The aim of this study was to compare acetabular offset, femoral offset, and global offset measurements obtained after total hip arthroplasty (THA) between a two-dimensional (2D) method and a three-dimensional (3D) method. The subjects were 89 patients with unilateral osteoarthritis who underwent primary THA at our institution. Acetabular, femoral, and global offsets were measured by each of the 2D and 3D methods in native and implanted hips. In native hips, mean acetabular, femoral, and global offsets were 32.4 ± 3.3, 32.7 ± 4.5, 65.1 ± 5.7 mm, respectively, by the 2D method, and 32.3 ± 3.1, 38.1 ± 4.0, 70.4 ± 4.9 mm, respectively, by the 3D method. In implanted hips, mean acetabular, femoral, and global offsets were 27.6 ± 4.1, 33.8 ± 7.8, 61.4 ± 8.5 mm, respectively, by the 2D method, and 27.6 ± 3.9, 41.8 ± 6.2, 69.4 ± 7.2 mm, respectively, by the 3D method. There was significant difference in femoral and global offsets between the 2D and 3D methods in both native and implanted hips. Comparison of the 2D and 3D methods for evaluation of acetabular, femoral, and global offsets after THA clarified the usefulness and accuracy of the 3D method. A primary goal of THA is to provide a stable hip by restoring joint biomechanics2. Reconstruction of joint offsets promotes joint stability, which reduces the risk of dislocation and enables a good range of motion with low risk of bony or soft-tissue impingement, sufficient abductor muscle strength without alteration in gait, and minimized polyethylene wear8. Clement et al. have reported that the long-held biomechanical theory of medialization of the acetabular component with compensatory increased femoral offset results in improved functional outcome9.Total hip arthroplasty (THA) is a highly successful surgical intervention performed to relieve pain and improve function in individuals with advanced arthritis of the hip joint12. However, it has also been reported that offset measurements obtained from radiographs can be underestimated compared with those obtained by computed tomodensitometry and a low-dose EOS imaging system14 A recent study reported that three-dimensional (3D) preoperative planning based on computed tomography (CT) imaging showed high accuracy and consistency for hip reconstruction15. 3D planning software has also been used for the postoperative evaluation of THA18. To the best of our knowledge, there are few reports to have undertaken a detailed comparison of offset values between the 2D method using postoperative radiographs and 3D bone models obtained from CT data. We hypothesized that the 3D method might be more accurate than the 2D method for evaluating offsets.Some reports have evaluated offset after THA using a two-dimensional (2D) method based on postoperative anteroposterior radiographsThe aim of this study was to measure acetabular, femoral, and global offsets after THA, using a 2D method based on postoperative anteroposterior radiographs and a 3D method based on bone models from CT data, and to compare offset values between the two methods. Offset values were also compared between native hips and implanted hips after THA.2). The diagnosis was secondary osteoarthritis due to hip dysplasia in 73/89 hips , and primary osteoarthritis in 16/89 hips. The exclusion criteria were severely deformed contralateral hip, osteonecrosis of the hip, rheumatoid arthritis, previous hip or pelvic fracture, previous undergone surgery, and no postoperative CT examinations  in 191 hips of 182 patients using an image-free navigation system  at our institution between June 2014 and March 2020. Of these, 89/182 patients with unilateral osteoarthritis formed the study group  as follows. Acetabular offset for the 3D method was defined as the perpendicular distance from the centre of rotation of the femoral head to the teardrop line in the functional pelvic plane after repositioning using the 3D-template system. Femoral offset for the 3D method was defined as the perpendicular distance from the centre of rotation of the femoral head to a line passing through the centre of two circles created in the proximal medullary cavity of the femur , a graphical user interface for R 21.The reproducibility of the 2D and 3D methods was evaluated. For intra-observer reliability, one orthopaedic surgeon measured each parameter twice in 10 hips, at an interval of ≥ 4 weeks. To test inter-observer reliability, two orthopaedic surgeons measured each parameter twice on 10 hips, at an interval of ≥ 4 weeks. Intra-class and inter-class correlation coefficients were calculated to analyse the variability between observers. Values of 0.81–1.00 indicated almost perfect correlation; 0.61–0.80, substantial correlation; 0.41–0.60, moderate correlation; 0.21–0.40, fair correlation; and 0.00–0.20, slight correlationThis study was approved by Institutional Review Board at Mie University hospital (H2018-083). All patients gave informed consent for the use of their surgical data in the study. The study was carried out in accordance with the principles of the Helsinki declaration.Table In native hips, mean acetabular, femoral, and global offset values by the 2D method were 32.4 ± 3.3, 32.7 ± 4.5, and 65.1 ± 5.7 mm, respectively, and by the 3D method were 32.3 ± 3.1, 38.1 ± 4.0, and 70.4 ± 4.9 mm, respectively.In implanted hips, mean acetabular, femoral, and global offset values by the 2D method were 27.6 ± 4.1, 33.8 ± 7.8, and 61.4 ± 8.5 mm, respectively; and by the 3D method were 27.6 ± 3.9, 41.8 ± 6.2, and 69.4 ± 7.2 mm, respectively. Statistical analysis revealed no significant difference between the 2D and 3D methods for acetabular offset in either native or implanted hips. However, a significant difference was found for femoral and global offset between the 2D and 3D methods for both native and implanted hips (Table Figure 22. Of the present patients, 82% had secondary osteoarthritis of the hip associated with acetabular dysplasia, which makes it difficult to achieve placement in the original anatomical position in THA. To obtain sufficient acetabular cup coverage and favorable clinical outcomes in THA for patients with hip dysplasia, we usually place the cup more medially, then more proximally, and eventually use superolateral bone grafting24. The resultant medialization of the hip centre has been estimated as 3–6 mm26. However, if the decreased acetabular offset was not compensate and only the original femoral offset was restored, the global offset will be reduced. Several studies have reported that change of the hip centre affect the biomechanics of the hip28. To maintain global offset, medialization should theoretically be compensated for by an equivalent increase in femoral offset10. If the surgeon compensates for this decrease in acetabular offset by increasing femoral offset by the same amount this both restores global offset and the abductor lever arm. There are some potential advantages of medialization of the hip centre and maintaining global offset. Bicanic et al. have reported that hip load decreases when the cup is placed more medially or distally, and when the femoral neck is longer or lateral offset is used29. Bonnin et al. have also reported that medialization of the cup decreased stresses on the head–cup interface and on the abductor muscles, even when global offset was not restored. Moreover, they found that optimal outcome in terms of stress was observed when the cup was medialised but global offset was restored30. Therefore, medialization of the hip centre and maintaining global offset have the positive effect of decreasing hip load and also decreases the risk of bone or soft tissue impingement, thus improving the range of motion of the hip joint and reducing the risk of dislocation32.In this study, we measured offset values after THA and compared these measurements between 2 and 3D methods. The main finding was that error between the 2D and 3D methods varied among the offset parameters. There was no significant difference in acetabular offset between the two methods. This result is very important because it indicates that the 2D method can be used to evaluate acetabular offset with the same accuracy as the 3D method. No previous studies have compared acetabular offset between 2 and 3D methods. In addition, postoperative acetabular offset in implanted hips was medialized by 4.8 mm compared to that in the native hip by both the 2D and 3D methods. This result suggests that secondary osteoarthritis such as acetabular dysplasia is common in Japan. Jingushi et al. have reported a 9% incidence of primary osteoarthritis and a 81% incidence of secondary osteoarthritis with hip dysplasia in Japan14. Femoral offsets for native and implanted hips were 40 and 41 mm, respectively, using radiographs; and 43 and 45 mm, respectively, with the EOS imaging system. Pasquier et al. have reported preoperative and postoperative femoral offset values of 38.97 and 41.83 mm, respectively, for radiographic measurement; and 42.9 and 44.68 mm, respectively, for CT-scan measurement13. In addition, Lecerf et al. have reported mean femoral offset of 42.6 mm with radiographic measurement and 45.8 mm with CT-scan measurement33. In the present study, femoral offset was significantly underestimated by the 2D method compared with the 3D method in both native and implanted hips. Although femoral offset can be generally measured on anteroposterior radiographs of the femoral neck with the lower limb in about 20 degrees of internal rotation, the reproducibility of this method is poor, having mean error of about 9.7 mm19. In addition, internal rotation of 15–20 degrees does not consistently compensate for femoral anteversion, which varies widely even in primary osteoarthritis34. These results indicate that the 3D method should be used to evaluate femoral offset.In contrast, some studies have reported underestimation of femoral offset by the 2D method compared with the 3D method. Lazennec et al. compared femoral offset between measurements obtained from radiographs and the EOS imaging system33. In a cadaveric study, Weber et al. have reported that 15% of the radiographic global offset change measurements and 35% of the femoral offset change measurements on plain radiographs were outside a tolerance limit of 5 mm compared to the 3D-CT35. In the present study, error in global offset showed considerable variation according to evaluation method. These results indicate that it is difficult to accurately evaluate global offset using the 2D method; therefore, we recommend the 3D method for this measurement.Global offset was also significantly smaller with the 2D method than with the 3D method in both native and implanted hips because femoral offset contributes to underestimation of global offset. Furthermore, the error in global offset between the native and implanted hips was larger with the 2D method than with the 3D method. In their comparison of radiographic and CT-scan measurements, Lecerf et al. have reported that radiographic measurement underestimated by > 5 mm in 28% of cases36. However, we consider that the benefit of this method outweighs the disadvantage of the higher radiation dose because our results clarified that offset evaluation is more accurate with the 3D method. Furthermore, 3D method for evaluation of offset parameters are not affected by limb position and antetorsion of the femoral head. In the future, we plan to examine the relevance to clinical outcomes using the offset parameters that can be accurately measured by 3D method.There are three limitations of the present study. First, it is difficult to plot accurately the centre of rotation when the femoral head is severely deformed. However, the native hip is an almost sphere because of normal hip in this study. In addition, the centre of the head by 3D method can be calculated in three-dimensions by plotting four points around the circumference of the head to create a sphere approximated the head. Second, no deformity of the hip does not necessarily mean that native hip is a normal hip. However, we consider that it has little influence on the results of this study because this study does not assess whether the contralateral hip is normal or not. Third, CT after THA has a higher radiation burden to patients compared with that from radiographic measurementsIn conclusion, the results of this study clarified differences in accuracy between 2 and 3D methods for evaluation of offset parameters. Although there was no significant difference between the methods for acetabular offset, the 2D method significantly underestimated femoral and global offsets compared with the 3D method. These results demonstrate the accuracy and potential of the 3D method for evaluation of offset parameters following total hip arthroplasty.


