Urine steroid metabolomics, combining mass spectrometry-based steroid profiling and machine learning, has been described as a novel diagnostic tool for detection of adrenocortical carcinoma (ACC).This proof-of-concept study evaluated the performance of urine steroid metabolomics as a tool for postoperative recurrence detection after microscopically complete (R0) resection of ACC.135 patients from 14 clinical centers provided postoperative urine samples, which were analyzed by gas chromatography–mass spectrometry. We assessed the utility of these urine steroid profiles in detecting ACC recurrence, either when interpreted by expert clinicians or when analyzed by random forest, a machine learning-based classifier. Radiological recurrence detection served as the reference standard.Imaging detected recurrent disease in 42 of 135 patients; 32 had provided pre- and post-recurrence urine samples. 39 patients remained disease-free for ≥3 years. The urine “steroid fingerprint” at recurrence resembled that observed before R0 resection in the majority of cases. Review of longitudinally collected urine steroid profiles by 3 blinded experts detected recurrence by the time of radiological diagnosis in 50% to 72% of cases, improving to 69% to 92%, if a preoperative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22% to 39% of patients. Specificities varied considerably, ranging from 61% to 97%. The computational classifier detected ACC recurrence with superior accuracy (sensitivity = specificity = 81%).Urine steroid metabolomics is a promising tool for postoperative recurrence detection in ACC; availability of a preoperative urine considerably improves the ability to detect ACC recurrence. Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy , 2. DiseMost ACCs are biochemically active, typically presenting a steroidogenic pattern dominated by steroid precursor metabolites rather than end products of steroidogenesis . This paIn this study, we evaluated the diagnostic performance of urine steroid metabolomics, the combination of mass spectrometry-based steroid profiling and data analysis by machine learning-based algorithms, in the postoperative surveillance of ACC patients following microscopically complete (R0) tumor resection. We assessed the performance of this approach in the detection of disease recurrence, also comparing direct interpretation of steroid profiles by clinical experts to fully automated, machine learning–based analysis of the steroid metabolome.www.ensat.org), with approval of local ethical review boards and after obtaining written informed patient consent. Participating countries included the United Kingdom (Birmingham), Germany , France (Paris), Italy , Greece (Athens), the Republic of Ireland , Poland (Warsaw), Croatia (Zagreb), and Portugal . Urine samples were collected between 2007 and 2016. Inclusion criteria were defined as (i) histologically confirmed diagnosis of ACC, (ii) complete (R0) tumor resection, and (iii) provision of at least 1 postoperative 24-hour urine sample when disease-free, that is, before any radiological evidence of disease recurrence  and within 2 years from surgery. Participating centers were prompted to provide urine samples every 3 months, but actual frequency of provided samples did not constitute an exclusion criterion as long as at least 1 postoperative sample had been provided at a time with no evidence of disease recurrence on surveillance imaging.Serial postoperative 24-hour urine samples were collected from patients with histologically confirmed ACC, who had undergone microscopically complete (R0) tumor resection in 14 clinical specialist referral centers participating in the European Network for the Study of Adrenal Tumors  emergence of new lesions on cross-sectional imaging , which either enlarge on follow-up scans or regress in response to systemic antitumor therapy; (ii) emergence of enhancing lesions on positron emission tomography or positron emission tomography CT scans; or (iii) histological evidence of recurrent/metastatic ACC from percutaneous biopsy or revision surgery.Measurement of 24-hour urinary steroid metabolite excretion was carried out by GC-MS, as described in detail previously . In brieThree clinical experts with extensive experience in adrenal disease  were provided with longitudinally collected postoperative urinary steroid profiles from patients who either (i) developed disease recurrence (“recurrence cohort”) or (ii) remained recurrence-free over a follow-up period of at least 3 years, which we considered our “recurrence negative” cohort, as the chances of ACC recurrence past this time-point are low . ProvisiThe 3 assessors were blinded to clinical and radiological information other than basic patient demographics  and were only provided with a previously established steroid metabolite reference range derived from a healthy adult control cohort . The clinical experts were asked to identify the first urine indicative of a recurrence (or state “no recurrence” in patients that they considered as non-recurred), taking into account differences of the steroid profiles to those observed in healthy controls and the previously observed “malignant steroid fingerprint” in patients with a primary ACC tumor in situ .Recurrence detection by the clinical experts was considered successful only if based on interpretation of the steroid profile in a urine sample collected before or at the time of the first radiological detection of recurrent disease. This means that late biochemical detection in relation to imaging did not count as true positive for the purposes of sensitivity calculations.Supervised machine learning was used to create an approach for automatic separation of recurrent from non-recurrent patients . The macWe used machine learning to approach 2 separate 2-class classification problems. First, we considered the differentiation of all 215 urine samples collected in the 39 non-recurred patients from all 76 urine samples collected post-recurrence in the 32 recurred patients. Second, to test the ability of our approach for very early detection, we aimed to differentiate all non-recurred samples against the first urine sample collected in each recurrent patient at the time of first radiological detection of ACC recurrence .Random forests were used as machine learning classifier , 22. TheData analysis and graphic representation was completed using GraphPad Prism Software Version 8. Data are summarized as median (interquartile range) values unless otherwise stated. Sensitivities and specificities are accompanied by 95% confidence intervals (95% CI), derived using the Wilson/Brown method , 24.We recruited 135 patients  who had undergone complete (R0) resection of a histologically confirmed ACC and provided at least one 24-hour urine sample while considered disease-free according to their most recent clinical and radiological assessment and no later than 2 years postoperatively . Median During a median follow-up period of 32 months (interquartile range 15–48 months), 42 of 135 patients (31%) developed disease recurrence; of these, 10 had to be excluded from the analysis as they had not provided 24-hour urines after the detection of recurrence. Of the cohort of patients who remained disease-free postoperatively, 39 were clinically and radiologically followed for more than 3 years; as ACC recurrence presenting beyond that time frame is rare , we defiThe 39 patients of the “recurrence-free” cohort provided a median of 4 (range 1–24) postoperative 24-hour urine samples. In the “recurrence” cohort, the patients collected a median of 5 (range 2–35) postoperative 24-hour urine samples; 13 of the 32 patients had also collected a preoperative 24-hour urine sample, facilitating the comparison of steroid profiles observed at diagnosis of the primary tumor and at detection of ACC recurrence. All samples provided by the recurred patients are depicted in We hypothesized that the development of radiologically detectable recurrent or metastatic disease would be heralded by an increase in one or more adrenal steroid metabolites excreted in 24-hour urine. Such changes were indeed observed; indicative example cases are shown in heat-map format in An important question here was whether the “malignant steroid fingerprint” observed at baseline  represents an inherent characteristic of the individual ACC that is largely preserved upon disease recurrence. We found that this was indeed the case, with re-emergence of steroid metabolites at recurrence mostly identical to those found increased at baseline in the vast majority of patients . The oven = 13) to 85% (95% CI 58–97%), 69% (95% CI 42–87%) and 92% (95% CI 67–100%), respectively  with sensitivities of 66% (95% CI 48–80%), 53% (95% CI 36–69%), and 75% (95% CI 58–87%) . This improved substantially for the subgroup of patients who had provided preoperative urine samples .Of note, a considerable proportion of correct recurrence detections (ranging from 22%–39% for the 3 experts) were made based on urine collections that predated the first radiological evidence of recurrence by more than 2 months . Only a n = 76) provided by the 32 recurred patients from postoperative urine samples (n = 215) provided by the 39 non-recurred patients with high accuracy  0.89, 95% CI 0.86–0.91; sensitivity = specificity = 81%)  .When considering only the first post-recurrence urine sample from each patient in the “recurrence” cohort, the accuracy of random forest classification was lower at 75%  , with thHigh-volume recurrences were more likely to be detected by the random forest classifier (sensitivity 60% [95% CI 36%–80%] vs. 42% [95% CI 19%–68%] in low-volume recurrences). However, detection was not affected by mitotane treatment (sensitivity 55% [95% CI 34%–74%] in mitotane-treated patients vs. 58% [95% CI 32%–81%] in patients not treated with mitotane). Interestingly, in 11 of 18 successfully diagnosed recurred patients, recurrence had also been unanimously detected by all 3 assessing clinicians and in 16 of 18 by at least 2 different clinicians.The machine learning analysis determined the 11-deoxycortisol metabolite THS as the single most important steroid metabolite in differentiating post-recurrence urine samples from samples provided by non-recurred patients, followed by the mineralocorticoid precursor metabolite tetrahydrocorticosterone, the pregnenolone metabolite pregnenediol and the androgen metabolite etiocholanolone .In this study, we explored the utility of urinary steroid profiling as a novel diagnostic tool for recurrence detection in patients with microscopically complete (R0) resection of ACC. Results show that analysis of the steroid profiling data by a machine learning–based algorithm represents a highly promising noninvasive and radiation-free tool. Once validated prospectively, this would be a useful addition to the current imaging-focused follow-up protocols, expediting scans in patients with suspicious biochemistry and informing discussions in cases with ambiguous imaging results.Urinary steroid profiling in conjunction with machine learning-based data analysis, also termed urine steroid metabolomics, has already yielded highly promising results in several retrospective studies in patients with primary adrenal masses, where it was employed to differentiate ACCs from benign adrenal tumors , 16. In In the present study, we studied 135 adult patients with microscopically complete R0 resection of ACC recruited from 14 centers associated with the European Network for the Study of Adrenal Tumors (ENSAT). Of the 81 patients who completed 3 years of postoperative surveillance, 42 (52%) recurred, a rate that is similar