Microglia are the brain’s resident immune cells with a tremendous capacity to autonomously self-renew. Because microglial self-renewal has largely been studied using static tools, its mechanisms and kinetics are not well understood. Using chronic in vivo two-photon imaging in awake mice, we confirm that cortical microglia show limited turnover and migration under basal conditions. Following depletion, however, microglial repopulation is remarkably rapid and is sustained by the dynamic division of remaining microglia, in a manner that is largely independent of signaling through the P2Y12 receptor. Mathematical modeling of microglial division demonstrates that the observed division rates can account for the rapid repopulation observed in vivo. Additionally, newly born microglia resemble mature microglia within days of repopulation, although morphological maturation is different in newly born microglia in P2Y12 knock out mice. Our work suggests that microglia rapidly locally and that newly born microglia do not recapitulate the slow maturation seen in development but instead take on mature roles in the CNS. Microglia are the brain's resident tissue macrophages  with a dAlthough microglia are long-lived cells, they do self-renew slowly under physiological conditions . To studMicroglia are highly dynamic, and understanding their behavior requires an equally dynamic approach that monitors their characteristics chronically over time in their native milieu. Here, we characterized microglial ontogeny and maturation in the adult visual cortex using time-lapse imaging in vivo in awake young adult mice after microglial depletion. We show that microglial self-renewal is slow under basal conditions, with microglia maintaining their territories and showing little movement, loss, or proliferation. In agreement with previous studies, we show that, following depletion, newly born microglia rapidly repopulate the brain and acquire equal cell-to-cell spacing reminiscent of baseline conditions. Self-renewal is driven locally by residual microglia, which have a capacity for fast and continued self-division. To determine whether other mechanisms such as migration from sites outside of the imaging area contributed to the increase in microglia numbers, we mathematically modeled microglial self-renewal in vivo and showed that the observed division rates could account for the rapid repopulation. Finally, we showed that newly born microglia quickly acquire ramified morphologies and dynamic surveillance capabilities in response to focal injury, suggesting that microglial functional maturation is remarkably rapid in the adult brain and does not recapitulate developmental features. To dissect the underlying mechanisms responsible for our observations of microglial repopulation, we considered signaling pathways in microglia associated with migration, microglial motility, and maturation. While there are many receptors expressed by microglia that respond to changes in their environment, we focused on the P2Y12 receptor. P2Y12 is highly expressed exclusively in microglia in the brain  and reguTo track microglial turnover in vivo under basal conditions, we imaged microglia daily in the same awake young adult mice using the microglial-labeled CX3CR1-GFP transgenic mouse line  and a chMicroglial distribution over time was assessed with a custom algorithm that compared the location of microglia in the same field of view over the 14-day imaging period. To measure microglial movement, we defined a ‘translocation index,’ which was an average distance between the location of each microglia on day 1 and the location of the nearest microglia on an nth day . On average, microglia shifted on the order of 15 μm from day to day . This nuMicroglial homeostasis is tightly regulated under basal conditions. Therefore, to explore microglia self-renewal in the visual cortex, we used an established paradigm that partially depletes microglia using PLX5622 (PLX), a Colony Stimulating Factor 1 Receptor (CSF1R/c-kit/Flt3) inhibitor . With thAfter discontinuing PLX treatment, we imaged microglial restoration daily in the same mouse to capture the dynamics of this process . Newly bPLX treatment in P2Y12-KO/CX3CR1-GFP (which will be abbreviated to P2Y12-KO henceforth) mice caused a depletion of microglia of ~70% , with a Microglial self-renewal may be driven by local residual microglia that remain following depletion, although the dynamics of this process are poorly understood. It is unclear whether specific subpopulations of remaining microglia are responsible for the division, or all remaining cells have the capacity to divide; whether repopulation is local or originates in a specific brain region and is coupled to large scale microglial migration; and whether repopulating cells do so by single-cell division or through an intermediate multinucleate body that gives rise to multiple new microglia. During the repopulation phase, we observed occasional splitting of existing microglia into two daughter cells , which hThe characteristic morphologies of microglia in the process of division allowed us to identify ‘potential’ proliferating cells, which we refer to as doublet somas. To determine whether these doublet microglia had distinct morphological characteristics, we analyzed the morphology of microglia in fixed sections from control mice and mice whose microglia repopulated for two days after depletion  to account for the loss of microglia with depletion, we found that individual microglia surveilled their territory in the same manner throughout the control, depletion, and repopulation conditions  and interleukin one receptor (IL1R), and those that regulate migration such as fractalkine through its receptor, CX3CR1 . A gradient or threshold of these factors may start intracellular cascades in microglia that trigger division and migration, such as NF-ΚB signaling, which has been shown to be important for microglia to repopulate fully . In addiTogether our results build on current adult microglia ontogeny research showing that rejuvenation of adult microglia is driven by stochastic doublet cell division locally during repopulation, with newly born cells rapidly able to take on their roles in the brain. Loss of P2Y12 impacts microglial morphologies during repopulation but seems to play a minor role in microglial division. In addition, our mathematical modeling supports the idea that division of residual microglia alone can fill the microglia niche following depletion. While our in vivo tracking approach illuminates the rapid and heterogeneous dynamics of microglial repopulation, future studies will be needed to fully understand the mechanisms that lead to the generation of new microglia and their ability to adopt their mature features. Such studies will provide a deeper understanding of the important role microglia play in homeostasis and disease.IMSR_JAX:005582) GFP reporter mice expressing GFP under control of the fractalkine receptor (CX3CR1) promoter . Animals were housed in a 12 hr light/12 hr dark cycle with food ad libitum. Three- to 6-month-old male and female heterozygous (CX3CR1-GFP)  containing 1200 mg/kg PLX5622  was given to mice as the sole food source for 1–2 weeks to deplete microglia. Control diet with the same base formula but without the compound was given to the control group  and during the repopulation phase for 3–4 weeks.−1), midazolam (5.0 mg kg−1), and dexmedetomidine (0.5 mg kg−1). Body temperature was maintained at 37°C with a heating pad and aseptic technique was maintained during all surgical procedures. Mice were placed in a stereotaxic frame and head-fixed for cranial window surgeries. Hair was removed and the skull was exposed through a scalp incision. A 3 mm Biopsy punch  was used to create a circular score on the skull over V1. A 0.5 mm drill bit  was used to then drill through the skull for the craniotomy, tracing the 3 mm score. A 5 mm coverslip attached to a 3 mm coverslip  by UV glue  was then slowly lowered down into the craniotomy (3 mm Side down). The coverslip was secured with Loctite 404 glue . A custom headplate produced by emachine shop (http://www.emachineshop.com)  was kept in place with C and B Metabond Dental cement . The dental cement was used to cover exposed skull and keep the headplate in place. Mice were administered slow-release buprenex by veterinary staff (s.q. mg kg-1 every 72 hr) and monitored for 72 hr.Animals were anesthetized using a fentanyl cocktail (i.p.) during the cranial window implantation surgical procedure. The fentanyl cocktail consisted of fentanyl . Excitation for fluorescent imaging was achieved with 100-fs laser pulses (80MHz) tuned to 920 nm for GFP with a power of ~30–40 mW measured at the sample. Fluorescence was detected using a photomultiplier tube in whole-field detection mode using a 580/180 filter. Images were collected from 20 μm to 300 μm into the brain. For repeated imaging, blood vessels were used as gross landmarks and stable microglia were also used as fine landmarks to re-identify the correct region for imaging. Image analysis was done offline using ImageJ and Matlab with custom algorithms as described in wska-lab .For awake imaging, mice were trained and habituated to awake imaging during the 2-week recovery from cranial window surgeries. Mice were head fixed under the microscope for 10 min on the first day and the time spent head fixed was increased by 5 min daily. Habituation sessions were terminated if mice exhibited signs of discomfort such as excessive movement and vocalization. Training was complete once mice tolerated 4 consecutive days of head fixation. The imaging session did not exceed 60 min daily. For imaging of microglial morphology and motility, a separate cohort of mice was used and anesthetized using Fentanyl Cocktail. Fentanyl cocktail contained Fentanyl , midazolam , and dexmedetomidine (0.5 m per kg) premixed and given intraperitoneally for each anesthetized imaging session.Two-photon XYZ images of microglia were collected. For analysis, microglia within a volume of 800 X 600 X 100 μm (approximately 60 to 160 μm from the surface of the brain) were used. The images were processed and aligned using Fiji ImageJ. The centroid for each microglia was identified and the x, y, and z coordinates were recorded. Images were aligned over consecutive days using the blood vessels as gross landmarks. A custom algorithm was created to quantify the distances between individual microglia and the remaining microglia in the image. The minimum value was then used as the 3D nearest neighbor value for that microglia and nearest neighbor values were averaged together for all microglia in a single animal at each time point.Distances were calculated as shown below:The location of selected microglia from the first time point were identified, and the XYZ coordinates for the microglia were recorded for the first day. The 3D Nearest neighbor values were calculated as shown above except the location of a microglia was compared to the location of all microglia at another time point. Usually, translocation was measured relative to Day1 of imaging or the time point immediately prior.We began with three parameters:SCR_001905), the algorithm was then initialized with the following values . The maximum number of intersections and the area under the curve (AUC) of the Sholl profile was analyzed to determine microglia arbor complexity.http://bigwww.epfl.ch/thevenaz/stackreg/). After thresholding and binarizing of the maximum intensity projections for all time points together, overlays of consecutive time points  were made, so that white pixels represented stability. A custom