Specifically, our method utilizes a matrix equation framework to calculate R2M values for SNPs within a chromosome region of interest (ROI) based upon the choices of 1-4 “index” SNPs (iSNPs) that serve as proxies for underlying regulatory variants. Exhaustive testing of sets of 1–4 candidate iSNPs identifies iSNP models that best account for estimated R2 values derived from single-variable linear regression analysis of correlations between mRNA expression and genotypes of individual SNPs. Subsequent genotype-based estimation of pairwise r2 linkage disequilibrium (LD) coefficients between each iSNP and the other ROI SNPs allows the SNPs to be parsed into semi-independent families. Analysis of mRNA expression and genotypes data downloaded from Gene Expression Omnibus (GEO) and database for Genotypes and Phenotypes (dbGAP) demonstrates the usefulness of this method for parsing SNPs based on experimental data. We believe that this method will be widely applicable for the analysis of the genetic basis of mRNA expression and visualizing the contributions of multiple genetic variants to the regulation of individual genes.In this study, we present a novel, multiple coefficient of determination (R Information generated from these studies is often useful for the annotation of SNPs that associate with human disorders in genomewide association studies (GWAS) and for identifying causal variants that contribute to human disorders4. Understanding how expression of specific genes, especially the high- or low-expression extremes, contribute to the etiology of human disorders is an important step toward developing new methods for diagnosing disorder subtypes and for identifying possible molecular targets for novel drugs to treat, slow or prevent their development6.Identifying genetic variants that correlate with gene expression and elucidating their underlying molecular mechanisms are major goals in the field of human genetics7 and these expression “quantitative trait loci” (eQTL) are often useful for annotating individual genetic markers, usually single nucleotide polymorphisms (SNPs), that correlate with disease liability or protection in genome-wide association studies (GWAS). Although the majority of “expression” SNPs (eSNPs) do not directly function as regulatory genetic variants, their correlation with mRNA expression may result from being in LD with one or more regulatory variants. The initial focus on the association of single eSNPs with human disorders, however, may result in the failure to detect associations that depend upon multiple genetic variants that influence the expression of individual genes, for example in cases where disorder liability or protection occurs only at the extremes of mRNA expression that are not captured by a single genetic marker9. Identifying sets of eSNPs that accurately capture the full range of gene expression for use in genetic association studies therefore remains an important goal. Current methods that assess the contributions of multiple genetic variants to mRNA expression of a single gene include haplotype-, regression- and Bayesian statistics-based approaches13. While powerful, these methods may still fail to capture the complete landscape of genetic regulation for individual genes.Quantitative genome-wide association studies (GWAS) have identified a large number of genetic variants that correlate with mRNA expression of nearby genes2) derived from single-variable linear regression analysis of individual SNPs located within defined chromosome regions of interest (ROI). We believe that this method will be useful for both assessing the minimum number of independent regulatory variants that influence the expression of a given gene and identifying families of SNPs that are in LD with these variants. A potential application of our approach is the identification of sets of SNPs that will serve as more effective markers in genetic association studies.In this study, we describe a novel method for analyzing the combined contribution of regulatory variants to mRNA expression based on the analysis of coefficients of determination (R14. The underlying assumptions of this method were: (i) non-regulatory SNPs correlate with mRNA expression to the extent to which they are in LD with regulatory variants, and (ii) semi-independent “families” of SNPs that correlate with mRNA expression reflect the underlying contributions of one or more regulatory variants. Although this ‘ad hoc’ method produced useful results for many of the genes we analyzed, mathematical modeling revealed significant inconsistencies, in particular with respect to SNPs with highly correlated genotypes and/or SNPs with minor contributions to the variance of mRNA expression.We previously described a multiple linear regression-based method for parsing SNPs that correlate with mRNA expression into semi-independent families2M) to be calculated based on the matrix equation R2M = CTR−1C, where C is a column vector , with elements equal to the Pearson correlation coefficients between a dependent variable Y and one or more independent variables, , CT is row vector equal to the transpose of C, and R−1 is the inverse of the correlation matrix R for the independent variables (modified nomenclature based on equations in:15). Elements of R comprise Pearson correlation coefficients for all pairwise comparisons of the independent variables: rX1X1, rX1X2 … rXiXj.…..rXpXp. In the derivations that follow, we found it useful to use the well-known identity equation: R−1 = adjugate of R/determinant of R = adjR /detR.During a search for a mathematically sound method for parsing SNPs that correlate with mRNA expression, we learned of a well-known result from multivariate statistical analysis that allows population multiple coefficients of determination  and genotypes of a small set of regulatory variants  and rG1G1, rG1G2 … rGiGj.…..rGpGp as the Pearson correlation coefficients between genotypes for all pairwise combinations of these regulatory variants.The first step toward using the above equations to develop a method for parsing SNPs that correlate with mRNA expression was to define rA, and bi-allelic regulatory variants as SNPB, SNPC, SNPD, etc. Based on this notation, we also designated rYG1 = rYA, rYG2 = rYB, etc., and rG1G1 = rAA, rG1G2 = rAB, etc. It should be noted that (rYA)2 is equal to the single variable linear regression-based coefficient of determination for SNPA = R2A and (rAB)2 is an r2 LD estimator for the pair of SNPs, SNPA and SNPB. We also used the notation: R2M = R2AB for systems comprising SNPA and SNPB, R2M = R2ABC for systems comprising SNPA, SNPB SNPC, etc., where the subscript “M” stands for “multiple.”To simplify our notation, we designated individual non-regulatory SNPs as SNP2A) for non-regulatory SNPA in LD with multiple regulatory variants SNPB, SNPC, etc., was to derive explicit equations for R2A that are consistent with the constraints: R2AB = R2B, R2ABC = R2BC, R2ABCD = R2BCD,or R2ABCDE = R2BCDE. The restrictions on the values of R2A in these equations reflect the assumption that, within each model, SNPA simply correlates with, but does not directly contribute to mRNA expression, beyond the contributions made by SNPB, SNPC, etc., which directly contribute to mRNA expression or are in high LD with actual regulatory variants. Manually solving these “constraint” matrix equations for R2A in systems comprising one non-regulatory SNP (SNPA) and one-, two- or three-regulatory variants  yielded the equations for R2A listed in rows 1–3 of Table 2A take on the forms for 2-, -3 and 4-SNP systems listed in Fig. The next step in using the matrix equation to calculate coefficients of determination (R2A in a two-SNP system describes the expected result that the contribution of a non-regulatory variant (SNPA) to mRNA expression (R2A) is equal to the contribution of a regulatory variant SNPB with which it is in LD (R2B) multiplied by the r2 LD coefficient for the two SNPs (r2AB). Equations for systems containing increasing numbers of regulatory variants, however, become increasing complex, with the number of additive terms in the polynomial expansions of the matrix equations defining R2A for 3- and 4-SNP systems, for example, equal to (4)2 = 16 and (18)2 = 324, respectively. Based on our inferred pattern for constructing these equations shown in Table 2A can be easily and rapidly carried out using a computer. .It should be noted that the equation for RA) and one or more biallelic regulatory variants. The goal of these analyses was to confirm that our method accurately predicts R2A values for pre-assigned sets of regulatory- and non-regulatory SNPs, prior to using the method to analyze experimental data, where the identities of regulatory- and non-regulatory SNPs are unknown.To determine whether the equations described above can accurately predict values for coefficients of determination obtained from linear regression analysis of mRNA expression vs. SNP genotype in experimental datasets, we carried out a series of calculations using simulated mRNA expression/genotype datasets for one non-regulatory SNP , each exhibiting a wide range of allele frequencies and pairwise r2 LD values were constructed from sets of haplotypes with randomly assigned population frequencies that sum to 1. Values for mRNA expression were assigned based on the genotypes of the regulatory variants  in each model using Fisher’s “genotypic value” framework. , data in each virtual spreadsheet allowed the calculation of Pearson correlation coefficients for: (i) correlations between mRNA expression and genotypes for individual regulatory variants and (ii) pairwise correlations between regulatory variant genotypes, that are required for matrix-equation-based calculation of R2A. As shown in Supplementary File 2A and values of R2A predicted using our matrix-based equations for two-, three- and four-regulatory variant systems yielded nearly identical results.In addition to providing a value for RAB) and third-order (DABC) D linkage disequilibrium coefficients17 for non-regulatory and regulatory SNPs used as input variables.  