RNA sequencing (RNA-Seq) in its varied forms has become an indispensable tool for analyzing differential gene expression and thus characterization of specific tissues. Aiming to understand the brain barriers genetic signature, RNA seq has also been introduced in brain barriers research. This has led to availability of both, bulk and single-cell RNA-Seq datasets over the last few years. If appropriately performed, the RNA-Seq studies provide powerful datasets that allow for significant deepening of knowledge on the molecular mechanisms that establish the brain barriers. However, RNA-Seq studies comprise complex workflows that require to consider many options and variables before, during and after the proper sequencing process.https://www.btrain-2020.eu/) where bioinformaticians and brain barriers researchers collaborated to analyze and establish RNA-Seq datasets on vertebrate brain barriers. The obstacles BtRAIN has identified in this process have been integrated into the present manuscript. It provides guidelines along the entire workflow of brain barriers RNA-Seq studies starting from the overall experimental design to interpretation of results. Focusing on the vertebrate endothelial blood–brain barrier (BBB) and epithelial blood-cerebrospinal-fluid barrier (BCSFB) of the choroid plexus, we provide a step-by-step description of the workflow, highlighting the decisions to be made at each step of the workflow and explaining the strengths and weaknesses of individual choices made. Finally, we propose recommendations for accurate data interpretation and on the information to be included into a publication to ensure appropriate accessibility of the data and reproducibility of the observations by the scientific community.In the current manuscript, we build on the interdisciplinary experience of the European PhD Training Network BtRAIN (Next generation transcriptomic profiling of the brain barriers provides a novel resource for understanding the development, function and pathology of these barrier cells, which is essential for understanding CNS homeostasis and disease. Continuous advancement and sophistication of RNA-Seq will require interdisciplinary approaches between brain barrier researchers and bioinformaticians as successfully performed in BtRAIN. The present guidelines are built on the BtRAIN interdisciplinary experience and aim to facilitate collaboration of brain barriers researchers with bioinformaticians to advance RNA-Seq study design in the brain barriers community. Central nervous system (CNS) homeostasis is ensured by endothelial, epithelial, mesothelial and glial brain barriers that divide the CNS into compartments . CNS barFor the purpose of clarity, we here define some general terms, as they lack a cohesive reference within the brain barriers community. For the purposes of this manuscript: The blood–brain barrier (BBB) is localized at the level of endothelial cells of the CNS microvasculature, which includes capillaries, pre-capillary arterioles and post-capillaries venules. BBB characteristics are not intrinsic to CNS microvascular endothelial cells but rather rely on the continuous crosstalk of cellular and acellular elements around CNS microvessels, which are referred to as the neurovascular unit (NVU). The NVU contains BBB endothelial cells, the endothelial basement membrane with a high number of embedded pericytes and the glia limitans composed of the parenchymal basement membrane and astrocytic endfeet . The bloThe known functions of the BBB and BCSFB include inhibition of free diffusion of molecules from the blood to the CNS while ensuring rapid efflux of toxic metabolites out of the CNS . In addiThe BBB endothelial cells are characterized by the presence of molecularly unique, complex and continuous tight junctions, in addition to adherens junctions, lack of fenestrations and a low rate of pinocytotic activity , 9. More+ endothelial cells from the brain of mice for subsequent single cell RNAseq (scRNA-Seq) [ERT2-Rosa-tdTomato mice to isolate endothelial cells from the brain and peripheral tissues in health and disease, including mouse models of stroke, multiple sclerosis, traumatic brain injury and seizures [Phenotypic characteristics of the brain barriers are ultimately regulated at the transcriptional level. In fact, the analysis of transcription profiles has been a useful tool in biomedical research and has had an increasing impact in the last few decades. Therefore, different research groups have begun to investigate the BBB transcriptome employing different methodologies. For instance, the first RNA-Seq study of CNS cells, including the BBB, was published 10 years ago  while a RNA-Seq) . This stRNA-Seq) . Other sseizures . This moseizures . These aThroughout the 1980s, Sanger sequencing was used to identify transcripts within tissues and cells, while quantitative methodologies, such as quantitative real-time polymerase chain reaction (qRT-PCR), came into prominence in the 1990s. These methods, usually referred to as low-throughput or ‘first-generation’ sequencing, are still being used to this day for specific purposes, although they are laborious, costly (for output level) and therefore, not suitable for establishing full transcriptomes of an entire tissue. The new millennium brought high-throughput techniques for transcriptomics analysis, with microarrays followed by next-generation sequencing (NGS) technologies. The most relevant in the present context and the most commonly used NGS technique is RNA-Seq, which allows advances in the characterization and quantification of transcriptomes, including whole transcriptome sequencing in a much less laborious and time-consuming fashion compared to previous methods –17.New technologies bring along opportunities that allow for a more in depth understanding of known mechanisms and the discovery of novel pathways. The new challenges and problems must be addressed, and so, the field of bioinformatics with its associated researchers who are highly specialized data analysts, started. In their short history, RNA-Seq methods have seen a sharp decline in costs coupled with the improvement of the underlying technology. This translated to an exponential increase of studies and groups taking advantage of this technology and of the amount of large datasets produced and published Fig. .Fig. 2RNbrain microvessels when isolating pure capillary fractions and others referring to capillaries when in fact the isolated microvessels are comprised of a mixture of arterioles, venules and capillaries. Considering the reported zonated gene expression of endothelial cells along the CNS vascular tree [A major challenge when aiming to compare transcriptome profiles from a given cell is understanding the source of the cell and how it was isolated. In the present context it is important to highlight that we found discrepancies in the protocols used for isolating brain endothelial cells to be common but remarkably underreported. The inaccuracy begins with a lack of consensus in the nomenclature for the different CNS vascular segments isolated and analyzed, with some laboratories referring to lar tree , transcrTo unveil the full power of transcriptome profiling it is, thus, essential to have a solid intersection in the fields of transcriptome profiling, bioinformatic analysis and classical brain barriers research. In this manuscript we highlight the intersection of transcriptomic profiling (with an emphasis on RNA-Seq) and the field of studying the brain barriers . We start by addressing considerations to be taken into account for the overall experimental design, and then elaborate on the multiple and essential intermediate steps throughout the workflow, including comparing different BBB isolation methodologies for RNA-Seq, data analysis and publishing recommendations. It is not our intention to establish rigid rules on how to perform an RNA-seq study in the field of brain barriers. Rather, our aim, based on our collaborative approaches in BtRAIN, is to raise awareness of the relevance of each experimental step and to highlight the relative strengths and weaknesses of the available alternatives. We then summarize what we consider essential information to be included in original manuscripts describing RNA-seq to define BBB signature genes in health and disease. We are convinced that appropriate availability of information will improve comparability and reproducibility of the different studies and thus advance quality and cost-efficiency of these studies in the field. By setting the stage for datasets that allow for meta-analysis-based research, our suggestions will furthermore allow for the implementation of the 3R rules of experimental animal research by reducing and refining animal experimentation.Experimental design is possibly the most important step of any transcriptomic experiment as the success of the project heavily depends on the choices made at this early stage. The first step is to have clear and defined goals. Questions that should be addressed before starting an experiment include: (i) Is the intent of the experiment to specifically define the transcriptome of the brain endothelial cells along the entire vascular tree or rather solely of BBB endothelial cells in CNS microvessels or even capillaries? (ii) Is the aim to compare the transcriptome of brain endothelial cells at different stages of e.g. development or under specific pathological conditions? (iii) Is the intent to define the transcriptome of a specific tissue , a specific time point  or a specific pathological condition (Multiple Sclerosis vs Alzheimer’s Disease)? (iv) What are the possible batch effects, such as the sacrificing groups of experimental animals on different days? These, and other similar questions, are known as intrinsic factors and their impact on the experimental design is direct, as they are defined by the question and objectives of the study.There are also extrinsic factors that influence experimental design in the form of practical limitations. They are (i) biological sample availability, e.g.: human CNS tissue is sparse and may not be obtained in the required quality to allow for RNAseq analysis. (ii) costs, e.g.: pre-sequencing optimization costs as well as sufficient sequencing of samples per group to the required depth. (iii) time, e.g. time required for breeding experimental animals to obtain the required brain barriers genotype, time for protocol optimization of the tissue of interest  isolation protocol and validation of the results