In mice, spared nerve injury replicates symptoms of human neuropathic pain and induces upregulation of many genes in somatosensory neurons. Here we used single cell transcriptomics to probe the effects of partial infraorbital transection of the trigeminal nerve at the cellular level. Uninjured neurons were unaffected by transection of major nerve branches, segregating into many different classes. In marked contrast, axotomy rapidly transformed damaged neurons into just two new and closely-related classes where almost all original identity was lost. Remarkably, sensory neurons also adopted this transcriptomic state following various minor peripheral injuries. By genetically marking injured neurons, we showed that the injury-induced transformation was reversible, with damaged cells slowly reacquiring normal gene expression profiles. Thus, our data expose transcriptomic plasticity, previously thought of as a driver of chronic pain, as a programed response to many types of injury and a potential mechanism for regulating sensation during wound healing. The somatosensory system is responsible for providing vertebrates with important information about thermal, chemical and mechanical cues by eliciting a very wide range of distinct sensations that allow animals to respond appropriately . For exaGal), neuropeptide Y (Npy) and neurotensin (Nts), transcription control factors including activating transcription factor 3 (Atf3), SRY-Box11 (Sox11) and cytokines like colony stimulating factor 1 (Csf1) that are all expressed in injured neurons. It is generally thought that these transcriptional changes are important for triggering neuropathic pain with recent work showing that upstream inhibition of this gene expression program attenuated pain , and protein S100β (S100b) were poorly represented in Dropseq sequencing of trigeminal neurons  and early growth response protein 1 (Egr1) in the sn-data. Both genes were prominent in the earlier single cell analysis . Remarkably, even without these injury-related transcripts playing a role, Atf3-expressing neurons still grouped together as two related classes and remained segregated from the uninjured trigeminal neural classes , calcitonin gene-related peptide 1 , kainate one receptor (Grik1) and Scn10a are normally expressed at high levels in significant numbers of trigeminal neurons , Piezo2, netrin G1 (Ntng1), microtubule associated protein 2 (Map2) and FAT atypical cadherin 2 (Fat3) that normally are prominent in larger diameter neurons involved in more discriminative types of sensation. ISH confirmed that a large subset of injured neurons still expressed Piezo2 (Tshz2), regulator of G protein signaling 4 (Rgs4) and calcium voltage gated channel subunit alpha 1 c (Cacna1c) that are normally markers of smaller diameter neurons (Gfra1) was primarily expressed in I1 neurons , which was rapidly upregulated but downregulated at later time-points and two injury-related neuropeptides Nts and Npy that were more slowly upregulated  and examined mice that exhibited a chronic itch-like phenotype where scratching had produced obvious shallow skin lesions in the head and neck area (scratch lesions). In both cases, Atf3-positive neurons were detected by ISH and just as in the case of sham surgery these cells also expressed other markers of nerve injury  in neonate knockin mice  activate tdTomato expression (red).Therefore, to explicitly test if neurons can recover after entering the injured state, we generated kin mice . This ap neurons . In contobserved . Note thtdTomato labeled neurons would always express Atf3 (and their number would decrease with time). However, if they also reverted to an uninjured transcriptional state, these cells would continue to express tdTomato even though they were no longer positive for Atf3. Our results  neurons rose with time after injury indicating a progressive recovery occurs. Quantitation of data revealed that the percentage of tdTomato-neurons that were Atf3-negative rose from 11.0 ± 5.3% at day-7 to 42.7 ± 14.7% at day-21  exclusively reflected cell death  then tdT results  show thay-21 see , demonsty-21 see . Furtherith Atf3 . In the ith Atf3  make thiAtf3-IRES-Cre line  also induced injury-related transcripts in a few neurons. Second, many of the genes that are upregulated by injury have roles in tissue remodeling, neural development, and axon guidance see . These gCre line  and sn-tCre line  and 2 shSingle cell sequencing has vastly expanded the definition of neural class, with the general assumption that distinct transcriptomic classes are fundamentally different types of cell . Here, sWhat is the role of the injured state in triggering neuropathic pain? Here we demonstrate that in this experimental model the injury-related gene expression program is essentially complete two days after IOT and thus precedes the peak development of pain . Thus, iAtf3-Cre driver to ablate and silence damaged neurons will help address these issues and reveal if and when these injured neurons are required for development (and perhaps resolution) of various types of pain.Finally, we note that the type of nerve damage caused by transection makes it unlikely that precise reinnervation of target sites can be required for neurons to initiate the transition back to normal function. Indeed, it is possible that aberrant signaling from neurons with inappropriate peripheral endings could contribute to some types of pain and sensory disturbances associated with severe injuries. In the future, we expect that genetic approaches using the Atf3-IRES-Cre mice were generated by homologous recombination in C57BL/6J mouse zygotes using clustered regularly interspersed short palindromic repeats (CRISPR) associated protein 9 (Cas9) to assist targeting  at position 209 with a leucine (CTA). The mice with scratch lesions were a double heterozygous transgenic strain where a TetTag, Fos-tTA  guidelines for the care and use of laboratory animals and were approved by the NIDCR ACUC. Male and female mice were used for all experiments but were not analyzed separately; animals were assigned to groups without randomization. Mice were C57BL/6NCrl except where specified and were 6 weeks or older at the time of surgery. IOT was carried out as previously described  and cut argeting  see Figu animals . A stand Fos-tTA  drove ex2, 10 mM Tris, pH 8.0, 1 µM DTT, 0.1% Triton X-100 (v/v). The homogenate was filtered through a 40 µm cell strainer , transferred to low bind microfuge tubes  and centrifuged at 800 g for 8 mins at 4°C. The supernatant was removed, the pellet gently resuspended in 500 µl of PBS with 1% BSA and SUPERaseIn RNase Inhibitor  and was incubated on ice for 10 min.Trigeminal ganglia were harvested and incubated overnight in RNAlater  at 4°C. Excess RNAlater was removed, ganglia were frozen on dry ice and stored at −80°C. To isolate nuclei, we adapted methods described previously  to the tNeuronal nuclei selection was performed by incubating the sample with a rabbit polyclonal anti-NeuN antibody  at 1:2000 with rotation at 4°C for 30 min. The sample was then washed with 500 µl of PBS with 1% BSA and SUPERaseIn RNase Inhibitor and centrifuged at 800 g for 8 mins at 4°C. The resulting pellet was resuspended in 80 µl of PBS, 0.5% BSA, 2 mM EDTA. 20 µl of anti-rabbit IgG microbeads  were added to the sample followed by a 20 min incubation at 4°C. Nuclei with attached microbeads were isolated using LS column  according to the manufacturer’s instruction. The neural nuclei enriched eluate was centrifuged at 500 g for 10 min, 4°C. The supernatant was discarded, and the pellet was resuspended in 1.5 ml of PBS with 1% BSA. To disrupt clumped nuclei, the sample was homogenized on ice with an Ultra-Turrax homogenizer (setting 1) for 45 secs. An aliquot was then stained with trypan blue and the nuclei were counted using a hemocytometer. The nuclei were pelleted at 800 g, 8 mins at 4°C and resuspended in an appropriate volume for Dropseq or 10X Chromium capture. A second count was performed to confirm nuclei concentration and for visual inspection of nuclei quality.Scn9a, Scn10a, Piezo2, etc. but instead expressing highly elevated levels of markers of non-neuronal cells including Prp1, Mbp, Apod, Apoe were tagged as non-neuronal and were removed prior to the clustering reported here. Outside single cell experiments no data were excluded. Reclustering data excluding specific genes was achieved by analyzing data for significantly up- or down-regulated transcripts and removal of these genes from the list of variable genes used in principle component selection prior to standard clustering. Numbers of sn-transcriptomes analyzed were typical for this type of experiment. GO-analysis was performed using the Gene Ontology Resource (Dropseq capture (for a subset of control data only) and library generation were performed as described previously  with theResource .Atf3 was strongly clustered in the trigeminal ganglion of IOT, sham surgery and facial injury models, images shown for controls and all injury models are of this region. Cell counts were determined from images of multiple sections from three animals. Total neuronal counts were determined using ISH for tubulin beta type 3 (Tubb3), injured neurons were identified using ISH for Atf3 and recovered neurons . 