solving complex polynomial equations derived from our “constraint” matrix equations ons Fig. . TogetheB, SNPC, SNPD, SNPE) selected from a set of genotyped or imputed SNPs within a chromosome region of interest (ROI) that best served as proxies for unknown regulatory variants. In the context of our method, candidate proxy SNPs are termed “index SNPs” (iSNPs). Criteria for identifying the best iSNPs that fit simulated and experimental data are described below.In this section, we provide an example of how our method can be used to analyze experimental mRNA expression/genotype data. Unlike the simulated data sets described above, the regulatory variants that influence mRNA expression for most genes are unknown. For this reason, we used our method to identify individual SNPs or combinations 1, 2, 3 or 4 SNPs . In our notation, these are the “estimated” sample R2A values for these SNPs. The next step was to calculate sample Pearson correlation coefficients for: i) mRNA expression vs. genotype (rYGi) and ii) genotype vs. genotype (rGiGj) for each SNP in the ROI. In our current study, individual-level mRNA expression and genotype data were obtained from two human brain datasets: “BrainCloud” (BC)18 and “4BrainR”19 and a human lymphoblastoid cell line (LCL) dataset20. See Supplementary File We began by calculating a single-variable linear regression-based, sample coefficient of determination for mRNA expression vs. genotype for each SNP in the ROI  were selected from a shortened list of ROI SNPs from which SNPs with duplicate genotypes and SNPs that fail to meet a specific P-values threshold  had been removed. To identify the best 1-iSNP model generated by this process, the normalized root mean-square error (NRMSE) was calculated for each model by comparing the estimated and predicted R2A values for all of the SNPs in the ROI, with the models ranked in order of decreasing 1/NRMSE. The quality of fit of each model was also assessed by calculating adjusted R2model values based on linear regression analysis of estimated R2A vs. predicted R2A.For 1-iSNP models, an independent calculation of R2A = [RB(rAB − rBCrAC) + RC(rAC − rABrBC)]2/(1 − rBC2)2  chosen from the reduced list of ROI SNPs. Again, models were ranked in order of decreasing 1/NRMSE and quality assessed by calculating adjusted R2model. Due to the large number of terms in the polynomial equations defining R2A in 3- and 4-iSNP models, R2A values in these models were calculated based on our derived equation, R2A = −β/2α, using values of the quadratic equation-related terms -β and 2α listed in Table 2A, which we believe holds for models containing an arbitrary number of iSNPs, is based on our observation that solutions of quadratic equations required to solve constraint matrix equation-derived polynomial equations for RA: RA = −β ± (β2 − 4αγ)1/2/2α, simplify to RA = −β/2α, because the terms under the square-root sign of the equation sum to 0 under the given constraints. (See Supplementary File Generation of 2-iSNP models based on the equation RABrBC)]2/ − rBC22 ABrBC)]2/ − rBC22 MTHFR mRNA expression in frontal cortex based on mRNA expression and genotype data from the 4BrainR18 data set  “estimated’ R2 values derived from single-variable linear regression analyses of the mRNA expression/genotype data calculated independently for each SNP (blue bars and gray) and (ii) “predicted” values of R2 obtained from matrix equation-based calculations (dark blue line). The quality of the fit between the estimated R2A values (blue/gray bars) and predicted R2A values (dark blue line) is obvious upon inspection of the upper (R2-R2) plot and quantified by adjusted R2model = 0.973. The lower graph in Fig. 2-Δ2 plot,” where values of pairwise r2 (=Δ2) LD coefficients calculated for each SNP with respect to SNPB (red line), SNPC (green line), SNPD (black line) are imposed on the estimated R2 value for each SNP (blue bars). To facilitate comparisons, the heights of the red, green and black lines were scaled to the heights of the estimated R2 value for each iSNP. Together, these lines define three semi-independent families of SNPs linked to the iSNPs: rs198368 (red), rs2066470 (green), and rs10864536 (black). Independent analyses of 4BrainR data for MTHFR mRNA expression in fontal cortex (FCTX), temporal cortex (TCTX), cerebellum (CERE) and pons (PONS) show that FCTX, TCTX and CERE share the same three red, green and black iSNP families, while PONS is best modeled by 3 iSNPs drawn from the rs10864536 iSNP family  derived from Japanese (JPT), Chinese (CHB) or Caucasian (CEU) populations  to correspond to the FTCX green iSNP family and the other two iSNP families (iSNP = rs4845881 and re1023252) to subsets of the FCTX black IiSNP family of SNPs. Together, these results suggest that regulatory variants associated with the green and black iSNP families contribute to mRNA expression in FCTX, TCTX, CERE and LCLs, while regulatory variants associated with the FCTX red iSNP family are active in FCTX, TCTX and CERE, but not in PONS or LCLs. The observation that FCTX black iSNP family SNPs are parsed into two separate families in LCLs suggest that this family contains at least two distinct regulatory variants. Obviously, more work will be required to elucidate the number and locations of regulatory variants associated with each SNP family, but we believe that the above example illustrates the usefulness of our method for fine-structure analysis of mRNA expression and its power to produce hypotheses for further investigations.CHI3L2 (Chitinase 3 like 2), the autism and schizophrenia candidate gene DGCR8  and the Alzheimer candidate genes GSTM3 and GSTM5 (Glutathione S-transferase mu-1/5) can be found in Supplementary Files A summary of the above findings is provided in Supplementary File 2model, (iii) maximum R2M, (iv) maximum sum of estimated R2 values for individual iSNPs, (v) minimum Akaike information criterion (AIC) and/or (vi) minimum Bayesian Information criterion (BIC). Minimum NRMSE is set as the default criterion. Importantly, the method also provides a quantitative account of how regulatory variants influence the estimated single-variable linear regression R2 values (R2A) for non-regulatory SNPs in linkage disequilibrium within datasets under investigation. Specifically, it clearly shows how the combined effects of multiple regulatory variants can only be understood through application of the “constraint” matrix equations defined in this study, i.e., R2AB = R2B, R2ABC = R2BC, R2ABCD = R2BCD, etc., for which we derived a general formula (R2X) terms, where x = B, C, D or E  minimum NRMSE, (ii) maximum adjusted Rla Table . These c2M, may produce insights not available from consideration of models based on a single criterion. A discussion of two additional limitations of our method, the assumption of additive effects of SNP alleles and applicability primarily to common, cis-acting, biallelic genetic variants, can be found in Supplementary File Limitations of the current method include the current lack of hybridization array-based mRNA expression data sets that produce reproducible results for many genes of interest and the subjective nature of choosing the best iSNP models. Although we have not yet used this method to analyze RNA-sequencing-based mRNA expression data, we are optimistic that these datasets will provide better replication compared to array-based datasets. Likewise, we believe that comparing the results of models selected based on different criteria, for example maximum 1/NRMSE versus maximum RFinally, we want to stress that our method only quantifies associations among SNPs within the datasets under investigation, rather than identifying specific regulatory variants. Thus, caution must be taken when interpreting the results of analyses. The identification of an iSNP family does not necessarily guarantee the existence of underlying regulatory variants: our program will dutifully parse statistical noise as well as true causal relationships SNP. For all of these reasons, we have made no effort to provide P-values for selected models. Rather, we hope that this method will be useful for bringing order to the “forest” of SNPs surrounding genes of interest and for generating hypothesis that can be investigated using additional bioinformatic tools and by experimentation.2M) based on Pearson correlation coefficients (rYX) between: 1) a dependent variable, (Y = mRNA expression) and multiple independent variables (X = SNP genotypes) and ii) pairwise comparisons of the independent variables. We subsequently developed a R language-based computer program for carrying out the required calculations and plotting the results of the analysis of simulated and experimental mRNA expression/SNP genotype data sets. A detailed description of the methods developed in this study can be found in Supplementary Files https://github.com/saffenlab/R2-D2-model.Briefly, we developed a mathematical method for parsing SNPs that correlate with mRNA expression into SNP families related by LD using a matrix equation from multivariate statistical analysis that allows the calculation of multiple coefficients of determination RM based oSupplementary Information


The comparison between δ18Os and instrumental data indicates that the δ18Os variability is primarily controlled by the precipitation seasonality  modulated by the El Niño/Southern Oscillation (ENSO) on interannual to interdecadal timescales. Higher (lower) δ18Os values thereby correspond to lower (higher) EASM/NSM ratios associated with El Niño (La Niña) events. Significant correlations with ENSO and the Pacific Decadal Oscillation (PDO) indicate that the precipitation seasonality in SEC is remarkably influenced by ocean-atmosphere interactions, with lower (higher) EASM/NSM ratios during warm (cold) phases of ENSO/PDO. The progressive increase in δ18Os since 2005 AD may reflect a strengthening of the central Pacific El Niño under continued anthropogenic global warming. The relationship between seasonal precipitation and δ18Os with ENSO/PDO requires further studies.In southeastern China (SEC), the precipitation amount produced by the East Asian summer monsoon (EASM) is almost equivalent to that during the non-summer monsoon (NSM) period, both of them significantly affecting agriculture and socioeconomy. Here, we present a seasonally-resolved stalagmite δ For example, in 1998, more than 1,000 people were killed and more than 200 million people were affected by the floods in the middle and lower reaches of Yangtze River Valley (YRV), and the direct socioeconomic losses amounted to about £10,000 million2. In addition, the spring rainy period in SEC3, associated with low temperatures, overcast and rain, is also a severe threat to agriculture  and transportation5. Studies indicate that the rainy season in SEC includes the EASM and spring rainfall and both of them show high variability6. Frequent flood and drought events resulting from this seasonally variable precipitation seriously impact agriculture and socioeconomy in this region. Therefore, it is essential to study the variability in precipitation seasonality and assess its influencing factors.Southeastern China (SEC), which is one of the most densely populated areas in East Asia, is significantly influenced by summer floods and droughts associated with the East Asian summer monsoon (EASM)17. Some investigated the combined effects of PDO9 and ENSO15 on EASM, spring or East Asian winter monsoon (EAWM) precipitation over Eastern China, respectively. Others examined the modulation of the relationship between ENSO and EASM16, spring6 or EAWM17 precipitation by the PDO. However, the relationship between precipitation variability and ENSO/PDO on interannual to interdecadal timescales remains poorly understood, partly due to the limited availability of instrumental datasets. In addition, most researchers focused on the variations of EASM or EAWM in the Asian monsoon region, and only few studies examined the variability in precipitation seasonality18. Long-term paleoclimatic reconstructions are needed to explore the variability of precipitation in SEC and its seasonality.Observations and simulations demonstrate that ENSO and PDO have a major impact on the interannual-interdecadal variability in precipitation over SEC18O records of tree-ring cellulose in SEC were recently published20, but they mainly constrain precipitation and relative humidity during the rainy season. High-precision