Age-related hearing loss is one of the most prevalent health conditions in older adults. Although hearing aid technology has advanced dramatically, a large percentage of older adults do not use hearing aids. This untreated hearing loss may accelerate declines in cognitive and neural function and dramatically affect the quality of life. Our previous findings have shown that the use of hearing aids improves cortical and cognitive function and offsets subcortical physiological decline. The current study tested the time course of neural adaptation to hearing aids over the course of 6 months and aimed to determine whether early measures of cortical processing predict the capacity for neural plasticity. Seventeen  older adults (mean age = 75 years) with age-related hearing loss with no history of hearing aid use were fit with bilateral hearing aids and tested in six testing sessions. Neural changes were observed as early as 2 weeks following the initial fitting of hearing aids. Increases in N1 amplitudes were observed as early as 2 weeks following the hearing aid fitting, whereas changes in P2 amplitudes were not observed until 12 weeks of hearing aid use. The findings suggest that increased audibility through hearing aids may facilitate rapid increases in cortical detection, but a longer time period of exposure to amplified sound may be required to integrate features of the signal and form auditory object representations. The results also showed a relationship between neural responses in earlier sessions and the change predicted after 6 months of the use of hearing aids. This study demonstrates rapid cortical adaptation to increased auditory input. Knowledge of the time course of neural adaptation may aid audiologists in counseling their patients, especially those who are struggling to adjust to amplification. A future comparison of a control group with no use of hearing aids that undergoes the same testing sessions as the study’s group will validate these findings. Aging can lead to sensory impairments such as age-related hearing loss, which is one of the most common sensory deficits in older adults . The gloHearing aids are the most common treatment for mild-to-moderate age-related hearing loss. Although hearing aid technology has advanced dramatically over the last decade, the Global Burden of Disease estimates suggest that there is an 83% unmet need for hearing aids globally, calculated as the proportion of individuals with moderate-to-severe hearing loss who do not use a hearing aid . Our preNew hearing aid users require time to become accustomed to their hearing aids e.g., . EvidencSeveral studies have examined perceptual adaptation in older adults who were first-time hearing aids users, but they have had differing conclusions. Some studies show significant improvement in perceptual measures over time . For exaWhile these studies have suggested that adaptation may be observed post-fitting from four to eighteen weeks up to 6 months e.g., , other sIn addition to perceptual and behavioral measures, research has been conducted to study neural changes induced by newly fit hearing aids in older adults using subcortical  and cortThe CAEP has been used to examine the effects of auditory stimulation and amplification while wearing hearing aids in normal-hearing and hearing-impaired participants e.g., . More spAs mentioned earlier, a number of studies have tracked the results of using bilateral newly fit hearing aids using CAEPs for a period of 4 weeks to 6 months in older adults. Specifically, Our previous study  showed cData from thirty-one older adults  between the ages of 60–84 years were included in this study. These data were taken from a larger research study ; their audiograms are shown in All participants in the experimental group were fit with bilateral hearing aids and tested in six testing sessions as shown in p > 0.08. The control group’s data analysis showed that there was no cortical change in amplitudes of the peaks P1, N1, and P2 between sessions 1 and 6 in quiet  or in noise . In addition, there was no significant change in latencies of the peaks P1, N1, and P2 between sessions 1 and 6 in quiet  or in noise . There were no significant differences between the control and the experimental group in session 1 in any of the cortical components across conditions  . Maximum power output measurements were conducted to ensure that the hearing aids did not exceed maximum tolerance limits. On the first day, the participants received an in-service on hearing aid use and were instructed to begin wearing their hearing aids at least 8 h per day. Participants were advised that the hearing aids were set according to their audiometric thresholds, and that for the purposes of the study aims, no changes could be made to the settings. They were also told that they would adjust to the prescribed amplification if they wore their hearing aids on a daily basis. Upon request, at the end of 6 months, changes were made to features such as gain, amplification, directionality, etc. To ensure compliance, monitoring of hearing aid use (average hours/day) was done at each follow-up session through the hearing aid data logging function available through the Widex software platform (group average = 9.31 h/day ± 2 h).The hearing aid fitting procedure was previously described in All tests were conducted in an electrically-shielded sound-attenuated booth. Participants wore their hearing aids during the recording session and were seated in an upright position at a distance of two meters from an Interacoustics SP90 speaker at 0° azimuth  was used to record responses at a sampling frequency of 2,048 Hz via a 32-channel electrode cap. The offsets for all channels were below 50 μV, and earlobes served as references. Six hundred artifact-free sweeps were collected for each condition.MATLAB  was used for offline processing. Zero-phase offline bandpass filtering was performed from 1 to 30 Hz, using a 4th-order Butterworth filter. An electro-oculography reduction method  was usedChanges across six sessions: Repeated measures analyses of variance (RMANOVA) were performed using time (6 sessions) as a within-subject factor for amplitude and latency of peaks N1 (in quiet and in noise) and P2 (in quiet and in noise), followed by planned paired t-tests. Predictive measures: As stated in the introduction, we also aimed to determine if these amplitude measures can be used by the clinician to provide information regarding potential adaptation to newly fit hearing aid individuals. Therefore, regression model analysis was conducted to determine whether earlier sessions predicted improvement in the final session. Confidence intervals  = 2.055, p = 0.006, η2p = 0.128]. Post hoc pairwise comparisons between sessions after adjusting for multiple comparisons (p = 0.031), but P2 amplitude in quiet did not increase until 6 weeks after hearing aid fitting (p = 0.033). Furthermore, a significant increase in P2 amplitudes in noise was observed in the final session—24 weeks following the initial hearing aid visit (p = 0.012) (p > 0.3). Taken together, these results demonstrate evidence of neuroplasticity in N1 amplitudes earlier (2 weeks following hearing aid fitting) than P2 amplitude changes (6 weeks after hearing aid fitting). The time course of neuroplasticity during the period of hearing aid use is reflected in F = 1.156, p = 0.334], consistent with our previous finding of no change in latency between sessions 1 and 6.The RMANOVA showed a main effect of time [parisons  showed t= 0.012) , 5, but R2 = 0.655, p = 0.004) (p = 0.01).We tested whether changes in N1 amplitude that were observed in the earlier sessions predicted the increase in amplitude in the final session (week 24), thus providing a prognosis for eventual hearing aid improvement. Therefore, we examined the amplitude of N1 in quiet at the first session along with the change in amplitude in session 2 and session 3; i.e., 2, and 4 weeks after fitting, to determine whether clinicians can use these measures in these early sessions to provide the patient with expectations regarding the adaptation process. The “Enter” method of linear regression was used since it specifies the order of variables in the model, and the independent variables were entered in the following order: amplitudes at session 1, change in amplitude at session 2 and change in amplitude at session 3, with change in amplitude at session 6 as the dependent variable. To rule out strong correlations between predictor variables, collinearity diagnostics were performed showing satisfactory variance inflation factor (highest = 1.30) and tolerance (lowest = 0.76) scores. The analysis showed that the earlier sessions highly predict variance in the sixth session (= 0.004) . Of the The 85% confidence intervals were calculated for session 1 across measures to determine the criterion for significant amplitude change in an individual see . This peThe main goal of the present study was to determine the time course of neural adaptation to hearing aids and to determine whether early measures of cortical processing predict the capacity for neural change. The current study tested neuroplastic changes induced by hearing aid use over the course of 6 months using CAEPs. Neural changes were observed as early as 2 weeks following the initial fitting of hearing aids. The results showed a neural relationship between responses in earlier sessions and the change predicted after 6 months of the use of hearing aids. These results are significant, because a period of 4 weeks is usually provided for adjustment to the hearing aids, during which the patient can decide whether or not to keep the hearing aids, and knowledge of the potential for neural adaptation may be useful in the decision making process.A previous study by the authors  reportedP2 appears to reflect stimulus detection and identification, based on the spectral information provided by temporal-lobe generators, specifically located in auditory cortices of Heschl’s gyrus e.g., . N1 geneWe note that significant increases in P2 amplitudes were observed only after session 4. Therefore, it is less likely that P2 amplitudes were affected by increased stimulus exposure or repeat testing effects, at least in the early weeks of the study, as has been reported in previous auditory training studies in normal-hearing young adults , 2014. BAs mentioned in the introduction, studies have shown mixed results concerning neural adaptation at subcortical e.g.,  and cortAge-related hearing loss is considered one of the most prevalent health conditions in older adults . AlthougThis study was a relatively small study with the aim of providing pilot data for a larger study that includes more participants and a longer adaptation period. Therefore, a larger cohort would be needed to overcome inter individual variability. Due to the limited nature of the study, it was only possible to test the control group at sessions 1 and 6. Therefore, the observed changes in the hearing aid use group may be due to repeated testing effects. Future studies should include a control group that is tested for the same number of sessions, or perhaps a delayed treatment group that includes multiple baselines. Another suggestion for future research is to conduct a similar study design with first time hearing aid users but with a parallel group of experienced users, and for a longer period of auditory rehabilitation. In addition, associating the neural findings with behavioral changes in speech perception is important, and a future study would benefit from the inclusion of perceptual/self-assessment measures at all-time points. Finally, a future study should consider employing an active listening protocol to eliminate possible confounds of watching a subtitled movie on attention-related cortical components.