Aedes aegypti. We compared isolate infectivity and vector competence (VC) among thirty two A. aegypti-viral isolate pairs. Mosquito populations from high dengue incidence area exhibited overall greater VC than those from low dengue incidence area at 58.1% and 52.5%, respectively. On the other hand, the overall infection rates for the isolates ThNR2/772  and ThNR2/391 , were significantly higher than those for isolates ThNR2/406  and ThNR2/479 . These results suggest that the efficacy of dengue virus circulation was likely to vary according to the combination between the virus strains and origin of the mosquito strains, and this may have epidemiologic implications toward the incidence of flu-like classic dengue fever (DF) and dengue shock syndrome (DSS).Dynamics of dengue serotype 2 virus isolated from patients with different disease severity, namely flu-like classic dengue fever (DF) and dengue shock syndrome (DSS) were studied in its mosquito vector  Flulike classic dengue fever (DF) and dengue hemorrhagic fever (DHF) are increasingly important public health problems issues in the tropical region. Annually, an estimated 390 million dengue infections per year and 96 million of symptomatic dengue infections occur . It has Aedes aegypti, the primary vector of dengue, is well-adapted to cohabitate with humans by breeding in man-made containers. Several population genetic and vector competence (VC) studies have been done on A. aegypti because of its ability to transmit dengue and yellow fever flaviviruses [A. aegypti populations particularly for DENV-2es [A. aegypti for flaviviruses [iviruses ,14,15,16iviruses . Vector iviruses . The geniviruses , presentDENV-2es ,11,20. GThere are several possible mechanisms that can affect the transmission potential of different dengue virus strains. Some strains may infect and replicate target cells more efficiently, sustain higher viremia in the human host, and infect more mosquitoes ,22,23. SA. aegypti for DENV-2 at a regional geographic scale [A. aegypti [Several studies have reported variation in the oral receptivity of ic scale ,28,29, aic scale . Further aegypti ,25,26. HA. aegypti pairs on a regional geographic scale in Thailand. In particular, eight geographically heterogeneous mosquito strains collected from areas with high and low dengue incidence were orally challenged with four virus isolates recently isolated from patients exhibiting flu-like classic dengue fever (DF) and dengue shock syndrome (DSS).In the present study, we address the potential variation in VC and patterns of oral infection of dengue virus isolates representing distinct disease severity in sympatric DENV-2-Aedes aegypti were observed according to the mosquito origin (dengue incidence at the area and location), mosquito strain, disease severity of the patients from whom the virus was isolated, and the virus isolate  to 39.2% . Mosquito strains collected from high dengue incidence areas showed a higher infection rate (58.1% of 854 mosquitoes) compared to mosquitoes from low dengue incidence area (52.5% of 953 mosquitoes). However, no significant statistical difference was observed A. Signif < 0.005 E.A. aegypti vector competence for dengue viruses isolated from patients exhibiting different disease severity (DF and DSS), the mosquito strains were grouped by dengue incidence at the area of mosquito origin (sub-district level). High dengue incidence sub-districts comprises TAL, KAO, BPA, and TAK strains, while low incidence sub-districts were KKR, GKE, RBK, and RBA strains. Mosquito strains were further classified location at the district administrative level Muang (TAL and KAO), Bang Pakong (BPA and TAK), Krong Kruang (KKR and GKE) and Ratchasan (RBK and RBA). Vector competence variation: As a proxy of vector competence (VC), the VC was calculated as the number of mosquitoes positive for the viral RNA in head tissues divided by the number of mosquitoes exposed to the respective viruses. To better understand the geographical variation in the For the first grouping, the VC among mosquitoes from high dengue incidence for DSS isolates ranged from 19.6% (KAO mosquito strain for the ThNR2/479 virus isolate) to 85.4% (TAK for the ThNR2/479 virus isolate). DF isolates ranged from 37.0% (TAK mosquito strain for the ThNR2/406 virus isolate) to 73.1% (TAK mosquito strain for the ThNR2/772 virus isolate). Although no statistically significant differences were observed between the overall VC for DF and DSS, mosquito strains collected from high dengue incidence areas showed an overall higher competence for viruses isolated from DSS patients than from DF patients. Whereas mosquito strains collected from low dengue incidence areas exhibited a similar overall VC for either DF or DSS virus isolates, with VC ranging from 40.6% (GKE mosquito strain for the ThNR2/391 virus isolate) to 67.3% (KKR mosquito strain for the ThNR2/391) for DSS isolates, and 18.8% (RBK mosquito strain for the ThNR2/406) to 81.8% (KKR mosquito strain for the ThNR2/406) for DF isolates . For thep < 0.05. Females of the TAL, KAO, GKE, and KKR strains exhibited a significantly higher VC for DF virus isolates than for DSS, while BPA, TAK, and RBK showed a significantly higher VC for DSS than for DF virus isolates, (p < 0.05) as shown on A. aegypti strain-virus isolate combinations which ranged from 18.8% (RBK-ThNR2/406) to 85.4% (TAK-ThNR2/479).The VC of individual mosquito strains significantly differed between virus isolated from patients with DSS and DF, A. aegypti was calculated as the number mosquitoes positive for viral RNA in the abdomen divided by the number exposed to the virus isolate. Overall, ThNR2/772 (DF) and ThNR2/391 (DSS) were the most infectious virus isolates, where 62.3% of 467 and 60.9% of 422 mosquitoes were positive, respectively; while ThNR2/406 (DF) and ThNR2/479 (DSS) were significantly less infectious where 55.2% of 458 and 54.8% of 449 mosquitoes were positive, respectively,  and ThNR2/391 (DSS) isolates were more uniform. ThNR2/772 ranged from 40% (RBK mosquito strain) to 73.1% (KAO mosquito strain), while ThNR2/391 ranged from 40% (GKE mosquito strain) to 80% (TAK mosquito strain). In contrast, virus isolates ThNR2/406 (DF) and ThNR2/479 (DSS), exhibited more phenotypic variation where infection rates ranged from 18.8% (RBK mosquito strain) to 88.6% (KKR mosquito strain), and from 21.6 (KAO mosquito strain) to 85.4% (TAK mosquito strain), respectively, . FurtherVector, viral, and environmental factors determine the capacity of dengue virus to infect and disseminate in its mosquito vector. In the present study, we challenged each mosquito strain with all four virus isolates at the same time and mosquitoes were held together during the same environmental fluctuations of the incubation period (14 days), to minimize the effect of environmental variation. Even though all mosquito strains may not have experienced the same environmental conditions no statistical significant differences were observed when mean temperature and humidity that each mosquito strain experienced during the incubation period were compared among strains (data not shown). Nonetheless, some of the observed mosquito VC and viral isolate infectivity variations may have been influenced by environmental variation among experimental groups.A. aegypti experimentally infected with DEN virus has been reported [A. aegypti populations [A. aegypti strains. Although the A. aegypti were collected in the same province, considerable variability in overall infection rates was observed according to the dengue incidence in the collection area, geographic origin (districts), mosquito strain, individual virus isolate, and disease severity of the patients from whom the virus was isolated  was ca. 20 km, and between sub-districts was ca. more than 30 km.Collection sites: Four districts in Chachoengsao province situated in eastern Thailand, with the highest (Muang and Bang Pakong) and lowest (Krong Kruang and Ratchasan) dengue (DF/DHF/DSS) incidence  reported during the past 20 years were selected. Within these districts the sub-districts (tambon) with the highest  and lowest  dengue incidence from 1999 to 2002 were selected as mosquito collection sites . Sub-disA. aegypti were collected from two sites in each sub-district,  were performed at the hospital, while virus isolation and serotype determination were performed by the staff of the Arbovirus Section, National Institute of Health, Medical Science Center, Nonthaburi. The clinical diagnosis and clinical severity gradings of each isolate was classified using the World Health Organization criteria  . The serAedes albopictus clone C6/36 cell line from the patient serum before using for experiments. To ensure that the virus specimens were not altered significantly from their wild type character as found in the host patient, none of the isolate has been purified by any methodology before inoculation into mosquito cells.All isolates were passaged four times in Ae. albopictus clone C6/36 cell line and incubated at 28 °C for 8 days in Eagle’s medium supplemented with 2% heat-inactivated fetal bovine serum (FBS) and 0.2 mM each of nonessential amino acids plus antibiotics . The infected culture fluid was harvested 8 days after inoculation, aliquoted, and