Alteration of RNA structure by environmental signals is a fundamental mechanism of gene regulation. For example, the riboswitch is a noncoding RNA regulatory element that binds a small molecule and causes a structural change in the RNA, thereby regulating transcription, splicing, or translation of an mRNA. The role of riboswitches in metabolite sensing and gene regulation in bacteria and other lower species was reported almost two decades ago, but riboswitches have not yet been discovered in mammals. An analog of the riboswitch, the protein-directed RNA switch (PDRS), has been identified as an important regulatory mechanism of gene expression in mammalian cells. RNA-binding proteins and microRNAs are two major executors of PDRS via their interaction with target transcripts in mammals. These protein-RNA interactions influence cellular functions by integrating environmental signals and intracellular pathways from disparate stimuli to modulate stability or translation of specific mRNAs. The discovery of a riboswitch in eukaryotes that is composed of a single class of thiamine pyrophosphate (TPP) suggests that additional ligand-sensing RNAs may be present to control eukaryotic or mammalian gene expression. In this review, we focus on protein-directed RNA switch mechanisms in mammals. We offer perspectives on the potential discovery of mammalian protein-directed and compound-dependent RNA switches that are related to human disease and medicine. The alteration of RNA structure by environmental signals via the action of riboswitches is recognized as an essential mechanism that regulates gene expression in prokaryotes and eukaryotes In prokaryotes, about 40 families of riboswitches have been identified that respond to diverse effectors, including coenzymes, nucleotide derivatives, amino acids, and signaling molecules To date, only one type of riboswitch, thiamine pyrophosphate (TPP), has been reported for eukaryotes, in fungi More than a decade has elapsed since the TPP riboswitch sequences were first identified in eukaryotes cis-acting RNA elements. The interactions involve either structural remodeling trans-acting regulators. Structural PDRSs involve secondary structure-recognizing RBPs, such as double-stranded RNA-binding proteins  and the Gamma-interferon Activated Inhibitor of Translation (GAIT) complex In recent years, compound protein-directed RNA switches (PDRSs) have been recognized as emerging translational regulators of gene expression in complex eukaryotic organisms In one example, PTBP/hnRNP I binds to CU-rich sequence and disrupts the local secondary structure, such as the stem-loop, thereby exposing the RNA to targeting by the miRNA-induced silencing complex There are well characterized PDRSs in bacteria that have been described in previous reviews 22.1Transcript-selective translational control is generally mediated by interaction of RNA-binding proteins or complexes and sequence/structural elements in 5′ or 3′UTR of target transcripts VEGFA mRNA in myeloid cells that respond to hypoxic and inflammatory signals -bearing complexes, respectively Hypoxia-Induced hnRNP L-DRBP76-hnRNP A2/B1 (HILDA) complex VEGFA mRNA and alters the RNA secondary structure to abolish binding of the GAIT complex to the hypoxia stability region (HSR), thereby activating VEGFA mRNA translation VEGFA 3′UTR, modulate the translation of a family of mRNAs via PDRS, including VEGFA, DNM1L, and PHF21A among others  PDRS controls translation of  signals A. IFN-γ-g others B [42].FiVEGFA mRNA 3′UTR and inhibits translation. Under hypoxic stress, hypoxia-induced cytoplasmic hnRNP L can block miR-297-RISC binding to the CARE in the HSR (Another examplar RNA switch is mediated by hnRNP L and miRNA-induced silencing complex (miRISC)  the HSR  [24].MKLN1, EIF5, and CDK6DGK-α) mRNA in human glioma cells via the same mechanism reported for the VEGFA mRNA switch, thereby promoting glioblastoma progression In addition, we have recently reported several other mRNA transcripts that contain miR-297-dependent, hnRNP L-directed RNA switch elements, including VEGFA PDRS is located in the mRNA 3'UTR VEGFA PDRS uses a novel switching mechanism to integrate the response to two different physiological stimuli. The VEGFA PDRS is a single RNA with interdependent binding sites in two distinct, ligand-responsive conformational states  Bacterial riboswitches sense physiological signals by direct binding of the effector molecule, without requiring signal “interpretation” by a regulatory protein. For the human VEGFA PDRS, the sensor and switch mechanisms are protein-mediated l states B. This mVEGFA mRNA among other transcripts – the first vertebrate RNA switch that controls translation. These novel RNA elements will help us understand how pairs (or larger clusters) of RNA elements in noncoding regions of mRNAs, and their cognate interacting proteins, integrate physiological and pathological stimuli to regulate translation in mammalian cells.In summary, we have described a multi-protein, multi-element, condition-dependent, conformational switch in the 3′UTR of human 2.2Now that the hnRNP L-dependent PDRSs have been identified, next big question is how are these RNA switches regulated? Based on the molecular mechanisms of the VEGFA RNA switch described above, we believe that regulation of target mRNA translation by hnRNP L-directed PDRS is controlled by five parameters: (a) cell-type- or tissue-specific expression of a spectrum of target mRNAs; (b) cytosolic concentration of a target mRNA under normoxic or hypoxic conditions; (c) cytosolic concentration of hnRNP L, determined by hypoxia-induced cytoplasmic translocation; (d) hnRNP L binding affinity for a target mRNA CARE; and (e) availability of hnRNP L to interact with target mRNAs that could be regulated by “sponging” factors such as miRNAs. Recent findings from multiple laboratories indicate that miRNAs can function as sequence-specific RNA decoys and modulate RBP function via seed sequence-independent interaction with RBPs VEGFA mRNA switch by miR-574-3p via interaction with hnRNP L under pathophysiological conditions VEGFA mRNA switch  located between an upstream open reading frame (uORF) and the main ORF in the 5′UTR of the target mRNA, CEBPA . This interaction may block scanning of the 43S pre-initiation complex through the 5′UTR, prevent the formation of a mature ribosome, and repress mRNA translation. An abundantly expressed, C-rich miR-328 acts as an RNA decoy to bind and capture hnRNP E2. The formation of miR-328-hnRNP E2 complex releases hnRNP E2 from the 5′UTR C-rich RNA element in CEBPA mRNA, thereby restoring the translation of CEBPA and driving myeloid cell differentiation. In contrast, loss or reduction of miR-328 expression in blast crisis chronic myelogenous leukemia permits hnRNP E2-mediated translational silencing of CEBPA mRNA, blocks myeloid cell differentiation, and leads to oncogenic growth of blood cells.In addition to hnRNP L-directed RNA switches that activate mRNA translation, another hnRNP-mediated RNA switch mechanism has been reported to silence mRNA translation during the differentiation process of myeloid progenitor cells A [45]. Ain vivo.Putting these findings together, we think that miRNAs not only act as a ligand to directly participate in the PDRS , but also can bind and inactivate RBPs to regulate PDRS . In the future, it will be interesting to test whether two or more miRNAs could synergistically conduct or regulate PDRS, and whether manipulating concentrations of those miRNAs could modulate RNA switches and reverse pathogenic phenotypes 2.4AdipoR1 (Adiponectin receptor protein 1) mRNA during muscle differentiation and in obesity AdipoR1 mRNA 3′UTR. These two binding sites form a double-stranded stem-loop structure in the absence of PTBP and miR-221. PTBP binding destroys the stem-loop structure and facilitates a linear RNA conformation, thus allowing miR-221-RISC to bind to its target site and inhibit the translation of AdipoR1 mRNA In contrast with the role of miRNAs in sponging RBPs and regulating RNA switches, RBPs have been shown to inhibit or promote the activity of miRNAs and the stability of their target mRNAs. Polypyrimidine tract binding protein PTBP  is well-characterized as a splicing repressor through binding to CU-rich elements in mRNA precursors SCP1) and REST corepressor (CoREST), whose encoded proteins play essential roles in preventing fibroblast-to-neuron transition GNPDA1) trans-differentiation of fibroblasts to functional neurons in vitroAnother example of this type of RNA switch is driven by PTBP for maintenance of specific cell lineages Taken together, these two studies suggest that PTBP exerts its non-splicing regulatory function to regulate miRISC activity in cytoplasm under different pathophysiological conditions. Furthermore, PTBP-dependent PDRS provides a molecular model for understanding co-regulation between different RNA-binding proteins and miRNAs. It has been observed that a large number of miRNAs are localized in the nucleus 2.5-methyladenosine (m6A) RNA methylation  motifs by a methyltransferase complex bearing METTL3 and METTL14 (m6A writers) 6A methylation can be removed by two major demethylases: fat mass and obesity-associated protein (FTO) and alkB homolog 5 (ALKBH5) 6A modification regulates RNA stability, splicing, export, and translation efficiency 6A is commonly believed to function by interacting with YTH-domain-containing proteins (m6A readers) An emerging nucleic acid modification that activates the RNA switch is N(6)hylation C, which 6A modification, in addition to being recognized directly by m6A reader proteins, has also been shown to mediate RNA-protein interactions. It increases the accessibility of its surrounding RNA sequences through altering local RNA secondary structures, a process termed m6A-directed RNA switch or m6A switch 6A modification was found to mediate interactions between heterogeneous nuclear ribonucleoprotein C (hnRNP C), an RNA binding protein that facilitates mRNA processing, and a U5-containing hairpin present in a lncRNA, namely, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) 6A modification at position A2577 inside an “AGG” sequence weakens its base pairing with the third U in the opposite U5-tract inside the stem-loop hairpin. Thus, the first three Us in the U5-tract become more accessible to hnRNP C, which leads to an eightfold increase in the hnRNP C binding affinity 6A immunoprecipitation (MeRIP)-Seq and photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) in normal and global m6A reduction conditions, the authors identified around 2800 high-confidence m6A-dependent RNA switches in hnRNP C-binding RNAs 6A destabilizes the U5-tract of the stem-loop and converts the structure of an m6A-containing hairpin towards the induced conformation in the hnRNP C–RNA complex 6A RNA switch and hnRNP C binding activity regulate mRNA splicing, maturation, and stability. This mechanism was uncovered in a model cell system HEK293T human embryonic kidney cell line, but may be generalizable to other cell types. Besides hnRNP C, another m6A-directed RNA switch has also been found to mediate interactions between a purine-rich secondary structure in MALAT1 and low-complexity Arg-Gly-Gly repeats of hnRNP G 6A motif.m6A modification-dependent RNA conformational switch may be a universal molecular mechanism to modulate the accessibility of RBPs to specific RNA and influence their function. Further investigations should explore whether other RBP-mediated RNA switches are regulated by m6A or other modified nucleic acids  Collectively, these studies suggest that