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The studies involving human participants were reviewed and approved by the Institutional Review Board of the University of Maryland, College Park. The patients/participants provided their written informed consent to participate in this study.HK designed the study, analyzed the data, interpreted the results, and wrote the manuscript. KJ collected the data. SA designed the study, collected the data, interpreted the results, and wrote the manuscript. All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


One major challenge in PET radiomics is its sensitivity to noise. Low signal-to-noise ratio (SNR) affects not only the precision but also the accuracy of quantitative metrics extracted from the images resulting in noise-induced bias. This phantom study aims to identify the radiomic features that are robust to noise in terms of precision and accuracy and to explore some methods that might help to correct noise-induced bias.18F-FDG filled 3D printed inserts, reflecting heterogeneous tracer uptake and realistic tumor shapes, was used in the study. The three different phantom inserts were filled and scanned with three different tumor-to-background ratios, simulating a total of nine different tumors. From the 40-minute list-mode data, ten frames each for 5 s, 10 s, 30 s, and 120 s frame duration were reconstructed to generate images with different noise levels. Under these noise conditions, the precision and accuracy of the radiomic features were analyzed using intraclass correlation coefficient (ICC) and similarity distance metric (SDM) respectively. Based on the ICC and SDM values, the radiomic features were categorized into four groups: poor, moderate, good, and excellent precision and accuracy. A “difference image” created by subtracting two statistically equivalent replicate images was used to develop a model to correct the noise-induced bias. Several regression methods  were tested. The best fitting model was chosen based on Akaike information criteria.A phantom containing three Several radiomic features derived from low SNR images have high repeatability, with 68% of radiomic features having ICC ≥ 0.9 for images with a frame duration of 5 s. However, most features show a systematic bias that correlates with the increase in noise level. Out of 143 features with noise-induced bias, the SDM values were improved based on a regression model (53 features to excellent and 67 to good) indicating that the noise-induced bias of these features can be, at least partially, corrected.To have a predictive value, radiomic features should reflect tumor characteristics and be minimally affected by noise. The present study has shown that it is possible to correct for noise-induced bias, at least in a subset of the features, using a regression model based on the local image noise estimates. Recene (MATV) –7.18F-FDG PET-derived radiomics might have a better predictive value than conventional SUV metrics for a variety of cancer types [PET radiomics provide a more comprehensive characterization of the tumor tracer uptake than conventional SUV metrics. Tumor uptake heterogeneity, based on the radiotracer used, may reflect the underlying biological processes. These tumor uptake heterogeneity characteristics can be quantified by radiomics. Radiomics can therefore be used to facilitate personalized medicine by supporting the identification of patient candidates for targeted therapy, providing prognostic information to assess response to therapy, and predicting patient survival outcome –10. Seveer types –15.89Zr-Immuno-PET has demonstrated to be useful in predicting the efficacy of immunotherapy in oncological studies [89Zr-Immuno-PET derived radiomics may have the potential to support personalized immunotherapy by eliminating the limitations caused by tumor heterogeneity [Treating cancer patients with immunotherapy based on monoclonal antibodies (mAbs) has been shown to have the potential to elicit an effective immune response against tumors, and  studies . 89Zr-Imogeneity .18F-FDG PET. However, in 89Zr-Immuno-PET, the low positron yield of 89Zr (22.6%) in combination with the low amounts of injected radioactivity (~37 MBq), results in images with a poor signal-to-noise ratio (SNR). And, this low SNR affects the precision (repeatability) and accuracy (bias) of the quantitative metrics extracted from these images [One major challenge in radiomics is its sensitivity to several factors such as image acquisition, reconstruction protocol parameters, segmentation methods, image pre-processing, and noise –22. The e images . Here, pThe current study focuses on the sensitivity of PET radiomics to noise and the effect of noise on their precision and accuracy. This experimental phantom study aims to identify the radiomic features (hereafter referred to as RF) that are (1) robust to noise in terms of precision and accuracy and (2) to explore methods that can correct the noise-induced bias of low accuracy features (the term feature hereafter only refers to radiomic features) provived they have high precision.18F-FDG filled 3D printed inserts was used in the study [A NEMA IQ phantom containing three he study , reflect89Zr-Immuno-PET studies, while the images with a frame duration of 120s have noise levels seen with clinical FDG studies. All images were reconstructed using the ordered subset expectation maximization (OSEM) iterative reconstruction method  with time-of-flight (TOF) and resolution modeling . Two European Association of Nuclear Medicine Research Ltd (EARL) compliant reconstruction protocols EARL1 [A list-mode scan of 40 minutes was acquired for each phantom filling on the Siemens Biograph mCT40 PET/CT system . From the list-mode scans, ten frames each for 5 s, 10 s, 30 s, and 120 s frame duration were reconstructed. The shorter frame durations were used to generate images with different noise levels. The images based on 5 or 10 s frame duration simulate noise levels typically seen in ls EARL1   delineations were made using the in-house software tool ACCURATE . VOIs weOverall, 480 features, belonging to 11 feature groups, were extracted from PET images using the RaCaT tool  version. The PytAll data analysis was performed using Python (version 3.7.6). In this section, the precision and accuracy of the features were analyzed.The precision or repeatability of the features was analyzed using the intraclass correlation coefficient (ICC). A two-way mixed-effects model was used to evaluate the absolute agreement between the features derived from the ten replicates. ICC was calculated using the R package: irr  , moderate (0.5 ≤ ICC < 0.75), good (0.75 ≤ ICC < 0.9), or excellent (ICC ≥ 0.9) precision.To study the effect of noise on feature performance, the percentage of features belonging to each of the four categories was calculated per feature group and in total for each of the scan durations.A similarity distance metric (SDM) was used to calculate the distance between the features derived from noisy short frame duration images and the long, nearly noise-free, 40-minute reference scan. Thus, SDM was used to quantify the noise-induced bias of the features. SDM ranges from 0 to 1, and the higher the SDM the better the accuracy, or in other words the closer the noisy scan feature to the reference scan feature.SDM was calculated using the formula:n is the number of tumor types m is the number of repeated measurements n tumor types in the reference scan (l)s) and reference scan (l) feature valuesThis metric can be interpreted as the ratio of the variance between the (nine simulated) tumor features in the reference scan to the total variance, where the total variance is the sum of variance between the tumor features in the reference scan and the squared Euclidean distance between the noisy and reference scan feature values. Thus, if a calculated feature is accurate for high noise levels, the squared Euclidean distance between the noisy and reference scan features is negligible when compared to the differences in feature values between the simulated tumors, and this results in an SDM value close to 1.Based on the SDM values, the features were categorized as having poor (SDM < 0.5), moderate (0.5 ≤ SDM < 0.75), good (0.75 ≤ SDM < 0.9), or excellent (SDM ≥ 0.9) accuracy, in the order from large to negligible noise-induced bias.To explore the effect of noise on feature accuracy, the percentage of features belonging to each of the 4 categories was calculated per feature group and per frame duration.Identification of robust features. Based on their sensitivity to noise, the features were first classified into the following categories based on ICC and SDM:ICC < 0.9: Low repeatability features. These features were excluded from further analysis due to the low reliability to use them in clinical practice as such.ICC ≥ 0.9 and SDM < 0.9: Potential features, i.e., features with excellent precision but displaying noise-induced bias. These features are potentially useful when corrected for noise-induced bias.ICC ≥ 0.9 and SDM ≥ 0.9: Ideal features. These features have excellent precision and accuracy and can be applied regardless of the noise level of the