APOE locus, there has been considerable success in identifying additional genetic variants associated with Alzheimer’s disease. Major challenges in understanding how genetic risk influences the development of Alzheimer’s disease are clinical and neuropathological heterogeneity, and the high level of accompanying comorbidities. We report a multimodal analysis integrating longitudinal clinical and cognitive assessment with neuropathological data collected as part of the Brains for Dementia Research study to understand how genetic risk factors for Alzheimer’s disease influence the development of neuropathology and clinical performance. Six hundred and ninety-three donors in the Brains for Dementia Research cohort with genetic data, semi-quantitative neuropathology measurements, cognitive assessments and established diagnostic criteria were included in this study. We tested the association of APOE genotype and Alzheimer’s disease polygenic risk score—a quantitative measure of genetic burden—with survival, four common neuropathological features in Alzheimer’s disease brains , clinical status  and cognitive performance . The APOE ε4 allele was significantly associated with younger age of death in the Brains for Dementia Research cohort. Our analyses of neuropathology highlighted two independent pathways from APOE ε4, one where β-amyloid accumulation co-occurs with the development of tauopathy, and a second characterized by direct effects on tauopathy independent of β-amyloidosis. Although we also detected association between APOE ε4 and dementia status and cognitive performance, these were all mediated by tauopathy, highlighting that they are a consequence of the neuropathological changes. Analyses of polygenic risk score identified associations with tauopathy and β-amyloidosis, which appeared to have both shared and unique contributions, suggesting that different genetic variants associated with Alzheimer’s disease affect different features of neuropathology to different degrees. Taken together, our results provide insight into how genetic risk for Alzheimer’s disease influences both the clinical and pathological features of dementia, increasing our understanding about the interplay between APOE genotype and other genetic risk factors.Alzheimer’s disease is a highly heritable, common neurodegenerative disease characterized neuropathologically by the accumulation of β-amyloid plaques and tau-containing neurofibrillary tangles. In addition to the well-established risk associated with the   APOE and the development of tauopathy and beta-amyloidosis and an independent pathway associated with tauopathy.We report a multimodal analysis profiling how genetic risk factors for Alzheimer’s disease influence the development of neuropathology and clinical performance in the Brains for Dementia Research (BDR) study, highlighting a shared pathway between  APOE gene. Relative to the most common genotype (ε3/ε3), the ε4 allele increases the risk of Alzheimer’s disease, with ε4 homozygosity associated with ∼20-fold increase in risk . The most frequent manifestation of Alzheimer’s disease is late onset Alzheimer’s disease where onset occurs after the age of 65. Late onset Alzheimer’s disease is highly heritable  cohort was established in 2007 recruiting both dementia patients and unaffected controls over the age of 65 to partake in routine longitudinal assessments collecting cognitive, clinical, lifestyle and psychometric data, prior to post-mortem brain donation  and the associated university brain banks handling the donations (Cardiff brain donations were banked in London). Participants over the age of 65 were recruited using national and local press, TV and radio coverage, articles in charity newsletters, national magazines with an older following, BDR posters, leaflets, memory clinics, talks at carer/support groups, Women’s Institute and the University of the Third Age. There was no screening to exclude or include individuals with particular diagnoses or those carrying genetic variants associated with neurodegenerative diseases. The cohort includes individuals with and without dementia, spanning the full spectrum of dementia diagnoses. Participants underwent a series of longitudinal cognitive and psychometric assessments and registered for brain donation. An extensive description of the recruitment strategy, demographics, assessment protocols and neuropathic assessment procedures can be found in (All assessments were conducted by a trained psychologist or research nurse. Exclusion criteria to undergo assessments included: (i) factors precluding brain donation , (ii) being younger than 65 for healthy controls (except where they were spouses/partners of participants with dementia), (iii) having insufficient English language skills for completing assessments and (iv) being geographically too remote from an assessment centre. Baseline assessments were conducted face-to-face (in the participant’s place of residence or a BDR centre), follow-up assessments were usually face-to-face but telephone interviews were also used for some healthy control participants. Follow-up interviews were annual for participants with cognitive impairment, and every 1–5 years (depending on age) for cognitively healthy participants. Clinical assessment was performed using the clinical dementia rating (CDR) . CognitiAfter removal, the brain was examined macroscopically and digitally recorded. After slicing, the brain was comprehensively sampled according to the BDR protocol by experienced neuropathologists in each of the five network brain banks. This protocol, arrived at by consensus across the BDR network and based on the BrainNet Europe initiative (n = 306 670 variants) and custom content covering 179 467 variants specific to neurological diseases , (iii) they had excess heterozygosity [>3 standard deviation (SD) from the mean], (iv) they were related to another individual in the sample (pi hat > 0.2), where one individual from each pair of related samples was excluded considering data quality and phenotype or (v) they were classed as non-European, determined by merging the BDR genotypes with data from HapMap Phase 3 (http://www.sanger.ac.uk/resources/downloads/human/hapmap3.html), linkage disequilibrium pruning the overlapping single nucleotide polymorphisms (SNPs) such that no pair of SNPs within 1500 bp had r2 > 0.20 and visually inspecting the first two genetic principal components along with the known ethnicities of the HapMap sample to define European samples  which uses Eagle2 (https://genome.sph.umich.edu/wiki/Minimac4) with the most recent 1000 Genomes reference panel . Imputed genotypes were then filtered with PLINK2.0alpha, excluding SNPs with an R2 INFO score < 0.5 and recoded as binary PLINK format. Proceeding with PLINK1.9, samples with >5% missing values, and SNPs with >2 alleles, >5% missing values, Hardy-Weinberg equilibrium P < 0.001 or a minor allele frequency of <5% were excluded. The final quality controlled imputed set of genotypes contained 6 607 832 variants.DNA extraction was performed using a standard phenol chloroform method on 100 mg of brain tissue. DNA quality was assessed using the Agilent 2200 TapeStation DNA integrity number and quantified using NanoDrop 3300 spectrometry. Genotyping was performed on the NeuroChip array which is a custom Illumina genotyping array with an extensive genome-wide backbone  , and then summed across all retained clumped variants with an Alzheimer’s disease GWAS P-value < PT. A range of P-value thresholds (PT) were used initially, to generate multiple possible PRS, where the optimal PRS was selected as the score that explained the highest proportion of variance (Nagelkerke’s pseudo R2) in Alzheimer’s disease case control status. In this analysis, Alzheimer’s disease cases and controls were defined as Braak high (Braak tangle stages V and VI) and low (Braak tangle stages 0–II) respectively, and PRS was tested using a logistic regression model with the first 8 genetic principal components as covariates. In the BDR cohort the optimal threshold for selecting SNPs for the PRS was P < 5 × 10−8 (GWAS results from 318 605)  which ‘c5 × 10−8 . Prior tAPOE SNPs rs7412 and rs429358 were genotyped with TaqMan assays using standard protocols. Where APOE genotype by TaqMan assay was not available, it was generated from the NeuroChip data (n = 44). The NeuroChip array includes multiple probes to assay the two APOE SNPs; based on the optimal concordance with the TaqMan assay (91% concordant across assays) we used the probes rs7412.B3 and rs429358.T2 to determine APOE status. In all statistical analyses, APOE status was modelled as two numeric variables counting the number ε2 alleles and number of ε4 alleles an individual had. Given the rarity of ε2/ε2 genotype [only four occurrences (0.58%) in this sample], the ε2/ε2 individuals were combined with the individuals with one ε2 allele.The https://github.com/ejh243/BDR-Genetic-Analyses).All statistical analyses were performed in R version 3.5.2. All analytical code is available via GitHub  APOE genotype modelled as two variables, (ii) Alzheimer’s disease PRS and (iii) APOE genotype and Alzheimer’s disease PRS simultaneously. All models included covariates for sex, BDR centre and eight genetic principal components.To test whether APOE status or Alzheimer’s disease PRS and any of the continuous neuropathology variables , clinical  or cognitive status at death  were tested using a linear regression model. TDP-43 proteinopathy as a binary variable was analysed with logistic regression, but the model framework was the same. Up to four regression models were fitted for each variable. First, the effects of APOE status and Alzheimer’s disease PRS were estimated separately using Model 1 and Model 2 below.Genetic associations between either Model 1:Model 2:APOE (either variable) and PRS were significantly associated with an outcome, then a multiple regression analysis was additionally fitted testing APOE and PRS simultaneously to confirm these were independent associations (Model 3).If Model 3:APOE and PRS was fitted to test if PRS associations differed depending on APOE genotype.Finally, an interaction model (Model 4) between Model 4:All analyses included age at death, sex and BDR centre as covariates and the first eight genetic principal components. Analyses for clinical or cognition measures also included a covariate that measured the time lapse between the last assessment and death.APOE and Alzheimer’s disease PRS affected clinical status and cognitive trajectories, we fitted multi-level regression models using all available pre-mortem assessment data. A time variable was created which measured the number of days after the first visit that an assessment took place. Each cognitive variable was then tested as the dependent variable against this time variable included as a fixed effect along with covariates for age, sex, BDR centre and the first eight genetic principal components and a random effect for individual. To test for genetic effects on the cognitive trajectory, either APOE (coded as two variables) or Alzheimer’s disease PRS, was included in the model as a main effect and as an interaction with time. Models were fitted using the R packages lme4 and lmTest.To test how APOE genotype). Therefore, we performed a multiple testing correction for 36 tests, reporting significant associations as those with P < 0.0014. Given the correlations between the neuropathological, clinical and cognitive variables this is likely to be a conservative approach.In total, we tested 12 outcomes against 3 genetic variables. Our outcomes comprised five neuropathological variables, one clinical variable at death, two cognitive measures, one longitudinal clinical, two longitudinal cognitive measures and a survival analysis of age at death. Against these 12 outcomes, we tested 3 genetic variables (Alzheimer’s disease PRS and two variables to model https://www.dementiasplatform.uk/) platform upon application.Genetic, clinical and cognitive data are available through the Dementia’s Platform UK . Within