Here, to overcome this lack of knowledge on the functionality of repressing riboregulators, translation inhibiting RNAs are developed from scratch. These de novo developed riboregulators explore features related to thermodynamical and structural factors previously attributed to translation initiation modulation. In total, 12 structural and thermodynamic features were defined of which six features were retained after removing correlations from an in silico generated riboregulator library. From this translation inhibiting RNA library, 18 riboregulators were selected using a experimental design and subsequently constructed and co-expressed with two target untranslated regions to link the translation inhibiting RNA features to functionality. The pure riboregulators in the design of experiments showed repression down to 6% of the original protein expression levels, which could only be partially explained by a ordinary least squares regression model. To allow reliable forward engineering, a partial least squares regression model was constructed and validated to link the properties of translation inhibiting RNA riboregulators to gene repression. In this model both structural and thermodynamic features were important for efficient gene repression by pure riboregulators. This approach enables a more reliable de novo forward engineering of effective pure riboregulators, which further expands the RNA toolbox for gene expression modulation.Metabolic engineering increasingly depends upon RNA technology to customly rewire the metabolism to maximize production. To this end, pure riboregulators allow dynamic gene repression without the need of a potentially burdensome coexpressed protein like typical Hfq binding small RNAs and clustered regularly interspaced short palindromic repeats technology. Despite this clear advantage, no clear general design principles are available to  de novo developed repressing riboregulators, which limits their applicability. Here, various engineering principles are identified and computationally explored. Subsequently, various design criteria are used in an experimental design, which were explored in an in vivo study. This resulted in a regression model that enables a more reliable computational design of repression small RNAs.To allow reliable forward engineering of microbial cell factories, various metabolic engineering efforts rely on RNA-based technology. As such, programmable riboregulators allow dynamic control over gene expression. However, no clear design principles exist for  Over the last decade, synthetic biology and systems biology spurred major advances in metabolic engineering, resulting in several economically competitive production processes for both bulk and fine chemicals from renewable resources, revolutionizing industrial biotechnology , 3, 4, 5Alternative approaches to control gene expression on the translational level employ small RNAs (sRNAs) to repress protein production by blocking translational initiation, enabling metabolic flux redirection at will , 17, 18.trans expressed RNA employed a variety of features [trans expressed RNA molecule [Early pure riboregulators were designed to hybridize the translation initiation region. The RNA architecture in this region plays a pivotal role in the translation initiation process, enabling gene expression through RNA-RNA interactions . This apfeatures , 30, 31.molecule , 34, 35.These features are classified as either structural or thermodynamic features. Several structural features of riboregulators modulating translation initiation through RNA-RNA interactions include post-transcriptional ribosome binding site (RBS) occlusion , 34, forde novo, the latter being the most interesting as this enables forward engineering in a broader context [de novo, which limits the construction of genetic circuitery using solely RNA.This broad range of employed features indicates the lack of consensus in literature, which limits the general applicability of the current design rules for pure riboregulators (without using coexpressed proteins). For instance, simply expressing the antisense strand does not fully repress gene expression on the post-transcriptional level , 23. As  context , 36. Morde novo design of pure riboregulators, referred to as translation inhibiting RNAs (tiRNAs), which repress gene expression by blocking translation initiation. To develop this predictive framework, the influence of all features previously attributed to post-transcriptional gene modulation were analyzed in a design of experiments (DOE). This experimental design allows exploration of the feature landscape and evaluation of their influence on gene repression. First, using a library of tiRNA, all features were analyzed in silico to create a collection of features with maximal information content. Next, the performance of de novo designed tiRNAs was evaluated in vivo, and used to construct an ordinary least squares (OLS) and a partial least squares (PLS) model which links riboregulator features to gene repression.Here, we propose a framework for the Escherichia coli strain DH10B (Invitrogen) was used for both plasmid construction and fluorescence measurement purposes. Unless otherwise stated, all products were purchased from Sigma-Aldrich . For plasmid construction and fluorescence measurements strains were grown in grown in lysogeny broth (LB) and MOPS EZ rich medium  at pH 7.4, respectively at 37°C with shaking. LB was composed of 1% tryptone-peptone , 0.5% yeast extract (Difco) and 1% sodium chloride . LB agar (LBA) plates contain the same components as LB with the addition of 1% agar. If required, medium was supplemented with 100 μg ml-1 ampicillin and 50 μg ml-1 kanamycin.mKate2 [pSilence plasmids were medium-copy vectors (pBR322 origin of replication (ori) and ampicillin resistance marker, originating from pSB6A1 ) with a mKate2  as repormKate2  as termimKate2 . A schem1 and pBlank2, which are the same vectors as the pSilence and pTarget plasmids, respectively. The pBlank1 plasmid comprises only the vector backbone and pBlank2 contains the mKate2 open reading frame (ORF) and rnpB T1 [The control plasmids used in this study were pBlank rnpB T1  as termi rnpB T1  and CPEC rnpB T1  assemblyin vivo assessment of translational inhibition, strains were plated on LBA plates containing 100 μg ml-1 ampicillin and 50 μg ml-1 kanamycin. After overnight incubation, three colonies were inoculated in 150 μl MOPS EZ rich medium, covered by a Breathe-Easy sealing membrane (Sigma-Aldrich), and grown overnight on a Compact Digital Microplate Shaker (Thermo Scientific) at 800 rpm and 37°C. Subsequently, these cultures were 1:100 diluted in 150 μl of fresh MOPS EZ rich medium and grown on a Compact Digital Microplate Shaker until late log phase (6 h) at 800 rpm and 37°C. Subsequently, fluorescence and OD were measured using a Tecan M200 pro microplate reader. Precultures were grown in Greiner bio-one  polystyrene F-bottom 96 well plates. Fluorescence and OD measurements were performed after growth in Greiner bio-one  black μclear 96 well plates. For measuring mKate2 expression an excitation wavelength and an emission wavelength of 588 nm and 633 nm were used, respectively. OD was measured at a wavelength of 700 nm to reduce bias in estimates of cell abundance [For bundance .E. coli DH10B cells without fluorescent protein expression (contains pBlank1 and pBlank2 plasmids). The medium blank was used to correct the background OD (ODbg) of the medium. The fluorescence of the strain without fluorescent protein expression (FPbg) was used to correct for the background fluorescence of E. coli. For all strains fluorescence per OD was calculated as follows:For fluorescence measurements, two types of controls were used on every 96-well microtiter plate, i.e., a MOPS EZ rich medium blank and -100, all other settings are set to the default setting. Suboptimal structures of RNA molecules are drawn with probabilities equal to their Boltzmann weights using RNAsubopt [For each tiRNA candidate 12 features were calculated  were calculated using the hydroGOF package in R.All statistical calculations and analyses were performed in R. Unless otherwise stated, error bars represent the standard deviation (n = 3). All coefficient of determinations  were centered and scaled based on the 0.1 and 0.9 p-quantiles  of the original distribution of feature X  values of FAB for all data points, including data from target UTR1 and UTR2 in a training set and validation set (5:1 ratio). Subsequently, the training set was used to create the model by leave-one-out cross validation where predictors were scaled prior to regression (by dividing each variable the sample standard deviation). In PLS regression the matrix of predictors X is decomposed into orthogonal score matrix T (projection of X) and loadings matrix P, circumventing potential collinearities in the data set:Y is regressed on the scores T (and not X). The specific PLS algorithm used is kernel PLS, which was described by Dayal et al [The PLS regression was done in R with the package pls . The PLSal et al .trans expressed tiRNAs are de novo designed to inhibit translation initiation of a gene of interest, the rate-limiting step in translation [de novo development of repressing riboregulators through DOE guided exploration of the sequence space was developed and optimized  was created i with limited correlations, i.e. FAA, FAB, EIS, PAU, RBS11, and PT, was used in a DOE to unravel the features with the most influence on the repression efficiency of these pure riboregulators (see 6-2 design), comprising two center points and 16 factorial points. After rescaling all features  see . The cortors see . More spin vivo as depicted in trans acting sRNA typically require relatively higher expression of the sRNA compared to its target, in both natural and synthetic sRNA regulation systems [Subsequently, the performance of these 18 tiRNAs in the DOE was evaluated  systems , 58.1 or UTR2 , which is in contrast to previous studies inversely relating translation to the Gibbs free energy of the UTR [2 forms much less base pairs in the region around the Shine-Dalgarno (SD) sequence  does not possess any of the features and serves as a control when combined with UTR1 to determine the burden of the promoter used, which is not significantly different from the strain containing pBlank1 and pTarget1. This is in accordance with literature determining the burden of RNA riboregulators, which is low compared to other types of gene expression regulation [de novo designed riboregulators shows that almost all tiRNAs were active. Specifically, eight of the 18 tiRNAs targetting UTR1 inhibit the translation initiation of UTR1 with more than 75%. The most repressing tiRNAs reduce protein expression of UTR1 to about 6% of the original expression level. The highest dynamic range of translation repression among all data points is 16, which is higher than previously described repressing riboregulators [de novo, without using a naturally occurring functional chassis.The outcome of the designed experiment is depicted in gulation . The actgulators . Moreove2 are comparable to those of UTR1, indicating that the truncated part distal to the RBS BBa_B0032 is less important for riboregulator activity. There is a clear difference in repression efficiency between tiRNA1  and tiRNA16 , showing the importance of at least one of the selected features for translational inhibition. Another interesting fact is the high repression rate of tiRNA17 and tiRNA18, which are the center points of the DOE. The good performance of these center points indicate that choosing less extreme values can also result in effective translational repression.Overall, the repression levels on UTR1 as target) in Figure F in 1 and UTR2 is depicted in Figure G and H in 2) are depicted in Figure I in To unravel underlying design principles of repressing tiRNAs, an OLS linear regression analysis was performed in a first approach. To this end, a linear regression model was applied using all data points in the experimental design. All relative expression percentages from all data points of the experimental design are plotted against all normalized features  describing tiRNA performance. The final model contained 4 latent variables and was selected based on the root mean squared error of prediction and the explained Y variance. By using the training set, a final PLS model contains 63.9% of the X variance, which explained 50.4% variance of the response variable and a R2 (describing the model efficiency) of 0.50 . The success of this data driven approach indicates the importance of machine learning techniques in modern synthetic biology to grasp the ever increasing complexity of biological design. Furthermore, the complex nature of riboregulation and the limited knowledge of the underlying working mechanisms makes engineering RNA devices challenging. To this end, novel technologies (for instance SHAPE-Seq) enable high-throughput study of the structure-function relationship of various types of riboregulators in detail by combining RNA structural probing techniques and next-generation sequencing technology, allowing better prediction of riboregulator performance [The developed approach allows radation ) or interadation , 36, 69.formance , 67.S1 Text(PDF)Click here for additional data file.


