Otherwise, ARG concentrations were variable with no differences between baseflow and wet weather conditions. The majority of ARG at the CSO outfall were observed on the attached fraction of samples: 64–79% of sul1 and 59–88% of tet(G). However, the timing of peak ARG and human fecal indicator marker gene BacHum did not necessarily coincide with observation of the microbial signature of wastewater in CSO effluent. Therefore, unit processes that remove settleable particles  operated throughout a CSO event would achieve up to (0.5–0.9)-log removal of ARG and fecal indicators by removing the attached fraction of measured genes. Secondary treatment would be required if greater removal of these targets is needed.Combined sewer overflows (CSOs) degrade water quality through the release of microbial contaminants in CSO effluent. Improved understanding of the partitioning of microbial contaminants onto settleable particles can provide insight into their fate in end-of-pipe treatment systems or following release during CSO events. Sampling was performed across the hydrograph for three storm events as well as during baseflow and wet weather in three surface waters impacted by CSO. qPCR was performed for select antibiotic resistance genes (ARG) and a marker gene for human fecal indicator organisms (BacHum) in samples processed the partitioning of microbial contaminants on settleable particles versus suspended in the aqueous phase. Amplicon sequencing was performed on both fractions of storm samples to further define the timing and partitioning of microbial contaminants released during CSO events. Samples collected at the CSO outfall exhibited microbial community signatures of wastewater at select time points early or late in the storm events. CSOs were found to be a source of ARG. In surrounding surface waters,  High leThe aim of this work is to expand our understanding of the intra- and inter-storm variability in the partitioning of a broader suite of microbial contaminants onto settleable particles during CSO events. To achieve this goal, water samples were collected across three CSO events and settleable and suspended bacteria were separated using previously validated methods . AnalysiFigure 1 and Supplementary Figure CSO effluent was collected during three storm events in the surface water outside of a CSO outfall in northern NJ (g for 10 min at 4°C. The supernatant (top 70% volume fraction) was collected by pipette and will be referred to as “suspended.” The suspended fraction was previously determined to contain 80% of non-settleable particles in stormwater .To separate settleable and suspended microbes, methods previously validated by ormwater . The remFigure 1). The mean daily temperature for baseflow and wet weather sampling events was ± 2°C. Nearby rainfall gauges reported cumulative rainfall of 1.8 cm for wet weather sampling in the Passaic River and Raritan Bay and 4.2 cm for the wet weather sampling on the Hudson River, all of which were sufficient to cause CSO. The Passaic and Hudson Rivers are tidally influenced at the sampling locations. Both the baseflow and wet weather sampling occurred during low tide in the Passaic River. The Hudson River baseflow sampling occurred during high tide, while the wet weather sampling occurred during low tide. The Raritan Bay baseflow sampling occurred during low tide and wet weather sampling occurred during high tide. Aqueous samples (∼500 to 900 mL) were concentrated on 0.22 μm nitrocellulose filters  prior to DNA extraction. Sediment samples were homogenized prior to DNA extraction, and analyzed as described in Section “Biomolecular Analyses.”To compare the concentration of ARG in CSO effluent to that in local waterways under different flow and tidal conditions, water (1 L) and composited bed sediment (∼50 mL) samples were collected in triplicate during baseflow  and during or following wet weather ], BacHum as a human fecal indicator (sul1 and sul2 (tet(G) and tet(O) , cell pellets (0.5 mL of cell pellet from wastewater samples), or sediment (∼0.5 g) using a commercial kit  following the manufacturer’s directions. To determine the concentration of ARG across the precipitation event, qPCR was performed on Storms I-III samples for select ARG  and 16Sd tet(O) . A standd tet(O) .t-test  or a Wilcoxon rank sum test was applied to test for differences in attached and suspended log-normalized ARG or 16S rRNA gene copy numbers intra-storm. A Kruskal–Wallis rank sum test followed by a post hoc pairwise t-test was performed on log-normalized total gene copy numbers to test for inter-storm differences. These statistical tests were performed in R  operational taxonomic unit data at the class level followed by cluster analysis with a SIMPROF test and non-metric multidimensional scaling (nMDS) in PRIMER 7. Rarefaction was performed in mothur. To determine which taxa were preferentially associated with a given sample type  biomarker analysis was performed on class-level relative abundance data for samples excluding the storm samples that formed significant clusters with wastewater. The linear discriminant analysis effect size (LEfSe) tool  tool  was usedsul1 gene copies accounted for 64% ± 6% of the total sul1 gene copies for Storm I , and 71% ± 18% of the total sul1 gene copies for Storm III . Across all storms, attached sul1 gene copies were greater than suspended sul1 gene copy concentrations (p = 1.1 × 10-5). For tet(G), attached gene copy concentrations were greater than suspended for the three storms (p = 0.03) with the attached fraction accounting for 88% ± 9% of total tet(G) gene copies during Storm I, 77% ± 13% of total tet(G) gene copies for Storm II, and 59% ± 52% of total tet(G) gene copies for Storm III . tet(G) was not detected in all Storm III samples or in both phases when it was detected. Normalizing tet(G) concentrations to 16S rRNA gene copy numbers resulted in no difference in the partitioning between attached and suspended phases across the storms (p = 0.15).Sampling during three storm events was performed to determine concentration of ARG in CSO effluent, the proportion of attached compared to suspended ARG, and the intra- and inter-storm variability in this partitioning. Attached sul1 and tet(G) concentrations observed during the three storm events were within the range observed during the baseflow and wet weather sampling in the Passaic River, Hudson River, and Raritan Bay . Significantly higher 16S rRNA normalized sul1 gene copy concentrations were observed in wet weather in water column from Passaic  and Hudson Rivers , but not Raritan Bay (p = 1.00). Significant differences were not observed in the water column for tet(G), tet(O) or sul2 between baseflow and wet weather in water or sediment at any site  16S rRNA gene copy normalized Figure 6). Intra-storm variability of BacHum was evidenced by observation of BacHum in only the suspended fraction early in Storm III then only in the settleable fraction later in the storm. Inter-storm variability in partitioning was observed for 16S rRNA gene copies where the attached portion accounted for 56% ± 0% of total 16S rRNA gene copies for Storm I, 57% ± 21% of total for Storm II, and 70% ± 9% of total for Storm III . Attached 16S rRNA gene copies were greater than suspended 16S rRNA gene copies (p = 0.014) for Storm III.Attached human fecal indicator marker gene BacHum accounted for 46 ± 8% of total BacHum gene copies in Storm I and 61% ± 29% in Storm II  peaked mid-storm event. For Storm III, sul1 concentrations increased across the storm event while the highest BacHum concentrations were observed in the middle of the storm. Inter-storm variability was observed with sul1 gene copy numbers significantly lower in Storm III than Storms I and II (both p < 0.03). No significant differences were observed between Storms I and II for sul1 gene copy numbers (p = 1). Differences between tet(G) and BacHum copies were not observed between the storms (p = 0.2–1.0).Intra- and inter-storm variability was observed in the concentration of ARG and fecal indicator marker genes. During Storm I, ARG and indicator gene concentrations peaked in the sample collected at the end of the storm. During Storm II, concentrations of Figures 3C,D and Supplementary Figures Figure 7). Four storm samples had 89.4% similarity , several waterborne pathogens , and many other bacteria that fit neither of these classifications. By finding the product of the relative abundance of Enterobacteriales and the concentration of 16S rRNA gene copies, one may estimate the concentration of Enterobacteriales in a sample . This observation was not preserved for Storm II samples with community structures similar to wastewater: the attached and suspended Enterobacteriales were similar to other storm samples .Of particular interest is the timing of the release of bacterial groups that contain indicator organisms and pathogens. The order Enterobacteriales contains fecal indicators  gene copies. The high percentages of settleable particle associated ARG and fecal indicator marker genes are likely due to ARG and fecal marker genes present in biofilm and cells that preferentially grow attached to particles. When tet(G) concentrations were normalized to 16S rRNA gene copies, no significant differences were observed between attached and suspended sul1 or tet(G) concentrations indicating the higher total bacterial densities in the attached fraction could explain the higher ARG tet(G) concentrations observed on the attached phase. In contrast, attached sul1 gene copies were greater than suspended concentrations with and without normalization to total bacterial population, indicating that the greater microbial density on the attached phase was not enough to explain why more sul1 genes were associated with settleable particles. Here, the greater association of sul1 with settleable particles may be due in part to sorption of extracellular DNA, which has been demonstrated to sorb to organic and inorganic clays, soil particles, sand  concentrations was variable, indicating that treating more than the first and final flush may be desirable. Given that the timing of peak ARG concentrations did not necessarily coincide with the observation of microbial community signatures indicative of wastewater microbial communities, there are likely other sources of ARG in the storm events . The urban area investigated likely has runoff containing feces from domestic and wild animals that could contribute to the indicator organism and ARG loads in CSO effluent. Domestic animals are receiving increasing antibiotic prescriptions and may serve as a reservoir for antibiotic resistant bacteria (The timing of peak ARG concentrations is of interest for CSO interventions including end-of-pipe treatment. bacteria . There asul1 and <1–5% of the tet(G) observed in wastewater influent. Dilution of ARG may explain why the lowest sul1 concentrations were observed in Storm III, which had the greatest cumulative rainfall. Concentrations of wastewater micropollutants such as hormones were found to decrease with increasing flow because of such dilution  concentrations observed in the storm samples were within the range observed during baseflow and wet weather conditions in local water ways. During wet weather ARG concentrations in surface water would be lowered by dilution, an observation noted during historic flooding in Colorado , if raindilution .Microbial community structures with high similarity to wastewater were observed at