images.Evaluation of feature correctability. To evaluate the feasibility to correct the features for noise-induced bias, the behavior of the features at different noise levels was studied and categorized  were selected.Positive rank correlation between the short and the long scan features. It was assumed that a feature derived from a short scan should correlate with that of a long one to be considered reliable. Since this relationship can be non-linear, the Spearman’s correlation test was used to check if there was a positive correlation between high and low SNR scans. Only a positive correlation was used since a negative correlation would mean that feature order is not maintained between short and long scans. A rank correlation threshold of 0.7 (r ≥ 0.7) was used.Monotonic trend direction of RF as a function of the noise level. With some types of tumors, the features can show a positive bias corresponding with the scan duration, while other tumors can have a negative bias. Features in which the direction of noise-induced bias depends strongly on the underlying uptake distribution will be impossible to correct as in clinical practice this would imply a unique correction (method) for each tumor. We, therefore, restricted the feature correction to only those features that presented the same direction of noise-induced bias across all simulated tumors. This was verified by checking if the feature shows the same monotonic trend direction for all the tumors using Spearman’s correlation test, i.e., either monotonically increasing (r ≥ 0.7) or monotonically decreasing (r ≤ 0.7).Coefficient of variation of the RF. Even if a feature has high ICC, the feature can still have poor precision, i.e., a high coefficient of variation (COV) or standard deviation (SD). Hence, the COV of features at higher noise levels was checked whether it was below 30%, with the purpose to further eliminate imprecise features and restrict the number of features to be corrected.The features which pass all the aforementioned steps were considered for correction purposes and are hereafter referred to as correctable features.diff and COVdiff respectively, could be used to characterize local noise. COVdiff was computed by dividing SDdiff by the mean of the two replicates. Next, the local noise estimate SDdiff (for each frame duration) was correlated with the tumor RF value bias, obtained by taking the difference between the reference scan and the noisy scan (RFdiff = RFlong—RFshort). Similarly, the local noise estimate COVdiff (for each frame duration) was correlated with the tumor RF value ratio, obtained by taking the ratio between the reference scan and the noisy scan (RFratio = RFlong / RFshort) to take into account the percentage difference.To have an estimate of the local noise, a “difference image” was created by subtracting two statistically equivalent replicate images (frame 1 and frame 2) for each short scan duration. The intensity level in the difference image gives an indication of the local noise in the replicates. Ideally, the difference image should have voxels with an intensity of 0 in the absence of noise. In other words, the lower the intensity values in the difference image, the lower the noise-induced variability in the replicates. The local noise was then characterized by the use of this difference image. Thus, the hypothesis was that the SD and COV of the voxel intensity values in the tumor VOI of the difference image, hereafter referred to as SDFrom each of the 10 replicates per scan duration, the median RF value was used and plotted as a function of image noise (either expressed as SD or COV as explained above). Next, a regression model was fitted using the variability of the feature values over the 10 replicates as a weighting factor. The following regressions were tested for each of the correctable features:Linear, only slope (intercept = 0)Linear, only intercept (fixed slope = 1)Linear, with slope and interceptModel 1: Model 2: ExponentialSum of two exponentials: Reciprocal: Model 1: Model 2: Double exponentialModel 1: Model 2: Sigmoid/logisticModel 1: Model 2: Power lawModel 1: Model 2: Rationalcurve_fit function from the scipy optimize (version 1.4.1) Python package [fsolve from the same package was used to find the intercept for the linear model with a fixed slope of 1, since this functionality was not available in the statsmodels package.For linear regression models, the weighted least squares (WLS) method was used for estimating the parameters (slope with or without intercept) with the statsmodels (version 0.11) Python package , and for package . The funThe Akaike information criteria (AIC) was used to determine the best model . Then, the selected model was used to predict the ideal RF value, and the SDM was recalculated to check if the bias was compensated, i.e., whether the SDM increased and if this new SDM was reclassified as good or excellent.Feature correction for different reconstructions. Modeling was done for EARL1 and EARL2 reconstructed scans and the corrected RF were calculated for each reconstruction. To explore if the correction parameters were in agreement between the different reconstruction protocols, the fit parameters from EARL1 were applied to the EARL2 data. Then, the resulting corrected RF were compared to those resulting from using EARL2 fit parameters and the percentage increase in SDM after feature correction was calculated.For EARL1 images, 95% of the features in the 120s scan duration had an excellent ICC, with the percentage decreasing at shorter durations: 85% for 30 s, 76% for 10 s, and 68% for 5 s. Similar results were obtained using the EARL2 reconstruction .Shorter scan EARL1 reconstructed images showed significantly better accuracy when compared to EARL2 reconstructed images. The percentage of features with excellent SDM was the highest for the 120 s scans and decreased for the shorter scan duration images, with the lowest for the 5 s scans. The percentages of 120 s scan features yielding an excellent similarity distance from the 40-minute reference scan were 60.66%, and 45.49% for EARL1 and EARL2 reconstructions respectively, and they reduced to 9.01% and 8.13% correspondingly for 5 s scans. The number of features belonging to each SDM category is given in Overall, 156 (34%) features in EARL1 and 158 features in EARL2 have been found to have low repeatability (ICC < 0.9), while only 41 (9%) of the features in EARL1 and 35 features in EARL2 showed an ideal performance with excellent ICC (ICC ≥ 0.9) and excellent SDM (SDM ≥ 0.9). The remaining 258 (57%) features in EARL1 and 262 features in EARL2 are found to have high repeatability (ICC ≥ 0.9) but poor accuracy (SDM < 0.9) in the presence of noise, and they were therefore checked for correctability. The percentage of features grouped into these three categories per feature group is shown in The number of features passing each of the tests performed to check the feature correctability, per reconstruction protocol is shown in ratio using COVdiff and the models linear (with slope and intercept), double exponential (model 1), power (model 1 and 2) logistic (model 1), rational (model 1) worked best for estimating RFdiff using SDdiff. The best fits were chosen as per AIC and their frequency as the best choice for each of the variable pairs are listed in the After feature correction, the SDM improved for all the features when the most appropriate correction method was applied. The models reciprocal, double exponential (model 2), exponential (model 2), and power (model 1) worked best for estimating RFdiff using SDdiff while that of 13 features improved to excellent and 29 to good by correcting the RFratio using COVdiff. diff was of 94.96. Using COVdiff on the other hand, resulted in a 111.51% increase in SDM on average for all the features.The results of SDM improvement due to noise-induced bias reduction have been restricted to the most noisy 5s scans. For the EARL1 reconstructed images, the SDM of 30 features improved to excellent and 7 features to good by correcting the RFdiff and COVdiff were 131 and 222 respectively and in the latter case 196 and 237 correspondingly.For the EARL2 reconstruction, a comparison of the results obtained by using the same fit parameters in EARL1 reconstruction and by selecting the best fit for EARL2 reconstructed data for correcting 126 features using regression is shown in Simulation of different noise levels, e.g., those seen in low photon count tracers, by varying the scan duration has shown that even though the noise-induced variability between the replicates remained low for several features, there is a systematic bias in the radiomic feature values of images with low SNR. For a feature to have a predictive value and to be useful in the oncological clinical studies, it is desired that the feature value depends on the tumor characteristics only and not on noise. This implies that radiomics analysis can only be used in high count studies where the image quality is high or after features are corrected for noise-induced variability and/or biases. In order to correct for noise-induced bias, we have evaluated the feasibility of using several regression models describing the relationship between the feature value and the local noise estimate.In this study, a large number of features have shown reasonably high repeatability in images with poor SNR for both EARL1 and EARL2 reconstructions. Features from EARL1 reconstructed images have comparatively shown better repeatability most probably because of increased smoothing with Gaussian filter, as Gaussian smoothing is known to reduce the image intensity variations between voxels, making the image more homogenous. While this is useful for image noise reduction, it is expected to also remove tumor heterogeneity details and, consequently, some PET textural features may no longer have a meaningful predictive value. Hence, there is always a trade-off between noise and image resolution. However, Pfaehler et. al has shown that an increase in the Gaussian smoothing do not necessarily eliminate all important heterogeneity information . Also, sdiff and COVdiff from the difference image resulted in bias reduction, with COVdiff providing overall better results than SDdiff, although the selection of either SDdiff or COVdiff is feature dependent in some cases. In addition, we see that the feature bias trend with local image noise was similar between the two EARL compliant protocols pointing to possible compatibility between the