to previous retrospective studies , 27, whiAn important finding of our study is that there were substantial similarities between the steroid profiles of recurrent ACCs and their respective steroid profiles collected preoperatively, with the primary tumor in situ. Indeed, half of all recurrent ACCs shared 4 or 5 of their “top 6” most elevated steroid metabolites with their primary tumor of origin. Most of the “malignant steroid biomarkers” that were identified in our 2011 study on primary adrenal tumors  were alsWe assessed the diagnostic potential of urinary steroid profiling as a recurrence surveillance tool using two approaches: (i) an “expert review” approach and (ii) automatic recurrence detection by computational analysis of steroid data using a machine learning–based algorithm. On retrospective, blinded assessment of serial 24-hour urine collections, clinicians were able to detect recurrence by the time of its first radiological manifestation with high sensitivity in cases where a preoperative urine sample was available. In patients who were only able to contribute postoperative urine samples, the ability of clinicians to detect recurrence was substantially lower.Adjuvant mitotane did not compromise the diagnostic performance of reviewing clinicians, despite the drug’s well documented ability to inhibit steroidogenesis . MitotanWe applied a machine learning–based approach to the urinary steroid profiling data to detect recurrent ACC in an automated and defined fashion. The biochemical complexity of steroidogenesis with multiple substrates, products, and pathways, in combination with the small underlying disease volumes in the setting of recurrent malignancy, render individual biomarkers diagnostically insufficient. Machine learning–based approaches are ideally suited to systematically evaluate the wealth of data provided by multisteroid profiling in an objective and reproducible fashion, as already demonstrated in the differential diagnosis of adrenal incidentalomas . Our clan = 47), while urine steroid metabolomics by GC-MS found abnormalities in 95% (To our knowledge, this is the first study systematically exploring the diagnostic potential of urine steroid profiling in the postoperative monitoring of ACC patients. Strengths of our study include the large cohort size and the application of computational analysis to meet the demands of the multivariable GC-MS steroid datasets. The limitations of our work pertain to the paucity of preoperative samples in the majority of patients, the variable frequency of postoperative sample collections and the fact that the machine learning classifier has not been validated on an additional data set. We also did not systematically compare the results of routine biochemical analysis of serum steroids to the 24-hour urine analysis by GC-MS; however, we previously demonstrated that routine serum biochemistry only identified abnormalities in 73% of ACC patients (s in 95% .On this background, and despite the generally small disease volume in the recurred patients, in comparison to patients presenting with a large primary tumors , our appIn conclusion, we demonstrated that urine steroid metabolomics, that is, the combination of mass spectrometry–based steroid profiling with machine learning–based steroid data analysis, is superior to interpretation of steroid profile results by individual experts. Following potential further refinement of this algorithm, this diagnostic approach should be taken forward to be assessed against radiological disease detection in a prospective test validation study with systematic collection of pre- and postoperative urines in defined intervals. This will also allow for systematic comparison of serum and 24-hour urine steroid profiles and ideally utilize high-throughput technology, such as liquid chromatography–tandem mass spectrometry or also, as recently published , high re


A massive new resource of diverse Cas9 binding and cleavage events establishes new rules for effective CRISPR targeting. The RNA-guided nuclease Cas9 has unlocked powerful methods for perturbing both the genome through targeted DNA cleavage and the regulome through targeted DNA binding, but limited biochemical data have hampered efforts to quantitatively model sequence perturbation of target binding and cleavage across diverse guide sequences. We present scalable, sequencing-based platforms for high-throughput filter binding and cleavage and then perform 62,444 quantitative binding and cleavage assays on 35,047 on- and off-target DNA sequences across 90 Cas9 ribonucleoproteins (RNPs) loaded with distinct guide RNAs. We observe that binding and cleavage efficacy, as well as specificity, vary substantially across RNPs; canonically studied guides often have atypically high specificity; sequence context surrounding the target modulates Cas9 on-rate; and Cas9 RNPs may sequester targets in nonproductive states that contribute to “proofreading” capability. Lastly, we distill our findings into an interpretable biophysical model that predicts changes in binding and cleavage for diverse target sequence perturbations. Streptococcus pyogenes (Spy) Cas9 has been widely adopted as a platform for perturbing gene expression and protein levels in human cells  and superb specificity  :target mispairing given a guide sequence have been less comprehensively addressed. Most biophysical studies have measured relatively few RNP:target pairs, and while recent work has extended the number of off-target binding measurements per guide, the total number of sgRNAs profiled remains limited . gRNAs were curated from a variety of sources, including genetic screens, Cas9 off-target screens, and efforts to characterize Cas9 biochemistry. Sequence transformations of the curated sequences were also included. Sequence transformations consisted of taking the complement, reverse, or reverse complement of parts of gRNA sequences to allow direct comparison between nucleotide composition-matched gRNA sequences. Each sublibrary included DNA targets with all single mismatches; 66 contiguous double mismatches; 10 noncontiguous double mismatches; all single RNA:DNA bulges plus select double and triple bulges; 230 contiguous mismatch series consisting of rA:dA, rC:dC, rG:dG, and rU:dT mismatches from a start to end position; and 12 fixed sequences common to all sublibraries. In total, 54,349 targets for 91 sublibraries (including a duplicate sublibrary for the λ1 sgRNA) were designed (table S1). For each sublibrary, a corresponding sgRNA was prepared and loaded in dCas9, while the DNA was divided and barcoded with 16 distinct time point primers using polymerase chain reaction (PCR)  to allowWe next designed a massively parallel filter-binding apparatus to permit processing binding time courses of sublibraries in 96-well plates . As partffinal) and an observed rate (kobs). Confidence intervals for each time point were constructed assuming Poisson noise. Targets for which the first time point neared the binding level of the last time points were fit to final fraction bound only. Furthermore, targets wherein time points’ confidence intervals for estimated fraction bound overlapped zero were separately flagged as binding below our detection limit . Off-targets with an extreme fit rate or final fraction bound were flagged as poor fits. Some targets could not be fit, usually because of very low counts, and were labeled missing data.Target counts from sequencing data were fit to a one-step association model , resulting in two fit parameters: final fraction bound . By screening sgRNA sequences in silico with RNAfold  of sequence complementarity comprising a seed sequence that is sufficient for binding at 5 nM RNP   RNAfold . An addiP = 0.030, Wilcoxon rank sum test). We then compared the discriminative power of our quantified kobs and ffinal. Final fraction bound for on-target sequences mostly exceeded 50% and did not correlate with guide efficacy. In contrast, association rates for effective sgRNAs were significantly faster than those of ineffective sgRNAs . This observation is consistent with recent CRISPRi data demonstrating that apparent association rates govern CRISPRi activity in human cells (We next aimed to compare the parameters estimated from these experiments with in vivo activity scores. We first classified the subset of sgRNAs with published CRISPR interference (CRISPRi) activities as either effective or ineffective . Howeverffinal and kobs for off-target sequences with series of 0 to 20 complementary nucleotides at the PAM-proximal end of the target .Across all sgRNAs, most RNA:DNA mismatches or bulges had small effects on final fraction bound . Recent P = 6 × 10−5, one-sided Wilcoxon rank sum test). This DNA insertion preference was most prominent at end positions  or center positions (−8 to −11) (fig. S2A and table S5). Positions outside of these regions did not exhibit such bias whether tested individually or in aggregate. As expected, design of a DNA bulge at the −1 position for targets with GGG PAMs, where a canonical PAM would be maintained, exhibited far greater binding than targets with HGG PAMs, where PAM usage would require a DNA bulge. Deletion of DNA target bases, which is expected to lead to the formation of an RNA bulge, typically led to a greater decrease in the final amount of target binding. For 3-bp RNA bulges in the 5 bp nearest the PAM, the majority of off-targets were at or below the limit of detection.We noted that designing a DNA bulge in the gRNA:target DNA complex perturbed binding nearly as much as designing a mismatch. Further investigation showed that DNA bulges that matched the identity of the PAM-proximal nucleotide fared better than bulges of nucleotides that did not match but were located at the same position  for a given sgRNA might be an accurate proxy for CRISPRi silencing capability. We first assessed predicted sgRNA CRISPRi activity for mismatched targets using a recently published model for CRISPRi efficacy at mismatched sites  predicted activity and low ffinal (<30%), or moderate to high (>10%) predicted activity and high ffinal (>60%) (fig. S2B), meaning that our biophysical measurements generally agreed with prior modeling efforts ; furthermore, across all sublibraries with at least 50 ffinal measurements, the correlations between these two metrics were overwhelmingly positive .Having characterized off-target binding to naked DNA, we next asked whether the final fraction bound  for perfect targets might be expected to fall significantly below 100%, the cleavage of template by active Cas9 over time would be expected to drive the reaction to completion in the limit of long incubations times. This subsaturating behavior  for perfect targets  and 1 (cleavage competent) but were instead broadly distributed . The exiR = 0.775; In addition to assaying the 91 sublibraries described above, we also constructed two “3-mer scanning libraries” to test for the effects of flanking sequence on association and cleavage for λ1 and FANCF sgRNAs. These libraries were designed to harbor all possible trimers spanning the 5′ and 3′ flanks of the 23-bp target, extending 3 nt 5′ (to position −23) and 6 nt 3′ (to position 8) . Binding2 fold changes relative to the default perfect target sequence for each guide. An additive model fit by LASSO regression captured most of the variance  and measured logCGGGAG) and least (NGGGAATTT) CRISPRi activity in the study of Xu et al. . This effect is not only due to GC content, as GC-matched controls showed smaller changes in their median relative association rates (two- to fourfold decreases). The unexpectedly large association rate decrease observed