Hepatitis delta virus (HDV) RNA forms an unbranched rod-like structure and complexes with the delta antigen (HDAg). Host ADAR1-catalyzed RNA editing at the amber/W site of the small HDAg leads to the production of the large HDAg, which inhibits replication and is required for virion assembly. For HDV genotype 1, amber/W editing is controlled by HDAg and the RNA structure immediate vicinity and downstream of the editing site. Here, the effects of 20 mutants carrying an increased length of consecutive base-pairing at various sites in HDV RNA on amber/W site editing were examined. All nine mutants carrying genomic regions that formed up to 15 consecutive base pairs, which is also the maximum length observed in 41 naturally occurring HDV genomes, showed normal editing rate. However, mutants carrying a 16 or 17 consecutive base-paired antigenomic segment located as far as 114 nt upstream could increase editing efficiency, possibly by interfering with HDAg binding. These data show for the first time that extended base-pairing upstream of the amber/W site could increase HDV RNA editing efficiency. Furthermore, it appears that the naturally occurring HDV RNA structures have been selected for suboptimal amber/W RNA editing, which favors the HDV replication cycle. Hepatitis delta virus (HDV) virion is composed of hepatitis B virus surface antigens (HBsAgs) surrounding a ribonucleoprotein (RNP) complex that comprises the circular RNA genome and multiple copies of the delta antigen (HDAg) ,2,3. TheAs the HDV replication cycle depends heavily on structural features of its RNA, the present study was undertaken to explore how differences in the patterns of the predicted HDV-1 unbranched rod-like structure could affect HDV-1 RNA editing. We created small (1–5 nt) deletions, insertions and/or base substitutions at various sites located opposite the HDAg coding region, which serve primarily to form the unbranched rod-like structure, with the goal of removing predicted unpaired bases, and consequently, increasing the length of consecutive base-pairing in the HDV RNA. Our findings indicate that extended base-pairing upstream of the amber/W site could increase editing efficiency, possibly by interrupting the interactions between the HDV RNA and HDAg. Based on these results, we propose a model explaining how the regulation of amber/W editing of HDV-1 is mediated.The pGEM-4Z-D1I contained an XbaI-XbaI (nt 781) monomer cDNA insert representing an HDV-1 sequence of Italian origin  ,21. pGEMThe inserts of the nonreplicating HDV amber/W editing reporter constructs, pCR3.1-WTNR and pCR3.1-m-17cNR, were obtained by PCR amplification of a region covering nt 216–1381 using HDV cDNA dimer constructs expressing WT and m-17c mutant sequences, respectively, as the template . Total RHuh-7 cells were cultured in 35-mm dishes and transfected with 4 μg of DNA per dish using Lipofectamine , as described elsewhere ,26. Six To analyze the amber/W editing, total RNA samples were extracted from the transfected cells at 12 days post-transfection. Residual plasmid DNA was removed by digestion with RQ DNase I (Promega), the RNA was reverse transcribed using oligo(dT) , and theHuh-7 cells were also co-transfected with pS1X, which encodes HBsAg , and theThe unbranched rod-like RNA structure, comprising short base-paired segments interspersed with small bulges, plays important roles in the HDV replication cycle ,20. HoweWe examined sequence reversion and/or the accumulation of more mutations using RNA isolated from cells six days post-transfection. Sanger sequencing of the RT-PCR product of a region covering 389–1310 nt indicated that no additional sequence change, other than at the amber/W site, compared to the original mutated sequence of m1 and m2. Sequencing of 40 cloned PCR products of m2 revealed no reversion and several base changes, but they were randomly distributed, and most were detected in only one clone.We then designed three more mutants, m5~7, which each carried 16 consecutive bp on their genomes . We alsoWe also selected regions in which base-paired segments were interrupted by asymmetric single-nucleotide bulges or symmetric bulges containing one unpaired nt on each side of the rod-like HDV RNA. Single-base substitutions, deletions, or insertions were used to remove each bulge and allow extended base-pairing. The sites at which m9~14 were mutated are presented in The study shown in As large HDAg is known to be required for HDV virion assembly , we nextAmong the replication-competent mutants containing a region of 16 consecutive bp on the genomic RNA, only m2 exhibited significantly increased amber/W site editing. Closer examination of the base-paired patterns of the mutants revealed that G-U bp were present in the elongated consecutive base-paired genomic regions of m4, but not in that of m2 B. A G-U To evaluate how the position of 17 consecutive bp upstream of the amber/W site affected RNA editing, we designed mutants with 17 consecutive bp located 11 nt (mutant m-17a), 50 nt (m2-17) and 76 nt (m-17b) 5′ of the amber/W site . Each caAn additional mutant, m-17c, carrying a region of 17 consecutive bp located further upstream at 114 bp upstream of the amber/W site  was replThe ADAR1-mediated amber/W editing regulates HDV RNA synthesis and virion assembly. For HDV-1, this amber/W site occurs as an A-C mismatch pair in the midst of 8 bp, and that increased base-pairing in the region 15 to 25 nt downstream, but not upstream, of the HDV amber/W editing site significantly increases editing . Here, wBased on our present results and the previous reports , we propCollectively, our mutagenesis data  suggest that the RNA structure of WT clone I could render the HDV genome able to tolerate the mutations that accumulate during replication while also restricting the editing efficiency, which is important for viral survival. The other mutants containing 16 or 17 consecutive base-paired regions on their antigenomes exhibited promotion of amber/W editing. We examined the GC contents of the experimentally designed duplex regions but did not find evidence that this parameter played a significant role in the level of RNA replication or the amber/W editing rate. We do not yet know whether the extended base-pairing on the HDV circular RNA reduces the stability of the processed RNA and/or disrupts yet-unidentified cis-elements required for replication. It also remains unknown whether different HDV RNA structural patterns could attract different kinds of cellular proteins to form a variety of HDV RNP complexes that differ in their abilities to trans-activate viral RNA synthesis. Although a higher level of large HDAg would favor virion assembly C, the edClearly, our findings improve our understanding of the interplay of bulges and consecutive bp in HDV RNA-protein  interactions, which in turn regulates RNA synthesis and amber/W editing. Our findings also have implications for the selection of naturally occurring HDV RNA. A recent report indicated that HDV activates the interferon response in hepatocytes, but its replication is resistant to self-induced innate immune responses . This in


Escherichia coli sRNAs in the presence and absence of a global regulator, Hfq, and find that two-thirds of tested sRNAs feature Hfq-dependent regions. Further, we identify in vivo hybridization patterns that hallmark functional regions to uncover mRNA targets. In this way, we biochemically validate 25 mRNA targets, many of which are not captured by typically tested, top-ranked computational predictions. We additionally discover direct mRNA binding activity within the GlmY terminator, highlighting the information value of high-throughput RNA accessibility data.Herein we introduce a high-throughput method, INTERFACE, to reveal the capacity of contiguous RNA nucleotides to establish in vivo intermolecular RNA interactions for the purpose of functional characterization of intracellular RNA. INTERFACE enables simultaneous accessibility interrogation of an unlimited number of regions by coupling regional hybridization detection to transcription elongation outputs measurable by RNA-seq. We profile over 900 RNA interfaces in 71 validated, but largely mechanistically under-characterized,  Mapping RNA accessibility is valuable for identifying functional/regulatory RNA regions. Here the authors introduce INTERFACE, an intracellular method that quantifies antisense hybridization efficacy of any number of RNA regions simultaneously via a transcriptional elongation output, measurable via RNA-seq To respond to environmental stress, sRNAs tune metabolic and regulatory pathways, typically by altering messenger RNA (mRNA) translation or stability via direct association4. To date, a relatively small proportion of confirmed sRNAs  have been mechanistically characterized 5. The challenges of functional sRNA characterization can be partly attributed to specific characteristics such as: regulation of targets in trans, regulation of multiple targets per sRNA, and relatively small regulatory regions2 (10–25 nt9 with 8–9 nt10 of imperfect complementary). sRNA interactions are initiated via seed sequences of pairing regions that canonically reside in single-stranded (unstructured) sequences or in hairpin apical loops, typically located in the first 2/3 of sRNA sequences1. In recent years, computational target prediction programs have incorporated various facets of sRNA regulation, including seed parameters11, to offer thousands of putative bacterial sRNA:mRNA pairs; however, only a small set has been successfully validated in vivo2. This can be attributed to true targets  ranking well into the hundreds of predicted targets in popular algorithms12 coupled with the demanding nature of traditional partner validation assays .Bacterial small regulatory RNAs (sRNAs) constitute a distinctive class of RNAs that possess intrinsic roles in cellular regulation14. Many mechanistic details of the Hfq-binding RNA sequence have been well-studied, uncovering characteristic binding motifs and interaction mechanisms, such as its ability to recognize a number of sRNA Rho-independent terminators15. However, the mechanistic necessity of Hfq for proper RNA interactions remains elusive due to a combination of factors, such as under-realized insights into the hierarchy of Hfq-sRNA regulation15, cross-regulation of sRNAs by other putative chaperones, such as ProQ16, absence or limited function of Hfq homologues within GC-rich Gram-positive bacteria17, and variable roles of Hfq on different sRNA:mRNA pairs15. Thus far, it has been proposed that sRNAs rely on Hfq to increase local concentrations of sRNA and respective target mRNA19, stabilize interaction partners, facilitate base pairing catalysis by structural rearrangement15 or present some combination thereof, depending on the target mRNA3.Another confounding factor hindering sRNA characterization is the regulatory impact of the Hfq chaperone, an Sm-protein known to affect sRNA:mRNA interactions. The extent to which Hfq globally impacts the ability of all sRNAs to establish interactions with cognate mRNA targets is a fundamental question concerning intracellular proteins’ effects on sRNA networks. Because its intracellular quantity is limited, sRNAs are in constant competition for Hfq and are likely cycled off of Hfq once they have established stable base pairing with an mRNA target24. However, these methods are somewhat limited by requisite targeting toward a single protein binding partner of interest25, as well as by difficulties sensing interactions involving low-abundance non-coding RNAs23. To this end, efforts have recently been made to complement computational predictions with in vivo insights to enhance prediction reliability. For example, in vivo chemical and enzymatic probing methods gauge the level of “protection” or reactivity of individual bases/backbones within a region of interest, and have recently been adapted for high-throughput use27. As these local nucleotide availabilities do not always correlate with regional-level accessibility that more