stored at −80 °C until used. The virus titer of the isolates was measured by the focus formation test by using BHK-21 cells on 96-well plates [Plaque assay: A seed virus was prepared by inoculation into a monolayer culture of l plates . The vir2 PFU/mL) with an aliquot of the same virus pool. The titers of the post feeding virus suspensions did not change significantly.Oral Infection: Only 4 to 5 day-old female mosquitoes were used to minimize age factors effects. About 60 females were placed in cylindrical pint cardboard cages covered at one end with fine non-wettable nylon mesh. These females were deprived of food for 36–40 h prior to the infectious meal, then were allowed to feed on the infectious meal (BVS) consisting of equal volumes of isolate virus suspension, washed rabbit erythrocytes, and 10% sucrose solution (as a source of energy). Drops of the infectious meal were placed on the mesh covering the cardboard cage containing mosquitoes as previously described ,25. FeedDetection of virus infection: After has completed the EIP (14 days) mosquito head and abdomen were severed and homogenized in Trizol LS reagent for total RNA extraction, and then assayed for dengue virus genome by using RT-PCR method. The detection of viral RNA in the homogenized abdomen was interpreted as indicating that the mosquito midgut had become infected. Detection of RNA viral in the homogenized head indicated disseminated from the infected midgut to a secondary target organs.RNA extraction and RT-PCR: Total RNA was extracted from individual mosquito head and abdomen severed using Trizol LS (Invitrogen) according to the manufacturer’s recommendations. Briefly, the head and abdomen of mosquito were separately homogenized in 300 µL of Trizol reagent then 99.9 µL of chloroform was added. The resulting mixed suspension was centrifuged at 14,000 rpm/min for 10 min at 4 °C. RNA was precipitated from the aqueous phase (ca. 145 µL) by mixing with 133.2 µL of isopropyl alcohol. Isopropyl alcohol-RNA precipitate was recovered by centrifugation and the RNA pellet was washed once with 90 µL of 75% ethanol followed by a second wash with absolute ethanol and then air dried. The RNA pellet was resuspended in 10 µL of RNase-free water and used as a template RNA in reverse transcriptase PCR (RT-PCR). Synthetic oligonucleotide primer pairs were designed based on published sequence data for dengue virus serotype specific primer, sense (D2-S) and complementary (D2-C) genome nucleotide (nt) regions 1203 to 1222 and 1432 to 1413 respectively ,37.2), 1 µL of dNTP mix (containing 10 mM of each dNTP), 0.5 µL (25 pmol) of corresponding primers, D2-S (5′-GTTCGTCTGCAAACACTCCA-3′) and D2-C (5′-GTGTTATTTTGATTTCCTTG-3′), and 1 µL of enzyme mix . Nine microliters of PCR product was subjected to agarose gel electrophoresis, and amplified DNA fragments were visualized with ethidium bromide staining.Dengue virus RNA was assayed by using one step RT-PCR kit (Quiagen) following the manufacturer’s recommendations with slight modifications. Briefly, 2 µL of RNA, 8 µL of reaction mix, and RNase free water was reverse transcribed and amplified following the thermal cycle protocol recommended in the kit instruction manual, with some modifications, as follows: one cycle at 53 °C for 30 min, one cycle at 94 °C for 2 min, followed by 45 cycles at 94 °C for minute, 53 °C for 1 min, and 68 °C for 2 min, followed by one cycle at 68 °C for 7 min. The reaction mixture solution contained a mixture of 5 µL of 5× buffer  11.5 software package was used to generate all the results and graphs in this study.In this study, we analyzed 32 recently collected vector–virus pairs . Among vector–virus pairs infection rates were different; indeed, many of those were significantly different. This leads us to conclude that the observed variations in susceptibility among mosquito strains is probably due to the combination of certain genetic elements of the vector and the virus as has been previously observed in similar researches. Furthermore, these results suggest that the efficacy of dengue virus circulation in a given locale may vary according to the interaction of virus strains with the origin of the vector mosquitoes. This study contributes to the knowledge of the role of the association between mosquito genetic determinants of susceptibility and genetic variation of dengue virus in the occurrence of different disease severity.


Assigning chromatin states genome-wide  is commonly performed to improve functional interpretation of these states. However, computational methods to assign chromatin state suffer from the following drawbacks: they typically require data from multiple assays, which may not be practically feasible to obtain, and they depend on peak calling algorithms, which require careful parameterization and often exclude the majority of the genome. To address these drawbacks, we propose a novel learning technique built upon the Self-Organizing Map (SOM), Self-Organizing Map with Variable Neighborhoods (SOM-VN), to learn a set of representative shapes from a single, genome-wide, chromatin accessibility dataset to associate with a chromatin state assignment in which a particular RE is prevalent. These shapes can then be used to assign chromatin state using our workflow.We validate the performance of the SOM-VN workflow on 14 different samples of varying quality, namely one assay each of A549 and GM12878 cell lines and two each of H1 and HeLa cell lines, primary B-cells, and brain, heart, and stomach tissue. We show that SOM-VN learns shapes that are (1) non-random, (2) associated with known chromatin states, (3) generalizable across sets of chromosomes, and (4) associated with magnitude and multimodality. We compare the accuracy of SOM-VN chromatin states against the Clustering Aggregation Tool (CAGT), an unsupervised method that learns chromatin accessibility signal shapes but does not associate these shapes with REs, and we show that overall precision and recall is increased when learning shapes using SOM-VN as compared to CAGT. We further compare enhancer state assignments from SOM-VN in signals above a set threshold to enhancer state assignments from Predicting Enhancers from ATAC-seq Data (PEAS), a deep learning method that assigns enhancer chromatin states to peaks. We show that the precision-recall area under the curve for the assignment of enhancer states is comparable to PEAS.Our work shows that the SOM-VN workflow can learn relationships between REs and chromatin accessibility signal shape, which is an important step toward the goal of assigning and comparing enhancer state across multiple experiments and phenotypic states. Regulatory elements (REs) denote regions of chromatin that promote, enhance, repress, or insulate transcription , 2. GeneThe location of REs can be measured genome-wide using chromatin-accessibility-based methods, such as FAIRE-seq , DNase-SThus, new computational methods are needed to assign chromatin state at the genome scale from a single chromatin accessibility experiment. Ideally, these computational methods should include signal shape rather than peak location alone. Traditional peak caller performance in DNase-seq assays is heavily dependent on method and parameter choice  and may k-means clustering and hierarchical clustering to associate chromatin accessibility signal shape  with other epigenetic factors , which yields more interpretable results. Unlike PEAS, CAGT makes use of signal shape across the genome rather than peaks alone. However, CAGT is normally used to study transcription start sites (TSS) rather than whole chromosomes and has not been applied to chromatin state assignment. This makes applicability to genome-scale assignments unclear, as only 5% of chromatin accessibility peaks are located within 2.5 kb of TSS [Currently, only 2 methods, Predicting Enhancers from ATAC-Seq Data (PEAS) and the Clustering Aggregation Tool (CAGT), can assign chromatin state genome-wide using chromatin accessibility data alone . PEAS usb of TSS .Here, we present an improvement on the Self-Organizing Map (SOM), which we call SOM with Variable Neighborhoods (SOM-VN), by incorporating a signal-dependent calculation of neighborhood sizes when learning chromatin accessibility signal shapes from regions segmented across entire chromosomes. A SOM is a single-layer neural network where each node and weight corresponds to a shape from a chromosomal region and its signal intensity, respectively . In our We tested SOM-VN using 14 publicly available DNase-seq assays representing diverse cell types, data quality, and read count . The percentage of peaks passing an Irreproducible Discovery Rate cutoff of 0.05 across replicates was greater than 50% in all samples. Likewise, when we learned shapes on each replicate separately using SOM-VN and used these to assign chromatin state, we found between 20 and 80% overlap for all REs in all cell types  and GM12878.Notably, all shapes learned using permuted signal were associated with weak REs or had no association, suggesting that the shapes associated with enhancers and promoters are only recoverable in the original signal. In brain tissue (H), the distribution of cross-correlations skews higher than in other samples, which may reflect lower