10 µm sections were used for multi-color in situ hybridization using RNAscope Multiplex Fluorescent Assay (Advanced Cell Diagnostics) according to the manufacturer’s instructions. Confocal images were acquired with a Nikon C2 Eclipse Ti (Nikon) at 1 µm optical section. All images are maximum projection (collapsed) stacks of 10 individual optical sections; consistency of staining was assessed using multiple sections from at least three mice as is considered standard; images were processed using Adobe Photoshop CC to adjust brightness, contrast and set channel color for display. Because expression of  neurons  were defn levels  are the All sequence data have been deposited in GEO (Accession number: GSE131272). Other data that support the findings of this study are available from the corresponding author upon request. There are no restrictions on data availability. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by four peer reviewers, including Peggy Mason as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Eve Marder as the Senior Editor.Thank you for submitting your article "Stereotyped transcriptomic transformation of somatosensory neurons in response to injury" 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.This work suggests that injured trigeminal neurons from animals with a wide range of injuries – from a scratch to a nerve crush – will de-differentiate into a phenotype that is common to all afferent types. The independence of phenotype from afferent type and the range of injuries that yield this are both exciting new developments. Yet the conclusions are more intriguing and appealing than the data are compelling. To do justice to this work, the authors are requested to expand, increase the quantification, and all-around sharpen the results and figures. Thus, the reviewers all believe that the authors would be best served by tightening the results up substantially. Areas that would benefit from attention include but are not limited to the following.- Place a clear methodological description of the minimal injury that yields the common phenotypic change in the results narrative. And explicitly compare the minimal injury to sham surgery.- A full analysis of RNAseq data, that goes beyond a list of genes in a table, such as GO, pathway analysis, transcription factor binding site analysis etc.- Provide detailed description of the mice used and validation of the Atf3-IRES-Cre knockin mouse generated.Please use the detailed comments of the reviewers. We look forward to a substantial revision,Reviewer #1:This is an exciting contribution that takes a new look at the idea that changes in gene regulation are critical to the generation of neuropathic pain. Support for this attractive idea derives from abundant studies looking at the upregulation of genes in DRG cells after peripheral injury. Here the authors look at several far more innocuous manipulations beyond the typical constriction/partial transection injury. And surprisingly these manipulations cause similar genetic changes as do the frank injuries. These findings suggest that the role of changes in gene regulation to the generation of neuropathic pain is at least more nuanced than currently formulated.Additional findings  round out this contribution.While these findings are exciting, this reviewer has a request and a concern:1) The impact of these findings on the idea that changes in gene regulation are critical to the generation of neuropathic pain should be discussed explicitly. These results suggest that by not looking at more innocuous / acute manipulations, previous research may have missed the full natural history of gene expression changes after peripheral damage. Please add this to the Discussion.2) The results are not compelling because the figures do not illustrate the results in a compelling fashion. The results wrt the sham controls are also confusing. Were there changes after sham surgeries? Why isn’t sham listed in Figure 5—figure supplement 1C.Reviewer #2:The authors combined single-nuclei RNAseq of isolated trigeminal neuronal nuclei with in situ hybridization (ISH) to characterize changes in gene expression after infraorbital trigeminal nerve transection (IOT) or a more moderate facial cut injury. The authors find that IOT leads to two new injury related clusters, with peak cell numbers 2-7dpi followed by a progressive decline starting 21dpi. They find that facial cut injury with or without scratching also generates two similar injury related clusters albeit with far less cells in each cluster. ISH was performed to validate these findings by showing either the presence or absence of colocalization of select genes with the injury-marker gene ATF3. Overall, the conclusions are modest, expected, and narrow in scope. Further, the RNAseq data provides little analysis beyond identification of neuronal clusters and the ISH experiments lack scientific rigor. Hence, the impact of the paper is limited. This work might be more suitable to be published as a resource rather than a research.The major finding of the RNAseq data is that injured peripheral neurons alter their gene expression and form distinct cluster(s), whether injury occurs to the nerve or the terminations in the facial skin, and that this clustering persists yet subsides over time. This conclusion lacks novelty and rather mainly validates decades of research on this topic. No additional analysis is done with the RNAseq data .The authors perform no quantification or statistical testing for the ISH experiments and basic controls are not used . In Figure 1D-G, Atf3 expression looks very high and might not match the number of injured cells reported in the sequencing (13.8%). Clarification of total number of neurons using a general neuronal marker can help.The 'tables' provided appear to be raw data excel files generated by the sequencing software more suitable to be labeled as supplements rather than 'tables'. Tables a1 and a2 are mentioned but not included in the current version.The sn-transcriptomics experiments represent a mix of sequencing from different platforms (DropSeq- for controls and 10X genomics for injuries). This inconsistency can lead to misinterpretation of the results. Information on sequencing depth and counts normalization is missing.In Figure 3, there is no mention of time post injury where the ISH was done. Quantification of co-localized markers would help.In Figure 4, does control represent sham-injury as in Figure 3?In Figure 5, quantifications of images are necessary. Some indication on the infection rate of the virus are needed, since on day 7 it looks like only about 50% of Atf3 cells are also TdTomato positive. Details on the Atf3 -IRES-cre mice are missing, are these homozygous or heterozygous. Claiming injury does not lead to cell death of injured cells should be shown by using a dead cell marker. Using the Atf3-cre; Flx TdTomato together with markers for different neuronal cell types could shed light on the injured cells identity following an injury.Reviewer #3:In general this is a well written study about an interesting topic and the results are of fundamental importance. Namely that all sensory neurons collapse into close to one phenotype after injury, which is likely a transcription profile related to regeneration. Following recovery, however, they return to their original phenotypes independent of developmental cues. The conclusions of this study are important and of interest to many, those interested in nerve regeneration, sensory neuron physiology and neural development.Within the tables please include a control and injured neuron expression levels. I.e. a p-value for inclusion in the group, a mean expression level for the cells in this group (Log2 TPM+1), a mean expression level in all other groups, and a fraction of cells in this group with non-zero expression. As in Lacar et al., 2016, and others.Is there a reason for just showing the top 100 genes in each category in Supplementary file 3, then the top 50 in Supplementary file 2? As the full data sets will be placed on GEO please include full summary lists in the tables, to enable straightforward downstream analysis of these lists by a reader.Please include a diagram of how the nuclear sequencing was performed early in the manuscript, as this reviewer wondered while reading this paper if there were any non-neuronal groups and if not why not? By reading the Materials and methods this became clear, but for the general understanding of the study a diagram of the experiments performed would help a great deal.Please perform pathway analysis on the regulated gene clusters. Also on the