Matlab algorithm was used to compare pixels across individual time points and across consecutive time points to generate a motility index . Additional indices were generated, including stability, as the proportion of extension pixels (green) in one overlay that became stable (white) in the subsequent overlay divided by the total extension (green) pixels in the first overlay. Conversely, an instability index was calculated as the proportion of stable (white) pixels in one overlay that became retracted (magenta) in the subsequent overlay divided by the total stable (white) pixels in the first overlay.For motility analysis, XYZT images consisting of 40-μm-deep z-stacks were collected every 5 min, 12 times for a total of 60 min. Single-image 10 μm Z-projections were created for each time point, and lateral motion artifact was corrected using the StackReg and TurboReg functions  by microglia during the 1 hr imaging session. This was done by calculating the total number of pixels representing microglia divided by the total pixels in the field of view. This number was further divided by the proportion of pixels representing microglia in the 1st time point to generate the normalized surveillance.http://bigwww.epfl.ch/thevenaz/stackreg/). The polygon tool was used to outline the soma of microglia, and the area was calculated in microns (μm).Two to 10 microglia were selected at random from high-magnification images. Single-image 10 μm Z-projections were created for each time point, and lateral motion artifact was corrected using the StackReg and TurboReg functions . The tissue was cut on a freezing microtome  at 50 μm. For immunohistochemistry, sections were rinsed, and endogenous peroxidase activity and nonspecific binding were blocked with a 10% BSA solution. Sections were then incubated in primary antibody solution to detect microglia  followed by a secondary antibody solution , mounted and coverslipped. To determine microglial depletion and repopulation, primary visual cortex sections were imaged on a Zeiss LSM 510 confocal microscope (Carl Zeiss). For each section, a 10 mm z stack in the center of the tissue was collected with a z step of 1 µm at ×40 magnification. The analysis was performed offline in ImageJ. The images were thresholded, and the total number of white pixels were then measured. Results from three to four sections per animal were averaged.For the quantification of astrocytes, tissue was processed for histology as described above. Sections were incubated in a primary antibody solution , 1:500, Sigma G3893, clone G-A-5) followed by a secondary antibody solution , mounted and coverslipped. To determine astrocyte coverage, somatosensory cortex sections were imaged on a Zeiss LSM 510 confocal microscope (Carl Zeiss). For each section, a 10 µm Immunohistochemical assessment of repopulated microglia was carried out in brains of CX3CR1-GFP animals received control chow for 2.5 days after 7 day PLX5562 or control that were never exposed to PLX . Tissue was processed for histology in similar manner as described above. Sections were incubated in goat-anti-Galectin 3  at 4°C overnight, then washed and incubated in AlexaFluor 594  secondary at RT, subsequently mounted and coverslipped. Consistent with our in vivo imaging, primary visual cortex sections were imaged  on a Nikon AR1 HD laser scanning confocal microscope (Nikon). Two or three non-overlapping images were collected per V1; using a 40X water-submersion objective lens and imaged at 0.5 µm z-step using the same laser setting across all sections. Analysis was performed using ImageJ, and statistical analysis was completed in R (R Core Team 2021). For image analysis, the intensity of Galectin-3 was measured in doublets, singlets, and control microglia separately. In addition, microglial soma morphology was also assessed using several parameters: perimeter, area, long axis, and short-axis as well as elongation (long/short axis ratio). Data were processed in R and plotted using Prism (GraphPad).CX3CR1-GFP mice  were fed PLX5622 chow or control chow for seven consecutive days. On the 7th day, mice were sacrificed using Euthasol (1:10 dilution) and perfused with 0.15M PBS containing 0.5% sodium nitrite (weight/volume) and 2 I.U. heparin/mL. Cortices were dissected from one hemisphere then immediately frozen in ice-cold isopentane. Frozen cortices were weighed and homogenized in T-PER  for 30 s, vortexed, and subsequently sonicated for an additional 30 s. Cortical lysates were centrifuged for 5 min at 14,000 g twice; each time, clarified supernatants were saved, and the pellets were discarded. Concentrations of inflammatory modulators in homogenates were measured using MILLIPLEX MAP Mouse TH17 Magnetic Bead Panel  as per manufacture recommendations. The following cytokines were assessed: IL-1β, IL-4, IL-6, IL-10, TNFα. For all cytokines, a 5-Parameter logistic model was used to fit the standard curves for absorbance values.n represents individual animals. Exclusion criteria were discussed prior to analysis and included (1) poor image quality  as determined qualitatively prior to quantification; (2) the health of the animal (defined as hunched posture and limited movement) as observed during imaging; (3) incomplete imaging sessions. All microglia analyzed in each animal were averaged to generate a single value per animal. Because not all animals could be imaged at all time points, we used a one-way ANOVA and treated each measurement at different time points as independent, which was supported by the examination of residuals. All values reported are the mean ± s.e.m. For all analyses, α = 0.05. Two-tailed unpaired or paired t-tests and one-way or two-way ANOVA with or without repeated measures (ANOVA) with Tukey post hoc comparisons were used to compare cohorts where appropriate. The exact p values are provided in a supplementary file. The data met the assumptions of normality and equal variances as tested by Prism eight as part of the statistical analyses.Statistical comparisons were made between animal and treatment groups using Prism eight software . Samples sizes are similar to those reported in the field and were based on previous experiments. All  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:All reviewers highlighted the interest of your study that provides an elegant longitudinal study and mathematical modeling of microglial self-renewal and maturation. Your response and revised manuscript fully answers the reviewers comments on the mathematical model, the statistical analyses of doublets as well as a more in depth description of the PLX diet model. Taken together, your findings elegantly describe a novel dynamics of microglial self-renewal and maturation and the role of a key pathway in this process.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Satyajit Rath as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Amanda Sierra (Reviewer #1).Thank you for submitting your article "P2Y12 is not a major regulator of the kinetics of microglial self-renewal and maturation in the adult visual cortex" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at All reviewers highlighted the interest of your study that provides an elegant longitudinal study and mathematical modeling of microglial self-renewal and maturation. However, they also raised specific major and major points that should be addressed in a revised version of the manuscript. In particular, they stressed the importance to provide:i) A clearer description of the mathematical model;ii) A clarification of the notion of doublets, their statistical analyses as well as an in vivo evidence that they correspond to cell division;iii) Stronger statistical analyses of the changes in cytokines after PLX diet and of the impact of P2Y12 inactivation on microglial morphology.eLife.We look forward to receiving a revised version of your manuscript and thank you for considering Reviewer #1:The manuscript "P2Y12 is not a major regulator of the microglial self-renewal and maturation in the adult visual cortex" by Mendes et al. is an elegantly written manuscript that shows very solid data on the kinetics of microglial self-renewal and acquisition of functional maturation after depletion with one of the most promising therapeutic tools in neurodegenerative diseases, the CSf1 inhibitor PLX5266. The authors also show that this process is largely independent of P2Y12, a receptor that has been claimed essential to maintain microglial homeostasis.The data presented is novel, the methods of analysis are the state-of-the-art and several new tools, including a mathematical model have been developed. There are however, a few issues that need to be addressed by the authors.1. The mathematical model, while very interesting, is very poorly described for a lay audience. As an author of papers that include mathematical models, I am well aware that this is extremely difficult, but the authors need to be clearer both in the methods and in the Results sections.In the methods, the authors need to describe the rationale for the different formulas proposed to analyze the dynamics of microglia, and the previous references in which their analysis was surely based.In the results, the authors need also to be more specific (without entering the formula details). I would suggest 1, the draw a cartoon that summarizes how the model was created; 2, create a table where the values used to create the model ; and 3, be more specific about the actual comparison between experimental and simulated data. For instance, I have trouble understanding what the "empirical division rates" are , as they first appear in line 353 but they were not mentioned earlier.2. The authors claim that they did not observe changes in cytokine production after 7 days of the PLX diet, but this is due to the low n of their sampling and the large variability (ouliers?) in the PLX group, as in fact strong tendencies can be observed in most cytokines analyzed in Supp. Figure 2. The authors should perform power analysis to determine the n-size, and reach that n if it is feasible. At the very least, they should be more cautious describing their results and concluding a lack of inflammatory response (line 182).3. While I agree that visually the P2Y12 KO mice data looks largely like the control mouse data throughout the paper, it is unclear whether in all experiments a proper statistical analysis comparing control and KO mice was performed. For instance, data in Figure 2B vs 2D, 2C vs 2E, 5B vs 5G, and all Figure 6 .Certainly, showing each set of data in separate graphs (as is done in most of the paper), does not allow a direct comparison. I understand this is done due to the complexity of the data, but the readers should be better informed on the test that was used to analyze the data.All figures should include more information on the test used, even when the comparison was not significant.4. Definition of a doublet: as doublets are a critical point to analyze microglial dynamics in this paper ), the authors should be more precise in their description. Are they simply enlarged microglial cell bodies? How large? In figure 2, their identification would be easier for the reader if the contours of what the authors consider to be the cell bodies are drawn.Similarly, how can the authors be sure microglia in Figure 3 is a multinucleated body , if a nuclei staining was not performed? In this particular cell, the authors claim that it did not divide, but at day 4 there is something that looks like another cell right on top – can the authors confirm this?5. The authors made some interesting observations on the mode of proliferation of microglia that should be quantified to support their conclusions:– "The appearance of new cells was associated with a characteristic increase and elongation of the cell soma" (line 257).