and iv) human resources, as a transcriptomics project might involve several scientists, from the principal investigator to wet lab researchers and technicians, sequencing facility technicians and bioinformaticians Fig. .Fig. 3WoDefine a clear goal for the transcriptomics study: What is the specific intention for the project.Consider both extrinsic and intrinsic factors when designing an experiment.Plan the experiment including the advice from the experts involved in the different steps, such as sequencing facility staff and bioinformaticians.The vasculature is heterogeneous throughout the CNS  Fig. 4)4). This Capillary density is related to the metabolic demands, and thus neuronal activity, of the respective CNS regions . The graThe cellular and molecular characteristics of the NVU components are also heterogeneous throughout the CNS. Endothelial cells of the BBB present some of the highest regional differences. Indeed, expression of endothelial junction proteins  is higher in the WM compared to the GM . In the +/K+ ATPase, expression of transporters or efflux pumps or of adhesion molecules are not the same in endothelial cells of brain arterioles, capillaries and venules  protrude into each of the brain ventricles, and thus there are two lateral  ChPs, one in the third ventricle  and one in the fourth ventricle . There is increasing evidence that the gene expression profile of each ChP reflects their positional identities. The mouse lateral and fourth ventricle whole ChPs present a differential transcriptome and secretome, as assessed by RNA-seq and mass spectrometry . A recenAlthough most transcriptomic studies have focused on the lateral ChPs, the choice of ChP will influence the sequencing results and the comparison with available datasets. The heterogeneity of the ChP among the four ventricles in the human brain remains to be characterized.Consider regional transcriptional heterogeneity among the four ChPs. The choice of ChP should be stated in the methods.Many genetically modified mouse models have been developed for studying brain barriers development and function. For a general overview on the available genetic tools that are used to study the BBB function, we recommend the review of Sohet et al. , while fhttp://www.informatics.jax.org/ can be used to have a detailed overview of the mouse line of interest. Original literature on how the mouse line has been created and the expression pattern of the respective reporter needs to be carefully evaluated. This includes consideration of the promoter used for driving the expression of the fluorescent reporter with respect to cellular specificity or efficiency of expression, which could depend also on the age or disease state investigated. Furthermore, inducible expression systems, e.g. based on Cre-recombinase or TET-ON or TET-OFF-regulation need to be tested for their specificity, leakiness and completeness of driving reporter expression [Before any experimental approach, a deep understanding of the genetic mouse model that is used is needed. To this end, online tools such as pression , 47. SpeA RNA-Seq study of CNS microvessels, capillaries or single BBB endothelial cells necessarily relies on the protocol used to isolate the target tissue or cell. Due to the highly complex structure of the NVU and the aforementioned heterogeneity of NVU components throughout the CNS vasculature, a detailed description of the protocol used to isolate the material for RNA-seq analysis is mandatory. Each step of the isolation protocol is critical and has a direct impact on the sample purity as well as on the yield of the material obtained for sequencing. To date, many different protocols for the isolation of endothelial cells of the BBB, or CNS microvessels, or brain endothelial cells have been published. Most classical isolation protocols consist of a combination of mechanical disruption and enzymatic digestion and a subsequent size selection of the isolated vascular segments by filtration through nylon membranes with different pore sizes Fig. . AdditioThe first step for the isolation of CNS microvessels or endothelial cells of the BBB is to properly slice or cut the tissue into small pieces in order to facilitate enzyme-based digestion or the following Dounce homogenization steps . TypicalFiltration of the dissociated tissue or CNS vasculature fragments enriches for a specific component from the brain homogenate. One of the most common methods for the isolation of CNS microvessels or specifically capillaries is performing either single or a series of filtrations through nylon or polyester membranes followed by gradient centrifugation steps with Percoll™, Dextran or serum albumin, in order to separate microvessel fragments from cellular debris, myelin and other non-desired cell types –58.The enrichment of a certain type of CNS microvessel (i.e. capillaries) over the others depends on their diameter and can be achieved by using different filter pore sizes. Therefore, the combination of larger (~ 100 μm) to smaller (~ 20 μm) filter pore sizes, in addition to using one or several filters in sequence, can determine the final vascular segment that is isolated . Indeed,Although size selection represents a possible choice for defining the CNS microvascular segment that is isolated, some technical details have to be kept in mind. Other steps of the protocol can highly influence the physical properties of the isolated microvessels or capillaries Fig. b. For inIn sum, size selection is a critical step in the isolation protocol of microvessels, capillaries or individual endothelial cells of the BBB, and ambiguity in terminology should be avoided by accompanying qualitative and quantitative information.Tissue processing methods can affect size selection. Indicate the method used and, if possible, the state of the tissue after dissociation.Indicate the size-dependent filtration steps, if used, including pore size of the filter mesh and the combination of more than one filter.http://www.informatics.jax.org).For animal studies, indicate the strain, age and sex of the animals used. For reporter mouse studies indicate the precise mouse line used as indicated on the MGI homepage  to define the cell population that will ideally be targeted.Clearly indicate the amount of material obtained after bead selection, including number of isolated cells and amount of RNA extracted from them; this is useful information for the study itself and for future reference.Clearly indicate the necessity of separating beads from cells or not, if choosing positive selection of the target cell.ERT2 mice [FACS is a powerful technique that uses flow cytometry to selectively separate cell populations from complex pools of different cells. FACS-based selection has been used in transcriptome profiling studies of the brain barriers , 14, 79.RT2 mice , 81 or bRT2 mice .In any case, the final sample after FACS consists of a highly enriched fraction of the cell type of interest. However, a high amount of starting material is often necessary to obtain a sufficient yield after the sorting, although it will also depend on the needs of the downstream application and/or analysis , 84. AlsAs already mentioned in other sections of this paper (e.g.: beads selection), obtaining a viable single cell suspension is also crucial when isolating cell populations of the brain barriers prior to FACS. Similar to the beads selection, a combination of mechanical and enzymatic digestion prior to FACS is often used to improve cell–cell dissociation. Indeed, a good single cell suspension reduces the amount of false positives/negatives produced by antibody staining (when not relying on reporter mice) or reporter proteins prior to FACS. In line with the necessity of a single cell suspension, duplet exclusion should be tightly controlled to ensure the best purity and reliability of the sorted material, as well as a strict gating strategy according to the experimental needs . In addiThe flow conditions and the instrument used should be indicated.The precise scatter and fluorescence gating strategy used for the FACS of the target cell should be included in the supplement of the research article, as well as a detailed description of the isolation protocol and potential staining steps performed prior to the sorting and their duration.Duration of sorting itself and yield of cells received should be described.Time of RNA extraction following the FACS should be clearly stated, as different experimental designs implement differences in time points, e.g.: extraction of RNA right after FACS of after several hours due to travel from the sorting facility back to the laboratory.Laser capture microdissection (LCM) allows for the dissection of CNS microvessels from a CNS tissue section with the help of a microscope and a laser. Dissected CNS microvascular endothelial cells can be later captured by adsorption, ejection, gravity or aspiration. LCM permits to take a snapshot of the transcriptomic profile of the BBB, in opposition to methodologies that require long incubation times.One of the main limitations of LCM is the low yield of this laborious technique, which can be circumvented by using kits designed to isolate RNA from small amounts of cells  or by peConsider the balance between yield and purity of the isolated CNS microvascular endothelial cells when deciding to use cross sections or longitudinal sections.It is recommended to use RNA isolation kits specifically designed for small amounts of cells, or performing RNA amplification prior to sequencing.Test for and consider the possible cellular contaminants co-isolated with the BBB endothelial cells.Isolated brain microvascular fragments or single cells can be cultured and used as a BBB endothelium in vitro model. Most of the in vivo BBB characteristics are maintained by primary cultures of brain endothelial cells, hence representing powerful tools to study various aspects of BBB properties. However, often these cultures offer restricted capacity of genetic manipulation (e.g. transfection) and can be maintained in culture for a limited amount of time and/or passages. Nevertheless, numerous primary cultures of brain endothelial cells have been established from both mouse , rat 9191 and huOn the other hand, BBB in vitro model established by immortalized cell lines allow for much easier handling, as in many cases the cells can be cultured and passed as needed, in addition to much better tolerance to genetic manipulations. This makes cell lines a very suitable tool