster Ovary (CHO) cells as a model system and further tested on HeLa cells. CHO cells have the benefit of having a short doubling time (14–17 h), allowing for efficient experimental timelines while being a highly popular cell line for microscopy and cell research. Both synchronised and unsynchronised cell populations were exposed to varying doses of near-UV (385 nm), blue (475 nm) and red (630 nm) light to assess wavelength-dependent effects. In synchronised cells and in agreement with established literature18,26, near-UV light dose induced significant dose-dependent delays in mitotic timing, with effects detectable at doses as low as 0.6 J/cm2 (Fig. 2c, d and Supplementary Movie 3). Notably, at 60 J/cm2, the characteristic peak in mitotic rounding became almost imperceptible, indicating widespread cell cycle arrest due to stress-induced cell rounding. These light doses are comparable to standard widefield microscopy imaging (Table S1). To validate that these effects were primarily due to light exposure rather than the potential light sensitivity of the CDK1 inhibitor used for synchronisation, we conducted parallel experiments with unsynchronised cells and manually tracked cell rounding and mitosis. In unsynchronised cell populations exposed to near-UV light, we observed a dose-dependent decrease in dividing cells (an 80% decrease in mitotic events on light doses of 60 J/cm2) and a simultaneous increase in arrested cells (a 30% increase of arrested cells on light doses of 60 J/cm2) (Fig. S1a). Consistent with our findings in synchronised populations, the time required for cells to divide after entering mitotic rounding increased with higher light doses from an expected 30 min division time to over 55 min in the case of 60 J/cm2 (Fig. S1b). These results confirm that the observed effects were primarily attributable to light exposure rather than light sensitivity increase by the synchronisation drug. Notably, synchronised populations exhibited higher sensitivity to near-UV-induced damage compared to their unsynchronised counterparts (2). This increased susceptibility was evidenced by a higher proportion of apoptotic cells, as identified by a SYTOX-based cell viability assay (Fig. S1c), where increasing the excitation light dose led to an increase of up to five fold in the number of dead cells. This observation can be attributed to the fact that synchronised cells were uniformly arrested at the G2/M checkpoint, a critical stage immediately following DNA replication and preceding mitosis. Near-UV radiation is known to induce DNA damage14, and cells in G2 phase are particularly vulnerable due to the presence of fully replicated chromosomes and the imminent onset of mitosis. The accumulation of DNA damage at this stage can trigger cell cycle arrest or apoptosis more readily than in unsynchronised populations, where cells are distributed across various cell cycle phases27. Additionally, as a control, we manually tracked cell rounding and mitosis in synchronised CHO cell populations to compare against our automated detection pipeline. The results followed the same trend of a dose-dependent effect of 375 nm excitation light dose. Higher doses led to significant increases in the number of arrested cells (a 300% increase at 60 J/cm2 compared to the control), delays in the mitotic process (from an expected 20-min division to up to 50-min division times at 60 J/cm2), changes in division patterns (overall mitotic peak shifts), and a general worsening of mitotic timing (Fig. S2a–d). These observations based on manual annotation supported the results of our automated analysis, further confirming the PhotoFiTT approach. While near-UV light dose demonstrated the most pronounced effects, longer wavelengths also induced cellular stress at higher doses. Cells exposed to 475 and 630 nm excitation light began showing comparable delays to those observed with near-UV light (i.e. mitotic rounding time points above 60 min) only at the highest tested dose of 60 J/cm2 while in 385 nm light excitation doses of 0.6 J/cm2 was enough to cause an effect (Fig. 2d). This finding highlights a clear wavelength dependence in phototoxicity, with longer wavelengths requiring substantially higher doses to induce comparable delays, supporting observations in previously published studies19,28. PhotoFiTT’s analysis of cell size dynamics revealed that near-UV exposure delayed the appearance of daughter cells in a dose-dependent manner (Fig. 2e). Under control conditions, mother cells (∼20 µm diameter) divide into daughter cells (∼15 µm diameter) within 50−75 min post-synchronisation. Near-UV light exposure induced dose-dependent delays in this process, with high doses (60 J/cm2) delaying daughter cell appearance by up to 120 min after synchronisation. Such delay in division and the persistence of cells in the rounding state over time indicates the increase in the number of cells arrested due to photodamage and explains the lost peak in the distribution of cell rounding with high doses (60 J/cm2) in Fig. 2c. Classifying mother and daughter cells according to their size lets PhotoFiTT evaluate cell division delays (Fig. 2f). When challenged with longer wavelengths (475 and 630 nm) and in agreement with our previous observations (Fig. 2d), cell division delays are also wavelength-dependant. Compared to shorter wavelengths, longer wavelengths need greater light doses to produce significant effects, such as a 20-min delay in the onset of cell division (Fig. 2f). Beyond acute effects on cell division, PhotoFiTT can also quantify post-mitotic cellular activity, providing insight into long-term impact on cellular behaviour. We observe that higher light doses and shorter wavelengths led to decreased cumulative cellular activity over a 7 h period (calculated as the cumulative activity in time) (Fig. 2g). While each of these metrics has its own benefits, tracking the 1-to-2 transition of a mother cell to two resolvable daughter cells, as done with manual annotations, provides a highly efficient and straightforward approach to assessing photodamage effects. This transition can be easily monitored by classifying cell rounding events pre- and post-division within a short time window (15−30 min). In control, synchronised populations, ~50% of cells transition from resolvable mother-to-daughter cells within the first 50 minutes post-synchronisation. At a light dose of 6 J/cm2, the proportion of cells completing the mother-to-daughter transition within this time-frame is reduced to around 40% for 630 nm illumination, 30% for 475 nm, and only 10% for 405 nm (Fig. 2e, f). Testing PhotoFiTT on HeLa cells yielded similar results. Using a dose of 0.6 J/cm2 did not produce discernible changes, but when challenged with higher doses of 6 and 60 J/cm2, HeLa cells exhibited the same dose-dependent delay found in CHO cells. Specifically, division times increased from ~50 to 90 min, and there was a decrease in activity of up to 30% with higher excitation light doses. These findings suggest that HeLa cells might be more resilient to light damage than CHO cells (Fig. S3a–d and Supplementary Movie 4).

To evaluate PhotoFiTT’s applicability across varied illumination strategies, we subsequently integrated the workflow with laser point-scanning confocal microscopy. This technique contrasts with widefield methods by scanning a focused laser beam point-by-point, thereby limiting illumination to a small volume at each instant. The duration of illumination per point, termed the dwell time, directly influences the localised light dose and photon collection efficiency. Our objective was to determine whether distinct illumination patterns, specifically variations in dwell time and irradiance while maintaining an equivalent total light dose, elicit differential cellular responses (Fig. S4 and Supplementary Movie 5). We implemented two distinct conditions featuring the same total light dose: (1) a high-power, rapid dwell time method (fast scan), where each point is illuminated intensely for a brief period; and (2) a low-power, extended dwell time method (slow scan), where each point is illuminated less intensely but for a longer time. With this experimental design, we tested 0.6 and 6 J/cm2 light doses. Cells exposed to 0.6 J/cm2 presented almost no changes to their physiology with either scanning approach (Fig. S4 Fast and Slow scan at 0.6 J/cm2 a–c). However, increasing the dose to 6 J/cm2 yielded an increase in cell division delays from 65 to 80 min when using the slow scan, suggesting that longer excitation light exposure is more damaging than shorter exposures when normalising for total light doses (Fig. S4 Fast and Slow scan at 6 J/cm2 a–c). This outcome creates an opportunity to minimise phototoxicity by fine-tuning the balance between scan speed and illumination irradiance.

Lastly, we demonstrate the applicability of the method in more realistic scenarios, accounting for multiple light exposures and the presence of excitable fluorophores. We applied the same PhotoFiTT workflow using MitoTracker dyes in a continuous fluorescence live-cell acquisition (Fig. 3 and Supplementary Movie 6). MitoTracker dyes are cell-permeant fluorescent probes that selectively accumulate in active mitochondria of live cells due to their cationic charge, which is attracted to the negative mitochondrial membrane potential29. The imaging workflow used RO-3306 to synchronise cell populations while adding MitoTracker Green FM and MitoTracker Red CMXRos in two different populations. MitoTracker Green FM (excitation/emission: 490/516 nm) and MitoTracker Red CMXRos (excitation/emission: 579/599 nm) were selected to evaluate wavelength-dependent phototoxic effects. These specimens were exposed to 475 nm light every 4 min during a 20-min acquisition period (150 ms per exposure event), resulting in a combined total exposure of 0.6 J/cm². This was followed by media exchange and release from the CDK1 inhibitor RO-3306, allowing cells to enter mitosis. The subsequent mitotic events were captured using only transmitted light (e.g. brightfield) to avoid further phototoxicity effects (Fig. 3a). By illustrating the timing of mitotic cell rounding in synchronised populations, Fig. 3b, c reveal a slight delay when the green fluorescent probes are excited at 475 nm wavelength. Figure 3b quantifies these delays across various MitoTracker probes, showing that the control population peaks at around 50 min. Notably, exposure to 475 nm light induced a modest delay of 15 min when exciting MitoTracker Green FM when compared to the non-exposed control. Figure 3c presents a temporal analysis of mother and daughter cell populations across different 475 nm light doses, whilst Fig. 3d depicts the cumulative cell activity over 7 h post-exposure, normalised for each replicate. The synchronised population exhibited physiological behaviour with alterations in the activity arising from using MitoTracker CMXRed and Green FM. Considering the nature of MitoTracker dyes and their binding to mitochondria, a small physiological change is expected29. Unlike conventional mitochondrial stains that washout upon loss of membrane potential, MitoTracker dyes contain a mildly thiol-reactive chloromethyl moiety that forms covalent bonds with mitochondrial proteins, potentially affecting mitochondrial function. When assessed manually and in the absence of RO-3306, MitoTracker Green FM-stained cells also presented a modest delay in their time of division and an increased number of arrested cells (Fig. S2e, f). These results collectively underscore the sensitivity of PhotoFiTT in detecting subtle phototoxic effects during fluorescence microscopy, even when using common organelle-specific probes. Taken together, our results uncover the nuanced and continuous nature of phototoxicity. They reveal a spectrum of cellular responses that vary with both light dose and wavelength, underscoring the importance of considering them in experimental design (Fig. S5 and practical applications Note S3).

Methods
Cell lines and culture
CHO (ATCC CCL-61) and HeLa (ATCC CCL-2) cells were cultured in DMEM (Gibco) supplemented with either 42 µM gentamicin (Gibco) or 1% penicillin/streptomycin (Gibco) and 10% foetal bovine serum (FBS; Gibco). All cells were grown at 37 °C in a 5% CO2 humidified incubator.

Cells synchronisation
Cells were seeded on an eight well-chambered cover glass (Cellvis) with a total of 6 × 104 cells per well and incubated with 10 µM RO-3306 (Sigma-Aldrich) to inhibit CDK1 activity for 16–18 h. Unsynchronised cells were seeded using the same protocol and allowed to grow for the same duration.

MitoTracker staining
Synchronised cells were incubated with media containing 10 µM RO-3306 (Sigma-Aldrich) and either 200 nM MitoTracker Green FM (ThermoFisher, M7514) or MitoTracker Red CMXRos (ThermoFisher, M7512) for 30 min at 37 °C.

Excitation light dose and live-cell imaging
Experiments were performed at the AxioObserver 7 (Zeiss), equipped with a Colibri 7 LED Illumination (Zeiss), a Quad Beam Splitter 405+493+575+653 (Zeiss), a 20x/0.8 Plan-Apochromat objective (Zeiss) and a Prime 95B sCMOS camera (Teledyne Photometrics). The temperature and CO2 levels were maintained at 37 °C and 5% respectively, throughout the excitation light exposure and imaging periods.
Irradiance at the sample level was measured at the beginning of every experiment, using a PM100D power metre with an S170C power sensor (Thorlabs). The measurements for each wavelength line were the following: 84.9 ± 2.8 mW for the 385 nm line, 85.2 ± 0.8 mW for the 475 nm, and 70.6 ± 0.4 mW for 630 nm (mean ± standard deviation). To convert the light energy into irradiance, we considered the field of view (FOV) size of 0.014 cm2.
Cells were then exposed to the excitation light according to experimental conditions. Ten FOVs were selected per exposure condition, ensuring that exposed areas would not overlap. The exposure time was set for different field of view in the same well according to the experimental conditions (100 ms corresponding to 0.6 J/cm2, 1 s to 6 J/cm2 and 10 s to 60 J/cm2). Immediately after light exposure and before imaging, cells were washed two times with PBS and RO-3306 containing media was replaced by phenol-red free fluorobrite DMEM (Gibco) supplemented with 2 mM GlutaMAX (Gibco), 42 µM gentamicin (Gibco) and 10% FBS (Gibco). The media exchange was done with a custom-made 4-syringe holder designed in TinkerCAD (https://www.tinkercad.com/) and 3D printed using a Prusa MK4 3D printer (.stl file in supplementary Information). Brightfield images were acquired every 4 min for 8 h and have a pixel size of 0.55 µm × 0.55 µm.
Cells stained with Mitotracker instead underwent six sequential exposures (4-min intervals) of 475 nm light at 0.6 J/cm² total light dose, while monitoring mitochondrial dynamics (using BP 514/30 filter). Post-illumination, cells were washed thrice with prewarmed PBS before brightfield time-lapse acquisition every 5 min for 7 h.

Cell fixation and Hoechst nuclei staining to generate fluorescent and brightfield paired images for training
Cells grown on an eight-well-chambered cover glass (Cellvis) until 60% confluency were fixed with a 4% paraformaldehyde solution (Electron Microscopy Sciences) for 10 min and washed three times with PBS. Subsequently, the samples were incubated with a 0.1 µg/ml Hoechst 33342 solution for 10 min in the dark. Cells were imaged using the same microscope setup described in live-cell imaging. Pairs of brightfield and Hoechst-stained images (the last acquired with a BP 467/24 filter in place) were captured.

Confocal microscopy experiments
Confocal microscopy experiments were conducted using a Zeiss LSM 880 Airyscan system equipped with a 405 nm diode laser and calibrated for precise light dosing. The system’s maximum output of 3.4 mW was verified using a PM100D power metre with an S170C power sensor (Thorlabs). Two distinct illumination regimes were implemented to deliver equivalent total light doses of 0.6 or 6 J/cm² through compensatory power-time relationships: the high-power (HP) condition uses short 3.528 µs dwell times at 0.3 mW (8.8% max) for 0.6 J/cm² or 3.0 mW (88.2% max) for 6 J/cm², operating at 94% of the system’s maximum permissible irradiance. Conversely, the low-power (LP) regime employed extended 10.584 µs dwell times (3× longer than HP) at reduced intensities of 0.1 mW (2.9% max) or 1.0 mW (29.4% max) to simulate prolonged imaging scenarios.
Key spatial parameters included a pixel dimension of 0.42 µm (0.1764 µm² area = 1.764 × 10⁻⁹ cm²), with energy per pixel calculated as the product of power (W) and dwell time (s). Both regimes maintained identical spatial sampling while achieving dose equivalence through inverse power-time compensation, enabling direct comparison of irradiance-dependent versus duration-dependent phototoxic effects. The experimental design specifically contrasted rapid high-irradiance exposure against sustained low-irradiance illumination to dissect temporal components of light-induced cellular stress.

Live-cell viability
Cell viability was performed by adding 0.1 µM SYTOX Orange (Thermo) to previously seeded cells and taken to the microscope for live-cell imaging for 12 hours. Live-cell imaging was performed on the Nikon Eclipse Ti2 microscope equipped with a CoolLED pe800 illumination (Nikon), a 20x/0.8 Plan-Apochromat objective (Nikon) and an Orca-fusion BT camera (Hamamatsu). Ten fields of view (FOV) were randomly selected per exposure condition, and the images were acquired every 4 min using the brightfield and TRITC channels in a multichannel acquisition. The temperature and CO2 levels were maintained at 37 °C and 5%.