– "The original microglia often had extensive processes which were maintained during the division and generation of a new microglia" (line 259).– "newly generated microglia frequently had a ramified microglia arbor on the same day that division was complete" (line 261).– "newly generated microglia frequently had a ramified microglia arbor on the same day that division was complete. In many cases, we observed that newly born microglia had a long terminal process extending" (line 263) ".The frequency of these events should be quantified.6. In the discussion, the comparison between the maturation of microglia during development and the maturation of microglia observed in this paper is kind of confusing, mostly because most developmental analysis has been done at the transcriptional level (using scRNAseq), whereas this paper is solely based on imaging. I agree that performing scRNAseq is out of the scope of this paper, but the authors should be more precise in their comparison.An example of this mix up is the sentence "While microglial expression in the early phase of development appears to recapitulate developmental programs (19), we did not observe the typical morphologies of developing microglia, or changes in process dynamics and responses to injury that could indicate a less mature phenotype." (line 591).Also, what are the "typical morphologies of developing microglia"? and what is known about the motility of developing microglia?Finally, the use of the word "profile" (line 593) is confusing, because it is usually related to the "transcriptional profile" but the authors seem to use it to refer to their own morphological results.eLife of citing papers from open repositories such as biorxiv.7. The paper on Mac2+ (possible) microglia progenitors, which is widely discussed in this paper, has not been peer-reviewed and I am unsure about the policies of Reviewer #2:In this manuscript, the authors have studied the behavior of microglial self-renewal upon PLX-induced depletion. They first performed in vivo imaging in head-fixed animal and thereby confirmed (ref 6 and 9) that microglia displayed limited migration and turnover. Then, they analyzed the dynamics of repopulation after PLX-induced depletion. They confirmed (ref. 18) that the rapid replenishment observed after depletion is only due to residual microglia, and they propose a mathematical model to support this observation. The authors then show that new-born microglia display a ramified morphology, and a dynamic behavior that is comparable to the one observed before depletion. Finally, the authors show that the parameters of replenishment are not altered by the loss of function of P2Y12R.A major concern is that most of the data in this manuscript simply confirmed already published experiments. This manuscript also provides new data related to brain replenishment after PLX-induced depletion of microglia. These data will certainly be of great interest for those working in the field of microglia function in the adult, but may have a limited impact outside of this community.Reviewer #3:This is an interesting study on microglia self-renewal and maturation in the adult cortex after microglia depletion. The aims are to understand the mechanisms explaining the rapid repopulation and to test whether new microglia are identical to those which have been depleted. There have already been several studies on this subject. The interest of this new study lies in the fact that the authors performed a longitudinal (over 30 days) analysis of microglia dynamics in awake mice.The results suggest that the rapid population is based on the local proliferation of microglia that escaped depletion and that the new microglia rapidly acquire functional properties typical of adult microglia. The dynamics of this repopulation is largely independent of P2Y12 receptors.1. An important part of the article is based on the analysis of microglia called "doublet", which would be microglia in the process of proliferation. However, the authors do not provide a very clear definition of what a doublet is and how these doublets were identified. Clearly defined quantitative criteria would make this part more convincing. In addition, as a certain number of these doublets remain in the doublet state, without dividing, during the observation period (up to 30 days), it would be interesting to show that they indeed correspond to cells in the process of proliferation. A correlation between the identification of doublets in vivo and histochemical analysis of nuclear markers after fixation could help.2. The approach consisting in using the values of the repopulation dynamics obtained with monitoring microglia every 4 hours for 24 hours in a mathematical model  to test whether proliferation alone can explain the observations made with a monitoring less frequent (1 / day) but longer (30 days)  is interesting. Unfortunately, the description of the mathematical modeling is unclear and it is difficult as it is to understand if the results of this modeling actually support the authors' conclusion that "it is reasonable to believe that the repopulation.… came purely from local doublet cell division" (page 16).3. The authors tend to minimize the role of P2Y12. It is true that this receptor does not modify the dynamics of microglia repopulation. However, after repopulation the morphology of P2Y12 KO microglia is very different from that of wild type microglia. Suppl. Figure 4 clearly shows that at the end of repopulation (D30), P2Y12 KO microglia look more like the control, wild type and P2Y12 KO, microglia than do repopulating wild type microglia. Maybe this could be stressed a bit more clearly as this may be important for microglia-neuron interactions. As for wild type microglia , pictures of P2Y12 KO microglia in control and at different stages of the repopulation should be shown. All reviewers highlighted the interest of your study that provides an elegant longitudinal study and mathematical modeling of microglial self-renewal and maturation. However, they also raised specific major and major points that should be addressed in a revised version of the manuscript. In particular, they stressed the importance to provide:i) A clearer description of the mathematical model;To address the comments on the mathematical model in the manuscript, we included a schematic and more clarification on the description of the model in the text accompanying figure 4 and supplementary figure 7.ii) A clarification of the notion of doublets, their statistical analyses as well as an in vivo evidence that they correspond to cell division;To address this comment, we quantified the morphology of doublets on a set of confocal images from fixed brains of control animals and animals that were treated with PLX and allowed to repopulate for 2 days (this is peak repopulation time with a large number of doublet somas present) to determine quantitatively the morphological characteristics that separated singlets from doublets. The somata of doublet cells in repopulated animals were enlarged and irregular in size. While no single morphological measurement  fully separated doublet and singlet somas, a combination of soma size and perimeter separates out the doublet population from non-doublet microglia in repopulated animals, and from microglia in control animals . Doublets are therefore defined as cells with large somas which are elongated, often to form an irregular soma shape. We also immunoreacted these sections and find an upregulation of Galectin-3 in repopulating microglia, although both doublet and singlet microglia showed high levels of expression , consistent with results on proliferation and Galectin-3 expression in Zhan et al., 2020.iii) Stronger statistical analyses of the changes in cytokines after PLX diet and of the impact of P2Y12 inactivation on microglial morphology.3Cr1GFP/+ animals in supplementary figures 4-5,9-13.Cytokine analysis: Based on the data presented in old Suppl. Figure 2, we performed a power analysis and determined that a sample size of 7 animals per group allows us to detect a 2-fold increase in cytokine production with a power of 0.8. We then repeated this experiment with a new cohort of animals and present the data in new Suppl. Figure 2. No significant upregulation of any of the cytokines tested was apparent after PLX treatment. P2Y12 comparisons: We now provide the comparisons between P2Y12KO and CXReviewer #1:[…] 1. The mathematical model, while very interesting, is very poorly described for a lay audience. As an author of papers that include mathematical models, I am well aware that this is extremely difficult, but the authors need to be clearer both in the methods and in the Results sections.In the methods, the authors need to describe the rationale for the different formulas proposed to analyze the dynamics of microglia, and the previous references in which their analysis was surely based.In the results, the authors need also to be more specific (without entering the formula details). I would suggest 1, the draw a cartoon that summarizes how the model was created; 2, create a table where the values used to create the model ; and 3, be more specific about the actual comparison between experimental and simulated data. For instance, I have trouble understanding what the "empirical division rates" are , as they first appear in line 353 but they were not mentioned earlier.We included a schematic that describes the mathematical model in its entirety as supplementary figure 7. In addition, we expanded on the mathematical model in the Results section that accompanies figure 4. We also included a detailed description of the formula used for the model which was created specifically for this analysis by Dr. Matthew McCall and Zachary Brehm, both co-authors on the manuscript.2. The authors claim that they did not observe changes in cytokine production after 7 days of the PLX diet, but this is due to the low n of their sampling and the large variability (ouliers?) in the PLX group, as in fact strong tendencies can be observed in most cytokines analyzed in Supp. Figure 2. The authors should perform power analysis to determine the n-size, and reach that n if it is feasible. At the very least, they should be more cautious describing their results and concluding a lack of inflammatory response (line 182).To address this question, we performed a power analysis in consultation with Dr. Matthew McCall. We found that 7 animals for each group (control versus PLX) were necessary to achieve a power of 0.8 to detect a 2-fold change in cytokine release. We decided on a conservative 2-fold changes although most papers using inflammatory stimuli show upregulations of cytokines that are at least several fold and in some cases an order of magnitude. Using a new cohort of animals, we investigated how PLX treatment affected IL-1b, IL-4, IL-6, IL-10 and TNF-a and found that there was no observed change in the cytokine production after PLX (supplementary figure 2). This is in line with previously published work using PLX5622.3. While I agree that visually the P2Y12 KO mice data looks largely like the control mouse data throughout the paper, it is unclear whether in all experiments a proper statistical analysis comparing control and KO mice was performed. For instance, data in Figure 2B vs 2D, 2C vs 2E, 5B vs 5G, and all Figure 6 .Certainly, showing each set of data in separate graphs (as is done in most of the paper), does not allow a direct