accurately mimics RNA:RNA interactions of regulatory interfaces28, efforts have also been placed on methods to quantify regional in vivo RNA hybridization. Corresponding datasets have yielded useful predictions of regions capable of establishing RNA:RNA interactions28. Nonetheless, acquisition of these data is typically limited by low-throughput experiments29 and reliance on sRNA overexpression that disturbs native transcript or protein stoichiometry30. As such, there remains a need for high-throughput methods to identify characteristic features of RNA regions that are likely to engage in regulatory interactions with respective mRNA targets.In recognition of the need for high-throughput mechanistic sRNA insights in vivo, recent efforts have unveiled large sets of RNA:protein and RNA:RNA interactions and even corresponding interacting regionsin vivo transcriptional elongation analyzed by RNA-seq for functional accessibility characterization in a single experiment. INTERFACE is a high-throughput method capable of surveying the regional accessibility of a large collection of RNAs simultaneously by mimicking in vivo antisense hybridization. In vivo antisense hybridization has previously been shown to be sensitive to sites involved in intermolecular interactions and to capture transient configurations often relevant to regulation31. INTERFACE is an engineered RNA system that exploits conserved bacterial mechanisms of translational stalling and Rho-dependent transcription termination mechanisms to quantify RNA hybridization via a transcriptional elongation response. In this work, we apply machine learning to design over 900 antisense oligonucleotide probes to interrogate the accessibility of regions fully covering a library of 71 experimentally validated sRNAs in E. coli in their native environment. These sRNAs represent two distinct subclasses: mechanistically characterized sRNAs, meaning that exact binding sites corresponding to at least one mRNA target have been confirmed , and mechanistically uncharacterized sRNAs   hybridization in regions harboring mapped mRNA-binding sites in 16 well-characterized sRNAs that comprise our training set. This information enables identification of likely functional sites in other sRNAs to serve as “in vivo filters” of computational predictions11 to identify true mRNA targets in vivo. Upon benchmarking the value of these “INTERFACE-informed” computational predictions against typically tested, top-ranked predictions via in vitro binding assays, we confirm thirteen novel mRNA targets for three uncharacterized sRNAs . Of these validated targets, six correspond exclusively to INTERFACE-informed computational predictions, one exclusively to top-ranked computational predictions, and six are shared by both approaches. We additionally confirm twelve INTERFACE-informed targets in six sRNAs that represent both uncharacterized and characterized sRNAs , of which half fall below the top-20 in computation prediction ranking. Finally, we showcase a trans antisense regulatory potential within the GlmY terminator as suggested by INTERFACE accessibility data; to our knowledge, GlmY has only been implicated in indirect mRNA regulation via RapZ protein sequestration33.To obtain molecular insight concerning global RNA function in vivo, we developed INTERFACE, 31. To allow large-scale identification of accessible RNA interfaces via next generation sequencing (NGS), we constructed a system that harnesses transcriptional regulation by coupling in vivo asRNA:RNA hybridization to transcription elongation control. In short, this approach allows sensing of molecular interactions between an asRNA toehold-switch “probe” (encoded by a plasmid-based expression system) and a complementary region in a target RNA (taRNA) by virtue of hybridization-activated transcription anti-termination (described below). The anti-termination output was inspired by trp operon attenuation34 that was previously used to synthetically convert translational to transcriptional control in bacteria35. In this engineered system, differential transcriptional elongation, quantified via RNA-seq, is a direct function of the propensity for a taRNA region to interact with a corresponding asRNA probe. Short and long transcripts indicate low and high asRNA probe hybridization in a targeted region, respectively.Assessment of hybridization potential has proven informative in identifying RNA interfaces accessible to RNA:RNA interactions but has previously been limited to low-throughput approaches36, in which the asRNA probe is the toehold and the RSE and RBS form the hairpin. This is followed by (iv) the elongation switch (ES), which consists of a tnaC (trp attenuator) leader peptide nucleotide sequence followed by a Rho utilization (rut) transcription termination site. Finally, located directly downstream is (v) the RNA elongation reporter (RER), consisting of a truncated GFP coding sequence that supports maximum possible transcript lengths. If a taRNA region is accessible to the asRNA probe, elongated transcripts are generated. This output is stimulated by successful binding of the probe to its target region, which disrupts the hairpin loop structure and enables translation of the tnaC leader peptide preceding the rut site  is incorporated within each INTERFACE transcript, NGS results not only contain information pertaining to transcript length, indicative of taRNA region accessibility, but also a unique sequence identifier of the corresponding target region. Although this sequence identifier is reminiscent of barcoding approaches that have been used in genome-wide RNA structure probing methods, this system yields specific, “bar-coded” outputs for each interrogated taRNA region, rather than variable RNA-specific fragment outputs26. In this way, INTERFACE supports the simultaneous characterization of regional accessibility profiles within any number of RNAs.The inducible system is composed of five main elements that together enable transcription elongation in response to asRNA probe:taRNA hybridization  and the system induced in early log growth. Once a user-specified growth phase is reached, total RNA is extracted from the library culture, and a cDNA library is generated for deep sequence analysis. Importantly, fragmentation of RNA is omitted to preserve the ability to correctly assign a 3′-end to the corresponding 5′-end of each transcript and reliably identify the extent of transcriptional elongation for each transcript (based on the taRNA to which the distinct asRNA probe hybridized). Following cDNA purification, samples are sequenced using a standard paired-end Illumina-platform protocol. Finally, corresponding forward and reverse RNA-seq reads are paired and filtered for INTERFACE transcripts corresponding to distinct asRNA probes; asRNA probe-specific transcriptional elongation can then be correlated with the accessibility of respective taRNA regions.As shown in Fig. 31. Second, we confirmed that RBS exposure governs transcriptional elongation by designing two control INTERFACE constructs to represent the extremes within the spectrum of RBS exposure: (i) a permanently sequestered RBS (RSE stably sequesters RBS due to enhanced complementarity) and (ii) a permanently opened RBS (RSE is mutated to expose the RBS). Both constructs contained identical, random asRNA probes predicted to have limited interaction with the transcriptome. From NGS data, we observed three major transcript length peaks present at different loci of the INTERFACE transcript , (ii) ~140 nt, terminating at the end of the rut terminator and (iii) ~200 nt, terminating at an RER position that denotes the full extent of transcriptional elongation. Consistent with expectations, the sequestered RBS and the open RBS controls exhibited partial (terminating at tnaC) and full (terminating in RER) transcript lengths, respectively  asRNA probe binding to a corresponding taRNA region disrupts the stem loop to expose the RBS, (2) RBS exposure governs transcriptional elongation of the INTERFACE RNA system, and (3) elongation patterns are dependent on the elongation switch and are reliable indicators of regional accessibility. First, asRNA probe hybridization to its taRNA region has been previously verified to expose the RBS using a low-throughput, fluorescence-based asRNA hybridization assayipt Fig. : (i) ~80ely Fig. .Fig. 2IN31 along the model Tetrahymena group I (gI) intron taRNA in vivo using an INTERFACE plasmid that supports taRNA overexpression  Fig. . The reg6. taRNA region sequences were selected for in vivo accessibility probing by coupling an established biophysical model of asRNA:RNA hybridization28 to a machine-learning algorithm  along the 71 chosen sRNAs 39 that is capable of delivering experimental suggestions based on value-of-information analysis40. Ultimately, the top 1.5% of SpKG-ranked regions , fulfilling the constraints of minimal overlap and full coverage, were chosen for INTERFACE accessibility probing to obtain accessibility landscapes of the sRNA ensemble  25.To apply INTERFACE to describe accessibility patterns of functional sRNA regions, we also deliberately selected ~40 regions previously mapped to sRNA:mRNA interactions in our training set of 16 well-characterized sRNAs for INTERFACE probing. These regions, termed “exact binding regions” are contiguous nucleotides previously confirmed to host interactions with specific mRNA targets (Supplementary Data st) Fig. . Furtherend Fig. . The exc12. To aid identification of most-likely true targets, we propose a pipeline that begins with the identification of likely functional RNA regions from INTERFACE accessibility data  for interaction validation by in vitro electrophoretic mobility shift assays (EMSAs) (Supplementary Data 45). In cases in which an sRNA region’s likelihood of being a functional interface was ambiguous based on accessibilities, we paired the accessibility signature with the YUNR motif previously identified as characteristic of asRNA:RNA recognition46 (Methods). INTERFACE-informed mRNA targets were then selected as those within the top 100 IntaRNA mRNA target predictions11 whose predicted interaction relied on at least five nucleotides of the likely functional sRNA region (Methods). IntaRNA11 was selected for computational predictions as it offers high accuracy without requiring homology information12. We benchmarked INTERFACE-informed predictions against conventional methods of putative target selection for validation   Fig.   for Tpke11  and gadX for GcvB  of predicted targets, we found that positive predictive values (PPVs) of INTERFACE-informed and top-ranked computational predictions for each sRNA are comparable, but positive mRNA targets added by INTERFACE-informed predictions are often low-ranking in IntaRNA predictions, and thus have low chances of being further considered as true targets in the absence of other experimental data Fig. , Table 1add and yphC50  to evaluate the global impact of Hfq on regional hybridization within a library of sRNAs in their native intracellular context  were observed in distinct regions of the well-established Hfq-dependent sRNA Spot 42 , there was a clear positive skew of hybridization changes in the hfq+ strain relative to the hfq-null mutant (~2-fold)  has been previously experimentally characterized  Fig. , support mutant ~-fold  due to low expression or functional independence from chaperone or degradation proteins.Overall, we demonstrate that INTERFACE can support further mechanistic understanding of regulatory RNA networks by: (i) capturing hybridization-based dependencies on intracellular regulatory factors, (ii) pinpointing sites capable of creating RNA:RNA interactions, and (iii) identifying regions that can be involved in mRNA targeting for uncharacterized (and even well-characterized RNAs) to propose novel RNA regulatory potential. Collectively, these findings demonstrate the advantage of obtaining large-scale in vivo accessibility data to complement computational prediction algorithms that aim at uncovering in vivo RNA:RNA interactions. This