the m2.6trans-acting factors have not been well characterized during the regulation of the translation of uORF and mORF.An 5′ upstream ORF (5′ uORF) is defined as an open reading frame located in 5′UTR of mRNAs and out-of-frame with the main coding sequence. In general, translation of 5′ uORF reduces the protein expression of the downstream main ORF (mORF) in vivo in yeast in vivo. They raise an intriguing question of whether this mechanism could be used by the eukaryotic cell as a riboswitch-like mechanism. This question was motivated by the findings that Ded1p/DDX3 switches between repression and initiation of translation, by either the formation of mRNP granules or their resolution, followed by translational activation upon Ded1p/DDX3-mediated ATP hydrolysis Previous studies have shown that the presence of a secondary structure downstream of an alternative AUG or a near-cognate initiation codon facilitates alternative translation initiation at uORF In summary, Ded1p/DDX3 unwinds RNA secondary structures in 5′UTR of mRNAs and controls translation initiation codon selection, thereby extending the N-terminal sequence of a large number of proteins. In the future, the function and mechanism of action of DDX3 protein in remodeling 5′UTR local secondary structure and regulating translation initiation codon selection need to be studied in mammals, compared to yeast.3The current strategy to discover bacterial riboswitches has relied on bioinformatic predictions based on sequence and structure conservation. Many laboratories have searched computationally for bacterial riboswitch sequences in the human genome without a productive outcome, possibly because the riboswitch function in bacteria has no corollary in humans. If there are human riboswitch-like RNA switches, such as an ATP sensor, they are likely to be different in sequence and structure from switches in lower order species. The challenge is to shift this paradigm by establishing proof that riboswitch-like RNA aptamers are present in mammals, including humans, where they function in both healthy and disease states.in vitro systematic evolution of ligands by exponential enrichment (SELEX) to identify human ATP-dependent RNA aptamers as components of putative riboswitches Dr. Luptak’s laboratory used bioinformatic predictions and Based on the two examples just discussed, we think that more riboswitch-like protein-dependent or even small compound-dependent RNA switches may exist in mammals. Specific structures of potential mammalian RNA switches will “switch” upon binding to a regulatory molecule, such as an RNA-binding protein/complex, a natural metabolite, or a medicinal chemical compound. The structural remodeling in human RNA switches may alter noncoding RNA functions , mRNA stability, or translatability, leading to gene regulation and pathophysiological consequences. We speculate that there will be four types of RNA switches in humans . We will3.1pyr 5′-leader region and stabilizes the conformation of an antiantiterminator stem-loop VEGFA mRNA translation, while hnRNP L in the HILDA complex activates the VEGFA mRNA translation through the RNA structural switch. Many genes encoding RNA-binding metabolic enzymes have been identified as genes containing genetic mutations that contribute to the pathogenesis in specific human diseases PDRSs in bacteria, such as the PyrR regulator RNA/protein complex, are well-characterized. PyrR protein is a transcriptional repressor of the pyr operon with uracil phosphoribosyltransferase enzymatic activity. The interaction of PyrR with nucleotide metabolites UMP/UTP determines whether it interacts with a structured RNA regulator in the RNR2 rRNA and ND6 mRNA, and consequently, a dysfunctional ETC complex that compromises mitochondrial activity. However, the molecular mechanism is still unclear. We expect that more metabolic enzymes from different cellular compartments will be uncovered to function as new RBPs that contain noncanonical RNA-binding domains. These enzymes may bind to specific mRNAs and regulate their translation, such as adenosine kinase  and aldolase A   complex. mRNA interactome capture, followed by mass spectrometry analysis, has identified a group of noncanonical RBPs in the FAS-activated serine/threonine kinase (FASTK) family of proteins toplasm)  [87]. ItThis possible new type of metabolic enzyme-mediated RNA aptamer may be a part of a PDRS if a structural rearrangement or a conformational switch occurs. As another example, isocitrate dehydrogenases  have been reported to be RNA-binding proteins in mouse cardiomyocyte cells 3.2Salmonella is an ATP-sensing riboswitch under acidic pH conditions ydaO RNA motif, which is a well-established cyclic di-AMP-sensing riboswitch that also weakly binds ATP cis-acting RNA elements to regulate translation efficiency, dependent on either specific RNA-binding proteins or ribosomes. Mitochondria produce the majority of ATP for conducting cellular functions, and the ATP level needs to be fine-controlled. In yeast, mRNAs of mitochondrial-encoded genes (MEGs) contain 5′UTRs, and many regulatory proteins bind to the 5′UTR to modulate MEG translation and ATP production 2-cytochrome c reductase (Complex III), cytochrome c oxidase (Complex IV), and ATP synthase (Complex V). Mammalian mitochondria also lack the homologs of translational regulators that bind to 5′UTR of MEG mRNAs in yeast. So, what is the mechanism for translational control of MEGs in mammalian mitochondria?Dr. Ronald Breaker suggested that potential physiological human riboswitch-like RNA switches, if they exist, may be found for second messengers and other cellular signaling compounds, rather than for common metabolites, such as ATP 0 complex and functional Complex V without producing idle component proteins that could cause mitochondrial unfolded protein stress A peculiar feature for two gene pairs, ATP8/ATP6 (two MEGs encoding Complex V constituents of the ETC complex) and ND4L/ND4 (two MEGs encoding Complex I constituents of the ETC complex), is that their genes overlap, resulting in a bicistronic transcript with no UTRs. For the former, transcription starts from the upstream gene, ATP8, covering the overlapping region between the two genes, and stopping at the end of a downstream gene, ATP6. This process yields one long mRNA molecule that is eventually translated into two different proteins ATP6, beginning with AUG start codon, and ATP8 ending with UAG stop codon with +1 nt frameshift. The 46-nt RNA region may form a stem-loop structure (mfold web server) The TurboFold algorithm integrates multiple sequence alignment results from a group of homologous sequences with nearest neighbor base pairing thermodynamics, to predict most probable RNA secondary structures ATP6 by sequestering the AUG start codon. The pioneering ribosome translating ATP8 may unwind the stem-loop structure and allow a second ribosome to translate ATP6 as a coupling mechanism, to ensure the 1:1 stoichiometric ratio of ATP8 and ATP6  for regulating mRNA translation We hypothesize that this 46-nt RNA element may contribute to translational regulation of the stoichiometry of ATP8 and ATP6 proteins in a ribosome-dependent manner . Since mand ATP6 . Therefo3.3Prokaryotic and eukaryotic cells have well-established metabolic sensing systems that are important for cell survival and environmental adaptations l-carnitine) from red meat is first converted into TMA by gut microbiota and then oxidized to TMAO by the hepatic enzyme, flavin-containing monooxygenase 3 (FMO3), and excreted into the circulation. Based on several clinical reports, the TMAO level in the circulatory system could be an important clinical biomarker for evaluating a patient’s risk for major adverse cardiovascular events Dr. Hazen’s laboratory has reported that the metabolite, trimethylamine N-oxide (TMAO), but not its precursor trimethylamine (TMA), triggers cardiovascular disease (CVD) in both mice and humans fMet, but does not affect its secondary structure 2+. As a consequence, changes in the translatome landscape may contribute to the progression of multiple CVDs promoted by TMAO . It has recently been reported that the levels of miR-146-5p and its targets NUMB and DLST are strongly linked to TMAO levels and may be involved in the progression of atherosclerosis A high concentration of TMAO is known to stabilize the tertiary structure of tRNA3.4Metabolite analogs and small chemical compounds have been screened as possible inhibitors of bacterial riboswitches, to assess their potential as candidates for the development of antibiotics For example, a benzimidazole (1) compound selectively binds to the precursor stem-loop structure of miR-96 and inhibits its processing and maturation, thereby inducing cancer cell apoptosis . These putative effects could explain the hormetic  activity of most therapeutic compounds, partly accounting for drug side-effects. These potential drug–RNA interactions might also underlie the therapeutic benefits of some drugs , or the development of drug-resistance .Drug–RNA interactions need to be investigated for popular medications, such as ibuprofen, aspirin, statin, metformin, and many other frequently prescribed medicines. We propose that potential pharmacological RNA switching may be mediated by some of these human-manufactured chemical compounds . For ins4CD36 3′UTR, lncRNAs).A riboswitch is a critical component of the RNA world. A spectrum of roles of mammalian RNA switches, which resemble riboswitches and riboswitch-like mechanisms in prokaryotes and simpler eukaryotes, have been uncovered over the last two decades in microbiology and cellular biology studies. Cellular stresses activate or inhibit protein-directed RNA switches under pathogenic conditions. In mammals, the principal executors of RNA switching are RNA-binding proteins and miRNAs. The mammalian PDRSs and potential riboswitch-like, metabolite-dependent RNA switches may have four unique properties: (a) They are not necessarily conserved in evolution from bacteria to eukaryotes and could be present in genes from particular species under specific conditions, such as the VEGFA RNA switch. (b) Unlike most bacterial riboswitches, which are unique to a single transcript, human RNA switches can respond to a protein ligand or a metabolite and regulate a cohort of transcripts that provide cells with the ability to adapt to changing environments. (c) Previous findings indicate that human PDRSs confer a 2–5-fold change in gene expression, which is less than the 10-fold change found in bacterial riboswitches, but on par with the action of other human regulatory mechanisms, such as those mediated by miRNAs, that likely function in parallel with other regulatory factors to fine-tune gene expression. (d) We anticipate that human RNA switches are not restricted to mRNA 5′UTR but are also present in mRNA coding regions  and other untranslated RNAs  How widely is PDRS distributed in different animal species and across distinct cell types in each species? (b) What are the global targets of PDRSs, including mRNAs and noncoding RNAs? (c) What is the relevance of PDRSs in human disease and medicine? Although a variety of PDRSs has been convincingly shown, it is unclear whether mammalian riboswitch-like RNA switches that depend on metabolites are present or not. To resolve these questions, we need a hypothesis-driven investigation , Fig. 4 We propose that defects in putative human RNA switches contribute to both normal and disease-associated changes of gene expression and cellular metabolism. A better understanding of the molecular mechanisms of human RNA