General image processing workflow
As depicted in Fig. 2, the image analysis to extract the measurements of interest is composed of: (1) detecting the number of cells in the field of view; (2) detecting cell rounding; (3) estimating the general changes in cell activity. These three steps enable extracting measurements for the numerical quantification of photodamage, such as cell size distribution across time.

Estimation of the number of cells in the field of view using virtual cell nuclei staining and segmentation
The total number of cells in the field of view was computed by first, inferring the cell nuclei from brightfield images using an in-house trained Pix2Pix model34, and then, segmenting each nucleus with a pre-trained StarDist model (StarDist-versatile) provided by the developers as '2D versatile fluo' model and trained on a subset from the Data Science Bowl Kaggle Challenge35. Using Zero CostDL4Mic36, Pix2Pix was trained with paired images of fixed cells stained with Hoechst and imaged with brightfield and widefield fluorescence microscopy. The imaged data were divided into 400 and 132 fields of view for training and testing, respectively. All the input images were pre-processed as follows: a bleach correction to remove the illumination artefacts was computed by applying a large low-pass filter (a wide Gaussian filter) and subtracting it from the original image; then, the image intensity values were normalised with the min-max projection to the [0, 1] range. Both pre-processed input and output images were then normalised using a percentile normalisation of 1 and 99.9%, respectively (denoted as Contrast enhancement in the ZeroCostDL4Mic Pix2Pix notebook). During Pix2Pix training, images were also reshaped to have a size of 1024 × 1024 pixels, so the images were reshaped to have a pixel size of 0.644 µm × 0.644 µm. Pix2Pix was trained from scratch with a patch size of 512 × 512 pixels, a batch size of 5 and for 2000 epochs with a learning rate of 0.001 and 1000 more epochs with a linear learning rate decay. The accuracy results for the test dataset are as follows: Structural similarity index measure (SSIM) (0.86), learned perceptual image patch similarity (LPIPS) (0.10), peak signal-to-noise ratio (PSNR) (25.17), normalised root mean squared error (NRMSE) (0.13). The output PDF reports of ZeroCostDL4Mic for both the model training and the quality control check are included in the Supplementary Information. The Pix2Pix model instance corresponding to the last epoch was chosen to process the first frame of each cell synchronisation video. The output of Pix2Pix was then segmented using the pre-trained StarDist model. The total number of individually segmented nuclei was used as the number of cells in the field of view, with an average of 126 cells per FOV.

Cell mitosis detection with StarDist
A 2D StarDist25 model -StarDist-CHO- was trained using Zero CostDL4Mic36 notebooks. From all the videos acquired for the study, 105 were chosen for the creation of the ground truth. For each video, a random time-point was selected and all the rounded cells (either before or after division) were manually annotated with a unique label (i.e. instance segmentation). The data was split into 85 and 20 videos for training and testing, respectively. The model was trained on a Tesla T4 GPU for 500 epochs with patches of 525 × 512 pixels, a batch size of 20, a grid size of 2 × 2 for the first convolutional layer of the model, the Mean Squared Error (MSE) loss function and an initial learning rate of 5 × 10−3. Data augmentation composed of rotations, translations and mirroring was also applied. Before the training, all the images were downsampled to a pixel size of 0.865 µm × 0.865 µm (i.e. downsampling of a factor of 1.5709), so the size of a cell during rounding in pixels is smaller than the input size used to train StarDist. This resampling factor was chosen to ensure that the receptive field of the U-Net in StarDist covers a wide enough region to identify the cell contours characteristic of the rounded cell. The accuracy results in the test set were as follows: intersection over union (IoU) (0.530), false positive (6.05), true positive (17.05), false negative (4.6), precision (0.6712), recall (0.790), accuracy (0.555), f1 score (0.699), true detection (21.65), predicted detections (23.1), mean true score (0.712), mean matched score (0.901) and panoptic quality (0.628). The output PDF reports of ZeroCostDL4Mic for the model training and the quality control check are attached to the Supplementary Information.

Cell activity estimation
Cell activity was calculated as the difference between consecutive pre-processed frames. First, a normalisation of the brightfield microscopy videos was applied to ensure a uniform and comparable illumination intensity across the entire video: (1) the image intensity values of each frame were normalised with the min-max projection to the [0, 1] range, (2) a bleach correction to remove the illumination artefacts was computed by applying a large low-pass filter (a wide Gaussian filter) and subtracting it from the original image, (3) the image intensity values of the entire video are normalised again with the min-max projection to the [0, 1] range. Second, the contrast of the video is enhanced by applying a contrast-limited adaptive-histogram equalisation (CLAHE) using the Skimage Python package with a kernel of size 25, a clip limit of 0.01 and 256 bins. Third, we applied a Gaussian filter with σ = 1 to smooth all the frames in the video and alleviate the impact of the noise in the images, which could provide wrong readings and change the activity values. Finally, for each time point t in the video, the general cell activity was computed as the difference between pairs of temporal frames as follows:
activity(t) = mean ||eIm(t) − eIm(t − 1)||2
where eIm is a normalised, contrast-enhanced and smoothed image. Cumulative cell activity is calculated as the aggregation of activity (activity(t)) in time:
Cumulative activity(T) = Σ t=0 t=T activity(t)

Quantitative data analysis
The percentage of cells in the field of view identified by StarDist-CHO at each time point t (C(t)) provides a distribution of cell rounding events across time. C(t) is calculated by dividing the number of StarDist-CHO detections by the number of nuclei detected by StarDist-versatile. The peak of cell division is determined as
tp : C(tp) = max t ∈ T {C(t)}
with tp being estimated for each video, with has a total number T of time points. Cell size was monitored using the instance segmentations from StarDist-CHO (Fig. 2c, d).
The instance segmentation of StarDist-CHO allows for the estimation of the cell size (S) (i.e. the sum of all the pixels forming the cell mask), which is then used to estimate the cell diameter D by resolving the equation
S = πR2
where R is the cell radius and D = 2R. Tracking the distribution of D across time we distinguish two populations: mother and daughter cells with a diameter of 20 and 15 µm, respectively (Fig. 2b, e). This is achieved by classifying all the detections with a diameter larger than 18 µm as mother cells and daughter cells otherwise (Fig. 2f).
The total n number of this analysis is the following: for 385 nm is 50 FOVs across five replicas for unsynchronised cultures, 50 FOVs across five replicas for 0 J/cm2, 20 FOVs across two replicas for 0.6 J/cm2, 30 FOVs across three replicas for 6 J/cm2 and 30 FOVs across three replicas for 60 J/cm2). For 475 nm 37 FOVs across five replicas for unsynchronised cultures, 42 FOVs across five replicas for 0 J/cm2, 30 FOVs across three replicas for 0.6 J/cm2, 27 FOVs across four replicas for 6 J/cm2 and 14 FOVs across two replicas for 60 J/cm2). For 630 nm is 44 FOVs across five replicas for unsynchronised cultures, 46 FOVs across five replicas for 0 J/cm2, 12 FOVs across two replicas for 0.6 J/cm2, 44 FOVs across five replicas for 6 J/cm2 and 24 FOVs across three replicas for 60 J/cm2). Cell activity at each time point is hardly difficult to compare due to the stochasticity of cell movement at short time windows. Therefore, we calculate the cumulative cell activity after T minutes given as
Cumulative activity(T) = Σ t=0 t=T activity(t)
The resulting value at T = 420 min (7 h) is shown in (Fig. 2g). The total n number of this analysis is the following: for 385 nm is 50 FOVs across five replicas for 0 J/cm2, 20 FOVs across two replicas for 0.6 J/cm2, 29 FOVs across three replicas for 6 J/cm2 and 29 FOVs across three replicas for 60 J/cm2). For 475 nm is 36 FOVs across four replicas for 0 J/cm2, 21 FOVs across three replicas for 0.6 J/cm2, 18 FOVs across two replicas for 6 J/cm2 and four FOVs across one replica for 60 J/cm2). For 630 nm is 26 FOVs across three replicas for 0 J/cm2, seven FOVs across two replicas for 0.6 J/cm2, 22 FOVs across three replicas for 6 J/cm2 and nine FOVs across one replica for 60 J/cm2).

SYTOX signal quantification
To assess cellular apoptosis, we quantified the percentage of SYTOX-positive cells by dividing the number of cells expressing SYTOX by the total cell count in each field of view. The SYTOX signal was segmented using a two-step process: first, applying Otsu’s thresholding algorithm, followed by a Watershed transformation to delineate individual apoptotic cells. This approach enabled an accurate approximation of apoptotic cell numbers in each frame. The image processing workflow was implemented in Fiji and automated using a custom ImageJ macro for high-throughput analysis. To determine the total cell count, we employed the method described in the previous section, 'Estimation of the number of cells in the field of view using virtual cell nuclei staining and segmentation.' A total of ten FOVs and one replica for each condition were assessed.