comparison. I understand this is done due to the complexity of the data, but the readers should be better informed on the test that was used to analyze the data.All figures should include more information on the test used, even when the comparison was not significant.3Cr1GFP/+ animals in supplementary figures 4-5,9-13.We provided the comparisons between P2Y12KO and CX4. Definition of a doublet: as doublets are a critical point to analyze microglial dynamics in this paper ), the authors should be more precise in their description. Are they simply enlarged microglial cell bodies? How large? In figure 2, their identification would be easier for the reader if the contours of what the authors consider to be the cell bodies are drawn.2 in cross section, compared to an average of 75um2 for control cells) which are elongated, often to form an irregular soma shape.To address this comment, we quantified the morphology of doublets on a set of confocal images from fixed brains of control animals and animals that were treated with PLX and allowed to repopulate for 2 days (this is peak repopulation time with a large number of doublet somas present) to determine the morphological characteristics that separated singlets from doublets. The somata of doublet cells in repopulated animals were enlarged and irregular in size. While no single morphological measurement  fully separated doublet and singlet somas, a combination of soma size and perimeter largely separates out the doublet population from non-doublet microglia in repopulated animals, and from microglia in control animals . Doublets are therefore defined as cells with large somas , if a nuclei staining was not performed? In this particular cell, the authors claim that it did not divide, but at day 4 there is something that looks like another cell right on top – can the authors confirm this?We only observed a large mass of microglia that may represent a multinucleated body once in all our imaging sessions and thus were not able to analyze this phenomenon. While multinucleated bodies have been observed in the Bruttger et al., 2015 paper, we do not believe they contribute to microglial proliferation after depletion with PLX5662.5. The authors made some interesting observations on the mode of proliferation of microglia that should be quantified to support their conclusions:We quantified these observations in a subset of doublets  that could be clearly followed across imaging time points with high spatial and temporal resolution .– "The appearance of new cells was associated with a characteristic increase and elongation of the cell soma" (line 257).91% of new cells were associated with a characteristic increase and elongation of the cell soma of the original cell.– "The original microglia often had extensive processes which were maintained during the division and generation of a new microglia" (line 259).95% of the original microglia had extensive process which were maintained during the division and generation of a new microglia.– "newly generated microglia frequently had a ramified microglia arbor on the same day that division was complete" (line 261).82% newly-generated microglia had a ramified microglia arbor on the same day that division was complete.– "newly generated microglia frequently had a ramified microglia arbor on the same day that division was complete. In many cases, we observed that newly born microglia had a long terminal process extending" (line 263) ".We modified the language of this in the text to reflect that it is difficult to identify and differentiate between parent cells and new cells.This data was included in the text in the Section “Residual microglia are capable of rapid division to generate new microglia and repopulate the cortex”.6. In the discussion, the comparison between the maturation of microglia during development and the maturation of microglia observed in this paper is kind of confusing, mostly because most developmental analysis has been done at the transcriptional level (using scRNAseq), whereas this paper is solely based on imaging. I agree that performing scRNAseq is out of the scope of this paper, but the authors should be more precise in their comparison.An example of this mix up is the sentence "While microglial expression in the early phase of development appears to recapitulate developmental programs (19), we did not observe the typical morphologies of developing microglia, or changes in process dynamics and responses to injury that could indicate a less mature phenotype." (line 591).We revised this section and clarified the maturation of microglia in development versus the maturation of microglia with depletion and repopulation.Also, what are the "typical morphologies of developing microglia"? and what is known about the motility of developing microglia?We have explained the morphological and dynamic changes in microglia during development (ameboid morphologies and motile somas vs. processes in early life microglia) to compare to our findings in adult newly-born microglia.Finally, the use of the word "profile" (line 593) is confusing, because it is usually related to the "transcriptional profile" but the authors seem to use it to refer to their own morphological results.We removed the word profile when referring to the morphological results throughout the manuscript.7. The paper on Mac2+ (possible) microglia progenitors, which is widely discussed in this paper, has not been peer-reviewed and I am unsure about the policies of eLife of citing papers from open repositories such as biorxiv.We updated the references to include the published work.Reviewer #2:[…] A major concern is that most of the data in this manuscript simply confirmed already published experiments. This manuscript also provides new data related to brain replenishment after PLX-induced depletion of microglia. These data will certainly be of great interest for those working in the field of microglia function in the adult, but may have a limited impact outside of this community.While we agree that some of our data confirms recently published findings, we are confident that the novel aspects of our experiments will not only add to the microglia repopulation field but to the microglia biology field altogether. We provide new information as to the dynamics of microglial division, showing that these divisions can be remarkably rapid and continuous. We also provide novel information on the maturation of newly generated microglia and show that their morphologies, surveillance and responses to injury are established very rapidly after repopulation. Lastly, we show that P2Y12 has a limited role in these processes, despite being a critical receptor for microglial homeostatic functions. An overall understanding of the basic biology and mechanism behind adult microglia biology will be beneficial in understanding how microglia self-renew in disease states.Reviewer #3:[…] 1. An important part of the article is based on the analysis of microglia called "doublet", which would be microglia in the process of proliferation. However, the authors do not provide a very clear definition of what a doublet is and how these doublets were identified. Clearly defined quantitative criteria would make this part more convincing. In addition, as a certain number of these doublets remain in the doublet state, without dividing, during the observation period (up to 30 days), it would be interesting to show that they indeed correspond to cells in the process of proliferation. A correlation between the identification of doublets in vivo and histochemical analysis of nuclear markers after fixation could help.To address this comment, we stained and characterized doublets during the peak of division on day 2 of repopulation and now provide a quantitative analysis of the morphological features that distinguish doublets from singlets and control microglia . Additionally, we also stained these cells for Galectin 3, to determine whether the doublet morphology corresponded to the recently described Gal3 positive progenitor-like microglia that is resistant to CSF1R inhibition . While we confirmed that Gal3 was indeed upregulated in a subset of microglia during the repopulation phase, it appeared that Gal3 was expressed equally in doublet and singlet cells . This agrees with the findings in the Zhan study that described EdU incorporation equally in Gal3+ and Gal3- microglia during repopulation, suggesting that proliferation occurs in both of these types of microglia. Unfortunately, Ki67 labels very few microglia in our hands in repopulated tissue  precluding an analysis of proliferating cells and morphology. We did not attempt to use EdU as the experimental design requires a pulse/chase administration which would label many cells rather than just those actively dividing at the time of analysis.2. The approach consisting in using the values of the repopulation dynamics obtained with monitoring microglia every 4 hours for 24 hours in a mathematical model  to test whether proliferation alone can explain the observations made with a monitoring less frequent (1 / day) but longer (30 days)  is interesting. Unfortunately, the description of the mathematical modeling is unclear and it is difficult as it is to understand if the results of this modeling actually support the authors' conclusion that "it is reasonable to believe that the repopulation.… came purely from local doublet cell division" (page 16).We included a schematic that describes the mathematical model in its entirety in the supplementary figures section. In addition, we expanded on the mathematical section that accompanies figure 5 We also included a detailed description of the formula used for the model. This description was created and reviewed by statisticians Dr. Matthew McCall and Zachary Brehm both co-authors on the manuscript.3. The authors tend to minimize the role of P2Y12. It is true that this receptor does not modify the dynamics of microglia repopulation. However, after repopulation the morphology of P2Y12 KO microglia is very different from that of wild type microglia. Suppl. Figure 4 clearly shows that at the end of repopulation (D30), P2Y12 KO microglia look more like the control, wild type and P2Y12 KO, microglia than do repopulating wild type microglia. Maybe this could be stressed a bit more clearly as this may be important for microglia-neuron interactions. As for wild type microglia , pictures of P2Y12 KO microglia in control and at different stages of the repopulation should be shown.Thank you for your comment. It is true that P2Y12 may play a role in the regulation of newly born microglia morphology. To address this comment, we altered the title of the paper, rewrote the abstract and discussion to reflect this point and included pictures of P2Y12 KO microglia in figure 6. Direct comparisons of CX3cr1 GFP/+ and P2Y12 KO animals are now presented in supplementary figures 4-5 and 9-13.References:65, 1744-1761. 10.1002/glia.23192.1. Lowery, R.L., Tremblay, M.E., Hopkins, B.E., and Majewska, A.K. (2017). The microglial fractalkine receptor is not required for activity- dependent plasticity in the mouse visual system. Glia 7, 10905. 10.1038/ncomms10905.2. Sipe, G.O.e.a. (2016). Microglial P2Y12 is necessary for synaptic plasticity in mouse visual cortex. Nat Commun 22, 1782-1792.3. Stowell, R.D., Sipe, G.O., Dawes, R.P., Batchelor, H.N., Lordy, K.A., Whitelaw, B.S., Stoessel, M.B., Bidlack, J.M., Brown, E., Sur, M., and Majewska, A.K. (2019). Noradrenergic signaling in the wakeful state inhibits microglial surveillance and synaptic plasticity in the mouse visual cortex. Nat Neurosci 10.1038/s41593-019-0514-0.