Different essential viral proteins are translated via programmed stop codon readthrough. Pea enation mosaic virus 1 (PEMV1) and potato leafroll virus (PLRV) are related positive-sense RNA plant viruses in the family Solemoviridae, and are type members of the Enamovirus and Polerovirus genera, respectively. Both use translational readthrough to express a C-terminally extended minor capsid protein (CP), termed CP-readthrough domain (CP-RTD), from a viral subgenomic mRNA that is transcribed during infections. Limited incorporation of CP-RTD subunits into virus particles is essential for aphid transmission, however the functional readthrough structures that mediate CP-RTD translation have not yet been defined. Through RNA solution structure probing, RNA secondary structure modeling, site-directed mutagenesis, and functional in vitro and in vivo analyses, we have investigated in detail the readthrough elements and complex structure involved in expression of CP-RTD in PEMV1, and assessed and deduced a comparatively simpler readthrough structure for PLRV. Collectively, this study has (i) generated the first higher-order RNA structural models for readthrough elements in an enamovirus and a polerovirus, (ii) revealed a stark contrast in the complexity of readthrough structures in these two related viruses, (iii) provided compelling experimental evidence for the strict requirement for long-distance RNA-RNA interactions in generating the active readthrough signals, (iv) uncovered what could be considered the most complex readthrough structure reported to date, that for PEMV1, and (v) proposed plausible assembly pathways for the formation of the elaborate PEMV1 and simple PLRV readthrough structures. These findings notably advance our understanding of this essential mode of gene expression in these agriculturally important plant viruses. RNA viruses use a variety of strategies to express their encoded proteins, and one mechanism used is translational readthrough of stop codons. Some viruses in the family Solemoviridae use this expression strategy to make a C-terminally extended minor capsid protein that, when incorporated into virus particles, mediates plant-to-plant transmission by aphids. In this study we investigated the RNA sequences and structures that facilitate readthrough production of the minor capsid proteins of pea enation mosaic virus 1  and potato leafroll virus . A combination of structural and functional analyses allowed for the identification of RNA elements that contribute to the readthrough process. In both cases, the RNA structures that facilitate readthrough were formed via long-distance RNA-RNA interactions, with the active readthrough structure for PEMV1 being significantly more complex than that for PLRV. These results provide the first structural models for readthrough signals in these virus genera, and provide new insights into how these critical RNA structures assemble. This information significantly advances our understanding of this important gene expression strategy that is employed by these agriculturally important viruses. Programmed stop codon readthrough is an alternative protein expression strategy utilized by different viruses. The readthrough process involves the decoding of a stop codon as a sense codon by near-cognate tRNAs, which then allows ribosomes to continue translating in the original reading frame. The resulting C-terminally extended readthrough product is functionally distinct from its pre-readthrough protein, which expands the coding capacity of viral mRNAs. Positive sense RNA viruses represent the largest proportion of the viruses that employ readthrough –3. Some Readthrough efficiency is promoted and fine-tuned by downstream regulatory RNA sequences and structures –3 that aMembers of Luteovirus (Tombusviridae), Polerovirus (Solemoviridae) and Enamovirus (Solemoviridae) genera, all of which are related by similarities in their CPs and corresponding readthrough products, have been proposed to regulate CP readthrough from their subgenomic (sg) mRNAs via long-distance RNA-RNA interactions (spanning ~700 nt); based on observed complementarity between corresponding PRTEs and DRTEs ,13. ViruTo date, only limited information is available about the regulation of readthrough-mediated translation of CP-RTD from sg mRNAs in Luteovirus, Polerovirus and Enamovirus genera ,13. The Fig 1A) -Methionine at 25°C for 1 hr according to the manufacturer’s instructions, except that the concentration of KOAc was increased to 133 mM for each reaction to optimize translation and readthrough efficiency. The viral proteins translated during the incubation were detected and quantified through 12% SDS-PAGE and phosphorimaging, respectively  complementary to both the genome and sg mRNA . Northern blots, from three independent repeats, were captured using Typhoon FLA 9500 Variable Mode Imager and viral RNA bands were quantified using QuantityOne software. Sg mRNA levels of each mutant PEMV1 were calculated to generate average values with SE.Total nucleic acids were extracted from infected protoplasts, separated by agarose gel electrophoresis, and transferred to nylon membrane (Hybond-Ni.e. single stranded) nucleotides. Two primers, fluorescently labeled at their 5′-ends, one complementary to a region downstream from the PRTE (genome coordinates– 4828–4857) and the other to a region downstream from DRTE (genome coordinates –5504–5533), were used for primer extension reactions following 1M7 treatment of wt PEMV1 sg mRNA. After fluorescent capillary electrophoresis of the products of primer extension, the raw data was analyzed using the ShapeFinder software [RNAStructure web server was used [RNA2Drawer software was utilized to draw RNA secondary structure models depicted throughout the paper [Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) was performed and the data was used to model the RNA secondary structures of PRTE and DRTE regions in full-length PEMV1 sg mRNA, as described previously ,6,53. SHsoftware  to generwas used  to combi


Memristor crossbars can be very useful for realizing edge-intelligence hardware, because the neural networks implemented by memristor crossbars can save significantly more computing energy and layout area than the conventional CMOS  digital circuits. One of the important operations used in neural networks is convolution. For performing the convolution by memristor crossbars, the full image should be partitioned into several sub-images. By doing so, each sub-image convolution can be mapped to small-size unit crossbars, of which the size should be defined as 128 × 128 or 256 × 256 to avoid the line resistance problem caused from large-size crossbars. In this paper, various convolution schemes with 3D, 2D, and 1D kernels are analyzed and compared in terms of neural network’s performance and overlapping overhead. The neural network’s simulation indicates that the 2D + 1D kernels can perform the sub-image convolution using a much smaller number of unit crossbars with less rate loss than the 3D kernels. When the CIFAR-10 dataset is tested, the mapping of sub-image convolution of 2D + 1D kernels to crossbars shows that the number of unit crossbars can be reduced almost by 90% and 95%, respectively, for 128 × 128 and 256 × 256 crossbars, compared with the 3D kernels. On the contrary, the rate loss of 2D + 1D kernels can be less than 2%. To improve the neural network’s performance more, the 2D + 1D kernels can be combined with 3D kernels in one neural network. When the normalized ratio of 2D + 1D layers is around 0.5, the neural network’s performance indicates very little rate loss compared to when the normalized ratio of 2D + 1D layers is zero. However, the number of unit crossbars for the normalized ratio = 0.5 can be reduced by half compared with that for the normalized ratio = 0. HBy doing so, the vector matrix multiplication can be calculated physically using the memristor crossbar’s current–voltage relationship, where each column current is the MAC operation result of the corresponding column. The physical MAC calculation by memristor crossbars can consume smaller energy than the conventional computing using CMOS digital circuits ,10,11. M0+ and G0- represent the memristor’s conductance on plus and minus columns, respectively, for ‘row #0′. X0 is input voltage applied to ‘row #0′. Similarly, Gm+ and Gm- are the memristor’s conductance for ‘row #m’. Xm is the input voltage to ‘row #m’ in 0+ can be calculated with 0- is 0+ and I0- is calculated with 0 is obtained and delivered to the next crossbar. Here, 0+ is larger than G0-, the weight is positive. If G0+ is smaller than G0-, the weight is negative. Similarly, Explaining the MAC calculation by memristor crossbars more in detail, in One thing to consider in implementing the neural networks in From the previous publications about the experimental line resistance measured from academia and industries, the crossbar’s size can be 128 × 128 or 256 × 256 ,21. UnfoOne of the important operations used in neural networks is convolution operation. Similarly with the previous discussion, for performing the convolution by memristor crossbars, the full-image convolution should be partitioned into several sub-image convolution blocks. By doing so, each sub-image convolution can be mapped to the unit crossbar instead of the large-size crossbar . In thisIn this paper, to mitigate the overlapping overhead caused from the mapping of sub-image convolution to unit crossbars, various convolution schemes using 3D, 2D, and 1D kernels are investigated and compared, because the overlapping overhead can be different for the different convolutions with 3D, 2D, and 1D kernels. The 3D kernel is composed of width, length, and height. In the sub-image convolution with 3D kernels, the overlapping overhead becomes significantly large, because two neighboring sub-images can share many borderline pixels in the both lateral and vertical dimensions, as will be explained in the next section. The 2D kernel composed of only width and length is used in depthwise convolution, where the overlapping can be found only in the lateral direction, not in the vertical direction. By doing so, the overlapping overhead of 2D convolution can be smaller than the convolution with 3D kernel. The 1D kernel can perform pointwise convolution. In this case, no overlapping overhead can occur in the both lateral and vertical directions. Of course, the overlapping overhead due to the 1D kernel is the smallest among the three kernels. Based on the comparative study on various convolution schemes with the 3D, 2D, and 1D kernels, an area-efficient mapping method of sub-image convolution to unit crossbars is proposed to minimize the overlapping overhead due to the borderline pixels shared between two neighboring sub-images in the following section. In As mentioned in the previous section, in this paper, we try to propose an area-efficient mapping method of neural network’s convolution to crossbars. To do so, first, we consider that the convolution is mapped to one big crossbar without sub-image partitioning. S, RW, and RN are the parasitic source, line, and neuron resistance, respectively [IN,0 is input voltage applied to ‘row #0′. I0 is the column current from ‘column #0′. In IN,0 are applied to the crossbar’s rows. The currents generated by the crossbar’s columns can be thought of as the MAC results calculated physically from the memristor crossbar.One problem of the memristor crossbar in ectively . VIN,0 iW. Here, the crossbar is assumed to have 784 cells per column, as shown in W means line resistance per cell. If the column has 784 cells and RW = 1.1 Ω, the total line resistance becomes as large as 862 Ω. In this figure, the normalized column current means the MAC calculation result is plotted with increasing the percentage number of active rows among 784 rows. The ‘active rows’ means the row’s input voltage is high. If the percentage number of active rows is 50%, 392 rows are applied by high voltage and the other 392 are driven by 0V, among the total 784 inputs. Here, 1T-1R means the crossbar composed of 1 transistor and 1 memristor. 