First, we generated a 3D-reference from several imaged hindbrains and segmented them using a trainable tool; these were aligned using rigid registration, revealing distribution of neuronal differentiation growth patterns along the axes. Second, we quantified the dynamic growth of the neuronal differentiation domain by in vivo neuronal birthdating experiments. We generated digital neuronal birthdating 3D-maps and revealed that the temporal order of neuronal differentiation prefigured the spatial distribution of neurons in the tissue, with an inner-outer differentiation gradient. Last, we applied it to specific differentiated neuronal populations such as glutamatergic and GABAergic neurons, as proof-of-concept that the digital birthdating 3D-maps could be used as a proxy to infer neuronal birthdate. As this protocol uses open-access tools and algorithms, it can be shared for standardized, accessible, tissue-wide cell population atlas construction.Reconstruction of prototypic three-dimensional (3D) atlases at the scale of whole tissues or organs requires specific methods to be developed. We have established a digital 3D-atlas maker (DAMAKER) and built a digital 3D-atlas to monitor the changes in the growth of the neuronal differentiation domain in the zebrafish hindbrain upon time. DAMAKER integrates spatial and temporal data of cell populations, neuronal differentiation and brain morphogenesis, through  The brain, like most other organs, is formed by the coordinated growth of a few unspecialized cells in the embryo, which give rise to billions of neurons. For the brain to work properly, it is crucial that, during embryonic development, each neuron ends up in the correct location. This migration to the right spot has to happen while the brain grows and changes shape, which affects how and how far neurons and their precursor cells need to move to reach their final position. If these movements and changes in shape are not coordinated correctly, neurons can end up in the wrong place, form the wrong connections, and ultimately impact how the brain works.Previous work done in fruit flies and zebrafish resulted in three-dimensional maps of these animals’ healthy brains, which allowed scientists to have a holistic view of how brains are organized. Although these maps are a valuable resource to study the structure of the brain, they do not provide information on how the brain transforms over time, especially during embryonic development. To get a clearer picture of how a few precursor cells give rise to the incredibly complex tissue that is the brain, a three-dimensional map spanning the entire developmental process is needed.To fill this gap in knowledge, Blanc et al. developed a digital atlas-maker pipeline (DAMAKER) that allows scientists to generate three-dimensional models of the embryonic brain from microscopy images of several individuals. They then used this pipeline to construct a three-dimensional digital atlas of how a part of the brain called the hindbrain develops in the zebrafish embryo.First, they collected images of the hindbrain showing neurons born at different times and matched these images to the existing static maps. Next, DAMAKER was used to follow neurons from the time of their birth to their final location, allowing Blanc et al. to create a map showing where neurons born at different stages during development end up. This type of map allows users to accurately determine when different populations of mature neurons are born, which allows scientists to estimate when different defects in brain development might originate.Based on these data, Blanc et al. concluded that in zebrafish most of the cells that will end up forming the hindbrain acquire their specialized neuronal identities very early in development, between 24 and 48 hours post fertilization. These temporal maps of healthy hindbrains were then compared to maps of brains in which the birth of neurons was disrupted, thus changing the final number of neurons in the brain. This experiment showed that changing the number of neurons that are born early in development alters the final positions of neurons and the overall shape of the brain. Therefore, for the brain to grow to its correct size, there must be a balance between the number of unspecialized cells in the developing brain, and the rate at which these cells become neurons.The DAMAKER pipeline not only provides scientists with a tool to study neurodevelopmental disorders, but also serves as a method that can be adjusted to map growth and shaping of other organs. Drosophila provided 3D-registration atlases and software that can automatically find the corresponding landmarks in a subject brain, and map them to the coordinate system of a target brain  embryonic brain is essential for understanding how brain morphogenesis impacts the position, and therefore function, of neuronal progenitors and neuronal clusters in normal and pathological conditions. Pioneer work in et brain . HoweverDigital 3D-atlases enable the exploration of brain structures in the 3D-context and can be updated to incorporate new information, which is essential for the exponential increase of knowledge. Existing gene expression 3D-atlases in larval and adult fish provide valuable tools . Howeverin vivo imaging data from distinct transgenic embryos with the Fijiyama registration tool  that fulfills the previous requirements and can be used to create an expandable atlas of virtually any tissue in any organism. Further, it is able to perform data analysis and quantification mostly on a single platform, FIJI, which is already established as the standard for image analysis in the field. The expandable zebrafish digital 3D-atlas was created by combining ion tool , which aion tool  that levion tool  and undeion tool . This moion tool . Neuronaion tool . Althougion tool , little in vivo  quantify them along any body axes  se embryos , and , and embryos were imaged either at 48hpf or 72hpf to assess the relative position of neurons born at different times within the differentiation domain (Red-cells vs. older-born as KaedeGreen-cells). To generate the birthdate 3D-map at 48hpf, we first photoconverted the HuC:KaedeGreen at 24hpf and imaged embryos at 48hpf. We observed that the onset of neuronal differentiation was before 24hpf and that the first differentiated neurons located more ventral and medial than late-born cells , only a small part comprised neurons differentiated before 24hpf (3.42 [SD ± 1.56]×106 μm3 early-born neurons vs. 8.64 [SD ± 0.86]×106 μm3 late-born neurons), whereas most of the differentiated neurons arose before 36hpf (9.61 [SD ± 0.68]×106 μm3) and the rest differentiated between 36 and 48hpf (3.10 [SD ± 0.35]×106 μm3). This timely growth in the differentiated neuronal domain could be clearly observed when the relative contribution of early- vs- late-born neurons as compared to the whole neuronal differentiation territory at 48hpf was plotted (6 μm3 for early-born vs. 11.88 [SD ± 1.28]×106 μm3 for late-born). Most neurons underwent differentiation between 24hpf and 48hpf, with half of the differentiated neurons arising before 36hpf (6.75 [SD ± 2.19]×106 μm3 born before 36hpf vs. 7.58 [SD ± 2.37]×106 μm3 after 36hpf) and the rest were differentiated between 36 and 48hpf (11.19 [SD ± 1.09]×106 μm3 born before 48hpf vs. 2.99 [SD ± 0.90]×106 