for high-throughput screening purposes, as they are also a much more homogenous cell population compared to primary cell cultures, where often contaminants are found in the culture. As a major drawback, cell lines of the BBB endothelium do not strictly retain BBB characteristics such as high tightness and very low permeability to the same degree as primary brain endothelial cells, therefore careful selection of the best suited BBB cell line is needed according to the specific scientific question being answered.In both cases, the presence of additional BBB cell types found in vivo is not always modeled in the in vitro systems, such as for example the presence and anatomical disposition of the astrocytic endfeet found in the NVU in vivo. To overcome these limitations, sophisticated co- or tri-cultures of brain endothelial cells together with pericytes or astrocytes from different sources have been established and, to a certain degree, mimic the in vivo NVU structure –96.In the context of transcriptomic studies, both BBB cell lines and primary brain endothelial cells cultures have been successfully used in transcriptomic approaches. For example bulk RNA-seq has been performed on the human cerebrovascular endothelial cells (hCMEC/D3)  and on pRecent advancements in stem cell technology have furthermore allowed derivation of human in vitro models of the BBB from stem cell sources including human cord blood-derived stem cells of circulating endothelial progenitors  and humaConsider the effect of culture-induced mRNA expression changes in the in vitro BBB models due to medium composition.Use RNA-Seq profiling of in vitro BBB models and especially of hiPSC- derived in vitro BBB models to benchmark them against the BBB in vivo.The ChP consists of a highly vascularized stroma populated by immune cells and is surrounded by a layer of highly specialized epithelial cells which form the BCSFB. Contrary to the BBB, the ChP endothelium is fenestrated and does not form a BBB .ChP transcriptomic studies of the entire ChP tissue will include the transcriptome of the epithelial cells forming the BCSFB but also from the endothelial cells, stromal fibroblasts and immune cells of the ChP. Alternatively, the ChP epithelial cells can be isolated to focus on the transcriptome of the BCSFB.Using the whole ChP greatly simplifies the protocol for tissue isolation, but the cellular heterogeneity within the ChP will complicate subsequent analysis and interpretation of the results especially when performing bulk RNAseq studies. However, these studies will provide additional information on the other components of the ChP, such as the vasculature or immune populations. Many RNA-seq studies have taken this approach, particularly those focused on humans , 105.Alternatively, if the barrier component of the ChP is the focus of the study, the epithelial cells can be isolated. While this results in cleaner data, dissociating the ChP may be challenging (see below). To overcome these difficulties, the novel single-nucleus RNAseq method emerges as an option for tissues that are hard to dissociate, such as the ChP .The research question and the technical limitations will determine whether the whole ChP or the isolated epithelium will be sequenced.TTR has been recently identified in ChP macrophages [The techniques for isolating the ChP epithelial cells are similar to those for the BBB. Of note, the ChP epithelium is composed of large cuboidal cells, which are easier to dissect microscopically with LCM compared to the thin BBB endothelium. Indeed, LCM has been used to isolate the human ChP epithelium for microarray studies of the BCSFB –108. In rophages , and theThe research question and the technical limitations will determine whether the whole ChP or the isolated epithelial cells will be sequenced. This should be specified in the methods.The ChP can be particularly challenging to dissociate. The techniques for isolating the ChP epithelium include LCM, mechanical and enzymatic digestion. Resulting purity should be assessed and reported.Before performing RNA-seq, it is good practice to ensure that the chosen isolation strategy results in the desired brain barrier cell purity. Indeed, knowledge about the degree of brain barrier cell purity is essential as it dramatically reduces possible biases in the downstream analysis, overall improving the biological meaningfulness of any RNA-seq study. Pre-sequencing purity assessments can be achieved by different techniques, often used in combination. Common contaminants when isolating endothelial cells of the BBB are pericytes or pericyte fragments and astrocytic endfeet, which can hardly be avoided Fig. , Table 1Another potential source of contaminants when isolating endothelial cells of the BBB are ChP cells, in particular the ChP epithelium. Indeed, in the vast majority of the preparation the ChP is not removed from the processed material, therefore potentially accounting for contamination. Performing qPCR for choroid plexus specific markers such as transthyretin or keratin-8 will allow to determine the presence of ChP mRNA in the brain endothelial preparation. For example, low expression of claudin-3 mRNA has been reported in freshly isolated brain microvessels, despite recent evidence proving lack of claudin-3 expression in the mouse brain microvasculature. This could be due to contamination of the isolated brain microvessels with ChP epithelial cells, which express claudin-3 .RNA isolation methods have to be chosen depending on the type and availability of starting material, on the one hand, and the intended RNA-seq analyses, on the other Fig. . The useContaminations with extrinsic RNA and DNA, or with nucleases that might lead to the degradation of RNA samples can have a negative impact on the results. General measures to avoid these issues include thorough and regular cleaning of work areas and equipment with decontamination solution as well as use of clean gloves, aerosol barrier pipette tips, and DNAse and RNAse-free plasticware. Additionally, it is recommended to carefully handle RNA samples at the temperature suggested by the manufacturer of the isolation kit .Commercially available RNA isolation protocols generally follow two main steps: (i) sample lysis, homogenization and clearing, and (ii) RNA purification. Isolation kits and protocols must be chosen according to the type of sample  and RNA molecules to be purified .Cell lysis is commonly performed using a guanidine-thiocyanate-based buffer combined with a strong reducing agent, such as tris(2-carboxyethyl)phosphine hydrochloride, 2-mercaptoethanol or dithiothreitol to ensure the complete cell lysis and protein denaturation (lysis buffer), but these procedures vary according to the characteristics of the starting sample  tissue)—Fig. .Fig. 7OvCultured adherent cells, like endothelial cells, can be subjected to trypsinization prior to cell lysis or they can be lysed directly in the culture container by replacing the liquid medium with lysis buffer directly to the cell monolayer. Cell lysis by addition of lysis buffer is recommended to be performed prior to storage to avoid transcriptomic alterations during freezing. The cell lysate is safe to be stored at − 80 °C.g). After complete removal of the supernatant, the cell pellet is re-suspended in lysis buffer. As for the lysis of adherent cells, cell lysis is recommended to be performed prior to storage. Sorted and isolated single cells can be directly collected in lysis buffer and mild lysis buffer, respectively.Cell suspensions, such as microvessel fragments or single endothelial cells, can be pelleted by gentle centrifugation  is the RNA that will be translated by the ribosomes into proteins. mRNA is characterized by having a coding sequence surrounded by 3′ and 5′ untranslated regions and a long sequence of adenine nucleotides at the 3′ end (poly-A tail). Several other types of non-coding RNA have important roles in cell biology, e.g. ribosomal RNA (rRNA) and transfer RNA (tRNA) are necessary for the translation process. Additionally, other RNA families are important for gene expression regulation as for example, microRNA (miRNA) with a size of ca. 22 nucleotides, other small RNAs (< 200 nt) and long non-coding RNA (lncRNA) with sizes greater than 200 nucleotides. Regarding the RNA content in a cell, it is important to notice that just mRNA and many lncRNA have a poly-A tail at the 3′ end. Additionally, rRNA represents the majority of the RNA content in the cell.The combination of a highly concentrated chaotropic salt (e.g. guanidinium thiocyanate) in the lysis buffer with a certain concentration of an organic solvent  allows the adsorption of nucleic acids to the silica matrix in spin columns. Although this solid phase extraction allows for efficient and easy isolation of purified nucleic acids, several details must be taken into consideration. The final concentration of ethanol or isopropanol in the mixture with lysis buffer is essential to promote the adsorption of RNA to the silica matrix . DiffereThe RNA extraction protocol should be selected based on tissue type and quantity, as well as the intended sequencing and analysis.Specific protocols are required for the isolation of total RNA including miRNA.Correct sample homogenization and clearing are essential for isolation efficiency of RNA and analysis reproducibility.Genomic DNA contamination can have a considerable impact on the sequencing results therefore, gDNA removal or digestion is mandatory.RNA concentrations are best determined using a fluorometric quantification or qPCR. For example, fluorometric quantification can reliably measure RNA concentrations as low as 0.2 ng/μl. Spectrophotometric quantification is not recommended due to its inaccuracy, especially for small amounts of RNA, and due to the unreliable results in the presence of contaminants with an absorption wavelength close to those of DNA and RNA, e.g. phenol. Nonetheless, the spectrophotometer can be a useful tool to determine contaminations. For example, measurements with absorption wavelengths of 230 and 280 nm can indicate contaminations with guanidinium salts and proteins, respectively.https://www.ncbi.nlm.nih.gov/geo/) or Sequence Read Archive (SRA—https://www.ncbi.nlm.nih.gov/sra). Given their impact in study reproducibility and cross-study meta-analyses it is advisable to include these in publications as well as public databases.Quality control is best performed using an automated capillary electrophoresis platform that