Manual annotations of unsynchronised videos
A subset of unsynchronised cells was chosen to identify the delays in mitosis and cell arrest due to photodamage. Those cells entering mitosis were manually labelled by detecting the first time point in which they show the mitotic rounding until the resolvable emergence of two daughter cells. Cells that failed to produce two daughter cells by the end of the time lapse (8 h) but still attained cell rounding are considered to be cell cycle arrested. This annotation lets us distinguish between successful mitoses and cell arrest and calculate the time for cell division or arrest. A total of three FOVs per condition were annotated and tracked.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Del Rosario and colleagues present a framework that integrates a standardized experimental protocol with advanced imaging analysis to quantify light-induced cellular stress. This work is particularly timely, as optical microscopy has become one of the most important tools for observing biomolecular processes in living cells. However, it is crucial to understand when the processes under study are influenced or altered by the observation itself—specifically by the light used to capture the images. For this reason, I am supportive of its publication in Nature Communications, as the journal's broad readership aligns well with the relevance and significance of this report.\nThat said, before publication, I believe there are a few points that need to be clarified or described in greater detail:\n• The framework is presented as a method to quantify phototoxicity in fluorescence microscopy, but this aspect is not clearly emphasized in the text. If I understand correctly, the three main measurements are performed on brightfield images, while fluorescence measurements are only used for generating the training set for virtual staining. Although the method’s applicability to non-fluorescent cells is an advantage, it may cause some confusion. Specifically, is the \"Acquisition\" phase (Fig. 2a) conducted on fluorescently labeled cells? This does not necessarily imply that fluorescence images are used in the analysis itself, and clarifying this distinction would help improve understanding of the method.\n• I would expect the degree of cellular stress to differ in fluorescently labeled cells, as fluorophores absorb light, reducing cell transparency compared to non-labeled cells. The authors seem to acknowledge this, as they mention light absorption in the context of the CDK1 inhibitor. However, I recommend further clarification of how labeling affects light absorption and, consequently, cellular stress, and how this factor may influence experimental outcomes.\n• The cellular activity measurement is presented as a method to quantify sub-cellular changes. However, I believe this claim may be overstated. In essence, the measurement seems to reflect changes in cell shape rather than directly assessing alterations in the behavior of sub-cellular components, such as mitochondria or vesicles. I suggest refining this claim to more accurately describe what the measurement captures.\n• Can you better explain why measurements need to be performed on both synchronized and unsynchronized cells? Is this an integral part of the protocol, or are these just two examples? It would be helpful to clarify whether this dual approach is essential for the method or simply demonstrates its flexibility.\nOther minor issues include:\n• Could you indicate the size of the field of view (FOV) used to convert power into intensity?\n• There are small typos: \"uu\" instead of \"µm\" appears in multiple instances on page 6.", "reviewer_2": "In this manuscript, the authors present an application of pre-existing deep-learning based tools to assess the photodamaging effects of light illumination on live cells. Using non-damaging bright field illumination, the authors record the behaviour of illuminated cells over many hours, using software tools to segment and analyse cells. Three measurements are used to assess photodamage: delays in mitosis, identification of cell cycle arrest, and levels of cellular activity. This is applied to Chinese Hamster Ovary (CHO) cells, exposing them to UV, blue or red light. For UV illumination, delays in mitotic timing were observed with doses as low as 0.6 J / cm^2. For red illumination, only 60 J / cm^2 was sufficient to cause noticeable damage.\nThe manuscript is clear and well-written. The supplementary videos provide a nice overview of the work, but video 2 is too fast — it is impossible to follow what is being pasted into the terminal, etc.. Figures 1 and 2 provide a nice description of the assessment pipeline, but it would be nice to include images of 'bad' cells as well as 'happy' cells. The authors should be commended for making their pipeline available as well-documented open-source code. I have, for reasons of time, been unable to install and operate the code on my own computer, but given the authors' track record in developing software I am confident that the software would operate correctly if I had. However, if I have understood correctly, applying to the pipeline to cells different to the ones used by the authors (CHO), then the neural networks employed (developed by other researchers) must be retrained. This significantly restricts the utility of the tool.\nThe manuscript consists of two main parts: 1) the development of a software pipeline for analysing photodamage, 2) the application of this approach to studying photodamage in live cells. While I think the first part may have general utility for many researchers, the second part is not very thorough. I certainly would not draw any general conclusions from these results. This is important, as the presented results contradict many imaging studies where similar irradiances have been used without ill effects. Indeed, Wäldchen et al. (2015) have shown that cells can withstand even many kJ / cm^2 of red illumination, while the authors here suggest that just 60 J / cm^2 is enough to damage them.\nOne possible reason for this discrepancy is that the methods section states that cells were cultured in DMEM, and this was only switched for phenol-red-free Fluorobrite DMEM after photodamaging illumination. This is very different from the usual scenario in which cells would be illuminated in imaging medium (such as Fluorobrite DMEM). Phenol red absorbs strongly at all wavelengths used and has previously been reported to significantly increase photodamage from imaging illumination. Another possibility is that Wäldchen et al. used a range of U2OS, COS-7 and HeLa cells, while here the authors used CHO cells. If a general claim about photodamage is to be made, these experiments must be repeated with a different cell line in order to ensure that the photodamage is not a CHO-specific effect.\nThe authors report illumination powers, but it is not clear how these were measured. If a standard power meter sensor was used then this is likely to be an underreport, as these power meters are not sensitive to the high-angle rays present in microscopic illumination. Instead, a specialist power meter, such as the Thorlabs S170C, should be used (and if this was used this should be clearly stated in the text). Furthermore, it is not clear how these powers were converted to irradiances. Different powers were used for different wavelengths, but this apparently led to the same irradiance. This can only be true if the field of illumination varies by wavelength and it is not clear that this should be the case. Indeed, it appears (by backcalculation) that the authors calculated the irradiance by dividing the power by the full field size of the objective (pi * (12.5 mm / 20)^2). This assumes that there is no illumination outside of the manufacturer's claimed field of view, which is not true (as there is no real field stop in the objective). Instead, an external field stop of known size should be inserted into the illumination path.\nPerhaps most critically, the illumination timing used in the mock illumination is very different from that used in real timelapse experiments. In this work, the illumination is delivered in a single continuous dose at the beginning of the experiment. In a typical live-cell imaging experiment, the illumination would instead be delivered in little chunks throughout. Delivering a large dose up-front will create a much larger number of reactive oxygen species and other underdesirable photoproducts that may overwhelm a cell's protective mechanisms in a way that a more distributed photodose would not. I strongly suggest that the authors perform experiments using their analysis framework where cells have been illuminated in a more realistic manner. Only then, in combination with further experiments using multiple cells lines and different media, will the results allow general conclusions to be drawn.\nGiven the currently narrow applicability of the developed pipeline (i.e. only applicable to CHO cells), and the aforementioned issues with the experiments, I feel unable to recommend this manuscript for publication in Nature Communications. However, if the pipeline can be made more general and the experiments made more robust and generalisable, then I see that this could become a valuable tool for the community. Some minor comments are detailed below:\n== Minor comments ==\n* Given that RO-3306 is known to cause apoptosis after long incubation times, have the authors considered using a different means of cell cycle sychronisation, such as tapping a plate to dislodge only mitotic, rounded cells? This would provide a smaller number of cells for further analysis, but it seems that they are not analysing an entire cell culture plate anyway. Importantly, this would completely rule out the possibility that the effects observed were related to RO-3306.\n* Can the authors comment on how the use of point-scanning illumination, such as that employed in confocal microscopes, might alter the conclusions drawn from the epi-illumination used in this work?\n* No line numbers are included in the manuscript file. While not strictly necessary, these would make it easier for reviewers to refer directly to specific parts of the text.\n* What level of mitotic delay is enough? Is a 25 minute delay biologically significant for CHO cells? What about other cell types?\n* Related to the last point, there is no indication of statistical or biological significance between the different conditions tested, using any of the metrics employed.\n* The authors' results suggest that live-cell single molecule localisation microscopy (SMLM) is impossible without perturbation of the cell's health, given that a 60 J / cm^2 dose is sufficient at 630 nm, while irradiances of the order of hundreds of W / cm^2 are used. How can this view be married with the extensive literature showing successful live-cell SMLM? Have these imaged cells all been irreparably damaged without anyone noticing?", "reviewer_3": "The work introduces PhotoFiTT, a standardized workflow for quantitatively evaluating the phototoxic effects on adherent and dividing tissue culture cells. The workflow utilizes machine learning tools to enhance cell segmentation from brightfield transmission microscopy time series and extract three different morphological parameters and their changes over time, which indicate cell fitness, particularly the ability of timely cell division. The authors demonstrate the application of their approach on CHO cells that were exposed to three different light doses at three different wavelengths, showing the expected correlation in the quantitative evaluation parameters.\nOverall, the work is very well presented, and the tool is potentially useful to be adopted by other labs to optimise experimental conditions for live cell microscopy studies.\nHowever, I feel it currently falls short of demonstrating its validity in real-world scenarios, as I will detail below. Thus, before recommending this work for publication, I would like to see the following issues and queries addressed.\nMajor issues:\n1. The authors evaluate the phototoxic effects of 3 different light doses that deviate by a factor of 10, at three different wavelengths to validate their approach. It seems that the effects of UV-light and higher light doses on the cell population are obvious even without a sophisticated quantitative evaluation. Can the approach reliably detect and quantify more subtle effects? Are there limitations to doing this?\n2. The PhotoFiTT assay is per se label-free, which is beneficial as it should not contribute to phototoxicity. However, in most realistic live cell imaging scenarios, cells are fluorescently labeled and observed at high resolution over a certain observation period with a certain temporal resolution. Importantly, bleaching of fluorophores itself generates reactive oxygen species that contribute to phototoxicity. Therefore, it is essential to determine the phototoxic effects in the specific experimental conditions where labelling is applied and observed with the same objective. This allows for the comparison of phototoxic effects with the baseline or with different illumination regimes. Eventually, this will inform the researcher on the data amount, the level of resolution, and SNR quality needs can be afforded without inducing unwanted biological side effects that otherwise might affect the interpretation of the experimental outcome. Showcasing the application of PhotoFiTT to one such realistic example is needed to be more convincing about its broader value.\n3. Since the workflow essentially assesses effects on cell division rates, why not simply count the increase in cell number density in the 10 irradiated areas vs 10 non-irradiated areas before and, say, 24 hours after irradiation? Also, what is the benefit of assessing three different division-linked morphological parameters? Are there specific situations where this redundancy is beneficial?\n4. An obvious shortcoming of the PhotoFiTT assay (as is also mentioned by the authors), is its restriction to dividing cells, as all three metrics effectively measure mitotic or postmitotic behaviour. Could an activity measurement alone not also be adapted to work for non-dividing, differentiated cells like neurons?\n5. The authors mention the transferability of the assay to other widefield (?) imaging systems. Does this also apply to confocal systems? If so, this should be explicitly mentioned and ideally be demonstrated. For example, a low-hanging fruit might be to quantitatively assess the difference between delivering the same light dose by a widefield illumination (low power density, longer dwell time) or by the focused laser beam of a confocal point scanning system (high power density, short dwell time), which has been a long-standing point of discussion in the field.\nMinor issues:\nThe text mentions that cell nuclei are segmented, while the figures give the impression that entire cells are segmented. This needs clarification.\nWhich objective was used for the irradiation? Is it the same as for imaging?\nHow big were the FOVs accordingly? How many cells are typically irradiated and assessed in a single experiment?\nThe NA of the objective should affect the energy distribution within cells, with a high NA compressing more energy into a smaller focus z-range? Has this been accounted for?\nWhat is the energy (J/cm2) for the transmission observation compared to the excitation light irradiation? Has it been controlled that this does not influence the “baseline” physiology?\nDid the authors control for potential cytotoxic effects of RO-3306 independent of light exposure?\n(This may only apply to my downloaded file) Video S1 stops prematurely at 01:44 of the 2:19 total runtime.\nTypo on p6, 2nd column: “…the images were reshaped to have a pixel size of 0.644 μu x 0.644 μm.”", "reviewer_4": "In this paper, the authors present a label-free and quantitative approach to evaluate phototoxicity for live-cell imaging experiments. The aim is to enhance the reliability and reproducibility of such live-cell imaging studies.\nMore approaches to quantify phototoxicity are welcome - especially updated ones that exploit recent technical advances.\nThe strengths of the presented study lie chiefly in using a label-free (brightfield) approach, a large amount of data that can be (semi-) automatically processed for quantification, and the use of trainable open-source software. This has the potential to make it widely applicable (within the area of live-cell imaging, and depending on the uptake of its community). The authors use three parameters: Duration of mitosis (the ideal ‘canary in the gold mine’, Cole 2014), cell size and ‘cumulative activity’. Manuals, videos and curated code are a very useful addition to aid replication and adaptation by others.\nBelow are some suggestions to improve the manuscript, and some issues that need addressing.\nMajor comments:\n1) As presented here, we only see this method applied to one type of cells and one type of microscopy. Perhaps this could be expanded. For example, does the presented approach work for any optical contrast method (phase contrast (as used) as well as differential interference contrast (DIC))? Was this method tested on e.g. motile cell lines? This expansion is optional, but would prove wider applicability. Alternatively, conclusions and claims should be modified.\n“Addressing this gap, we introduce PhotoFiTT, a quantitative imaging-based framework designed to assess phototoxicity effects on cellular behaviour in live-cell microscopy experiments.”\n– for adherent cell lines in traditional slide-based setups.\n2) It would be useful to show how well the algorithm fares compared to a ground truth. The ground truth may likely require manually determined mitotic durations and segmentations. However, without any comparison, the question of accuracy remains unanswered.\n3) The authors' results nicely illustrate aspects of phototoxicity that have been previously published. The way it is currently written, it is not always clear whether the novelty of this submission lies in the methods used or in (largely unreferenced) observations. It would be good to more clearly promote the first aspect, while ensuring that stated observations align well with previously published studies.\nA few omitted references are used below as examples. While not exhaustive, and not all recent, these are important papers and good starting points for the reader/authors.\nMitotic delay / using time spent in mitosis as measure:\nCole R. 2014. Live-cell imaging. Cell Adhes Migr 8: 452–9.\nGorgidze LA, Oshemkova SA, Vorobjev IA. 1998. Blue light inhibits mitosis in tissue culture cells. Biosci Rep 18: 215–24.\nMora-Bermudez F, Ellenberg J. 2007. Measuring structural dynamics of chromosomes in living cells by fluorescence microscopy. Methods 41: 158–67.\nWavelength dependence of phototoxicity:\nDouthwright S, Sluder G. 2017. Live cell imaging: assessing the phototoxicity of 488nm and 546nm light and methods to alleviate it. J Cell Physiol 232: 2461–8.\nSchneckenburger H, Weber P, Wagner M, Schickinger S, et al. 2012. Light exposure and cell viability in fluorescence microscopy. J Microsc 245: 311–8.