Elsevier

Biosensors and Bioelectronics

Volume 94, 15 August 2017, Pages 94-106
Biosensors and Bioelectronics

Functional nucleic acids as in vivo metabolite and ion biosensors

https://doi.org/10.1016/j.bios.2017.02.030Get rights and content

Highlights

  • The review focuses on the incorporation of functional nucleic acids into in vivo biosensors.

  • The recent advances in the development and application of in vivo biosensors are discussed.

  • There is a need for functional nucleic acids that recognize diverse targets inside cells.

  • In vivo nucleic acid-based biosensors are a promising real-time and non-invasive strategy to study cell function.

Abstract

Characterizing the role of metabolites, metals, and proteins is required to understand normal cell function, and ultimately, elucidate the mechanism of disease. Metabolite concentration and transformation results collected from cell lysates or fixed-cells conceal important dynamic information and differences between individual cells that often have profound functional consequences. Functional nucleic acid-based biosensors are emerging tools that are capable of monitoring ions and metabolites in cell populations or whole animals. Functional nucleic acids (FNAs) are a class of biomolecules that can exhibit either ligand binding or enzymatic activity. Unlike their protein analogues or the use of instrument-based analysis, FNA-based biosensors are capable of entering cells without disruption to the cellular environment and can report on the concentration, dynamics, and spatial localization of molecules in cells. Here, we review the types of FNAs that have been used as in vivo biosensors, and how FNAs can be coupled to transduction systems and delivered inside cells. We also provide examples from the literature that demonstrate their impact in practical applications. Finally, we comment on the critical limitations that need to be addressed to enable their use for single-cell dynamic tracking of metabolites and ions in vivo.

Introduction

The cell is the fundamental unit of life; thus, understanding the dynamic interplay between metabolites, metals, and proteins inside the cell, both in normal and diseased states, is a crucial research frontier (Armitage and Barbas, 2014, Newman et al., 2011, Purvis and Lahav, 2013). In particular, the detection and quantification of important molecules can provide insight on the dynamics and kinetics of physiological processes, disease progression, and can reveal potential therapeutic targets (Zhang et al., 2013). A great deal has already been uncovered about cell function, structure, and metabolic processes through imaging and analytical chemistry (Oikawa and Saito, 2012). For example, while not quantitative, early microscopy and immunocytochemistry provided information about the localization of and interactions between biomolecules (de Matos et al., 2010). Additionally, biomolecules and metabolites inside cells have been both identified and quantified using instrument-based analytical methods, including high-pressure liquid chromatography (Lu et al., 2006), gas chromatography (Vielhauer et al., 2011), mass spectrometry (Luo et al., 2007), and more recently, magnetic resonance imaging (Foster et al., 2008). This fundamental analytical research has provided information about metabolic processes in digested bulk sample (Carter et al., 2014, Trapnell, 2015).

Recent single cell studies reveal that differences between individual cells, in both unicellular and multicellular organisms, often have profound functional effects. Importantly, the differences occurring within the same population of cells can have important consequences for the health and function of the entire population (Kolodziejczyk et al., 2015, Proserpio and Lönnberg, 2016). The last few years have seen rapid development in technologies that permit a detailed analysis of the genome and transcriptome of a single cell; however, the corresponding techniques for defining single-cell metabolite and protein concentrations are lacking. Unfortunately, classical imaging and analysis methods cannot be carried out in real-time and are performed on groups of cells, under the assumption that all cells of a particular “type” are identical. To address this, researchers have begun to employ fluorescent biosensors, incorporating either small molecule probes or genetically-encoded proteins, to image and quantify various targets inside individual cells in real-time (Palmer et al., 2011, Chan et al., 2012, Carter et al., 2014, Rose et al., 2014, Lock et al., 2015). Over the past decade, the number of available genetically-encoded biosensors and the types of cellular processes they can monitor has dramatically increased. The most common method couples ligand-responsive promoters to fluorescent genes, such as the green fluorescent protein (GFP) (Espuny-Camacho et al., 2013, Zadran et al., 2013, Zhao et al., 2011). This strategy has enabled researchers to track and localize intracellular metabolites in a relatively non-invasive and selective manner (and has been extensively reviewed by (Y. Zhao et al., 2011; Carter et al., 2014)). While promising, there are several challenges associated with the protein-based approach. First, GFP is sensitive to the redox environment, rendering it prone to photobleaching in live cells (particularly due to presence of flavins) (Carter et al., 2014). Secondly, the signal-to-noise ratio cannot be amplified, as each fluorescent protein reporter can produce only one fluorophore (Tsien, 1998). This is compared to enzymes, such as luciferase, whose signal is based on the enzymatic cleavage of multiple substrates using a single reporter protein (Bongaerts et al., 2002). Most importantly, re-engineering promoters so that they can bind to and detect different targets is not always feasible and is extremely time-consuming. Finally, the binding domains are generally species-specific, and thus, they cannot be used across several model systems or cell types (Kushwaha and Salis, 2015). Together, these properties prevent them from being reliable and generally applicable intracellular single-cell biosensing systems. These fluorescent in vivo biosensors have been recently reviewed (Specht et al., 2017).