approach is a viable high-throughput alternative to effective but lower throughput experimentation that is typically necessary to inform computational predictions yncD, mepM, and fecE), and DNA ligation (SroE target ligB)50. These results suggest that INTERFACE may be valuable in revealing new stress-responsive sRNA networks. The ability of INTERFACE in revealing novel regulation is further exemplified in GlmY, whose high-terminator accessibility prompted the in vitro validation of two novel mRNA binding partners (RNAs coding for Zn-binding proteins:50add and yphC) that depend on the terminator sequence of GlmY for interactions. To date, sRNA terminator activity has been seen only in rare instances; for example, chb mRNA antagonizes the ChiX:chiP sRNA:mRNA interaction by pairing with the ChiX terminator, decreasing ChiX levels and activity52. Furthermore, rare RprA regulation of csgD relies on three distinct regions, one of which is in the terminator; however, mutation to the terminator region alone does not affect in vivo target repression51. It is not clear whether these antisense GlmY-TER:mRNA interactions detected by INTERFACE represent an mRNA feedback, or whether the terminators, like other RNA stem-loops, are capable of initiating antisense pairing and regulation.Interestingly, the target repertoire of some of the studied sRNAs suggests that they may have important roles in stress responses such as metal ion binding and transport (SroG targets rpoS mapped binding site (23 nt) has relatively high-normalized accessibility  compared to a 16 nt stretch completely contained within this mapped site    also have accessibility landscapes that are affected by Hfq deletion  60, suggesting that they share similar ribosomal stalling-based AT mechanisms with E. coli. Furthermore, as eukaryotic RNA polymerases have been shown sensitive to hairpin terminators in vitro62, an adaptation in which the stability of the hairpin structure is determined by the extent of asRNA binding to its target region could be viable for use in higher organisms. Combining high-throughput information about regulatory RNA regions, intracellular factor dependency, and environment-associated activity within a multitude of RNAs may be key to the design of synthetic antisense RNA or other regulatory molecules.We anticipate the utility of INTERFACE in characterizing unique hybridization behaviors of other distinct classes of RNAs  as well as investigating hybridization changes in response to various environmental conditions to support identification of stress responsive regulatory RNAs and associated functional regions. More broadly, given that INTERFACE mostly exploits formation and disruption of Watson-Crick base pairing, we foresee that INTERFACE will support investigation of RNA function in other organisms in vivo, pending appropriate replacement of the anti-termination (AT) mechanism. Importantly, TnaC peptides have been identified in species of many pathogenically relevant bacterial genera  and the Hfq-deficient isogenic strain (JW4130-1Δhfq from the Keio collection32) for experiments performed to characterize accessible interfaces in native RNAs. The Hfq-deficient strain was cured of a kanamycin resistance gene cassette following a FLP recombination protocol32 and the resistance deletion was confirmed via genomic PCR. Two main plasmids were constructed from iRS3GG and modified iRS3GG (modified for targeting native RNAs) plasmids, published previously28, for this work: Overexpression-INTERFACE  and INTERFACE (for native taRNAs), respectively  a truncated version of a (GFP) reporter gene in which start and stop codons have been preserved to allow for transcript size characterizations.Three different 28 to explain hybridization efficacy, ν, as follows:In order to efficiently design asRNA probes for 71 sRNAs, we employed, in combination with a machine learning algorithm (described below), an un-optimized version of a model previously reportedG terms represent the free energies which must be considered for the interaction of the target region with the reporter probe, in which subscripts “tf,” “asT,” and “asF” represent target unfolding, binding between the asRNA and target, and the asRNA reporter probe unfolding, respectively. This model also includes a pseudo-frequency factor 39 to a weighted set cover problem. The SpKG algorithm is developed to solve the sequential ranking and selection problem, in which, at each iteration, one or several experimental suggestions are provided based on value-of-information analysis by taking into account the new observations . SpKG can be used to adaptively select the targeted regions within a large molecule to identify which regions are more amenable to establish interactions with other molecules66. By adapting the SpKG algorithm to a weighted set cover problem, we attempt to maximize the value of information of all suggested probes under the constraints of (i) full taRNA regional coverage of each sRNA molecule and (ii) minimum overlap between suggested probes. In this way, this work is the first demonstration of applying SpKG in a batch setting .To optimally select for accessible regions in 71 experimentally confirmed bacterial small RNAs in L, suppose there are n potential taRNA regions with the length specification (9–16 nucleotides). Let ,,…, be intervals on  that denote these n potential target regions. Let x1,x2,…,xn∈{0.1} be binary variables that denote either the kth target region is selected or not. We use vk, k = 1,…,n to represent the knowledge gradient value of the kth target region. These values can be computed via the SpKG algorithm38. Our optimization problem can be written asIn the following, we provide the mathematical formulation of the problem. For any RNA molecule with length λ is a tunable parameter that penalizes the number of target regions selected to minimize overlapping. As described, this optimization problem is a weighted set cover problem, contained within Karp’s 21 NP–complete problems67, and cannot be solved in polynomial time. In order to solve it efficiently, we use a Greedy algorithm68 to approximate a solution.Here C is the set of nucleotides covered so far; I is the set of index for the selected targeting regions.)k ∉ I, let for all choose C ← C ∪ , I ← I∪{k*}.update While Output.Algorithm:To quantify the performance of this algorithm in synthetic simulations, we compare it with two comparatively naive algorithms for designing aRNA probes—exploration and exploitation. The exploration (uniform) algorithm selects random taRNA regions with a uniform length of 12 nt for each RNA molecule. The exploitation algorithm selects target regions using the aforementioned biophysical model with identical coverage and overlap constraints as the SpKG algorithm, but without any machine learning optimization. In these controlled synthetic simulations, we sample the “true” accessibility coefficients from a stochastic process. Taking into account simulated noise of each experiment, we then normally sample the observations from the three sets of target regions generated using the SpKG, exploration (uniform), and exploitation algorithms. For each set of suggested target regions, we consider a metric called experimental effort to estimate “how close” we are in terms of identifying the most accessible target region in a given number of simulation iterations . In this case, the metric considers, for each sRNA, the difference between the observed maximum accessibility and the “true” accessibility of the optimal region (expected to exhibit the highest accessibility in the molecule) chosen by each respective algorithm. The experimental effort for each algorithm is rescaled from zero to one.28. Specifically, 30 designed probes targeting the gI intron were cloned into the O-INTERFACE plasmid. Additionally, five previously designed31 probes  were introduced into both the O-INTERFACE plasmid and a corresponding modified plasmid lacking the elongation switch completely  was incorporated into both INTERFACE constructs to generate the sequestered RBS control69. For the free RBS control only, a randomized RSE was introduced to the sequestered RBS control via Gibson assembly to prevent formation of the stem-loop that serves to block the RBS69.All probes were inserted into one of two INTERFACE plasmids for hybridization evaluation of regions within an overexpressed gI intron taRNA (O-INTERFACE) or a native taRNA (INTERFACE), via golden gate (GG) cloningely Fig. . For theE. coli electro-competent cells  and, upon plasmid extraction, into the appropriate experimental strain. Each LB-agar plate containing the sRtar library (~100 plates) was supplemented with small LB volumes and thoroughly scraped using cell spreaders. The resulting combined cell mass was recovered for 1.5 h at 37 °C and stored at −80 °C upon supplementation with 30% (v/v) glycerol for a final 15% (v/v) glycerolized culture. INTERFACE strain libraries containing DsrA and RyhB-specific asRNA probes were stored separately.All INTERFACE constructs, including ~100 sRtar DNA library pools, were transformed initially into hfq strains, respectively. The inability to confirm a small percentage of clones was likely due to limited sequencing depth. All synthesized constructs with their corresponding probe sequence, taRNA, and primers used are listed in Supplementary Data For strain confirmation, each O-INTERFACE or control INTERFACE plasmid was individually sequenced upon plasmid extraction from the relevant experimental strain (K-12 MG1655). For the sRtar library, strain diversity was confirmed by sequencing randomly selected subsets of colonies per plate . sRtar library diversity was further supported by RNA-seq analysis, allowing confirmation of >95 and >98% of clones for parent and Δ69. For the experiments using the O-INTERFACE plasmid to validate the system, we prepared individual overnight cultures  for each construct. Equal parts of each resulting culture were combined and seeded into 40 mL of LB to comprise 1% of total volume. Cell cultures were grown in triplicate under four different induction conditions (N = 12): (1) no anhydrotetracycline (aTc), no arabinose (ara); (2) 20 μL of aTc , no ara; (3) no aTc, 800 μL of 20% ara  and (4) 20 μL aTc, 800 μL 20% ara.INTERFACE experiments were performed following a previously reported protocol for a related low-throughput regional hybridization quantifying system, in which GFP fluorescence is the system outputhfq) for a total of twelve samples. Samples were induced 1–1.5 h post seeding (OD ~0.2–0.3), recovered 5 h post induction (OD ~0.5–0.7), and immediately processed for total RNA extraction.For the experiment intended to characterize native taRNAs (INTERFACE plasmid), 100 mL of LB were seeded with 600 μL of the sRtar library directly from gradually thawed freezer stocks in triplicates. Control probes were seeded into these triplicates cultures separately as follows (1) 400 μL free RBS (from an overnight culture), (2) 400 μL sequestered RBS (from an overnight culture), and (3) 400 μL each of the libraries of probes targeting DsrA and RyhB . Kanamycin was added to all cultures to obtain a final concentration of 50 μg/mL. Samples were grown in triplicate under two induction conditions (no ara and 2 mL of 20% ara), and in two separate strains .Following RNA extraction, and in preparation for RNA-seq, we used the NEBNext Multiplex Small RNA Library Prep set for Illumina (NEB E7330) to prepare the DNA libraries. The RNA Fragmentation step was omitted to guarantee that each 5′-read and 3′-read from RNA-seq could be reliably assumed as the true starts and ends, respectively, of the corresponding transcripts. The protocol provided for preparation of DNA libraries by the supplier (NEB) was followed with a few adaptions. Briefly, a 1:2 dilution of the 3′ SR adaptor for Illumina was ligated overnight (18 h at 16 °C) using between 0.5 μg and 1 μg of non-fragmented RNA as the starting material. Next, the SR RT Primer for Illumina was annealed to the 3′ adaptor ligated RNA samples and then the 5′ SR adaptor for Illumina was ligated (1 h at 25 °C). Subsequently, a reverse transcription reaction was performed (60 min at 50 °C) to obtain cDNA, which was immediately enriched via a standard PCR amplification as recommended by the supplier with a modified extension time of 1 min per cycle instead of 15 s for a total of 15 cycles. The resulting DNA was purified using the AMPure Bead XP system and a magnetic rack in at least two wash cycles with freshly prepared 80% ethanol.tnaC sequence) and 310 nt (observed maximum size). Finally, DNA libraries were prepared for RNA-seq using standard Illumina kits and were run using a NextSeq equipment in a 75 × 2 paired-end scheme.DNA libraries were submitted to the GSAF core facility (UT Austin) for sequencing. The samples were analyzed for their size distribution using a bioanalyzer (Agilent). To enrich for the transcripts of interest (INTERFACE plasmid transcripts), in the case of the sRNA experiment, and enhance mapping depth of every single probe, the GSAF facilities performed a Pippin Prep (Sage Science) preferentially selecting for transcript sizes between 120  using CUTADAPT70 to trim adaptor sequences despite low adaptor contamination (<0.5%), (3) creating a reference genome for unique samples of interest. For the gI intron experiments, the reference genome consisted of 30 O-INTERFACE transcripts corresponding to target regions outlined in Fig. 31 (with and without the adaptor sequence), as well as 2 control O-INTERFACE sequences corresponding to the sequestered- and free-RBS controls . For the sRNA accessibility experiment, the reference genome was comprised of INTERFACE transcripts targeting all sRNA regions in Supplementary Data 71 and subsequently to a more manageable bed file using BEDTools72. The next step (6) exploited awk to develop a script to filter for the R1 reads that contained at least 7 nucleotides of the asRNA probe sequence, and, in the last step, (7), R2 reads corresponding to R1 reads were obtained using their unique identifier (contingent on R1 and R2 reads mapping to the same INTERFACE sequence) and the Linux command “join.”The computational pipeline used to process the RNA-seq results includes the following steps: (1) performing a quality check on base sequencing quality using fastqc, a program offering analysis on attainment of passing quality scores /fastqc,  using CU/fastqc,  using CU73 and plTetO74) and the transcription end site, both obtained from the RNA-seq results processed following the procedure described above. To calculate relative accessibility, Python was used to calculate the weighted averages of the read length per probe. In the case of experiments probing the gI intron, a baseline (85 nt) was calculated as the minimum O-INTERFACE transcript size and subtracted from each transcript length weighted average. Next, relative accessibility per probe was estimated as the ratio of the adjusted weighted average in the presence of the taRNA (double induction: aTc and ara) to the adjusted weighted average of the transcript length in the absence of any induction. In contrast, for the sRNA experiment, the relative accessibility was calculated utilizing only the weighted average of the transcript length in the presence of the INTERFACE transcript, as the taRNA is natively present. This weighted average of the transcript length was similarly adjusted by a baseline (164 nt) then linearly normalized between 0 and 1 within each sRNA molecule for each replicate to obtain a “raw accessibility” per region and replicate. This was done to discern the hybridization landscape while controlling for transcript abundance and other inconsistencies between strains. Reported accessibilities are average raw accessibility values, linearly normalized within each sRNA for production of figures and tables.A Python code was used to generate a file containing a summary of the number of reads per end position within each unique INTERFACE sequence provided for mapping. The transcript length with respect to each target region was calculated as the number of nucleotides between the observed transcription start site (TSS) for each promoter  in normalized accessibility replicates between the two strains were categorized as Hfq-dependent.The differences in relative accessibility between parent and Hfq-deficient strains were calculated for every region targeted in this study. sRNAs that exhibited any significant differences   to significance of P-value <0.1.Reported accessibility values were calculated as detailed in “Calculation of relative accessibility.” Standard errors , a representative functional region was chosen as the 5′-most region with containing a significant YUNR motif . Motif scanning was performed using the FIMO tool within the MEME suite75.Molecular features of regulatory regions within well-characterized sRNAs, specifically (i) location and (ii) average normalized accessibility  were used as criteria to identify potential regulatory regions in five under-characterized sRNA based on aforementioned observed features in mapped binding sites. Specifically, for the three uncharacterized sRNAs whose predicted mRNA targets were benchmarked, up to three representative likely functional regions were selected for their (i) position within 5′- or 3′-end windows  and (ii) for “most extreme” high or low accessibility. If regions contained within an sRNA’s 5′ and 3′ windows exhibited mid-accessibility clustering , the YUNR RNA:RNA recognition motifDue to the accessibility profile of SroH , the allowable position range for likely functional region search was extended from 20 to 40% of total sRNA length from the 5′-end. For the 5′-end of Tpke11 (see Selection of Representative mRNA and sRNA Sequence for EMSAs) that was probed in this work, two regions were believed “likely functional” due to associated absolute transcript lengths within top and bottom ~30%  of the entire sRNA region library, despite generally poor representation of INTERFACE transcripts targeting other regions in this sRNA.11. Specifically, the query ncRNA input was the sequence of each represented sRNA . Importantly, because the top 5 top-ranked and INTERFACE-informed predictions for SroG were identical , we additionally chose to validate all INTERFACE-informed predicted targets that corresponded to the other proposed SroG functional regions Fig. . For SroFor the subset of sRNAs with highly accessible regions in the 3′-end chosen for biochemical validation , all viable targets within the top 100 (see Selection of Representative mRNA and sRNA Sequence for EMSAs) adhering to the aforementioned overlap criteria were chosen for experimental verification. Furthermore, to account for strong structure in these terminator regions (presumably the reason for which no CyaR predictions in the top 100 were predicted to utilize the accessible 3′ region for interaction), IntaRNA predictions were additionally run for the sequences corresponding to the accessible 3′ regions only (starting at the first nucleotide of the accessible sequence and continuing to the 3′-end of the sRNA). Between 1 and 3 functionally interesting targets with the top-10 were ultimately chosen for experimental validation . All computational and sequence details of mRNAs tested  can be found in Supplementary Data E. coli K-12 MG1655 chromosomally modified to encode a CsrA-FLAG fusion (CML377) in which RNA was extracted in exponential growth phase (OD ~0.6)47. Specifically, candidates were deemed unviable if the mRNA expression profile did not include the predicted interaction region of the mRNA, as evaluated by visualizing mapped reads in Integrative Genomics Viewer 2.3 (IGV)76 . Furthermore, candidates were considered unviable if the predicted interaction region within the −200 to +100 mRNA sequence was contained within an upstream coding sequence or if they were annotated as hypothetical proteins in the BW25113 genome.Experimental logistics of sRNA and mRNA sequences for heterologous expression were aided by transcriptomic data from 44.5′-UTR transcription starts for IVT were chosen to represent those identified by visualizing mapped reads in IGV in an attempt to most accurately mimic in vivo mRNA length and structure. For mRNAs (i) known to be co-transcribed and (ii) showing no obvious transcription start, a pseudo 5′-UTR start was chosen as first nucleotide following the stop codon of the preceding gene. At least 18 nt of the mRNA coding sequence (including start codon) was added following the 5′-UTR to make up the experimentally tested mRNA fragment .2, 20 mM KCl, 10 mM Na2HPO4-NaH2PO4 at pH 8.0, 10% glycerol). The reactions were denatured for 5 min at 70 °C, then incubated at 37 °C for 1.5 h. Samples were run for 4 h at 25–35 mA in a 5% non-denaturing polyacrylamide gel with 0.5X TBE running buffer. The gel was dried at 80 °C for 1.5 h (Gel Dryer 583 BioRad) and phospho-imaged using Typhoon FLA 700 .Internally labeled sRNAs and respective mRNAs were mixed in 1:10 molar ratios  in 12–15 µL reactions containing 1X binding buffer (20 mM Tris-HCl at pH 8.0, 1 mM MgClhttps://github.com/mihailom/INTERFACE-pipeline-tools. Scripts pertaining to the SpKG algorithm are available upon request.All codes used for “Calculation of relative accessibility” can be accessed at Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7


Background and Objectives: The presence of endometrial-like tissue outside the uterine cavity is a key feature of endometriosis. Although endometriotic lesions appear to be histologically quite similar to the eutopic endometrium, detailed studies comparing both tissues are required because their inner and surrounding cellular arrangement is distinct. Thus, comparison between tissues might require methods, such as laser capture microdissection (LCM), that allow for precise selection of an area and its specific cell populations. However, it is known that the efficient use of LCM depends on the type of studied tissue and on the choice of an adequate protocol. Recent studies have reported the use of LCM in endometriosis studies. The main objective of the present study is to establish a standardized protocol to obtain good-quality microdissected material from eutopic or ectopic endometrium. Materials and Methods: The main methodological steps involved in the processing of the lesion samples for LCM were standardized to yield material of good quality to be further used in molecular techniques. Results: We obtained satisfactory results regarding the yields and integrity of RNA and protein obtained from LCM-processed endometriosis tissues. Conclusion: LCM can provide more precise analysis of endometriosis biopsies, provided that key steps of the methodology are followed. Endometriosis is a disease characterized by the presence of endometrial-like tissue outside the uterine cavity . BecauseThe laser capture microdissection (LCM) is a technique used in molecular and cellular biology laboratories. By employing high quality resolution microscopy and laser cutting, LCM allows for the selection and excision of defined tissue areas and the study of specific cells populations. The precise selection of interest areas enables researchers to increase the sensitivity of subsequent molecular or protein analysis , therebyTissue processing and LCM protocols depend on the tissue type, taking into consideration its texture and the presence of nucleases or proteases which, depending on the tissular concentration, may compromise RNA and protein integrity  ,7. ThereThe aim of the present work is to systematize the main steps involved in LCM processing of endometrial and endometriotic lesion biopsies in order to improve the amount and quality of the recovered material for RNA or protein analysis, as well as the viability of this material for further studies.Ten and eight patients with endometriosis were, respectively, included for RNA and protein analysis. Eight patients without endometriosis as confirmed by videolaparoscopy were included. The inclusion criteria for all women were to be in their reproductive years, non-smoking and free from