Out of 8,815 genes with sufficient data, we identified 621 genes exhibiting significant cis-divergence. Systematic analysis of these genes revealed that the genetic variants affecting microRNA binding and RNA secondary structures contribute to the observed divergences. Finally, we demonstrated that although the divergences in RNA abundance were predominantly determined by allelic differences in RNA transcription, most genes with significant ASD did not exhibit significant difference in RNA abundance. For these genes, the apparently compensatory effect between the allelic differences in RNA transcription and ASD suggests that changes in RNA decay could serve as important means to stabilize RNA abundances during mammalian evolution.Cellular RNA abundance is determined by both RNA transcription and decay. Therefore, change in RNA abundance, which can drive phenotypic diversity between different species, could arise from genetic variants affecting either process. However, previous studies in the evolution of RNA expression have been largely focused on transcription. Here, to globally investigate the effects of  During the past decades, a number of cis-elements have been identified  at a false discovery rate (FDR) of 15%  of 15% . To incr) of 15% . Out of cis-regulatory effect on RNA degradation is to compare the allele-specific decay rates of RNA transcripts in an F1 hybrid  for F1 hybrids between different genetically diverse yeast strains  and Mus spretus SPRET/EiJ mouse strain (SPRET). These two mouse strains diverged ∼1.5 million years ago, resulting in ∼35.4 million SNPs and ∼4.5 million insertions and deletions (indels) between their genomes  and the local RNA secondary structure in both coding regions and 3′ UTRs could affect RNA decay. Finally, via investigating the role of ASD in the allele-specific RNA abundances (ASA), we demonstrated that on one hand, the observed ASA divergences were predominantly determined by the allelic differences in RNA transcription (AST) and on the other hand, most (>80%) of the genes with significant ASD did not exhibit significant ASA, indicating the pervasive compensatory effects between AST and ASD also existing in mammalian evolution and suggesting that changes in RNA decay rates could serve as important means to stabilize RNA abundances during evolution.Here, to globally investigate the effects of  genomes . Such a To investigate the allelic divergence of RNA decay rates in a mammalian system, we measured the ASD in a fibroblast cell line derived from an F1 hybrid mouse between the BL6 and SPRET strains. As shown in Statistics of allelic read mapping. Percentages are calculated as fractions over the number of read pairs after trimming. Percentages are calculated as fractions over the number of read pairs concordantly mapped to autosome.Table S1 To estimate the allele-specific RNA decay rate in a quantitative manner, we used the reads with unambiguous allelic origin. More specifically, we used only the reads that were mapped on SNP loci within genic regions. After filtering out the SNP loci with potential allelic read mapping bias due to the incomplete SNP annotation in paralogous genes or pseudogenes, 8,815 genes containing at least five SNPs supported with sufficient allelic reads were retained . The longer read length allowed the assignment of the PacBio reads to the parental alleles without any ambiguity. Allelic ratios of the read counts were then compared between the two time points. As shown in cis-regulatory elements within the RNA transcripts. To study the potential cis-features accounting for the observed allelic biases, we first calculated the frequencies of sequence variants for the genes with or without significant ASD. As shown in The ASD divergences observed in F1 cells should reflect the effect of the sequence variants influencing cis-elements accounting for such ASD divergences. Given the well-known importance of miRNA in regulating RNA stability, we first focused on the variants affecting miRNA target sites cis-eleme  . It has s (AREs) . To inveAREScore  , out of the 8,815 genes for which we could confidently measure ASD, we identified 1,241 genes exhibiting ASA divergence  results from the balance between AST and ASD. Previous studies in yeast using similar hybrid systems have demonstrated that the allelic biases in the two processes often possess opposite effects on the RNA abundance and some of the evolutionary changes in RNA decay are mechanistically coupled with those in RNA transcription . Considevergence  and S14.cis-divergence in RNA decay did not contribute much to the observed ASA. Instead, the ASA should largely result from the significant allelic biases in RNA transcription. On the other hand, among the 621 ASD genes, most  did not exhibit significant ASA  did not exhibit significant ASD , suggestcant ASA . For thecis-divergence of RNA decay in mammals, we conducted a first genome–wide ASD profiling in a hybrid mouse system, the F1 cross between the BL6 and SPRET inbred mouse strains. Among all the mouse strains with high-quality genome assembly, SPRET has the largest number of sequence variants relative to BL6, which provides a large number of potential regulatory variants between the two strains  of rdQTLs exhibited opposite effects to those of RNA transcription (inferred from steady-state mRNA expression levels), this proportion is much smaller than that identified from yeast studies (>80%). There are at least four different possible  scenarios explaining the different observations between yeast and human: 1) Mechanical coupling for opposing effects in transcription and decay is not as prevalent in mammals as in Saccharomyces yeasts. 2) Saccharomyces than mammals. Such scenario would be consistent with the vastly greater effective population size of yeasts relative to that of mammals. 4) The divergence time and reproductive isolation between two yeast species or strains is much larger than between variants stemming from the same human population. Therefore, no evidence for compensatory evolution would be expected in the latter case, assuming random mating with regard to the QTLs studied and the absence of population substructure.To distinguish the effects of transcription from those of decay on the changes of RNA abundance, the Tirosh lab investigated the evolutionary divergence in mRNA decay between closely related yeast species and their F1 hybrid . Interescis-divergences on RNA transcription and decay in these genes have opposite effects on RNA abundance. This indicates that pervasive opposing effects between transcription and decay observed in yeast also exist in mammals. The second scenario discussed above most likely explained the observation in the previous human QTL study.In this study, comparing the genes with significant ASD to those with significant ASA, we observed that the majority  of the genes exhibiting ASA showed no significant ASD, indicating that allelic difference in transcription should be the predominant contributor to the observed ASA. This observation is in agreement with the previous human QTL study of RNA decay, in which the authors found that most (84.5%) of the identified eQTLs (expressed RNA abundance QTLs) were not rdQTLs . Taken tcis-divergent effect could result from two possible scenarios. First, a mechanistic coupling between RNA transcription and decay, where the same cis change simultaneously leads to an increase (decrease) in transcription and an increase (decrease) in RNA decay. Second, to stabilize the RNA abundance, a change causing increased (decreased) transcription (or decay) is followed by an independent change causing increased (decreased) decay (or transcription). By comparing the parental differences and the allelic differences for both RNA transcription and RNA decay in the yeast hybrid system, cis-/trans-origins of the divergences in RNA transcription and RNA decay. Interestingly, for those genes with opposite effects on RNA transcription and decay, the divergences of RNA transcription and decay often originated either both from cis or both from trans, suggesting that these opposite divergences might result from the same genetic variants, thus mechanistically coupled. Further analyses indeed suggested that the changes in some trans-factors (such as Rpb4/7 and Ccr4-Not protein complexes) might be involved in the coupled evolution of RNA transcription and decay in yeast, a clear demonstration of the first scenario . Therefore, it would be possible to investigate the regulatory mechanisms underlying the cis-divergence in RNA decay by analyzing the sequence differences of ASD genes between the two alleles. Indeed, by such analysis, we demonstrated that sequence variants affecting miRNA binding could contribute to the observed ASD divergence. In contrast, in our previous analysis of allele-specific translation efficiency using the same F1 cells, we did not observe the significant impact of miRNA binding on translation, indicating, at least in the cellular system as used in our studies, that miRNAs regulate gene expression mostly through RNA degradation  between the stability of RNA secondary structure and the rate of RNA decay  with modification of cell culture medium .The F1(B×S) hybrid mice were obtained as described before . Adult md  was directly added to cell cultures. Cells were collected at 0, 0.5, and 1.5 h after the addition of actinomycin D. Total RNA from the collected cell samples was extracted using TriZOL reagent (Life Technologies) following the manufacturer's protocol. Stranded mRNA sequencing libraries were prepared with 500 ng total RNA according to the manufacturer's protocol (Illumina). The libraries were sequenced in a 2 × 100 +7 manner on a HiSeq 2000 platform (Illumina).Actinomycin http://ftp.ensembl.org, version GRCm38, release 74). The RefSeq gene annotation was downloaded from the University of California, Santa Cruz (UCSC), genome browser (http://hgdownload.soe.ucsc.edu/goldenPath/mm10/database/). The single nucleotide variants and indels between BL6 and SPRET were downloaded from the Mouse Genome Project Web site (http://www.sanger.ac.uk/). The vcf2diploid tool (version 0.2.6) in the AlleleSeq pipeline was used to construct SPRET genome by incorporating the single nucleotide variants and indels into BL6 genome (The reference sequences and the Ensembl gene annotation of the C57BL/6J genome (mm10) were downloaded from the Ensembl FTP server  using TopHat (version 2.0.8) with default mapping parameters and Ensembl gene annotation . ConcordP-value < 0.05, after Benjamini–Hochberg correction for multiple testing).To estimate ASD, only the reads that could be unambiguously assigned to SNP loci from either allele were counted (see above). To avoid bias due to the potential misalignment of reads to the wrong allele, we used previously published datasets generated from fibroblast cell lines of the two parental strains . SpecifiP-value < 0.05, after Benjamini–Hochberg correction for multiple testing).Because of potentially incomplete annotation of SNPs at paralogous genes or pseudogenes in the SPRET/EiJ genome, some reads, which could be mapped to multiple gene loci if the C57BL/6J sequence was used as a reference, were mapped to