Recently, C-reactive protein (CRP) was shown to affect intracellular calcium signaling and blood pressure in vitro and in vivo, respectively. The aim of the present study was to further investigate if a direct effect on G-protein coupled receptor (GPCR) signaling by CRP can be observed by using CRP in combination with different GPCR agonists on spontaneously beating cultured neonatal rat cardiomyocytes. All used agonists  affected the beat rate of cardiomyocytes significantly and after washing them out and re-stimulation the cells developed a pronounced desensitization of the corresponding receptors. CRP did not affect the basal beating-rate nor the initial increase/decrease in beat-rate triggered by different agonists. However, CRP co-incubated cells did not exhibit desensitization of the respective GPCRs after the stimulation with the different agonists. This lack of desensitization was independent of the GPCR type, but it was dependent on the CRP concentration. Therefore, CRP interferes with the desensitization of GPCRs and has to be considered as a novel regulator of adrenergic, angiotensin-1 and endothelin receptors. The homopentameric C-reactive protein (CRP) is a classic acute phase protein that has been known in human medicine for decades. It has been established primarily as a biomarker for active and chronic inflammation of bacterial origin  This pic2+ mobilization, which was further increased in the presence of CRP [Publications in recent years showed that CRP is more than a biomarker and affects both physiological and pathological processes ,7,8,9. Te of CRP .CRP progressively emerges as a molecule with regulatory properties besides its role as an acute phase molecule. In the case of acute inflammation and the associated high CRP levels in the bloodstream, this pentamer primarily has contact with mobile leukocytes and sessile cells lining the vessels, especially endothelial cells. Although interaction with receptors on these cells seems obvious, CRPs molecular action has so far only been investigated on, e.g., Fc receptor γRII (FcγRII) and in the context of macrophage activation and its role as an archaic antibody-like molecule ,12,13. HIn this respect, it is obvious to examine the influence of CRP on receptor-controlled cell-physiological activation processes. In this paper, we report on the influence of CRP on the signaling system of selected GPCRs, which are involved in the regulation of cardiac, smooth or skeletal muscle cells.GPCRs are a large group of membrane-bound proteins, the amino acid chain of which crosses the cell membrane seven times. The group is named after the receptor activation triggered extracellularly by agonists, which leads to an interaction with G proteins intracellularly . AdrenocThe β1-adrenoceptor is the major adrenergic receptor of the myocardium. Besides this receptor, the β2 and β3 adrenoceptors and the α1-adrenergic, angiotensin II AT1- and endothelin 1 ETA receptors are also expressed in this organ. This receptor expression can be modulated by a long-term treatment with the corresponding agonist and antagonists and also by the agonistic autoantibodies ,22,23,24The AT1-, ETA- and α1 receptors are receptors of the blood vessels and are known as vasoconstrictors . HoweverThe physiological effect of receptor agonists has been studied for decades on the cultures of neonatal rat cardiomyocytes. These cells beat spontaneously in culture and the change in the pulsation rate after the addition of the agonists can be measured visually on an inverted microscope. This bioassay is a standard cell biological method for the identification and characterization of functional autoantibodies against GPCR or the effect of pharmacological receptor antagonists ,28.The aim of the study was to investigate if the inflammatory acute phase protein CRP interferes with the regulation of GPCRs on the cellular level. Here we present first observations about the inhibition of the elementary process of receptor desensitization in rat cardiomyocytes.Human CRP  was purified from human pooled plasma with a selective CRP-binding matrix as described elsewhere in detail . EndotoxThe murine monoclonal antibody (0.64 mg/mL) against CRP was provided by Biometec Inc.  and generated by Dr. B. Micheel and Dr. W. Schroedl.The pharmacological agonists were purchased from Sigma, Germany  or MP Biomedicals, Germany (Angiotensin II) and used in concentrations indicated in 2, the culture medium was renewed after 24 h. The cells were cultured for 2–4 days at 37 °C before using them in the experiment. Target point was the beating rate for 15 s of 6–10 synchronously contracting cell clusters per flask, placed on a heated stage of an inverted microscope at 37 °C. First, the basal beating rate of the cardiomyocytes was measured and after this the agonists were added. The difference between the basal beating rate and increase or decrease in the beating rate after the addition of the agonists is expressed as Δ beating rate/15 s. Respective agonists were then added to the cell culture medium and beating rate was measured again after 5 and 120 min. This was followed by a change of medium , then adding fresh prewarmed (37 °C) complete SM20 culture medium. Thereafter, the pulsation rate returned to the initial value (measured at t = 125 min). After this the stimulation with the agonists was repeated in the same agonist concentration.The cardiomyocyte bioassay was carried out on neonatal rat cardiomyocytes in cell culture as described elsewhere in detail . BrieflyFor CRP co-incubated cells, human CRP was added in a final concentration of either 50 µg/mL or in decreasing concentrations of 40, 20, 10, 5 and 2 µg/mL and preincubated 10 min before agonist stimulation. CRP was not added again after 125 min.To block the activity of CRP, it was pretreated with a blocking monoclonal antibody (0.64 mg/mL) directed against CRP for 30 min at room temperature. This mixture was added to the cardiomyocytes like the CRP solution as described above.First, cardiomyocytes were stimulated with isoprenaline (ISO) with and without CRP for 120 min. As expected, ISO increased the beating frequency compared to the basal rate 0; A. The inThis CRP-induced prevention of desensitization was not only seen for the β-adrenoceptors but also for other GPCRs. In our experiments, we additionally tested the effect of CRP on the β2-adrenoceptor (stimulated with clenbuterol (CLE)), the α1-adrenoceptor (stimulated with phenylephrine (PHE)), and the angiotensin II AT1 receptor (stimulated with angiotensin II (ANG)). These receptor agonists also exert a positive chronotropic response and the desensitization of these GPCR was also prevented by CRP . AlthougTo investigate whether this was exclusive to positive chronotropic agonists that realize their effects via the α1-, β1-, β2- adrenoceptors or the AT1-receptor, cells were also stimulated with endothelin 1 (ET-1), which binds to ETRs and exerts a negative chronotropic effect in the spontaneously beating rat cardiomyocytes.ET-1 decreased the cardiomyocyte beating rate initially with and without CRP . Again, n = 7)).CRP stimulation alone did not affect the cardiomyocyte beating rate as visible in Detailed data values of all replicate experiments are listed in This study was designed to further investigate possible direct effects of CRP on GPCR signaling. Therefore, an established in vitro model was used, spontaneously beating neonatal rat cardiomyocytes, which react to stimulations with agonists or antagonists of different GPCRs with a change of beating rates .Stimulation of cardiomyocytes with different adrenergic, AT-1 and ETR agonists readily affected their beating rate as expected. Co-incubation with CRP did not influence the basal beating rate nor the initial effect of the agonistic stimulation. However, desensitization of GPCRs, which was observed with all used agonists, did not occur in CRP co-incubated cardiomyocytes , 130 minThis leads to the conclusion that this acute phase protein somehow inhibits the mechanism of desensitization. This phenomenon, which is the same in all GPCRs examined, suggests ubiquitous mechanisms that take place via the cell membrane and therefore, potentially affect all GPCRs.GPCR signaling is naturally regulated in a highly complex manner and on several levels. Desensitization is a basic physiological principle, employed by cells in order to protect themselves from overstimulation and possible exhaustion. Receptors can be rendered unresponsive by sequestering or degrading them or downstream intracellular messengers. Rapid desensitization, which would be the case in our applied experimental timeline, is mainly achieved by GPCR phosphorylation, uncoupling the receptor from its respective G protein . This phThe blockade of desensitization of the examined GPCRs achieved by CRP goes beyond the known inflammatory properties of CRP. To hypothesize which molecular route CRP modulates in order to produce a second chronotropic reaction to GPCR agonists would be purely speculative and cannot be deduced from these results. It is, however, in line with previous findings, showing a direct effect of CRP on intracellular calcium signaling, which was further increased by ISO or PE stimulation, no matter the order of stimulation . InteresObviously, the protective mechanism of the fast desensitization of GPCRs is inhibited by an elevated level of CRP in vitro, with possible pathophysiological consequences in vivo. Hemodynamic parameters are often unstable in extreme inflammatory settings such as sepsis, which also present dramatically high circulating CRP levels with peaks of ≥150 mg/L CRP . AlthougIn conclusion, the acute phase protein CRP may play an important role in the regulation of GPCR signaling. By blocking the desensitization of different GPCRs, CRP—in combination with the corresponding agonist—induces a permanent receptor stimulation that may represent a dangerous pathogenic factor. This permanent stimulation of cells could induce a calcium overload, apoptosis and subsequent cell death. Investigations could be conducted to determine whether this effect on the GPCR regulation could play an additional role in the context of the CRP-mediated tissue destruction during ischemic processes . TherefoThe findings described herein should be a springboard for more elaborate experiments to characterize and understand the molecular details of CRP-mediated inhibition of GPCR desensitization.The studies were carried out on primary neonatal rat cardiomyocytes. Their cell-specific receptor equipment is to be seen primarily as a signaling system. Whether this phenomenon also occurs in other cell types such as endothelial cells was not investigated. Other test systems such as calcium signaling are suitable for this. It should also be a focus to find out more about the molecular mechanism of receptor desensitization including whether CRP directly interacts with the respective GPCRs or other receptors on the cardiomyocytes. If similar data are obtained this way, then the conclusions on the pathophysiological effects of CRP on the homeostasis of GPCR could be further elaborated.