hormonal contraceptive medication for three months prior to inclusion in the study. Endometriosis was confirmed by histopathological examination of biopsies (collected during surgery) according to the criteria of the American Society for Reproductive Medicine . Upon si2). For protein concentration analysis, control endometrium  and endometriotic lesions from patients with endometriosis were analyzed, and the protein extraction methods (with or without sonicator) and areas (3.5 × 103 μm2 or 5.0 × 103 μm2 or 8.0 × 103 μm2) were compared. A diagram of the experimental design is presented in The tissue samples for RNA analysis were classified and compared according to: tissue type (endometrium or endometriotic lesion); staining protocol (Hematoxylin/Eosin (HE) or Cresyl Violet (CV)); fresh or frozen samples and respective preservation time after cryopreservation; and area . Therefore, although different types of lesions were microdissected, all lesions were equally treated as lesions from where RNA and protein concentrations were measured, regardless of lesion type.Eutopic endometrium and endometriotic lesion biopsies were immediately immersed in cryomedium agent Tissue-Tek OCT  and the vials were placed in dry ice to be transported to the Research Laboratory of HIAE. Some fragments were kept at −80 °C for 3 days until processing , while other fragments of the same material were immediately processed .Samples embedded in OCT were serially sectioned into 10 μm sections on cryostat  at minus 25 °C. The cryosections were mounted on glass slides , covered with a polyethylene naphthalate  membrane  and were exposed to UV radiation for 20 min in order to help tissue fixation on the membrane.Two staining protocols were tested: Cresyl Violet (CV) and Hematoxylin/Eosin (HE). Briefly, for CV staining, the sections were hydrated in decreasing alcohol solutions (100% and 70%) for 25 s in each solution. The CV was added to the slide for 30 s and finally the sections were dehydrated in increasing alcohol solutions (70% and 100%) for 25 s in each solution.The HE staining protocol started with hydration of sections in decreasing ethanol solutions  for 30 s each. The slides were washed in distilled water (DW) to remove excess alcohol, then immersed in hematoxylin for 30 s, washed in DW for 60 s, and placed in eosin for 3 s, washed with DW and finally dehydrated in increasing alcohol solutions  for 30 s each.2 were microdissected for RNA analysis and areas of 3.5 × 103 μm2, 5.0 × 103 μm2 and 8.0 × 103 μm2 were cut for protein concentration analysis. The microdissected material was catapulted into the adhesive covers of microdissection tubes (Sample AdhesiveCap 500 clear D Zeiss). Tube lids had either RNAlater or RIPA buffer containing 1% protease and phosphatase inhibitor , depending on the subsequent technique to be performed, RNA or protein extraction, respectively.The laser capture microdissection was performed with computer system PALM RoboSoftWare 4.6 MicroBeam LSM 710, which consists of a source of laser beams adapted to an inverted optical microscope coupled to a video system. The cutting region, viewed in 20× objective, was carefully defined, considering a safety margin to ensure that the laser did not burn areas of interest with consequent loss of material . For thi® spectrophotometer , at optical density 260 nm. Sample purity was checked by reading the plate between 230/260 nm and 260/280 nm. After quantification, total RNA was frozen at −80 °C until processing for transcription into cDNA.Total RNA was extracted from all microdissected samples using RNAaqueous Micro Total RNA Isolation Kit , according to manufacturer’s instructions. In order to compare the amount of RNA recovered per sample, all extracted RNA samples were eluted in the same volume (15 µL) of water. The procedure was done in RNase free environment using RNAseZAP , under ice. After extraction, the total RNA concentration was determined using a NanoDropAfter treatment of the samples with DNase I , complementary DNA (cDNA) was synthesized using a volume of 12 µL of total RNA per sample, irrespective of its concentration in the sample. All of the RNA obtained from each sample was used for the reverse transcription following the protocol of the Superscript III First-Strand Synthesis SuperMix kit . Water and RNA were briefly added in the final volume of 14 µL per sample. Samples were placed in thermocycler  at 50 °C for 50 min to obtain cDNA. The reaction was stopped in ice and the cDNA stored at −80 °C.A primer was designed to amplify GAPDH housekeeping gene sequence. GAPDH gene sequence was retrieved from the National Center database is Biotechnology Information . The primer was designed from these sequences, using the Primer3 platform and tested with an in-silico PCR using the Genome Browser platform being: F: 5-GAAGGTGAAGGTCGGAGTCA and R-3: 5-3-TGAGGTCAATGAAGGGGTCA. GAPDH primer was synthesized with 100 bp by Invitrogen-Brazil. The primers’ efficiency was tested in triplicate by dilution curves of a cDNA pool obtained from endometriotic lesions and endometrium samples that were not microdissected.® Green Master Mix  detection system probes. A total of 8.4 μL of the mix solution, along with 0.84 μL of each primer (Forward and Reverse GAPDH), 3.42 μL of ultrapure water DNase/RNase free and 1.5 μL of cDNA were added, with a final volume of 15 μL per well of the plate. An extended number of cycle amplifications (50 cycles) were set in the PCR System to achieve amplification of even small amounts of RNA in the sample.SYBR Green qPCR assays were performed on 7500 Real Time PCR System  using SYBRThe specificity of the generated product was confirmed by analyzing the primers dissociation melting curve of the formed products. For gene expression adjustment between plates, a positive control was included in the experiment; in this case, a pool of non-microdissected endometriosis cDNA samples.n = 11) was run in agarose gel electrophoresis. The agarose gel was run at 100 W voltage and stained with 2.5% ethidium bromide. The visualization was carried out using an ImageQuant 300 trans-illuminator (GE), coupled to a suitable photodocumentary system and software to capture 300 I Quant.In order to check for PCR-real time amplification specificity as a way to complement the results from dissociation curves, a subset of samples . To compare protein extraction protocol, sonication was used in a subset of samples, always kept on ice to avoid any protein degradation. After extraction, the total protein concentration was calculated using Pierce’s method.The supernatant’s protein lysates were quantified by Pierce’s method . Briefly, in a 96 flat-bottom plate, 5 µL of the corresponding standard curve protein and of the samples were added in triplicate. Then, 100 µL of reagent B:A in 1:50 proportion were added to all wells. The plate was kept in a water bath at 37 °C for 30 min, and then read at Expectra Max adjusting absorbance to 562 nm after 2 s shaking.2). The data are presented as mean values accompanied by the 95% confidence interval and p value corrected by sequential Bonferroni method. For protein analysis GraphPad Prism 8.0 was used, as well as Mann–Whitney or Kruskal–Wallis multiple comparisons adjusted with Dunn’s test for non-parametric observations.The data were analyzed using SPSS  generalized estimation models, with log function, Gamma distribution and assuming correlation structure symmetry to contemplate the correlation between the different measurements of the same patient. The response variable was RNA concentration (ng/µL) and the dependent variables used to adjust the model were: tissue type , staining (CV and HE), type of preservation of samples (fresh or frozen) and microdissected area  and 50 μm2 (p = 0.185) (We observed that both CV and HE can be used for staining slides in LCM protocols . No stat= 0.185) . Time isp < 0.001) was obtained for RNA concentration when comparing microdissected areas of 25 μm2 or 50 μm2 regardless of the previous staining technique   amount of RNA was recovered from endometriotic lesions as compared to the amount recovered from eutopic endometrial tissue (When comparing RNA concentrations recovered from eutopic endometrium versus endometriotic lesion microdissected areas of 25 μm= 0.094) . Howeverl tissue .2 and 50 μm2 (p < 0.001) of both tissues, although in this analysis previous preservation or staining methods were not taken into account  for the housekeeping gene GAPDH were 35.2 ± 2.53 for microdissected areas of 25 μm3 µm2), no difference was found in total protein concentration among samples from endometrium (control group) and from eutopic endometrium or endometriotic lesions from patients. Although some variation occurred among experiments run at different occasions, protein recovery from the different tissues was similar within the same experiment, as shown in Considering the same microdissected area (5.0 × 103 µm2 to 8 × 103 µm2 (circa 60%). It was observed that even this relatively small increase resulted in significantly more recovery of protein  as well as the protein concentration (measured by BCA Protein Assay) obtained from microdissected sections with the use of LCM directly depend on the size of the excised area. The staining method (Cresyl Violet or HE), and whether the tissue sample was fresh or kept frozen before sectioning or the tissue type (endometrium or endometriotic lesion) did not influence the RNA and protein recovery. RNA quality was also assessed by amplification of GADPH, and checking for genomic DNA purity and the presence of contaminants and integrity (RT-PCR and agarose gel electrophoresis).Tissue staining facilitates the identification of areas of interest and also the different types of cells and tissue structures. We corroborated the findings from Ordway et al. 2009) who also did not find differences in RNA integrity due to the staining used before LCM  who also. In contCurrently only one published study evaluated freezing time and the quality of RNA obtained by microdissection. It was, however, done on lactating ruminant’s breast tissue. The authors found that the tissue could be stored for several days at −80 °C without affecting the morphology and RNA quality, but freezing decreased the amount of material recovered . The freResearch involving RNA or DNA quantitation and gene expression commonly does not require large amounts of material, since PCR enables amplification to such a degree that it allows for obtaining additional material for testing. However, currently, protein quantification methods are still limited, as they have low sensitivity to detect rare proteins. For this reason, larger sampling areas are needed in LCM for protein identification in downstream techniques .3 μm2 and that there is no need to use additional processing for protein extraction (such as sonicating).The experiments carried out in the present study have shown that it is possible to obtain significant amounts of protein in microdissected endometrial tissue or endometriotic lesions. It is important to emphasize that the areas must be equal to, or greater than, 5.0 × 10Nevertheless, the present study has some limitations. A single technique was used to measure protein concentration and no downstream experiments were executed in order to evaluate protein quality. However, we believe that the choice of a particular downstream technique for protein integrity assessment varies according to the original specific protein concentration. The choice of methods with different sensitivities, such as Western Blotting (less sensitive) or bead-based assays , depends on the concentration of the specific protein object of the study, by the mass of the hole tissue used for extraction . Also, iWe conclude that LCM can be used as a viable platform for the study of protein and gene expression in endometriosis. With the use of LCM in endometriosis, it will be possible to isolate areas of interest or separate different cell populations for molecular studies by adjusting the processing steps which precede the technique.