select time points early or late in the storm event outside of the CSO outfall during wet weather. This observation is consistent with other studies that reported peak concentrations of indicator organisms of fecal pollution early and late in the hydrograph for CSO effluent, CSO-impacted surface waters , and stoE. coli and select waterborne pathogens such as Salmonella and Shigella. Enterobacteriales concentrations were relatively consistent across the storms except for during Storm III where their concentrations peaked at times when the wastewater signatures were observed. The weak correspondence with wastewater signatures is likely because there are many commensal organisms in the Enterobacteriales order. Given the limitations of the short reads generated by amplicon sequencing, sequences were not identified below this taxonomic level.The timing of peak BacHum observations did not coincide with the microbial signatures of wastewater. Partitioning of BacHum to settleable particles was variable across the storms. BacHum is a highly sensitive but moderately specific human fecal indicator gene exhibiting some cross amplification with feces from deer, dogs, geese, and gulls , which cFigure 7 and Supplementary Figure Understanding the differences between the attached and suspended microbial communities is of interest for understanding potential removal of groups containing pathogens and for understanding the impact on surface water microbial ecology. The attached and suspended microbial communities observed in CSO effluent clustered together without significant differences for 4 out of 14 samples collected across the three storms  concentrations. A microbial community structure with a wastewater signature was observed at select time points at the beginning or end of the storm but did not necessarily coincide with peak ARG and BacHum concentrations indicating that treatment of more than the first and final flush may be desirable. Results of this study indicate further treatment would be needed after separation of settleable particles for ARG given that ARG would remain in the suspended fraction of water during CSO events.This study highlights the presence, partitioning, and intra- and inter-storm variability of ARG, indicator marker genes, and microbial communities at a CSO outfall during wet weather. CSO were found to be a source of ARG. Generally, ARG were detected in higher concentrations in the attached compared to the suspended fraction of CSO effluent while BacHum was more variably associated with the different fractions. Higher total bacterial concentrations on the settleable particles could explain the difference between attached and suspended Conception, design, data analysis, and interpretation by AE, NF, and HDR. Data collection performed by AE and HDR. Article drafted by AE and NF.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


EGFR-mutant lung adenocarcinomas (LUAD) display diverse clinical trajectories and are characterized by rapid but short-lived responses to EGFR tyrosine kinase inhibitors (TKIs). Through sequencing of 79 spatially distinct regions from 16 early stage tumors, we show that despite low mutation burdens, EGFR-mutant Asian LUADs unexpectedly exhibit a complex genomic landscape with frequent and early whole-genome doubling, aneuploidy, and high clonal diversity. Multiple truncal alterations, including TP53 mutations and loss of CDKN2A and RB1, converge on cell cycle dysregulation, with late sector-specific high-amplitude amplifications and deletions that potentially beget drug resistant clones. We highlight the association between genomic architecture and clinical phenotypes, such as co-occurring truncal drivers and primary TKI resistance. Through comparative analysis with published smoking-related LUAD, we postulate that the high intra-tumor heterogeneity observed in Asian EGFR-mutant LUAD may be contributed by an early dominant driver, genomic instability, and low background mutation rates. EGFR mutant lung adenocarcinoma (LUAD) exhibit diverse clinical outcomes in response to targeted therapies. Here the authors show that these LUADs involve a complex genomic landscape with high intratumor heterogeneity, providing insights into the evolutionary trajectory of oncogene-driven LUAD and potential mediators of EGFR TKI resistance. Further, certain clinical phenotypes, such as never-smoker Asian EGFR-mutant LUAD are under-represented in these cohorts8. Activating mutations in the epidermal growth factor receptor (EGFR) are the most common therapeutically tractable driver mutation in lung adenocarcinomas (LUAD) with distinct ethnic differences, occurring at higher frequencies in Asians (40–60%) compared to Caucasians (7–10%)12. Due to the preponderance of never smokers, EGFR-mutant LUADs are often associated with low-mutation burdens. On the contrary, the copy number landscape has been shown to harbor considerable genomic complexity15, although the extent to which these observations are confounded by intra-tumor heterogeneity (ITH) is unclear.Although comprehensive genomic sequencing studies have identified recurrent somatic alterations in LUAD18, these methods lack sensitivity and specificity especially in tumors with high ITH, where regionally dominant clones may exist. More recently, multi-region sequencing studies on smoker dominated Caucasian non-small cell lung cancers (NSCLC) have revealed high-mutation burden and low ITH21—primarily attributed to inordinately long trunks  as a result of accruing multiple passenger and driver alterations from chronic tobacco exposure. On the other hand, late diversification was contributed by branch/private driver alterations  and increased APOBEC activity, representing evolutionary processes that are potentially amenable to therapeutic targeting19.While it is feasible to computationally resolve clonal composition in single-tissue samples23. In EGFR-mutant LUAD, selection pressures imposed by targeted therapies can either result in expansion of pre-existing rare TKI resistant subclones  or be acquired stochastically26. The rapid emergence of resistance to EGFR TKIs28 seems counter intuitive in the context of low-mutation burden, and the impact of intra-tumor heterogeneity on the extent of tumor shrinkage and eventual emergence of drug resistance is not well established.The extent of ITH, and the factors that drive cancer evolution, is of clinical interest as it has been inextricably linked to treatment failureEGFR-mutant LUAD through multi-region exome sequencing and SNP arrays on 16 tumors. Despite low-mutation burdens in these predominantly never-smoker oncogene-driven LUADs, we demonstrate an under-appreciated level of genomic complexity, both in terms of copy number landscape, as well as relatively high-proportional ITH due to early diversification in these tumors. Through integrative genomics, we show that early EGFR and TP53 mutations are often followed by genome doubling events, with ongoing genomic instability typified by a variegated copy number landscape and late high-amplitude amplifications and deletions. We further highlight how multiple co-occurring drivers may portend poor clinical outcomes, including primary EGFR TKI resistance. By comparing the clonal architecture of our EGFR-mutant LUAD with that of previously published smoking-related LUAD, we provide insights into determinants of ITH and suggest that the evolutionary trajectories of LUADs are shaped by cumulative effects of background mutation rates, strength and timing of driver mutations and ongoing genomic instability.Here we present the first comprehensive description of the genomic architecture of EGFR-mutation positive Asian LUAD cases (15 never smokers) to multi-sector whole-exome sequencing. A total of 79 sectors with 3–11 regions from each tumor were sequenced  in every case regardless of the mutation type , underscoring its role as an early tumor initiating driver event  were recurrently mutated across two patients , we found a median of 62.3% heterogeneity (range: 32.26–82.2%) in our largely never-smoker Asian EGFR-mutant LUADs, contrary to previous findings of low ITH in LUAD . However, 4 of 16 patients  demonstrated relatively higher contribution of APOBEC activity (signature-2) in late subclonal events  for 61 of the 79 tumor sectors across 15 patients Fig. , as well16. The inferred cancer cell fractions and mutant allele copy numbers suggest that EGFR and TP53 mutations occurred prior to WGD and local SCNA )  attained by EGFR-mutant LUAD was quite striking. In exploring potential reasons for the unexpectedly high-branch mutations in EGFR-mutant LUAD, we did not find enrichment for subclonal drivers  and has less co-drivers across tumors compared to other LUAD drivers, we developed a “driver dominance score”, which measures the number of co-occurring drivers for each defined driver gene per tumor. Drivers with higher dominance score will tend to have less co-drivers across cases (Methods section). Applying this procedure across 412 published LUADs4, EGFR ranked second, underscoring its role as a dominant  LUAD driver , consistent with its previously reported association with poor outcomes44. The remaining patient who relapsed (A114) had stage IIB disease and prognostically unfavorable clinical features, including lymphovascular invasion, involvement of hilar and intrapulmonary lymph nodes, and was the largest tumor in our series at 6.0 cm  although some patients had disproportionately more branch/private mutations, such as patient A121 who harbored a branch TP53 mutation, and A065 who carried four different LUAD driver mutations in four different sectors  or higher overall mutation burdens . Taken together, our data suggest that in EGFR-mutant LUAD, an early TP53 mutation may impact clinical outcomes through facilitating genomic instability and the acquisition of additional co-occurring driver events.Interestingly, patient A014, who in addition to a TP53 wt tumors, five harbored only the activating EGFR mutation as the single-truncal driver. One of these patients, A006 charted an indolent clinical course, having been radiologically diagnosed with ground glass opacities for 5 years prior to surgery. Here, the genomic landscape was distinctly “silent”, with the lowest GII score, no WGD, low-mutation burden and no LUAD co-drivers  high proportion of late branch and private mutations and (ii) large proportion of genome altered through a combination of early genome doubling events and low-copy gains and losses, followed by late sector-specific copy number changes. Our findings illustrate how timing of genomic events and mutation rates can influence the natural history and diverse clinical trajectories of EGFR-mutant LUAD. Founding mutations in EGFR and frequent early TP53 mutations coupled with other truncal alterations deregulating the cell cycle and evading cell death, facilitate tolerance of pervasive WGD and CIN. Despite a relatively high fraction of branch mutations in these treatment naïve tumors, we generally observed a low prevalence of subclonal drivers or putative resistance mutations , consistent with a neutral evolution model45.Through multi-region sequencing, we have, for the first time, characterized the clonal and subclonal genomic landscape of Asian EGFR provide high-response rates to the targeted EGFR TKIs, these responses are often short-lived28 unlike those to Imatinib in BCR-ABL1 fusion driven chronic myeloid leukemia (CML)47. Although the determinants of