this cohort, 57.3% of individuals had dementia at their first assessment (i.e. at baseline), with 63.3% of the cohort affected by dementia at death. At recruitment, individuals had a mean CDR of 1.42 (SD = 1.36), a mean MMSE score of 22.3 (SD = 8.81) and a mean MoCA score of 17.2 (SD = 10.6). These scores indicate that the majority of participants only suffered mild cognitive impairment, although the full range of cognitive performance was represented in the cohort. Participants underwent a mean of 2.85 assessments (SD = 1.71) prior to death. Individuals who had at least two assessments (N = 486) were followed for a mean of 3.40 years (SD = 2.00 years) with a mean of 1.42 years between assessments (SD = 0.67 years). Our genetic analyses focused on four semi-quantitative and one indicator neuropathology variable. In 672 samples NFT pathology was quantified using Braak NFT stage . Alzheimer’s disease PRS was nominally associated with an increased mortality , although this was not significant after correcting for multiple testing.To determine whether higher genetic risk for Alzheimer’s disease was associated with increased mortality we analysed survival with Cox’s proportional hazard models . APOE geAPOE ε4 alleles was positively associated (P < 0.00014) with all four semi-quantitative neuropathology measures (P = 4.16 × 10−24). Associations were also found between ε4 status and Thal β-amyloid phase , neuritic plaque density  and Braak Lewy body stage . Alzheimer’s disease PRS was associated with two measures of neuropathology (P = 1.36 × 10−6) and neuritic plaque density . TDP-43 was not associated with either APOE genotype or Alzheimer’s disease PRS. Although variants in the APOE region were excluded from the PRS, we tested both APOE and PRS against Braak NFT stage and neuritic plaque density simultaneously to confirm that the identified associations were independent. The estimated effects of ε4 on both Braak NFT stage and neuritic plaque density were unaffected, while the Alzheimer’s disease PRS associations were slightly attenuated . In this analysis, none of the five neuropathological variables had statistically significant differences across APOE genotype groups (P > 0.05)  and β-amyloid plaques.The number of measures . The mosathology : a highetenuated  but rema > 0.05) . Taken tAPOE or Alzheimer’s disease PRS had a specific (or primary) effect on a particular aspect of neuropathology. To this end, we repeated the analysis of how Alzheimer’s disease PRS and APOE influence pathology, sequentially controlling for other neuropathology variables. This analysis revealed some interesting patterns. First, after controlling for any of the other three quantitative neuropathological variables, Braak Lewy body stage was not significantly associated with APOE ε4 , albeit with an attenuated magnitude of effect. Considering the two measures of plaque burden, only Thal β-amyloid phase remained significantly associated with ε4 after controlling for neuritic plaque density . Neither Braak NFT stage nor neuritic plaque density remained significantly associated with Alzheimer’s disease PRS after controlling for the other measure of pathology (APOE ε4 has a specific influence on tauopathy (NFTs) as well as a shared effect on both plaque and NFT development, whereas the PRS is more generally associated with an increased burden of Alzheimer’s disease neuropathology.Given that the two distinct molecular pathologies—tauopathy and β-amyloidosis—that define Alzheimer’s disease are highly correlated , we want APOE ε4  suggesti in pre-mortem CDR score . In contrast, Alzheimer’s disease PRS was not significantly associated with any of the measures of clinical or cognitive status prior to death. To test whether the association between APOE and clinical measures was mediated by neuropathology we repeated these analyses including Braak NFT stage as an additional covariate; this variable had the largest effect in the genetic analyses described above, and its effect additionally captured associations with plaque pathology. In this model, the associations between APOE ε4 and CDR or MMSE were attenuated and neither remained significant , while MMSE declined by a mean of 1.07 per year (P = 3.00 × 10−29). APOE genotype was associated with worse cognitive scores at the start of the study and faster rates of decline as the study progressed (P = 4.92 × 10−5) at the start of the study, and individuals then accumulated an additional decrease in 0.803 in their score per allele per year (P = 1.58 × 10−8). In contrast, although APOE was associated with a higher CDR score at the start of the study , there was no significant difference in the change in clinical status related to APOE as the study progressed. There was no significant association with MoCA scores and APOE genotype. There was no significant association between Alzheimer’s disease PRS and longitudinal clinical or cognitive profiles or clinical or cognitive status at study entry. On repeating these analyses using the participant’s age rather than time in the study, we found no significant linear associations with either cognitive status at study entry or performance as the study progressed (Participants had a mean of 2.85 (SD = 1.71 visits) clinical assessment visits spread over a mean of 3.40 years (SD = 2.00 years) with a mean time between visits of 1.42 years (SD = 0.67 years). Over the course of all participants' involvement in the BDR study, there was an overall decline in clinical status and cognitive performance. On average the CDR increased by a mean of 0.139 per year (ogressed . For eveogressed .P = 7.30 × 10−42) and declined quicker; each additional Braak NFT stage was associated with an additional increase in 0.0247 in CDR per year (P = 3.99 × 10−5). We observed similar results for cognitive performance measured by MMSE; at study entry, each additional Braak NFT stage was associated with a decrease in 2.58 in MMSE score (P = 7.27 × 10−26) and participants accumulated an additional decrease in 0.384 in MMSE per Braak NFT stage per year (P = 3.90 × 10−15). Repeating the APOE analysis with a covariate for the potential confounder of neuropathology found that in line with the cross-sectional analyses, the associations with both clinical severity and cognition were no longer significant after adjusting for Braak NFT stage  and that although APOE genotype is also associated with β-amyloidosis, synucleinopathy and cognition, these relationships are largely confounded by their correlation with tangle burden. Furthermore, our results indicate that APOE has a specific direct effect on NFT independent of other neuropathologies. Although this finding contradicts the predictions of the ‘amyloid cascade hypothesis’ in which tau tangle formation is considered secondary to β-amyloid pathology  and β-amyloidosis, there was no evidence of independent effects on either, suggesting that, in combination, common genetic variants have a broader, more general effect on the neuropathological burden present in Alzheimer’s disease. This contrasts with findings from a previous study testing the consequences of an Alzheimer’s disease PRS without APOE, which only reported a significant association with NFTs and not β-amyloid plaques , and a second pathway with direct effects on NFTs independent of β-amyloidosis. It is as a consequence of these neuropathological changes that cognitive performance is then impaired. The relationship between common genetic variants associated with Alzheimer’s disease and neuropathology is more complex, with each individual variant potentially having a different effect on neuropathology and cognition. Taken together, these results provide insights into how the symptoms of Alzheimer’s disease dementia manifest and how genetic risk factors influence the development of pathology.In summary, our data indicate that fcaa167_Supplementary_DataClick here for additional data file.


The escalating scale of millet consumption on the eastern Eurasian steppe over time, and an expansion of isotopic niche widths, indicate that historic Mongolian empires were supported by a diversification of economic strategies rather than uniform, specialized pastoralism.Populations in Mongolia from the late second millennium B.C.E. through the Mongol Empire are traditionally assumed, by archaeologists and historians, to have maintained a highly specialized horse-facilitated form of mobile pastoralism. Until recently, a dearth of direct evidence for prehistoric human diet and subsistence economies in Mongolia has rendered systematic testing of this view impossible. Here, we present stable carbon and nitrogen isotope measurements of human bone collagen, and stable carbon isotope analysis of human enamel bioapatite, from 137 well-dated ancient Mongolian individuals spanning the period c. 4400 B.C.E. to 1300 C.E. Our results demonstrate an increase in consumption of C While such stereotypes likely arose from the hyperbolized accounts of neighboring adversaries, starting with the Han, who fought against the Xiongnu5, they have persisted and now pervade academic evaluations of the economic basis of these ancient peoples. The modern economic focus on pastoralism in rural areas of Mongolia today is frequently viewed as a relic of the past and has been drawn upon to interpret the often-fragmentary archaeological record of this region7, although ethnoarchaeological approaches often ignore the role of urban markets and motorized transport in modern mobile pastoralism. The view of uniformly specialized pastoral economies has also furthered the scholarly fascination with historical Mongolian populations, resurrecting the long-standing question of whether an empire can meet the costs and challenges of long-term political and economic organization in the absence of grain surpluses7.Mongolian empires, such as the Xiongnu and Mongols, are some of the most renowned imperial entities in public and academic thought. This is, in part, due to their historical portrayal as highly mobile, predatory horseback polities with a specialized dairy and meat-based economy10. Crop surplus has traditionally been viewed as an essential component of stable political entities and complex imperial food production and procurement systems are often over-simplified by historians and archaeologists, leading to their characterization as single-resource systems (such as maize for the pre-Columbian empires of South America). Refined analyses generally reveal diverse and dynamic economies supporting imperial expansions, which draw together a variety of food sources11. As a consequence, it is perhaps unsurprising that archaeological, archaeobotanical, and historical records are beginning to strongly hint at the possibility that historical Mongolian empires were not solely reliant on dairy pastoralism, but also featured agriculture, as well as craft specialization, and participation in trade systems that spanned thousands of kilometers14.Empires are, however, inherently complex and, by definition, extend their control over multiple societies, cultures, and economies, as well as heterogeneous landscapesPanicum miliaceum L.) and foxtail , and other domesticated grains in the surrounding steppe lands of Siberia, Kazakhstan, and northwestern China during the second and first millennia B.C.E.24. There are some archaeobotanical data suggesting the use of crops in Mongolia starting around c. 100 B.C.E. – 200 CE26, though these have been dismissed as reflective of trade rather than local production27. Overall, due to issues of wind deflation and a lack of sampling during excavation, archaeobotanical evidence from Mongolia is severely lacking. Moreover, where present, it is difficult to determine the degree to which an archaeobotanical assemblage represents overall dietary reliance. To date, there have been no systematic, direct analyses of the consumption of domesticated crops among peoples in this region over the past three millennia, leaving the economic basis for some of the world’s most famous empires unresolved.Of particular interest in this context has been the