a unique position in the SPRET/EiJ genome. In such cases, removal of multiple mapped reads  could lead to inaccurate calculation of ASD. To avoid such bias, for each SNP locus, based on the mock datasets, we compared the ratio of allele-specific reads, including multiple mapped reads, with that counting only uniquely mapped reads. Fisher's exact test was used to filter the SNP loci with potential bias  were considered for further analysis.After SNP loci filtering (see above), only the genes with at least five SNPs supported by sufficient allelic reads in all different time course samples  be the number of mRNA transcripts for allele i  at time t. We assumed an exponential decay λ, such that t, the number of RNA-seq reads that we can assign to an allele ni(t) is a fraction f(t), of the total number of mRNA transcripts for that allele, such that To determine whether a gene exhibited allelic differences in mRNA decay rate, we combined a previously published logistic model and a bootstrapping strategy . SpecifiTaking the log it gives:t is measured in hours). If decay rates are the same in both strains In this linear logistic model, the mRNA decay rate differences between the two alleles can be directly estimated using the parameter β. The parameter exp(β) represents the change in the odds of observing an mRNA allele of the strain 1 type, given a 1-h increase in time (n (n ≥ 5) SNP loci, we generated 5,000 new lists, each consisting of n SNP loci that were chosen at random with replacement from the original list. For each of the 5,000 random lists, mRNA decay rate differences between the two alleles were estimated using the above logistic model, and then yielded a bootstrap distribution, from which we got the bootstrapping mean and standard deviation. To determine the statistical significance of genes with ASD, we calculated a P-value based on the z-score that represented how many folds of standard deviation the bootstrapping mean deviated from zero. The raw P-values were then adjusted using the Benjamini–Hochberg method. To estimate the FDR, we used a similar permutation strategy as described before  and decay rate difference requirement x, the FDR was estimated as false positives divided by the number of real genes passing the same threshold. Finally, Benjamini–Hochberg–adjusted P-value < 0.05 and To assess the uncertainty of estimated mRNA decay rate differences, a bootstrapping procedure was applied . Specifid before . In brieStarting with 500 ng of total RNA, DNase treatments were first performed according to the manufacturer's protocol  for samples collected at 0 and 1.5 h after actinomycin D treatment. Reverse transcription (RT) reactions were followed using random hexamer primers (Thermo Fisher Scientific) and SuperScript II reverse transcriptase (Thermo Fisher Scientific). PCR reactions were then performed using 1 μl of RT products as template in 50 μl of GoTaq PCR system (Promega). PCR primers were designed for amplifying the genic region containing sequence variants between B6 and SP transcripts. All primer sequences are listed in Table S2. The PCR program was as follows: 4 min at 95 °C; followed by 28 cycles of 30 s at 95 °C, 30 s at 55 °C, and 45 at 72 °C; and a final elongation of 10 min at 72 °C. Different PCR products from the same RT product using different primers were then mixed and purified using Agencourt AMPure XP system (Beckman Coulter) and quantified by Qubit HS dsDNA measurement system (Life Technologies). These mixed PCR products were then sequenced on PacBio RS SMRT platform according to the manufacturer's instruction.PCR primers for PacBio validation.Table S2 Sequence reads from the PacBio RS SMRT chip were processed through PacBio's SMRT-Portal analysis suite to generate circular consensus sequences. The circular consensus sequences were then mapped to a reference database containing both alleles of target genes using BLAST with default parameters. The best hit was retained for each aligned sequence read. These reads were then assigned to C57BL/6J or SPRET/EiJ allele with fewer mismatches. The numbers of reads assigned to either allele of each gene at 0 and 1.5 h were counted, respectively. The following equation was used to estimate ASD:P-value from bootstrapping analysis >0.05 for both replicates; 2) To compare with the genes exhibiting ASD, we selected a separate group of control genes that were also supported by sufficient allelic reads (see above) but did not show any difference in decay rate between the two alleles: 1) To analyze the sequence features of the genes exhibiting ASD, we further selected a subset of these control genes, which possessed similar density of sequence variants as ASD genes, to avoid the potential bias due to the different variant densities between ASD and control genes. Specifically, based on the distribution of sequence variant density across the whole transcript in ASD genes, we randomly selected from all the control genes a subset with the same variant density distribution as ASD genes.RNAfold from ViennaRNA package version 2.1.9 with default parameters at a temperature of 37 °C  flanking each SNP between C57BL/6J allele and SPRET/EiJ allele. The variant of interest was placed at the center of each window if the whole window was within the transcript; however, if the variant was <(i – 1)/2 nt  from the end of the RNA transcript, the i-nt window was shifted such that its boundary lay at the end of the transcript. We then calculated the absolute difference of MFE between the two alleles Local RNA secondary structure MFE was calculated using of 37 °C . SpecifiTargetScan v7  using tScan v7  as previtScan v7 . For botAREScore algorithm with default parameters .The effects of AREs were estimated using the rameters . BrieflyCodonW version 1.4.4 (http://codonw.sourceforge.net/). For each gene, the coding sequence (CDS) of each allele was used as input for CodonW.Codon usage bias was estimated using the CAI calculated using In the analysis of these sequence features, the difference between the stable (slow-decaying) and unstable (fast-decaying) alleles was calculated for ASD and control genes (see the selection of control genes without ASD section for details) separately. RefSeq annotation was used to separate each coding gene into 5′ UTR, CDS, and 3′ UTR. For the genes with multiple isoforms, the longest one was used. When 5′ UTR (CDS and 3′ UTR) region is considered, only the genes with 5′ UTR (CDS and 3′ UTR) are used. Note that some genes do not have annotated 5′ UTR or 3′ UTR, and noncoding RNAs are not considered when separating genes into different regions.http://www.ebi.ac.uk/ena) and assigned the accession no. ERP017147.The RNA-seq data from this publication have been submitted to the European Nucleotide Archive (https://doi.org/10.26508/lsa.201800052.Supplementary Information is available at


Improved assays are critical to better characterize the HIV reservoir and to reliably evaluate candidate intervention strategies. Here we describe different methods to quantify the HIV reservoir.We developed an optimized quantitative viral outgrowth assay (QVOA) to quantify the frequency of cells harboring replication-competent HIV, which is simpler and more sensitive than classical QVOAs. We also developed new inducible RNA assays that concomitantly measure the frequency of cell-associated [ca-] (gag and tat-rev) and cell-free [cf-] HIV RNA after three days of anti-CD3/CD28 stimulation.6 CD4+ T-cells for ca-RNA gag and tat-rev, and cf-RNA, respectively. There are a large proportion of transcription-competent proviruses (ca-RNA) that seemed unable to form complete virions (cf-RNA), suggesting post-transcriptional blocks or defective proviruses. Importantly, the median frequency of infected CD4+ T-cells as estimated by 3-day inducible cf-RNA assay was not statistically different from the frequency measured by the QVOA (median of 3.3 [1.9–6.2] IUPM). The latently infected cells detected by the inducible cf-RNA assay correlated highly with the QVOA , and both assays were equivalent in 60% of the samples tested, suggesting that most cells induced to produce virions are generating replication-competent virus.The median frequency of the infected cells measured after induction was 94 IQR[60–132], 16 IQR [9–29] and 2.9 IQR[1.9–6.8] cells/10These inducible RNA assays provide more sensitivity and a greater dynamic range for the monitoring of reduction of the reservoir by eradication strategies. Such assays may serve as robust and useful tools for clinical investigations of the HIV reservoir. •A large proportion of HIV DNA is defective and is unable to produce HIV RNA or replication competent virus after stimulation•Inducible RNA assays are more sensitive and show a greater dynamic range for the monitoring HIV reservoir decay than viral-outgrowth methods•Cells producing viral particles after activation are similar in frequency to cells harboring replication competent virus.The other bullets should end with a period. HIV persists in infected individuals in a stable pool of CD4 T cells. This HIV reservoir is the major barrier to the eradication of HIV and it has been extremely challenging to accurately quantify the size of the replication-competent reservoir . Therefore, improved assays are critical both to better characterize the poorly understood HIV reservoir and to reliably measure candidate intervention strategies.We describe different methods to quantify the HIV reservoir, including an optimized quantitative viral outgrowth assay  and novel assays that measure the inducible reservoir. Similar to the Tat/rev Induced Limiting Dilution Assay , our new inducible RNA assays can measure the frequency of cells with inducible HIV RNA upon stimulation. The great advantage of our assay is that allows for the concomitant measurement of different HIV RNA transcripts and viral production after only three days. Importantly, we show that the quantification of viral production shows comparable performance to our modified QVOA. Importantly, the induction assays described can provide increased sensitivity and a greater dynamic range for the monitoring of reduction of the HIV reservoir by intervention and eradication strategies. The performance characteristics of these assays, including relative sensitivity, precision and specimen requirements, are presented.Alt-text: Unlabelled Box1+ T cells infected with integrated latent virus. Targeting and eliminating this pool of latently infected cells is an essential component of the quest for a cure ) [tat-rev (multiply spliced RNA [msRNA]) [Cf-RNA from supernatants (in dilution [200 μl] or in bulk [600 μl]) was extracted using the Aptima HIV-1 target capture system as described for mQVOA. Ca-RNA was extracted using Magmax RNA extraction kits (Life Technologies) following manufacturer's instructions. Droplet digital PCR  was performed to quantify gag  for cf-R[usRNA])  and tat-[msRNA])  in multi[msRNA]) . As with[msRNA]) . A schem2.5http://www.R-project.org/)  IUPM, which was statistically lower than IUPMs at either days 7 or 14 . Since this assay readout has been described to detect the viral production earlier than the quantification by p24 ELISA , we calc+ T cells obtained from a blood draw