calculates a score for the RNA quality. Depending on the automated capillary electrophoresis platform used for RNA characterization, the RNA quality score might have different names . The RNA quality score has values between 0  and 10  and it is calculated using an algorithm that incorporates several features of the RNA electropherogram, such as the ratio of 28S:18S ribosomal RNA . A qualiHuman BBB and BCSFB samples from a clinical setting often present suboptimal preservation, which may affect the resemblance of the transcriptome to the in vivo situation. With some exceptions –163, theOther factors that may lead to RNA degradation are the handling and storage conditions and, if applicable, the sectioning process. However, there are reports of a remarkable RNA stability in postmortem human brain samples , 168.The RNA quality should be accessed, and the library preparation protocol should be chosen accordingly.The quantity of isolated RNA must be sufficient to perform library preparation. Commercially available kits allow for preparation of libraries with 0.1 to 500 ng of total RNA.The RNA samples must be clear of contaminants such as proteins, salts, sugars or DNA.In summary we advise the starting material to fulfill three criteria:Integrity values for RNA are essential both for the selection of the enrichment method as well as sample selection.Assess RNA quantity and quality of the samples prior to sequencing. A quality threshold should be debated between the wet-lab researcher, the sequencing technician and the bioinformatician.If the data is made publicly available, always disclose the integrity values of the included samples.Consider the integrity of the samples when choosing the library preparation method (discussed below).Consider pre- and postmortem factors: they may affect heterogeneity in the whole transcriptome.When designing an RNA-Seq experiment, the following aspects must be considered: sequencing strategy, library preparation, number of replicates and sequencing depth Fig. .The sequencing strategy is important to guarantee the quality of the analysis at a reasonable cost. Paired-end sequencing means sequencing from both ends of the cDNA fragment. Since both fragments are aligned as a pair of reads, this strategy is preferable for de novo transcriptomic assembly, to study isoform expression or poorly annotated transcriptomes. Single-end sequencing implies that just one end of the cDNA fragments is sequenced. This approach is more cost-effective than paired-end sequencing and it is suitable for studying gene expression in well annotated organisms, such as human, mouse, rat and zebrafish. Additionally, the length of the reads produced during sequencing can be chosen depending on the purpose of the study. Longer reads (from 150 to 300 bases) are more cost-intensive but offer a higher resolution to study alternatively spliced RNA isoforms or poorly annotated transcriptomes. Shorter reads (e.g. 75 base pairs) are suitable to study gene expression in well annotated organisms.The sequencing depth (number of sequenced reads per sample) used in RNA sequencing experiments is important for detection of differentially expressed genes, especially for lowly abundant transcripts. RNA-Seq experiments are recommended to use a minimum sequencing depth between 10 and 30 million reads per sample , 170. HoTechnical variability in sequencing is usually low. Nonetheless, the technical variability introduced during sample collection and library preparation can be estimated using technical replicates or RNA spike-in like the one developed by the external RNA controls consortium (ERCC)  The ERCCBiological variability is the natural variation due to physiologic differences among subjects or over time in the same subject. Biological variance is usually more pronounced than technical variance and must be addressed with greater concern. Biological variability can be observed at different levels according to the sample type . For instance, while studying the BBB from human samples, biological heterogeneity might be introduced by several variants, such as genetic background, lifestyle, hormonal level, medical history, sex or age. Differently, when studying the BBB from animal models (e.g. mouse), the variants that might introduce biological variability are mostly age, sex and strain. This reduced number of variants is expected to introduce less biological variability in samples from animal models when compared to samples from humans. Although the variability introduced from such variants can be minimized by using, inter alia, sex and age matched samples, uncontrollable biological heterogeneity should be accounted for by using biological replicates. For example, human BBB samples cannot always be collected in the same stage of the male or female hormonal cycles. Since it is known that hormones like steroids and estradiol regulate BBB permeability and tight junction protein expression, respectively, it is important that such biological variability is taken into consideration by using biological replicates . AgainstInclusion of replicates in RNA-seq experiments is important to assess technical and biological variations:Since ribosomal RNA makes up most of the cellular RNA, rRNA depletion or mRNA enrichment should be performed prior to library preparation. Ribosomal RNA depletion or mRNA enrichment methods must be carefully chosen depending on the RNAs of interest and the integrity of the RNAs. For instance, ribosomal RNA depletion can be preferable for degraded material if poly-A bias is observed.It is important to understand and choose carefully between stranded and unstranded library preparation protocols. Stranded RNA-Seq library preparation is characterized by the use of deoxyuridine triphosphate (dUTP) instead of deoxythymidine triphosphate (dTTP) during the second strand synthesis. Thanks to this alteration, the second strand can be degraded using uracil-N-glycosylase prior to the PCR amplification and only the first strand of cDNA will be amplified and sequenced Fig. . A stranAlthough not the main focus of this manuscript, since they have important applications in the field of brain barriers, we also want to briefly highlight 3′ RNA sequencing and small RNA sequencing protocols.3′ end RNA sequencing is an alternative to RNA-Seq. Library preparation techniques are almost identical but unlike RNA-Seq, 3′ end RNA sequencing includes an enrichment step that, following fragmentation, excludes all but the outermost 3′ fragments adjacent to the poly-A tail. Due to the information lost during the enrichment for terminal fragments, 3′ end RNA-seq is not suited for studying open reading frames and, consequently, alternatively spliced transcript isoforms. Since 3′ end RNA-Seq generates exactly one read for each transcript and allows the incorporation of unique molecular identifiers (UMI) as applied in the Massive Analysis of cDNA Ends (MACE) method . The tecSeveral studies have shown the important role of small RNA in the molecular mechanisms that control the function of the BBB . For exaCorrect RNA quantification and quality control are essential to ensure the quality of the sequencing results and their reproducibility.Strandedness and sequencing strategy have a big impact on the analysis and the results. For that reason, they must be carefully chosen.Biological replicates are essential and minimum sample size is affected by extrinsic and intrinsic factors.The selection of the correct library preparation technique is crucial for accurate analysis and should be carefully chosen according to the topic under research.After completion of the first human genome sequence, the demand for cheaper and faster sequencing methods accelerated the development of NGS. Nowadays, NGS platforms enable low-cost and high-throughput analyses by sequencing billions of reads in parallel. Two of the currently most used short read sequencing methods in research labs are Ion Torrent (LifeTechnologies) and Illumina sequencing . AlthougFull transcript RNA sequencing can be performed using nanopore or single-molecule real-time sequencing and might reveal the impact of alternative splicing variants in the BBB or BCSFB function.Gene expression profiling studies from BBB or BCSFB samples can be performed using both Illumina and Ion Torrent platforms.Sequencing strategy (single-end or paired-end).Read length .Desired thought-put.Run time..Sequencing technology and platform should be chosen according to four main criteria:Data analysis for RNA-Seq is a multi-step process that can be achieved with a plethora of technologies and strategies. A proper analysis should take into consideration all the steps mentioned throughout this manuscript as they can influence the results and their interpretation. There is no optimal “one size fits all” pipeline to be used for all different transcriptomic projects in the field of brain barriers, although the overarching steps will mostly be the same Fig. . AnalysiA major key for a successful data analysis is clear communication between the wet lab brain barriers researcher, the sequencing facility staff and the bioinformaticians allowing for appropriate considerations of the respective limitations for optimal achievement of the project goals. We believe that there are several good publications and resources on how to optimize an analytical pipeline and benchmarking the different tools , 190. AsFor the purposes of this paper we will start after the demultiplexing process with the FASTQ files. These are the raw files that sequencing facilities and companies most commonly give to the researchers.Along with the FASTQ files, researchers usually also get a text file with hashes  for each of the FASTQs. These can be created with different algorithms  and are used to ensure that the files were not corrupted during transfer. As such, these hashes should always be used to check the integrity of the FASTQ files.Some companies also do some pre-processing steps, for instance trimming reads with low quality . It is important to check if these steps were implemented, and if the answer is no, which steps should be. Removing low quality reads, for instance, can improve the efficiency of the analysis while also removing possible errors.The most commonly used software for the effect of pre-alignment quality control (QC) is FastQC . It is eBased on this information it should be decided if the raw data is ready for alignment, if it requires pre-processing, such as removal of repetitive sequences, or if the sequencing