for this unfavorable 6-mer block was poorly predicted by the model trained on the scanning 3-mer data, suggesting that interactions beyond neighboring context nucleotides affect association rates. We also observed that association rates measured across perfect targets containing identical 6-bp blocks were more guide dependent and exhibited much larger variance than single base changes (1.8 versus 0.74 log2 fold units)  . These oOur initial survey of the binding of Cas9 loaded with 90 different gRNAs at 5 nM RNP affirmed two main points: The vast majority of library species exhibit intermediate levels of both binding  and cleavage. To determine whether these behaviors can be described by a simple two-state binding model and to quantify the presence of nonproductive bound states, we selected 12 of the 90 gRNAs for association profiling at 1.25 and 20 nM and cleavage profiling at 20 nM RNP.kon, and koff. As protein concentration increases, the final fraction bound of a substrate also increases until it saturates at 100%. Yet, our extended dCas9 association data show that many Cas9 targets  do not saturate and instead plateau in their occupancy at levels far below 100%. Furthermore, for most sgRNA:target pairs, the observed final cleavage level was independent of Cas9 concentration (fig. S4A and table S11).Under a two-state binding model, the final fraction bound is a consequence of three independent parameters: protein concentration, To address these discrepancies, we added an additional parameter to our fit capturing this “maximal productive binding” to allow saturation below 100% of the DNA targets present in solution. Fitting the data in this manner thus models two phenomena: concentration-dependent initial binding affinity and concentration-independent entry into a stable noncanonical bound state. Our data were generally well fit by jointly fitting the three concentrations  were observed only for off-targets of λ1 Cas9 RNP (and RNPs derived from λ1 sequence transformations) (fig. S4B and table S13). Unlike the other tested sequences, λ1-derived sequences are devoid of internal, noncanonical PAMs. It is likely that RNP-PAM interactions can dominate the initial binding affinity observed for targets with multiple PAMs. In these circumstances, most target mismatches may not change the kinetics of binding because initial binding is mediated by PAM interactions rather than target complementarity. Our maximal productive binding measurement instead appears to align with the conventional understanding of Cas9 targets, which have an 8- to 10-bp seed region that is sensitive to disruption, an 8- to 11-bp PAM-distal region that is largely resilient, and an intermediate zone sensitive to large perturbations .Kd)  . In the We integrated the maximal productive binding estimates from our joint association fit data with the final cleavage level estimates from our 20 nM cleavage data to investigate the likelihood of each of the models described above. For a wide assortment of off-target sequences, the distribution of fit values strongly favors a model with subsaturating behavior of Cas9 for both productive association and cleavage . TargetsWe previously noted that longer RNP incubation times ultimately led to reduced dissociation . We confOverall, we tested 6865 off-target sequences across the same 12 sgRNAs measured across multiple dCas9 concentrations. Of these, 2300 did not have sufficient binding prior to dissociation, 618 were fit to a negligible off-rate in the joint association fit, and 2548 exhibited dissociation below the limit of detection given the time scale for the dissociation experiments we conducted. The remaining 1399 off-targets spread across the 12 gRNAs exhibited a similar pattern as seen for λ1: The loss of complementarity PAM-distally, from 20 to 16 bp, increased the observable dissociation, from 9 to 44% (for 15-min association experiments) and from 0 to 22% (for 60-min association experiments) . This inOur results suggest a model wherein Cas9 traps off-target sequences in slowly acting or nonproductive states that both are not bound by nitrocellulose and block progression to cleavage . Under tGRNP:perfect target). To group targets across Cas9 RNPs, we annotated mismatches  and RNA and DNA bulges (from 1 to 3 nt) at each position for each RNP:target pair and defined targets with identical annotations as sharing the same “target perturbation” (tables S15 and S16). We then fit an energy penalty that decreases the likelihood of productive binding or scission (ΔΔGperturbation) to every target perturbation. Initial attempts at modeling suggested that different Cas9 RNPs exhibited differential sensitivity to sequence perturbations; for this reason, we also included an energy scaling parameter (mRNP) that allowed the overall magnitude of these energy perturbations to vary by guide.To learn about the sequence determinants of productive binding and scission, we designed a biophysical framework adaptable to both parameters. We first assigned each of the 12 Cas9 RNP:perfect target pairs a baseline energy value to capture the partial productive binding and scission observed for perfect targets , as was the logarithm of the scission energy scaling parameter . From this, we infer that more energetically favorable Cas9 RNP:perfect target pairs suffer commensurately larger penalties when mismatches disrupt their pairing, unexpectedly linking binding sensitivity to specificity.Leave-one-out cross validation of RNP datasets suggested that perturbation penalties were stable and well correlated with maximal productive binding estimates on held-out data . Using this framework, we fit productive binding energy penalties for 446 distinct target perturbations and both RNP-specific energy parameters for 11 dCas9 RNPs using 4871 binding measurements, and, separately, scission energy penalties for 439 perturbations and RNP-specific energy parameters for 10 Cas9 RNPs using 3603 binding and cleavage measurements , especially for productive binding . In contrast, nonconsecutive double mismatches that were at least 4 nt apart appeared additive, suggesting that sufficiently distant mismatches may have independent effects on productive binding.We also explored whether maximal productive binding energy penalties for doubly mismatched targets were additive with respect to their constitutive single mismatches. Consecutive double mismatches clearly diverged from additivity. Most notable were PAM-distal positions where single mismatch energy penalties were set to 0, but double mismatch energy penalties exceeded 1 Gperturbation values could predict relative CRISPRi knockdown in human cells. Across 3011 promoters with singly mismatched sgRNA series, we compared measured CRISPRi phenotypes both to our estimated ΔΔGperturbation of productive binding and to activity predicted by a convolutional neural network (CNN) that incorporated additional features beyond the identity of the RNA-DNA mismatches (such as GC content and position relative to transcription start site) and was trained on this dataset , suggesting that biochemical parameters governing Cas9 binding and cleavage are the dominant features influencing in vivo efficacy. However, because the CNN model was trained only on single mismatch data, it is unable to predict more complex perturbations, whereas our ΔΔGperturbation predictions span a broad variety of off-targets including 1 to 3 nt bulges and mismatch series of arbitrary size that greatly expand the scope of off-target assessment. Unexpectedly, 204 of the 386 more complex perturbations we estimate (53%) have predicted off-target activity at least as great as that predicted for a singly mismatched target, highlighting the vital importance of considering off-targets with these and similar perturbations in in vivo off-target assessments.Lastly, we evaluated whether productive binding ΔΔ dataset  (27). Th3 off-targets per RNP per experiment and speculate that future applications of this technology to Cas9 or other DNA- or RNA-binding proteins of interest could assess more than 250,000 targets with straightforward protocol modifications. Thus, we believe that massively parallel filter binding represents cost-effective and operationally straightforward tool for profiling protein–nucleic acid binding kinetics.Here, we present a large corpus of Spy Cas9 binding and cleavage across diverse sgRNA sequences and corresponding DNA off-targets enabled by further parallelizing our pooled, sequencing-based filter-binding assay. We now report an amortized cost of 8 cents per off-target measurement. In contrast to imaging-based methods that require maintaining fluidics and microscopes, our new design requires minimal equipment: principally, a single 96-well vacuum manifold. We profiled ~10In this study, we show that differences in perfect target association kinetics appear to explain some of the differences in screening efficacy across sgRNAs. Two underappreciated phenomena—sgRNA folding and disadvantageous extended PAM sequences—appear to modify efficacy at the level of binding, with implications for both CRISPRi/a and CRISPR KO (knockout) screens , that positively correlates with target complementarity. We speculate that P(stop) may be functionally equivalent to what we report as the probability of productive binding and that nonproductive Cas9 RNP:target interactions are easily dismantled by either colliding with RNA polymerase or passing through nitrocellulose. Thus, our work adds to the growing biochemical evidence of nonproductive bound states  according to the manufacturer’s instructions, starting with 0.15 reaction units per sgRNA and scaled up to 0.5 units as needed to generate sufficient material for each sgRNA. sgRNAs were purified using Agencourt RNAClean XP beads for the all-sgRNA round (part no. A63987) and Zymo RNA Clean & Concentrator-5  for additional syntheses. Cas9 and dCas9 were provided by the Doudna laboratory.2, 5% glycerol, heparin (0.05 mg/ml), 1 mM dithiothreitol, and 0.005% Tween 20], a final working concentration of 50 nM. Loaded dCas9 was further diluted to attain the desired concentration  for association experiments and 20 nM for dissociation experiments in 1× binding buffer.For loading, each sgRNA was incubated at 98°C for 1 min and slowly cooled to room temperature. dCas9 was diluted to 100 nM and incubated with an equal volume of sgRNA at 20% excess in 1× binding buffer  was inferred as followsc(t) for each time point and calculating final fraction bound as before.For data visualization, fraction bound [Some targets were not fit due to the following criteria:1) Binding was not dynamic over the course of the experiment. The average final fraction bound at the two latest time point did not exceed the upper limit of the 90% confidence interval for each of the first two time points.2) Counts for the target sequence were too small to be reliable. There were fewer than five time points in the first half of the experiment that exceeded 30 reads.Targets that did not meet both of the above requirements were further stratified. Of the targets that were not dynamic, those that had at least five time points in the second half of the experiment where the entire 90% confidence interval exceeded 0 were fit to a horizontal line based on the time points in the second half of the experiment (no rate parameter). Of the remaining targets, those that averaged below 15% final fraction bound in the second half of the experiment were flagged as low affinity. The remainder  were annotated as noisy.ffinal times kobs. Cleavage data were fit in the same manner as binding data.We observed that after performing initial fits, some