signal-to-noise ratio in brain tissue (H) and the high read counts of the replicates in this sample , and no promoter-associated shapes were found in any sample. In contrast, the associations between unpermuted REs and shape included 60 promoter-associated shapes and 3 enhancer-associated shapes. All other associations in the permuted annotation model were with weak REs. This result is expected because of the imbalance in RE type coverage, as weak REs cover a higher percentage of the genome than promoter or enhancer REs  sample, and the brain tissue (H) sample. Within cell types, precision and recall calculated in the test autosomes did not vary greatly for different sets of autosomes chosen for training Fig. . All RE We visualized a subset of shapes learned by SOM-VN on all chromosomes from brain tissue (H) Fig. . A notabTo evaluate whether these patterns were observable in other cell types, we computed the range of maxima for each RE and cell type, and we visually determined the percentage which were bimodal or multimodal. We found that, in general, promoter-associated shape maxima were higher than enhancer-associated shape maxima, which were higher than weak-associated shape maxima; however, some overlap existed between the groups  and H1 (L), brain tissue (H) to A549 and H1 (L), and H1 (L) to A549 and brain tissue (H). We indicate the mean values across all chromosomes in Table Specific types of shapes learned using SOM-VN were generalizable between 2 cell types. For example, enhancer-associated shapes learned on brain tissue were generalizable to A549. Similarly, when shapes learned on H1 were applied to brain tissue, weak RE precision and recall did not differ considerably from when the same shapes were applied back to H1 itself. Interestingly, even though no enhancer-associated shapes were learned on H1, enhancer-associated shapes learned on brain tissue were generalizable to H1. These results indicate that enhancer and weak RE associated shapes learned using SOM-VN can generalize across some cell types.While only SOM-VN is designed to learn chromatin accessibility signal shapes across whole chromosomes to assign chromatin to RE-associated states, CAGT was designed for a similar task. We therefore compared the performance of SOM-VN to this method. To compare against CAGT, we learned shapes using the default parameters for CAGT and then associated them with REs (see Methods). For each sample, we evaluated the precision and recall of chromatin state assignment for each chromosome using shapes learned on all chromosomes Table .Table 2Sk-means procedure used by CAGT was unable to converge on a set of shapes for brain tissue (H), we did not include that evaluation. We also did not include brain tissue (L) because neither SOM-VN nor CAGT learned any shapes associated with promoters or enhancers. Notably, CAGT did not learn any promoter shapes in GM12878, B-cells, H1 (H), heart tissue , HeLa (L), or stomach tissue (L), whereas SOM-VN learned promoter-associated shapes for all samples in this evaluation. Furthermore, CAGT did not learn any enhancer-associated shapes in GM12878, H1, HeLa, or stomach tissue (L), whereas SOM-VN learned enhancer-associated shapes in all cell types but H1 and GM12878. Finally, in samples where CAGT learned promoter or enhancer associated shapes, SOM-VN exhibited considerable improvement. SOM-VN improved A549 promoter precision by 0.05 and recall by 0.33, B-cell (L) enhancer precision by 0.13 and recall by 0.10, heart tissue (L) enhancer precision by 0.03 and recall by 0.40, HeLa (H) promoter precision by 0.17 and recall by 0.61, and stomach tissue (H) promoter precision by 0.39, promoter recall by 0.17, enhancer precision by 0.38, and enhancer recall by 0.08. Weak precision and recall were also improved in these samples, with the exception of a 0.07 drop in recall for heart tissue (L) and a 0.18 drop in recall for HeLa (H).Because the To verify that the associations found with REs were due to shape and not to magnitude alone, we also learned associations between REs and maximum signal within a region. We then used these associations to assign chromatin state to GM12878, B-cells, H1 (H), heart tissue, HeLa cells, and stomach tissue. We found that not only did shapes learned on SOM-VN yield markedly better precision and recall than magnitude alone for enhancers and promoters in the majority of cases, but that using magnitude alone did not result in promoter or enhancer associations being made for the majority of cell types  PEAS assigns chromatin state to peaks only, rather than assigning chromatin states to the entire genome, and (2) PEAS distinguishes enhancers from other REs after removing promoters, rather than assigning chromatin states that distinguish between multiple types of REs. To compare SOM-VN to PEAS, we adapted SOM-VN to distinguish enhancers from non-enhancers after removing promoters. We evaluated the performance of the SOM-VN workflow on the GM12878 cell line in order to directly compare to the published results for PEAS . In thisThe precision-recall (PR) area under the curve (AUC) score approached the PR AUC scores reported in  with genTo determine whether thresholding by signal intensity also increased precision and recall in the general case , we also evaluated precision and recall on only signal above 5 RPKM for the general case and compared the resulting precision and recall to that of the general case without this cutoff. We found that this cutoff increased B-cell (L), A549, H1 (L), GM12878, and stomach tissue (L) promoter precision by more than 0.10 and H1 (H) promoter recall by more than 0.10, but did not find any changes of this magnitude in enhancers, weak REs, or promoters of other samples . For instance, in B-cells, brain tissue, heart tissue, and stomach tissue, SOM-VN learned more enhancer-associated shapes in high SPOT samples than in low SPOT samples and also exhibited better overall enhancer precision and recall. However, in H1 cells, no enhancer-associated shapes were learned for either the high SPOT sample or the low SPOT sample.We note that SOM-VN has some limitations that could be improved upon in future iterations. First, we selected a 4 kb region size by evaluating the Davies–Bouldin clustering index  for a raAssignment of chromatin state is a challenge due to the limitations of experimental validation on a large scale and the number of assays needed to computationally assign states associated with REs using histone modifications. For this reason, assigning chromatin state using chromatin accessibility signal alone would provide a less resource-intensive option for researchers. To this end, we developed SOM-VN to learn shapes and associate them with REs . We found that learned shapes were non-random and consistent across chromosomes and that using these shapes to assign chromatin state could improve precision and recall over existing methods. Our work is thus an important step toward expanding the utility of chromatin accessibility signal shape alone to assign chromatin state associated with REs.gosr binbam (available from https://github.com/wresch/gosr) was used to compute RPKM signal and smooth the signal into 50 bp bins. We segmented this signal into 4 kb training regions.We used publicly available chromatin accessibility data aligned to hg38 from 8 different cell types with variations in SPOT score. These included cell lines A549 (low SPOT score), H1 (low and high SPOT scores), HeLa (low and high SPOT scores), and GM12878 (low SPOT score), primary B-cells from a 21-year-old adult male (high SPOT score) and a 37-year-old adult male (low SPOT score), and the following tissue samples: brain tissue from 1 male embryo at 58 days gestation and 1 embryo at 56 days gestation , brain tissue from 1 male embryo at 105 days gestation (low SPOT score), heart tissue from 1 embryo at 96 days gestation , heart tissue from a 3-year-old male (low SPOT score), stomach tissue from a female embryo at 98 days gestation (low SPOT score), and stomach tissue from a female embryo at 108 days gestation (high SPOT score). The function https://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html).We used publicly available ChromHMM annotations in hg38 as ground truth. We downloaded annotations for the following Roadmap epigenome ID’s: E114 (A549), E081 , E003 (H1), E116 (GM12878), E083 , E117 (HeLa), E032 , and E092 . These annotations were based on a 15-state ChromHMM model from the Roadmap Epigenomics Project , which was not the focus of our work, we simplified the chromatin state space to include the following:Additional details are available in Additional file To find associations between REs and the maximum magnitude of a region, we first computed the maximum RPKM signal of each 4 kb region in each chromosome. We then computed the overlap between these regions and the ChromHMM annotations to find associations. Additional steps were conducted in the same manner as when associating shape with REs and are described in Additional file To assign chromatin state to new chromosomes or cell types not used to learn shape (i.e. the testing data), we segmented the testing data into 8 kb regions overlapping each other by 4 kb. In this manner, we evaluated containment of shape rather than exact