different versions of the recommended reconstruction settings for harmonization. Moreover, in addition to the use of the local noise information, we also evaluated the possibility of using the background noise for feature bias trend characterization. However, the outcome of this approach is not reported here, as it yielded poor results, probably because the PET textural features are highly sensitive to intensity variation between neighboring voxels and background noise estimates may not completely capture local intensity variations [89Zr-Immuno-PET, might result in high noise-induced variability between the different splits making it unsuitable for noise-induced bias mitigation.Even though several features were stable with high repeatability, they showed a systematic noise dependent bias. Shorter EARL1 reconstructed images showed better accuracy when compared to EARL2 reconstructed images. We have shown that it is possible to characterize the trend of the feature bias with local image noise. Using both SDriations . Our ass89Zr-Immuno-PET, the concept can be easily expanded to other tracers with poor count statistics and even to high count tracers, such as 18F-FDG, with the purpose to reduce either scan duration or the injected activity. Another possible application could be to reduce the impact of feature bias due to noise based on the sensitivity of PET detectors, thus eliminating PET/CT system bias which can be especially useful in multicenter studies. Moreover, we have explored two different recommended reconstruction protocols (EARL standards 1 and 2) yielding similar contrast recoveries for harmonization in multicenter studies to check if our methodology will be applicable for different reconstruction settings [The possibility to characterize the trend of the feature bias with local image noise opens the door to a wide range of situations where noise is a dominant factor. While this study was done based on the noise conditions seen in settings .3 [One possible limitation of this study was that we used only nine tumor inserts, all of them with fairly high uptake (the lowest TBR was 2.5) and relatively big size. Yet, smaller lesions may occur in clinical data. However, Hatt et. al has recommended to apply radiomics analyses to lesions larger than 10 cm3 . Moreove3 . In clin3 .Another limitation is that we only used limited regression techniques for our hypothesis to describe the noise-induced bias. More complex models might give better results, although with a higher risk to overfit the data. Since substantial bias reduction for the correctable features was achieved with simple methods, and this was not the main aim of the study, it was decided to not include more complex approaches. Yet, it will be easy to extend the approach to include them in future studies. For now, we were able to show that models using a noise-estimate as input can be used to reduce the noise-induced bias under very poor signal conditions.While in our study we only analyzed the impact of the noise on the repeatability and accuracy of the radiomic features, the noise present in the images also makes segmentation a difficult task, leading to wide inter-observer variability. These discrepancies in tumor segmentation can also affect the repeatability and accuracy of the radiomic features derived from low SNR images.89Zr-labelled mAbs, or to develop a clinical radiomics model in patient studies obtained on different PET systems with substantial differences in image quality e.g., because of differences in sensitivity.Several radiomic features derived from low SNR images have high repeatability, but many of them show a substantial noise-induced bias. For a feature to have a predictive value, we want the feature value to depend on the tumor characteristics only and not on noise. We have shown that it is possible to correct the radiomic feature noise-induced bias with simple models using the local image noise estimate as input. These models may allow the use of radiomics analysis for low count studies, such as with S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigExample for ideal feature, potential feature, and unusable feature (left to right) showing the feature behaviour change with scan duration. Note how the ideal feature and potential feature have smaller error bars unlike the unusable feature. Their feature values are also consistent for all scan durations, or in other words the lines do not cross each other and stay parallel to each other, while the opposite is true for the unusable feature. Also note how the ideal feature has the same value for all scan durations, or in other words the lines are straight. On the other hand, there is systematic bias in the potential feature with scan duration, or in other words there is a (linear/non-linear) relationship between the feature value and the scan duration.(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S1 TableNote: The feature Dependence count percentage belonging to the family NGLDM  was always 1 irrespective of the tumor. So, it is excluded in the analysis, which leaves 455 features.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


Drosophila melanogaster that allow temporal characterization of key processes involved in stem cell and tissue homeostasis. However, these organ culture techniques have been limited to imaging sessions of <16 hours, an interval too short to track dynamic processes such as damage responses and regeneration, which can unfold over several days. Therefore, we developed an organ explant culture protocol capable of sustaining midguts ex vivo for up to 3 days. This was made possible by the formulation of a culture medium specifically designed for adult Drosophila tissues with an increased Na+/K+ ratio and trehalose concentration, and by placing midguts at an air-liquid interface for enhanced oxygenation. We show that midgut progenitor cells can respond to gut epithelial damage ex vivo, proliferating and differentiating to replace lost cells, but are quiescent in healthy intestines. Using ex vivo gene induction to promote stem cell proliferation using G12VRas or string and Cyclin E overexpression, we demonstrate that progenitor cell lineages can be traced through multiple cell divisions using live imaging. We show that the same culture set-up is useful for imaging adult renal tubules and ovaries for up to 3 days and hearts for up to 10 days. By enabling both long-term imaging and real-time ex vivo gene manipulation, our simple culture protocol provides a powerful tool for studies of epithelial biology and cell lineage behavior.In recent years, live-imaging techniques have been developed for the adult midgut of  Drosophila melanogaster midgut is a prime example of this as its population of intestinal stem cells (ISCs) are able to interpret signals from their surrounding environment such as cytokines released by neighboring damaged enterocytes (EC)  . When thtes (EC) .Drosophila tissues and organs such as imaginal discs  introduced during dissection results in structural damage which lead to breaks of the epithelium during prolonged culture. Hence, we have optimized the dissection procedure to limit the handling of the midgut and thus the risk of damaging it .Drosophila midgut specifically, an agarose gel also minimizes the effects of peristaltic movements, which, if uncontrolled, will impair imaging and can lead to epithelial tearing. Our approach is a slight departure from previously published techniques for Drosophila tissues  as a measure of cell death , scanning confocal (Leica SP8), and lattice lightsheet (Zeiss LLS7). We did not notice signs of phototoxicity with any of these microscope types in samples imaged for 48 hr, but acquisition parameters had to be chosen with care. For example, by optimizing laser power and dwell time, single intestines could be imaged at high frame-rates (15 min) without noticeable phototoxicity for at least 48 hr even with a confocal microscope (Video 5). However, we found confocal imaging to be less optimal than widefield microscopy for high frame-rate imaging due to the longer acquisition time required for multiple midguts in a single sample.i.e. previously ingested food) in the posterior midgut  and mex-Gal4 Gal80TS driver lines, respectively. This indicates that transcription and protein synthesis are maintained at normal levels in our culture system and shows that this system can be used to assay the effects of transgene induction in real time. Interestingly, progenitor cells were found to be asynchronous in their expression of the reporter GFP  to be quiescent  where kept on fresh food, thus allowing the intestine to clear any trace of SDS and recover. This is different to the ex vivo condition, where a small amount of SDS is retained in the lumen and continues to damage the intestine during imaging. Although we found it impossible and impractical to perfectly match the luminal SDS content in vivo and ex vivo, the in vivo condition did confirm our ex vivo observations . After SA limitation of the SDS-feeding protocol described above is that midguts were damaged prior to imaging. Therefore, it cannot be excluded that the observed differentiation events were EBs that were poised to differentiate prior to SDS feeding, and then differentiated due to the damage stimulus. To confirm that, at the time of damage, pre-existing EBs were indeed capable of responding to tissue stress, we used a thin tungsten needle to create a small lesion in explanted intestines. The lesions perforated both visceral muscle and epithelial layers, but left the peritrophic matrix intact . IntestiAll in all, our observations suggest that both ISCs and EBs are quiescent in undamaged intestines. While previous studies did show varying rates of ISC proliferation in healthy midguts in vivo , this maDelta/Notch signaling that occurs between ISCs and adjacent EBs (Notch (N) by RNA interference (RNAiN) in esg+ cells has been shown to promote the formation of undifferentiated tumor-like masses in vivo  strongly promotes stem cell proliferation in adult midguts . When explanted midguts were shifted to the permissive temperature (29 °C) at the start of imaging, progenitor cells, marked by GFP co-expressed with G12VRas, started to rapidly proliferate  cell tracking, we reconstructed the lineages of 17 dividing cells that expressed le times , the fouSC state . We direSC state . Interes further . For sim further . MoreoveG12VRas stimulation results in multiple cellular changes, we also tested whether