or pre-sequencing steps need to be optimized and/or repeated.After mapping the raw data (see next section), a genome browser, such as Integrated Genomics Viewer (IGV) , can be Mapping will also generate metrics regarding overall alignment rates, reads aligned to genes, reads aligned uniquely versus reads aligned multiple times and unmapped reads. A close look at these metrics can uncover problems such as sample contamination or DNA still present in the sample.Finally, for post-differential expression analysis, one can perform a batch effect test and, if necessary, a batch effect correction based either on known variables  or blindly. Plot visualization is also very informative regarding sample variability, by means of PCA or heatmaps colored by group and identified by sample, as an example.Quality control must be performed at every step in the analysis process.There are no hard rules on quality control metrics, though all applied quality measures must be mentioned in the publication of the data.Mapping metrics can inform about problems not detected at the raw data level and therefore should be taken into account when analyzing the data.Data visualization  can be informative regarding read distribution and sample variability, respectively.While analytical design can be modified to serve the purposes of the analysis, most RNA-Seq experiments with the objective of identifying differentially expressed genes will follow the same general workflow.https://www.ncbi.nlm.nih.gov/refseq/ and Ensembl—https://www.ensembl.org/index.html—being the most used for the referenced species) as well as multiple versions and builds. When writing a manuscript, it is imperative to reference the source, version and build used for the reference genome and annotation or precise replication will not be possible.From the raw files, the data is aligned, either by means of mapping to an available and curated reference genome or by means of performing a de novo transcriptome assembly. In the field of brain barriers, the vast majority of experiments are done using vertebrate, well established animal models, human tissue or in vitro models of the BBB and BCSFB, including cell line models. As such, usually, the alignment is done by means of mapping to a reference genome of the species of interest. There are multiple sources for reference genomes . This choice should be mentioned in publications in order to allow the possibility of replication.After quantification, the count matrix contains the raw counts, which need to be normalized. Several normalization algorithms are commonly used with different purposes and efficiencies. FPKM (Fragments Per Kilobase of transcript per Million mapped reads) and RPKM (Reads Per Kilobase of transcript per Million mapped reads) are commonly used for intra-sample comparison by normalizing for both gene length and library depth. However, they have been shown to be inconsistent when compared to other methods such as TPM (Transcripts per Million), and as such, if intra-sample comparison is the objective, the latter has proven more robust . On the With the normalized values calculated, the following steps are the differential expression analysis and the gene ontology (GO) and pathway analysis. We will discuss cut off values and their importance in the next section and briefly mention GO and pathways.Always reference the source, version and build of the reference genome and annotation.Indicate the counted feature .Normalization of the counts is mandatory. The selected normalization algorithm needs to take into account the different strengths and utilities.After the differential expression analysis to select differentially expressed genes (DEG) a common approach is to apply cut-offs to the statistical values obtained, usually a measure of probability of a false positive, one of relative expression and one of absolute expression. One of the statistical values obtained is a p-value. These values should not be used for DEG selection. P-values are obtained for comparison between groups on a per gene basis without taking into account the number of genes. However, when undergoing parallel multiple comparisons (as is the case with tens of thousands of genes) the likelihood of obtaining false positives will increase. To appreciate this, it is necessary to understand what p-values actually represent. A p-value is merely a statistical probability that the result/observation for that gene would be at least as extreme (the observed difference would at least be the same) if the null hypothesis was true . As such, if we have a p-value of 0.01 but 20,000 genes tested, it would be expected that 200 of those genes were false positives.In order to solve this issue, a multiple testing correction is applied. There are a variety of algorithms, some of them very strict. Bon Ferroni is probably the simplest and the strictest one. It consists of dividing the α  by the number of tests (genes) to get a new α. However, while increasing confidence on the genes that do pass the new significance threshold (increased True Positive Rate) it is probable that there is a concomitant increase of the false negative ratio. A more appropriate correction is the Benjamini–Hochberg correction , which pAnother option is to ignore the p-value (adjusted or not) and focus solely on the fold change. Massively simplifying it, the fold change indicates the order of magnitude of the difference for that gene between conditions. This allows for the discard of genes where there are small differences that may be difficult or impossible to validate by qPCR and especially at the protein level, due to problems pertaining to low depth and the RNA–Protein abundance discrepancy . Also, iA commonly used solution is to apply cut-offs for both the FDR and the log2FC, potentially also combined with an extra cut-off for overall expression.If the goal of the experiment is mechanism or process discovery, pathway and enrichment analysis can provide guides of where to proceed in the functional validation. However, there are a few issues that we found problematic and should be considered.Firstly, these analyses are heavily dependent on the quality of the respective databases used. For instance, the more a respective field is studied (e.g. cancer) the more information about that field is included in the database. The corollary is that the less that is known in a specific research field or about a molecule or mechanism, the less information will be available .Secondly, there is a trend to use commercial tools for pathway and gene ontology analysis. While the tools appear to be solid, their restricted availability complicates reproducibility of the results. Thus, we recommend the use of open source tools for the same purpose instead.Finally, while it is possible to have an estimation of cell and tissue purity based on the results from transcriptomics studies, the methodologies to do just that should not be relied upon. The advent of extensive datasets, such as the one from Vanlandewijck and colleagues  can provIn RNA-Seq studies p-values require multiple test correction. However, overly harsh multiple test correction can increase the False Negative Ratio of differentially expressed genes.The use of individual cut-offs and or mixes have their strengths and should be used in accordance to the objective.The intrinsic limitation of pathway and enrichment analysis should be taken into account.It is often requested by journals or reviewers to validate RNA-Seq results in two ways. The first one is by qPCR. This RNA level validation can be performed either on the same samples as the RNA-Seq or on different ones. It is an open debate if qPCR validation is actually necessary. It is our opinion that, while not absolutely necessary, qPCR validation can provide valuable information. When done on the same samples, qPCR can validate the sequencing process, while validation of the results using a different set of biological replicates can help confirm the results .The other validation approach is at the protein level. This can be achieved by different methodologies including Western Blots (WB), immunohistochemistry (IHC) and immunofluorescence (IF) staining. WBs tend to be more informative when it comes to quantification values, IHC and IF staining on the other hand allow for cellular and subcellular location of the protein being investigated. Protein level validation should be performed prior to functional studies based on the chosen candidates from the RNA-Seq results. Common issues are the absence of correlation between protein and RNA levels or of the differential expression detected amongst groups due to the RNA–protein discrepancy .https://www.ncbi.nlm.nih.gov/geo/), SRA (https://www.ncbi.nlm.nih.gov/sra) and the European Nucleotide Archive (ENA—https://www.ebi.ac.uk/ena). As most of the data repositories allow for the data to remain private until publication, it is recommended that the process of uploading the data is done in advance. This also allows to provide the reviewers with access tokens. Outside of public data repositories, local copies of the data should also be stored and maintained.Upon publication of the manuscript, all relevant data should be made accessible. This necessarily includes the raw data but can also include processed data and any relevant metadata. There are a number of specialized data repositories, including GEO (http://bioinformaticstools.mayo.edu/bbbomics/) [http://betsholtzlab.org/VascularSingleCells/database.html) [https://www.brainrnaseq.org/) [http://linnarssonlab.org/cortex/) [https://portal.brain-map.org/). These web interfaces are usually representative of studies that focus on the assessment of molecular expression in specific tissues or cell types and can be valuable tools when designing a new experiment or doing quality control and purity assessment of bulk RNA-Seq data.As a complement, some groups have in addition created web-interfaces allowing other researchers to explore their results. This includes BBBomics (bomics/) , the Vasse.html) , 76, Braeq.org/) , 205, Sicortex/)  and the Include a description of the selected region/tissue/cell type  as well as discarded regions/tissues/cell types  as well as quantification.Library enrichment—RNAs of interest.Platform.Pairedness.Strandedness.Depth (range).Replicates per group..Add sequencing specific information, including:When possible a standardization of a simplified script as supplementary material in all publications.Simplified schematic analytical pipeline with