match, thereby avoiding mis-assignment due to signal shift. We then computed an ambiguity metric on each region: the ratio of the cross-correlation between a region and its matching shape, and the cross-correlation between the region and its second-best matching shape. To consolidate overlap, we used a dynamic programming approach that minimized the sum of ambiguities. This assignment process resulted in a BED file containing the start and end points of each 8 kb region as well as the RE most associated with its chromatin state.A as a region which was both matched to A and for which its percentage of coverage in ChromHMM across all RE types was maximized in A. Likewise, we defined a false positive as a region that was matched to A but for which its percentage of coverage in ChromHMM across all RE types was not maximized in A, a true negative as a region that was not matched to A and for which its percentage of coverage in ChromHMM across all RE types was not maximized in A, and a false negative as a region that was not matched to A but for which its percentage of coverage in ChromHMM across all RE types was maximized in A.We used the true positive rate (TPR), false positive rate (FPR), true negative rate (TNR), and false negative rate (FNR) to evaluate the precision (TPR/(TPR + FPR)) and recall (TPR/(TPR + FNR)) on chromatin state assignments made using the SOM-VN workflow with respect to each RE. We defined a true positive for a given RE type We included 2 types of null models in our analysis: the permuted signal model, which represented shapes learned randomly assuming no pattern of variations in the signal, and the permuted ChromHMM model, which represented a case where no true associations between shape and annotation existed. To build the permuted signal model, we permuted signal intensities in the 50 bp bins of the RPKM signal file before segmenting the signal into 4 kb regions and learning shapes using these permuted signal regions. We then associated these shapes with REs from ChromHMM annotations to obtain associations between random signal and chromatin state. To build the permuted ChromHMM model, we permuted the positions of ChromHMM annotations without modifying the annotation sizes.Additional file 1. Supplementary Tables. This file contains Supplementary Tables 1–7.Additional file 2. Supplementary Figures and Methods. This file contains the supplementary figures referenced in the manuscript and details the methodology for segmenting regions, computing the Davies-Bouldin Index, training the SOM-VN, merging learned shapes, and associating learned shapes with RE.


Post-traumatic stress disorder is likely to affect clinical courses in the somatic hospital ward when appearing as comorbidity. Thus, this study aimed to assess the costs associated with comorbid post-traumatic stress disorder in a somatic hospital and to analyze if reimbursement appropriately compensated additional costs.The study used data from a German university hospital between 2011 and 2014, analyzing 198,819 inpatient episodes. Inpatient’s episodes were included for analysis if they had a somatic primary diagnosis and a secondary diagnosis of post-traumatic stress disorder. Costs were calculated based on resource use and compared to reimbursement. Analyses were adjusted for sex, age and somatic comorbidities.N = 219 Inpatient’s episode were found with primary somatic disorder and a comorbid post-traumatic stress disorder. Inpatients episodes with comorbid post-traumatic stress disorder were compared to 34,229 control episodes, which were hospitalized with the same main diagnosis. Post-traumatic stress disorder was associated with additional hospital costs of €2311 [95%CI €1268 - €3355], while reimbursement rose by €1387 [€563 - €2212]. Results indicate that extra costs associated with post-traumatic stress disorder are not fully reimbursed. Male patients showed higher hospital costs associated with post-traumatic stress disorder. On average, post-traumatic stress disorder was associated with an extra length of stay of 3.4 days [2.1–4.6 days].Costs associated with post-traumatic stress disorder were substantial and exceeded reimbursement, indicating an inadequate reimbursement for somatic patients with comorbid post-traumatic stress disorder. Post-traumatic stress disorder (PTSD) is diagnosed in individuals who were exposed to a traumatic event or who witnessed a traumatic event, when the following symptoms are observable: high physical arousal, negative thoughts leading to avoidance of trauma-related stimuli, and intrusive symptoms such as flashbacks or nightmares [DSM-V]. Individuals with PSTD have previously been shown to suffer from severely impaired health-related quality of life and struggle with a decreased life satisfaction , 2. PTSDThere is some evidence that psychiatric comorbidities are associated with additional hospital costs. A meta-analysis found depression in particular to be associated with significantly higher hospital costs . In patiPTSD as main diagnosis was associated with annual direct excess health care costs ranging from 512 US-$ to 19,435 US-$ per annum in a recent systematic review assessing the costs of illness . Higher An increase in LOS is especially significant since LOS is not taken into account in the German Diagnosis Related Groups (G-DRG)-based reimbursement system in full proportion to the additional days of stay. DRGs are the most common hospital reimbursement system internationally, implemented in Germany in 2004. DRGs aim to create homogenous groups of patients on national level and are yearly reviewed, by approximately 16% of all hospitals in Germany providing case-cost data, clinical case data  and additional service data on patient level , 19. WitTo our knowledge there is no study assessing the associated hospital costs or adequate reimbursement of only PTSD as comorbid disorder in somatic patients. A previous study found additional reimbursement associated with psychiatric comorbidities in general, however, the additional reimbursement under the current G-DRG system did not cover the higher costs . Thus, aCost of illness studies (COIs) display the economic costs of an illness, providing scientific evidence for political decision makers in order to take further actions, such as treatment or research , 23. ThuData collection was conducted at a university hospital in Germany between 2011 and 2014. Inpatients episodes were included in this study if they were hospitalized for somatic reasons and had a documented PTSD as a comorbid disorder. Presence of comorbid PTSD was defined as the presence of the International Statistical Classification of Diseases and Related Health Problems, 10th revision, German modification (ICD-10-GM) code for PTSD (ICD-10 F43.1) as secondary diagnosis. Only adult patients aged > = 18 years were included. Inpatients episodes with a psychiatric main diagnosis were excluded. All inpatient episodes between 2011 and 2014 with an minimum age of 18 year and that had no documented comorbid psychiatric disorder were considered as potential controls (non-PTSD). Stratification by the ICD-10 main diagnosis code was applied to ensure comparability based on diagnostic and therapeutic regimes. As a result, only non-PTSD control episodes which were hospitalized with the same main diagnosis as PTSD inpatient episodes were considered for the analysis.Resource use was obtained from the hospital’s department of management. Using a bottom-up micro costing approach, full operating costs were calculated per inpatient episode according to the standardized costing system using actual costs per case occurred in conformity with the calculations of the German Institute for Hospital Reimbursement (InEK) . Cost caCosts and reimbursements were valued and adjusted to 2014 €. Data included main and secondary diagnoses, age, sex, LOS, discharge status and intensive care hours. Main diagnoses based on the 4-digit ICD-10 were included as categorical fixed effects, while age and sex were included as potential confounders. Furthermore, the Charlson comorbidity (CCI) index was calculated and included into the analyses as a continuous covariate, to control for severe comorbidities.As primary endpoints we used in-hospital costs per episode calculated according to the standardized InEK costing system, as described above , 29. As As no official checklist for COIs exits, results reported in this study follow the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) where applicable .N = 219 were found to be patients with a primary somatic disorder and a comorbid PTSD. N = 34,229 inpatient episodes with the same main diagnosis as those with PTSD were found. Thus, as shown in Table p < 0.001), younger patients (p < 0.001), and had less severe comorbidities as indicated by the CCI (p < 0.001). PTSD was associated with longer length of hospital stay. Mean costs per inpatient episode of €7919 in patients with comorbid PTSD were found, whereas patients without comorbid PTSD showed mean costs per inpatient episode of €5253. Male patients with comorbid PTSD showed higher mean costs of €11,021 per episode. Reimbursement was lower than costs per episode in patients with comorbid PTSD, with an average reimbursement of €7081 per episode. In