time points were consistent outliers across DNA targets in the same experiment. This could be explained by a biased control target count for such outlier points, which would affect the inference of fraction bound for all other targets. To address this, time points for which the magnitude of the averaged residuals exceeded 2.5 times the median magnitude of the averaged residuals were excluded, and count data were refit using the remaining time points. An average of 1.9 time points out of 16 and a median of 1 out of 16 were excluded per experiment across all association experiments. The association rates we report refer to MdCas9 is the concentration (M) of dCas9. fbound,maximal is the maximal productive binding.For joint association analysis across three dCas9 concentrations, measurements outside an inferred fraction bound from 0 to 150% were excluded as outliers. After filtering, the following equation was usedkon was initialized to 2 × 107 per M per minute, koff to 0.02 per minute, and fbound,maximal to 0.85.For joint fits, kon and koff in the manner of a Kd, reported in units of kTt > 0) were included in the fit.Initial binding affinity was calculated from fbound,minimum is the fit fraction of dCas9 that did not reverse on the time scale of the experiment. fbound,initial is the fit fraction of dCas9 bound at t = 0.Dissociation experiments were fit to a distinct equationkobs or koff below 0.02 per minute, an observed rate above 2 per minute, an ffinal above 1.2, or an ffinal below −0.2 were excluded as poor fits.After fitting curves, fits with an The LASSO model for 3′ context effects was fit using the R package glmnet. Coefficients for dimer identities by position were retrieved by running the coef command with parameter s = “lambda.1se”.GRNP:perfect target, whereby more negative energies favor productive binding. We anticipate that most Cas9 RNPs should have a value near or below zero such that the probability of productive binding is near or above 50%. In addition, we assign each sequence perturbation a fixed adjustment to the energy gap that applies independent of Cas9 RNP: ΔΔGperturbation. However, it stands to reason that different Cas9 RNP:target pairings will be differentially affected by mismatches and bulges. Specifically, it is expected that RNP:target pairs that are more energetically favorable should conversely suffer larger energy penalties when disrupted. We first attempted a parameter-free correction by using DNA:DNA and RNA:DNA duplex hybridization energies estimated by MELTING5, but performance was poor. Instead, we introduced another parameter (mRNP) to scale ΔΔGperturbation per RNP.We assume that the choice for a Cas9 RNP:perfect target pair between entering a productively bound state or a nonproductively bound state can be modeled with a simple energy gap: ΔFrom these parameters, we derived the probability of productive bindingMaximal productive binding data were organized into a matrix of sequence perturbations by dCas9 RNP guide sequences. Maximal productive binding levels were subject to a series of quality control steps:1) Overly large estimates of maximal productive binding (>150%) were replaced with missing values (NAs).GRNP:perfect target.2) Alternate perfect target contexts were removed to ensure one value for Δkon  or fast initial koff (>1 min−1) estimates were replaced with 2% maximal productive binding.3) Targets with slow initial 4) Targets with high estimates of maximal productive binding (>98%) were replaced with 98%.5) Targets with low estimates of maximal productive binding (<2%) were replaced with 2%.6) Sequence perturbations with fewer than four valid maximal productive binding estimates (from different RNPs) were removed.d.After filtering, 465 perturbations remained for fitting, with 19 redundantly encoded perturbations. One RNP (the reverse complement of VEGFA site 1) was excluded entirely because of low levels of productive binding and few valid fitted values, leaving 11 columns for a 465 by 11 data matrix mRNP parameters was constrained to 1 by fitting only 10 free parameters and inferring the 11th. mRNP values were initialized to 1, and ΔGRNP:perfect target values were initialized to 0. ΔΔGperturbation values were initialized by converting the difference in probability of productive binding from the perfect target to the perturbed target to a ΔG and taking the average across all dCas9 RNPs. If the average for a perturbation was below −0.1 kT, it was set to −0.1 kT.All productive binding parameters were fit jointly using the nls.lm function in R. The geometric mean of the mRNP values were bounded between 0.2 and 5. ΔGRNP:perfect target values were bounded between −6 and 3 kT. ΔΔGperturbation values were bounded between −0.1 and 6 kT.The data matrix m was predicted as followsd, removing NA values, and converting the matrix to a vector. After fitting, ΔΔGperturbation values below 0 were set to 0.Residuals were reported to nls.lm by taking the difference between After an initial fit, the top 20 perturbations ranked by mean absolute deviation were manually examined for potential outliers. Out of the 220 measurements examined, 9 dCas9 RNP:off-target pairs appeared to have extreme values and were designated as outliers. In a majority of cases, refitting the data had a marginal impact on the fitted values, suggesting that, overall, fits were robust to random error.d and taking the Spearman correlation between learned ΔΔGperturbation values and estimated probabilities of productive binding (which would be unaffected by ΔGRNP:perfect target or mRNP).Leave-one-out cross validation was performed by removing one column from Probability of scission data was fit in much the same way, with some added steps and modifications. Measurements where the probability of productive binding was below 2% were replaced with NAs. Final cleavage levels above 99% were replaced with 99%. The probability of scission was calculated as maximal productive binding divided by final cleavage level. Probabilities of scission above 99% were replaced with 99%. Probabilities of scission below 10% were replaced with 10% because low levels of scission were difficult to resolve, especially when the probability of productive binding was low.Gperturbation values were bounded between −0.1 and 7 kT because probability of scission for perfect targets generally exceeded the probability of productive binding for perfect targets, which increased the range of detection from ΔGRNP:perfect target values. Lastly, out of the 200 measurements for the 20 perturbations with greatest error, only 4 measurements were deemed outliers.In total, 458 perturbations were able to be fitted, although one additional Cas9 RNP  was removed because of low levels of cleavage across all targets. ΔΔ


Glycerol pretreatment is a promising method for the environmentally-friendly transformation of lignocellulosic materials into sustainable cellulose-rich raw materials  to fabricate biocomposites. Here, a comparison of aqueous acidified glycerol (AAG) pretreatment of wheat straw (WS) with alkaline, hot water, and dilute acid pretreatments on the thermal and mechanical characteristics of their fabricated composite board is presented. A comparison of total energy expenditure during WS pretreatment with AAG and other solutions was estimated and a comparative influence of AAG processing on lignocellulosic constituents and thermal stability of WS fiber was studied. Results imply that AAG pretreatment was superior in generating cellulose-rich fiber (CRF) as compared to other pretreatments and enhanced the cellulose contents by 90% compared to raw WS fiber. Flexural strength of acidic (40.50 MPa) and hot water treated WS composite (38.71 MPa) was higher compared to the value of 33.57 MPa for untreated composite, but AAG-treated composites exhibited lower values of flexural strength (22.22 MPa) compared to untreated composite samples. Conversely, AAG pretreatment consumed about 56% lesser energy for each kg of WS processed as compared to other pretreatments. These findings recognize that glycerol pretreatment could be a clean and new pretreatment strategy to convert agricultural waste into high-quality CRF as a sustainable raw material source for engineered biocomposite panels. Global development and quest for biodegradable materials have increased over the past few decades owing to public concern about the environment, depletion of fossil reserves, political commitment and climate change. To realize the UN climate panel target of 50–80% reduction in greenhouse gas emissions, the shift from bondage of petro-fuels to sustainable resources is indispensable. Lignocellulosic materials offer an alternative, abundant, renewable, environmentally-benign, and low-cost raw fiber source for manufacturing of a plethora of biobased products. The use of waste lignocellulosic fibers for biocomposites promotes the biorefinery concept with the vision of a sustainable economy (bioeconomy). About 140 billion tons of lignocellulosic biomass is generated annually worldwide and a significant part of it has been considered as waste with an absence of any conflict with food availability .However, the incorporation of lignocellulosic fibers into the polymer matrix phase for composite manufacturing is challenging due to non-cellulosic constituents present over the fiber’s surface which impart weak fiber-binder interfacial conglutination . ConversMost of these fiber-cooking processes operate under extreme conditions of temperature and pressure or employ highly concentrated chemicals thus evoke critical environmental hazards and human health issues. The sustainable development goals (SDGs) of the UN in 2015 focus on numerous issues, many of which highlight the need for green and sustainable solvents for processing of renewable resources. Among various methods, organosolv pretreatment using glycerol can be conducted under normal pressure due to the inherent advantages of negligible volatility and economic benefits. Organosolv lignocellulose processing is realized as an emerging technology in a biorefinery context due to its advantages such as the ability to disintegrate lignocellulose into high-purity biopolymer components  with prominent efficiency, low capital investment, and the possibility of executing it in small-scale units, as well as facile solvent recovery and reuse . The higWheat straw strands and polyester resin (SIR-8340) were kindly acquired from Fiber Craft Industries, Lahore, Pakistan. Resin SIR-8340 is a general purpose unsaturated polyester resin with styrene monomer, produced by Saudi Industrial Resins Ltd. Jeddah, Saudi Arabia. Glycerol and all other chemicals were of laboratory scale with purity >95% and used as received.w/w), dilute acid (H2SO4 1.82% w/w), and hot water was performed under similar conditions as those of AAG pretreatment under 10% fiber loading [Lignocellulosic wheat straw (WS) was ground, sieved (particle size ≤ 0.5 mm), and washed with distilled water to remove any dust particles. For pretreatment, dried wheat straw was added into aqueous acidified glycerol  with 10% solid loading in a 500 mL conical flask . Pretrea loading . In eachAssuming the biomass pretreatment reactor as a closed system for which general thermodynamic energy balance equation can be written as :(1)∆U=Q+v2 is the flow velocity. Thus,v2/u2 in which u2 is the tip velocity. The following relation can be obtained from Equation (5) by applying the dimensional analysis:Work done (ller (q) : (3) q=NThermal energy supplied for the pretreatment of WS fiber can be obtained by using the following expression: mixture . Thus, Ew/w) solution of H2SO4.Contents of lignocellulosic