durability of response in CML remain poorly understood, current studies suggest that the burden of point mutations and SCNAs is moderate in CML compared to solid tumors48 which possibly contributes to the longer TKI responses of CML compared to LUAD patients47. As a result of ongoing genomic instability in EGFR-mutant LUAD, we observed late sector specific copy number amplifications in previously reported genes mediating TKI resistance such as ERBB2, MET, and HGF40, providing a potential substrate for developing a drug tolerant state. In addition, the subclonal nature of high-amplitude amplifications and deletions underscores the challenge in interpreting gene copy number thresholds e.g., MET and ERBB2, from single biopsies in NSCLC49. Importantly, we implicate the role of multiple co-truncal drivers in a patient exhibiting primary EGFR TKI resistance, with validation in a corresponding patient-derived cell line, and by supporting findings from public data sets. It is thus plausible that co-existing truncal drivers may be associated with a propensity for primary resistance, while minor clones with additional drivers or resistance-mediating alterations can emerge later after initial tumor response to EGFR TKIs. However, larger sample sizes with more functional validations will be needed to test this and to decouple the effects of different genomic features like TP53 mutations, driver burdens and GII.While the truncal activating mutations in EGFR-mutant LUAD with comparable branch/private mutation burden to smoker LUADs. Although our study does not allow delineation of all the intermediate steps and selective sweeps preceding the final clonal composition, it nevertheless illustrates the contrasting life histories and a distinct evolutionary trajectory of EGFR-mutant LUAD, compared to smoking-related LUADs20. In the smoking scenario, a tumor-initiating cell population acquires mutations at a high rate and hence accumulates a large number of passenger and relatively weak driver mutations, with low likelihood for early acquisition of dominant drivers like EGFR . 3–6 samples were pooled together and hybridized using the SeqCap EZ Human Exome Library v3.0  kit. Captured regions were washed, purified, amplified, and subjected to 2 × 101 sequencing on the Hiseq 2000 to obtain a mean coverage of 114X.EGFR were also added to the panel. Amplicons were generated according to manufacturer’s recommendation and libraries were prepared using the NEBnext Ultra DNA-seq kit (New England Biolabs). Libraries were then pooled and sequenced on Hiseq 2500 by 150 bp paired end reads to obtain a mean depth of 3860X.Primers were designed around the somatic variants (SNVs and Indels) annotated to be in the exonic and splice regions, using the Generead DNA-seq custom panel v2 (Qiagen). In addition, primers covering the T790M locus in 53 using default parameters. Duplicate reads were marked using Picard tools after which realignment around known indels and base quality recalibration was performed at an individual sample level using GATK 2.7 version54.Reads were mapped to the human reference sequence GRCh37 (hg19) using the bwa-aln algorithm55 allowing up to 5 reads supporting the variant allele in the normal sample up to a maximum of 0.05 allele frequency. The passed variants were further filtered using the described criteria to obtain a more confident set of somatic variants. A minimum of 6 reads supporting variant allele in the tumor was mandatory. To improve accuracy in low-frequency calls, variants with allele frequency (VAF) in tumor below 0.2 were treated as somatic only if the tumor allele frequency was at least 10 times greater than normal allele frequency for the variant allele.Somatic mutation calling was performed using MuTectTo eliminate false positive variant calls due to polymerase chain reaction (PCR)-chimeras formed in exome-seq protocols, germline variants were called from the normal sample of all patients as described below and were filtered out from the somatic variant list.56 and were further filtered for >4 reads (MAPQ >20) supporting the indel with a minimum allele frequency of 0.1.Somatic indels were called using Strelka57.The final list of somatic SNVs and indels was then annotated by multiple databases using the Annovar tool54. Variants were then subjected to hard filtering using GATK recommendations to obtain a more confident set of SNVs and indels.Germline variants for each patient were called from the normal sample using GATK Unified Genotyper after indel realignment and recalibration of bam filesFastq files were mapped to the human reference sequence GRCh37 (hg19) using bwa-mem algorithm with default parameters. The bam files were realigned and recalibrated using GATK. Since the amplicon size is around 150–200 bp, the reads obtained were overlapping. For each base at the overlapping region, if the pair bases were identical, quality score for both bases were updated to original quality score ×1.2; else if the pair bases were not identical, the base with the lower quality score was replaced by the base with the higher quality score, and both quality scores were updated to original higher quality scores ×0.8.58. Default parameters were used except the minimum variant frequency was set to 0.01. Only those variants also called in the exome-seq were considered. Variants with <10 reads supporting the alternate allele were filtered out and variants with allele frequency below 0.05 were mandated to have a minimum alternate read count of 15. Further, mutation calls were required to have a VAF five times higher in the tumor compared to the normal.Somatic SNVs were then called using VarScan v2.3.7For somatic indels, read counts supporting the indel identified by exome-seq for that patient were obtained from both the tumor and normal bam files using a custom script. Reads properly paired and mapped with MAPQ >20 were counted. Somatic indels with >90% read counts from one strand were removed from further analyses. Further only those indels were treated as validated which had at least 10 reads supporting the alternate allele at a frequency five times greater in tumor than in normal with a minimum VAF of 0.03 in the tumor.A validation rate of 94% for indels and 85% for SNVs was achieved for the exome-seq data. Only validated variants were considered for generation of phylogenetic trees and any downstream analysis.59 was used to infer phylogenetic relationships between tumor sectors for each patient.Using the presence and absence of somatic mutations across samples, we first calculated the genetic distances between samples using the hamming distance. The neighbor joining algorithm from the APE package20 were retrieved from European Genome-phenome Archive (EGA). The corresponding EGA data set-IDs are: EGAS00001000930 and EGAD00001000900. In order to directly compare the pattern with our data set, we selected only those patients, where the tumor was restricted to a single site and had pure adenocarcinoma histology. The downloaded targeted deep sequencing data were processed using the same pipeline as our Singapore cohort data. Since the fraction of trunk mutations in the phylogenetic trees is a function of the number of sectors, we calibrated the trunk ratio by performing random subsampling of sectors for each patient. The average proportion of the trunk was compared across cases conditioning on the same number of sectors.The published LUAD data sets4. With the information from the point mutation and the flanking 5′ and 3′ bases, the Emu package60 was used to infer the mutation signatures in the 79 tumor sectors.In order to uncover mutational processes active within the EGFR mutant LUAD patients, we combined somatic mutations from the Singapore cohort with two published large-scale data setsn = 78) through significantly mutated genes in LUAD collected from seven publications63. Mutations in these genes are shown to occur more than just by chance or due to the size of gene. Other cancer driver genes include additional significantly mutated genes in at least one cancer type from two pan cancer studies63 and remaining genes in cancer gene census64 (n = 735). Non-silent mutations in these two lists of driver genes were annotated on the trees. For comparison across data sets, either LUAD genes or combined set of both groups of drivers (extended driver list) was used as indicated in main text or figure legends.We defined LUAD driver genes  were eliminated from further analysis. 61 out of 79 samples from 15 patients remained after these filtering. For 5 of these 61 samples , ASCAT solution was manually picked using second or third most optimal purity/ploidy solution since either the raw data suggested these sectors to be similar to other sectors from same patient or variant allele frequencies suggested alternate purity solution. While presence of multiple clones within a sector might lead to such alternate solutions in some scenarios65, we chose to be conservative in absence of a gold standard and removed any potentially artificial heterogeneity. Copy number losses or gains were determined relative to the median integer ploidy of the tumor which was obtained as the median integer copy number of the SNPs used in ASCAT analysis.Illumina omniexpress arrays were run using DNA from all 79 tumor sectors along with matched normal tissue using protocols suggested by the manufacturer. Log ratio (LRR) and B allele frequency (BAF) for all SNPs on the array were obtained from Genome Studio. These LRR and BAF values were used as input for ASCAT41 were curated and only those regions were considered which were altered in same direction in at least two studies.Genomic Instability Index (GII) was calculated as the fraction of the total genome which was altered by any copy number gains or losses with copy change ≥1 defined relative to median integer ploidy. adGII (amplification and deletion based genomic instability index) scores were calculated as fraction of genome affected by high-copy gains and losses (or amplification and deletions with copy change ≥2 relative to ploidy). To obtain cytoband or gene level copy numbers, chromosomal locations of cytobands and genes were overlapped with those of the segments and the segment copy number was assigned to that cytoband or gene. In case multiple segments overlapped with the cytoband/gene, a minimum of 25% overlap was made mandatory and the segment with the highest overlap was used to assign the copy number to the cytoband/gene. Known driver regions in LUAD from previous large scale studies38. In brief, a p-value was obtained using 10,000 simulations with observed probabilities of copy number events. For samples with ploidy ≤3, a p-value threshold of 0.001 was used. To avoid underestimating genome doubling in high-ploidy samples, a p-value threshold of 0.05 was used for samples with ploidy = 4, and all samples were classified as genome doubled if the ploidy exceeds 4.The genome doubling status for each tumor sample was determined using a published algorithm16, where the corresponding integer copy number and tumor purity were derived using SNP-array and ASCAT algorithm. A given mutation was classified as “clonal” if the 95% confidence interval of CCF overlapped 1, and “subclonal” otherwise. The timing of a given mutation relative to copy number alteration was classified on its clonal status and the integer rounded mutant allele copy number. Indels, regions with no copy number alteration (major copy number = minor copy number = 