contrast, a positive net reimbursement was observed in patients without comorbid PTSD.In total 198,819 inpatient episodes were recorded between 2011 and 2014, of whom p = 0.032) and LOS (p = 0.041). The effect of sex on hospitalization costs was not significant (p = 0.091). Age and further comorbidities, operationalized trough the CCI, showed no impact on the effects of comorbid PTSD. Among male patients, PTSD was associated with additional hospitalization costs of €3846 , €2956 higher reimbursements , and an extra LOS of 5.4 days . In contrast, no gender differences were found for the control group. See Table After risk-adjustment, PTSD was associated with additional hospitalization costs of €2311 [95%CI €1268 - €3355] per episode compared to non-PTSD patients hospitalized with the same main diagnosis, as shown in Table Figure We assessed the additional hospital costs associated with PTSD in a somatic hospital using the hospital cost and reimbursement data. Inpatient episodes of patients with PTSD differed significantly from the control group. Additional costs of PTSD during hospitalization in somatic patients were €2311 on average, whereas reimbursement rose by only €1387 on average. Thus, costs were much higher and exceeded reimbursements by more than 10%, whereas in the control group reimbursements exceeded costs by 2%, indicating an inadequate reimbursement in the German reimbursement system for comorbid PTSD in somatic patients. Across all measures, male PTSD patients were less frequent than their female counterparts, but were associated with substantial higher PTSD-related excess costs, a larger cost-reimbursement gap, and a more profound PTSD-related prolongation of hospital stays than in female PTSD patients. Contrary, in psychiatric samples female gender was found to be associated with a higher LOS in previous studies . GeneralPrevalence of PTSD in the German population is around 2.3% in representative surveys –36. PrevWe further assessed if reimbursements under the G-DRG system are sufficient for patients suffering from PTSD. We found that reimbursement did increase in the presence of PTSD as comorbid disorder. However, treatment of patients with comorbid PTSD still resulted in an average lack of funding of €924€ per episode. These findings are comparable those from previous research. Hochlehnert et al.  found thPhysical functionality and existence of mental disorders are correlated and therefore existence of comorbid mental disorders could be seen as a proxy for severity of physical impairment . For insHealth effects of chronic sleep disturbances, often associated with PTSD –44 couldPrevious researchers speculate that effects of psychiatric comorbidity on doctor-patient communication might play a role , and psyAlthough single-center, our study describes a large all-comer patient cohort. We were able to describe the associated costs of comorbid PTSD in the somatic hospital under routine conditions. No special diagnostic or treatment of PTSD was conducted and thus the probability that we described the real inward treatment and financial situation is good. Due to the retrospective design using management data, we were able to include a high number of observations in our analysis. The high number should improve the robustness of the analysis. Nevertheless, the data used most likely did not contain information on different possible cofounders, such as information on previous diagnoses and treatments, and thus the omitted-variable bias cannot be excluded. For instance, higher health care costs of psychiatric comorbidities were associated with previous surgeries in a sample of back pain patients .Furthermore of a total of 198,819 inpatient episodes with somatic diagnoses within the study period, only 0.11% had a documented comorbid PTSD. This is markedly lower than should be expected from estimates of PTSD prevalence in the population and means that there was likely a dark field of patients with existing, but undocumented PTSD. Internists are known not to detect psychological disorders in routine care , 55 and Another limitation is, that we did not account for potential time-related biases and thus did not control for PTSD being diagnosed later in the clinical course. Additionally, we did not know the treatment state of the PTSD cases and were not able to track whether and to what degree proper treatment of PTSD might improve the outcomes or cost of somatic hospitalizations. Thus, the effects of PTSD on the associated costs might be overestimated. Furthermore, the German DRG system is in constant development, with the InEK adjusting the system in accordance with inflation and new laws every year. As major development in recent years, costs of care is additionally reimbursed to the general DRGs since 2020  and thusIn this study we found PTSD to be associated with additional health care costs in a somatic inpatient hospital. These additional health care costs were not sufficiently reimbursed by the German DRG system. If the German DRG system does not properly account for PTSD as a comorbid disorder, this constitutes a disincentive effect for hospitals to properly treat PTSD patients. This effect was already discussed in previous research , 21 and This study extends the knowledge of associated health care costs in somatic patients with comorbid mental disorders, giving a first estimate of the impact of PTSD. PTSD was associated with additional hospital costs, and reimbursement of the rendered health care services was insufficient. More evidence is needed to have a full picture of the impact of PTSD on health care costs.


CZI) at a maternity hospital in Salvador, Brazil, during the 2016 microcephaly outbreak.To identify newborns with congenital Zika infection (CZI between January and December 2016. Serology (immunoglobulins IgM and IgG) and quantitative reverse transcriptase polymerase chain reaction (RT‐qPCR) for the Zika virus were performed. Demographic and clinical characteristics of newborns with and without microcephaly were compared.A prospective study enrolled microcephalic and normocephalic newborns with suspected RT‐qPCR in urine or placenta samples. Thirteen (11.8%) normocephalic newborns also tested positive for Zika virus by PCR in urine, plasma, or placenta samples, while IgM antibodies against Zika were detected in 4 (4.2%) others.Of the 151 newborns enrolled, 32 (21.2%) were classified as microcephalic. The majority of these cases were born between January and May 2016. IgM and IgG Zika virus antibodies were detected in 5 (23.8%) and 17 (80.9%) microcephalic newborn blood samples, respectively. Six (24%) microcephalic newborns tested positive for Zika virus by CZI cases, confirmed by IgM serology or RT‐qPCR for Zika virus, provides evidence that CZI can present asymptomatically at birth. This finding highlights the need for prenatal and neonatal screening for Zika virus in endemic regions.Identification of 17 normocephalic  Neonatal surveillance for congenital Zika infection in Brazil and the identification of Zika virus in microcephalic and normocephalic newborns. Between October and December 2015, 2975 cases of microcephalic newborns were notified in Brazil, mostly in the states of Pernambuco (1153 cases), Paraíba 476 cases), and Bahia (271 cases), cases, aIn response to the microcephaly outbreak in northeastern Brazil, the Pan American Health Organization (PAHO) and the World Health Organization (WHO) released an epidemiological alert,The association between microcephaly and congenital Zika infection (CZI) was established by the identification of Zika virus in the amniotic fluid of a pregnant woman in BrazilThe aim of the present study was to identify and characterize cases of CZI at a maternity hospital in Salvador, Bahia, during the 2016 microcephaly outbreak.2This prospective study was conducted at the Jose Maria Magalhães Netto public maternity hospital located in Salvador, Bahia, Brazil, between January 18 and December 16, 2016.In accordance with the protocol established by the Brazilian Ministry of Health in November 2015,Sociodemographic data and clinical information from mothers were collected through interviews, while clinical data on newborns and birth conditions were obtained by reviewing medical records. Data entry and data management were performed using REDCap version 6.18.1 . Biological samples, including placental tissue, blood, umbilical cord blood, and newborn urine were collected by nurses in the maternity ward and the laboratory team at the hospital. Serological and molecular tests were done at the Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador (IGM‐FIOCRUZ) and at the Infectious Diseases Immunology Laboratory of the Federal University of Bahia, Salvador.During the study period, the Brazilian Ministry of Health changed the protocol for the specific criteria used to identify cases of microcephaly. From January to March 12, 2016, the cutoff for microcephaly notification for term newborns was a head circumference of less than or equal to 32 cm for both sexes; and less than or equal to the third percentile on the Fenton growth chart by gestational age and sex for preterm newborns. Between March 13 and December 2016, new criteria were employed to determine microcephaly cases: head circumference