and high-resolution δ18O records of stalagmite (δ18Os), calibrated against instrumental data, can be used to reconstruct the variability of ENSO/PDO in the past. In this study, we present a first seasonally-resolved δ18Os record for the period 1810–2009 AD based on an annually laminated stalagmite from E’mei cave, Jiangxi Province, SEC. This record allows us to explore the variability in the precipitation seasonality modulated by ENSO and PDO on interannual to interdecadal timescales.Some δ18O values accounts for 54% of the annual precipitation and the non-summer monsoon (NSM) (October to next April) precipitation with higher δ18O values accounts for 46%  value is controlled by both EASM and NSM precipitation. Data from the nearest GNIP station  during 1988–1992 indicate lower δ18Ow values, more June-September precipitation and less March-April precipitation during La Niña years as compared to El Niño years  is located in the north of Jiangxi Province, SEC Fig.  was coll232Th levels of 2–4 × 103 ppt resulted in large dating uncertainties of 172–438 years for 230Th dates . The 230Th dates at 6 and 18.5 mm from the top are 1997 ± 267 and 1932 ± 158 AD, respectively, and the inferred age of the top of EM1 is 2028 ± 267 AD. This is consistent with the observation of active dripping when this stalagmite was collected in December 2009. The top age of EM1 was therefore assumed to be late 2009 AD.Elevated detrital 22  and white, porous sub-layers (WPSL)18O and δ13C records of EM1 with stalagmite YQ15-1, which was obtained from Yongquan Cave, 50 km northeast of E’mei Cave. The dating errors of YQ15-1  between δ18O and δ13C values along the growth axis. The co-variation of δ18O and δ13C can also result from the climatic and environmental change24. The inverse relationship between δ18O and δ13C values on seasonal timescales , e.g., precipitation amount, moisture sources, moisture pathways and rainfall seasonality. Dayem et al.29 suggested that several processes associated with precipitation seasonality might jointly influence the fluctuation of Chinese δ18Os29. Based on the analyses of modern precipitation δ18O data30, Tan31 suggested that δ18Os in the monsoon region of China records the moisture source changes related to ENSO circulation but not the monsoon intensity or precipitation amount. The higher δ18Os values reflect more moisture originating from the adjacent Pacific Ocean relative to the remote Indian Ocean during El Niño phases, and vice versa31. However, some studies suggested that EASM precipitation is primarily derived from the Indian Ocean, while the moisture from the West Pacific Ocean is a minor contributor to monsoonal rainfall only33, and the δ18Op variability is caused by both precipitation amount and moisture transport history32. Cheng et al.34 and Yang et al.35 suggested that δ18Os is primarily controlled by large-scale monsoon intensity and upstream precipitation. As introduced above, δ18Ow in the study area is influenced by both the EASM and the NSM associated with ENSO circulation  in order to explore its significance on interannual to interdecadal timescales.Although δ18Os and the SOI shows a significantly negative correlation  events. This is consistent with previous studies based on instrumental data35 and δ18O records of tree-ring cellulose36 and stalagmites38 in SEC. δ18Os is significantly and negatively correlated with the ratio of EASM/NSM precipitation  using a 5-year moving average for 1951–2009 AD  index for 1810–2000 AD. The annual D/F index, based on drought or flood events in spring, summer and autumn recorded in historical documentaries39, can reflect local precipitation variations in May-September, with a decreased (increased) D/F index representing more (less) precipitation. The good coherence indicates that lower (higher) δ18Os values are associated with increased (decreased) EASM precipitation in the study area  corresponding to warm ENSO phases . On an interdecadal timescale, previous studies demonstrated that the PDO/ENSO circulation shifted from a cold phase during 1951–1976 AD to a warm phase during 1977–2000 AD11. During the cold phase of PDO/ENSO, EM1 δ18Os values are generally lower than during the subsequent warm phase  and tends to be weaker (stronger) during El Niño (La Niña) years41, and the onset varies from late April to early June41. Thus, May-September precipitation cannot be always used to represent the EASM precipitation, especially in SEC characterized by a strong precipitation seasonality. This is also supported by instrumental δ18Op data from the Changsha GNIP station  are lower than those during El Niño years (1991–1992 AD), indicating that the EASM starts early (late) during La Niña events (El Niño events). Therefore, EASM precipitation during 1951–1976 AD likely represented May-September precipitation, while it shifted to June-September precipitation during 1977–2010 AD; the NSM precipitation should be adjusted accordingly. Hence, less EASM and more NSM precipitation results in higher δ18Os values during long-term El Niño phases  and the EASM precipitation , and is significantly positively correlated with the NSM precipitation , suggesting that higher (lower) δ18Os values are associated with lower (higher) EASM/NSM ratios during El Niño (La Niña) phases on interannual to interdecadal timescales.Similarly, the poor correlation between δale Fig.  might be18Os variation on interannual-interdecadal timescales. Studies demonstrated that EASM precipitation is mainly sourced from the remote Indian Ocean while NSM precipitation primarily originates from the adjacent Pacific Ocean33. Thus, the EASM/NSM ratio may also represent the ratio of moisture sources/pathways during the EASM season to that of the NSM season, partly similar to the “circulation effect” proposed by Tan31. He suggested that the δ18Op and δ18Os values are affected by the variation of moisture source in the summer monsoon season. We also emphasize the influence of NSM precipitation, which accounts for 45~65% of the annual precipitation in the study area. As a consequence, we suggest that the variation in precipitation seasonality  modulated by ENSO plays a key factor in controlling the δ18Os variability in the study area on interannual to interdecadal timescales, with higher (lower) δ18Os values reflecting lower (higher) EASM/NSM ratios associated with El Niño (La Niña) phases.Considering the strong precipitation seasonality (including precipitation amount and moisture sources/pathways) in the study area, we suggest that the EASM/NSM ratio better explains the δ18Os and the PDO index shows a significantly positive correlation  PDO phases. A positive correlation between the PDO and the δ18O record was also observed in other records from speleothem45 and tree-ring cellulose46 in the monsoon region of China. The EM1 δ18Os is significantly negatively correlated with the SOI index  phases. This is consistent with the relationship between δ18Os and the SOI index during 1951–2004 AD  and the western ridge point moves westward (eastward) during El Niño (La Niña) phases41. Based on a comparison between instrumental δ18Op data and the WPSH strength (WPSHs) index, Tan30 showed that increased δ18Op values in SEC are associated with a strengthening and westward expansion of the WPSH during the summer following an El Niño event. The comparison between the WPSHs index and δ18Os yielded a significantly positive correlation  WPSH. Positive correlations between WPSH intensity and δ18O records were also reported from the monsoon region of China based on stalagmites and tree-ring cellulose31. As a consequence, the comparison between δ18Os, WPSHs, ENSO and PDO reveals that higher (lower) δ18Os values represent a strengthened (weakened) WPSH associated with warm (cold) phases of PDO/ENSO on interannual to interdecadal timescales. This indicates a lower (higher) EASM/NSM ratio in the study area during a warm (cold) phase of PDO/ENSO. The significant 55–66 year cycle observed in the EM1 δ18Os time series is generally consistent with the 50–70 year cycle seen in the PDO records22. The significant 2.4, 3.2 and 5.7-year cycles in the EM1 δ18Os time series are consistent with 2–7 year cycles in ENSO records, further supporting the important influence of PDO and ENSO on precipitation variation  δ18O values corresponding to less (more) precipitation during El Niño (La Niña) events. Studies based on instrumental and reanalysis data suggest increased spring13 and EAWM precipitation15 in SEC associated with El Niño events, indicating increased NSM precipitation during El Niño events on interannual timescales. Therefore, we suggest that the interannual variability recorded by the EM1 δ18Os data might also reflect the variability in seasonal precipitation amount, with more (less) EASM and less (more) NSM precipitation during La Niña (El Niño) phases.What is the relationship between δses Figs  and 3a a50. These studies indicate that decreased summer rainfall in North China and increased summer rainfall in South China 49 result from a strong EASM during cold phases of PDO/ENSO, and vice versa. However, Chan and Zhou11 found that less (more) early summer monsoon precipitation in May-June in South China results from the warm (cold) phases of PDO/ENSO. This indicates that the pattern of interdecadal variability of June-August precipitation is opposite to that of May-June precipitation associated with PDO/ENSO. Yang et al.9 also demonstrated that the effect of the PDO on precipitation anomalies in East China is depends on the month: May-June precipitation patterns in East China are opposite July-August or annual mean patterns. In the study area, the interdecadal variation of precipitation in May also shows an opposite trend to that in June-September for 1951–2009 AD  phases of ENSO/PDO on interdecadal timescales, indicating more (less) NSM precipitation during warm (cold) phases of ENSO/PDO. This is consistent with our observations  under continued anthropogenic climate warming during recent decades52. The anomalous low-level anticyclone and Walker circulation associated with CP El Niño events contributes to anomalously dry conditions over southeastern Asia and an intensified winter monsoon with a deeper East Asian trough at 500 hPa resulting in lower temperatures in South China53. CP El Niño events can also lead to decreased spring precipitation in South China and increased spring precipitation in the lower reaches of Yangtze River13. More NSM precipitation might also contribute to increased δ18Os values. We suggest that the highest δ18Os values during the late 20th century result from dry conditions in summer, consistent with the results based on tree-ring δ13C data from SEC51. The synchronously increased δ13C values in the EM1 and YQ-15 stalagmites . Details about the instrumental setup are explained in Cheng et al.56.Four subsamples (50–100 mg), drilled using a hand-held carbide dental drill, were analyzed at the Institute of Global Environmental Change, Xi’an Jiaotong University. The chemical procedure used to separate uranium and thorium was similar to that described in Edwards 18O and δ13C), a total of 780 subsamples were micro-milled along the central axis of stalagmite EM1. 