We recently developed a computational model of cisplatin pharmacodynamics in an endobronchial lung tumor following ultrasound-guided transbronchial needle injection (EBUS-TBNI). The model suggests that it is more efficacious to apportion the cisplatin dose between injections at different sites rather than giving it all in a single central injection, but the model was calibrated only on blood cisplatin data from a single patient. Accordingly, we applied a modified version of our original model in a set of 32 patients undergoing EBUS-TBNI for non-small cell lung cancer (NSCLC). We used the model to predict clinical responses and compared them retrospectively to actual patient outcomes. The model correctly predicted the clinical response in 72% of cases, with 80% accuracy for adenocarcinomas and 62.5% accuracy for squamous-cell lung cancer. We also found a power-law relationship between tumor volume and the minimal dose needed to induce a response, with the power-law exponent depending on the number of injections administered. Our results suggest that current injection strategies may be significantly over- or under-dosing the agent depending on tumor size, and that computational modeling can be a useful planning tool for EBUS-TBNI of cisplatin in lung cancer. Moreover, even though lung cancer accounts for 13% of new cancer cases, it is responsible for 22% of the deaths2. There is a significant interest in therapies that could potentially improve lung cancer response rates.Lung cancer is the leading cause of cancer-related mortality in the world, with 1.76 million deaths in 2018. This is more than the next three deadliest types of cancer combined 4. The potential advantage of intratumoral delivery of chemotherapy is that it can achieve high concentrations of cytotoxic agent within a tumor while reducing the off-target tissue burden that can lead to adverse side effects 6. However, there is still no consensus as to exactly how intratumoral cisplatin should be delivered to NSCLC tumors. In fact, only two strategies have been employed clinically to date: (1) administering the entire cisplatin dose as a single central injection7, and (2) dividing the dose among five distinct injection sites throughout the tumor8. In addition, the dose itself has been chosen empirically, most commonly being up to 40 mg9. There would thus seem to be significant opportunity to advance the efficacy of EBUS-TBNI of cisplatin by determining the dose that achieves the best tradeoff between cytotoxicity and systemic side-effects, and by employing an injection strategy that ensures all tumor cells receive a lethal concentration of agent. Indeed, our previous computational modeling work10 suggests that there are enormous gains to be made in distributing a given cisplatin dose at optimally chosen injection sites throughout the tumor as opposed to depositing it all at a single location.Endobronchial ultrasound-guided transbronchial needle injection (EBUS-TBNI) of cisplatin has recently emerged as a safe alternative to systemic delivery for treating recurrent centrally located non-small cell lung cancer (NSCLC)The goal of the present work was therefore to investigate how various doses and injection strategies influence treatment response to EBUS-TBNI of cisplatin. As we are not yet at the point where this question can be studied prospectively, we examined the responses achieved in a case series of patients who had received EBUS-TBNI of cisplatin as salvage therapy for late-stage NSCLC. We adapted our previously developed computational model to predict the likelihood of tumor reduction in each patient based on the dose and number of injections they were given, and then compared these predictions to the clinically observed responses. We also used the model to estimate the minimal cisplatin dose required to induce a positive response in each tumor, as well as how this dose depends on the number of spatially distributed injections, with the goal of establishing an initial foundation upon which to set rationalized guidelines for cisplatin dose as a function of tumor volume and delivery strategy.The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was approved by the University of Vermont (UVM) Committee on Human Research in the Medical Sciences (CHRMS 17-075) and by The University of Florida (UFL) Institutional Review Board (201700864). Informed consent was not required by these oversight committees due to the retrospective nature of the study. A cohort of patients that underwent EBUS-TBNI from January 2009 to November 2018 was retrospectively assessed.10. Briefly, this model considers a tumor to have a boundary defined by segmentation of the tumor in a 3D CT scan11. The voxel size reflects the resolution of the 3-D CT scan so that tumor volume and shape can be accurately represented in the model. Volume and shape are the characteristics that distinguish the different tumors from the perspective of our model. Each voxel in the segmented tumor is assumed to be comprised of the superposition of two distinct compartments corresponding to extracellular and intracellular spaces. Cisplatin diffuses within the extracellular compartment We implemented our previously developed computational model of cisplatin pharmacodynamics following intratumoral injection12, the cisplatin diffusion coefficient within each tissue space was estimated as a function of molecular weight to be 2/s. The intracellular uptake rate constant was taken from the literature14 to be 10.Based on empirical observations10. Briefly, this algorithm identifies those injection sites that will give rise to the most homogeneous cisplatin distribution pattern within the tumor. Because we assume cisplatin to bind irreversibly to DNA in the intracellular compartment, its concentration in that compartment increases monotonically with time toward an asymptote, 10:The model is initialized by loading the initial dose of cisplatin into the extracellular compartment mentclass2pt{minim of Eqs. 10:4\docu50 values estimated in vitro from experiments in immortalized human cancer cell lines (Genomics of Drug Sensitivity in Cancer)15. We assume that treatment response is related to a 30% reduction in tumor diameter, following RECIST v.1.1 criteria16. This is roughly equivalent to a 66% volume reduction for a spherical tumor. Therefore, treatment response will be achieved in the model if at least 66% of the tumor volume reaches an intracellular concentration above Even though several different features play a role in determining tumor response to treatment, for the sake of simplicity we assume that apoptosis of cancer cells occurs when the intracellular cisplatin concentration exceeds a threshold value, 7 age 18–80 years, pathologically confirmed non-small cell lung cancer, histologic or cytologic recurrence of cancer following therapy at initial diagnosis, recurrence in an EBUS-accessible site, and computed tomography (CT) scans performed less than five weeks prior to treatment. The characteristics of the cohort are shown in Table We screened 37 patients treated for lung cancer with EBUS-TBNI of cisplatin, the inclusion criteria beingOut of the 37 patients in the dataset, 33 met the inclusion criteria. We excluded one additional patient with squamous cell lung cancer who passed away prior to assessment of therapeutic response. Of the remaining 32 patients, 15 had confirmed adenocarcinoma, 16 had confirmed squamous-cell carcinoma, and 1 had large-cell carcinoma.16. Partial or complete responses by RECIST were considered positive, so the patients were divided into a group that responded to treatment  and a group that did not respond (stable or progressive disease). Of the 22 patients treated at the University of Florida, 1 received 2 injections and 21 received 4 injections according to the injected dose, which was 20 or 40 mg, respectively. Of the 10 patients treated at the University of Vermont, all were administered 5 injections regardless of the dose that ranged from 10 to 40 mg. Multiple injections were administered at sites distributed roughly uniformly throughout the tumor as determined empirically by the treating physician, and dose was selected without formal regard to tumor size.All patients received a total cisplatin dose, determined empirically by the treating physician, ranging from 10 to 40 mg per tumor. Treated lesion response to therapy was determined based on RECIST v1.1 criterahttps://chestimagingplatform.org)17.All CT scans were evaluated by an observer who was blinded to the tumor response. Semi-automated segmentation of the treated volume was performed as previously described using the freely available Chest Imaging Platform (Treatment response was observed in 25 patients (78%), with an 80% response rate in patients with adenocarcinoma, a 68.7% response rate in those with squamous-cell carcinoma, and a 100% response rate in patients with large-cell carcinoma.15 showed that the 72-h IC50 for cisplatin in 75 immortalized human NSCLC cell lines (adenocarcinoma and squamous-cell) ranged from 0.66 to 155.1 μM . Figure 50 values and the fitted Gaussian curve 50 values for different cell lines, we drew the threshold intracellular concentration for cell apoptosis in each voxel The dataset from the Genomics of Drug Sensitivity in Cancer platformThe model correctly predicted treatment response in 72% of the 32 patients in the dataset. Among the adenocarcinomas, the model performed correctly in 80% of the cases Table . The con3, respectively) than the group mean of 14.1 cm3 (SD 19.2 cm3) . Among the six small squamous-cell tumors, which all had volumes lower than 4 cm3, there were three false positives and three true positives. In the group with tumor volumes > 4 cm3 there were two false negatives and one false positive.Interestingly, the two tumors associated with the false negatives in the adenocarcinoma group (ADN-1 and ADN-15) were substantially larger  the diffusion coefficient 10.The results are presented in Table 3 in volume, this relationship is accurately approximated by a power-law of the form The minimum cisplatin dose estimated by the model to induce a treatment response is non-linearly dependent on tumor volume Fig. A. For tuo 5 Fig. B–F.Figur10. In the present study, we put these assumptions to the test by comparing the predictions of the model to clinical outcomes in a cohort of non-small cell lung cancer