switches, which could be physiologic, pathogenic or pharmacological, will improve and promote the rational design of drugs that target RNA switches for treatment, and may help to reduce the side-effects of medication. We predict that new research fields will be created to elucidate the interplay between small molecules and RNAs in human health and disease . UnderstThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Genetically encodable sensors have been widely used in the detection of intracellular molecules ranging from metal ions and metabolites to nucleic acids and proteins. These biosensors are capable of monitoring in real-time the cellular levels, locations, and cell-to-cell variations of the target compounds in living systems. Traditionally, the majority of these sensors have been developed based on fluorescent proteins. As an exciting alternative, genetically encoded RNA-based molecular sensors (GERMS) have emerged over the past few years for the intracellular imaging and detection of various biological targets. In view of their ability for the general detection of a wide range of target analytes, and the modular and simple design principle, GERMS are becoming a popular choice for intracellular analysis. In this review, we summarize different design principles of GERMS based on various RNA recognition modules, transducer modules, and reporting systems. Some recent advances in the application of GERMS for intracellular imaging are also discussed. With further improvement in biostability, sensitivity, and robustness, GERMS can potentially be widely used in cell biology and biotechnology. The detection and quantification of cellular proteins, nucleic acids, and metabolites is critical in understanding cellular signaling pathways and many other physiological processes. These cellular molecules are tightly regulated in living systems. Both their cellular levels and distributions play essential roles for their biological functions. As a result, the development of sensors to characterize the spatial and temporal distributions of cellular targets and to accurately quantify their cellular levels has been a major focus in current biochemical studies ,2,3.Although the expression levels of many biomolecules can be measured using traditional methods such as gel electrophoresis, mass spectrometry, liquid chromatography, and NMR spectroscopy , most ofFluorescence imaging, on the other hand, overcomes most of these challenges ,6,7. SynAequorea victoria . B. B79]. BAnother recent advance in these fluorogenic RNA complexes is the Corn/DFHO system . This coSimilarly, several other fluorogenic RNA complexes have been developed as potential reporting systems in engineering GERMS. For example, RNA Mango D is a clThe target-specific recognition module is another critical unit in GERMS. In general, to detect cellular nucleic acid targets, RNA strands with complementary sequences can be directly used as highly specific recognition modules. On the other hand, for most non-nucleic acid targets, RNA aptamers can be engineered as the recognition modules in GERMS.Aptamers, first reported in 1990 , are oli14–1015) oligonucleotides with a random sequence in the same region, which is flanked by known fixed sequences. After the PCR and in vitro transcription of the synthetic DNA library into an RNA library, several selection steps are introduced to remove unwanted unbound oligonucleotides. The RNA sequences that are bound to the target are then released and reverse transcribed into DNA, before being further amplified by PCR. Such multiplied DNA molecules are then transcribed, in vitro, back into RNA, and a new selection round begins. Up to 20 selection rounds are usually performed in conventional SELEX to enrich aptamers with a high target binding affinity. Negative and counter SELEX are often processed at the same time to ensure a selective binding toward the target [SELEX has been widely used in aptamer selection. In general, SELEX begins with a chemically synthesized DNA library. The library contains numerous  [Using SELEX, RNA aptamers have been identified toward various targets, ranging from metal ions  , small o2+) , ATP [992+) , vitamin2+) , and org2+) ), to pro2+) , transcr2+) , and HIV2+) ), and ev2+) ). ThrougIn addition to the conventional SELEX procedure, several other advanced SELEX methods have been developed that are particularly suitable for engineering GERMS. Among others, three notable methods are Capture-SELEX, ribozyme-based SELEX, and graphene oxide-based SELEX.Capture-SELEX is different from conventional SELEX in that it does not require the immobilization of the target compounds to beads or surfaces . In a reRibozyme-based SELEX is another powerful method for developing RNA-based sensors. As mentioned above, ribozymes are naturally occurring RNA strands that can catalytically trans- or cis-cleave at a particular position or sequence . One potGraphene oxide (GO) is another platform which has recently become popular in screening for aptamers. GO-SELEX is based on the non-specific adsorption of the oligonucleotide library by graphene oxide . The libmetH S-adenosylhomocysteine (SAH) riboswitch can selectively recognize SAH in preference to S-adenosylmethionine (SAM) by 1000-fold [Riboswitches are naturally occurring recognition modules for many critical cellular metabolites and signaling molecules ,113. Ano000-fold , while S000-fold .During the conventional in vitro SELEX process, it is difficult to perform negative or counter SELEX against all the diverse and structurally related molecules in the cell. In addition, obtaining aptamers that have a suitable target-binding affinity is in many cases still a challenge. Most in vitro identified aptamers should be further tested and optimized in the real cellular environment. The major advantage of riboswitches over SELEX-generated aptamers is that riboswitches have been evolved to have the type of in vivo selectivity and binding affinity needed to recognize cellular targets. +–C metallo-base pair-based fluorogenic RNA sensor for the intracellular imaging of Ag+ ions [RNA-based recognition modules can also be designed based on sequence-specific base pairings. In addition to the traditional Watson-Crick (A to U and C to G) base pairs, wobble base pairs , G-quadruplexes, and metallo-base pairs can also be engineered as specific recognition modules for the development of RNA-based sensors. For example, we have recently developed a C–AgAg+ ions . In thisGERMS have been successfully applied in multiple intracellular studies. For example, the Jaffrey lab developed a type of allosteric Spinach sensor. Similar to the one shown in thiM TPP riboswitch, we developed TPP-targeting GERMS. Similar to that shown in We previously engineered Spinach riboswitches, nature-inspired GERMS for detecting metabolites in the cytosol of cells with high target selectivity. For example, by engineering the Spinach aptamer into the expression platform in a natural We recently engineered another class of RNA-based fluorescent sensors, termed RNA integrators, for the intracellular detection of low-abundance metabolites . In thisIn addition to these nature-inspired designs, GERMS can also be engineered based on recent advancements in DNA and RNA nanotechnology. For example, our lab has recently developed the first GERMS based on an RNA logic circuit, termed the Catalytic Hairpin Assembly RNA circuit, that is Genetically Encoded (CHARGE) . In our In another example showing that dynamic RNA nanotechnology can contribute to the design of GERMS, the Yin group has developed toehold switches to detect target RNAs with an average ON/OFF ratio of over 400 . The toeE. coli cells, demonstrating its potential for intracellular imaging.Synthetic RNA nanotechnology, i.e., the design and construction of artificial RNA nanostructures, can also provide a useful scaffold to improve the performance of GERMS. For example, the Andersen group has recently reported a single-stranded RNA origami FRET system . In theiOther than the examples described above, GERMS can also function, in a way, as logic gates. Alam et al. showed that Split Broccoli aptamers can be converted into an AND gate for monitoring the assembly of RNA–RNA hybrids . The KhiOver the past few years, GERMS have emerged for live-cell imaging and the detection of various RNAs, proteins, metabolites, synthetic compounds, and ions. The high versatility of these RNA nanostructures has provided GERMS with a wide choice regarding the recognition modules, transducer modules, and the reporting systems. GERMS can be developed toward various targets with both a high binding affinity and selectivity. The sensitivity, modularity, and dynamic range of these RNA-based sensors have been dramatically improved.One critical challenge in the rational design of GERMS is to understand how the recognition module changes its conformation after binding to its target. Indeed, it can be difficult to design transducer modules if the structures of both the apo- and holo- forms of the recognition module remain unknown. The crystal structures for most existing riboswitches have been solved. However, for many SELEX-generated aptamers, we still have limited knowledge about their tertiary structures. On the other hand, computational simulations have been used to assist the design and engineering of GERMS. Unfortunately, it is still challenging to accurately simulate many complex intramolecular interactions among different modules within these functional RNA structures, without mentioning the challenge in predicting how target binding can thermodynamically and kinetically change the conformation of GERMS.Currently, it is still taking a long time and many trials to develop a functional RNA-based sensor. The number of selection rounds will be greatly reduced if there are guidelines for the pairing of different modules in GERMS. In other words, if we could design the transducer module simply by looking at the sequence of the recognition module and its binding pocket, this would greatly improve the design efficiency. Potential milestones in engineering GERMS will likely depend on revolutionary algorithms in computational simulations and a more comprehensive understanding of the relationships between RNA sequences and their corresponding tertiary structures.Another limitation in applying GERMS for mammalian cells or in vivo imaging is RNA degradation and low-level expression. Short RNA constructs, like those in most GERMS, can be rapidly degraded in eukaryotic cells. One potential solution for improving the expression level of GERMS is based on circular RNA constructs. Circular RNAs have been identified in vivo as naturally evolved stable RNA molecules. Circular RNAs do not have either a free 5’- or 3’-end, which makes them invulnerable to most cellular exonucleases. Recent studies have shown that these circular RNAs can be stable for days-to-weeks and that they accumulate at high levels in diverse eukaryotic organisms ,137,138.In conclusion, we have summarized in this review the basic design principles and recent applications of GERMS for bioimaging and the detection of cellular targets. The versatility of these RNA-based sensors makes GERMS highly useful for studying essentially any molecule in living cells. GERMS have shown great potential for future live-cell imaging. After improving their biostability, sensitivity, target selectivity, and kinetics, the next steps will likely be the engineering of GERMS into working sensors in eukaryotic cells, as well as the generation of universal protocols for developing GERMS toward any target of interest.