370 subsamples were micro-milled at a spatial resolution of 0.1 mm from 0 to 13 mm, and 0.05 mm from 13 to 25 mm distance from the top. These samples were measured using a MAT253 isotope ratio mass spectrometer equipped with a MultiPrep system at Xi’an Jiaotong University. Analytical precision of the δ18O and δ13C analyses was better than 0.06‰ and 0.03‰ (1σ), respectively. 410 subsamples were micromilled along the right side of central axis of stalagmite EM1 at a resolution of 0.07 mm from 25 to 53.7 mm distance from the top, and measured on a Thermo Fisher Deltaplus XL isotope ratio mass spectrometer linked to a Gasbench II at the University of Innsbruck. Analytical precision was 0.08‰ and 0.06‰ for δ18O and δ13C, respectively (1σ)57. All isotopic values are reported in the δ notation relative to the Vienna Pee Dee Belemnite (VPDB) standards.For stable isotope analyses  and a white, porous sub-layer (WPSL) can be observed . Monthly precipitation δ18O data (1988–1992) from Changsha stations were obtained from the Global Network for Isotopes in Precipitation . The Southern Oscillation Index (SOI) was obtained from http://www.bom.gov.au/climate/current/soihtm1.shtml. Positive SOI values represent El Niño events and negative values represent La Niña events. The Pacific Decadal Oscillation (PDO) index, defined as the leading standardized principal component of monthly sea-surface temperature (SST) anomalies in the North Pacific Ocean, was taken from http://www.ncdc.noaa.gov/teleconnections/pdo. The west Pacific subtropical high (WPSH) plays an important role in governing the variability over East China. The WPSH strength (WPSHs) index58 is used to represent the intensity of WPSH, with stronger WPSHs concurrent with a south-westward extension of WPSH and vice versa.Monthly instrumental data from the Nanchang and Jiujiang meteorological station during the period 1951–2010 were obtained from the National Climate Center (NCA, 59 on the PAST platform60. The 90% confidence level is shown in Supplementary Fig. Were computed using REDFIT softwareSupplementary Information


Retinal imaging scores are correlated with brain Aβ loads. The findings are validated in an independent cohort, using a second hyperspectral camera. A similar spectral difference is found between control and 5xFAD transgenic mice that accumulate Aβ in the brain and retina. These findings indicate that retinal hyperspectral imaging may predict brain Aβ load.Studies of rodent models of Alzheimer’s disease (AD) and of human tissues suggest that the retinal changes that occur in AD, including the accumulation of amyloid beta (Aβ), may serve as surrogate markers of brain Aβ levels. As Aβ has a wavelength-dependent effect on light scatter, we investigate the potential for in vivo retinal hyperspectral imaging to serve as a biomarker of brain Aβ. Significant differences in the retinal reflectance spectra are found between individuals with high Aβ burden on brain PET imaging and mild cognitive impairment ( The use of PET for detection of Aβ in the brain in AD has limitations; studies also indicate that retinal changes, including Aβ deposition, occur in AD. Here the authors demonstrate the potential to use in vivo retinal hyperspectral imaging as a surrogate for brain accumulation of Aβ. Advances in positron emission tomography (PET) imaging with tracers that are selective for Aβ have great utility in confirming a clinical diagnosis of the disease, however the application of this approach is limited by its cost and accessibility. As a consequence, PET imaging is not presently scalable for population screening of individuals at risk of AD. Given that Aβ accumulates in the brain over 15–20 years prior to the onset of manifest cognitive impairment3, the lack of a convenient in vivo biomarker of Aβ stands as a lost opportunity for early intervention.The accumulation of Aβ in the brain is a hallmark feature of AD5 and is the only part of the central nervous system that can be imaged non-invasively at sub-cellular resolutions14. Human post-mortem histopathological studies have shown accumulation of Aβ in the retinas of those with confirmed AD, principally in the inner-retinal layers17. Aβ is also known to occur in the subretinal deposits of people with age-related macular degeneration18. Studies of transgenic mouse models of AD have demonstrated the presence of retinal Aβ21 and shown quantitative and temporal correlations between brain and retinal Aβ deposition17. Although some disagreement remains regarding the extent to which retinal Aβ accumulation occurs and the extent to which it phenocopies accumulation in the brain23, emerging evidence highlights the potential for retinal imaging as a biomarker of brain Aβ status.The retina is a developmental extension of the brain24. Other work has attempted to identify evidence of Aβ in brain and retinal tissue without the need for a label using hyperspectral (HS) imaging25. The approach is based on the acquisition of a series of images of an object across many contiguous wavelengths of light. HS images are thus stacks of images that combine spectral and spatial information into a single data cube. Each HS pixel corresponds to the reflectance spectrum of a locus across all the measured wavelengths  or the control group , on the basis of brain Aβ PET scan results. All but one participant in the control group (owing to excessive blinking) had both eyes successfully imaged using the Metabolic Hyperspectral Retinal Camera .To elucidate the spectral changes in Alzheimer’s disease (AD) with retinal HS imaging, we recruited a cohort of participants who had undergone Aβ PET scanning and neuropsychological tests . Three participants (Aβ PET+) were excluded from the study owing to posterior capsular opacification after artificial lens implantation that prevented successful imaging. The recruited cohort consisted of 35 participants who were assigned to either the case group  compared with the cases , raw reflectance spectra are not useful for visualisation of group differences , which provides phenomenological measures of variations across the sample. This method was chosen as it is robust to over-fitting and is useful in situations where the sample size is small relative to the number of variables (number of wavelengths) studied. The DROP-D method identified the two main spectral components that account for most of the within-group variability and discrimination between cases and controls was optimal when these spectral components were corrected for  for w1 and RMSE = 1.1 ± 0.4% for w2, Supplementary Figs. To correct for sources of spectral variability unrelated to AD we used a spectral analysis method called Dimension Reduction by Orthogonal Projection for Discrimination DROP-D; 27. The i27 , indicating a general spectral difference between cases and controls, which was only visible after correcting for the inherent spectral variation between eyes.The spectral model derived for each sampling location was used to calculate a HS score for each participant, for each location Fig. . Overallp = 0.002, 95% CI: 0.06–0.22, unpaired two-tailed t test, Fig. p = 0.02, 95% CI: 0.04–0.36, unpaired two-tailed t test, Fig. p = 0.03, 95% CI: 0.01–0.26, unpaired two-tailed t test, Fig. p = 0.01, 95% CI: 0.03–0.24, unpaired two-tailed t test, Fig. r = 0.18, p = 0.3, 95% CI: −0.17–0.48), MMSE  or RNFL thickness . HS scores were not significantly influenced by the presence of drusen in the case  or control groups .Using pairwise comparisons at each location, controlled for false discovery rate, we showed that the most significant difference between groups was found at the S1 sampling location  where the largest group difference was found. All subsequent references to HS score relate to the S1 location unless specified otherwise. Validation data sets included the fellow eye of each participant and an independent case (Aβ PET+) and control (Aβ PET−) cohort imaged on a separate MHRC using the same acquisition protocol Fig. .Fig. 5Vap = 0.02, 95% CI: 0.02–0.21, unpaired two-tailed t test, Fig. p = 0.03, 95% CI: 0.02–0.38, unpaired two-tailed t test, Fig. Significant differences in HS scores were found between all case and control data sets. In the principal cohort, HS scores for fellow eyes of cases were significantly higher than the fellow eyes of controls  for the principal  between the HS scores for study and fellow eyes  than the study eye. Despite these differences there was a significant correlation . Importantly, a strong correlation was found between HS scores measured pre- and post-operatively  and non-transgenic littermate controls (n = 10) underwent retinal imaging using a custom-made HS camera (450–680 nm). This was done to establish whether the retinal spectral difference between human subjects with high and low brain Aβ load is also seen between mice when the same data processing method is applied. Retinal images of one mouse (5xFAD) had spectral intensities below three median absolute deviations for > 50% of the recorded wavelengths and were excluded from further analysis. Owing to the difference in the wavelength ranges of the human and mouse HS-imaging systems, recalibration of the human spectral model was needed to calculate HS scores acquired with the mouse imaging system. Recalibration did not remove the ability to distinguish between human Aβ PET+ and PET− subjects . Spectral representations of the principal axes of variation that were adjusted for in this correction were closely approximated with linear combinations of the known spectra of the ocular media (chiefly the lens), macular pigment, melanin and haemoglobin. Following correction, we used a simulation to show that much of the difference between cases and controls could be described by the spectral profile of Aβ in solution  and 9. W33. Any ocular constituent with a similar spectral profile to that measured for Aβ in solution could account for the observed effect  from PET− (control) participants. Accordingly, participants who had undergone brain Aβ PET imaging were recruited for HS imaging of the retina. A spectral analysis method was used to minimise spectral variability and identify a difference between Aβ PET+ cases and PET− controls. The method was further validated against an independent cohort to determine whether the same discrimination performance could be obtained. To understand the underlying components of the main spectral difference between groups, modelling was performed using a combination of spectra of ocular structures that are known to affect ocular reflectance as well as the spectrum of Aβ in solution derived from spectrophotometry. This information was used to ascertain whether an Aβ signature could be seen with HS imaging of the retina in Aβ PET+ participants.All protocols and methods described in this study were approved by the Human Research Ethics Committee of the Royal Victorian Eye and Ear Hospital (Project number 15/1253 H and 16/1265 H) and by Veritas IRB for the validation cohort in Canada (16093) and were conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants following a thorough explanation of all test procedures. All procedures involving mice were performed in accordance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. Ethics approval was obtained from the Howard Florey Institute Animal Ethics Committee .Participants in the principal cohort were recruited from two sources, the Royal Melbourne Hospital Neuropsychiatry Unit and the Australian Imaging, Biomarker & Lifestyle Study of Ageing (AIBL) study. All cases had either mild cognitive impairment or early AD as determined by a neuropsychological test battery and moderate–high Aβ burden measured on brain PET imaging prior to study commencement. The tracer-specific standardised uptake value ratio (SUVR) diagnostic thresholds for PET positivity were PiB ≥ 1.40, NAV4694 ≥ 1.40, Flutemetamol ≥ 0.55 and Florbetapir ≥ 1.05. The control group included age- and sex-matched participants with Aβ burden on PET scan below the diagnostic threshold for AD, or where Aβ distribution was atypical for AD.Exclusion criteria included age under 30 years; opacification of the ocular media such as visually significant cataract  or corneal disease sufficient to preclude retinal imaging; angle-closure glaucoma or angle-closure suspects; retinal surgery in the preceding 6 months; previous major ocular trauma; retinal dystrophy and participants with any medical conditions likely to preclude satisfactory retinal imaging.36 and as the subretinal deposits (drusen) that are found in age-related macular degeneration have been reported to contain Aβ, among other spectrally active constituents37. Different retinal sampling locations were used to assess the effect of these potential confounders. Optical coherence tomography (OCT) images were collected on a Spectralis SD-OCT  using three standard protocols: a horizontal posterior pole raster scan; peripapillary retinal nerve fibre layer scan; and optic nerve head (ONH) scan with Enhanced Depth Imaging. Colour retinal images were collected using the Digital Retinography System . To ensure optimal quality retinal images, pupils were dilated (>5 mm) using tropicamide 0.5% w/v  and phenylephrine 2.5% w/v .All participants underwent a comprehensive ocular examination, by a registered optometrist (FH) or ophthalmologist (PvW) prior to imaging to identify any ocular exclusion criteria. Conventional retinal imaging techniques and clinical examinations were employed to assess the presence of other retinal pathologies and to evaluate comparability between groups. This is important as retinal nerve fibre layer thinning may exert confounding spectral influencesHS imaging was performed using the Metabolic Hyperspectral Retinal Camera . The MHRC captures images of the retina with a 30-degree field of view and a pixel resolution of ~8.3 µm. Ninety-one consecutive frames were recorded for each HS image, scanning from 900 nm to 450 nm in steps of 5 nm. Exposure time was set to 0.01 s per frame, leading to an overall acquisition time of under 1 s  was presented with a high contrast false-coloured image of the retina Fig. . The incThe foveola was sampled using a circular area of interest (60-pixel diameter) in the centre of the fovea (F1). The parafoveola was sampled using an annulus (100–200 pixels in diameter) centred on the fovea (F2). Areas in the temporal vascular arcades superior (S1) and inferior to the fovea (I1) as well as superior (S2) and inferior to the ONH (I2) were sampled using squares (200 × 200 pixels), on a fixed template horizontally orientated with the temporal raphe Fig. .39 image was calculated using variance parameters 1 and 20 to increase contrast between the blood vessels and background whilst removing noise and artefacts. To be conservative, 40% of pixels in the DoG image with the highest intensity were considered to be blood vessels40.Given that retinal vascular anatomy varies considerably between individuals, each sampling location may contain varying numbers of pixels overlying blood vessels. Retinal blood vessels have a prominent spectral signature and thus failure to exclude vessels results in unnecessary variability in the spectral profile. In each sampling location, blood vessels visible in the inner retina were automatically segmented using the green channel of the false-coloured image. A difference of Gaussian (DoG)26, each location was analysed independently. To transform the multiplicative interaction between retinal structure and reflectance, measured spectra were first log-transformed41. The average spectrum at each sampling location was then computed and smoothed using a Savitzky-Golay spectral smoothing procedure with a 5th order kernel of width of 1342. At each location, a machine learning method called Dimension Reduction by Orthogonal Projection for Discrimination (DROP-D)27 was trained to classify Aβ PET− controls from PET+ cases on the basis of the spectral data from the study eye of the principal cohort. This linear machine learning discrimination method was used to derive the HS scores as it is robust to over-fitting and well-suited to problems in which the number of observations (participants) is small compared to the number of variables (wavelengths). In brief, DROP-D works in two steps  was used to derive the spectral model. The right eye was chosen in cases in which both eyes were similar. The PET status (positive or negative) of each participant in the principal cohort was used to develop the spectral model to discriminate between cases and controls. As the interaction between light and retinal tissue is highly dependent on the sampling locationt tests, adjusted for false discovery rate for multiple comparison across all three data sets. The agreement of the HS scores between study and fellow eyes was estimated using Pearson’s correlation coefficient. In developing the model using data from the principal cohort, knowledge of Aβ PET status (positive vs negative) alone was known to the investigators. The quantitative PET data were provided (by co-author CJF) after the analytical model was finalised and correlation between HS score and PET quantitative values were performed in a blinded fashion. Blinding was not necessary for cross-validation studies  as parameters of the analytical model were fixed.The spectral model derived using the study eyes was applied to fellow eyes to provide internal validation of the model and to an independent cohort based in Montreal, Canada. One eye of each of 13 PET− controls and 4 PET+ cases of the independent cohort were imaged using a second MHRC of the same model  and following the same imaging protocol. The tracer-specific SUVR diagnostic thresholds for PET positivity were Florbetaben ≥ 1.45. Between-group differences were tested using unpaired two-sided t test. The agreement of the HS scores pre- and post-operatively was estimated using Pearson’s correlation coefficient.To assess the robustness of the model to the presence of lens opacification (cataract) and artificial intraocular lenses, a cohort of 10 participants scheduled to undergo elective outpatient cataract surgery were recruited from the Royal Victorian Eye and Ear Hospital, Melbourne, Australia. The Aβ PET status of these subjects was unknown and all were presumed to be cognitively normal. Retinal HS images were taken before and 8 weeks after cataract surgery. The spectral model obtained from the principal cohort was used to derive the HS scores pre- and post-cataract surgery. Differences in HS scores pre- and post-operatively were tested using a paired two-sided 45 colony was established in-house using breeder pairs of 5xFAD mice  and C57BL/6 J mice. This strain 6799Vas/Mmjax) backcrossed on a C57BL/6 J background does not carry the retinal degeneration allele Pde6brd1. Mice were genotyped using tail tissue samples shortly after birth by an external vendor . Non-transgenic littermates served as controls.The 5xFAD micen = 12, WT n = 10) aged between 9 and 14 months old were imaged with the HS imaging camera. A subset of these  were killed and retinal immunohistochemistry was performed.Mice were housed in temperature-controlled cages (20 °C) with ad libitum access to food and water and a 12-hour light–dark cycle. During the light cycle, ambient light was kept below 50 lux to avoid the possibility of photoreceptor damage (43). Twenty-two animals . The advantage of this strain is the lack of an inherited retinal degeneration, which may affect the HS signal from the retina45.On the B6SJL background (MMRRC Stock 34840-JAX), these mice are known to accumulate Aβ as early as 2 months of age in the brain and this increases with age48. In brief, whole eyes were fixed in 10% neutral-buffered saline overnight and embedded in paraffin for sectioning. Sagittal sections (7 μm) were treated with 80% formic acid  and 3% hydrogen peroxide  prior to incubation  in blocking buffer . Monoclonal mouse antibody 1E8 raised to human Aβ17–24 was used at a dilution ratio of 1:500 to detect total Aβ50. After washing, sections were incubated for 15 min at room temperature with biotinylated secondary antibody and streptavidin/horseradish peroxidase reagent  prior to chromogen development . Slides were counterstained with Harris’s haematoxylin , and mounted in distyrene-plasticiser-xylene media . Sections stained with the omission of the primary antibody served as negative experimental controls.Immunohistochemistry was performed on retinas from three 5xFAD mice and one WT mouse, as previously described−1 of ketamine  and 10 mg kg−1 of xylazine  and diluted in sterile injectable saline at a ratio of 1:10. Topical anaesthetic and mydriatic agents were instilled . Ocular gel  and a coverslip was placed over the surface of each eye to prevent corneal dehydration and to prevent cataract formation. A heated platform was used to maintain body temperature during imaging.Prior to imaging, animals were anaesthetised with an intraperitoneal injection of 1 ml/100 g of a mixture containing 80 mg kgMice were imaged using a custom-built bench ophthalmoscope comprised of a 150 W xenon light source and a fast switching monochromator  oriented perpendicular to the pupil plane. A semi-reflective pellicle beam splitter  was used to combine the illumination and imaging paths. A condensing lens focused the image onto a 16-bit monochromatic CMOS sensor . Image acquisition was performed from 320 to 680 nm . Images at each wavelength were acquired using a 100 ms exposure for a total acquisition time of 36.1 s. Images were exported in TIFF format.39 calculated using variance parameters 1–5 (0.2 step) for the small and 4–14 (1 step) for the large filters. This method increased contrast between blood vessels and the background while removing noise and artefacts. Pixels overlying blood vessels and the optic nerve were excluded. Owing to the smaller size of the mouse retina compared to human, the pixel intensities were averaged at each wavelength to yield a single reflectance spectrum per retina. The reflectance spectra were resampled between 450 and 680 nm in 5 nm steps using a linear interpolation method. The human HS model was recalibrated (450–680 nm) to suit the mouse imaging system as described above. Differences in illumination over the two imaging sessions were compensated for using the average spectral difference of control mice for each session. Retinal HS scores were obtained by applying the recalibrated human HS model to mouse reflectance spectral data.Light intensity at each wavelength was normalised using a model eye lined with Spectralon. The residual motion between adjacent frames was then registered. Blood vessels visible in the inner retina were automatically segmented using an average image from wavelengths 390–460 nm. The mask used to extract blood vessels was derived using series of DoG51. The statistical tests used in this study are indicated in the results section and within figure and table legends.All analyses were performed using Matlab  or Prism . Correlation was assessed using Pearson’s correlation coefficient. A significance level of 0.05 was considered statistically significant. When multiple comparisons were performed, the family-wise significance level was maintained at 0.05 by adjusting the false discovery rate using the Benjamini–Yekutieli procedureFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Reporting SummarySource Data