1S-1R is the array made of a self-rectifying memristor. For 1T-1R, the effective LRS resistance considering both LRS and transistor’s ON resistance is assumed to be 26.3 KΩ in the circuit simulation of W = 0 Ω, the normalized column current seems very linear upon increasing the percentage of active rows among 784 rows for both 1S-1R and 1T-1R cells. It indicates clearly that the MAC calculation accuracy is not degraded regardless of 1S-1R and 1T-1R cells. However, when RW = 0.5 Ω and RW = 1.1 Ω, the normalized column currents seem to saturate rapidly with increasing the percentage active rows over 25%. It means the MAC calculation accuracy is degraded very much when RW is not zero. If RW becomes larger, the MAC calculation accuracy becomes degraded more, as shown in When RFor overcoming the large line resistance problem due to large-size crossbars, the MNIST image should be partitioned into several sub-images. For example, the 28 × 28 MNIST image can be divided into sixteen 7 × 7 sub-images, as indicated in Here, it should be noted that the borderline pixels overlapping between two neighboring sub-images should be considered in counting the crossbar’s row number as many as 81(=9 × 9). In Comparing the crossbar size for between the full-image convolution (784 × 784) in As explained earlier, the memristor’s size should be very large for performing the full-image convolution. If the memristor’s size is very large, the line resistance can be very large, too. If so, the MAC calculation accuracy can be degraded significantly. To avoid the line resistance problem, the full image should be partitioned into smaller sub-images. Each sub-image convolution can be performed by each unit crossbar, where the unit crossbar’s line resistance can be much smaller than the crossbar of full-image convolution. However, when the sub-image convolution is mapped to small-size unit crossbars, the borderline pixels overlapping between two neighboring sub-images can cause the overlapping overhead, because the crossbar’s row number is increased.The overlapping overhead mentioned just earlier can be different for various convolution schemes with different kernels. The comparison of 3D, 2D, and 1D kernels in In the mapping method in Before ending the discussion of x-axis of As explained in Here, it should be noted that the MATLAB and pytorch simulation results were verified by the circuit simulation results of CADENCE SPECTRE. Because the circuit simulation is much slower than the MATLAB and pytorch, in this paper, only a part of the hybrid circuit composed of memristors and CMOS devices is simulated for MNIST testing vectors. From the simulation, it was observed that the MAC result calculated from the circuit simulation is the same with the MATLAB and pytorch simulation. Here, the circuit simulation was performed by CADENCE SPECTRE software . In the 3. The bottom electrode is SrTiO3. A butterfly curve from the device in From the circuit simulation using CADENCE SPECTRE, the LRS read current is estimated around 38 μA per cell, when the read voltage is 1V and a 1T-1R crossbar is used. If the memristor has HRS, the read current can be as small as 1 μA per cell. For the memristors not selected, the memristor’s current can be negligibly small, because the selector’s OFF resistance is larger than HRS by three orders of magnitude. For the transient characteristic of memristors, the programing and read pulse widths were measured around ~100ns . The powMemristor crossbars can be very useful for realizing edge-intelligence hardware, because the neural networks implemented by memristor crossbars can save significantly more computing energy and layout area than the conventional CMOS digital circuits. One of the important operations used in neural networks is convolution. For performing the convolution by memristor crossbars, the full image should be partitioned into several sub-images. By doing so, each sub-image convolution can be mapped to small-size unit crossbars, where the crossbar’s size should be defined as a fixed size such as 128 × 128 or 256 × 256 to avoid the line resistance problem of large-size crossbars. To propose the area-efficient mapping method of sub-image convolution to unit crossbars, the various convolution schemes with 3D, 2D, and 1D kernels were investigated and compared in terms of the neural network’s performance and overlapping overhead in this paper. Based on the investigation and comparison, the 2D+1D kernels indicated that they could perform the convolution using a much smaller number of unit crossbars with less rate loss than the 3D kernels. When training and testing the CIFAR-10 dataset, the mapping of sub-image convolution of 2D+1D kernels to unit crossbars could save the number of unit crossbars almost by 90% and 95%, for the unit crossbar’s size of 128 × 128 and 256 × 256, respectively, compared with the 3D kernels. On the contrary, the rate loss of 2D+1D kernels was less than 2%. To minimize the rate loss more, the 2D+1D kernels could be combined with 3D kernels in one neural network. When the normalized ratio of 2D+1D layers is around 0.5, the neural network’s performance indicated very little rate loss using only half of unit crossbars compared to the normalized ratio of 2D+1D layers of zero.


Detection of viruses by RNA and DNA sequencing has improved the understanding of the human virome. We sought to identify blood viral signatures through secondary use of RNA-sequencing (RNA-seq) data in a large study cohort. The ability to reveal undiagnosed infections with public health implications among study subjects with available sequencing data could enable epidemiologic surveys and may lead to diagnosis and therapeutic interventions, leveraging existing research data in a clinical context. We detected viral RNA in peripheral blood RNA-seq data from a COPD-enriched population of current and former smokers. Correlation between viral detection and both reported infections and relevant disease outcomes was evaluated. We identified Hepatitis C virus RNA in 228 subjects and HIV RNA in 30 subjects. Overall, we observed 31 viral species, including Epstein-Barr virus and Cytomegalovirus. We observed an enrichment of Hepatitis C and HIV infections among subjects reporting liver disease and HIV infections, respectively. Higher interferon expression scores were observed in the subjects with Hepatitis C and HIV infections. Through secondary use of RNA-seq from a cohort of current and former smokers, we detected peripheral blood viral signatures. We identified HIV and Hepatitis C virus (HCV), highlighting potential public health implications for the approach described this study. We observed correlations with reported infections, chronic infection outcomes and the host transcriptomic response, providing evidence to support the validity of the approach. Through secondary use of human RNA and DNA sequencing to detect viruses, evidence has emerged regarding a healthy human blood virome4. In a study of the peripheral blood DNA virome in 8,240 individuals, Moustafa and colleagues3 repurposed human WGS data and were able to map the WGS data to sequences of 94 different viruses, including 19 human viruses, and observed differences in virus profiles across age, sex and ancestry3. Human Cytomegalovirus (CMV), HHV-6A, HHV-6B, Epstein–Barr virus (EBV) and Torque Teno Virus  were among those detected. In a multi-tissue study repurposing RNA sequencing (RNA-seq) data by Kumata and colleagues4, EBV, CMV, TTV and human papillomavirus were among the viruses observed in blood.Studies of viruses in human tissues have helped to better understand the human virome and its role in human disease5. To assess the potential impact of this question in existing study populations, we sought to detect peripheral blood viral signatures by repurposing RNA-seq data in a population of current and former smokers with and without chronic obstructive pulmonary disease (COPD).We hypothesize that detection of viruses known to cause long-term latent infections within existing study populations would be of particular interest, as this could enable epidemiologic surveys and perhaps lead to diagnosis and therapeutic intervention for subjects from study populations of various diseasesAfter quality control procedures, peripheral blood RNA-seq data were available for 3,984 samples, including 1601 COPD cases and 1609 controls (Table p < 0.0001) and suggesting high specificity. Although the 112 subjects self-reporting liver disease and lacking HCV RNA detection indicates the sensitivity may not be high, reduced HCV RNA loads would be observed in subjects undergoing treatment. Subjects with detected HCV RNA were younger with fewer comorbidities, and tended to be male, current smokers and African-American .A medical history of Hepatitis C infection was not obtained in COPDGene. However, 171 subjects with RNA-seq data had a self-reported history of liver disease at the 5-year follow-up and an additional 18 subjects reported liver disease at the 10-year follow-up. Of the 228 subjects with detectable HCV RNA, 77 were among the 189 total subjects with liver disease  in the subjects with HCV RNA detection  lower levels of a published HCV down expression score and higher levels of an HCV up expression score in the subjects with HCV detection p < 0.0001). The eight remaining subjects . We observed higher expression scores for interferon alpha and interferon gamma pathways  in the subjects with HIV RNA detection  lower levels of a published HIV down expression score and higher levels of IFI27 in the subjects with HIV detection  project9 prevents a more complete comparison of findings, particularly with respect to HCV. The study of human viruses by Moustafa and colleagues3 involved the use of whole-genome DNA sequencing. Although we detected viral gene expression mRNA from infections caused by some DNA viruses identified in that study, such as EBV, CMV and TTV, the RNA from HCV and HIV infections would not be represented in their results to allow a direct comparison.Through repurposing of blood RNA-seq data, we detected RNA of 31 viral species including HCV and HIV. The protocols used in the creation of the RNA-seq data in this study did not include polyA enrichment. A previous study demonstrated difficulty detecting viral RNA in polyA enriched RNA-seq data compared with non-polyA enriched RNA-seq data11 and HIV infections13. The concordance of virus detection and levels of the HCV and HIV host response scores from published transcriptomic studies also suggests we have observed chronic viral infections.We observed significant enrichment of subjects with reported liver disease among those with detectable HCV RNA. We also observed a significant enrichment of subjects self-reporting HIV infections among those with detectable HIV RNA. These findings along with the elevated interferon pathway score in those with detectable RNA suggest we have identified HCV and HIV infections and a signature of the host response, given the role of interferons in response to HCV14 and between 10 and 20% of HCV infections will lead to cirrhosis over 20–30 years15 with an increased risk of hepatocellular carcinoma. Direct-acting