less than or equal to two standard deviations below the average (WHO standard) for term newborns; and less than or equal to two standard deviations below the average (INTERGROWTH‐21st) by gestational age and sex for preterm newborns.For data analysis purposes, all enrolled newborns were reclassified to define microcephaly according to the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH‐21st) charts, taking into account the newborn's gender, gestational age, and head circumference at birth.Newborns were classified as small, appropriate, or large for their gestational age based on gender‐specific birth weight for their gestational age using WHO reference curves.Transfontanellar ultrasound was performed as a standard procedure by the medical staff at the maternity hospital during admission. Information regarding these procedures was obtained from patient medical records.For serological analysis, detection of specific anti‐Zika virus immunoglobulin G (IgG) antibodies using indirect enzyme‐linked immunosorbent assays (ELISA)  was performed in accordance with the manufacturer protocol. In addition, an immunoglobulin M (IgM) antibody capture ELISA (MAC‐ELISA), provided by the Arbovirus Reference Collection (ARC) division of the Centers for Disease Control and Prevention (CDC), was used in accordance with the established CDC protocol.Cytomegalovirus IgM  and Toxoplasma IgM (Dia.Pro) were also assayed by ELISA in blood samples of newborns whose mothers tested positive (IgM serology). Serum Venereal Disease Research Laboratory (VDRL) test results and HIV status were obtained from patient medical records. Cytomegalovirus IgG and Toxoplasma IgG results from mothers were obtained from medical records.For Zika virus diagnosis, viral RNA was extracted from clinical samples using the QIAmp viral RNA mini kit  and quantitative reverse transcriptase polymerase chain reaction (RT‐qPCR) was performed for Zika virus, as previously described.2 or Fisher exact test for categorical variables or the Mann‐Whitney U test or Kruskal‐Wallis test for continuous variables. P<0.05 was considered statistically significant.Data analysis was performed using SPSS version 21  software. Comparison of demographic and clinical characteristics of newborns with and without microcephaly was done using either the χThe study was approved by the Institutional Review Board of the Gonçalo Muniz Institute, Oswaldo Cruz Foundation  and by the Ethical Review Committee of the Pan‐American Health Organization (PAHOERC 2017‐05‐0044). The legal guardians of all newborns provided written informed consent.3Between January and December 2016, 172 newborns were identified as eligible by health professionals at the maternity hospital in Salvador, Brazil. Of these, 21 of the infants’ guardians declined to participate in the study, resulting in 151 newborns subsequently enrolled: 32 (21.2%) classified as microcephalic and 119 (78.8%) as normocephalic.P>0.05).The temporal distribution of cases by month of birth is shown in Figure The median gestational age at the time of delivery was 39 weeks  and most n=103, 68.2%) deliveries were vaginal. Seventy‐nine (52.3%) newborns were male. A total of 115 (76.1%) newborns were normal birth weight for their gestational age, while 32 (21.2%) were classified as SGA  newborns were classified as microcephalic, and 5 (15.6%) of these were classified as having severe microcephaly; 119 (78.8%) were considered normocephalic. Median head circumference at birth was 34.0 cm  and 31.0 cm  for normocephalic and microcephalic newborns, respectively.P=0.30). However, significantly higher proportions of microcephalic newborns had low birth weight (P<0.001) and were SGA (P<0.001) compared with normocephalic newborns  microcephalic newborns and 9 (7.6%) normocephalic newborns presented abnormal Apgar scores within the first minute of life. Transfontanellar ultrasound was performed in 123 (81.5%) of the enrolled newborns. Of 28 microcephalic newborns who underwent ultrasound, 7 (25.0%) had abnormal results, with the presence of calcifications observed in all seven cases, ventriculomegaly in 4 (14.3%) and the absence of corpus callosum in one (3.5%) case.Arthrogryposis was observed in 2 (6.3%) microcephalic newborns: detected in the upper and lower limbs of one and in only the lower extremities of the other. Both newborns presented bilateral clubfoot and bilateral hip dislocation.Twenty‐five samples of plasma, urine, or placental tissue were available for molecular diagnosis from microcephalic newborns. Six 24.0%) tested positively for Zika virus by RT‐qPCR in two samples of urine and four of placental tissue, while no positivity was detected in any of the plasma samples. Serological testing for Zika virus was performed in 21 plasma samples from microcephalic newborns, with IgM positivity observed in 5 (23.8%) versus IgG positivity in 17 (80.9%). In all, 19 (76.0%) microcephalic newborns presented some evidence of Zika virus infection, and 6 (24.0%) tested negative for Zika virus under all procedures. The results of IgG and IgM Zika‐specific antibodies and RT‐qPCR results for microcephalic newborns are presented in Table 4.0% testFor normocephalic newborns there were 110 samples of plasma, 87 samples of urine, and 100 samples of placental tissue available for molecular diagnosis. Thirteen (11.8%) normocephalic newborns tested positive for Zika virus by RT‐qPCR in seven samples of urine, two of placental tissue, and six of plasma. Serological testing for Zika virus was performed in 95 samples, with IgM positivity observed in 4 4.2%) versus IgG positivity in 71 (74.7%). A total of 76 (69.1%) normocephalic newborns presented some evidence of Zika virus infection, with 17 confirmed with Zika virus by IgM serology or RT‐qPCR. The results of IgG and IgM Zika‐specific antibodies and RT‐qPCR results for normocephalic newborns are presented in Table % versus Toxoplasma and Cytomegalovirus results were available for 123 (81.5%) women. While the majority had previous exposure to Cytomegalovirus  or Toxoplasma , only one presented anti‐toxoplasma IgM positivity, and four were anti‐Cytomegalovirus IgM‐positive on serology. By contrast, all of their newborns were IgM seronegative for Cytomegalovirus and Toxoplasma, and none presented any clinical signs of congenital infection.All mothers and newborns were tested for syphilis and HIV at hospital admission. Five (3.3%) women had positive VDRL and all were HIV negative. None of the enrolled newborns presented any serological evidence of syphilis or HIV infection. Twelve (7.9%) newborns were admitted to a neonatal intensive care unit, and 3 (2.0%) died. One of the deaths was attributed to severe microcephaly, and two others to premature birth .4The present year‐long hospital study was conducted in response to an initial surge in microcephaly cases in October 2015 in Salvador, Brazil. Microcephalic newborns with a clinical suspicion of CZI were enrolled, in addition to normocephalic cases in which the mothers reported the presence of a skin rash (a possible sign of Zika virus infection) at some point during pregnancy.Elevated cases of microcephalic and normocephalic newborns were observed to cluster together during the first 5 months of 2016, just months after the 2015 Zika virus outbreak in Salvador. This epidemic link was reported previouslyDifficulties in confirming the diagnosis of Zika virus in the microcephalic cases have been previously reported.P=0.03), we found a similar rate of Zika virus RT‐qPCR positivity in both groups. Most of the Zika virus RNA identified herein were in urine samples. While Zika virus RT‐qPCR is considered a valuable option for viral RNA identification in samples of urine during acute Zika virus infection, due to the short period of viremia,Although microcephalic newborns had a higher rate of anti‐Zika IgM than normocephalic newborns  newborns, with 1 (3.1%) evolving to death. In addition, arthrogryposis, one of the previously described clinical manifestations of CZI,Vigilant monitoring is necessary in cases of normocephalic CZI, as late‐onset microcephaly has been reported in infants with CZI,The inclusion of normocephalic cases in the present study allowed for the additional identification of 17 normocephalic cases of CZI confirmed by IgM serology or RT‐qPCR for Zika virus. Our findings reinforce the notion that CZI holds the potential to present asymptomatically at birth and highlight the necessity to implement prenatal and neonatal Zika virus screening in endemic regions as a standardized protocol.JVO, FWML, and ICS contributed to the study design, writing of the manuscript, and data analysis. JVO, TCXC, LBP, CMF, JGSL, DAC, EMF, ACB, RP, GBS, and JIC contributed with the enrollment of participants, reviewed medical records, and collected samples and data. LCJA, MG, JJG, DCS, PV, AOD, CSS, FWML, and ICS contributed with laboratory analysis.The authors have no conflicts of interest.Table S1. Laboratory and clinical characteristics of 25 newborns with microcephaly and available samples, Salvador, 2016.Table S2. Laboratory and clinical characteristics of 17 normocephalic newborns with congenital Zika infection (IgM anti‐Zika virus or Zika virus RT‐qPCR positive).Click here for additional data file.


