Vascular and bleeding complications after transcatheter aortic valve implantation (TAVI) are common and lead to increased morbidity and mortality. Analysis of plaque at the arterial access site may improve prediction of complications.® or MANTA®) in this retrospective cohort study. The ability of pre-procedure femoral CT angiography to predict complications was investigated including a novel method of quantifying plaque composition of the common femoral artery using plaque maps created with patient specific X-ray attenuation cut-offs.We investigated the association between demographic and procedural risk factors for Valve Academic Research Consortium (VARC-3) vascular complications in patients undergoing transfemoral TAVI with use of a vascular closure device (ProGlide®vs MANTA®) and vascular complications. Vascular complications after TAVI were associated with sheath size  and strongly associated with CT-derived necrotic core volume in the common femoral artery of the procedural side .23 vascular complications occurred in the 299 patients in the study group (7.7%). There were no demographic risk factors associated with vascular complications and no statistical difference between use of closure device (ProGlidePlaque map analysis of the common femoral artery by CT angiography reveals patients with greater necrotic core are at increased risk of VARC-3 vascular complications.The novel measurement of necrotic core volume in the common femoral artery on the procedural side by CT analysis was associated with post-TAVI vascular complications, which can be used to highlight increased risk. Advances in the procedure and technology including vascular closure devices (VCDs), such as the suture based ProGlide® and plug-based MANTA® devices allow it to be performed entirely percutaneously. However, complications associated with vascular access and bleeding still occur and result in significant increases in morbidity and mortality. Therefore, the Valve Academic Research Consortium (VARC-3) have defined clinical end points for vascular access site and access-related complications, bleeding and transfusion.Transcatheter aortic valve implantation (TAVI) is an alternative to surgical aortic valve replacement, frequently used in high-, and intermediate-risk patients. and sheath to femoral artery ratio (SFAR) and anatomical features such as distance from common femoral artery (CFA) to skin and tortuosity are significantly associated with an increased risk of VARC vascular complications. Arterial calcification defined by CT imaging is also a risk factor for vascular complications. However, to date only qualitative measures of calcification have been utilised. The role of other non-calcified plaque components as a risk factor for VARC-3 vascular access site and access-related complications is not known.Previous studies have investigated demographic and procedural risk factors associated with VARC Vascular Access Site & Access-Related Complications, demonstrating that female sex and carotid arteries. In this study, we investigate whether identifying quantitative plaque composition from CT-derived plaque maps of the CFAs created using these ratios predicted risk of VARC-3 Vascular access site and access-related complications in patients undergoing transfemoral TAVI access with VCD closure.We have previously described a technique utilising CT angiography lumen contrast/plaque attenuation ratios that can discriminate calcified plaque, fibrous plaque and necrotic core in both the coronary,n = 333) at our institute were considered for inclusion. For inclusion into the study, patients were required to have undergone both a CT planning scan , and then felt to be suitable percutaneous transfemoral access and closure with a vascular closure device (VCD).In this single centre, retrospective cohort study all sequential patients who had undergone transcatheter aortic valve implantation (TAVI), between 21 March 2017 and 12 August 2020 (n = 9) and were excluded. Patients were excluded from VCD if there was anterior wall calcification in the common femoral at the anticipated puncture site of the CFA (n = 7). Finally, patients were excluded from the study if they had previous peripheral vascular surgery (n = 1) or stenting (n = 1). Therefore, 299 patients were included after the above exclusion (® or MANTA®) was chosen at the operators’ discretion.16 patients were excluded as their iliofemoral arteries were imaged by catheter angiography, not CT. Patients were considered ineligible for transfemoral access if the ileofemoral diameter was ≤5 mm due to atherosclerotic plaque or small calibre arteries and these underwent alternative access TAVI  and the location in the CFA was confirmed fluoroscopically. Patients undergoing closure using a MANTA® Software v. 7.14.5.13 .Patients had a TAVI planning CT angiogram, which consisted of an electrocardiogram (ECG)-gated cardiac CT and a contrast-enhanced helical scan of the whole aorta to below the femoral artery bifurcation to establish procedural feasibility. Images were analysed using VitreaThe anteroposterior diameter of the CFA, including any plaque, was measured manually at the mid-femoral head, whilst distance to skin from the CFA was measured from the most anterior point of the CFA perpendicular to the skin surface at the mid-femoral head. The length of the arterial vessels was measured from the most proximal point of the common iliac artery to the most distal point of the CFA. Tortuosity was expressed as the tortuosity index, which is the true length of these vessels divided by the straight-line distance. This sets attenuation thresholds for plaque components individualised to each patient. These are used by the Vitrea® software to create plaque maps of the CFAs allowing the volumes of the necrotic core, fibrous plaque and calcified plaque to be calculated . The upper and lower limits were defined with the upper limit the origin of the inferior epigastric artery and the lower limit the most distal point of the CFA prior to the femoral bifurcation. Attenuation (Hounsfield units) of contrast of the lumen was sampled at the mid-femoral head. The attenuation cut-offs for each plaque component were calculated according to ratios of luminal contrast and plaque attenuation  derived using histological validation and described in detail previously.lculated . To asseThe artefact created by a hip replacement on the same side as the TAVI procedure interferes with attenuation base plaque analysis, so these patients (29) were excluded from analysis of plaque composition in the study. Some patients (3), despite having sufficient contrast to carry out analysis of CFA dimensions, did not have sufficient contrast for advanced plaque analyse of the CFA. Therefore, 267 patients were included in the analysis of plaque composition.The primary end point of the study was a composite of major vascular complications and minor vascular complications as determined by the VARC-3 vascular access site and access-related complications definitions as shown in Time to haemostasis, which was measured in minutes and defined as the time from end of valve deployment until successful haemostasis obtained at the access site was compared for both VCD used for closure and compared to a historical cohort that underwent the gold standard of surgical closure .Supplementary Material 1.Click here for additional data file.STROBE Statement.Click here for additional data file.t test was used to compare continuous variables with normal distribution and the Mann–Whitney U test was used to compare continuous variables with non-normal distribution. Fisher’s exact test was used to compare proportions of categorical variables. Pearson correlation coefficient was used to compare two continuous variables. Multivariate analysis was carried out using multiple logistic regression for categorical data and multiple linear regression for continuous data. Differences in data were expressed as an odds ratio (OR) with 95% confidence limits (CI). A 2-sided p < .05 was deemed significant for all statistical tests.Statistical analysis was performed using Prism 9, GraphPad . Unpaired Student’s Baseline characteristics for the 299 patients undergoing TAVI procedure with vascular closure devices and CFA dimensions analysis between 1 October 2016 and 12 August 2020 are presented in ® device, whilst 64 (21.4%) of patients had closure with ProGlide® devices. The majority of patients receiving ProGlide® devices 53/64 (83%) achieved haemostasis with two devices. Additional ProGlide® devices were required in nine patients (14%), with seven patients requiring three ProGlide® devices and two patients requiring five ProGlide® devices to achieve haemostasis.Of the patients undergoing a TAVI procedure, 235 (78.6%) were closure with the MANTA145 (48.5%) patients were male and the average age was 83.1 ± 6.9 years (mean ± standard deviation). Overall, there were six in-hospital deaths (2.0%) following the procedural group, of which 2 (0.7%) were related to an access site complication.There were 11 VARC-3 major events (3.7%) and 12 VARC-3 minor events (4.0%) meaning the primary endpoint was met in 23 patients (7.7%) . All eveThe VARC-3 major events that occurred were percutaneous closure device failure with fatal bleeding (1), percutaneous closure device failure with life-threatening bleeding (2), percutaneous closure device failure with major bleeding (2), retroperitoneal haematoma with fatal bleeding (1). retroperitoneal haematoma with major bleeding (2), pseudoaneurysm with major bleeding (1), pseudoaneurysm requiring surgical repair and major bleeding (1), and pseudoaneurysm requiring surgical repair (1).The VARC-3 minor events that occurred were femoral dissection requiring endovascular ballooning (5), femoral occlusion requiring endovascular ballooning (1), femoral dissection and occlusion requiring endovascular ballooning (1), femoral dissection managed conservatively (1), retroperitoneal haematoma with minor bleeding (1), arterial clot managed conservatively (2), and small pseudoaneurysm of femoral artery managed conservatively 1).