1), regions with just single copy were eliminated as these could not be evaluated for timing. Mutations were called early only when (i) the mutation is clonal, and (ii) the rounded integer mutant allele copy number ≥2.The cancer cell fraction (CCF) and mutant allele copy number for a given SNV was calculated following the algorithm described previouslyn = 69) and ccRCC drivers (n = 16) which were found mutated in ≥5 patients in the TCGA data. This was based on the logic that, for each cancer patient, the number of driver mutations found in each case implies the self-sufficiency of the drivers. Mutations which possess high capability to drive tumorigenesis would exist with few co-occurring driver mutations since these will be the sufficient to initiate a tumor. Using the combined public data sets4, we computed a measure for driver dominance of each gene asDi is the dominance score for gene i, Ni is the number of patients carrying non-silent mutations in gene i while dj is the number of LUAD driver mutations in patient j.The dominance of a driver (driver self-sufficiency) was calculated for all known LUAD drivers (Di) we define is inversely proportional to the number of co-occurring driver mutations across samples.Therefore, the genetic dominance  (42.ccRCC drivers were picked from the TCGA publication as significantly mutated genes24. Using targeted sequencing we had confirmed that mutation found in the cell lines matched with the primary patient tumor (Supplementary Data 471L cells were derived from second generation of xenograft from A014 tumor (cells from first xenograft were transplanted into a second mouse). The tumor was collected, digested with 1 mg/ml Collagenase IV  and cultured on 100 mm plastic dish with culture conditions similar to those described earlier for patient-derived cell linesPC9-GefR cell line was generated by exposing PC9 cells  to stepwise increment doses of Gefitinib (0.1–6.4 µM). Both isogenic cells were maintained in RPMI (Sigma-Aldrich), supplemented with 10% FBS , 100 units/ml of Penicillin, 100 µg/ml of Streptomycin, and 0.25 µg/ml of Amphotericin B .All cell lines were cultured in clean, well established cell culture labs with no sort of mycoplasma or other contamination.471L, PC9 and PC9-GefR cells were seeded into 96-well plates and were treated with varying concentration of drugs the next day (0.0001–20 µM). CellTiter-Glo Reagent (Promega) was added directly to the cells per manufacturer’s instructions after a 72 h treatment period. Luminescence was measured to determine the amount of viable cells. Percentage cell viability was calculated relative to 0.2% DMSO vehicle control. All cell viability assays were performed in triplicates. Dose response curves were generated using GraphPad Prism version 7.http://www.ebi.ac.uk/ega/), which is hosted by the EBI, under the accession code EGAS00001001736.All the sequencing and SNP array data have been deposited at the European Genome-phenome Archive (EGA, Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8


Leukemia Inhibitory Factor (LIF)/Signal transducer and activator of transcription 3 (STAT3) signaling pathway maintains the stemness and pluripotency of mouse embryonic stem cells (mESCs). Detailed knowledge on key intermediates in this pathway as well as any parallel pathways is largely missing. We initiated our study by investigating the effect of small molecule Curcumin on various signalling pathways essential for self-renewal. Curcumin sustained the LIF independent self-renewal of mESCs and induced pluripotent stem cells (miPSCs) in a STAT3 activity dependent manner. Gene expression analysis showed LIF/STAT3 and redox signaling components to be majorly modulated. Amongst ROS genes, expression of Manganese Superoxide Dismutase (MnSOD) specifically relied on STAT3 signaling as evidenced by STAT3 inhibition and reporter assay. The silencing of MnSOD, but not Cu-ZnSOD expression, resulted in the loss of mESC pluripotency in presence of LIF, and the overexpression of MnSOD is sufficient for maintaining the expression of pluripotent genes in the absence of STAT3 signaling. Finally, we demonstrate MnSOD to stabilize the turnover of pluripotent proteins at the post-translational level by modulating proteasomal activity. In conclusion, our findings unravel a novel role of STAT3 mediated MnSOD in the self-renewal of mESCs. Curcumiression) . Expresspromoter . These rp21cip1, p27kip1 and p53 in without LIF and Curcumin treated cells as compared to that of cells treated with LIF  3 and Shp (Protein phosphatase) 2, the proteins known to negatively regulate the JAK/STAT pathway. With increasing concentration of curcumin we found significant decrease in the expression of Socs3 denoting the probable mechanism underlying curcumin mediated maintenance of pSTAT3 level of mESCs . To spece of LIF . Down ree of LIF  and loste of LIF . Colocale of LIF . In addie of LIF . This suTo conclusively demonstrate the role of MnSOD in the pluripotency of mESCs, we overexpressed MnSOD in mESCs cultured in presence or absence of LIF and further analyzed the pluripotent gene expression. As consistent with previous results, cells cultured in the absence of LIF and feeders showed decreased expression of pluripotent genes as compared to cells exposed to LIF. Surprisingly, cells overexpressing MnSOD and cultured in absence of LIF maintained higher expression of pluripotent genes at the transcript and protein level in contrast to cells transfected with vector control . We alsoTo identify the downstream mechanism of how MnSOD maintains the expression of pluripotent genes, we sought to analyze the stability of pluripotent proteins. We followed the degradation of pluripotent proteins OCT4 and NANOG during the blocking of nascent protein synthesis by cycloheximide. Post Cycloheximide treatment for 9 hrs. pluripotent proteins appeared stable in mESCs exposed to LIF compared to without LIF condition. Overexpression of MnSOD in LIF deprived condition rescued the stability of OCT4 protein similar to mESCs exposed to LIF . MajoritElucidation of different signaling events involved in the self-renewal of ESCs is important for understanding their physiology and optimizing culture conditions. A significant amount of research has been conducted on identifying the “switches” that dictate stem cells either to differentiate or to maintain pluripotency and self-renewal. Although, several studies have highlighted the dynamic changes in the ROS and antioxidant enzymes during the self-renewal and differentiation of ESCs, seclusion of prime antioxidant enzyme that plays the crucial role in the self-renewal of mESCs had not been achieved. In the present study we identified MnSOD as a critical LIF/STAT3 downstream target that mediates LIF/STAT3 self-renewal of mESCs . We alsoApart from LIF/STAT3 pathway, enormous effort has been put forth to unravel the various other conduits that could be involved in pluripotency of mESCs resulting in a LIF independent growth of mESCs. Small molecules like GSK-3 inhibitor CHIR99021 and MEK (MAPK/ERK kinases) inhibitors SU5402 and PDI184352 have provided great opportunity to gain new insights into alternative pathways that facilitates ES cell self-renewal282931Socs3, but not Shp2 and conveying the probable mechanism underlying the maintenance of pSTAT3 in absence of LIF. Conflicting reports exist in different cell types regarding Curcumin's role in modulating the SOCS3 expression. In hematopoietic progenitors obtained from myeloproliferative neoplasm patients, Curcumin elevated the transcript levels of Socs1 and Socs3 by inhibiting the histone deacetylase activity39Screening for different signaling pathways known to play significant role in ESC pluripotency showed LIF/STAT3 and oxidative pathways to be majorly modulated by Curcumin. Further analysis demonstrated Curcumin to mediate its effect in a STAT3 signaling dependent manner. Previous reports had mentioned Curcumin, in a dose dependent manner to suppress STAT3 phosphorylation and concomitant decrease in proliferative capacity in cancer derived cellsSod2-/-) mice develop several pathologies and neonatal lethality Our experimental results using JAK inhibitor I showed MnSOD, among different antioxidant genes analyzed, to be the downstream target of STAT3 involved in the sustenance of self-renewal of mESCs. MnSOD is the main antioxidant enzyme that scavenges ROS  in the inner mitochondrial matrixTo understand the intriguing puzzle of how MnSOD maintains the expression of pluripotent genes in the absence of LIF, we looked at the post translational stability of these proteins. Cycloheximide experiments demonstrated MnSOD to enhance the stability of the pluripotent proteins. Also, overexpression and repression studies clearly demonstrated MnSOD to supress proteasomal activity. These results signify a strong correlation between the stability of pluripotent proteins, proteasomal activity and MnSOD protein level. Previous reports have suggested that the homeostasis of pluripotent proteins including OCT4, NANOG and c-MYC to be regulated by ubiquitination48Recently various necessitating transcription factors for pluripotency that are downstream target of STAT3 have been reported115 cells were plated onto gelatin coated 35 mm dishes and were induced with LIF (Millipore), No LIF and 5 µM Curcumin (Acros Organics) and 24 hrs post induction, the cells were treated with 2 µM Insolution JAK inhibitor I  or 20 µM 2-Methoxyestradiol (Sigma Aldrich) respectively. 24 hrs post JAK inhibitor treatment, the cells were analyzed for gene and protein expressions while the 2-Methoxyestradiol treated cells were harvested 48 hrs post treatment. For details on clonal analysis, embryoid body differentiation, alkaline phosphatase assay, Teratoma formation, cumulative population doubling and BrDU based cell proliferation, see mESCs(R1) and Oct4- GFP tagged miPSCs  were cultured on mitotically inactivated MEF feeders, in a suitable medium -DMEM-High Glucose (Gibco) containing 15% FBS (HiMedia), 2 mM Glutamine, 1X NEAA, 1% Penicillin and 1X Streptomycin (All from Gibco) along with 8 µM LIF (Chemicon). For inhibition of STAT3 or MnSOD, 10The Open Reading Frame (ORF) of MnSOD was PCR amplified from mESC cDNA using gene specific primers and cloned in BamHI and XhoI sites of pCDNA3.1 (+) (for transient transfection) and pMIG vector  and positive clones were confirmed by sequencing (Amnion Biosciences). To clone MnSOD promoter region, 1779 bp upstream of MnSOD start siteFor transient transfection, 5 µg of plasmid was transfected into confluent mESCs using XtremeGENE HP transfection reagent (Roche Diagnostics) and 24 hrs post transfection, the transfected cells were subjected to different conditions  and were analysed for their gene and protein expression pattern. MnSOD overexpression was also performed by retroviral transduction using pMIG MnSOD and pMIG-GFP vector control. For knockdown of MnSOD and Cu-Zn SOD, the lentiviral vectors pLKO-MnSOD shRNA and pLKO-Cu-ZnSOD shRNA (Sigma Aldrich) along with the scrambled control were transduced in mESCs. For retroviral and lentiviral production and details on transduction see RNA isolation was performed using RNeasy micro kit (Qiagen) as per manufacturer's instruction. For additional details on PCR, see supporting information Experimental Procedures. Primer sequences are described in For immunofluorescence, cells were fixed in 4% paraformaldehyde, permeabilized and incubated with primary antibody at 4°C overnight, followed by incubation for 2 hrs at RT with Secondary antibody and incubated for 2 hrs at RT and observed under fluorescent microscope (Nikon Instruments Inc.). The primary and secondary antibodies and their dilutions used are described in For western blot, proteins were fractionated on 8–12% SDS-PAGE gels and transferred onto charged PVDF membrane (Millipore). Post transfer, the membrane was blocked using 3% Skimmed milk/BSA in 0.1% TBST solution and probed with primary antibody overnight at 4°C and subsequently with horseradish-peroxidase (HRP)-conjugated secondary antibody for 1 hr at RT. The primary antibodies, secondary antibodies and their dilutions are described in Enumeration of number of GFP positive cells or measuring SSEA1 postive cells stained with primary Anti-SSEA1 antibody  and PE conjugated secondary antibody  was performed using flow cytometer . For Propidium iodide staining, cells were stained with Propidium Iodide (Sigma Aldrich) and cell cycle was analysed by flow cytometry. For additional details, see Cells were transfected with 950 ng of pGL3-MnSOD-luciferase and 50 ng of TK Renilla, along with vector control, using XtremeGENE HP transfection reagent. 