growing archaeobotanical evidence from across Central Asia that demonstrates an influx of millet, both broomcorn (31. The distinction in stable carbon isotope ratios (δ13C) between C3 plants on the one hand – including crops such as rice (Oryza sativa L.), wheat (Triticum spp. L.) and barley (Hordeum vulgare L.) – and C4 plants on the other – including millets and maize (Zea mays L.) – can be tracked through the bone collagen and bioapatite of humans relying on these resources and/or the animals feeding off of them32. While bone collagen δ13C is primarily derived from the protein portion of the diet, tooth enamel bioapatite δ13C reflects the whole diet33. Stable nitrogen isotope (δ15N) analysis provides additional insights into the trophic level position of an individual, orienting them within the local food chain34.Stable isotope analysis of archaeological human and associated faunal remains has emerged as an increasingly powerful methodology for tracking palaeodietary and subsistence change in Central and East Asia4 resources (i.e. millet and millet-based foods). We are especially interested in dietary trends during the Xiongnu and Mongol imperial periods, as there has long been a dominant assumption that these empires wholly depended on dairy pastoralism. We use stable carbon isotope analysis of human tissues to directly test whether, in line with some previously published archaeobotanical and historic evidence, the Xiongnu and Mongol empires in fact relied quite significantly on millet-based agricultural systems. We present δ13C and δ15N analysis of human bone collagen and δ13C and δ18O analysis of human tooth enamel bioapatite from 137 previously-excavated individuals from across Mongolia dated to between c. 4400 B.C.E. and 1375 C.E. in order to directly assess changing diets through the region’s key imperial transitions.It is our aim to discover when Eastern Steppe populations began utilizing cultivated Cc. 800–200 B.C.E.) and the Xiongnu (c. 200 B.C.E. – 250 C.E.). The Early Iron Age samples include 16 individuals  from one site. From the subsequent Xiongnu, we analyzed 59 individuals  from 28 sites. Individuals from the later Mongol Empire (c. 1200–1375 C.E.) are grouped together and consist of 28 individuals  from 19 sites.We analyzed 80 bone collagen and 108 dental enamel samples from 137 individuals from 60 archaeological sites Tables ; Fig. 1.35  and dry  regions, we also determined the average values for each environmental type , to adequately assess human δ13C bone collagen), Supplementary Table 13C enamel bioapatite), and Supplementary Table 15N bone collagen). For the bone collagen data, both the Xiongnu and the Mongol average δ13C values were significantly higher than those of Early individuals (p < 0.05). The same trend was seen for tooth enamel δ13C, with Early Iron, Xiongnu, and Mongol samples having δ13C significantly higher than that of the Early group . Bronze Age δ15N values were also significantly higher (p < 0.05) than those of the Early Iron, Xiongnu, and Mongol periods . Modern plant samples from Mongolia have yielded δ13C values ranging from −28.3 to −23.4‰ for C3 photosynthetic pathways and an average δ13C of −14.7‰ for plants following the C4 photosynthetic pathway39. Notably, wild C4 plants make up a much smaller proportion of Mongolian and other Central Asian environments than C3 plants41. Overall, contemporary studies suggest that leaf δ13C values decrease with increasing mean annual precipitation42, both as a product of reduced C4 plants in wetter landscapes and aridity-driven changes in δ13C among C3 plants 29, the Gobi 48, Gobi-Altai 30, and north central Mongolia 30 areas to show that regional herbivores generally consumed C3 plants, with some having higher stable carbon isotope values, indicative of C4 plant consumption, in the hyper-arid desert regions48.While C13C values than the available fauna in all periods, with the greatest difference occurring in the Xiongnu and Mongol periods (p ≤ 0.005) which is a product of higher levels of aridity leading to a higher availability of C4 plants in the natural vegetation cover. However, no such correlation is observed in humans  or between δ15N bone collagen and δ13C enamel values . Given this, alongside the consistent elevation of human δ13C values over the available fauna δ13C values, this indicates that higher δ13C values in human bone collagen and enamel is a product of direct consumption of non-wild C4 plants.Statistical tests further support this assessment, with humans having higher δ05)Figs.  and 3A. ans Fig. , either 13C values for faunal remains from steppe regions are typically C3 (−19.3 ± 1.3‰), and the stable carbon isotopic offset between bone collagen of herbivores and carnivores is c. 1‰49. Thus, human bone collagen steppe samples dating to the Early period (prior to 800 B.C.E.) do not show δ13C values indicative of a millet dietary contribution (−18.3 ± 0.1‰). This same offset applies to human bone collagen samples from dry regions from all periods since these are elevated by up to 1‰  when compared to the bone collagen mean for faunal samples from dry regions (−17.2 ± 1.5‰). However, for later time periods in steppe regions average human bone collagen δ13C values are elevated by c. 3‰  when compared to faunal values. Thus, the higher (c. 2‰) human bone collagen δ13C values observed for later periods in steppe regions when compared to the early period is indicative of a temporal increase in millet-based food consumption. Furthermore, given that bone collagen reflects primarily consumed protein, and that millet has a poor protein content, the dietary caloric contribution from millet was likely much  higher than its protein contribution50. This is corroborated by human enamel δ13C values given that this isotopic proxy reflects the carbon dietary mix50. Steppe human enamel samples for the later periods show mean δ13C values higher by c. 3.5‰ when compared to the Early period. For dry areas, mean human enamel samples δ13C values are higher by c. 1‰ when compared to the Early period, which indicates a temporal increase in millet-based food consumption although considerably smaller than that observed in the steppe regions as shown also in the model estimates for millet caloric contributions  between δ15N and δ13C bone collagen values which is expected given that an increase in aridity leads to a higher availability of C4 plants in the vegetation cover. A similar correlation, albeit with isotopic offsets, would be expected if humans relied exclusively on animal products. However, no clear environmentally-driven correlation is observed for the human groups. There is no significant correlation for the Early  and Xiongnu  periods, and although the correlation is significant for the Mongol period , it only explains 39% of the variability. For similar δ15N bone collagen values across the human individuals, there are wide ranges in δ13C collagen values. Whereas during the Xiongnu period one can observe a significant negative correlation , which implies the contribution from a food source with higher δ13C values but lower δ15N values when compared to animal food sources. These isotopic relationships are indicative of varying individual intake of a food with elevated δ13C values, such as millet, and having relatively uniform δ15N and δ13C values across regions with varying levels of aridity.Further evidence for C13C values in human bone collagen and tooth enamel through time is a product of the consumption of crops rather than changing availabilities of baseline C4/C3 plant ratios or the availability of samples in different local environments, we developed a Bayesian model to produce a C4 dietscape, representing estimates of spatial distribution of C4 plants based on per capita caloric consumption (See SI for detailed discussion). Stable carbon isotope data of dental enamel was used, and individuals were separated into two periods, Early (Neolithic - Bronze Age) or Late . The results for the two models show that during the Early period C4 caloric contributions were very low across Mongolia, likely including consumption of local plants and livestock consuming natural vegetation, with mean estimates varying between c. 2.5 and 5.0% of calories   , an area where environmental increase of carbon values would not be expected naturally  Fig. . During es) Fig. . The C4 4 plants during the imperial periods in ancient and historic Mongolia  analyzed in this study show the first visible evidence of C4 plant consumption as part of a mixed agro-pastoral diet. However, this particular site in northwest Mongolia is more a part of the Minusinsk Basin region of southern Siberia, an area where millet consumption was common by the Late Bronze Age, than the rest of Mongolia. Previously published contemporaneous human and faunal isotope analyses within this region are indicative of animals consuming primarily C3 plants20, with the human population postulated as having a diet additionally composed of C4 plants in concert with meat and milk.Our results clearly demonstrate an increase of human consumption of Cia Figs. , and 5. ata Fig.  and 3A s13C values suggestive of moderate to high C4 plant consumption, with the number of individuals with such values reaching their peak during these imperial periods. We also observe the largest range and diversity of δ13C and δ15N values during the imperial periods. This is likely due to diverse subsistence strategies being pursued across each empire, reflecting different environmental zones and levels of imperial support. This is result of the extensive range of each empire, and includes the knowledge that not everyone that died in Mongolia would have been “Mongolian”, but these individuals likely lived and died within the empires. Since the majority of the individuals analyzed were excavated from elite imperial tombs, and human remains representing other sectors of society are lacking at present, attributing all outliers to non-local outsiders would be to dismiss the agency of Mongolian populations and provide something of a ‘colonial’ narrative.In the rest of Mongolia during the tenure of the Xiongnu and Mongol empires human stable carbon values became more varied with increasing numbers of individuals displaying bone collagen and tooth enamel δ13C values for the Xiongnu and Mongol empires range between those indicative of a pure C3 diet to those that suggest heavy C4 plant consumption. Interestingly, during this period, a few individuals had δ13C values lower than those of the Early period which, alongside lower δ15N values, indicates a staple intake of C3 plants, likely crops such as wheat and barley. Historical and archaeobotanical sources suggest that cereal crops were commonly cultivated or obtained through trade during the Mongol period56. In addition to grains, carbonized fruit and nut remains have been recovered from sediments at the Mongol capital of Kharakhorum  showing the diversity of imported plants through the presence of rice (Oryza sativa L.), over a dozen cultivated fruits, including grapes (Vitis vinifera L.), figs (Ficus carica L.), and jujube (Ziziphus jujube Mill.), as well as vegetable and oil-seed crops. There are also remains of spices – notably a few, such as black pepper (Piper nigrum L.) and caraway (Carum carvi I.), that were imported along the trade routes with South Asia, and would have involved transport across distances of up to 2000 kilometers12.Individual bone collagen and tooth enamel δ13C and δ15N values have been plotted to show this increase of dietary diversity over time  in the Iron Age, which represent more labor investment in farming practices than millets63. At Ivolga, this occurs alongside evidence of ploughshares at permanent settlements63, as well as written accounts of crops suitable for the northern steppe being managed by imperial Xiongnu administrators, such as the ‘Lord of Millet Distribution’, referred to in the 1st century C.E. Chinese accounts54. Millet grains, still articulated in their chaff, have been found within the graves of Xiongnu rulers at Gol Mod and