® over MANTA®, the urgency of TAVI procedure, the side of transfemoral access, or hip replacement having been previously performed on the same side as TAVI access . We did not find any significant association between volume of vessel, plaque burden, total plaque volume, volume of fibrous plaque, or volume of calcification with the composite of VARC-3 outcomes (p 0.01) was associated with an increased risk of vascular composite outcome but the strongest predictor was volume of necrotic core in the common femoral artery   . The rep and it is possible that quantifying CFA necrotic core may also be of use in these procedures.In our study of demographic factors and femoral artery plaque composition as a risk factor for vascular complications, we found a significant association between increased size of outer sheath diameter, as well as the necrotic core volume in the CFA, with vascular complications during TAVI procedures. Our finding of increased sheath size as a risk factor is of interest for other vascular procedures using large sheaths, such as EndoVascular Aneurysm Repair, particularly given the reported complication rate of this procedure between 16 and 30% has identified female sex as a risk factor of vascular complications after TAVI. We did find that increased sheath size was a risk factor for vascular complications at both uni- and multivariate level of analysis.There were no significant associations between demographic risk factors and vascular complications in our study. We note that a previous study whilst other studies have also demonstrated a significantly increased risk of vascular complications with an increased SFAR.Larger sheath size has previously been reported as a risk factor for vascular complications,Analysis of dimensions with CT imaging, such as CFA diameter, and perpendicular distance from CFA to skin was not a significant risk factor for the composite of VARC-3 major and minor vascular complications. Importantly, we found that an increase in the total necrotic core volume throughout the CFA was an independent risk factor for vascular complications after TAVI. this study investigated a novel quantitative method of measuring femoral plaque. Despite finding an association with necrotic core volume and VARC-3 outcomes, we did not find a significant association between the volume of calcification and VARC-3 composite outcome. A possible explanation for this finding may be that calcified plaque is a risk factor only if it is present on the anterior wall of the CFA and percutaneous closure was avoided in these patients. Given that anterior calcified plaque precluded selection for percutaneous closure but anterior non-calcified plaque did not this may have influenced the results. Another possibility is that the presence of calcified plaque acts as a surrogate for other plaque types, a feature seen in the coronaries.Although qualitative femoral calcification has previously been reported as a risk factor for vascular complications, and their equivalent in the CFA may have a predisposition to disruption during the stress of large bore access and instrumentation. It has been demonstrated that the components of necrotic core (lipid and thrombus) are less stiff with lower resistance to deformation than fibrous tissue and arterial media. For plaque map analysis of necrotic core to be utilised clinically, it should be generalisable to other centres that may have other reporting software, have acceptable interobserver variability and not be too time-consuming. This study was performed with Vitrea® software, however, we have previously demonstrated similar plaque maps in the coronary arteries using a different software vendor. Once the attenuation of luminal contrast is known the ratios described can be utilised to calculate plaque volume on any software where the attenuation thresholds for quantifying plaque can be altered. Our interobserver analysis revealed acceptably low variability with narrow levels of agreement. This mirrors results in coronaries where plaque map analysis identified high-risk plaque with lower interobserver variability than conventional CT coronary angiography features. Performing plaque map analysis using the Vitrea® software requires around 5 min per patient.The use of plaque map improves the classification of plaque composition and our study demonstrates a strong association between necrotic core and VARC-3 outcomes (OR 17.49), this suggests that CFA plaque necrotic core may be a key predisposing factor to vascular complications. Previous studies have revealed that coronary plaques with high necrotic core content “thin-cap fibroatheroma” are the most likely to rupture) in 7.7% of patients. This was lower than the 21% recorded in two other studies, This may be due in part to the mandated use of ultrasound-guided puncture and the exclusion of VCD use in patients with common femoral artery anterior wall calcification. The complication rate was similar (6.6%) to a recent study comparing vascular closure with MANTA® or ProGlide®.In this study, we found that there was a vascular complication  or MANTA® (5.7%), p = 0.11). Our results are supported by other data, which has demonstrated no significant difference between closure devices and vascular complications. Conversely, the recent CHOICE-CLOSURE study demonstrated an increased risk of VARC-2 vascular complications in the MANTA® group compared to the ProGlide® group.In contrast to the above study, which found a significantly lower rate of vascular complications in the ProGlideWe noted that similar types of complications were encountered with the use of each device, with percutaneous closure device failure, retroperitoneal haematoma and pseudoaneurysm occurring in both device types. Similarly, with regards to VARC-3 minor events, femoral dissections and femoral occlusions occurred in both device groups.There were some limitations to this study. Firstly, diagnosis of plaque composition in this study was only radiological, and was not supported by other means, such as intravascular imaging. However, our method has been utilised in previous work, which has demonstrated the suitability of CT imaging with attenuation ratios to classify different compositions of coronary plaque validated against the gold-standard of post-mortem histology. Thirdly, we defined arterial size as the anteroposterior diameter of the CFA at the level of the mid-femoral head. This was chosen as we felt it would obtain a standardised, repeatable measurement. However, it is possible that different diameters may have been obtained from cross-sectional analysis. Fourthly, we found a high variability in the CT data with wide CIs. However, these statistically significant findings remained after multivariate analysis had been performed. Finally, patients with VCD closure were recruited for this study sequentially and therefore were not randomised. Therefore, despite the use of multivariate analysis, bias cannot be excluded in this study.Secondly, the sheaths used for deployment of the Sapien three valve (Edwards Lifesciences) incorporate a dynamic expandable technology, so the final actual outer diameter will be larger than that stated following valve deployment.CT defined femoral artery plaque composition utilising plaque maps predicted vascular complications during transcatheter aortic valve implantation. Necrotic core, rather than calcified plaque volume was associated with a significant increase in VARC vascular complications along with increased sheath size.


Osteosarcoma (OS) is a heterogeneous, aggressive malignancy of the bone that disproportionally affects children and adolescents. Therapeutic interventions for OS are limited, which is in part due to the complex tumor microenvironment (TME) that has proven to be refractory to immunotherapies. Thus, there is a need to better define the complexity of the OS TME. To address this need, we used single-cell RNA sequencing (scRNA-seq) to describe the cellular and molecular composition of the TME in 6 treatment-naïve dogs with spontaneously occurring primary OS. Through analysis of 35,310 cells, we identified 30 distinct immune cell types, 9 unique tumor populations, 1 cluster of fibroblasts, and 1 cluster of endothelial cells. Independent reclustering of major cell types revealed the presence of follicular helper T cells, mature regulatory dendritic cells (mregDCs), and 8 transcriptomically distinct macrophage/monocyte populations. Cell-cell interaction inference analysis predicted that mregDCs and tumor-associated macrophages (TAMs) play key roles in modulating T cell mediate immunity. Furthermore, we used publicly available human OS scRNA-seq data to complete a cross-species cell type gene signature homology analysis. The analysis revealed a high degree of cell type gene signature homology between species, suggesting the cellular composition of OS is largely conserved between humans and dogs. Our findings provide key new insights into the biology of canine OS and highlight the conserved features of OS across species. Generally, the data presented here acts as a cellular and molecular roadmap of canine OS which can be applied to advance the translational immuno-oncology research field. Slow advancements in the development of OS therapeutics are, in part, due to the relatively rare incidence which limits patient accrual into clinical trials. In recent years, there has been increased interest in using large animal models to evaluate and validate the potential of immunotherapeutics for various cancers3. Spontaneously occurring canine OS is regarded as an ideal model of human OS due to higher disease prevalence in dogs, similar genetics and pathology, and the immune competent status of dogs4. Although dogs have been identified as a valuable pre-clinical model, immunological reagent limitations have restricted researcher’s ability to effectively characterize the canine OS tumor microenvironment (TME).Osteosarcoma (OS) is an aggressive malignancy of the bone that disproportionally impacts children and adolescences. Despite a profound impact on the lives of affected individuals, effective therapeutics are lacking, with minimal advancements since the introduction of combined surgical excision and adjuvant chemotherapy in 19865. Researchers have turned to the TME with the objective of understanding and targeting the cellular constituents that promote immune suppression7. Unlike many other cancer types, there have been reports in both humans and dogs that suggest increased macrophage abundance in OS reduces metastatic rate and enhances survival10. This unexpected finding and the ill-defined mechanisms of immune suppression in the OS TME highlights the need for a deeper understanding of OS pathobiology.Osteosarcoma has a complex TME that consists of malignant osteoblasts, osteoclasts, fibroblasts, macrophages, and lymphocytes as well as numerous other stromal and immune components. Together, the OS TME creates an immune suppressive milieu that hinders antitumor immune responses11. Importantly, the human scRNA-seq landscapes of primary, recurrent, and metastatic OS have recently been described and act as a point of reference for cell type homology analysis between canine and human OS13. The aim of the current study was to use scRNA-seq to complete a molecular dissection of the canine OS TME and evaluate cell type transcriptomic homologies between humans and dogs.In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool to investigate the transcriptomes of individual cells within heterogenous tissues. The approach overcomes species-specific regent limitations by relying on a universal transcript capture method that is only limited by the completeness of genome annotationsIn the study, we generated a single-cell RNA sequencing atlas of six treatment-naïve dogs with primary osteosarcoma. Our analysis reveals the presence of 41 transcriptomically distinct cell types in canine OS and provides evidence of conserved cell type gene signatures and composition between human and canine OS. Overall, the data generated here can be used to inform the identification of conserved OS TME features and facilitate further study of the canine osteosarcoma tumor microenvironment.To establish a treatment-naïve canine osteosarcoma reference, we completed single-cell RNA sequencing (scRNA-seq) on 6 dogs and collected data on a total of 35,310 cells. The average number of cells collected per tumor was 5,885 and on average each cell was sequenced to a depth of 72,649 reads per cell. All tumors were confirmed to be osteosarcoma by histology and histological subtyping was completed on each tumor. In total, the curated dataset consisted of 1 fibroblastic, 1 chondroblast, and 4 osteoblastic tumors, with one dog exhibiting radiographical evidence of lung metastasis .Initial low-resolution cell type annotation revealed the presence of 7 major cell types consisting of T cells, B cells, tumor infiltrating monocytes (TIMs)/tumor associated macrophage (TAMs), dendritic cells (DCs), osteoclasts (OCs), tumor cells, cycling tumor cells, and an additional 3 minor cell populations consisting of neutrophils, mast cells, and endothelial cells . Evaluat12. These approaches consistently enabled the identification of T cells, B cells, osteoclasts, and endothelial cells. However, our high-level unsupervised clustering failed to distinguish between stromal fibroblasts and malignant osteoblasts. This unexpected