from a clinical trial can be limited, we wanted to evaluate the minimum number of cells required to maintain the precision and accuracy of the mQVOA assay. We calculated the IUPM using 6, 4, 3, 2 or 1 assay replicates, corresponding to 9.0, 7.5, 6.0, 4.5, 3.0 or 1.5 × 106 CD4+ T cells assayed, respectively . No statistical differences were observed when the IUPM were measured using fewer replicates; however, as expected the CI95 enlarged as the number of replicates diminished and the CI95 was significantly wider when using fewer than 4 replicates compared to the original set up . Thefore, a minimum of 7.5 × 106 CD4 T cells, distributed in 5 replicates and 3-fold dilutions, should be assayed to provide the best balance of required cell numbers with optimal assay precision and accuracy.Since the number of CD4+ T cells.Finally, we evaluated the impact of freezing on these different assays by comparing cryopreserved cells with fresh cells from the same specimen . We found no statistical difference in the frequency of cells harboring replication competent virus when the mQVOA assay was run with fresh or frozen CD43.26 CD4 T cells, respectively). Nineteen individuals randomly selected from the cohort were used to run the all of the inducible RNA assays. For that. CD4+ T cells were stimulated with anti-CD3/CD28 antibodies in a limiting dilution format in the presence of raltegravir, to avoid new rounds of infection. After 3 days of culture, the RNA from the cells and supernatant were both extracted and quantified to determine the frequency of cells expressing ca-RNA (both unspliced gag ca-RNA and multiply spliced tat-rev ca-RNA) and cf-RNA (gag), respectively . gag ca-RNA was the most common RNA species found in all individuals, followed by tat-rev ca-RNA, and finally gag cf-RNA  were 31 and 5 times higher for gag and tat-rev ca-RNA respectively.Ex vivo quantification of HIV transcripts (in bulk) revealed that 40% and 90% of the samples were detectable for ca-RNA tat-rev and gag, respectively, and when detectable the values were low  and virion production (cf-RNA) concomitantly in the same culture well; two examples are depicted in Supplementary fig. 3 and gag ca-RNA transcript, while only 66% [58–70] and 33% [27–51] of all positive wells were positive for the tat-rev ca-RNA transcript and cf-RNA, respectively  was significantly higher than the total copies of ca-RNA gag and tat-rev . We found no difference in the frequency of cells expressing ca-RNA gag and tat-rev when calculations were performed with fewer replicates (Supplementary fig. 2A). In contrast, we observed an increase in the frequency of cells producing cf-RNA when using fewer replicates for the calculation (Supplementary fig. 2A). We then calculated the percentage change in the CI95 for each RNA transcript when decreasing the number of replicates (Supplementary fig. 2B). As expected, the CI95 expanded as the number of replicates decreased, which was statistically significant when using fewer than 6 replicates for ca-RNA gag and tat-rev. In contrast, the CI95 for cf-RNA was significantly wider when using fewer than 9 replicates. Therefore, a minimum of 4.50 and 6.75 × 106 CD4+ T cells for ca- and cf-RNA, repectively should be assayed to provide the best balance of required cell numbers with optimal assay precision and accuracy. Of note, the number of cells required for the inducible RNA assays was lower than that used for the mQVOA because of the greater sensitivity of the assays for induced RNA transcripts.As with the mQVOA, we calculated the impact of replicate number on assay precision and accuracy for the inducible RNA assays. We calculated the frequency of infected cells per million using 12, 9, 6, 3, 2 or 1 assay replicates in a subset of individuals, corresponding to 9.00, 6.75, 4.50, 2.25, 1.50 or 0.75 × 10Finally, as with mQVOA, no differences were observed when the inducible RNA assays were run using fresh or frozen samples (Supplementary fig. 2C).3.3+ T cells measured by mQVOA and the inducible RNA assays (ca-RNA and cf-RNA) with values obtained from HIV DNA quantification by ddPCR. The median frequency of total HIV DNA (gag) measured in our cohort was 640 [366–1487] copies of HIV DNA per million CD4+ T cells . The inducible RNA assays showed intermediate frequencies between HIV DNA and mQVOA quantification . However, they were 6.8 and 40 times lower, respectively, than the frequency of cells harboring total HIV DNA (p < .0001 in both cases). The supernatants collected from the inducible RNA assays permitted the calculation of the frequency of cells releasing viral particles (cf-RNA) after stimulation (n = 35), with a median measurement of 2.9 [1.9–6.8] copies/106 CD4+ T cells (p < .0002), but no statistical difference was observed when compared to the frequency of cells carrying replication competent virus (p = .5). Moreover, there was a strong statistically significant correlation between mQVOA and the inducible cf-RNA assay , and mQVOA and the inducible tat-rev ca-RNA assay , we evaluated whether the number of viral particles produced by bulk stimulation of 5 × 106 CD4+ T cells  correlated with the frequency of cells harboring replication competent virus. We found a positive correlation between the cf-RNA copies in the supernatant from the bulk stimulation and the mQVOA assay , which is higher than values reported for standard QVOA [.4 IUPM), T cells ,40. We d T cells .gag usRNA and tat-rev msRNA) and virion production (cf-RNA) from the same well, permitting an extensive comparison of RNA species. The differences observed in the quantification of the distinct RNA species may provide important information regarding the transcriptional status of infected cells as well the proportion of transcriptionally competent genomes. The frequency of cells capable of producing gag usRNA was higher than that producing tat-rev msRNA, similar to other studies [gag transcripts detected by PCR methods may contain internal deletions or other mutations [gag might be overestimating the size of the replication competent reservoir. Many defective HIV genomes also have deletions that encompass the tat and rev genes [tat-rev RNA reduces the likelihood of measuring proviruses with large internal deletions, suggesting that multiply spliced transcripts may be better surrogate markers for productive infection over unspliced transcripts. Accordingly, we found that tat-rev ca-RNA expression, but not gag ca-RNA expression, correlated with inducible virion production. However, tat-rev transcripts are required, but not sufficient, for the production of viral particles, especially with proviruses with frequent deletions in other areas of the viral genome [In parallel, we aimed to develop similar assays in a limiting dilution format for the concomitant detection of inducible ca-RNA (usRNA and msRNA) and virion production. The main advantage of inducible RNA assays are that there is no need for outgrowth of virus with feeder cells before measurement. These assays are therefore much faster than QVOA (only 3 days) and fewer cells are required. We measured HIV transcription . However, we clearly showed that there is already an accumulation of viral particles in the supernatant after three days of culture, despite the lower frequency of cells producing viral particles compared to ca-RNA. Importantly, the number of cf-RNA copies produced by each positive cell was higher than the copies of ca-RNA produced, which may increase the sensitivity of viral particle detection. The longer incubation times (i.e. 7 days) adopted in other similar inducible assays [We found that both ca-RNA species were expressed more frequently than cf-RNA. This could indicate a population of infected cells with defective integrated provirus capable of viral transcription but not subsequent virion assembly . Latent splicing . Our ressplicing . Alternae assays  may favoe assays .10 lower than the production of virus from a replication competent provirus. Consequently, most defective proviruses are unable to give rise to significant levels of virion production and cells that do produce virions are thus likely to carry replication competent virus.Importantly, the inducible cf-RNA assay described here showed comparable performance to the mQVOA: both assays are equivalent in >60% of the samples tested. This result suggests that the inducible cf-RNA assay could be a good alternative to mQVOA, with shorter culture set up and lower cell requirements. The release of viral particles upon stimulation is the closest step to a replication competent virus; however, not all virions released in in vitro experiments appear to be replication competent . With thThe frequency of infected cells in ART-suppressed individuals is much lower than that in in vitro cultures. Therefore, even if the vast majority of proviruses are defective in ART-treated subjects, we might not able to detect the suboptimal virion production from these defective proviruses. Interestingly, the copies of cf-RNA produced upon bulk stimulation also correlate with the IUPM. This result might be important for high throughput assays to test different strategies of latency reversing agents, since the cfRNA assay is faster and less expensive. However, the bulk assay does not permit precise calculation of the frequency of cells being reactivated. Although the inducible cf-RNA assay constitutes an attractive alternative to other methods to quantify the size of the latent HIV reservoir, there are potential limitations associated with the use of this assay. First, we cannot conclude that all viral particles released are replication competent. In addition, it is likely that not all HIV proviruses are reactivated during a single round of stimulation , similar10 greater reduction than a QVOA. Second, the expression of HIV antigens even without replication competent virus could be contributing to the generalized immune activation and the concomitant pathologic complications observed even in individuals on suppressive ART. As such, the inducible RNA assay may represent an alternative to existing assays used to evaluate the efficacy of therapeutic strategies aimed at reducing the size of the HIV reservoir.In conclusion, we describe novel inducible RNA assays to measure the size of the HIV reservoir that are quantitative, sensitive and allow for the concomitant measurement of different HIV RNA transcripts and viral production with the great advantage that results can be generated in three days. While inducible cf-RNA may be a correlate marker of replication competence, inducible ca-RNA assays may provide more sensitivity and a greater dynamic range. They also may detect cells that can produce viral antigens even if the virus is not replication competent. This assay may thus be valuable for candidate intervention strategies for two reasons. First, it may correlate with the reduction in assays for replication competent virus when an effective intervention that reduces the reservoir is identified. The inducible ca-RNA assay would thus provide the ability to measure 1–2 logSummary of some relevant papers performing QVOA.Supplementary Table 1Supplementary materialImage 1The following is the supplementary data related to this article.