\nImage processing to classify phototoxicity from transmitted light images:\nRichmond D, Jost AP-T, Lambert T, Waters J, et al. 2017. DeadNet: identifying phototoxicity from label-free microscopy images of cells using deep ConvNets. arXiv 170106109.\nGrah JS, Harrington JA, Koh SB, Pike JA, Schreiner A, Burger M, Schönlieb CB, Reichelt S. Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy. Methods. 2017 Feb 15;115:91-99. PMID: 28189773.\nCell outline changes:\nKnoll SG, Ahmed WW, Saif TA. 2015. Contractile dynamics change before morphological cues during fluorescence illumination. Sci Rep 5: 18513.\nRe “critical need for standardised and generalisable methods able to quantitatively link cell damage to high-intensity light exposure across different fluorescence microscopy techniques.” Important additional publications:\nKiepas A, Voorand E, Mubaid F, Siegel PM, Brown CM. Optimizing live-cell fluorescence imaging conditions to minimize phototoxicity. J Cell Sci. 2020 Feb 21;133(4):jcs242834. PMID: 31988150.\nAlghamdi RA, Exposito-Rodriguez M, Mullineaux PM, Brooke GN, Laissue PP. Assessing Phototoxicity in a Mammalian Cell Line: How Low Levels of Blue Light Affect Motility in PC3 Cells. Front Cell Dev Biol. 2021 Dec 17;9:738786. PMID: 34977004.\n4) An important advantage of the proposed method may be its sensitivity. However, the authors are not clear on their method’s sensitivity, to the point of contradicting themselves:\np. 4, middle left:\n“A key strength of PhotoFiTT is its ability to reveal the non-binary nature of phototoxicity, demonstrating a continuum of effects that vary with light dose and wavelength in a label free manner.”\nVersus p.4 bottom left:\n“It is important to acknowledge limitations of PhotoFiTT. While highly sensitive to major cellular perturbations, it may not detect subtle light-induced events.”\nIt is important for this publication to have an unambiguous statement regarding its method’s sensitivity (or lack thereof) and evidence it.\nCellular morphology alone is not a clear indicator of safe illumination levels. The physiology of living specimens can be altered already at much lower illumination levels. This subtle phototoxicity, affecting healthy-looking specimens, can change the kinetics of cellular and developmental processes, leading to data misinterpretation. Dynamic processes have been shown to be sensitive indicators of phototoxicity (Kiepas 2020, Alghamdi 2021, Icha 2017) compared to morphology, and can indicate positive (hormetic) effects.\nWith this in mind, it might also be useful to revisit the statement “…integration of PhotoFiTT principles into real-time analysis pipelines to apply corrective measures to avoid cell death.”, as this goes back to the binary definition of phototoxicity.\n5) On p.1, the authors state: “Importantly, tolerance to photodamage varies across specimens and is influenced by damage severity, complicating the development of replicable imaging protocols (12, 13)”.\nLater, they write that “a critical need remains for standardised and generalisable methods able to quantitatively link cell damage to high-intensity light exposure across different fluorescence microscopy techniques.”\nIt might be worth expanding on this. A method to assess phototoxicity can only be standardised and generalised to a limited degree, since phototoxic effects always depend on the sample (and many other factors such as media, type and localisation of fluorescent proteins used etc.). See Waldchen et al. 2015 with ~6-fold difference of phototolerance between HeLa cells and U2OS (or COS-7) cells.\nFor the same reason, providing a hard limit (“For imaging domains where illuminations are well above the 60 J/cm2 threshold studied here, researchers should carefully consider and mitigate photodamage.”) is dangerously reductive and misleading. In phototoxicity, there is no 'one size fits all'. For a large variety of samples, photodamage may affect samples long before this threshold.\n_______________\nMinor comments:\nThe instructions are not sufficient in parts - e.g. measuring the power at the sample would benefit from more detail in the methods section and the video.\nWhat would also be very helpful: Guidelines on how many images need to be taken to ensure sufficient spatial and temporal resolution (at least in the case of the cell line used here), and how many cells analysed to reach robust conclusion(s).\n“Cumulative cellular activity” is not well defined. It needs to be clearly defined in the main text. How is it distinct from cell diameter dynamics, and what does it signify? Also, why ‘cumulative’? This is used earlier for phototoxic effects, but for cellular activity seems to be with respect to ‘change of area over time’. Also, is cell activity directional (i.e. only shrinkage taken into account)?\np. 1 bottom left: “Traditional methods for assessing phototoxicity include viability assays and morphological observations.”\nRequires some references.\nFig. 1 B:\nCells here look elliptical, but diameter calculation is for perfectly round cells. If an ellipse is fitted, indicate whether long or short axis is chosen (or result discarded).\n“Expose cells to a light irradiation event replicating the illumination pattern of the imaging experiment to be analysed for phototoxity;“ - phototoxicity\n“...pursposely-trained”: correct the first word.\n“Video S1-2 provide in-detail tutorials to reproduce these procedures.” Change to: “Videos S1 and S2 provide...”\np. 2 top left:\n“low-illumination brightfield microscopy” – meaning low-intensity?\nAlso, what is subcellular about Fig. 1 C? The outline demarcates the cellular border.\np 2. top right:\n“Notably, synchronised populations exhibited higher sensitivity to near-UV-induced damage compared to their unsynchronised counterparts.” Read e.g. Icha et al. 2017 for references on this.\n\"the persistence of mother cells upon time\" - unclear\n\"Upon challenged\" - unclear\n\"with longer wavelengths (475, and 630nm)\" - change to: 475nm and 630nm\n\"cell division delays are also wavelength-dependant, requiring higher light doses to induce noticeable effects\". Not clearly formulated - why comparative? Do the authors mean 'higher light doses at longer wavelengths'? Also, 'noticeable effects' - meaning delays? Clarify.\nSuppl. mat. pdf, p. 2:\n\"Recomentation\" - Recommendation\n\"For other cell lines: For other cell types or experimental conditions, manually annotate a representative image set and train a new StarDist model following the protocol outlined in Methods Section D.\" Which is Methods Section D? This one?\nUnclear.\nvideo 710560_sh8m2f: There's 30 seconds of a frozen frame at the end of the video.\nvideo 710566_sh8sb8: Too fast to follow - can it be slowed down please.\nvideo 710567_sh8sbm: nice illustration of effects; however, blue shading at bottom overpowers the plot line."}, "author_response": "We are profoundly grateful for your thorough evaluation of our manuscript. We have\nmeticulously addressed each point raised, implementing comprehensive revisions that\nstrengthen our findings and broaden their applicability.\nThe revised manuscript now incorporates several new experimental expansions. We\nhave extended our cellular model validation by including HeLa cells alongside the\noriginal CHO line, providing critical cross-validation that demonstrates the robustness\nof our PhotoFiTT framework across diverse mammalian systems. This comparison\nrevealed intriguing differential photosensitivity profiles, with HeLa cells exhibiting\ngreater resilience to light-induced damage than CHO cells at equivalent doses yet\nmaintaining consistent dose-dependent response patterns. We have also thoroughly\nexamined the phototoxic contribution of fluorescent probes through targeted\nMitoTracker experiments. These investigations specifically quantify how fluorophore\nexcitation amplifies cellular photodamage, demonstrating that even at modest\nillumination doses (0.6 J/cm²), MitoTracker Green FM produces measurable 15-minute\ndelays in mitotic timing when excited at 475 nm. This observation aids in using our\nassay to inform optimal probe selection for imaging protocols. Furthermore, we have\nconducted confocal microscopy experiments comparing varied illumination regimes at\nequivalent total doses. By contrasting fast scanning (3.528 μs/pixel) with slow\nscanning (10.584 μs/pixel) at identical energy inputs (6 J/cm²), we revealed that dwell\ntime influences photodamage manifestation. These findings provide practical\nguidance for parameter optimisation in point-scanning microscopy applications.\nWe sincerely appreciate the opportunity to enhance our manuscript and believe the\nresulting work represents a substantial advancement in understanding and mitigating\nphototoxicity in quantitative microscopy.\nPlease find below our point-by-point reply to reviewers:\nReviewer #1\nReviewer #1: Del Rosario and colleagues present a framework that integrates a\nstandardized experimental protocol with advanced imaging analysis to quantify light-\ninduced cellular stress. This work is particularly timely, as optical microscopy has\nbecome one of the most important tools for observing biomolecular processes in living\ncells. However, it is crucial to understand when the processes under study are\ninfluenced or altered by the observation itself—specifically by the light used to capture\nthe images. For this reason, I am supportive of its publication in Nature\nCommunications, as the journal's broad readership aligns well with the relevance and\nsignificance of this report.\nThat said, before publication, I believe there are a few points that need to be clarified\nor described in greater detail:\ndedication to providing valuable feedback. As follows, we address directly each of the\ncomments made.\n• The framework is presented as a method to quantify phototoxicity in fluorescence\nmicroscopy, but this aspect is not clearly emphasized in the text. If I understand\ncorrectly, the three main measurements are performed on brightfield images, while\nfluorescence measurements are only used for generating the training set for virtual\nstaining. Although the method’s applicability to non-fluorescent cells is an advantage,\nit may cause some confusion. Specifically, is the \"Acquisition\" phase (Fig. 2a)\nconducted on fluorescently labeled cells? This does not necessarily imply that\nfluorescence images are used in the analysis itself, and clarifying this distinction would\nhelp improve understanding of the method.\nOur reply: PhotoFiTT aims to assess the phototoxicity caused by fluorescence light\nwithout relying on fluorescent markers or additional illumination to avoid measurement\nbiases. Thus, the “Acquisition” step in Figure 2a only involves transmitted light\nmicroscopy. The source of damage, such as fluorescent excitation light illumination, is\nmeant to be applied before in what we call the “Photodamage irradiation” step. The\ndescription in the text has been changed to “This implementation (Figure 2a), followed\nthese key steps: 1) Synchronise cells using the CDK-1 inhibitor RO-3306, which\narrests cells in the G2/M interface, or study unsynchronised cells that can be tracked\nfrom mitotic rounding until division; 2) Expose cells to a light irradiation event\nreplicating the illumination pattern of the imaging experiment to be analysed for\nphototoxicity; 3) Identify cells undergoing mitotic rounding and division (Figure 2b)\nusing a Stardist deep learning model purposely-trained for brightfield round cell\ndetection; 4) Analyse three key parameters: mitotic timing, cell diameter dynamics,\nand cellular activity”. Fig. 2a has also been updated and the caption now reads as\n“Synchronised cells are washed prior to 6-hour time-lapse imaging, including\ntransmitted light and fluorescence markers for model training. Mitotic rounding is\nidentified using trained models to quantify cell activity and division dynamics. Detailed\nworkflow descriptions are in Notes S1 and S2”.\nReviewer #1: I would expect the degree of cellular stress to differ in fluorescently\nlabeled cells, as fluorophores absorb light, reducing cell transparency compared to\nnon-labeled cells. The authors seem to acknowledge this, as they mention light absorption in the\ncontext of the CDK1 inhibitor. However, I recommend further clarification of how labeling affects light absorption and,\nconsequently, cellular stress, and how this factor may influence experimental outcomes.\nOur reply: Following also the recommendation of Reviewer 3, we now include new\nexperimental results (new Fig. 3, Figure S2 e,f, Video S6) assessing the effects of\nfluorescence tags (MitoTracker), which show increased cellular damage, as\n2\nhighlighted by the reviewer. Additionally, we have expanded the discussion in the\nmanuscript by adding the following information: “While ROS are crucial for signalling\nand normal cellular functions at physiological levels, excessive amounts can cause\noxidative stress, disrupting the biological processes under investigation. Namely,\nfluorophore excitation by light, such as those arising from fluorescent proteins or\nfluorescent molecular probes, boosts ROS production through their interaction with\nambient oxygen, increasing the sensitivity of cells to light”\nReviewer #1: The cellular activity measurement is presented as a method to quantify\nsub-cellular changes. However, I believe this claim may be overstated. In essence,\nthe measurement seems to reflect changes in cell shape rather than directly assessing\nalterations in the behavior of sub-cellular components, such as mitochondria or\nvesicles. I suggest refining this claim to more accurately describe what the\nmeasurement captures.\nOur reply: The description in the text has been corrected to “Cellular activity: quantify\noverall cellular changes over time as a measure of overall cellular health (Figure 1c).\nCellular activity reflects general dynamic changes in cellular shape across time.” and\nin the caption of Fig 1. as “Activity: Quantifies observable cellular changes between\nframes (red outlines).”\nReviewer #1: Can you better explain why measurements need to be performed on\nboth synchronized and unsynchronized cells? Is this an integral part of the protocol,\nor are these just two examples? It would be helpful to clarify whether this dual\napproach is essential for the method or simply demonstrates its flexibility.\nOur reply: While cell arrest can be assessed in unsynchronised populations,\nsynchronising the cell cycle allows for a significant increase in mitotic events,\nconsiderably boosting the number of observations. Since the CDK1 inhibitor is known\nto make cells more sensitive to photodamage, we are now comparing the results from\nboth experiments (synchronised and unsynchronised) to validate our readouts. The\nnew Figure S1 displays the effects of phototoxicity, revealing equivalent trends\nregardless of cell cycle synchronisation via RO-3306 induction. This clarification is\nmade in the text “To validate that these effects were primarily due to light exposure\nrather than the potential light sensitivity of the CDK1 inhibitor used for synchronisation,\nwe conducted parallel experiments with unsynchronised cells.”\nReviewer #1: Could you indicate the size of the field of view (FOV) used to convert\npower into intensity?\nOur reply: This information is now in the methods section as “To convert the light\npower into intensity we considered the field of view (FOV) size of 0.014 cm2.”\nappears in multiple\ninstances on page 6.\nOur reply: Corrected.\nReviewer #2 (Remarks to the Author)\nReviewer #2: In this manuscript, the authors present an application of pre-existing\ndeep-learning based tools to assess the photodamaging effects of light illumination on\nlive cells. Using non-damaging bright field illumination, the authors record the\nbehaviour of illuminated cells over many hours, using software tools to segment and\nanalyse cells. Three measurements are used to assess photodamage: delays in\nmitosis, identification of cell cycle arrest, and levels of cellular activity. This is applied\nto Chinese Hamster Ovary (CHO) cells, exposing them to UV, blue or red light. For\nUV illumination, delays in mitotic timing were observed with doses as low as 0.6 J /\ncm^2. For red illumination, only 60 J / cm^2 was sufficient to cause noticeable damage.\nOur reply: We thank the reviewer for the time dedicated to reviewing our work and\nproviding valuable feedback. As follows, we address directly each of the comments\nmade.\nThe manuscript is clear and well-written. The supplementary videos provide a nice\noverview of the work, but video 2 is too fast — it is impossible to follow what is being\npasted into the terminal, etc..\nOur reply: We have slowed down video 2.\nReviewer #2: Figures 1 and 2 provide a nice description of the assessment pipeline,\nbut it would be nice to include images of 'bad' cells as well as 'happy' cells.\nOur reply: Images of \"happy” and “bad” cells can be seen in the old supplementary\nFigure S2, corresponding to Figure S5 in the updated manuscript.\nReviewer #2: The authors should be commended for making their pipeline available\nas well-documented open-source code. I have, for reasons of time, been unable to\ninstall and operate the code on my own computer, but given the authors' track record\nin developing software I am confident that the software would operate correctly if I had.\nHowever, if I have understood correctly, applying to the pipeline to cells different to the\nones used by the authors (CHO), then the neural networks employed (developed by\nother researchers) must be retrained. This significantly restricts the utility of the tool.\nOur reply: The reviewer is correct about the need to retrain or fine-tune the deep\nlearning models when the cell type changes. For this reason, we decided to build\nPhotoFiTT’s pipeline using networks that are retrainable and reusable through the user-\n4\nfriendly platform ZeroCostDL4Mic (in the cloud) and DL4MicEverywhere (locally)\n(i.e.,\nStarDist and Pix2Pix networks). ZeroCostDL4Mic is a well-recognised tool in the\ncommunity that disseminates (re-)trainable DL pipelines among non-expert life\nscientists requiring minimal non-coding input. On the other hand, the instructions to\nreproduce PhotoFiTT’s pipeline, including data annotation and training, are provided in\ndetail (Methods sections “Cell mitosis detection with StarDist”, “Estimation of the number\nof cells in the field of view using virtual cell nuclei staining and segmentation”,\n“Manual\nannotations of unsynchronised videos”; and Note S1, Section D. Image Analysis\nWorkflow), and all the additional notebooks to compute the analysis have been also\ndesigned for code-naïve users (https://github.com/HenriquesLab/PhotoFiTT).\nFor\nexample, the new experiments involving HeLa cells, required fine-tuning StarDist and\nPix2Pix. This was straightforward by solely reannotating the images and changing the\ndata paths in the StarDist and Pix2Pix notebooks. Additionally, all data, including\nannotations and training data, is available through a thoroughly documented BioImage\nArchive open data repository (https://www.ebi.ac.uk/biostudies/bioimages/studies/S-\nBIAD1269), which serves as an example of how data and annotations should look like,\nand can be used for researchers to reproduce PhotoFiTT.