constituents of untreated and pretreated WS samples were determined by the method reported elsewhere . BrieflyThe thermal degradation profiles of untreated and treated WS samples were measured from room temperature to 600 °C using TGA (Model: SDT Q600) under heating rate of 10 °C/min with nitrogen flow rate of 20 mL/min.w/w) solid loading [w/w) was added and mixed thoroughly followed by addition of untreated or treated biomass fiber. Subsequently, the initiator (methyl ethyl ketone peroxide) was introduced dropwise in the matrix-fiber mixture under vigorous mixing and gel-like mixture was put into stainless steel mold and thermally pressed at 70 °C and 20 MPa for 10 min. Polyester resin (SIR-8340) was used as a polymer matrix which was reinforced with WS particles under 25%  of the composite samples was performed on a universal testing machine (Model: Tira test 2810) using composite sample with 4 mm thickness and 40 mm gauge length. The cross-head speed for the test was 1 mm/min following ASTM-D790 standard .The thermal properties of untreated and composite panels made from various pretreated fibers were characterized by thermogravimetric analysis (Model: SDT Q600) under nitrogen atmosphere in the temperature span of 25–600 °C at the rate of 10 °C/min.Glycerol pretreatment also significantly reduced the hemicellulose contents from 40% in raw fiber to 13%. Enhancement in the cellulosic contents by partial removal of hemicellulose and lignin after acidified pretreatment of different lignocellulosic biomass has been reported in the literature ,18. In tTp  values of 315.8 °C, 333.1 °C and 313.2 °C compared to the value of 338.4 °C for glycerol treated WS fibers. The highest value of Tp shows the superiority of glycerol pretreatment in producing CRF as also indicated from lignocellulosic composition analysis . The minimum value of residue at 600 °C  was obtained for alkali-treated composites, which may be due to the least amount of lignin left in the alkali-treated fiber, as evident from TGA profiles of composite sheets prepared using untreated and AAG, dilute acid, alkaline and hot water-pretreated WS biomass are shown in Tmax value from 347.1 °C to 363 °C was observed for particulate composites fabricated form oil palm trunk after pretreatment with hot water [T10 value from 238 °C for untreated composites to 249 °C for biocomposites made from treated fiber [The present results are in accordance with the literature as various studies reported a boost in the thermal potency of lignocellulosic fiber based composites after different pretreatments. An increase in the ot water . An alkaed fiber  also noted fiber .3, respectively, was noted for biocomposite sample prepared from acid treated WS fiber. Hot water pretreatment was also suited to manufacture the biocomposite sheets with elevated flexural strength and modulus, as evident from Adequate mechanical characteristics of the biocomposites are mandatory for their widespread industrial usage in furniture, construction, and automotive sectors. Stress–strain profiles for the biocomposites made from raw and treated WS fibers are presented in The impact of different pretreatments of lignocellulosic fiber on the mechanical properties of biocomposites have been described in the literature and asserts the present results. Pretreatment of bagasse fiber with 1% sulfuric acid solution considerably enhanced the flexural strength of the fabricated composite sheets by 56% . IncreasTotal energy expenditure for biomass pretreatment is one of the critical factors to identify its economic feasibility. Energy consumed to process 1 kg of WS fiber by AAG, alkali, dilute acid, and hot water pretreatments is provided in Organosolv processes using glycerol have been extensively studied to produce cellulose-rich fiber and fermentable sugars from lignocellulosic residues but commercialization of this technology for conversion of lignocellulose into green composites is currently confined. Some commercialized processes are being developed such as Organocell , Chempolis , and glycerol-based Glycell™ ; but considerably high pretreatment cost  is still the main hurdle . The ene3 compared to a value of 23.85 MJ/m3 for untreated composites while the mechanical performance of the glycerol treated biocomposites was reduced with a SED value of 17.82 MJ/m3. Glycerol based processes could be a favorable technology for conversion of agri-waste into cellulosic fiber for green materials fabrication but uniform dispersion of treated fiber into polymer matrix to avoid agglomeration, limited knowledge regarding chemical reactivity of glycerol with solvated lignocellulosic components and optimization of pretreatment parameters are the captious issues need to be explored in the future.Sustainable biorefinery approach encourages the development of efficient pretreatment methods for lignocellulosic biomass and this study presents a comparative investigation for glycerol assisted pretreatment of wheat straw residue as a new approach to produce cellulose-rich fiber (CRF) for green composite fabrication. Lignocellulosic composition analysis of untreated and pretreated WS fibers enumerated the superiority of AAG pretreatment to produce CRF as compared to other pretreatment techniques such as hot water, dilute acid and alkaline solution. AAG pretreatment consumed about 56% lesser energy than other pretreatments indicating that this process could be favorable to operate at commercial scale. The composite samples prepared from dilute acid treated WS fibers showed an enhanced strain energy density (SED) of 24.15 MJ/m


Myeloid cells and fibroblasts involvement in different clusters were confirmed and linked to corresponding pathway activities. Collectively, our findings revealed more complex disease subtypes in SSc; Key gene mediators such as IL6, FGFR1, TLR7, PLCG2, IRK2 identified by network analysis underscored the scientific rationale for exploring additional targets in treatment of SSc.Pathophysiology of systemic sclerosis , an autoimmune rheumatic disease, comprises of mechanisms that drive vasculopathy, inflammation and fibrosis. Understanding of the disease and associated clinical heterogeneity has advanced considerably in the past decade, highlighting the necessity of more specific targeted therapy. While many of the recent trials in SSc failed to meet the primary end points that predominantly relied on changes in modified Rodnan skin scores (MRSS), sub-group analysis, especially those focused on the basal skin transcriptomic data have provided insights into patient subsets that respond to therapies. These findings suggest that deeper understanding of the molecular changes in pathways is very important to define disease drivers in various patient subgroups. In view of these challenges, we performed meta-analysis on 9 public available SSc microarray studies using a novel pathway pivoted approach combining consensus clustering and machine learning assisted feature selection. Selected pathway modules were further explored through cluster specific topological network analysis in search of novel therapeutic concepts. In addition, we went beyond previously described SSc class divisions of 3 clusters  and expanded into a much finer stratification in order to profile SSc patients more accurately. Our analysis unveiled an important 80 pathway signatures that differentiated SSc patients into 8 unique subtypes. The 5 pathway modules derived from such signature successfully defined the 8 SSc subsets and were validated by  Systemic sclerosis (SSc) is a heterogeneous autoimmune disease characterized by the dysregulated immune system, vasculopathy, and systemic fibrosis of the connective tissues. SSc is a rare disease with a prevalence around 240 patients per million in USA . Still, It is well accepted that both genetic and environmental factors contribute to the pathophysiology of SSc. Genome-wide association studies (GWAS) have identified some key genetic associations that confer susceptibility to SSc including major histocompatibility complex (MHC) II region as being the most significant in this disease –9. Toll-The disease of SSc is heterogeneous both clinically and genetically due to the involvement of multiple pathogenic pathways. Thus, a better understanding of the interplay of these factors is considered necessary in developing novel therapies for improved treatment options. Extensive efforts have been made to define and comprehend the disease mechanism at molecular level. Several microarray analysis studies or recent meta-analysis have delineated the existence of a few major key subtypes of SSc skin samples such as immune response dominant, fibro-proliferative and normal phenotypes –21. ThisS1 Table. Many of the studies we selected were clinical trials that involve medical intervention on patients; other studies have paired biopsy position (e.g. forearm vs back) design wherein non-involved skin area was sampled. We generally only considered baseline SSc patients and focused exclusively on involved (affected) skin samples on forearms to avoid any confounding factor. The availability of clinical information such as disease stage, severity and internal organ involvement varied a lot across studies, we hence concentrated on the factor of major disease phenotypes  in our research. Overall, we had a total of 141 SSc samples and 80 controls entered our meta-analysis, including 126 dSSc and 15 lSSc; the detailed sample distributions are listed in .All of the 9 SSc data sets were obtained by searching on 2 popular gene expression repositories: GEO or ArrayExpress databases, using key words “systemic sclerosis”, “systemic scleroderma” or “SSc”. We looked into studies published in the last 12 years and focused exclusively on microarray data. Next generation sequencing or other types of high throughput gene expression data were not considered since the absolute majority of public SSc datasets were based on microarray, and it is computationally more consistent to combine data sets of the same platform for meta-analysis. During the initial search, a total 26 SSc studies were identified from the two data repositories. They were subsequently screened for redundancy to exclude 3 studies sharing the same set of samples with others. An additional filter was applied to remove another 12 studies of non-skin samples. Lastly, we disqualified two studies (GSE9285 and GSE76806) as these data sets share much less common genes with others. The final list of studies included in our current investigation is specified in TM [TM entries using an incremental size of 50. The criterion was to include at least one pathway that contained a given key word when we searched from top to bottom. 