μm3 after 48hpf). When the ratio of neurons born at different stages over the whole neuronal differentiation domain was assessed, a steady increase of neuronal differentiation domain was observed , which is a dynamic and expandable 3D-tool for monitoring the temporal changes in growth and shape during tissue morphogenesis. We built a digital representation of the developing vertebrate hindbrain from 3D-imaging data collected at different times, which can be used for further quantitative analysis and multilevel modeling. Our goal is to share an established protocol using open-access tools and algorithms that allow standardized and accessible tissue-wide cell population atlas construction and can be used to create an expandable atlas of any tissue in any organism.In this work, our interest was to explore how neuronal differentiation and morphogenesis might be intertwined during hindbrain development. For this, we registered experimental whole hindbrain images and mapped neuronal differentiation patterns with a unified spatial representation, using confocal imaging setup and leveraging cross-platform segmentation and registration tools. To improve comparison of various signals across embryos that requires an alignment/registration, which is often done manually and is very time-consuming, we used the Fijiyama plugin in FIJI . This alThe quantitative evaluation of datasets often involves manual annotation, which is time-consuming, introduces biases and often constitutes the major constraint in an evaluation pipeline. To overcome this problem, we used the Trainable Weka Segmentation 3D (TWS), , which cin vivo cell birthdating system that allowed us to monitor intervals of neuronal differentiation  dissect the final location of distinct birthdated cell populations, and (ii) obtain a whole-cell population coverage by analyzing the whole differentiated territory, while maintaining the in vivo positioning of cells within the structure. Until now, most of the currently used approaches either lack whole population coverage or are limited by expression timing of the gene or transgene; more importantly, although they permit coverage of the whole cell population, they do not allow the user to foreshadow the birthdate of a given cell population at the time of interest. Here, with the combination of photoconversion experiments as a tool to register temporality of neuronal differentiation and DAMAKER, we provide neuronal birthdating 3D-maps and revealed that the order of neuronal differentiation prefigured the spatial distribution of neurons in the hindbrain, with an inner-outer gradient of temporal differentiation.The digital 3D-brain atlases are essential for neurobiology because they allow to address new questions that can not be answered by the use of the available 2D-atlases. The addition of time as a key parameter is an asset, since it will permit to follow cells upon time, and therefore cell displacements and tissue growth. For this, we made use of an ntiation . Thus, tUsually, expression of the reporter genes in transgenic lines is delayed as compared to the gene expression onset, unless the reporter has been inserted within the given gene locus. Now, we can specifically knock-in genes within a targeted genome site thanks to the CRISPR-Cas technology, but most of the available —and very valuable—transgenic lines were (and still are) generated with random insertion systems that do not always recapitulate the right onset of gene expression. Thus, our tool can be very useful for predicting neuronal birthdates just by analyzing the position of neurons in the differentiation domain as we showed with glutamatergic and GABAergic neurons. Besides that, this developed digital platform will be useful to interrogate phenotypes in neurological diseases, and zebrafish avatars from human neurological disorders could be analyzed in a very simple manner. As a proof-of-concept, we show tissue shape comparisons after modulating neurogenesis using pharmacological treatments. Thus, the combination of several features can provide computational models allowing to merge information from several high-throughput experiments to enlarge our reductionist view, and implement these digital tools for neuronal disorder studies.In addition, brain morphogenesis studies could use our digital atlas-maker tool, which maps neuronal differentiation domains in the embryonic brain over time and can be easily shared. In the future, DAMAKER can be further improved by using batch processing algorithms  or by adjusting for more geometrically complex tissues or for tissues with less alignment features (such as heart). Last but not least, this pipeline could be applied to time-lapse videos by repeating the process for each frame. This approach will help us to fill the knowledge gap between gene regulatory networks, cell populations and tissue architecture, as it will allow to follow the dynamic events that pattern the early embryonic organs as they happen within the intact system.All procedures were approved by the institutional animal care and use ethic committee  and the Generalitat of Catalonia (Departament de Territori i Sostenibilitat), and implemented according to National and European regulations. Government and University veterinary-inspectors examine the animal facilities and procedures to endure that animal regulations are correctly followed. The PRBB animal house holds the AAALAC International approval B9900073. All the members entering the animal house have to hold the international FELASA accreditation. The Project License covering the proposed work  pays particular attention to the 3Rs.egr2a/krx20 gene .z-stacks were recorded with 0.57×0.57 × 1.19 μm voxel size. Gain was adjusted to each signal in order to display as little burnt signal as possible.Zebrafish embryos were treated with phenylthiourea (PTU) at 24hpf, prior to being anesthetized with tricaine and mounted dorsally in 0.9% low melting point agarose on glass-bottom Petri dishes (Mattek) at the desired time. Images were acquired with a Leica SP8 system using PMT or HyD3 detectors and a 20×glycerol immersion objective, HCX PL APO Lambda blue 20×/0.7 argon laser 30%, Diode 405 nm . yz confocal cross-sections, z-stacks were acquired with a 1.194 μm z distance.Stained fixed samples were imaged on a Leica SP8 inverted confocal microscope with 20×glycerol immersion objective and hybrid detectors. For xGreen to HuC:KaedeRed photoconversion was carried out at the indicated embryonic stages with UV light (λ=405 nm) using a 20×objective in a Leica SP8 system. Upon exposure to UV light, Kaede protein irreversibly shifts emission from green to red fluorescence (λ=516–581 nm). Proper photoconversion was monitored by the appearance of strong KaedeRed signal under excitation with a 543 nm laser, and the disappearance of KaedeGreen. After photoconversion, embryos were either imaged or returned to embryo medium with PTU in a 28.5 °C incubator to let them grow until the desired stage.HuC:Kaedein situ hybridization was adapted from gad1b riboprobe  to the reverse primer . The vglut probe was as described in in situ hybridization, the DIG-labeled probe was detected with TSA-Cy3.Embryo whole mount iboprobe  encompasFor immunostaining, 4% PFA-fixed embryos were permeabilized with 10mg/ml proteinase K during different times according to the