RNA quality and yield are key determinant factors for accurate expression profiling. Limited cell number and FACS induced cellular stress make RNA isolation of sorted zebrafish cells a delicate process. We aimed to optimize a workflow to extract sufficient amounts of high-quality RNA from a limited number of FACS sorted cells from TgWe evaluated two suitable RNA isolation kits (the RNAqueous micro and the RNeasy plus micro kit) and determined that sorting cells directly into lysis buffer is a critical step for success. For low cell numbers, this ensures direct cell lysis, protects RNA from degradation and results in a higher RNA quality and yield. We showed that this works well up to 0.5× dilution of the lysis buffer with sorted cells. In our sort settings, this corresponded to 30,000 and 75,000 cells for the RNAqueous micro kit and RNeasy plus micro kit respectively. Sorting more cells dilutes the lysis buffer too much and requires the use of a collection buffer. We also demonstrated that an additional genomic DNA removal step after RNA isolation is required to completely clear the RNA from any contaminating genomic DNA. For cDNA synthesis and library preparation, we combined SmartSeq v4 full length cDNA library amplification, Nextera XT tagmentation and sample barcoding. Using this workflow, we were able to generate highly reproducible RNA sequencing results.The presented optimized workflow enables to generate high quality RNA and allows accurate transcriptome profiling of small populations of sorted zebrafish cells.The online version of this article (10.1186/s12864-019-5608-2) contains supplementary material, which is available to authorized users. Over the recent years, it has become clear that transcriptional control relies on the orchestrated activity of transcription factor complexes, DNA methylation, chromatin modification dynamics as well as higher-order DNA looping . GeneticDanio rerio) has become increasingly important as a model organism to study vertebrate development and cancer [In recent years, zebrafish  zebrafish line to optimize our workflow. This zebrafish line expresses GFP in 10–15% of its cells, thereby requiring only a limited number of zebrafish embryos to obtain a sufficient number of cells for our experimental set-up. In this paper, we present a detailed workflow for gene expression analysis of a small number  of fluorescence-based sorted zebrafish cells by means of RNA-sequencing, with a detailed description of the critical steps from embryo dissociation to the actual sequencing procedure.Despite the above-mentioned advantages, some considerations have to be made when using these zebrafish reporter lines for RNA-sequencing purposes. First, cells undergoing the process of sorting encounter stress and show reduced viability . In dyinTg(fli1a:EGFP) transgenic zebrafish line were used as input for comparative RNA-isolation between the two kits. Each set of 20,000 cells was directly sorted into the lysis buffer of the RNA isolation kit (see further).In search for an appropriate method to isolate RNA from a low number  of FACS sorted zebrafish cells, two RNA isolation kits were compared. The RNAqueous Micro Total RNA Isolation Kit from Ambion and the RNeasy Plus Micro Kit from Qiagen are specifically designed for purification of total RNA from a small amount of cells . In both cases, only longer RNA fragments (> 200 nucleotides) are purified, although the workflow can be modified to isolate small RNAs such as microRNAs, 5,8S rRNA, 5S RNA, tRNA’s,…  measurements by means of capillary electrophoresis using the Fragment Analyser . RNA isolation of 20,000 sorted cells resulted in a low RNA yield (see further), making it difficult to measure the obtained RNA yield with frequently used devices such as using the Nanodrop or Qubit. Therefore, the Fragment Analyzer was also used for RNA concentration determination. Based on the RQN values, both kits enable purification of intact RNA with a mean RQN score of 9.45 (range: 8–9.7) for the RNAqueous micro kit and 9.45 (range: 8.5–9.7) for the RNeasy plus micro kit Fig. . The medhprt1 reference gene [p-value = 0.0023) . We designed 2 RT-qPCR assays, one targeting the 5′ end and one targeting the 3′ end of the nce gene . We perf23) Fig. .These results indicate that both RNA isolation kits work well for RNA isolation of a low number of sorted cells. RNA with comparable high RQN values and yield is obtained. However, when evaluating RNA quality based on 5′-3′ delta Cq values, the RNAqueous micro kit performs slightly better, indicating less mRNA degradation.Both tested RNA isolation methods incorporate a genomic DNA (gDNA) elimination step during extraction. The RNeasy plus micro kit works through a ‘gDNA eliminator’ spin column, while the RNAqueous micro kit provides reagents for an optional post-elution DNase step. Since gDNA contamination can lead to a bias in gene expression measurements, we tested whether the DNase treatment supplied by the RNA isolation kit was sufficient or whether an additional gDNA elimination step was required. For each kit, 5 RNA samples were isolated following standard procedure with the gDNA elimination step from the kit and for 5 RNA samples no gDNA removal step was carried out. The resulting RNA was split into two samples. Half of the volume was treated with an additional gDNA removal step (Heat & Run) while the other half was used in the next step without additional manipulations. Heat & Run (Articzymes) DNase treatment was chosen as additional gDNA removal step because it does not require an additional purification step in which RNA could be lost.elfa) and the expressed repeat element (ERE) loopern4, the latter recently proposed as a robust reference repeat [We used 2 ng of this purified RNA form 1 representative embryo as input for a qPCR reaction with primers for a classic reference gene , gDNA contamination was noted, indicating that this is only a limited amount and no additional Heat&Run gDNA removal step is required. Yet, for the RNAqueous micro kit, the gDNA elimination step provided by the kit is not sufficient and an additional gDNA removal step is required. When combining the gDNA removal procedure provided by the kit together with Heat&Run DNase treatment, most but not all of the contaminating gDNA could be removed .Taken together, since gDNA contamination could bias gene expression studies , 25, it FACS sorting is a stressful process that may reduce cell viability and subsequently the quality of the isolated RNA. To overcome this problem, we tested whether sorting directly into the lysis buffer could preserve RNA quality. However, a critical consequence of this approach is dilution of the lysis buffer by the FACS buffer thus possibly influencing its lysis potential as well as the obtained RNA yield and quality. To investigate this, we analysed the maximal diluting factor of each lysis buffer with retention of its lysing capacity. We sorted a range of cells  in either a collection medium or in the recommended volume of the lysis buffer for both kits and compared both RNA yield and RQN values .For the RNAqueous micro kit, based on RQN comparison, sorting into the lysis buffer was beneficial up to a sort volume of 146 μl (=maximum diluting point), which is equal to 30,000 cells in the tested sorting set-up. This volume roughly corresponds to the used volume of the lysis buffer (100 μl), indicating that the lysis buffer could be diluted to roughly 0.5× its volume without loss of its lysis potential. Sorting up to 30,000 cells (146 μl) resulted in a better RNA quality (higher RQN) when sorting directly into the lysis buffer compared to sorting into collection buffer. Of note, with increasing cell numbers, the lysis buffer is further diluted, resulting in lower RQN values Fig. .Fig. 2SoThe RNeasy plus micro kit uses a higher recommended lysis buffer volume than the RNAqueous kit and consequently the turning point for switching to sorting into a collection medium is much higher. Here, sorting up to 75,000 cells in a volume of 360 μl resulted in higher RNA quality when sorting into the lysis buffer. The volume of the sorted cells corresponds again roughly to the used volume of the lysis buffer (350 μl), indicating that the lysis buffer keeps its lysing potential up to 0.5× dilution Fig. .In addition to the positive effect on RNA quality obtained by sorting directly into the lysis buffer, the yield is considerably enhanced by avoiding a centrifugation step for a low number of cells Fig. . To keepWe could confirm that by keeping the lysis buffer below its maximum diluting point (0.5×), the RNA quality remained high (RQN of > 7.5) and in the case of the RNeasy plus micro kit even equally high as cells sorted into collection buffer Fig. c & d.When comparing the average yield and RQN, we conclude that sorting up to 75,000 cells into 350 μl of lysis buffer from the RNeasy plus micro kit is beneficial, compared to sorting into 350 μl collection buffer. In addition, when correcting the volume of lysis buffer to avoid dilution and reduction of RNA quality, the RNA yield when sorting into lysis buffer stays higher than sorting into collection buffer Fig. . For theIt is important to note that the volume of the sorted cells is defined by the type of cell sorter that was used, for example, the FACS that was used here (Bio-Rad S3e) sorts on average 1,000,000 cells in a volume of 4,8 ml FACS buffer. Both lysis buffers could endure roughly a 0.5× dilution, the number of cells correspond to this sorted volume is defined by the chosen type of cell sorter and should therefore be determined in advance.In view of these data, we can conclude that sorting a small cell number directly into the lysis buffer of the RNA isolation kit is beneficial for RNA quality and yield up to 0.5× dilution of the lysis buffer. When sorting a higher volume (and thus a higher cell number) it is advised to either adapt the lysis buffer volume or sort into a collection medium first and then pelleting the cells before adding lysis buffer.Given that sorting based isolation of fluorescent cells induces a stress response and that RNA isolation directly after sorting might not always be feasible, we analysed the effect of the time interval between cell sorting and RNA isolation on the quality of the obtained RNA.We isolated RNA at different time points  post FACS sorting of 20,000 cells with both RNA isolation methods and assessed RNA integrity with the Fragment Analyser. Since sorting into the lysis buffer of the kit will directly lyse the cells and protect the released RNA, we investigated whether the RNA integrity could be preserved for a longer period of time compared to sorting into a collection buffer. RNA was isolated directly after sorting or after the cells were kept for 15 min, 30 min, 1 h, 2 h or 24 h on ice. In addition, samples were frozen into liquid nitrogen and kept at − 80 °C for 1 week to evaluate if longer storage was possible. The time between cell sorting and RNA isolation did not influence RNA integrity for both kits. However, sorting directly into the lysing buffer of the RNA isolation kit consistently resulted into high(er) quality RNA. In contrast, when sorting into a collection buffer, the RNA quality was highly variable and the RQN ranged from 0 and 10. Obtaining high-quality RNA after long-term storage or a longer period of time between sorting and subsequent RNA isolation was only possible when the RNA was protected by the lysis buffer from the kit  expression in the EGFP negative population  and sample Q_1 (RNeasy plus micro), was processed using the same work flow but now Nextera XT library prep was performed on the SMART seq V4 generated cDNA. These two initial samples were then sequenced using the Illumina Nextseq500 to get a first idea whether this workflow fits our purposes (GSE121917). Both RNA isolation kits resulted in a high percentage of uniquely mapped reads (89.5% for the RNAqueous micro kit and 88.2% RNeasy plus micro) and FastQC revealed good sequencing quality Fig. . In bothTo test reproducibility of our applied method, additional samples were processed GSE121917). For each RNA isolation kit, 1 RNA sample was split to generate 2 technical replicates  and they were treated as individual samples for cDNA synthesis, library prep and sequencing. For sample details  with both RNA isolation kits to compare with sorted samples. To look at reproducibility of the kit, we performed hierarchical clustering and PCA analysis. Both analyses clearly show that both technical replicates cluster closely together and that all the sorted samples cluster closer to each other than the unsorted whole embryo samples Fig. a & b. Whr = 0.98 both kits)  and the 2 two unsorted samples (r = 0.96). The introduced variation could be partly due to the RNA isolation kit or due to the difference in the sorted cell population since fli1a is expressed in different cell types. As expected, sorted cells did not correlate well with unsorted whole embryos (r < 0.74). All the above-presented data shows that our applied method generates good quality data and is highly reproducible.A very high correlation, as quantified by the Pearson’s correlation coefficient, was noted for the technical replicates (ts) Fig. . The corfli1a gene is a DNA binding transcription factor that is involved in vascular development. Expression is observed in neural crest derived cells such as developing cartilage in the jaw, vasculature, and developing mesenchyme. Gene Ontology (GO) annotations linked to the fli1a zebrafish gene include DNA binding transcription factor activity (RNA polymerase II specific), angiogenesis, blood vessel morphogenesis, regulation of transcription by RNA polymerase II, and cell differentiation. Gene set enrichment analysis (GSEA) was performed on unsorted (whole embryo) versus fli1a + sorted cells to see if the same GO annotated gene sets were enriched in the sorted fli1:GFP sampes compared to the unsorted samples. This was indeed the case, indicating that sorting enriched for fli1a expressing cells. Gene sets like GO_vasculature, GO_cartilage_development and GO_mesenchyme_development were enriched in the EGFP sorted cells  embryos and were able to show expression of tissue specific genes through RT-qPCR analysis. Expression of lck (thymocyte specific gene) and GFP is detected in the GFP positive samples but not in the GFP negative samples  or Tg adult zebrafish (data not shown).To further validate our method, we tested the workflow on different transgenic lines and confirmed with RT-qPCR the expression of key genes for the specified cell population. We sorted thymocytes from Tg, easy and consistently delivers high-quality RNA samples. In addition, sorting into the lysis buffer will protect RNA up until the moment of purification, making it possible to postpone RNA isolation after sorting.To maximize the RNA quality and yield, direct sorting into the lysis buffer from the RNA isolation kit is advised. In this way, the sorted cells will be lysed immediately and the released RNA will be protected from degradation. However, as the sorted cells reside in sorting buffer, dilution of the lysis buffer reduces the lysis potential. Therefore, sorting directly into the lysis buffer is only beneficial up to a certain number of sorted cells. This maximum cell number is different for each RNA isolation kit used. Both RNA isolation kits yield good quality RNA up to a 0.5 times dilution of their respective lysis buffers. For the sorter (Bio-Rad S3e) used in our experiments, this corresponds with 30,000 cells for the RNAqueous micro kit and 75,000 cells for the RNeasy plus micro kit.later (Ambion), could also help stabilizing the RNA after sorting and thus prevent RNA degradation [later is more viscous, pelleting by spin down of at least 30 min is required to recovering the cells for RNA isolation. When working with a small number of cells, this pelleting step may lead to cell loss during this procedure. In addition, when sorting into RNAlater, you will dilute this protecting agent. Similar as to sorting into lysis buffer, this requires analysis of how much dilution can be endured without the risk of losing its protecting capacity.Sorting into a RNA protecting agent, like RNAradation . As RNAlAfter successful isolation of high quality RNA, an additional gDNA elimination step can be incorporated into the workflow to completely eliminate residual gDNA in the RNA sample. We could show that the gDNA elimination step from the RNaqueous micro kit does not sufficiently remove contaminating gDNA. To fully purify the RNA from residual gDNA, an additional DNA removal step is required. For the RNeasy plus micro kit, the gDNA elimination step provided by the kit is most efficient to remove contaminating gDNA. Yet, this was tested on low input material (two embyros) so when more input is provided, the gDNA removal column might be saturated and gDNA removal will be insufficient. Thus, it is advised to test gDNA contamination for your chosen experimental set up. The presence of gDNA in a RNA sample may lead to overestimation of the abundance of certain transcripts and thus causes bias in expression studies , 25. TheThe SMART seq v4 kit is suitable for RNA sequencing up to single cell level. When sorting a single cell into the SMART seq lysis buffer, the cell will be lysed immediately and cDNA synthesis and amplification can be performed directly on the cell lysate . The resThe proposed workflow is currently applicable for polyA RNA-sequencing of small  numbers of sorted zebrafish cells. In addition to polyA tailed transcripts, representing roughly one third of the entire transcriptome, many other non-polyadenylated RNAs are expressed including a wide variety of long non-coding RNAs. In principle, this can be achieved using a total RNA-sequencing workflow, but this requires a ribosomal RNA depletion step which is not yet available for zebrafish , 37.In conclusion, we optimized a workflow for polyA+ RNA sequencing of a low number  of sorted zebrafish cells. We identified that sorting the cells directly into the lysis buffer of the kit maximizes the quality and the yield of the RNA. Yet, this is only beneficial up to a certain volume of sorted cells due to dilution of lysis buffer. For both RNA isolation kits, we could show that the lysis buffer could endure 0.5× dilution without losing its lysis potential and lowering the RNA quality and yield. When sorting a higher number of cells, we advise to increase the lysis buffer volume or to sort into a collection medium first. After isolation, the quality of the isolated RNA should always be assessed before proceeding to the next step. Incorporation of an additional gDNA removal step  is advised to completely clear the RNA from contaminating gDNA. Full length cDNA synthesis and amplification can be done by Smart seq v4 followed by tagmentation and sample barcoding by Nextera XT. This workflow enables to retrieve high quality RNA from FACS sorted zebrafish cells and yields highly reproducible RNA sequencing data. Since RNA quality is critical when performing transcriptome studies, we strongly recommend to do an in depth quality assessment of the isolated RNA as presented in this study, when implementing a new RNA sequencing workflow.fli1a:EGFP) zebrafish were obtained from the zebrafish international resource center (ZIRC). The Tg(fli1a:EGFP) were crossed and embryos were collected. The clutch of embryos (50–150) were transferred to a petri dish with E3 media and kept in an incubator at a temperature of 28 °C and a daily light and darkness cycle (see higher) until the moment of further processing. For all our experiments, roughly 10 times 100 embryos were used for FACS sorting. All embryos used were younger than 120 h post fertilization (hpf), and thus no ethical approval was needed.Zebrafish were maintained in a zebtec semi-closed recirculation housing system  with a constant water temperature of 28 °C and a conductivity of 500 μS. The fish are exposed to a daily 14 h light and 10 h darkness cycle and fed 2 times/day with dry food  and once with Artemia (Ocean Nutrition). The Tg and 300 μl FBS . The cells were transferred to a 15 ml tube, pelleted (5 min 800 g), washed with PBS and resuspended in 2 ml resuspension solution . The cell solution was passed several times through a 40 μM mesh filter prior to cell sorting. This cell suspension was analysed by flow cytometry on a Bio-Rad S3e cell sorter. Forward and side scatter was used to gate for live, single cells. GFP positive cells, which ranged between 5 and 15% of the parental population, were sorted and collected. No other markers were used for flow cytometry. Representative samples (a negative control vs. GFP positive cells) with the gating strategy are shown in the supplemental data  was euthanized using an overdose of tricaine and transferred to a 35 mm culture dish. The tricaine was removed as much as possible and 3 ml of preheated (28 °C) dissociation solution  was added. During an incubation period of 90 min at 28 °C, the larvae dissociation was advanced by pipetting up and down with a 1 ml pipette every 15 min. When the larvae were completely dissociated, the reaction was stopped by adding 3 μl 1 M CaClThe sorted cells were collected in either the lysis buffer of the RNA isolation kit or into a collection medium (the used resuspension buffer). The cells were kept on ice during the whole procedure.RNA from sorted cells was isolated with the use of the RNeasy plus micro kit  or the RNAqueous-Micro Total RNA Isolation Kit . For the RNeasy plus micro kit, the RLT buffer was supplemented with 2-mercaptoethanol (10 μl per 1 ml RLT) as suggested. The integrity and the concentration of the RNA was analyzed with the Fragment Analyzer  High Sensitivity RNA Analysis Kit (DNF-472-0500). The PROSize software version 3.0.1.5 determined a RQN RNA integrity score considering the entire electropherogram.hprt1, as a representative of all mRNAs in the sample. By using the oligo dT primed reverse transcriptase iScript Select kit , the cDNA synthesis starts from the 3′ polyA tail, proceeding to the 5′ end of the mRNA transcript. Upon mRNA degradation, the cDNA synthesis is interrupted and not completed to the 5′ end of the transcript. We designed two RT-qPCR assays, 1 targeting the 5′ end , and 1 targeting the 3′ end of the hprt1 cDNA . The assays were developed using the NCBI ‘pick primer’ tool and efficiencies were determined based on a relative 6-point 4-fold dilution series . The higher the 5′-3′ dCq value, the more mRNA degradation. RNA used for this assay was isolated from 1 embryo using the RNAqueous micro or the RNeasy plus micro kit as described in the manual. Starting from 250 ng RNA, cDNA was synthesized using the oligodT primers of the iScript select cDNA synthesis kit.This RT-qPCR assay aims to assess the integrity of a low abundant reference gene mRNA, For RT-qPCR, 2.5 μl 2× SsoAdvanced SYBR Green supermix (Bio-Rad) was mixed with 5 ng input cDNA and 250 nM  of each forward and reverse primer in a 384 well plate (Bio-Rad) and run on a LightCycler 480 (Roche). Thermocycler conditions were set as follows: 95 °C for 2 min, followed by 44 cycles of 95 °C for 5 s, 60 °C for 30 s, 72 °C for 1 s and finally a melting curve analysis was performed at 95 °C for 5 s followed by 60 °C for 1 min, gradual heating to 95 °C at a ramp-rate of 0.11 °C/s followed by cooling to 37 °C for 3 min. Cq values were exported and RT-qPCR data were analysed with qbase+ version 2.6.1 (Biogazelle).elfa  and the ERE reference gene [loopern4  was performed on 2 ng RNA from each sample to test for contaminating gDNA. As