2) We only kept the pathway that has enrichment scores over 3 in at least 5 samples out of 141 cases. In the end, a total of 693 pathways passed the filter and entered subsequent analysis.From each GEO study, the normalized gene matrix data  was carefully examined using PCA and boxplot profiling. We then applied GSVA algorithm   based on 90 percent of the samples, Euclidean distance and ward.D metrics. Optimal number of clusters were determined by visual inspection of consensus matrix pattern, CDF plot and delta plot from default function output as well as the plot of an external metrics: silhouette   . These mTable 1). In each cluster & pathway-module combination, the average of pathway enrichment scores was calculated (shown in S4 Table) and the value range was divided into 3 levels: High (> = 0.15), Moderate (-0.15~0.15), and Low (= <0.15) to reflect the pathway enrichment levels in each cluster and pathway module.Fast implementation of random forest method (ranger)  was chosTable 1) and performed two nodes prioritization algorithms  for each pathway set. In case of target list having more than 500 genes, only top 500 genes were used to cross-reference with MRSS in the following step.We extracted genes corresponding to the 5 pathway modules respectively  on  scoring  and querModified Rodnan skin score (MRSS) was extracted from one study from GEO. We then calculated Pearson correlation coefficients for each gene with MRSS based on quantile normalized gene expressions. MRSS associated genes were selected based on p value < = 0.05 and correlation coefficient > 0.We selected common genes across all 9 SSc studies and combined individual gene expression matrices into a gene level compendium matrix (12306 genes by 141 samples). Combat method   for the final meta-analysis. With a novel application of gene-pathway space projection, we sought to unify different SSc studies into a single pathway-based compendium before further dissecting their molecular signatures. Consensus clustering procedure was then applied to classify SSc samples into more discrete subgroups than previously documented 3 or 4 clusters [We surveyed PubMed and related GEO database of the past 15 years 2004 ~ 2019), selecting 9 relevant skin SSc studies through a multi-step screening and filtering process . Similarly, we failed to observe notable difference among diffuse, limited SSc samples and Controls, suggesting that the clinically groups may only differ on a subset of variable pathways. In addition, some of the disease samples were known to be highly similar to controls as documented by previous studies [Gene expression from individual study was transformed into pathway enrichment scores, and subsequently merged into one joint pathway compendium. We performed PCA on the compendium and found no potential batch effect or any outlier sample  were used in this process so that we did not exclude any interesting biological feature. As a result, a sum of 693 pathways passed the filters and entered downstream clustering analysis.Moderate filter steps based on Mean Absolute Deviation (MAD) and enrichment scores were applied to remove uninformative pathways. Relatively lenient cutoffs . Additionally, an external metric of silhouette scores was also generated  to demonstrate the optimal cutoff of SSc subtype numbers. Previous SSc studies mostly focused on 3 to 4 major SSc divisions [Fig 3A–3D). Eventually, we selected the optimal cluster number at N = 8 to strike a balance between minimal cluster size and a good stratification setting of SSc patients. Details on the cutoff determination methods of the 4 plots are specified in Methods section. The 8 subtype classification of 141 SSc patients defined a spectrum of disease clusters ranging from the biggest cohort of 28 (19.8%) samples to the smallest cohort of 8 (5.67%) patients (S2 Table). It was also noted that none of the clusters is significantly enriched with an individual SSc study; the overall association between cluster membership and GEO study sources is non-significant (Chi-square test: p = 0.15). In addition, neither dSSc nor lSSc patients was found to enrich a certain subtype (Chi-square test: p = 0.12), although some level of cluster associated lSSc depletion was observed in cluster 2 and cluster 4 (S3 Table). Overall, the 8 SSc clusters were established independent of study effects or clinical phenotypes (dSSc vs lSSc).We performed consensus clustering  on 221 Sivisions , 21, 37,Methods). A couple of algorithms reached more than 80% accuracy using all 693 pathways, however, only gbm, ranger and svmRadial could roughly uphold accuracy level when we rerun the cross-validation using top 50 or 100 features . Eventually, random forest (ranger) classifier was adopted since it on average achieved the highest level of accuracy in all 3 pathway modules (80.1%); In addition, it relied on a model based feature selection metric (Gini index) instead of random heuristic searching. We then adopted a step-up loop process using repeated cross-validation to determine an optimal number of top pathways that can reconstruct 8 SSc groups with comparative accuracy. As a result, a signature of top 80 pathways was elected whereby each SSc cluster can be interpreted biologically through its pathway enrichment pattern . We also compared the profiles of all SSc subtypes with that of control; highlighting 5 pathway modules that can literally differentiate the 8 clusters by their expression level combinations . It was readily evident that cluster 1 and 6 resembled control cohort, showing elevated level of cell metabolism pathways (Pathway-module Black). Meanwhile a minor difference was noted between the two subtypes that cluster 1 has slightly elevated level of immune response than cluster 6, suggesting a possible transitional role among cluster 1 samples. Such variation was also noted between cluster 2 and 4 wherein the average enrichment of Blue moderately differed between the 2 groups while both of them demonstrated a strong inflammation signature. Cluster 3, showing elevated level in Blue module only, formed a distinct cohort. Clusters 5 and 8 are again diverging solely on Pathway module Yellow although they are not so dissimilar to each other; both subsets demonstrated strong inflammatory, pro-fibrotic and cell-metabolism signatures. Lastly, cluster 7 appeared to be a separate “normal-like” cohort, enriching merely in a couple of metabolism pathways of Pathway module Yellow. Collectively, the 8 SSc cluster subtyping system was a more pertinent and finer stratification compared to the paradigmatic 3-type classifications  by previous literatures [S5 Table.A total of 693 pathways, many of which redundant, were used to establish the 8 cluster SSc classification. The sheer number of pathways involved in consensus clustering actually hampered our effort in deriving meaningful biological interpretations, not to mention questing relevant gene targets. In addition, we arrived at the 8 SSC subsets through a consensus clustering approach based on Euclidean distance metrics and the robustness of their cluster memberships may need further validation by a different method. To address these issues, we adopted a machine learning procedure aiming to reconstruct the cluster membership and reduce the number of pathways through an accompanying feature selection process. The goal of such process was to find a highly predictive pathway subset that can achieve the same level of classification accuracy as that of all 693 pathways. We evaluated a diverse set of popular multiclass machine learning algorithms based on 10-fold cross-validation of the training set  and used them as start nodes to query against the MetabaseTM biological network [After identifying 8 SSc subtypes using the pathway signature, we further explored the underlying gene interactions of each cluster through network analysis. The aim of such inquiry was to uncover latent gene hubs or upstream regulators that lay behind the pathway pattern; these genes, if established, could serve as potential drug target candidates for related SSc patient groups. To achieve this goal, we extracted all genes from each of the 5 pathway modules identified previously  n network . Both meS6 Table) based on 5 pathway modules, of which 92 unique genes emerge if we combine module sources. We picked a list of 9 genes  based on their MRSS associations and biological assessment, showing their profiles in Fig 5.Still, each algorithm led to hundreds of potential target genes ranked by their hub scores, we further reduced the target lists by overlapping with external clinical features such as modified Rodnan Skin Score (MRSS) which is an important clinical marker of SSc skin phenotype. We extracted MRSS from one study and correlated with genomic expressions. Genes significantly associated with MRSS were selected, and overlapped with the potential targets identified in pathway module based network analysis. This procedure generated a concentrated target list of 164 genes . Identified as key hub in its subnetwork, pursuing those three targets with small molecule compound or monoclonal antibody will possibly destabilize the topology of subnetwork, and normalize the aberrant biological pathways. A recent approval of Nintedanib (OFEV®) targeting FGFR for SSC-associated interstitial lung disease (ILD) substantiates our effort for drug targets based on PPI network analysis. Another possible target is IRAK2, a key serine/threonine kinase involved with IL1R and its downstream upregulation of NF-kB and its pathway. The function of IRAK2 in myofibroblast had been illustrated in a murine IRAK2 knockout model in which fibroblasts showed reduced reaction to multiple TLR ligands. In summary, our analyses has not only demonstrated some important biological pathways relevant to the disease, but also identified some important key hub genes/proteins within the biological pathway networks.We further focused on 3 genes of interest  from the 9 gene list and investigated their subnetwork connections . Endothelial cells and macrophages were also included in the analysis as a comparison. A follow-up pairwise t-test was carried out to compare among the clusters and corresponding results  were recorded in S7 Table. The overall cellular enrichment pattern appeared highly consistent with the cluster pathway features . Specifically, cluster 1 showed low level of fibroblasts and endothelial signatures, corresponding to the under-enriched Pathway module Blue, on the other hand, it had moderate enrichment in myeloid and macrophage that corresponds to Pathway module Red. Cluster 2 exhibited elevated level of myeloid + macrophages (Pathway module Red & Green) and moderate enrichment in fibroblasts + endothelial (Pathway module Blue). Cluster 3 only enriched in fibroblasts + endothelial which was reflected by Pathway module Blue in Fig 4B. Cluster 4, with strong expression in all 4 cell types, had elevated enrichment in Pathway modules Blue, Red and Green. In the similar manner, cluster 5, 6, 7 and 8 respectively displayed enrichment pattern of the 4 cell types that linked to 3 Pathway modules. In summary, Red and Green pathway groups are strongly associated with myeloid signature while Blue pathways correspond steadily with fibroblasts activity. The establishment of such cells-to-pathway affiliation also helped us defining the main biological functions of each pathway module (Table 1). In contrast to the previous studies which demonstrated differential expression levels of multiple immune cell lineage gene signatures, our cellular deconvolution demonstrated the prevalent myeloid cell signature among multiple SSc clusters, especially the co-variation between myeloid and macrophage gene signatures. The dysfunction of macrophage can lead to aberrant tissue repair and uncontrolled tissue modelling by communicating with epithelial cells, endothelial cells and fibroblasts by producing pro-fibrotic mediators and enhance the survival and activation of myofibroblasts. Hence, our analysis substantially underscored the contribution of myeloid cells to SSc pathogenesis.When examining genes identified in network analysis, we recognized that many of them were highly related to myeloid cell and fibroblast signatures. A cell type enrichment analysis was then performed to profile fibroblasts and myeloid components in each cluster . For instance, cluster 1 and 6 resembled the control cohort, showing active cellular metabolism pathways (Pathway module Black) similar to the normal control group. However, we noticed a subtle difference between the two subtypes that cluster 1 was actually mixed with