Study results revealed striking differences in the transcriptomic profiles from the three different methods that imply ex vivo changes in gene expression occurred during the blood collection, preservation, and mRNA extraction processes. When comparing the ability of the three preservation methods to accurately capture individuals’ expression differences, RNAgard® outperformed PAXgene® RNA, and both showed better individual separation of transcriptomic profiles than PBMCs. Hence, our study recommends using a single blood collection platform, and strongly cautions against combining methods during the course of a defined study.Peripheral Blood gene expression is widely used in the discovery of biomarkers and development of therapeutics. Recently, a spate of commercial blood collection and preservation systems have been introduced with proprietary variations that may differentially impact the transcriptomic profiles. Comparative analysis of these collection platforms will help optimize protocols to detect, identify, and reproducibly validate true biological variance among subjects. In the current study, we tested two recently introduced whole blood collection methods, RNAgard Peripheral blood remains a popular tissue to interrogate biological state, mainly due to its availability and minimally invasive mode of collection. Also, its systemic connectivity to numerous bodily tissues renders it a valuable and rich source for detection and identification of human biomarkers. These blood biomarkers can be used extensively in a wide range of applications from diagnostics, such as monitoring disease progression to therapeutics, drug development, and assessing patient responses to medical treatment. While mRNA quantification provides a powerful functional readout which bridges DNA sequence and protein levels, maintaining RNA integrity of the peripheral blood during its collection and preservation prior to analysis poses numerous technical challenges.ex vivo. Together, these experimental variations complicate the detection of true biological variance. Various stages after phlebotomy, including blood collection, transportation and storage, along with processing steps such as RNA isolation method and choice of microarray platform, can influence gene expression profiles [®RNA Blood RNA tube  and RNAgard® Blood Tube . Both of these RNA collection systems use proprietary reagents that lyse blood cells immediately after collection in the tubes, inhibit RNA degradation, and block induction of new transcripts [Post-collection, the nucleic acids in blood are susceptible to oxidative damage and nuclease attack . Additioprofiles . Hence, nscripts , 6. Mult®RNA tubes to Tempus™ Blood RNA Tubes (Applied Biosystems) show that the choice of collection tubes can critically impact differential expression patterns. According to Asare et al., the Tempus™ system preserved the in-vivo transcription profiles better, thus leading to the identification of a greater number of gene expression changes and reflecting the true biology of specific sets of inducible genes [et al. identified a mere 54% overlap in the glucocorticoid receptor-stimulated gene expression profiles between these two commonly used commercial systems [®RNA and Tempus™ tubes. Recently, Nikula and colleagues corroborated these findings; they noted hundreds of transcripts associated with many blood immune cell functions and canonical pathways were differentially expressed because of technical bias between PAXgene®RNA and Tempus™ RNA preservation methods [et al. claimed that the choice between the PAXgene®RNA and Tempus™ RNA collection tubes does not affect mRNA expression profiling, but does alter miRNA levels [et al. observed significant differences between these two methods during their attempt to optimize a combination of the blood collection system and RNA extraction procedure to generate reproducible gene expression results from human blood samples [Prior studies comparing PAXgenele genes . In anot systems . They re methods . ConversA levels . More re samples .®RNA and Tempus™ Blood RNA systems studied in these reports, RNAgard® Blood Tubes is another commercially available blood collection system designed for rapid stabilization to yield RNA suitable for genome-wide expression profiling. RNAgard®, however, remains largely unexplored and, to the best of our knowledge, the utility of RNAgard® has yet to be systematically compared to any other method. Hence, in this investigation, we compared genome-wide expression profiles obtained from two different whole blood RNA collection systems: PAXgene®RNA and RNAgard®. For reference purposes, peripheral blood mononuclear cells (PBMCs) preserved in Trizol have been included as an additional RNA preservation system. Although RBC-depletion from blood samples has been shown to improve preserving the quality of RNA in Trizol [® collection system in comparison with the widely used PAXgene®RNA, and the conventional method of PBMCs preserved in TRIzol.In addition to the PAXgenen Trizol , processn Trizol , 13. ThuThis study comprises active duty Army personnel of the 101st Airborne at Fort Campbell, Kentucky, assessed before and after being deployment. The first phase of recruitment occurred during a two-week period immediately prior to deployment with a follow-up phase 2 and 3 after deployment. This study used samples from phase 1 sample collection. The study was conducted in accord with ethical principles for the conduct of human research as specified in the latest version of the Declaration of Helsinki.The study was approved by the Institutional Review Board of NYU School of Medicine, Human Research Protection Office of the United States Army at Ft. Detrick MD and Army Command of the 101st Airborne at Ft. Campbell Kentucky.®RNA tube (2.5ml whole blood) , an RNAgard® Blood Tube (2.5ml whole blood) (Biomatrica), and duplicate CPT tubes (8.0 ml whole blood in each tube) . All PAXgene and RNAgard tubes were inverted about 10 times after collection and incubated at room temperature for 2 hours. Tubes were subsequently frozen at -20°C for at least 24 hours followed by storage at -80°C … In parallel, PBMCs separation was carried out using BD Vacutainer® CPT™ (BD Biosciences) at the collection site following the manufacturer’s instructions. This was accomplished by centrifugation of the samples at 1700 × G for 20 minutes at room temperature within two hours of blood collection, followed by carefully removing PBMCs using a transfer pipette. The PBMCs were then washed using PBS and the cell pellet was immediately stabilized by adding 500ul of Trizol reagent . All tubes and sample aliquots were transported from the clinical site on dry ice.Peripheral blood was collected from eight healthy volunteers using a 21-gauge butterfly needle and catheter after obtaining informed consent. For each subject, four samples of blood were collected and dispensed in into a PAXgene®RNA tubes were handled following our basic laboratory protocol based off PreAnalytiX specimen handling and enhance yield procedures from PAXgene Blood RNA MDx Kit Handbook (08/2016). The tubes were thawed overnight at room temperature to ensure complete lysis of blood cells and maximize the mRNA yield. A 2.0 mL (PAXgene®RNA + blood) aliquot was used for automated RNA extractions with the PAXgene®RNA blood miRNA kit (Qiagen) employing an amended version of the manufacturer’s guidelines on the QIAcube Workstation (Qiagen) [Frozen PAXgene(Qiagen) –16 Brief® tubes were processed in a similar manner as the PAXgene®RNA tubes with a few minor exceptions After thawing the tubes overnight, 4 mL of blood was transferred to a separate tube for RNA extractions. The procedure was adapted after consultation with manufacturer and BioMaxi Precipitation Buffer (Biomatrica) was added to the 4 mL blood in 1:4 ratios. This was followed by incubating the mixture for 15 minutes at room temperature and a quick vortexing was performed. The contents were centrifuged at 4,500 g for 30 minutes, and the supernatant (used for DNA extraction simultaneously) was separated from the pellet. The pellet was used to extract RNA on the Qiacube liquid handler by using the exact same procedure described above for the PAXgene®RNA tubes. The quality and quantity of the resulting RNA was measured using the same techniques as for the PAXgene®RNA tubes. In order to minimize batch effects, all blood samples were extracted on the same day.Frozen RNAgardRNA from PBMCs stored in TRIzol was isolated according to the manufacturer's protocol. Briefly, cells were lysed by vortexing in TRIZOL Reagent, which is a monophasic solution of phenol and guanidine isothiocyanate, disrupts the cells and solubilizing cell components. This step is followed by the addition of chloroform which separates the solution into an aqueous phase and an organic phase. The RNA remains in the aqueous phase and is recovered by precipitation with isopropanol. The resulting nucleic acids were quantified using NanoDrop . The integrity of the RNA was analyzed using Agilent RNA ScreenTape assay .in vitro transcription and incorporation of Cy-5 fluorescent dye into the test sample. Universal Human Reference RNA  was labeled with Cy-3 fluorescent dye and used as a common reference RNA across all arrays. The samples were purified, dye incorporation and cRNA yield were checked with Nanodrop , then simultaneously hybridized to Agilent 8 × 60k slides for 17 hours at 65°C using Agilent’s Gene Expression Hybridaization Kit (Agilent 5188–5442) according to the manufacturer’s instructions. The arrays were washed and scanned according to Agilent protocol using SureScan Microarray Scanner  and two color scan setting for 8x60K array. Microarray performance was assessed by QC metric tool. Following the manufacturer's protocol, Agilent's Stabilization and Drying Solution (Agilent 5185–5979) was used to protect against the ozone-induced degradation of dyes on microarray slides during hybridization and processing steps. The intensity data were extracted using the Feature Extraction 11.5.1.1 software (Agilent Technologies) default parameters. RNA samples obtained from all three collection tube types for each subject were run in duplicates hybridized to separate arrays on a single 8 array slide.Microarray hybridization and sample labeling were performed according to the Two-Color Microarray-Based Gene Expression Analysis–Low input Quick Amp Labeling- protocol version 6.9 (Agilent Technologies). Agilent RNA Spike-In mix was added to 200 ng of total RNA prior to the labeling reactions to monitor both labeling reactions and microarray performance, following the Two-Color RNA Spike-In Kit protocol (Agilent 5188–5279). Samples were labeled using the Low Input Quick Amp Labeling kit (Agilent 5190–2306) and then hybridized to the Agilent SurePrint G3 Human gene expression v2 8 × 60K Microarray Kit, design ID:039494  following the manufacturer’s protocol. Total RNA was reverse transcribed to cDNA, followed by The spike-in RNA demonstrated a linear relation between signal intensity and concentration of the RNA and all datasets were qualified for further analysis. All subsequent analysis was performed using custom R scripts. Median red and green channel intensity values were within array normalized using local polynomial regression, converted to log2 fold differences , and quantile across-array normalized. Duplicate probes were then averaged together. Other common normalization methods were investigated and their use did not change the general findings. Likewise, enforcing a 50% probe variance cutoff filter to highlight technical variation produced very similar results. Gene Ontology (GO) term enrichment analysis was performed using the online bioinformatics resource DAVID .