As a result, ARHGAP5 was upregulated to promote chemoresistance and its upregulation was also associated with poor prognosis in gastric cancer. In summary, impaired autophagic degradation of lncRNA ARHGAP5-AS1 in chemoresistant cancer cells promoted chemoresistance. It can activate the transcription of ARHGAP5 in the nucleus and stimulate m6A modification of ARHGAP5 mRNA to stabilize ARHGAP5 mRNA in the cytoplasm by recruiting METTL3. Therefore, targeting ARHGAP5-AS1/ARHGAP5 axis might be a promising strategy to overcome chemoresistance in gastric cancer.Chemoresistance remains the uppermost disincentive for cancer treatment on account of many genetic and epigenetic alterations. Long non-coding RNAs (lncRNAs) are emerging players in promoting cancer initiation and progression. However, the regulation and function in chemoresistance are largely unknown. Herein, we identified ARHGAP5-AS1 as a lncRNA upregulated in chemoresistant gastric cancer cells and its knockdown reversed chemoresistance. Meanwhile, high ARHGAP5-AS1 expression was associated with poor prognosis of gastric cancer patients. Intriguingly, its abundance is affected by autophagy and SQSTM1 is responsible for transporting ARHGAP5-AS1 to autophagosomes. Inhibition of autophagy in chemoresistant cells, thus, resulted in the upregulation of ARHGAP5-AS1. In turn, it activated the transcription of ARHGAP5 in the nucleus by directly interacting with ARHGAP5 promoter. Interestingly, ARHGAP5-AS1 also stabilized ARHGAP5 mRNA in the cytoplasm by recruiting METTL3 to stimulate m At present, the general treatment of cancers include neoadjuvant chemotherapy and adjuvant chemotherapy, before and after surgery, respectively3. However, chemoresistance will eventually appear to confer the treatment failure. Chemoresistance usually display resistance to many chemotherapeutic drugs through various mechanisms including both genetic and epigenetic changes, alterations in multiple signaling pathway or cell metabolism4.Despite of significant advances, cancers such as gastric cancer remains one of the uppermost causes of mortality7. Recently, autophagy is emerging as a preeminent factor to modulate tumorigenesis and cancer therapy in response to metabolic stress, cellular damage, and detoxication7. Autophagy might promote chemoresistance by enabling cancer cell survival under chemotherapeutic stresses9. However, it can also promote cell apoptosis, thus enhancing chemosensitivity11. Therefore, the regulation and function of autophagy in drug response and chemoresistance development warrant further investigations.Autophagy, an evolutionarily conserved process for the intracellular recycling and degradation, plays a pivotal role in molecular, cellular, and tissue homeostasis by coordinating metabolism and signaling pathways14. Mechanically, the localization of lncRNAs vary from chromatin to subnuclear domains or cytoplasm, participating in the regulation of chromatin remodeling, gene transcription, RNA splicing, microRNA interaction, RNA stability or transportation and protein functions16. Among them, natural antisense transcripts (NATs) are simply complementary to their RNA transcripts in opposite strand and non-coding in majority. NATs could involve in regulating gene expression or stability through DNA–RNA, RNA–RNA, or protein–RNA interactions and the dysregulation is implicated in many pathogeneses including tumorigeneses18. Historically, accumulating evidence indicated that chemoresistance was associated with the alteration in expression of some lncRNAs, such as UCA119, linc-ROR20, and GAS522.Discovery of enormous LncRNAs in diverse kinds upend the traditional insight about genome organization. This “dark matter” gradually demonstrated the “bright side” with multiple functions in cell fate determinationIn this study, we identified a chemoresistance-promoting NAT named ARHGAP5-AS1. It was upregulated in chemoresistant gastric cancer cells, resulting from impaired autophagic degradation. Intriguingly, ARHGAP5-AS1 increased the expression of its target gene ARHGAP5 to promote chemoresistance. Inhibiting either ARHGAP5-AS1 or ARHGAP5 succeeded to reverse chemoresistance.23. Cells were all cultured in RPMI-1640 medium (Invitrogen) medium supplemented with 10% FBS and 100 U/mL penicillin–streptomycin. All the antibodies used in our study were listed as: anti-cleaved PARP1 ; anti-cleaved Caspase3 ; anti-β-actin ; anti-RhoGAP (ARHGAP5) ; anti-LC3B ; anti-SQSTM1 ; anti-FLAG ; anti-GAPDH ; anti-H3 ; anti-HuR ; anti-METTL3 ; anti-METTL14 , and anti-WTAP . The chemicals used in this study include chloroquine (CQ) (C6628), cisplatin , rapamycin (Sigma-Aldrich), actinomycin D , doxorubicin hydrochloride , and 5-fluoracil .Human gastric cancer cell lines SGC7901 and BGC823 were all purchased from the Type Culture Collection of the Chinese Academy of Sciences . Multidrug-resistant cells SGC-R and BGC-R were induced from SGC7901 and BGC823, respectivelyTotal RNAs were isolated using the Trizol reagent (Invitrogen) and concentrations were quantified using NanoDrop 2000 , followed with DNase I digestion and reverse transcribed by random primers to generate cDNA templates strictly according to the manufacturer’s instructions . Nuclear or Cytoplasmic RNA were isolated using the Thermo Scientific™ NE-PER™ Nuclear or Cytoplasmic RNA Purification Kit according to manufacturer’s protocols. Real-time quantitative PCR was performed using SYBR Green Master Mix Kit and Light Cycler 480 II system . To determine relative gene expression, RNA integrity was normalized to internal control β-actin or 18S. All primer sequences used for PCR are listed in Supplemental Table TM RNAiMAX transfection reagent (Thermo Fisher Scientific) at a final concentration of 100–200 nM. All siRNAs sequences used for knockdown are listed in Supplemental Table The full length of ARHGAP5-AS1 was cloned to pcDNA3.1 vector. Various truncation segments of SQSTM1 was cloned to EX05-flag vector. Plasmids for preparing probe were all segment-pGEM-T. All of the plasmids were purified using the EndoFree Plasmid Maxi Kit (QIAGEN) and transfected into cells using X-tremeGENE HP DNA Transfection Reagent . Specific siRNAs were designed and synthesized by Gene Pharma Company . SiRNAs were transfected using LipofectamineCell viability was measured using the 3--5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) standard method. For apoptosis analysis, the cells were harvested and assessed for apoptosis using Annexin V-FITC-PI dual-staining kit  by flow cytometer.5 cells were seeded overnight in six-well plate and transfected with given siRNAs or plasmids for 48 h, and incubated with doxorubicin hydrochloride  at 37 °C for 2 h. After collecting cells in a single cell suspension, the raw fluorescence intensity of single cells was measured using flow cytometry in FL2 channel.About 5 × 102 1 nM, MgCl2·6H2O 1 mM, d-glucose 5 mM, hepes 20 mM, BSA 1% in 1 × PBS) treatment for 8 or 12 h, or rapamycin (50 or 200 nM) treatment for 48 h.Autophagy was effectively induced using EBSS treatment for 2 or 4 h, amino acid deprivation medium  to block the synthesis of new RNA. Cells were then harvested to extract with total RNA at 0, 2, 4, 6, 8, 12 h after Act D addition for quantitative RT-PCR.TM RNA-Binding Protein Immunoprecipitation Kit . Briefly, 1 × 107 cells after the given treatment were lysed in 100 μL RIP lysis buffer with Protease Inhibitor and RNase Inhibitor, and immunoprecipitated with antibodies of interest and protein G magnetic beads for overnight at 4 °C, followed by six washes in Washing Buffer and protein digestion at 55 °C. Total RNA was isolated from the aqueous after digestion and subjected to RT-PCR analysis for quantification.RIP was performed using Magna RIPThe assay mainly combined RNA florescence in situ hybridization (FISH) (LGC Science Ltd.) and immunofluorescence (IFC) with some modifications. Firstly, cells in 12-well plate were fixed using fixation buffer and incubated with the Stellaris RNA FISH probe (LGC) in hybridization buffer for at least 4 h at 37 °C after using 0.2% Triton X-100 for 20 min to permeate the cells. After washing with Wash Buffer A, the cells were blocked with 3% BSA for 1 h at RT and incubated with anti-SQSTM1 or anti-LC3B antibody at 4 °C overnight. After washing with PBST three times, the cells were incubated with secondary fluorescent antibodies for 1 h at RT before proceeding to imaging.2, 10 mmol/L HEPES (PH 7.5), 0.5% NP40, 2 mmol/L DTT, 1 mmol/L EDTA, 100 U/mL RNase Inhibitor, Protease Inhibitor, 100 μg/mL tRNA, and 400 μmol/L Vanady ribonucleoside complexes). RNA–protein complex was formed by incubating 1–2 μg biotin-labeled probe with cell lysates at 30 °C for 30 min. After incubating pretreated Streptavidin Sepharose at RT for 50 min, RNA–protein mixture was precipitated and extracted proteins for Western blotting with 20 μL 6×Loading buffer after six times strictly washing with RNA Washing Buffer , 0.5% NP40).Firstly, Streptavidin Sepharose  was pretreated using RNA-binding buffer , 1 mM DTT, protease inhibitors (Roche)). 