qPCR is not possible using RNA as template, amplification signals the presence of gDNA in the RNA sample. The RNA was mixed with 2.5 μl 2× SsoAdvanced SYBR Green supermix (Bio-Rad) and 250 nM  of each forward and reverse primer in a 384 well plate (Bio-Rad) and run on a LightCycler 480 (Roche). Thermocycler conditions were as follows: 95 °C for 2 min, followed by 44 cycles of 95 °C for 5 s, 60 °C for 30 s, 72 °C for 1 s and finally a melting curve analysis was performed at 95 °C for 5 s followed by 60 °C for 1 min, gradual heating to 95 °C at a ramp-rate of 0.11 °C/s followed by cooling to 37 °C for 3 min. Cq values were exported and RT-qPCR data were analysed with qbase+ version 2.6.1 (Biogazelle).For the gDNA contamination assay, RNA was isolated from 2 embryos and diluted to 1 ng/μl to match input amount of sorted cells. Five samples for each kit were isolated without including the gDNA elimination step of the procedure, another five samples for each kit were isolated with the standard RNA isolation method including its gDNA elimination step. Each RNA sample was split into two: half of the RNA was subjected to a Heat & Run  gDNA removal step to remove possible contaminating gDNA. The other halve of the sample was used in further steps without extra gDNA removal. The Heat & Run kit enables gDNA removal without the need of an extra purification step, therefore preventing the loss of sparse RNA. qPCR for the reference gene nce gene  loopern4fli1a:EGFP) embryos was dissociated and processed for FACS sorting as described above. A range of cells  were sorted either directly into the lysis buffer of the RNA isolation kit or in a collection medium . When cells were sorted in a collection medium, they were centrifuged (5 min at 1200 rmp), supernatant was removed and the pellet was dissolved in the lysis buffer of the RNA isolation kit. RNA was isolated following the manual of the RNA isolation kit. The integrity and RNA concentration was measured with the Fragment Analyzer as described earlier.A clutch of Tg(fli1a:EGFP) embryos were FACS sorted following the procedure described above. The cells were either sorted in lysis buffer of the RNA isolation kit or into a collection medium . The sorted cells were kept on ice for a specific amount of time  before RNA isolation was carried out. The cells that were sorted into a collection medium were pelleted  and dissolved in lysis buffer right at the start of the RNA isolation process. In addition, to test if longer storage was possible, samples were frozen in liquid nitrogen and stored at − 80 °C for a week before proceeding to RNA isolation. RNA quality was determined with the Fragment Analyzer as described above.20,000 GFP+ cells from Tg were added to 20 μl of each RNA sample. Samples were incubated for 10 min at 37 °C and 5 min at 58 °C for gDNA removal. cDNA synthesis and amplification was prepared using the SMART-Seq v4 Ultra Low Input RNA Kit . cDNA concentrations were measured using the Qubit dsDNA high sensitivity assay kit . Each sample was diluted to 0.5 ng/μl and 2 μl was mixed with 2.5 μl 2× SsoAdvanced SYBR Green supermix (Bio-Rad) and 250 nM  of each forward and reverse primer in a 384 well plate (Bio-Rad) and run on a LightCycler 480 (Roche). Thermocycler conditions were as follows: 95 °C for 2 min, followed by 44 cycles of 95 °C for 5 s, 60 °C for 30 s, 72 °C for 1 s and finally a melting curve analysis was performed at 95 °C for 5 s followed by 60 °C for 1 min, gradual heating to 95 °C at a ramp-rate of 0.11 °C/s followed by cooling to 37 °C for 3 min. Cq values were exported and RT-qPCR data were analysed with qbase+ version 2.6.1 (Biogazelle). The ERE reference genes 1 ng of SMART-Seq v4 amplified cDNA  was used for Nextera XT DNA library prep . For sample and input specifications see Table https://www.bioinformatics.babraham.ac.uk/projects/fastqc/).Libraries were sequenced using a NextSeq 500 (Illumina). Quality control on fastq files was performed with FastQC .The DESeq2 R-package (version 1.20.0) was used for count normalization and differential gene expression analysis. The normalized read counts were used to generate PCA plots, heatmaps and the correlation matrix. Pearson’s correlation coefficient was calculated on log transformed normalized read counts. Pre-ranked Gene Set Enrichment Analysis (GSEA) was preformed using GenePattern 2.0 . Fragment analyser electropherogram shown for the RNAqueous micro kit (top) and the RNeasy plus micro kit (bottom) for normal isolation procedure (left) and for RNA isolation with the adapted protocol for small RNA’s as provided in the manual (right). The red arrow indicates presence of small RNA’s in the isolated RNA sample (PDF 1667 kb)Additional file 2:Supplementary note 1: Statistical analysis (DOCX 147 kb)Additional file 3:Figure S2. RT-qPCR confirms tissue specific gene expression in tissue specific sorted cells. (A) From a clutch of Tg(fli1a:EGFP) embryos, EGFP positive and EGFP negative cells were sorted. RT-qPCR analysis shows expression of EGFP and fli1a is in the EGFP positive samples but not or negligible in the EGFP negative samples. (B) RT-qPCR analysis confirms GFP and lck expression in GFP + but not in GFP – sorted Tg(rag2:GFP) cells. For all samples (A) & (B) 20,000 cells were sorted directly into the lysis buffer of the RNA isolation (RNAqueous micro or RNeasy plus micro) kit (PDF 874 kb)Additional file 4:Figure S3. Sequencing libraries run in multiple rounds of sequencing introduces substantially larger inter-run variation than the effects imposed by the use of different RNA isolation kit. Heatmap (left) and PCA analysis (right) of all samples sequenced. A_1 and Q_1 are sequenced on a different sequencing run than A_2a, A_2b, Q_2a, Q_2b, A_unsorted and Q_unsorted (PDF 889 kb)Additional file 5:Supplementary note 2: protocol for RNA isolation and sequencing of a low number (5000- 200000) of sorted zebrafish cells (PDF 606 kb)Additional file 6:Figure S4. FACS gating strategy used to collect EGFP positive cells from a clutch of Tg(fli1a:EGFP) zebrafish. Representative samples of a negative control sample (left) and a Tg(fli1a:EGFP) sample (right). This gating strategy was used to first select live cells based on forward and side scatter, with subsequent selection of single cells. Last, GFP positive cells were selected and sorted. No GFP+ cells are seen in control fish (left) while 5–15% of the cell population was found to be GFP positive cells in Tg(fli1a:EGFP) samples (right) (PDF 1027 kb)


The successful implementation of next-generation sequencing techniques in plant and woody tissues depends on the quality of initial starting material. This study demonstrated the use of a modified protocol that enabled the simultaneous extraction of both genomic DNA and total RNA from recalcitrant woody material. The genetic material obtained by this protocol is of high quality and can be directly used in downstream analysis . This protocol is particularly useful not only when the initial plant material is limited but also when genomic DNA features  have to be compared with the total RNA . For such studies, the extraction from the same materials is highly preferred to minimize sample variation. The advancement of next-generation sequencing techniques has greatly facilitated almost all branches of biological and life-science studies. To guarantee reliable results and conclusions, high-quality starting genetic material  is essential. Our modified protocol, which is based on existing methods, enabled the efficient simultaneous extraction of genomic DNA and total RNA from the same recalcitrant woody sample. Thereby, it greatly minimizes the amount of initial plant materials needed for a specific study. The increasing affordability of next-generation sequencing (NGS) techniques such as RNA-seq and bisulfite sequencing has enabled the application of these techniques to a wide range of studies. The sequencing and the downstream analysis heavily rely on the quality of the starting biological materials and the efficacy of the extraction methods of the nucleic acids. Usually the genomic DNA (gDNA) and total RNA (totRNA) are extracted using separated batches of the same biological material. Few efforts have been made to combine the existing methods to simultaneously extract high-quality gDNA and totRNA from the same sample. This is especially important in the case of very limited amount of the starting material that cannot be split in different batches or in the case of studies where the analysis of gDNA and totRNA have to be compared so that the extraction from the same materials is highly preferred to minimize sample variation  . ProtocoPicea abies L. Karst.) tree inoculated with the fungal pathogen Heterobasidion parviporum Niemelä & Korhonen isolate 96026 in a forest of Lapinjärvi, Finland  owned by Natural Resources Institute, Finland (Luke). The tree stems were drilled creating 15 mm diameter holes. The drilled holes were filled with Norway spruce sawdust precolonized by H. parviporum and sealed with parafilm. After three-and-half months, the inoculated stem tissues of 12 trees  were harvested in November. The surrounding area of the inoculated drill holes containing the mixture of tree woody tissues and fungal mycelia were collected and ground to powder by IKA® A11 basic mill  in liquid nitrogen. The ground materials were then collected in 50 ml falcon tubes (2.0 g/tube). The step-by-step procedure for co-extraction of gDNA and totRNA is provided as a supplementary file (Supplementary Protocol). The gDNA concentration and integrity was determined by Qubit fluorimeter quantification and gel electrophoresis (1% Tris-acetate-EDTA agarose gel for 40 min at 150 V), respectively. The totRNA quality was assessed by Agilent 2100 bioanalyzer following the manufacturer's instructions.The material used was obtained from a 30-year-old Norway spruce (www.genomics.cn/en/index) for bisulfite sequencing and RNA-seq, respectively. All submitted samples have reached the quality standard of Level A in Beijing Genomic Institute for Whole genome bisulfite sequencing and HiSeq Transcriptome library construction, respectively.Four extractions for each biological replicate were performed and pulled together to recover both the gDNA and the totRNA. A final total volume of 500 μl of gDNA and 320 μl of totRNA was obtained for each biological replicate. The concentration for gDNA was between 31.4 ng/μl and 276 ng/μl, while for totRNA was between 81 ng/μl and 772 ng/μl . The proWith this paper, we provide the scientific community with an efficient, fast, safe and cheap protocol for simultaneous extraction of gDNA and totRNA from very difficult and recalcitrant samples like woody tissues. This protocol allows the extraction of high-quality genetic material for example, for whole-genome bisulfite sequencing and RNA-seq studies. The simultaneous extraction of both gDNA and totRNA can be an advantage in the case of limited source material or to minimize sample variation when the gDNA has to be compared with the totRNA from the same biological source or condition.NGS has and will continue to revolutionize the study of biology. The costs of sequencing are decreasing dramatically. To keep pace with increasing sequencing possibilities, upstream biological material preparations are heading toward more efficient and simplified directions. Our protocol contributes to that aspect and shall be readily applied in NGS studies.Our protocol is based on established methods for extracting genomic DNA and total RNA, separately.It allows simultaneous extraction of genomic DNA and total RNA from very difficult and recalcitrant samples like wood material.The resulting genetic materials are of high quality and could be easily applied in next-generation sequencing studies.Click here for additional data file.