24 hrs post transfection, the medium was replaced with fresh mESC medium and induced with different concentrations of JAK inhibitor I. 24 hrs post inhibitor treatment, the cells were harvested and luciferase activity was performed using dual luciferase reporter assay system (Promega Corporation) as per manufacturer's instructions.To look at the direct interaction, 293T cells cultured in 24 well plates were transfected with 500 ng of pMIG-SOD2, 50 ng of TK Renilla and different concentrations of pMX-STAT3 (Addgene) along with vector control. 24 hrs post transfection, the cells were harvested and luciferase activity was performed using the dual luciferase activity kit (Promega) as per manufacturer's instructions.Cells were harvested 48 hr post treatment of different concentration of 2-methoxyestradiol and equal concentrations of protein were used for SOD activity analysis, using SOD activity kit as per manufacturer's instructions (Biovision).The cytosolic extract of MnSOD plasmid transfected and MnSOD ShRNA transduced mESCs were collected using lysis bufferStudent's t-test was applied at appropriate places. The values with p ≤ 0.05 were considered statistically significant.Supplementary Information


Intercellular communication is essential to coordinate the behaviour of individual cells during organismal development. The preimplantation mammalian embryo is a paradigm of tissue self-organization and regulative development; however, the cellular basis of these regulative abilities has not been established. Here we use a quantitative image analysis pipeline to undertake a high-resolution, single-cell level analysis of lineage specification in the inner cell mass (ICM) of the mouse blastocyst. We show that a consistent ratio of epiblast and primitive endoderm lineages is achieved through incremental allocation of cells from a common progenitor pool, and that the lineage composition of the ICM is conserved regardless of its size. Furthermore, timed modulation of the FGF-MAPK pathway shows that individual progenitors commit to either fate asynchronously during blastocyst development. These data indicate that such incremental lineage allocation provides the basis for a tissue size control mechanism that ensures the generation of lineages of appropriate size. Early embryonic cell fate and lineage specification is tightly regulated in the preimplantation mammalian embryo. Here, the authors quantitatively examine the ratio of epiblast to primitive endoderm lineages in the blastocyst and show composition of the inner cell mass is conserved, independent of its size. Coordinated cell behaviour is an essential characteristic of multicellular organisms. During embryonic development, cellular proliferation, death and differentiation must be precisely coordinated, to generate an organism of the appropriate size and cellular composition. Embryos of different animal taxa display a range of regulative abilities that allow them to produce consistent, reproducible structures, even when faced with changes in cell number or morphological alterationsin vivo and to embryonic stem (ES) cells in vitro2345The preimplantation mammalian embryo is a paradigm of regulative development and self-organization. During preimplantation development, the fertilized egg gives rise to the blastocyst—the embryonic structure capable of implanting into the uterus—without the need for maternal input. The blastocyst stage is highly conserved across mammals and comprises two extraembryonic epithelia, trophectoderm (TE) and primitive endoderm , both of which encapsulate the embryonic lineage: the pluripotent epiblast (EPI). The EPI gives rise to most somatic cell types Cell differentiation during preimplantation development is largely dictated by intercellular interactions and spatial cues, rather than pre-determined, and consequently the embryo is capable of accommodating changes in cell number and other experimental manipulations, while developing to term7891011Fgf4 (fibroblast growth factor-4) is the first gene to be differentially expressed within the ICM241516172426282921242627282930313233Lineage choice in the ICM is partly determined by lineage-specific transcription factors. ICM cells in the early blastocyst E3.0–3.25) co-express marker genes for both PrE and EPI, such as the zinc-finger GATA-binding family member GATA6 and the homeodomain transcription factor NANOG, respectively15161819202122 co-expreThe key elements driving the specification of PrE and EPI within the ICM  and the sequential phases of gene expression  have been established. However, studies addressing the functional significance of these two phases and how they affect the regulative nature of the blastocyst have yielded somewhat contradictory results. Lineage tracing and chimera experiments have argued that EPI cells exhibit restricted developmental potential from very early stages of blastocyst development (∼E3.25–E3.5)26in vivo once specifiedIn the present study, we have probed the plasticity of ICM cells at single-cell resolution and at defined stages of mouse blastocyst development, taking advantage of our recently developed single-cell image analysis pipeline223637The current model for lineage specification in the ICM of the mouse blastocyst involves three successive phases based on gene expression patterns and changes in cell position: coexpression of PrE and EPI markers is followed by mutually exclusive expression of markers in cells that are initially scattered in a salt-and-pepper distribution and which subsequently sort into two spatially coherent populations1315242639152223Z-stacks of confocal images spanning the entire embryo acquired , 64–90 cells (N=41), 90–120 cells (N=32), 120–150 cells (N=14) and >150 cells  coexisted within the ICM with PrE and EPI cells in blastocysts of up to 90–120 cells  and NANOG+ (EPI) cells throughout most of blastocyst development. As these DP cells are lost as PrE and EPI arise, we posit the DP compartment represents a transient, progenitor state for both lineages. In such a scenario, only the fate of these progenitors would be affected by changes in FGF4–RTK–MAPK pathway activity, whereas PrE or EPI cells might be insensitive to this signal.We next investigated whether the temporal depletion of ICM progenitors determined the outcome of modulating the FGF4–RTK–MAPK pathway. We subjected blastocysts to a series of treatment regimes  or EPI (MEKi) cells324346To establish each treatment regime, we used four of the developmental stages described above  and 2a: For all treatment regimes, control embryos displayed spatially segregated EPI and PrE populations, where NANOG and GATA6 were mostly mutually exclusive . We used22Notably, NANOG levels were affected by all treatment conditions, irrespective of the treatment regime: FGF4 treatment reduced NANOG levels in EPI cells—thus increasing the % of DN cells —whereas 32in vivo in the blastocystTaken together, our data indicate that FGF modulation can only alter the fate of ICM progenitors (DP cells) and not EPI or PrE cells. They suggest that single-positive cells (NANOG+ or GATA6+) may be committed and support previous findings showing that EPI cells, once specified, do not change fate To assess whether modulation of the FGF–RTK–MAPK pathway altered marker expression beyond GATA6 and NANOG, we treated mid to late blastocysts (80–100 cells) as described above and assessed the expression of GATA4 and OCT4, which are later PrE and ICM/EPI markers, respectively15164922bona fide PrE and EPI cells.Overall, these data further indicate that ICM cells that acquire PrE or EPI fate do not switch fate as a result of changes in FGF4–RTK–MAPK activity, as they maintain the marker expression pattern of in vitro culture-induced developmental delay; for instance, control embryos with <100 cells maintained a small proportion of progenitor (DP) cells  cells , whereasP) cells , as did P) cells . These dTo test this hypothesis, we experimentally manipulated the size of the ICM . Mouse e7810515253545152After 48 h in culture, all embryos developed into blastocysts  of corre52Having established that allocation to the EPI and PrE occurs incrementally, we next wanted to determine how this process is initiated. The current view posits that FGF4, secreted by EPI cells, stimulates the RTK–MAPK pathway to maintain GATA6 in neighbouring ICM cells, which acquire PrE fate172122232426293022232426296To experimentally test this model, we prevented specification of EPI cells by exposing 8-cell stage embryos to FGF4 for 30 h  become allocated to EPI or PrE in an asynchronous manner during blastocyst development  and 6a. 52Fgf4 mutants fail to maintain GATA6 after the early blastocyst stage, giving rise to an ICM with only NANOG+ cells2429Gata6-null mutant embryos lack the PrE lineage entirely, which cannot be rescued by exogenous FGF4 , based on previous observations that the relative ratio of GATA6 to NANOG affects the rate of PrE specificationThe incremental allocation of cells to each lineage creates a system with the inherent flexibility to respond to exogenous perturbations, providing robustness and a cellular basis to frame the regulative abilities of the preimplantation mammalian embryo. Allocation of cells from a common progenitor pool permits shifting the rate of specification towards PrE or EPI as a function of the population composition at any time . This wo5222Our data suggest that EPI cells induce PrE specification through the secretion of FGF4, and that PrE cells cannot induce EPI fate, which relies on active inhibition of ERK signalling by the MEK inhibitor . However152563Previous studies have explored changes in cell plasticity during blastocyst development, with disparate conclusions26333326Heterogeneity in ICM composition reflects the presence of three dynamic and functionally distinct subpopulations, and suggests a mechanism by which individual cell behaviours are coordinated to achieve a consistent outcome at the population level. Our results lead us to hypothesize the existence of a tissue size-control mechanism operating in the mammalian blastocyst, which allows for control of lineage composition in a self-organizing system and provides a cellular basis to the regulative abilities of the early mammalian embryo.All animal work was approved by Memorial Sloan Kettering