\nReviewer #2: The manuscript consists of two main parts: 1) the development of a\nsoftware pipeline for analysing photodamage, 2) the application of this approach to\nstudying photodamage in live cells. While I think the first part may have general utility\nfor many researchers, the second part is not very thorough. I certainly would not draw\nany general conclusions from these results. This is important, as the presented results\ncontradict many imaging studies where similar irradiances have been used without ill\neffects. Indeed, Wäldchen et al. (2015) have shown that cells can withstand even\nmany kJ / cm^2 of red illumination, while the authors here suggest that just 60 J / cm^2\nis enough to damage them.\nOne possible reason for this discrepancy is that the methods section states that cells\nwere cultured in DMEM, and this was only switched for phenol-red-free Fluorobrite\nDMEM after photodamaging illumination. This is very different from the usual scenario\nin which cells would be illuminated in imaging medium (such as Fluorobrite DMEM).\nPhenol red absorbs strongly at all wavelengths used and has previously been reported\nto significantly increase photodamage from imaging illumination. Another possibility is\nthat Wäldchen et al. used a range of U2OS, COS-7 and HeLa cells, while here the\nauthors used CHO cells. If a general claim about photodamage is to be made, these\nexperiments must be repeated with a different cell line in order to ensure that the\nphotodamage is not a CHO-specific effect.\nOur reply: We thank the reviewer for their insightful comments. We would like to clarify\nthe positioning of our work within the field of photodamage assessment. The primary\ncontribution of our manuscript is the PhotoFiTT framework itself - a quantitative\napproach for assessing phototoxicity through standardised metrics that can be\nimplemented across diverse experimental conditions. Following the reviewer's\n5\nvaluable suggestion, we have expanded our analysis to include HeLa cells (new\nFigure S3), which demonstrates that our approach can successfully quantify\nphotodamage across different cell types. As expected, our results show that HeLa\ncells exhibit different phototoxicity thresholds compared to CHO cells, with greater\nresilience to light damage (Figure S3a-d). This highlights PhotoFiTT's capability to\ndetect cell-type specific responses to phototoxicity, which is an important\nconsideration when designing live-cell imaging experiments.\nWe appreciate the reviewer highlighting the work by Wäldchen et al. (2015). Rather\nthan contradicting this important previous study, we view our work as complementary,\nrevealing how different experimental conditions and cell types can yield varying\nphototoxicity thresholds. The power of PhotoFiTT lies precisely in its ability to quantify\nthese differences through standardised metrics.\nOur framework is designed to be experimental-condition agnostic, meaning\nresearchers can apply it to their specific imaging protocols to establish appropriate\nphototoxicity thresholds for their particular experimental setup, whether that involves\ndifferent cell types, media formulations, or other experimental variables. This allows\nfor informed decisions about imaging parameters that balance data quality with sample\nhealth for each specific experimental system.\nWe believe this approach addresses a critical need in the field, as highlighted by the\nreviewer's own observation that different studies have reported varying cellular\nresponses to similar light doses. The PhotoFiTT framework provides tools to\nsystematically quantify and understand these differences.\nReviewer #2: The authors report illumination powers, but it is not clear how these\nwere measured. If a standard power meter sensor was used then this is likely to be an\nunderreport, as these power meters are not sensitive to the high-angle rays present in\nmicroscopic illumination. Instead, a specialist power meter, such as the Thorlabs\nS170C, should be used (and if this was used this should be clearly stated in the text).\nFurthermore, it is not clear how these powers were converted to irradiances. Different\npowers were used for different wavelengths, but this apparently led to the same\nirradiance. This can only be true if the field of illumination varies by wavelength and it\nis not clear that this should be the case. Indeed, it appears (by backcalculation) that\nthe authors calculated the irradiance by dividing the power by the full field size of the\nobjective (pi * (12.5 mm / 20)^2). This assumes that there is no illumination outside of\nthe manufacturer's claimed field of view, which is not true (as there is no real field stop\nin the objective). Instead, an external field stop of known size should be inserted into\nthe illumination path.\nOur reply: To measure the illumination power, we use a Thorlabs PM100D power\nmeter with a S170C power sensor (100mW, 350nm-1100nm). We have provided the\nremaining calculations for power and included them clearly in the experimental\nprotocol description “Light intensity at the sample level was measured at the beginning\nof every experiment, using a PM100D power meter with a S170C power sensor\n(Thorlabs). The measurements for each wavelengths line were the following: 84.9 ±\n6\n2.8 mW for the 385 nm line, 85.2 ± 0.8 mW or the 475 nm, and 70.6 ± 0.4 mW for 630\nnm (mean ± standard deviation). To convert the light power into intensity we\nconsidered the field of view (FOV) size of 0.014 cm2”.\nReviewer #2: Perhaps most critically, the illumination timing used in the mock\nillumination is very different from that used in real timelapse experiments. In this work,\nthe illumination is delivered in a single continuous dose at the beginning of the\nexperiment. In a typical live-cell imaging experiment, the illumination would instead be\ndelivered in little chunks throughout. Delivering a large dose up-front will create a much\nlarger number of reactive oxygen species and other undesirable photoproducts that\nmay overwhelm a cell's protective mechanisms in a way that a more distributed\nphotodose would not. I strongly suggest that the authors perform experiments using\ntheir analysis framework where cells have been illuminated in a more realistic manner.\nOnly then, in combination with further experiments using multiple cells lines and\ndifferent media, will the results allow general conclusions to be drawn.\nOur reply: To address the critical issue of timing differences in illumination between\nour experimental design and standard timelapse protocols, we emphasise that\nPhotoFiTT's framework is inherently adaptable to various illumination conditions.\nWhile our initial experiments used single-dose exposures to establish clear dose-\nresponse relationships, we now also demonstrate the method’s ability to analyse the\neffects of distributed illumination through new confocal microscopy comparisons\n(Figure S4) and MitoTracker experiments (Figure 3). On confocal, by comparing fast\n(3.528 μs/pixel) and slow (10.584 μs/pixel) scanning regimes at the same total doses,\nwe show that prolonged dwell times may increase phototoxic effects, even with\nidentical energy deposition. Additionally, the new MitoTracker experiments (Figure 3)\nsimulate realistic imaging conditions with periodic illumination (6 exposures over 20\nminutes). This demonstrates PhotoFiTT's capability to detect cumulative phototoxic\neffects from interrupted light delivery.\nThese findings illustrate the relevance of our method to standard imaging protocols\nwhile maintaining the ability to identify sub-lethal perturbations that may go unnoticed\nby conventional viability assays.\nReviewer #2: Given the currently narrow applicability of the developed pipeline (i.e.\nonly applicable to CHO cells), and the aforementioned issues with the experiments, I\nfeel unable to recommend this manuscript for publication in Nature Communications.\nHowever, if the pipeline can be made more general and the experiments made more\nrobust and generalisable, then I see that this could become a valuable tool for the\ncommunity. Some minor comments are detailed below:\nOur reply: We have made every effort to thoroughly address the reviewer's comments\nand suggestions. We appreciate the opportunity to revise the document and hope that\nwe have adequately responded to the points raised.\n7\n== Minor comments ==\nReviewer #2: Given that RO-3306 is known to cause apoptosis after long incubation\ntimes, have the authors considered using a different means of cell cycle\nsychronisation, such as tapping a plate to dislodge only mitotic, rounded cells? This\nwould provide a smaller number of cells for further analysis, but it seems that they are\nnot analysing an entire cell culture plate anyway. Importantly, this would completely\nrule out the possibility that the effects observed were related to RO-3306.\nOur reply: We thank the reviewer for raising this. We selected RO-3306\nsynchronisation due to its widespread adoption in cell cycle studies and its ability to\nyield sufficient cell numbers for robust statistical analysis of mitotic events. Crucially,\nour parallel experiments with unsynchronised populations (Figure S1a-b) revealed\nidentical dose-dependent phototoxicity trends, including mitotic delays and arrest\nrates, despite the absence of CDK1 inhibitor treatment. This consistency across\nsynchronisation methods confirms that the observed effects predominantly stem from\nlight exposure rather than RO-3306-related artefacts. While prolonged RO-3306\nexposure can induce apoptosis (addressed in Figure S1c), our 16-18 hour treatment\nwindow remains within established safe limits for this synchronisation protocol.\nReviewer #2: Can the authors comment on how the use of point-scanning illumination,\nsuch as that employed in confocal microscopes, might alter the conclusions drawn\nfrom the epi-illumination used in this work?\nOur reply: To address the question regarding point-scanning illumination in confocal\nmicroscopy, we conducted dedicated experiments comparing phototoxic effects under\ndifferent scanning regimes (Figure S4). While widefield illumination distributes light\nuniformly, confocal point-scanning concentrates energy into discrete voxels with\nvariable dwell times. Our framework revealed that slower scanning (10.584 μs/pixel)\nat 6 J/cm² leads to ~20% greater mitotic delays than faster scanning (3.528 μs/pixel)\nat equivalent doses, demonstrating that temporal illumination patterns significantly\ninfluence phototoxicity outcomes. This underscores the need for modality-specific\noptimisation, as conclusions from widefield studies may not directly translate to\nconfocal systems. Crucially, PhotoFiTT's adaptable design enabled these\ncomparisons by quantifying dose-dependent physiological impacts across both\nillumination strategies, validating its utility for diverse microscopy platforms.\nReviewer #2: No line numbers are included in the manuscript file. While not strictly\nnecessary, these would make it easier for reviewers to refer directly to specific parts\nof the text.\nOur reply: Continuous line numbers have been added.\nsignificant for CHO cells? What about other cell types?\nOur reply: The biological significance of mitotic delays depends on the experimental\ncontext and the cellular processes under investigation. In CHO cells, a 25-minute\ndelay (e.g., from ~30 to ~55 minutes at 60 J/cm², Figure S1b) represents a 83%\nprolongation of mitotic progression, which could disrupt time-sensitive processes like\nchromosome segregation or cytokinesis. PhotoFiTT quantifies these deviations as part\nof a continuous dose-response spectrum (Figure 2d), enabling researchers to define\nexperiment-specific tolerance thresholds. For developmental studies requiring precise\ncell cycle timing, even minor delays may invalidate conclusions, while acute\nobservations of static structures might tolerate larger perturbations. The framework’s\nstrength lies in its capacity to map illumination parameters to quantitative physiological\noutcomes (e.g., 6 J/cm² at 475 nm causing >50% mitotic delay), allowing users to\nbalance resolution needs against cellular tolerances for their specific biological\nquestion.\nReviewer #2: Related to the last point, there is no indication of statistical or biological\nsignificance between the different conditions tested, using any of the metrics\nemployed.\nOur reply: In response to the reviewer's comment, we provide classical statistical\nsignificance measures for Figures 2 and 3 below, which may enhance the reviewer’s\ninterpretation of results. This type of analysis can now also be easily done by users in\nPhotoFiTT, which will automatically identify the most suitable test (e.g., Kolmogorov-\nSmirnov or t-Test), depending on the data features. This information was excluded\nfrom the main manuscript due to the fact that p-values can be significantly affected by\nsample size. Consequently, an overreliance on p-values may create a false sense of\nvalidation, which has drawn criticism from the scientific community, including from one\nof the primary authors (Gómez-de-Mariscal et al., Scientific Reports 2021). In this\ncase, even though our results predominantly demonstrate statistical significance,\nthese authors believe they do not provide additional meaningful insights to the\nmanuscript.\nResults of Kolmogorov-Smirnov Statistical Test for the Synchronised CHO population\n(95% statistical significance highlighted) (Figure 2d and 2g):\nMitotic rounding timepoint (min)\nGroup 1 Group 2 p-value\n385 nm 0 jcm2 0.6 jcm2 1.505454e-10\n0 jcm2 6 jcm2 9.000135e-09\n0 jcm2 60 jcm2 1.046327e-09\n0.6 jcm2 6 jcm2 5.788676e-01\n0.6 jcm2 60 jcm2 3.237701e-01\n6 jcm2 60 jcm2 9.578463e-01\n9\n475 nm 0 jcm2 0.6 jcm2 7.651221e-02\n0 jcm2 6 jcm2 6.596662e-01\n0 jcm2 60 jcm2 3.287127e-04\n0.6 jcm2 6 jcm2 9.496976e-01\n0.6 jcm2 60 jcm2 7.629005e-05\n6 jcm2 60 jcm2 5.375329e-04\n630 nm 0 jcm2 0.6 jcm2 5.473916e-01\n0 jcm2 6 jcm2 2.291002e-01\n0 jcm2 60 jcm2 3.206385e-06\n0.6 jcm2 6 jcm2 2.201206e-01\n0.6 jcm2 60 jcm2 3.117831e-04\n6 jcm2 60 jcm2 2.237082e-03\nCumulative cell activity at t=420 min [%]\nGroup 1 Group 2 p-value\n385 nm 0 jcm2 0.6 jcm2 0.001772\n0 jcm2 6 jcm2 0.000296\n0 jcm2 60 jcm2 0.000002\n0.6 jcm2 6 jcm2 0.662267\n0.6 jcm2 60 jcm2 0.229588\n6 jcm2 60 jcm2 0.013140\n475 nm 0 jcm2 0.6 jcm2 0.039855\n0 jcm2 6 jcm2 0.027037\n0 jcm2 60 jcm2 0.024182\n0.6 jcm2 6 jcm2 0.372625\n0.6 jcm2 60 jcm2 0.255652\n6 jcm2 60 jcm2 0.305947\n630 nm 0 jcm2 0.6 jcm2 0.428160\n0 jcm2 6 jcm2 0.000189\n0 jcm2 60 jcm2 0.028805\n0.6 jcm2 6 jcm2 0.017664\n0.6 jcm2 60 jcm2 0.368007\n6 jcm2 6 jcm2 0.066274\nResults of Kolmogorov-Smirnov Statistical Test for the Synchronised CHO population\nlabelled with MitoTracker and illuminated with 475 nm wavelength (95% statistical\nsignificance highlighted) (Figure 3b and 3d)\nMitotic rounding timepoint (min)\nGroup 1 Group 2 p-value\nUnlabelled MitoTrackerTM Red CMXRos 0.083001\nUnlabelled MitoTrackerTM Green CMXRos 0.083001\nUnlabelled MitoTrackerTM Red CMXRos illumination 0.429719\nSynchro MitoTrackerTM Green CMXRos illumination 0.042240\nMitoTrackerTM Red CMXRos MitoTrackerTM Green CMXRos 0.999992\nMitoTrackerTM Red CMXRos MitoTrackerTM Red CMXRos illumination 0.831970\nMitoTrackerTM Red CMXRos MitoTrackerTM Green CMXRos illumination 0.033542\nMitoTrackerTM Green CMXRos MitoTrackerTM Red CMXRos illumination 0.983137\nMitoTrackerTM Green CMXRos MitoTrackerTM Green CMXRos illumination 0.174533\nMitoTrackerTM Red CMXRos illumination MitoTrackerTM Green CMXRos illumination 0.174533\nCumulative cell activity at t=420 min [%]\nGroup 1 Group 2 p-value\nUnlabelled MitoTrackerTM Red CMXRos 4.224042e-02\nUnlabelled MitoTrackerTM Red CMXRos illumination 9.738308e-01\nUnlabelled MitoTrackerTM Green CMXRos illumination 6.332985e-01\nMitoTrackerTM Red CMXRos MitoTrackerTM Green CMXRos 3.354166e-02\nMitoTrackerTM Red CMXRos MitoTrackerTM Red CMXRos illumination 5.569063e-05\nMitoTrackerTM Red CMXRos MitoTrackerTM Green CMXRos illumination 1.745330e-01\nMitoTrackerTM Green CMXRos MitoTrackerTM Red CMXRos illumination 5.803556e-10\nMitoTrackerTM Green CMXRos MitoTrackerTM Green CMXRos illumination 3.967294e-03\nMitoTrackerTM Red CMXRos illumination MitoTrackerTM Green CMXRos illumination 3.354166e-02\nReviewer #2: The authors' results suggest that live-cell single molecule localisation\nmicroscopy (SMLM) is impossible without perturbation of the cell's health, given that\na 60 J / cm^2 dose is sufficient at 630 nm, while irradiances of the order of hundreds\nof W / cm^2 are used. How can this view be married with the extensive literature\nshowing successful live-cell SMLM? Have these imaged cells all been irreparably\ndamaged without anyone noticing?\nOur reply: The Reviewer highlights a key issue regarding our phototoxicity thresholds\nin relation to existing live-cell SMLM studies. Our findings indicate that doses over 60\nJ/cm² at 630 nm cause significant physiological changes in mitotic timing and cellular\nfunction (Figures 2d, f, g). Most live-cell SMLM experiments use much higher light\ndoses (Table S1), and their short durations may obscure acute phototoxic effects\naffecting the cell cycle. This observation aligns with the lack of literature tracking cell\ndivision using high-illumination SMLM.\nOn the other hand, SMLM predominantly employs Total Internal Reflection\nFluorescence (TIRF) or Highly Inclined and Laminated Optical Sheet (HILO)\ntechniques, which limit the illumination volume and reduce cellular phototoxicity, a\nsubject that may be interesting to explore in future PhotoFiTT studies.\nRather than dismissing live-cell SMLM, PhotoFiTT provides guidelines to minimise\ncellular disruptions. The 60 J/cm² threshold acts as a conservative maximum, enabling\nresearchers to create dose-response curves tailored to their conditions (Figure 2d)\nand optimise illumination settings for both resolution and cell viability.\nReviewer #3 (Remarks to the Author)\nReviewer #3: The work introduces PhotoFiTT, a standardized workflow for\nquantitatively evaluating the phototoxic effects on adherent and dividing tissue culture\ncells. The workflow utilizes machine learning tools to enhance cell segmentation from\nbrightfield transmission microscopy time series and extract three different\nmorphological parameters and their changes over time, which indicate cell fitness,\nparticularly the ability of timely cell division. The authors demonstrate the application\nof their approach on CHO cells that were exposed to three different light doses at three\ndifferent wavelengths, showing the expected correlation in the quantitative evaluation\nparameters.\nby other labs to optimise experimental conditions for live cell microscopy studies.\nHowever, I feel it currently falls short of demonstrating its validity in real-world\nscenarios, as I will detail below. Thus, before recommending this work for publication,\nI would like to see the following issues and queries addressed.\nOur reply: We sincerely thank the reviewer for recognising our work and for their\ndedication to providing valuable feedback. As follows, we directly address each of the\ncomments made.