50 μL lysates were used for input control. Lysates were incubated with prepared streptaviden beads . RNase-free bovine serum albumin (BSA) and yeast tRNA (both from Sigma) was used when incubation in blocking lysates at 4 °C for 3 h to avoid the non-specific RNA–protein binding. Then washed twice with ice-cold lysis buffer, three times with the low salt buffer , and once with the high salt buffer . Finally, the bound DNAs or RNAs were extracted and purified for qPCR.The detailed procedures were according to previously described® Nascent RNA Capture Kit (ThermoFisher) according to the manufacturer’s instructions. Briefly, cells (5 × 105/well) were seeded in six-well plate and labeled by adding EU to cell culture medium (0.5 mM for 2 h), followed by cell collection for RNA extraction using TRIzol regent. Then 5 μg EU-RNAs were prepared to click to Biotin-Azide (0.5 mM) in Click-iT reaction cocktail for 30 min, and the Biotin-EU RNAs were purified. Afterwards, about 500 ng quantified RNAs were yielded to incubate with 50 μL Streptavidin T1 magnetic beads and washed five times, finally reverse transcriptase-mediated cDNA synthesis on beads should be immediately performed to be further used for qRT-PCR to detect nascent-transcribed RNAs of interested.This assay was performed with the click-iTt-tests to analyze statistical difference only if specified. Differences were regarded as significant when p < 0.05.All experiments were performed at least in triplicate and used the Student’s 26. In total, 109 differentially expressed lncRNAs were found, of which 49 were upregulated and 60 were downregulated in chemo-resistant cells. ARHGAP5-AS1 was one of the most eminent transcripts significantly upregulated in resistant cells  as previously reportedlls Fig. . We firslls Fig. . Besideslls Fig.  and proglls Fig.  in gastrlls Fig.  in resislls Fig. , enhancells Fig.  and incrlls Fig. . On the lls Fig.  dramaticlls Fig. , reducedlls Fig. , and declls Fig. . Taken t25, gene set enrichment analysis (GSEA) based on expression profiling indicated that autophagy was impaired and mTOR was activated in chemo-resistant cancer cells  in the cytoplasm35. Since autophagy was traditionally recognized to degrade protein, the degradation of RNAs in autolysosomes was specifically named as RNautophagy36. For example, lysosomes localized SIDT2 could effectively deliver RNAs into lysosomes during RNautophagy37. SQSTM1 is the well-known autophagy receptor which recruits polyubiquitinated proteins to be degraded to LC3-positive autophagosomes39. In our study, we found SQSTM1 could directly interact with ARHGAP5-AS1 and recruit it to autophagosome for degradation, thus extending SQSTM1 as an RNautophagy adapter. Interestingly, recent studies have revealed the key role of SQSTM1 as a putative RNA-binding protein in aggrephagy, protein–RNA aggregate clearance by autophagy39. While we defined the particular sequence of ARHGAP5-AS1 is responsible for its interaction with SQSTM1, it remains unknown the molecular mechanism about dynamic regulation in SQSTM1 recognition of ARHGAP5-AS1.Interestingly, the autophagy-dependent RNA catabolism has been discovered in yeast and RNAs are broken down by Pnp1/Urh1 through Rny1-mediated transportation to the vacuole40. Dysregulation of these non-coding molecules, thus, contributed to the development of many disorders such as cancers. LncRNAs are multifunctional to affect the functions and stabilities of various proteins or nuclear acids. Among them, one particular class is natural anti-sense transcripts (NATs). NATs are able to regulate the expression of their target genes both in cis and in trans42. NAT is involved in gene transcription and alternative splicing, based on RNA–DNA/RNA and RNA–protein interactions. For example, AS1DHRS4 repressed the transcription of DHRS4 gene cluster by simultaneously pairing with ongoing sense transcripts and recruiting epigenetic regulators, such as DNA and histone methyltransferases to remodel chromatin43. In addition, NATs can regulate the stability of various RNAs including mRNA and other non-coding RNAs, such as microRNAs by direct antisense–sense RNA interactions44. Herein, we identified ARHGAP5-AS1 as a head to head NAT of ARHGAP5 to regulate both the generation and stability of ARHGAP5 mRNA in the nucleus and cytoplasm, respectively.Increasing evidences have indicated lncRNAs as the dominator to modulate vast majority of physiological and pathological processes6A) is the most prevalent and conservative RNA modification in mammalian cells45. However, the recognition of particular RNAs to be m6A modified by methyltransferase complex has not been clarified. Interestingly, ARHGAP5-AS1 interacted with METTL3 and ARHGAP5 mRNA, thus facilitating m6A modification of ARHGAP5 mRNA in a sequence-specific manner. As a consequence, m6A modification could be recognized by various readers including YTHDF1-3 proteins to confer target RNAs distinct destinations, such as transportation, splicing, degradation, and protein translation47. For example, YTHDF2 binds and destabilizes m6A-modified RNAs. Interestingly, the interaction of ARHGAP5 mRNA with the well-established RNA stabilizer protein HuR was abrogated upon the knockdown of ARHGAP5-AS1. Meanwhile, the m6A modification of ARHGAP5 mRNA was also reduced. Generally, the m6A modification was thought to stabilize mRNA by blocking HuR binding. Indeed, certain mRNA fragments from cells with METTL3 displayed increased capability of HuR binding48. However, both in vitro and in vivo experiments confirmed that HuR could directly interact with m6A-modified mRNAs49. Therefore, spatial constraints govern m6A and HuR binding, indicating that HuR could directly bind to m6A or indirectly interact with m6A as one part of an m6A-binding ribonucleoprotein complex. Certainly, the specific m6A readers facilitate HuR binding warrants further investigations.While the mechanism for lncRNAs in chromatin remodeling has been well explored, how lncRNAs affect RNA stability in the cytoplasm remains largely unknown. We found that ARHGAP5-AS1 can recruit RNA modifying enzymes to affect RNA stability, resembling the recruitment of epigenetic modifiers to remodel the chromatin. As catalyzed by METTL3/METTL14/WTAP methyltransferase complex, N6-methyladenosine (m51. There are three major members in the Rho subfamily of small GTPases, called RhoA, RhoB, and RhoC, respectively. They are highly homologous and share same upstream regulators and downstream effectors. However, they have different roles in cancer progression. RhoA and RhoC were frequently activated in many cancers and can stimulate malignant transformation. In contrast, RhoB exhibited tumor suppressor functions by promoting cell apoptosis. Moreover, the inactivation of RhoA by αGCF2/LRRFIP1 conferred resistance to cisplatin mainly through enhancing DNA damage repair or silencing cytoskeleton/trafficking genes53. Therefore, ARHGAP5 can promote cancer progression by enhancing proliferation, migration, and invasion of various cancer cells56. Furthermore, ARHGAP5 can also facilitate cancer development independent on RhoA activation58. We presented evidence that it was upregulated in chemoresistant cancer cells and its knockdown succeeded to reverse chemoresistance, extending its tumor-promoting functions. At large, targeting ARHGAP5 and its upstream regulators might be a novel strategy to overcome chemoresistance.ARHGAP5 could significantly dysregulate the activity of Rho subfamily of small GTPases that plays an important role in cancer progression mainly by regulating cytoskeleton organization6A modification and HuR binding of ARHGAP5 mRNA. Inhibiting either ARHGAP5-AS1 or ARHGAP5 succeeded to reverse chemoresistance (Fig. In summary, ARHGAP5-AS1 is a new chemoresistance-promoting antisense lncRNA. Autophagy adaptor SQSTM1 recruited it for autophagic degradation and it was upregulated in chemoresistant cancer cells resulting from impaired autophagy. It stimulated ARHGAP5 transcription in the nucleus and stabilized ARHGAP5 mRNA in the cytoplasm by recruiting METTL3 for mnce Fig. . TherefoSupplementary Files


RNA sequencing (RNA-seq) is a sensitive and accurate method for quantifying gene expression. Small samples or those whose RNA is degraded, such as formalin-fixed paraffin-embedded (FFPE) tissue, remain challenging to study with nonspecialized RNA-seq protocols. Here, we present a new method, Smart-3SEQ, that accurately quantifies transcript abundance even with small amounts of total RNA and effectively characterizes small samples extracted by laser-capture microdissection (LCM) from FFPE tissue. We also obtain distinct biological profiles from FFPE single cells, which have been impossible to study with previous RNA-seq protocols, and we use these data to identify possible new macrophage phenotypes associated with the tumor microenvironment. We propose Smart-3SEQ as a highly cost-effective method to enable large gene expression profiling experiments unconstrained by sample size and tissue availability. In particular, Smart-3SEQ's compatibility with FFPE tissue unlocks an enormous number of archived clinical samples; combined with LCM it allows unprecedented studies of small cell populations and single cells isolated by their in situ context. Omnis cellula e cellula (every cell from a cell): This idea promoted by Rudolf Virchow, the grandfather of modern pathology, rings no less true today than 160 years ago  material and allows microscopic selection of specific cells. It combines the template-switching SMART method  and protSupplemental Figs. S1, S2; for a technical description, see Supplemental File 1; for a step-by-step comparison with similar protocols, see Supplemental Table S1).Previous single-cell RNA-seq protocols, Smart-seq  and SmarSupplemental Table S2). Because of the method's efficiency, with no inefficient dsDNA ligation and only one cleanup step, it is sensitive at very small amounts of starting RNA. The short number of steps and small reaction volumes allow a batch of libraries to be prepared in half a day with high throughput in a 96-well plate, at a reagent cost of about 5 USD per sample . Library yields are predictable enough that the optimal number of PCR cycles can be reliably estimated from the amount of input material over five orders of magnitude. Furthermore, because Smart-3SEQ reads only a single fragment of each transcript, the one containing the polyadenylation site, it is not sensitive to transcript length, which is a confounder for whole-transcript RNA-seq .We validated the accuracy and sensitivity of the Smart-3SEQ method by using it to quantify the reference RNAs used in the SEQC Consortium's benchmarking study , althougSupplemental Fig. S5A). Of all aligned reads, 99.99% were in the expected forward-strand orientation. The abundance of each transcript as measured by Smart-3SEQ corresponded linearly with its expected copy number in the