developmental stage , blocked in 2% BSA, 5% goat serum, 0.1% Tween20 in PBS for 3hr at room temperature, and then incubated overnight at 4°C with the primary antibody. The primary antibody used was mouse anti-HuC (1:400). After extensive washing with PBST, embryos were incubated overnight at 4°C with secondary antibody conjugated with Alexa Fluor 633 (1:500).Tg[HuC:Kaede] sibling embryos were photoconverted at 24hpf, and after 6 hr acclimatation the embryos were placed in embryo medium supplemented with 400 μM EdU , 2% DMSO and PTU. The embryos were immediately placed on ice for 30 min, and later incubated at 28.5 °C for 5 hr 30 min. Then, embryos were rinsed twice in embryo medium with PTU and grown until 48hpf stage. Embryos were fixed in 4%PFA for 4 hr on a shaker, washed, and treated with proteinase K (1:1000 in PBST) for 18 min. After washing them in PBST, they were fixed in 4%PFA for 30 min, washed and revealed with the Click-iT EdU Imaging Kit following manufacturer’s protocol. Finally, to recover protein fluorescence, embryos were washed in PBS with 2%BSA and 40 mM EDTA overnight at 4 °C on shake . The nexin vivo imaged under a Leica SP8 confocal microscope.Tg[HuC:GFP] embryos were treated with 10μM of the gamma-secretase inhibitor LY411575 (Sigma-Aldrich) and Tg[HuC:GFP] non-treated were used as control. The treatment was applied into the fish water at 48hpf during 4hr at 28.5°C. Then, embryo medium was changed and they were let to develop until 72hpf, mounted and Fijiyama registration FIJI plugin , a regisThe Trainable Weka Segmentation FIJI plugin , a machiUsers’ and embryos’ averaging, as well as consensus thresholds, were performed in FIJI. Homemade batch-processing macros were used to process the whole datasets at once. Macros for batch processing of each of the steps were kept independent to ensure easy adaptability, such as variation in sample size or selected thresholds. Signal consensus among all users was used for both quantifications and models. Signal consensus among 3 or more embryos was used for the generation of the models. Intersection of 3D-models was performed using the image calculator option in FIJI.Embryos’ 3D-models were registered together and resliced along different axes. Slices were then quantified using FIJI’s 3D object counter. The slicing and quantification of individual slices was automatized with a simple homemade FIJI macro. Quantification along axes was processed through R. We used ratios of signal of interest vs. total differentiation domain volumes of individual embryos to generate a nonparametric regression curve with local linear kernel estimation method . The R (R core team 2020) package npregfast version 1.5.1  and the Quantification of 3D-volumes after the application of the pipeline were plotted using either GraphPad Prism 9.0 for global quantifications or R for the axes distributions.https://zenodo.org/record/6529705#.YtqYZ-xBxgF) to compute the signed distance between the two mesh surfaces. The computed measurements were then displayed as a color-coded map on the reference mesh and histogram of measurement counts was generated.Models generated through the pipeline were exported as.OBJ mesh and fed into a python script leveraging the VEDO library  or Cristina Pujades (cristina.pujades@upf.edu).All datasets supporting our work are included in the manuscript and supported files. z-stacks resulting from the pipeline, and subsequent intersections can be found in the 3D-model collection (Supporting ZIP document). The codes can be found in GitHub  This methodological manuscript is of interest to the fields of neural development, tissue morphogenesis, and image analysis technologies. The authors developed an image registration tool and created a digital atlas to reflect the anatomical distribution of neuronal birthdates in the developing zebrafish hindbrain. The provided resources can be very useful to monitor temporal changes in tissue growth. 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:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Richard White as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Venera Weinhardt (Reviewer #3).Thank you for submitting your article "The Digital 3D-Atlas MAKER (DAMAKER): a dynamic and expandable digital 3D-tool for monitoring the temporal changes in tissue growth during hindbrain morphogenesis" for consideration by 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:All reviewers agreed that the paper needs to improve significantly on the following three points:1 – To improve the temporal content of the method by additional experiments on time-lapse imaging and corresponding controls.2 – To show inter-individual variability, which should be possible without additional experiments by incorporation of (most probably) existing data.The details on points 1 and 2 are outlined in the constructive recommendations to the authors and public reviews. Please also note that the reviewers consented that while analyses of mutants would be nice to add, they are not required for the publication of this article.3 – To deposit software along with explanations/manuals on how to use and extend it further. The two links that are currently provided send to the identical webpage on which neither Macros nor R code can be found.Reviewer #2 (Recommendations for the authors):1. Much higher 3D magnifications are needed in order to evaluate the morphogenetic changes of particular cell types and domains in a greater resolution.2. More experimental data is required in order to further convince how the new imaging protocols provide many more detailed answers compared to traditional imaging tools.3. Validation of the technology's strength should be provided by asking a physiological question about the developing hindbrain. Many relevant mutant lines are available (including in the author's lab) that can serve to demonstrate how the DAMAKER protocol is indeed an innovative and original strategy to study brain development in a manner that was absent in previous studies.4. As opposed to the numerous notions of the authors regarding the development of a novel code to provide a temporal 3D atlas, the actual data presented to demonstrate this capacity is rather limited. Hence, the text should be more balanced in that aspect.Reviewer #3 (Recommendations for the authors):The manuscript is readable but at places is difficult to understand due to a lack of information and broader terms. The following aspects can be improved:– It is stated that manual segmentation of masks was performed. Masks of what exactly? The fluorescence imaging is per se is either signal (1) or not (0). Therefore, the whole purpose of masking is therefore unclear.– Supplementary figure 1, panels E and F please make sure to clarify what is measured and what is calculated value? As I understand NDD is measured and PD is simply calculated by substruction, thus the same std and inverted look of plots.– A supplementary figure with corresponding stages of zebrafish development would be very helpful for those outside of the zebrafish community.– I think it is important to show individual variability of the data, which would help to understand the difficulties of the atlas making and justify the development of a pipeline. Please include data on multiple datasets with the same marker.