antiviral (DAA) therapy has been highly effective over the last decade14, highlighting the importance and benefits of HCV detection and diagnosis. In 2019, approximately 1 million people were living an HIV infection16. However, approximately 13% were not yet diagnosed16, highlighting the potential impact of HIV detection for both population surveillance and individual counseling within existing and future research study populations.From 2013 to 2016, approximately 2.4 million people in the United States were living with a HCV infection17. The lack of control samples in this study would also be addressed in a properly designed metagenomic experiment. Although this study does not involve clinical diagnostic methods, we believe the public health implications of the viruses detected justifies the use of our approach, as it leverages existing data to enable targeted clinical interventions for study participants.Limitations of this study include the lack of information regarding HCV infections, though an ongoing effort to return results to study subjects will provide clinical test results to allow further evaluation of the sensitivity and specificity of the RNA-seq approach. Clinically determined HIV-negatives were also lacking and prevented a full evaluation of HIV RNA detection accuracy. We also lack a validation cohort with infection-related questionnaire data. Although we are focused on secondary use of existing sequencing data, if these data do not exist for a particular study population, metagenomic approaches are available to provide more optimized viral detectionThis study is the first to identify RNA signatures of HCV and HIV in the peripheral blood of a COPD-enriched population of current and former smokers through secondary use of RNA-seq data. In this study we were able to identify signatures of viral species with pathogenic and public health implications and observed correlations with reported infections, chronic infection outcomes and host transcriptomic response. Secondary mining of existing sequencing data in disease study populations using bioinformatic approaches may reveal chronic viral infections for cost-efficient epidemiologic studies and lead to additional clinical validation and therapeutic interventions where necessary.18. The COPDGene cohort includes primarily current and former cigarette smokers. The five-year follow-up visit included questionnaires, spirometry, chest computed tomography scans, and collection of blood for complete blood cell counts and RNA sequencing. Subjects were at least one month removed from an acute respiratory infection. The RNA-sequencing and data processing methods were reported previously19. Briefly, paired end reads with nominal 75 bp length were generated on an Illumina HiSeq 2500 flow cell. Sequencing was performed to an average depth of 20 million reads. For the human gene expression analyses, STAR aligner20 was used to map the reads to GRCH38 and RSubreads produced gene-level counts21 with Ensembl gene annotation22. We confirmed concordance between sex-specific expression features and reported sex, and concordance between variants called from RNA sequencing reads and corresponding DNA genotyping.The Genetic Epidemiology of COPD (COPDGene) study is a longitudinal cohort study that includes more than 10,000 non-Hispanic White and African American subjects enrolled at 21 centers across the United States23, as described in a previous microbiome study6. Filtering of the unmapped reads was performed using PathSeq and the host reference in the GATK Resource Bundle23. The filtering step helps to addresses quality, host contamination or repetitive sequence issues. We then mapped the cleaned read data to viral reference genomes using PathSeq. The viral genomic reference was created using representative genomes  from the National Center for Biotechnology Information (NCBI). Taxonomy information for the viral genomic data was also obtained from NCBI . PathSeq output included the mapped read counts for each sample and viral species.Using reads that were not mapped to the human genome during the gene expression analysis, we detected viral signatures in the COPDGene 5-year follow-up whole blood RNA-seq data using PathSeq, the microbial detection pipeline from the Genome Analysis Toolkit (GATK4)24 using gene set variation analysis via the R package GSVA25, as previously described6. We focused on the Hallmark interferon alpha and interferon gamma pathway gene sets. This method creates a composite transcriptomic expression score for the set of genes within each of the pathways. We also created scores using genes observed to be differentially expressed in the blood of HCV and HIV infected individuals. In a study of peripheral blood mononuclear cells (PBMCs), Bolen and colleagues26 identified 109 genes differentially expressed in HCV patients relative to uninfected controls. Using GSVA and the 56 genes down-expressed in HCV and 53 genes up-expressed, we created HCV down expression and up expression scores. For HIV, we used the set of 10 genes identified by Ockenhouse and colleagues27, observed to be down expressed in the PBMCs of HIV seropositive individuals and differentiating seropositive from seronegative, to create an HIV down expression score. Previous studies showed higher expression of the gene IFI27 in the peripheral blood of HIV infected individuals compared with seronegative controls29; we also analyzed expression of this gene to provide evidence of HIV infections. For this analysis, RNA-seq data were retained for genes with variance in the upper 80th percentile and for genes with average read counts greater than five. GSVA output provides expression pathway scores for each subject and gene set.To observe transcriptomic signatures of the host response to the detected infections, we projected the RNA-seq gene expression data onto the Hallmark pathway gene set collection from MSigDBAll subjects in this study provided written consent for study procedures, including genetic analysis. COPDGene was approved by the Institutional Review Boards at all participating centers. The research methods were carried out in accordance with the relevant guidelines.All subjects in this study provided written informed consent. COPDGene was approved by the Institutional Review Boards at all participating centers.Supplementary Information.


The innate immune response controls the acute phase of virus infections; critical to this response is the induction of type I interferon (IFN) and resultant IFN-stimulated genes to establish an antiviral environment. One such gene, zinc finger antiviral protein (ZAP), is a potent antiviral factor that inhibits replication of diverse RNA and DNA viruses by binding preferentially to CpG-rich viral RNA. ZAP restricts alphaviruses and the flavivirus Japanese encephalitis virus (JEV) by inhibiting translation of their positive-sense RNA genomes. While ZAP residues important for RNA binding and CpG specificity have been identified by recent structural studies, their role in viral translation inhibition has yet to be characterized. Additionally, the ubiquitin E3 ligase tripartite motif-containing protein 25 (TRIM25) has recently been uncovered as a critical co-factor for ZAP’s suppression of alphavirus translation. While TRIM25 RNA binding is required for efficient TRIM25 ligase activity, its importance in the context of ZAP translation inhibition remains unclear. Here, we characterized the effects of ZAP and TRIM25 RNA binding on translation inhibition in the context of the prototype alphavirus Sindbis virus (SINV) and JEV. To do so, we generated a series of ZAP and TRIM25 RNA binding mutants, characterized loss of their binding to SINV genomic RNA, and assessed their ability to interact with each other and to suppress SINV replication, SINV translation, and JEV translation. We found that mutations compromising general RNA binding of ZAP and TRIM25 impact their ability to restrict SINV replication, but mutations specifically targeting ZAP CpG-mediated RNA binding have a greater effect on SINV and JEV translation inhibition. Interestingly, ZAP-TRIM25 interaction is a critical determinant of JEV translation inhibition. Taken together, these findings illuminate the contribution of RNA binding and co-factor interaction to the synergistic inhibition of viral translation by ZAP and TRIM25. The type I interferon (IFN) response is one of the first lines of cellular defense against invading pathogens. The IFN-induced zinc finger antiviral protein (ZAP) is a potent inhibitor of diverse RNA and DNA viruses . ZAP encEarlier efforts to elucidate ZAP RNA binding activity showed that ZAP binds RNA with its four N-terminal CCCH ZnFs, and mutations of ZnFs 2 and 4 most dramatically reduce ZAP antiviral activity . The firWhile much attention has been given to characterizing ZAP RNA binding, less has been given to its critical co-factors such as TRIM25. TRIM25 was identified as a ZAP co-factor in the context of inhibiting alphavirus translation . HoweverWhile some have attempted to illuminate the contribution of ZAP or TRIM25 RNA binding to the ZAP-TRIM25 interaction, these studies have focused on only a few select mutations. Still, most agree that RNA binding in either ZAP or TRIM25 is not required for the ZAP-TRIM25 interaction. One group demonstrated not only that RNase A treatment has little effect on the ZAPL-TRIM25 interaction, but also that an example ZAPL RNA binding mutant retains and even increases its interaction with TRIM25 . AnotherIn light of the recent novel structural insights, we asked how different ZAP and TRIM25 RNA binding mutations affect the ability of ZAP and TRIM25 to interact with one another and to restrict SINV and JEV translation. We curated a panel of ZAP and TRIM25 RNA binding mutants from prior studies, including mutants with a range of RNA binding and antiviral capabilities . We firsTo investigate the role of ZAP and TRIM25 RNA binding in the context of viral translation, we generated a panel of constructs with mutations previously demonstrated to impact RNA binding. For ZAP, each of the following mutations was introduced in both ZAPS and ZAPL to probe potential isoform differences in RNA binding and antiviral function. These mutations fall into two general categories: 1) ZnF mutants that individually disrupt each CCCH motif and 2) CpG RNA binding cavity mutants. We made four individual mutations to disrupt each N-terminal CCCH ZnF: H86K, C88R, C168R, and H191R, which are located in ZnF 1, 2, 3, and 4, respectively . Two putFor TRIM25, we generated two constructs, one in which we replaced the lysine-rich motif in the L2 linker with alanines (TRIM25 7KA) and one in which we deleted the previously identified RNA binding domain in the PRY/SPRY domain (TRIM25 ΔRBD) .in vitro RNA pull-down assay. We incubated lysates of cells transfected with ZAP or TRIM25 mutants with biotin-labeled SINV (Toto1101 strain) genomic RNA, allowing for RNA and bound protein to be immunoprecipitated using streptavidin beads and probed for the presence of bound ZAP or TRIM25. As a negative control, we also assessed the ability of the WT constructs to bind firefly luciferase (Fluc) RNA. We quantified the resultant ZAP and TRIM25 bound to RNA and normalized to input ZAP and TRIM25 protein levels with ImageJ   . Becausee (Rluc) . We attee (Rluc) .We found that the ZAP and TRIM25 RNA binding mutants inhibit JEV translation to varying degrees Figure 5Upon assaying TRIM25 variants, we found that TRIM25 ΔRBD inhibits JEV translation similarly to TRIM25 WT  and its parental 293T lines . TRIM25 Infections with SINV expressing firefly luciferase (Toto1101/Luc) and