elevated levels of immune response genes. Cluster 2 and 4 classified as Pathway module Blue, demonstrated moderate difference in terms of the extent of the enriched inflammation signature. Cluster 3, showing elevated levels in the Blue module only, formed a distinct cohort. Consistent with literature, Cluster 5 and 8, characterized as Pathway module Yellow exhibited a mixture of strong inflammatory, pro-fibrotic and cell-metabolism signatures. Lastly, cluster 7 appeared to be a separate “normal-like” cohort. Collectively, the 8 SSc cluster subtyping system was a more pertinent and finer stratification compared to the paradigmatic type classifications  by previous literatures [Previous SSc meta-analysis integrate multiple datasets with varying source effects by merging data together after normalization and baseline correction procedure . We adoperatures , 37, 38.The 8 SSc subtypes we assessed in the study can be grouped to inflammatory, fibro-inflammatory, metabolic-fibrosis, metabolic, and normal-like phenotypes. For the inflammatory dominant clusters, such as cluster 2 and cluster 4, it is evident that IL17, IL22 pathways were highlighted, suggesting the hyperactive Th17 cells in such groups of patients. Current knowledge indicates complex roles of Th17 in fibrogenesis: One study demonstrated that IL-17 derived from Th17 cells promoted collagen production and fibroblast growth . AnotherAnother distinct computational approach we adopted was to analyze the topology of PPI networks derived from each cluster using two algorithms: 1) random walk  and 2) nin silico cellular deconvolution was applied to determine to what extent each immune or stromal cell subset is associated with the identified 8 disease subtypes. Our analyses showed that the SSc metabolic phenotype normally correlated with low enrichment of cellular components–except for cluster 8, which are enriched by all cellular components. The general moderate-to-low patterns of cell type enrichment shown in Fig 7 suggest that the SSc subtypes with dominant metabolic pathways are associated with a “normal” like phenotype. In contrast, clusters 2, 3, 4, and 5, which are annotated as immune response dominant, exhibited substantially elevated levels of myeloid lineage enrichment. In particular, Cluster 4, with strong expression in all 4 cell types, has elevated enrichment in the Pathway module of pro-fibrotic and/or immune responses. In general, this analysis underscores the important involvement of myeloid cells, particularly macrophages, linked to fibroblast pathway genes. The establishment of such cells-to-pathway affiliation helped to define the main biological functions of each pathway module.To expand the biological discoveries from our meta-analysis, Our meta-analysis on SSc public studies, on the other hand, is not without limitations: Firstly, it has always been challenging to achieve enough sample size in meta-analysis of this type of rare human disease. In addition, there is a severe unbalanced distribution of lSSc and dSSc patient numbers in our analysis, which limited the power to delineate the molecular differences between the two categories. Thirdly, all the SSc public datasets available so far are based on microarray platforms that have limited resolution due to probe designs. Last and most importantly, the lack of important clinical information–e.g. disease duration or treatment history, in majority of the public datasets may hamper our effort in accurately defining the disease phenotypes. For example, some subtype of patients such as the normal-like group, may simply represent the early stage of the disease if we have disease duration data available. In view of these concerns, a more comprehensive future longitudinal study of SSc patients is needed to unveil critical biological mechanisms behind the etiology, disease progression, and prognostics of this dangerous autoimmune disorder.In summary, our findings based on the current pathway driven meta-analysis indicate that there exists more complex disease structure in SSc than previously described. These results also confirmed the important role of myeloid cells in SSc pathogenesis. Nevertheless, the extent of contribution to disease phenotype through myeloid cell involvement still warrants further examination. Meanwhile, important hub genes identified through our module specific network analysis overlapped strongly with current clinical evidences; some of the genes are potential drug targets and may serve as the foundation for next generation of SSc treatment.S1 Checklist(DOC)Click here for additional data file.S1 FigSamples are colored by study sources and clinical phenotypes are denoted in different shapes.(TIF)Click here for additional data file.S1 TableMethod section. 1 Whitefield Lab from department of Genetics, Dartmouth Medical School, Hanover, NH, USA; 2 Assassi Group from University of Texas Health Science Center, Houston, TX, USA; 3 Bioinformatics Core facility, University of Manchester, Manchester, UK.Some samples are repeated at different time series points, before-after treatments or biopsy positions. Detailed inclusion or exclusion standards are specified in (DOCX)Click here for additional data file.S2 Table*Percentages in the table represent total percentages.(DOCX)Click here for additional data file.S3 Table*Percentages in the table represent total percentages.(DOCX)Click here for additional data file.S4 TableTable 1 was calculated and shown.The average of pathway enrichment scores for each cluster & pathway-module combination from (DOCX)Click here for additional data file.S5 TablePathways modules identified from (DOCX)Click here for additional data file.S6 TablePearson correlation coefficient and related p value are used to denote association between MRSS and gene expression, the algorithm sources  and pathway module (1~5) are shown in column 1 and 2.(XLSX)Click here for additional data file.S7 TableOne-sided Welch T test was performed to compare cell type enrichment level among the 8 SSC clusters and the control group. P value matrix (corrected by Bonferroni procedure) was generated. The comparison was based on the alternative hypothesis that cluster in row label > cluster in column label(XLSX)Click here for additional data file.


BIRC5 (Survivin) andYBX1,as well as long-noncoding RNAsHOTAIR,ZFAS1, andAGAP2-AS1 were detected in BT-474 EVs by quantitative RT-PCR. Bioinformatics meta-analyses showed thatBIRC5 andHOTAIR RNAs were substantially upregulated in breast tumours compared to non-tumour breast tissue, warranting further studies to explore their usefulness as biomarkers in patient EV samples. We envision this effective procedure for obtaining large amounts of cancer-specific EVs will accelerate discovery of EV-associated RNA biomarkers for cancers including HER2+ breast cancer.Extracellular vesicles (EVs) are emerging as key players in breast cancer progression and hold immense promise as cancer biomarkers. However, difficulties in obtaining sufficient quantities of EVs for the identification of potential biomarkers hampers progress in this area. To circumvent this obstacle, we cultured BT-474 breast cancer cells in a two-chambered bioreactor with CDM-HD serum replacement to significantly improve the yield of cancer cell-associated EVs and eliminate bovine EV contamination. Cancer-relevant mRNAs EVs have been associated in many studies with resistance of cancer to chemo or radio therapies2.Interactions between tumour and stromal cells sculpt the tumour microenvironment and contribute to cancer malignancy, metastasis and immune evasion. Extracellular vesicles (EVs)3, either alone or combined with the detection of circulating tumour DNA (ctDNA) or circulating tumour cells (CTCs)4. Upregulation of RNA transcripts including long-noncoding RNA (lncRNA) offers a means for distinguishing EVs originating from tumour and non-tumour cells. LncRNAs are greater than 200 nucleotide-long transcripts constituting two thirds of the transcriptome and appear to play a critical role in carcinogenesis of many cancers including breast malignancies11, and constitute an understudied class of EV cargo13. HER2-positive breast cancers were reported to differentially express over 1,300 unique lncRNAs compared to non-tumour breast tissue15. Some of the HER2-associated lncRNAs identified to date includeZFAS1 which was found to associate with ribosomes in breast cancer cells17; HOX transcript antisense intergenic RNA(HOTAIR) which is involved in regulation of chromatin states and targets genes related to tumour metastasis and correlates with poor prognosis19; and AGAP2 antisense RNA 1 (AGAP2-AS1) which promotes resistance of breast cancer cells to trastuzumab through EV signalling20. Some mRNAs specific to or upregulated in breast cancer cells may also serve as HER2-EV biomarkers, includingEpCAM which affects intercellular adhesion and is relevant to tumour progression21;BIRC5  which is involved in regulation of mitosis and apoptotic inhibition22; andYBX1 (the Y-box binding protein) which is an oncogenic transcription and translation regulator that regulates cell invasion and migration23. The overexpression of these mRNAs  were all associated with poor outcomes for breast cancer patients24. RNAs represent promising EV-associated biomarkers but difficulties in producing sufficient amounts of pure cancer associated EVs complicate validation of RNA presence in EVs.EVs contain cargo specific to their parental cell, are very stable, and circulate in blood and other bodily fluids. These properties make EVs prime candidates for cancer detection in liquid biopsies25. This strategy allowed us to obtain sufficient EV yields to demonstrate that tumour cells release EVs associated with several potential breast cancer biomarkers.Here, we present a simple solution for obtaining high quantities of cancer-associated EVs by culturing the HER2-positive breast cancer cell line BT-474 in a CELLine AD 1000 two-chamber bioreactor flask. The CELLine bioreactor system mimics physiological growth conditions by allowing three dimensional (3D) cell growth on a fibre-mimetic surface, resulting in increases in cell number and EV production8 cells/mL) in 15 mL Advanced DMEM/F-12 medium  supplemented with 2% CDM-HD serum replacement  in the lower cell chamber of a CELLine AD 1000 bioreactor flask . The same media (150 mL) was used in the upper media chamber but supplemented with 2% FCS  from contaminating the cancer-specific EVs, we cultured BT-474 cells from ATCC (ATCC® HTB-20™)  as outlined inNegative staining TEM of pooled EV fractions was conducted by adsorption onto Formvar-coated copper grids  for 2 min, then treated with 2% uranyl acetate for 2 min. Grids were then visualised on a Tecnai G2 Spirit TWIN  transmission electron microscope at 120 kV accelerating voltage and images were captured using a Morada digital camera .26. Breast cancer cell lines were grown to log-phase, washed twice with ice-cold PBS, and lysed in an sodium dodecyl sulphate (SDS) lysis buffer . Proteins (25 μg) were separated by SDS-polyacrylamide gel electrophoresis (PAGE) and transferred to PVDF membranes. Membranes were subsequently immunoblotted with antibodies recognising human HER2 , human EpCAM , human alpha-Tubulin  and human TSG101  and corresponding secondary antibodies. Bound antibodies were visualized using Pierce™ ECL Western Blotting Substrate  and the chemiluminescence was measured using a BioRad ChemiDoc MP imaging system .This procedure was carried out as described previouslyEpCAM21,BIRC522,YBX123,GAPDH, andHPRT1, and lncRNAsZFAS117,HOTAIR19, andAGAP2-AS120. Three independent experiments were performed with duplicate PCR reactions