– Line 206 "Once the alignment of the HuC signal was accomplished,…". How the alignment was verified? Fijiyama to my memory uses an iterative approach, was the number of iterations fixed to 4? Was its success verified and how?– Throughout the manuscript when reporting on the quantitative analysis of a tissue, in particular volume, you use "neurons are born". That is misleading. The analysis pipeline does not count the number of neurons but rather the volume occupied by a tissue. Please make sure to state it at least in the beginning.– In all figures and videos please do not use a red-green colour combination. 20% of readers are colorblind.– Please include the AP and LML coordinate system on the figures to ensure that readers with other backgrounds can understand it as well.Finally, I could not find supporting software on your web page: https://www.upf.edu/web/pujadeslab/open-science. Considering the focus of your manuscript this is essential. Next time, please ensure the availability of your software during the review process.eLife. Your revised article has been evaluated by the three original reviewers, as well as by Richard White (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "A dynamic and expandable Digital 3D-Atlas MAKER for monitoring the temporal changes in tissue growth during hindbrain morphogenesis" for further consideration by eLife. There is still one request on the assessment of the inter-individual variability that will be highly useful to implement, also in the perspective of the future usefulness of your work for others.We much appreciate the work you put into the revised version, and think that it is now almost suitable for publication in The authors quantified the interindividual variability more carefully in Figure 1-Supplement 1 and Figure 3 – Supplement 1, with the purpose of demonstrating that "interindividual variability is low". However, in all the grayscale representations, variability is clearly visible in that some non-white volume area have at least 2~3 layers of cells, and even the HuC volume of the fish quantified at the same experimental condition could have a variability of 20% . Instead of only claiming the "low variability", the authors should have a quantitative measurement of this variability in the manuscript, and provide the confidence level of the birthdate map. This will essentially help the users to make a decision on whether an area of interest should be followed up or is beyond the precision that can be measured by the atlas. Essential revisions:All reviewers agreed that the paper needs to improve significantly on the following three points:1 – To improve the temporal content of the method by additional experiments on time-lapse imaging and corresponding controls.In order to improve the temporal content of the method we have performed and included the following experiments:1.1 To show that there is a progressive temporal growth of the HuC-domain, we have included an additional temporal window of neuronal differentiation and provide the neuronal birthdating 3D-maps at 48hpf and 72pf . We have quantified the contribution of the distinct birthdated neuronal populations to the HuC-domain .1.2 In the same line, to show the progressive temporal growth of the glutamatergic and GABAergic neuronal domains within the HuC we provide an additional temporal window for these cell populations . We have quantified the contribution of the neurons born at different times to the corresponding vglt2a/gad1b-domains and compared their growth dynamics .Green photoconversion with an additional age-labeling method such as EdU. We photoconverted HuC:KaedeGreen at 24hpf, pulsed embryos at 30hpf with EdU to label proliferating cells, and let them grow until 48hpf. We could observe that new neurons born after 24hpf were found by 48hpf in the newly generated neuronal differentiation domain . This responds to point 3 of Reviewer 1 asking to show the new cells incorporated between the two time points are reflected in the growing unphotoconverted population.1.3 To further support the observation that indeed newly born cells were orderly incorporating into the neuronal differentiation domain, we combined HuC:KaedeRed cells labeled in an early conversion are strictly included in the HuC:KaedeRed domain labeled in a late conversion, we overlaid the differentiation domains contributed at each time interval . This responds to point 3 of Reviewer 1.1.4 To reveal that the HuC:Kaedein situ hybridizations with vglut2a and gad1b probes at early stages of embryonic development (24hpf) while monitoring the neuronal differentiation domain. Indeed, glutamatergic and GABAergic neurons were already specified at 24hpf and they were located within the HuC territory , although the transgenic lines did not display the reporter expression at this stage.1.5 To demonstrate that indeed the birthdating 3D-maps could be used as proxy to infer birthdate, we verified that glutamatergic and GABAergic neurons were born before 24hpf, as our tool predicted and included it in new Figure 4. We performed Green was performed at different stages . These additional volume analyses show that overall hindbrain volumes are comparable even after photoconversion, and that embryos are properly/homogenously staged.1.6 As additional controls to demonstrate that different type of experiments can be combined, we evaluated any possible differences within the conditions, through quantification of HuC volumes at 48hpf and 72hpf in embryos in which photoconversion of Hu:Kaede2 – To show inter-individual variability, which should be possible without additional experiments by incorporation of (most probably) existing data.2.1. To evaluate the accuracy of the registration, interindividual variability of different signals was assessed. Dorsal and transverse projection views overlaying five different specimens and displaying the interindividual variability as a gray scale is now shown by comparison of average signal prior to threshold among individuals .2.2. To show interindividual volume variability, the size of the neuronal differentiation territory (HuC-domain) in different transgenic backgrounds was assessed displaying no significative differences .2.3. To demonstrate that birthdating 3D-maps displayed low interindividual variability, we verified that photoconversion of the HuC-:KaedeGreen at different stages was consistent in all the analyzed embryos. For this, we first verified that the overall HuC-volumes did not dramatically change among embryos that underwent HuC:KaedeGreen photoconversion at different stages . Second, embryos in which HuC:KaedeGreen was photoconverted at different times were imaged at 72hpf, and the HuC:KaedeRed territories of distinct embryos were overlaid and displayed as a gray gradient .The details on points 1 and 2 are outlined in the constructive recommendations to the authors and public reviews. Please also note that the reviewers consented that while analyses of mutants would be nice to add, they are not required for the publication of this article.2.4 We fully agree with the Referees, and although we do not include the analysis of mutants, we have decided to take the newly developed protocol one step further to serve as a proof-of-concept study by using fish with normal or aberrant neurogenesis as Referee 2 commented. For this, we include the comparative analysis of control embryos with those in which the Notch-signaling was downregulated and therefore they have increased neurogenesis. Since it has been reported that hindbrain cells respond to Notch , we conditionally downregulated Notch-signaling by treating Tg[HuC:GFP] embryos with an inhibitor of the γ-secretase, LY411575, and assessed the changes in the neuronal differentiation domain volume and 3D-shape. Results are displayed in new Figure 5 and Figure 5– video 1. This experiment shows that our pipeline can be of use for the analyses of 3D-shape changes too.3 – To deposit software along with explanations/manuals on how to use and extend it further. The two links that are currently provided send to the identical webpage on which neither Macros nor R code can be found.We apologize for not including the right links to obtain the macros and the R code that were used in the manuscript. The used software along with explanations on how to use and extend it further can be found at:https://github.com/cristinapujades/Blanc-et-al.