temperature-sensitive SINV (Toto1101/Luc:ts6) have been previously described . Each inBamHI and HindIII restriction sites. ZAPS and ZAPL were cloned into pcDNA3.1-3XFLAG from pTRIP-TagRFP-hZAPS and pTRIP-TagRFP-hZAPL ”, to replace wild-type sequence in TRIM25. The 7KA mutated sequence is bolded in the below gene block, and nonessential nucleotides on the 5’ and 3’ ends are written in lowercase.Point mutations in ZAPS and ZAPL were generated using the Q5 Site-Directed Mutagenesis Kit (New England Biolabs) and all plasmids were verified by sequencing (Genewiz). Primers for mutations were synthesized by Integrated DNA Technologies  was genealanines , and utiTGTACAGTCAGATCAACGGGGCGTCGAGAGCACTGGATGATGTGAGAAACAGGCAGCAGGATGTGCGGATGACTGCAAACAGAAAGGTGGAGCAGCTACAACAAGAATACACGGAAATGAAGGCTCTCTTGGACGCCTCAGAGACCACCTCGACAAGGAAGATAAAGGAAGAGGAGAAGAGGGTCAACAGCAAGTTTGACACCATTTATCAGATTCTCCTCAAGAAGAAGAGTGAGATCCAGACCTTGAAGGAGGAGATTGAACAGAGCCTGACCAAGAGGGATGAGTTCGAGTTTCTGGAGAAAGCATCAAAACTGCGAGGAATCTCAACAAAGCCAGTCTACATCCCCGAGGTGGAACTGAACCACAAGCTGATAAAAGGCATCCACCAGAGCACCATAGACCTCAAAAACGAGCTGAAGCAGTGCATCGGGCGGCTCCAGGAGCCCACCCCCAGTTCAGGTGACCCTGGAGAGCATGACCCAGCGTCCACACACAAATCCACACGCCCTGTGGCAGCAGTCTCCGCAGAGGAAGCAGCATCCGCAGCACCTCCCCCTGTCCCTGCCTTACCCAGCAAGCTTCCCACGTTTGGAGCCCCGGAACAGTTAGTGGATTTAAAACAAGCTGGCTTGGAGGCTGCAGCCAAAGCCACCAGCTCACATCCGAACTCAACATCTCTCAAGGCCAAGGTGCTGGAGACCTTCCTGGCCAAGTCCAGACCTGAGCTCCTGGAGTATTACATTAAAGTCATCCTGGACTACAACACCGCCCACAACAAAGTGGCTCTGTCAGAGTGCTATACAGTAGCTTCTGTGGCTGAGATGCCTCAGAACTACCGGCCGCATCCCCAGAGGTTCACATACTGCTCTCAGGTGCTGGGCCTGCACTGCTACAAGAAGGGGATCCgttt-3’5’gtttX-tremeGENE9 DNA Transfection Reagent (Roche Life Science) was used to transfect cells at a ratio of 3 μL to 1 μg DNA according to the manufacturer’s instructions. To keep the total plasmid amount in co-transfections constant, empty vectors pcDNA3.1-myc or 3XFLAG were transfected as necessary .XhoI linearization of pToto1101 (in vitro by Sp6 RNA polymerase (New England Biolabs) in the presence of the cap analog [m7G(5’)ppp(5’)G] (New England Biolabs). Fluc DNA templates for transcription were amplified from the pGL3-Control plasmid (Promega). Fluc RNA was transcribed in vitro using the mMESSAGE mMACHINE T7 Transcription Kit (Invitrogen). Biotin-labeled RNAs were generated by adding 10mM biotin-16-UTP (Roche Life Science) to in vitro transcription reactions. JEV replicon DNA templates for transcription were generated by XhoI linearization and transcribed in vitro using the mMESSAGE mMACHINE T7 Transcription Kit (Invitrogen). Transcribed RNAs were purified using the Quick-RNA Miniprep Kit (Zymo Research) and biotinylation was confirmed by streptavidin dot blot  and incubated with RNA probes in a final volume of 100 μL RNA binding buffer supplemented with 1 unit/μL RNAseOUT (Thermo Fisher Scientific), 1 μg/μL heparin (Sigma-Aldrich), and 100 ng/μL yeast tRNA (ThermoFisher) for 30 min at 30°C. Lysate-RNA mixtures were then incubated with 300 μL of Dynabeads M-280 Streptavidin (Invitrogen) for 30 min at room temperature on a shaker. Protein-RNA complexes were washed three times by RNA binding buffer, and proteins were eluted by incubation with 30 μL of 4x Laemmli Sample Buffer (Bio-Rad) for 5 min at 95°C. The proteins were further analyzed by immunoblot and quantified by ImageJ as previously described  and 4-15% precast Mini-PROTEAN TGX Gels (Bio-Rad) before transferring to a PVDF membrane (Bio-Rad). Immunodetection was achieved with 1:2,500 anti-myc , 1:20,000 anti-FLAG (Sigma-Aldrich), and 1:20,000 anti-actin-HRP (Sigma-Aldrich). Primary antibodies were detected with 1:20,000 goat anti-mouse HRP (Jackson ImmunoResearch) or 1:20,000 goat anti-rabbit HRP (Thermo Fisher Scientific). Proteins were visualized on a ChemiDoc (Bio-Rad) using ProSignal Pico ECL Reagent (Genesee Scientific). ImageJ was used to quantify western blots as previously described . BrieflyTo assess ZAP or TRIM25 co-immunoprecipitation (co-IP) with RNA binding mutants of TRIM25 or ZAP, respectively, cells were transfected in 6-well plates, collected, and then lysed by rotating in FLAG IP buffer  supplemented with a complete protease inhibitor cocktail (Roche Life Science) at 4°C for 30 min, before spinning down at 14,000 rpm at 4°C for 15 min. To equilibrate beads prior to use, anti-FLAG beads  or anti-myc beads  were washed 3 times in FLAG IP buffer. Three hundred μL of whole cell lysate (WCL) were incubated with 30 μL of anti-FLAG or -myc beads rotating at 4˚C for 45 minutes. FLAG IP buffer was used to wash immunoprecipitates 3 times before eluting bound proteins with SDS loading buffer, and boiling for 5 minutes for immunoblot analysis. Western blot ImageJ analysis was performed as previously described . BrieflyTransIT-mRNA Transfection Kit (Mirus Bio). Cells were lysed 4 hours post-transfection of replicon RNA and luciferase activity was measured by Dual-Luciferase Reporter Assay (Promega). Each independent experiment included triplicate wells of biological replicates per condition.Following transfection of ZAP or TRIM25 constructs into ZAP or TRIM25 KO 293T for 48 hours, JEV replicon RNA was transfected by Statistical analyses in The original contributions presented in the study are included in the article/EY, LN, and ML conceptualized and designed the study. CW and RK assisted in cloning ZAP and TRIM25 RNA binding mutants. LN performed RNA binding and JEV replicon experiments, while EY performed co-IP and SINV replication and translation experiments. LN performed the correlation analysis. EY and LN co-wrote the first draft of the manuscript. ML provided critical feedback. All authors contributed to manuscript revision, read, and approved the submitted version.This work was supported in part by NIH R01AI158704 (ML), UCLA AIDS Institute and Charity Treks 2019 Seed Grant (ML), Ruth L. Kirschstein Multidisciplinary Training Grant in Microbial Pathogenesis , Ruth L. Kirschstein Cellular and Molecular Biology Training Program , Warsaw Fellowship (EY), and Whitcome Fellowship .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Posttranscriptional adenosine-to-inosine modifications amplify the functionality of RNA molecules in the brain, yet the cellular and genetic regulation of RNA editing is poorly described. We quantify base-specific RNA editing across three major cell populations from the human prefrontal cortex: glutamatergic neurons, medial ganglionic eminence-derived GABAergic neurons, and oligodendrocytes. We identify more selective editing and hyper-editing in neurons relative to oligodendrocytes. RNA editing patterns are highly cell type-specific, with 189,229 cell type-associated sites. The cellular specificity for thousands of sites is confirmed by single nucleus RNA-sequencing. Importantly, cell type-associated sites are enriched in GTEx RNA-sequencing data, edited ~twentyfold higher than all other sites, and variation in RNA editing is largely explained by neuronal proportions in bulk brain tissue. Finally, we uncover 661,791 cis-editing quantitative trait loci across thirteen brain regions, including hundreds with cell type-associated features. These data reveal an expansive repertoire of highly regulated RNA editing sites across human brain cell types and provide a resolved atlas linking cell types to editing variation and genetic regulatory effects. Here the authors provide a deep catalogue of cell-specific A-to-I editing sites in the human cortex. Thousands of sites are enriched and elevated in neurons relative to glial cells, and are genetically regulated across multiple brain regions. Adenosine to inosine (A-to-I) editing is abundant in the human brain and predicted to occur at millions of locations across the genome4. A-to-I editing occurs at single isolated adenosines (selective editing) as well as in extended regions with multiple neighboring adenosines (RNA hyper-editing)7 and is catalyzed by adenosine deaminase acting on RNA (ADAR) enzymes. These base-specific changes exponentially amplify RNA sequence diversity and expand the functionality of many brain-expressed genes, by allowing the same coding sequence to produce different mRNA and products. Although the growth of RNA-sequencing datasets has increased the catalog of known editing sites, the functional relevance of most sites remains unknown. We reasoned that sites with precisely regulated differences in RNA editing levels across brain cell types or brain regions signal a potentially critical role in supporting the functional diversity of brain circuits. Moreover, dissecting the genetic regulation of RNA editing at these sites gives insight into their potential role in healthy and diseased brains.The complexity of the central nervous system (CNS) is largely coordinated through multiple layers of transcriptional regulation, generating functionally distinct RNA molecules with specialized posttranscriptional modifications11. RNA editing in protein-coding regions can result in recoding specific amino acids, which influences essential neurodevelopmental processes, including actin cytoskeletal remodeling at excitatory synapses12, regulation of gating kinetics of inhibitory receptors13, and modulation of neurotransmission at inhibitory synapses14. RNA editing sites are dynamically regulated throughout human cortical development16, with marked increases in editing levels occurring between mid-fetal development and infancy. These profiles are conserved in nonhuman primates and murine models of cortical development, indicating an evolutionarily selected function15. Moreover, widespread changes in RNA editing are linked to several neuropsychiatric and neurodevelopmental disorders22. Yet, the cellular specificity of RNA editing sites in the human brain remains largely unknown due to the lack of cell type-specific studies.In the CNS, RNA editing regulates neuronal transcription, splicing, and subcellular localization of mRNA transcripts24. In Drosophila, RNA editing has been explored across several neuronal populations, each containing a unique editing signature composed of distinct site-specific editing levels in neuronal transcripts25. In the human brain, the challenge of purifying specific cell populations has so far prevented broad-based analysis of cell type-specific RNA editing. Single-cell RNA-sequencing has been applied in a modest number of cells26 (ncells = 268), indicating that highly edited sites in individual cells often go undetected by bulk brain RNA-seq. The systematic identification of bona fide, high-confidence cell type-associated RNA editing sites in the human brain is critical for understanding the scope and specificity of this layer of cell regulation.The vast majority of RNA editing sites have been detected in bulk brain tissue, a mixture of dozens of neuronal and glial cell types with distinct transcriptional, and potentially epitranscriptional, programs. In the mouse brain, editing levels are higher in neurons relative to glial cells, and these trends are consistent across brain development27. The integration of paired genomic and transcriptomic data can identify RNA editing quantitative trait loci (edQTLs). It is estimated that anywhere between ~10–30% of RNA editing sites identified in bulk tissue samples of the human brain are regulated by common genetic variants28, and this number will continue to grow with increasing sample sizes. However, there has been no investigation of cell type-associated edQTLs in the human brain.In addition to cell-specific factors, common genetic variation has also recently emerged as an important regulator of RNA editing levels in the brain29. The PFC contains two major neuronal populations, excitatory glutamatergic (GLU) and the inhibitory GABAergic