sample  : If we assume the lowest-input samples are sequenced to completion so that all unique sequences in the library have been detected at least once, our average observation of 189,322 nonduplicate reads among the low-PCR libraries implies a capture efficiency of 30%; for comparison, the theoretical maximum efficiency of GGG template switching has been estimated as 46% (r′ = 0.926) . Likewise, the replicate correlations were very high for high inputs but decreased for low inputs , and the signal loss compared to the first dilution increased with subsequent dilutions . However, in the lowest-input samples, the expected copy number of several transcripts was less than 1, so we expect considerable sampling error even in the true copy numbers of low-abundance transcripts, which is magnified by the recursive sampling of the serial-dilution process.The proportion of reads aligned to the reference sequences decreased mainly with RNA input, but the number of PCR cycles also made a large difference (n of r′) A. The lod as 46% . As the Supplemental Fig. S9), roughly the amount in a single mammalian cell , so they did not affect measurements of gene expression. The diversity of the libraries also decreased with input amount, but the genome loci detected by the reads stayed fairly constant . Of the reads that aligned outside transcripts’ 3′ ends, only a small fraction aligned upstream of genome-encoded poly(A) sites , suggesting off-target priming was not the major cause of these artifacts. The true abundances of the transcripts in these human samples are not known a priori, but it is still possible to validate Smart-3SEQ's measurements by comparing them with those of an alternative method, qPCR, on the same reference RNA samples. Smart-3SEQ showed a strong correlation (average Spearman's ρ = 0.845 for high-input samples)  with TaqMan-based qPCR measurements of 999 genes (ρ = −0.827)  and the Universal Human Reference RNA (UHRR), which were also used in the SEQC benchmarks. We mixed in the ERCC standards at the same relative concentration used by SEQC (1/500 ERCC stock concentration with 100 ng/µL human RNA) to make our RNA samples comparable with theirs, then we serially diluted this mixture down to 10 pg total RNA . Likewise with ample RNA, Smart-3SEQ corresponded with both SYBR-and TaqMan-based qPCR roughly as well as high-input RNA-seq , and with low input it performed slightly worse than Smart-seq , although both methods performed worse with low input than Smart-3SEQ and RNA-seq did with high input. Although the direct correlation between Smart-3SEQ and the other methods was lower , the qPCR platforms were equally correlated with each other as they were with the sequencing platforms . Thus, we conclude that although Smart-3SEQ and whole-transcript RNA-seq may have different error profiles, they both quantify biological signal well.We compared the accuracy of Smart-3SEQ side-by-side with the SEQC data from standard RNA-seq performed on the same ERCC transcripts and human reference RNAs , as well as Smart-seq performed on only 10 pg of human reference RNAs with Smart-seq as implemented in the SMART-Seq v4 kit from Takara Bio USA . With moderate amounts of input RNA, Smart-3SEQ showed equivalent accuracy to RNA-seq in the ERCC standard curves , likely attributable to shorter cDNA inserts from FFPE . Although there was some bias in the gene expression profiles according to the preparation method, the separation between biological categories was much stronger. The artifactual signal in the no-input controls, which may represent contamination of the library preparation reagents, cross-contamination of spuriously assigned sequence reads from the other libraries, or spuriously aligned sequencing artifacts, was distinct from the lowest-input samples . These results show that Smart-3SEQ maintains its sensitivity and accuracy even with low-quality RNA samples.To test Smart-3SEQ's robustness to degraded RNA, we repeated the serial dilutions with pairs of fresh-frozen and FFPE samples. That is, from each fresh-frozen tissue sample, an FFPE block was also prepared, as previously described  of homogeneous cell populations as identified by cytology and location within the tumor microenvironment. This is similar to the approach that others have used to assess the quality of single-cell RNA-seq libraries . The pre200 30%–50%). We microdissected single-cell and bulk samples from ducts involved by DCIS and from an adjacent focus of stromal macrophages, six bulk tissue samples and 10 single cells of each type plus six no-cell controls . Unlike fresh-tissue single-cell RNA-seq, in which the tissue is processed without first visualizing the cells or their context within the tissue, the use of LCM allowed us to microscopically identify specific areas and cells to capture within the mastectomy specimens. This effectively allowed us to pick specific single cells of interest from the millions of cells present in the clinical archival specimen. Based on the preceding serial-dilution experiments with the human reference RNAs, a sample of 500 cells is well beyond enough input for high-quality data from a bulk library. This number of cells can also be obtained from a section of a single breast duct involved by DCIS or a nearby collection of macrophages.We reviewed FFPE tissue from a mastectomy specimen obtained within the prior year from a patient with ductal carcinoma in situ (DCIS), which had typically low-quality RNA .There are minor differences between the quality of the single-cell and bulk libraries. Of reads from the bulk libraries, 27% were uniquely alignable A; this iPadj < 0.01) between bulk stromal macrophage and ductal tissue samples. As expected, the gene expression profiles between ductal cells and stromal macrophages are considerably different and identify biomarkers indicative of DCIS and macrophage populations. The top 100 significantly differentially expressed genes, according to only the bulk samples, are shown as a heatmap to illustrate the differences . The significantly differentially expressed genes include common clinical biomarkers for macrophages (CD68 and CD163) and DCIS cells  . The single-cell samples generally have gene expression profiles similar to the cell type–matched bulk samples. All the gene expression patterns of single stromal macrophages match the bulk stromal macrophages. No stromal macrophages match ductal-cell signal. However, five ductal cells show a gene expression pattern similar to the bulk stromal macrophages rather than DCIS as expected.To assess the degree to which Smart-3SEQ's gene expression profiles of dissected single cell can recapitulate the information from bulk cell populations, we first determined which genes were significantly differentially expressed  confirms that there are macrophages alongside DCIS cells  locus, which encodes an important therapeutic target. Examination of gene expression from the single and bulk DCIS cells shows a coordinated increase in signal at a possible amplicon surrounding the ERBB2 locus  between single stromal macrophages and single ductal cells inferred to be macrophages . Many of the most significant genes are related to extracellular matrix interactions and leukocyte signaling. These data raise the possibility that intraductal macrophages have a phenotype that is distinct from stromal macrophages and further highlight the ability of single-cell LCM and Smart-3SEQ to dissect complex cell populations that would otherwise be missed in bulk analysis.There are 52 genes that were significantly differentially expressed . In particular, PCA  showed several distinct trends. Among the bulk tissue samples, PC1 separated healthy macrophage from the tumors, as expected, whereas PC2 showed a continuum of the tumor samples with invasive ductal carcinoma (a more advanced tumor stage) at the extreme. On these same PCA axes, the single epithelium cells formed a new cluster intermediate between single IDC and single macrophage. However, the single DCIS cells were more heterogeneous, forming a cluster that spanned along the bulk DCIS–IDC continuum and subsumed the cluster of single IDC cells. This is consistent with the biology of the DCIS analyzed in this case: Unlike the samples in the previous experiment, these samples were dissected from several different ducts in the same patient's tissue; therefore, these DCIS cells display more heterogeneity than the previous samples . Here, we show that Smart-3SEQ quantifies transcript abundance accurately across at least five orders of magnitude of RNA input amounts, from common working concentrations down to single cells, including those with degraded RNA. This accuracy is maintained even with inexpensive sequencing conditions ; for example, at current list prices, an Illumina NextSeq run divided over a pool of 96 libraries (4 million × 76 nt reads each) comes to about 25 USD per library, after about 5 USD for the library preparation. These attributes make Smart-3SEQ useful for a variety of biological research applications.Smart-3SEQ is a streamlined, sensitive, and robust method for gene expression profiling that compares favorably with previous methods. The protocol has fewer steps and takes less time than any other RNA-seq method to date, and it keeps costs very low by using minimal volumes of common reagents (Combining Smart-3SEQ with laser-capture microdissection (LCM Smart-3SEQ) enables a new kind of single-cell genomics research on clinical FFPE samples. In most “single-cell” genomics experiments, a piece of tissue is homogenized and thousands of cells are used to make individual sequencing libraries, but the positional identities of the cells are lost in the context of the tissue and must be inferred from their genomic data—we propose to call this “reverse RNA-seq” , by loosWe have shown the success of our method for single-cell expression profiling on a number of levels: (1) Our approach is efficient in that we have obtained distinct and reproducible gene expression