The emergence of new sustainable approaches for insect management using RNA interference (RNAi) based insecticides has created the demand for high throughput analytical techniques to fully characterise and accurately quantify double stranded RNA (dsRNA) prior to downstream RNAi applications. In this study we have developed a method for the rapid characterisation of single stranded and double stranded RNA using high resolution RNase mapping in conjunction with ion-pair reverse-phase chromatography utilising a column with superficially porous particles. The high resolution oligoribonucleotide map provides an important ‘fingerprint’ for identity testing and bioprocess monitoring. Reproducible RNA mapping chromatograms were generated from replicate analyses. Moreover, this approach was used to provide a method to rapidly distinguish different RNA sequences of the same size, based on differences in the resulting chromatograms. Principal components analysis of the high resolution RNA mapping data enabled us to rapidly compare multiple HPLC chromatograms and distinguish two dsRNA sequences of different size which share 72% sequence homology. We used the high resolution RNase mapping method to rapidly fingerprint biomanufactured dsRNA across a number of different batches. The resulting chromatograms in conjunction with principal components analysis demonstrated high similarity in the dsRNA produced across the different batches highlighting the potential ability of this method to provide information for batch release in a high throughput manner. •Developed high resolution RNase mapping in conjunction with IP RP HPLC•Provides an important ‘fingerprint’ for identity testing and bioprocess monitoring•Rapidly distinguishes different RNA sequences of the same size•Method used to rapidly fingerprint biomanufactured dsRNA across a number of different batches This is due to the difficulty in purifying and enriching biological RNA, the formation of RNA-metal ion adducts and problems associated with sequencing RNAs due to poor fragmentation in tandem mass spectrometry experiments. Furthermore, there is traditionally a trade-off between chromatographic performance and mobile phase conditions necessary to obtain high sensitivity during electrospray mass spectrometry analysis.With the emergence of suitable large scale production systems for the biomanufacturing of double stranded RNA (dsRNA) for RNAi applications, the development of high throughput methods that facilitate the rapid characterisation of dsRNA is required. Mass spectrometry based methods offer a powerful approach to characterise RNA. RNase mass mapping methods have been developed and used for the identification and quantification of RNA and RNA post transcriptional modifications . TypicalRecent approaches have further developed the use of RNase mass mapping for the characterisation of dsRNA . RNase dIn the production of biopharmaceutical proteins peptide mapping with reversed-phase (RP) chromatography has been used for the analyses of recombinant protein biopharmaceuticals delivering comprehensive characterisation of products . In addiIn this study we have developed a method for the rapid characterisation of single stranded and double stranded RNA using high resolution RNase mapping in conjunction with ion-pair reverse-phase chromatography utilising superficially porous particles. This mapping approach can be applied to RNA in a similar way that peptide mapping is used in the production of proteins. Principal components analysis was employed to analyse the repeatability of replicate analysis, detect minor differences between different RNA and analyse batch-to-batch variability in the production of ssRNA and dsRNA.22.1d‑1‑thiogalactopyranoside (IPTG) ≥99%, sodium dodecyl sulphate (SDS) sodium chloride (NaCl), dimethyl sulfoxide (DMSO) were from Sigma Aldrich.Genes were synthesised by GeneArt® Gene Synthesis (Invitrogen Life Technologies). Ampicillin sodium salt, tetracycline hydrochloride, isopropyl β‑2.2E. coli strain, HT115 (DE3) [E. coli HT115 (DE3) cells. The pDome11 transformed cells were grown in culture and induced with IPTG to express dsRNAs as previously described [The 15 (DE3)  was obtaescribed .9E. coli cells was re-suspended in 200 μL of pre-warmed lysis buffer  and incubated at 70 °C for 3 min followed by 3 minute incubation at 37 °C. 200 μL warm solution  was added followed by addition of 100 μL 5 M NaCl. This was centrifuged at 20,238g for 4 min and supernatant transferred to a new Eppendorf tube. 100 μL 60% isopropanol was added and purified using solid phase extraction (SPE) as essentially described [RNA purification was performed using the RNASwift method as previously described  with minescribed . It is i260/280 and A260/230 ratios obtained [RNA concentrations were determined using a NanoDrop™ 2000c spectrophotometer (ThermoFisher Scientific) by absorbance at 260 nm normalized to a 1.0 cm (10.0 mm) path and Aobtained . Additio2.3Samples were analysed by IP-RP-HPLC on a passivated Agilent 1100 series HPLC using a Proswift RP-1S Monolith column (50 mm × 4.6 mm I.D. ThermoFisher). Chromatograms were generated using UV detection at a wavelength of 260 nm. The chromatographic analysis was performed using the following conditions: Buffer A 0.1 M triethylammonium acetate (TEAA) pH 7.0 ; Buffer B 0.1 M TEAA, pH 7.0 containing 25% acetonitrile (ThermoFisher). RNA was analysed using the following gradient. Gradient starting at 22% buffer B to 27% in 2 min, followed by a linear extension to 62% buffer B over 15 min, then extended to 73% buffer B over 2.5 min at a flow rate of 1.0 mL/min at 50 °C.2.4via in vitro transcription, DNA template was amplified using PCR from the plasmid pCOIV that contains a 686 bp sequence flanked on both sides with T7 promoter sequences and optimised synthetic T7 terminator sequences. In addition, a 561 bp DNA template flanked on both sides by T7 promoters was generated by PCR from within the 686 bp sequence of the pCOIV plasmid. For in vitro synthesis of ssRNA, a DNA template was amplified using PCR from the plasmid pCsm40 that contains 521 bp sequence with a T7 promoter. To create a complementary ssRNA, a 521 bp DNA template identical to the first template was used with a T7 promoter on the opposite strand. The following conditions were used: 0.02 U/μL Q5 High-Fidelity DNA Polymerase, 200 μM dNTPs, 0.5 μM each of forward and reverse primer and 10 ng DNA template in a final volume of 50 μL. The following PCR parameters were used: the initial denaturation was 1 cycle of 30s at 98 °C, 30 cycles of 30 s at 98 °C, 30 s at 68 °C, and 30 s at 72 °C and a final extension at 72 °C for 2 mins. dsRNA and ssRNA were then generated using in vitro transcription in conjunction with HiScribe™ T7 High Yield RNA Synthesis Kit (New England Biolabs): 10 mM NTPs, 1× reaction buffer, 1 μg DNA template and 2 μL HiScribe™ T7 polymerase in 20 μL RNase-free water. Compositions of the dsRNA and ssRNAs are shown in the Supplementary Table 1.For dsRNA synthesis 2.5−1, using UV detection at a wavelength of 260 nm. All RNase mapping IP-RP-HPLC analyses were performed on a U3000 RSLC system (Thermo Scientific). Data analysis was performed in Chromeleon Software v7.2 (Thermo Scientific). Average Relative Standard Deviation (%RSD) were calculated for both retention time and peak area using Chromeleon using the individual RSD for selected peaks (>5 mAU) in the chromatogram.Following purification, 2.5 μg of dsRNA in RNase-free water was incubated with 100 ng RNase A at 37 °C for 30 min in a volume of 10 μL. For RNase T1 mapping, 2.5 μg of purified dsRNA in 50% DMSO was incubated at 90 °C for 30 s and allowed to cool to room temp. 2000 U RNase T1 was added and reaction mix incubated for 15 min at 37 °C in a volume of 10 μL. Subsequently, 2.5 μg of digested dsRNA was analysed using IP-RP-HPLC-UV using an Accucore C18 column . LC buffer A: 80 mM 1,1,1,3,3,3‑Hexafluoro‑2‑propanol  with 20 mM TEAA (Sigma-Aldrich), LC buffer B: LC buffer A with 50% acetonitrile (v/v) (ThermoFisher). Starting with 6% buffer B, an isocratic step was performed for 2 mins followed by a linear extension to 20% B in 23 min, then extension to 30% B over 3 min. Extend to 50% B for 1 min and subsequently to 6% B in 0.5 min and hold at 6% B for 3 min at a flow rate of 300 μL min2.6The Principal Components Analysis was carried out using SIMCA-P 14.0 Software . The data were exported from Chromeleon 7.0 (ThermoFisher) as Excel files. The data were reduced by binning into 10 datapoints width bins using the prospectr package in the R-language for statistical computing. This left at least 19 points across the narrowest chromatographic peak but reduced the size of the dataset to a more manageable size .33.1in vitro transcription and subsequently purified to remove proteins and contaminants from in vitro transcription reagents. IP-RP-HPLC analysis in conjunction with a monolithic polystyrene‑divinylbenzene column was used to demonstrate the purity of the ssRNA samples used in this study . Subsequently the purified ssRNA was digested with RNase A prior to IP-RP-HPLC analysis in conjunction with IP-RP-HPLC using superficially porous silica particles to generate a high resolution fingerprint of the RNA. We have previously demonstrated the application of superficially porous silica particles for the analysis of nucleic acids [A 521 nt ssRNA was generated using ic acids . The purin vitro transcription and purified prior to analysis by IP-RP-HPLC to confirm the purity of the dsRNA used in this study . The purified dsRNA was subsequently digested with RNase A, which cleaves each strand of dsRNA 3′- to C or U bases, and analysed using IP-RP-HPLC analysis  and digested using RNase A prior to IP-RP-HPLC. The resulting chromatograms are shown in To further demonstrate the ability of the high resolution fingerprinting method to rapidly detect differences between RNA samples, experiments were performed using ssRNAs of the same size but different sequence. The 521 nt ssRNA (1) sequence and its complementary sequence ssRNA (2) were via RNase A or RNase T1, making discrimination between these two different RNAs challenging. The 686 and 561 bp dsRNA were in vitro transcribed, purified  and digested using RNase T1 prior to IP-RP-HPLC  of the 686 bp dsRNA is unique in this example. Therefore, only limited numbers of different oligoribonucleotide fragments will be generated either HPLC see B. The reHPLC see .3.3in vitro transcription, in cell-free