patients receiving EBUS-TBNI of cisplatin , and this was used to gauge treatment response according to the RECIST v1.1 criteria16. Finally, the model does not take into account regional heterogeneities of structure and/or biophysical properties within a tumor, such as may arise spontaneously or as a result of prior chemotherapy or radiation therapy. Nevertheless, the model remained consistent in predicting the therapeutic outcomes of the cohort when parameters were modified by an order of magnitude, demonstrating a degree of robustness in the face of parameter value uncertainties. Going forward, as we develop a greater understanding of how these various factors vary within a tumor and between patients, and develop methods for assessing them in individual tumors, we expect the accuracy of our model predictions to improve accordingly.This study has a number of limitations. For example, we did not have the data necessary to calibrate model parameters to the individual patients in our cohort, so the flux of cisplatin to the extratumoral space, and the volume of this space, was based on previous data from a single patient in whom serial plasma concentrations of cisplatin following injection were measured22 where cisplatin diffusion from sites of injection to distant malignant cells could be particularly limited, yet our model assumes fixed diffusivity throughout the tumor. Relating local diffusivity to Hounsfield unit (HU) values on CT might offer a means of refining the model in this regard, although it remains unclear as to the precise link between diffusivity and HU. Estimating local tissue clearance due to perfusion is even more problematic, as CT does not have the resolution to quantify the microvasculature. In principle, we can estimate global cisplatin clearance from a tumor from the kinetics of cisplatin levels in the blood, as we have shown previously10, but unfortunately these data were not available for the patients of the present study. Accordingly, we used an estimated clearance parameter obtained previously in a single patient, but it is likely that this parameter varies significantly between patients, and indeed can even vary over the course of treatment in single patient due to angiogenesis on the time-scale of weeks.The computational model itself also has limitations because of the numerous simplifying assumptions that had to be made in its construction, mostly reflective of a lack of biologic data. Thus, even though we have been able to demonstrate promising performance, we recognize that the ability of the model to predict clinical outcomes could probably be improved substantially with better and more detailed information about the tumor microenvironment. As cytotoxic cisplatin levels within a tumor depend heavily on the competition between drug diffusion and clearance, spatial variations in the cisplatin diffusion coefficient and clearance rate likely impact outcomes significantly. The tumor microenvironment is highly heterogeneous, with densely fibrotic areas3, while for larger tumors we advocate for as many injections as possible subject to tumor accessibility and patient tolerance. Furthermore, we found a power law relationship between the optimal dose to elicit a clinical response and tumor volume, with the exponent of the power law depending on the number of injections.In conclusion, we have demonstrated that our previously developed computational model of cisplatin pharmacodynamics following intratumoral injection has promise as a treatment planning tool for EBUS-TBNI cisplatin of lung tumors, particularly adenocarcinomas. Even in its current form the model suggests that 1–2 injections might be the optimal choice for tumors smaller than 4 cm


N = 2110). OCs were assessed with the full or modified Lewis-Murray scale, or with birth weight < 2.5 kg as a proxy. In a large cohort we tested whether the pathways from placenta-relevant variants in the original report were associated with case-control status. Unlike in the original study, we did not find significant effect of PRS on the presence of OCs in cases, nor a substantial difference in the association of PRS with case-control status in samples stratified by the presence of OCs. Furthermore, none of the PRS by OCs interactions were significant, nor were any of the biological pathways, examined in the Swedish cohort. Our study could not support the hypothesis of a mediating effect of placenta biology in the pathway from genes to schizophrenia. Methodology differences, in particular the different scales measuring OCs, as well as power constraints for interaction analyses in both studies, may explain this discrepancy.Ursini et al reported recently that the liability of schizophrenia explained by a polygenic risk score (PRS) derived from the variants most associated with schizophrenia was increased 5-fold in individuals who experienced complications during pregnancy or birth. Follow-up gene expression analysis showed that the genes mapping to the most associated genetic variants are highly expressed in placental tissues. If confirmed, these findings will have major implications in our understanding of the joint effect of genes and environment in the pathogenesis of schizophrenia. We examined the interplay between PRS and obstetric complications (OCs) in 5 independent samples (effective  Despite the relatively small effect sizes with odds ratios of less than 2, the association appears robust, with numerous replications summarised in a recent meta-analysis.2 These observations were important in supporting the theoretical framework of the “neurodevelopmental hypothesis” of schizophrenia, first presented in 1987,4 which suggests that the combination of genetic predisposition coupled with pre- or perinatal events affects brain development and increases vulnerability to schizophrenia later in life.Obstetric complications have been consistently associated with schizophrenia and are considered nongenetic risk factors for the disease.6 The total genetic effect of common variants is usually summarised in polygenic risk scores (PRSs), which have proved powerful predictors of disease.7 However, there is still distance to be covered in understanding the pathways linking genetic variation with disease development. Furthermore, because PRS is a composite measure, it tells us nothing about the interactions of individual genes with the environment. One idea gaining traction these days is that traditional distinctions between genetic and environmental risk are blurred. It has been suggested that GWAS of disease outcomes have identified genetic variants that capture the effect of modifiable risk factors as well as direct genetic effects.8Genome-wide association studies (GWAS) have identified many risk variants robustly associated with schizophrenia.9 recently reported that the intrauterine and perinatal environment modulates the association of schizophrenia with genomic risk, by examining the interaction between PRS for schizophrenia with the presence of obstetric complications, which they termed early-life complications (ELCs). They reported that the association of PRS with schizophrenia is 5-fold stronger in patients with ELCs compared to patients without ELCs and that the finding replicated in 2 independent samples. Follow-up gene expression and pathway analyses showed that the genes mapping to the most associated genetic variants with schizophrenia are highly expressed in placental tissues and have differential expression in placentae from complicated pregnancies.In this context, Ursini and colleaguesUrsini et al thus proposed a mechanism of action for the genetic variants most associated with schizophrenia leading to altered neurodevelopment through modulation of placental physiology and function. This report suggests that some of the top risk variants for schizophrenia may influence the health of the placenta and render a foetus less resilient to early hazards. If confirmed, these findings would have major implications for our understanding of the joint effect of genes and environment in the pathogenesis of schizophrenia.9 it is striking that the interaction with ELCs was found only in PRS based on a relatively small number of variants at or near genome-wide significance (PT < 5 × 10−8 and PT < 10–6), at thresholds which are usually less predictive than PRS built on a larger set of variants.5 Accepting that schizophrenia is a polygenic disorder and its aetiopathological mechanisms are likely to be diverse,10 one explanation is that the more strongly associated polymorphisms act via altered placental transcriptional regulation, while variants associated at lesser thresholds of significance act through different pathophysiological pathways. To confirm this hypothesis, we tested whether genetic risk from the most associated loci with schizophrenia is modulated through pre- or perinatal events in independent samples with genetic and obstetric complications (OCs) data.Examining the data of Ursini et al,11 comprising 310 schizophrenia cases and 237 controls with data on obstetric complications; (2) a sample from London 12 with 60 cases with psychotic disorder, 47 unaffected first degree relatives of cases and 17 controls; and (3) a sample of first-episode psychosis from Italy (Verona)13 comprising 141 cases and 89 controls. In addition, we analyzed (4) a population sample from the UK Biobank (UKB),14 where we identified 326 cases with schizophrenia based on self-reporting or Hospital Episode Statistics and 220,582 unaffected controls; and (5) a case-only sample (Cardiff)15 of 804 individuals with schizophrenia or schizoaffective disorder, to test whether PRS predicts OCs in cases. Detailed descriptions of the samples are reported in the Neff = 4/(1/Ncases + 1/Ncontrols).Our study included 3 clinical case-control samples: (1) a subsample of the Swedish Schizophrenia Study (Sweden)16 a 15-item scale consisting of complications that happened during the ante- and intrapartum period. This is a widely used and validated scale.17 Individuals were scored as “positive” if they had at least one definite complication, and OCs were used as a binary outcome. In the UKB, in the absence of detailed data of pre- or perinatal events, we used birth weight < 2.5 kg as a proxy for OCs, taking into account the effect size and the proportion of the population exposed to the risk, and in line with