profiles from our single-cell experiments; (2) the single-cell libraries recapitulate bulk gene expression data; (3) we identified two distinct cell-type signatures, the DCIS profile and the macrophage profile, with clinically recognized biomarkers of each cell type; (4) the data are sufficiently quantitative to be used in conventional single-cell RNA-seq approaches; (5) the data are sufficiently quantitative that we can detect a DNA copy-number change; and (6) we can use the data to discover potentially new cell phenotypes, such as stromal macrophages versus ductal macrophages, and describe gene expression profile differences between the two cell subtypes. There are limitations to LCM Smart-3SEQ. LCM is labor-intensive, and this limits the number of cells that can be collected and observed. The quality of RNA from FFPE material is poor. Recent FFPE clinical material that has been well archived can have an RNA integrity number (RIN) above 3. However, RNA quality from FFPE material decreases with time and decades-old FFPE material will yield degraded RNA that is challenging for the LCM Smart-3SEQ method. As single cells already have a limited amount of RNA, the age of the FFPE specimen may inﬂuence the cell-to-cell variation in absolute RNA expression and cell profile differences will be altered. The RNA obtained from a single cell may represent a fraction of the total possible RNA because the LCM may only capture part of the cell. For these reasons we do not propose LCM as a substitute for microﬂuidic single-cell methods. Rather, they are complementary approaches that solve different problems; microﬂuidic RNA-seq remains the best way to characterize thousands of cells from a single tissue sample and discover clusters of cell types, while LCM Smart-3SEQ allows the experimenter to choose the specific cells or tissue regions of interest, from many different tissue samples in a single experiment, including FFPE tissue. Likewise, LCM Smart-3SEQ is not a substitute for truly in situ hybridization or sequencing methods , which cOur approach addresses a number of experimental-design problems that are difficult to overcome with conventional single-cell RNA-seq. FFPE archival clinical tissue cannot be examined by conventional single-cell RNA-seq because the cells cannot be physically dissociated. Nonarchival tissue samples, both fresh and frozen, are difficult to collect for clinical studies, especially those that require large numbers of samples for assessing clinical outcomes. LCM Smart-3SEQ also enables studies on uncommon or microscopic lesions that are difficult to collect as fresh or fresh-frozen material. Thus, a much wider variety of cells and diseases can be assessed using archival material with Smart-3SEQ. There are also technical advantages of LCM Smart-3SEQ owing to the ability to bank isolated single cells for future experiments. This includes the spiking in of known cells to control for batch effects with biological replicates.The LCM Smart-3SEQ method creates a new histologically focused approach to studying small cell populations and individual cells. Cells of interest can be chosen by morphology, microenvironment location, or in situ biomarker status and then profiled to uncover previously unappreciated heterogeneity in gene expression, including subtle changes that might escape reverse RNA-seq analyses. This approach will allow transcriptome profiling to isolate and measure each cell individually, moving histology and molecular pathology to the level of cell-to-cell variation.ERCC standards and Human Brain Reference RNA were purchased from Thermo Fisher Scientific . Universal Human Reference RNA was purchased from Agilent Technologies (740000) and resuspended in RNA Storage Solution (Thermo Fisher Scientific AM7001), which was also used for all RNA dilutions. HBRR and UHRR stocks were measured with a Quant-iT RNA Assay Kit (Thermo Fisher Scientific Q10213) on a Qubit ﬂuorometer, and these concentrations were used to calculate dilutions. All RNA stocks were stored at −80°C.Samples were collected with the approval of a HIPAA-compliant Stanford University Medical Center institutional review board. The FFPE tissue blocks were archived with the Stanford University Hospital Department of Pathology. RNA quality for the FFPE block was measured by extracting total RNA from a separate section with the RNeasy FFPE Kit (Qiagen 73504) and testing it on an Agilent Bioanalyzer with the RNA Pico kit.Consecutive sections of the FFPE block were taken on a microtome at 7 µm thickness and mounted on glass slides with polyethylene naphthalate membranes (Thermo Fisher Scientific LCM0522). Slides were stored overnight in a nitrogen chamber. The next day, slides were immersed 20 sec each in xylenes (three times), 100% ethanol (three times), 95% ethanol (two times), 70% ethanol (two times), water, hematoxylin (Dako S3309), water, bluing reagent (Thermo Fisher Scientific 7301), water, 70% ethanol (two times), 95% ethanol (two times), 100% ethanol (three times), xylenes (three times).Slides were dissected immediately after staining. Histology was categorized by a board-certified pathologist (R.B.W.) according to initial sections stained with hematoxylin and eosin. Cells were dissected on an ArcturusXT LCM System using both the ultraviolet (UV) laser to cut out each sample and the infrared laser to adhere it to a CapSure HS LCM Cap (Thermo Fisher Scientific LCM0215). For bulk samples, roughly 500 cells were captured by area, according to density estimates by cell counting on small areas. For single cells, a cell was dissected from the same area as the corresponding bulk sample, then any additional cells adhering to the cap were ablated with the UV laser. For the ablation validation experiment, two regions of both types (still roughly 500 cells each) were captured on the same cap, and then one region or the other was ablated with the UV laser, except the no-ablation controls (therefore they had roughly 1000 cells). After LCM, the cap was sealed in a 0.5 mL tube (Thermo Fisher Scientific N8010611) and stored at −80°C until library preparation, which was performed within 3 d of dissection.Supplemental Figure S29 were color-corrected to enhance contrast with the automatic “Stretch HSV” function in GNU Image Manipulation Program 2.8.20. All photographs were recompressed to reduce file size with the “jpeg-recompress” feature in JPEG Archive 2.1.1, using “veryhigh” quality, “accurate” mode, and the mean pixel error algorithm. All photographs are presented at the original resolution. A complete archive of microscopy photos from all dissections is included in Supplemental File 4.LCM photographs were captured in JPEG format with the built-in camera and software of the ArcturusXT system. Photographs in Supplemental File 2). Reference RNA libraries were prepared using the standard protocol for nondegraded RNA and the pre-SPRI pooling option, one batch for the ERCC experiment and one for the human reference RNAs, with the numbers of PCR conditions shown in Supplemental Figures S4, S9. The fresh-frozen versus FFPE experiment used the corresponding versions of the protocol and only libraries of the same version were pooled; no-template controls were prepared with both versions. The fresh-frozen versus FFPE experiment used the same numbers of PCR cycles for the same dilutions as the reference RNA experiment. The LCM experiments used the special protocol for FFPE tissue on an Arcturus LCM HS cap and the pre-SPRI pooling option, a single batch for all LCM samples, using 22 PCR cycles for the bulk and ablated samples with roughly 500 cells, 25 cycles for the single cells, and 20 cycles for the no-ablation controls with roughly 1000 cells. No-template controls were prepared identically to single-cell samples in the same batch using empty LCM caps. Libraries were characterized immediately and stored at −20°C until sequencing.Sequencing libraries were prepared according to the Smart-3SEQ protocol ; adapter trimming was enabled and short trimmed sequences were retained for diagnostic purposes. For the MiSeq run, FASTQ generation, demultiplexing, and adapter trimming were performed by the MiSeq Reporter in “Generate FASTQ” mode. Only reads that passed the chastity filter were included in the total read count and further analysis. The first 5 nt of each read were removed from its sequence and appended to the read name as its UMI; the following 3 nt were assumed to derive from the G-overhang of the template-switch oligonucleotide and were discarded. Reads shorter than 8 nt after adapter trimming were assumed to be primer dimers and were excluded from other analysis, but still counted toward the total number of sequenced reads. Software versions and command-line arguments are listed in 30 as the “adapter” sequence, and alignments with MAPQ < 10 (posterior probability < 0.9) were discarded. Reads from the experiment that mixed human reference RNAs with ERCC spike-ins were aligned by STAR . Zero counts are included, so each UMI with zero hits votes for a limit of one nonduplicate read per UMI. Therefore, when the total number of reads aligned to the genome position is small, this formula produces the same results as the traditional approach of allowing only one hit per UMI. However, unlike the traditional approach, it does not underestimate the nonduplicate read count when the total is large.We solved this problem with an extension of the algorithm proposed by ci is the number of reads aligned to transcript i and li is the length of the annotated transcript.A and C are the random variables of which the abundance measure a and read count c, respectively, are particular realizations, and corr is Pearson's product-moment correlation.Transcripts per million were computed differently between Smart-3SEQ and whole-transcript RNA-seq because the latter must be normalized by transcript length:t-SNE was performed with the Rtsne package (https://github.com/jkrijthe/Rtsne) on the Euclidean distance matrix of the normalized data.Differential gene expression was analyzed with DESeq2 1.6.1 . All defhttps://www.ncbi.nlm.nih.gov/bioproject) under accession number PRJNA413176. Data processing pipeline scripts are collected at https://github.com/jwfoley/3SEQtools. Scripts used to perform the analyses in this study are collected in Supplemental Code and at https://github.com/jwfoley/Smart-3SEQ_paper_scripts. Additional support for the Smart-3SEQ protocol is available in an online forum at https://groups.google.com/forum/#!forum/smart-3seq.The sequencing data generated in this study have been submitted to the NCBI BioProject database (