remaining 'non-regulated' transcripts that still clustered into the I1 and I2 groups.Would transcription factor binding site analysis demonstrate what TF binding sites are over represented in the injured groups? Can the I1 and I2 groups be separated this way?Do the expression levels of the genes differ with different injury paradigms? Please quantify.Are the uninjured profiles in the injured ganglia the same as their control uninjured counterparts? As there is extensive monocyte/non-neuronal activation in injured ganglia after nerve injury do the 'uninjured cell profiles' vary because of these changes and/or via other mechanisms? This study mentioned Gal but expand to the full group of genes and quantify the extent of these similarities/differences.Related to the point above; in the long term injured cells (21 and 75 days) that have returned to 'normal' are there any retained strongly regulated gene signatures in common.Ganglia from male and female mice were analyzed in these studies, do any sex specific transcripts separate into specific groups?Non-neuronal cells? Do you have any data on those? The study would be significantly enhanced with the addition of such profiles.Reviewer #4:The manuscript by Nguyen et al. analyzes transcriptomic changes that occur at a single cell level in primary afferents upon injury. Although many groups have looked at changes in gene expression upon injury, this is the first to do so at the single cell level, and the results are extremely novel and interesting. The major conceptual advance that is revealed through these analyses is that injured neurons take on a new, somewhat de-differentiated phenotype that appears to be independent of the primary afferent cell type involved – an injured state, that is common across afferents. The study also has the potential to provide a very valuable resource to the pain community.1) In many places, it feels like the authors are describing their impression of the data. Although I have full confidence that the authors have looked at their data carefully, the analyses would appear more rigorous if they were backed up with quantitative data, where possible.2) In other instances, quantification is not possible. Nevertheless, a fuller description of the examples that led the authors to their interpretation would be helpful. For instance, "I1 neurons expressed markers that suggested they were originally involved in more discriminative types of sensation, while I2 neurons had an expression profile more related to nociceptors " Which genes led to this interpretation?3) Although the raw data have been made available, I think this form of data will only be useful to those who have extensive experience with clustering algorithms and wish to reanalyze all the data from scratch. It would be much better to provide the datasets in which gene expression in individual cells has been assigned to a cluster. This resource would allow people to readily look up genes of interest and mine the data for other interesting observations.4) The mice that have scratch lesions should be described in more detail. They seem to be a double transgenic strain where TetTag, Fos-tTA drove expression of tetO-mCherry-2A-Gnaq. Were there mCherry-positive neurons in these mice that was (presumably) indicative of neurons that were driving the itch-like behavior? If so, were they primary afferents with somatotopy consistent with the lesions? Even if the authors have no idea why these mice scratch to the point of injury, I think it would be better to be more transparent about it. Also, although I acknowledge that this allele is not a major part of your study, given that you used it, I think it is important to describe it in more detail. For instance, was the mCherry-2A-Gnaq allele targeted to the Rosa locus or randomly inserted?5) Please provide a map for the construction of the Atf3-IRES-Cre knockin mouse and some indication that you have validated it. This work suggests that injured trigeminal neurons from animals with a wide range of injuries – from a scratch to a nerve crush – will de-differentiate into a phenotype that is common to all afferent types. The independence of phenotype from afferent type and the range of injuries that yield this are both exciting new developments. Yet the conclusions are more intriguing and appealing than the data are compelling. To do justice to this work, the authors are requested to expand, increase the quantification, and all-around sharpen the results and figures. Thus, the reviewers all believe that the authors would be best served by tightening the results up substantially. Areas that would benefit from attention include but are not limited to the following.- Place a clear methodological description of the minimal injury that yields the common phenotypic change in the results narrative. And explicitly compare the minimal injury to sham surgery.- A full analysis of RNAseq data, that goes beyond a list of genes in a table, such as GO, pathway analysis, transcription factor binding site analysis etc.- Provide detailed description of the mice used and validation of the Atf3-IRES-Cre knockin mouse generated.Please use the detailed comments of the reviewers. We look forward to a substantial revision,Reviewer #1:This is an exciting contribution that takes a new look at the idea that changes in gene regulation are critical to the generation of neuropathic pain. Support for this attractive idea derives from abundant studies looking at the upregulation of genes in DRG cells after peripheral injury. Here the authors look at several far more innocuous manipulations beyond the typical constriction/partial transection injury. And surprisingly these manipulations cause similar genetic changes as do the frank injuries. These findings suggest that the role of changes in gene regulation to the generation of neuropathic pain is at least more nuanced than currently formulated.Additional findings  round out this contribution.While these findings are exciting, this reviewer has a request and a concern:1) The impact of these findings on the idea that changes in gene regulation are critical to the generation of neuropathic pain should be discussed explicitly. These results suggest that by not looking at more innocuous / acute manipulations, previous research may have missed the full natural history of gene expression changes after peripheral damage. Please add this to the Discussion.2) The results are not compelling because the figures do not illustrate the results in a compelling fashion. The results wrt the sham controls are also confusing. Were there changes after sham surgeries? Why isn’t sham listed in Figure 5—figure supplement 1C.Reviewer #2:The authors combined single-nuclei RNAseq of isolated trigeminal neuronal nuclei with in situ hybridization (ISH) to characterize changes in gene expression after infraorbital trigeminal nerve transection (IOT) or a more moderate facial cut injury. The authors find that IOT leads to two new injury related clusters, with peak cell numbers 2-7dpi followed by a progressive decline starting 21dpi. They find that facial cut injury with or without scratching also generates two similar injury related clusters albeit with far less cells in each cluster. ISH was performed to validate these findings by showing either the presence or absence of colocalization of select genes with the injury-marker gene ATF3. Overall, the conclusions are modest, expected, and narrow in scope. Further, the RNAseq data provides little analysis beyond identification of neuronal clusters and the ISH experiments lack scientific rigor. Hence, the impact of the paper is limited. This work might be more suitable to be published as a resource rather than a research.The major finding of the RNAseq data is that injured peripheral neurons alter their gene expression and form distinct cluster(s), whether injury occurs to the nerve or the terminations in the facial skin, and that this clustering persists yet subsides over time. This conclusion lacks novelty and rather mainly validates decades of research on this topic. No additional analysis is done with the RNAseq data .The authors perform no quantification or statistical testing for the ISH experiments and basic controls are not used . In Figure 1D-G, Atf3 expression looks very high and might not match the number of injured cells reported in the sequencing (13.8%). Clarification of total number of neurons using a general neuronal marker can help.The 'tables' provided appear to be raw data excel files generated by the sequencing software more suitable to be labeled as supplements rather than 'tables'. Tables a1 and a2 are mentioned but not included in the current version.The sn-transcriptomics experiments represent a mix of sequencing from different platforms (DropSeq- for controls and 10X genomics for injuries). This inconsistency can lead to misinterpretation of the results. Information on sequencing depth and counts normalization