Point mutations in the coding sequence for solute carrier 6 (SLC6) family members result in clinically relevant disorders, which are often accounted for by a loss-of-function phenotype. In many instances, the mutated transporter is not delivered to the cell surface because it is retained in the endoplasmic reticulum (ER). The underlying defect is improper folding of the transporter and is the case for many of the known dopamine transporter mutants. The monoamine transporters, i.e. the transporters for norepinephrine (NET/SLC6A2), dopamine (DAT/SLC6A3) and serotonin (SERT/SLC6A4), have a rich pharmacology; hence, their folding-deficient mutants lend themselves to explore the concept of pharmacological chaperoning. Pharmacochaperones are small molecules, which bind to folding intermediates with exquisite specificity and scaffold them to a folded state, which is exported from the ER and delivered to the cell surface. Pharmacochaperoning of mutant monoamine transporters, however, is not straightforward: ionic conditions within the ER are not conducive to binding of most typical monoamine transporter ligands. A collection of compounds exists, which are classified as atypical ligands because they trap monoamine transporters in unique conformational states. The atypical binding mode of some DAT inhibitors has been linked to their anti-addictive action. Here, we propose that atypical ligands and also compounds recently classified as partial releasers can serve as pharmacochaperones. All SLC6 transporters are Namulation . Expressmulation –5. The pmulation –4; this mulation –9. Thesemulation .Fröhlich and Loewi discovered that cocaine potentiated of the action of adrenaline already at the beginning of the 20th century , but it Like most membrane proteins, SLC6 transporters are assembled in the ER before they are delivered to the plasma membrane . There aMany drugs have been discovered, which remedy defects in protein folding, including those arising from mutations in membrane proteins . At leasThere are two examples of pharmacochaperones, which have been approved for the treatment of affected patients. Migalastat, an inhibitor of α-galactosidase A , corrects the folding defect in several mutated versions of α-galactosidase A, which cause Fabry's disease . Lumacaf0,+/SLC6A14 [The following observations support the assumption that this model is also relevant for SLC6 transporters other than monoamine transporters: (i) The C-terminal SEC24-binding site is also present in transporters from other branches, i.e. in the GABA-transporter 1/SLC6A1 , the gly/SLC6A14 . (ii) Mu/SLC6A14 ,42,43. , only ibogaine was effective in restoring ER export/cell surface delivery and function (i.e. substrate uptake and high-affinity inhibitor binding) to the mutant SERT . Moreove+ for high-affinity binding. The ER does not contain any appreciable concentration of Na+. It is, therefore, not surprising that this class of compounds has proven ineffective in pharmacochaperoning folding-deficient SERT. In fact, when tested on disease-relevant folding-deficient mutants of DAT [+, but it is unlikely to be the inward-facing conformation: substrates dissociate rapidly from the inward-facing state at low Na+ concentrations [p-chloroamphetamine and amphetamine fail to rescue folding-deficient mutants of SERT [+ and renders the inner vestibule accessible to cysteine-modifying reagents [+[in vivo in Drosophila melanogaster [In contrast with ibogaine and noribogaine, typical inhibitors of monoamine transporters such as cocaine, mazindol, paroxetine and reboxetine  bind to s of DAT –8, typics of DAT . It is ntrations . In fact of SERT  and of D of SERT , respectreagents . Similaragents [+. Ibogainagents [+. Accordiagents [+,35,59. Tnogaster ,35. Buprnogaster . Bupropinogaster –9 were aThe rewarding and addictive properties of cocaine are caused by inhibition of DAT. However, it has been appreciated that inhibitors of DAT differ substantially in their reinforcing properties –64. Comp+ [+ concentrations; accordingly, the inward-facing state is the predicted minimum energy (=stable) conformation of monoamine transporters in the ER. Based on these interrelated properties (i.e. trapping of SERT in the inward-facing conformation and Na+-independent binding), PAL-1045 was predicted to be an effective pharmacochaperone. In fact, PAL-1045 rescued a folding-deficient mutant of SERT with an efficacy approaching that of noribogaine [It has also been appreciated that amphetamine-like releasers differ in their efficacy; the term partial releaser was coined to differentiate the compounds with reduced ability to trigger the substrate-exchange mode from those, which cause maximum efflux of monoamines ,67. Part+ . As mentibogaine .2+-binding site on the extracellular side; individual transition metals exert distinct effects on DAT and discriminate between the forward and the reverse transport mode [Pharmacochaperones that target the orthosteric binding site (i.e. the endogenous ligand binding site) have the disadvantage of also blocking the function of the rescued protein. In vivo, the limiting factor is the ratio of the half-lives of the rescued protein and of the pharmacochaperone: the refolded protein must persist after the pharmacochaperone has been eliminated. Migalastat, for instance, is administered every other day; this allows for unblocking the rescued α-galactosidase A . Thus, tort mode ,83.Taken together, these findings indicate that the vestibule of monoamine transporters can also accommodate small molecules, which act as allosteric modulators. In fact, allosteric modulators have been identified such as the quinazolinamines SoRI-9824, SoRI-20040 and SoRI-20041 . These cAt the current stage, there are only a few starting points, which guide the rational search for pharmacochaperones. These include the chemical structures of effective pharmacochaperones (There are more than 60 disease-relevant mutations in SLC6 transporters, which result in folding deficiency. The phenotypic consequence of a folding defects, in most instances, a devastating disorder. Monoamine transporters have both a rich pharmacology and a large collection of folding-deficient mutants. Hence, they are well suited to explore the concept of pharmacochaperoning.At the current stage, there are only a few starting points, which guide the rational search for pharmacochaperones. These include the chemical structures of effective pharmacochaperones (A major future challenge is to extract the structure–activity relationship of pharmacochaperones. Pharmacochaperones provide a means to map the traps in the energy landscape of the folding trajectory: we posit that an orthogonal map of mutants versus effective pharmacochaperones will offer an insight into the conformational search space, which is traversed by the nascent transporter. Thus, pharmacochaperones are not only of interest because of their potential therapeutic application, but they also provide an approach to address the transporter folding problem.