systems or in planta for RNA interference applications has generated significant demand for the development and application of high throughput analytical tools for analysis of dsRNA. In particular, the development of a method that rapidly characterises dsRNA and assesses potential lot-to-lot variation in the biomanufacturing of dsRNA. The ability to rapidly assess the biomanufactured dsRNA in a high throughput manner without the requirement for specialist mass spectrometers and complex data analysis will provide significant advantages in a biomanufacturing environment. Therefore, in an approach to develop a rapid method for the analysis of biomanufactured dsRNA, 4 separate batches of E. coli engineered to express dsRNA were grown and the dsRNA extracted and purified . The IP-RP-HPLC analysis of the RNase A/T1 digests of the dsRNA is shown in The ability to produce large quantities of dsRNA in either bacterial systems, by 3.42 value which is the cross validated equivalent of R2. The point at which this value of predictivity drops is deemed the optimum number of components. This PCA approach has previously been applied to analyse complex chromatographic data [Further comparative analysis of the RNAse mapping data was performed using principal components analysis (PCA) to detect patterns of similarities/differences between the resulting chromatograms in an objective manner. PCA is a method of projecting high dimensional data down to simple two dimensional plots that describe the similarity of observations based upon their pattern of variables. The components are projected in order of variance explained so that the first components explain the most prominent features of the data and successive components less important features until eventually all that is left is random variation. Within the SIMCA software the point at which no more useful information is to be extracted is determined by 7 fold validation which is a re-prediction technique. One seventh of the data is left out and re-predicted by the model and the difference between the actual data and the predicted data is expressed as a Qhic data .In this analysis, the data were transformed using Standard Normal Variate (SNV) transform which removes any overall amplitude variation caused by sample dilution or injection variation. The data were also subjected to mean centering and Pareto scaling. The reason for applying Pareto scaling was to up-weight medium peaks without increasing baseline noise as would be the case if unit variance scaling were used. Both transformations were applied within the SIMCA software. The software extracted five principal components by cross validation explaining 89% of the variance but the first two components were sufficient to describe the overall similarity of the chromatograms. The higher order components mainly described minor or artifact features in the chromatograms. The two component Scores plot explains 52% of the variance in the data. Each chromatogram is represented as a point in the plot. The closer together the points the more similar are the chromatograms. Dissimilarity is shown by the distance between the points. For clarity the PCA has been performed on the whole dataset but the result split into three plots which only show the relevant comparisons, namely injection replicates, batch replicates and different size/sequences of RNA see .Fig. 5PrE. coli HT115 cells digested using either RNase A (16–18 shown in purple) or RNase T1 digestion (20–23 shown in orange)  see B.4E. coli across a number of different batches. Moreover the PCA analysis revealed tight clustering of the injection replicates, demonstrating the reproducibility of the chromatography. A cluster of PCA points from a large group of in-specification samples could be used to define the limits of acceptable process variation with any future samples giving analyses outside of this cluster being defined as outside what was expected for that product. The results demonstrate the potential ability to rapidly analyse large numbers of dsRNA, lot-to-lot variability in the production of dsRNA, and detect variations in dsRNA generated for batch release. All without the requirement for interfacing with mass spectrometry and the associated technical expertise required for such analysis.In this study we have performed high resolution RNase mapping in conjunction with ion-pair reverse phase chromatography utilising superficially porous particles for the rapid fingerprinting of single stranded and double stranded RNA. The ability to generate a fingerprint based on the IP-RP chromatogram was used to rapidly detect different RNA sequences of the same size, based on differences in the resulting chromatograms. To facilitate the ability to identify small changes in the chromatographic data which cause significant variance among the samples, PCA analysis was performed. Using this approach we were able to distinguish two dsRNA fragments of different size which share 72% sequence identity. Due to the overlapping peaks in these chromatograms, small single nucleotide differences may sometimes be detected. However, it is more likely larger changes in the dsRNA are required before a material could be identified as an incorrect sequence. We used the high resolution RNase fingerprinting method to rapidly analyse dsRNA biomanufactured in


Relatively few studies to date have investigated the dose rate effect in RNT. Therefore, the aim of this study was to benchmark 90Y doses in the ranges 1–10.4 and 6.2–62.3 Gy with initial dose rates of 0.013–0.13 Gy/hr  and 0.077–0.77 Gy/hr , respectively. Results were compared to a 6-MV photon beam doses in the range from 1–9 Gy with constant dose rate of 277 Gy/hr. The cell survival parameters from the linear quadratic (LQ) model were determined. Additionally, Monte Carlo simulations were performed to calculate the average dose, dose rate and the number of hits in the cell nucleus.Three human colorectal carcinoma (CRC) cell lines  were exposed to α/β ratio was found to be ≈ 31 for HDR–90Y and ≈ 3.5 for EBRT. LDR–90Y resulting in insignificant cell death compared to HDR–90Y and EBRT. Simulation results also showed for LDR–90Y, for doses ≲ 3 Gy, the average number of hits per cell nucleus is ≲ 2 indicating insufficiently delivered lethal dose. For 90Y doses 90Y is radiobiologically less effective than EBRT. However, HDR–90Y at ≈ 56 Gy was found to be radiobiologically as effective as acute ≈ 8 Gy EBRT.For the HT29 cell line, which was the most radioresistant, the 90Y, a higher initial activity is required to achieve an outcome as effective as EBRT.These results demonstrate that the efficacy of RNT is dependent on the initial dose rate at which radiation is delivered. Therefore, for a relatively long half-life radionuclide such as  Targeteμm for alpha particle emitters, 0.2 keV/ μm for beta emitters and 4–26 keV/ μm for Auger electron emitters [223Ra (α),177Lu (β) and 67Cu (β) which emit particles with a short range (relatively high LET) are used in Targeted RNT [Current clinical therapeutic radionuclides can be classified based on the nature of their radiation properties such as linear energy transfer ; 50–230 keV/ emitters , 8. Radieted RNT . AlternaAnother important property of therapeutic radionuclides is their physical half-life which determines the dose rate at which RNT is delivered along with its initial activity. For a given initial activity, a radionuclide with shorter half-life delivers dose at higher rate compared to longer half-life radionuclides. The effect of dose rate in RNT and external beam radiotherapy (EBRT) dose response can be classified into low and high dose rates (respectively LDR and HDR) , 11. It 90Y RNT in vitro studies and investigate the differences in delivering EBRT and 90Y RNT (both HDR and LDR). Additionally, this study aimed to compare cell kill mechanisms between the two irradiation processes. To do this, we investigate 90Y RNT, which is a common treatment for primary and metastasised liver cancer that initiates from colorectal cancer.The aim of this study was to establish a platform for experimental and theoretical ∘C in a humidified atmosphere containing 5% CO2. The cells were assessed by MTS -5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) cell viability assay which is a colorimetric quantification of viable cells. The assay is based on the reaction of MTS tetrazolium compound by viable cells to generate a coloured formazan product that is soluble in cell culture media. Since the cell culture medium needs to be changed after 8 days, the maximum incubation time was 8 days. However, 90Y requires ≈ 15 days to deliver 98 % of the targeted dose. The initial specific activity of 90Y was calculated to deliver a target dose in 8 days. All experiments were repeated twice to assess consistency.As this study involved the measurement of radiation survival for a high number of samples, the MTS assay was used instead of the standard clonogenic assay. The human CRC cell lines HT29, HCT116 and SW480 were cultured in RPMI  medium supplemented with 10% fetal bovine serum (FBS) and incubated at 37 Each cell line was cultured in a 96-well plate and allowed 4 h incubation before irradiation. Additionally, a control cell plate for each cell line was prepared. All of the cell cultures were irradiated with a 6 MV flattening filter-free (FFF) linear accelerator (linac) photon beam with a single dose in the range of ≈ 1–9 Gy . All the cell plates were irradiated at a dose rate of ≈ 277 Gy/hr, at 100 cm source–to–surface distance (SSD), with back-scatter and buildup solid water : (1) 90YCl3 solutions with LDR specific activity of 0.24–2.4 MBq/ml ; and (2) relatively HDR-specific activity of 1.44–14.4 MBq/ml . The pH of prepared solutions were also measured to be ≈ 7. A volume of 20 μl from each 90Y dilution was added to each well  and A(t) is activity as a function of time representing the number of beta particles (i.e. number of decays) emitted per second with energies randomly selected from the 90Y spectrum. The corresponding total doses were calculated according to where 90Y concentrations using the full beta energy spectrum of 90Y. The physical 90Y absorbed doses were calculated to be ≈ 1–10 Gy for LDR and ≈ 6–62 Gy for HDR. Results were also validated by OLINDA [for varying y OLINDA  and GATEy OLINDA  calculat90Y concentrations. GATE is a Monte Carlo-based platform developed by the international OpenGATE collaboration for radionuclide imaging and dosimetry applications [A Monte Carlo model was developed in the GATE toolkit  to calcuications . All simications , which uications . The lowications  of GEANTThe energy spectrum of emitted beta particles was obtained using GEANT4 , 23 and A0 is the initial activity.where μl cylindrical well . Then, 1010 beta particles with energies randomly selected from the energy spectrum of 90Y were emitted from the source volume in random directions  represents a cubic sensitive detector that can be voxelised into 3D rectangular or cubic voxels. Parameters such as dose, deposited energy, number of ionisations and number of beta particle hits were stored into this 3D matrix according to the spatial position of the hits for each individual simulated cell cytoplasm and nucleus. These parameters were calculated