is missing.In Figure 3, there is no mention of time post injury where the ISH was done. Quantification of co-localized markers would help.In Figure 4, does control represent sham-injury as in Figure 3?In Figure 5, quantifications of images are necessary. Some indication on the infection rate of the virus are needed, since on day 7 it looks like only about 50% of Atf3 cells are also TdTomato positive. Details on the Atf3 -IRES-cre mice are missing, are these homozygous or heterozygous. Claiming injury does not lead to cell death of injured cells should be shown by using a dead cell marker. Using the Atf3-cre; Flx TdTomato together with markers for different neuronal cell types could shed light on the injured cells identity following an injury.Reviewer #3:In general this is a well written study about an interesting topic and the results are of fundamental importance. Namely that all sensory neurons collapse into close to one phenotype after injury, which is likely a transcription profile related to regeneration. Following recovery, however, they return to their original phenotypes independent of developmental cues. The conclusions of this study are important and of interest to many, those interested in nerve regeneration, sensory neuron physiology and neural development.Within the tables please include a control and injured neuron expression levels. I.e. a p-value for inclusion in the group, a mean expression level for the cells in this group (Log2 TPM+1), a mean expression level in all other groups, and a fraction of cells in this group with non-zero expression. As in Lacar et al., 2016, and others.Is there a reason for just showing the top 100 genes in each category in Supplementary file 3, then the top 50 in Supplementary file 2? As the full data sets will be placed on GEO please include full summary lists in the tables, to enable straightforward downstream analysis of these lists by a reader.Please include a diagram of how the nuclear sequencing was performed early in the manuscript, as this reviewer wondered while reading this paper if there were any non-neuronal groups and if not why not By reading the Materials and methods this became clear, but for the general understanding of the study a diagram of the experiments performed would help a great deal.Please perform pathway analysis on the regulated gene clusters. Also on the remaining 'non-regulated' transcripts that still clustered into the I1 and I2 groups.Would transcription factor binding site analysis demonstrate what TF binding sites are over represented in the injured groups? Can the I1 and I2 groups be separated this way?Do the expression levels of the genes differ with different injury paradigms? Please quantify.Are the uninjured profiles in the injured ganglia the same as their control uninjured counterparts? As there is extensive monocyte/non-neuronal activation in injured ganglia after nerve injury do the 'uninjured cell profiles' vary because of these changes and/or via other mechanisms? This study mentioned Gal but expand to the full group of genes and quantify the extent of these similarities/differences.Related to the point above; in the long term injured cells (21 and 75 days) that have returned to 'normal' are there any retained strongly regulated gene signatures in common.Ganglia from male and female mice were analyzed in these studies, do any sex specific transcripts separate into specific groups?Non-neuronal cells? Do you have any data on those? The study would be significantly enhanced with the addition of such profiles.Reviewer #4:The manuscript by Nguyen et al. analyzes transcriptomic changes that occur at a single cell level in primary afferents upon injury. Although many groups have looked at changes in gene expression upon injury, this is the first to do so at the single cell level, and the results are extremely novel and interesting. The major conceptual advance that is revealed through these analyses is that injured neurons take on a new, somewhat de-differentiated phenotype that appears to be independent of the primary afferent cell type involved – an injured state, that is common across afferents. The study also has the potential to provide a very valuable resource to the pain community.1) In many places, it feels like the authors are describing their impression of the data. Although I have full confidence that the authors have looked at their data carefully, the analyses would appear more rigorous if they were backed up with quantitative data, where possible.2) In other instances, quantification is not possible. Nevertheless, a fuller description of the examples that led the authors to their interpretation would be helpful. For instance, "I1 neurons expressed markers that suggested they were originally involved in more discriminative types of sensation, while I2 neurons had an expression profile more related to nociceptors " Which genes led to this interpretation?3) Although the raw data have been made available, I think this form of data will only be useful to those who have extensive experience with clustering algorithms and wish to reanalyze all the data from scratch. It would be much better to provide the datasets in which gene expression in individual cells has been assigned to a cluster. This resource would allow people to readily look up genes of interest and mine the data for other interesting observations.4) The mice that have scratch lesions should be described in more detail. They seem to be a double transgenic strain where TetTag, Fos-tTA drove expression of tetO-mCherry-2A-Gnaq. Were there mCherry-positive neurons in these mice that was (presumably) indicative of neurons that were driving the itch-like behavior? If so, were they primary afferents with somatotopy consistent with the lesions? Even if the authors have no idea why these mice scratch to the point of injury, I think it would be better to be more transparent about it. Also, although I acknowledge that this allele is not a major part of your study, given that you used it, I think it is important to describe it in more detail. For instance, was the mCherry-2A-Gnaq allele targeted to the Rosa locus or randomly inserted?5) Please provide a map for the construction of the Atf3-IRES-Cre knockin mouse and some indication that you have validated it.The editorial summary requested additional quantification and general sharpening of the results to better highlight the findings and do justice to the work. We have added significant new data and analysis and have edited the text taking all the individual reviewers’ comments into account as follows:1) Reviewer 1 requested Figure 1—figure supplement 1 be added to the main figures and reviewer 3 wanted a diagram of how the nuclear sequencing was performed: these are now included in a new Figure 1. To accommodate this change we added a short section explaining the uninjured control results and highlighting advantages of nuclear sequencing vs. cell sequencing  for studying injury related changes in trigeminal neurons.2) We have now provided quantitation, and statistical analysis of ISH data as requested by all reviewers. This is detailed in Supplementary file 1; the quantitation fully supports conclusions drawn from ISH results in the manuscript.3) We have added uninjured controls to Figure 2  and Figure 4  as requested by reviewers 1 and 2. We also relabeled Figure 4 and hope that the new labeling making clear that sham IOT surgery is a mild peripheral injury rather than a control clears up any confusion about these data. That sham surgery is a mild injury model was stated in the original text, but we understand that readers often concentrate on the figures. We have also made sure colors are explained and time after injury is detailed wherever these were missing.4) We have added GO-pathway analysis as requested by the editor and reviewers 2 and 3: Figure 2—figure supplement 3 and Figure 5—figure supplement 1. These support the conclusions about differences in the I1 and I2 classes of injured neurons as well as the role of upregulated vs. downregulated genes. The new Supplementary file 2 goes with Figure 2—figure supplement 3 and provides additional analysis of I1 vs. I2 requested by reviewer 3. Unfortunately, transcription factor analysis that this reviewer suggested might be interesting was rather uninformative  and was thus not included.5) We have added Figure 6—figure supplement 1 detailing the generation and validation of Atf3-IRES-Cre knockin mice as requested by the editor and reviewers 2 and 4 as well as more detail about the mice with scratch lesions .6) We have added Figure 2—figure supplement 2 which augments quantitation of gene expression changes in the injury models as requested by reviewer 3. We have also explained in the text that the clustering and UMAP representation of the data provide analysis of gene expression based on the quantitative expression of many genes, thus the co-clustering of the different injury models is very significant. The clustering approach we used  minimizes impact of technical variation without forcing cells into shared clusters assuaging concerns of reviewer 2.7) We added a clear description of the mild injuries that induce the I1/I2 injured state in the results and add comparison to the sham IOT surgery (another mild injury) as requested by the editor.8) Reviewers 3 and 4 wanted more extensive data for readers: as requested we have included the full set of genes in Supplementary files 2-4, and as reviewer 3 wanted these include p-value for inclusion, adjusted p-value, relative expression in a class vs. other classes, fractional expression in the class in question, fractional expression in other classes.9) As requested by the editor we added discussion focused on the idea that changes in gene regulation are critical to the generation of neuropathic pain and highlight that by not looking at more innocuous / acute manipulations, previous research may have missed the full natural history of gene expression changes after peripheral damage.10) We supply higher resolution figures .