we could stimulate ISC proliferation more directly. For this we used the TSesg driver to co-express string (stg), a Cdc25C homolog, and Cyclin E (CycE) . We detected no significant difference in cell cycle duration in progenitors’ cell cycles driven by stg and CycE (9.4±4.6 hr) and cell cycles driven by G12VRas  . String E (CycE) . NotablyE (CycE) , althougon ~25%;  was not and CycE , a signiG12VRas- and 8 from stg and CycE-expressing guts. Interestingly, for 8 of these 17 pairs, the non-dividing cells displayed increases in nuclear size over time, and also lost GFP expression, which is indicative of differentiation towards the EC cell fate . Measuring the distance between sister cells at each time interval, we found that cells in non-co-dividing pairs remained close to one another until the following mitotic event . If co-dting EBs . ConversDifferences in the motility of co-dividing and non-co-dividing siblings could explain these observed effects. We therefore measured cell motility as the distance in 3D space that a cell travelled between one time-point and the next. We found that the genotype used to induce proliferation did not have an effect on the cell motility of either sibling type . LikewisDrosophila organs, we first focused on the Malpighian  tubules. Being physically connected to the intestine, we reasoned the two organs may share similar requirements for their survival ex vivo. A shared characteristic between Malpighian tubules and the adult midgut is the presence of a population of progenitor cells marked by esg expression , indicating their long-term survival , which lay along the abdominal cuticle, could be seen still beating regularly after up to 10 days in culture . SimilarDrosophila tissues and will be useful in investigating a wide range of biologically relevant phenomena.Therefore, we believe our culture protocol could be applied to other adult Drosophila midgut has emerged as a powerful tool to understand the biology of epithelia and their resident stem cells. In recent years this system has been enriched by the development of advanced live-imaging approaches . As ISC proliferation could be stimulated by tissue damage ex vivo, this suggests that, in homeostatic conditions, stem cells may only proliferate when the need to replace damaged or dying cells arises. Given the lack of cell death in undamaged midguts ex vivo, the previously reported proliferation-suppressive effect of enterocytes  has been shown to drive ISC proliferation  ‘co-dividing’ pairs in which both daughter cells divided again, like the progeny of symmetric divisions; and (2) ‘non-co-dividing’ pairs in which only one daughter cell divided while the other did not for the remainder of the imaging session (>6 h), as would be the case for the progeny of asymmetric divisions. By analyzing the reconstructed lineages, we found that upon G12VRas stimulation most lineages did not present co-dividing siblings. Interestingly, if co-dividing siblings were the result of symmetric divisions, this would be in accordance to the previously described prevalence of asymmetric division events in normal intestines . Using thinner agarose pads could help to reduce the required working distance, but may result in hypoxia. This in turn could be solved by increasing the oxygen concentration using microscope incubation chambers equipped with an atmosphere control unit. Midgut survival ex vivo also seems to be limited by the growth of enteric bacteria, especially since, once dissected, midguts cannot properly move food through the intestine and defecate. Indeed, we observed that the visceral muscle, which is not in direct contact with luminal contents, could survive and contract regularly for up to a week in optimal conditions, despite the death of the adjacent epithelium. Generating axenic flies may help to further extend the survival of the midgut epithelium ex vivo. Newer, gentler imaging technologies such as light-sheet microscopy could also improve survival during live-imaging sessions by reducing phototoxicity. It s also possible that different organs may have specific requirements in terms of media composition and additives, in which case tailoring culture media to a specific organ may be beneficial. Indeed, even the same organ can have different requirements in male and female flies . Lastly,Nonetheless, the increased survival ex vivo our protocol allows is significant and we believe it can enable experiments that will lead to a better understanding of the mechanisms that mediate epithelial homeostasis, such as the regulation of asymmetric and symmetric ISC division events. Our protocol may also provide a platform to dissect inter-organ interactions, given the positive effect that co-culture with ovaries and fat bodies had on midgut survival . Moreove1118w (Bloomington Drosophila Stock Center 3605)esg-Gal4 tubGal80ts UAS-GFP / CyO; UAS-flp Act >CD2>Gal4/TM6 B (PMID: 19563763)TS UAS-nlsGFP / CyOesg-Gal4 Tub-Gal80TS UAS-GFP / CyOmex-Gal4 Tub-Gal80esg-Gal4 UAS-GFPTS UAS-nlsGFP / CyO; His2Av-mRFP / TM6Besg-Gal4 Tub-Gal80His2Av-mRFP (Bloomington Drosophila Stock Center 23650)RNAi / TM6BUAS-N (PMID: 26237646)G12V / CyOUAS-Ras (PMID: 21167805)UAS-stg, UAS-CycE (PMID: 24975577)PCNA-GFP His2Av-mRFP; PCNA-GFPFlies were raised on standard cornmeal and molasses fly food. Prior to dissection, flies were flipped to fresh vials without live yeast daily for 3 days at 18 °C to reduce the load of commensal bacteria. On the morning of the dissection, flies were fed a sucrose 0.05% aqueous solution on a cotton pad at room temperature (25 °C) for 4–6 hr to clear most luminal contents. This also helped to reduce accumulation of food in the posterior section of the gut, which could lead to the mechanical stress of the epithelium. For SDS feeding experiments, flies were fed overnight either standard food or food mixed with SDS to a final concentration of 0.2% v/w. Both foods were also mixed with a blue food-safe dye to control for feeding. The following morning, flies were fed a sucrose 0.05% aqueous solution on a cotton pad to clear most of the luminal SDS.Schneider’s medium  was modified by adding the following reagents to the stated final concentrations: 1 mM trisodium citrate dihydrate , 91.2 mM sodium chloride , 55.8 mM D-trehalose anhydrous , 10 mM glutamine , and 2 mM N-acetyl cysteine  . GlutamiFor fly extract, well-fed female flies were anesthetized on ice. Using mortar and pestle, flies were homogenized on ice in the presence of 10µλ per mg of flies of modified Schneider’s medium (as described above) with bovine serum albumin (BSA) added to 1%. The homogenate was centrifuged at 0.6 G and 4 °C for 10'. Supernatant was saved and fly carcasses discarded. The centrifugation step was repeated 3 times until all solid fly residues were eliminated. Extract was heat inactivated by heating at 60 °C for 5’, then centrifuged at 0.6 G and 4 °C for 10'. Supernatant was saved and filtered using 0.22 µm syringe filters. Extract was aliquoted and stored at –20 °C before use.For live-imaging, 100% fly extract prepared in modified Schneider’s medium was used as a base for the complete culture medium. Fly extract was slowly thawed at 4 °C, then 10% fetal calf serum , 1:100 Antibiotic-Antimycotic , 100 µg/ml Ampicillin , and 25 µg/ml Chloramphenicol  were added. To suppress peristaltic movements, 10 µg/ml isradipine  was added to the complete medium immediately before imaging.Prior to dissection, a 35 mm dish with lockable lid  is prepared by first placing a thin wet paper tissue around its inner rim to reduce evaporation during long imaging sessions , heated to 70 °C, over four 4 mm areas in the observation region of the dish. For each dish, 4 pads can be easily cast , using complete medium based on fly extract is not required;Using micro-scissors the head is removed with a clean cut, thus ensuring that the crop and proventriculus still reside in the fly thorax ;The cuticle around the anus is pulled, exposing the hindgut ;Holding the fly gently with forceps around the thorax-abdomen junction, the soft ventral abdominal cuticle is ripped using another forceps, pulling it along the length of the fly towards the anus, thus exposing the midgut ;The abdomen is then gently separated from the thorax ;The crop is gently pinched and pulled out of the thorax, thus freeing the anterior midgut along with it ;The midgut is gently freed from the abdominal cuticle . Care haCrop and malpighian tubules are cut away using micro-scissors, and the hindgut is similarly removed just below its connection to the midgut . This stOnce dissected, a midgut can then be transferred to a well containing modified Schneider’s medium with 10 µg/ml isradipine. Ovaries and the fly abdomen are transferred to this well too;Once all midguts have been dissected, they can be carefully transferred to the agarose pads for sample preparation. To avoid damage during transfer, it is recommended that midguts be moved by keeping them in a small drop of liquid in between the prongs of a forceps. Ovaries and fly abdomens to be co-cultured with the intestines are transferred to the space between the agorose pads, free-floating in the culture medium;For localized damage experiments, midguts were poked in their posterior section using an electrolytically sharpened tungsten needle once transferred to the agarose pads. To keep guts still during the procedure, the hidgut section can be clamped with forceps held in the non-dominant hand. For this, a longer hidgut section can be preserved (see point 9). It is imperative to avoid perforating the peritrophic matrix, thus preventing the contamination of the culture from commensal bacteria. For this, guts are not perforated perpendicularly to the epithelium, but at a~45° angle. Alternatively, guts can be carefully scratched repeatedly in the same spot to produce a tear;For imaging of midgut, hearts, and Malpighian tubules we used an inverted widefield Nikon Ti Eclipse microscope equipped with an incubation chamber (Okolab) for moisture and temperature control, a CoolSnap HQ2 camera (Photometrics), a SOLA LED light engine (Lumencore), and a Prior motorized stage. To limit phototoxicity, albeit at the cost of reduced signal/noise ratio, we used a 4 x neutral density filter, limited light intensity to 5%, and limited exposure times to 100–150ms and 200–300ms for GFP and RFP signals, respectively. Follicles were imaged using a Leica SP8 confocal microscope equipped with a white laser light source and an incubation chamber (Okolab). To