-2022We uploaded the updated macros with a short description for better comprehension. In addition, we included a brief description on how to adapt them for other uses.Reviewer #2 (Recommendations for the authors):1. Much higher 3D magnifications are needed in order to evaluate the morphogenetic changes of particular cell types and domains in a greater resolution.See point 1 in Reviewer 2.2. More experimental data is required in order to further convince how the new imaging protocols provide many more detailed answers compared to traditional imaging tools.See point 1 in Essential Revisions.3. Validation of the technology's strength should be provided by asking a physiological question about the developing hindbrain. Many relevant mutant lines are available (including in the author's lab) that can serve to demonstrate how the DAMAKER protocol is indeed an innovative and original strategy to study brain development in a manner that was absent in previous studies.See point 2.4 in Essential Revisions.4. As opposed to the numerous notions of the authors regarding the development of a novel code to provide a temporal 3D atlas, the actual data presented to demonstrate this capacity is rather limited. Hence, the text should be more balanced in that aspect.We provide code and strategies to automatize processes and to allow this type of approaches to be feasible in a decent amount of time. We do not provide a new registration or segmentation approach as stated in the text.Reviewer #3 (Recommendations for the authors):The manuscript is readable but at places is difficult to understand due to a lack of information and broader terms. The following aspects can be improved:We have improved the writing and included more and better explanations.– Supplementary figure 1, panels E and F please make sure to clarify what is measured and what is calculated value? As I understand NDD is measured and PD is simply calculated by substruction, thus the same std and inverted look of plots.See response to point 1 to Reviewer 1.– A supplementary figure with corresponding stages of zebrafish development would be very helpful for those outside of the zebrafish community.We followed the Reviewer’s advice, provided schemes of embryos at the different stages of embryonic development covered in the manuscript, and we have better depicted the experiments outline.– I think it is important to show individual variability of the data, which would help to understand the difficulties of the atlas making and justify the development of a pipeline. Please include data on multiple datasets with the same marker.See point 2 in Essential Revisions.– Line 206 "Once the alignment of the HuC signal was accomplished,…". How the alignment was verified? Fijiyama to my memory uses an iterative approach, was the number of iterations fixed to 4? Was its success verified and how?The number of iterations used was 4 as mentioned in the protocol. Alignment accomplishment can be verified through Fijiyama itself, and we further confirmed it through tissue shape analysis. Since we concluded that the approach was robust enough, we did not consider necessary to discuss it, especially since it is done on Python and we wanted to keep the pipeline as much as possible in FIJI. We have included it in this new version.– Throughout the manuscript when reporting on the quantitative analysis of a tissue, in particular volume, you use "neurons are born". That is misleading. The analysis pipeline does not count the number of neurons but rather the volume occupied by a tissue. Please make sure to state it at least in the beginning.Yes, the Reviewer is right and we followed her advice.– In all figures and videos please do not use a red-green colour combination. 20% of readers are colorblind.We have changed them.– Please include the AP and LML coordinate system on the figures to ensure that readers with other backgrounds can understand it as well.We have either included them or mentioned the orientation in the figure legends.Finally, I could not find supporting software on your web page: https://www.upf.edu/web/pujadeslab/open-science. Considering the focus of your manuscript this is essential. Next time, please ensure the availability of your software during the review process.See point 3 in Essential Revisions.ReferencesDevelopment, 135(19): 3259–3269.Caron, S. J. C., Prober, D., Choy, M., and Schier, A. F. 2008. In vivo birthdating by BAPTISM reveals that trigeminal sensory neuron diversity depends on early neurogenesis. Proceedings of the National Academy of Sciences of the United States of America, 106(32): 13365–13370.Distel, M., Wullimann, M. F., and Köster, R. W. 2009. Optimized Gal4 genetics for permanent gene expression mapping in zebrafish. Science, 370(6515): 463–467.Fabian, P., Tseng, K.-C., Smeeton, J., Lancman, J. J., Dong, P. D. S., et al. 2020. Lineage analysis reveals an endodermal contribution to the vertebrate pituitary. Cell Reports, 39(10): 110915.Hevia, C. F., Engel-Pizcueta, C., Udina, F., and Pujades, C. 2022. The neurogenic fate of the hindbrain boundaries relies on Notch3-dependent asymmetric cell divisions. Frontiers in Neuroanatomy, 11: 52.Kesavan, G., Chekuru, A., Machate, A., and Brand, M. 2017. CRISPR/Cas9-Mediated Zebrafish Knock-in as a Novel Strategy to Study Midbrain-Hindbrain Boundary Development. Development, 147(11). https://doi.org/10.1242/dev.186882.Kesavan, G., Machate, A., Hans, S., and Brand, M. 2020. Cell-fate plasticity, adhesion and cell sorting complementarily establish a sharp midbrain-hindbrain boundary. Development, 136(15): 2523–2533.Nikolaou, N., Watanabe-Asaka, T., Gerety, S., Distel, M., Köster, R. W., et al. 2009. Lunatic fringe promotes the lateral inhibition of neurogenesis. Development, 146(14). https://doi.org/10.1242/dev.176735.Voltes, A., Hevia, C. F., Engel-Pizcueta, C., Dingare, C., Calzolari, S., et al. 2019. Yap/Taz-TEAD activity links mechanical cues to progenitor cell behavior during zebrafish hindbrain segmentation. The authors quantified the interindividual variability more carefully in Figure 1-Supplement 1 and Figure 3 – Supplement 1, with the purpose of demonstrating that "interindividual variability is low". However, in all the grayscale representations, variability is clearly visible in that some non-white volume area have at least 2~3 layers of