observation may be in part due to the presence of a fibroblastic osteosarcoma tumor in our dataset and the broad expression of fibroblast markers  across all tumor cell clusters .Cell types were annotated based on expression of canonical markers, reference mapping using a human OS dataset, and gene set enrichment analysis . Cell ty14. The analysis revealed that the majority of cells in the tumor/fibroblast cluster exhibited evidence of CNV aberrations with only a small subset of cells predicted to be diploid  analysis to infer which cells exhibited aneuploidy based on their transcriptional properties 14. The a16. Interpretation of GSEA further revealed that fibroblasts (c6) exhibited the most pronounced “epithelial-mesenchymal transition” (EMT) and “angiogenesis” signatures, which suggests the fibroblasts might play a role in promoting tumor growth. Additionally, GSEA supported the annotation of hypoxic osteoblasts (c4), as the cluster exhibited the strongest hypoxic transcriptomic signature.Independent reclustering on cycling tumor cells and tumor/fibroblasts identified 10 distinct cell clusters which we defined as 4 cycling malignant osteoblasts clusters, 5 non-cycling malignant osteoblast clusters, and 1 fibroblast cluster . The def17. This analysis confirmed Cluster 6 exhibited the strongest fibroblast gene signature . We then completed differential gene expression (DGE) analysis contrasting fibroblasts (c6) and non-hypoxic osteoblasts  to better define the canine fibroblast gene signature  by contrasting with non-hypoxic osteoblasts . Few differentially expressed genes were identified, suggesting cell types are similar, but subsequent pathway analysis identified enrichment of “response to oxygen levels” to be a top enriched pathway, suggesting that the tumor cells were indeed hypoxic  and follicular helper CD4 T cells (CD4fh) both exhibited moderate enrichment for exhausted and costimulatory terms, with minimal distinction between the two T cell types. The analysis also revealed the presence of a T cell cluster with an IFN gene signature, a population that has been reported to be hypersensitive to stimulation16.To ensure we captured all biologically relevant T cell populations, we completed independent reclustering, which led to the identification of 10 transcriptomically distinct clusters: 3 CD8 T cell, 4 CD4 T cell, 1 NK cell, and 2 mixed CD4/CD8 T cell clusters /b. Next, markers . Althougregs and CD4fh. Comparisons between Tregs (c3) and activated CD4 T cells (c2) revealed overexpression of IL21R, TNFRSF4, and TNFRSF18, with CTLA4 being the most definitive marker of Tregs (fh (c5) cells, we identified CXCL13, IL4I1, and TMEM176A to be defining features , they also displayed a gene signature consistent with follicular helper T cells 22. A similar population of CXCL13+ CD4fh T cells has been identified in multiple human tumors and the cell type has been implicated in tertiary lymphoid follicle formation and modification of intratumoral adaptive immune responses25. Our analysis confirms expression of CTLA4/FOXP3 on Tregs and CXCL13/IL21 on CD4fh is conserved across species, while also providing complete gene signatures for the canine T cell subtypes17.After identifying each T cell subset, we completed pseudobulk conversion and DGE analysis to further establish the transcriptomic signatures of Tex) and effector CD8 T cells (CD8eff) to be among the most abundant populations, along with activated CD4 T cells and Tregs. We then curated a heatmap of defining features predicted to be expressed on the cell surface with the objective of identifying potential cell markers to be used in alternative cell identification approaches, such as flow cytometry , TNFSF8 (CD153), and TMEM140 may represent valuable surface markers for further investigation of canine Tregs, CD4fh, and TIFN-sig, respectively. Together, the relative cellular percentages and potential surface markers provide a foundation for further functional study of the cell types identified in our transcriptomic analysis.Following initial cell classification, we determined the cellular composition of each cell type as a percentage of all immune cells and of all cells in each sample , mature regulatory DCs , cDC1s (c2), plasmacytoid DCs , and precursor DCs   were not observed, suggesting a potential tissue specificity20. The identification of mregDCs, also reported as migratory (mig) DCs, is of note as this cell type is predicted to modulate T cell responses30. Thus, we provide evidence that a key immune regulatory cell type is present in canine OS.Five dendritic cell (DC) subtypes were identified when completing independent reclustering on FLT3; preDC) . Key fea; preDC) 27,28. Th31. To ensure that none of the DC populations were of B cell origin, we evaluated MS4A1 (CD20) expression and found it to be minimally expressed . We then used SCENIC to predict active regulons in each DC subtype . This analysis revealed RUNX2, a master regulator of pDC development, to be enriched in both pDCs and preDCs32. Overall, these findings suggest canine preDCs are closely related to the recently defined plasmacytoid-like human preDCs27.We next used hierarchical clustering and Toll-like receptor expression to investigate differences between preDCs and pDCs. Hierarchical clustering indicated preDCs are closely related to myeloid cDC2s and cDC1s, suggesting a myeloid lineage, while the pDCs were located on a unique clade, suggesting a lymphoid origin . In huma33. This analysis revealed that mregDCs had a marked enrichment for migration, regulatory, and maturation associated gene signatures  and intermediate TAMs  did not fit into any of the macrophage subtypes presented in Ma et al, so they were instead annotated based on an activated signature  and an intermediate polarization signature, respectively. Tumor infiltrating monocytes (TIMs) were divided into two populations based on CD4 expression, a division of monocytes unique to dogs36. Unsupervised clustering divided lipid-associated (LA-) TAMs into two subclusters defined by either C1QChi expression (c3) or SPP2hi expression (c2). To better define the distinctions between the two LA-TAM populations we completed pseudobulk-based DGE analysis  and osteoclasts (OCs), we analyzed these two cell types in the same UMAP space. In doing so, our analysis highlighted the relatedness of OCs and TAMs which would have been overlooked if analyzed independently. Through independent reclustering we identified 8 transcriptomically distinct macrophage/monocyte populations which were annotated using modified nomenclature derived from Ma et al -c35. Act37. We identified the C1QChi LA-TAM (c3) cluster to have the strongest anti-inflammatory transcriptomic signature while CD4+ monocytes (c11) exhibited the most prominent pro-inflammatory transcriptomic signature ignature . To furtignature . Finding39. Several terms were identified suggesting that both SPP2hi LA-TAMS and intermediate TAMs preferentially utilize oxidative phosphorylation and mitochondrial metabolic pathways. C1QChi LA-TAMs had a distinct profile suggestive of lipid and polysaccharide metabolism. Lastly, GSEA confirmed c10 to be consistent with IFN-TAMs based on strong enrichment of IFN signaling associated terms. In summary, we described the transcriptional profiles of macrophages in the canine OS TME which provides a foundation for further investigation of the functional relevance of each cell type.Gene set enrichment analysis was used to provide further insights into the inferred functional capacity of each macrophage subtype . Cell cl40. The cycling OCs (c5/c8) in our canine OS dataset likely correspond to previously reported pre/progenitor OCs, while the mature OCs (c6) are consistent with previous reports  have not been described in macrophage or osteoclast clusters from human and mouse tissues and may represent a canine specific cell type, or more likely a previously unresolved OC subtype  to macrophages  we identified canine mature OCs to be defined by ATP6V1C1, CD84, HYAL1, and CAMTA2 expression, with subsequent GSEA analysis suggesting an association with bone resorption and remodeling (+ OCs (c9) with macrophages and mature OCs. . By evaluating the intersection of the differentially expressed genes we determined CD320+ OCs are defined by HMGA1, TNIP3, and CD320 expression  and human (CD163 and CD68) macrophage markers (45. AIF1 (Iba1) was the most non-specific marker with diffuse expression across all cell types, except for mature OCs. Lastly, CD204 (MSR1) was determined to be largely specific to TAMs, but the expression also extended to CD320+ OCs and CD4+ monocytes. To investigate the translational relevance of this finding, we evaluated expression of the markers in human OS . We observed similar expression patterns, with marked variability in specificity of each marker, suggesting the variability is conserved across species.In contrast to other tumor types, there have been multiple reports in humans and dogs suggesting that increased TAM infiltrates in OS are correlated with reduced metastasis rates and increased patient survival markers . With thhi, TNFSF13B for IFN-TAMs, and CD84 for mature OCs. Lastly, we calculated the relative percentages of each cell type to further facilitate cell identification  versus outgoing (express ligand) signals were used to infer the activity of cells within the TME (+) macrophages and C1QChi LA-TAMs were predicted to have the strongest outgoing signals otations . All can47. Briefly, the approach used DGE analysis between two cell populations in each species, then signing the adjusted P value to determine if transcriptomic signatures were conserved. When contrasting fibroblasts and endothelial cells, we found substantial overlap in gene expression patterns with key endothelial cell markers  enriched in both species  in human pDCs, but not in canine pDCs. Lastly, we applied the same approach to compare mature OCs with TIMs  using single-cell RNA sequencing which revealed the complex network of cells within the tumor microenvironment (TME). Through analysis of 6 treatment-naïve canine OS samples we were able to identify 30 distinct immune cell types, 9 unique malignant osteoblast populations, 1 cluster of fibroblasts, and 1 population of endothelial cells . We described the transcriptomic heterogeneity within malignant osteoblasts, identified cell types that have not been previously reported in dogs, and applied our data set to investigate the transcript abundance of widely used macrophage surface markers. Ultimately, the data presented here act as a molecular roadmap of the canine OS tumor microenvironment and help to overcome the reagent and technical limitations associated with using the dog as a model.50. Further investigation of this cell population is warranted.Prior to this study, evidence of a conserved OS TME between humans and dogs has been limited. By obtaining a publicly available human OS dataset we were able to directly compare the cellular composition and relative relatedness of cell types between species. Lymphocytes exhibited the highest degree of conservation between species, while subtle differences in the transcriptomic signatures of plasmacytoid dendritic cells, monocytes, and endothelial cells were identified. Although we identified distinct transcriptional signatures of mast cells in humans and dogs, this difference may be the result of either discordant annotation or distinct transcriptional profiles between species. Mast cells in both species were defined by GATA2 and MS4A2 expression. GATA2 (a transcription factor with implications in basophil and mast cell differentiation) and MS4A2 (the IgE receptor found on basophils and mast cells) are not specific to mast cells, so it is possible that the cluster could instead