Noyon Uul27 as well as of local elites throughout the steppe60. There are also uncharred grains found within Xiongnu pit-house villages, all of which were unprocessed  and thus most likely not transported long distances60 instead representing local production and consumption  and nitrogen (δ15N) stable isotopes, and the carbonate of dental enamel bioapatite was measured for carbon stable isotopes (δ13C), with some individuals analyzed for both bone collagen and bioapatite  represent a period of diet of approximately the last 20 years of life80.Samples of bones and teeth were collected from archaeological sites across the country of Mongolia, through varying environmental and topographical zones. Where possible, we collected a tooth and long bone fragment from individuals from each time period. While we aimed to assemble an equal number of samples from all time periods, the collection was dominated by individuals from the imperial periods, resulting in fewer individuals prior to the Iron Age. Bone collagen was preferably extracted from rib bones, but occasionally other bone fragments were employed . δ13C values are indicative of the whole dietary carbon  consumed during enamel formation49. First molars mineralize before an individual is 3 years old, second molars are fully formed around age 8, and third molars, if present, are completely mineralized between the age of 7 and 1681. To avoid tooth samples that might show a breastfeeding isotopic signal contribution in older children and adolescents we preferentially selected second and third molars. First molars were chosen only when both the M2 and M3 were unavailable.By contrast, tooth enamel  δc. 20 years of life80. Collagen was extracted from each rib sample following standard procedures35. Approximately 1 gram of pre-cleaned bone was demineralized in 10 ml aliquots of 0.5 M HCL at 4 °C, with changes of acid until CO2 stopped evolving. The residue was then rinsed three times in deionized water before being gelatinized in pH 3 HCl at 75 °C for 48 hours. The resulting solution was filtered, with the supernatant then being lyophilized over a period of 24 hours.We selected ribs for bone collagen analysis as representative of the last 13C and δ15N values were compared and calibrated against International Standards , IAEA N2 , IAEA C6  Replicate analysis of an in-house fish gelatin standard suggests that machine measurement error is c. 0.1‰ for δ13C and 0.3‰ for δ15N.After calculating the collagen yield, all purified collagen samples (~1 mg) were located in tin capsules to be analyzed in duplicate at the Department of Archaeology, Max Planck Institute for the Science of Human History by the elemental analyzer/continuous flow isotope ratio mass spectrometry (EA-IRMS) using a ThermoFisher Elemental Analyzer coupled to a ThermoFisher Delta V Advantage Mass Spectrometer via a ConFloIV system. δ21. This consisted of a series of washes in 1.5% sodium hypochlorite for 60 minutes, followed by three rinses in purified H2O and centrifuging, before 0.1 M acetic acid was added for 10 minutes, followed by another three rinses in purified H2O (as per35).Teeth or tooth fragments were cleaned using air-abrasion to remove any adhering external material. 8 mg of enamel powder was obtained using gentle abrasion with a diamond-tipped drill along the full length of the buccal surface or fragment in order to maximize the period of formation represented by the resulting isotopic analysis for bulk samples. Enamel powder was pre-treated using a protocol to remove any organic or secondary carbonate contaminatesc. ± 0.2‰ for δ13C and ± 0.2‰. Using a Thermo Gas Bench 2 in tandem with a Thermo Delta V Advantage Mass Spectrometer at MPI-SHH, gases produced from a reaction with 100% phosphoric acid were analyzed for stable carbon and oxygen isotopic composition. We compared the resulting values against International Standards ; IAEA-CO-8 ; USGS44 (δ13C = −42.2)); as well as an in-house standard of ). The data from these standards suggest that the machine measurement error is c. ± 0.1‰ for δ13C and ± 0.2‰ for δ18O. We increased the precision of our analyzed samples by measuring repeats of extracts using a tooth enamel bovid standard .Following reaction with 100% phosphoric acid, gases evolved from the samples were analyzed to stable carbon and oxygen isotopic composition using a Thermo Gas Bench II connected to a Thermo Delta V Advantage Mass Spectrometer at the Max Planck Institute for the Science of Human History, Jena (MPI-SHH). Carbon and oxygen isotope values were compared against an International Atomic Energy Agency (NBS 19) and in-house standard (MERCK). Replicate analysis of internal bovid enamel standards suggests that machine measurement error is 13C between each period were significant, we performed a Wilcoxon rank sum test, with multiple test correction using the Benjamini-Hochberg procedure. All tests were performed using the free R statistical software82.To determine whether the differences in human δ13C values and local food isotopic values adjusted for spatial variations due to varying environmental conditions80. To achieve the latter, we grouped site locations into the categories of “steppe” and “dry” depending on modern day annual precipitation. Steppe sites have a range from 250–350 mm in precipitation per annum and arid sites have below 250 mm of yearly rainfall. It was assumed that the enamel δ13C signal is defined by the dietary carbon mix50. To extrapolate the spatial distribution of per capita millet caloric intakes (dietscape) a Bayesian additive mixed model with error-in variables85 available as an online app via the Pandora & IsoMemo initiatives was employed86. Dietscapes were generated for two main periods corresponding to a temporal divide defined by the intensification of millet consumption as observed from the interpretation of raw isotopic data, into Early (Bronze Age) and Late . Modelling at a higher chronological resolution was not possible given a lack of data for shorter time periods. Further details on dietscape modelling are available in Supplementary Text Caloric estimates of millet intakes were obtained using the Bayesian mixing model FRUITS having as input data individual tooth enamel δ87 and at the University of Groningen, Faculty of Science and Engineering, Groningen, The Netherlands 88. All pre-Xiongnu samples and 30% of the Xiongnu samples were radiocarbon dated to solidify the dating of individuals from early time periods. Most Xiongnu and Mongol samples were separated into periods based on archaeological materials and burial styles as assessed by excavators and curators from the National University of Mongolia. See Supplementary Text AMS radiocarbon dates were conducted at the Oxford Radiocarbon Accelerator Unit (ORAU), Oxford, England, UK All of the data included in the study have been made available in Tables Supplementary Information.Supplementary Table.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.


Myelin basic protein (MBP) is the second most abundant protein in central nervous system myelin. Since the 1980s, it has been regarded as a marker of brain tissue injury in both trauma and disease. There have been no recent reports regarding MBP in aneurysmal subarachnoid haemorrhage (SAH).One hundred four SAH patients with ruptured aneurysms underwent endovascular treatment within 24 h of rupture, and 156 blood samples were collected: 104 on days 0–3, 32 on days 4–6 and 20 on days 9–12 post-SAH. MBP levels were assayed using ELISA and compared with the clinical status on admission, laboratory results, imaging findings and treatment outcome at 3 months.p < 0.001), non-survivors (p = 0.005), patients who underwent intracranial intervention (p < 0.001) and patients with intracerebral haemorrhage . On days 4–6 post-SAH, significantly higher levels were found following intracranial intervention (p = 0.009) and ICH (p = 0.039). There was clinically relevant correlation between MBP levels on days 0–3 post-SAH and 3-month Glasgow Outcome Scale (cc = − 0.42) and also ICH volume (cc = 0.48). All patients who made a full recovery had MBP levels below detection limit on days 0–3 post-SAH. Following endovascular aneurysm occlusion, there was no increase in MBP in 86 of the 104 patients investigated (83%).MBP levels on days 0–3 post-SAH were significantly higher among poor outcome patients  and correlates with the treatment outcome. Endovascular aneurysm occlusion was not followed by a rise in MBP in most cases, suggesting the safety of this technique. Myelin basic protein (MBP) is the second most abundant protein in the central nervous system (CNS) myelin after the proteolipid protein. It is essential in the formation of the myelin sheath by oligodendrocytes. On the cellular level, the main role of MBP is to provide adhesion of the cytosolic surfaces of the multilayered myelin sheath; it also binds the cytoskeleton to the cell membrane and mediates extracellular signals to the cytoskeleton . Since tThis study was approved by the local bioethics committee and performed in accordance with the Declaration of Helsinki. One hundred four patients met the inclusion criteria for this study: (1) informed written consent from the patient or family; (2) SAH confirmed by head computerised tomography (CT); (3) aneurysm identified by digital subtraction angiography and managed endovascularly within 24 h of rupture; (4) aged over 18 years; (5) no history of neurological disease or active systemic inflammatory or neoplastic disease; (6) not pregnant. Patients were managed in the neurointensive care unit, and continuous intravenous infusion of nimodipine was carried out for at least 14 days, whilst euvolemia was maintained. Intracranial pressure (ICP) was monitored in the unconscious patients, and standard stepwise management of raised ICP was instituted, leading to decompressive craniectomy  if necessary. Acute hydrocephalus was managed with external ventricular drainage (EVD). Head CT scan was performed in all patients within 48 h of endovascular treatment and any other intracranial intervention. Further imaging and laboratory tests were applied as appropriate to the clinical situation. The clinical status on admission and functional outcome at 3 months were assessed by the neurosurgeon.g for 10 min, and aliquots were taken immediately for storing at − 80 °C. MBP concentrations were measured using the commercially available ELISA kit . The assay range is 78 to 5000 pg/ml.Peripheral blood samples were collected in all patients on days 0–3 post-SAH  and on days 4–6 and 9–12 post-SAH where possible. When intracranial intervention was performed, efforts were made to draw samples within 12 h. Serum-separating tubes were used to collect peripheral venous blood. The contents of the tubes were allowed to clot at 4 °C for 30–60 min. The samples were then centrifuged at 2000U test was used to compare two independent samples where there was lack of normal distribution in at least one of them. Probability value less than 0.05 was considered statistically significant. Correlation was assessed by Spearman’s rank order correlation coefficient test. Correlation coefficient > 0.4 or < − 0.4 was considered clinically relevant. The authors describe an MBP concentration above sensitivity threshold of the ELISA kit (> 78 pg/ml) as “elevated”, whilst concentrations below this level are addressed as “below detection limit” or “undetectable”. Sensitivity thresholds for MBP are marked in all figures. Results in the figures and tables are presented as median and interquartile range and base exclusively on data from patients with elevated MBP.Analysis was carried out after dichotomising patients into survivors and non-survivors or into poor outcome  and favourable outcome (GOS score 4–5) patients. STATISTICA 10 software  was used to perform statistical analysis and create figures. The normality of data distribution was assessed using the Shapiro-Wilk test. The Mann-Whitney One hundred fifty-six blood samples were collected from 104 SAH patients: 104 on days 0–3 post-SAH, 32 on days 4–6 post-SAH