cells, and even the HuC volume of the fish quantified at the same experimental condition could have a variability of 20% . Instead of only claiming the "low variability", the authors should have a quantitative measurement of this variability in the manuscript, and provide the confidence level of the birthdate map. This will essentially help the users to make a decision on whether an area of interest should be followed up or is beyond the precision that can be measured by the atlas.We made quantitative measurements of the interindividual variability and included in Figure 1 – Supplement 1G and Figure 3 – Supplement 1B and D. These quantifications indicate that there is an average of maximum of 15% of difference between the HuC volume of the individuals and the HuC volume of the generated model. We included these data in the manuscript  and in the corresponding figure legends.We made changes in the color-code in Figure 3E and switched to colors with more contrast. We specified the difference between H, H' and H'’ in Figure 4H. We tried several color combinations for Figure 4H; however, we did not change it because the current one was the best.We have updated the wiki for a better comprehension.


Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are different from microarray data, it is an unanswered question what preprocessing methods for RNA-seq data should be applied prior to GNI to attain optimal performance, or what the required sample size for RNA-seq data is to obtain reliable GNI estimates.We ran 9144 analysis of 7 different RNA-seq datasets to evaluate 300 different preprocessing combinations that include data transformations, normalizations and association estimators. We found that there was no single best performing preprocessing combination but that there were several good ones. The performance varied widely over various datasets, which emphasized the importance of choosing an appropriate preprocessing configuration before GNI. Two preprocessing combinations appeared promising in general: First, Log-2 TPM (transcript per million) with Variance-stabilizing transformation (VST) and Pearson Correlation Coefficient (PCC) association estimator. Second, raw RNA-seq count data with PCC. Along with these two, we also identified 18 other good preprocessing combinations. Any of these algorithms might perform best in different datasets. Therefore, the GNI performances of these approaches should be measured on any new dataset to select the best performing one for it. In terms of the required biological sample size of RNA-seq data, we found that between 30 to 85 samples were required to generate reliable GNI estimates.This study provides practical recommendations on default choices for data preprocessing prior to GNI analysis of RNA-seq data to obtain optimal performance results. Gene network inference (GNI) methods can identify putative interactions between different genes and the gene products they encode. Gene regulatory networks (GRN), which can be inferred by GNI methods, help in the basic biological understanding of genes and their functions as well as inferring potential drug targets of a disease . It is wGNI methods have mostly been developed for and applied to microarray gene expression datasets . The maiganet v2.2, in order to measure the performance of each GRN prediction. Using some of the identified good performing preprocessing combinations, we conducted further analysis and determined the required sample size of RNA-seq data for optimal GNI performances. This study addresses a need of our own lab to rationale decide which pre-processing steps to apply to GNI, and we believe it provides useful guidelines and practical tools for everyone interested in infer gene networks from RNA-seq datasets.To the best of our knowledge, this is the first study that performs a systematic comparison of preprocessing combinations, which include data transformation, normalization and association estimation for GNI. We compared the combination of 15 popular preprocessing methods, along with no preprocessing case, by using 3 different GNI algorithm and 7 RNA-seq datasets for the analysis. Optimal performance was measured by comparing the identified GRN connections to a compiled literature interaction database. For this, we updated the R  softwareStep 1 as shown in Step 2, we consider 6 different basic data transformations: First, no conversion at all, leaving the counts as they are. Second, converting the raw counts to CPM (counts per million), which corrects for different numbers of reads in different samples. Third, we also used TPM (transcript per million) which is similar to CPM but also take gene lengths into account. For each of these three approaches, we also implemented a second version in which the transformation was followed by a logarithm in base 2 (Log-2). We refer to this group as data types (or datatypes) in the analysis and figures in the paper.We assembled a set of RNA-Seq data pre-processing steps, to evaluate how they perform when applied prior to GNI analysis. We considered pre-processing steps commonly used prior to GNI analysis applied to microarray data, and combining them with pre-processing steps commonly applied to RNA-Seq data in the context of differential expression analysis. Step 3, we considered more advanced normalizations, namely 3 popular normalization techniques of RNA-seq and another one that is frequently used in microarray datasets. Specifically, Variance-stabilizing transformation (VST) [calcNormFactors function of edgeR [At on (VST)  from DeSon (VST) , the trion (VST)  and alsoon (VST)  from theof edgeR  R softwaof edgeR  that is At Step 4, we implemented the rank based data transformation technique, copula–transform (CT) as it was used in the popular GNI algorithms ARACNE [s ARACNE  and C3NEs ARACNE . They ars ARACNE . We alsoAt Step 5, we implemented association estimators to measure the similarity of the expression of gene pairs. Among the 27 available estimators [timators , we seletimators . They artimators , Chao-Shtimators , 28. TheAt Step 6, we used the preprocessed datasets to infer GRNs and input them to Step 8. At Step 7, we input the literature interaction information to Step 8, where overlap analysis was performed between the predicted networks and the literature to evaluate the performances of each preprocessing combinations.A pipeline was developed to implement this workflow, which is based on the workflow management system Snakemake .recount [We downloaded 7 different RNA-seq datasets using the recount  R packagFor the main analysis, we used 6 different RNA-seq datasets (Dataset 2 to 7) and obtained a total of 5400 different performance scores to select best performing preprocessing combinations with the representative GNI algorithms. The horizontal bar plots in The wide heterogeneity in the RNA-seq data makes it very difficult to choose a suitable preprocessing combination that works best for all the datasets. In In If we had looked at a result of a single dataset we would end up selecting a best preprocessing combination. But, we used 6 different datasets that allowed us to find out that there is no best preprocessing combination valid for all the datasets. Rather, there are good combinations that work well mostly but each dataset might need a different preprocessing combination for the best GNI