A recent exciting opportunity for in vivo biosensors integrates functional nucleic acids (FNAs) (Chang et al., 2016, Dolgosheina et al., 2014, Kuwahara and Sugimoto, 2010, Liu et al., 2009, Schlosser and Li, 2009, Silverman, 2016, Tang et al., 2014, Zhan et al., 2016, Zhou et al., 2014). FNAs are a class of biomolecules that can either recognize and bind to their target molecule (aptamers, riboswitches) (Stoltenburg et al., 2007, Serganov and Nudler, 2013), or exhibit catalytic activity on their substrate (DNAzymes) (Silverman, 2016). There are several reviews describing FNAs applied to therapeutics (Sullenger and Gilboa, 2002, Keefe et al., 2010), as in vitro biosensors (Zhang et al., 2011), and for delivery (Zhou and Rossi, 2011). However, due to their small size, biocompatibility, and ability to respond or bind to a wide range of target molecules, they have emerged as in vivo biosensors, allowing real-time and less invasive detection of metabolites. Here, we discuss the recent and exciting progress in the field of FNAs and how these “next-generation biosensors” may be employed for single-cell and high-throughput biology. We first review the various types of FNAs that can be leveraged for in vivo biosensing. Next, we discuss the different strategies for delivering FNAs in vivo and coupling them to transducer systems. We highlight several specific recent examples, and lastly, discuss challenges and future opportunities.

Section snippets

Functional nucleic acids

Although DNA and RNA are known for their ability to store and transfer genetic material inside the cell, they can be exploited for other uses, such as target binding and catalysis. These molecules are collectively known as functional nucleic acids (FNAs), which include aptamers, DNAzymes, ribozymes, riboswitches, and DNA/RNA nanomaterials. Below we describe FNAs that have been specifically employed to create in vivo biosensors.

Genetically-encoded FNA biosensors

Motivated by the promise of protein-based genetically encoded biosensors, there has been a growing interest in developing synthetic RNA-biosensors. Two approaches discussed below have been implemented; each differing in the mode for achieving the signal read-out. In either approach, the biosensors can be encoded as foreign DNA and expressed on an exogenous vector (e.g., plasmid) that is taken up directly into a cell or organism through transformation or transfection. This molecular cloning

Non genetically-encoded FNA-based biosensors

Rather than engineering cells or organisms of interest to detect intracellular metabolites, FNAs can also be developed, synthesized, and individually delivered into cells as reporters (Fig. 3). Regardless of the FNA employed, most biosensing schemes rely on the molecular beacon-inspired mechanism. Briefly, this design typically involves the formation of a hairpin structure and includes a fluorescence donor at one end of the beacon and a Förster resonance energy transfer (FRET) acceptor on the

Current challenges and limitations

Although FNAs are promising tools for the detection and quantification of targets inside cells, like any other technology, they are not without limitations. Several of these limitations must be considered or addressed to enable the full potential to be realized.

Implementing each biosensor strategy

We have reviewed a number of strategies for employing FNAs for in vivo biosensing. Researchers can leverage genetically encoded strategies vs. delivered FNAs. The chemistry of the FNA itself can be modified (i.e., RNA, DNA, XNA, or other modifications). There is also significant flexibility in deployment of the FNA-based biosensor (e.g., colloidal carriers, graphene oxide conjugates, or invasive delivery). Given that this technology is in its infancy, it is unclear which particular strategy

Conclusions

The capability of detecting molecules inside living cells is required for accurately discerning molecular function and activity, and consequently, for disease progression and prevention. Real-time biosensing inside individual cells remains a challenge because typical analytical measurements only capture a single-time-point in bulk samples. Functional nucleic acids are a new class of molecules that can be incorporated into a number of fluorescent platforms providing an in vivo read-out of the

Future perspectives

While this is a promising and cutting-edge technology, two major challenges remain that prevent FNAs from being broadly applied for dynamic single-cell studies: (1) the availability of reliable and well-validated FNAs that are responsive to diverse and relevant target molecules; (2) understanding differences in the specificity and sensitivity of various detection platforms and FNA candidates.