represent basophils or possibly eosinophilsfh T cells, and IFN-TAMs, opening avenues for further investigation of these cell populations using the transcriptomic signatures identified in this study. With the caveat that transcript expression may not correlate with protein expression, we used the surfaceome reference database to identify possible surface markers for further study of these cell types 26. In addition to antibody-based assays, the transcriptomic signatures presented here provide a reference for the application of deconvolution algorithms (such as CIBERSORTx and TIMER) when evaluating bulk RNA sequencing data obtained from canine OS samples52.Our analysis revealed the presence of many rare cell populations, including mregDCs, CD453. The biological role of mregDCs is still being identified, but recent reports suggest a potential role in shaping T cell antitumor immune responses24. In our analysis, we were able to identify a CCR7+/IL4I1+/FSCN1+ dendritic cell population which closely resembles the descriptions of human mregDCs. We found that canine mregDCs express high levels of immune suppressive and costimulatory molecules, which may play a role in modulation of adaptive immune responses through communication with follicular helper and regulatory T cells. This study provides evidence that mregDCs are present in canine OS, demonstrating a conserved role within osteosarcoma across species.Mature regulatory dendritic cells represent a recently defined cell type which has been identified across several human tumor types, including OS54. To provide further context for the discrepant findings reported in the literature, we evaluated the transcript abundances of key macrophage markers used in human and canine analysis. Although the analysis was completed at the transcript level, we observed notable differences in the specificity of each cell type marker within myeloid population. Inconsistences between cell types evaluated using immunohistochemistry could explain why some groups identify negative prognostic correlates while other groups report positive outcomes. Further validation of the variability in cell type markers should be completed using refined immunohistochemistry panels coupled with spatial transcriptomics. Ultimately, a better understanding of the prognostic and functional roles of myeloid cells within the TME will aid in the development of effective targeted therapeutics.The heterogeneity within the myeloid compartment of tumors is only beginning to be understood and the role of TAMs in OS clinical outcomes is debatedWhile the single-cell RNA atlas presented here provides key insights into canine OS, the dataset is not without limitations. First, although we sampled male and female dogs across a range of ages, our dataset still only consisted of 6 dogs and may not fully represent all cell populations found in canine OS. Secondly, the tumor sample obtained from one dog (dog 6) exhibited markedly more neutrophils relative to other samples which may suggest sample contamination with blood, bone marrow, or necrotic tissue. Lastly, cellular annotations largely relied on human gene signatures due to the lack of canine specific data available. This may have exaggerated similarities between species and may have resulted in the forcing of distinct canine-specific cell types into human nomenclature subtypes. Thus, the discordant findings regarding mast cell gene signatures represents an important distinction that should be investigated further and considered when using the dog as a model for human disease.The data presented here represent a valuable resource for comparative oncology research. A major goal of this project is to make the data accessible to the greater research community and multiple avenues are provided for researchers to explore and use the dataset (see data availability statement). Our comparisons between human and canine OS revealed the conserved nature of cell type gene signatures in OS while also identifying potential differences. Overall, our analysis supports the dog as a model for human OS and provides a novel reference dataset that can be used to increase the value of canine immuno-oncology research.Dogs for the study were selected based on the presence of an appendicular primary tumor and the absence of previous therapeutic intervention. All dogs presented with radiographic evidence of OS and subsequent histopathology was completed to confirm the diagnosis. Dog demographics are presented in Between 3 and 5 tumor biopsies were collected from the amputated limb, washed with phosphate buffered saline (PBS), minced using a scalpel, and digested with collagenase type II (250 U/mL) in Hanks' Balanced Salt Solution (HBSS) for 45 minutes at 37°C with agitation (Thermo Fisher Scientific Inc.). Samples were passed through a 70-μm cell strainer, washed with PBS, then centrifuged for 5 minutes at 400 rcf. To enrich for live cells, samples were pooled into 4-mL HBSS layered onto 3-mL Ficoll Paque , and centrifuged for 30 minutes at 400 rcf with acceleration at 9 and brake at 0. Following density centrifugation, the cell interface layer was collected and washed one time with PBS, resuspended in 10-mL of Ammonium-Chloride-Potassium lysis buffer for 3–7 minutes at room temperature. To remove small debris and platelets, a final wash at 100 rcf for 15 minutes was completed. Cells were resuspended in 0.04% molecular grade BSA  in PBS and transported to a Chromium iX instrument  for cell capture. All samples were captured within 30 minutes of preparation.Single cells were isolated and tagged with unique cell barcodes using a Chromium iX instrument with a target of 5,000 cells per sample. Two of the six dogs (dogs 1 and 2) had two samples processed each with a 5,000-cell target, for a total target of 10,000 cells. Single cells were isolated and processed using a Chromium Next GEM Single Cell 3 Kit v3.1 following manufacture recommended protocols. Individual cell transcriptomes were captured and labeled with molecular barcodes, then a standard Illumina library preparation was completed using a dual index library construction kit (10x Genomics). Samples quality was analyzed using a LabChip  and submitted for sequencing on an Illumina NovaSeq 6000 sequencer  with a target of 100,000 150 bp paired-end reads per cell. Raw data were demultiplexed by the sequencing core then transferred for downstream analysis.20.A Cell Ranger analysis pipeline  was utilized to process raw FASTQ sequencing data, align reads to the canine genome, and generate a count matrix. The default settings were used when running “cellranger count” and aligned to a CanFam3.1 reference prepared as previously described17. To estimate the number of dead/poor quality cells, the percentage of mitochondrial reads per cell was calculated using PercentageFeatureSet to count all reads mapped to features with the prefix "MT-". Each object was filtered to only retain cells which met the following requirements: 200 < nFeature_RNA < 5500, percent.mt < 12.5, and 100 < nCount_RNA < 75000. Next, DoubletFinder, was used to identify and remove putative cell doublets55. After completing QC filtering on each sample, all samples were integrated into one object using the SCTransform and integration workflow46. During this step, we regressed out the percent mitochondrial reads to minimize the impacts on clustering results and used 2000 features as integration anchors. Following data integration, three low quality clusters were identified and removed then the samples were split out to repeat data integration. Ideal clustering parameters  were determined using the R package clustree56. Dimension reduction and visualization was completed, and the data were projected using 2-dimensional, non-linear uniform manifold approximation and projection (UMAP) plots.For each sample, the count matrix was imported into R using the Read10X function then converted to a Seurat object using the CreateSeuratObject function57. Briefly, markers included CD3E for T cells, CTSK for osteoclasts, CD68 for macrophages, S100A12 for neutrophils, COL1A1/ALPL/FAP for tumor/fibroblasts, FLT3 for dendritic cells, MS4A1/JCHAIN for B cells, ESAM/PLVAP for endothelial cells, TOP2A for cycling cells, and GATA2/MS4A2 for mast cells. Further high-resolution cell identification was completed through independent reclustering on cells within each major  population.High level cell type annotations were established using unsupervised clustering results, gene set enrichment analysis, and manual annotation based on the literature for accepted human cell type markersFeature expression was visualized using violin plots, feature plots, and dot plots. Selected features were chosen based on prior biological knowledge and statistically significant features identified using the FindMarkers function. Y-axis scales for violin plots within a figure are on fixed scales. Feature plots show normalized expression for each feature on variable scales. For all feature plots, grey/light purple coloration indicates low expression and dark purple coloration indicates high expression. Dot plots use scaled expression data which depicts deviation from the average value for a gene across the cells being sampled.58. Prior to running DESeq2, low abundance features, defined as features with less than 10 raw counts across all cells sampled, were filtered out. Features that had an adjusted P value of less than 0.05 (as determined using a Benjamini and Hochberg correction method) and a log2(fold change) greater than 0.58 were considered to be statistically significant.Differential gene expression (DGE) analysis was completed using pseudobulk conversion followed by a DESeq2 pipeline60. Terms which reached an adjusted P value of 0.05 or lower (Benjamini and Hochberg correction method) were discussed as significantly enriched.When completing follow-up gene set enrichment analysis (GSEA) on the gene lists generated from DGE analysis, the significantly upregulated and downregulated features were processed separately. The upregulated and downregulated gene lists were used with clusterProfiler and MSigDB gene sets to infer pathway activity61. The log2(fold change) and mean expression for every feature within each cell type was calculated and used to complete GSEA. P values were corrected for multiple comparisons using a false discovery rate (FDR) method and corrected P values were filtered to only retain terms in which at least one cell types had a value less than 0.05. The enrichment values were scaled and the top pathways  were plotted using a heatmap.In addition to using GSEA following DGE analysis we also used the R package singleseqgset to complete GSEA on cell type clusters. The tool uses a competitive gene set enrichment test that was based on a Correlation Adjusted MEan RAnk gene set test14. Briefly, the approach segments the genome into 220-kb variable genomic bins to establish a genome-wide copy number profile for each single cell at an approximate resolution of 5 Mb. Eash sample was run individually with a known normal cell population consisting of osteoclasts, neutrophils, macrophages, and T cells when inferring CNV status. Individual cell classifications were extracted from each .rds output file, then transferred to the Seurat object containing the integrated data. The CNV status was visualized on a UMAP. This approach was only used to infer if a cell was aneuploid or diploid and individual chromosome mutations were not evaluated due incompatibilities of software across species.Copy number variation (CNV) analysis was completed using CopyKAT on high quality cells that contained more than 2000 unique molecular identifiers (UMIs)62. The two regulatory feather files used for analysis were obtained from https://resources.aertslab.org/cistarget/ and were named "hg19-500bp-upstream-7species.mc9nr.feather" and "hg19-tss-centered-10kb-10species.mc9nr.feather". Default settings were then applied to run the analysis pipeline. Regulons specificity scores (rss) were calculated using AUCell and the rss values were used to infer regulon activity in the cell types analyzed63.Single-cell regulatory network inference and clustering (SCENIC) was used to infer activity of gene regulatory networks by cell types12. The count matrices reported from the previous study were loaded in as Seurat objects and were filtered using the same parameters as used for the 6 canine OS tumor samples. The human dataset was annotated using high-level unsupervised clustering while referencing the primary article in an attempt to recreate the original annotations. Following annotation of each species, the 12 (6 human and 6 canine) OS samples were integrated into one object to using a SCTranscfrom workflow with 3000 variable features as anchors. Only features with homologues across both species were used for integration. SCT normalized counts were then used to complete hierarchical clustering using the hclust function with method set to “complete”. Subsequent DGE analysis contrasting cell types within each species was completed within individual species datasets. The adjusted P values obtained from DGE analysis were assigned a sign (+/−) based on the log2(fold change) then the signed P values were used to generate a scatter plot.Six treatment-naive human OS samples were obtained from the NCBI GEO database accession GSE16245464. A project specific GitHub page containing all analysis code and software versions used to analyze the data presented in this manuscript is available at https://github.com/dyammons/canine_osteosarcoma_atlas. Any additional data requests can be made by contacting a corresponding author.Raw sequencing data and processed Seurat objects are available on the NCBI Gene Expression Omnibus database under the accession number TBD. The annotated dataset is available for browsing at the UCSC Cell Browser (url TBD)


Principles of biomolecular condensation have been revealed through in vitro reconstitution2. However, intracellular environments are much more complex than test-tube environments: They are viscoelastic, highly crowded at the mesoscale, and are far from thermodynamic equilibrium due to the constant action of energy-consuming processes3. We developed synDrops, a synthetic phase separation system, to study how the cellular environment affects condensate formation. Three key features enable physical analysis: synDrops are inducible, bioorthogonal, and have well-defined geometry. This design allows kinetic analysis of synDrop assembly and facilitates computational simulation of the process. We compared experiments and simulations to determine that macromolecular crowding promotes condensate nucleation but inhibits droplet growth through coalescence. ATP-dependent cellular activities help overcome the frustration of growth. In particular, actomyosin dynamics potentiate droplet growth by reducing confinement and elasticity in the mammalian cytoplasm, thereby enabling synDrop coarsening. Our results demonstrate that mesoscale molecular assembly is favored by the combined effects of crowding and active matter in the cytoplasm. These results move toward a better predictive understanding of condensate formation in vivo.The mesoscale organization of molecules into membraneless biomolecular condensates is emerging as a key mechanism of rapid spatiotemporal control in cells This high excluded volume can inhibit molecular motion, but on the other hand can entropically favor assembly through depletion attraction forces7. The majority of cytoplasmic volume is taken up by mesoscale (10 – 1000 nanometer diameter) particles5. This means that the effects of crowding strongly affect the behavior of mesoscale particles and assemblies, while having less impact on nanoscale processes because nanoscale particles can move relatively freely between mesoscale crowders, but mesoscale particles cannot. Studies have shown that macromolecular crowding can change biochemical reaction kinetics, protein conformations, and motor functions9.Cells are highly crowded, with up to 40% of cellular volume excluded by macromolecules10 and chromatin in the nucleus11. The presence of these networks and the high concentration of particles together make the intracellular environment viscoelastic. This contrasts with simple buffer solutions, which are only viscous.The cell also contains elastic networks that constrain and organize the cell interior. These include the actomyosin cytoskeleton in the cytoplasm12. Overall, the intracellular environment is highly complex, and its impact on the assembly of membraneless biomolecular condensates remains largely unexplored.Finally, cells are non-equilibrium open systems, and use adenosine triphosphate (ATP)-dependent cellular activities to maintain a non-equilibrium steady state by exchanging energy, information and material with the extracellular environment, thereby locally reducing entropy13. This wide range of length- and time-scales makes it difficult to predict how the crowded, active cellular environment will affect biomolecular condensate formation. Several studies have focused on the impact of elastic mechanical properties on condensate growth17. For example, elastic chromatin mechanics has been shown to frustrate the growth of nuclear condensates17. However, the combined impacts of macromolecular crowding, elastic networks, and non-equilibrium cellular activities on condensate formation are less well understood.The assembly of membraneless biomolecular condensates bridges length scales between the nanoscale and mesoscale, where nanometer diameter molecules come together to form higher-order structures of tens to thousands of nanometers in diameter18, but adds the ability to chemically induce the interaction of components.It is difficult to derive general physical principles from the study of endogenous condensates because these systems are formed through complex coacervation of many molecules. Furthermore, these components are often dynamically altered by post-translational regulation, the details of which are typically unknown. Thus, when perturbing intracellular environments, it is difficult to fully attribute structural changes in endogenous condensates to only biophysical cues, since biological functional changes associated with perturbations can also lead to structural changes in endogenous condensates. To overcome these issues, we developed an orthogonal synthetic intracellular condensate system called synDrops. synDrops adapted a previous approach to create a molecular condensate of well-defined geometryS. cerevisiae cells and mammalian cervical cancer HeLa cells. Complementary to the experimental system, we also developed two independent agent-based molecular dynamics models to simulate synDrops within cellular environments from first principles. Combining experiments and simulations, we show that macromolecular crowding facilitates the nucleation process while inhibiting the growth phase of condensate dynamics. However, ATP-dependent cellular activity promotes growth by assisting long-range structural rearrangements. In conclusion, we found that the assembly of mesoscale biomolecular condensates is favored by the crowded and active cellular environment.We successfully induced synDrop formation in both budding yeast 19, which governs polymer network growth. Multivalency is essential for the formation of mesoscale condensates through phase separation24. We used homomultimerizing domains to create multivalency in our system  and GID (Gibberellin Insensitive Dwarf 1) domains. These domains undergo a heterotypic interaction that is potentiated in the presence of the plant hormone Gibberellin (GA)25 . GAI is 18, we adapted our plasmid design for expression in mammalian cells by combining the two genes onto the same plasmid and separating them by a P2A self-cleavage sequence  platforms to provide complementary information in silico. The first simulation setup used a HOOMD-blue engine29 combined with a dynamic bonding plugin that we previously developed30  for 2 hours. We found rapamycin-treated cells did not form droplets after GA induction  to quantify crowding5. We found that macromolecular crowding was decreased in rapamycin-treated cells, consistent with previous reports5  pathwaynduction . Howevernduction . Thus, rreports5 . We thenreports5 , and foureports5 . Howeverreports5 . In thesreports5 , but was35 and the other is through Ostwald ripening36. However, droplet coalescence has been suggested as the dominant mechanism for droplet growth in biological systems16. In this mechanism, the rate of droplet growth depends on the collision rate between two smaller droplets, which in turn depends on the diffusivities of these droplets35. We therefore hypothesized that macromolecular crowding inhibits droplet growth by reducing droplet diffusivities. To test this hypothesis, we quantified synDrop diffusivities through particle tracking in both control conditions and after increasing macromolecular crowding. Since droplet size increases with time, we analyzed droplet diffusivities at different time points after induction. Similar to previous results  models to simulate synDrops. These models allowed us to investigate molecular details that are not easily accessible from experimental data. The well-defined structures and binding interactions between the two synDrop components enabled us to quantify droplet network structures with graph-theory based analyses. Here, we defined each synDrop component as a node and the bond between two components as an edge. We calculated the topological shortest distances between each pair of components and mapped out bond connectivity to define each molecular cluster. The distance matrix from this analysis was then used for hierarchical clustering. Within the resulting clustergram, squares along the diagonal correspond to clusters of interacting molecules. Each pixel on the x and y axes represents an interaction between two individual molecules in the simulation system and is colored according to the topological distance between them . Blank pixels indicate that there is no path connecting the two corresponding molecules .When there were no crowders in the system, there was very limited cluster formation . In contWe next wondered if MD simulations could provide further insights into the molecular basis of the frustration of synDrop growth when there is excessive macromolecular crowding. We plotted the average diffusivity for each cluster as a function of the cluster size and found that diffusivities decrease as a function of cluster size as expected, and were reduced overall when crowder volume fractions were increased , consist8. To assess this idea, we performed MD simulations on a simplified system where two protein components each had only a single available binding site  (d) under different volume fractions of crowders by quantifying the number of bonds formation at equilibrium. The effective Kd was indeed reduced (affinity was increased) in simulations with increased crowder volume fractions (off) by allowing the system to reach equilibrium, switching the binding rate to zero, and then measuring the rate at which bonds dissociate , which is koff divided by Kd. Interestingly, when comparing conditions with 30% volume fraction crowder to those with no crowders, we found koff did not change  . We thenractions . Furtherssociate . Calculat change . This inin vivo experiments and simulations support the model that macromolecular crowding promotes droplet nucleation by reducing the effective Kd of chemical bond