Cancer Center's Institutional Animal Care and Use Committee. Animals were housed in a specific pathogen-free facility under a 12 h light cycle. All embryos used for this study were obtained from natural matings of outbred CD1 virgin females (Charles River) of 4–8 weeks of age, to CD1 studs. Embryos were flushed out of the oviducts (morula stage) or uterine horns (blastocyst stage) using 500 μl of flushing and holding medium 2 atmosphere. Before embryo culture, KSOM medium was buffered in the incubator for at least 15 min. Embryos were cultured in groups of five to ten embryos per drop. Zona pellucidae were removed from blastocysts on collection and before in vitro culture. Morula-stage embryos were cultured within zonae, which were only removed on cavitation (after 24–30 h).Morula- and blastocyst-stage embryos were cultured on 35 mm Petri dishes , within 10–15 μl microdrops of Potassium Simplex Optimized Medium  at 37 °C in a humidified 5% CO−1)−1 of Heparin (Sigma). For FGF4–RTK–MAPK pathway inhibition experiments, the MEKi inhibitor PD0325901 (Stemgent) was diluted in KSOM at 1 μM−1 of Heparin.For FGF4–RTK–MAPK pathway stimulation experiments, recombinant human FGF4  was diluted in culture medium (KSOM) at saturating concentration  were applied in several regimes, defined by the developmental stage of the embryo at the time of collection/start of the treatment period and the length of the culture period . The devTo ensure accurate staging of embryos and to reduce variability due to stage differences between litters of embryos, each modulation experiment was performed on a single litter. Individual litters were collected from single females, two to three embryos per litter were fixed on collection, as reference specimens to assess the developmental stage of the entire litter, and the rest of embryos distributed between control and either FGF4- or MEKi/FGFRi-supplemented medium for culture. All reference littermates are included in the data set shown . The treTo ensure that the differential response obtained for each regime was not due to the progressively shorter periods of exposure to FGF4 or inhibitors, we cultured embryos collected from each stage for longer periods. We observed no difference in the response to treatment as a result (not shown). Moreover, we observed that culture of embryos from the eight-cell stage for only 30 h was sufficient to alter gene expression  and thus32Eight-cell stage embryos were collected on embryonic day E2.5 from CD1 females as described above. To generate half embryos, embryos were denuded by brief incubation in acidic Tyrode's, placed back in FHM, and physically dissociated by forcing them through a finely drawn glass Pasteur pipette. Blastomeres were transferred to microdrops of buffered KSOM-AA and re-aggregated in groups of four cells. Dissociation can cause cell lysis, in which case dead blastomeres were replaced with blastomeres from another embryo. Control embryos for these experiments were made by re-aggregating eight blastomeres after dissociation. It should be noted that some re-aggregated, ‘single' control embryos were observed to have fewer cells than intact controls after 48 h of culture , which cTo generate double embryos, eight-cell stage embryos were denuded and placed in pairs in depressions on 35 mm Petri dishes, within KSOM-AA microdrops, the same way as half embryos . ControlBoth half and double embryos were fixed for 10 min in 4% PFA after 48 h in culture and processed as described below.−1 of Hoechst 33342 (Invitrogen) in PBS.Whole-mount immunofluorescence was performed as described in ref. Z-stacks of whole embryos were imaged using a Zeiss LSM510 laser-scanning confocal microscope equipped with a Zeiss EC Plan-Neofluar 40 × /1.3 numerical aperture objective with a 0.17 mm working distance. Z-sections were acquired every 1 μm. Semi-automated nuclear segmentation for cell counting and fluorescence intensity measurement was performed using the MATLAB-based algorithm MINShttp://katlab-tools.org (requires MATLAB license).To determine the total cell count of each embryo, The same imaging parameters were used across experiments, as discussedOver- and under-segmentation of nuclei by MINS was manually corrected as previously describedZ-axis, which is as an artefact of the imaging process on a confocal microscope. We modelled this decay for each individual channel and embryo by fitting a linear regression to the logarithm of fluorescence values as a function of the Z-value. However, as the slope of this model is based on a moderate number of cells, it is not stable; therefore, we used an empirical Bayes approachFluorescence intensity decays along the All treatments were repeated in at least two independent litters. Transfer from FGF4 to MEKi  was doneWe tested three approaches to classify cells into lineages. First, for exploratory purposes, all cells were manually assigned to either the TE or one of the ICM lineage denominations  when correcting for over- and under-segmentation. Cells were classified as TE based on their location on the periphery of the blastocyst, their morphology, the lack of expression of ICM markers and/or the expression of CDX2, whenever this information was available. ICM cells were visually classified as EPI when showing exclusive expression of NANOG, PrE when showing exclusive expression of GATA6, DP when showing expression of both NANOG and GATA6 (a ratio of GATA6:NANOG approximately between 0.5 and 2) and DN when showing expression of neither marker. However, we found this approach to be inconsistent and subject to investigator bias. We therefore applied a thresholding approach to freshly collected littermates as a reference sample . Using t15k-means clustering. From the centres for the EPI and PrE clusters, we generated the location of the cluster centre for a DN cluster. We then applied these centres to classify cells across all stages such that each cell is assigned to the class of the centre closest to it. Reassuringly, the identities determined using the k-means clustering approach essentially recapitulated those obtained using the thresholding approach for the reference littermates —when DP cells dominate the ICM—towards two isolated clusters of NANOG+ and GATA6+ cells at late blastocyst stages 120–150 cells)—when both EPI and PrE are completely segregated . We thertermates . We appl cells—whtermates , which wtermates . These v0 cells—wTo estimate the proportion of PrE, DP and EPI+DN cells as a function of the embryo size . Tables of the compiled data, experimental references and annotated scripts containing the code used for all data transformations, Z-correction and identity assignment, as well as for generating all plots in the figures, are available at How to cite this article: Saiz, N. et al. Asynchronous fate decisions by single cells collectively ensure consistent lineage composition in the mouse blastocyst. Nat. Commun.7, 13463 doi: 10.1038/ncomms13463 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures 1-7, Supplementary Table 1.


Photorhabdus bacteria.In search for new natural products, which may lead to the development of new drugs for all kind of applications, novel methods are needed. Here we describe the identification of electrophilic natural products in crude extracts via their reactivity against azide as a nucleophile followed by their subsequent enrichment using a cleavable azide-reactive resin (CARR). Using this approach, natural products carrying epoxides and α,β-unsaturated enones as well as several unknown compounds were identified in crude extracts from entomopathogenic  Microorganisms are a major source for novel natural products and the subsequent development of new drugs for all kinds of applications –2. For eThe increasing sensitivity of analytical methods, especially in mass spectrometry, enables the detailed analysis of various natural product producing microbes. Much more compounds have been identified than originally thought, but often these are produced only at a very low level. This is also reflected by the genome sequences of bacteria and fungi that often encode numerous biosynthesis gene clusters (BGC) with most of the corresponding natural products unknown for several reasons: these BGCs are silent under standard laboratory conditions , the comTherefore it is desirable to have multiple and complementary methods available that allow the detection of several different natural product classes. Besides the traditional chemical screening and bioactivity-guided isolation, the exploitation of the inherent properties of natural products is also feasible. Consequently, simple functional groups of natural products like dehydroalanine –7, keton92), The recently introduced cleavable azide-reactive resin  in acetonitrile overnight followed by extensive washing of the resin with methanol and dichloromethane. After disulfide-bond cleavage with a solution of tris(2-carboxyethyl)phosphine (TCEP) in a 1:5:10 mixture of phosphate-buffered saline (PBS)/chloroform/methanol at pH 7, the filtrate was analyzed by HPLC–MS. As expected the corresponding mass m/z 493.2 [M + H]+ of the cleaved cycloaddition product could be directly detected in the base-peak chromatogram (BPC) showing the characteristic fragmentation pattern of the CARR adducts (trans-stilbene oxide were added to liquid Lysogeny Broth (LB) medium. The obtained methanolic Amberlite XAD-16 extracts were azidated, enriched and analyzed. Up to a final concentration of 5 µg/L (≈25 nmol/L) the epoxide could be detected . Compound 1 could hardly be detected in extracts from standard growth media but was detected from infected insects and media mimicking the insect hemolymph [m/z 271.1 [M + H]+ could be detected at 8.5 min, which can be associated with 1.The obtained XAD extract was treated the same way as ass of 3  derived emolymph –20. Cleaemolymph . Only a Photorhabdus strains . Here, an even lower amount of 3 could be detected in Photorhabdus PB 68.1  [5) [6) [Photorhabdus strains they have previously only been detected from infected insects [E. coli [4) showing the same azide reactivity and retention time compared to 4 from the crude extract  added into LB medium revealed a detection limit of 10 µg/L (≈20 nmol/L)  , cepafun4) [5) –24 and l [5) [6) –26 were  insects , low-salPhotorhabdus. Epoxystilbene (1) and glidobactins have never been observed before in XAD extracts of Photorhabdus grown under standard conditions. Most likely this is due to very low production levels of these compounds thus illustrating the power of this method. We deem that many more electrophilic compounds were just overlooked in the past due to their low concentrations. In combination with labeling experiments even the nature of the parent natural product could be revealed. Moreover, a possible scale-up of this procedure should enable the preparative purification of yet unidentified compounds as well as the structural confirmation of the identified structures. This approach can also be applied to extracts of other bacteria, fungi and plants and can give at least hints on new electrophilic natural products, where their reactivity against azide might also reflect their biological activity.The combination of the reactivity-guided introduction of an azide functionality into electrophilic natural products and the subsequent azide enrichment on a solid phase facilitates the detection of epoxides and α,β-unsaturated enones in XAD extracts of File 1Materials, methods and supplementary figures.