RNA–protein interactions is incomplete. This especially concerns the binding between RNA and unstructured protein regions, a widespread class of such interactions. Here, we review the recent experimental and computational work on RNA–protein interactions in an unstructured context with a particular focus on how such interactions may be shaped by the intrinsic interaction affinities between individual nucleobases and protein side chains. Specifically, we articulate the claim that the universal genetic code reflects the binding specificity between nucleobases and protein side chains and that, in turn, the code may be seen as the Rosetta stone for understanding RNA–protein interactions in general.Despite their importance, our understanding of noncovalent  ADE, adenineCYT, cytosineFMRP,Fragile X Mental Retardation ProteinFUS, Fused‐In‐SarcomaGO, Gene OntologyGUA, guanineIDPs, unstructured/disordered proteinslncRNAs,long noncoding RNAsMD, molecular dynamicsPUR, purine; PYR, pyrimidineRBDs, RNA‐binding domainsRBPs, RNA‐binding proteinsRBR, RNA‐binding regionsRF, random forestRRM, RNA recognition motifSVM, support vector machineURA, uracilUTRs, untranslated regionsOur understanding of biology at the molecular level is transforming rapidly and central to this is a radical reappraisal of the importance and ubiquity of RNA–protein interactions Unstructured RBPs are closely involved at all stages of the life cycle of different RNAs in the cell. In the case of mRNAs and long noncoding RNAs (lncRNAs), this includes transcription, processing by the splicesosome and the exon junction complex, nuclear export, translation at the ribosome, and decay  depend much more on their linear sequence features than in the case of folded proteins et al. A distribution of disorder probability along the FUS sequence, that is, its disorder profile as calculated in this case by IUPRED vice versa). This also agrees with the analysis of the distribution of GUA‐preferring amino acids in structured proteins, which shows that they are mostly enriched in unstructured loop regions A 1D distribution of charged residues along protein sequence gives one the possibility to identify prominent negatively or positively charged regions. Interestingly, the FUS RRM together with the flanking RBRs displays a prominent alternation in the net charge Fig. . Many IDinter alia GO terms, protein localization, and chromosome position information Computational prediction of RNA–protein interactions in an unstructured context is still relatively underdeveloped due in part to our lack of understanding of the basic physicochemical principles at play and a general lack of high‐resolution information when it comes to unstructured partners. This situation, however, will likely change in the near future and the lessons learned and the methods developed when it comes to RNA–protein interactions in general will likely be important in the case of structural disorder as well. Most modern methods for predicting RNA–protein binding from sequence information, physicochemical properties of individual RNA and protein building blocks, or global RNA–protein characteristics are based on machine learning strategies. In particular, predictive models are trained on the known interactors by using features such as composition, hydrophobicity, and evolutionary information Although they tackle an important and difficult problem, the above methods still frequently suffer from a limited accuracy, a lack of general applicability, and the fact that only a few of them The recent findings about the mode of interaction between RNA and IDPs highlight the relevance of predicting and understanding such interactions from the perspective of first principles. For instance, it is known that short linear motifs with <10 residues are one of the primary ways of how IDPs interact with partners ab initio methods have been used to study the quantum‐mechanical aspects of such binding, including hydrogen bonding ab initio calculations to estimate the binding preferences between all 20 natural amino acids and the four DNA bases Significant progress in understanding nucleobase–amino acid interactions has over the years been made using computational approaches et al. et al. et al. et al. have evaluated chromatographically the interaction propensities of amino acids and different pyridine derivatives in water BT When it comes to experimental determination of nucleobase–amino acid‐binding preferences, only limited progress has been made over the years. Akinrimisi As discussed above, RNA–protein interactions in an unstructured context are a ubiquitous feature of modern biological systems. However, they also provide an important perspective for understanding the establishment of the RNA–protein relationship in primordial systems in which structural disorder is likely to have been even more pervasive R = −0.74 (note that all affinity scales are defined such that negative Rs mean matching A common feature of most studies of the code's origin has been their focus on individual codons and amino acids only R = −0.80 in human This is unexpected and potentially far‐reaching: although mRNAs and their cognate proteins are completely different biopolymers, they exhibit a strong, quantitative complementarity in that the density of a particular type of groups in one of them can be accurately predicted from the affinity profile for similar groups in the other one. Importantly, we could show that this finding is statistically extremely robust and holds equally well for organisms from all three domains of life Fig. B 102, 10The above results provide support for the stereochemical hypothesis of the origin of the genetic code, but they emphasize the importance of an extended, polymeric context in which the relatively weak affinities of individual building blocks can be amplified. Moreover, these findings support a novel hypothesis that in the unstructured state, mRNAs and the proteins they encode may be complementary to each other and bind in a coaligned manner, whereby the complementarity level is negatively regulated by mRNA ADE content , 105. SiAn improvement in our understanding of the RNA–protein interactions in the unstructured context, including the ability to predict binding sites and relative affinities, would represent a major step forward from both fundamental and practical perspectives. Currently, most of our knowledge of RNA–protein interactions concerns structured RNA‐binding motifs and, consequently, computational methods for predicting RNA‐protein interactions in a physicochemically and structurally realistic manner are necessarily limited and biased by such knowledge , 59, 106There are a number of important open challenges regarding the RNA–protein interactions in an unstructured context. Here, we outline what we believe are the three most relevant and fundamental such challenges that have thus far, somewhat surprisingly, escaped wider attention. We firmly believe that these issues hold a key to a deeper understanding of RNA–protein interactions in an unstructured context, but could also prove to be essential on a much wider scale.Nucleotides and amino acids are arguably the most fundamental building blocks in all living matter. Moreover, the specificity in interactions between nucleic acids and proteins is directly shaped by the specificity in interactions between these individual building blocks. Considering this, it is remarkable that the pioneering, yet incomplete experimental studies aimed at determining the affinities between individual nucleotides and amino acids, that is, their fragments in vitro. In other words, phase separation and RNA–protein interactions in an unstructured context go hand in hand and should therefore be studied from a joint perspective: understanding of RNA–protein interactions in the unstructured state will provide critical information for the understanding of granule formation and vice versa. Importantly, a growing body of work has shown that the presence of RNA, and in particular single‐stranded RNA, markedly reduces the critical concentration required for the formation of phase‐separated protein granules Phase‐separated granules represent arguably the best, biologically relevant system for studying the interactions between RNA and proteins in an unstructured context The third open challenge concerns the development of computational frameworks for predicting the sites and the strength of interaction between RNA and unstructured protein regions that would be based on fundamental physicochemical principles. While the top‐down machine learning‐based approaches definitely have significant merit, the more physicochemically motivated, bottom‐up strategies could provide a deeper mechanistic insight and have a greater predictive power. When it comes to the interaction between single‐stranded RNAs and largely unstructured proteins, successful strategies could be based on the knowledge of the intrinsic interaction affinities between the individual building blocks of the two polymers, as discussed above. Presently, we do not have a clear prescription for how this could be implemented practically, but are motivated by a simple analogy. Namely, hybridization of two strands of DNA or folding of an RNA molecule can be well predicted from a simple thermodynamic quantification of Chargaff pairing rules and local stacking propensities