tools and processes.Relevant Quality Control information.Metrics used throughout the paper, such as normalization values, cut-offs . Keep consistent, explain if not.Further criteria for selection should be made clear ..Provide data analysis details:While referring to protocols in the methods section, point to original paper and make the differences to it clear. Alternatively, include a detailed protocol in the supplementary material.At the time of publication make both the raw data, the analyzed data and any relevant metadata publicly available.As mentioned above, the present manuscript has focused on the application of bulk RNA-Seq in the field of brain barriers research. New applications in the field of transcriptomics are however emerging and may thus be of equal significance in the near future.scRNA-Seq is perhaps the most established and known in brain barriers research. Several studies have used this methodology to great effect allowing to discover a previously unknown diversity of cells in the brain and the brain vasculature, respectively , 206. SiSpatial RNA-Seq is a recent technique that allows to sequence tissue sections in a way that the transcriptomic profiles can be attributed back to different locations in the section, with each location corresponding to a uniquely barcoded spot. Briefly, a tissue section is placed on a special microscopic slide  that allows to maintain positional information throughout the sequencing process. As such, in conjugation with a previously acquired image of the section taken beforehand, it is possible to attribute specific transcriptomic profiles to regions in the tissue section. At present the resolution of spatial RNA-Seq allows to distinguish about 5000 spots per slide with each spot having a 55 µm diameter and a 100 µm center to center distance between spots. Thus, this technology is not well suited for precise assignment of transcriptomic information to the fine structures of the microvasculature (such as BBB microvessels), but rather to focal areas in the brain and spinal cord allowing to determine regional differences in the transcriptome in the brain and spinal cords in health and disease , 208. AsAs a final note of this section, it is important to point out that none of the mentioned technologies will replace bulk RNA-Seq. Rather, they provide alternative or supplementary technology to bulk RNA-Seq and are accompanied by different strengths and weaknesses. For instance, scRNA-Seq usually requires live, viable, dissociated cells and transcripts of low and medium expression will be underrepresented. The choice of which technology is appropriate should be made based on what the objectives of the study in question are as well as extrinsic factors, such as cost. This decision should be made early on the process and the input of a sequencing facility technician and of a bioinformatician is valuable to the wet-lab researcher.This manuscript intends to provide a valuable and exhausting source of information for brain barriers researchers and bioinformaticians when planning RNA-Seq analysis in the context of brain barriers research based on the experiences made in the BtRAIN network. Our intention is that this manuscript incites closer interaction between classical brain barriers researchers and bioinformaticians in planning and performing RNA-Seq analysis. We are convinced that considering the issues raised here will allow for future publication of studies providing more specific and accurate information which is prerequisite for data comparison and replication.Alignment and Mapping: Alignment is the process of attributing reads to the corresponding place in the genome/transcriptome. Mapping usually refers exclusively to doing this process by aligning the reads to a reference genome while alignment can also refer to a de novo assembly.Batch effect: Type of variability not related to the biological question that can obfuscate the results. It tends to have a technical source and it is usually hard to pinpoint. Can be due to different labs, different time days for RNA extraction as well as many other factors.BAM file: Binary form of a SAM file (Sequence Alignment/Map), which is the file resulting from the alignment. These files can be sorted by gene name of by genomic coordinates.Checksum (i.e. md5sum): algorithm that creates an “hash” (a string of characters) corresponding to a file. Since small changes to the file completely alter this string it can be used to detect corruption of the file during transfer.Demultiplexing: in silico process of assigning the reads back to the corresponding sample. During sequencing samples are often multiplexed (pooled and sequenced together) making demultiplexing an essential step.Downstream Analysis: Usually refers to the more flexible statistical steps that start with the counts matrix. It includes, amongst others, normalization, differential expression analysis, batch effect correction, multiple testing correction and annotation.FASTQ: file type that stores the biological sequence alongside the corresponding PHRED scores. It is usually the file with which the analysis starts.Fluorescence-activated cell sorting (FACS): technique that uses flow cytometry to sort a heterogeneous mixture of cells into two or more groups based upon light scattering and fluorescent characteristics.Genome Browser: Tool that allows for direct visualization of the reads aligned to a reference genome using the coordinate sorted BAM files.Laser capture microdissection (LCM): technique that allows the dissection and isolation of cells or tissue. It uses direct microscopic visualization and a laser. Dissected material can be captured by absorption, ejection, gravity or aspiration.Paired or single-end reads: single-end reads are only sequenced from one end of the DNA fragment while paired-end reads are sequenced from both ends. This will have an impact on certain features, for instance, paired-end reads are better suited for the discovery of novel alternative splicing sites.PHRED score: Logarithmic quality score for base-calling errors. The higher the score, the less the probability of an error for each base. A PHRED of 3, for instance, corresponds to a 50% probability of a correct call while a PHRED score of 30 corresponds to a 99.9% probability of a correct call.Principal component analysis (PCA): statistical method that uses the provided data  to define unrelated variables. Usually presented as either 2 or 3 dimensional, it can be used in an exploratory fashion to access the sources of variability in the data.Quantification: Also sometimes referred to as counting. In this step reads are attributed to features. These features can be genes, transcripts or exons.Transcriptome: complete set of transcripts (RNA molecules) in a cell, and their quantity, for a specific developmental stage or physiological condition.Transcriptomics: the study of transcriptomes and their functions.Raw files: files that have not been processed, usually referring to the fastq files.RNA-sequencing (RNA-Seq): high-throughput method for both mapping and quantifying transcriptomes.Upstream Analysis: Usually refers to the analysis up until a count file is generated. This includes, but is not restricted to, trimming, alignment, sorting, counting and the initial quality control steps.


Streptomyces species are gram-positive bacteria with GC-rich linear genomes and they serve as dominant reservoirs for producing clinically and industrially important secondary metabolites. Genome mining of Streptomyces revealed that each Streptomyces species typically encodes 20–50 secondary metabolite biosynthetic gene clusters (smBGCs), emphasizing their potential for novel compound discovery. Unfortunately, most of smBGCs are uncharacterized in terms of their products and regulation since they are silent under laboratory culture conditions. To translate the genomic potential of Streptomyces to practical applications, it is essential to understand the complex regulation of smBGC expression and to identify the underlying regulatory elements. To progress towards these goals, we applied two Next-Generation Sequencing methods, dRNA-Seq and Term-Seq, to industrially relevant Streptomyces species to reveal the 5´ and 3´ boundaries of RNA transcripts on a genome scale. This data provides a fundamental resource to aid our understanding of Streptomyces’ regulation of smBGC expression and to enhance their potential for secondary metabolite synthesis. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13259393 Streptomyces species are gram-positive filamentous bacteria and hold a great importance for their ability to produce a wide range of clinically or industrially important secondary metabolites2. During the middle 20th century, the number of available antibiotics rapidly increased and especially, more than 70% of the antibiotics from bacteria were discovered from Streptomyces species, emphasizing their importance as the dominant source of antimicrobial compounds3. However, the discovery of novel antibiotics rapidly decreased during the latter part of 20th century as research progress with Streptomyces species declined as reflected by a decreasing number of novel secondary metabolite discovered4. Fortunately, with the emergence of Next-Generation Sequencing (NGS) technique, the genome sequences of many Streptomyces species have been collected and increased the potential to produce novel secondary metabolites5. Computational prediction revealed that a single Streptomyces species typically possesses about 20–50 secondary metabolite biosynthetic gene clusters (smBGCs), and the great number of smBGCs in Streptomyces genomes encourages researchers to revisit these organisms to cope with the threat of emerging multi-drug resistant bacteria7.8. Since most secondary metabolites are not essential for growth and produced to respond to environmental stimuli, such as osmotic pressure or nutrient limitations or inter-species competition, the smBGCs are expected to be under tight and complex regulation11. To utilize the genomic potential of Streptomyces, an understanding of the genetic regulatory mechanisms for activating smBGCs is crucial. Especially, understanding transcriptional regulatory mechanisms is important since the transcription is the first step of gene expression and diverse regulations take place in transcription13. Here we report NGS datasets describing the differential RNA-Seq (dRNA-Seq) and Term-Seq of seven important Streptomyces species to broaden our understanding