and 20 on days 9–12 post-SAH. Elevated MBP levels were found in 18 out of 104 patients (17%) on days 0–3, 14 out of 32 on days 4–6 (44%) and 10 out of 20 on days 9–12 (50%). Table p = 0.005, Table U test also confirmed significantly higher MBP levels on days 0–3 post-SAH in unfavourable outcome patients (p < 0.001). Spearman’s test showed there was clinically relevant correlation between 3-month GOS and MBP level on days 0–3 and 4–6 post-SAH . In nine out of ten cases of DC, elevated MBP levels were observed early after intervention. In contradistinction, serum MBP was detectable in only four out of eleven patients with EVD and/or ICP monitor implanted.Intracranial interventions were carried out in 21 patients. These included intraparenchymal ICP monitor implantation, EVD implantation and DC with intracerebral haemorrhage (ICH) or subdural hematoma (SDH) evacuation where appropriate. In these patients, significantly higher levels of MBP were observed on days 0–3 and 4–6 post-SAH /2 formula was 34.5 cmAt no stage was there any difference of MBP between the sexes; neither was there any correlation with the patients’ age or modified Fisher score. Among the six routine blood tests, the only significant correlations were for sodium on days 4–6 and 9–12 post-SAH  and CRP on days 4–6 post-SAH (cc = 0.66).The principal finding of this study is that the degree of parenchymal brain damage in SAH is reflected by the magnitude of the early MBP increase in peripheral blood. We have observed significantly higher levels of MBP on days 0–3 post-SAH in patients following surgical intervention and with large ICH compared with no intervention and small ICH/no ICH. The early MBP levels also correlated with the treatment outcome and with the ICH volume.The role of MBP as a marker of brain damage is well accepted in the literature, yet the number of publications regarding SAH is very limited, only three papers were identified , 5, 12. Our study also supports the safety of endovascular intervention in SAH. In 1984, Thomas et al. showed that cranial surgery in contrast to spinal/peripheral nerve surgery led to significant MBP elevation in peripheral blood . The samAccording to our local practice, patients who are in good clinical condition after endovascular aneurysm occlusion return to their referring hospital, whilst those that are seriously ill remain for monitoring and treatment in our neurointensive care unit. This inevitably leads to an overrepresentation of poor outcome patients on days 4–6 and 9–12 post-SAH. Nevertheless, analysis of the raw data shows that patients without elevated MBP on days 4–6 and 9–12 post-SAH more frequently survive and achieve favourable outcome than those with increased MBP on the later dates. In the previously mentioned study by Hirashima et al., delayed increase of MBP in CSF (on days 4–9 post-SAH) was attributed to the occurrence of delayed cerebral ischaemia (DCI). MBP has been suggested to first be released into the CSF prior to clearance to the blood, and we suspect a similar relationship between DCI and serum MBP [One surprising feature was the high correlation between sodium and MBP on days 4–6 and 9–12 post-SAH. This may be due to the relatively small number of subjects in this late phase, but may also be related to the use of hypertonic saline in the management of severely brain-injured patient, or to central diabetes insipidus in patients with massive brain injury.There are several limitations in our study and other points that need to be addressed. First, we have noted a different range of MBP values in our study (up to 5 ng/ml), compared with previous, older publications up to 100 ng/ml). This may be attributable to the different assay techniques; in the current study, ELISA was used, whereas previous studies used radioimmunoassay. Furthermore, the reference serum MBP in healthy adults has not been well established (ranges from < 30 to 60 pg/ml based on ELISA studies) , 9, 11. 0 ng/ml. Concentration of MBP in peripheral blood after intracranial aneurysm rupture reflects the severity of the brain parenchymal damage (due to surgery or ICH) and correlates with the treatment outcome. Endovascular aneurysm occlusion was not followed by a rise in MBP in most cases, suggesting the safety of this technique.


Airfoil tip vortex formation noise is a significant noise source in many aerodynamic applications such as aircraft, fans, rotors and propellers. The data collection presented in this paper examines the effects of sinusoidal geometry and porosity on the tip vortex formation noise produced by finite length airfoils. The use of serrated and porous materials is inspired by silent owl-wings and is a promising approach to control flow-induced noise. Noise measurements have been taken using a 47-channel planar microphone array in the anechoic wind tunnel at the Brandenburg University of Technology. Over 2600 unique test cases with variations in sinusoidal tip geometry (amplitude and wavelength) and flat tip porosity were measured during the experimental campaign for a NACA0012 and NACA614 airfoil. The microphone data have been processed using acoustic beamforming software named Acoular to produce one-third-octave band tip noise spectra. Specifications table•This dataset provides new information on the effect of sinusoidal tip geometry and flat tip porosity on airfoil tip vortex formation noise.•The data can be used to assist the development of low-noise airfoil tip devices using passive noise control.•The data can be used to validate computational fluid dynamics and computational aeroacoustic simulations of different airfoil tip shapes.•Students, researchers and those working in industry, who are interested in the acoustic behaviour of wall-mounted finite airfoils and specifically the wingtip, will benefit from this data collection.1The data presented in this article is a set of tip vortex formation noise measurements taken for a wall-mounted finite airfoil employing novel sinusoidal and porous tip devices This dataset extends and complements the authors’ previous benchmark dataset on the tip vortex formation noise produced by a wall-mounted finite airfoil with flat and rounded tip geometries α = 0: 5: 20°, and a Reynolds number of CRe = 2.25 × 105, based on chord. α = 0° and 15° and a Reynolds number of CRe = 2.25 × 105. Raw and processed data for each table and figure can be accessed via the direct URL to the data: http://dx.doi.org/10.17632/2jzkxbwgp8.1. The dataset for the reference NACA0012 and NACA6412 airfoil models with flat tip can be accessed via: http://dx.doi.org/10.17632/6x59x7x3ny.2The dataset contains raw acoustic pressure time histories (.h5 format) and one-third-octave band sound pressure level spectra (.txt and .tif format) processed using Acoular. 2The airfoils used in this experimental campaign have NACA0012 and NACA6412 profile with either a sinusoidal or porous tip (as listed in The sinusoidal tips shown in  and wereAcoustic measurements were taken in the open-jet aeroacoustic wind tunnel at the Brandenburg University of Technology in Cottbus, Germany (see Acoustic measurements were recorded with a planar 47-channel microphone array (see X axis, while the spanwise direction is along the positive Y axis. The positive Z axis points from the wind tunnel ceiling down towards the airfoil. The trailing edge of the airfoil at zero angle of incidence is 710 mm below the planar microphone array. The positions of the 47 microphones, from 0 to 46, are stated in The centre of the microphone array is the origin of the coordinate system. The streamwise direction is along the positive 4 to 2.25 × 105, based on chord.A detailed test matrix is given in α = 0: 5: 20° and a Reynolds number of 5. One-third-octave band tip noise spectra for tripped airfoils with sinusoidal and porous tips at 5 are also shown in Acoular


The metabolic index (ϕ) captures how temperature and oxygen interact to limit marine organism distributions at local scales. These findings indicate that ϕ has utility as a conservation tool for local management. Chrysoblephus laticeps, which is found in the highly heterogenous temperature and oxygen environment along the South African coastal zone, as a model species. In laboratory experiments, we find a bidirectional (at 12°C) hypoxia tolerance response across the temperature range tested (8 to 24°C), permitting a piecewise calibration of ϕ. We then project this calibrated ϕ model through temperature and oxygen data from a high spatial resolution (11 to 13 km) ocean model for the periods 2005 to 2009 and 2095 to 2099 to quantify various magnitudes of ϕ across space and time paired with complementary C. laticeps occurrence points. Using random forest species distribution models, we quantify a critical ϕ value of 2.78 below which C. laticeps cannot persist and predict current and future distributions of C. laticeps in line with already observed distribution shifts of other South African marine species. Overall, we find that C. laticeps’ distribution is limited by increasing temperatures towards its warm edge but by low oxygen availability towards its cool edge, which is captured within ϕ at fine scales and across heterogenous oxygen and temperature combinations. Our results support the application of ϕ for generating local- and regional-scale predictions of climate change effects on organisms that can inform local conservation management decisions.The distributions of ectothermic marine organisms are limited to temperature ranges and oxygen conditions that support aerobic respiration, quantified within the metabolic index (ϕ) as the ratio of oxygen supply to metabolic oxygen demand. However, the utility of ϕ at local scales and across heterogenous environments is unknown; yet, these scales are often where actionable management decisions are made. Here, we test if ϕ can delimit the entire distribution of marine organisms when calibrated across an appropriate temperature range and at local scales (~10 km) using the endemic reef fish,  Climate change is driving the redistribution of species across the globe  which represents the minimum level of oxygen availability to sustain a standard metabolic rate (pO2). The critical pO2 for maximum metabolic rates of aquatic organisms has evolved to match prevailing pO2 and thus at normoxia ϕ is akin to factorial aerobic scope (MMR/SMR) (crit) ranging between 2 and 5 limits the equatorward distribution of four marine species from diverse habitats. crit, accounting for the spatial variation of mass marine extinctions at the time. These studies explored spatial trends in ϕ across broad north to south extents and from coarse-resolution ocean models, with the patterns observed largely attributed to increasing temperature driving metabolic demand above supply.The metabolic index (ϕ) represents the ratio of oxygen supply to resting oxygen demand, incorporating the effect of both temperature and oxygen availability. Oxygen demand is quantified as the critical oxygen partial pressure (lic rate  and oxygMMR/SMR) . Unlike Chrysoblephus laticeps) and predict future distribution responses. We hypothesize that ϕ will be limited towards (1) the warm-temperate edge of C. laticep’s distribution through a temperature-induced increase in oxygen demand and (2) the cool-temperate edge through the low oxygen availability in this region.At local scales, organisms respond to abrupt environmental heterogeneity rather than broader climate signals, which occur on scales of metres to kilometres, particularly around the coastal zone  as the m−1) relative to temperatures throughout most parts of the east coast (−1), but there are localized areas (towards the west of the south coast) where oxygen levels can get to critically low (0–2 ml.l−1) levels (−1)  with these characterized by spatial and temporal heterogeneous ocean temperatures and oxygen availability (st coast . Althougst coast . Oxygen ) levels . The souChrysoblephus laticeps) as a model species for this study. Typical of most species in the sparid genus, C. laticeps is considered slow growing, has a maximum age of 19 