\nMajor issues:\nReviewer #3: The authors evaluate the phototoxic effects of 3 different light doses\nthat deviate by a factor of 10, at three different wavelengths to validate their approach.\nIt seems that the effects of UV-light and higher light doses on the cell population are\nobvious even without a sophisticated quantitative evaluation. Can the approach\nreliably detect and quantify more subtle effects? Are there limitations to doing this?\nOur reply: PhotoFiTT detects subtle physiological changes below conventional\nviability assay thresholds. Our data indicate mitotic delays at doses as low as 0.6 J/cm²\nwith 385 nm illumination, resulting in a 5-10 minute shift in cell division timing without\nimmediate cell death. This sensitivity is consistent across wavelengths; for example,\nmorphological changes.\nThe method's accuracy comes from its capacity to track mitotic progression and\ncellular dynamics simultaneously. Our MitoTracker experiments (Figure 3), for\nexample, reveal a 15-minute mitotic delay at doses that seem benign in label-free\nconditions. To further highlight the sensitivity of our approach, in new confocal\nexperiments (Figure S4) we show that different pulsed illumination patterns can\nchange phototoxic effects at equivalent total doses, indicating that the timing of energy\ndeposition affects cellular responses.\nReviewer #3: 2. The PhotoFiTT assay is per se label-free, which is beneficial as it should\nnot contribute to phototoxicity. However, in most realistic live cell imaging scenarios,\ncells are fluorescently labeled and observed at high resolution over a certain\nobservation period with a certain temporal resolution. Importantly, bleaching of\nfluorophores itself generates reactive oxygen species that contribute to phototoxicity.\nTherefore, it is essential to determine the phototoxic effects in the specific\nexperimental conditions where labelling is applied and observed with the same\nobjective. This allows for the comparison of phototoxic effects with the baseline or with\ndifferent illumination regimes. Eventually, this will inform the researcher on the\ndata amount, the level of resolution, and SNR quality needs can be afforded without\ninducing unwanted biological side effects that otherwise might affect the interpretation\n12\nof the experimental outcome. Showcasing the application of PhotoFiTT to one such\nrealistic example is needed to be more convincing about its broader value.\nOur reply: The reviewer points out the need to validate PhotoFiTT in fluorescence\nlabelling experiments. In response, we propose experiments using MitoTracker probes\nwith controlled 475 nm illumination (see Figure 3). While PhotoFiTT is label-free during\nanalysis, it evaluates phototoxic effects from prior fluorescence imaging.\nOur data show that exciting MitoTracker Green FM at 475 nm led to a 15-minute delay\nin mitotic timing and a 20% reduction in cumulative cellular activity compared to\nunexposed controls (refer to Figures 3b and 3d). These effects occurred at doses (0.6\nJ/cm²) below the threshold detectable in label-free conditions (Figure 2d). This\nsensitivity arises from PhotoFiTT's ability to track physiological consequences of\nreactive oxygen species (ROS) generation and other light-induced disturbances, even\nhours after exposure. PhotoFiTT's temporal resolution reveals the disconnect between\nimmediate fluorophore excitation and delayed effects. While viability assays may miss\ntransient ROS bursts, our precise monitoring uncovers cumulative damage that can\ndisrupt cell cycle progression. This capability allows researchers to create\n\"phototoxicity budgets\" that account for direct illumination effects and fluorophore-\nmediated damage. By testing different labelling strategies, such as MitoTracker Green\nFM versus CMXRos (Figure 3), researchers can identify optimal probe-wavelength\ncombinations that minimise physiological disruption while maintaining signal quality.\nWe have expanded the discussion in the manuscript by adding the following\ninformation: “Longer wavelengths, while generally less harmful, have been described\nto exert significant effects on cellular physiology, including alterations in mitochondrial\nmembrane potential, cytoskeletal dynamics, and reactive oxygen species (ROS).\nWhile ROS are crucial for signalling and normal cellular functions at physiological\nlevels, excessive amounts can cause oxidative stress, disrupting the biological\nprocesses under investigation. Namely, fluorophore excitation by light, such as those\narising from fluorescent proteins or fluorescent molecular probes, boosts ROS\nproduction through their interaction with ambient oxygen, increasing the sensitivity of\ncells to light.”\nReviewer #3: 3. Since the workflow essentially assesses effects on cell division rates,\nwhy not simply count the increase in cell number density in the 10 irradiated areas vs\n10 non-irradiated areas before and, say, 24 hours after irradiation? Also, what is the\nbenefit of assessing three different division-linked morphological parameters? Are\nthere specific situations where this redundancy is beneficial?\nOur reply: While cell density measurements over extended periods (e.g., 24h post-\nirradiation) could quantify gross proliferation changes, this approach lacks the\ntemporal resolution to detect sub-lethal perturbations and conflates phototoxicity with\nunrelated viability factors. PhotoFiTT's multi-parametric strategy instead resolves\ndistinct mechanistic pathways: mitotic timing precision reflects DNA repair/checkpoint\n13\nactivation (Figure 2d), cell size dynamics report on cytokinetic fidelity (Figure 2e-f),\nand cumulative activity integrates motility/membrane fluctuations sensitive to\ncytoskeletal or metabolic stress (Figure 2g). This granularity is crucial, as phototoxicity\nmanifests through varied mechanisms. For example, UV wavelengths delay mitosis\nvia DNA damage checkpoints, while visible light overall perturbs activity through ROS-\nmediated cytoskeletal changes.\nThe parameter redundancy provides robustness across cell types and experimental\ncontexts. For example, while HeLa cells exhibit less baseline motility than CHO\n(reducing activity metric sensitivity), their dose-dependent mitotic delays remain\nclearly quantifiable (new Figure S3). Conversely, in models where division rates are\nintrinsically variable (e.g., primary cells), activity metrics become the primary\nphototoxicity indicator. By cross-validating across orthogonal readouts, PhotoFiTT\nensures reliable detection even when single parameters approach detection limits in\nspecific systems.\nReviewer #3: 4. An obvious shortcoming of the PhotoFiTT assay (as is also mentioned\nby the authors), is its restriction to dividing cells, as all three metrics effectively\nmeasure mitotic or postmitotic behaviour. Could an activity measurement alone not\nalso be adapted to work for non-dividing, differentiated cells like neurons?\nOur reply: In reference to our previous comment, PhotoFiTT takes advantage of a\nwell-established essential cellular process found in various cell types, namely mitosis.\nWe agree that cellular activity could be assessed across different cell types that exhibit\nat least minimal activity under physiologically relevant conditions. This could also\ninvolve techniques like cell segmentation and tracking of shape dynamics. However,\nthe extent of these changes is less understood and is challenging to interpret in terms\nof cellular processes.\nReviewer #3: 5. The authors mention the transferability of the assay to other widefield\n(?) imaging systems. Does this also apply to confocal systems? If so, this should be\nexplicitly mentioned and ideally be demonstrated. For example, a low-hanging fruit\nmight be to quantitatively assess the difference between delivering the same light dose\nby a widefield illumination (low power density, longer dwell time) or by the focused\nlaser beam of a confocal point scanning system (high power density, short dwell time),\nwhich has been a long-standing point of discussion in the field.\nOur reply: The Reviewer raises a critical point regarding PhotoFiTT's applicability\nacross microscopy modalities. To address this issue, we have extended our analysis\nto point-scanning confocal microscopy (new Figure S4). When applying PhotoFiTT to\nlaser point-scanning confocal microscopy, we quantified distinct phototoxic outcomes\nbetween fast (3.528 μs/pixel) and slow (10.584 μs/pixel) scanning regimes matched\nfor total dose (6 J/cm² at 405 nm). While the current study focuses on validating core\nprinciples across modalities rather than exhaustive cross-platform comparisons, the\nconfocal data establish PhotoFiTT's capacity to resolve system-specific phototoxicity\n14\nprofiles. The open-source implementation facilitates direct adaptation to any\nmicroscope with time-lapse brightfield capabilities, enabling researchers to map dose-\nresponse curves for their specific hardware configurations. Future applications could\nsystematically compare widefield vs. confocal phototoxicity thresholds, leveraging\nPhotoFiTT's quantitative framework to establish modality-specific optimisation\nguidelines.\nMinor issues:\nReviewer #3: The text mentions that cell nuclei are segmented, while the figures give\nthe impression that entire cells are segmented. This needs clarification.\nOur reply: We apologise for the confusion. In the pipeline two segmentation steps are\nfollowed. In one we segment the entire cells going into mitosis. This would provide us\nwith measurements of cell arrest and or successful mitotic rates (in Section “Cell\nmitosis detection with StarDist” of the Methods and Figure S2). Yet another\nsegmentation step is followed after virtually staining the first frame of each video (i.e.,\nan image translation that infers the cell nuclei from brightfield microscopy images of\ncells) in which we segment the inferred cell nuclei (in Section “Estimation of the\nnumber of cells in the field of view using virtual cell nuclei staining and segmentation”\nof the Methods). This second segmentation step occurs to count individual cells. This\ninformation is now more explicit in Figure 2a, which highlights that two segmentation\nsteps are followed.\nReviewer #3: Which objective was used for the irradiation? Is it the same as for\nimaging?\nHow big were the FOVs accordingly? How many cells are typically irradiated and\nassessed in a single experiment?\nOur reply: We used a 20x objective with a FOV size of 0,014 cm2. The total number\nof cells is approximately 125. This is now stated in the companion protocol, section\nD.1.\nReviewer #3: The NA of the objective should affect the energy distribution within cells,\nwith a high NA compressing more energy into a smaller focus z-range? Has this been\naccounted for?\nOur reply: The NA objective was not considered, but the energy was measured at the\nlaser's endpoint after passing through the objective. This indicates that our energy\nmeasurement is as close as possible to what our sample receives.\nReviewer #3: What is the energy (J/cm2) for the transmission observation compared\nto the excitation light irradiation? Has it been controlled that this does not influence the\n“baseline” physiology?\nwith the simultaneously imaged control experiments as shown in Figure 2 c-g, S1 b,c,\nFigure S2 b-f, Figure S3 a-d and Figure S4 a-c (0 J/cm2).\nReviewer #3: Did the authors control for potential cytotoxic effects of RO-3306\nindependent of light exposure?\nOur reply: Indeed, we replicated the suggested experiment on unsynchronised cells,\nas shown in Figure S1. Although RO-3306 heightens the cell's sensitivity to excitation\nlight, the tendency for cell damage remains consistent in both synchronised and\nunsynchronised cells.\nReviewer #3: (This may only apply to my downloaded file) Video S1 stops prematurely\nat 01:44 of the 2:19 total runtime.\nOur reply: Thank you for highlighting it. We have corrected it.\nReviewer #3: Typo on p6, 2nd column: “…the images were reshaped to have a pixel\nsize of 0.644 μu x 0.644 μm.”\nOur reply: Thank you. We have corrected it.\nReviewer #4 (Remarks to the Author):\nReviewer #4: In this paper, the authors present a label-free and quantitative approach\nto evaluate phototoxicity for live-cell imaging experiments. The aim is to enhance the\nreliability and reproducibility of such live-cell imaging studies.\nMore approaches to quantify phototoxicity are welcome - especially updated ones that\nexploit recent technical advances. The strengths of the presented study lie chiefly in\nusing a label-free (brightfield) approach, a large amount of data that can be (semi-)\nautomatically processed for quantification, and the use of trainable open-source\nsoftware. This has the potential to make it widely applicable (within the area of live-\ncell imaging, and depending on the uptake of its community). The authors use three\nparameters: Duration of mitosis (the ideal ‘canary in the gold mine’, Cole 2014), cell\nsize and ‘cumulative activity’. Manuals, videos and curated code are a very useful\naddition to aid replication and adaptation by others.\nBelow are some suggestions to improve the manuscript, and some issues that need\naddressing.\nMajor comments:\nand one type of microscopy. Perhaps this could be expanded. For example, does the\npresented approach work for any optical contrast method (phase contrast (as used)\nas well as differential interference contrast (DIC))? Was this method tested on e.g.\nmotile cell lines? This expansion is optional, but would prove wider applicability.\nAlternatively, conclusions and claims should be modified.\n“Addressing this gap, we introduce PhotoFiTT, a quantitative imaging-based\nframework designed to assess phototoxicity effects on cellular behaviour in live-cell\nmicroscopy experiments.”\n– for adherent cell lines in traditional slide-based setups.\nOur reply: We appreciate the reviewer's insightful observation regarding the\nframework's applicability across imaging modalities and cell types. While our current\nvalidation focused on CHO and HeLa cells using brightfield microscopy, PhotoFiTT's\ndesign inherently supports adaptation to various label-free contrast methods. The\nframework's deep learning segmentation pipeline can be retrained for phase contrast\nor DIC imaging. We additionally demonstrated the inclusion of different experimental\napproaches, evidenced by our successful implementation of MitoTracker-stained\nspecimens to assess enhanced photosensitivity due to emitter excitation (Figure 3).\nThis adaptability stems from the method's reliance on fundamental cellular features\n(mitotic timing, size dynamics) rather than specific contrast mechanisms.\nWe have also changed the sentence as suggested: “Addressing this gap, we introduce\nPhotoFiTT (Phototoxicity Fitness Time Trial), a quantitative imaging-based framework\ndesigned to assess phototoxicity effects on cellular behaviour in live-cell microscopy\nexperiments (Figure 1) for adherent cell lines in traditional coverslip-based setups.”\nReviewer #4: It would be useful to show how well the algorithm fares compared to a\nground truth. The ground truth may likely require manually determined mitotic\ndurations and segmentations. However, without any comparison, the question of\naccuracy remains unanswered.\nOur reply: We now provide the accuracy metrics of the segmentation algorithm in the\nmethods section: “The accuracy results in the test set were as follows: intersection\nover union (IoU) (0,530), false positive (6.05), true positive (17,05), false negative\n(4.6), precision (0.6712), recall (0.790), accuracy (0.555), f1 score (0,699), true\ndetection (21.65), predicted detections (23.1), mean true score (0.712), mean matched\nscore (0.901), panoptic quality (0.628). The output PDF reports of ZeroCostDL4Mic\nfor the model training and the quality control check are attached to the supplementary\nmaterial”. Likewise, we include the manual annotation of synchronised cell populations\nin Figure S2, where the delay timings are comparable (around a 30-minute delay for\ncells exposed to UV light of 60 J/cm2). These quantifications together provide\nevidence that the results of the proposed method recapitulate the effects of\nphototoxicity reliably.\nbeen previously published. The way it is currently written, it is not always clear whether\nthe novelty of this submission lies in the methods used or in (largely unreferenced)\nobservations. It would be good to more clearly promote the first aspect, while ensuring\nthat stated observations align well with previously published studies.\nA few omitted references are used below as examples. While not exhaustive, and not\nall recent, these are important papers and good starting points for the reader/authors.\nMitotic delay / using time spent in mitosis as measure:\nCole R. 2014. Live-cell imaging. Cell Adhes Migr 8: 452–9.\nGorgidze LA, Oshemkova SA, Vorobjev IA. 1998. Blue light inhibits mitosis in tissue\nculture cells. Biosci Rep 18: 215–24.\nMora-Bermudez F, Ellenberg J. 2007. Measuring structural dynamics of chromosomes\nin living cells by fluorescence microscopy. Methods 41: 158–67.\nWavelength dependence of phototoxicity:\nDouthwright S, Sluder G. 2017. Live cell imaging: assessing the phototoxicity of\n488nm and 546nm light and methods to alleviate it. J Cell Physiol 232: 2461–8.\nSchneckenburger H, Weber P, Wagner M, Schickinger S, et al. 2012. Light exposure\nand cell viability in fluorescence microscopy. J Microsc 245: 311–8.\nImage processing to classify phototoxicity from transmitted light images:\nRichmond D, Jost AP-T, Lambert T, Waters J, et al. 2017. DeadNet: identifying\nphototoxicity from label-free microscopy images of cells using deep ConvNets. arXiv\n170106109.\nGrah JS, Harrington JA, Koh SB, Pike JA, Schreiner A, Burger M, Schönlieb CB,\nReichelt S. Mathematical imaging methods for mitosis analysis in live-cell phase\ncontrast microscopy. Methods. 2017 Feb 15;115:91-99. PMID: 28189773.\nCell outline changes:\nKnoll SG, Ahmed WW, Saif TA. 2015. Contractile dynamics change before\nmorphological cues during fluorescence illumination. Sci Rep 5: 18513.\nRe “critical need for standardised and generalisable methods able to quantitatively link\ncell damage to high-intensity light exposure across different fluorescence microscopy\ntechniques.” Important additional publications:\nKiepas A, Voorand E, Mubaid F, Siegel PM, Brown CM. Optimizing live-cell\nfluorescence imaging conditions to minimize phototoxicity. J Cell Sci. 2020 Feb\n21;133(4):jcs242834. PMID: 31988150.\nAlghamdi RA, Exposito-Rodriguez M, Mullineaux PM, Brooke GN, Laissue PP.\nAssessing Phototoxicity in a Mammalian Cell Line: How Low Levels of Blue Light Affect\nMotility in PC3 Cells. Front Cell Dev Biol. 2021 Dec 17;9:738786. PMID: 34977004.\nmethod and for pointing out the overlooked publications. These have been\nincorporated into the text, integrating our findings with prior research as follows:\nCole R. 2014. Live-cell imaging. Cell Adhes Migr 8: 452–9\n“PhotoFiTT leverages the predictable nature of cell division as a \"biological clock\" to\nquantify phototoxicity-induced perturbations (Figure 1)”\nGorgidze LA, Oshemkova SA, Vorobjev IA. 1998. Blue light inhibits mitosis in tissue\nculture cells. Biosci Rep 18: 215–24.