interneurons, which account for about 80% and 20% of all cortical neurons, respectively30. Medial ganglionic eminence (MGE)–derived interneurons comprise ~60–70% of all cortical GABAergic neurons and contain parvalbumin- and somatostatin-expressing interneurons, which have been implicated in neurodevelopmental and neuropsychiatric disorders32. Oligodendrocytes (OLIG) are the major glial cell type in the central nervous system that provides support and myelin-based insulation to axons33. Here, GLU, MGE-GABA, and OLIG populations were isolated from nine donors using fluorescence-activated nuclei sorting (FANS), followed by transcriptomic analysis by RNA-seq34. By combining our high-resolution cell type-specific data with complementary single nucleus RNA-seq, as well as bulk RNA-seq from multiple brain regions from the GTEx project, we provide comprehensive analysis and validation of cell type-specific RNA editing across the human brain, including key genetic regulators of editing quantitative trait loci (edQTLs). Overall, our study suggests that RNA editing plays a critical role in supporting the diverse molecular identities of brain cell types.Here we sought to expose the main cellular and genetic drivers of RNA editing variation in the human brain. We first quantified RNA editing among three major cell populations purified from the adult prefrontal cortex (PFC)—the brain region critical for cognition, memory, and executive function and is broadly implicated in neuropsychiatric illnessAlu editing index (AEI) as a global measure of site-selective RNA editing activity for each cell type (see Methods). The AEI is defined as the ratio of the total number of A-to-G edited reads over the total coverage of all adenosines in Alu elements across the transcriptome. The AEI was higher in neurons compared to OLIG  and elevated in MGE-GABA relative to GLU  , higher expression of ADAR2 in MGE-GABA neurons relative to GLU and OLIG , and higher ADAR3 expression in OLIG relative to neurons   and ADAR1 (R2 = 0.64) expression and negatively associated with ADAR3 (R2 = 0.33)  Fig. . Notably33) Fig. .Fig. 1Glsee Methods)    and ADAR2 (R2 = 0.54), but negatively associated with ADAR3 (R2 = 0.21)  Fig. . To minion) Fig. . Normalisee Methods). All sites were subjected to rigorous downstream filtering and quality control. In brief, thresholds were set to control total read coverage (>10 supporting reads), minimum edited read coverage (>3 supporting edited reads), and a minimum editing ratio (at least 5%). We further required that sites meet these criteria in at least eight out of nine donors. Sites in homopolymeric and blacklisted regions of the genome were discarded, along with sites marked as common genomic variants    Fig.  and resi8%) Fig. . Second,8%) Fig. . Third, 8%) Fig. , we also8%) Fig. . Notably8%) Fig.  and exhion) Fig. . Furtheron) Fig. . Moreoveon) Fig. . We alsoADAR2 (~1.8%), and ADAR1 expression (~1.5%) , respectively , pH (~3.5%), 18. FMRP eCLIP peaks were significantly enriched for MGE-GABA , GLU , and OLIG sites   can also act as global mediators of RNA editing. We found a total of 470 RBPs were differentially expressed , and roughly half (~49%) were more highly expressed in OLIG relative to GLU and MGE-GABA , suggesting the differential editing at the majority of these sites depends on ADAR activity (Supplementary Data ngenes = 775), GLU (ngenes = 660), and OLIG (ngenes = 378) populations, respectively , GLU (nsites = 23), and OLIG (nsites = 23) populations  and adjusted each linear model for the possible influence of postmortem interval (PMI), age, and donor as a repeated measure Fig. . OverallN-acetyltransferase activity and methylated histone binding.Functional annotation revealed that cell type-enriched sites in each cell type, predominantly those in introns, were associated with genes involved in neuronal differentiation and cell adhesion, but were also enriched for unique processes (Supplementary Data n = 36 sites), which alter amino acid states and displayed increased conservation relative to sites in other genic regions  actin cytoskeletal remodeling at excitatory synapses (K→E in CYFIP2) and gating kinetics of inhibitory receptors (I→M in GABRA3) , a K→R site in mitofusin 1 (MFN1), a E→G site in calcium-dependent secretion activator (CADPS). Notably, an I→V site in coatomer subunit alpha (COPA) was more highly edited in OLIG relative to neurons. We tested the cellular specificity for four recoding sites using an independent method (site-specific PCR amplification of regions harboring editing sites followed by sequencing) applied to orbitofrontal cortex samples from six independent adult donors , GRIA3, and GRIA4, which were more highly edited in neurons relative to OLIG. We also validated one S→G site in UNC80, a component of the NALCN sodium channel complex (UNC80), which was more highly edited in MGE-GABA neurons.A small fraction of differentially edited sites was cataloged as RNA recoding events  Fig. . Twelve p = 0.009 and p = 6.3 × 10−5, respectively). Also, MGE-GABA and GLU sites were enriched for sites in the DLPFC previously found to be dynamically regulated throughout prenatal and postnatal development16 (p = 2.3 × 10−214). Enrichment for OLIG RNA editing sites was consistently lower across all independent studies and cohorts, as expected. These results reconfirm the validity of our approach and shed light on some of the cellular origins of altered RNA editing in neurodevelopment and disease22.Further, we explored whether our data could resolve the cellular specificity of editing sites previously described to be dysregulated in bulk brain tissue across neurodevelopment and in neurological disorders Fig. . We obseR2 = 0.16), GLU (R2 = 0.15), and OLIG cells (R2 = 0.04) , we found a higher density of RNA editing sites in OLIG cells at several genes associated with OLIG-specific expression: PCDH9 (n = 560 sites), RNF220 (n = 419 sites), FRMD5 (n = 277 sites) , SNHG14 (n = 1083 sites), and DPP10 (n = 243 sites) were enriched for editing sites, while in GLU, genes KCNIP4 (n = 1831 sites), PHACTR1 (n = 433 sites), and PTPRD (n = 488) were enriched, among others  Fig. . In MGE-MD5 n = 27 sites . A total of 24 discrete cell clusters comprising six major cell types were identified through unsupervised dimension reduction and annotated using previously defined cell marker genes , ~21% expressed MGE-GABA markers (nnuclei = 3708), and ~9% expressed OLIG markers (nnuclei = 1709). Notably, a small subset which were assigned to the GLU pseudo-bulk pool were also positive for markers of CGE-derived inhibitory neurons , which make up ~7% of nuclei overall. This was consistent with our FANS strategy, which separated MGE-GABA form other neurons using the MGE-GABA specific marker SOX6. Thus, a small population of non-MGE-derived GABA neurons  was sorted together with GLU neurons29. We generated three additional pseudo-bulk pools representing astrocytes , endothelial cells (nnuclei = 464), and microglia (nnuclei = 130). We examined ADAR expression and global editing rates within each of the six cellular pools (see Methods), confirming higher global editing activity as well as higher expression of ADAR1 and ADAR2 in MGE-GABA and GLU relative to OLIG cells , and relative to all remaining non-neuronal cell types (p = 0.0002)  of the adult PFC generated by PsychENCODEnes Fig. . To overely Fig. . We obse02) Fig. .Fig. 4CoR2 = 0.54), GLU (R2 = 0.59), and OLIG (R2 = 0.43)  Fig. . RNA edi43) Fig. .The challenges of cell type purification and single cell sequencing have precluded the broad application of those techniques. To leverage the extensive bulk RNA-seq datasets generated from human brains, we sought to use our data to infer cell type-specific RNA editing in 1129 bulk RNA-seq samples across 13 brain regions from the Genotype-Tissue Expression (GTEx) project  . Importantly, a significant fraction of the variance in global editing activity was explained by differences in neuronal fractions both within and across all brain regions  and ADAR2 expression (R2 = 0.70), and a negative association with ADAR3 (R2 = 0.22)  Fig. , on par 22) Fig. . For fursee Methods). As expected, the number of editing sites in the cerebellum and cerebellar hemisphere  was threefold greater than all other regions  , ADAR2 (R2 = 0.48), and ADAR1 (R2 = 0.30) expression   Fig. . Intra-don) Fig. . Importaon) Fig. . Moreoveon) Fig. .Fig. 6Anp = 6.8 × 10−6), followed by RNA editing sites mapping to downstream transcription start site positions, sites in exons and those in splice-site regions , a site which shows strong preferential editing in neurons, is consistently associated with the same SNP (chr8:30536581) across all 13 brain regions with the similar direction of effect  and expand upon these efforts by 13-fold (Supplementary Data We used imputed genotype data from the GTEx project to detect common single nucleotide polymorphisms (SNPs) that are associated with RNA editing levels  (see Methods). To identify genetic variants that could explain the variability of selective RNA editing, we ran association tests across each brain region and identified 661,791 cis-edQTLs  at a genome-wide FDR <5% Fig. . Each maect Fig. . Importa37 (Supplementary Data ADAR1 (R2 = 0.59) and ADAR2 (R2 = 0.85) expression, as well as with the proportion of neurons per brain region (R2 = 0.41)  Fig. . We confADAR2 and display increased neuronal content. These results illuminate differences in RNA editing sites and rates among cortical cell populations and establish frameworks for future large-scale studies of bulk RNA-seq brain tissue to interpret RNA editing sites and their variability.Exposing highly regulated editing sites across different cell populations, brain regions and those that are genetically regulated can promote studies to dissect their functional relevance within the right cellular context. Given the dearth of studies examining cellular features of RNA editing in the brain, we first set out to increase the resolution of RNA editing among three main cell populations in the human cortex. Through FANS, unique populations of cell types were isolated from the PFC with distinct functional differences. Using RNA-seq, we determined how RNA editing facilitates transcriptomic diversity more commonly in MGE-GABA and GLU neuronal populations relative to the major glial population in the brain—OLIG cells, documenting numerous sites and genes with cell-specific editing properties. snRNA-seq data was used to validate global trends in editing among CNS cell types and confirm the cellular resolution for a subset of RNA editing sites and genes. Subsequently, we applied rules of cell-specific RNA editing to quantify variability in RNA editing rates observed in bulk brain tissues from the GTEx project, and successfully show that: (i) tissues and donors with higher neuronal content display increased global editing rates and exhibit increased detection rates of RNA editing sites; (ii) commonly detected sites quantified in bulk brain tissue can be classified as cell type-specific or -enriched RNA editing sites; (iii) hundreds of edQTLs classify as cell type-specific or -enriched and display larger effect sizes than edQTLs without cellular resolution; and (iv) similar to RNA editing site detection, edQTL detection is also better powered in regions that express higher levels of Drosophila25. These global trends were accompanied by increased detection in the number of RNA-edited sites in neurons. Such differences were largely associated with differences in ADAR expression levels, rather than being driven by differential expression of the edited transcripts. We also uncovered several