SARS-CoV-2 is the causative of the COVID-19 disease, which has spread pandemically around the globe within a few months. It is therefore necessary to collect fundamental information about the disease, its epidemiology and treatment, as well as about the virus itself. While the virus has been identified rapidly, detailed ultrastructural analysis of virus cell biology and architecture is still in its infancy. We therefore studied the virus morphology and morphometry of SARS-CoV-2 in comparison to SARS-CoV as it appears in Vero cell cultures by using conventional thin section electron microscopy and electron tomography. Both virus isolates, SARS-CoV Frankfurt 1 and SARS-CoV-2 Italy-INMI1, were virtually identical at the ultrastructural level and revealed a very similar particle size distribution with a median of about 100 nm without spikes. Maximal spike length of both viruses was 23 nm. The number of spikes per virus particle was about 30% higher in the SARS-CoV than in the SARS-CoV-2 isolate. This result complements a previous qualitative finding, which was related to a lower productivity of SARS-CoV-2 in cell culture in comparison to SARS-CoV. Betacoronavirus which entered the human population most probably at the end of 2019 and is spreading pandemically around the world1. The virus causes the disease termed COVID-19 which primarily affects the respiratory system2 but can extend to other organs3. Severity of the disease is highly variable from non-symptomatic to fatal outcomes1.The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a 4) which appeared in the human population in 2003. Both viruses use the same receptor  for host cell entry5. Infection of different cell lines and of patient material could be shown8. Ultrastructural hallmarks of entry, replication and assembly seem to be virtually identical to SARS-CoV9. Like all viruses of the family Coronaviridae, the virus is a biomembrane-enveloped virus with prominent surface projections, called spikes or peplomers, which are formed by a glycoprotein (S protein) trimer  in the enveloped lumen of the virus 17SARS Coronavirus 2 Italy-INMI1 (SARS-CoV-2)The following virus isolates were used:2 to reach approximately 70% confluence. To infect the cultures with virus, the medium was removed and 10 ml of fresh medium with diluted virus stock suspension was added to the cells (the multiplicity of infection was about 0.01). After incubation for 30 min, as indicated above, 20 ml of medium was added and cells were further incubated. Cultivation was stopped 24 h after addition of the virus suspension by replacing the medium with 2.5% glutaraldehyde in 0.05 M Hepes buffer (pH 7.2). Incubation with the fixative lasted at least 1 h at room temperature. Fixed cells were scraped from the culture flasks and collected in centrifuge tubes.Vero E6 cells  were cultivated in cell culture flasks with D-MEM, including 1% L-glutamine and 10% fetal bovine serum, for 1 d at 37 °C and 5% COen bloc contrasting, dehydration and embedding in epoxy resin (Epon18) were done following a standard protocol19  using a swing-out rotor and washed twice with 0.05 M Hepes buffer. The cell pellet was heated to 40 °C in a water bath and mixed with 3% low-melting point agarose (1:1 [v/v]) at 40 °C. After a brief (approx. 2–3 min) incubation at 40 °C, the suspension was centrifuged in a desktop centrifuge using a fixed-angle rotor for 5 min at 5000 g and cooled on ice to form a gel. The cell pellet was cut off from the agarose gel block by using a razor blade and stored in 2.5% glutaraldehyde in 0.05 M Hepes buffer. Postfixation, 0 min usiUltrathin sections were produced with an ultramicrotome  using a diamond knife . Sections were collected on bare copper grids , contrasted with 2% uranyl acetate and 0.1% lead citrate and coated with a thin (2–3 nm) layer of carbon. For electron tomography, gold colloid  was added to the carbon-side of the sections by incubating the sections on a drop of the gold colloid suspension for 1–5 min at room temperature.6 filament and operated at 120 kV. Magnification calibration of the microscope was done by using the MAG*I*CAL calibration reference standard for transmission EM . Images were recorded with a side-mounted CCD camera  and 1376 × 1032 pixel. Tilt series for electron tomography were acquired by using the tomography acquisition software of the Tecnai  and a bottom-mounted CCD camera  at 2048 × 2048 pixel. A continuous tilt scheme at one degree interval was used and at least 120 images were recorded (minimum + 60 to − 60°). Tracking before image acquisition was performed to compensate image shifts introduced by the mechanics of the stage. Alignment and reconstruction were done with the Inspect3D software  by using a defined procedure and the “Simultaneous Iterative Reconstruction Technique” (SIRT) with 25 iterations  which was equipped with a LaB20 (version 3.7.11). Alignment of the tilt series  and reconstruction of the tomograms were performed with the IMOD software package21 (version 4.9.12) using SIRT with 25 iterations after low pass filtering  of the aligned image stack.Additional single-axis tilt series  of thicker sections (200–250 nm) were acquired with a transmission electron microscope  at 200 kV with a pixel size of 0.57 nm by using a side-mounted CCD camera  and SerialEMSize of virus particle profiles was measured in images of ultrathin sections  and in tomograms of thicker (150–180 and 200–250 nm) plastic sections.22 by selecting the outer leaflet of the virus membrane with the “polygonal selection” tool and the measurement setting “fit ellipse”. Maximal and minimal diameter of the fitting ellipse and shape descriptors, such as aspect ratio and circularity (4π*area/perimeter2), were determined.Extracellular virus particles in ultrathin sections were selected randomly at the microscope and recorded with the side-mounted camera , if they met the following criteria: (1) the particle was morphologically intact; (2) the particle was not pressed against other structures. Eight datasets were recorded  extension of the polygonal selection, which was made for each virus particle to determine its maximal diameter. The precision of the method was validated by individual line measurements of spike length.Extracellular virus particles in thicker 150–180 nm) plastic sections were recorded by single-axis electron tomography using the Tecnai and the bottom-mounted Eagle 4 k CCD camera, at a magnification of 18,500 × and 23,000 × (1.17 and 0.96 nm pixel size) and a binning of 2. Virus particles were selected randomly. If particles appeared morphologically intact and tilting to at least -60 and + 60° was possible, a tilt series of the region of interest was recorded. Two datasets, one for SARS-CoV and one for SARS-CoV-2, with a minimum of 12 tilt series each, were recorded (Table 50–180 nmShrinkage of virus particles in x/y direction during irradiation with the electron beam was measured with the Tecnai using similar electron dose as applied for electron-tomography. Suitable sample positions were selected at low magnification and focusing was done at a distant position (10 µm) using the low-dose module of the Tecnai. Immediately after switching automatically back to target position, a high-resolution image  was recorded (t = 0 min). The selected sample position was continuously illuminated by the electron beam for 30 min before another image was recorded (t = 30 min). The maximal diameter of individual virus particles was measured at both time points using the “polygonal selection” tool and the “fit ellipse” measurement setting of Fiji.The spike number of virus particles was estimated using ultrathin plastic sections of 45 nm thickness, which was the lowest sectioning thickness set point producing regular sections. Extracellular virus particles were randomly selected and recorded with the side-mounted CCD camera at a magnification of 135,000 × if the particles met the following criteria: (1) the particle was morphologically intact; (2) the particle was not deformed (e.g. by pressing against other structures); (3) the particle membrane was visible (at least 90% of the perimeter). Two datasets, each with about 150 particles, were recorded  between t = 0 and t = 30 min.The size distribution and the median resulted by measuring the virus particle profiles in conventional ultrathin sections could be biased by an overrepresentation of virus section profiles of a particular virus particle size and/or of undetected deformed particles. Therefore, we recorded tomographic tilt series of viruses in thicker sections 150–180 nm) and calculated single-axis tomograms to measure virus particles at their maximal diameter Fig.  and to e50–180 nmTo check whether the different size values measured in section profiles and in tomograms were caused by a variable distortion of the sections at different thickness settings, we measured particle shape descriptors of virus particles in sections of different section thickness. The results showed that the slightly oval particle shape (aspect ratio of about 1.1) is independent of the section thickness in the investigated range of 45 to 110 nm Table , which iTo get an idea about the spike number on the two different coronaviruses, we counted the spikes present on particle profiles in plastic sections of 45 nm thickness and related their number to the maximal diameter of the individual profile. Figure , in the surrounding of virus producing Vero cells, by using thin section EM. Viruses and cells were chemically inactivated and stabilized by glutaraldehyde in situ and embedded in plastic. This preparation procedure changes the ultrastructure of biological objects24, including their dimensions25, e.g. by adding chemicals or by removing the water, and it