\n“In synchronised cells and in agreement with established literature, near-UV irradiation\ninduced significant dose-dependent delays in mitotic timing”\nMora-Bermudez F, Ellenberg J. 2007. Measuring structural dynamics of chromosomes\nin living cells by fluorescence microscopy. Methods 41: 158–67.\n“Previous studies have offered valuable quantitative assessments of phototoxicity”\nDouthwright S, Sluder G. 2017. Live cell imaging: assessing the phototoxicity of\n488nm and 546nm light and methods to alleviate it. J Cell Physiol 232: 2461–8.\n& Schneckenburger H, Weber P, Wagner M, Schickinger S, et al. 2012. Light exposure\nand cell viability in fluorescence microscopy. J Microsc 245: 311–8.\n“with longer wavelengths requiring substantially higher doses to induce comparable\ndelays, supporting observations in previously published studies”\nRichmond D, Jost AP-T, Lambert T, Waters J, et al. 2017. DeadNet: identifying\nphototoxicity from label-free microscopy images of cells using deep ConvNets. arXiv\n170106109.\n“including the use of advanced deep-learning algorithms as classifiers”\nGrah JS, Harrington JA, Koh SB, Pike JA, Schreiner A, Burger M, Schönlieb CB,\nReichelt S. Mathematical imaging methods for mitosis analysis in live-cell phase\ncontrast microscopy. Methods. 2017 Feb 15;115:91-99. PMID: 28189773.\n“and mitosis analysis”\nKnoll SG, Ahmed WW, Saif TA. 2015. Contractile dynamics change before\nmorphological cues during fluorescence illumination. Sci Rep 5: 18513.\n“These effects, often subtle and cumulative, can lead to significant changes in cell\nbehaviour, organelle integrity, and developmental processes.”\nKiepas A, Voorand E, Mubaid F, Siegel PM, Brown CM. Optimizing live-cell\nfluorescence imaging conditions to minimize phototoxicity. J Cell Sci. 2020 Feb\n21;133(4):jcs242834. PMID: 31988150.\n“Historically, photobleaching has been used as a proxy reporter of phototoxicity”\nAssessing Phototoxicity in a Mammalian Cell Line: How Low Levels of Blue Light Affect\nMotility in PC3 Cells. Front Cell Dev Biol. 2021 Dec 17;9:738786. PMID: 34977004.\n“In synchronised cells and in agreement with established literature”\nReviewer #4: An important advantage of the proposed method may be its sensitivity.\nHowever, the authors are not clear on their method’s sensitivity, to the point of\ncontradicting themselves:\np. 4, middle left:\n“A key strength of PhotoFiTT is its ability to reveal the non-binary nature of\nphototoxicity, demonstrating a continuum of effects that vary with light dose and\nwavelength in a label free manner.”\nVersus p.4 bottom left:\n“It is important to acknowledge limitations of PhotoFiTT. While highly sensitive to major\ncellular perturbations, it may not detect subtle light-induced events.”\nIt is important for this publication to have an unambiguous statement regarding its\nmethod’s sensitivity (or lack thereof) and evidence it.\nOur reply: We appreciate the reviewer's keen observation regarding the need for\nprecise characterisation of PhotoFiTT's sensitivity. Our framework detects\nphototoxicity through deviations in mitotic timing, cell size dynamics, and cumulative\nactivity metrics, enabling quantification of cellular-level perturbations across three\norders of magnitude in light dose (0.6-60 J/cm²), as demonstrated in Figure 2d. This\ncontinuum of phototoxic effects manifests as graded physiological responses rather\nthan binary live/dead outcomes, with near-UV doses as low as 0.6 J/cm² inducing\nmeasurable 15-minute mitotic delays in synchronised CHO populations (Figure 2c).\nHowever, \"subtle light-induced events\" refer to molecular-scale perturbations (e.g.,\ntransient ROS bursts, single-organelle dysfunction) that do not cascade into\ndetectable cell cycle alterations. Such phenomena fall beyond PhotoFiTT's detection\nthreshold unless they ultimately impact mitotic progression or global cellular activity.\nThe framework's strength lies in bridging the gap between acute photochemical\ndamage and measurable physiological consequences, as evidenced by its ability to\nresolve 20% differences in mitotic delays between confocal scanning modes at\nequivalent doses (Figure S4b). While not replacing molecular reporters of oxidative\nstress or DNA damage, PhotoFiTT provides systemic readouts of phototoxicity\nthrough tightly coupled cellular processes, establishing physiologically relevant\nthresholds for acceptable imaging conditions.\nWe have altered the discussion to reflect this with the following paragraphs:\n“The PhotoFiTT framework quantitatively connects how illumination parameters\ninfluence distinct physiological endpoints: mitotic timing precision, daughter cell\nemergence kinetics, and cumulative cellular activity metrics (Figure 2c-g). PhotoFiTT\n20\nachieves this through label-free brightfield analysis enhanced by deep learning-based\nsegmentation, avoiding fluorescence-induced confounding factors. While ROS-\nmediated mechanisms contribute to phototoxicity, our synchronisation experiments\nreveal phase-specific vulnerability windows that transcend simple oxidative stress\nmodels. The 300% increase in arrested cells at 60 J/cm2 (Figure S2a) underscores\nthe need for cell cycle-aware illumination protocols.”\n“A principal advantage lies in PhotoFiTT’s capacity to establish quantitative\nphototoxicity thresholds, for instance, defining 6 J/cm2 at 475 nm as the critical dose\ncausing > 50% mitotic delay in CHO cells (Figure 2d). This granularity enables rational\noptimisation of imaging parameters rather than heuristic approaches. The framework’s\nmodular design permits extension to diverse microscopy modalities, as demonstrated\nthrough confocal dwell time comparisons where slow scanning (6 J/cm2) induced\n~20% greater delays than fast scanning at equivalent doses (Figure S4b).”\nReviewer #4: Cellular morphology alone is not a clear indicator of safe illumination\nlevels. The physiology of living specimens can be altered already at much lower\nillumination levels. This subtle phototoxicity, affecting healthy-looking specimens, can\nchange the kinetics of cellular and developmental processes, leading to data\nmisinterpretation. Dynamic processes have been shown to be sensitive indicators of\nphototoxicity (Kiepas 2020, Alghamdi 2021, Icha 2017) compared to morphology, and\ncan indicate positive (hormetic) effects.\nWith this in mind, it might also be useful to revisit the statement “…integration of\nPhotoFiTT principles into real-time analysis pipelines to apply corrective measures to\navoid cell death.”, as this goes back to the binary definition of phototoxicity.\nOur reply: This is a great point, and we deeply thank the reviewer for it. We tried not\nto be overly bold with this statement. Still, we have altered two paragraphs to address\nthe issue: “A principal advantage lies in PhotoFiTT’s capacity to establish quantitative\nphototoxicity thresholds, for instance, defining 6 J/cm2 at 475 nm as the critical dose\ncausing > 50% mitotic delay in CHO cells (Figure 2d). This granularity enables rational\noptimisation of imaging parameters rather than heuristic approaches. The framework’s\nmodular design permits extension to diverse microscopy modalities, as demonstrated\nthrough confocal dwell time comparisons where slow scanning (6 J/cm2) induced\n~20% greater delays than fast scanning at equivalent doses (Figure S4b).”\nReviewer #4: 5) On p.1, the authors state: “Importantly, tolerance to photodamage varies across specimens and is influenced by damage severity, complicating the development of replicable imaging protocols (12, 13)”.\nLater, they write that “a critical need remains for standardised and generalisable methods able to quantitatively link cell damage to high-intensity light exposure across different fluorescence microscopy techniques.”\nIt might be worth expanding on this. A method to assess phototoxicity can only be standardised and generalised to a limited degree, since phototoxic effects always\n21\ndepend on the sample (and many other factors such as media, type and localisation\nof fluorescent proteins used etc.). See Waldchen et al. 2015 with ~6-fold difference of\nphototolerance between HeLa cells and U2OS (or COS-7) cells.\nFor the same reason, providing a hard limit (“For imaging domains where illuminations\nare well above the 60 J/cm2 threshold studied here, researchers should carefully\nconsider and mitigate photodamage.”) is dangerously reductive and misleading. In\nphototoxicity, there is no 'one size fits all'. For a large variety of samples, photodamage\nmay affect samples long before this threshold.\nOur reply: We appreciate the reviewer's critical assessment of phototoxicity\nthresholds and their context-dependent nature. Our revised manuscript now\nacknowledges the inherent variability in phototoxicity susceptibility across\nexperimental systems, as demonstrated by the excitation light sensitivity difference in\nmitotic timings between CHO and HeLa cells under equivalent illumination (Figure 2f,g\nand S3c,d) as “Testing PhotoFiTT on HeLa cells yielded similar results. Using a dose\nof 0.6 J/cm2 did not produce discernible changes, but when challenged with higher\ndoses of 6 and 60 J/cm2, HeLa cells exhibited the same dose dependent delay found\nin CHO cells. Specifically, division times increased from approximately 50 minutes to\n90 minutes, and there was a decrease in activity of up to 30% with higher excitation\nlight doses. These findings suggest that HeLa cells might be more resilient to light\ndamage than CHO cells (Figure S3 a-d, Video S4).”\nThe 60 J/cm² threshold presented in our study is an illustrative example within our\nspecific experimental context (synchronised CHO cells at 630 nm), rather than a\nuniversal limit. We have amended the discussion to frame the 60 J/cm² value as a\nmethodological benchmark for our experimental system while emphasising the need\nfor context-specific optimisation using PhotoFiTT's quantitative metrics as “The current\nimplementation focuses on adherent proliferating systems, with demonstrated efficacy\nin CHO and HeLa models. While this covers many experimental scenarios, adaptation\nto non-dividing or 3D cultures remains future work. The 80% reduction in mitotic events\nat 60 J/cm2 (Figure S1a) highlights particular relevance for developmental studies and\nlong-term time-lapse imaging”.\nThis approach aligns with emerging standards in phototoxicity assessment that\nprioritise mechanistic understanding over universal thresholds.\n_______________\nMinor comments:\nReviewer #4: The instructions are not sufficient in parts - e.g. measuring the power at\nthe sample would benefit from more detail in the methods section and the video.\nProtocol: Initiate each experimental session by calibrating the photodamage irradiation\nlight’s intensity. This step is paramount to guarantee uniformity in experimental\nconditions. Employ a power meter to accurately measure the light intensity (W/cm2)\nat the sample plane, ensuring the microscope’s internal chamber has stabilised at 37\n◦C. Control for the fact that light intensity can vary over time. This calibration is critical\nbefore commencing each experiment to maintain consistency and reliability in results.”\nReviewer #4: What would also be very helpful: Guidelines on how many images need\nto be taken to ensure sufficient spatial and temporal resolution (at least in the case of\nthe cell line used here), and how many cells analysed to reach robust conclusion(s).\nOur reply: The PhotoFiTT framework requires specific temporal and spatial resolutions\nto ensure robust phototoxicity quantification. Temporally, acquisition intervals ≤5\nminutes are critical for resolving mitotic delays, as mammalian cell division typically\noccurs within 30-60 minutes. This granularity enables detection of sub-10 minute\nperturbations, as demonstrated by the 15-minute mitotic delay induced by 0.6 J/cm²\nMitoTracker excitation (Figure 3b). For longitudinal studies, we recommend ≥12 hours\npost-irradiation tracking to capture delayed phenotypic manifestations, validated by\nour 7-hour activity profiles showing dose-dependent declines (Figure 2g).\nSpatially, imaging three non-overlapping fields containing ≥50 mitotic cells each\nensures the capacity to detect ≥15% differences in arrest rates. Our automated pipeline\nprocesses 500-1000 cells per condition (Figure S1a-b), while manual validations\nestablished that ≥300 cells/condition yield <5% variation across replicates. The multi-\nparametric approach compensates for cell-type-specific response variations: while\nHeLa cells show 30% lower activity metric sensitivity than CHO (Figure S3d), their mitotic\ndelay profiles remain equally quantifiable.\nReviewer #4: “Cumulative cellular activity” is not well defined. It needs to be clearly\ndefined in the main text. How is it distinct from cell diameter dynamics, and what does\nit signify? Also, why ‘cumulative’? This is used earlier for phototoxic effects, but for\ncellular activity seems to be with respect to ‘change of area over time’. Also, is cell\nactivity directional (i.e. only shrinkage taken into account)?\nOur reply: We have integrated explicit descriptions of cumulative activity in different\nparts of the text as:\nframes (red outlines). Here cumulative cell activity represents the sum of\nactivity in time. ”\nLine 158: “We observe that higher light doses and shorter wavelengths led to\ndecreased cumulative cellular activity over a 7-hour period (calculated as the\ncumulative activity in time) (Figure 2g).”\nLine 524-525 of the methods: “Cumulative cell activity is calculated as the aggregation\nof activity (activity(t)) in time: Cumulative_activity(T ) = ∑activity(t) (Equation 2)”\nReviewer #4: Also, be clear on the definition and units of irradiation vs irradiance and\nintensity (e.g. “Table S1: Irradiation range (W/cm2)”).\nOur reply: We revised references to it throughout the text.\nReviewer #4: p. 1 bottom left: “Traditional methods for assessing phototoxicity include\nviability assays and morphological observations.” Requires some references.\nOur reply: We have included the missing references.\nReviewer #4: Fig. 1 B:\nCells here look elliptical, but diameter calculation is for perfectly round cells. If an\nellipse is fitted, indicate whether long or short axis is chosen (or result discarded).\nOur reply: The diameter in this case is calculated using the equation 4 in the methods\n(D=2*(sqr(area/pi))) rather than with the axes (short or long) of the cell segmentation\nmask. Sometimes cells indeed display ellipse shapes, however, this approach\nprovides an estimate that depends directly on the entire cell size, which is more stable,\nproviding a more homogeneous measurement in time and better displaying the size\nhalving.\nReviewer #4: “Expose cells to a light irradiation event replicating the illumination\npattern of the imaging experiment to be analysed for phototoxity;“ - phototoxicity\nOur reply: Corrected.\nReviewer #4: “...pursposely-trained”: correct the first word.\nOur reply: Corrected.\nReviewer #4: “Video S1-2 provide in-detail tutorials to reproduce these procedures.”\nChange to: “Videos S1 and S2 provide...”\nReviewer #4: p. 2 top left:\n“low-illumination brightfield microscopy” – meaning low-intensity?\nOur reply: Yes, corrected.\nReviewer #4: Also, what is subcellular about Fig. 1 C? The outline demarcates the\ncellular border.\nOur reply: We have corrected it as: Activity: “Quantifies overall cellular changes\nbetween frames.”\nReviewer #4: p 2. top right:\n“Notably, synchronised populations exhibited higher sensitivity to near-UV-induced\ndamage compared to their unsynchronised counterparts.” Read e.g. Icha et al. 2017\nfor references on this.\nOur reply: We added the missing reference.\nReviewer #4: \"the persistence of mother cells upon time\" - unclear\nOur reply: Changed to “the persistence of cells in the mitotic rounding state upon\ntime“\nReviewer #4: \"Upon challenged\" - unclear\nOur reply: Changed to “When challenged“.\nReviewer #4: \"with longer wavelengths (475, and 630nm)\" - change to: 475nm and\n630nm\nOur reply: Corrected.\nReviewer #4: \"cell division delays are also wavelength-dependant, requiring higher\nlight doses to induce noticeable effects\". Not clearly formulated - why comparative?\nDo the authors mean 'higher light doses at longer wavelengths'? Also, 'noticeable\neffects' - meaning delays? Clarify.\nOur reply: We have changed it to “Compared to shorter wavelengths, longer\nwavelengths need greater light doses to produce significant effects, such as a 20-\nminute delay in the onset of cell division (Figure 2f).”\nReviewer #4: Suppl. mat. pdf, p. 2:\n25\n\"Recomentation\" - Recommendation\nOur reply: Corrected.\nReviewer #4: \"For other cell lines: For other cell types or experimental conditions,\nmanually annotate a representative image set and train a new StarDist model following\nthe protocol outlined in Methods Section D.\" Which is Methods Section D? This one?\nUnclear.\nOur reply: Corrected as “new StarDist model following the protocol outlined in the\nMethods”.\nReviewer #4: video 710560_sh8m2f: There's 30 seconds of a frozen frame at the end\nof the video.\nOur reply: The video has been updated to remove the frozen part.\nReviewer #4: video 710566_sh8sb8: Too fast to follow - can it be slowed down please.\nOur reply: The video has been slowed down.\nReviewer #4: video 710567_sh8sbm: nice illustration of effects; however, blue\nshading at bottom overpowers the plot line.\nOur reply: The video has been altered.\nWe would like to express our sincere gratitude for your thorough and constructive\nfeedback on our manuscript. Your insightful comments helped us make significant\nimprovements to our work. Below, we summarise the main changes made in response to\nthe reviewer’s comments:\n- Statistical Analysis and Figure Legends: We have updated all figure legends to clearly\nindicate whether error bars represent standard deviations or standard errors.\nAdditionally, we now provide statistical significance levels in the figures (line 288).\n- Clarification of Experimental Protocols: The description of the light exposure protocol\nhas been revised for clarity and conciseness.\n- Practical Example Calculations: To assist users working with CHO and HeLa cells, we\nhave added step-by-step example calculations to the supplementary material (Note S3).\n- Terminology Consistency: We have systematically revised the manuscript to use precise\nand consistent terminology regarding irradiance (W/cm²), light dose/radiant exposure\n(J/cm²), and related concepts.\n- Cellular Feature Analysis: We have clarified the distinction between cell stress rounding\nand mitotic rounding. The manuscript now specifies when the 1-to-2 transition (mother\nto daughter cell) is analysed and details the methods used for both manual and\nautomated tracking.\n- Discussion of Phototoxicity Mechanisms: The discussion has been significantly\nexpanded to address the temporal aspects of phototoxicity. We provide a more in-depth\ncomparison with recent literature, highlighting that the temporal pattern of light delivery,\nnot just total dose, critically influences photodamage. This supports the adoption of fast\nscanning and intermittent exposure strategies in live-cell imaging.