editing sites in close proximity on the same transcript and co-regulated in the same cell population , groups of sites which illustrate a regulation of editing that can exert its effect differently in different parts of the same transcript, as recently shown across neuronal populations in Drosophila25. While such events would be successively edited by ADAR, regulation by RBPs may also have a similar effect38. Several RBPs were preferentially expressed in either MGE-GABA and GLU populations or OLIG and were either positively or negatively correlated with global editing activity. Such RBPs might explain a fraction of the differential RNA editing levels observed at different sites in distinct populations. However, follow-up functional validation is required to fully understand the trans regulation of editing levels by RBPs among brain cell types.We observed increased global editing rates in neurons relative to OLIG populations in humans, similar to previous reports among CNS cell types in mice and UNC80, a gene encoding for the NALCN channel complex subunit, was preferentially edited in MGE-GABA neurons  in ons Fig. . This ge44. Moreover, intronic regions, which were highly edited in FANS-derived data, are often over-represented in RNA-seq and snRNA-seq generated from the nuclear fraction relative to a combined cytoplasmic and nuclear fraction as in bulk tissue sample and has been demonstrated across various tissues and model systems48. Validation rates by snRNA-seq were highest for GLU and MGE-GABA populations, which constituted pools with the largest number of nuclei, and concordance of editing levels for these sites was exceptionally high between snRNA-seq and FANS-derived datasets. Until now, there has been limited investigation of the accuracy of RNA editing calls from snRNA-seq data26. We anticipate that future work applying statistical models to snRNA-seq to quantify editing will benefit from increased sample sizes  and longer, more deeply sequenced reads, which may also expand the catalog of RNA editing sites necessary to build full cell-specific profiles.snRNA-sequencing data were used to confirm global RNA editing trends among MGE-GABA, GLU and OLIG populations, and shed additional light on global editing patterns across three additional CNS cell types. We also validated several thousand cell-specific RNA editing sites using an in silico cellular pooling technique to overcome hurdles related to snRNA-seq. Specifically, calling RNA editing sites from snRNA-seq is challenging as the technique is limited by extremely low capture efficiency and low sequencing depth with reads covering only a fraction of the entire genomeADAR2 expression and whether a region exhibits increased neuronal content (similar to variation in the AEI in bulk tissue). Differences in pools of cellular RNA across samples and randomly sequenced RNA fragments offer likely explanations for such intra-donor variability in site detection. Another possible explanation to account for such heterogeneity is that RNA editing is a transient process and such differences may simply reflect distinctions in the timing of cellular processes and signaling cascades. Indeed, at the level of individual cells, dynamic responses to environmental cues occur on a timescale that is faster than activity-induced transcription via the coordinated, activity-induced switching of internal molecular states and cellular metabolism2. Moreover, we detected 13,438 unique eSites, which occurred predominantly in 3′UTRs across brain regions. While this level of discovery significantly expands upon existing counts of genetically regulated editing sites in the brain27, we anticipate that this number is still an underestimate. Our edQTLs do not include rare RNA editing sites that were detected in a small subset of samples (which are the majority) that may also be genetically regulated. Future work considering edQTL models that account for rare editing sites may lead to a more resolved atlas of RNA editing sites and their genetic regulation in the brain.Our results also underscore several important features of RNA editing in bulk brain RNA-seq, and in doing so, highlight the tremendous heterogeneity of RNA editing site quantification and detection at the population scale. The majority of randomly selected sites are detected only across a subset of donors within a particular brain region, and these detection rates are largely a function of 50. As such, editing activity in neurons might play a role in controlling the organizational architecture of neuronal networks implicated in higher-order learning and memory. A related explanation of enhanced diversity of RNA editing in neurons may hint at its potential relation with essential neuronal functions. This idea is reinforced by the high density of editing in neuronal transcripts that encode proteins directly involved in excitatory/inhibitory (E/I) functions and the significant differences in editing between excitatory GLU and inhibitory GABA neurons. Such differences may modulate E/I balance within the cortical circuitry. The imbalance of E/I activity is thought to play a critical role in the pathophysiology of several different neuropsychiatric and neurological disorders, including autism spectrum disorder, schizophrenia, and epilepsy52. Thus, the functional relevance of the observed RNA editing differences between different populations of brain cells in health and disease warrants further investigation. Lastly, it remains unclear how RNA editing may play out across distinct cellular compartments and locations. Recent work has found discrete gene expression differences across synapses, dendrites, axons, and neuronal bodies54, and RNA editing may represent a driver of activity-induced RNA localization11.Finally, while our data offer avenues for understanding cellular specificity of RNA editing, one key question remains: “What is the biological explanation as to why neurons exhibit a preponderance of RNA editing activity?”. Here we extend some putative solutions. One possible explanation for enhanced RNA sequence diversity and ADAR expression in neurons might be to afford increased neural plasticity. Neurons, more than other cell types, must be able to quickly respond to altered environmental inputs, therefore, RNA editing may represent a mechanism capable of aligning with the timing required for experience-dependent plasticity, similar to other RNA modificationsn = 9), GLU (n = 9), and OLIG (n = 9) populations (syn12034263). Antibodies against brain cell population markers NeuN, SOX6, and SOX10 were used in the FANS protocol34. In brief, NeuN  is a well-established marker of neuronal nuclei and was used to isolate neuronal (NeuN+) from non-neuronal nuclei (NeuN−); SOX6 is a transcription factor expressed in MGE-GABA neurons during development and into adulthood, and anti-SOX6 antibodies are used to separate nuclei of MGE-GABA (SOX6+) from GLU (SOX6−) neurons55; finally, SOX10 is a transcription factor specifically expressed in OLIG and is used to isolate OLIG (SOX10+) from other non-neuronal nuclei.FANS-derived cortical cell populations: Raw FASTQ files were obtained for 27 paired-end (125 bp) nuclei from MGE-GABA  of three adult brains (syn15672826). The 10X Genomics chromium platform was used to capture and barcode single nuclei using the Chromium Single Cell 3′ Library and Gel Bead Kit v2 (10x Genomics) and the Chromium Single Cell A Chip Kit (10x Genomics)Transcriptome and genotype data from Genotype-Tissue Expression Project: We obtained approval to access the Genotype-Tissue Expression (GTEx) Project through the database of Genotypes and Phenotypes (dbGaP) (phs000424.v8). Raw FASTQ files were obtained for a total of 1431 paired-end (75 bp) samples across 13 brain regions (anterior cingulate cortex (ACC), amygdala, caudate, cerebellar hemisphere, cerebellum, cortex, frontal cortex, hippocampus, nucleus accumbens, putamen, spinal cord, substantia nigra). The VCFs for the imputed array data were available through dbGAP, in phg000520.v2.GTEx.MidPoint.Imputation.genotype-calls-vcf.c1.GRU.tar (the archive contains a VCF for chromosomes 1-22 and a VCF for chromosome X).The following RNA-seq datasets were leveraged to quantify RNA editing. All data is de-identified. Raw FASTQ files or mapped bam files of the following datasets were downloaded from either the NCBI sequencing read archive or Synapse.org:56 and mapped files were used as input for the subsequent analysis. To quantify site-selective RNA editing sites from FANS-derived cortical cell populations, we used a combination of de novo calling via Reditools v2.057 (parameters: -S -s 2 -ss 5 -mrl 50 -q 10 -bq 20 -C -T 2 –os 5) and supervised calling of known RNA editing sites from the REDIportal database58 as a second pass using samtools mpileup, described below. A number of filtering steps were applied to retain only high-quality, high-confident bona fide RNA editing sites: (i) all multi-allelic events were discarded; (ii) a minimum total read coverage of ten reads and at least three edited reads were required to classify as an editing event; (iii) any sites mapping to homopolymeric regions or in hg38 blacklisted regions of the genome59 were discarded; (iv) any sites mapping to common genomic variation in dbSNP(v150) and those in gnomAD with minor allele frequency greater than 0.05 were discarded; (v) RNA editing sites within 5 bp of an annotated splice site were removed to avoid issues with mis-mapped reads that should have mapped across splice junctions; and (vi) for FANS-derived cell populations, sites were further filtered using a rate of detection in at least eight out of nine samples per cell type. All remaining sites were annotated using ANNOVAR60 to gene symbols using RefGene and repeat regions using RepeatMasker v4.1.161. Conservation metrics were gathered from the phastConsElements30way table of the UCSC Genome Browser, which consists of evolutionary conservation using phastCons and phyloP from the PHAST package62. Importantly, for sites uniquely detected in one cell type, we performed the third round of RNA editing quantification in the remaining two cell types to determine whether those sites in the neighboring cell types displayed high coverage and little-to-no editing (escaping the minimum coverage and edited read threshold) or simply little-to-no read coverage (escaping the minimum coverage threshold). The resulting RNA editing data frames per cell type contained no more than ~6% missing data. These values were imputed in a cell-specific manner using median imputation . The resulting sites from these steps were subsequently referred to as high-confidence selective RNA editing sites and were used for downstream analysis.All FASTQ files were mapped to the human reference genome (GRCh38) using STAR v2.7.317. This approach quantifies the total number of edited reads and the total number of unedited reads that map to each RNA editing site detected. All analyses considered read strandedness when appropriate. This supervised analysis required at least ten supporting reads and a minimum of three edited reads to qualify as a RNA editing site.To quantify RNA editing in snRNA-seq and GTEx data, we called known sites from the REDIportal database in addition to any novel sites  identified from FANS-derived cell populations. To this end, to quantify RNA editing sites and rates from a list of given sites, nucleotide coordinates for all such sites were used to extract reads from each sample using the samtools mpileup functionSites uniquely detected in one cell population and with zero coverage (or with insufficient detection rates) in the other two cellular