does not preserve their accurate molecular structure26. However, at the resolution level sufficient to study the ultrastructure of organelles , this procedure provides reliable information which is, at this resolution, in many cases very similar to the information obtained by cryo-EM24, the gold standard in structural biology.We determined the size distribution and the spike number of SARS-CoV and SARS-CoV-2 virus particles in situ28. However, for single particle cryo-EM, virus particles usually have to be concentrated and purified, which is not trivial, especially for enveloped viruses. Purification and/or enrichment can select for a certain particle size and shape, introduce deformations29, which was also observed for SARS-CoV-211, and might cause loss of membrane protein30. Biosafety still requires inactivation of the virus preparation before conducting the sample preparation for cryo-EM, and the effects on the ultrastructure must be carefully controlled. The recently published work on isolated SARS-CoV-213 only partially addressed those aspects11.Cryo-EM provides maximal structural information about the virus architecture down to the molecular level31. This work is technically extremely challenging and very time consuming31 and usually restricted to a limited set of samples which not necessarily fully represent the biological variability of the sample. However, in a recent study both approaches could be applied to SARS-CoV-2 infections of different cell lines14 providing valuable structural data on cell-associated virus.Studying virus particles by cryo-EM in situ attached to or present in the cells is extremely difficult to perform, since whole cell cryo-EM (i.e. cryo-electron tomography) needs either thin parts of an infected cell or lamella preparation by FIB-SEM to generate datasets of frozen hydrated and therefore virtually unchanged virus particles4 and reports on the virus ultrastructure in plastic sections which are available15. However, the similarity of the size distribution of the two coronaviruses tested was a surprise because enveloped viruses are usually more variable in shape and size than non-enveloped viruses32. The median maximal diameter of the virus particles which we determined in sections is in the wide range of particle sizes reported in the literature (60–140 nm)36. The large variability of the reports could be due to differences in the measurement techniques used or to variations of the ultrastructural preservation achieved by the various fixation and embedding protocols. The tannic acid and uranyl acetate en bloc contrasting applied in our preparations may have increased the particles artificially. However, preliminary experiments indicate that the effect on the measurement of the particle size is small  Measurement of virus particle section profiles in ultrathin (65 nm) sections and (2) measurement of widest particle profile in tomograms of thin (150–180 nm) sections. The size distribution median was a few nanometers higher in tomograms (99 nm) than in ultrathin sections (95 and 97 nm). Since shape descriptors indicate a constant particle shape in thin and thicker ultrathin sections and measurement of shrinkage in x/y-direction during long-time irradiation showed no significant shrinkage of virus particles during recording of tomography tilt series, we conclude that the particle size measured in tomograms represent the correct size of virus particles in plastic sections. Median and shape of the size distribution of particles in ultrathin sections and of particles in tomograms of thicker sections converge with increasing thickness of ultrathin section. The difference between the two measurement approaches can be explained by an over- and/or underrepresentation of particle section profiles of a particular size class at a particular section thickness. Measurements in 45 nm thick sections resulted in larger size values (103 nm) than determined in tomograms and in thicker sections for both viruses. Besides a statistical over- and/or underrepresentation of certain profiles in the 45 nm sections, the strict selection criteria for particles in these sections, which only includes particles with an almost completely visible membrane, most probably lead to the exclusion of small profiles of virus particle caps, because their membrane is hardly or only partially visible in extreme thin sections. However, our results show that measurement of particle size in ultrathin sections of the standard section thickness between 65 and 110 nm provides a good estimator for the size of the coronavirus particles embedded in plastic.38; SARS-CoV-2: 90–97 nm14). As already mentioned above, it is highly likely that the plastic embedding changed the shape and size of the virus particles. Obviously, the virus particles in thin plastic sections appear more oval than the particles shown by cryo-EM14. Therefore, a simple explanation for the difference of the particle size measured by thin section EM and cryo-EM could be the change of particle shape from round to oval in thin section EM. The aspect ratio of the virus particle enveloping ellipse in thin section EM was about 1.1 over a wide range of section thickness which indicates an oval particle shape. A change from round (aspect ratio = 1) to an oval shape with the aspect ration 1.1 would be associated with an increase in maximal particle diameter of about 10% which roughly amounts the difference between particle size measured by cryo-EM (~ 90 nm) and thin section EM (~ 100 nm). Other reasons to explain the difference could be the different virus strains which we have used in comparison to the strains used in the cryo-EM studies38 or differences in the cell culture which seem to have an effect on the size distribution11. It is also not possible to exclude that concentration and purification of virus particles before cryofixation have an impact on the size distribution of the virus particle population. A comparison of non-purified and purified SARS-CoV-2 showed only small differences (91 vs. 92 nm11) and the measurement of SARS-CoV-2 in situ resulted in similar values (90 nm14) which suggest that virus particles were not affected during preparation in those experiments.The size values measured for SARS-CoV in our study  differ from the values measured by cryo-EM . However, it is possible that the tannic acid, which was used for en bloc contrasting, has increased the spike size artificially because tannic acid is known to bind to glycoproteins39.The maximal length of the spikes associated with the virus particles in ultrathin sections was 23 nm in SARS-CoV and SARS-CoV-2, which is close to the values determined by Cryo-EM , which may explain the observed differences. Spike density of SARS-CoV-2 was determined recently by cryo-EM for different virus isolates than the virus isolate used in our study. The reported mean values vary from 25 to 40 spikes per virus particle with significant variation among particles14. We also detected a high variability of the spike number among virus particles and with a median of 25 spikes per virus particle our measurements fit to the lower values measured for the other isolates.The measurement of the spike number associated with virus particle profiles in ultrathin sections revealed differences between the SARS-CoV and SARS-CoV-2 virus populations studied, which could be supported in their tendency by determination of the spike number of entire virus particles in tomograms. A qualitative difference of the spike density between SARS-CoV and SARS-CoV-2 was already observed by the study of Ogando et al40 and relate it with virus infectivity and receptor-binding affinity, to get an idea if adaptation of the spike protein coverage of the particles is responsible for the fitness of particular virus isolates in the population.The relevance of the observed difference in spike number between different virus isolates is not known but could be related to virus infectivity and fitness. Our results show that these differences can be detected by measuring the spike number in thin sections, which is much easier than by using (cryo) electron tomography. Further studies should measure the spike number of isolates already present or rapidly evolving in the human populationBesides the important characterization of newly evolved virus strains, the morphometric analysis presented in this work may generally serve as a reference and technical guidance for the analysis of SARS-CoV-2 particles in other cell types and infection schemes because particle size, shape and spike number are most likely depending on the cell type of virus production (see above) and the particular infection conditions, such as the number of viruses infecting a cell. So far, no systematic assessment of these factors and their implication for virus transmission and infectivity is available. While such an assessment could be done by using reconstituted human tissue in culture, it must be supported by the analysis of patient tissue. Our work proves, that particle size and spike number of SARS-CoV-2 can be determined using standard ultrathin sections, which allows the morphometric analysis of SARS-CoV-2 at a wider scale, especially in human tissue culture and tissue from patients.In summary, we provide morphometric data for SARS-CoV and SARS-CoV-2 particles in plastic sections, which are very similar to the data obtained by cryo-EM. All raw datasets can be used for re-investigation or other purposes . The major outcome is that the investigated isolates of SARS-CoV and SARS-CoV-2 are ultrastructurally very similar in shape and size and show a small difference in their spike number.Supplementary Information 1.Supplementary Video 1.Supplementary Video 2.Supplementary Video 3.Supplementary Video 4.