years  and demand is the minimum pO2 required to sustain a standard metabolic rate (pO2crit) taking into account temperature and mass (The metabolic index (ϕ) (Equation 1) represents the ratio of oxygen supply to oxygen demand where supply is the prevailing oxygen partial pressure (Ao and −Eo) are derived from the slope and intercept of the linear relationship between critical oxygen partial pressure (pO2crit) which has been mass standardized [divided by mass (B) and its scaling exponent (n)] and the inverse of BTk [the product of temperature (T) in kelvin and the Boltzmann constant (Bk)]. These parameters are species-specific and thus need to be derived from laboratory experiments that quantify a species’ pO2crit across a temperature range.The parameters of ϕ  or offshore of the Noordhoek Ski-boat Club outside of Port Elizabeth  and brought back alive to the Aquatic Ecophysiology Research Platform laboratories of the South African Institute for Aquatic Biodiversity. Fish were left to adjust for ~6 weeks to holding tank conditions kept at the average bottom sea temperature of sampling locations (16°C) before standard and maximum metabolic rates were quantified. These metabolic rate measurements formed part of a complimentary study quantifying the metabolic response of C. laticeps to abrupt temperature swings which are a common occurrence through its distribution (see 2crit) was started. Respirometer flush pumps were turned off, and oxygen was depleted within a respirometer by the organism until distinct signs of stress or impaired performance were observed. Metabolic rates were measured in 5-min intervals during this progressive hypoxia and combined with all earlier metabolic rate measurements to quantify the critical oxygen level.Fifty 2crit was defined as the oxygen saturation level where standard metabolic rates could no longer be maintained and below which metabolic rates decreased in proportion with prevailing oxygen saturation levels. To quantify O2crit, we used the ‘calcO2crit’ function, developed by 2crit is taken as the point of intersection between standard metabolic rate and the linear declines in metabolic rates below the standard metabolic rate in hypoxic water (2.min−1.kg−1) from crit’ function was run for each individual dataset with the maximum number of points for the regression below the standard metabolic rate set to 15 to limit the potential for including too many points above ‘true O2crit’ and overestimating. Subsequent O2crit plots were visually inspected, and O2crit was considered overestimated if a large number of points were included in the regression below SMR, but metabolic rates were not decreasing in proportion to oxygen saturation levels. This can happen if metabolic rate measurements greater than standard metabolic rate are lacking at low oxygen levels and, in such cases, the maximum number of points was limited to four (one more than the minimum required for a regression). All O2crit thresholds (% saturation) were converted to corresponding critical oxygen partial pressures .OpO2crit, we estimated the mass scaling exponent (n) for C. laticeps by first temperature standardizing data (done by dividing with the Arrhenius function) and finding the best fit mass scaling exponent from the mass–pO2crit power-law relationship (pO2crit between the Tsitsikamma (TNP) and Port Elizabeth (PE) sampling areas, the linear relationship between pO2crit and temperature was modelled including area as an additive effect and its interaction with temperature, but only for temperatures between 12 and 24°C to ensure the residuals of the linear model were normally distributed. We also followed the same approach to determine if sex had a significant effect on pO2crit. The sex and stage of each study organism were determined by inspecting the gonads of euthanized individuals following the O2crit trial. Without microscopic gonad sections, it was difficult to determine the sexual stage  with 100% accuracy. Sex was therefore classified as either female (F), male (M), predominately male but possibly intersex stage (M/I) or predominately female but possibly intersex stage (F/I), and included as an additive and interactive effect with temperature in a linear model with pO2crit as the dependent variable. If no mass and sex stage effects were found, all data were pooled to estimate the parameters of ϕ.To standardize mass effects on Spatial environmental data were obtained from a high-resolution  coupled ocean-biogeochemistry model  coupled −3), temperature (°C), salinity (psu) and depth data were extracted from the layer of cells closest to the sea floor because C. laticeps is a benthic reef-associated species. Data were extracted for depths between 0 and 100 m below sea level and from around the southern African coast (26.5–37.5 °S and 13.5–37.5 °E). Oxygen concentrations were converted to partial pressure (kPa) based on grid cell temperature, oxygen concentration, salinity and pressure at depth using the ‘o2_unit_conv’ function in the ‘presens’ package  for the period between 2000 and 2010, made available by The Department of Environment, Forestry and Fisheries . The NMLS contains mandatory species level catch and effort data from the boat-based commercial fisheries operating in South African waters since 1985 and is one of the largest geo-referenced marine datasets in the world (C. laticeps occurs.Occurrence points for he world . The boacrit) and predict future distributions of C. laticeps up to 2099 using the ‘randomForest’ package , 10 full random forest models were trained and assessed with maximum, minimum and mean monthly ϕ, and depth as predictor variables, for the contemporary period. Each of the 10 models was trained with a unique generation of pseudo-absence data and subsequent random training/testing data split. Ten more reduced random forest models were then re-trained and tested using only the top two predictor variables from the full models. The predictive accuracy (measured as a proportion of correctly identified test data cells) of the full versus the reduced models were compared and the simplest combination of predictor variables with the highest accuracy subsequently used to project current and future distributions of C. laticeps. Current and future distributions of C. laticeps were estimated by combining all 10 model projections, trained on unique combinations of presence–pseudo-absence data (described above), with most confidence given to areas where all 10 models agreed. To quantify the critical threshold of ϕ (ϕcrit), the feature contribution  of each training data point was first extracted from the random forest models using the ‘forestFloor’ package , factorial aerobic scope (FAS) or prevailing temperature as predictor variables. Aerobic scope environmental layers were generated by fitting quadratic polynomials through the relationship between mass standardized standard (SMR) or maximum metabolic rates (MMR) and temperature, from data corresponding to the same individuals in this study but measured in 2crit trials across the temperature range tested  OpO2crit, with the mass scaling exponent estimated at 0.17 , which was not significantly different between sampling areas and their interaction with temperature  permitted fitting a piecewise relationship to estimate ϕ parameters on either sides of 12°C [Bk.T)]. The estimated ϕ model parameters Ao,  Eo, taken from the intercept and slope respectively, could thus be estimated for each linear relationship. At temperatures greater than 12°C, we estimated Ao to be 1.55951E+9 and Eo to be −0.4885, and below 12°C Ao was estimated to be 5.109275E−18 and Eo to be 1.01. A graphical representation of this calibrated piecewise ϕ for C. laticeps is presented across a matrix of temperatures (°C) and oxygen levels (% saturation), indicating how temperature or oxygen availability can limit ϕ  spatial bottom temperature  and oxygg domain . The meaT test, P-value > 0.05, crit for C. laticeps. Extracting contributions from parameters of the reduced models indicated that suitable depths for C. laticeps occur between around 13 and 75 m below sea level (crit) below which C. laticeps can no longer persist is 2.78  of contemporary ϕ and depth had a mean predictive accuracy of 0.93 across all 10 runs. Depth and minimum ϕ were the two most important predictor variables in all full-model runs. The mean predictive accuracy of the reduced model with just depth and minimum ϕ as predictor variables was 0.92 and was statistically indistinguishable from the full models , absolute aerobic scope (AAS) and temperature only models predicted distributions to stretch past Cape Town into areas where catches of C. laticeps are not common  = 8.064, P-value < 0.0005] but only between AAS and ϕ or temperature predictor models .The distribution models with different sets of predictor variables  predicted a similar core distribution for t common a–d. The C. laticeps is constrained by a minimum critical metabolic index (ϕcrit) threshold of 2.78, whereby ϕ is limited in the west by supply (low oxygen) and in the east by demand (high temperatures). Importantly, we show that the distribution of C. laticeps is limited by different environmental variables across its contemporary distribution but through their influences on a common mechanism–aerobic metabolism.The geographic distribution of crit value for C. laticeps (2.78) found here; however, is similar to the summer ϕcrit (3.2) found for the related sharpsnout seabream (Diplodus puntazzo) in the north Atlantic (crit ranging from 2.2 for common eelpout (Zoarces viviparus) to 3.3 for Atlantic cod (Gadus morhua), due to the antagonistic effect of rising temperature on ϕ through exponentially increasing oxygen demand. These results suggest that, on average, marine fishes require environmental conditions that permit a factorial aerobic scope of around three, although the number of fishes tested is small. Estimating in situ metabolic rates of wild fishes (field metabolic rates) remains a technological challenge  but rather by the oxygen supply capacity and how it scales with temperature compared to the maximum metabolic rate  of ϕ for this analysis, the monthly temporal resolution gives an indication of ϕ temporal availability  temperature and oxygen stressors in the lab, suggesting a capacity to withstand sub-optimal ϕ on a daily scale. The dimension of ‘time’ is an important consideration for future lab studies to calibrate ϕ as longer-term acclimation can reduce the temperature sensitivity of metabolic measurements  and the marbled spinefoot (Siganus rivulatus), in the Mediterranean to develop a spatial thermal habitat suitability index and predict the distributions of both species. Mugil cephalus) in the Mediterranean generally tracks the environmental conditions that optimize aerobic scope. Although these physiological-based mechanistic models overcome many of the limitations associated with purely correlative SDMs, they often assume that temperature is the only environmental variable that modulates how a fish population relates to its immediate environment  approaches to bridge the gap between correlation and process and filter out interaction complexities   (Oncorhynchus gorbuscha) (Oncorhynchus kisutch)  shifted its distribution from the southern Benguela to the Agulhas Bank during the mid-1990s, which was attributed to an increase in upwelling off the Agulhas Bank  has the potential to explain the whole distribution of marine fish, if calibrated across a wide temperature range. We also show that ϕ can predict the distribution of an endemic marine organism across a dynamic heterogenous marine environment. Importantly, whilst we highlight how different environmental variables (oxygen and temperature) limit different edges of the distribution for Ethics for this research was approved under the regulations of Rhodes University Animal Ethics (DIFS152025) and the Animal Use and Care Committee of the South African National Parks (004/16).Conservation Physiology online.Supporting_Information_Revision_v2_coaa090Click here for additional data file.