\n- Corrections and Editorial Improvements: Numerous typographical errors have been\ncorrected, including consistent spelling of “Hoechst” and other technical terms. Figure\ncaptions and references have been revised for clarity and completeness, and the\navailability of analysis macros is now explicitly stated.\nWe believe these revisions have substantially improved the clarity, rigour and utility of the\nmanuscript. All changes are detailed in the accompanying point-by-point response to\nreviewers. Once again, thank you for your valuable feedback. We hope that the revised\nversion meets your expectations.\nREVIEWER COMMENTS\nReviewer #2 (Remarks to the Author):\nI commend the authors for their efforts in revising the manuscript, which have addressed\nmany of the points raised by myself and the other reviewers. The addition of the confocal\nscanning and HeLa experiments is welcome, and it is nice that the authors could retrain\nthe neural networks used to assess HeLa cell data without much effort. While I still have\nconcerns that the results provided by PhotoFITT seem at odds with those previously\nreported in the literature, the revised text and experiments make it clearer that this is a\ntool that can be used to optimise a particular experiment, rather than one that can be\nused to study phototoxicity in general. Indeed, I remain unconvinced that mitotic delay is\na relevant measure of damage for many samples, but that is up to the user to decide. As\nsuch, I would be happy to see the manuscript published in Nature Communications.\nReviewer #2 (Remarks on code availability):\nThe code is clearly written and well-documented.\nOur reply: Thank you for your constructive feedback on our revised manuscript. We\nappreciate your recognition of the improvements made, especially regarding confocal\nscanning, HeLa cell experiments, and neural network retraining. Your comments clarified\nthe scope and application of PhotoFiTT, and we are grateful for your support.\nWe acknowledge your reservations about using mitotic delay as a universal metric for\nphotodamage and agree its relevance depends on the experimental context. We clarified\nthat PhotoFiTT is a flexible tool to optimise conditions, not a definitive assay for\nphototoxicity across all samples.\nThank you again for your valuable feedback, which has improved our work's clarity and\nutility. We hope the revised manuscript addresses your concerns and is suitable for\npublication.\nReviewer #3 (Remarks to the Author):\nThe authors made an excellent effort to respond to my and the other referees’ criticisms\nand implement their suggestions, thus significantly improving the quality of the\nmanuscript. However, I still have a few minor remaining questions and comments that I\nwould like to see addressed before ultimately recommending the work for publication.\nOur reply: Thank you for your detailed feedback on our revised manuscript. We\nappreciate your recognition of our e_orts and suggestions. We have addressed your\npoints with revisions to improve clarity, rigour, and utility. Thank you again for your\nvaluable feedback.\n(1) Statistics: The figure legends should indicate whether error bars in diagrams show\nstandard deviations or standard errors.\nRelated to this, I don't fully buy the argument given in response to Reviewer #2's request\nfor significance tests, that those would not add any insight. An alternative might be to\nindicate the level of significance (by number of star signs) and briefly discuss the\nlimitations for interpretation in the methods section. It also begs the question of why the\nsample sizes were not appropriately chosen in the first place to achieve valid\nsignificance.\nOur reply: Every figure has been updated to show the level of statistical significance, and\nwe've also highlighted the limitations of interpreting p-values, as mentioned in line 288:\n“We implemented the statistical analysis of the results provided by PhotoFiTT, which lead\nto similar conclusions with statistically significant p-values. It should be noted that whilst\nsignificance analysis provides a valuable statistical overview, a critical interpretation of\nthe data is essential. p-values are notably sensitive to empirical bias, and therefore our\nconclusions are drawn from a holistic assessment of the data, a principle of reproducible\nanalysis we have previously discussed36.”\n(2) Line 207ff: “These specimens were imaged for 20 minutes using a constant light\nexposure every 4 minutes for a total acquisition of 20 minutes with a combined exposure\nof 0.6 J/cm2.” This sentence reads somewhat redundantly. What was the actual effective\ntime of exposure? Would it matter if the effective exposure time is applied continuously\nfor a shorter time or with intervals over a longer period?\nOur reply: We thank the reviewer for identifying this redundancy. We have revised this\nsentence to “These specimens were exposed to 475 nm light every 4 minutes during a 20-\nminute acquisition period (150 ms per exposure event), resulting in a combined total\nexposure of 0.6 J/cm²”. We have also clarified that the e_ective exposure time was 150\nms per exposure event. Importantly, the intermittent exposure pattern allows for cellular\nrecovery between exposures, which di_ers significantly from continuous illumination\nregimes of the same total dose. This idea is further elaborated in the discussion section\n“This observation aligns with recent literature showing that phototoxicity is not dictated\nsolely by total energy delivered, but also by how that energy is distributed over time.\nStudies have demonstrated that pulsed or intermittent illumination protocols reduce\ncellular stress and photobleaching compared to continuous exposure2,12,27, as\nrecovery periods allow for more e_ective detoxification of reactive oxygen species and\nrepair of sub-lethal damage.”\n(3) Table 1 and more generally: From assaying two different cell lines, CHO and HeLa\ncells, it would be useful to present an example calculation on how to derive a starting\npoint or recommendation for users who work with those cell lines. For example, for\nwide-field live-cell imaging of a green-fluorescent target in HeLa cells, setting the\nillumination to a 5 W/cm² power density would reach a “safe dose” of 5 J/cm² with a\ntotal acquisition time of 1 s. With 10 ms exposure time (provided this yields sufficient\nSNR), this would allow for distributing the dose over 100 exposures, which can be used\nto either image 100 time points for a 2D time series, or correspondingly fewer time\npoints for a 3D time series. Decreasing the power density by 10-fold, and perhaps using\nDL denoising to overcome SNR penalty, would allow 1000x 10-ms-exposures, while\nadding a colour channel would reduce the number of afforded time points again,\ndepending on the wavelength (not sure if my back of the envelope calculation is\ncorrect, but something along those lines).\nOur reply: We have added comprehensive example calculations to the supplementary\nmaterial (Note S3: Practical Implementations Examples) that include step-by-step\ncalculations for common imaging scenarios. We also include calculations showing how\nreducing power density 10-fold (0.5 W/cm²) extends the safe acquisition time to a total of\n10 seconds, allowing for 1000 exposures, and demonstrating the practical benefits of our\napproach.\n(4) There are still many typos that will likely be amended in the editorial process.\nHowever, one that might be prone to being overlooked is “Hoeschst”, which should read\n“Hoechst”.\nOur reply: We have corrected all instances of 'Hoeschst' to 'Hoechst' throughout the\nmanuscript. We have also verified the proper spelling of all other fluorophore names and\ntechnical terms.\nReviewer #4 (Remarks to the Author):\nThere are two major issues that need to be resolved:\n1) L 235: low doses (0.6 J/cm2) align with their known capacity for direct DNA\ninteraction30, while longer wavelengths (475 nm, 630 nm) required 100-fold higher\nirradiance (60 J/cm2)\n> As requested for the first revision, please be clear on the definition and units of\nirradiation vs irradiance. Irradiance is a measure of the power of electromagnetic\nradiation (such as light) that is received over a given area. The SI unit for irradiance is\nwatts per square meter (W/m2) - of course, variations thereof can be used. How are the\nauthors using the term irradiation? As exposure / fluence / light dose (J/cm2)? A useful\ntable can be found in ref. 5. Note that in the context of microscopy, the term intensity is\noften (albeit incorrectly) used interchangeably with irradiance.\nOur reply: Thank you for your detailed and constructive feedback. We believe these\nrevisions address your concerns and significantly strengthen the manuscript. Thank you\nagain for your valuable input and for helping us improve the quality and clarity of our work.\nWe have thoroughly revised our manuscript to use precise and consistent terminology\nthroughout:\n- Irradiance (W/cm²): The power of electromagnetic radiation per unit area incident on a\nsurface - this describes the intensity of illumination at any given moment.\n- Light dose or Radiant exposure (J/cm²): The total energy delivered per unit area over the\nentire exposure period, calculated as irradiance × exposure time.\nWe have systematically replaced ambiguous uses of \"irradiation\" with the appropriate\nterm:\n- When referring to power density: \"irradiance\" (W/cm²)\n- When referring to total energy delivered: \"light dose\" or \"radiant exposure\" (J/cm²)\n- When referring to the process of exposing samples to light: \"light exposure\" or\n\"illumination\"\n2) PhotoFiTT analyses three cellular features: Rounding, size (large mother cells, smaller\ndaughter cells) and activity (cellular outlines).\nRounding is always called 'mitotic rounding' - but cellular rounding can be a stress\nresponse and due to cellular swelling (osmotic imbalance) and loss of homeostasis. For\nit to be called 'mitotic rounding', the rounding has to be one cell separating into two that\nis being assessed.\nIndeed, in line 160, the authors write: While each of these metrics has its own benefits,\ntracking the 1-to-2 transition of a mother cell to two resolvable daughter cells provides a\nhighly efficient and straightforward approach to assessing photodamage effects.\nBut it is unclear if PhotoFiTT uses tracking / pedigree analysis as a default analysis, or if\nthis is an additional analysis used in this study. At what point does the study switch from\ngeneric rounding/cell size metrics to the 1-to-2 transition analysis? This needs to be\nmade very clear for the reader.\nAlso, line 62 states that 'Cellular activity reflects general dynamic changes in cellular\nshape across time.' How is this differentiated from mitotic rounding?\nOur reply: To avoid potential issues, we now refer to mitotic rounding as cell rounding,\nusing a generic term for the e_ect. We also added the term mitotic rounding when\nreferring to cells that were able to complete mitosis, and stress-induced cell rounding\nwhen no division was observed. A line was added as follows:\nLINE 87: “Identify undergoing cell rounding and division to separate mitotic rounding\nfrom stress-induced cell rounding from stress-induced cell rounding”\nWe analyse the 1-to-2 transition with the results from manually tracking cell rounding\nand division (Figure S1 and S2). This analysis allows us to validate the results and\nconclusions drawn from the automatic cell rounding segmentation pipeline.\nAdditionally, by thresholding the size of segmented cell rounding, we can distinguish\ndaughter cells from mother cells (Figure 2b and 2f), which provides yet another way of\nanalysing the 1-to-2 transition. We have now rewritten the description of these steps in\nthe main text as follows:\nLine 109: To validate that these e_ects were primarily due to light exposure rather\nthan the potential light sensitivity of the CDK1 inhibitor used for synchronisation,\nwe conducted parallel experiments with unsynchronised cells and manually\ntracked cell rounding and mitosis.\nLine 153: Additionally, as a control, we manually tracked cell rounding and\nmitosis in synchronised CHO cell populations to compare against our\nautomated detection pipeline.\nLine 189: While each of these metrics has its own benefits, tracking the 1-to-2\ntransition of a mother cell to two resolvable daughter cells, as done with manual\nannotations, provides a highly e_icient and straightforward approach to\nassessing photodamage e_ects.\nCellular activity is measured for the full duration of the videos, considering the initial\ncellular dynamics leading up to rounding, the rounding itself, and subsequent changes,\nincluding arrest, apoptosis, division, and growth. Cellular activity considers the overall\ndisrupted cellular behaviour beyond cell arrest or irregular mitotic timing. For example,\ncells a_ected by phototoxicity show a slowed-down rounding and cytoplasm dynamics\nright after division.\nA third point regards intensity vs. exposure time.\nIn line 184, the authors state: longer excitation light exposure is more damaging than\nshorter exposures when normalising for total light doses (Figure S4 Fast and Slow scan\nat 6 J/cm2 a-c). This outcome creates an opportunity to minimise phototoxicity by fine-\ntuning the balance between scan speed and illumination intensity.\nand in line 456: ...dose equivalence through inverse power-time compensation, enabling\ndirect comparison of intensity-dependent versus duration-dependent phototoxic\neffects. The experimental design specifically contrasted rapid high-intensity exposure\nagainst sustained low-intensity illumination to dissect temporal components of light-\ninduced cellular stress.\nThis is a very interesting finding that in my view would benefit from being discussed more\nin-depth. Embedding this result in the wider literature (studies reducing intensity vs\nexposure time, and/or studies finding that only the total light dose determines the degree\nof phototoxicity, independent of how light is delivered) addresses important practical\nconsiderations for a wide readership.\nOur reply: We have significantly expanded our discussion of this important finding, which\naligns with emerging literature on temporal aspects of phototoxicity:\n“Our findings demonstrate that the temporal pattern of light delivery is a critical\ndeterminant of phototoxicity, even when the total light dose is held constant. Specifically,\nwe observed that prolonged, low-intensity illumination is more damaging to cells than\nbrief, high-intensity exposures at equivalent total doses, challenging the conventional\nnotion that simply lowering intensity is su_icient to minimise phototoxicity.\nMechanistically, this e_ect is likely due to the limited capacity of cellular antioxidant and\nDNA repair systems, which can recover between short, intense exposures but become\noverwhelmed during extended or continuous illumination, resulting in greater cumulative\ndamage.\nThis observation aligns with recent literature indicating that phototoxicity is not solely\ndictated by the total energy delivered, but also by how that energy is distributed over time.\nStudies have demonstrated that pulsed or intermittent illumination protocols reduce\ncellular stress and photobleaching compared to continuous exposure2,12,27, as\nrecovery periods allow for more e_ective detoxification of reactive oxygen species and\nrepair of sub-lethal damage. These findings advocate for the adoption of fast scanning\nand intermittent exposure strategies in live-cell imaging, as they better preserve cellular\nphysiology than simply reducing light intensity.”\nL 31: comparable effects.\n> comparably deleterious effects.\nCorrected.\nL 42: photobleaching has been used as a proxy reporter of phototoxicity12\n> far more publications than 12 have used this. As 2 and 5 are reviews, adding these\nshould be enough.\nReferences added.\nL 44: Importantly, tolerance to photodamage varies across specimens and is\ninfluenced by damage severity\nWhat do the authors mean? This is circular argumentation.\nWe have clarified the answer by rewriting the sentence “Importantly, specimen type,\ndevelopmental stage, and experimental conditions influence phototoxicity tolerance,\ncomplicating the development of replicable imaging protocols14,15”.\nL 46: Previous studies have offered valuable quantitative assessments of\nphototoxicity3,10,14,16,17\n> 12,25,28 are also valuable quantitative assessments. They should also be referenced.\nReferences added.\nFig.1 caption: biological imaging assay\n> what do the authors mean by 'biological' imaging assay? In the context of\nphototoxicity, it will have to be biological.\nWe altered the caption to say “experimental imaging assay” to avoid redundancy.\nL 66: It detects deviations in these patterns caused by light exposure, enabling the\nquantification of phototoxic effects in fluorescence microscopy.\n> This is only the case where a dependence on light dose (or irradiance) has been\ndemonstrated. The patterns can also be caused by other factors (temperature, media,\ncumulative damage etc).\nWe altered the caption and now reads as the following: “It detects deviations in these\npatterns caused by light exposure, enabling the quantification of phototoxic e_ects in\nfluorescence microscopy. This is only the case where a dependence on light dose (or\nirradiance) has been demonstrated. The patterns can also be caused by other factors\n(temperature, media, cumulative damage, etc.) and thus, comparison against a control\nis also recommended.”\nFig. 2 caption, line 104: t 50 minutes\n> remove t\nRemoved.\nL 276: The PhotFiTT computational framework\n> PhotoFiTT\nCorrected.\nL 168: challenged with higher doses of 6 and 60 J/cm2, HeLa cells exhibited the same\ndose dependent delay\n> dose-dependent delay\nCorrected.\nL 438: Cell fixation and Hoescht nuclei\n> Hoechst, not Hoescht. Replace throughout.\nCorrected.\nL 510: attached to the supplementary material.\n> 'attached to'?\nReplaced “attached” with “included”.\nL 522: alleviate the impact of the noise in the images\n> unclear\nAdded a line to make it clearer: “images which could provide wrong readings and\nchange the activity values”.\nL 527: where eIm is a normalised, contrast-enhanced and smoothed image.\nCumulative cell activity is calculated as the aggregation of activity (activity(t)) in time:\n> italicise eIm and activity(t)\nCorrected.\nL 530: Cummulative\n> Cumulative\nCorrected.\nL 539: with tp being estimated for each video, V.\n> V is not in the equation.\nCorrected as:\n𝑡 :𝐶(𝑡 ) = max{𝐶(𝑡)}, (3)\n! !\n∀#∈%&\nwith 𝑡 being estimated for each video, which has a total number 𝑇 of timepoints.\n!\nL 573: The image processing workflow was implemented in Fiji and automated using a\ncustom ImageJ macro for high-throughput analysis.\n> Indicate the availability of the macro\nWe added this information in line 280 as “ImageJ macro to analyse SYTOX data is\navailable at https://github.com/HenriquesLab/PhotoFiTT/tree/main/IJ-macros/SYTOX.”"}]

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