for varying An is the total number of decays within day n  and where 90Y cell irradiation experiments including their control cell plates, the MTS cell viability assay was added and cells were incubated at 37 ∘C for ≈ 3 h. Next, the viability of each cell culture was measured and α and β parameters were calculated from the linear quadratic (LQ) model [For each of the EBRT, LDR and HDR–Q) model : 5\docuα and β are the radiobiological parameters corresponding to the linear and nonlinear (quadratic) responses to the radiation dose. The α and β parameters were obtained by fitting the LQ model, Eq. where SF is the cell survival fraction, where 90YCl3 for 8 days; however, since the initial dose rate is exponentially decreasing over time, at a critical time (Tcrit) or dose rate (Rcrit) the DNA damage (the probability of causing DSB) effectively becomes insignificant [Tcrit) after the start of treatment [Tcrit and Rcrit for all the three lines are calculated for the highest initial dose rate using Eqs. The cells are incubated with nificant . For an reatment . Tcrit aλ, α, R0 and Tav are the decay constant for 90Y, the radiobiological parameter of the cell line, the initial dose rate of 90Y RNT and the average doubling time of the cell line, respectively.where 90Y RNT experiments. The optimal cell densities in an exponential phase of growth for an 8 day density viability assay were calculated to be 30, 100 and 200 cells/well (in a 96-well plate) for HCT116, HT29 and SW48, respectively.Cell density measurements shown in Fig. 90Y. The activity for each delivered dose was selected to achieve the target total dose at T = 8 days incubation. In addition, a summary of total dose calculations performed by GATE, Matlab and OLINDA [90Y activities correspond to different initial dose rates, with a maximum ≈ 220 μGy/s, that decay exponentially over incubation time. In comparison, EBRT has a higher and constant dose rate, 277 Gy/h, (i.e. ≈ 105μGy/s) with shorter irradiation time, T ≈ 1.7 min.A summary of total dose calculations and simulation results are presented in Tables d OLINDA  is prese90Y RNT. For each cell line, an EBRT dose > 8 Gy and a HDR–90Y dose > 56 Gy resulted in similar survival fractions and thus are not shown. Comparing the EBRT and HDR–90Y survival curves, similarities can be observed. Both EBRT and HDR–90Y resulted in an exponential dose response. However, at doses < 19 Gy, the HDR–90Y survival curve slope is shallower compared to that for EBRT. As the HDR–90Y dose rate decreases exponentially over the incubation time, for a given dose, HDR–90Y irradiation resulted in less cell kill than EBRT for all three cell lines. Figure 90Y RNT and EBRT, showing a negligible response from LDR–90Y RNT and resulting in negligible dose response compared to EBRT over the dose range studied, 1–8 Gy. The calculated α,β values are listed in Table α and β parameters for HDR–90Y are smaller. This is due to the relatively lower and exponentially decreasing dose rate of 90Y  which further confirmed that HT29 is the most radioresistant cell line.Figure α/β ratios were determined from the LQ model  is larger than the DNA double helix’s diameter (i.e. ≈ 2 nm)[90Y that the average number of hits per cell nucleus is ≲ 2 for doses ≲ 3 Gy during the 8 days irradiation time. Although for doses N/nucleus is ≲ 2 after ≈ 7–8 days. For HDR–90Y, similarly, the number of hits per cell nucleus decreases during the 8 days of irradiation time but the average number of hits per cell nucleus remains above 2 for all 90Y doses investigated. For EBRT, Figure . ≈ 2 nm). Therefo. ≈ 2 nm). Thus, tFurthermore, Fig. In comparison to the established clonogenic assay, the MTS (or MTT) assays are sensitive and accurate methods with several advantages such as requiring a short time to assess the viability, acquiring the results easily and accurately , 27–32. α/β ratios derived from a metabolic activity assay at a single time point should be used cautiously in further radiobiological modelling. Additionally, the cells sustaining radiation-induced damage may exhibit delayed effects and there could be a lag between irradiation and subsequent biological events  leading to cell death [Tcrit for all three lines ranges approximately 3–5.5 days. In this study, the MTS assays were performed eight days after the initial addition of 90YCl3, on average ≈ 4 days beyond these critical times where the treatment is effectively being insignificant and the delayed effects could occur. Thus, performing the MTS assay after the actual treatment time could still include the cell death due to the delayed effects.Previous studies have shown that the cells exposed to the higher radiation doses cannot regain their exponential growth in a single MTT or MTS assay, and multiple MTS assays are required to obtain measurements of radiation survival that is comparable to that of clonogenic assays , 29. Altll death , 36. Thell death , 36. In 90Y RNT. With EBRT, the dose was delivered at a constant dose rate of 277 Gy/h in ≈ 1.7 min, whereas 90Y was delivered at a lower dose and varying rate (i.e. 0.013–0.13 Gy/h and 0.077–0.77 Gy/h) over 8 days. The dose rate and nature of radiation clearly affects the nature of cell damage. There are two types of cell damages: (1) double-strand break (DSB), where two proximal DNA strands are broken simultaneously by a single radiation event causing lethal damage (LD) (i.e. cell death) and (2) single-strand break (SSB), where each DNA strand broken by independent radiation events. When only a single DNA strand is damaged, the cell is considered sublethally damaged (SLD) and has the ability to repair itself typically within in 0.5-3 hours [90Y RNT is less effective at causing lethal damage than HDR–90Y RNT and EBRT.The cell survival results provide insight into different damage responses to EBRT and -3 hours . Therefoα. The rate of cell kill through the SSB process is measured from the quadratic part of the survival curve, parameterised by β which is highly dependent on the initial dose rate. The value of α depends on the the radiation type. For high LET radiation such as alpha particles, DSB dominates the cell kill process and therefore α has higher significance than β. Therefore, for a low and exponentially decreasing dose rate 90Y source, there is less contribution to SF from the βD2 component than from the α term. This is evident from Fig. 90Y survival curve. However, as the EBRT dose rate is significantly higher than HDR–90Y RNT (by a factor 400), the rate of lethal SSB production is much higher. Consequently, this results in delivering more lethal damage to cells at an even lower EBRT dose range (compared to 90Y doses). Moreover for LDR–90Y (0.013-0.13 Gy/h), there is even less contribution from the βD2 component since the rate of SSB repair occurs even more rapidly  the average number of hits per cell nucleus is ≲2. Additionally for doses Tcrit for each cell lines (i.e. 3–5.5 days). This suggests that for LDR– 90Y only ≈ 1–2 half-lives of delivered dose can potentially cause direct cell nucleus DNA damage.The rate of cell kill through the DSB process is measured from the response component of the survival curve parameterised by 90Y. The HT29 and HCT116 cell lines were the most radioresistant and radiosensitive cell lines, respectively. This is also consistent with previous studies [90Y irradiation results. The α/β ratios for both EBRT and 90Y were derived from Eq. All three cell lines showed higher sensitivity to EBRT than  studies , 39. SimG is the Lea–Catcheside factor and μ is the DNA repair time constant [t′ refers to the first SSB of two DSBs required to cause lethal damage. Also, the integral over t refers to the second SSB of remaining of two DSBs to cause the unrepairable lethal damage. The exponential term describes the repair and reduction in 2 SSB → DSB process due to decreasing dose rate. For a constant EBRT dose rate where the dose is delivered acutely and the irradiation time is very short, the G–factor approaches unity and Eq. where constant . The fir90Y RNT dose rate is much lower and exponentially decreasing  the G factor reduces to: For long irradiation times  as the dose rate is relatively lower compared to EBRT. In comparison, G∞≈1 for EBRT, using an irradiation time ≈ 1.7 min. This is more evident for the LDR–90Y survival curve since for a given dose, the initial dose rate is ≈ 0.013–0.13 Gy/h. This is also consistent with the simulation results. For example, for LDR–90Y, for smaller delivered activities and doses (i.e. ≲ 3 Gy), the average number of hits per cell nucleus is ≲ 2 during the 8 days irradiation time /15 hours). The G∞ factor also plays an important role for high LET radiation. For example 213Bi with a 46 min half-life which decays ≈2% as high LET alpha and ≈98% low energy beta particles, delivers dose at a reasonably high dose rate. Currently 213Bi is the only radionuclide that can deliver dose at a dose rate close to the EBRT dose rate . The ability to deliver a very localised dose at very high dose rate has a significant impact on the RNT [90Y, the HT29 and HCT116 cell lines were the most radioresistance and radiosensitive cell lines, respectively. HT-29 cells have the TP53 mutation which may increase the cell resistance to radiation [α/β derived from the HDR–90Y cell survival curve was larger than that derived from EBRT by a factor of ≈ 8 for all three cell lines. This can be attributed to a relatively lower dose rate of 90Y. Also for each given dose, the total HDR–90Y absorbed dose (6.2–55.5 Gy) was up to six times greater than the EBRT dose (1–9 Gy).The  the RNT . For botadiation . The α/βα and β values derived for HDR–90Y RNT and EBRT reflect on the different relative radiobiological effectiveness of 90Y RNT compared to EBRT.Additionally, different 90YCl3 was uniformly distributed in the well. This assumption was used to calculate the absorbed dose to cells for each 90YCl3 activity.These results are subject to several assumptions. Firstly, for all the calculations and formalisms, it was assumed that there is no significant cell repopulation during 8 days of incubation. A more complex formulation is required to offset the repopulation effect , 42. Sec90Y RNT in vitro specific to CRC cell lines. For the HT29 cell line, we found α/β ≈ 31 for HDR–90Y compared to ≈ 3.5 for EBRT. For LDR–90Y, we found insignificant cell death at doses < 10 Gy compared to EBRT. Simulation results also demonstrated that for 90Y doses ≲ 3 Gy, the average number of hits per cell nucleus ≲ 2 indicating insufficient delivered lethal dose. Similarly, for 90Y doses 90Y RNT. Therefore, our results demonstrate that LDR–90Y is radiobiologically less effective than EBRT. However, HDR–90Y at ≈ 56 Gy was shown to be radiobiologically as effective as acute ≈ 8 Gy EBRT. These results demonstrate that the efficacy of RNT is dependent on the initial rate at which dose is delivered. The RNT dose rate is inversely proportional to the radionuclide half-life for a given total dose. Therefore, a longer half-life radionuclide such as 90Y requires a higher initial activity to attain higher initial dose rate, and a larger total dose in Gy delivered is required to achieve a treatment outcome that is equally as effective as EBRT.We benchmarked the effectiveness of EBRT to