High-throughput processing of the separated cells is enabled using microfluidics platforms. However, dissociation of tissue results in loss of information about cell location and morphology and potentially alters the transcriptome. An alternative approach for collecting RNA from single cells is to re-purpose the electrophysiological technique of patch clamp recording. A hollow patch pipette is attached to individual cells, enabling the recording of electrical activity, after which the cytoplasm may be extracted for single cell RNA-Seq (“Patch-Seq”). Since the tissue is not disaggregated, the location of cells is readily determined, and the morphology of the cells is maintained, making possible the correlation of single cell transcriptomes with cell location, morphology and electrophysiology. Recent Patch-Seq studies utilizes PCR amplification to increase amount of nucleic acid material to the level required for current sequencing technologies. PCR is prone to create biased libraries – especially with the extremely high degrees of exponential amplification required for single cell amounts of RNA. We compared a PCR-based approach with linear amplifications and demonstrate that aRNA amplification ( The introduction of RNA sequencing (RNA-Seq) expanded the toolset for characterization of neurons; however, the original RNA-Seq techniques were not able to perform expression profiling of individual cells, limiting our ability to investigate cellular heterogeneity. We7, or using microfluidic devices such as those employed in Drop-Seq8 or the Chromium controller (10x Genomics)9. Unfortunately, the treatments used to dissociate tissues lead to loss of complex morphology of individual neurons, eliminate location information, destroy cell connectivity, and irreversibly change the electrophysiological properties of neurons. Furthermore, dissociation may trigger changes in the transcription profile of cells11.Most of such approaches involve tissue dissociation to produce a single cell suspension, followed by sorting of the cells by fluorescence-activated cell sorting (FACS)1. This approach has been used by others (Patch-Seq)12, utilizing the SMARTer technique13, in which PCR amplification is used to produce sufficient amounts of DNA for sequencing.Patch clamp was invented to record cellular electrical activity, which is often specific to a given cell or neuron type; however, whether or not electrophysiological recording is performed, the patch clamp pipettes can be used to collect individual cells or their contents from within intact or sliced tissue. Cells may be chosen for study based on their morphology, electrophysiological properties and/or location in the tissue or organ. We developed a protocol using patch clamp for collection of cellular contents followed by RNA-Seq15. These challenges led us to investigate alternative linear amplifications methods of DNA or RNA, which are less prone to such bias16. Two major approaches are available for linear amplification of nucleic acids: (a) isothermal DNA amplification, which is implemented in Ovation® RNA-Seq System V2  kits, and (b) aRNA amplification, which utilizes in vitro transcription (IVT)17.Whole transcriptome sequencing of single cell material requires amplification of minute amounts of RNA/cDNA – typically in the range of 10 pg, or even less. If PCR amplification is employed and the number of thermal cycles is large, biased amplification of certain RNA species becomes noticeable, for example, favoring cDNA fragments of shorter length or of lower GC content8 and CEL218 protocols, outperformed the PCR-based protocols. CEL-Seq and CEL2 require multiplexing, involving use of barcoded primers to enable parallel processing steps. However, the benefits of multiplexing diminish, and complexity of the protocol becomes an unnecessary complication with lower numbers of samples, such as for electrophysiological patch clamp measurements, when every single cell is collected through a lengthy process.Support for the utility of linear amplification methods for single cell analysis came from an analysis using ERCC  spike-in controls, which showed that aRNA amplification used for single cell transcriptome applications, such as the CEL-Seq19.In this study we focused on protocols that can be applied to single cell or sub-single cell material and that do not require multiplexing. We chose the Ovation® RNA-Seq System V2 kit for linear DNA amplification, and the aRNA method for linear RNA amplification. The NuGEN kit was originally designed for 500 pg input RNA (the amount of RNA in ~50 cells), thus we modified the protocol to work with single cell RNA amountsin vitro transcription of cDNAs for linear RNA amplification, as first described in 199220. Several modifications of this technique had been described previously22. We made further adjustments and compared our modified aRNA protocols with the NuGEN and SMARTer protocols with nucleic acid material extracted from single cells using patch clamp technique.The aRNA method uses 4; in fact, collection is usually incomplete, as RNA in the nucleus and in the branches of neurons may not be collected. Therefore, we also tested robustness of amplification using only 5 pg of input RNA.In order to compare the efficiency of the different protocols, we used a standard input of 10 pg (similar to the amount in single cells) of Universal Human Reference RNA . Patch clamp collection of cells does not yield identical amounts of RNAin vitro transcription protocol and SMARTer protocol19 and single cell data transcriptomes collected from embryonic brain neurons using Fluidigm C1 which utilizes the SMARTer method23. We, in addition, performed the original in vitro transcription protocol and SMARTer protocol side by side with our modified aRNA protocol  and transcriptome mapping ratio calculated as a percentage of mapped reads which mapped to the transcriptome, excluding both rRNA  and mtRNA . The third metric was gene model discovery rate, assessed as the number of genes with more than 5 mapped reads detected per 3 million mapped reads. A Recent single cell RNA-Seq (scRNA-Seq) study indicated that more than one million reads are required to analyze the variance in expression2. We used 3 million as our baseline for our analysis to increase the gene complexity because we are targeting highly complex neuronal cells24.We used 5 metrics to assess the RNA-Seq data of amplified products. The first two metrics were reproducibility, measured as the Pearson coefficient of correlation of expression profiles between technical replicates, and accuracy, measured as a correlation between gene expression measured in 10 pg or 5 pg samples as compared to bulk RNA-Seq of UHR RNA. Prior to assessing reproducibility and accuracy metrics the samples were normalized by downsampling to 3 million mapped reads.The other two metrics, which were applied only to UHR data, assess 22 was developed for single cell applications and consists of 3 rounds of linear amplification cycles. The protocol employs column purification of nucleic acids (cDNA or aRNA at different stages of the protocol), and RNA ethanol precipitation between the cycles. Column purification and alcohol precipitation are prone to loss of nucleic acid26, which could be especially detrimental when the initial amount of material is minute. To address this potential issue, we replaced column purification of nucleic acids and ethanol precipitation with magnetic bead purification , which increases effective concentration of template, resulting in higher yield Fig. . Decreas19. Samples with less than 3 million mapped reads were removed from the analysis and the remaining samples were downsampled to 3 million mapped reads to normalize the data, increasing consistency. Data quality was evaluated using the five metrics described above. A comparison indicates that the modifications we introduced to the aRNA method substantially increased data quality in every metric for 10 pg input material , and the reproducibility of the modified aRNA protocol was practically identical with the SMARTer protocol (0.76 vs 0.78) Fig. . However19 protocol for single cells followed by NuGEN Ovation® Rapid Library Systems , (333 single cells). We also processed cells using original aRNA protocol but only few cells (5 cells) successfully generated single cell libraries. We compared our single cell RNA-Seq data from these methods.We used patch clamp to collect cytoplasmic material from 1,013 single cells from different layers of embryonic brain Fig. . We then23 (GSE67835) processed by the Fluidigm C1 instrument utilizing the SMARTer protocol followed by the Nextera library preparation. We processed raw data from all three methods  through the same analytical pipeline in Partek Flow. Again, we downsampled each sample to 3 million mapped reads, after removal of those with fewer reads.To expand the comparison, we also accessed publicly available raw data (FASTQ files) from 466 single cells dissociated from fetal and adult brainAmounts of RNA extracted from single cells by patch clamp can be highly variable resulting in high variation in amount of produced aRNA. We excluded samples (n = 135) that were failed to yield aRNA compared to negative control (water), which were run in parallel. In addition, there were a few outliers with an abnormally high library concentration (>100 ng/µl) (n = 2). We excluded these libraries for analyses as they had a large amount of dimer sequences and these failed the QC due to low mapping rate. Lastly, samples (n = 14) producing library amount lower than 200 ng (the minimum amount required for sequencing by Illumina protocol), were also excluded.total mapping rate, transcriptomic mapping ratio and gene model discovery rate. The total mapping rate was similar between SMARTer (62%) and modified aRNA methods (63%) and much lower with the NuGEN approach (24%), as a result of spurious amplification (as seen in UHR samples). The Transcriptomic mapping ratio was similar with SMARTer and modified aRNA protocols (62%), whereas it was lower with the NuGEN protocol (42%) . The SMARTer and original aRNA approaches detected fewer genes , and NuGEN detected the lowest number of genes (608).Single cell RNA-Seq data were analyzed using following metrics: methods 6% and mucin vivo or in relatively intact tissue slices, obviating the need to disperse tissues to separate cells, which previous work has shown can alter gene expression.Using patch clamp to extract single cell material for