on transcriptional regulation of Streptomyces in genome-scale by providing the transcript boundary information  of transcripts by differentiating the TSSs from the 5′-ends of processed transcripts. For dRNA-Seq, two libraries are constructed, one from the 5′-ends of unprocessed bacterial primary transcripts and the other from the 5′-ends of processed transcripts. By comparing the two libraries, TSSs can be differentiated from the processed 5′-ends. In contrast, Term-Seq captures the 3′-ends of transcripts, which lead to identification of the genuine transcription termination sites (TTSs) and processed 3′-ends25.The seven species studied include wth Fig. 24. dRNA-26. In addition, TSS information enables to determine 5′-untranslated region (5′-UTR) of each gene in nucleotide resolution, which contains transcriptional or translational regulatory elements, such as the ribosome binding site (RBS), riboswitches and upstream open reading frames29. Likewise, transcriptional terminator sequences and 3′-UTR can be determined from the 3′-end information of transcripts obtained from Term-Seq. With the aid of genome-wide transcriptome and translatome information which can be obtained from RNA-Seq and Ribo-Seq, respectively, the transcriptional and translational effect of each regulatory element, including the promoter sequence, RBS or transcription terminator sequence, can be evaluated. Furthermore, the determined regulatory elements can be utilized for improving the production of secondary metabolites in Streptomyces through synthetic biology approaches. The transcript boundary information obtained from dRNA-Seq and Term-Seq will serve as fundamental resources to understand the complex regulatory mechanisms in bacteria and improve the industrial applications.From the TSSs determined from dRNA-Seq, the promoter sequences can be identified with the aid of computational motif discovery toolsS. avermitilis MA-4680, S. clavuligerus ATCC27064, S. coelicolor M145, S. griseus NBRC13350, S. lividans TK24, S. tsukubaensis NBRC108819 and S. venezuelae ATCC15439 were used in this study. The mycelium of each Streptomyces was maintained in 25% glycerol at −80 °C. Cells were cultured in 50 mL R5− media with 8 g glass beads (3 ± 0.3 mm diameter) at 30 °C. The R5− medium consists of 5.73 g TES (pH 7.2), 103 g sucrose, 10 g glucose, 5 g yeast extract, 10.12 g MgCl2∙6H2O, 0.25 g K2SO4, 0.1 g casamino acids, 0.08 mg ZnCl2, 0.4 mg FeCl3∙6H2O, 0.02 mg CuCl2∙2H2O, 0.02 mg MnCl2∙4H2O, 0.02 mg Na2B4O7∙10H2O, and 0.02 mg (NH4)6Mo7O24∙4H2O in 1 L distilled water. Cell growth was determined by measuring optical density at 600 nm with biological triplicates. The cells were sampled at four different time points according to the growth profile of each strain, which were early-exponential (E), transition (T), late-exponential (L), and stationary (S) phases. The E, T, L, and S time points were 13, 17, 19.5, 33.5 h for S. avermitilis, 26, 80, 105.5, 125 h for S. clavuligerus, 12, 16, 20, 34 h for S. coelicolor, 15.5, 28, 32.5, 48.5 h for S. griseus, 9.5, 14, 16, 20 h for S. lividans, 13, 19, 23, 31 h for S. venezuelae, and 15, 18.5, 28, 48 h for S. tsukubaensis after inoculation, respectively , and resuspended with lysis buffer . The cell suspension was frozen with liquid nitrogen, and then physically lysed by grinding using mortar and pestle. The cell lysate was centrifuged at 4 °C for 10 min at 16000 × g and the supernatant was saved and stored at −80 °C until used for RNA extraction. For RNA extraction, the supernatant was mixed with equal volume of phenol:chloroform:isoamyl alcohol = 25:24:1 solution. The mixture was then centrifuged and RNA was extracted from the upper aqueous phase with ethanol precipitation.After harvesting, the cells were washed with polysome buffer  and incubated at 30 °C for 10 minutes. After incubation, the cells were centrifuged down and the supernatant was discarded. The cell pellet was resuspended with AE-SDS  and the suspension was mixed with equal volume of phenol:chloroform = 5:1 solution. Cells were lysed by incubating at 65 °C for 5 min and centrifuged. RNA was extracted from the upper aqueous phase with isopropanol precipitation, and genomic DNA aggregate formed upon addition of isopropanol was removed before precipitation.For Term-Seq of To remove any DNA contamination, the RNA samples were treated with DNase I .The four DNase I treated RNA samples from the four growth phases were mixed equally to obtain one 10 μg RNA mixture and a total of two RNA mixtures were prepared from the eight RNA samples as the biological duplicates for each strain. The rRNA in the RNA mixture was depleted using Ribo-Zero rRNA Removal Kit for Bacteria . The rRNA-depleted RNA was incubated in 1 × RNA 5′ polyphosphatase  reaction buffer and 1 U of SUPERase-In  at 37 °C for 1 h, with or without TAP for TAP( + ) or TAP(−) libraries, respectively. The reaction was cleaned up with ethanol precipitation and 5 pmol of 5′ RNA adaptor (5′-ACACUCUUUCCCUACACGACGCUCUUCCGAUCU-3′) was ligated to the purified RNA using T4 RNA ligase  by incubating at 37 °C for 90 min in 1 × RNA ligase buffer and 0.1 mg/mL BSA. The ligation product was then purified using Agencourt AMPure XP beads  according to the manufacturer’s instructions. The purified product was reverse-transcribed with SuperScript III Reverse Transcriptase (Invitrogen) according to the manufacturer’s instructions and purified using Agencourt AMPure XP beads. The purified cDNA was amplified and indexed using Phusion High-Fidelity DNA Polymerase (Thermo Fisher Scientific) for Illumina sequencing. The amplification step was monitored using a CFX96 Real-Time PCR Detection System  and stopped before the PCR reaction was fully saturated. Finally, the amplified library was purified using Agencourt AMPure XP beads.S. coelicolor were prepared as previously described17. The equal amounts of DNase I-treated RNA from the sampling time points were mixed and used for the input of Term-Seq library construction. The RNA was treated with Ribo-Zero rRNA Removal Kit for Bacteria (Epicentre) to deplete rRNA. The resulting 500~900 ng of rRNA-depleted RNA was mixed with 1 μL of 150 μM amino-blocked DNA adaptor (5′-p-NNAGATCGGAAGAGCGTCGTGT-3′), 2.5 μL of 10 × T4 RNA ligase 1 buffer, 2.5 μL of 10 mM ATP, 2 μL of DMSO, 9.5 μL of 50% PEG8000, and 2.5 μL of T4 RNA ligase 1 (New England BioLabs). The mixture was incubated at 23 °C for 2.5 h and reaction was cleaned-up using Agencourt AMPure XP beads. The adaptor ligated RNA was then fragmented by incubating at 72 °C for 90 seconds in fragmentation buffer . The fragmentation reaction was cleaned-up using Agencourt AMPure XP beads. The fragmented RNA  was reverse transcribed with SuperScript III Reverse Transcriptase using 1 μL of 10 μM reverse transcription primer (5′-TCTACACTCTTTCCCTACACGACGCTCTTC-3′) according to the manufacturer’s instructions. The cDNA was then purified with Agencourt AMPure XP beads. Another amino-blocked adaptor with different sequence (5′-p-NNAGATCGGAAGAGCACACGTCTGAACTCCAGTCAC-3′) was ligated to the cDNA with increased incubation time (8 h). The ligation product was purified using Agencourt AMPure XP beads and indexed by PCR with Phusion High-Fidelity DNA Polymerase using forward (5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCT-3′) and reverse (5′-CAAGCAGAAGACGGCATACGAGATNNNNNN (6 nt index) GTGACTGGAGTTCAGAC-3′) primers. The PCR reaction was monitored using a CFX96 Real-Time PCR Detection System and stopped before the PCR reaction was fully saturated. The PCR product was purified with Agencourt AMPure XP beads.Term-Seq libraries for six species except S. coelicolor, 1 μg of the total RNA instead of rRNA depleted RNA was ligated with 1 μL of 150 μM amino-blocked DNA adaptor (5′-p-NNAGATCGGAAGAGCGTCGTGT-3′) as described above. After ligation, rRNA was removed by using Hybridase™ Thermostable RNase H . 13.5 μL of the purified ligation product was mixed with 1.5 μL of 10 × DNase I Reaction Buffer (New England BioLabs), 15 μL of Hybridase complement buffer , 1 μL of anti-rRNA oligo mix  and 2 μL of 50 mM MgCl2 and incubated in thermal cycler 30. 2 μL of Hybridase™ Thermostable RNase H was added to the mixture and the mixture was incubated at 65 °C for 20 minutes, 90 °C for 1 second and 65 oC for 10 minutes. rRNA depletion reaction was cleaned up using RNA Clean & Concentrator Kits  and the product was processed according to the remaining procedures as described above.For S. tsukubaensis. For the dRNA-Seq of S. tsukubaensis, both TAP(+) libraries and TAP(−) libraries were sequenced using Illumina MiSeq platform with 1 × 150 bp read length. The reads were processed using CLC Genomics Workbench. The raw reads were first mapped to phiX sequence, which is used in Illumina sequencing platform for quality control. The detailed mapping parameters are as follow. Mismatch cost: 2; Insertion cost: 3; Deletion cost: 3; Length fraction: 0.9; Similarity fraction: 0.9; Map randomly for non-specific matches. After mapping to phiX sequence, unmapped reads were collected and trimmed to remove adaptor sequences, short reads and low quality reads. The detailed parameters are as follow. Quality score limit: 0.05; Maximum number of ambiguities: 2; Remove adaptors; Discard read lengths below 15. For Term-Seq, two nucleotides at both ends were removed since the adaptors include random 2 nucleotides. The trimmed reads were mapped to the available reference genomes  with same parameters for phiX mapping, except the non-specific match handling (non-specific matches were discarded). After mapping to reference genomes, the directions of mapped reads of Term-Seq were inverted since the sequencing output comes in reverse direction.All libraries were sequenced using either Illumina MiSeq or Illumina HiSeq. 