limit dwell time, samples were imaged at a frequency of 700 Hz. Intestines could also be successfully imaged with the same setup. For both widefield and confocal setups, videos were captured at room temperature (25 °C) or at 29 °C for temperature-sensitive gene induction experiments using a 20 X air objective (APO 20 X NA 0.75 WD 1). Multipoint acquisition was used to image the R4-5 posterior midgut section of 12 intestines during each imaging section. For each midgut, a 45 µm Z-stack was captured using a 3 µm Z-step. Focus was manually checked and corrected during the course of the imaging session with the Nikon Ti Eclipse, or using a contrast-based autofocus routine for the Leica SP8. Frame rate was typically one full multi-channel Z-stack/midgut/hour. For the imaging of Malpighian tubules and ovaries contractions and beating hearts, the frame rate was 1 slice every 0.08ms instead.1118 flies were cultured in media containing NucGreen  diluted 1:20. Midguts were imaged once a day for 3 days, using a 10 X air objective (Plan Fluor 10 X NA 0.3 WD 16) to capture a stitched z-stack of the whole organ with z-steps of 20 µm. Images were normalized to their median pixel value to account for changes in background, then midgut areas were manually selected from maximum intensity projections. The NucGreen signal was computed as the sum of pixel values in the selected areas, normalized to the first time-point in the series.For cell survival experiments, guts from WMidguts were explanted from 5 to 15 days old flies reared at 18 °C and cultured at 29 °C for 24 hr. Midguts were then carefully removed from the culture setup and fixed in para-formaldehyde 6% in PBS for 30 min at room temperature. Similarly, in vivo control flies were shifted to 29 °C for 24 hr, then dissected and their midguts fixed. Fixed intestines were stained for DAPI 0.1 mg/ml  in PBS with 0.1% Triton-X100, then mounted with VECTASHIELD antifade mounting medium . The posterior region of the intestines was imaged and the GFP and DAPI signals were thresholded using Otsu’s method . Regions+ cells from 48 hr images were identified in previous time-points and their GFP intensity measured.For GFP induction tests, malpighian tubules were detached from midguts using micro-scissors and cultured in sandwiched agarose structures as described above. Tubules were cultured at 18 °C for 24 hr then at 29 °C. Images were captured daily. Individual GFPImage analysis was performed using either ImageJ or Python (v3.7.10). Using a custom Python script , each tiCells were then analyzed as follows:Lineages were reconstructed with a custom Python script  using thNuclear size was measured during manual tracking from the most in-focus z-slice using an upscaled image so as to achieve sub-pixel precision;Mean nuclear GFP intensity was measured during manual tracking and adjusted to the median value in a 50 µm window around the cell of interest to account for changes in local background;Cell cycle duration was measured as the number of frames  from the appearance of a cell resulting from a mitotic event and its subsequent division;+esg + have a high nuclear/cytoplasm ration, measuring internuclear distance is a good approximation for quantifying cell motility. A few errors during image registration resulted in improper measurements, which were then discarded. For this, outlier values were first identified using the interquartile range method, then confirmed on the registered movie file;Cell motility was defined as the distance between the positions of a cell in two subsequent frames. We used the center nuclei to mark cells’ positions. Since For analysis of the angle between sister cells, the z-profile along the axis connecting the center of the two cells was reconstructed Python. Only cells in flat sections of epithelium in reference to the imaging plane (based on enterocytes’ nuclei positions) were considered. The pixel values of the stack along a line connecting the two cells were used to reconstruct the 3D profile. Once all profiles were reconstructed, the angle between the 3D centers of the sister cells was manually computed in reference to the imaging plane. Measurements were repeated three times and averaged. Drosophila midgut and other abdominal organs. Highlights include demonstrated organ viability for up to 72 hours and protocols for ex vivo injury and genetic manipulation. These advances enable the first real-time lineages tracking multiple stem cell divisions, which reveal intriguing spatial-temporal behavior patterns. The manuscript provides a thorough and thoughtful evaluation of these dynamic data and includes additional information about imaging parameters that will be invaluable to those seeking to replicate this method.Marchetti and colleagues present a promising, ex vivo culture method for the adult  public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:Drosophila intestine" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Lucy O'Brien as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by K Vijay Raghavan as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Karen Bellec (Reviewer #2).Thank you for submitting your article "An improved organ explant culture method reveals stem cell lineage dynamics in the adult The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) Either substantiate or revise interpretations of symmetric/asymmetric fate outcomes.Because the movies use guts without markers for terminal fates, distinguishing asymmetric and symmetric outcomes is particularly challenging. Reviewers 1 and 3 felt that the criteria used to classify fate outcomes as symmetric or asymmetric were not sufficient to justify exclusion of the other outcome. Can the authors justify their interpretations, for instance by using midguts that express enteroblast- and enteroendocrine-specific markers? Alternately, if supporting evidence would not be available in a reasonable time frame, the authors are instead asked to describe the data in a manner that does not assign fate outcomes to division events that are ambiguous.2) Clarify the use of nuclear size as a measure of enteroblast differentiation.Reviewers 1 and 2 had questions about how nuclear size was used as a measure of enteroblast differentiation. For Figure 3 and Figure 4 movies, can the authors split the esg>GFP and his::RFP channels so that the nuclei are visible? This is critical to see nuclear size. For Figures 5-7 movies, only esg>GFP(nls?) is shown; can the his::RFP channel also be included? This is critical to evaluate that loss of GFP signal is not due to the trivial reason that the cell moved out of view. Can the authors provide data about the frequency of cell differentiation in the SDS movies? Can they provide additional examples of nuclear measurements for Figure 6E-H and Figure 7D-E (only one example of each type is shown)?3) Provide additional information about organ viability in general and about ex vivo vs in vivo damage response.Reviewers 1 and 2 raised some questions about organ viability. Can the authors provide data for the typical viability of midguts subjected to confocal imaging at frame rates more frequent than 1 hour? Can they comment on how trachea and muscle may be altered by ex vivo culture conditions? The authors develop an ingenious SDS pulse-chase injury protocol; can the authors compare the ex vivo damage response shown in Figure 4 to an in vivo response to the same pulse-chase treatment?Reviewer #1 (Recommendations for the authors):– The terms motility and cell contact are used to discuss the data in Figure 7. However, since non-esg+ cells in the tissue are not visible, it is unclear whether the analyzed cells are autonomously motile or whether they are passively moving with or displaced by other cells. Similarly, since only cell nuclei are visible, cell-cell contact cannot be ascertained (e.g. lines 417-418). Can the authors replace "motility" and "cell contact" with terms that encompass these ambiguities, or else provide information that substantiates these interpretations?– The text states that the dissection technique used by the authors (which is nicely illustrated in Video 1) is an improvement over the standard technique. Can the authors revise the text to clarify this comparison, for instance by describing what the standard technique is?– In Figures 4-7, I'm confused why GFP fluorescence appears to localize to cell nuclei, not the cytoplasm, given that the genotype in the figure caption does not indicate a GFP:nls. Why is GFP used to measure nuclear size rather than the (presumably more accurate) His::RFP? Why is the RFP channel not shown?– Which asymmetric divisions were analyzed in Figure 7 ? Can the authors revise Figure 7D to show individual data points rather than averages?– Peristaltic contractions: Do guts remain viable if isradipine is not included in the media? Is the digestive tract still able to 'defecate' ex vivo?– A reference for the use of N-acetyl cysteine and sodium citrate as anti-phototoxicity agents would be helpful.