FNAs can be developed against a whole spectrum of targets, ranging from metal ions, small molecules,

Author contributions

The manuscript was written through contributions of all authors.

Conflicts of interest

The authors declare no conflict of interest.

Acknowledgments

The authors thank Arwa Alsaafien for assistance with the figures. This work was possible due to funds from the Natural Sciences and Engineering Research Council of Canada (438054-2013, PDF to M.M.).

References (184)

  • E.G. Armitage et al.

    J. Pharm. Biomed. Anal.

    (2014)
  • S. Ausländer et al.

    Trends Biotechnol.

    (2013)
  • G.M. Barratt

    Pharm. Sci. Technol. Today

    (2000)
  • C. Bechara et al.

    FEBS Lett.

    (2013)
  • R.J. Bongaerts et al.

    Methods Enzymol.

    (2002)
  • R.R. Breaker

    Mol. Cell

    (2011)
  • R.R. Breaker et al.

    Chem. Biol.

    (1994)
  • P.E. Burmeister et al.

    Chem. Biol.

    (2005)
  • A. Chen et al.

    Biosens. Bioelectron.

    (2015)
  • H. Dong et al.

    Biomaterials

    (2011)
  • I. Espuny-Camacho et al.

    Neuron

    (2013)
  • B.-Y. Fang et al.

    Colloids Surf. B Biointerfaces

    (2016)
  • P.J. Foster et al.

    Neoplasia

    (2008)
  • C.C. Fowler et al.

    Chem. Biol.

    (2013)
  • N.P. Gabrielson et al.

    Mol. Ther.

    (2012)
  • K. Groff et al.

    Biotechnol. Adv.

    (2015)
  • F. Groher et al.

    Biochim. Biophys. Acta

    (2014)
  • C. Guerrier-Takada et al.

    Cell

    (1983)
  • T.G. Iversen et al.

    Nano Today

    (2011)
  • A.A. Kolodziejczyk et al.

    Mol. Cell

    (2015)
  • Y. Liang et al.

    Biosens. Bioelectron.

    (2011)
  • S. Liao et al.

    Biosens. Bioelectron.

    (2016)
  • J.T. Lock et al.

    Cell Calcium

    (2015)
  • W. Lu et al.

    J. Am. Soc. Mass Spectrom.

    (2006)
  • D.L. Ludwig et al.

    Plasmid

    (1991)
  • B. Luo et al.

    J. Chromatogr. A

    (2007)
  • L. Ma et al.

    Biosens. Bioelectron.

    (2017)
  • W. Ma et al.

    Biosens. Bioelectron.

    (2016)
  • B.K. Nanjwade et al.

    Eur. J. Pharm. Sci.

    (2009)
  • S. Ausländer et al.

    Nat. Methods

    (2014)
  • D.A. Baum et al.

    Cell. Mol. Life Sci.

    (2008)
  • B.D. Bennett et al.

    Nat. Chem. Biol.

    (2009)
  • C. Berens et al.

    Biotechnol. J.

    (2015)
  • S. Bhakdi et al.

    Arch. Microbiol.

    (1996)
  • A.J. Camden et al.

    Biochemistry

    (2016)
  • J.M. Carothers et al.

    Nucleic Acids Res.

    (2010)
  • K.P. Carter et al.

    Chem. Rev.

    (2014)
  • J. Chan et al.

    Nat. Chem.

    (2012)
  • D. Chang et al.

    Sensors

    (2016)
  • J.C. Cochrane et al.

    RNA

    (2008)
  • L. Cui et al.

    Anal. Chem.

    (2016)
  • E. Cukierman et al.

    Biochem. Pharmcol.

    (2011)
  • Y. D’Agostino et al.

    Brief. Funct. Genom.

    (2017)
  • R. Deng et al.

    Angew. Chem. Int. Ed.

    (2014)
  • E.V. Dolgosheina et al.

    ACS Chem. Biol.

    (2014)
  • J. Elbaz et al.

    Nat. Commun.

    (2016)
  • A. Ellington et al.

    Curr. Protoc. Nucleic Acid Chem.

    (2001)
  • A.D. Ellington et al.

    Nature

    (1990)
  • A.H. El-Sagheer et al.

    Chem. Sci.

    (2014)
  • G.S. Filonov et al.

    J. Am. Chem. Soc.

    (2014)
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