formation, but also inhibits droplet growth by reducing droplet diffusivity, therefore kinetically frustrating coalescence of small droplets into larger structures.In conclusion, our combination of 37. We therefore hypothesized that ATP-dependent cellular activities might affect synDrop formation by promoting their motion and therefore driving coalescence of small droplets into larger structures.We next wondered if other features of the cytoplasmic environment could impact synDrop assembly. In addition to being crowded, the cytoplasm is also far from equilibrium due to ATP-dependent activities. Cellular metabolism was previously shown to strongly affect the motion of mesoscale particles38. We depleted ATP at different time points to assess the importance of ATP during different phases of synDrop assembly. We observed that synDrop growth was inhibited within 10 min of ATP depletion (We used metabolic inhibitors: 2-deoxyglucose (2-DG) and antimycin to deplete intracellular ATP in yeast cells, taking care to maintain neutral pH within cells and isotonic conditions to avoid osmotic perturbations to cell volumeepletion , top, anepletion , bottom.epletion . Therefo39. We therefore hypothesized that actomyosin contractility might agitate the cytoplasm and increase synDrop motion. To test this idea, we inhibited actomyosin dynamics using the JLY drug cocktail, which simultaneously prevents actin depolymerization, polymerization, and myosin II-based restructuring40. This treatment reduced diffusivity almost as much as total ATP-depletion, suggesting that actomyosin dynamics is a dominant factor that increases mesoscale diffusivity in the cytoplasm of mammalian cells , ATP-dependent cellular activities may change the spatial distribution and dynamics of macromolecular crowders. At larger length-scales (> 100 nm), larger cellular structures including membranes and the actomyosin cytoskeleton, both of which undergo dynamic ATP-dependent fluctuations, have strong impacts on mesoscale confinement and elasticity44. We therefore examined droplet trajectories more closely to gain insight into how longer-range confinement and elasticity relate to synDrop growth.Given that coalescence dominates synDrop growth, the growth process is intrinsically linked to droplet motion. Multiple intracellular factors can influence droplet motion, including macromolecular crowding, viscoelasticity, and poroelasticity45. If the average angle between steps is in the range of (π/2 to π) the angular correlation function will be less than zero (−1 to 0). This indicates anti-persistent motion in particle trajectories, suggesting confined or caged particle movement. Conversely, if the averaged angle between steps falls in the range of (0 to π/2), the angular correlation function will be larger than zero (0 to 1). This implies persistent motion in particle trajectories, suggesting motion driven by active processes. We analyzed droplet trajectories at three time points after synDrop induction in control conditions or after ATP depletion in an isotonic buffer in yeast cells . In contrast to endogenous condensates, the synDrops components were designed to minimize non-specific interactions with endogenous molecules within cells, including ATP-consuming enzymes. Moreover, the well-defined protein structures and network geometry make synDrops highly amenable to simulation and analysis with graph-theoretical approaches.50, reflecting local heterogeneity in macromolecular crowding and cellular activity. These physical variations may underlie the distinct behavior of droplet formation within cells compared to the theoretical prediction that droplets should thermodynamically fuse into a single entity. By actively modulating local macromolecular crowding and cellular activity levels, cells could potentially control the formation of endogenous condensates at different locations via biophysical signals. For example, increased cellular activity, such as actin dynamics near the cell cortex, could facilitate endogenous condensate formation, which might in turn contribute to the nucleation and growth of the cytoskeleton network.Our study highlights how the intracellular environment modulates mesoscale molecular assembly through a combination of macromolecular crowding and cellular active-matter. Notably, the intracellular environment is highly heterogeneous in mesoscale diffusivity51. On the other hand, the biophysical properties of the cell interior may also change during disease progression, leading to aberrant phase separation of endogenous condensates. Our study provides a framework to guide future investigations into the effects of intracellular biophysical properties on endogenous condensate formation and dissolution and their relevance to normal biology and disease pathology.We speculate that changes in the biophysical properties of cells could be sensed by their impacts on condensate assembly. Indeed, a synthetic droplet can modulate the rates of kinase reaction in response to changes in macromolecular crowding, demonstrating the feasibility of this idea in cells26, dimer (PDB 4LTB) and hexamer (PDB 3BEY)18, Green fluorescent protein (Superfolder GFP) and blue fluorescent protein (mTagBFP) were first amplified using PCR. Using Gibson assembly, we then fused each ORF: Gai-BFP-3BEY and Gid-4LTB-GFP with a strong yeast promoter TDH3, an N-terminal nuclear export signaling sequence, and a yeast terminator CYC1, and subsequently assembled into yeast backbone vectors pRS304 and pRS306 respectively. For mammalian plasmids, we codon optimized yeast ORFs based on codon usage of Homo sapiens . We then introduced P2A sequence between two ORFs through oligo overhangs and combined Gai-BEY-3BEY and Gid-4LTB-GFP using Gibson assembly onto the same mammalian lentiviral pLVX plasmid with a CMV promoter.For yeast plasmids, open reading frames (ORF) encoding full length Gid and Gai 1–92MATa leu2–3, 112 trip1–1 can1–100 ura3–1 ade2–1 his3–11-,15) sequentially using a LiAc based protocol52. Single yeast cell colony was selected based on whether condensates were able to form after one hour of 300 μM GA induction.Two yeast synDrops plasmids were first linearized using restriction enzyme cutting within auxotrophic marker region. The linearized plasmids were then transformed into W303 yeast strains  based on manufacturer’s protocol. After 24 hours, cells were ready for imaging by replacing with fresh supplemented DMEM. Induction of synDrops in HeLa cells were performed by adding GA till 100 M final concentration.Mammalian HeLa cells were plated on a six-well plate in high glucose Dulbecco’s Modified Eagle Medium (DMEM) with L-glutamine (Gibco) supplemented with 10% fetal bovine serum , penicillin (100 U/ml) and streptomycin (100 g/ml) (Gibco). Cells were incubated at 37 with 5% CO53. The media was further supplemented with either 80mM sorbitol for isotonic buffer condition or 10mM sorbitol for hypo-osmotic buffer condition. Additionally, the pH was balanced to 7.5 using a 50mM Tris-HCl buffer. For ATP depletion in mammalian HeLa cells, a mixture of 6 mM 2-deoxyglucose (2-DG) and 1 μM carbonyl cyanide-trifluoromethoxy phenylhydrazone (FCCP) in CO2-independent medium supplemented with L-glutamine were added to cells for 1 hour54.To deplete ATP, S. cerevisiae cells were treated with 20 mM 2-deoxyglucose (2-DG) and 10 μM antimycin A in Synthetic Complete (SC) media without glucose5. For JLY treatment, mammalian HeLa cells were first treated with 10 μM y27632 for 10 min, followed by the addition of jasplakinolide and latrunculin B to final concentrations of 10 μM y27532, 8 μM jasplakinolide, and 5 μM latrunculin B40 for 1 hour in CO2-independent medium supplemented with L-glutamine.To inhibit TOC1 signaling, S. cerevisiae cells were treated with 1 μM rapamycin for 2 hours in Synthetic Complete Dextrose (SCD) mediaμm and total distance of 3 μm (7 slices). Average projection of Z stacks was used for subsequent imaging analyses. Droplet movies were also recorded using GFP channel with 50 ms frame rate without delay for a total of 20 s . 100% power of 488 nm laser light source  with GFP filter set (EF-EGFP (FITC/Cy2), Chroma, part number = 49002) was used for imaging yeast cells with 40 nm diameter Genetically Encoded Multimeric nanoparticles (GEMs). To record GEM movement, we used Highly inclined thin illumination (HILO) mode. Each GEM movie was imaged on a single focal plane at 10 ms frame rate with no delay (100 Hz) for a total of 4 s .Yeast cells were imaged using TIRF Nikon TI Eclipse microscope with a 100x oil objective  and a sCMOS camera . Epifluorescence LED light source  was used for imaging yeast cells with synDrops. The GFP channel was imaged through GFP filter set (EF-EGFP (FITC/Cy2), Chroma, part number = 49002), while the BFP channel was imaged through quad-band filter set . Z stacks were taken for each channel with an interval of 0.5 μm and total distance of 6 μm (13 slices). Average projection of Z stacks was used for subsequent imaging analyses. Droplet movies were also recorded using GFP channel with 50 ms frame rate without delay for a total of 20 s .Mammalian cells with synDrops were imaged using confocal Nikon TI Eclipse microscope with a spinning disk unit , under a 60x oil objective  and a sCMOS camera . The GFP channel was excited through laser light source  and imaged through GFP emission filter . The BFP channel was excited through LED light source  and imaged through DAPI filter set . Z stacks were taken for each channel with an interval of 0.5 55 on ImageJ57. Due to higher signal-to-noise ratio in GFP channel compared to BFP channel as well as droplets overlap in GFP and BFP channels, droplets were then only detected using GFP channel. We applied a LoG (Laplacian of Gaussian filter) detector on Trackmate to identify the droplets, with one micron ‘Estimated object diameter’ and a fixed ‘Quality threshold’ across all different conditions in each experiment. To determine the droplet total intensity, we employed a circular particle detection algorithm that identified each droplet with a fixed circular region larger than the droplet itself. Subsequently, we computed the mean pixel intensity within this identified region and subtracted the background mean pixel intensity. We denoted this measurement as the droplet total intensity because it reflects both the number of molecules and molecular concentration within the condensate. Results of particle detection from Trackmate included all time points and were then saved as xlm files. Using home-written MATLAB (2019a) code, we subsequently extracted and compiled droplets information, including droplet raw mean pixel intensities and their locations in the images. We also determined the background mean pixel intensity by randomly selecting 20 circles with the same one-micron diameter in each image from areas away from droplets. Thus, final droplet total intensities were calculated by subtracting the background mean pixel intensity from droplet raw mean pixel intensities identified above. Yeast-Spotter58 were used to identify single yeast cells, which generated ImageJ mask files indicating locations of each individual yeast cell. By combining with droplets’ location information, we computed the number of droplets per cell using home-written MATLAB code.To characterize synDrops’ properties within cells, images after average Z projections were analyzed using the Trackmate pluginTo obtain droplet diffusivities, we used simple linear assignment problem (LAP) particle tracking function on Trackmate in addition to particle detection, with max linking and gap-closing distance of 390 nm and maximal gap-closing frame interval of 1. Only trajectories with more than 10 time points were included for subsequent mean squared displacement