per sample. RT-qPCR data were presented as cycle threshold (CT) values. Expression values were normalized relative to GAPDH mRNA expression. Statistical analysis was performed using multiple T-test. Technical triplicates of Trizol-purified RNA from each experimental condition were reverse transcribed into cDNA using qScript Flex cDNA kit  primed with equal molar ratio of oligo-dT and random primers according to the manufacturer’s instructions. Quantitative RT-PCR was carried out using SYBR Green MasterMix  and gene-specific primers previously validated in the literature . These ihttps://xenabrowser.net/datapages/) and was manually annotated. This procedure has resulted in a dataset called “Figure 2B and C_meta_analysis_rawdata.xlsx” deposited in the DRYAD Digital Repository and used for all subsequent analyses. All data manipulations, plotting and statistical analyses were carried out in R computing environment (v 3.5.3) running in R Studio (v 1.1.414) on a Windows 10 x64 machine. The ggplot2 package (v 3.3.0) was used to renderFor this meta-analysis, the “RSEM expected count (DESeq2 standardized)” dataset was downloaded on 31st March 2020 from the TCGA_GTEx_TARGET cohort included in the UCSC Xena portal (https://doi.org/10.1101/2020.09.27.309252).An earlier version of this article can be found on bioRxiv  was found between cells and EVs in the RNA analysed  and Genotype Tissue Expression (GTEx) databases, respectively. Tumour and non-tumour tissues in all 20 tissues analysed expressed similar levels of cancer .27. In addition, the common issue of contaminating bovine EVs29 was avoided by using the serum replacement CDM-HD, which is chemically defined, protein free, and animal component free. This bioreactor system provided highly enriched EVs in 15 mL of conditioned media, avoiding the sample loss and extra time associated with pre-centrifugation concentrators. Bioreactors were shown to improve the EV yield by over ten-fold (per volume) compared to conventional cell culture34. Cell lines including those from prostate cancer, mesothelioma, oral squamous cell carcinoma, melanoma and breast cancers were shown to grow in CELLine bioreactor35. Although it has been reported that cell morphology and surface markers are comparable, cells cultured in the bioreactor and conventional flasks appear to produce EVs with different metabolite content35. This could be due to 3D arrangement of cells in the bioreactor compared to monolayers in conventional flasks. The main drawback is the inability to visually observe the cells. Although the CELLine flask can be used for over 3 months of continuous cell culture, the initial cost of the CELLine flask is significantly higher than the conventional flask.While EVs hold promise as liquid biopsy targets for breast cancer, efficient production of EVs for molecular characterisation of EV-associated RNA can be challenging using conventional culture systems. In this technical feasibility study, we circumvented this obstacle by culturing BT-474 cells, a commonly used HER2-positive cell line, in a CELLine AD 1000 two-chambered bioreactor, which increased the cell density and EV production due to the unique growth surface and fluid interactions36. We then demonstrated that the BT-474 small EVs were associated with lncRNAsZFAS1,HOTAIR, andAGAP2-AS, as well as mRNAsBIRC5, YBX1, HPRT, andGAPDH using qRT-PCR.We verified that the EVs contained HER2, EpCAM, and TSG101 proteins. Transmission electron microscope imaging also allowed us to be confident that we had truly isolated small and large EVs in accordance with the MISEV guidelinesEpCAM mRNA was not detected in the small EVs although the EpCAM protein was detectable in the corresponding cell lysates, large EVs, and small EVs. Differential RNA expression in cancer, especially upregulation, has potential to infer a gene’s utility as a biomarker. Our finding indicates that RNAsBIRC5 andHOTAIR are promising EV-biomarkers, particularly in breast cancer, where they are substantially upregulated compared to non-tumour breast tissue. Of interest, EV associated lncRNAHOTAIR was reported to correlate with HER2-positive breast cancer37. Upregulation of serum exosomalHOTAIR was also reported to associate with poor response to chemotherapy in breast cancer patients38.Interestingly, the cancer-specific39. Further, it is more economical to detect RNA than protein biomarkers because each protein biomarker requires a specific antibody. These findings demonstrate the efficient production of enriched BT-474 EVs and highlight their unique cargo, especiallyBIRC5 mRNA andHOTAIR lncRNA. Further studies to determine their clinical significance are warranted.Currently, proteins dominate the EV biomarker field. However, novel EV-associated breast cancer biomarkers like lncRNAs and other RNAs are being explored more thoroughly to aid in detection and management. RNA biomarkers have higher sensitivity and specificity than proteins because PCR can amplify traces of RNA sequences with high specificity and sensitivityhttps://doi.org/10.5061/dryad.jdfn2z39340.DRYAD: Towards establishing extracellular vesicle-associated RNAs as biomarkers for HER2+ breast cancer.This project contains the following underlying data:-Figure 1B_image_57.tif (Raw data for TEM image)- Figure_1C_NTA_Capture_MEV_ExperimentReport.pdf  with red vertical lines and blue numbers denote standard deviation and diameters at specific peaks, respectively)- Figure_1D_NTA_Capture_SEV_ExperimentReport.pdf  with red vertical lines and blue numbers denote standard deviation and diameters at specific peaks, respectively)- Figure_1E_qEV_BCA_and_particle_data.xlsx  column)- Figure_1F_raw_not_cropped.pptx (Raw western blot images)- Figure 2A_RT_qPCR raw data.xlsxEpCAM, BIRC5, YBX1, GAPDH, HPRT1) and three long non-coding RNAs  in BT-474 cells and their EVs.)For Figure 2A  and three long non-coding RNAs  in BT-474 cells and their EVs.)For-UCSC Xena portal) Figure 2B and C_meta_analysis_rawdata.xlsx  expression values of 10 genes in 8,867 tumours and 6,874 normal tissues downloaded on 31st March 2020 from theThe R script containing the code for all the above computations and visualisationsCreative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the No further comments.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:NAI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. I don't have any further comments. Authors have addressed some of my comments so I'm happy to approve it.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:NAI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. No further comments.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Breast biobanking, Mammary gland development, Breast cancer preventionI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors report a novel culture model to isolate and increase the yield of extracellular vesicles (EVs) using a breast cancer cell line. They also claim that some RNA molecules present in EVs are predominantly expressed in tumour tissues compared to their normal tissues’ counterparts. My major concerns are the following: In Figure 1 – Experiments comparing the results with conventional 2D culture system should be shown to support the statements in the paper. And comparison using bovine serum should be shown too.1. Use of specific EV markers is needed to confirm the presence of EVs – see Gonzalez et al, Plos One, 2014 Use of at least another cell line is warranted to validate the data and use of primary cells would be ideal to assess potential clinical translation of the data. Figure 2A – The rational for selection of this list of EV-associated RNAs is not clear and needs to be defined. Real-time PCR data should be normalized to a reference transcript. Also, it is not clear if these experiments were done with large EVs, small EVs or both? If data corresponds only to small EVs then the experiments should be repeated with large EVs too. Authors state “RNAs BIRC5 and HOTAIR are promising EV-biomarkers” but this is not proven by the data. The housekeeping genes GAPDH and HPRT are also expressed in EVs. Testing the presence of BIRC5 and HOTAIR RNAs in tumour vs non-tumour EVs is warranted. Number of independent experiments and replicates needs to be stated throughout the manuscript.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Breast cancer biologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Figure 1 could be improved by enlarging region of Fig 1A focusing on the part of the chamber growing the cells and the isolation of EVs. Could an antibody directed against a histone or tubulin be used in the western blot  to show that the isolated EVs are not contaminated with any cellular debris?In Figure 2A are the differences in HOTAIR (and the others) significant between BT474 cells and EVs? To what p-value? The growth of cells in this system shows that there are differential levels of HOTAIR in the cells vs EVs, and gene expression data is presented to demonstrate the over expression of HOTAIR in tumours.The identification of extracellular vesicle (EV) biomakers is certainly of interest in breast cancer research. This paper presents methodology to grow and isolate sufficient EVs to enable the investigation of their cargo. This study describes the growth of the breast cancer BT474 cell line in a CELLine AD 1000 bioreactor flask with media containing CDM-HD serum. This allowed the cells to be grown in 3D conditions and large numbers of EVs to be isolated without contamination from bovine EVs. It will be of interest to test the presence of HOTAIR in malignant vs non-malignant EVs, to support the HOTAIR as a breast cancer EV biomarker. The authors discuss the advantages of RNA biomarkers and while a candidate approach is taken here, this ability to isolate sufficient EVs allows them to be used for RNAseq etc.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Cancer and chromatin biologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Extracellular vesicles (EV) are fast emerging as both therapeutic agents and biomarkers. Low yields of EVs in commonly used experimental models have somewhat diminished interest and their further scrutiny. Methodologies to improve EV yield in short term cultures are desirable and much needed for this field. In this brief communication, the authors report a culture method to enrich EV from low EV yielding breast cancer cell line, and using public datasets of tissues to bioinformatically demonstrate that these EVs are associated with RNA species enriched in tumors compared to their respective normal tissues.Please provide the rate of EV production using standard 2D culture method vs your novel bioreactor-based method. It may perhaps appeal to labs working in this area to know the rate of production of EV per ml media per 1million seeded cells per hour in culture (both standard vs bioreactor). Figures: It is not clear how many times these experiments were repeated or number of samples used. Please state them in the Figure Legends. Figure 1C-D showing hydrodynamic diameter distribution profiles should have intervals in Y-axis.The authors should discuss possible drawbacks to this method or choice of cell lines used in this study. Could you further elaborate on how the TCGA data was analyzed in your methods in order to help reproduce your findings by others. The EV enrichment method appears to be simple and straightforward, and therefore the applications in cancer field is apparent. I have the following comments for the authors.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Breast biobanking, Mammary gland development, Breast cancer preventionI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.