previous evidence that low birth weight is among the most significant and consistent OCs associated with risk for schizophrenia.18OCs were assessed with the Lewis-Murray scale,19–21 In brief, genotyped SNPs were removed if they had missingness > 0.02, minor allele frequency (MAF) < 0.01, or Hardy-Weinberg equilibrium P-value < 10−06 (10−08 in UKB). Participants who were related based on identity-by-descent values, with mismatching reported and genotyped sex, or with call rates < 98%, were removed from the datasets. PCA using LD pruned variants was performed in PLINK.22 Only genotypically confirmed European ancestry individuals based on the first 2 principal components were included in the analyses. PRS with the clumping and thresholding method23 for the 4 case-control samples were built using the published Psychiatric Genomics Consortium (PGC) schizophrenia GWAS5 and for the case-only sample from Cardiff the meta-analysis with the CLOZUK discovery samples.21 For samples included in the discovery GWAS, we used leave-one-out training datasets.Genotyping, quality-control, and imputation methods for each of the included samples have been published previously.PT < 5 × 10−8 and PT < 10−6, that showed interaction with ELCs in the Ursini et al9 study. We also repeated the analyses at PT < 0.1, which consistently showed high association with case-control status in our samples. All PRS included in the analyses were adjusted for population stratification by using the first 10 principal components (PC) as covariates, with the exception of MFS  where the model did not converge and we restricted our covariates to 4 PCs. Analyses were performed with logistic regression models for: (1) association of PRS with OCs in total samples and stratified by case or control status, (2) association of PRS with case-control status in the total samples and stratified by the presence or absence of OCs, and (3) PRS by OCs interaction on case-control status including as covariates all possible PC by PRS and PC by OCs interactions.24 Effect sizes from our different samples were pooled together with fixed effects meta-analyses due to low heterogeneity as measured with the I2 statistic.25All analyses were repeated with PRS formed from SNPs associated with schizophrenia at the 2 levels of significance, 9 reported that the SNPs driving the PRS-OC interaction lie in pathways enriched for “placental” genes . This was conducted by using the subset of SNPs from PRS at PT < 5 × 10−8 and PT < 10−6 deemed relevant to placenta biology and conducting a pathway analysis that yielded 7 and 67 significant pathways, respectively. To examine whether these gene-sets are associated with schizophrenia risk, we conducted pathway analyses using these placenta-relevant SNPs indexed in the summary statistics from a GWAS of the full Swedish Schizophrenia Study with a total sample of 11 244 subjects (5001 cases and 6243 controls) and 9 871 789 imputed SNPs.11Ursini et al26 consists of 3 steps: first, mapping SNPs onto genes by an annotation step; second, computing gene level P-values by a gene analysis step; and third, computing pathway level P-values by a gene-set analysis step. Gene locations for the annotation step used NCBI Build 37 / UCSC hg19 to ensure that the same human genome build as the SNP locations in the previous GWAS study were used. A competitive test was performed, which tests the hypothesis that the statistics of genes within a pathway are significantly different from genes outside the pathway. A Bonferroni correction was applied, resulting in adjusted significance thresholds of P < .007 for the first and P < .0007 for the second PRS threshold for the 7 and 67 pathways tested, respectively.Pathway analysis using MAGMA v1.09PT < 5 × 10−8 and PT < 10−6 is associated with the presence of OCs preferentially in cases, (2) whether the association between PRS and case-control status is stronger in the presence of OCs and (3) the interaction between PRS and OCs in predicting case-control status. Reported P-vales are 2-sided.The presence of OCs was not associated with case-control status, similar to the Ursini et al discovery sample, possibly reflecting power limitations in both studies . Following similar methodology, we examined (1) whether PRS at PT < 5 × 10−8 associated with the presence of OCs either in total samples or specifically in cases. We only observed a positive association of PRS at PT < 10−6 with history of OCs in the total Swedish sample (P = .03) and in MFS cases (P = .04), which would not survive any correction for multiple testing . Stratification by the presence of OCs did not appear to have a substantial or consistent effect. For example, PRS at PT < 10−6 in the Swedish sample appeared to have a stronger effect in the presence of OCs (odds ratio 1.62 vs 1.57 in the absence of OCs), but when examining PRS at PT < 5 × 10−8 at the same sample we notice the opposite effect  (Associations of PRS at e of OC) . Subgroue of OC) . Furthere of OC) .5 we repeated the analyses at PT < .1. At this threshold, PRS was associated with case-control status in all the samples with odds ratios varying depending on sample definition and sample size (range 1.62–2.87). With the exception of MFS, we did not observe any preferential association between PRS and OCs in cases or any consistent effect of OC stratification on the association between PRS and case-control status  as reported in the study by Ursini et al were tested for association with case-control status in the total Swedish cohort. No significant associations were found even at a nominal level of significance  are captured by the Lewis-Murray scale, with few important exceptions . The pooled effect from 9 studies of definite obstetric complications as specified in the Lewis-Murray scale was a significant predictor of case-control status . This suggests that the Lewis-Murray scale is efficient in capturing relevant OCs, and differences between the findings of our study and that of Ursini et al is possibly due to the additional items covered only in the McNeil–Sjöström scale. Further comparing the 2 studies, we notice that our samples had more consistent prevalence of OCs, between 25% and 36% for the full Lewis-Murray scale and 10% for low birth weight in the UKB. More variability in OCs was observed in the Ursini et al study, with the range of ELCs prevalence between 20% and 66%, reflecting either more variability in the sample selection or in the ratings of the McNeil–Sjöström scale.From the latest meta-analysis,2 and the high genetic correlation of schizophrenia with bipolar disorder,31 it is unlikely that this has an important impact on the findings. In terms of the power to detect gene by environment interaction, both studies utilized a variety of samples with genetic and OC data. It is noteworthy that, although the initial findings were replicated in 2 additional case-control samples in the Ursini et al study, we failed to confirm the association, despite similar total effective sample size . To further test the power of our samples for PRS analyses, we repeated the analyses with a more powerful PRS at PT < .1 and we found significant main effects in all samples, as expected, but no interaction effects.Other considerations are the differences in the sample characteristics between the 2 studies and power constraints. Two of our samples  included a broader definition of psychosis, not restricted to schizophrenia and schizoaffective disorder. However, given the evidence that OCs is a risk factor across non-organic psychoses32 Studies of gene-environment interaction suffer from low power to detect these effects,33 while the violation of underlying model assumptions increases the likelihood of spurious findings.34 For studies of interaction between polygenic scores and environment, methodological and conceptual challenges, including the choice of the environment and outcome variables, measurement error, and sample selection processes, can result in misleading outcomes.35Our failure to confirm the interaction is not surprising, given the vanishingly rare evidence in the literature that average genetic risks for chronic diseases vary substantially according to environmental exposures.Given the above limitations in the PRS by OCs interaction analyses, to further examine the hypothesis that placenta biology is a significant mediator of the genetic risk and the development of schizophrenia, we tested whether the significant pathways identified by Ursini et al were enriched in schizophrenia cases. Our hypothesis was that if these pathways are important in the pathogenesis of schizophrenia, they should be associated with case-control status in a large sample of 5001 schizophrenia cases and 6243 controls. However, our null pathway analysis results call into question the relevance of these pathways.In summary, although we cannot categorically refute the original findings due to differences in the study design, we would like to add a note of caution that early environment modulation of the effect of PRS may be population specific or related to specific early life events not captured by the scale we used. Our study, in line with empirical data to this point in psychiatric and behavioral genetics, not having identified many replicable examples for gene by environment interaction, highlights the complexity of the field. Further research with larger, well-phenotyped samples is advisable to examine the interaction of genetic liability with obstetric complications and similarly with other environmental risks with the aim of unravelling specific aetiopathogenic mechanisms of schizophrenia.This study represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Part of this research has been conducted using the UK Biobank Resource under Application Number 18177 (PI Cathryn Lewis). The work at Cardiff University was supported by Medical Research Council Centre Grant no. MR/L010305/1 and Program Grant no. G0800509. R.M. is partly supported by a grant from David Posnett.The authors have declared that there are no conflicts of interest in relation to the subject of this study. M.C.O.D., J.T.R.W. and M.J.O. have received a collaborative research grant from Takeda Pharmaceuticals. Takeda played no part in the conception, design, implementation, or interpretation of this study.sbab052_suppl_Supplementary_MaterialClick here for additional data file.