analyses using home-written MATLAB code.59 on ImageJ57 with particle detection parameters of radius 3, cutoff 0, per/Abs (percentile) 0.1 and particle linking parameters of link range 1, displacement 5 with Brownian dynamics. We only selected trajectories with more than 10 time points for subsequent mean squared displacement analyses using home-written MATLAB code.To determine GEM diffusivities, GEM trajectories were detected using the Mosaic plugin60. The contour lines were further obtained using MATLAB function contour.To generate density plots based on the scattered data points, the data space was separated into 25 × 25 different regions based on the minimum and maximum values on both x and y axis. By counting the number of data points within each region, 2D density matrices were generated and smoothed using the MATLAB function scattercloud61 package in python. To identify clusters, a distance matrix was first calculated based on the topological shortest path that links each pair of molecules. Subsequently, a hierarchical clustering algorithm was employed on the distance matrix. This led to the reordering of molecule sequences, with molecules within each cluster being grouped together. Cluster size was then determined based on the number of molecules that were within each cluster. Locations of each molecule were also recorded at each time point.We used graph-theory based methods for analyzing MD simulations. Each molecule within MD simulations had a unique number identifier and was treated as the node for the graph. Bonds formed at each time point were recorded based on molecule pairs that formed each bond, and were treated as the edges for the graph. The graph at each time point was then constructed by providing both node and edge information inputs using the igraphTo calculate cluster diffusivity, clusters with size larger than 10 molecules were first identified at each time point. Pairwise clusters from consecutive time points were connected from the last time point by determining the largest number of same nodes, thus forming trajectories. If a cluster’s size changed by 20 within a time interval, it was considered as a new cluster and tracked as a distinct trajectory. Only trajectories with more than 10 time points were selected for calculating cluster diffusivities, where mean squared displacement of the cluster’s center of mass for each trajectory was fitted over the first 10 time intervals. All analyses were performed using home-written python3 code.d) of the chemical bonds, we analyzed the kinetics of bond formation in monomeric MD simulations until equilibrium was achieved. In the monomeric system (where the dimers and hexamers are in a 1:1 stoichiometric ratio), we reduced the available binding sites of two components from 6 and 2 to 1 and 1 each. By fitting the data to an exponential decay function, we extracted the number of bonds formed at equilibrium. Subsequently, Kd was calculated based on the concentration of all species at equilibrium, using the formula for a dimerization reaction A + B ↔ AB, To determine the effective dissociation constants (KMSD(t) = 6Dt for long times, and D was obtained in the units of μm2/τ. The unit of time τ = 7. 5 × 10−8 seconds was then obtained by matching this D to an approximate cellular value of μm2/s.To roughly match simulation time scales to experimental ones, the mean-squared displacement of 40 nm GEM particles in simulation were fit to the Einstein diffusion relation in 3d 29, making use of a single graphics processing unit (GPU) to achieve considerable acceleration in simulation speeds. We use coarse-grained (CG) representations of each synDrops component—(i) a sphere with six rigid evenly distributed binding sites to represent the hexamer and (ii) 3 spheres in a rod-like arrangement with two complementary binding sites at two ends to represent the rigid coiled-coil dimer. We have 1170 dimers and 390 hexamers within a cubic box with 860 nm sides (maintaining a 3:1 stoichiometric ratio of dimers and hexamers to have 1:1 ratio of complementary binding sites). This results in concentrations of 3 μM for dimers and 1 μM for hexamers, similar to our estimated values in experiment. Finally, 20 spheres of diameter 40 nm are added to mimic the trace presence of GEMs in the experiment.An agent-based molecular dynamics (MD) simulation approach has been developed to study the synDrops system. MD Simulations were performed using HOOMD-blue v2.9.662 package. We ran MD simulations with varying volume fractions of ribosomes to study the effect of crowding in synDrops assembly. We also varied the effective temperatures that only govern the ribosome movements to study how cellular activities affect synDrops assembly.In addition, spherical components of various sizes without any binding site are added in the system to mimic the crowded cellular environment. For the initial configuration, the CG components are arranged in a lattice whose positions are generated from a CsCl-type lattice generator using the ‘lattice’ module from the ASE (Atomic Simulation Environment)30 in HOOMD-blue that builds upon a model for epoxy binding developed in63. The Bond Updater, for every n steps during the MD simulation, stochastically adds or removes dynamic bonds. Binding events occur with a fixed probability Pon at a critical distance dbind between interaction sites, while unbinding events occur with a probability Poff. Using our dynamic bonding framework, we thus have controls over our binding and unbinding rate constants kon and koff respectively; the bond affinity ε is defined byoff. We ensure that the dynamic bonding model satisfies detailed balance using a particular Metropolis-like criterion66, so that the system moves towards an equilibrium ensemble as bonds form and dissolve dynamically. We use the cell neighbor list67 to accelerate non-bonded agents’ calculations and possible bonding pairs’ constructions.Binding occurs through complementary interaction sites between dimers and hexamers. We modeled such covalent interactions by developing an open-source C++ plugin, called the Dynamic Bond Updater30 defined by 29 smoothing function. The soft potential was implemented by using HOOMD-blue’s tabulated potential option . Here, ron is chosen as the point at which the smoothing starts. We set ron = 0.95rcut for our simulations, and rcut = σ. There is no soft repulsion between complementary binding interaction sites on hexamers and dimers, where we implemented a Lennard-Jones (LJ)71 attraction between the hexamer and dimer rigid bodies, with a cut-off distance equal to 2.5σ.Interactions between crowders and synDrops proteins occur via a soft repulsion potential72 dynamics, with drag forces proportional to the diameter. The dimensions of every CG component approximate their respective crystal structures. Within our MD simulations, we typically use periodic boundary conditions (PBC). However, we also have the option of adding ‘walls’ to confine our system in a ‘closed box’. For volume fractions up to 35%, we are able to place ribosomes in the box without overlaps through random sequential insertion. For higher concentrations, we first set up initial simulation box size using lengths of 1400 nm on a side (4.3x the target volume) and the appropriate number of ribosomes, and then compress the system to the target size of 860 nm linearly over 5 × 105 simulation steps (using the ‘hoomd.variant’ module of HOOMD-blue), and finally turn on dynamic bonding in the system to record synDrops dynamics.All objects in the system undergo thermal fluctuations using Langevin42 impacts formation of synDrops via MD simulations, we assign the crowders a different effective temperature Tc from the rest of the system, which can be achieved through separate Langevin ‘thermostats’ in HOOMD-blue. We ran a different set of MD simulations at a fixed volume fraction of ribosomes (= 30%) but varying the crowder effective temperatures Tc.To study how non-thermal cellular activityA custom 3-dimensional agent-based Java program was developed to simulate aggregation and cluster formations of proteins in a cellular environment. All objects in these simulations are spheres, or spherical aggregates, that move in space as a result of applied forces. These forces arise in three distinct ways: through collisions with other spheres and with the boundaries of the simulation, through bonds to other spheres, and through a random force and torque applied to approximate the Brownian motion of each object. The movements of all molecules then follow Langevin dynamics with a defined effective temperature.73. All the pairwise forces are summed and then attenuated for numerical stability such that collisions resolve over several time-steps (not instantaneously). Translational bond forces are resolved in the same way: by calculating the force required to bring a stretched bond back to its relaxed position. By contrast, torques are calculated with a linear torsional spring. Brownian forces and torques are randomly taken from a Gaussian distribution with a mean of zero and variance of 2DΔt, where D is the diffusivity of the object  and Δt the time-step.Collisions are resolved with a simple rule: at a low-Reynolds number we can calculate the exact force to resolve any pairwise collision. This method is described in detail in Alberts & Odell (2004)μM. As in the HOOMD-Blue system, we use 1170 dimers, 390 hexamers, and varying numbers of ribosomes in a box with side length 860 nm.The two protein components of the synDrops system were modeled as spheres having 6 or 2 uniformly distributed binding sites on their surface . The sizSupplement 1Supplement 2


Arabidopsis thaliana (Arabidopsis) identified amino acid substitutions in or around these motifs that resulted in CESA proteins resistant to Endosidin 20, an inhibitor of cellulose synthesis  protein complexes. At the catalytic core of each CESA protein is a glycosyltransferase type II (GT2) domain that synthesizes uridine diphosphate glucose into β-1,4-glucans . There aynthesis  Fig.). .. Arabidynthesis . These rl growth .cesa6 mutant plants with mutagenized YFP-AtCESA6 expressed via the native promoter. They selected 18 single amino acid substitutions in or near conserved motifs within the GT2 domain. Ten of these mutations were identified in a screen for Endosidin 20–resistant plants (cesa6 mutation results in seedlings with short dark-grown hypocotyls and reduced cellulose content (To test how motifs in the core of AtCESA6 contribute to protein function, the authors complemented t plants . The addt plants . The ces content . TherefoThe authors generated substantial data for each CESA mutant and employed principal component analysis to identify relevant trends in the data. In this way, they were able to categorize the 18 mutations into 2 groups. Group I includes mutations in or near the DDG motif as well as those in the IF3 motif. Mutations in this group cause trafficking defects because mutant proteins were largely retained in the Golgi and there was less YFP-CESA6 signal at the plasma membrane. Group II includes mutations in the DXD, TED, and QXXRW motifs. Mutations in these motifs likely lead to protein misfolding or inhibition of oligomerization of CESA complexes in the endoplasmic reticulum or Golgi.Taken together, the work presented by Huang and colleagues provides compelling insights into how the catalytic cores of CESAs contribute to CESA trafficking and function. This work provides a launchpad from which we can ask transformative questions: Are there amino acid substitutions that enhance CESA trafficking and function? Do similar mutations in other members of the CESA gene family have a similar effect? And are the effects of the mutations investigated here the same across the plant lineage?