Cervus canadensis) spring count and classification data (2004 to 2016) and fall harvest data from hunting districts in western Montana to construct a population model to estimate vital rates and assess evidence for an association between a series of environmental covariates and indices of predator abundance on per capita recruitment rates of elk calves. Our results suggest that per capita recruitment rates were negatively associated with cold and wet springs, and severe winters, and positively associated with summer precipitation. In contrast, an analysis of the raw age ratio data failed to detect these relationships. Our approach based on a population model provided estimates of the region-wide mean per capita recruitment rate , temporal variation in hunting-district-specific recruitment rates , and annual population growth rates . We recommend using routinely collected population count and classification data and a population modeling approach rather than interpreting estimated age ratios as a substantial improvement in understanding population dynamics.Understanding the dynamics of ungulate populations is critical given their ecological and economic importance. In particular, the ability to evaluate the evidence for potential drivers of variation in population trajectories is important for informed management. However, the use of age ratio data  as an index of variation in population dynamics is hindered by a lack of statistical power and difficult interpretation. Here, we show that the use of a population model based on count, classification and harvest data can dramatically improve the understanding of ungulate population dynamics by: 1) providing estimates of vital rates  that are easier to interpret and more useful to managers than age ratios and 2) increasing the power to assess potential sources of variation in key vital rates. We used a time series of elk ( The population dynamics of ungulates reflect complicated interactions between abiotic and biotic factors such as environmental variation, predation and harvest –3. UnderThe trajectories of populations through time are the integrated result of a group of co-varying vital rates , and effective management requires the identification of those rates responsible for demographic performance . AlthougGiven the practical challenges of studies on individually marked animals, many ungulate populations are routinely monitored and managed using observed age ratios  as a proxy for juvenile survival ,22. In cIn addition to the challenges of using age ratios as indices of demographic performance, a separate challenge is posed by their use as a response variable in the log-linear or linear models that are typically used to evaluate sources of variation in population dynamics ,28. PerfAn alternative modeling approach uses a state-space approach to connect the counts of animals through time using a population model that explicitly incorporates key biological processes while separately modeling the observation process, rather than distilling the counts to a single ratio ,30,32. TThe per capita recruitment rate can drive the population dynamics of ungulates and is the result of a series of processes that are potentially affected by environmental conditions and predator pressure . MaternaCervus canadensis) herds in western Montana that contained spring count and fall harvest data to assess the strength of evidence for a variety of potential sources of variation in recruitment rates using (1) a state-space approach  and (2) a standard age ratio approach (“age ratio model”). Second, we compared results from the two models to evaluate and understand important differences in what could be learned from each analytical approach. We predicted the population model would provide more information on population growth rates and trajectories that can inform management  as well as the observation processes.Here, our goals were two-fold. First, we analyzed a time-series of data on harvested elk  that were defined by the state wildlife agency  based on biological and logistical boundaries  and are distributed across a series of physiographic gradients. Elevation ranges across the study area from 767 m to 3,200 m, with a mean within-district range of 976 m (sd = 516 m). The terrain ranges from flat (minimum 100m slope variance = 0) to rugged (maximum 100m slope variance = 0.25). Over the 13-years of our study (2004 to 2016) precipitation in late spring (May to June) ranged from 40 mm to 568 mm , whereas winter precipitation from December to March ranged from 33 mm to 1,650 mm .Our 27,318 kmundaries . The hunWe used annual spring elk count and age/sex classification data collected from fixed wing aircraft (March-April). Surveys were conducted annually on the winter range for each district in the late spring prior to the migration to summer range and the birth pulse. All surveys were conducted to minimize the potential for double counting, and most surveys were completed within a single day. Due to logistical limitations, not every district had count and age/sex classification data for each of the 13 years (2004 to 2016), which generated a discontinuous time series for most districts . Age ratios were derived from the total counts from the surveys. In our analysis, we included all hunting districts that had a minimum of 6 years of count data collected during 2004 to 2016  a model for the biological processes of elk survival, recruitment and harvest, and 2) the observation process that gave rise to data.E) of calves (Nc), adult females  and adult males  in year t and district u is given as:ϕa), the proportion of calves that were female , and the per capita recruitment rate (τ), and hc, haf, and ham are age/sex specific harvest. The timing of the surveys (late spring) did not match the model anniversary (June 1), therefore the survival terms for calves represented an approximate 10-month survival probability. Individuals that were harvested in year t were subtracted from the population total in year t-1, i.e., treated as if harvest occurred instantaneously after surveys in late spring. Adult survival terms therefore represented survival after accounting for harvest. We assumed the survival of all age/sex classes other than calves was the same through time, a simplification given the evidence for age-related changes in adult survival in ungulates) . In cont, 0.09]) .We also found strong evidence for an association between several environmental covariates that corresponded to conditions during the first year the calf is on the ground and per capita recruitment. Cumulative spring precipitation had a negative association with per capita recruitment rates ((0.22 m) . In comp, 0.07]) . Althoug, 0.08]) .Finally, we also found strong evidence for an association between environmental variation during the year in which the calf is in-utero and recruitment. We found strong evidence for a negative association with lagged winter severity and per capita recruitment rates  .In contrast to the population model, the variance term for the age ratio model incorporated process variance and observation error (e ratios . In content rate . Similar10–2016) .Ntotal, provided a qualitative ability to assess the quality of the observation process. If observed counts are markedly different that the predicted number of animals in a population, it indicates a lack of model fit to the observation process, which can arise from a diversity of factors, including double-counting (in the case of an overestimate), or partial counting (in the case of an underestimate). It can also indicate a lack of fit due to a violation of the closure assumption from immigration/emigration, which can inform how populations are defined in the management process. Moreover, estimated sizes  and the female prior to conception and when the calf is in-utero (year t-1) were strongly