DMPK gene elicits DM1, whereas elongated CCTG repeats in intron 1 of ZNF9/CNBP triggers DM2. Pathogenesis of both disorders is manifested by nuclear retention of expanded repeat-containing RNAs and aberrant alternative splicing. The precise determination of absolute numbers of mutant RNA molecules is important for a better understanding of disease complexity and for accurate evaluation of the efficacy of therapeutic drugs. We present two quantitative methods, Multiplex Ligation-Dependent Probe Amplification and droplet digital PCR, for studying the mutant DMPK transcript (DMPKexpRNA) and the aberrant alternative splicing in DM1 and DM2 human tissues and cells. We demonstrate that in DM1, the DMPKexpRNA is detected in higher copy number than its normal counterpart. Moreover, the absolute number of the mutant transcript indicates its low abundance with only a few copies per cell in DM1 fibroblasts. Most importantly, in conjunction with fluorescence in-situ hybridization experiments, our results suggest that in DM1 fibroblasts, the vast majority of nuclear RNA foci consist of a few molecules of DMPKexpRNA.Myotonic dystrophy type 1 (DM1) and type 2 (DM2) are human neuromuscular disorders associated with mutations of simple repetitive sequences in affected genes. The abnormal expansion of CTG repeats in the 3′-UTR of the  DMPK gene; whereas DM2 is associated with intronic elongation of CCTG repeats in ZNF9/CNBP gene2. Pathogenesis of both diseases is mediated by a toxic RNA gain-of-function mechanism manifested by nuclear retention of expanded CUG- and CCUG-harboring RNAs which aggregate within nucleoprotein foci3. Their adverse effects are mediated through sequestration of various proteins including the muscleblind-like (MBNL) family of splicing factors. These proteins are functionally depleted by the expanded repeats in DM1 and DM2 which leads to abnormalities in many pathways of RNA metabolism including alternative splicing, a molecular hallmark of DM8.Myotonic dystrophy type 1 (DM1) and type 2 (DM2) are adult onset muscular dystrophies leading to disability and shortened lifespan. Both belong to a larger group of human disorders associated with mutational expansions of simple repetitive sequences within specific genes. The mutation that causes DM1 results from the expansion of CTG repeats in the 3′-UTR of the 4. Given the importance of alternative splicing as one of the biomarkers of disease severity and therapeutic response, accurate quantification of splicing variants could facilitate their refinement for clinical applications. Specific oligonucleotide-based microarrays and next-generation sequencing have been used for profiling and identification of many aberrantly spliced transcripts. However, their routine application for precise quantification remains limited due to low quantitative parameters of these methods, restriction to highly expressed genes, high costs and the large amount of RNA required. On the other hand, traditional analysis of splicing isoforms via primer design, PCR and agarose gel visualization of PCR products shows low sensitivity, low repeatability and reproducibility between different laboratories. Thus, new methods of alternative splicing analysis enabling robust and reliable quantification of splicing isoforms are needed for many aspects of DM research, and the first aim of this study is to optimize such new approaches.In DM adult skeletal muscle abnormal expression of embryonic splicing isoforms has been reported for a few hundred genesDMPK transcripts (DMPKexpRNA) associated with DM1 pathogenesis involves quantification of the toxic molecules per cell. Thus, selection of the most sensitive and reliable methodologies is essential to compute the absolute number of disease-associated molecules. The most recent study measuring the abundance of endogenous DMPK transcripts revealed that in human DM1 myoblasts both normal (DMPKnormRNA) and mutant mRNAs are low-abundance species9. Several lines of evidence including Northern blotting, quantitative RT-PCR and RNA-sequencing showed that there are between one and a few dozen DMPK mRNAs per cell, half of which contain the repeat expansion. In a similar study looking at the abundance and processing of an antisense transcript across the DMPK repeat expansion, only a handful of repeat containing antisense transcripts were computed per cell10. Herein, we calculate concentration of DMPK mRNA in human DM1 samples using a novel ddPCR approach which is an absolute quantification method. Optimization of this technique to develop a further biomarker of DM1 is the second aim of this current work.Understanding the biology of mutant DMPKexpRNA copy number. MLPA and ddPCR are reliable dosage quantification methods and various applications include large mutation detection, genotyping of copy number variants, evaluation of NGS library exome enrichment efficiency, methylation analysis, and high-sensitivity gene expression studies18. MLPA is a multiplex method simultaneously utilizing multiple probes that allows quantification of different target sequences. Each probe is composed of two half-probes that hybridize to directly adjacent target sequences. Only half-probes specifically recognizing their targets undergo subsequent ligation and dosage-dependent amplification with a pair of universal primers. The MLPA products consisting of a mixture of probe-specific PCR fragments are separated by capillary electrophoresis and quantified. The normalized signals from MLPA probes are proportional to the dosage of their targets.Two methods based on Multiplex Ligation-Dependent Probe Amplification (MLPA) and droplet digital PCR (ddPCR) were used herein for quantification of RNA biomarkers of DM pathogenesis, including aberrant alternative splicing and 20. After endpoint PCR, the starting concentration of template is determined by Poisson statistical analysis of the number of positive (containing amplified target) and negative (no amplified target detected) reactions. The creation of several thousands of droplets in one reaction means that a single sample can generate several thousands of data points rather than a single result, bringing the power of statistical analysis of ddPCR into practical application. Thus, the digital PCR procedure displays many potential advantages over real-time PCR, including the capability to obtain absolute quantification without external references and robustness to variation in PCR efficiency.ddPCR is a highly quantitative and precise method which enables absolute nucleic acid measurement based on the partitioning of individual molecules into many low-volume droplet reactions at limiting dilution, resulting in one or zero molecules in most reactions in individual dropletsDMPK transcripts in copies per cells using ddPCR. Most importantly, in conjunction with fluorescence in-situ hybridization experiments, our results suggest that in DM1 fibroblasts, the vast majority of nuclear RNA foci consist of a few molecules of mutant DMPK transcripts.Knowing the precise number of disease-causing molecules is of high importance since it will allow a better understanding of DM pathology and successful drug discovery. Herein, we show that a robust and reliable quantification of RNA biomarkers of DM pathogenesis can be achieved by using two medium-throughput methods based on MLPA and ddPCR. Both methods have proven to generate highly reproducible results allowing simultaneous quantification of splicing isoforms of many genes that are aberrantly spliced in DM. In addition, we provide a novel quantitative approach for computing the absolute number of 4. We divided these exons into three categories according to the timing of alternative splicing origin  consisted exons of four genes: CACNA1S E29, ANK2 E21, PHKA E28 and MBNL1 E7 with inclusion rates changed in all affected DM1 patients showing high correlation with disease severity. The third category (late responding exons) comprised exons in two genes: KIF13A E32 and ARHGEF7 3′UTR which are altered strongly in the most affected DM1 patients. We used several criteria to select these eight exons: (i) a high correlation between splicing changes and muscle strength index in DM1 patients, (ii) a low variance of exon inclusion rate in the muscles of healthy individuals, (iii) constitutive exons flanking exons of interest not affected by alternative splicing, and (iv) a similar expression level of genes carrying analyzed exons to allow for their simultaneous analysis. Among the eight genes, INSR E11 was the only exception where criteria (i) and (ii) were not fulfilled.Aberrant alternative splicing is a hallmark of DM pathogenesis and precise calculation of splicing isoforms remains challenging. The first aim of this work was to design sensitive, reproducible and highly quantitative methods to monitor alternative splicing changes in skeletal muscles of DM1 and DM2 patients which may serve as accurate biomarker of disease progression that could be used in clinical trials. For this purpose we selected eight alternative exons whose splicing profiles correlate well with disease severity measured as a loss of muscle strength21 and ddPCR17 reactions we show that simultaneous and accurate quantitation of two splicing isoforms either exon inclusion (ex-ON) or exclusion (ex-OFF) can be achieved with both methods. First, for multiplex analysis of the exons, we designed a splicing-specific MLPA (ssMLPA) assay consisting of 16 MLPA probes  specific to transcripts with (ex-ON) or without (ex-OFF) an alternative exon, and labeled at the 5′ end with either FAM or HEX fluorophore Fig. . AdditioWe compared two quantitative approaches to evaluate the extent of changes in alternative splicing in DM human skeletal muscles and in non-DM samples. We selected a representative group of samples derived from either distal  or proximal (quadriceps and biceps bronchi) muscles of DM1 (n = 7) and DM2 (n = 5) and control biopsies from the same muscles of non-DM individuals (n = 7) (Supplementary Table t test). Interestingly, DM2 samples also diverged significantly from the controls, although to a lesser extent , . For most exons analyzed statistically significant differences were found between