®RNA at 7.306 (0.595) and RNAgard® at 7.443 (0.614). The PAXgene®RNA and the RNAgard® RIN values did not differ significantly from each other. ® (triplicate), PAXgene®RNA (duplicate), and PBMC/Trizol (triplicate), comprising a total of 64 samples, eight in each set. Overall patterns of expression fold values appear consistent across the three preservation methods. However, as seen in ® and PAXgene®RNA preserved samples collected from the same individual using the same microarray platform. As expected based on differing cell type composition, the PBMC samples differed qualitatively from the two whole blood based collection methods. Technical repeat samples (color coded) from individuals tended to cluster together in these two different collection methods; in contrast, PBMCs preserved in Trizol showed less overall variation but poorer separation among individuals (Mean (±SD) TapeScreen RNA integrity number (RIN) values were 8.875 (±0.595) for PBMC/Trizol, significantly higher (two sided unpaired t-test p < 0.0001) than PAXgeneividuals . OverallRelative variability across samples alone does not directly address the ability to accurately quantify biological signal as methods which reduce message abundance variability may do so at the cost of real biological variation. ®RNA/RNAgard® differences. For example, a slightly positive correlation between gene variance and GC percentage is seen among the RNAgard® samples , p-values were calculated for each probe using a t-test across the same individuals. Numbers provide Pearson’s correlation values between probe variability measures (columns) and probe distance to 5’ and 3’ ends of transcript, gene and transcript size, G+C nucleotide percent, mean probe FD vs. universal reference mRNA (M), and mean probe intensity (A). Bold values represent significant correlation at p<0.01. The data is also represented as the Within each platform the mRNA probe variability was measured by mean within individual variance (Var), p-value , and absolute fold difference (FD). Across platforms  that can be identified and, at least partially removed. Our attempt to identify links between technical variability and the probe properties pointed to some possible contributors affecting differential expression. Also, an examination of the enrichment of annotation terms indicated considerable differences in genes related to specific functions. The RNAgard® system showed remarkably high numbers for apoptosis associated genes, while the PAXgene®RNA product showed mostly high numbers for enriched genes related to metal ion binding. It is possible that variations in the rate of cell lysis between the two methods may explain the observed differential expression of apoptosis genes, while inclusion of chelators may account for the observed differences in metal ion binding gene expression.Similarly, when results must be compared between diverse methods of preservation, particularly if differences are noted in specific gene types and their function, additional care should be exercised in the interpretation. Although the order of gene variances did not change much significantly between the PAXgene®RNA and RNAgard®. Additionally, PBMC separation presents an additional processing step. Both of these differences are likely to add technical variation. In this case, any potential gains from having “purified” cell types and higher RIN values did not compensate for this increased variation. Due to these underlying cellular and methodological differences, this study focused on comparing PAXgene®RNA and RNAgard® with PBMCs in Trizol to provide a larger frame of reference.Despite differences in cellular makeup, previous studies have shown general comparability when looking at PBMCs vs whole blood gene expression . The low®RNA and RNAgard® systems for the first time, and demonstrated distinct differences between the whole blood gene expression profiles generated using these two collection platforms. By comparing gene expression measurements within and across technical replicates from the same individuals, we found systematic differences between PAXgene®RNA and RNAgard®. Minimal signal-to-noise differences were observed in the samples in the study. The results of these studies strongly suggests the deployment of a single preservation platform throughout the course of any specific study without mixing or changing of the collection method during the course of sample acquisition and preservation.In conclusion, our study compared the PAXgeneS1 Fig® vs PBMCs (B) RNAgard® vs PAXgene® (C) PBMCs vs RNAgard®.Scatterplot: Scatterplot representing correlation between donors and preservation methods (A) PAXgene(TIF)Click here for additional data file.


Candidates for preoperative or intraoperative nodal assessment among patients with non-small cell lung cancer (NSCLC) manifesting as a subsolid tumor are not established. The present study was conducted to demonstrate the distribution of nodal metastasis rate according to newly proposed T categories for subsolid tumors, and we further aimed to identify radiologic parameters that can be predictive of nodal metastasis.We retrospectively reviewed cases of NSCLC manifesting as a subsolid tumor in computed tomography scans in a university-affiliated tertiary hospital between April 2013 and August 2016. All patients underwent mediastinal lymph node dissection during resection surgery. Multivariate analysis was performed among clinical and radiologic parameters.n = 23, 8.6%), cT1 , and cT2 . Ten patients (3.7%) had nodal metastasis: pN1 , pN2 . Nodal metastasis was not observed in tumors with a solid part ≤1.0 cm (cT1mi and cT1a) or in nonsolid tumors ≤3.0 cm (cTis). The nodal metastasis rate in cT1b, cT1c, and cT2 tumors was 6.1% (4/65), 8.3% (1/12), and 11.7% (5/43), respectively. Multivariate analysis showed that a solid part size > 1.5 cm  was significantly associated with nodal metastasis.Of the 269 eligible patients, T-categories were classified as cTis  among proposed T categories for subsolid tumors and a solid part size is an important radiologic parameter predictive of nodal metastasis in NSCLC manifesting as a subsolid tumor. Considering the low rate of nodal metastasis, pathologic nodal assessment may be unnecessary in early T category tumors with a small solid part size. The considerable increase in lung cancer screening has recently led to issues such as an increased number of lung nodules discovered via computed tomography (CT) and the management of these lesions. The subsolid nodule, defined as a well-demarcated lung lesion containing a ground-glass opacity, exhibits different behaviors from a solid nodule and accordingly, has garnered much attention. In 2013, the Fleischner Society recommendations emphasized that from a management perspective, both pure and part-solid ground-glass nodules are best considered as a category separate from purely solid lesions  was found to associate significantly with nodal metastasis.We performed a multivariate analysis to identify the risk factors predictive of nodal metastasis Table . The mulIn our cohort, the nodal metastasis rate was 3.7% (1.9% with both pN1 and pN2). Among part-solid nodules (cT1a-cT1c), the pN1 and pN2 rates were 1.5 and 1.0%, respectively, whereas there was no nodal metastasis in cTis (nonsolid nodule ≤3 cm). These findings of low incidence of nodal metastasis are consistent with previous studies , 5, 9.When tumors were classified by the proposed new T categories, any nodal metastasis was observed from cT1b to cT2 tumors, in which the solid part size was > 1.0 cm or total tumor size > 3.0 cm. In the final pathologic reports, patients with nodal metastasis had adenocarcinoma with invasive subtypes. There was no early stage adenocarcinoma , minimally invasive adenocarcinoma (MIA), lepidic predominant adenocarcinoma (LPA)). These findings suggest that preoperative pathologic nodal evaluation or extended lymph node assessment might be unnecessary when early stage adenocarcinomas are suspected by radiologic features.It is debatable whether extended lymph node assessment should be performed in patients with early stage NSCLC. As noted earlier, the incidence of nodal metastasis in NSCLCs manifesting as a subsolid tumor is lower than in NSCLCs with pure solid tumor. Furthermore, the extent of intraoperative node assessment  in NSCLC patients remains controversial . AlthougCurrently available guidelines recommend that preoperative pathologic mediastinal evaluation  should be considered regarding tumor size, radiologic N status, and tumor centrality , 15. HowTumor size has been considered an important risk factor for predicting nodal metastasis, and preoperative pathologic nodal assessment was considered in cases of tumor exceeding 3 because of low negative predictive value of PET-CT for detecting mediastinal nodal metastasis , 19. HowIn our study, we did not find an association between tumor centrality and nodal metastasis, although tumor location has been considered an important factor related to NSCLC. In a study by Lee et al.  of 221 pThere are some limitations in our study. First, because of the retrospective design nature, selection bias may have affected the results. EBUS and lobectomy were performed more frequently in more advanced T categories Table . TherefoAmong the proposed T categories for subsolid nodules, we observed nodal metastasis from cT1b, in which the solid part size exceeded 1 cm. The nodal metastasis rate was 3.7%, and solid part size is an important radiologic parameter predictive of nodal metastasis in NSCLC manifesting as a subsolid tumor. Considering the low rate of nodal metastasis, pathologic nodal assessment may be unnecessary in early T category tumors with a small solid part size.