RNA sequencing offers multiple advantages over approaches that involve tissue dissociation to isolate single cells. Patch clamp provides the opportunity to record location, morphology and electrophysiology of a single cell for correlation with the expression profile of the same cell. Patch clamp can be used for recording In order to work with the minute amount of extracted single cell RNA, which often is less than the total cellular RNA content, we improved the efficiency of the procedures for RNA collection, handling and amplification. The protocols for patch clamp and collection of cytoplasm are critical, as they determine the amount of input RNA and how well it is preserved. For instance, we sliced brain samples under HEPA-filtered air hood to eliminate contamination of material during collection of cytoplasm from single cells. Additional precautions included using an RNase inhibitor in our intracellular solution, and flash-freezing the collected cytoplasm in lysis buffer in liquid LN2, immediately after mixing, to reduce potential RNA degradation.Using reference RNA, we compared RNA sequence quality across protocols that variously employ PCR amplification, linear DNA amplification and linear RNA amplification. These protocols were then evaluated using measures of gene model discovery rate, precision, accuracy and resistance to amplification of parasitic, non-transcriptomic fragments. In our hands, linear RNA amplification based on the modified aRNA method produced the most robust results, was the most sensitive, and consistently produced higher quality data in comparison to PCR amplification.We then tested the methods on RNA extracted from single neuronal cells from fetal human brain and compared our results with publicly available single cell RNA-Seq data. These analyses found that methods based on either linear amplification or PCR are sensitive enough and produce consistently meaningful results with single cell material. When data quality was compared, however, it becomes clear that the modified aRNA method, based on IVT, has a higher mapping rate, transcriptome ratio and gene model discovery rate, than the PCR method, confirming conclusions from comparison of these methods using UHR.et al.18 developed a method utilizing only one aRNA amplification cycle, by taking advantage of the increased initial cDNA amount due to multiplexing single cell samples prior to aRNA amplification. Further incremental improvements in the methodology, such as high-yield RNA transcriptases  or sequencing library methods that require less input cDNA  may result in decreased number of linear amplification rounds to one, even without multiplexing.Reducing the number of rounds of RNA amplification may be a key factor contributing to the improved performance of the modified aRNA method. Every additional round requires RNA purification, cDNA synthesis and aRNA amplification, and every step is prone to variable loss of nucleic acids, increasing noise and bias. Recognizing this problem, Hashimshony The universal human reference RNA  was purchased and dissolved in RNase-free water , according to manufacturer’s recommendations to 1 µg/µl stock solution. Working solutions with concentration 10 pg/µl were made by serial 1:10 dilutions and kept in low-binding microcentrifuge tubes at −80 degrees not more than two weeks.Deidentified tissues from adult human brains were acquired from medical waste after written informed consent was received from adults undergoing neurosurgery. Deidentified human fetal tissue was obtained following informed consent from parents, from elective terminations. Collection of this tissue was performed under approval by the Institutional Review Boards of both Children’s Hospital Los Angeles and the Keck School of Medicine of the University of Southern California. University of Southern California institutional review board approvals HS-12-00474 and HS-13-00399 were obtained for this work. All methods were performed in accordance with the relevant guidelines and regulations.2 1 mM; MgATP 4 mM; GTP 0.3 mM; phosphocreatine 8 mM; HEPES 10 mM; EGTA 11 mM; pH 7.25 and 300 mOsm) containing recombinant RNase inhibitor (Clontech) (0.4 U/µl); and mounted in a standard patch pipette holder, connected to an automatic pressure control unit . Under microscopic visualization, the patch pipette tip was maneuvered to the vicinity of a neuron, while positive pipette pressure (25–50 mmHg) was maintained. Once the pipette touched the cell membrane, gentle suction (−15 to −30 mmHg) was applied to enable formation of a giga-seal. We made the whole-cell configuration  by application of strong negative pressure to remove small patch of membrane. The content was expelled into a PCR tube containing 5 µl of lysis buffer  by breaking the end of pipette tip and applying positive pressure (25–50 mmHg). The cellular material was centrifuged followed by 1 or 2 freeze thaw cycles to assure complete lysis of the material. For every cell we recorded metadata containing information about morphology, electrophysiological recording, location in tissue, details of the method used for RNA processing and library preparation along with details about batch of processing.The embryonic brains from second trimester were sliced in a HEPA filter protected fume hood with VT1000 vibratome (Leica) into 400um slices. Neurons in tissue slices from resected embryonic brain were visually identified under illumination with an infrared Dodt gradient contrast system (homebuild). Patch pipettes  were filled with intracellular solution  were diluted in water to final volume of 5 µl. Alternatively, a whole volume of single cell lysate (5 µl) was used as a starting material for the protocol.To the 5 ul of each sample, we added: 4.9 µl of RNase free water, 2.4 µl of 5x First Strand buffer , 1.2 µl of dNTPs , 0.45 µl of 100 mM DTT  and 0.3 µl of dt-T7 oligo . This entire mixture was incubated for 5 minutes at 70 degrees to denature the RNA, then immediately placed on ice and the first strand enzyme mix was added: 0.3 µl of Rnasin , 0.45 µl of Superscript III , 1 µl of RNAse-free water. First strand synthesis was performed at 42 °C for 30 minutes, followed by 70 °C for 15 minutes.To the 9.35 ul of the above reaction, we added second strand mix , 0.75 µl of dNTP mix, 1 µl of DNA polymerase I , 0.25 µl of RNase H , 8.26 µl of RNase-free water) and incubated for exactly 2 hours at 16 °C. After incubation, 1 µl of T4 DNA polymerase  was added and samples incubated for an additional 10 minutes at 16 °C. Double stranded DNA was then purified using 52 µl of Agencourt XP RNAclean beads  according to manufacturer’s protocol and eluted in 4 µl of water.Reagents from Ambion MEGAscript® T7 Transcription Kit  were used for aRNA amplification. The 1 µl of each ATP, GTP, CTP, TTP, 10X reaction buffer, 10x Enzyme mix were added to each sample followed by incubation at 37 °C for 14 hours. Amplified RNA was purified with 18 µl of Agencourt RNACleanXP Beads and eluted in 4 µl of water.After addition of 1 µl of Random primers , samples were incubated at 70 °C for 10 minutes. First strand synthesis was performed by adding first strand mastermix , 1 μl of Superscript III) and successive incubations at RT (25 °C) for 10 minutes, 42 °C for 30 minutes, 95 °C for 5 minutes.1 µl of dt-T7 oligo (10 ng/µl) was added to the above 10 ul first strand reaction for each sample. After 5 min of incubation at 70 °C, second strand master mix was added  and samples were incubated exactly 2 hours at 16 °C. After incubation, 1 µl of T4 DNA polymerase  was added and samples incubated for an additional 10 minutes at 16 °C. Samples were then immediately purified with 70 µl of Agencourt RNACleanXP Beads and eluted in 4 µl of water.In vitro transcription was performed the same way as in the first round, and samples were purified with Agencourt RNAClean XP beads. RNA samples were eluted in 50 µl of RNase-free water and the amount of aRNA was measured using Agilent RNA ScreenTape  on an Agilent 2200 Tapestation.For bulk RNA samples (100 ng of UHR) and 10 pg UHR samples processed with modified aRNA protocol libraries were generated using Illumina Truseq Stranded mRNA Library Preparation kit . Samples from aRNA (round 2) were dried with SpeedVac  into low volume (less than 1ul) and added to the Elute-Prime-2 stage of the TruSeq Stranded mRNA protocol  to bypass the poly-A mRNA selection process. Rest of the steps were performed, following the manufacturer’s protocol using TruSeq RNA Single Indexes Set A and B .19.For samples processed with Ovation® RNA-Seq System V2 , sequencing libraries were made using NuGEN Encore Rapid Library system  according to the protocol previously reportedSize distribution of sequencing libraries was assessed by Agilent D1000 Screen Tape (5067-5582) on an Agilent 2200 Tapestation. Library concentrations were measured by KAPA Library Quantification Kits for Illumina® Platforms . 24 libraries were multiplexed and sequenced on an Illumina HiSeq 2500 System on Rapid flowcells (GD-402-4001) using HiSeq Rapid SBS Kit V2 with 100 bp single-ended reads.Sequencing reads were trimmed from both ends based on quality score using filter module in Partek Flow with settings of 25 minimum read length. The trimmed reads were mapped to human genome (hg38) and transcriptome (GENCODE Genes - release 26) using STAR aligner (STAR - 2.5.3a) within Partek Flow pipeline . Total mapping rate was measured as part of standard Post Alignment QA/QC modules in Partek Flow. Read counts were quantified for each sample using STAR transcript/gene expression quantification function, and the transcriptome mapping ratio was taken from Quantification summary. For further analysis BAM files were downsampled to 3,000,000 mapped reads using the SAMtools module in Partek Flow. Samples with less than 3,000,000 mapped reads were excluded. The reads were assigned to genes using htseq module within Partek Flow. In addition, number of reads was normalized with quantile normalization prior to comparison. For every sample we calculated the number of genes detected per 3,000,000 mapped reads (defined as genes with more than 5 uniquely mapped reads), Pearson coefficients of correlation between gene expression of technical replicates and between each sample and bulk RNA. For comparison with in-house SMARTer and original aRNA protocol, we compared the gene discovery rate with samples downsampled to 100,000 reads as there was limited number of reads on these samples.Supplementary Information.