2500 platform with either 1 × 100 bp (dRNA-Seq) or 1 × 50 bp (Term-Seq) read length except the dRNA-Seq of 31. The detailed calculation is as follow.Z(x) is the modified z-score at position x, r(x) is the read count of position x. The read counts were determined for 5′-ends of mapped reads for dRNA-Seq and 3′-ends of mapped reads for Term-Seq. m(C(x)) and σ(C(x)) are the mean and standard deviation of read counts of other positions, C(x), near the position x. C(x) is the set of positions y, satisfying 2 < |x − y| ≤ 50. The z-score of each position was first calculated separately for biological replicates and then averaged. Note that the position x, where m(C(x)) ≤ 0.25 in any of the biological replicates was ignored. For dRNA-Seq, the z-scores were calculated only for the positions of TAP(+) libraries, where normalized read counts are more than two-fold higher compared to the normalized read counts of the same positions in TAP(−) libraries. Finally, positions with z-scores higher than 12 were retained and if there are multiple positions within 3 nt distance, the positions with less z-scores were discarded.To determine the read count enriched peak positions where represent possible TSSs for dRNA-Seq or TTSs for Term-Seq, the read count enrichment to a specific position was represented with the z-score of the read count at the specific position as previously describedS. avermitilis, S. clavuligerus, S. tsukubaensis) were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive under the accession number SRP15802332, SRP18829033, and SRP10379534, respectively. The dRNA-Seq raw read FASTQ files of S. griseus, S. lividans and S. venezuelae were deposited in the European Nucleotide Archive (ENA) under the study accession number PRJEB4091835, PRJEB3150736 and PRJEB3637937, respectively. For Term-Seq of S. avermitilis and S. lividans, raw read FASTQ files were deposited under the same accession as the dRNA-Seq. For S. clavuligerus, the Term-Seq raw read FASTQ files were also deposited in the NCBI Sequence Read Archive under the accession number SRX693712338 and SRX693712439. For S. coelicolor and S. griseus, the Term-Seq raw read FASTQ files were deposited under the same accession as the dRNA-Seq of S. griseus. The Term-Seq raw read FASTQ files of S. tsukubaensis and S. venezuelae were deposited in the European Nucleotide Archive (ENA) under the study accession number PRJEB3637937.For dRNA-Seq, raw read FASTQ files of three species  31. In average, about 525 potential TSSs and 1285 potential TTSs were determined for each species, and about 7% of the predicted TSSs and 8% of the predicted TTSs were found in the smBGC regions (the smBGCs for each Streptomyces species were predicted using antiSMASH)43. The determined TSS and TTS information and smBGC information are available at Figshare30. Then, the RNA-Seq read density near the potential TSSs and TTSs was calculated (the RNA-Seq data were obtained from same culture conditions)41. Across the TSSs predicted from dRNA-Seq, RNA-Seq read density drastically increased for all the six Streptomyces species in four growth phases, indicating that the TSSs were successfully captured from dRNA-Seq (Fig. Streptomyces species (Fig. For technical validation of dRNA-Seq and Term-Seq results, the correspondence of read count enriched positions in dRNA-Seq and Term-Seq to increment and decrement of RNA-Seq profiles, respectively, was evaluated. The dRNA-Seq and Term-Seq read count enriched positions were determined for each Seq Fig. . Likewisies Fig. .Fig. 3RN


Peripheral blood is the most promising source of RNA biomarkers for diagnostic and epidemiological studies, because the presence of disease and prognostic information is reflected in the gene expression pattern. Quality RNA is used by a number of different downstream applications, so the selection of the most appropriate RNA stabilization and purification method is important. We have analyzed the RNA purified from 300 blood samples from 25 donors processed by two technicians using three methodologies with Tempus and PaxGene tubes.The best quality sample results were obtained with the Tempus Spin RNA Isolation Kit and the PaxGene Blood miRNA Kit, although larger amounts of RNA were obtained with the Tempus Spin RNA Isolation Kit. Lower Cq values were observed for RNA and miRNA genes in samples that were tested with PaxGene Blood miRNA Kit and Tempus Spin RNA Isolation Kit respectively. We identify the Tempus Spin RNA Isolation Kit as the most robust methodology, whilst the MagMax for Stabilized Blood Tubes RNA Isolation Kit showed the most instability. For biobanks, which process a large cohort and conduct epidemiological studies, the Tempus Spin RNA Isolation Kit is the most appropriate methodology. The study demonstrates the robustness of real-life procedures. Twelve samples from each donor were collected: four blood samples that were collected in PaxGene tubes , and eight samples in Tempus tubes .The potential influence that individual technicians may have over the outcome of the sample analysis was evaluated. Consequently, two technicians processed 150 blood tubes, each using the three extraction methodologies mentioned above. Duplicate samples were processed for the 25 donors (n = 50), to check the reproducibility of each technicians’ with regards to the three tested extraction methodologies (n = 150)  by each technician.Methodology A: PaxGene® tubes and the PaxGene® Blood miRNA Kit . The PaxGene tubes were semi-automated processed using the Qiacube robot (Qiagen) [(Qiagen) .Methodology B: Tempus® tubes and the MagMax for Stabilized Blood Tubes RNA Isolation Kit  [er, USA)  and a DyMethodology C: Tempus® tubes and the Tempus™ Spin RNA Isolation Kit  [ms, USA) . No opti260 nm value, while A260/A280 nm and A260/A230 ratios were used to calculating the purity.The RNA yield and its purity were measured using an Infinite F200 spectrophotometer . The concentration was measured against the AForty-two samples from 7 donors were measured by fluorimetry technique using the Quant-iT™ RiboGreen™ RNA Assay Kit  Fig. .Samples RNA integrity was measured using the Bioanalyzer 2100  and the Agilent RNA 6000 Nano Kit (Agilent Technologies Inc.). The RNA integrity  was calculated.Samples from 7 donors (n = 42) were randomly selected for cDNA synthesis and expression analysis. Samples were extracted by the two technicians using the three methodologies. RNA was reverse-transcribed using miScript II RT Kit (Qiagen) , with the miScript SYBR Green PCR Kit (Qiagen) .A statistical analysis was performed using the IBM SPSS Statistics program 17.0.3 . A descriptive statistic analysis was also performed with the XLSTAT v2017.6 program. The ANOVA test (p < 0.05) and the ‘post hoc test after ANOVA’—Scheffé’s method—(p < 0.05) was applied to compare the mean differences between methodologies. Grubbs test was applied for outliers identification. Box plots were constructed with the XLSTAT v2017.6 program for the visualisation of replicas variability.Samples were extracted using three methodologies. The main characteristics are summarized in Additional file A comparison of the RNA yield between methodologies and technicians’ were performed. The average yields, are indicated in Additional file 260/A280 and A260/A230 ratios were analyzed for the three methodologies . The 6 samples from Methodology A but for which no RIN values was calculated correspond to samples with high RNA concentrations (118.16–172.16 ng/µl), for which the Bioanalyzer showed an anomalous profile. Those 6 RNA samples were subjected to an extraordinary DNase treatment. The RIN was analyzed again obtaining a good result , while higher Cq values were observed for Methodology C.Previous comparative studies have been performed on different methodologies . However260/A230 ratios with Methodology A and especially for Methodology B than these previous studies. The presence of impurities with Methodology B has been described previously [260/A230 ratio between the two technicians were very marked for Methodology B, possibly due to small differences in the protocol implementation. It has been reported that the observed differences in A260/A230 ratio with Methodology A, are due to the high salt content of PaxGene kit elution buffer [With respect to the purity of the RNA, the best results were obtained with Methodology C. Overall, ‘very good’ and comparable purity values were observed for Methodology A and Methodology C, in accordance with previous research , 12, 20.eviously . The difn buffer .Methodologies A and C showed extraordinary RNA integrity, not observed in other studies –12. It wAdditionally, Methodology C show greatest robustness between replicas and technicians. The differences observed between technicians, may be attributed to a better understanding and/or execution of a particular protocol. Clearly, there was a huge variability in the purity and integrity measures with Methodology B, possibly due to a non-efficient contaminants removal.Slightly worse expression results were obtained for mRNAs with Methodology A, although this methodology showed a higher detection capacity for miRNAs. The purification capacity of miRNA for Methodology C should be noted, although the manufacturer doesn’t guarantee the purification of miRNA. Skogholt et al. suggest that differences in expression between methodologies are due to sampling systems, influenced by physical properties of the transcripts . AlthougTo summarize, superior results were obtained with Methodologies A and C. For Biobank large-cohort and epidemiological studies, Methodology C is the most appropriate methodology. However, the reduced hands-on time that is needed for Methodology A must also be considered.DNase treatment was included in Methodologies A and B; however the additional treatment with DNase was not performed with Methodology C.Additional file 1. (i) Reversotranscription, (ii) mRNA and miRNA real time PCR conditions, (iii) and oligo sequences details.Additional file 2: Table A. Summary of the technical characteristics, technical requirements and cost analysis for the three methodologies tested. Table B. Average and standard deviation obtained for RNA yield, A260/A280, A260/A230 and RIN obtained by both technicians with the three methodologies. The average values encompassing the values obtained by both technicians are also indicated. Table C. Robustness of each methodology analysis. Average percentage and standard deviation for variation between methodologies and technicians for variables yield, A260/A280, A260/A230 and RIN.Additional file 3. Diagram of an RNA electropherogram for six samples purified with Methodology A. Images a–f correspond to the RNA purified by Methodology A, following the manufacturer’s instructions. The images a’–f’ correspond to the same RNA samples after additional treatment with DNase. a and a’: Donor 16 Sample 1, b and b’: Donor 16 Sample 2, c and c’: Donor 17 sample 1, d and d’: Donor 23 Sample 1, e and e’: Donor 23 Sample 2, f and f’: Donor 23 Sample 3. The anomalous peaks of the electropherograms are shaded in the picture.Additional file 4. Representation of the average Cq value for the RNA obtained by the three methodologies. mRNA genes  and miRNA  were tested. Error bars are indicated.