Understanding the correlates of self-rated health (SRH) can help public health professionals prioritize health-promotion and disease-prevention interventions. This study aimed to investigate the association between multiple comorbidities and global SRH and age-comparative SRH.A total of 512,891 participants aged 30–79 years old were recruited into the China Kadoorie Biobank study from ten regions between 2004 and 2008. Multivariate logistic regression models were used to estimate the odds ratios (ORs) for the associations between comorbidities  and SRH. Population attributable risks (PARs) were used to estimate the contribution of multiple comorbidities to poor global SRH and worse age-comparative SRH.After adjusting for covariates, suffering from various diseases increased the chance of reporting a poor global SRH [OR (95% CI) ranged from 1.10  for fracture to 3.21  for rheumatic heart disease] and a worse age-comparative SRH [OR (95% CI) ranged from 1.18  for fracture to 7.56  for stroke]. From the population perspective, 20.23% of poor global SRH and 45.12% of worse age-comparative SRH could attributed to the cardiometabolic diseases, with hypertension , diabetes , coronary heart disease  and stroke  making the largest contribution.Various diseases were major determinants of global and age-comparative SRH, and cardiometabolic diseases had the strongest impact on both global SRH and age-comparative SRH at the population level. Prevention measures concentrated on these conditions would greatly reduce the total burden of poor SRH and its consequences such as poor quality of life and use of health care services. Self-rated health (SRH), a common but comprehensive measure of a person’s global  health status, has been widely used to describe the health status of an individual and a population in epidemiology surveys because of the easily collected data . Respondn = 6061) [Understanding the determinants and correlates of SRH can help public health professionals prioritize the interventions for health promotion and disease prevention. Previous studies have found a wide range of determinants of SRH, including sociodemographic factors, lifestyle factors, physical illness and psychological factors –12. It i = 6061) .Using data from the China Kadoorie Biobank (CKB) with over half million participants, we aimed to investigate the relations of multiple comorbidities with global SRH and age-comparative SRH among middle-aged and elderly population in China.The CKB study is a general population-based prospective cohort study that was designed to investigate the complex interplay of lifestyle behavior, environmental and genetic factors and risks of various chronic diseases. The detailed study design, sampling strategy and features of the study participants are previously reported , 23. In In the baseline survey, trained interviewers conducted a standardized questionnaire using a laptop-based direct data-entry system for the questionnaires, with built-in functions to avoid data missing and logical errors. The questionnaires covered general demographic characteristics, socio-economic status, diet, general health, physical activity, family medical history and mental health. After the interview, a series of physical measurements including weight, height, waist and hip circumference and blood pressures were recorded for each participant by trained technicians, and a 10-mL non-fasting blood sample was taken.We excluded 2 participants with missing values of body weight or height. A total of 512,889 participants  remained in the current analysis.Physician diagnosed disease status was enquired in baseline questionnaire. Respondents were asked: “Has a doctor ever told you that you had the following disease?” followed by a list of diseases including diabetes, hypertension, coronary heart disease (CHD), stroke, rheumatic heart disease, tuberculosis, emphysema/bronchitis, asthma, cirrhosis/chronic hepatitis, peptic ulcer, gallstone/gallbladder disease, kidney disease, fracture, rheumatic arthritis, psychiatric disorders, neurasthenia, head injury and cancer. If someone reported that he/she was diagnosed with cancer, the site of cancer was also asked. Prevalent diabetes was defined if the participant met one of the following criteria : 1) selfn = 15,691, 3.06%) were excluded from the analysis. To be consistent with global SRH, participants answering “about the same”  were excluded in the main analysis and thus age-comparative SRH was categorized into two groups: better and worse. In order to ensure the accuracy and robustness of the results, we also conducted sensitivity analysis of including participants who reported “about the same” and thus age-comparative SRH was categorized into three groups: better, about the same, and worse.To assess SRH status of each participant, two questions were asked in baseline interview: 1) How is your current general health status: excellent, good, fair, or poor? 2) How is your current health status compared with someone of your own age: better, about the same, worse, or don’t know? We considered the first question as global SRH and the second one as age-comparative SRH. No data were missing for two SRH variables. We categorized the global SRH into two categories to be comparable to the literature on this topic: good  and poor . For the age-comparative SRH, participants answering “don’t know” (2). The physical activity level was measured by adding up metabolic equivalent tasks (METs) for daily work or leisure activities. All participants were also inquired about their family history of five diseases, including diabetes, heart attack, stroke, mental disorders and cancer.Other variables included age, sex, study location (10 regions), marital status , education level , annual household income , employment status , cigarette smoking  and alcohol drinking  were also inquired in the baseline questionnaires. Body mass index (BMI) was calculated as weight divided by the square of height  for continuous variables, and compared using ANOVA and Chi-square tests for continuous and categorical variables, respectively. Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for relations of multiple comorbidities with global and age-comparative SRH. We adjusted for potential confounders in sequential steps: model 1 adjusted for sociodemographic factors ; model 2 further adjusted for lifestyle factors ; model 3 additionally adjusted for all comorbidities in the same model to test the independent associations. In the sensitivity analysis of including participants answering “about the same” for the age-comparative SRH, multinomial logistic regression models were used.Population attributable risks (PARs) were used to estimate the proportion of the total prevalence of poor global SRH (or worse age-comparative SRH) that could be attributed to multiple comorbidities, and were calculated for each disease using the following formula: PAR=(p(OR-1))/(p(OR-1)+1) , where pP values < 0.05 were considered as statistical significance.Stratified analyses were performed according to sex, administrative regions , age , education level  and household income . Tests for interaction were conducted by adding interaction terms of the certain disease and the stratifying variable in the final model. We also analyzed the impact of the duration of disease diagnosis, treatment, the number of diseases and the site of cancer on global and age-comparative SRH. Furthermore, to test the influence of undiagnosed diseases on the associations, we used hypertension and diabetes as examples in a sensitivity analysis. In the main analysis, the definitions of hypertension and diabetes included both the self-reported physician-diagnosed diseases and screening-detected diseases from the physical examination (which could be considered as undiagnosed cases). In the sensitivity analysis, we only used self-reported physician-diagnosed hypertension and diabetes (yes vs. no) as the exposures and evaluated their associations with SRH. All analyses were performed using SAS 9.3 (SAS Institute Inc), and two-sided n = 302,631) were women. Most participants were married (90.59%) and more than half (55.90%) were recruited from rural areas; two-thirds of the participants were employed (68.74%), and about one-fourth (26.43%) were current smokers and 17.13% were current regular drinkers. The mean (SD) MET and BMI of the participants was 21.08 (13.87) and 23.66 (3.38), respectively.The baseline characteristics of participants are presented in Table The most prevalent disease was hypertension (34.02%), followed by fracture 6.91%), gallstone/gallbladder disease (6.04%), diabetes (5.91%), peptic ulcer (3.90%), CHD (3.02%), emphysema/bronchitis (2.59%), rheumatoid arthritis (2.07%), stroke (1.73%), cirrhosis/chronic hepatitis (1.21%), tuberculosis (1.49%), kidney disease (1.48%), neurasthenia (1.11%), head injury (1.10%), depressive symptoms (0.64%), asthma (0.55%), cancer (0.50%), psychiatric disorders (0.37%) and rheumatic heart disease (0.18%) , explained the largest part of poor global SRH and worse age-comparative SRH in our study population, followed by emphysema/bronchitis and gallstone/gallbladder disease. Whereas the contribution of rheumatic heart diseases, psychiatric disorders and cancer was small because of the low prevalence Table .Table 4PP for trend < 0.001; P for trend < 0.001; We further examined whether treatment would affect SRH, the results suggested that people with diseases and still on treatment tended to report a poor global SRH and worse age-comparative SRH compared with those having a disease but not receiving treatment  Table 13When looking at different types of cancer, liver cancer had the greatest impact on global SRH , followed by lung cancer, stomach cancer, other cancer types, intestine cancer, breast cancer, cervix cancer and esophagus cancer. For age-comparative SRH, esophagus cancer had the greatest impact , followed by lung cancer, stomach cancer, other cancer types, liver cancer, breast cancer, intestine cancer and cervix cancer  Table 16When examining the influence of undiagnosed diseases on the association, we found that the association become stronger in the sensitivity analysis of using self-reported hypertension and diabetes compared to the main analysis of using both self-reported and screening-detected  hypertension and diabetes  Table 17In this large population-based prospective study, we found that having multiple diseases significantly increased the chance of reporting a poor global SRH and a worse age-comparative SRH, although the magnitude of different diseases varied substantially. We also found cardiometabolic diseases  had the strongest impact on both global SRH and age-comparative SRH at the population level.Overall, presence of cardiovascular disease  had the biggest impact on global SRH. Other diseases that have a great impact on global SRH including respiratory disease (emphysema/bronchitis and asthma), mental disorders and cancer . Consistent with our findings, Haseli-Mashhadi et al.  found thIn general, the associations between diseases and age-comparative SRH were stronger than the associations with global SRH. Consistently, cardiovascular disease, cancer and mental diseases had the major impact on reporting worse age-comparative SRH. Our findings are consistent with some other studies which found that chronic diseases substantially affected people’s perception of their health status compared to their peers of similar age. In our study, stroke was the most important factor affecting age-comparative SRH among total population, rural residence and male participants. A study in Sweden also found that stroke had the biggest impact on reporting worse age-comparative SRH , while aChronic disease like CHD, stroke, rheumatic heart disease, cancer, and emphysema or bronchitis have a great influence on people’s functional ability, which in turn is significantly associated with both global and age-comparative SRH , 15, 17.In our population of middle-aged and elderly men and women, hypertension had the strongest impact on both global SRH and age-comparative SRH at the population level. The contributions of diabetes, CHD and stroke to the burden of poor global SRH and worse age- comparative SRH were also large due to either high prevalence rates or large effect size. However, it should be noted that the population level contribution of different diseases may vary in populations with different prevalence rates.We found that people with diseases and still on treatment generally reported worse SRH compared to those with a disease but not receiving treatment, which might be because people still on treatment had worse disease status or had more serious conditions. In our study, longer duration was associated with much worse SRH for diabetes and hypertension, and this may be because longer duration of diabetes and hypertension was associated with higher risks of complications, cardiovascular and cerebrovascular diseases –35. In aThe major strength of our study is that we have covered a wide range of diseases and the analysis was based on a large population sample of both men and women in ten regions of China. We also included both global and age-comparative SRH measures in our study, and the large sample size allowed us to conduct stratified analyses with sufficient power. Many of the previous studies have only studied the contribution of diseases on global SRH at individual level, whereas we studied the contribution of diseases on both global and age-comparative SRH at both the individual and population level. However, the study has several limitations that should be noted. The main limitation is the cross-sectional design of the study, which does not allow for investigating the temporal associations between various diseases and SRH. Another limitation is that most of the studied diseases were self-reported which may be subject to misclassifications. The sensitivity analysis of using self-reported hypertension and diabetes vs. the main analysis of using both self-reported and screening-detected cases indicated that this type of misclassification might overestimate the association. This is probably because people with self-reported physician-diagnosed diseases were more likely to be severe cases, and thus the association would be attenuated if we included both self-reported and undiagnosed diseases in the analysis. However, the magnitude of attenuation may depend on the proportion of undiagnosed cases. For hypertension and diabetes, the proportion of undiagnosed cases was not negligible, in other words, the awareness of the disease was not very high in the Chinese populations , 38. WhiWe found chronic physical diseases  and mental illnesses are major determinants of poor global SRH and worse age-comparative SRH. Cardiometabolic diseases  had the strongest impact on both global SRH and age-comparative SRH at the population level. Our study suggests that prevention measures concentrated on these conditions would have the largest effect on the total burden of poor SRH and its consequences such as poor quality of life and use of health care services. Knowledge of diseases that have a great impact on both global SRH and age-comparative SRH might help in exploring the factors behind low SRH status. Knowing the patient’s SRH status and the common determinants of SRH could help health professionals in the health care sectors to focus on the patient’s own goals for health and function.