Birds of the order Passeriformes represent the most speciose order of land vertebrates. Despite strong scientific interest in this super-radiation, genetic traits unique to passerines are not well characterized. A duplicate copy of growth hormone (GH) is the only gene known to be present in all major lineages of passerines, but not in other birds. GH genes plausibly influence extreme life history traits that passerines exhibit, including the shortest embryo-to-fledging developmental period of any avian order. To unravel the implications of this GH duplication, we investigated the molecular evolution of the ancestral avian GH gene (GH or GH1) and the novel passerine GH paralog (GH2), using 497 gene sequences extracted from 342 genomes. Passerine GH1 and GH2 are reciprocally monophyletic, consistent with a single duplication event from a microchromosome onto a macrochromosome in a common ancestor of extant passerines. Additional chromosomal rearrangements have changed the syntenic and potential regulatory context of these genes. Both passerine GH1 and GH2 display substantially higher rates of nonsynonymous codon change than non-passerine avian GH, suggesting positive selection following duplication. A site involved in signal peptide cleavage is under selection in both paralogs. Other sites under positive selection differ between the two paralogs, but many are clustered in one region of a 3D model of the protein. Both paralogs retain key functional features and are actively but differentially expressed in two major passerine suborders. These phenomena suggest that GH genes may be evolving novel adaptive roles in passerine birds. Passerines comprise the majority of bird species, but their unique genetic features are relatively unexplored. A duplicate copy of the gene for growth hormone (GH) was previously identified as the only known gene that originated in the common ancestor of passerines and has been retained in all major lineages of the order. We examined the evolution of avian GH genes using sequences from 342 genomes. We find that the passerine GHs maintain functional features, are expressed in both major lineages of passerine species, and show evidence of positive selection. We identify mutations at sequence and chromosomal levels that likely affect passerine GH expression and function, supporting the evolution of new, adaptive roles for these genes in passerine evolution. Accelerated evolution of these genes coincides with the extreme, GH-influenced life history traits passerines display, such as accelerated development.Gene duplication is among the most important forces in evolution . DuplicaPasseriformes represent a monophyletic  super-raHowever, many of these proposed key adaptations are not restricted to passerines. Vocal learning is present in parrots and hummingbirds . SexuallWhat then remain as possible mechanisms or key adaptations that might explain passerine diversity? Whether high speciation rates, low extinction rates, or both have driven passerine diversity, there may be underlying adaptive mechanisms. Compared with other birds, passerines have rapid developmental periods, short generation times, and many offspring . Such trGH, also called somatotropin, is the ancestral hormone of the GH protein family, controlling adult body size across all vertebrates . The expHere, we evaluate molecular evolution of the GH paralogs using complete GH sequences from 183 passerines and 159 other birds. The rich genomic resources now available allowed us to examine the chromosomal context of both paralogs in multiple species and resolve the phylogenetic placement of key events including the GH duplication, lineage-specific deletions, and rearrangements of syntenic genes. We provide strong evidence of positive selection on both passerine GH paralogs, the first RNA-seq evidence on transcriptional functionality and tissue specificity of both paralogs in a passerine, and speculate on the potential neofunctionalization or subfunctionalization of these genes in the major lineages of passerines.We retrieved a total of 497 full length GH coding sequences (CDS) from 342 bird genomes including all major lineages of living birds, comprising 159 non-passerines with a single copy, 154 passerines with two paralogs retrieved, and 29 passerines with only one full length paralog retrieved  online. Lanius ludovicianus failed to yield evidence of a second paralog (GH2 in this case). Thus, we found evidence that at least partial copies of both paralogs are present in 182 of the 183 passerines we studied. The majority of “missing” paralogs (21 of 29) were GH1s, possibly because GH1 resides on a microchromosome, while GH2 is on a macrochromosome. Microchromosomes usually have higher GC content and are more difficult to sequence and assemble  and zebra finch (Taeniopygia guttata). As the linkage of CD79B and GH occurs across Tetrapoda, for example Xenopus tropicalis genome assembly UCB_Xtro_10.0 and Homo sapiens genome assembly GRCh38.p13  consistently occurs with MAP6 and MOGAT2 downstream in a syntenic context that maps to zebra finch chromosome 1 . UpstreaC). Retention of introns in the derived passerine paralogs (GH2) suggests that the duplication occurred at the DNA level, and did not involve retroposition. The segmental nature of the original duplication discussed above is also consistent with this conclusion. Like human GH1, avian GH genes are typically 217 amino acids in length (observed range 214–218 residues). Small indels of 1–3 codons in single taxa or clades account for the length variation  annotated in passerine GH paralogs were somewhat longer in GH2 than in GH1  online. Acanthsitta (suborder Acanthisitti) and oscines, while GH2 is higher in suboscines (suborder Tyranni).The base composition of the passerine paralogs has diverged post-duplication . While tP < 0.00001; supplementary table S6AVisual examination of the GH gene tree based on protein-CDS suggests that passerine GH paralogs generally have longer branches than non-passerine GH . We confN/dS or ω) must be less than one, simulating no positive selection, versus three alternate models where sites with ω > 1 were allowed on foreground branches. The best-fitting model tested (likelihood ratio test against the null P < 0.0001) allowed positive selection on both passerine GH1 and GH2. Of the single-clade models, both passerine GH1 and GH2 were individually highly significant for the model with ω > 1 allowed, but the model with positive selection allowed on GH2 displayed a larger improvement . This result suggests a burst of positive selection immediately following the duplication event. Overall, the relative rate and selection test results provide evidence for positive selection on both passerine GH1 and GH2, with a stronger signal on the GH2 clade.Next, we performed a more conservative aBSREL (adaptive branch-site random effects likelihood) test of positive selection  on the tCThe Bayes empirical Bayes method . One core alpha helix that is buried in the model has no such sites. Both potentially phosphorylatable sites also appear on the surface of the model (fig. 5B). Finally, 14 of the 21 sites inferred to be under positive selection are clustered in one region of the structural model (lower left of fig. 5A), although these sites are widely spaced in the primary sequence, spanning residues 85–209. They include sites under positive selection in both paralogs  and one site (209) that lies near the end of a highly conserved region of the primary sequence that is bent into a surface loop by a disulfide bridge that lies near the carboxy terminus of the polypeptide (fig. 5B). Also in this region of the 3D model is site 90 (fig. 5A), which carries the only fixed amino acid replacement between GH1 and GH2 (Gly > Lys). This site is involved in receptor binding in humans .The promoter region of chicken GH has been well characterized . In thisic genes . All pasPipra filicauda). GH1 was expressed at very high levels in the pituitary gland , at moderate levels in testes (5–34 transcripts) and at very low levels (≤5 transcripts) in ten brain areas tested , as is one of the cysteine pairs presumed to form a disulfide bridge (B). Disulfide bridges are often integral to maintaining protein tertiary structure. Also found in this region is site 90, which is known to be involved in binding to the GH receptor in humans  available on NCBI , plus thM. candei and A. chloris, we used blast, querying genes annotated on a previous assembly from the same species’ genome (in the case of Acanthisitta) and from relatives’ genomes (for both Manacus and Acanthisitta). Many annotated genomes did not convey enough information to reconstruct the full syntenies shown in We examined the syntenic context of these annotated GH genes. When chromosome-level assemblies were available, we noted the chromosomal location of the GH paralog(s). For each annotated avian genome, we recorded the genes flanking GH genes. For non-passerines, we also noted the two genes flanking either side of the homologous genomic region where a segment containing GH2 is inserted in passerines . AdditioT. guttata), suboscine (Chiroxiphia lanceolata), and acanthisittine (A. chloris). To obtain dotplots focused on the displacement of the segment containing GH2 in oscines compared with suboscines and Acanthisitta, we ran pairwise alignments of these three sequences in D-GENIES  was discarded because it was highly divergent from all other bird sequences and contained multiple stop codons, also likely due to mis-assembly. We calculated base composition on our dataset of avian GH sequences that were free of ambiguities.For the initial categorization of GH paralogs as GH1  or GH2 (duplicate), we used sequences from the set of 76 annotated avian genomes from NCBI . GH paraPtilorrhoa leucosticta raw reads (no GH1 copy previously retrieved) with a 100 bp sequence from Rhagologus leucostigma GH1, and determined whether short read hits had higher sequence identity with previously retrieved P. leucosticta GH2 or the query R. leucostigma GH1. In cases where we found raw reads that closely matched the “missing” paralog from the related species, we considered this evidence that at least a partial copy of the “missing” paralog exists in this species genome, although it is not present in the currently available genome assembly.Failure to retrieve one full sequence from certain non-passerine genomes or two full sequences from certain passerine genomes was most likely due to genome incompleteness or mis-assembly. When the genome assembly of passerine species yielded only one complete paralog . Given the clear evolutionary origin to the GH paralogs we found (see Results: Copy Number and Duplication Origin), for passerine sequences, we chose to use the symbol GH1 to refer to the ancestral paralog and GH2 to refer to the novel derived paralog. GH is used to refer to the non-passerine avian paralog as per the Chicken Gene Nomenclature Consortium . PreviouOpisthocomus hoazin within Neoaves). The constraint topology was invoked in RAxML with the -g setting.We estimated the trees used in the evolutionary analyses from the Geneious alignment of GH CDS using RAxML v. 8  option “We performed tests of the relative molecular clock rate of protein-coding nucleotide sequence evolution on the constrained and unconstrained trees with the BASEML package in PAML version 4.9j . The nulN/dS or ω). In the branch-site test, the null model M7  is compared with various alternate M8 models. M8 specifies the M7 condition on background branches and sites, allowing an additional category of ω on user-specified foreground branches  where ω > 1, indicating elevated nonsynonymous changes and positive selection on branches and sites where this rate category occurred. We specified the following foreground clades: (A) the passerine GH1 clade, (B) the passerine GH2 clade, (C) all passerine GH (both GH1 and GH2), and (D) the two branches immediately following the duplication event to explicitly test the hypothesis of a burst of positive selection following duplication  were collected from the wild and sacrificed at Tiputini Biodiversity Station, Ecuador. Whole brains, pituitary, and testes were extracted from birds 4–6 min postmortem and stored in dry ice until placed in liquid nitrogen.As part of a separate study on male cooperative display behavior , 12 maleThe brains were cryosectioned and microdissected for ten individual brain nuclei known to be relevant to social behavior and sex-hormone regulation: the ventromedial hypothalamus (VMH), anterior hypothalamus (AH), paraventricular nucleus (PVN), medial preoptic area (POM), dorsomedial intercollicular nucleus (ICO), midbrain central gray (GCT); part of the mesolimbic reward system: arcopallium intermedium (AI), or part of both: bed nucleus of the stria terminalis (BSTm), lateral septum (LS), nucleus taenia (TNA). The mRNAs from these ten nuclei, pituitary gland, and testis were extracted using a Qiagen miRNeasy Micro Kit .P. filicauda genome (GCA_003945595.1) using splice-aware RNA-seq mapper STAR v. 2.75  showed no bias for mapping rates in exon 1 of either paralog. We calculated the total number of reads per gene and per exon using featureCounts v2.0.1 (The ten brain nuclei and two peripheral tissues were prepared as TruSeq Stranded mRNA Sample Prep Kit libraries per tissue and per individual. Sequencing was performed on an Illumina HiSeq 4000. We mapped resultant single-ended reads to the annotated  v. 2.75  on two ps v2.0.1 . We usedevad033_Supplementary_DataClick here for additional data file.