performance. This is pretty different than we would expect to find in the beginning of this study. For example, the best performing GNI and preprocessing combination in The complete tables for each GNI algorithm and the table of the similar analysis of all the GNI and preprocessing combinations together can be seen as a spreadsheet file in The preprocessing combinations in Ranking approach, as in On the other hand, there are still best performing combinations available and that small difference in accurate predictions may still biologically have profound effect. Because even a single newly detected interaction may be the main regulator of that biological condition. Therefore, it is still interesting to use the best performing ones among the good ones when inferring GRN from RNA-seq data. In order to be able to find best ones, we use the ranking approach on them as explained above. The difference among rank values may look larger than the real performance scores. Therefore, we consider all the information as in datatypes) derived from the basic data transformations. CS and QN were removed from further analysis as mentioned previously. Results in the box plots in In NoNorm). Regarding the best performances, there is no one clearly differentiated from others considering all the 6 datasets. Nonetheless, regarding worst performances, QN clearly provides 3 worst median scores and it does not provide a best score over all the 6 datasets. It is a clear evidence of inconsistence performance of QN. This observation lead us to exclude QN from most of the analysis as we have no intention to use it in real applications regarding these outcomes. Considering the median values, VST has 4 highest and 2 lowest ranks. NoNorm has 2 best and 4 worst ranks. It is not safe to choose VST as the best performing normalization method in general as we see it also performs worst in two out of 6 datasets. One thing is worth mentioning that when VST has best median value 4 times, then NoNorm has always the worst values. Additionally, when NoNorm has the best median values twice then VST has the worst median values. This suggest that we need to consider preprocessing combinations with and without VST normalization. RLE and TMM seem to show moderate performances and similar to each other but RLE seem to be slightly better. This analysis was done regarding median performance results but we are normally interested in the best performance scores. As we see from In We have a closer look into the best performing normalization methods in In If we look at to the pie charts in CT is an important part of GNI over microarray datasets. Here, we investigated its effect on RNA-seq datasets. In Our analysis until here focused on the problem of which GNI preprocessing combinations should be chosen and what are the possible consequences if not properly done. Nevertheless, current RNA-seq datasets may be of any size from a few to several hundreds. It is also important to know if the sample size of the available count data is sufficient to attain to the best or optimal GNI performances. This important question was answered in  for micrDataset 1 was used as an additional different data set (with sample size 85). It is used for only sample size analysis along with the other datasets. In Dataset 1, using the preprocessing combination B-spline, RLE and Log-2 CPM (first row and column), we observe that there is constant and slow increase in performance until the precision score 0.025 around maximum. C3NET and ARACNE show similar trend until the sample size 85 but C3NET provided the precision score of 0.025 in 70 samples compared to the score for sample size 85 of ARACNE. In the Dataset 1, when we use PCC  along with the same other combination (RLE ad log-2 CPM), then we can obtain the same performance score of around 0.025 in 40 samples with C3NET. With this combination, the performance of C3NET showed somewhat convergence at sample size around 40 but it still produced a performance increase from 0.025 to 0.030 after sample size 70 to 85. ARACNE does not seem to converge but produce a slow performance increase until the sample size 85 and hits the score around 0.027. We do not mention about RELNET (RN) as it was already included the study as a base algorithm and used as a reference to somewhat justify the performance scores of the main representative GNI algorithms ARACNE and C3NET. Because RN is used as the first base step of those and many other algorithms [For Dataset 2, the scores of C3NET from the preprocessing combination B-spline, RLE and Log-2  start converging to precision 0.027 at sample size 60. The performances show an increase from around 0.027 to 0.03 between the sample sizes 70 to 80. A convergence is observed afterwards until the sample size 96. On the other hand, ARACNE seems to converge at precision around 0.027 after sample size 80. For Dataset 2, using the same preprocessing combination but replacing SCC instead of B-spline , we observe slower increase in performances than before until the sample size 96.For Dataset 3, the scores of C3NET with the combination B-spline, RLE and Log-2 TPM (second row and column), rapidly converge to precision around 0.022 from sample size 8 to 30. It then increases to the precision score of around 0.026 from sample size 60 to 80 and converges second time afterwards. Here, the scores of ARACNE seem to converge at sample size 60. For Dataset 3, when we replace PCC instead of B-spline in the previous combination , the performance scores of C3NET converge at sample size 50 to the precision of around 0.027. Whereas, ARACNE seem to converge at sample size 60 to the precision of around 0.026.nonnormalized in these figures) and Log-2 TPM , C3NET and ARACNE converged at sample size 50. On the other hand, using PCC instead of B-spline allowed them to converge at sample size 30 with the similar performance scores. PCC made significant difference with respect to sample size at similar converged performance scores.For Dataset 4, the combination B-spline, no normalization . Therefore, we selected some popular ones as they are expected to be promising. As illustrated in By using 6 different datasets, we found out that there is no best but there are good combinations that work well in mostly but each dataset might need a different preprocessing combination. That directs us to use the literature and perform a quick performance analysis with the good preprocessing combinations and select the best combination specifically for the dataset of interest. Alternatively, one may just choose one of the good ones that we present in this study; knowing the fact that it may not give the best performance but a good one.Comparing to the performance of randomly generated networks, we observed some close poor performance scores to random networks but we also observed dramatic gains in performance when right preprocessing combinations used. This shows the importance of this kind of studies that will help better selecting the preprocessing combinations to be able to apply GNI algorithms on RNA-seq datasets with some confident. From our extensive analysis, we selected some good preprocessing combinations as specified in the ganet, along with this study.We concluded that preprocessing is the most important part of any GNI application over RNA-seq datasets. Because, if not