populations.Sites detected in at least two cell populations but with significantly higher RNA editing levels in one of the two cell types, determined by linear regression analysis.Alu Editing Index for each sample using the STAR mapped bam files as input. The AEI is computed as the ratio of edited reads (A-to-G mismatches) over the total coverage of adenosines and is a robust measure that retains the full Alu editing signal, including editing events residing in low-coverage regions with a low false discovery rate.The AEI method v1.0 was leveraged to compute the 7 with minor additional processing steps15, including: (1) extending cluster boundaries by the average distance between editing sites per cluster and subsequently merging clusters with overlapping coordinates (cluster length is a commonly a product of read length); (2) all resulting hyper-editing sites were annotated using ANNOVAR (described above); (3) sites mapping to common genomic variation in dbSNP(v150) (maf > 0.05) were discarded.RNA reads that undergo extensive hyper-editing of many neighboring adenosines within an extended region or cluster on the same transcript will not align to the reference genome due to the high degree of dissimilarity. Therefore, to identify hyper-edited reads in the current study, all unmapped reads from the original STAR alignment were converted to FASTQ and used as input for hyper-editing analysis. We adopted a well-established RNA hyper-editing pipeline7. The normalized hyper-editing signal was computed by dividing the total number of resulting high-quality RNA hyper-editing sites over the total number of mapped bases from the STAR alignment and multiplying the resulting value by one million. The number of total uniquely mapped bases for each sample were collected using Picard Tools v2.22.3 (http://broadinstitute.github.io/picard/) on each mapped bam file.To minimize any batch effects, we computed a normalized hyper-editing signal per million mapped bases63. We pulled sequences 4 bp (±) of the target adenosine to evaluate the enrichment and depletion of specific nucleotides neighboring A-to-G editing sites.EDLogo was used to quantify local sequence motifs2(gene length + 1)). Subsequent pairwise comparisons of the normalized number of RNA editing events per gene were performed . Genes displaying enrichment of normalized RNA editing sites were those dented as outlier genes beyond the 99% confidence intervals from the grand mean.The total number of selective A-to-G RNA editing events were computed for each gene within each cell population. The total number of edits per gene were compared in a series of pairwise comparisons across each cell populations. To adjust for gene length, we normalized the number of A-to-G editing events per gene for each cell type by the log of gene length  were first defined based on a consensus list of 837 high-confidence human RBPs from the hRBPome databaseADAR). Data from two eCLIP experiments and an input control experiment were obtained using the postmortem frontal cortex from control subjects18. At the time of writing this paper, this experiment represents the only eCLIP-seq data generated from human postmortem brain tissue. The regioneR R package66 was used to test overlaps of FMRP binding regions for each replicate separately with cell type-specific RNA editing sites based on permutation sampling. We repeatedly sampled random regions from the genome 1000 times, matching the size and chromosomal distribution of the region set under study. By re-computing the overlap with FMRP binding sites in each permutation, the statistical significance of the observed overlap was computed.Next, we collected transcriptome-wide binding patterns of FMRP  to explore potential ADAR-dependent editing activity. All significance values were adjusted for multiple testing using the Benjamini and Hochberg (BH) method to control the false discovery rate (FDR). Sites passing a multiple test corrected P value <0.05 were labeled significant.To identify sites with differing levels of RNA editing between two given cell types, we implemented linear model through the https://syngoportal.org/) using default parameters. Further, the ToppFunn module of ToppGene Suite software67 was also used to examine enriched biological processes. For this analysis, set a genomic background defined as all genes harboring at least one editing site and tested for significance using a one-tailed hypergeometric distribution with a Bonferroni correction. This is a proportion test that assumes a binomial distribution and independence for the probability of any gene belonging to any set. We use a one-sided test because we are explicitly testing for over-representation of genes that harbor editing sites across hundreds of GO categories, without any a priori selection of candidate gene sets. Using this framework, we tested enrichment among (i) all differentially edited sites and (ii) all differentially edited sites with the exception of intronic sites  to gauge their overall influence on enrichment results.Genes harboring differentially edited sites were functionally annotated using SynGO to test for synaptic gene enrichment  in an independent cohort of six adult individuals without a neuropsychiatric diagnosis. Nuclei of MGE-GABA, GLU, and OLIG cells were isolated by FANS, followed by RNA extraction and library construction. PCR primers were designed to target four recoding RNA sites in 35. To optimize cell classification and reduce unwanted variance, we quality filtered, normalized, and scaled data according to Seurat’s guidelines. For these data, we used a set of previously implemented methods35 consisting of the following steps. First, a cell was excluded if the number of expressed genes was less than 300 or more than 7000, with the number of UMI less than 300 or more than 20000, or the percentage of mitochondria reads more than 5%. The normalization method was LogNormalize and the scale factor was 10000. The linear regression was performed by choosing the percentage of mitochondria reads as a variable. Rfrom was used to compute the specificity score for each gene in each cell cluster. Hierarchical clustering was manually checked along with the top-ranked genes in each cell cluster to determine cellular specificity based on well-known gene markers to verify the assignment of cell types and subtypes. Cells with inconsistent or no assignment were removed from the analysis.Mapped bam files were obtained following the alignment of short reads to the reference genome (GRCh38/hg38). Reads were filtered for low quality, and counts were quantified (cell barcode counts and unique molecular identifier counts for each annotated gene) using CellRanger count, as previously describedNEUROD6 expression. Additionally, we further collapsed these cell clusters into six main cellular pools that constitute MGE-GABA, GLU, and OLIG populations  and astrocyte (GFAP), microglia (TNEN119) and endothelial cell (CLDN5) populations.Like the original report, we identified 29 transcriptionally distinct cell clusters representing various populations of glutamatergic excitatory projection neurons, GABAergic interneurons, oligodendrocyte progenitor cells, oligodendrocytes, astrocytes, microglia, endothelial cells, and mural cells. Here, we further reduced these clusters into 24 cell clusters by collapsing five highly similar GLU cell subsets based on To quantify RNA editing sites from snRNA-seq data, each mapped bam files was parsed into six unique bam files (per donor) reflective of the six main cellular pools (described above) using each cell's unique molecular identifier (UMI). In this way, we pooled cells with matching cell type markers and gene expression patterns for subsequent RNA editing analysis. Next, we quantified RNA editing levels for all high-confidence sites identified in the FANS-derived cell populations using the samtools mpileup function (described above). Given the challenges related to the limited number of cells per pool and low sequencing depth, we explored validation rates under varying read coverage thresholds (minimum of 3–20 reads), but ultimately required minimum coverage of 10 reads per site. We defined sites that were validated as those with median editing rates across the three replicates that were within 50% of the median editing rate of the FANS-derived site.http://rseqc.sourceforge.net/) to compute RNA-seq read coverage over the entire gene body for all transcripts for both the FANS RNA-seq data and snRNA-seq pools. Indexed bam files were used as input and the “wgEncodeGencodeBasicV31” table was downloaded from the UCSC table browser and used as our reference gene model in bed format. Second, for all sites, we computed the distance to the respective transcription start site (TSS). Sites were next parsed based on those that were validated by snRNA-seq and subsequently binned into three groups based on total read coverage: high (first quintile), medium , and low (fifth quintile). A two-sided Mann–Whitney U-test was used to test whether sites that validate by snRNA-seq (and each respective coverage bin) was significantly further from the TSS compared to those sites which did not validate.We tested whether sites that validate based on snRNA-seq were skewed toward the 3′ end of a transcript. First, we used RseqQC  using STAR v2.7.370 and utilized the Darmanis et al., signature matrix71 which contained a mixture of six major cell types: astrocytes, oligodendrocytes, microglia, endothelial cells, excitatory and inhibitory neurons. NNLS, executed through the est_frac function, was applied to log2 count per million (CPM) transformed data using the limma package in R. For each sample, both excitatory and inhibitory neuronal predictions were summed into one “neuronal” cell population. We focus our predictions on these major cell types in an effort to reduce noise and to evaluate distribution of cell types that reflect an approximate expected distribution in the human brain based on prior work.To compute cellular composition, we applied non-negative least squares (NNLS) from the bMIND R packagecis-eQTL mapping within the 13 brain regions. We leveraged existing VCFs for high-quality imputed genotype array data from dbGAP , using SNPs with an and estimated minor allele frequency ≥0.05. Only RNA editing sites detected in at least 50% of samples per region were considered for cis-edQTL mapping. Each data matrix for each region exhibited ~17% missing values, which were imputed using the well-validated predictive mean matching method in the mice R package using five multiple imputations and 30 iterations72. The genomic coordinates for all RNA editing sites were lifted over to GRCh37. Subsequently, to map genome-wide edQTLs, a linear model was used on the imputed genotype dosages and RNA editing levels using MatrixEQTL73. RNA editing levels were covaried for sex, age, RIN, and type of death. To control for multiple tests, the FDR was estimated for all cis-edQTLs (defined as 1 Mb between SNP marker and editing position), controlling for FDR across all chromosomes. Significant cis-edQTLs were identified using a genome-wide significance threshold (FDR <0.05). To assess whether cis-edQTLs relate to brain promoter and enhancer regions, the overlap between max cis-edQTLs and promoter and enhancer regions was tested from the FANTOM project collected from the SlideBase database37. A permutation-based approach with 1000 random permutations was used to determine the statistical significance of the overlap between edSNP coordinates and enhancer regions using the R package regioneR59.We conducted Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Reporting Summary