– Video 20 caption states that egg chamber rotation starts at Stage 5. My understanding is that rotation starts at Stage 1 .Reviewer #2 (Recommendations for the authors):1) Authors presented the different steps of the dissection procedure in Figure 1 and Video 1 where they removed the abdomen, the Malpighian tubules, the ovaries, and the crop. What is the argument for not keeping them attached or near the intestine?Keeping these tissues would limit the handling of the gut and induce less stress to the tissue. In addition, communication between the gut and the ovaries could be preserved. This is further relevant as the coculture with ovaries presented in Figure 2A shows a decrease cell death in explanted intestines. Authors could explain these additional steps.2) Authors explained the negative effects of peristaltic movements on the intestine upon live-imaging to justify the use of the peristalsis inhibitor isradipine.Authors could comment the impact of peristalsis inhibition on the normal gut functions.3) To test the GFP expression in explanted intestines in progenitors , authors used 5 to 15 days old flies. Did you use the same protocol for in vivo experiments? Did you see any impact of the age on the GFP expression in explanted intestines?The n could be increased to further validate the use of this system.4) Figure 3: Nuclei seem bigger upon SDS treatment  compared to control  at the T0 and also after. This difference is not seen in the graphs presented in Figure 3D-E. By looking the figure 3B, it is difficult to see the increase the size of the nuclei of cell losing the GFP signal.Figure 3D-E present only few progenitor cells examples. What is the proportion of cells differentiating upon SDS treatment? By looking at figure 3B 48h, it seems that all of them are differentiating.Is cell differentiation could be directly assessed with specific markers?Did the authors check the cell death in explanted intestines after SDS treatment? and compared with in vivo conditions? Is it possible that SDS treatment can have a negative impact on extended culture of intestines?Did the authors check the cell death in other genetic conditions used in the paper? In figure 3-supplement 2B? and others?These experiments could be a good control to make sure that the different treatments applied have no effect on the extended culture of the intestines.Reviewer #3 (Recommendations for the authors):1. In the paper, the authors have used esgTs to drive expression of RasG12V or stg and CycE and examined the ISC proliferation and analyzed asymmetric verse symmetric divisions. However, esg-Gal4 is expressed in both ISCs and EBs. Overexpression of stg and CycE in EBs can drive EBs to divide, so using the division of progeny to define the symmetric or asymmetric divisions is not very precise here and the symmetric division rate is exaggerated . In addition, it might be the reason that the authors observed the similar speed of the migration in the progeny of both symmetric and asymmetric divisions . Therefore, the ISC specific Gal4 should be used to drive expression of stg and CycE in ISCs and the division of their progenies will be examined.2. Loss of N induces accumulation of ISCs or ee cells, but these phenotypes are not observed in these explanted midguts . Is it possible that this protocol for culturing explanted midguts affects stem cell's identity? Essential revisions:1) Either substantiate or revise interpretations of symmetric/asymmetric fate outcomes.Because the movies use guts without markers for terminal fates, distinguishing asymmetric and symmetric outcomes is particularly challenging. Reviewers 1 and 3 felt that the criteria used to classify fate outcomes as symmetric or asymmetric were not sufficient to justify exclusion of the other outcome. Can the authors justify their interpretations, for instance by using midguts that express enteroblast- and enteroendocrine-specific markers? Alternately, if supporting evidence would not be available in a reasonable time frame, the authors are instead asked to describe the data in a manner that does not assign fate outcomes to division events that are ambiguous.TSesg driver and G12VUAS-Ras or UAS-stg UAS-CycE, is not feasible within a reasonable time, as it would require us to develop and combine several new reporter lines.This is a reasonable criticism and we’ve done our best to address it. The suggestion to use enteroblast and enteroendocrine reporters (cell fate markers) along with the TSesg-Gal4 UAS-2XEYFP; Su(H)GBE-Gal80 tub-Gal80) combined with UAS-YFP and the His2Av.mRFP nuclear marker to allow us to directly identify daughter cells as ISCs or EBs. Unfortunately, the combination of this ISC-specific driver and the His2Av.mRFP nuclear marker resulted in flies whose intestines were extremely prone to breaking during explant culture. These flies were also susceptible to infection, often leading to ex vivo culture contamination. These problems were observed in 6 separate live-imaging sessions with flies from 3 separate crosses. We did not observe these issues with any other genotype we tested, including the original ISC-specific driver and the His2Av.mRFP parent lines. Thus, unfortunately, we were not able to gather additional data to substantiate our assignments of symmetric vs. asymmetric fate outcomes. Given this, we have taken the alternative approach and modified our description of the lineages using the more general, conservative terms “co-dividing” and “non-co-dividing” instead of “symmetric” and “asymmetric” respectively (lines 277-283 and 301-307). Essentially, we have now classified cell divisions in the lineages based on their observed behavior, rather than their presumed fate outcomes. We do mention our expectation that “co-dividing” cells represent ISC duplications, but this is clearly presented as a likely possibility, not a definitive result.As an alternative, we therefore tested an ISC-specific driver (2) Clarify the use of nuclear size as a measure of enteroblast differentiation.Reviewers 1 and 2 had questions about how nuclear size was used as a measure of enteroblast differentiation. For Figure 3 and Figure 4 movies, can the authors split the esg>GFP and his::RFP channels so that the nuclei are visible? This is critical to see nuclear size. For Figures 5-7 movies, only esg>GFP(nls?) is shown; can the his::RFP channel also be included? This is critical to evaluate that loss of GFP signal is not due to the trivial reason that the cell moved out of view. Can the authors provide data about the frequency of cell differentiation in the SDS movies? Can they provide additional examples of nuclear measurements for Figure 6E-H and Figure 7D-E (only one example of each type is shown)?nlsGFP and His2Av.mRFP) and their merge are shown.To better show nuclear size of progenitor cells in control and SDS-treated intestines, we have added a new video  showing a close up of the cells described in Figure 3D-E. In this video, both individual channels  is still in the field of view as can be seen by the His2Av.mRFP signal. Please note its large nuclear size and low level of GFP expression.Regarding Video 18 (previously Video 16), to show that the non-dividing daughter of the first division event is differentiating (losing nlsGFP expression and growing in size), please refer to +esg cells in the control and SDS-treated intestines that we imaged . This data shows that many, but not all, +esg cells growing in size and begin to lose GFP expression in response to SDS damage.Lastly, to quantify the frequency of cell differentiation in SDS movies, we have added a quantification of nuclear growth and GFP intensity of all the clearly trackable 3) Provide additional information about organ viability in general and about ex vivo vs in vivo damage response.Reviewers 1 and 2 raised some questions about organ viability. Can the authors provide data for the typical viability of midguts subjected to confocal imaging at frame rates more frequent than 1 hour? Can they comment on how trachea and muscle may be altered by ex vivo culture conditions? The authors develop an ingenious SDS pulse-chase injury protocol; can the authors compare the ex vivo damage response shown in Figure 4 to an in vivo response to the same pulse-chase treatment?e.g. laser power, dwell-time, etc.) are optimized, it is possible to image intestines for 48h at higher frame-rates (e.g. 15 min). We have added a new movie  captured using a Leica SP8 confocal microscope to show this possibility. There are, however, some limitations: (1) acquisition with scanning confocal microscopes is slow and when imaging several intestines at the same time this can limit the framerate; (2) the file size of 48h movies captured at high frame rates is substantial and requires a powerful data storage infrastructure for storage and analysis. These considerations have been added to the Results section, lines 139-145.If imaging parameters (Trachea are severed when the intestine is detached from the abdomen and ovaries (line 96). The cell death marker, NucGreen, can be seen incorporated in tracheal nuclei (line 112). The visceral muscle, on the other hand, is healthy after explant and resumes contracting with regular peristalsis after the isradipine wears off. This is common in 48-72 hour cultures. A new video has been included to show regular peristaltic movements after 3 days ex vivo .We have added a new supplementary figure showing the effect of our SDS protocol in vivo . The data supports our ex vivo observations. It must be noted, however, that the in vivo and ex vivo conditions are not perfectly matched: the explanted intestines still contain low amounts of SDS, which continues to damage the epithelium while imaging, while in vivo flies were fed clean food after the SDS treatment. Nonetheless, after SDS feeding, intestines in vivo also show a large number of differentiating cells and late onset cell proliferation. These results are now discussed in greater detail in the main text, lines 210-220 and 842-852.Reviewer #1 (Recommendations for the authors):– The terms motility and cell contact are used to discuss the data in Figure 7. However, since non-esg+ cells in the tissue are not visible, it is unclear whether the analyzed cells are autonomously motile or whether they are passively moving with or displaced by other cells. Similarly, since only cell nuclei are visible, cell-cell contact cannot be ascertained (e.g. lines 417-418). Can the authors replace "motility" and "cell contact" with terms that encompass these ambiguities, or else provide information that substantiates these interpretations?+esg cells in the tissue are enterocytes. We did not observe enterocytes moving in the tissue, except for minor rearrangements (< 5µm) due to the loss of a neighboring cell. Indeed, enterocyte nuclei are a good reference for image registration algorithms. Contrary to this, +esg cells are highly motile in the presence of tissue damage  or when stimulated by G12VRas or stg and CycE.Most non-+esg cells have very high nucleus/cytoplasm ratio, using nuclear distance is a good approximation for measuring cell motility.On the point of cell motility, since – The text states that the dissection technique used by the authors (which is nicely illustrated in Video 1) is an improvement over the standard technique. Can the authors revise the text to clarify this comparison, for instance by describing what the standard technique is?We do not make any mention of a “standard” technique. It can be expected that any one individual researcher may dissect intestines in their own unique way. As dissection is a crucial step during which it’s easy to accidentally damage the intestine, we devised an optimized dissection protocol to reduce variation between users and improve midgut survival and integrity in culture.