connected to per capita recruitment rates. Contrary to our expectations, cumulative spring precipitation in year t was negatively associated with recruitment. A post-hoc analysis of the precipitation signal strongly suggested that these high values of spring precipitation were the result of heavy snow on the summer range, an observation consistent with previous work on elk in this system . Cold anWe found mixed evidence for a relationship between primary production (NDVI) and per capita recruitment rates. Although we found no evidence for a direct relationship between NDVI in either in the spring or summer during the year the calf is on the ground and recruitment, we did find evidence for an interaction between spring NDVI and winter severity such that years with combined low spring NDVI and severe winters were associated with lower recruitment. Moreover, we found an interaction between summer NDVI and winter severity during the year the calf is in-utero (year t-1) that suggested that high values of summer NDVI and severe winters reduced recruitment. NDVI is frequently interpreted as an index of forage quality , though In addition to the important influences of environmental conditions on calf recruitment, predation has been shown to be a major factor influencing juvenile elk survival in individual-based studies that allow for the estimation of cause-specific mortality ,44. It iUsing derived recruitment estimates from population models, rather than estimated age ratios, is a practical alternative for managers and uses routinely collected survey data. However, the results are subject to a series of potential biases and need to be carefully interpreted. It is unknown how the detection process is related to the actual (latent) abundance of elk. If a fraction of the population is persistently unavailable during surveys estimates of the population size from a count-based model are biased low. Yet, provided that the observed population is a consistent index of the size of the total population, estimated population growth rates and trends and underlying vital rates should be unbiased . Count dWe recommend managers consider using routinely collected time-series of observed count data and harvest data in a population model. This approach, as compared to the age ratio modeling approach or monitoring of trends in observed count and age ratios over time provides improved biological inference into sources of variation in vital rates, as well as critical information on population dynamics, resulting in useful information to aid management decisions. The population model approach allows managers to estimate population growth rates and use model predictions and goodness-of-fit metrics to inform the survey methodology. Furthermore, we suggest that any lack of fit between the model and the observations can be highly informative for managers. Poor fit can indicate where the closure assumptions are violated due to emigration or immigration and challenge ideas about the delineation of populations by informing managers about the quality of the observation process. This framework is also flexible enough to accommodate data that are missing due to either logistical limitations that prevented a yearly survey or to an age/class structure that is partly unobservable, such that managers can estimate temporal trends in populations with discontinuous or incomplete data. Combined, the practical benefits of the population model approach render it an attractive option for the informed management of populations using routinely collected survey data.S1 FigWe restricted our analysis to those hunting districts with at least 6 years of data over the duration of the study (2004 to 2016). The resulting subset of elk hunting districts used for analysis is depicted as the shaded gray hunting districts.(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigWe used posterior predictive checks: 1) to compare variation in the observed total counts of animals to replicated total counts using the Freeman-Tukey statistic as a discrepancy measure (left panel), and, 2) to compare variance in the observed number of calves to the variance in the observed number of calves .(TIF)Click here for additional data file.S4 FigWe used a posterior predictive check to compare variation in the observed age ratios  to replicated age ratios using the sum-of-squared-residuals as a discrepancy measure.(TIF)Click here for additional data file.S5 FigMountain lion harvest was on a standardized scale, with 0 corresponding to the average mountain lion harvest (4.0 harvested) and 1 corresponding to one standard deviation of harvest above the average (8.9 harvested) (top panel). The second panel shows the predicted difference in recruitment rates between a mild winter  and a mean winter (swe = 0) as a function of mountain lion harvest, and the bottom panel shows the difference in recruitment rates between a mild winter and a severe winter .(TIF)Click here for additional data file.S6 FigBlack bear harvest was on a standardized scale, with 0 corresponding to the average black bear harvest (20.7 harvested) and 1 corresponding to one standard deviation of harvest above the average (38 harvested) (top panel). The second panel shows the predicted difference in recruitment rates between a mild winter  and a mean winter (swe = 0) as a function of black bear harvest, and the bottom panel shows the difference in recruitment rates between a mild winter and a severe winter .(TIF)Click here for additional data file.S7 FigWolf counts were on a standardized scale, with 0 corresponding to the average (15.8 wolves) and 1 corresponding to one standard deviation above the average (30.4 wolves) (top panel). The second panel shows the predicted difference in recruitment rates between a mild winter  and a mean winter (swe = 0) as a function of wolf numbers, and the bottom panel shows the difference in recruitment rates between a mild winter and a severe winter .(TIF)Click here for additional data file.S8 FigSpring precipitation was on a standardized scale, with 0 corresponding to the average (0.18 m) and 1 corresponding to one standard deviation above the average (0.22 m) (top panel). The second panel shows the predicted difference in recruitment rates between a mild winter  and a mean winter (swe = 0) as a function of spring precipitation, and the bottom panel shows the difference in recruitment rates between a mild winter and a severe winter .(TIF)Click here for additional data file.S9 FigSummer precipitation was on a standardized scale, with 0 corresponding to the average (0.15 m) and 1 corresponding to one standard deviation above the average (0.19 m) (top panel). The second panel shows the predicted difference in recruitment rates between a mild winter  and a mean winter (swe = 0) as a function of summer precipitation, and the bottom panel shows the difference in recruitment rates between a mild winter and a severe winter .(TIF)Click here for additional data file.S10 FigSpring NDVI was on a standardized scale, with 0 corresponding to the average (1.06) and 1 corresponding to one standard deviation above the average (1.40) (top panel). The second panel shows the predicted difference in recruitment rates between a mild winter  and a mean winter (swe = 0) as a function of spring NDVI, and the bottom panel shows the difference in recruitment rates between a mild winter and a severe winter .(TIF)Click here for additional data file.S11 FigSummer NDVI was on a standardized scale, with 0 corresponding to the average (4.10) and 1 corresponding to one standard deviation above the average (4.85) (top panel). The second panel shows the predicted difference in recruitment rates between a mild winter  and a mean winter (swe = 0) as a function of lagged summer NDVI, and the bottom panel shows the difference in recruitment rates between a mild winter and a severe winter .(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