properly chosen, one might end up with very poor results. We also conclude that there is no best preprocessing method but several good combinations for GNI over RNA-seq datasets, as shown in We also concluded that the minimum required sample size may vary from around 30 to 85 for each RNA-seq data for sufficient performances of GNI. We also concluded that GNI algorithm and preprocessing combinations can make significant differences in the required sample size.For the practical applications in general, instead of selecting a single GNI preprocessing combination, we suggest to select several good ones and apply them for testing their performance with the literature as demonstrated in this study.Considering all the results, especially minet [c3net [We provided important practical information about the used methods in this section. However, further details are provided for all the methods in minet  and c3net [c3net . For ARAt [c3net . In ordet [c3net . One musAt Step 5 in copula in c3net software package [calcNormFactors function of edgeR [cpm function of the popular edgeR software package [At Step 4 in  package . At Step package  of DeSeq package , the tri package  and also package  from theof edgeR  R softwaof edgeR . Further package , 35. ForAn important practical note is that, before implementing any of these preprocessing steps, we add one to each value in the dataset matrices, which otherwise may cause erroneous results because of possible zeros in the matrix. Adding one to matrix do not change the results but may prevent from failing because of zeros. Also, if a result is obtained much better than the ones presented in this study, one must be very cautious and check the predicted network. We had come across cases where a few of the genes, who have extremely larger variations than most of the others, may dominate the network as big hubs. This biased case may cause to higher scores if the gene is one of the well-studied ones in the literature. Our default filtering and adding one before each preprocessing helped overcome these cases in the studied datasets. However, one should always check out the network once predicted for such dominated genes and also other unusual structures of the network.We first filtered out the NA named genes, meaning not annotated genes from the data. If there are multiple rows with the same gene symbol, we kept only one of the raw of that gene with the highest variance. After cleaning up the dataset, we filtered them as follows: We first removed those genes whose CPM expression values less than or equal to 0.1 for at least 20% of the samples. In In order to assess the performance of GNI results, we used the combination of all the suitable wet-lab validated interaction databases we found in the literature as in . The hypWe tested whether it makes sense to accept the hypothesis of using the literature as a relative reference network for performance evaluation using the randomly generated gene networks and its overlap with the literature. The result was not significant with respect to the overlap analysis. We gave more details on this below while describing random networks. Comparing it with the inferred networks by GNI algorithms, we observed very highly significant overlap results over the literature in general. These tests support the hypothesis to use the literature and we have some level of justification to use the literature as a reference network for globally assessing, or at least comparing, the inference performance of GNI algorithms on real datasets. Nonetheless, we are aware of the fact that when a gene interaction pair is labelled as TP according to the approach, it is not an absolute TP and we might still make mistake. But for the purpose of comparison of various algorithms on real and especially de novo datasets, this idea is very helpful and works well.The idea of using the literature for GNI performance evaluation is also not new to this study. It was initially used in  and sincSince one of the conclusions of this study is to evaluate the performance each de-novo dataset with the suggested good combinations, we developed a pipeline than can be run efficiently over high performance clusters or on a single computer for a smaller size evaluation. It is based on Snakemake , which iganet (version1.0) was including the following databases by 2011: BioGRID [ganet.hyperg, which runs hyper-geometric test, and for practical performance assessment, ganet.getperformance. The software ganet v.2.2 is available to be downloaded and used from a public repository. The details of how to install and use ganet with a practical example can be seen in The literature database in R software package  BioGRID , Human P BioGRID , Molecul BioGRID , IntAct  BioGRID  and B-Ce BioGRID . With th BioGRID , innateD BioGRID , CORUM [ BioGRID  and the  BioGRID  database BioGRID . We alsoWe generated 10 different random networks with 4748 interactions that is the same as the number of predicted interactions of the best scoring preprocessing combination in the best performing dataset, which is Dataset 6. We then performed the same overlap analysis for the random networks as done so far in this study. The literature database in ganet and the random networks overlap analysis resulted average precision of 0.003 and p value of 0.58 from hyper geometric tests. These results are very useful as we can employ them as base reference levels on performances. Any performance result close to or worse than the performance of the random network is considered as very poor and the associated combination is excluded with no doubt. For example, it appeared that the combinations that include CS estimator provided very poor results and excluded from further analysis. In fact, CS estimator was among the good ones in microarray analysis . This shrecount [RNA-seq datasets were downloaded conveniently using the recount  R packagDataset 1: This dataset has GEO accession number GSE79970 [GSE79970 . The titDataset 2: The GEO number of this dataset is GSE57148 [https://jhubiostatistics.shinyapps.io/recount/) is SRP041538. The title of this dataset in GEO database is ‘Characterizing gene expression in lung tissue of COPD subjects using RNA-seq’. We used 96 of COPD samples of this dataset as Dataset 2. After filtering, there were 17520 unique genes and 96 samples in the raw dataset.GSE57148  and its Dataset 3: This dataset has the 91 normal samples of GSE57148 from the same publication [lication  of DatasDataset 4: GEO number is GSE54456 [Dataset 5: This dataset has 83 normal samples of GSE54456 from the same publication [Dataset 6: GEO number is GSE69529 and SRA number is SRP059039. The title of this dataset in GEO database is ‘Elucidating the etiology and molecular pathogenicity of infectious diarrhea by high throughput RNA sequencing’. This study is based on whole blood samples. We used 55 of Rotavirus type samples of this dataset for Dataset 6. It is worth reminding that this dataset performed best overall despite its smaller number of samples. After filtering, there were 15040 unique genes in the raw dataset.Dataset 7: This dataset has 41 Salmonella samples of GSE69529 from the same study of Dataset 6. After filtering, there were 15112 unique genes in the raw dataset. Despite its low number of sample, this dataset has second best performance.Supplement 1Supplement 2