DM1 and DM2 samples with both methods, except for ANK2 E21 and PHKA1 E28. For all splicing events, there was strong correlation between the results of the MLPA and ddPCR analyses and Pearson correlation coefficient varied between R = 0.628  and R = 0.937 , respectively, for PHKA1 E28 and for MBNL1 E7. Average values of cumulative |ΔPSI|   was 0.052 (for MLPA), and 0.072 (for ddPCR). We conclude that both of the methods are equally reliable and sensitive enough to generate accurate and reproducible results when used separately. Thus, we believe that there is no need to use them simultaneously.DMPK transcripts. For this purpose, we selected a representative group of DM1 samples derived either from proximal (quadriceps) human skeletal muscles (n = 7) or from proliferating fibroblasts (n = 5) 22. To distinguish normal and mutant DMPK alleles, the FAM and HEX-labeled ZEN probes were designed to bind within the SNP site ” using the assay output in copies/µl  ”  in all DM1 samples tested. We confirmed this result with two other assays including Sanger sequencing and capillary electrophoresis of BpmI digested RT-PCR products , there was no link between these two variables and the sample from a patient with over 1400 CTG repeats had a comparable fraction of DMPKexpRNA as those with shorter repeats .In summary, the ddPCR estimated number of cellular DMPK mRNAs containing the CUG expanded repeats are retained in the nucleus, where they form foci, a molecular hallmark of DM1 pathology23. It remains an open question, whether foci are composed of multiple aggregating mutant RNAs or whether each focus equals a single RNA molecule. To get an insight into this issue, we applied standard FISH23 and compared signals from single molecules of fluorescently labeled probes with signals in repeat expansion DNA (CAGexpDNA) and RNA (CUGexpRNA) foci. In the five DM1 fibroblasts cell lines (the same as used for DMPK quantification via ddPCR assay) there was only one or occasionally two CAGexpDNA foci per nucleus, but different numbers of CUGexpRNA foci (1–12/nucleus)  or rather with different quantity of the mutant molecules present in these foci, we set up a confocal microscopy analysis and determined fluorescence intensity of single molecules of Alexa 488-labeled probes  and co-exist with those composed of two to eight molecules. Interestingly, for a subset of foci (5–10%) there appeared to be less than one molecule of mutant RNA per focus. The relative number of CUGexpRNA molecules per focus was used to calculate their amount per nucleus. As illustrated in Supplementary Figure expRNA and contained from one to over two dozen of the molecules. While there were on average 4 to 6 RNA foci per nucleus in the fibroblast cells, the estimated median numbers of mutant transcripts ranged from 8 to 17  Fig. . This rells Fig. . To find 17 Fig. . We founPCR Fig. .Figure 63. Here, we demonstrate that two quantitative methods, MLPA and ddPCR, can be successfully applied to accurately measure RNA biomarkers of DM pathogenesis which are mechanistically very informative.The selection of the most reliable methods for counting RNA molecules is of paramount importance for understanding the mechanism of pathogenesis of non-coding repeat expansion disorders such as DM1 and DM25. In DM adult skeletal muscle abnormal expression of embryonic splicing isoforms has been reported for a number of genes4. The timing of appearance of certain mis-spliced events correlates well with disease severity measured as a loss of muscle strength4 which makes them very useful biomarkers. This hallmark of DM pathogenesis is most informative when the efficacy of investigated compounds is measured to demonstrate their therapeutic value27. Thus far, profiling of aberrantly spliced transcripts has been performed with high-throughput methods such as specific oligonucleotide-based microarrays and next-generation sequencing, or with traditional RT-PCR and agarose gel quantification of PCR products. Neither method provides both highly reproducible and low cost analyses. We show that MLPA and ddPCR overcome this problem. Our results demonstrate a robust and reliable quantification alternative to calculate splicing isoforms of many genes that are a hallmark of different stages of muscle deterioration in DM.Aberrant alternative splicing is a key feature of DM1 and DM2 pathogenesisDMPK mRNAs containing the expanded CUG repeats are retained in the nucleus as distinct foci29. While the vast majority of published data have documented the presence and number of CUG foci via FISH (reviewed in3) there is still a gap in precise determination of the quantity of DMPKexpRNA per cell and per nuclear RNA focus. To tackle this issue, we used ddPCR which is an absolute quantification method and we determined DMPK copy number in a well-defined quantity of input cells in several human DM1 fibroblast cell lines. In a similar study by Gudde et al.9, DMPK mRNA molecules were calculated in proliferating human DM1 myoblasts using Northern blotting, qPCR and RNA-seq data. Although both the studies indicate a low abundance of DMPK transcripts, higher numbers were found in myoblast cells . Similarly, in human fibroblasts from frontotemporal degeneration (FTD) and amyotrophic lateral sclerosis (ALS) (FTD/ALS)31, the absolute copy number of c9orf72 mRNA was estimated at about 15 molecules/cell when measured by ddPCR and qPCR32. Interestingly, the intronic part of the transcript containing expanded GGGGCC repeats found in nuclear RNA foci in patients’ cells was detected in only single copy/cell. Thus, in FTD/ALS fibroblasts it appears the mutation-containing intron is degraded more rapidly than the mature transcript is processed. Our ddPCR data suggest that in DM1, the CUG mutation-harboring DMPK transcript is turned over slower than its normal-sized counterpart. In patients’ cells and skeletal muscles we consistently detected a higher number of copies of DMPKexpRNA reaching up to 70% of its entire population. However, except for the differences in RNA processing (i.e. maturation and degradation) variations in expression levels of mutant DMPK transcript may also be attributed to differences in RNA synthesis rate as a result of repeat-related chromatin effects.Mutant in situ hybridization has mostly been used to determine CUG RNA foci numbers3. A further application of the technique for more detailed characterization of foci is rather limited due to random incorporation of probes and a lack of tools allowing precise quantification of hybridized molecules. Nonetheless, FISH remains the only method to look at deposits of mutant molecules of DMPK transcripts in their natural environment. Here, we applied this technique to get an insight into a problem about quantity of DMPK mutant transcripts in nuclear CUG RNA foci. Our estimations based on normalization of fluorescence signals of RNA foci using values of single molecules of Alexa 488 probes led to the conclusion that in DM1 proliferating fibroblasts the vast majority of foci (~80%) consists of a few molecules of mutant DMPK transcripts. Importantly, FISH-estimated quantity of DMPKexpRNA per cell was convergent with what we computed based on ddPCR absolute quantification   according to manufacturer’s instruction. Extracted RNA was treated with DNase I (Invitrogen) at 37 °C for 30 min followed by enzyme inactivation at RT for 10 min. Reverse transcription reactions were performed using a SuperScript® III First-Strand Synthesis System (Invitrogen). Briefly, after DNase I treatment, random primers and dNTPs were added to 1 µg of RNA to make volume of 13 µl and heated at 65 °C for 5 min. Then 7 µl of RT mixture  was added and RT reaction was performed at 50 °C for 50 min followed by enzyme inactivation at 70 °C for 10 min. cDNA synthesis (as RT+) was accompanied by RT- controls lacking the reverse transcriptase. GoTaq Flexi DNA Polymerase (Promega) and 0.1 mM dNTP mix, 1.5 mM MgCl15. As opposed to commercial tests, this strategy exclusively utilizes short oligonucleotide probes. Each probe consists of two half-probes ; between-probe distance equals 3–5 bp; and the total probe length ranges from 93 to 128 nt. The sequences and detailed characteristics of all probes are shown in Supplementary Table 14 using 50× diluted cDNA samples and a mixture of 1 nM half-probe oligonucleotides. The products of the MLPA reactions were then diluted 20× in HiDi formamide containing GS Liz600, which was used as a DNA sizing standard, and were separated by size with capillary electrophoresis . The electropherograms were analyzed using GeneMarker software . The fluorescent signal of each probe was normalized against the average signal of control probes and then the ratios of signals for each pair of exon-specific probes representing alternative exon inclusion in analyzed samples were calculated.MLPA analysis was performed using the in-house designed assays. The probe-set layout was designed according to a previously validated strategyDMPK quantifications two alternative sets of probes were designed . Sequences of primers and probes are listed in Supplementary Table MLPA and ddPCR results of splicing assays for selected genes were used to compare “percent spliced in” (PSI) values of alternatively spliced cassette exons between non-DM and DM1 as well between non-DM and DM2 (changes in splicing denoted as |ΔPSI|).DMPK (copies per µl) and converted to copies per cell , whereas gDNA was column isolated (Qiagen). RNA and DNA concentrations were measured by NanoDrop 2000 Spectrophotometer. 250 ng of total RNA was used for cDNA synthesis as described above. Eventually, gDNA and cDNA from corresponding samples were used in ddPCR assays to quantify concentration of All experiments were performed at least three times, and the representative results are shown. The data are presented as the mean or median ± SD .t test, whereas significance of DMPK quantifications via ddPCR and FISH was determined by unpaired two-tailed t test; P values of <0.05 were considered to be statistically significant in all tests.Statistical significance of results of aberrant alternative splicing was determined by the Welch’s unpaired two-tailed Supplementary Information