The composition of adult mouse aggregation chimaeras is much more variable than X-inactivation mosaics. An early theoretical model proposed that almost all the extra variation in chimaeras arises, before X-inactivation occurs, by spatially constrained, geometrical allocation of inner cell mass (ICM) cells to the epiblast and primitive endoderm (PrE). However, this is inconsistent with more recent embryological evidence. Analysis of published results for chimaeric blastocysts and mid-gestation chimaeras suggested that some variation exists among chimaeric morulae and more variation arises both when morula cells are allocated to the ICM versus the trophectoderm (TE) and when ICM cells are allocated to the epiblast versus the PrE. Computer simulation results were also consistent with the conclusion that stochastic allocation of cells to blastocyst lineages in two steps, without the type of geometrical sampling that was originally proposed, could cause a wide variation in chimaeric epiblast composition. Later allocation events will cause additional variation among both chimaeras and X-inactivation mosaics. We also suggest that previously published U-shaped frequency distributions for chimaeric placenta composition might be explained by how TE cells are allocated to the polar TE and/or the subsequent movement of cells from polar TE to mural TE. Summary: Analysis of published results suggests that two stochastic allocation steps in chimaeric mouse blastocysts, without the geometrical sampling mechanism proposed previously, could explain much of the variation among aggregation chimaeras. Mouse aggregation chimaeras and mouse X-inactivation mosaics both comprise two distinct cell populations. Mouse aggregation chimaeras are typically produced by aggregating two genetically distinct eight-cell-stage mouse embryos  and so cInitially, the two aggregated cell populations predominantly appear to occupy two hemispheres, while the ICM and TE are formed from inner and outer cells, respectively, so they form two concentric spheres. The geometry of these relationships led Falconer and Avery to reason that the composition of the ICM and TE would usually be similar in aggregation chimaeras. They proposed that most of the variation among chimaeric epiblasts (which produces the entire foetus and adult) would arise at the second allocation step, when ICM cells are allocated to either the epiblast or primitive endoderm (PrE), which forms a layer of cells between the epiblast and blastocoel cavity at the late blastocyst stage . Their hOne issue with Falconer and Avery's analysis is that their distributions of mouse adult coat pigmentation probably included non-chimaeric, technical failures. Analysis of foetal-stage chimaeras can distinguish between non-chimaeric conceptuses (non-chimaeric foetus and extraembryonic tissues) and non-chimaeric foetuses with chimaeric extraembryonic tissues. One such study showed that the distribution of foetal compositions from several pooled series of chimaeras was broad but not U-shaped, when non-chimaeric conceptuses  were excluded but other non-chimaeric foetuses were included . The appNevertheless, we have two concerns with Falconer and Avery's spatially constrained, geometrical allocation hypothesis. These stem from an improved understanding of how cells are allocated to the primary developmental lineages in mouse blastocysts . Early lOur second criticism is that the geometrical mechanism proposed by An alternative hypothesis is that both allocation steps  contribute significantly to the variation among chimaeric epiblasts, both steps are at least partly stochastic in nature and the second allocation step does not depend on the geometrical constraints proposed by Results from a preliminary attempt to test Falconer and Avery's hypothesis, by analysing the composition of mid-gestation chimaeras, were more consistent with a two-step hypothesis, with significant variation arising both before and during allocation of ICM cells to epiblast and PrE, but relatively few chimaeras were analysed .The first aim of the present study was to analyse the composition of chimaeric blastocysts and a larger number of mid-gestation chimaeras, produced in several previously published studies, to characterise the variation among chimaeras at these stages and determine whether both the first and second allocation steps contributed to this variation. The second aim was to determine whether stochastic allocation of cells to different blastocyst lineages could explain the broad distribution of chimaeric epiblast compositions, without the type of geometrical mechanism that Table S1, further details are given in the Materials and Methods and results are shown in Table S2.Falconer and Avery's geometrical allocation hypothesis predicted that little variation arises before the second allocation step, so the extent of variation in composition of ICMs should be similar to that for whole blastocysts, whereas a stochastic, two-step hypothesis predicts that variation should be significantly greater among ICMs than whole blastocysts. We compared the composition of the whole blastocyst, ICM and TE in three published series of blastocyst chimaeras made by embryo aggregation, where the composition of the whole blastocysts were analysed. These studies are listed in In each series, the variance in composition was lowest for the whole blastocyst, higher for the TE and highest for the ICM. This implies that the first allocation event increases the variance in the TE and ICM lineages. Together, the results from preimplantation chimaeras indicate that there is significant variation among chimaeric ICMs and this is likely to arise both before and during allocation of cells to the ICM and TE. However, these preimplantation stage chimaeras provided no information about the relationship between the epiblast and PrE, so we also investigated post-implantation stage chimaeras.a/aGpi1↔b/bGpi1 aggregation chimaeras. Chimaeric tissues contained both GPI1A and GPI1B cells whereas non-chimaeric tissues contained only GPI1A or GPI1B cells. Foetuses and four extraembryonic tissues were analysed for the eight series of E12.5 chimaeras, listed in Table S1  electrophoretic marker in mid-gestation Table S1 . The foeTable S1 . ResultsTables S3 and S4). The frequency distribution for a specific sample type (e.g. amnion) from a series of E12.5 chimaeras was classified as balanced if the numbers of samples with <50% GPI1A did not differ significantly from the number with >50% GPI1A (Table S3), the four unbalanced series had a lower proportion of epiblast-derived samples with >50% GPI1A (Table S4). In most cases, the balance of the YSE and placenta followed those of the epiblast-derived samples but there were a few exceptions (Tables S3 and S4). In all eight series, most placental samples had <25% or >75% GPI1A, so these placental distributions were considered ‘atypical’. Compared with the pooled balanced set of four chimaera series, the pooled unbalanced set had significantly more non-chimaeric conceptuses (Table S5) and more non-chimaeric samples from chimaeric conceptuses (Table S6). Moreover, fewer of the non-chimaeric samples were 100% GPI1A rather than 100% GPI1B (Table S6). As there were major differences between the balanced and unbalanced strain combinations, we analysed them separately.In the original publications, the eight series of E12.5 chimaeras were divided into four balanced and four unbalanced strain combinations according to the distributions of the percentage GPI1A in epiblast-derived samples (0% GPI1A . The serTable S5). This shows that technical failure occurs during chimaera production and suggests that experimental variation that arises during chimaera production is likely to be significant among the chimaeric conceptuses.Even for the balanced strain combinations, production of E12.5 chimaeras yielded 15 non-chimaeric conceptuses  as well as 115 chimaeric conceptuses  are shown as white bars at the ends of the distributions and non-chimaeric samples (0 or 100% GPI1A) from chimaeric conceptuses are shown as yellow bars. This differs from how Falconer and Avery presented their results for coat pigmentation in adult chimaeras, as they did not distinguish between non-chimaeric mice and chimaeras with non-chimaeric coat pigmentation . In contrast, the placentas from the pooled balanced series of chimaeras showed a broad U-shaped distribution  from the pooled balanced series were relatively flat A–D but tribution F. All saribution G–L. Thisribution  and did Tables S7 and S8). This implies that the second allocation step is a significant source of variation among chimaeric epiblasts.If the second allocation step, involving the segregation of the epiblast and PrE lineages, caused variation among chimaeric epiblasts, samples within either of these lineages should show stronger positive correlations in composition (higher correlation coefficients) than samples derived from different lineages. Tables S7 and S8), this implies that significant variation existed among ICMs. This variation must have arisen either before or during the first allocation step when cells were allocated to the ICM or TE. This result is consistent with the analysis of preimplantation stage chimaeras, described above.If there were very little variation in composition among ICMs before the second allocation step, the correlation between the epiblast-derived samples and PrE-derivatives would be expected to be negative. As none of those correlations in the eight series of E12.5 chimaeras was negative  to simulate low and higher levels of variation, respectively, at the first allocation step. Each model comprised three series of simulations (A1–A3 and B1–B3), which simulated different numbers of ICM cells allocated to the epiblast versus the PrE at the second allocation step. For each of these six series, ten sets of 1000 chimaeric embryos with ‘black’ and ‘white’ cells were simulated. The second allocation