– In Figures4-7, I'm confused why GFP fluorescence appears to localize to cell nuclei, not the cytoplasm, given that the genotype in the figure caption does not indicate a GFP:nls. Why is GFP used to measure nuclear size rather than the (presumably more accurate) His::RFP? Why is the RFP channel not shown?nlsGFP). As such, the signal overlaps with His2Av.mRFP and nuclear sizes measured with the two markers are equivalent. The Figure captions have been corrected accordingly for clarity. The RFP channel was not displayed in some panels to more easily show progenitor cells, which would otherwise be harder to visualize intermixed with the larger RFP+ enterocytes. The His2Av.mRFP signal is usually much brighter in the large enterocytes than in the smaller progenitor cells, compounding this problem. Moreover, due to the major tissue rearrangements induced by G12VRas or stg and CycE, +esg cells can frequently overlap with enterocytes, in which case the RFP-labeled enterocyte and progenitor cell nuclei become indistinguishable.The samples used for figures 3-7 do express a nuclear GFP ? Can the authors revise Figure 7D to show individual data points rather than averages?All asymmetric divisions were included in the analysis. We also tested the differences in internuclear distance using only those asymmetric divisions for which we observed the non-diving cell losing GFP expression. The same significant difference to symmetric divisions was observed, albeit with higher p values due to the reduced number of samples, but still below the 0.05 significance threshold.Figure 7D has been modified as requested.– Peristaltic contractions: Do guts remain viable if isradipine is not included in the media? Is the digestive tract still able to 'defecate' ex vivo?i.e. distant several hundred microns from the break site) remain viable, with regular muscle movements and normally behaving epithelial cells.Peristaltic movements result in more frequent midgut breaks, usually in the posterior section. However, undamaged areas . In the absence of isradipine, or when its effect wears off, midguts can indeed be seen “defecating”. However, this is undesirable as it results in the culture medium being contaminated by enteric bacteria, which quickly grow and result in the death of the cultured intestines.– A reference for the use of N-acetyl cysteine and sodium citrate as anti-phototoxicity agents would be helpful.Drosophila hemolymph at detectable levels . We have added these citations to the text in the appropriate places in the Results section (lines 132-135).One of the main causes of phototoxicity is the generation of reactive oxygen species . Adding antioxidants to the culture medium is therefore a viable strategy to reduce the risk of phototoxicity. N-acetyl cysteine is an antioxidant widely used in cell culture . Citrate is also known to have antioxidant properties  and to be present in – Video 20 caption states that egg chamber rotation starts at Stage 5. My understanding is that rotation starts at Stage 1 .We apologize for the mislabeled Video. We meant to say that a rotating stage 4 egg chamber was seen starting to elongate, with elongation being a sign of progression to stage 5. As for egg chamber rotation, we did observe this phenomenon also in egg chambers at earlier stages. We have corrected the text accordingly .Reviewer #2 (Recommendations for the authors):1) Authors presented the different steps of the dissection procedure in Figure 1 and Video 1 where they removed the abdomen, the Malpighian tubules, the ovaries, and the crop. What is the argument for not keeping them attached or near the intestine?Keeping these tissues would limit the handling of the gut and induce less stress to the tissue. In addition, communication between the gut and the ovaries could be preserved. This is further relevant as the coculture with ovaries presented in Figure 2A shows a decrease cell death in explanted intestines. Authors could explain these additional steps.The main reason these structures are removed is to better access the intestine for imaging. When midguts are separated from neighboring organs and the abdominal cuticle, handling is easier and any region of the intestine is readily accessible. Consequently, imaging is also significantly clearer as ovaries and other organs could easily overlap with the midguts. Malpighian tubules also frequently wrap around midguts and stick to forceps during transfer from dissection dishes to imaging chambers. The crop and hindgut are also removed as they are easily damaged, which results in the release of enteric bacteria. We have expanded the relevant Material and Methods section (lines 604-608) to better explain these reasons.2) Authors explained the negative effects of peristaltic movements on the intestine upon live-imaging to justify the use of the peristalsis inhibitor isradipine.Authors could comment the impact of peristalsis inhibition on the normal gut functions.We have added comments on the possible impact of peristalsis inhibition in the Discussion section (lines 480-486).3) To test the GFP expression in explanted intestines in progenitors , authors used 5 to 15 days old flies. Did you use the same protocol for in vivo experiments? Did you see any impact of the age on the GFP expression in explanted intestines?The n could be increased to further validate the use of this system.All flies used for both in vivo or ex vivo experiments shown were aged 5 to 15 days post-eclosion. We did not observe any obvious differences in GFP expression in either younger (1-5 days post-eclosion) or older (20-30 days post-eclosion) flies. Older flies did, however, show tissue alterations such as dysplasia at the time of dissection, while younger ones had usually a smaller intestinal diameter and strong luminal autofluorescence.4) Figure 3: Nuclei seem bigger upon SDS treatment  compared to control  at the T0 and also after. This difference is not seen in the graphs presented in Figure 3D-E. By looking the figure 3B, it is difficult to see the increase the size of the nuclei of cell losing the GFP signal.+esg cells have a size larger than control cells already at T0. We have also added a new video (Video 7) that more clearly shows the nuclear size and GFP signal of cells from Figure 3D-E.We have added quantifications of nuclear size showing that some Figure 3D-E present only few progenitor cells examples. What is the proportion of cells differentiating upon SDS treatment? By looking at figure 3B 48h, it seems that all of them are differentiating.We have now quantified the GFP levels of more cells at both 0h and 48h. This shows that many cells, but not all, lose GFP expression. This new data is shown in Figure 3G, and discussed on lines 206-208.Is cell differentiation could be directly assessed with specific markers?Cell differentiation could be assessed using specific fluorescent reporters. Unfortunately, available reporters fluoresce at wavelengths that overlap with either the GFP or RFP reporters used to mark cells in our driver line, and so we could not use these reporters in our experiments without losing the ability to track cells.Did the authors check the cell death in explanted intestines after SDS treatment? and compared with in vivo conditions? Is it possible that SDS treatment can have a negative impact on extended culture of intestines?Any kind of damage, be it chemical or mechanical, does result in reduced survival ex vivo. This also includes SDS feeding, especially since, at the time of dissection, the intestines still contain SDS in the lumen and we do not allow flies time to completely flush the SDS from it. Mechanical damage like improper dissection or the puncture of the epithelium described in Figure 3 —figure supplement 1 also reduces gut viability.Did the authors check the cell death in other genetic conditions used in the paper? In figure 3-supplement 2B? and others?G12VRas or stg and CycE results in extrusion of enterocytes in regions were cell crowding is high, as it is known to occur also in vivo .Unless the intestine was damaged during dissection , we did not observe cell death with any of the genotypes we used. However, we did observe that the strong cell proliferation induced by either Reviewer #3 (Recommendations for the authors):1. In the paper, the authors have used esgTs to drive expression of RasG12V or stg and CycE and examined the ISC proliferation and analyzed asymmetric verse symmetric divisions. However, esg-Gal4 is expressed in both ISCs and EBs. Overexpression of stg and CycE in EBs can drive EBs to divide, so using the division of progeny to define the symmetric or asymmetric divisions is not very precise here and the symmetric division rate is exaggerated . In addition, it might be the reason that the authors observed the similar speed of the migration in the progeny of both symmetric and asymmetric divisions . Therefore, the ISC specific Gal4 should be used to drive expression of stg and CycE in ISCs and the division of their progenies will be examined.stg and CycE can indeed force some EBs to divide . We agree with the reviewer that our definition of symmetric and asymmetric divisions was not precise since cell identity cannot be assessed with certainty in these experiments. To address this issue, we did a number of live imaging trials using a stem cell specific Gal4 driver (see answers to editor above for details). However, these trials failed because those midguts were to fragile to image successfully. Therefore, we have changed our discussion in the text by using the more precise terms “co-dividing” and “non-co-dividing” cells to describe the divisions we previously called “symmetric” and “asymmetric” (see lines 277-283 and 301-307). These more conservative terms are accurate operational definitions, and make convey the same information without presuming unknown cell fate outcomes.Overexpression of 2. Loss of N induces accumulation of ISCs or ee cells, but these phenotypes are not observed in these explanted midguts . Is it possible that this protocol for culturing explanted midguts affects stem cell's identity?+ cells in damaged RNAiN-expressing intestines . These cells were proliferative and remained small in size, similar to what has been described in the literature using fixed samples. However, it’s possible that the 48h time-frame may not have been sufficient to observe the formation of large ISC/EE tumor masses in our experiments. In our previous work  we noted a great deal of variability in the N- phenotype, which was dependent upon not only timing, but also background genotype, microbiota, and the specific genetic elements used to suppress Notch signaling.In fact we did observe the accumulation of small-sized GFP