step (formation of epiblast and PrE) contributed variation to the epiblast composition in both models A and B but it was not constrained spatially in the way that was proposed by At the time of the second allocation step it was assumed that there were always 28 ICM cells and 36 TE cells in both models A and B. It is unclear whether more mouse ICM cells are allocated to become prospective epiblast or prospective PrE cells . Thus, to produce variation at the second allocation step, different numbers of simulated ICM cells were allocated to epiblast and PrE in the three series of simulations compared for each model .Figs S3 and S4 and results for all 60 sets are summarised in Figs S5 and S6.Our analysis of the balanced series of E12.5 chimaeras showed that frequency distributions of epiblast derivatives were consistent with fairly flat distributions but not the U-shaped distributions reported for the percentages of adult coat pigmentation . FalconeFalconer and Avery assumed that there is initially little variation in composition among chimaeric aggregates, little variation arises when morula cells are allocated to the ICM or TE and that almost all the variation arises when ICM cells are allocated to the epiblast or PrE. However, this is not supported by results reported here. Evidence from preimplantation stage chimaeras suggests that some variation already exists among aggregates before segregation of the ICM and TE. This would include both biological variation between aggregated embryos and experimental variation introduced by the chimaera production procedures. Even if this initial variation is overestimated for technical reasons, combined evidence from studies with preimplantation and post-implantation stage chimaeras implies that significant additional variation arises both when cells are allocated to the ICM or TE and when ICM cells are allocated to the epiblast or PrE.The computer simulations should be interpreted with caution, because several potential sources of biological variability were not included. For example, the simulations did not allow for biological differences between the aggregated embryos, in developmental stage, rate of cell proliferation or frequency of cell death, nor did they allow for experimental variation caused by the chimaera production procedure. Despite our biological evidence to the contrary, for simplicity, it was assumed that there was no difference in composition among chimaeras before cells were allocated to the ICM and TE and, as cell loss was not simulated, each simulated chimaeric embryo had 50% black cells, overall. Although the proportion of simulated inner cells was increased at each round of cell divisions in model B , the simulations did not allow for variation in inner cell numbers within a stage, although such variation is known to exist , 1981. TDespite these conservative simplifications, the computer simulations confirmed that, in principle, a fairly broad frequency distribution of simulated chimaeric epiblast compositions could be produced by two stochastic allocation steps, without the type of spatial constraints proposed by Together, our analyses of chimaera results and computer simulations showed that stochastic allocation of cells to different blastocyst lineages could cause much of the variation in chimaeric epiblast composition, without invoking the type of geometrical allocation mechanism proposed by The U-shaped frequency distribution reported for the composition of chimaeric placentas, both here and previously described , differsThe flow of TE cells, moving from the pTE to the mTE , could aOur analysis of chimaera composition data implies that both allocation of morula cells to the ICM or TE and allocation of ICM cells to the epiblast or PrE significantly increase the variation among chimaeric epiblasts. It also suggests that some heterogeneity exists among aggregates, even before the first allocation step. Moreover, computer simulation results were consistent with the possibility that significant variation among chimaeric epiblasts could arise by two stochastic allocation steps. Together, these analyses show that much of the variation in chimaeric epiblasts can be explained by stochastic allocation of cells to different blastocyst lineages without the type of spatial constraints proposed by 3H was a series of six eight-cell 3H-thymidine-labelled↔eight-cell unlabelled chimaeric blastocysts analysed by autoradiography at approximately E4.3 83Clo reiterated marker transgene (Tg (Table S1). Those in the series designated Bl-Tg1 were produced as a control series  by aggregating eight-cell, hemizygous Tg/- embryos with eight-cell, non-transgenic (−/−) embryos at E2.5  electrophoresis was used to estimate the contribution of a/aGpi1 and b/bGpi1 cells to different samples from the percentages of GPI1A and GPI1B allozymes. Maternal contamination or contribution to the placenta was identified as a separate GPI1C allozyme (c/cGpi1 cells) and excluded. Three dead conceptuses , one conceptus in series XR that was fused to a mole and twelve conceptuses that either shared extraembryonic membranes or had fused placentas (two each in series PCT-V and PCT-VI and four each in series XM and XR) were excluded from the analysis. Of the remaining 285 conceptuses, 233 were chimaeric and 52 were non-chimaeric in all of the samples analysed. Some technical losses occurred during analysis so data were incomplete for a few conceptuses.Data from eight series of E12.5 aggregation chimaeras  from four published studies  were anaWhen designing the computer simulations, our aims were to restrict the number of variables to a manageable number and use values that were biologically plausible. Two simulation models of chimaeric blastocysts composed of ‘black’ and ‘white’ cells were developed to model how variation in the epiblast of chimaeric blastocysts could arise in two allocation steps without the type of geometrical sampling that was proposed by In simulation model A, the first allocation step introduced little variation. In this model, all 64-cell chimaeric blastocysts contained 28 ICM cells and 36 TE cells. Overall there were 50% black cells and 13, 14 or 15 of the ICM cells were black cells so the percentage of black cells in the ICM only varied from 46.4–53.6%.In simulation model B, more variation was introduced at the first allocation step. A simulated eight-cell black embryo was aggregated with a simulated eight-cell white embryo to form an aggregate of 16 cells, which all underwent two rounds of cell divisions, without cell loss, to produce a 64-cell chimaeric blastocyst. As a chimaeric aggregate has twice the normal number of cells, it will have some inner cells from the outset and the simulation assumed that all aggregates initially had five inner and 11 outer cells (31.3% inner cells). In most biological aggregates, both embryos will contribute to the inner cells, so at least one black and one white inner cell were included in the inner cells of the simulated 16-cell aggregates. Three other cells were allocated randomly to the inner lineage and the remainder became outer cells.For non-chimaeric mouse embryos, it has been reported that there are typically four to seven inner cells at the 16-cell stage, and the percentage of inner cells rises from 25–44% to about 43–48% by the 32-cell stage , 1981. GIn both simulation models, additional variation was introduced at the second allocation step, by simulating random allocation of black and white ICM cells to the epiblast  in 64-cell stage chimaeric blastocysts. This is equivalent to the 32-cell stage in non-chimaeras, which is approximately when presumptive epiblast and PrE cells become specified . The proAs it is unclear whether more epiblast or PrE founder cells are produced, different numbers  of the 28 simulated ICM cells were allocated to epiblast, in the three series of simulations compared for each model. We did not simulate cell death but the effects of unequal levels of death among newly specified epiblast and PrE cells is likely to be similar to unequal cell allocation of ICM cells to these two lineages and this was simulated.The simulation was written with the programming language Python. For each of the six series of simulations, ten sets of 1000 chimaeric embryos were simulated. In the simulation code, a set of 1000 simulated chimaeras is referred to as a ‘run’.t-test and two-way ANOVA. Online statistical calculator, http://vassarstats.net/index.html, was used for continuity corrected chi square goodness-of-fit tests and http://graphpad.com/quickcalcs/chisquared1.cfm, was used for uncorrected chi square goodness-of-fit tests.The chimaera data were published previously so sample sizes were those used in the earlier studies. The choice of parametric or non-parametric tests was guided, in part, by normality tests. GraphPad Prism  was used for most statistical tests including Fisher's exact test, Spearman correlation test, Student's